From 70f0bd795332ef249676af4fd7c4021f8a9d3ec6 Mon Sep 17 00:00:00 2001 From: liupengyuan Date: Sun, 1 Oct 2017 08:40:32 +0800 Subject: [PATCH 01/44] =?UTF-8?q?Update=20=E7=88=AC=E8=99=AB=E5=85=A5?= =?UTF-8?q?=E9=97=A8.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- "chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git "a/chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" "b/chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" index 342218015..ee0100508 100644 --- "a/chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" +++ "b/chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" @@ -37,7 +37,7 @@ with open('0.md', 'w') as f: 首先我们打开网易首页，在页面内copy一个新闻标题到剪贴板，然后用鼠标右键点击页面，并选择`查看源代码`(如用chrome浏览器可选择`检查`，进入开发者模式)。，会看到一片很乱的代码(其实就是html标记的文本)，如果有一些html基础(推荐教程：http://www.w3school.com.cn/html/index.asp)， 看起来就不会那么乱了。 -可以对示例代码D-1稍加修改，就能够抓取到这个页面的源代码，但我们需要对这个网页源代码进行解析，定位到我们希望的内容，获取我们希望得到的数据。这里将使用优秀的第三方网页解析包Beautiful Soap (ver4)，该包已经随Anaconda安装，名称为bs4，并辅以对抓取页面的仔细分析。 +可以对示例代码D-1稍加修改，就能够抓取到这个页面(代码)，但我们需要对这个网页源代码进行解析，定位到我们希望的内容，获取我们希望得到的数据。这里将使用优秀的第三方网页解析包Beautiful Soap (ver4)，该包已经随Anaconda安装，名称为bs4，并辅以对抓取页面的仔细分析。 在刚打开的源代码页按`Ctrl+F`查找刚才copy在剪贴板上的新闻标题，找到这个新闻标题的位置。这行大致是这样：`
  • yyyyyyyyy
    • `， 其中`xxxxx`及`yyyyyyy`随新闻标题不同而不同。其中，`yyyyyyyy`是新闻标题，`http://news.163.com/xxxxxx.html`是新闻标题对应的网页链接，访问该网页链接，就能进入该新闻的内容页面，因此这两项是我们感兴趣并希望抓取的内容。 From de891462a3a17b2eec5cccbabbbd6834a6595049 Mon Sep 17 00:00:00 2001 From: liupengyuan Date: Sun, 1 Oct 2017 08:44:07 +0800 Subject: [PATCH 02/44] =?UTF-8?q?Update=20=E7=88=AC=E8=99=AB=E5=85=A5?= =?UTF-8?q?=E9=97=A8.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- "chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git "a/chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" "b/chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" index ee0100508..3aa2e42ad 100644 --- "a/chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" +++ "b/chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" @@ -62,7 +62,7 @@ f.close() ``` 示例代码D-2中： - `from bs4 import BeautifulSoup`，从bs4包中导入BeautifulSoup模块 -- `soap = BeautifulSoup(r.text, 'html.parser')`，对网页文本，利用python内置的`html.parser`解析器，建立一个`BeautifulSoup`对象`soap`。 +- `soap = BeautifulSoup(r.text, 'html.parser')`，对网页文本，利用python内置的`html.parser`解析器，将`r.text`初始化为`BeautifulSoup`对象`soap`。 - `soup.find_all('li')`，调用`BeautifulSoup`对象`soap`的`find_all()`方法，找到标签`li`之间的所有内容，并返回一个结果集合(`ResultSet`)，集合中每一个元素为一个`Tag`对象，与html中对tag定义相同，此处即利用`
  • `及`
    • `进行标记的所有对象。 - 对每一个Tag对象model，取出其内容(contents属性,会返回一个list，内容是两个tag之间的所有内容)，并转换为字符串写入文件。 From bca921ef0d90fcc355ae0a62013837cd5355fe9e Mon Sep 17 00:00:00 2001 From: liupengyuan Date: Sun, 1 Oct 2017 11:30:15 +0800 Subject: [PATCH 03/44] =?UTF-8?q?Update=20=E7=88=AC=E8=99=AB=E5=85=A5?= =?UTF-8?q?=E9=97=A8.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...54\350\231\253\345\205\245\351\227\250.md" | 49 ++++++++++++++++++- 1 file changed, 48 insertions(+), 1 deletion(-) diff --git "a/chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" "b/chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" index 3aa2e42ad..2298fa7a1 100644 --- "a/chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" +++ "b/chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" @@ -15,6 +15,7 @@ 输入以下代码，并观察执行结果。 ```python +# coding: utf-8 # 示例代码 D-1 import requests @@ -46,6 +47,7 @@ with open('0.md', 'w') as f: 输入以下代码，并观察执行结果。 ```python +# coding: utf-8 # 示例代码 D-2 import requests @@ -71,9 +73,10 @@ f.close() 由于我们是找到了`
  • `及`
    • `进行标记的所有对象，而经过观察可以发现，这样的对象非常多，并非只有新闻标题与链接符合这一模式。我们只能更细致地分析网页源代码。 我们重新在网页源文件中查找新闻标题，进行观察，可以发现，新闻标题及链接均在`
      `及`
    `之间(指向一种名称为`cm_ul_round`的样式)，且源文件中，其他非新闻标题的内容，均不在这种规定了`class`的`
      `标签之间，OK，找到关键标签了。 -输入以下代码，并观察执行结果。 +输入以下代码，并观察执行结果（查看所保存文件内容）。 ```python +# coding: utf-8 # 示例代码 D-3 import requests @@ -97,6 +100,50 @@ f.close() - `text.get('href')`，利用tag对象的get()方法得到text对象的链接。 - `text.string`，利用tag对象的string属性，得到text对象的文本。 +我们得到了首页上每个新闻标题及链接URL，我们下一步就是要利用这些URL来抓取新闻页面，并从中提取出正文。整个过程与前面示例代码D-3处理的过程基本类似，关键在于，找到正文在新闻网页源代码中所处的标签。 + +输入以下代码，并观察执行结果（查看所保存文件内容）。 +```python +# coding: utf-8 +# 示例代码 D-4 + +import requests +from bs4 import BeautifulSoup + +# 第一部分，得到首页面新闻URL及标题，并存入dict +r = requests.get('http://www.163.com') +soup = BeautifulSoup(r.text, 'html.parser') +url_titles = {} + +for model in soup.find_all(attrs={"class":"cm_ul_round"}): + model = model.find_all('a') + for text in model: + try: + url_titles[text.get('href')]=text.string + except KeyError: + pass + +# 第二部分，对URL及标题的dict，抓取每个新闻页面，提取正文并保存 +f = open('news_163.txt', 'w', encoding = 'utf-8') + +for url, title in url_titles.items(): + try: + r = requests.get(url) + except: + pass + soup = BeautifulSoup(r.text, 'html.parser') + + f.write('title:{}\n'.format(title)) + for model in soup.find_all(attrs={"class":"post_text"}): + model = model.find_all('p') + for text in model: + if text.string: + f.write(text.string) + f.write('\n') + +f.close() +``` + 4. 正则表达式 5. 用beautifulsoap解析数据 6. scrapy爬虫框架 From e0ef997df18d93561e1935dbd2a5f94f69bc778c Mon Sep 17 00:00:00 2001 From: liupengyuan Date: Sun, 1 Oct 2017 17:22:41 +0800 Subject: [PATCH 04/44] =?UTF-8?q?Update=20=E7=88=AC=E8=99=AB=E5=85=A5?= =?UTF-8?q?=E9=97=A8.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...\254\350\231\253\345\205\245\351\227\250.md" | 17 +++++++++++++---- 1 file changed, 13 insertions(+), 4 deletions(-) diff --git "a/chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" "b/chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" index 2298fa7a1..57ff24bc9 100644 --- "a/chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" +++ "b/chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" @@ -34,7 +34,7 @@ with open('0.md', 'w') as f: 3. 爬虫进阶 -- 任务：抓取网易首页(www.163.com) 上的全部新闻，并以将所有新闻标题及新闻内容存入到一个文本文件中。 +- 任务1：抓取网易首页(www.163.com) 上的全部新闻，并以将所有新闻标题及新闻内容存入到一个文本文件中。 首先我们打开网易首页，在页面内copy一个新闻标题到剪贴板，然后用鼠标右键点击页面，并选择`查看源代码`(如用chrome浏览器可选择`检查`，进入开发者模式)。，会看到一片很乱的代码(其实就是html标记的文本)，如果有一些html基础(推荐教程：http://www.w3school.com.cn/html/index.asp)， 看起来就不会那么乱了。 @@ -123,7 +123,7 @@ for model in soup.find_all(attrs={"class":"cm_ul_round"}): except KeyError: pass -# 第二部分，对URL及标题的dict，抓取每个新闻页面，提取正文并保存 +# 第二部分，对URL及标题的dict遍历，抓取每个新闻页面，提取正文并保存 f = open('news_163.txt', 'w', encoding = 'utf-8') for url, title in url_titles.items(): @@ -144,6 +144,15 @@ for url, title in url_titles.items(): f.close() ``` +示例代码D-4中： +- `url_titles[text.get('href')]=text.string`，这里以URL为键，以新闻标题为值，存入字典 +- `soup.find_all(attrs={"class":"post_text"})`，通过观察源代码（或利用谷歌浏览器的检查），发现正文在`"class="cm_ul_round"`属性的标签之间，因此提取其间所有内容 +- `model.find_all('p')`，提取`model`中所有标签`

      与

      `之间的内容 + +经过以上几个步骤，我们成功地将`www.163.com`首页所有新闻标题及正文抓取并存储到文件中了。当然，由于我们并没有特别仔细分析页面，最终抓取的文件中，仍然含有一部分不太正确的地方，留给读者自行解决。 + +- 任务2：抓取有道词典查询词的双语例句 + 4. 正则表达式 -5. 用beautifulsoap解析数据 -6. scrapy爬虫框架 +5. scrapy爬虫框架 +6. 扩展 From 5b15bfccdc245a0406e65b122f4ccb2fa994d495 Mon Sep 17 00:00:00 2001 From: liupengyuan Date: Sun, 1 Oct 2017 22:06:00 +0800 Subject: [PATCH 05/44] =?UTF-8?q?Update=20=E7=88=AC=E8=99=AB=E5=85=A5?= =?UTF-8?q?=E9=97=A8.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...54\350\231\253\345\205\245\351\227\250.md" | 45 ++++++++++++++++++- 1 file changed, 44 insertions(+), 1 deletion(-) diff --git "a/chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" "b/chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" index 57ff24bc9..eaec81448 100644 --- "a/chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" +++ "b/chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" @@ -32,7 +32,7 @@ with open('0.md', 'w') as f: 打开`https://github.com/liupengyuan/python_tutorial/blob/master/chapter1/0.md`网页，在网页正文区域点击右键，选择查看源代码，会发现代码示例D-1确实已经获取/抓取这个网页的文本文件。至此，一个最简爬虫已经完成。 -3. 爬虫进阶 +3. 静态定向爬虫基础 - 任务1：抓取网易首页(www.163.com) 上的全部新闻，并以将所有新闻标题及新闻内容存入到一个文本文件中。 @@ -152,6 +152,49 @@ f.close() 经过以上几个步骤，我们成功地将`www.163.com`首页所有新闻标题及正文抓取并存储到文件中了。当然，由于我们并没有特别仔细分析页面，最终抓取的文件中，仍然含有一部分不太正确的地方，留给读者自行解决。 - 任务2：抓取有道词典查询词的双语例句 +通过在有道词典(dict.youdao.com)进行单词查询，发现对任意单词`xxxxx`，给出该单词翻译信息的页面`URL`为：`http://dict.youdao.com/w/xxxxx` + +由于我要抓取词`xxxxx`中查询结果的所有双语例句，则需要在页面下部的显示最后一个例句后，点击`更多双语例句`，这会更新当前页面的`URL`为：`http://dict.youdao.com/example/blng/eng/xxxxx/#keyfrom=dict.main.moreblng`，所有双语例句均在其中，因此，我们只对这个`URL`进行爬取分析即可。 + +输入以下代码，并观察执行结果（查看所保存文件内容）。 + +```python +# coding: utf-8 +# 示例代码 D-5 +import requests +from bs4 import BeautifulSoup + +def get_word_sents(word): + ''' +    根据给定的词汇返回该词汇(word)在有道词典查询页面中的所有双语例句 +    参数：word，类型：str +    返回：含有双语例句的list +    ''' + url = 'http://dict.youdao.com/example/blng/eng/{}/#keyfrom=dict.main.moreblng'.format(word) + r = requests.get(url) + soup = BeautifulSoup(r.text, 'html.parser') + + sents = [] + for model in soup.find_all(attrs={"class":"ol"}): + models = model.find_all('p') + for model in models: + try: + model['class'] + except KeyError: + sents.append(model.text.replace('\n','')) + return sents + +if __name__ == '__main__': + word = '葡萄' + sents = get_word_sents(word) + filename = '1.txt' + with open(filename, 'w', encoding = 'utf-8') as f: + for sent in sents: + f.write(sent+'\n') +``` +示例代码 D-5 中： +- + 4. 正则表达式 5. scrapy爬虫框架 From 82393e144b8bde9c434d2f87e2dc655e644a28ad Mon Sep 17 00:00:00 2001 From: liupengyuan Date: Sun, 1 Oct 2017 22:06:51 +0800 Subject: [PATCH 06/44] =?UTF-8?q?Update=20=E7=88=AC=E8=99=AB=E5=85=A5?= =?UTF-8?q?=E9=97=A8.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- "chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" | 1 + 1 file changed, 1 insertion(+) diff --git "a/chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" "b/chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" index eaec81448..150116827 100644 --- "a/chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" +++ "b/chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" @@ -152,6 +152,7 @@ f.close() 经过以上几个步骤，我们成功地将`www.163.com`首页所有新闻标题及正文抓取并存储到文件中了。当然，由于我们并没有特别仔细分析页面，最终抓取的文件中，仍然含有一部分不太正确的地方，留给读者自行解决。 - 任务2：抓取有道词典查询词的双语例句 + 通过在有道词典(dict.youdao.com)进行单词查询，发现对任意单词`xxxxx`，给出该单词翻译信息的页面`URL`为：`http://dict.youdao.com/w/xxxxx` 由于我要抓取词`xxxxx`中查询结果的所有双语例句，则需要在页面下部的显示最后一个例句后，点击`更多双语例句`，这会更新当前页面的`URL`为：`http://dict.youdao.com/example/blng/eng/xxxxx/#keyfrom=dict.main.moreblng`，所有双语例句均在其中，因此，我们只对这个`URL`进行爬取分析即可。 From 6f312fc1ff55f2eeda2c09f07901be47da9f9d4e Mon Sep 17 00:00:00 2001 From: liupengyuan Date: Sun, 1 Oct 2017 22:14:41 +0800 Subject: [PATCH 07/44] =?UTF-8?q?Update=20=E7=88=AC=E8=99=AB=E5=85=A5?= =?UTF-8?q?=E9=97=A8.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../\347\210\254\350\231\253\345\205\245\351\227\250.md" | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git "a/chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" "b/chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" index 150116827..4483dcc62 100644 --- "a/chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" +++ "b/chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" @@ -182,16 +182,16 @@ def get_word_sents(word): try: model['class'] except KeyError: - sents.append(model.text.replace('\n','')) +                sents.append(model.text.replace('\n','')+'\n) return sents if __name__ == '__main__': word = '葡萄' sents = get_word_sents(word) + filename = '1.txt' with open(filename, 'w', encoding = 'utf-8') as f: - for sent in sents: - f.write(sent+'\n') + f.writelines(sents) ``` 示例代码 D-5 中： - From 1f35bdce2d4887758f3a687b3bfe37770158a43c Mon Sep 17 00:00:00 2001 From: liupengyuan Date: Sun, 1 Oct 2017 23:25:21 +0800 Subject: [PATCH 08/44] =?UTF-8?q?Update=20=E7=88=AC=E8=99=AB=E5=85=A5?= =?UTF-8?q?=E9=97=A8.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../\347\210\254\350\231\253\345\205\245\351\227\250.md" | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git "a/chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" "b/chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" index 4483dcc62..5bedcae0f 100644 --- "a/chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" +++ "b/chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" @@ -194,8 +194,12 @@ if __name__ == '__main__': f.writelines(sents) ``` 示例代码 D-5 中： -- +- url = `'http://dict.youdao.com/example/blng/eng/{}/#keyfrom=dict.main.moreblng'.format(word)`，构造查询URL +- `model['class']`，取`model`的`class`键的值，此处是为了过滤掉非双语例句的文本，形如`class="example-via"`等包含的文本将被过滤掉。 +至此，给定任意词汇，已经可以抓取到其双语例句平行文本，并存到文件中。 + +对任意静态网页，均可以利用以上类似的方法，抓取感兴趣的内容。对更复杂的网页，需要对BeautifulSoap进一步进行了解，可参考其官方文档：`https://www.crummy.com/software/BeautifulSoup/bs4/doc/`，而有些网页很不规律，也不太容易用beautifulSoap对一些内容进行提取，这时，可以利用正则表达式进行模式匹配进行提取。 4. 正则表达式 5. scrapy爬虫框架 From 0b150483a9aa094f66c11624f6273b94f3eea492 Mon Sep 17 00:00:00 2001 From: liupengyuan Date: Tue, 3 Oct 2017 18:43:12 +0800 Subject: [PATCH 09/44] Create novel_site_list --- chapter4/novel_site_list | 5 +++++ 1 file changed, 5 insertions(+) create mode 100644 chapter4/novel_site_list diff --git a/chapter4/novel_site_list b/chapter4/novel_site_list new file mode 100644 index 000000000..6e92703d2 --- /dev/null +++ b/chapter4/novel_site_list @@ -0,0 +1,5 @@ +- `http://www.bookdown.com.cn/mingzhu/` +- `http://www.sogoutxt.com/wenxue/` +- `http://www.xbwx.com/a/wxlw/` +- `http://www.jjxsw.cc/category/wenxuejingdian.html` +- 待补充 From 4710b458f71446d4ac590d3b1fa9418976dd869c Mon Sep 17 00:00:00 2001 From: liupengyuan Date: Tue, 3 Oct 2017 18:43:34 +0800 Subject: [PATCH 10/44] Rename novel_site_list to novel_site_list.md --- chapter4/{novel_site_list => novel_site_list.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename chapter4/{novel_site_list => novel_site_list.md} (100%) diff --git a/chapter4/novel_site_list b/chapter4/novel_site_list.md similarity index 100% rename from chapter4/novel_site_list rename to chapter4/novel_site_list.md From f94cd58fb1cfbf491eb2761d785b4eb7ccc2659e Mon Sep 17 00:00:00 2001 From: liupengyuan Date: Tue, 3 Oct 2017 18:48:13 +0800 Subject: [PATCH 11/44] Update novel_site_list.md --- chapter4/novel_site_list.md | 1 + 1 file changed, 1 insertion(+) diff --git a/chapter4/novel_site_list.md b/chapter4/novel_site_list.md index 6e92703d2..da2db893d 100644 --- a/chapter4/novel_site_list.md +++ b/chapter4/novel_site_list.md @@ -1,3 +1,4 @@ +- 百度网盘、微盘等各类资源 - `http://www.bookdown.com.cn/mingzhu/` - `http://www.sogoutxt.com/wenxue/` - `http://www.xbwx.com/a/wxlw/` From 97bbbc52feb80ab6f147285ab4deccd9a43a0bbb Mon Sep 17 00:00:00 2001 From: liupengyuan Date: Wed, 4 Oct 2017 10:47:48 +0800 Subject: [PATCH 12/44] =?UTF-8?q?Update=20=E7=88=AC=E8=99=AB=E5=85=A5?= =?UTF-8?q?=E9=97=A8.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- "chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git "a/chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" "b/chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" index 5bedcae0f..2cd79f31d 100644 --- "a/chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" +++ "b/chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" @@ -199,7 +199,7 @@ if __name__ == '__main__': 至此，给定任意词汇，已经可以抓取到其双语例句平行文本，并存到文件中。 -对任意静态网页，均可以利用以上类似的方法，抓取感兴趣的内容。对更复杂的网页，需要对BeautifulSoap进一步进行了解，可参考其官方文档：`https://www.crummy.com/software/BeautifulSoup/bs4/doc/`，而有些网页很不规律，也不太容易用beautifulSoap对一些内容进行提取，这时，可以利用正则表达式进行模式匹配进行提取。 +对任意静态网页，均可以利用以上类似的方法，抓取感兴趣的内容。对更复杂的网页，需要对BeautifulSoap进一步进行了解，可参考其官方文档：`https://www.crummy.com/software/BeautifulSoup/bs4/doc/`，而有些网页很不规律特别是结构有问题的网页，也不太容易用beautifulSoap进行提取，这时，可以利用正则表达式，一般解析网页最经常的就是用BeautifulSoap和正则表达式联合解析。 4. 正则表达式 5. scrapy爬虫框架 From 300594c608461e6983e39a8347f2070509714167 Mon Sep 17 00:00:00 2001 From: liupengyuan Date: Wed, 4 Oct 2017 10:57:08 +0800 Subject: [PATCH 13/44] =?UTF-8?q?Update=20=E7=88=AC=E8=99=AB=E5=85=A5?= =?UTF-8?q?=E9=97=A8.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- "chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" | 2 ++ 1 file changed, 2 insertions(+) diff --git "a/chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" "b/chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" index 2cd79f31d..6e3733863 100644 --- "a/chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" +++ "b/chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" @@ -34,6 +34,8 @@ with open('0.md', 'w') as f: 3. 静态定向爬虫基础 +本小节中的爬虫是开始就指定了要抓取网页的地址（定向），要抓取的内容直接存在在网页源代码中（静态），因此称为静态定向爬虫。 + - 任务1：抓取网易首页(www.163.com) 上的全部新闻，并以将所有新闻标题及新闻内容存入到一个文本文件中。 首先我们打开网易首页，在页面内copy一个新闻标题到剪贴板，然后用鼠标右键点击页面，并选择`查看源代码`(如用chrome浏览器可选择`检查`，进入开发者模式)。，会看到一片很乱的代码(其实就是html标记的文本)，如果有一些html基础(推荐教程：http://www.w3school.com.cn/html/index.asp)， 看起来就不会那么乱了。 From 0c41b2f4f440effc570a6d519d13b7885298c94c Mon Sep 17 00:00:00 2001 From: liupengyuan Date: Wed, 4 Oct 2017 17:45:38 +0800 Subject: [PATCH 14/44] =?UTF-8?q?Update=20=E7=88=AC=E8=99=AB=E5=85=A5?= =?UTF-8?q?=E9=97=A8.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...54\350\231\253\345\205\245\351\227\250.md" | 26 +++++++++++++++++-- 1 file changed, 24 insertions(+), 2 deletions(-) diff --git "a/chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" "b/chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" index 6e3733863..55ed8bf7e 100644 --- "a/chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" +++ "b/chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" @@ -203,6 +203,28 @@ if __name__ == '__main__': 对任意静态网页，均可以利用以上类似的方法，抓取感兴趣的内容。对更复杂的网页，需要对BeautifulSoap进一步进行了解，可参考其官方文档：`https://www.crummy.com/software/BeautifulSoup/bs4/doc/`，而有些网页很不规律特别是结构有问题的网页，也不太容易用beautifulSoap进行提取，这时，可以利用正则表达式，一般解析网页最经常的就是用BeautifulSoap和正则表达式联合解析。 -4. 正则表达式 +4. 用正则表达式处理字符串 + +正则表达式，这个术语不太容易望文生义(没有去考证是如何被翻译为正则表达式的)，其实其英文为Regular Expression，直接翻译就是：有规律的表达式。这个表达式其实就是一个字符序列，反映某种字符规律，用(字符串模式匹配)来处理字符串。很多高级语言均支持利用正则表达式对字符串进行处理的操作。 + +4.1 示例 + +我们先看一个简单的正则表达式：E，所反映的字符规律是：以标签`

      `及`

      `包含的字符串(包含标签)。同时，为了便于观察是否反映这一规律，我们将以一个字符串作为输入，其中会有包含这种字符规律的部分，并利用正则表达式E，将符合这种规律的字符串进行提取。 + +```python +#coding: utf-8 +#示例程序 D-6 +import re + +line ='asdfjd

      adfasdQ sdkf74j

      okik' +print(re.search(r'

      .+

      ', line)) +``` +示例程序D-6中： +- `import re`，引入re模块，即引入正则表达式模块。 +- `re.search(r'

      .+

      ', line)`，调用re模块的search()函数，其中有两个参数，第一个参数是一个字符串r'

      .+

      '，反映字符模式，也就是正则表达式；第二个参数是目标字符串，即变量line。整个功能是要从目标字符串line中搜索符合模式r'

      .+

      '的字符串。 + +从示例程序D-6中，我们发现程序会将以标签`

      `及`

      `包含的字符串(包含标签)全部提取出来，因此可以合理猜测上面的正则表达式中`

      `及`

      `表示的仍然是自己，而`.+`可能表示多个字符(获取是任意字符，不限定个数)。 + +示例程序D-6也是 + 5. scrapy爬虫框架 -6. 扩展 From db46e0f05ab0639a6e52297b8424a2951e0f99de Mon Sep 17 00:00:00 2001 From: liupengyuan Date: Wed, 4 Oct 2017 17:47:05 +0800 Subject: [PATCH 15/44] =?UTF-8?q?Update=20=E7=88=AC=E8=99=AB=E5=85=A5?= =?UTF-8?q?=E9=97=A8.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- "chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git "a/chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" "b/chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" index 55ed8bf7e..0494ec9b7 100644 --- "a/chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" +++ "b/chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" @@ -221,7 +221,7 @@ print(re.search(r'

      .+

      ', line)) ``` 示例程序D-6中： - `import re`，引入re模块，即引入正则表达式模块。 -- `re.search(r'

      .+

      ', line)`，调用re模块的search()函数，其中有两个参数，第一个参数是一个字符串r'

      .+

      '，反映字符模式，也就是正则表达式；第二个参数是目标字符串，即变量line。整个功能是要从目标字符串line中搜索符合模式r'

      .+

      '的字符串。 +- `re.search(r'

      .+

      ', line)`，调用re模块的search()函数，其中有两个参数，第一个参数是一个字符串`r'

      .+

      '`，反映字符模式，也就是正则表达式；第二个参数是目标字符串，即变量line。整个功能是要从目标字符串line中搜索符合模式`r'

      .+

      '`的字符串。 从示例程序D-6中，我们发现程序会将以标签`

      `及`

      `包含的字符串(包含标签)全部提取出来，因此可以合理猜测上面的正则表达式中`

      `及`

      `表示的仍然是自己，而`.+`可能表示多个字符(获取是任意字符，不限定个数)。 From 6d9860cafc64f8572ed1c942def960cb903e00e8 Mon Sep 17 00:00:00 2001 From: liupengyuan Date: Thu, 5 Oct 2017 13:35:56 +0800 Subject: [PATCH 16/44] =?UTF-8?q?Update=20=E7=88=AC=E8=99=AB=E5=85=A5?= =?UTF-8?q?=E9=97=A8.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- "chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git "a/chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" "b/chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" index 0494ec9b7..cecfe2dc3 100644 --- "a/chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" +++ "b/chapter4/\347\210\254\350\231\253\345\205\245\351\227\250.md" @@ -225,6 +225,6 @@ print(re.search(r'

      .+

      ', line)) 从示例程序D-6中，我们发现程序会将以标签`

      `及`

      `包含的字符串(包含标签)全部提取出来，因此可以合理猜测上面的正则表达式中`

      `及`

      `表示的仍然是自己，而`.+`可能表示多个字符(获取是任意字符，不限定个数)。 -示例程序D-6也是 +示例程序D-6也是利用正则来进行字符串处理的一般范式：1. 观察字符串；2. 设计正则表达式；3. 进行匹配提取。 5. scrapy爬虫框架 From c7253c17517fbe4bff8cab21a1a6188aa7a2c998 Mon Sep 17 00:00:00 2001 From: liupengyuan Date: Sat, 7 Oct 2017 09:50:30 +0800 Subject: [PATCH 17/44] Delete task4.html --- chapter2/task4.html | 12201 ------------------------------------------ 1 file changed, 12201 deletions(-) delete mode 100644 chapter2/task4.html diff --git a/chapter2/task4.html b/chapter2/task4.html deleted file mode 100644 index c53bdb275..000000000 --- a/chapter2/task4.html +++ /dev/null @@ -1,12201 +0,0 @@ - - - -task4 - - - - - - - - - - - - - - - - - - - -
      -
      - -
      -
      -
      -
      -
      -

      4.求$ \sum_{i=1}^mi + \sum_{i=1}^ni + \sum_{i=1}^ki$

      -

      4.1 任务简介与分析
      -当然，这个任务仍然是不允许采用等差数列求和的公式来计算。任务没有什么难度，就是多次求和的过程。

      -

      4.2 软件复用

      -
        -
      • 键入如下代码并观察执行结果。
        • -
      -
      #求sigma1~m + sigma1~n + sigma1~k
-
-n = int(input('请输入第1个整数，以回车结束。'))
-i = 0
-total_n = 0
-while i < n:
-    i = i + 1
-    total_n = total_n + i
-
-m = int(input('请输入第2个整数，以回车结束。'))
-i = 0
-total_m = 0
-while i < m:
-    i = i + 1
-    total_m = total_m + i
-
-k = int(input('请输入第3个整数，以回车结束。'))
-i = 0
-total_k = 0
-while i < k:
-    i = i + 1
-    total_k = total_k + i
-
-print('最终的和是：', total_n + total_m + total_k)
-
      -

      好，一次闯关成功，任务解决，so easy，赶紧进入下一个任务。但等等，先别急，逐行敲入代码或者是拷贝剪贴代码再修改的读者，是否觉得这样的工作是无聊的重复劳动？到本节为止，上段代码中间的6行，算上这三次，至少已经重复输入了四次，如果有其他任务或者实践需要类似的求和，则我们还将重复输入。
      -人们不喜欢一直简单重复，人们编程的初衷之一就是要利用计算机从简单重复中解救自己。如何能对类似的代码进行重复利用呢？既可以避免重复，又可以一次写好到处实用呢？这种不“重复制造轮子”的思想与方法称为软件复用。

      -

      4.3 函数、参数与返回值

      -

      要是计算机看到compute_sum(1...end)或compute_sum(1, end)或干脆就compute_sum(end)就能求得从1到n的和就好了。
      -compute_sum是说明要做什么，(end)是仿照python的print()及input()函数的形式，参数指明加到end为止。等等，这不就是函数吗？只不过这个函数是我们自己想的而已，如果可以，那上面的程序就可以简化成下面这样:

      -
      n = int(input('请输入第1个整数，以回车结束。'))
-m = int(input('请输入第2个整数，以回车结束。'))
-k = int(input('请输入第3个整数，以回车结束。'))
-
-print('最终的和是：', compute_sum(m) + compute_sum(n) + compute_sum(k))
-
      -

      完美！看到这样的简化，不禁有些小激动了。这样非常清楚，非常符合逻辑。
      -computer_sum()虽然名称符合python命名规范与习惯，形式也与我们已经接触到的print()及input()函数相同，但这只是我们临时写的函数，函数是由我们自行定义的，一般称为自定义函数，不需要区分的时候，可简称函数。
      -到目前为止，python语言还不知道这个自定义函数是什么，因此无法解释执行它，那么，我们就教给python这个函数的定义，让python理解吧。但在真正尝试前，我们先从最简单的自定义函数开始。

      -
        -
      • 键入如下代码并观察执行结果。
        • -
      -
      # 第一个自定义函数
-def fake_print(content):
-    print(content)
-
      -

      一片空白，这不是你大脑的状态，因为大脑的状态也许是一堆问号---没有出错，但居然也没有其他任何反应(我一定写了假代码，笑)。
      -由于没有报错，毫无疑问，本段代码符合python语法，其中def是定义函数使用的关键字，也就是英文define的缩写；fake_print是作者刚刚起的函数名；括号内的content是函数参数。
      -函数定义的基本结构是：

      -
      def 函数名(参数表):
-    语句块
-
      -
        -
      • 函数名(参数表)，一般称为函数头；
        • -
      • 函数定义内部的语句块又称为函数体；
        • -
      • 参数表就是一系列参数，可以有0，1或多个参数，多个参数之间用,分隔。
        • -
      -

      函数定义好之后，只是能使python解释器知道有一个自定义函数，知道这个函数的名字，知道函数参数表，但是并不执行，也并不知道函数参数表内参数的具体值。 -因此，函数定义时的参数，一般称为形式参数，简称形参。
      -python解释器知道很多已经定义好的函数，比如input()，比如print()，但是如果我们不在代码中写出来，不去调用使用，函数就不会被执行。
      -这样，前面执行第一个自定义函数的代码，就只是让python解释器知道有一个自定义函数，名字是fake_print，有一个参数content，这些都属于函数头信息。
      -对自定义函数来说，仅让python解释器知道这个函数头的信息还不够，还需要在函数体实现函数的功能。这个功能是为以后函数被使用被调用时准备的。

      -
        -
      • 键入如下代码并观察执行结果。
        • -
      -
      # 函数定义
-def fake_print(content):
-    print(content)
-
-# 主程序
-fake_print('假装是print()函数。')
-
      -

      本段代码执行的大体流程是：

      -
        -
      • 对第一行代码def fake_print(content):进行处理，发现这是函数定义，于是保存此处地址，保存函数信息：函数名是fake_print，一个函数形参是content；
        • -
      • 跳过fake_print()的函数体定义，直接从函数体外的第一行开始执行；
        • -
      • 在执行到主程序[^1]第一行fake_print('假装是print()函数。')时，发现这里调用了fake_print()函数，有一个实参与函数定义的形参content相对应，其值为'假装是print()函数。'，于是将'假装是print()函数。'赋值给content；
        • -
      • 保存调用函数的地址，然后根据fake_print()函数的地址，跳转到def fake_print(content):,准备运行函数体。此时，形参content已有实际值，已经可以作为有实际值的变量在函数体内使用了。执行函数时，其形参值是由实际参数在函数调用时由对应的实参传递过来的，一般把这个参数传递过程称为参数传递。注意参数传递时，实参会赋值给相对应的形参，最一般的就是位置对应，因此，不需要形参与对应实参的变量名相同；
        • -
      • 运行函数体，调用print()函数打印'假装是print()函数。'；
        • -
      • 函数体执行完毕，程序返回到调用fake_print()函数地址的行末，继续执行；
        • -
      • 执行到程序尾，整个程序执行完毕。
        • -
      -

      由自定义函数的执行过程可知：如果自定义函数在当前文件中定义，则函数定义的代码必须写在函数调用之前。有些“神秘”函数比如print()及input()，则不受此限制。实际上print()及input()等函数由于太过常用，函数已经被预先定义及实现好，并内置在Python语言中，称为内置函数。
      -作为示例，fake_print()函数只完成了一个简单的功能，即与只带一个参数的print()函数功能相同。
      -回到求和问题中来，现在可以定义我们梦想中的compute_sum()函数了。

      -
        -
      • 键入如下代码并观察执行结果。
        • -
      -
      #函数定义
-def compute_sum(end):
-    i = 0
-    total_n = 0
-    while i < end:
-        i = i + 1
-        total_n = total_n + i
-
-# 主程序
-compute_sum(100)
-
      -

      很不幸，一片空白给兴致勃勃的我们当头一棒。但我们已经进行了函数定义，已经进行了函数调用，已经没有任何python语法错误提示了啊？
      -别急，让我们一起简单的分析下程序执行过程：

      -
        -
      • 第一行，python解释器保存函数头信息，保存调用地址，跳转到函数体；
        • -
      • 执行compute_sum(100)调用函数，将100传递给n；
        • -
      • 进入compute_sum函数体，从i=0开始执行，执行完毕时，应该能够得到total_n的值为5050；
        • -
      • 函数体结束，程序跳转到函数调用处末尾，即compute_sum(100)的)后面；
        • -
      • 函数结束。
        • -
      -

      好吧，我们没有把compute_sum()函数中计算的结果打印出来，了解了。

      -
        -
      • 键入如下代码并观察执行结果。
        • -
      -
      # 函数定义
-def compute_sum(end):
-    i = 0
-    total_n = 0
-    while i < end:
-        i = i + 1
-        total_n = total_n + i
-        print(total_n)
-
-# 主程序
-compute_sum(100)
-
      -

      很奇妙，打印出了一大波数字，最后一个确实是5050，我们犯了一个常犯错误(请自行思考，python中空格缩进太重要了)。

      -
        -
      • 键入如下代码并观察执行结果。
        • -
      -
      # 函数定义
-def compute_sum(end):
-    i = 0
-    total_n = 0
-    while i < end:
-        i = i + 1
-        total_n = total_n + i
-    print(total_n)
-
-# 主程序       
-compute_sum(100)
-
      -

      必须承认这次得到了预期的计算结果，5050。
      -可以预想到，每次调用compute_sum()函数时，随着参数的不同，可以计算出任意从1累加到end的值。但是先等等，我们怎么来完成我们最初的任务呢？
      -compute_sum()函数虽然能计算任意从1累加到end的值，但却无法将这些值累加。其实在compute_sum()函数中打印total_n并无必要，中间结果无需打印。
      -那么，这样试试：

      -
        -
      • 键入如下代码并观察执行结果。
        • -
      -
      # 函数定义
-def compute_sum(end):
-    i = 0
-    total_n = 0
-    while i < end:
-        i = i + 1
-        total_n = total_n + i
-
-# 主程序
-print(compute_sum(100))
-
      -

      屏幕显示：None，None是python语言中的一个特殊的值，可简单解释为没有对象或值[^2]。
      -无需沮丧，我们接近成功了。这首先说明函数自身也是有值的，称为函数值，目前compute_sum()函数的值是None；其次说明我们的思路符合python语言设计的意图，函数可以有值，更进一步，函数值可以是任意对象/值。
      - 键入如下代码并观察执行结果。

      -
      # 函数定义
-def compute_sum(end):
-    i = 0
-    total_n = 0
-
-    while i < end:
-        i = i + 1
-        total_n = total_n + i
-
-    return total_n
-
-主程序
-print(compute_sum(100))
-
      -

      终于得到了预期中的结果。
      -本段代码中的return是python的关键字。由return将total_n赋值给函数compute_sum()，此时compute_sum()的函数值即为total_n当前的值。由于在得到函数的值以后，程序要返回到函数调用的位置，同时，函数的值也被顺利‘带回’，因此函数调用后得到的函数值，一般称为函数返回值。
      -我们顺利的完成了真正意义上的第一个实用函数(前面那个fake_print原来真是假的，笑)，但我们并没有特别激动。
      -一切本该如此，水到渠成，顺理成章。

      -

      4.4 拓展与总结 -（定义、以及图形等均可在这里）

      -
        -
      • return语句。
        -一个函数根据需要可以有0,1或多个return语句。
        -键入如下代码并观察执行结果。
        • -
      -
      def example_0(number):
-    number += 10
-
-def example_1(number):
-    number += 10
-    return
-
-# 主程序
-n=100
-print('0:',example_0(n))
-print('1:',example_0(n))
-
      -

      没有return语句或者return语句后面没有参数，调用函数后函数的值都是None。因此，在程序不需要返回值的时候，可以不用return语句。
      -键入如下代码并观察执行结果。

      -
      def example_multi(number):
-    return '多个返回值', number*10, number, number/10
-
-
-# 主程序
-n=100
-print(example_0(100))
-
      -

      可见可以利用return语句返回多个返回值[^3]，且返回值的类型可以不同。
      -键入如下代码观察执行结果。

      -
      def zero_flag(number):
-    if number < 0:
-        return -1
-    elif number == 0:
-        return 0
-    else:
-        return 1
-
-# 主程序
-print('First:', zero_flag(100))
-print('Second:', zero_flag(0))
-print('Third:', zero_flag(-100))
-
      -

      通过以上代码我们可以了解，在函数中，运行到return语句时，该return语句除了提供函数返回值以外，同时还会使程序跳出函数体(不会执行return语句之后函数体内的语句)，直接返回到函数调用处。因此，即使一个函数有多个return语句，每次调用时，最多仅会执行某一个return语句。

      -
        -
      • 函数调用与作用域初探
        • -
      -

      我们已经实现了自定义函数，并在主程序中调用了自定义函数。在自定义函数中，多次对内置函数进行了调用。
      -一个自定义函数也可以调用另一个自定义函数。例如：

      -
      # 自定义函数调用示例
-
-def my_abs(number):
-    if number < 0:
-        number = -number
-
-    return number
-
-def compute_sum(end):
-    i = 0
-    total_n = 0
-    number = my_abs(end)
-
-    while i < number:
-        i = i + 1
-        total_n = total_n + i
-
-    return total_n
-
-
-# 主程序
-i=1000
-print('1+2+...+', i, '=',compute_sum(i))
-
      -

      上述代码中，自定义求和函数compute_sum()调用自定义函数my_abs()，求得传入参数end的绝对值来作为累加的终点值，就此实现了自定义函数之间的调用。
      -细心的读者可能已经注意到一件事，在my_abs()函数与compute_sum()函数内，有相同的变量名number。在compute_sum()函数与主程序中，有相同的变量名i。但是注意，这几个名字相同的变量，并非同一个变量。

      -
        -
      • python程序可根据函数定义(0、1或多个)及主程序划分为多个区域，每个区域内都的变量(对象)的在各个区域的使用(作用)有一定规则限制，因此这种区域称作作用域，又分为函数作用域(对应函数体)与全局作用域(对应主程序)。
        • -
      • 每个函数内部的变量只属于该函数的函数体，主程序无法使用。例：
        • -
      -
      #函数作用域示例1
-
-def inner(n):
-    n = 10
-    print('inner', n)
-
-#主程序
-inner(n)        #报错
-
      -

      将提示句法错误：name 'n' is not defined，即n没有定义。虽然在主程序前面的inner()函数定义中，变量n被赋值10，但主程序无法使用inner()函数中的变量n，因此报错。

      -
        -
      • 每个函数内部的变量，其他函数也无法使用。例：
        • -
      -
      #函数作用域示例2
-
-def inner1(n):
-    n = 10
-    print('inner', n)
-
-def inner2():
-    print(n)
-
-#主程序
-inner1(10)
-inner2()        #报错
-
      -

      所提示的出错信息与前一段示例类似。
      -因此函数内部变量一般称为局部变量。

      -
        -
      • 自定义函数能够读取并使用主程序(同一个程序中的)中变量的值，因此主程序中的变量，也称为全局变量。例：
        • -
      -
      #函数作用域示例3
-
-def inner():
-    print('inner', n)
-
-#主程序
-
-n=100
-inner()
-
      -

      从示例3代码中可以看到，inner()函数内部并没有对n进行定义，但是能够从全局变量n中得到值100。但是自定义函数一般不能[^4]改变全局变量的值。例：

      -
      #函数作用域示例4
-
-def inner():
-    n=10
-    print('inner', n)
-
-#主程序
-
-n=100
-inner()
-print('outer', n)
-
      -

      可知，虽然在自定义函数内部，将n赋值了10，但是在主程序中的全局变量n的值仍然是100。
      -这个是怎么回事呢？是调用自定义函数inner()过程中，，全局变量n的值由100被赋值为10，然后从inner()函数返回的时候，n又被改回100了嘛？这似乎合理，但请看：

      -
      #函数作用域示例5
-
-def inner():
-    print('inner', n)
-    n=10
-
-#主程序
-
-n=100
-inner()
-print('outer', n)
-
      -

      程序出现错误，local variable 'n' referenced before assignment，即局部变量n赋值前被使用/引用。示例4与5中，变量n被定义了(赋值就是一种定义)，因此其中的n虽然与全局变量n同名，但其实是局部变量。
      -因此，示例4中使用的n是局部变量，其值为10，与全局变量n(值为100)无关；示例5中使用的n也是局部变量，在打印之前，并没有值，因此出错。只有示例3，由于函数内部没有对n进行定义，因此使用的是全局变量n的值。
      -最后要说明的一点是，对在自定义函数中与全局变量同名的变量，只要在自定义函数内部对该同名变量进行了任何形式[^4]的赋值，无论在该函数体内的哪个位置，该同名变量即成为局部变量，与同名全局变量无关。以下是更多示例：

      -
      #函数作用域示例6
-
-def inner():
-    print('inner', n)
-    n=n
-
-#主程序
-
-n=100
-inner()
-print('outer', n)
-
      -
      #函数作用域示例7
-
-def inner():
-    n=n
-    print('inner', n)
-
-#主程序
-
-n=100
-inner()
-print('outer', n)
-
      -

      最后一个示例比较有趣，请读者自行分析。

      -
        -
      • python内置函数
        • -
      -

      python语言解释器内置了一大批函数，可以直接使用。函数具体可见：https://docs.python.org/3/library/functions.html

      -

      本小节先介绍几个常用内置函数，后续还会陆续介绍。

      -
        -
      • abs(x)
        • -
      -

      Return the absolute value of a number. The argument may be an integer or a floating point number. If the argument is a complex number, its magnitude is returned.

      -
        -
      • divmod(a, b)
        • -
      -

      Take two (non complex) numbers as arguments and return a pair of numbers consisting of their quotient and remainder when using integer division. With mixed operand types, the rules for binary arithmetic operators apply. For integers, the result is the same as (a // b, a % b).

      -
        -
      • round(number[, ndigits])
        • -
      -

      Return the floating point value number rounded to ndigits digits after the decimal point. If ndigits is omitted or is None, it returns the nearest integer to its input.

      -

      4.5 完整代码

      -
      #求sigma1~m + sigma1~n + sigma1~k
-
-def compute_sum(end):
-    i = 0
-    total_n = 0
-
-    while i < end:
-        i = i + 1
-        total_n = total_n + i
-
-    return total_n
-
-n = int(input('请输入第1个整数，以回车结束。'))
-m = int(input('请输入第2个整数，以回车结束。'))
-k = int(input('请输入第3个整数，以回车结束。'))
-
-print('最终的和是：', compute_sum(m) + compute_sum(n) + compute_sum(k))
-
      -

      4.6 实践与练习

      -
        -
      • 练习 1：仿照求$ \sum_{i=1}^mi + \sum_{i=1}^ni + \sum_{i=1}^ki$的完整代码，写程序，可求m!+n!+k!
        • -
      • 练习 2：写函数可返回1-1/3+1/5-1/7...的前n项的和。在主程序中，分别令n=1000及100000，打印4倍该函数的和。
        • -
      • 练习 3：将task3中的练习1及练习4改写为函数，并进行调用。
        • -
      • 挑战性练习：写程序，可以求从整数m到整数n累加的和，间隔为k，求和部分需用函数实现，主程序中由用户输入m，n，k调用函数验证正确性。
        • -
      -

      [^1] : 主程序可简单理解为指一个程序代码中第一层次的语句，该语句前没有空格，同时也不是函数定义语句。

      -

      [^2] : None这个对象不太容易清晰定义，举例进行说明会更容易理解。假设我们数东西(对象)，如果数苹果，可以说1个苹果，2个苹果。当没有的时候，我们不说这是0个苹果，因为这时没有苹果这个对象存在；不能说这是0，因为我们不一定是在数数；也不能说没有苹果，因为我们不一定数什么我们只能说'没有'或'没有东西'，这时候没有具体的对象和数。但是我们知道，如果确定了要数任何对象A，我们就可以说'没有A'。所以，python语言中，None可以被赋值给如函数及变量等任何对象。

      -

      [^3] : 此处多个返回值是在()里面，表示是python中元组(tuple)类型，将在后续章节介绍。

      -

      [^4] : 用global关键字，可以在函数内部改变全局变量的值，相对特殊，将在后续章节介绍。

      - -
      -
      -
      -
      -
      - - - - - - From 4f1c20695013539bc7067c103117ee4fd57abd77 Mon Sep 17 00:00:00 2001 From: liupengyuan Date: Sun, 8 Oct 2017 12:42:21 +0800 Subject: [PATCH 18/44] Add files via upload --- chapter4/pandas Tutorial (Series).ipynb | 1957 +++++++++++++++++++++++ 1 file changed, 1957 insertions(+) create mode 100644 chapter4/pandas Tutorial (Series).ipynb diff --git a/chapter4/pandas Tutorial (Series).ipynb b/chapter4/pandas Tutorial (Series).ipynb new file mode 100644 index 000000000..6c37d046c --- /dev/null +++ b/chapter4/pandas Tutorial (Series).ipynb @@ -0,0 +1,1957 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Pandas统计分析入门（1）\n", + "- ## Pandas及相关python库简介\n", + "- ## 一维数据统计分析(Series基础)\n", + "- ## 二维数据统计分析(DataFrame基础)\n", + "- ## Visualization基础\n", + "---\n", + "## Pandas 简介\n", + "\n", + " pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. It is already well on its way toward this goal.\n", + "\n", + "## Numpy 简介\n", + "\n", + " NumPy is the fundamental package for scientific computing with Python. It contains among other things:\n", + "- a powerful N-dimensional array object\n", + "- sophisticated (broadcasting) functions\n", + "- tools for integrating C/C++ and Fortran code\n", + "- useful linear algebra, Fourier transform, and random number capabilities\n", + "\n", + "## Matplotlib 简介\n", + "\n", + "Matplotlib是一个Python的2D绘图库，它以各种硬拷贝格式和跨平台的交互式环境生成出版质量级别的图形。\n", + "通过Matplotlib，开发者可以仅需要几行代码，便可以生成绘图，直方图，功率谱，条形图，错误图，散点图等。\n", + "\n", + "## Seaborn 简介\n", + "\n", + "Seaborn is a library for making attractive and informative statistical graphics in Python. It is built on top of matplotlib and tightly integrated with the PyData stack, including support for numpy and pandas data structures and statistical routines from scipy and statsmodels.\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- ## 程序头部引入" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "from pandas import Series, DataFrame\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "以上引入各类利用pandas做数据分析可视化的模块，一般可作为每次数据分析项目/任务的起始代码，其中：\n", + "- `%matplotlib inline`是jupyter notebook的一个魔法命令，能够使基于matplotlib的绘图直接显示在网页中\n", + "- `from pandas import Series, DataFrame`，从pandas中引入最为常用的两个对象：Series及DataFrame\n", + "\n", + "**注意**，在以上的引入中，类似`pd`、`np`、`plt`及`sns`均为约定俗成的习惯性名称，**不建议**更改。\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 一维数据统计分析(Series基础)\n", + "\n", + "- 数据的描述、分析、可视化展示、概括性度量、输入与输出" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. Series对象及数据最基本展示\n", + "\n", + "我们将以一个词频统计结果的实例，进行介绍。教程中的各个代码段，请自行建立新的python程序，依次键入并顺序执行，观察执行结果。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "words_freq = [200,300,400,350,390,600,900,400,300,120]\n", + "freq_dict = {'天长地久':words_freq}\n", + "total_words_freq = [12345000,23456000,22333000,45632000,11144000,65433000,44444000,55555000,34522000,55566000]\n", + "years = [2006,2007,2008,2009,2010,2011,2012,2013,2014,2015]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "以上代码中：\n", + "- `words_freq`，是“天长地久”在2006-2015年10年之间的频次list\n", + "- `freq_dict`，键为“天长地久”，值为其历时频次的list\n", + "- `total_words_freq`，是每年统计文本语料的总字数list\n", + "- `years`，是年份list" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s = Series(words_freq)\n", + "s" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "利用list来初始化Series对象。Series是pandas内置的处理一维数据的数据类型。\n", + "- 第一列为Series对象的索引(index)，默认为数字索引；\n", + "- 第二列为Series对象的值(value)\n", + "- 最后一行说明，Series对象的值为int64类型" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s = Series(freq_dict['天长地久'], index = years)\n", + "s" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "可以在初始化Series对象时，同时指定索引(index)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s.name = '2006-2015'\n", + "s.index.name = 'year'\n", + "s" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "还可以指定Series对象的名称(name)，指定其索引(index)的名称(name)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Series对象可利用pandas内置的plot()直接绘图，默认为折线图。\n", + "\n", + "**折线图**多用于展示**数值型数据**，特别是用于展示历时数据。\n", + "\n", + "统计学中，依据计量尺度，一般可将数据分为三种类型：\n", + "- 分类数据\n", + "- 顺序数据(定序数据)\n", + "- 数值型数据(定量数据)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s.plot(kind='bar')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "plot()函数的参数`kind`，可以指定plot()函数所绘制图形的种类，`'bar'`为柱状图。也可以采用这种形式：`s.plot.bar()`，效果与指定kind参数为bar类似。\n", + "\n", + "**柱状图**用宽度相同的柱的高度或长短来表示数据多少。\n", + "\n", + "柱状图一般用于展示**分类数据**，展示不同类别数据的多少(可将以上数据的每个年份理解为不同类别)。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s.plot(kind='barh')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "'barh'为条形图，与柱状图类似。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s.plot('pie',figsize=(6,6))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "'pie'为饼图。\n", + "**饼图**使用圆形及圆内扇形的角度表示数值大小的图形。\n", + "\n", + "一般也用于展示**分类数据**，用来表示各分类部分数据占全部数据的比例(频率分布)。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s.plot('pie',figsize=(6,6))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "可利用plot()函数的`figsize`参数，设定图形的大小(长与宽)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. 数据基本分析、度量与展示\n", + "\n", + "**2.1 归一化**\n", + "\n", + "**归一化**一般要使数值的绝对值变成**相对值**，将数值映射到[0,1]区间上。\n", + "\n", + "在本例中，由于每个年度的语料大小不同，单纯观察每个年度某个词汇出现的频次(绝对值)，无法确定其出现的相对高低(相对值)。\n", + "\n", + "因此可将频次归一化，即某词汇每年出现的次数/每年语料的总字数。这样，该词汇就可以利用频次归一化的数值进行横向比较了。\n", + "\n", + "在实际中，也可以将频次归一化的结果乘以一个较大的整数。在本例中，可乘以10000000，表示语料中每10000000字出现该词汇的频次。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "#python实现\n", + "def norm_freq(freqs, total_freqs, per = 1):\n", + " return [per * freq/total_freq for freq, total_freq in zip(freqs, total_freqs)]\n", + "\n", + "per = 10000000\n", + "s_norm = norm_freq(freq_dict['天长地久'], total_words_freq, per = 10000000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "上面的代码用python实现了将每年的频次归一化为每年每一千万字该词汇出现的频次。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s_norm = Series(s_norm, index = years)\n", + "s_norm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "利用归一化处理后的结果初始化一个Series对象s_norm。注意，此时value的数据类型自动转换为float。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#pandas的Series运算\n", + "s_norm = per * s / total_words_freq\n", + "s_norm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "也可以直接利用pandas的序列运算进行归一化。运算结果与python编写代码一直，但是更简洁。\n", + "- `s_norm = per * s / total_words_freq`，其中`s`为Series类型，`total_words_freq`为list类型，直接相除运算的结果是将s中每个对应的值除以list中每个值。这种向量/矩阵运算是numpy及pandas中非常常见的情况，一般被称为**矢量化操作**。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s_norm.plot()\n", + "s.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "再次绘图，并将两根折线展示在一个图上。\n", + "\n", + "可以看出，归一化前，峰值出现在2012年；归一化后，峰值出现在2010年。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**1.2 求平均值**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#python实现\n", + "freq_mean = sum(freq_dict['天长地久'])/len(freq_dict['天长地久'])\n", + "freq_mean" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "计算均值的python代码。\n", + "\n", + "除非特别指出，一般平均值指的就是**算数平均值**。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#pandas内置求均值函数\n", + "s.mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "利用内置均值函数mean()更为简洁。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s_mean = Series([freq_mean]*10, index = years)\n", + "s_mean" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "声明一个每年数值均为均值的Series对象s_mean。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s.plot()\n", + "s_mean.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "将频次均值与各年频次画在一个图中" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**1.3 样本方差、标准差、标准分及变异系数**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "**方差**（variance)是在概率论和统计方差衡量随机变量或一组数据时离散程度的度量。\n", + "- 统计中的方差（样本方差）是每个样本值与全体样本值的平均数之差的平方值的平均数。\n", + "\n", + "**总体**：包含所研究的全部个体(数据)的集合。\n", + "\n", + "**样本**：从总体中抽取的一部分元素的集合。\n", + "\n", + "**样本容量**：构成样本的元素的数量称为样本容量(size)。\n", + "\n", + "样本方差计算公式为：\n", + "$$s^2 = \\frac{1}{n-1}\\sum_{i=1}^{n}(x_i-\\widetilde x)^2$$\n", + "\n", + "注意样本方差的公式中，分母为`n-1`。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#python实现方差\n", + "def variance(freqs):\n", + " mean = sum(freqs)/len(freqs)\n", + " return sum([(freq-mean)**2 for freq in freqs])/(len(freqs)-1)\n", + "\n", + "freq_var = variance(freq_dict['天长地久'])\n", + "freq_var" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "上述代码用python实现了求样本方差。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#pandas内置方差\n", + "s.var()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "而pandas内置求方差的函数var()更为简洁。· " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "**标准差**：标准差是方差的算术平方根。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#pandas内置标准差\n", + "s.std()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pandas内置std()函数,可得到序列s的标准差。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**标准分**：变量值与平均数的差除以标准差后的值，也称为**z分数**(z score)。\n", + "\n", + "标准分的计算公式为：\n", + "$$z_i=\\frac{x_i-\\widetilde x}{s}$$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "(s-s.mean())/s.std()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "上述代码可得到s中所有数值的标准分，存放在一个Series对象中。\n", + "标准差的**经验法则**(当一组数据对称分布时)：\n", + "- 约68%的数据在均值附近正负1个标准差内\n", + "- 约95%的数据在均值附近正负2个标准差内\n", + "- 约99%的数据在均值附近正负3个标准差内\n", + "在正负3个标准差以外的数据，称为**离群点(outlier)**。\n", + "\n", + "**切比雪夫不等式**：对任意分布的一组数据，至少有(1-1/k^2)的数据落在正负k个标准差以内(k>1)。" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "**变异系数**(离散系数)：一组数据的标准差与该组数据均值之比。公式为：\n", + "$$v_i=\\frac{s}{\\widetilde x}$$\n", + "\n", + "该系数是测度离散程度的相对统计量，主要用于比较不同演变数据的离散程度(特别对均值差异较大的数据组)。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#变异系数\n", + "s.std()/s.mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "变异系数可由标准差/均值直接得到。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**1.4 中位数、分位数、轴距与箱型图**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**中位数**(median)：中数是按顺序排列的一组数据中居于中间位置的数，即在这组数据中，有一半的数据比他大，有一半的数据比他小。如果观察值有偶数个，通常取最中间的两个数值的平均数作为中位数。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s_norm.median()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pandas内置了median()函数，即求中位数函数。" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "**分位数**是将总体的全部数据按大小顺序排列后，处于各等分位置的变量值。\n", + "- 将全部数据分成相等的两部分，它就是中位数；如果分成四等分，就是四分位数；八等分就是八分位数。\n", + "- 四分位数也称为四分位点，它是将全部数据分成相等的四部分，其中每部分包括25%的数据，处在各分位点的数值就是四分位数。\n", + "- 四分位数有三个，第一个四分位数就是通常所说的四分位数，称为下四分位数，第二个四分位数就是中位数，第三个四分位数称为上四分位数，分别用Q1、Q2、Q3表示。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s_norm.quantile(0.25)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s_norm.quantile(0.75)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pandas内置的quantile()函数可以求任意分位数，参数为0.25即Q1，参数为0.75即Q3。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s_norm.plot(kind='box')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "参数kind为`box`时，对应的就是**箱型图**。\n", + "\n", + "箱型图中间的箱型一般由三根线组成，由下至上分别对应Q1，Q2，Q3；而箱型以外的上下两根线，分别对应最大值与最小值(不含离群点)\n", + "\n", + "箱型图一般用于展示数值型数据，有助于对数据的整体分布有只管了解，特别有利于对离群点/异常点的观察。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#Series箱型图离群点/异常点显示，利用seaborn\n", + "sns.boxplot(s_norm)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "由于pandas内置的箱型图默认不含离群点，可直接利用seaborn中的boxplot函数，为Series对象绘制带离群点的箱型图。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**1.5 样本的累积和**（累积频数与累积频率）\n", + "\n", + "**累积频数**(cumulative frequencies)，将各有序类别或组的频数逐级累加起来的频数。\n", + "\n", + "**累积频率**(cumulative percentages)，将各有序类别或组的百分比逐级累加起来。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s.cumsum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pandas内置了cumsum()函数，可以直接得到累积频数。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s.cumsum().plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "利用cumsum()得到的仍然是Series对象，可以直接绘图。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s_ratio = s/s.sum()\n", + "s_ratio.cumsum().plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "累积频率(累积百分比)可以类似的得到。\n", + "\n", + "`sum()`为内置的求Series对象中value和的函数。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**1.6 最大值、最小值、众数及总体描述**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s.max()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "内置最大值函数max()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s.min()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "内置最小值函数min()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s.mode()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**众数**(mode)：一组数据中出现次数最多的数据值。一般用于测度分类数据的集中趋势，在数据量较大的时候有意义。\n", + "Pandas内置mode()函数，可以直接得到众数，注意众数可能不唯一，如此时s的众数有两个。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "Pandas的descibe()函数，可以给出汇总与描述统计的常用值。其中count为数据个数。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**1.7 随机数、直方图、峰度与偏度**(公式以后有时间补充)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "np.random.seed(66)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "调用np中random模块的seed()方法，生成一个随机数种子，在这个种子下，每次第一次生成的随机数均相同。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "arr1 = np.random.randn(10000)\n", + "arr1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "利用random的randn()函数，生成一个numpy的array类型的对象，大小为10000，可理解为长度为10000的数组(所有数据元素类型相同)。\n", + "\n", + "数组内数据分布为正态分布。randn即random normal。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "arr2 = np.random.randn(50)\n", + "arr2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "与前类似，生成了一个长度为50的array，数据为正态分布。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "s_1 = Series(arr1)\n", + "s_2 = Series(arr2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Numpy的array类型可以直接初始化为Series类型的对象。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s_1.plot(kind='hist')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**直方图**(histogram)：又称质量分布图，是数值数据分布的精确图形表示。\n", + "- 将值的范围分段，即将整个值的范围分成一系列间隔(一般要相等)\n", + "- 计算每个间隔中有多少值\n", + "\n", + "当plot()函数的kind参数为hist时，即代表直方图。\n", + "\n", + "此外，Pandas内置hist()函数，可对Series对象直接绘制直方图：`s_norm.hist()`。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s_1.plot(kind='hist',bins=50)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot()函数有一个重要参数`bins`，指定了间隔个数。此处将间隔数设为50." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s_1.plot(kind='kde')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**核密度估计图**(Kernel Density Estimation, KDE)：采用平滑的峰值函数(“核函数”)来拟合观察到的数据点，从而对真实的概率分布曲线进行模拟。\n", + "Pandas的plot()函数中，参数kind的值为kde时，即可绘制KDE图。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "sns.distplot(s_1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Seaborn中内置的distplot()函数，可以直接对Series对象绘制直方图及KDE图的合一图。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "sns.distplot(s_2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "类似地，可以为s_2绘制histogram及kde图。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s_1.skew(),s_2.skew(),s_1.kurt(),s_2.kurt()," + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**偏度**(Skewness)、偏态、偏态系数，是统计数据分布偏斜方向和程度的度量，是统计数据分布非对称程度的数字特征。正态分布的偏度为零。\n", + "- Skewness=0 分布形态与正态分布偏度相同\n", + "- Skewness>0 正偏差数值较大，为正偏或右偏。长尾巴拖在右边。\n", + "- Skewness<0 负偏差数值较大，为负偏或左偏。长尾巴拖在左边。\n", + "\n", + "**峰度**(Kurtosis)、峰态、峰态系数，是描述总体中所有取值分布形态陡缓程度的统计量。正态分布的峰度为3(也有很多将计算得到的峰度-3，而使正态分布峰度为零的做法)。\n", + "- Kurtosis=3(或0) 分布形态与正态分布峰度相同\n", + "- Kurtosis>3(或0) 正偏差数值较大，尖峰，厚尾。\n", + "- Kurtosis<3(或0) 负偏差数值较大，扁峰，薄尾。\n", + "\n", + "本例中，s_1比s_2更接近于正态分布。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3. Series类型基本操作整理\n", + "**3.1 创建与查看**\n", + "\n", + "创建一个Series的基本方法是：s = Series(data, index=index)。其中，data指代许多不同的数据类型：\n", + "- a Python dict\n", + "- an ndarray\n", + "- a scalar value (like 6)\n", + "\n", + "index指一个标签序列，一般用list类型。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3.1.1 利用list创建Series**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s = Series([x*x for x in range(100)])\n", + "s" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 第一行利用一个list，创建了一个Series对象s\n", + "- 第二行查看该对象。\n", + "- 该对象由两部分组成，第一列为index，第二列为values。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "数据太长，可用head()函数查看前几个数据。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s.tail(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "也可用tail()函数查看最后几个数据，head()及tail()函数内可放整型参数，代表列出的数据个数。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s.values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "查看序列s的值" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "type(s.values)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 查看s.values的类型\n", + "- Series对象的values的类型确实为numpy的array（数组）类型，即ndarray(n维数组)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s.index" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 查看s的index(索引)的值" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "type(s.index)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 查看s.index的类型\n", + "- Series对象的索引在未指定时，为pandas内置的RangeIndex类型，类似于python的range类型。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s2 = Series([x*x for x in range(26)], index=set('abcdefghijklmnopqrstuvwxyz'))\n", + "s2.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 利用list创建Series对象时，可用index参数指定一个list或set对应索引。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "type(s2.index), s2.index" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 指定索引后，Series对象的索引是pandas内置的Index类型" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3.1.2 利用dict创建Series**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "data = {'喜欢':3000,'爱':7000,'讨厌':1000,'恨':500}\n", + "s3 = Series(data)\n", + "s3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 可用dict创建Series对象，则字典的键自动成为index" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s3 = Series(data, index = ['a','b','c','爱'])\n", + "s3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 初始化时，也可以额外指定index。但是如果指定的index找不到对应的值，则该index对应值设为NaN（Not a Number）" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3.1.3 利用标量创建Series对象**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s0 = Series(5, index = ['a','b','c','d','e'])\n", + "s0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 利用标量创建Series对象时，对象的值将均为标量的值" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3.1.4 利用Series对象创建Series对象**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s4 = Series(s3)\n", + "s4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 这是最简单的利用Series对象创建Series对象的代码" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s5 = Series(s4, index = ['喜欢','爱','讨厌','中'])\n", + "s5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 与利用dict初始化类似，也可以额外指定index。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3.2 访问Series对象的元素**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s4[0], s4['爱'], s4.爱" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 利用位置索引或者index均可以访问对应元素\n", + "- 还可以利用index的值作为属性来访问对应元素" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s4[0:2]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s4['喜欢':'爱']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 位置索引及index的切片访问也适用，但利用index切片是包含末端的" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s4.index" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 可以利用index属性访问Series对象的索引" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s4.values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 可以利用values属性访问Series对象的值" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "for k, v in s4.items():\n", + " print(k, v)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "for k in s4.keys():\n", + " print(k)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "for v in s4:\n", + " print(v)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 可以类似dict一样迭代访问Series对象的各个元素" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "for k in s4.index:\n", + " print(k)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "for v in s4.values:\n", + " print(v)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 也可以利用index与values属性迭代访问" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3.3 修改、删除、排序等基本运算**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s4[0] = 10000\n", + "s4" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s4['爱'] = 4078\n", + "s4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Series对象内的值可以直接根据位置或index被赋值（修改）" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s4[0:3] = 666\n", + "s4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 可以切片式赋值" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s4.index = ['a', 'b', 'c', 'd']\n", + "s4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Series对象的index可以直接被赋值（修改），注意新的index的长度要与Series对象中index的长度一致。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s4 = s3.reindex(['喜欢','爱','讨厌','中', 'e'])\n", + "s4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 可用reindex方法来更改index，找不到对应值的index，则其值为NaN。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s5 = s4.drop('e')\n", + "s4" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 可利用drop函数删除指定索引及值，生成新的Series对象，但原series对象不变。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s4+s5" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s4-s5" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s4*s5" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s4/s5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Serires对象可以直接进行四则运算，运算规则是对应索引的值进行运算，整体运算结果仍然是Series对象。\n", + "- 如果索引值无法对应，则结果为NaN。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s4.sort_values()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 按照values（值）进行排序，按值排序时，NaN将排在尾端" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s4.sort_index()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 按照index（索引）进行排序" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s4.sort_values(ascending=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 将ascending参数指定为False，则变为倒序排序" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3.4 处理NaN值**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s5 = s4.fillna(1)\n", + "s5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 可以利用内置fillna()函数，将NaN值替换为指定数值" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s6 = s4.reindex(['喜欢','爱','不讨厌','中'], fill_value = 0)\n", + "s6" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 可在reindex方法内利用fill_value参数，指定值来替换在重新索引时新出现的NaN值，\n", + "- 注意，fill_value参数与不会对原有NaN值进行替换。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s4.add(s6)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s4.add(s6, fill_value=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 两个Series对象相加可用add方法，设置fill_value参数的值，替换非对齐index部分的NaN值。fill_value参数不会对运算前的NaN进行填充。\n", + "- 类似还有sub，div，mul方法，分别对应减法、除法及乘法。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3.5 输出**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s.plot()\n", + "plt.savefig('test.jpg')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 利用内置的plot()函数展示图片后，可利用plt的savefig()函数将图片保存为文件输出。\n", + "- 可以查看当前目录下的test.jpg文件，与当前显示一致。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "s.to_csv('test.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 利用to_csv()函数，可将Series对象输出为.csv文件\n", + "- 可在当前目录下，找到test.csv，双击打开查看" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 3795204028bd6b534fdaedec636201243f512313 Mon Sep 17 00:00:00 2001 From: liupengyuan Date: Sun, 8 Oct 2017 13:41:39 +0800 Subject: [PATCH 19/44] Add files via upload --- chapter4/pandas Tutorial (Series).ipynb | 136 +++++++++++++++++++++++- 1 file changed, 134 insertions(+), 2 deletions(-) diff --git a/chapter4/pandas Tutorial (Series).ipynb b/chapter4/pandas Tutorial (Series).ipynb index 6c37d046c..3a57c8b1d 100644 --- a/chapter4/pandas Tutorial (Series).ipynb +++ b/chapter4/pandas Tutorial (Series).ipynb @@ -85,7 +85,9 @@ "source": [ "### 1. Series对象及数据最基本展示\n", "\n", - "我们将以一个词频统计结果的实例，进行介绍。教程中的各个代码段，请自行建立新的python程序，依次键入并顺序执行，观察执行结果。" + "- 将以一个词频统计结果的实例，进行介绍。\n", + "- 教程中的各个代码段，请自行建立新的python程序，依次键入并顺序执行，观察执行结果。\n", + "- Series是pandas最重要最基础的数据对象，可用来表示数据表中的一列或一行。" ] }, { @@ -1890,7 +1892,137 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "**3.5 输出**" + "**3.5 过滤**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s > 1000" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Series对象进行比较等逻辑运算，结果仍然是一个Series对象，每一维度的值根据运算结果为True或False" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "s[s>1000]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 可以利用逻辑运算的结果Series，对Series对象进行过滤，得到满足条件的Series" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3.6 时间序列(Time Series)**示例" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "rng = pd.date_range('1/1/2012', periods=100, freq='S')\n", + "rng" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Pandas内置的date_range()函数，可以很方便的得到时间标签\n", + "- 第一个参数'1/1/2012'为时间字符串，表示开始时间\n", + "- 第二个参数periods为时间标签个数，此处设为100\n", + "- 第三个参数freq为时间频率间隔，此处的'S'表示间隔为秒，常用的为S,Min,H,D,M分别为秒、分、小时、天，月。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "ts = Series(np.random.randint(0, 500, len(rng)), index=rng)\n", + "ts.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 将时间标签作为Series对象的index，随机生成0-500间100个整数，作为Series对象的值。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "ts.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 利用plot()函数绘图" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "ts1 = ts.resample('Min').mean()\n", + "ts1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 利用resample()函数，可将时间序列重新抽样。\n", + "- 样本时间间隔根据resample()函数参数决定，本例中，样本时间间隔设为分钟(Min)\n", + "- 重新设定样本间隔后，可利用mean()函数，求得每个时间间隔内值的均值，作为新的index对应的值" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3.7 输出**" ] }, { From 1481277bc707c6e290c5d8cb4947845acbeede28 Mon Sep 17 00:00:00 2001 From: liupengyuan Date: Sun, 8 Oct 2017 17:54:13 +0800 Subject: [PATCH 20/44] Add files via upload --- chapter5/Latex Equation Tutorial .ipynb | 253 ++++++++++++++++++++++++ 1 file changed, 253 insertions(+) create mode 100644 chapter5/Latex Equation Tutorial .ipynb diff --git a/chapter5/Latex Equation Tutorial .ipynb b/chapter5/Latex Equation Tutorial .ipynb new file mode 100644 index 000000000..2ce8b6d0e --- /dev/null +++ b/chapter5/Latex Equation Tutorial .ipynb @@ -0,0 +1,253 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Latex公式编辑简易教程\n", + "### Latex简介\n", + "\n", + "LaTeX – A document preparation system\n", + "- LaTeX is a high-quality typesetting system;\n", + "- It includes features designed for the production of technical and scientific documentation. \n", + "- LaTeX is the de facto standard for the communication and publication of scientific documents.\n", + "- LaTeX is available as free software.\n", + "\n", + "### 1. 行内公式与独立公式\n", + "$a=c+d$\n", + "\n", + "上式Latex代码：`$a=c+d$`\n", + "$$a=c+d$$\n", + "上式Latex代码：`$$a=c+d$$`\n", + "\n", + "### 2. 上标与下标\n", + "$n_i$\n", + "\n", + "上式Latex代码：`$n_i$`\n", + "\n", + "$n^i$\n", + "\n", + "上式Latex代码：`$n^i$`\n", + "\n", + "$n_{i,j}$\n", + "\n", + "上式Latex代码：`$n_{i,j}$`\n", + "\n", + "$n^{i+j}$\n", + "\n", + "上式Latex代码：`$n^{i+j}$`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3. 根号与分数\n", + "3.1 根号\n", + "\n", + "$\\sqrt2$\n", + "\n", + "上式Latex代码：`$\\sqrt2$`\n", + "\n", + "$\\sqrt{x+y}$\n", + "\n", + "上式Latex代码：`$\\sqrt{x+y$`\n", + "\n", + "$\\sqrt[5]{x^2+y^2+z^2}$\n", + "\n", + "上式Latex代码：`$\\sqrt[5]{x^2+y^2+z^2}$`\n", + "\n", + "3.2 分数\n", + "\n", + "$\\frac a c$\n", + "\n", + "上式Latex代码：`$\\frac a c$`\n", + "\n", + "$\\frac {a}{c+d+e}$\n", + "\n", + "上式Latex代码：`$\\frac {a}{c+d+e}$`\n", + "\n", + "$\\frac {a}{c+d+\\frac e f}$\n", + "\n", + "上式Latex代码：`$\\frac {a}{c+d+\\frac e f}$`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.各类运算符号，字母形式\n", + "$\\lambda,\\xi,\\pi,\\mu,\\Phi,\\Omega,\\alpha, \\beta, \\gamma,\\Gamma, \\Delta, \\infty, \\to$\n", + "\n", + "上式Latex代码：`$\\lambda,\\xi,\\pi,\\mu,\\Phi,\\Omega,\\alpha, \\beta, \\gamma,\\Gamma, \\Delta, \\infty, \\to $`\n", + "\n", + "$\\neq, \\geq, \\leq, \\approx, \\cdot, \\cdots, \\vdots, \\ddots$\n", + "\n", + "上式Latex代码：`$\\neq, \\geq, \\leq, \\approx, \\cdot, \\cdots, \\vdots, \\ddots$`\n", + "\n", + "$\\widetilde x, \\widehat x, \\overrightarrow A, \\overleftarrow{bc}$\n", + "\n", + "上式Latex代码：`$\\widetilde x, \\widehat x, \\overrightarrow A, \\overleftarrow{bc}$`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5. 求和、累积及积分\n", + "5.1 求和\n", + "\n", + "$\\sum$\n", + "\n", + "上式Latex代码：`$\\sum$`\n", + "\n", + "$\\sum_{i=1}^{n}$\n", + "\n", + "上式Latex代码：`$\\sum_{i=1}^{n}$`\n", + "\n", + "$\\sum_{i=1}^{n}x_i$\n", + "\n", + "上式Latex代码：`$\\sum_{i=1}^{n}x_i$`\n", + "\n", + "$\\sum_{i=1,j=1}^{n,m}{x_i \\cdot y_j}$\n", + "\n", + "上式Latex代码：`$\\sum_{i=1,j=1}^{n,m}{x_i \\cdot y_j}$`\n", + "\n", + "5.2 累积\n", + "\n", + "$\\prod$\n", + "\n", + "上式Latex代码：`$\\prod$`\n", + "\n", + "$\\prod_{i=1,j=1}^{n,m}{x_i \\cdot y_j}$\n", + "\n", + "上式Latex代码：`$\\prod_{i=1,j=1}^{n,m}{x_i \\cdot y_j}$\n", + "\n", + "5.3 积分\n", + "\n", + "$\\int$\n", + "\n", + "上式Latex代码：`$\\int$`\n", + "\n", + "$\\int_0^x{f(x)}$\n", + "\n", + "上式Latex代码：`$\\int_0^x{f(x)}$`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 6 矩阵\n", + "\n", + "$\\begin{matrix} 1 & 2 & 3 \\end{matrix}$\n", + "\n", + "上式Latex代码：`$\\begin{matrix} 1 & 2 & 3 \\end{matrix}$`\n", + "\n", + "$\\begin{matrix} 1 \\\\ 2 \\\\ 3 \\end{matrix}$\n", + "\n", + "上式Latex代码：`$\\begin{matrix} 1 \\\\ 2 \\\\ 3 \\end{matrix}$`\n", + "\n", + "$\\begin{matrix} 1 & 2 & 3 \\\\ 4 & 5 & 6 \\\\ 7 & 8 & 9 \\\\ \\end{matrix}$\n", + "\n", + "上式Latex代码：`$\\begin{matrix} 1 & 2 & 3 \\\\ 4 & 5 & 6 \\\\ 7 & 8 & 9 \\\\ \\end{matrix}$`\n", + "\n", + "$$\\begin{pmatrix} 1 & 2 & 3 \\\\ 4 & 5 & 6 \\\\ 7 & 8 & 9 \\\\ \\end{pmatrix}$$\n", + "\n", + "上式Latex代码：`$$\\begin{pmatrix} 1 & 2 & 3 \\\\ 4 & 5 & 6 \\\\ 7 & 8 & 9 \\\\ \\end{pmatrix}$$`\n", + "\n", + "$$\\begin{pmatrix} 1 & 2 & 3 \\\\ 4 & 5 & 6 \\\\ 7 & 8 & 9 \\\\ \\end{pmatrix} \\tag3$$\n", + "\n", + "上式Latex代码：`$$\\begin{pmatrix} 1 & 2 & 3 \\\\ 4 & 5 & 6 \\\\ 7 & 8 & 9 \\\\ \\end{pmatrix} \\tag3$$`\n", + "\n", + "$$\\begin{pmatrix} \n", + "1 & 2 & \\cdots & m \\\\ \n", + "1 & 4 & \\cdots & m^2 \\\\\n", + "\\vdots & \\vdots & \\ddots & \\vdots\\\\\n", + "1 & 2^n & \\cdots & m^n \\\\\n", + "\\end{pmatrix}$$\n", + "\n", + "上式Latex代码：\n", + "```\n", + "$$\\begin{pmatrix} \n", + "1 & 2 & \\cdots & m \\\\ \n", + "1 & 4 & \\cdots & m^2 \\\\\n", + "\\vdots & \\vdots & \\ddots & \\vdots\\\\\n", + "1 & 2^n & \\cdots & m^n \\\\\n", + "\\end{pmatrix}$$```\n", + "\n", + "$$\\begin{array}{cc|c}\n", + " 1 & 2 & 3 \\\\\n", + " 4 & 5 & 6\n", + " \\end{array}$$\n", + " \n", + "式Latex代码：\n", + "```\n", + "$$\\begin{array}{cc|c}\n", + " 1 & 2 & 3 \\\\\n", + " 4 & 5 & 6\n", + " \\end{array}$$\n", + "```\n", + "\n", + "$$\\left[\n", + "\\begin{array}{cc|c}\n", + " 1 & 2 & 3 \\\\\n", + " 4 & 5 & 6\n", + " \\end{array}\n", + " \\right] $$\n", + " \n", + "上式Latex代码：\n", + "```\n", + "$$\\left[\n", + "\\begin{array}{cc|c}\n", + " 1 & 2 & 3 \\\\\n", + " 4 & 5 & 6\n", + " \\end{array}\n", + " \\right] $$\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 7. 空格、括号、绝对值、范数及转义\n", + "\n", + "$x b, x\\quad b, x\\qquad b$\n", + "\n", + "上式Latex代码：`$x b, x\\quad b, x\\qquad b$`\n", + "\n", + "$\\left(\\frac a b \\right), \\left[\\frac a b \\right], \\left|\\frac a b \\right|, \\left\\| \\frac a b \\right\\|$\n", + "\n", + "上式Latex代码：`$\\left(\\frac a b \\right), \\left[\\frac a b \\right], \\left|\\frac a b \\right|, \\left\\| \\frac a b \\right\\|$`\n", + "\n", + "$\\{a+b\\}$\n", + "\n", + "上式Latex代码：`$\\{a+b\\}$`\n", + "\n", + "Latex中一些保留字符如：`# $ % ^ & _ { } ~ \\`可直接用`\\`+上述符号进行转义，即可输出" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 9562209aa436069b64cc718c6170e09fa10eb54c Mon Sep 17 00:00:00 2001 From: liupengyuan Date: Sun, 8 Oct 2017 17:56:05 +0800 Subject: [PATCH 21/44] Create Markdown Basic Tutorial.md --- chapter5/Markdown Basic Tutorial.md | 107 ++++++++++++++++++++++++++++ 1 file changed, 107 insertions(+) create mode 100644 chapter5/Markdown Basic Tutorial.md diff --git a/chapter5/Markdown Basic Tutorial.md b/chapter5/Markdown Basic Tutorial.md new file mode 100644 index 000000000..d9a65b129 --- /dev/null +++ b/chapter5/Markdown Basic Tutorial.md @@ -0,0 +1,107 @@ +# Markdown极简教程 +### by 刘鹏远(liupengyuan[at]pku.edu.cn) +--- +本教程将采取**内容与形式一体化**的方式进行。具体来说，从本段开始，正文分为多个段，**奇数段**为实际显示内容，其紧邻的**偶数段**为奇数自然段内容对应的Markdown形式，奇数偶数段间以分割线分割。偶数段可视为Markdown代码，因此以`代码`显示，把偶数自然段的内容直接拷贝到支持Markdown的编辑环境中，即可显示对应奇数自然段。[Markdown](https://en.wikipedia.org/wiki/Markdown)编辑环境如喜欢在浏览器中编辑，可用[简书](http://www.jianshu.com/)，喜欢软件工具形式的，在Windows下可用[smark](https://github.com/elerao/Smark)，ios下可使用[Mou](http://25.io/mou/)。建议边看本教程边动手拷贝修改。 + +--- + +`` +本教程将采取**内容与形式一体化**的方式进行。具体来说，从本段开始，正文分为多个段，**奇数段**为实际显示内容，其紧邻的**偶数段**为奇数自然段内容对应的Markdown形式，奇数偶数段间以分割线分割。偶数段可视为Markdown代码，因此以`代码`显示，把偶数自然段的内容直接拷贝到支持Markdown的编辑环境中，即可显示对应奇数自然段。[Markdown](https://en.wikipedia.org/wiki/Markdown)编辑环境如喜欢在浏览器中编辑，可用[简书](http://www.jianshu.com/)，喜欢软件工具形式的，在Windows下可用[smark](https://github.com/elerao/Smark)，ios下可使用[Mou](http://25.io/mou/)。建议边看本教程边动手拷贝修改。 +`` + +--- +# Markdown极简教程 +## 欢迎转载，本文链接： +### https://github.com/LanguageResources/tools/blob/master/markdown.md +--- + +``` +# Markdown极简教程 +## 欢迎转载，本文链接： +### https://github.com/LanguageResources/tools/blob/master/markdown.md +``` + +--- +# 内容提纲 +- **段落中的文本** + - 改字体 + - 添超链接 + - 转义字符 +- **图片、公式与表格** + - 插入图片 + - 插入公式 + - 插入表格 +- **引用** +--- +``` +# 内容提纲 +- **段落中的文本** + - 改字体 + - 添超链接 + - 转义字符 +- **图片、公式与表格** + - 插入图片 + - 插入公式 + - 插入表格 +- **引用** +``` +*** +- **段落中的文本** + - 改字体，示例：**粗体**，*斜体*，~~删除~~ + - 添超链接，示例：[本教程链接](https://www.gitbook.com/book/4078/ability_tools/details) + - 转义字符，想输出用于Markdown标记的字符，而不是要用这些字符标记，可前面加`\`符号。示例：\`\`, \[\], \-, \*, \\等等。 +--- +``` +- **段落中的文本** + - 改字体，示例：**粗体**，*斜体*，~~删除~~ + - 添超链接，示例：[本教程链接](https://www.gitbook.com/book/4078/ability_tools/details) + - 转义字符，想输出用于Markdown标记的字符，而不是要用这些字符标记，可前面加`\`符号。示例：\`\`, \[\], \-, \*, \\等等。 +``` +--- +- **图片与表格** + - 插入图片，但须有一个图片的地址链接，示例：![图片](http://daringfireball.net/graphics/logos/) + - 插入表格 + 用字符`-`与`|`排成表格，但注意第一行最好没有空格，线可以不用太对齐，示例： + +AB | ED +--- |-- + a | b + d | e + +--- +``` +- **图片与表格** +- 插入图片，但须有一个图片的地址链接，示例：![图片](http://daringfireball.net/graphics/logos/) + + - 插入表格 + 用字符`-`与`|`排成表格，但注意第一行最好没有空格，线可以不用太对齐，示例： + +AB | ED +--- |-- + a | b + d | e +---- +``` +--- + - 引用，示例： + +> 引用的文字从此行开始 +>>再次引用，注意空行的作用 +>> +>>>三次引用，`还可以插入代码等。注意 +>>>如没空行，则文本与上一行连上了。 + +--- +``` + - 引用，示例： + +> 引用的文字从此行开始 +>>再次引用，注意空行的作用 +>> +>>>三次引用，`还可以插入代码`等。注意 +>>>如没空行，则文本与上一行连上了。 +``` +更多Markdown的知识，可参考[Markdown语法](http://www.appinn.com/markdown/)，或网上直接搜索。 + +``更多Markdown的知识，可参考[Markdown语法](http://www.appinn.com/markdown/)，或网上直接搜索。 +`` From 1042b7373a4a6afdb6c3d636f688bab9e4743917 Mon Sep 17 00:00:00 2001 From: liupengyuan Date: Sun, 8 Oct 2017 18:01:45 +0800 Subject: [PATCH 22/44] Update Latex Equation Tutorial .ipynb --- chapter5/Latex Equation Tutorial .ipynb | 1 + 1 file changed, 1 insertion(+) diff --git a/chapter5/Latex Equation Tutorial .ipynb b/chapter5/Latex Equation Tutorial .ipynb index 2ce8b6d0e..9114c1054 100644 --- a/chapter5/Latex Equation Tutorial .ipynb +++ b/chapter5/Latex Equation Tutorial .ipynb @@ -5,6 +5,7 @@ "metadata": {}, "source": [ "# Latex公式编辑简易教程\n", + "## by liupengyuan[at]pku.edu.cn\n", "### Latex简介\n", "\n", "LaTeX – A document preparation system\n", From 0c03da1343b03be88c965f0c6480ef63dc87e0ec Mon Sep 17 00:00:00 2001 From: liupengyuan Date: Sun, 8 Oct 2017 18:13:40 +0800 Subject: [PATCH 23/44] Update pandas Tutorial (Series).ipynb --- chapter4/pandas Tutorial (Series).ipynb | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/chapter4/pandas Tutorial (Series).ipynb b/chapter4/pandas Tutorial (Series).ipynb index 3a57c8b1d..b35e556de 100644 --- a/chapter4/pandas Tutorial (Series).ipynb +++ b/chapter4/pandas Tutorial (Series).ipynb @@ -62,9 +62,9 @@ "metadata": {}, "source": [ "以上引入各类利用pandas做数据分析可视化的模块，一般可作为每次数据分析项目/任务的起始代码，其中：\n", - "- `%matplotlib inline`是jupyter notebook的一个魔法命令，能够使基于matplotlib的绘图直接显示在网页中\n", - "- `from pandas import Series, DataFrame`，从pandas中引入最为常用的两个对象：Series及DataFrame\n", - "\n", + "- `%matplotlib inline`是jupyter notebook的一个魔法命令，能够使基于matplotlib的绘图直接显示在网页中，非jupyter notebook环境下编程，可以省略\n", + "- `from pandas import Series, DataFrame`，从pandas中引入最为常用的两个对象：Series及DataFrame。本行可以省略，则调用Series和DataFrame就需要加上前缀：pd.Series和pd.DataFrame。\n", + "- 在以上的引入中，第一行一般是为jupyter notebook\n", "**注意**，在以上的引入中，类似`pd`、`np`、`plt`及`sns`均为约定俗成的习惯性名称，**不建议**更改。\n", "\n", "---" From 469862a4f3cb2f40566814427bb1486074bd728b Mon Sep 17 00:00:00 2001 From: liupengyuan Date: Sun, 8 Oct 2017 18:16:56 +0800 Subject: [PATCH 24/44] Update pandas Tutorial (Series).ipynb --- chapter4/pandas Tutorial (Series).ipynb | 1 - 1 file changed, 1 deletion(-) diff --git a/chapter4/pandas Tutorial (Series).ipynb b/chapter4/pandas Tutorial (Series).ipynb index b35e556de..adc44cbd0 100644 --- a/chapter4/pandas Tutorial (Series).ipynb +++ b/chapter4/pandas Tutorial (Series).ipynb @@ -64,7 +64,6 @@ "以上引入各类利用pandas做数据分析可视化的模块，一般可作为每次数据分析项目/任务的起始代码，其中：\n", "- `%matplotlib inline`是jupyter notebook的一个魔法命令，能够使基于matplotlib的绘图直接显示在网页中，非jupyter notebook环境下编程，可以省略\n", "- `from pandas import Series, DataFrame`，从pandas中引入最为常用的两个对象：Series及DataFrame。本行可以省略，则调用Series和DataFrame就需要加上前缀：pd.Series和pd.DataFrame。\n", - "- 在以上的引入中，第一行一般是为jupyter notebook\n", "**注意**，在以上的引入中，类似`pd`、`np`、`plt`及`sns`均为约定俗成的习惯性名称，**不建议**更改。\n", "\n", "---" From 93b97c2ffaf011d22eae3972c2108f61668b5814 Mon Sep 17 00:00:00 2001 From: liupengyuan Date: Sun, 8 Oct 2017 18:17:51 +0800 Subject: [PATCH 25/44] Update pandas Tutorial (Series).ipynb --- chapter4/pandas Tutorial (Series).ipynb | 1 + 1 file changed, 1 insertion(+) diff --git a/chapter4/pandas Tutorial (Series).ipynb b/chapter4/pandas Tutorial (Series).ipynb index adc44cbd0..4ac93aff4 100644 --- a/chapter4/pandas Tutorial (Series).ipynb +++ b/chapter4/pandas Tutorial (Series).ipynb @@ -64,6 +64,7 @@ "以上引入各类利用pandas做数据分析可视化的模块，一般可作为每次数据分析项目/任务的起始代码，其中：\n", "- `%matplotlib inline`是jupyter notebook的一个魔法命令，能够使基于matplotlib的绘图直接显示在网页中，非jupyter notebook环境下编程，可以省略\n", "- `from pandas import Series, DataFrame`，从pandas中引入最为常用的两个对象：Series及DataFrame。本行可以省略，则调用Series和DataFrame就需要加上前缀：pd.Series和pd.DataFrame。\n", + "\n", "**注意**，在以上的引入中，类似`pd`、`np`、`plt`及`sns`均为约定俗成的习惯性名称，**不建议**更改。\n", "\n", "---" From 598c2a8c537efba2599391fd53dcb75f29c9d0d8 Mon Sep 17 00:00:00 2001 From: liupengyuan Date: Mon, 9 Oct 2017 00:29:39 +0800 Subject: [PATCH 26/44] Add files via upload --- chapter4/pandas Tutorial (DataFrame).ipynb | 1229 ++++++++++++++++++++ 1 file changed, 1229 insertions(+) create mode 100644 chapter4/pandas Tutorial (DataFrame).ipynb diff --git a/chapter4/pandas Tutorial (DataFrame).ipynb b/chapter4/pandas Tutorial (DataFrame).ipynb new file mode 100644 index 000000000..6a6a00500 --- /dev/null +++ b/chapter4/pandas Tutorial (DataFrame).ipynb @@ -0,0 +1,1229 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Pandas统计分析入门（2）\n", + "- ## 二维数据统计分析(DataFrame基础)\n", + "\n", + "---" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "from pandas import Series, DataFrame\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 二、二维数据统计分析(DataFrame基础)\n", + "\n", + "- 数据的描述、分析、可视化展示、概括性度量、输入与输出\n", + "df：多维条形图，多维折线图，多层图" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### 1. DataFrame对象及数据展示\n", + "\n", + "- DataFrame是pandas最重要最基础的数据对象之一，可用来表示数据表\n", + "- 如果将Series视为表格中的一列，则DataFrame可以视为表中多列，或者是具有相同index的多个Series\n", + "- 如果将Series视为向量(带索引)，则DataFrame即可视为矩阵(带索引)。\n", + "- 本小节仍将以一个词频统计结果作为实例，进行介绍。\n", + "- 教程中的各个代码段，请自行建立新的python程序，依次键入并顺序执行，观察执行结果。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "words_freq = np.array([200,300,400,350,390,600,900,400,300,120])\n", + "freq_dict = {'boy':words_freq,'girl':words_freq*2,'children':words_freq+100, 'child': words_freq+300}\n", + "total_words_freq = [12345000,23456000,22333000,45632000,11144000,65433000,44444000,55555000,34522000,55566000]\n", + "years = pd.date_range('2006', periods=10, freq='A')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df = DataFrame(freq_dict)\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 利用dict创建一个DataFrame对象，其index默认为从0开始的整数" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df = DataFrame(freq_dict, index = years)\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 与Series对象类似，可以在初始化DataFrame的时候，指定index\n", + "- 数据可视为3个有相同index的Series" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 与Series对象类似，DataFrame对象可以利用plot()函数直接绘制折线图" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df.plot(kind='bar')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 绘制柱状图(统计学中一般称为复式柱状图)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df.plot(kind='barh')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 绘制条形图" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df.plot(kind = 'area')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 绘制(面积)堆积图" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df.plot(kind='box')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 绘制箱型图" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "sns.boxplot(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 利用seaborn绘制箱型图" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df.plot(kind='scatter', x='boy',y='girl')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 当plot()函数kind为scatter时，可绘制散点图\n", + "- 散点图展现的是2组数据(2个变量)之间的关系\n", + "- 可见当前`boy`与`girl`两组数据之间是一种线性关系" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df.plot(kind='scatter', x='boy',y='girl',s=df['child'].values)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 对散点图，如果设置s参数，将一组list类型的数据传入，代表对应点的大小，则绘制出气泡图\n", + "- 气泡图展示的是三组数据(三个变量)之间的关系。上图可以解释为：当男孩数量增加时，女孩增加，同时小孩儿的数量增加。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. 查看DataFrame数据" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df.tail(4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 与Series类似，一样可以利用head()与tail()查看数据的前几行与后几行。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df.index" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 查看index属性，index也可称为行索引" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df.values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 查看values属性，可见DataFrame对象的values是一个numpy的ndarray类型。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 查看columns属性，columns也可称为列索引。此时列标签是pandas的Index对象，类似list，其中有个三个元素，类型为`object`，在pandas中，非数字类型一般均为object类型。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 利用describe()函数，快速查看DataFrame中数据表的数据概要" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "dft = df.T\n", + "dft" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 可将DataFrame对象转置。\n", + "- 注意，转置并不改变原DataFrame对象，而是新生成一个DataFrame对象。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "dft.index" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "dft.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 转置后，index与columns的值互换" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df.sort_index(ascending = False) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 根据索引进行排序，默认是升序，并对行索引进行排序。定义逐行变化方向为0轴方向：axis=0\n", + "- `ascending = False`，使之降序排列" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df.sort_index(axis = 1, ascending = False) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 将参数axis设为1，排序即按照列索引进行排序" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df.sort_values(by='boy')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 根据值进行排序，如是根据某对应列的值进行排序，需要在参数by中指定数据列索引" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3. 选择DataFrame数据\n", + "**3.1 列选择**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df['boy']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 利用列索引选择一列，将返回该列，且为Series对象。也可以用`df.boy`来访问，效果相同。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df[['boy']]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 指定一个列标签，放入list中，可以获取对应colums标签的列，返回DataFrame对象。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df[['boy','children']]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 利用指定多个列标签colums的list，可以获取对应colums标签的列，返回DataFrame对象。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df[[0,2]]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 利用代表列整数索引的list，可以获取对应colums标签的列，返回DataFrame对象。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + " df.loc[:,'boy']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 也可以利用DataFrame的loc属性进行列选取\n", + "- loc属性主要通过标签进行数据选取\n", + "- 其中第一个位置参数表示行方向(即axis=0)元素，为`:`即为全部选取\n", + "- 第二个位置参数表示列方向(即axis=1)元素，选取当前指定列标签的数据\n", + "- 返回一个Series对象" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + " df.loc[:,['boy']]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 与前类似，如在list中指定列标签，则返回一个DataFrame对象" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df.loc[:,['boy', 'girl']]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 与前类似，可以选择多个列，返回一个DataFrame对象" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df.loc[:,'boy': 'girl']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 可以利用列索引进行切片选取\n", + "- 注意切片是两端包含\n", + "- 注意切片不需要在list中" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df.iloc[:,0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 还可以利用DataFrame的iloc属性进行列选取\n", + "- iloc属性也称为位置(position)属性，主要通过位置(即对应整数索引)进行数据选取\n", + "- 其中第一个位置参数仍然表示行方向(即axis=0)元素，为`:`即为全部选取\n", + "- 第二个位置参数仍然表示列方向(即axis=1)元素，选取当前指定列标签的数据\n", + "- 返回一个Series对象" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df.iloc[:,[1]]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 与前类似，如在list中指定列位置，则返回一个DataFrame对象" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df.iloc[:,[1,2]]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 与前类似，可以根据位置选择多个列，返回一个DataFrame对象" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df.iloc[:,0:2]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 与前类似，可以通过列位置进行切片选择多个列，返回一个DataFrame对象" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3.2 行选择**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df.loc[years[0]]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 也可以利用DataFrame的loc属性进行行选取\n", + "- loc属性主要通过标签进行数据选取\n", + "- 其中第一个位置参数表示行方向(即axis=0)元素\n", + "- 第二个位置参数表示列方向(即axis=1)元素，不指定时，默认为全部选取\n", + "- 返回一个Series对象" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df.loc[[years[0]]]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 与前类似，如在list中指定行标签，则可选择该行，并返回一个DataFrame对象" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df.loc[[years[0], years[2]]]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 与前类似，如在list中指定多个行标签，则可选择多行，并返回一个DataFrame对象" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df.loc[years[0]: years[2]]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 与前类似，还可以利用标签切片来选择多行，并返回一个DataFrame对象" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df.iloc[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 还可以利用DataFrame的iloc属性进行行选取\n", + "- iloc属性也称为位置(position)属性，主要通过位置(即对应整数索引)进行数据选取\n", + "- 其中第一个位置参数仍然表示行方向(即axis=0)元素\n", + "- 第二个位置参数仍然表示列方向(即axis=1)元素，不指定时，默认为全部选取\n", + "- 返回一个Series对象" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df.iloc[[0]]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 与前类似，如在list中指定行位置，则返回一个DataFrame对象" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df.iloc[[0,1,2]]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 与前类似，可以根据位置选择多个行，返回一个DataFrame对象" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df.iloc[1:3]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 与前类似，可以根据位置进行切片选择多个行，返回一个DataFrame对象" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df[0:3]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 可以利用行切片，选取多行数据，返回一个DataFrame对象\n", + "- 个人觉得容易与选取列数据的混淆，不建议使用" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3.3 选择区块**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3.3.1 利用loc**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df.loc[years[0],'boy']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 利用loc，可以选择指定行标签、列标签的数据" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df.loc[years[0],['boy']]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 利用loc，指定行标签，在list中指定一个列标签，可以选择指定行列的数据\n", + "- 返回一个Series" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df.loc[[years[0]],['boy']]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 利用loc，在list中指定一个行标签，在list中指定一个列标签，可以选择指定行列的数据\n", + "- 返回一个DataFrame对象" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df.loc[years[0],['boy', 'girl']]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 利用loc，指定行标签，在list中指定多个列标签，可以选择指定行的多个列的数据\n", + "- 返回一个Series" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df.loc[years[0],'boy':'girl']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 与前类似，还可以切片选择给定行的多列数据\n", + "- 返回一个Series" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df.loc[[years[0]],'boy':'girl']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 与前类似，但是将返回一个DataFrame对象" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df.loc[years[0]:years[2],'boy':'girl']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 行列标签切片，选择对应数据\n", + "- 利用loc方式的行与列均可以如上类似处理" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3.3.2 利用iloc**\n", + "\n", + "利用iloc进行区域选择的方式与loc基本类似，只是利用位置而非标签信息进行选取，请参照前面理解" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df.iloc[0,0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df.iloc[0,[0,1,2]]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df.iloc[[0,1,2],0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df.iloc[[0,1,2],[1, 2]]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df.iloc[0:2,[1, 2]]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df.iloc[0:5,0:3]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3.3.3 利用iat选择单个元素**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df.iat[1,1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. 过滤DataFrame数据" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + " df[df.boy > 400]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 可利用DataFrame对象的某一列数据，进行布尔运算，根据布尔运算的结果过滤数据\n", + "- 返回一个DataFrame对象" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df[df[['boy','girl']] > 700]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 可设置多个列的条件，来过滤数据\n", + "- 返回DataFrame对象" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df[df > 400]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 可对所有元素进行条件过滤\n", + "- 返回DataFrame对象" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df[df['girl'].isin([700,800])]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 利用isin()函数，选择某列数据进行数据过滤" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From c7f4f4aa39936f1f840db0d85ed21f2052518c03 Mon Sep 17 00:00:00 2001 From: liupengyuan Date: Sat, 14 Oct 2017 21:37:27 +0800 Subject: [PATCH 27/44] Delete pandas Tutorial (DataFrame).ipynb --- chapter4/pandas Tutorial (DataFrame).ipynb | 1229 -------------------- 1 file changed, 1229 deletions(-) delete mode 100644 chapter4/pandas Tutorial (DataFrame).ipynb diff --git a/chapter4/pandas Tutorial (DataFrame).ipynb b/chapter4/pandas Tutorial (DataFrame).ipynb deleted file mode 100644 index 6a6a00500..000000000 --- a/chapter4/pandas Tutorial (DataFrame).ipynb +++ /dev/null @@ -1,1229 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Pandas统计分析入门（2）\n", - "- ## 二维数据统计分析(DataFrame基础)\n", - "\n", - "---" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "from pandas import Series, DataFrame\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 二、二维数据统计分析(DataFrame基础)\n", - "\n", - "- 数据的描述、分析、可视化展示、概括性度量、输入与输出\n", - "df：多维条形图，多维折线图，多层图" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "### 1. DataFrame对象及数据展示\n", - "\n", - "- DataFrame是pandas最重要最基础的数据对象之一，可用来表示数据表\n", - "- 如果将Series视为表格中的一列，则DataFrame可以视为表中多列，或者是具有相同index的多个Series\n", - "- 如果将Series视为向量(带索引)，则DataFrame即可视为矩阵(带索引)。\n", - "- 本小节仍将以一个词频统计结果作为实例，进行介绍。\n", - "- 教程中的各个代码段，请自行建立新的python程序，依次键入并顺序执行，观察执行结果。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "words_freq = np.array([200,300,400,350,390,600,900,400,300,120])\n", - "freq_dict = {'boy':words_freq,'girl':words_freq*2,'children':words_freq+100, 'child': words_freq+300}\n", - "total_words_freq = [12345000,23456000,22333000,45632000,11144000,65433000,44444000,55555000,34522000,55566000]\n", - "years = pd.date_range('2006', periods=10, freq='A')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df = DataFrame(freq_dict)\n", - "df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 利用dict创建一个DataFrame对象，其index默认为从0开始的整数" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df = DataFrame(freq_dict, index = years)\n", - "df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 与Series对象类似，可以在初始化DataFrame的时候，指定index\n", - "- 数据可视为3个有相同index的Series" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 与Series对象类似，DataFrame对象可以利用plot()函数直接绘制折线图" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df.plot(kind='bar')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 绘制柱状图(统计学中一般称为复式柱状图)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df.plot(kind='barh')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 绘制条形图" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df.plot(kind = 'area')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 绘制(面积)堆积图" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df.plot(kind='box')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 绘制箱型图" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "sns.boxplot(df)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 利用seaborn绘制箱型图" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df.plot(kind='scatter', x='boy',y='girl')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 当plot()函数kind为scatter时，可绘制散点图\n", - "- 散点图展现的是2组数据(2个变量)之间的关系\n", - "- 可见当前`boy`与`girl`两组数据之间是一种线性关系" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df.plot(kind='scatter', x='boy',y='girl',s=df['child'].values)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 对散点图，如果设置s参数，将一组list类型的数据传入，代表对应点的大小，则绘制出气泡图\n", - "- 气泡图展示的是三组数据(三个变量)之间的关系。上图可以解释为：当男孩数量增加时，女孩增加，同时小孩儿的数量增加。" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2. 查看DataFrame数据" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df.tail(4)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 与Series类似，一样可以利用head()与tail()查看数据的前几行与后几行。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df.index" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 查看index属性，index也可称为行索引" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df.values" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 查看values属性，可见DataFrame对象的values是一个numpy的ndarray类型。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df.columns" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 查看columns属性，columns也可称为列索引。此时列标签是pandas的Index对象，类似list，其中有个三个元素，类型为`object`，在pandas中，非数字类型一般均为object类型。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df.describe()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 利用describe()函数，快速查看DataFrame中数据表的数据概要" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "dft = df.T\n", - "dft" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 可将DataFrame对象转置。\n", - "- 注意，转置并不改变原DataFrame对象，而是新生成一个DataFrame对象。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "dft.index" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "dft.columns" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 转置后，index与columns的值互换" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df.sort_index(ascending = False) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 根据索引进行排序，默认是升序，并对行索引进行排序。定义逐行变化方向为0轴方向：axis=0\n", - "- `ascending = False`，使之降序排列" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df.sort_index(axis = 1, ascending = False) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 将参数axis设为1，排序即按照列索引进行排序" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df.sort_values(by='boy')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 根据值进行排序，如是根据某对应列的值进行排序，需要在参数by中指定数据列索引" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3. 选择DataFrame数据\n", - "**3.1 列选择**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df['boy']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 利用列索引选择一列，将返回该列，且为Series对象。也可以用`df.boy`来访问，效果相同。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df[['boy']]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 指定一个列标签，放入list中，可以获取对应colums标签的列，返回DataFrame对象。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df[['boy','children']]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 利用指定多个列标签colums的list，可以获取对应colums标签的列，返回DataFrame对象。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df[[0,2]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 利用代表列整数索引的list，可以获取对应colums标签的列，返回DataFrame对象。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - " df.loc[:,'boy']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 也可以利用DataFrame的loc属性进行列选取\n", - "- loc属性主要通过标签进行数据选取\n", - "- 其中第一个位置参数表示行方向(即axis=0)元素，为`:`即为全部选取\n", - "- 第二个位置参数表示列方向(即axis=1)元素，选取当前指定列标签的数据\n", - "- 返回一个Series对象" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - " df.loc[:,['boy']]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 与前类似，如在list中指定列标签，则返回一个DataFrame对象" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df.loc[:,['boy', 'girl']]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 与前类似，可以选择多个列，返回一个DataFrame对象" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df.loc[:,'boy': 'girl']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 可以利用列索引进行切片选取\n", - "- 注意切片是两端包含\n", - "- 注意切片不需要在list中" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df.iloc[:,0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 还可以利用DataFrame的iloc属性进行列选取\n", - "- iloc属性也称为位置(position)属性，主要通过位置(即对应整数索引)进行数据选取\n", - "- 其中第一个位置参数仍然表示行方向(即axis=0)元素，为`:`即为全部选取\n", - "- 第二个位置参数仍然表示列方向(即axis=1)元素，选取当前指定列标签的数据\n", - "- 返回一个Series对象" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df.iloc[:,[1]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 与前类似，如在list中指定列位置，则返回一个DataFrame对象" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df.iloc[:,[1,2]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 与前类似，可以根据位置选择多个列，返回一个DataFrame对象" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df.iloc[:,0:2]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 与前类似，可以通过列位置进行切片选择多个列，返回一个DataFrame对象" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**3.2 行选择**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df.loc[years[0]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 也可以利用DataFrame的loc属性进行行选取\n", - "- loc属性主要通过标签进行数据选取\n", - "- 其中第一个位置参数表示行方向(即axis=0)元素\n", - "- 第二个位置参数表示列方向(即axis=1)元素，不指定时，默认为全部选取\n", - "- 返回一个Series对象" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df.loc[[years[0]]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 与前类似，如在list中指定行标签，则可选择该行，并返回一个DataFrame对象" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df.loc[[years[0], years[2]]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 与前类似，如在list中指定多个行标签，则可选择多行，并返回一个DataFrame对象" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df.loc[years[0]: years[2]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 与前类似，还可以利用标签切片来选择多行，并返回一个DataFrame对象" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df.iloc[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 还可以利用DataFrame的iloc属性进行行选取\n", - "- iloc属性也称为位置(position)属性，主要通过位置(即对应整数索引)进行数据选取\n", - "- 其中第一个位置参数仍然表示行方向(即axis=0)元素\n", - "- 第二个位置参数仍然表示列方向(即axis=1)元素，不指定时，默认为全部选取\n", - "- 返回一个Series对象" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df.iloc[[0]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 与前类似，如在list中指定行位置，则返回一个DataFrame对象" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df.iloc[[0,1,2]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 与前类似，可以根据位置选择多个行，返回一个DataFrame对象" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df.iloc[1:3]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 与前类似，可以根据位置进行切片选择多个行，返回一个DataFrame对象" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df[0:3]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 可以利用行切片，选取多行数据，返回一个DataFrame对象\n", - "- 个人觉得容易与选取列数据的混淆，不建议使用" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**3.3 选择区块**" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**3.3.1 利用loc**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df.loc[years[0],'boy']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 利用loc，可以选择指定行标签、列标签的数据" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df.loc[years[0],['boy']]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 利用loc，指定行标签，在list中指定一个列标签，可以选择指定行列的数据\n", - "- 返回一个Series" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df.loc[[years[0]],['boy']]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 利用loc，在list中指定一个行标签，在list中指定一个列标签，可以选择指定行列的数据\n", - "- 返回一个DataFrame对象" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df.loc[years[0],['boy', 'girl']]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 利用loc，指定行标签，在list中指定多个列标签，可以选择指定行的多个列的数据\n", - "- 返回一个Series" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df.loc[years[0],'boy':'girl']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 与前类似，还可以切片选择给定行的多列数据\n", - "- 返回一个Series" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df.loc[[years[0]],'boy':'girl']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 与前类似，但是将返回一个DataFrame对象" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df.loc[years[0]:years[2],'boy':'girl']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 行列标签切片，选择对应数据\n", - "- 利用loc方式的行与列均可以如上类似处理" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**3.3.2 利用iloc**\n", - "\n", - "利用iloc进行区域选择的方式与loc基本类似，只是利用位置而非标签信息进行选取，请参照前面理解" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df.iloc[0,0]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df.iloc[0,[0,1,2]]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df.iloc[[0,1,2],0]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df.iloc[[0,1,2],[1, 2]]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df.iloc[0:2,[1, 2]]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df.iloc[0:5,0:3]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**3.3.3 利用iat选择单个元素**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df.iat[1,1]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. 过滤DataFrame数据" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - " df[df.boy > 400]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 可利用DataFrame对象的某一列数据，进行布尔运算，根据布尔运算的结果过滤数据\n", - "- 返回一个DataFrame对象" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df[df[['boy','girl']] > 700]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 可设置多个列的条件，来过滤数据\n", - "- 返回DataFrame对象" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df[df > 400]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 可对所有元素进行条件过滤\n", - "- 返回DataFrame对象" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df[df['girl'].isin([700,800])]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 利用isin()函数，选择某列数据进行数据过滤" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 3a0bb63237c008ab90971e1949002cb04b5acde2 Mon Sep 17 00:00:00 2001 From: liupengyuan Date: Sat, 14 Oct 2017 21:47:48 +0800 Subject: [PATCH 28/44] Add files via upload --- chapter4/pandas+Tutorial+DataFrame.ipynb | 4830 ++++++++++++++++++++++ chapter4/pandas+Tutorial+Series.ipynb | 3726 +++++++++++++++++ 2 files changed, 8556 insertions(+) create mode 100644 chapter4/pandas+Tutorial+DataFrame.ipynb create mode 100644 chapter4/pandas+Tutorial+Series.ipynb diff --git a/chapter4/pandas+Tutorial+DataFrame.ipynb b/chapter4/pandas+Tutorial+DataFrame.ipynb new file mode 100644 index 000000000..23a227fc0 --- /dev/null +++ b/chapter4/pandas+Tutorial+DataFrame.ipynb @@ -0,0 +1,4830 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Pandas统计分析入门（2）\n", + "- 转载注明转自：https://github.com/liupengyuan/\n", + "- ## 二维数据统计分析(DataFrame基础)\n", + "\n", + "---" + ] + }, + { + "cell_type": "code", + "execution_count": 181, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "from pandas import Series, DataFrame\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 二、二维数据统计分析(DataFrame基础)\n", + "\n", + "- 数据的描述、分析、可视化展示、概括性度量、输入与输出\n", + "df：多维条形图，多维折线图，多层图" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### 1. DataFrame对象及数据展示\n", + "\n", + "- DataFrame是pandas最重要最基础的数据对象之一，可用来表示数据表\n", + "- 如果将Series视为表格中的一列，则DataFrame可以视为表中多列，或者是具有相同index的多个Series\n", + "- 如果将Series视为向量(带索引)，则DataFrame即可视为矩阵(带索引)。\n", + "- 本小节仍将以一个词频统计结果作为实例，进行介绍。\n", + "- 教程中的各个代码段，请自行建立新的python程序，依次键入并顺序执行，观察执行结果。" + ] + }, + { + "cell_type": "code", + "execution_count": 182, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "words_freq = np.array([200,300,400,350,390,600,900,400,300,120])\n", + "freq_dict = {'boy':words_freq,'girl':words_freq*2,'children':words_freq+100, 'child': words_freq+300}\n", + "total_words_freq = [12345000,23456000,22333000,45632000,11144000,65433000,44444000,55555000,34522000,55566000]\n", + "years = pd.date_range('2006', periods=10, freq='A')" + ] + }, + { + "cell_type": "code", + "execution_count": 183, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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      " + ], + "text/plain": [ + " boy child children girl\n", + "2006-12-31 200 500 300 400\n", + "2007-12-31 300 600 400 600\n", + "2008-12-31 400 700 500 800\n", + "2009-12-31 350 650 450 700\n", + "2010-12-31 390 690 490 780\n", + "2011-12-31 600 900 700 1200\n", + "2012-12-31 900 1200 1000 1800\n", + "2013-12-31 400 700 500 800\n", + "2014-12-31 300 600 400 600\n", + "2015-12-31 120 420 220 240" + ] + }, + "execution_count": 184, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = DataFrame(freq_dict, index = years)\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 与Series对象类似，可以在初始化DataFrame的时候，指定index\n", + "- 数据可视为3个有相同index的Series" + ] + }, + { + "cell_type": "code", + "execution_count": 185, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 185, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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BEoEDxVunJS4xBGPt2Cwx+OGH7xMTE8OTTz7PunU/pbGxodvniYnJ/Pe/z6PTebaal6cR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\n9D5V5e9/d4TzrQ7+6tFiFs27NKXZvm8vLb/bRkx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Tjnod2b523H6eXu1YPH10p7ZXbX9Fm9n7V9fW1lb6KOs2Tpt2j39heWo9CTre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\npSN4lzvOXUhDQ32lwzggjrd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k6IF83ZBgyCCTtksiWqqWr+8ymYGiqgptK2pYvaGRVesaaGwJl3W2n4xn6e/J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\nlSPIq3k2AvC7aa7tf57h0Aqs5tvJGNF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Attempting to adjust your arguments for the new API (which might not work). Please update your code. See the version 0.6 release notes for more info.\n", + " warnings.warn(msg, UserWarning)\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 190, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df.plot(kind='scatter', x='boy',y='girl')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 当plot()函数kind为scatter时，可绘制散点图\n", + "- 散点图展现的是2组数据(2个变量)之间的关系\n", + "- 可见当前`boy`与`girl`两组数据之间是一种线性关系" + ] + }, + { + "cell_type": "code", + "execution_count": 192, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 192, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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DataFrame汇总与描述统计" + ] + }, + { + "cell_type": "code", + "execution_count": 193, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "boy 3960\n", + "child 6960\n", + "children 4960\n", + "girl 7920\n", + "dtype: int64" + ] + }, + "execution_count": 193, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Series对象的各类求值及描述统计函数，DataFrame对象均可以使用\n", + "- 默认参数是axis=0，表示以行方向为数据轴（基准），取每列做为一个Series对象对其进行统计" + ] + }, + { + "cell_type": "code", + "execution_count": 194, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2006-12-31 1400\n", + "2007-12-31 1900\n", + "2008-12-31 2400\n", + "2009-12-31 2150\n", + "2010-12-31 2350\n", + "2011-12-31 3400\n", + "2012-12-31 4900\n", + "2013-12-31 2400\n", + "2014-12-31 1900\n", + "2015-12-31 1000\n", + "Freq: A-DEC, dtype: int64" + ] + }, + "execution_count": 194, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.sum(axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 参数如果指定axis=1，表示以列方向为数据轴（基准），取每行做为一个Series对象对其进行统计" + ] + }, + { + "cell_type": "code", + "execution_count": 195, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "boy 120\n", + "child 420\n", + "children 220\n", + "girl 240\n", + "dtype: int32" + ] + }, + "execution_count": 195, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.min()" + ] + }, + { + "cell_type": "code", + "execution_count": 196, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "boy 396.0\n", + "child 696.0\n", + "children 496.0\n", + "girl 792.0\n", + "dtype: float64" + ] + }, + "execution_count": 196, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 197, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "boy 47871.111111\n", + "child 47871.111111\n", + "children 47871.111111\n", + "girl 191484.444444\n", + "dtype: float64" + ] + }, + "execution_count": 197, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.var()" + ] + }, + { + "cell_type": "code", + "execution_count": 198, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "boy 218.794678\n", + "child 218.794678\n", + "children 218.794678\n", + "girl 437.589356\n", + "dtype: float64" + ] + }, + "execution_count": 198, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.std()" + ] + }, + { + "cell_type": "code", + "execution_count": 199, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "boy 0.552512\n", + "child 0.314360\n", + "children 0.441118\n", + "girl 0.552512\n", + "dtype: float64" + ] + }, + "execution_count": 199, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.std()/df.mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 200, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "boy 370.0\n", + "child 670.0\n", + "children 470.0\n", + "girl 740.0\n", + "dtype: float64" + ] + }, + "execution_count": 200, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.median()" + ] + }, + { + "cell_type": "code", + "execution_count": 201, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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      " + ], + "text/plain": [ + " girl children child boy\n", + "2006-12-31 400 300 500 200\n", + "2007-12-31 600 400 600 300\n", + "2008-12-31 800 500 700 400\n", + "2009-12-31 700 450 650 350\n", + "2010-12-31 780 490 690 390\n", + "2011-12-31 1200 700 900 600\n", + "2012-12-31 1800 1000 1200 900\n", + "2013-12-31 800 500 700 400\n", + "2014-12-31 600 400 600 300\n", + "2015-12-31 240 220 420 120" + ] + }, + "execution_count": 214, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.sort_index(axis = 1, ascending = False) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 将参数axis设为1，排序即按照列索引进行排序" + ] + }, + { + "cell_type": "code", + "execution_count": 215, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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      boychildchildrengirl
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      " + ], + "text/plain": [ + " boy child children girl\n", + "2015-12-31 120 420 220 240\n", + "2006-12-31 200 500 300 400\n", + "2007-12-31 300 600 400 600\n", + "2014-12-31 300 600 400 600\n", + "2009-12-31 350 650 450 700\n", + "2010-12-31 390 690 490 780\n", + "2008-12-31 400 700 500 800\n", + "2013-12-31 400 700 500 800\n", + "2011-12-31 600 900 700 1200\n", + "2012-12-31 900 1200 1000 1800" + ] + }, + "execution_count": 215, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.sort_values(by='boy')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 根据值进行排序，如是根据某对应列的值进行排序，需要在参数by中指定数据列索引" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4. 选择DataFrame数据\n", + "**4.1 列选择**" + ] + }, + { + "cell_type": "code", + "execution_count": 216, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2006-12-31 200\n", + "2007-12-31 300\n", + "2008-12-31 400\n", + "2009-12-31 350\n", + "2010-12-31 390\n", + "2011-12-31 600\n", + "2012-12-31 900\n", + "2013-12-31 400\n", + "2014-12-31 300\n", + "2015-12-31 120\n", + "Freq: A-DEC, Name: boy, dtype: int32" + ] + }, + "execution_count": 216, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['boy']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 利用列索引选择一列，将返回该列，且为Series对象。也可以用`df.boy`来访问，效果相同。" + ] + }, + { + "cell_type": "code", + "execution_count": 217, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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      boychildren
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      " + ], + "text/plain": [ + " boy children\n", + "2006-12-31 200 300\n", + "2007-12-31 300 400\n", + "2008-12-31 400 500\n", + "2009-12-31 350 450\n", + "2010-12-31 390 490\n", + "2011-12-31 600 700\n", + "2012-12-31 900 1000\n", + "2013-12-31 400 500\n", + "2014-12-31 300 400\n", + "2015-12-31 120 220" + ] + }, + "execution_count": 218, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[['boy','children']]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 利用指定多个列标签colums的list，可以获取对应colums标签的列，返回DataFrame对象。" + ] + }, + { + "cell_type": "code", + "execution_count": 219, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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      boychildren
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      " + ], + "text/plain": [ + " boy children\n", + "2006-12-31 200 300\n", + "2007-12-31 300 400\n", + "2008-12-31 400 500\n", + "2009-12-31 350 450\n", + "2010-12-31 390 490\n", + "2011-12-31 600 700\n", + "2012-12-31 900 1000\n", + "2013-12-31 400 500\n", + "2014-12-31 300 400\n", + "2015-12-31 120 220" + ] + }, + "execution_count": 219, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[[0,2]]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 利用代表列整数索引的list，可以获取对应colums标签的列，返回DataFrame对象。" + ] + }, + { + "cell_type": "code", + "execution_count": 220, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2006-12-31 200\n", + "2007-12-31 300\n", + "2008-12-31 400\n", + "2009-12-31 350\n", + "2010-12-31 390\n", + "2011-12-31 600\n", + "2012-12-31 900\n", + "2013-12-31 400\n", + "2014-12-31 300\n", + "2015-12-31 120\n", + "Freq: A-DEC, Name: boy, dtype: int32" + ] + }, + "execution_count": 220, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + " df.loc[:,'boy']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 也可以利用DataFrame的loc属性进行列选取\n", + "- loc属性主要通过标签进行数据选取\n", + "- 其中第一个位置参数表示行方向(即axis=0)元素，为`:`即为全部选取\n", + "- 第二个位置参数表示列方向(即axis=1)元素，选取当前指定列标签的数据\n", + "- 返回一个Series对象" + ] + }, + { + "cell_type": "code", + "execution_count": 221, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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      boygirl
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      " + ], + "text/plain": [ + " boy girl\n", + "2006-12-31 200 400\n", + "2007-12-31 300 600\n", + "2008-12-31 400 800\n", + "2009-12-31 350 700\n", + "2010-12-31 390 780\n", + "2011-12-31 600 1200\n", + "2012-12-31 900 1800\n", + "2013-12-31 400 800\n", + "2014-12-31 300 600\n", + "2015-12-31 120 240" + ] + }, + "execution_count": 222, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.loc[:,['boy', 'girl']]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 与前类似，可以选择多个列，返回一个DataFrame对象" + ] + }, + { + "cell_type": "code", + "execution_count": 223, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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      boychildchildrengirl
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      " + ], + "text/plain": [ + " boy child children girl\n", + "2006-12-31 200 500 300 400\n", + "2007-12-31 300 600 400 600\n", + "2008-12-31 400 700 500 800\n", + "2009-12-31 350 650 450 700\n", + "2010-12-31 390 690 490 780\n", + "2011-12-31 600 900 700 1200\n", + "2012-12-31 900 1200 1000 1800\n", + "2013-12-31 400 700 500 800\n", + "2014-12-31 300 600 400 600\n", + "2015-12-31 120 420 220 240" + ] + }, + "execution_count": 223, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.loc[:,'boy': 'girl']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 可以利用列索引进行切片选取\n", + "- 注意切片是两端包含\n", + "- 注意切片不需要在list中" + ] + }, + { + "cell_type": "code", + "execution_count": 224, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2006-12-31 200\n", + "2007-12-31 300\n", + "2008-12-31 400\n", + "2009-12-31 350\n", + "2010-12-31 390\n", + "2011-12-31 600\n", + "2012-12-31 900\n", + "2013-12-31 400\n", + "2014-12-31 300\n", + "2015-12-31 120\n", + "Freq: A-DEC, Name: boy, dtype: int32" + ] + }, + "execution_count": 224, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.iloc[:,0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 还可以利用DataFrame的iloc属性进行列选取\n", + "- iloc属性也称为位置(position)属性，主要通过位置(即对应整数索引)进行数据选取\n", + "- 其中第一个位置参数仍然表示行方向(即axis=0)元素，为`:`即为全部选取\n", + "- 第二个位置参数仍然表示列方向(即axis=1)元素，选取当前指定列标签的数据\n", + "- 返回一个Series对象" + ] + }, + { + "cell_type": "code", + "execution_count": 225, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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      " + ], + "text/plain": [ + " boy child children\n", + "2006-12-31 200 500 300\n", + "2007-12-31 300 600 400\n", + "2008-12-31 400 700 500\n", + "2009-12-31 350 650 450\n", + "2010-12-31 390 690 490" + ] + }, + "execution_count": 249, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.iloc[0:5,0:3]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**4.3.3 利用iat选择单个元素**" + ] + }, + { + "cell_type": "code", + "execution_count": 250, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "600" + ] + }, + "execution_count": 250, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.iat[1,1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 在以上进行行、列选取的各类方法中，推荐使用loc及iloc方法，虽然稍显繁琐，但是逻辑较为严密，且性能更优。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. 条件选择DataFrame数据(布尔索引)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 对DataFrame按照条件来选择数据较为方便记忆的方式是：`对象[布尔索引]`，其中对象可以是整个DataFrame对象，也可以是DataFrame经过前一部分选择出的列、行或者区块。注意布尔索引要与选择出的数据对齐。\n", + "### 达到数据选择结果的方法较为多样，但为避免造成记忆及使用的混乱，其他条件选择数据的方式本教程不予介绍。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5.1 列" + ] + }, + { + "cell_type": "code", + "execution_count": 251, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2006-12-31 False\n", + "2007-12-31 False\n", + "2008-12-31 False\n", + "2009-12-31 False\n", + "2010-12-31 False\n", + "2011-12-31 True\n", + "2012-12-31 True\n", + "2013-12-31 False\n", + "2014-12-31 False\n", + "2015-12-31 False\n", + "Freq: A-DEC, Name: boy, dtype: bool" + ] + }, + "execution_count": 251, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.boy > 400" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 与Series中的类似，是每个元素比较后的布尔值\n", + "- 返回一个Series" + ] + }, + { + "cell_type": "code", + "execution_count": 252, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
      \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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      " + ], + "text/plain": [ + " boy child children girl\n", + "2011-12-31 600 900 700 1200\n", + "2012-12-31 900 1200 1000 1800" + ] + }, + "execution_count": 252, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + " df[df.boy > 400]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 可利用DataFrame对象的某一列数据，进行布尔运算，根据布尔运算的结果过滤数据\n", + "- 返回一个DataFrame对象" + ] + }, + { + "cell_type": "code", + "execution_count": 253, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
      \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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      " + ], + "text/plain": [ + " boy child children girl\n", + "2011-12-31 600 900 700 1200\n", + "2012-12-31 900 1200 1000 1800" + ] + }, + "execution_count": 253, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[(df.boy >300) & (df.girl > 900)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 可利用DataFrame对象的多列数据，进行多个布尔运算，注意用括号括起每个部分，表示`and`操作用`&`，'or'用`|`，`not`用`~`。\n", + "- 返回一个DataFrame对象" + ] + }, + { + "cell_type": "code", + "execution_count": 254, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2008-12-31 800\n", + "2009-12-31 700\n", + "2010-12-31 780\n", + "2011-12-31 1200\n", + "2012-12-31 1800\n", + "2013-12-31 800\n", + "Freq: A-DEC, Name: girl, dtype: int32" + ] + }, + "execution_count": 254, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['girl'][df.boy >300]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 选择df中的一列，再根据布尔索引进行过滤选择\n", + "- 返回一个Series" + ] + }, + { + "cell_type": "code", + "execution_count": 255, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
      \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
      girl
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      \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
      girlchild
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      " + ], + "text/plain": [ + " girl child\n", + "2011-12-31 1200 900\n", + "2012-12-31 1800 1200" + ] + }, + "execution_count": 256, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[['girl', 'child']][(df.boy >300) & (df.girl > 900)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 选择多列，并用组合的布尔索引进行数据选择" + ] + }, + { + "cell_type": "code", + "execution_count": 257, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2006-12-31 False\n", + "2007-12-31 False\n", + "2008-12-31 True\n", + "2009-12-31 True\n", + "2010-12-31 False\n", + "2011-12-31 False\n", + "2012-12-31 False\n", + "2013-12-31 True\n", + "2014-12-31 False\n", + "2015-12-31 False\n", + "Freq: A-DEC, Name: girl, dtype: bool" + ] + }, + "execution_count": 257, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['girl'].isin([700,800])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 对Series对象应用`isin()`函数，是判断该Series中的values是否在给定的数据表中\n", + "- 返回一个都是布尔值的Series" + ] + }, + { + "cell_type": "code", + "execution_count": 258, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
      \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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      \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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      " + ], + "text/plain": [ + " girl children\n", + "2008-12-31 800 500\n", + "2009-12-31 700 450\n", + "2013-12-31 800 500" + ] + }, + "execution_count": 259, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[['girl', 'children']][df['girl'].isin([700,800])]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 选择后利用isin生成的布尔索引进行数据选择" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5.2 行\n", + "\n", + "- pandas以布尔索引选择行数据的功能至今较弱" + ] + }, + { + "cell_type": "code", + "execution_count": 260, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "boy False\n", + "child True\n", + "children False\n", + "girl True\n", + "Name: 2007-12-31 00:00:00, dtype: bool" + ] + }, + "execution_count": 260, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.loc[years[1]]>500" + ] + }, + { + "cell_type": "code", + "execution_count": 261, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "child 500\n", + "girl 400\n", + "Name: 2006-12-31 00:00:00, dtype: int32" + ] + }, + "execution_count": 261, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.loc[years[0]][df.loc[years[1]]>500]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 但是暂时还无法将以往行对象选择后，应用`对象[行条件]`的方式进行条件数据选取，个人认为是pandas设计没有考虑周全的地方。虽然布尔索引选择行数据的应用场景较少。" + ] + }, + { + "cell_type": "code", + "execution_count": 262, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
      \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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      " + ], + "text/plain": [ + " child girl\n", + "2006-12-31 500 400\n", + "2007-12-31 600 600" + ] + }, + "execution_count": 262, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.T[[years[0],years[1]]][df.loc[years[2]]>500].T" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 如确有需求，可以对DataFrame对象进行转置，然后利用列布尔选择，再转置" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/chapter4/pandas+Tutorial+Series.ipynb b/chapter4/pandas+Tutorial+Series.ipynb new file mode 100644 index 000000000..e97ad8ad7 --- /dev/null +++ b/chapter4/pandas+Tutorial+Series.ipynb @@ -0,0 +1,3726 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Pandas统计分析入门（1）\n", + "- 转载注明转自：https://github.com/liupengyuan/\n", + "- ## Pandas及相关python库简介\n", + "- ## 一维数据统计分析(Series基础)\n", + "- ## 二维数据统计分析(DataFrame基础)\n", + "- ## Visualization基础\n", + "---\n", + "## Pandas 简介\n", + "\n", + " pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. It is already well on its way toward this goal.\n", + "\n", + "## Numpy 简介\n", + "\n", + " NumPy is the fundamental package for scientific computing with Python. It contains among other things:\n", + "- a powerful N-dimensional array object\n", + "- sophisticated (broadcasting) functions\n", + "- tools for integrating C/C++ and Fortran code\n", + "- useful linear algebra, Fourier transform, and random number capabilities\n", + "\n", + "## Matplotlib 简介\n", + "\n", + "Matplotlib是一个Python的2D绘图库，它以各种硬拷贝格式和跨平台的交互式环境生成出版质量级别的图形。\n", + "通过Matplotlib，开发者可以仅需要几行代码，便可以生成绘图，直方图，功率谱，条形图，错误图，散点图等。\n", + "\n", + "## Seaborn 简介\n", + "\n", + "Seaborn is a library for making attractive and informative statistical graphics in Python. It is built on top of matplotlib and tightly integrated with the PyData stack, including support for numpy and pandas data structures and statistical routines from scipy and statsmodels.\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- ## 程序头部引入" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "from pandas import Series, DataFrame\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "以上引入各类利用pandas做数据分析可视化的模块，一般可作为每次数据分析项目/任务的起始代码，其中：\n", + "- `%matplotlib inline`是jupyter notebook的一个魔法命令，能够使基于matplotlib的绘图直接显示在网页中\n", + "- `from pandas import Series, DataFrame`，从pandas中引入最为常用的两个对象：Series及DataFrame\n", + "\n", + "**注意**，在以上的引入中，类似`pd`、`np`、`plt`及`sns`均为约定俗成的习惯性名称，**不建议**更改。\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 一、一维数据统计分析(Series基础)\n", + "\n", + "- 数据的描述、分析、可视化展示、概括性度量、输入与输出" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. Series对象及数据最基本展示\n", + "\n", + "- 将以一个词频统计结果的实例，进行介绍。\n", + "- 教程中的各个代码段，请自行建立新的python程序，依次键入并顺序执行，观察执行结果。\n", + "- Series是pandas最重要最基础的数据对象，可用来表示数据表中的一列或一行。" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "words_freq = [200,300,400,350,390,600,900,400,300,120]\n", + "freq_dict = {'天长地久':words_freq}\n", + "total_words_freq = [12345000,23456000,22333000,45632000,11144000,65433000,44444000,55555000,34522000,55566000]\n", + "years = [2006,2007,2008,2009,2010,2011,2012,2013,2014,2015]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "以上代码中：\n", + "- `words_freq`，是“天长地久”在2006-2015年10年之间的频次list\n", + "- `freq_dict`，键为“天长地久”，值为其历时频次的list\n", + "- `total_words_freq`，是每年统计文本语料的总字数list\n", + "- `years`，是年份list" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 200\n", + "1 300\n", + "2 400\n", + "3 350\n", + "4 390\n", + "5 600\n", + "6 900\n", + "7 400\n", + "8 300\n", + "9 120\n", + "dtype: int64" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s = Series(words_freq)\n", + "s" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "利用list来初始化Series对象。Series是pandas内置的处理一维数据的数据类型。\n", + "- 第一列为Series对象的索引(index)，默认为数字索引；\n", + "- 第二列为Series对象的值(value)\n", + "- 最后一行说明，Series对象的值为int64类型" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2006 200\n", + "2007 300\n", + "2008 400\n", + "2009 350\n", + "2010 390\n", + "2011 600\n", + "2012 900\n", + "2013 400\n", + "2014 300\n", + "2015 120\n", + "dtype: int64" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s = Series(freq_dict['天长地久'], index = years)\n", + "s" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "可以在初始化Series对象时，同时指定索引(index)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "year\n", + "2006 200\n", + "2007 300\n", + "2008 400\n", + "2009 350\n", + "2010 390\n", + "2011 600\n", + "2012 900\n", + "2013 400\n", + "2014 300\n", + "2015 120\n", + "Name: 2006-2015, dtype: int64" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.name = '2006-2015'\n", + "s.index.name = 'year'\n", + "s" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "还可以指定Series对象的名称(name)，指定其索引(index)的名称(name)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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ZzNObSmnp6CV7XBwPrizy6bq0EeEhzC3MYG5hBt29Dg6cNK4mOlrZzPpt5azf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\n3H2T4nyPg1++eJjuXoff3ls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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "s.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Series对象可利用pandas内置的plot()直接绘图，默认为折线图。\n", + "\n", + "**折线图**多用于展示**数值型数据**，特别是用于展示历时数据。\n", + "\n", + "统计学中，依据计量尺度，一般可将数据分为三种类型：\n", + "- 分类数据\n", + "- 顺序数据(定序数据)\n", + "- 数值型数据(定量数据)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "s.plot(kind='bar')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "plot()函数的参数`kind`，可以指定plot()函数所绘制图形的种类，`'bar'`为柱状图。也可以采用这种形式：`s.plot.bar()`，效果与指定kind参数为bar类似。\n", + "\n", + "**柱状图**用宽度相同的柱的高度或长短来表示数据多少。\n", + "\n", + "柱状图一般用于展示**分类数据**，展示不同类别数据的多少(可将以上数据的每个年份理解为不同类别)。" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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7DTrJ33sBiFjBtqB56235DReKkuO+WFsW9VZXrhxBj3Q09a9j/051kKFIiIIN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\nUZ/eNxL7pqff8NTdBa19bpTeXh9jHlU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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "s.plot('pie',figsize=(6,6))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "'pie'为饼图。\n", + "**饼图**使用圆形及圆内扇形的角度表示数值大小的图形。\n", + "\n", + "一般也用于展示**分类数据**，用来表示各分类部分数据占全部数据的比例(频率分布)。" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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7DTrJ33sBiFjBtqB56235DReKkuO+WFsW9VZXrhxBj3Q09a9j/051kKFIiIIN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\nUZ/eNxL7pqff8NTdBa19bpTeXh9jHlU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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "s.plot('pie',figsize=(6,6))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "可利用plot()函数的`figsize`参数，设定图形的大小(长与宽)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. 数据基本分析、度量与展示\n", + "\n", + "**2.1 归一化**\n", + "\n", + "**归一化**一般要使数值的绝对值变成**相对值**，将数值映射到[0,1]区间上。\n", + "\n", + "在本例中，由于每个年度的语料大小不同，单纯观察每个年度某个词汇出现的频次(绝对值)，无法确定其出现的相对高低(相对值)。\n", + "\n", + "因此可将频次归一化，即某词汇每年出现的次数/每年语料的总字数。这样，该词汇就可以利用频次归一化的数值进行横向比较了。\n", + "\n", + "在实际中，也可以将频次归一化的结果乘以一个较大的整数。在本例中，可乘以10000000，表示语料中每10000000字出现该词汇的频次。" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "#python实现\n", + "def norm_freq(freqs, total_freqs, per = 1):\n", + " return [per * freq/total_freq for freq, total_freq in zip(freqs, total_freqs)]\n", + "\n", + "per = 10000000\n", + "s_norm = norm_freq(freq_dict['天长地久'], total_words_freq, per = 10000000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "上面的代码用python实现了将每年的频次归一化为每年每一千万字该词汇出现的频次。" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2006 162.008910\n", + "2007 127.899045\n", + "2008 179.107151\n", + "2009 76.700561\n", + "2010 349.964106\n", + "2011 91.696850\n", + "2012 202.502025\n", + "2013 72.000720\n", + "2014 86.901107\n", + "2015 21.595940\n", + "dtype: float64" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s_norm = Series(s_norm, index = years)\n", + "s_norm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "利用归一化处理后的结果初始化一个Series对象s_norm。注意，此时value的数据类型自动转换为float。" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "year\n", + "2006 162.008910\n", + "2007 127.899045\n", + "2008 179.107151\n", + "2009 76.700561\n", + "2010 349.964106\n", + "2011 91.696850\n", + "2012 202.502025\n", + "2013 72.000720\n", + "2014 86.901107\n", + "2015 21.595940\n", + "Name: 2006-2015, dtype: float64" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#pandas的Series运算\n", + "s_norm = per * s / total_words_freq\n", + "s_norm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "也可以直接利用pandas的序列运算进行归一化。运算结果与python编写代码一直，但是更简洁。\n", + "- `s_norm = per * s / total_words_freq`，其中`s`为Series类型，`total_words_freq`为list类型，直接相除运算的结果是将s中每个对应的值除以list中每个值。这种向量/矩阵运算是numpy及pandas中非常常见的情况，一般被称为**矢量化操作**。" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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137skjYLKZt7dWca01DBCAqQXiLCNlXVrim34iPXFH3C8qYif7S9lacINbnkw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\n3ibmZUYzLTWMjKQQPE0GwsP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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "s_norm.plot()\n", + "s.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "再次绘图，并将两根折线展示在一个图上。\n", + "\n", + "可以看出，归一化前，峰值出现在2012年；归一化后，峰值出现在2010年。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**1.2 求平均值**" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "396.0" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#python实现\n", + "freq_mean = sum(freq_dict['天长地久'])/len(freq_dict['天长地久'])\n", + "freq_mean" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "计算均值的python代码。\n", + "\n", + "除非特别指出，一般平均值指的就是**算数平均值**。" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "396.0" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#pandas内置求均值函数\n", + "s.mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "利用内置均值函数mean()更为简洁。" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2006 396.0\n", + "2007 396.0\n", + "2008 396.0\n", + "2009 396.0\n", + "2010 396.0\n", + "2011 396.0\n", + "2012 396.0\n", + "2013 396.0\n", + "2014 396.0\n", + "2015 396.0\n", + "dtype: float64" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s_mean = Series([freq_mean]*10, index = years)\n", + "s_mean" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "声明一个每年数值均为均值的Series对象s_mean。" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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UWg08BDiAx7XWG/wRWoixoqO7n827q0lJiGLBzGyz4/hNZnIM2WmxHDvVwoDDSUR4mNmR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\nvKIsevocHDjZxN6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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "s.plot()\n", + "s_mean.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "将频次均值与各年频次画在一个图中" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**1.3 样本方差、标准差、标准分及变异系数**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "**方差**（variance)是在概率论和统计方差衡量随机变量或一组数据时离散程度的度量。\n", + "- 统计中的方差（样本方差）是每个样本值与全体样本值的平均数之差的平方值的平均数。\n", + "\n", + "**总体**：包含所研究的全部个体(数据)的集合。\n", + "\n", + "**样本**：从总体中抽取的一部分元素的集合。\n", + "\n", + "**样本容量**：构成样本的元素的数量称为样本容量(size)。\n", + "\n", + "样本方差计算公式为：\n", + "$$s^2 = \\frac{1}{n-1}\\sum_{i=1}^{n}(x_i-\\widetilde x)^2$$\n", + "\n", + "注意样本方差的公式中，分母为`n-1`。" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "47871.11111111111" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#python实现方差\n", + "def variance(freqs):\n", + " mean = sum(freqs)/len(freqs)\n", + " return sum([(freq-mean)**2 for freq in freqs])/(len(freqs)-1)\n", + "\n", + "freq_var = variance(freq_dict['天长地久'])\n", + "freq_var" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "上述代码用python实现了求样本方差。" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "47871.11111111111" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#pandas内置方差\n", + "s.var()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "而pandas内置求方差的函数var()更为简洁。· " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "**标准差**：标准差是方差的算术平方根。" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "218.7946779771188" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#pandas内置标准差\n", + "s.std()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pandas内置std()函数,可得到序列s的标准差。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**标准分**：变量值与平均数的差除以标准差后的值，也称为**z分数**(z score)。\n", + "\n", + "标准分的计算公式为：\n", + "$$z_i=\\frac{x_i-\\widetilde x}{s}$$" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "year\n", + "2006 -0.895817\n", + "2007 -0.438768\n", + "2008 0.018282\n", + "2009 -0.210243\n", + "2010 -0.027423\n", + "2011 0.932381\n", + "2012 2.303530\n", + "2013 0.018282\n", + "2014 -0.438768\n", + "2015 -1.261457\n", + "Name: 2006-2015, dtype: float64" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(s-s.mean())/s.std()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "上述代码可得到s中所有数值的标准分，存放在一个Series对象中。\n", + "标准差的**经验法则**(当一组数据对称分布时)：\n", + "- 约68%的数据在均值附近正负1个标准差内\n", + "- 约95%的数据在均值附近正负2个标准差内\n", + "- 约99%的数据在均值附近正负3个标准差内\n", + "在正负3个标准差以外的数据，称为**离群点(outlier)**。\n", + "\n", + "**切比雪夫不等式**：对任意分布的一组数据，至少有(1-1/k^2)的数据落在正负k个标准差以内(k>1)。" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "**变异系数**(离散系数)：一组数据的标准差与该组数据均值之比。公式为：\n", + "$$v_i=\\frac{s}{\\widetilde x}$$\n", + "\n", + "该系数是测度离散程度的相对统计量，主要用于比较不同演变数据的离散程度(特别对均值差异较大的数据组)。" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.5525118130735324" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#变异系数\n", + "s.std()/s.mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "变异系数可由标准差/均值直接得到。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**1.4 中位数、分位数、轴距与箱型图**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**中位数**(median)：中数是按顺序排列的一组数据中居于中间位置的数，即在这组数据中，有一半的数据比他大，有一半的数据比他小。如果观察值有偶数个，通常取最中间的两个数值的平均数作为中位数。" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "109.7979476168295" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s_norm.median()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pandas内置了median()函数，即求中位数函数。" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "**分位数**是将总体的全部数据按大小顺序排列后，处于各等分位置的变量值。\n", + "- 将全部数据分成相等的两部分，它就是中位数；如果分成四等分，就是四分位数；八等分就是八分位数。\n", + "- 四分位数也称为四分位点，它是将全部数据分成相等的四部分，其中每部分包括25%的数据，处在各分位点的数值就是四分位数。\n", + "- 四分位数有三个，第一个四分位数就是通常所说的四分位数，称为下四分位数，第二个四分位数就是中位数，第三个四分位数称为上四分位数，分别用Q1、Q2、Q3表示。" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "79.25069739255241" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s_norm.quantile(0.25)" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "174.83259076226759" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s_norm.quantile(0.75)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pandas内置的quantile()函数可以求任意分位数，参数为0.25即Q1，参数为0.75即Q3。" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "s_norm.plot(kind='box')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "参数kind为`box`时，对应的就是**箱型图**。\n", + "\n", + "箱型图中间的箱型一般由三根线组成，由下至上分别对应Q1，Q2，Q3；而箱型以外的上下两根线，分别对应最大值与最小值(不含离群点)\n", + "\n", + "箱型图一般用于展示数值型数据，有助于对数据的整体分布有只管了解，特别有利于对离群点/异常点的观察。" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Series箱型图离群点/异常点显示，利用seaborn\n", + "sns.boxplot(s_norm)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "由于pandas内置的箱型图默认不含离群点，可直接利用seaborn中的boxplot函数，为Series对象绘制带离群点的箱型图。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**1.5 样本的累积和**（累积频数与累积频率）\n", + "\n", + "**累积频数**(cumulative frequencies)，将各有序类别或组的频数逐级累加起来的频数。\n", + "\n", + "**累积频率**(cumulative percentages)，将各有序类别或组的百分比逐级累加起来。" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "year\n", + "2006 200\n", + "2007 500\n", + "2008 900\n", + "2009 1250\n", + "2010 1640\n", + "2011 2240\n", + "2012 3140\n", + "2013 3540\n", + "2014 3840\n", + "2015 3960\n", + "Name: 2006-2015, dtype: int64" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.cumsum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pandas内置了cumsum()函数，可以直接得到累积频数。" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "s.cumsum().plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "利用cumsum()得到的仍然是Series对象，可以直接绘图。" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "s_ratio = s/s.sum()\n", + "s_ratio.cumsum().plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "累积频率(累积百分比)可以类似的得到。\n", + "\n", + "`sum()`为内置的求Series对象中value和的函数。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**1.6 最大值、最小值、众数及总体描述**" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "900" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.max()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "内置最大值函数max()" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "120" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.min()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "内置最小值函数min()" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 300\n", + "1 400\n", + "dtype: int64" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.mode()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**众数**(mode)：一组数据中出现次数最多的数据值。一般用于测度分类数据的集中趋势，在数据量较大的时候有意义。\n", + "Pandas内置mode()函数，可以直接得到众数，注意众数可能不唯一，如此时s的众数有两个。" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "count 10.000000\n", + "mean 396.000000\n", + "std 218.794678\n", + "min 120.000000\n", + "25% 300.000000\n", + "50% 370.000000\n", + "75% 400.000000\n", + "max 900.000000\n", + "Name: 2006-2015, dtype: float64" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "Pandas的descibe()函数，可以给出汇总与描述统计的常用值。其中count为数据个数。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**1.7 随机数、直方图、峰度与偏度**(公式以后有时间补充)" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "np.random.seed(66)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "调用np中random模块的seed()方法，生成一个随机数种子，在这个种子下，每次第一次生成的随机数均相同。" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1.41561436, -1.0852864 , -0.61630804, ..., 0.08479998,\n", + " -1.16755255, 1.13461007])" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "arr1 = np.random.randn(10000)\n", + "arr1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "利用random的randn()函数，生成一个numpy的array类型的对象，大小为10000，可理解为长度为10000的数组(所有数据元素类型相同)。\n", + "\n", + "数组内数据分布为正态分布。randn即random normal。" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 2.28112862, 0.51138372, 0.68988585, 0.67582702, 0.63808164,\n", + " -1.0410448 , -0.91601431, -1.64294384, 2.16465592, -0.67004449,\n", + " 0.46737616, 0.90362682, -0.34551556, -0.05367347, 0.46465691,\n", + " 1.37674635, -0.21249989, -2.14939938, -0.25040615, 1.43040674,\n", + " 0.27824394, 0.20567202, -0.68650085, 0.75210394, -1.12895709,\n", + " 0.68402948, -0.28490141, 0.29969392, 0.14210905, -0.11570231,\n", + " -0.48913203, -0.74924755, -1.52762847, -0.99353886, 1.81620157,\n", + " -1.20129803, -0.93263586, -0.36954506, 0.08467705, 0.61107937,\n", + " 0.50382233, -1.12408078, 0.398812 , 1.497698 , 0.78490215,\n", + " 0.55041055, -0.25888733, 0.39002582, -1.41102818, -2.09786379])" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "arr2 = np.random.randn(50)\n", + "arr2" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "与前类似，生成了一个长度为50的array，数据为正态分布。" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "s_1 = Series(arr1)\n", + "s_2 = Series(arr2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Numpy的array类型可以直接初始化为Series类型的对象。" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "s_1.plot(kind='hist')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**直方图**(histogram)：又称质量分布图，是数值数据分布的精确图形表示。\n", + "- 将值的范围分段，即将整个值的范围分成一系列间隔(一般要相等)\n", + "- 计算每个间隔中有多少值\n", + "\n", + "当plot()函数的kind参数为hist时，即代表直方图。\n", + "\n", + "此外，Pandas内置hist()函数，可对Series对象直接绘制直方图：`s_norm.hist()`。" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.distplot(s_2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "类似地，可以为s_2绘制histogram及kde图。" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-0.0063696106929140347,\n", + " 0.027355468589977068,\n", + " -0.020933532783578457,\n", + " -0.20411177773807099)" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s_1.skew(),s_2.skew(),s_1.kurt(),s_2.kurt()," + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**偏度**(Skewness)、偏态、偏态系数，是统计数据分布偏斜方向和程度的度量，是统计数据分布非对称程度的数字特征。正态分布的偏度为零。\n", + "- Skewness=0 分布形态与正态分布偏度相同\n", + "- Skewness>0 正偏差数值较大，为正偏或右偏。长尾巴拖在右边。\n", + "- Skewness<0 负偏差数值较大，为负偏或左偏。长尾巴拖在左边。\n", + "\n", + "**峰度**(Kurtosis)、峰态、峰态系数，是描述总体中所有取值分布形态陡缓程度的统计量。正态分布的峰度为3(也有很多将计算得到的峰度-3，而使正态分布峰度为零的做法)。\n", + "- Kurtosis=3(或0) 分布形态与正态分布峰度相同\n", + "- Kurtosis>3(或0) 正偏差数值较大，尖峰，厚尾。\n", + "- Kurtosis<3(或0) 负偏差数值较大，扁峰，薄尾。\n", + "\n", + "本例中，s_1比s_2更接近于正态分布。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3. Series类型基本操作整理\n", + "**3.1 创建与查看**\n", + "\n", + "创建一个Series的基本方法是：s = Series(data, index=index)。其中，data指代许多不同的数据类型：\n", + "- a Python dict\n", + "- an ndarray\n", + "- a scalar value (like 6)\n", + "\n", + "index指一个标签序列，一般用list类型。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3.1.1 利用list创建Series**" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 0\n", + "1 1\n", + "2 4\n", + "3 9\n", + "4 16\n", + "5 25\n", + "6 36\n", + "7 49\n", + "8 64\n", + "9 81\n", + "10 100\n", + "11 121\n", + "12 144\n", + "13 169\n", + "14 196\n", + "15 225\n", + "16 256\n", + "17 289\n", + "18 324\n", + "19 361\n", + "20 400\n", + "21 441\n", + "22 484\n", + "23 529\n", + "24 576\n", + "25 625\n", + "26 676\n", + "27 729\n", + "28 784\n", + "29 841\n", + " ... \n", + "70 4900\n", + "71 5041\n", + "72 5184\n", + "73 5329\n", + "74 5476\n", + "75 5625\n", + "76 5776\n", + "77 5929\n", + "78 6084\n", + "79 6241\n", + "80 6400\n", + "81 6561\n", + "82 6724\n", + "83 6889\n", + "84 7056\n", + "85 7225\n", + "86 7396\n", + "87 7569\n", + "88 7744\n", + "89 7921\n", + "90 8100\n", + "91 8281\n", + "92 8464\n", + "93 8649\n", + "94 8836\n", + "95 9025\n", + "96 9216\n", + "97 9409\n", + "98 9604\n", + "99 9801\n", + "dtype: int64" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s = Series([x*x for x in range(100)])\n", + "s" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 第一行利用一个list，创建了一个Series对象s\n", + "- 第二行查看该对象。\n", + "- 该对象由两部分组成，第一列为index，第二列为values。" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 0\n", + "1 1\n", + "2 4\n", + "3 9\n", + "4 16\n", + "dtype: int64" + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "数据太长，可用head()函数查看前几个数据。" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "90 8100\n", + "91 8281\n", + "92 8464\n", + "93 8649\n", + "94 8836\n", + "95 9025\n", + "96 9216\n", + "97 9409\n", + "98 9604\n", + "99 9801\n", + "dtype: int64" + ] + }, + "execution_count": 87, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.tail(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "也可用tail()函数查看最后几个数据，head()及tail()函数内可放整型参数，代表列出的数据个数。" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0, 1, 4, 9, 16, 25, 36, 49, 64, 81, 100,\n", + " 121, 144, 169, 196, 225, 256, 289, 324, 361, 400, 441,\n", + " 484, 529, 576, 625, 676, 729, 784, 841, 900, 961, 1024,\n", + " 1089, 1156, 1225, 1296, 1369, 1444, 1521, 1600, 1681, 1764, 1849,\n", + " 1936, 2025, 2116, 2209, 2304, 2401, 2500, 2601, 2704, 2809, 2916,\n", + " 3025, 3136, 3249, 3364, 3481, 3600, 3721, 3844, 3969, 4096, 4225,\n", + " 4356, 4489, 4624, 4761, 4900, 5041, 5184, 5329, 5476, 5625, 5776,\n", + " 5929, 6084, 6241, 6400, 6561, 6724, 6889, 7056, 7225, 7396, 7569,\n", + " 7744, 7921, 8100, 8281, 8464, 8649, 8836, 9025, 9216, 9409, 9604,\n", + " 9801], dtype=int64)" + ] + }, + "execution_count": 88, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "查看序列s的值" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "numpy.ndarray" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(s.values)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 查看s.values的类型\n", + "- Series对象的values的类型确实为numpy的array（数组）类型，即ndarray(n维数组)" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "RangeIndex(start=0, stop=100, step=1)" + ] + }, + "execution_count": 90, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.index" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 查看s的index(索引)的值" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "pandas.indexes.range.RangeIndex" + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(s.index)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 查看s.index的类型\n", + "- Series对象的索引在未指定时，为pandas内置的RangeIndex类型，类似于python的range类型。" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "e 0\n", + "d 1\n", + "n 4\n", + "b 9\n", + "w 16\n", + "dtype: int64" + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s2 = Series([x*x for x in range(26)], index=set('abcdefghijklmnopqrstuvwxyz'))\n", + "s2.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 利用list创建Series对象时，可用index参数指定一个list或set对应索引。" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(pandas.indexes.base.Index,\n", + " Index(['e', 'd', 'n', 'b', 'w', 'o', 'm', 't', 'i', 'p', 'a', 'l', 'z', 'y',\n", + " 'u', 'x', 'k', 'j', 'f', 'v', 'c', 's', 'q', 'r', 'g', 'h'],\n", + " dtype='object'))" + ] + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(s2.index), s2.index" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 指定索引后，Series对象的索引是pandas内置的Index类型" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3.1.2 利用dict创建Series**" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "喜欢 3000\n", + "恨 500\n", + "爱 7000\n", + "讨厌 1000\n", + "dtype: int64" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = {'喜欢':3000,'爱':7000,'讨厌':1000,'恨':500}\n", + "s3 = Series(data)\n", + "s3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 可用dict创建Series对象，则字典的键自动成为index" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "a NaN\n", + "b NaN\n", + "c NaN\n", + "爱 7000.0\n", + "dtype: float64" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s3 = Series(data, index = ['a','b','c','爱'])\n", + "s3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 初始化时，也可以额外指定index。但是如果指定的index找不到对应的值，则该index对应值设为NaN（Not a Number）" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3.1.3 利用标量创建Series对象**" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "a 5\n", + "b 5\n", + "c 5\n", + "d 5\n", + "e 5\n", + "dtype: int64" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s0 = Series(5, index = ['a','b','c','d','e'])\n", + "s0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 利用标量创建Series对象时，对象的值将均为标量的值" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3.1.4 利用Series对象创建Series对象**" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "a NaN\n", + "b NaN\n", + "c NaN\n", + "爱 7000.0\n", + "dtype: float64" + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s4 = Series(s3)\n", + "s4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 这是最简单的利用Series对象创建Series对象的代码" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "喜欢 NaN\n", + "爱 7000.0\n", + "讨厌 NaN\n", + "中 NaN\n", + "dtype: float64" + ] + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s5 = Series(s4, index = ['喜欢','爱','讨厌','中'])\n", + "s5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 与利用dict初始化类似，也可以额外指定index。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3.2 访问Series对象的元素**" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(nan, 7000.0, 7000.0)" + ] + }, + "execution_count": 99, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s4[0], s4['爱'], s4.爱" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 利用位置索引或者index均可以访问对应元素\n", + "- 还可以利用index的值作为属性来访问对应元素" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "a NaN\n", + "b NaN\n", + "dtype: float64" + ] + }, + "execution_count": 100, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s4[0:2]" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "爱 7000.0\n", + "dtype: float64" + ] + }, + "execution_count": 101, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s4['喜欢':'爱']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 位置索引及index的切片访问也适用，但利用index切片是包含末端的" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['a', 'b', 'c', '爱'], dtype='object')" + ] + }, + "execution_count": 102, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s4.index" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 可以利用index属性访问Series对象的索引" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ nan, nan, nan, 7000.])" + ] + }, + "execution_count": 103, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s4.values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 可以利用values属性访问Series对象的值" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a nan\n", + "b nan\n", + "c nan\n", + "爱 7000.0\n" + ] + } + ], + "source": [ + "for k, v in s4.items():\n", + " print(k, v)" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a\n", + "b\n", + "c\n", + "爱\n" + ] + } + ], + "source": [ + "for k in s4.keys():\n", + " print(k)" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "nan\n", + "nan\n", + "nan\n", + "7000.0\n" + ] + } + ], + "source": [ + "for v in s4:\n", + " print(v)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 可以类似dict一样迭代访问Series对象的各个元素" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a\n", + "b\n", + "c\n", + "爱\n" + ] + } + ], + "source": [ + "for k in s4.index:\n", + " print(k)" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "nan\n", + "nan\n", + "nan\n", + "7000.0\n" + ] + } + ], + "source": [ + "for v in s4.values:\n", + " print(v)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 也可以利用index与values属性迭代访问" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3.3 修改、删除、排序等基本运算**" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "a 10000.0\n", + "b NaN\n", + "c NaN\n", + "爱 7000.0\n", + "dtype: float64" + ] + }, + "execution_count": 109, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s4[0] = 10000\n", + "s4" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "a 10000.0\n", + "b NaN\n", + "c NaN\n", + "爱 4078.0\n", + "dtype: float64" + ] + }, + "execution_count": 110, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s4['爱'] = 4078\n", + "s4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Series对象内的值可以直接根据位置或index被赋值（修改）" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "a 666.0\n", + "b 666.0\n", + "c 666.0\n", + "爱 4078.0\n", + "dtype: float64" + ] + }, + "execution_count": 111, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s4[0:3] = 666\n", + "s4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 可以切片式赋值" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "a 666.0\n", + "b 666.0\n", + "c 666.0\n", + "d 4078.0\n", + "dtype: float64" + ] + }, + "execution_count": 112, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s4.index = ['a', 'b', 'c', 'd']\n", + "s4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Series对象的index可以直接被赋值（修改），注意新的index的长度要与Series对象中index的长度一致。" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "喜欢 NaN\n", + "爱 4078.0\n", + "讨厌 NaN\n", + "中 NaN\n", + "e NaN\n", + "dtype: float64" + ] + }, + "execution_count": 113, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s4 = s3.reindex(['喜欢','爱','讨厌','中', 'e'])\n", + "s4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 可用reindex方法来更改index，找不到对应值的index，则其值为NaN。" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "喜欢 NaN\n", + "爱 4078.0\n", + "讨厌 NaN\n", + "中 NaN\n", + "e NaN\n", + "dtype: float64" + ] + }, + "execution_count": 114, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s5 = s4.drop('e')\n", + "s4" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "喜欢 NaN\n", + "爱 4078.0\n", + "讨厌 NaN\n", + "中 NaN\n", + "dtype: float64" + ] + }, + "execution_count": 115, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 可利用drop函数删除指定索引及值，生成新的Series对象，但原series对象不变。" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "e NaN\n", + "中 NaN\n", + "喜欢 NaN\n", + "爱 8156.0\n", + "讨厌 NaN\n", + "dtype: float64" + ] + }, + "execution_count": 116, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s4+s5" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "e NaN\n", + "中 NaN\n", + "喜欢 NaN\n", + "爱 0.0\n", + "讨厌 NaN\n", + "dtype: float64" + ] + }, + "execution_count": 117, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s4-s5" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "e NaN\n", + "中 NaN\n", + "喜欢 NaN\n", + "爱 16630084.0\n", + "讨厌 NaN\n", + "dtype: float64" + ] + }, + "execution_count": 118, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s4*s5" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "e NaN\n", + "中 NaN\n", + "喜欢 NaN\n", + "爱 1.0\n", + "讨厌 NaN\n", + "dtype: float64" + ] + }, + "execution_count": 119, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s4/s5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Serires对象可以直接进行四则运算，运算规则是对应索引的值进行运算，整体运算结果仍然是Series对象。\n", + "- 如果索引值无法对应，则结果为NaN。" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "爱 4078.0\n", + "喜欢 NaN\n", + "讨厌 NaN\n", + "中 NaN\n", + "e NaN\n", + "dtype: float64" + ] + }, + "execution_count": 120, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s4.sort_values()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 按照values（值）进行排序，按值排序时，NaN将排在尾端" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "e NaN\n", + "中 NaN\n", + "喜欢 NaN\n", + "爱 4078.0\n", + "讨厌 NaN\n", + "dtype: float64" + ] + }, + "execution_count": 121, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s4.sort_index()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 按照index（索引）进行排序" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "爱 4078.0\n", + "喜欢 NaN\n", + "讨厌 NaN\n", + "中 NaN\n", + "e NaN\n", + "dtype: float64" + ] + }, + "execution_count": 122, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s4.sort_values(ascending=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 将ascending参数指定为False，则变为倒序排序" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3.4 处理NaN值**" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "喜欢 1.0\n", + "爱 4078.0\n", + "讨厌 1.0\n", + "中 1.0\n", + "e 1.0\n", + "dtype: float64" + ] + }, + "execution_count": 123, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s5 = s4.fillna(1)\n", + "s5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 可以利用内置fillna()函数，将NaN值替换为指定数值" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "喜欢 NaN\n", + "爱 4078.0\n", + "不讨厌 0.0\n", + "中 NaN\n", + "dtype: float64" + ] + }, + "execution_count": 124, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s6 = s4.reindex(['喜欢','爱','不讨厌','中'], fill_value = 0)\n", + "s6" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 可在reindex方法内利用fill_value参数，指定值来替换在重新索引时新出现的NaN值，\n", + "- 注意，fill_value参数与不会对原有NaN值进行替换。" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "e NaN\n", + "不讨厌 NaN\n", + "中 NaN\n", + "喜欢 NaN\n", + "爱 8156.0\n", + "讨厌 NaN\n", + "dtype: float64" + ] + }, + "execution_count": 125, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s4.add(s6)" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "e NaN\n", + "不讨厌 0.0\n", + "中 NaN\n", + "喜欢 NaN\n", + "爱 8156.0\n", + "讨厌 NaN\n", + "dtype: float64" + ] + }, + "execution_count": 126, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s4.add(s6, fill_value=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 两个Series对象相加可用add方法，设置fill_value参数的值，替换非对齐index部分的NaN值。fill_value参数不会对运算前的NaN进行填充。\n", + "- 类似还有sub，div，mul方法，分别对应减法、除法及乘法。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3.5 过滤**" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 False\n", + "1 False\n", + "2 False\n", + "3 False\n", + "4 False\n", + "5 False\n", + "6 False\n", + "7 False\n", + "8 False\n", + "9 False\n", + "10 False\n", + "11 False\n", + "12 False\n", + "13 False\n", + "14 False\n", + "15 False\n", + "16 False\n", + "17 False\n", + "18 False\n", + "19 False\n", + "20 False\n", + "21 False\n", + "22 False\n", + "23 False\n", + "24 False\n", + "25 False\n", + "26 False\n", + "27 False\n", + "28 False\n", + "29 False\n", + " ... \n", + "70 True\n", + "71 True\n", + "72 True\n", + "73 True\n", + "74 True\n", + "75 True\n", + "76 True\n", + "77 True\n", + "78 True\n", + "79 True\n", + "80 True\n", + "81 True\n", + "82 True\n", + "83 True\n", + "84 True\n", + "85 True\n", + "86 True\n", + "87 True\n", + "88 True\n", + "89 True\n", + "90 True\n", + "91 True\n", + "92 True\n", + "93 True\n", + "94 True\n", + "95 True\n", + "96 True\n", + "97 True\n", + "98 True\n", + "99 True\n", + "dtype: bool" + ] + }, + "execution_count": 127, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s > 1000" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Series对象进行比较等逻辑运算，结果仍然是一个Series对象，每一维度的值根据运算结果为True或False" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "32 1024\n", + "33 1089\n", + "34 1156\n", + "35 1225\n", + "36 1296\n", + "37 1369\n", + "38 1444\n", + "39 1521\n", + "40 1600\n", + "41 1681\n", + "42 1764\n", + "43 1849\n", + "44 1936\n", + "45 2025\n", + "46 2116\n", + "47 2209\n", + "48 2304\n", + "49 2401\n", + "50 2500\n", + "51 2601\n", + "52 2704\n", + "53 2809\n", + "54 2916\n", + "55 3025\n", + "56 3136\n", + "57 3249\n", + "58 3364\n", + "59 3481\n", + "60 3600\n", + "61 3721\n", + " ... \n", + "70 4900\n", + "71 5041\n", + "72 5184\n", + "73 5329\n", + "74 5476\n", + "75 5625\n", + "76 5776\n", + "77 5929\n", + "78 6084\n", + "79 6241\n", + "80 6400\n", + "81 6561\n", + "82 6724\n", + "83 6889\n", + "84 7056\n", + "85 7225\n", + "86 7396\n", + "87 7569\n", + "88 7744\n", + "89 7921\n", + "90 8100\n", + "91 8281\n", + "92 8464\n", + "93 8649\n", + "94 8836\n", + "95 9025\n", + "96 9216\n", + "97 9409\n", + "98 9604\n", + "99 9801\n", + "dtype: int64" + ] + }, + "execution_count": 128, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s[s>1000]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 可以利用逻辑运算的结果Series，对Series对象进行过滤，得到满足条件的Series" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3.6 时间序列(Time Series)**示例" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "DatetimeIndex(['2012-01-01 00:00:00', '2012-01-01 00:00:01',\n", + " '2012-01-01 00:00:02', '2012-01-01 00:00:03',\n", + " '2012-01-01 00:00:04', '2012-01-01 00:00:05',\n", + " '2012-01-01 00:00:06', '2012-01-01 00:00:07',\n", + " '2012-01-01 00:00:08', '2012-01-01 00:00:09',\n", + " '2012-01-01 00:00:10', '2012-01-01 00:00:11',\n", + " '2012-01-01 00:00:12', '2012-01-01 00:00:13',\n", + " '2012-01-01 00:00:14', '2012-01-01 00:00:15',\n", + " '2012-01-01 00:00:16', '2012-01-01 00:00:17',\n", + " '2012-01-01 00:00:18', '2012-01-01 00:00:19',\n", + " '2012-01-01 00:00:20', '2012-01-01 00:00:21',\n", + " '2012-01-01 00:00:22', '2012-01-01 00:00:23',\n", + " '2012-01-01 00:00:24', '2012-01-01 00:00:25',\n", + " '2012-01-01 00:00:26', '2012-01-01 00:00:27',\n", + " '2012-01-01 00:00:28', '2012-01-01 00:00:29',\n", + " '2012-01-01 00:00:30', '2012-01-01 00:00:31',\n", + " '2012-01-01 00:00:32', '2012-01-01 00:00:33',\n", + " '2012-01-01 00:00:34', '2012-01-01 00:00:35',\n", + " '2012-01-01 00:00:36', '2012-01-01 00:00:37',\n", + " '2012-01-01 00:00:38', '2012-01-01 00:00:39',\n", + " '2012-01-01 00:00:40', '2012-01-01 00:00:41',\n", + " '2012-01-01 00:00:42', '2012-01-01 00:00:43',\n", + " '2012-01-01 00:00:44', '2012-01-01 00:00:45',\n", + " '2012-01-01 00:00:46', '2012-01-01 00:00:47',\n", + " '2012-01-01 00:00:48', '2012-01-01 00:00:49',\n", + " '2012-01-01 00:00:50', '2012-01-01 00:00:51',\n", + " '2012-01-01 00:00:52', '2012-01-01 00:00:53',\n", + " '2012-01-01 00:00:54', '2012-01-01 00:00:55',\n", + " '2012-01-01 00:00:56', '2012-01-01 00:00:57',\n", + " '2012-01-01 00:00:58', '2012-01-01 00:00:59',\n", + " '2012-01-01 00:01:00', '2012-01-01 00:01:01',\n", + " '2012-01-01 00:01:02', '2012-01-01 00:01:03',\n", + " '2012-01-01 00:01:04', '2012-01-01 00:01:05',\n", + " '2012-01-01 00:01:06', '2012-01-01 00:01:07',\n", + " '2012-01-01 00:01:08', '2012-01-01 00:01:09',\n", + " '2012-01-01 00:01:10', '2012-01-01 00:01:11',\n", + " '2012-01-01 00:01:12', '2012-01-01 00:01:13',\n", + " '2012-01-01 00:01:14', '2012-01-01 00:01:15',\n", + " '2012-01-01 00:01:16', '2012-01-01 00:01:17',\n", + " '2012-01-01 00:01:18', '2012-01-01 00:01:19',\n", + " '2012-01-01 00:01:20', '2012-01-01 00:01:21',\n", + " '2012-01-01 00:01:22', '2012-01-01 00:01:23',\n", + " '2012-01-01 00:01:24', '2012-01-01 00:01:25',\n", + " '2012-01-01 00:01:26', '2012-01-01 00:01:27',\n", + " '2012-01-01 00:01:28', '2012-01-01 00:01:29',\n", + " '2012-01-01 00:01:30', '2012-01-01 00:01:31',\n", + " '2012-01-01 00:01:32', '2012-01-01 00:01:33',\n", + " '2012-01-01 00:01:34', '2012-01-01 00:01:35',\n", + " '2012-01-01 00:01:36', '2012-01-01 00:01:37',\n", + " '2012-01-01 00:01:38', '2012-01-01 00:01:39'],\n", + " dtype='datetime64[ns]', freq='S')" + ] + }, + "execution_count": 129, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rng = pd.date_range('1/1/2012', periods=100, freq='S')\n", + "rng" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Pandas内置的date_range()函数，可以很方便的得到时间标签\n", + "- 第一个参数'1/1/2012'为时间字符串，表示开始时间\n", + "- 第二个参数periods为时间标签个数，此处设为100\n", + "- 第三个参数freq为时间频率间隔，此处的'S'表示间隔为秒，常用的为S,Min,H,D,M分别为秒、分、小时、天，月。" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2012-01-01 00:00:00 18\n", + "2012-01-01 00:00:01 98\n", + "2012-01-01 00:00:02 182\n", + "2012-01-01 00:00:03 371\n", + "2012-01-01 00:00:04 193\n", + "Freq: S, dtype: int32" + ] + }, + "execution_count": 130, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ts = Series(np.random.randint(0, 500, len(rng)), index=rng)\n", + "ts.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 将时间标签作为Series对象的index，随机生成0-500间100个整数，作为Series对象的值。" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 131, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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q6kpNfcbrGUQJPC+f9FP14KKiqSaaehOpr9hPvWWilJh6y/tMbENh4ljb3p/C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\nDtIUXE559uou4iTF1s648F3i39Y00TkNWBLQlto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vf/0lPHZlFZc2yklvFWj9UgEZY+r6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\n84LoXqkCW5Nq6UxMlO5lxG9NsZtfXW4gvwhBvZX8om3oJcovakcZ+12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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ts.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 利用plot()函数绘图" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2012-01-01 00:00:00 244.950\n", + "2012-01-01 00:01:00 218.425\n", + "Freq: T, dtype: float64" + ] + }, + "execution_count": 132, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ts1 = ts.resample('Min').mean()\n", + "ts1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 利用resample()函数，可将时间序列重新抽样。\n", + "- 样本时间间隔根据resample()函数参数决定，本例中，样本时间间隔设为分钟(Min)\n", + "- 重新设定样本间隔后，可利用mean()函数，求得每个时间间隔内值的均值，作为新的index对应的值" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3.7 输出**" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "s.plot()\n", + "plt.savefig('test.jpg')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 利用内置的plot()函数展示图片后，可利用plt的savefig()函数将图片保存为文件输出。\n", + "- 可以查看当前目录下的test.jpg文件，与当前显示一致。" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "s.to_csv('test.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 利用to_csv()函数，可将Series对象输出为.csv文件\n", + "- 可在当前目录下，找到test.csv，双击打开查看" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From efa31027a6efc4f27f57f3a637a27bdf796bee07 Mon Sep 17 00:00:00 2001 From: liupengyuan Date: Sat, 14 Oct 2017 21:48:08 +0800 Subject: [PATCH 29/44] Delete pandas Tutorial (Series).ipynb --- chapter4/pandas Tutorial (Series).ipynb | 2089 ----------------------- 1 file changed, 2089 deletions(-) delete mode 100644 chapter4/pandas Tutorial (Series).ipynb diff --git a/chapter4/pandas Tutorial (Series).ipynb b/chapter4/pandas Tutorial (Series).ipynb deleted file mode 100644 index 4ac93aff4..000000000 --- a/chapter4/pandas Tutorial (Series).ipynb +++ /dev/null @@ -1,2089 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Pandas统计分析入门（1）\n", - "- ## Pandas及相关python库简介\n", - "- ## 一维数据统计分析(Series基础)\n", - "- ## 二维数据统计分析(DataFrame基础)\n", - "- ## Visualization基础\n", - "---\n", - "## Pandas 简介\n", - "\n", - " pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. It is already well on its way toward this goal.\n", - "\n", - "## Numpy 简介\n", - "\n", - " NumPy is the fundamental package for scientific computing with Python. It contains among other things:\n", - "- a powerful N-dimensional array object\n", - "- sophisticated (broadcasting) functions\n", - "- tools for integrating C/C++ and Fortran code\n", - "- useful linear algebra, Fourier transform, and random number capabilities\n", - "\n", - "## Matplotlib 简介\n", - "\n", - "Matplotlib是一个Python的2D绘图库，它以各种硬拷贝格式和跨平台的交互式环境生成出版质量级别的图形。\n", - "通过Matplotlib，开发者可以仅需要几行代码，便可以生成绘图，直方图，功率谱，条形图，错误图，散点图等。\n", - "\n", - "## Seaborn 简介\n", - "\n", - "Seaborn is a library for making attractive and informative statistical graphics in Python. It is built on top of matplotlib and tightly integrated with the PyData stack, including support for numpy and pandas data structures and statistical routines from scipy and statsmodels.\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- ## 程序头部引入" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "from pandas import Series, DataFrame\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "以上引入各类利用pandas做数据分析可视化的模块，一般可作为每次数据分析项目/任务的起始代码，其中：\n", - "- `%matplotlib inline`是jupyter notebook的一个魔法命令，能够使基于matplotlib的绘图直接显示在网页中，非jupyter notebook环境下编程，可以省略\n", - "- `from pandas import Series, DataFrame`，从pandas中引入最为常用的两个对象：Series及DataFrame。本行可以省略，则调用Series和DataFrame就需要加上前缀：pd.Series和pd.DataFrame。\n", - "\n", - "**注意**，在以上的引入中，类似`pd`、`np`、`plt`及`sns`均为约定俗成的习惯性名称，**不建议**更改。\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 一维数据统计分析(Series基础)\n", - "\n", - "- 数据的描述、分析、可视化展示、概括性度量、输入与输出" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1. Series对象及数据最基本展示\n", - "\n", - "- 将以一个词频统计结果的实例，进行介绍。\n", - "- 教程中的各个代码段，请自行建立新的python程序，依次键入并顺序执行，观察执行结果。\n", - "- Series是pandas最重要最基础的数据对象，可用来表示数据表中的一列或一行。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "words_freq = [200,300,400,350,390,600,900,400,300,120]\n", - "freq_dict = {'天长地久':words_freq}\n", - "total_words_freq = [12345000,23456000,22333000,45632000,11144000,65433000,44444000,55555000,34522000,55566000]\n", - "years = [2006,2007,2008,2009,2010,2011,2012,2013,2014,2015]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "以上代码中：\n", - "- `words_freq`，是“天长地久”在2006-2015年10年之间的频次list\n", - "- `freq_dict`，键为“天长地久”，值为其历时频次的list\n", - "- `total_words_freq`，是每年统计文本语料的总字数list\n", - "- `years`，是年份list" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s = Series(words_freq)\n", - "s" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "利用list来初始化Series对象。Series是pandas内置的处理一维数据的数据类型。\n", - "- 第一列为Series对象的索引(index)，默认为数字索引；\n", - "- 第二列为Series对象的值(value)\n", - "- 最后一行说明，Series对象的值为int64类型" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s = Series(freq_dict['天长地久'], index = years)\n", - "s" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "可以在初始化Series对象时，同时指定索引(index)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s.name = '2006-2015'\n", - "s.index.name = 'year'\n", - "s" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "还可以指定Series对象的名称(name)，指定其索引(index)的名称(name)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Series对象可利用pandas内置的plot()直接绘图，默认为折线图。\n", - "\n", - "**折线图**多用于展示**数值型数据**，特别是用于展示历时数据。\n", - "\n", - "统计学中，依据计量尺度，一般可将数据分为三种类型：\n", - "- 分类数据\n", - "- 顺序数据(定序数据)\n", - "- 数值型数据(定量数据)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s.plot(kind='bar')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "plot()函数的参数`kind`，可以指定plot()函数所绘制图形的种类，`'bar'`为柱状图。也可以采用这种形式：`s.plot.bar()`，效果与指定kind参数为bar类似。\n", - "\n", - "**柱状图**用宽度相同的柱的高度或长短来表示数据多少。\n", - "\n", - "柱状图一般用于展示**分类数据**，展示不同类别数据的多少(可将以上数据的每个年份理解为不同类别)。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s.plot(kind='barh')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "'barh'为条形图，与柱状图类似。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s.plot('pie',figsize=(6,6))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "'pie'为饼图。\n", - "**饼图**使用圆形及圆内扇形的角度表示数值大小的图形。\n", - "\n", - "一般也用于展示**分类数据**，用来表示各分类部分数据占全部数据的比例(频率分布)。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s.plot('pie',figsize=(6,6))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "可利用plot()函数的`figsize`参数，设定图形的大小(长与宽)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2. 数据基本分析、度量与展示\n", - "\n", - "**2.1 归一化**\n", - "\n", - "**归一化**一般要使数值的绝对值变成**相对值**，将数值映射到[0,1]区间上。\n", - "\n", - "在本例中，由于每个年度的语料大小不同，单纯观察每个年度某个词汇出现的频次(绝对值)，无法确定其出现的相对高低(相对值)。\n", - "\n", - "因此可将频次归一化，即某词汇每年出现的次数/每年语料的总字数。这样，该词汇就可以利用频次归一化的数值进行横向比较了。\n", - "\n", - "在实际中，也可以将频次归一化的结果乘以一个较大的整数。在本例中，可乘以10000000，表示语料中每10000000字出现该词汇的频次。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "#python实现\n", - "def norm_freq(freqs, total_freqs, per = 1):\n", - " return [per * freq/total_freq for freq, total_freq in zip(freqs, total_freqs)]\n", - "\n", - "per = 10000000\n", - "s_norm = norm_freq(freq_dict['天长地久'], total_words_freq, per = 10000000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "上面的代码用python实现了将每年的频次归一化为每年每一千万字该词汇出现的频次。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s_norm = Series(s_norm, index = years)\n", - "s_norm" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "利用归一化处理后的结果初始化一个Series对象s_norm。注意，此时value的数据类型自动转换为float。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "#pandas的Series运算\n", - "s_norm = per * s / total_words_freq\n", - "s_norm" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "也可以直接利用pandas的序列运算进行归一化。运算结果与python编写代码一直，但是更简洁。\n", - "- `s_norm = per * s / total_words_freq`，其中`s`为Series类型，`total_words_freq`为list类型，直接相除运算的结果是将s中每个对应的值除以list中每个值。这种向量/矩阵运算是numpy及pandas中非常常见的情况，一般被称为**矢量化操作**。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s_norm.plot()\n", - "s.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "再次绘图，并将两根折线展示在一个图上。\n", - "\n", - "可以看出，归一化前，峰值出现在2012年；归一化后，峰值出现在2010年。" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**1.2 求平均值**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "#python实现\n", - "freq_mean = sum(freq_dict['天长地久'])/len(freq_dict['天长地久'])\n", - "freq_mean" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "计算均值的python代码。\n", - "\n", - "除非特别指出，一般平均值指的就是**算数平均值**。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "#pandas内置求均值函数\n", - "s.mean()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "利用内置均值函数mean()更为简洁。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s_mean = Series([freq_mean]*10, index = years)\n", - "s_mean" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "声明一个每年数值均为均值的Series对象s_mean。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s.plot()\n", - "s_mean.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "将频次均值与各年频次画在一个图中" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**1.3 样本方差、标准差、标准分及变异系数**" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "**方差**（variance)是在概率论和统计方差衡量随机变量或一组数据时离散程度的度量。\n", - "- 统计中的方差（样本方差）是每个样本值与全体样本值的平均数之差的平方值的平均数。\n", - "\n", - "**总体**：包含所研究的全部个体(数据)的集合。\n", - "\n", - "**样本**：从总体中抽取的一部分元素的集合。\n", - "\n", - "**样本容量**：构成样本的元素的数量称为样本容量(size)。\n", - "\n", - "样本方差计算公式为：\n", - "$$s^2 = \\frac{1}{n-1}\\sum_{i=1}^{n}(x_i-\\widetilde x)^2$$\n", - "\n", - "注意样本方差的公式中，分母为`n-1`。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "#python实现方差\n", - "def variance(freqs):\n", - " mean = sum(freqs)/len(freqs)\n", - " return sum([(freq-mean)**2 for freq in freqs])/(len(freqs)-1)\n", - "\n", - "freq_var = variance(freq_dict['天长地久'])\n", - "freq_var" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "上述代码用python实现了求样本方差。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "#pandas内置方差\n", - "s.var()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "而pandas内置求方差的函数var()更为简洁。· " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "**标准差**：标准差是方差的算术平方根。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "#pandas内置标准差\n", - "s.std()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Pandas内置std()函数,可得到序列s的标准差。" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**标准分**：变量值与平均数的差除以标准差后的值，也称为**z分数**(z score)。\n", - "\n", - "标准分的计算公式为：\n", - "$$z_i=\\frac{x_i-\\widetilde x}{s}$$" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "(s-s.mean())/s.std()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "上述代码可得到s中所有数值的标准分，存放在一个Series对象中。\n", - "标准差的**经验法则**(当一组数据对称分布时)：\n", - "- 约68%的数据在均值附近正负1个标准差内\n", - "- 约95%的数据在均值附近正负2个标准差内\n", - "- 约99%的数据在均值附近正负3个标准差内\n", - "在正负3个标准差以外的数据，称为**离群点(outlier)**。\n", - "\n", - "**切比雪夫不等式**：对任意分布的一组数据，至少有(1-1/k^2)的数据落在正负k个标准差以内(k>1)。" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "**变异系数**(离散系数)：一组数据的标准差与该组数据均值之比。公式为：\n", - "$$v_i=\\frac{s}{\\widetilde x}$$\n", - "\n", - "该系数是测度离散程度的相对统计量，主要用于比较不同演变数据的离散程度(特别对均值差异较大的数据组)。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "#变异系数\n", - "s.std()/s.mean()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "变异系数可由标准差/均值直接得到。" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**1.4 中位数、分位数、轴距与箱型图**" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**中位数**(median)：中数是按顺序排列的一组数据中居于中间位置的数，即在这组数据中，有一半的数据比他大，有一半的数据比他小。如果观察值有偶数个，通常取最中间的两个数值的平均数作为中位数。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s_norm.median()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Pandas内置了median()函数，即求中位数函数。" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": false - }, - "source": [ - "**分位数**是将总体的全部数据按大小顺序排列后，处于各等分位置的变量值。\n", - "- 将全部数据分成相等的两部分，它就是中位数；如果分成四等分，就是四分位数；八等分就是八分位数。\n", - "- 四分位数也称为四分位点，它是将全部数据分成相等的四部分，其中每部分包括25%的数据，处在各分位点的数值就是四分位数。\n", - "- 四分位数有三个，第一个四分位数就是通常所说的四分位数，称为下四分位数，第二个四分位数就是中位数，第三个四分位数称为上四分位数，分别用Q1、Q2、Q3表示。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s_norm.quantile(0.25)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s_norm.quantile(0.75)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Pandas内置的quantile()函数可以求任意分位数，参数为0.25即Q1，参数为0.75即Q3。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s_norm.plot(kind='box')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "参数kind为`box`时，对应的就是**箱型图**。\n", - "\n", - "箱型图中间的箱型一般由三根线组成，由下至上分别对应Q1，Q2，Q3；而箱型以外的上下两根线，分别对应最大值与最小值(不含离群点)\n", - "\n", - "箱型图一般用于展示数值型数据，有助于对数据的整体分布有只管了解，特别有利于对离群点/异常点的观察。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "#Series箱型图离群点/异常点显示，利用seaborn\n", - "sns.boxplot(s_norm)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "由于pandas内置的箱型图默认不含离群点，可直接利用seaborn中的boxplot函数，为Series对象绘制带离群点的箱型图。" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**1.5 样本的累积和**（累积频数与累积频率）\n", - "\n", - "**累积频数**(cumulative frequencies)，将各有序类别或组的频数逐级累加起来的频数。\n", - "\n", - "**累积频率**(cumulative percentages)，将各有序类别或组的百分比逐级累加起来。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s.cumsum()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Pandas内置了cumsum()函数，可以直接得到累积频数。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s.cumsum().plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "利用cumsum()得到的仍然是Series对象，可以直接绘图。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s_ratio = s/s.sum()\n", - "s_ratio.cumsum().plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "累积频率(累积百分比)可以类似的得到。\n", - "\n", - "`sum()`为内置的求Series对象中value和的函数。" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**1.6 最大值、最小值、众数及总体描述**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s.max()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "内置最大值函数max()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s.min()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "内置最小值函数min()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s.mode()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**众数**(mode)：一组数据中出现次数最多的数据值。一般用于测度分类数据的集中趋势，在数据量较大的时候有意义。\n", - "Pandas内置mode()函数，可以直接得到众数，注意众数可能不唯一，如此时s的众数有两个。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s.describe()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "Pandas的descibe()函数，可以给出汇总与描述统计的常用值。其中count为数据个数。" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**1.7 随机数、直方图、峰度与偏度**(公式以后有时间补充)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "np.random.seed(66)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "调用np中random模块的seed()方法，生成一个随机数种子，在这个种子下，每次第一次生成的随机数均相同。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "arr1 = np.random.randn(10000)\n", - "arr1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "利用random的randn()函数，生成一个numpy的array类型的对象，大小为10000，可理解为长度为10000的数组(所有数据元素类型相同)。\n", - "\n", - "数组内数据分布为正态分布。randn即random normal。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "arr2 = np.random.randn(50)\n", - "arr2" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "与前类似，生成了一个长度为50的array，数据为正态分布。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "s_1 = Series(arr1)\n", - "s_2 = Series(arr2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Numpy的array类型可以直接初始化为Series类型的对象。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s_1.plot(kind='hist')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**直方图**(histogram)：又称质量分布图，是数值数据分布的精确图形表示。\n", - "- 将值的范围分段，即将整个值的范围分成一系列间隔(一般要相等)\n", - "- 计算每个间隔中有多少值\n", - "\n", - "当plot()函数的kind参数为hist时，即代表直方图。\n", - "\n", - "此外，Pandas内置hist()函数，可对Series对象直接绘制直方图：`s_norm.hist()`。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s_1.plot(kind='hist',bins=50)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot()函数有一个重要参数`bins`，指定了间隔个数。此处将间隔数设为50." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s_1.plot(kind='kde')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**核密度估计图**(Kernel Density Estimation, KDE)：采用平滑的峰值函数(“核函数”)来拟合观察到的数据点，从而对真实的概率分布曲线进行模拟。\n", - "Pandas的plot()函数中，参数kind的值为kde时，即可绘制KDE图。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "sns.distplot(s_1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Seaborn中内置的distplot()函数，可以直接对Series对象绘制直方图及KDE图的合一图。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "sns.distplot(s_2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "类似地，可以为s_2绘制histogram及kde图。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s_1.skew(),s_2.skew(),s_1.kurt(),s_2.kurt()," - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**偏度**(Skewness)、偏态、偏态系数，是统计数据分布偏斜方向和程度的度量，是统计数据分布非对称程度的数字特征。正态分布的偏度为零。\n", - "- Skewness=0 分布形态与正态分布偏度相同\n", - "- Skewness>0 正偏差数值较大，为正偏或右偏。长尾巴拖在右边。\n", - "- Skewness<0 负偏差数值较大，为负偏或左偏。长尾巴拖在左边。\n", - "\n", - "**峰度**(Kurtosis)、峰态、峰态系数，是描述总体中所有取值分布形态陡缓程度的统计量。正态分布的峰度为3(也有很多将计算得到的峰度-3，而使正态分布峰度为零的做法)。\n", - "- Kurtosis=3(或0) 分布形态与正态分布峰度相同\n", - "- Kurtosis>3(或0) 正偏差数值较大，尖峰，厚尾。\n", - "- Kurtosis<3(或0) 负偏差数值较大，扁峰，薄尾。\n", - "\n", - "本例中，s_1比s_2更接近于正态分布。" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3. Series类型基本操作整理\n", - "**3.1 创建与查看**\n", - "\n", - "创建一个Series的基本方法是：s = Series(data, index=index)。其中，data指代许多不同的数据类型：\n", - "- a Python dict\n", - "- an ndarray\n", - "- a scalar value (like 6)\n", - "\n", - "index指一个标签序列，一般用list类型。" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**3.1.1 利用list创建Series**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s = Series([x*x for x in range(100)])\n", - "s" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 第一行利用一个list，创建了一个Series对象s\n", - "- 第二行查看该对象。\n", - "- 该对象由两部分组成，第一列为index，第二列为values。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "数据太长，可用head()函数查看前几个数据。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s.tail(10)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "也可用tail()函数查看最后几个数据，head()及tail()函数内可放整型参数，代表列出的数据个数。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s.values" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "查看序列s的值" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "type(s.values)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 查看s.values的类型\n", - "- Series对象的values的类型确实为numpy的array（数组）类型，即ndarray(n维数组)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s.index" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 查看s的index(索引)的值" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "type(s.index)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 查看s.index的类型\n", - "- Series对象的索引在未指定时，为pandas内置的RangeIndex类型，类似于python的range类型。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s2 = Series([x*x for x in range(26)], index=set('abcdefghijklmnopqrstuvwxyz'))\n", - "s2.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 利用list创建Series对象时，可用index参数指定一个list或set对应索引。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "type(s2.index), s2.index" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 指定索引后，Series对象的索引是pandas内置的Index类型" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**3.1.2 利用dict创建Series**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "data = {'喜欢':3000,'爱':7000,'讨厌':1000,'恨':500}\n", - "s3 = Series(data)\n", - "s3" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 可用dict创建Series对象，则字典的键自动成为index" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s3 = Series(data, index = ['a','b','c','爱'])\n", - "s3" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 初始化时，也可以额外指定index。但是如果指定的index找不到对应的值，则该index对应值设为NaN（Not a Number）" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**3.1.3 利用标量创建Series对象**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s0 = Series(5, index = ['a','b','c','d','e'])\n", - "s0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 利用标量创建Series对象时，对象的值将均为标量的值" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**3.1.4 利用Series对象创建Series对象**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s4 = Series(s3)\n", - "s4" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 这是最简单的利用Series对象创建Series对象的代码" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s5 = Series(s4, index = ['喜欢','爱','讨厌','中'])\n", - "s5" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 与利用dict初始化类似，也可以额外指定index。" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**3.2 访问Series对象的元素**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s4[0], s4['爱'], s4.爱" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 利用位置索引或者index均可以访问对应元素\n", - "- 还可以利用index的值作为属性来访问对应元素" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s4[0:2]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s4['喜欢':'爱']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 位置索引及index的切片访问也适用，但利用index切片是包含末端的" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s4.index" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 可以利用index属性访问Series对象的索引" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s4.values" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 可以利用values属性访问Series对象的值" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "for k, v in s4.items():\n", - " print(k, v)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "for k in s4.keys():\n", - " print(k)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "for v in s4:\n", - " print(v)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 可以类似dict一样迭代访问Series对象的各个元素" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "for k in s4.index:\n", - " print(k)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "for v in s4.values:\n", - " print(v)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 也可以利用index与values属性迭代访问" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**3.3 修改、删除、排序等基本运算**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s4[0] = 10000\n", - "s4" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s4['爱'] = 4078\n", - "s4" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- Series对象内的值可以直接根据位置或index被赋值（修改）" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s4[0:3] = 666\n", - "s4" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 可以切片式赋值" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s4.index = ['a', 'b', 'c', 'd']\n", - "s4" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- Series对象的index可以直接被赋值（修改），注意新的index的长度要与Series对象中index的长度一致。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s4 = s3.reindex(['喜欢','爱','讨厌','中', 'e'])\n", - "s4" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 可用reindex方法来更改index，找不到对应值的index，则其值为NaN。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s5 = s4.drop('e')\n", - "s4" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s5" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 可利用drop函数删除指定索引及值，生成新的Series对象，但原series对象不变。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s4+s5" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s4-s5" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s4*s5" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s4/s5" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- Serires对象可以直接进行四则运算，运算规则是对应索引的值进行运算，整体运算结果仍然是Series对象。\n", - "- 如果索引值无法对应，则结果为NaN。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s4.sort_values()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 按照values（值）进行排序，按值排序时，NaN将排在尾端" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s4.sort_index()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 按照index（索引）进行排序" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s4.sort_values(ascending=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 将ascending参数指定为False，则变为倒序排序" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**3.4 处理NaN值**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s5 = s4.fillna(1)\n", - "s5" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 可以利用内置fillna()函数，将NaN值替换为指定数值" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s6 = s4.reindex(['喜欢','爱','不讨厌','中'], fill_value = 0)\n", - "s6" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 可在reindex方法内利用fill_value参数，指定值来替换在重新索引时新出现的NaN值，\n", - "- 注意，fill_value参数与不会对原有NaN值进行替换。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s4.add(s6)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s4.add(s6, fill_value=0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 两个Series对象相加可用add方法，设置fill_value参数的值，替换非对齐index部分的NaN值。fill_value参数不会对运算前的NaN进行填充。\n", - "- 类似还有sub，div，mul方法，分别对应减法、除法及乘法。" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**3.5 过滤**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s > 1000" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- Series对象进行比较等逻辑运算，结果仍然是一个Series对象，每一维度的值根据运算结果为True或False" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s[s>1000]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 可以利用逻辑运算的结果Series，对Series对象进行过滤，得到满足条件的Series" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**3.6 时间序列(Time Series)**示例" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "rng = pd.date_range('1/1/2012', periods=100, freq='S')\n", - "rng" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- Pandas内置的date_range()函数，可以很方便的得到时间标签\n", - "- 第一个参数'1/1/2012'为时间字符串，表示开始时间\n", - "- 第二个参数periods为时间标签个数，此处设为100\n", - "- 第三个参数freq为时间频率间隔，此处的'S'表示间隔为秒，常用的为S,Min,H,D,M分别为秒、分、小时、天，月。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "ts = Series(np.random.randint(0, 500, len(rng)), index=rng)\n", - "ts.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 将时间标签作为Series对象的index，随机生成0-500间100个整数，作为Series对象的值。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "ts.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 利用plot()函数绘图" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "ts1 = ts.resample('Min').mean()\n", - "ts1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 利用resample()函数，可将时间序列重新抽样。\n", - "- 样本时间间隔根据resample()函数参数决定，本例中，样本时间间隔设为分钟(Min)\n", - "- 重新设定样本间隔后，可利用mean()函数，求得每个时间间隔内值的均值，作为新的index对应的值" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**3.7 输出**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "s.plot()\n", - "plt.savefig('test.jpg')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 利用内置的plot()函数展示图片后，可利用plt的savefig()函数将图片保存为文件输出。\n", - "- 可以查看当前目录下的test.jpg文件，与当前显示一致。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "s.to_csv('test.csv')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 利用to_csv()函数，可将Series对象输出为.csv文件\n", - "- 可在当前目录下，找到test.csv，双击打开查看" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 6850a901d65677fc0b66b44cc857ca64294102a8 Mon Sep 17 00:00:00 2001 From: liupengyuan Date: Tue, 17 Oct 2017 21:15:01 +0800 Subject: [PATCH 30/44] Update 2.md --- chapter1/2.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/chapter1/2.md b/chapter1/2.md index f54f9aa7c..67e91bece 100644 --- a/chapter1/2.md +++ b/chapter1/2.md @@ -338,7 +338,7 @@ print('再见！', name) ``` - 练习2：仿照实践1，写出由用户指定整数个数，并由用户输入多个整数，并求和的代码。 - 练习3：用户可以输入的任意多个数字，直到用户不想输入为止。 - - 练习4：用户可以输入的任意多个数字，直到输入所有数字的和比当前输入数字小，且输入所有数字的积比当前输入数字的平方大。 + - 练习4：用户可以输入的任意多个数字，直到: 输入所有数字的和比当前输入数字小，且输入所有数字的积小于500。 [^1]:实际上有多种代码缩进方式，我们推荐并只介绍python创始人采用的这种方式。 From b69e558d25167ec76c197c09174f81aea404ffe2 Mon Sep 17 00:00:00 2001 From: liupengyuan Date: Tue, 17 Oct 2017 21:24:22 +0800 Subject: [PATCH 31/44] Update 2.md --- chapter1/2.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/chapter1/2.md b/chapter1/2.md index 67e91bece..0d32d7998 100644 --- a/chapter1/2.md +++ b/chapter1/2.md @@ -338,7 +338,7 @@ print('再见！', name) ``` - 练习2：仿照实践1，写出由用户指定整数个数，并由用户输入多个整数，并求和的代码。 - 练习3：用户可以输入的任意多个数字，直到用户不想输入为止。 - - 练习4：用户可以输入的任意多个数字，直到: 输入所有数字的和比当前输入数字小，且输入所有数字的积小于500。 + - 练习4：用户可以输入的任意多个数字，直到: 输入所有数字的和比当前输入数字小，且输入所有数字的积大于500。 [^1]:实际上有多种代码缩进方式，我们推荐并只介绍python创始人采用的这种方式。 From d6c638cd73c4fe06bc1f9d00f13af79c7daf3702 Mon Sep 17 00:00:00 2001 From: liupengyuan Date: Fri, 20 Oct 2017 16:01:17 +0800 Subject: [PATCH 32/44] Update 9.md --- chapter2/9.md | 15 +++++++-------- 1 file changed, 7 insertions(+), 8 deletions(-) diff --git a/chapter2/9.md b/chapter2/9.md index b378ef156..9fa29c48c 100644 --- a/chapter2/9.md +++ b/chapter2/9.md @@ -905,7 +905,7 @@ def count_words_freq_dict(filename): with open(filename) as f: for line in f: - words_freq_dict += Counter([word.split('/')[0] for word in line.split()]) + words_freq_dict.update([word.split('/')[0] for word in line.split()]) return words_freq_dict ``` @@ -913,10 +913,9 @@ def count_words_freq_dict(filename): - 首先从`collections`模块中引入了`Counter`类型 - `words_freq_dict = Counter()`是建立了一个空的Counter对象，Counter类型类似于`dict`，也可以视为一种特殊的`dict`。 -- `Counter([word.split('/')[0] for word in line.split()])`，实际上是将一个`list`转为`Counter`类型，转换过程中，`Counter()`会自动统计`list`中的对象频次，并以键值对的形式存入`Counter`对象，且与`dict`类似，这个`Counter`对象的`key`是可哈希的，即可利用哈希函数存取，时间复杂度为O(1)。 -- `Counter`对象支持加法操作，其结果就是新的`Counter`对象中相同键的值相加，同时保留各自不同的键值对。 +- `words_freq_dict.update([word.split('/')[0] for word in line.split()])`，其中`update()`是`Counter()`类型变量的一个函数，会自动更新其参数(一般为一个序列)中的对象频次，并以键值对的形式存入`Counter`对象，且与`dict`类似，这个`Counter`对象的`key`是可哈希的，即可利用哈希函数存取，时间复杂度为O(1)。 -好吧，我们将词频统计的代码精简到了7行，且效率很高，这就是python语言的威力。 +好吧，我们将词频统计的代码精简到了6行，且效率很高，这就是python语言的威力。 在本节任务中，我们利用词频统计任务，将程序的效率逐步提升，与此同时，所用数据类型逐步复杂，代码量逐步减少。 之所以这样安排而不是直接给出最优答案确实是期望在这个过程中，读者能对各种相关数据类型和算法有更清醒的认识和理解，也能够进一步锻炼使用python进行编程的能力。 通过1-9这几个任务，我们已经基本介绍并掌握了python语言的常用基本语法，并已对python编程有一定的实践能力。 @@ -1016,14 +1015,14 @@ print(palindrome(seq)) #coding: utf-8 #示例程序9-32 -from collections import Counter +from collections import defaultdict #假设之前已经词频统计完成，并已放入Counter或dict对象word_table中 -ch_table = Counter() +ch_table = defaultdict(int) for word, freq in word_table.items(): for ch in word: - ch_table += Counter({ch:freq}) + ch_table[ch] += freq for ch, freq in ch_table.items(): print(ch, freq, sep=':', end='|') @@ -1202,7 +1201,7 @@ def count_words_freq_dict(filename): with open(filename) as f: for line in f: - words_freq_dict += Counter([word.split('/')[0] for word in line.split()]) + words_freq_dict.update([word.split('/')[0] for word in line.split()]) return words_freq_dict def main(): From e3e12cb4feb46bbf3e09019aa21a33d783a249a1 Mon Sep 17 00:00:00 2001 From: liupengyuan Date: Fri, 20 Oct 2017 16:52:16 +0800 Subject: [PATCH 33/44] Update 9.md --- chapter2/9.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/chapter2/9.md b/chapter2/9.md index 9fa29c48c..a72801614 100644 --- a/chapter2/9.md +++ b/chapter2/9.md @@ -1031,7 +1031,7 @@ for ch, freq in ch_table.items(): 示例程序9-32中： -- `Counter({ch:freq})`是利用一个词典来初始化一个Counter对象。 +- 对词频统计结果中的每个词进行遍历，对组成每个词的字进行遍历，每个词中包含的字的频次为当前的对应词的词频。 9.9 拓展与汇总 From 1f4e7dc51126152174b017f310d187327f801c43 Mon Sep 17 00:00:00 2001 From: liupengyuan Date: Fri, 20 Oct 2017 16:58:59 +0800 Subject: [PATCH 34/44] Update task10.md --- chapter3/task10.md | 24 ++++++++++++++++++++++++ 1 file changed, 24 insertions(+) diff --git a/chapter3/task10.md b/chapter3/task10.md index d645c27bc..ca7a6bdd6 100644 --- a/chapter3/task10.md +++ b/chapter3/task10.md @@ -256,6 +256,30 @@ python解释器默认的搜索位置顺序是：1、当前目录；2、path环 因此，将我们自定义的模块/程序放置到当前目录或者上述代码列出的任意一个目录中，均能够被导入。 +注意，由于对Counter对象的加法计算耗时较多，因此，可以考虑在示例代码3-7中直接原task9中示例程序9-36中的关键语句。 + +```python +#coding: utf-8 +示例代码3-8 +import os +from Collections import Counter + +def count_words_dir(path): + words_freq_dict = Counter() + for root, dirs, files in os.walk(path): + for file in files: + file = os.path.join(root, file) + for line in file: + words_freq_dict.update([word.split('/')[0] for word in line.split()]) + return words_freq_dict + +# 测试 +if __name__ == '__main__': + path = r'd:\temp' + print(list(count_words_dir(path))[:20]) +``` + +当文件较多时，示例代码3-8的性能，将比示例代码3-7，有几倍的提升。 **10.4 输出结果为csv格式、xlsx格式及JOSN文件格式** From 710d1252367b7d8ee7c940038d5de508bac0a7e1 Mon Sep 17 00:00:00 2001 From: liupengyuan Date: Sat, 21 Oct 2017 20:46:26 +0800 Subject: [PATCH 35/44] Update task10.md --- chapter3/task10.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/chapter3/task10.md b/chapter3/task10.md index ca7a6bdd6..0d08e6e07 100644 --- a/chapter3/task10.md +++ b/chapter3/task10.md @@ -514,7 +514,7 @@ def count_gram_freq_dict(filename, n=2): words = [word.split('/')[0] for word in line.split()] if len(words) >= n: for i in range(len(words)-n+1): - gram_freq_dict['_'.join(words[i:i+n-1])] += 1 + gram_freq_dict['_'.join(words[i:i+n])] += 1 return gram_freq_dict ``` From 113afd594d4425ae3eb8ae13c865a2f8c6511eb8 Mon Sep 17 00:00:00 2001 From: liupengyuan Date: Sun, 22 Oct 2017 01:03:33 +0800 Subject: [PATCH 36/44] Add files via upload --- chapter4/pandas_Tutorial_1(Series).ipynb | 3757 +++++++++++++++ chapter4/pandas_Tutorial_2(DataFrame).ipynb | 4830 +++++++++++++++++++ chapter4/pandas_Tutorial_3(group).ipynb | 3653 ++++++++++++++ 3 files changed, 12240 insertions(+) create mode 100644 chapter4/pandas_Tutorial_1(Series).ipynb create mode 100644 chapter4/pandas_Tutorial_2(DataFrame).ipynb create mode 100644 chapter4/pandas_Tutorial_3(group).ipynb diff --git a/chapter4/pandas_Tutorial_1(Series).ipynb b/chapter4/pandas_Tutorial_1(Series).ipynb new file mode 100644 index 000000000..03e745c7f --- /dev/null +++ b/chapter4/pandas_Tutorial_1(Series).ipynb @@ -0,0 +1,3757 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Pandas统计分析入门（1）\n", + "- 转载注明转自：https://github.com/liupengyuan/\n", + "- ## Pandas及相关python库简介\n", + "- ## 一维数据统计分析(Series基础)\n", + "- ## 二维数据统计分析(DataFrame基础)\n", + "- ## Visualization基础\n", + "---\n", + "## Pandas 简介\n", + "\n", + " pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. It is already well on its way toward this goal.\n", + "\n", + "## Numpy 简介\n", + "\n", + " NumPy is the fundamental package for scientific computing with Python. It contains among other things:\n", + "- a powerful N-dimensional array object\n", + "- sophisticated (broadcasting) functions\n", + "- tools for integrating C/C++ and Fortran code\n", + "- useful linear algebra, Fourier transform, and random number capabilities\n", + "\n", + "## Matplotlib 简介\n", + "\n", + "Matplotlib是一个Python的2D绘图库，它以各种硬拷贝格式和跨平台的交互式环境生成出版质量级别的图形。\n", + "通过Matplotlib，开发者可以仅需要几行代码，便可以生成绘图，直方图，功率谱，条形图，错误图，散点图等。\n", + "\n", + "## Seaborn 简介\n", + "\n", + "Seaborn is a library for making attractive and informative statistical graphics in Python. It is built on top of matplotlib and tightly integrated with the PyData stack, including support for numpy and pandas data structures and statistical routines from scipy and statsmodels.\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- ## 程序头部引入" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "from pandas import Series, DataFrame\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "以上引入各类利用pandas做数据分析可视化的模块，一般可作为每次数据分析项目/任务的起始代码，其中：\n", + "- `%matplotlib inline`是jupyter notebook的一个魔法命令，能够使基于matplotlib的绘图直接显示在网页中\n", + "- `from pandas import Series, DataFrame`，从pandas中引入最为常用的两个对象：Series及DataFrame\n", + "\n", + "**注意**，在以上的引入中，类似`pd`、`np`、`plt`及`sns`均为约定俗成的习惯性名称，**不建议**更改。\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 一、一维数据统计分析(Series基础)\n", + "\n", + "- 数据的描述、分析、可视化展示、概括性度量、输入与输出" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. Series对象及数据最基本展示\n", + "\n", + "- 将以一个词频统计结果的实例，进行介绍。\n", + "- 教程中的各个代码段，请自行建立新的python程序，依次键入并顺序执行，观察执行结果。\n", + "- Series是pandas最重要最基础的数据对象，可用来表示数据表中的一列或一行。" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "words_freq = [200,300,400,350,390,600,900,400,300,120]\n", + "freq_dict = {'天长地久':words_freq}\n", + "total_words_freq = [12345000,23456000,22333000,45632000,11144000,65433000,44444000,55555000,34522000,55566000]\n", + "years = [2006,2007,2008,2009,2010,2011,2012,2013,2014,2015]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "以上代码中：\n", + "- `words_freq`，是“天长地久”在2006-2015年10年之间的频次list\n", + "- `freq_dict`，键为“天长地久”，值为其历时频次的list\n", + "- `total_words_freq`，是每年统计文本语料的总字数list\n", + "- `years`，是年份list" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 200\n", + "1 300\n", + "2 400\n", + "3 350\n", + "4 390\n", + "5 600\n", + "6 900\n", + "7 400\n", + "8 300\n", + "9 120\n", + "dtype: int64" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s = Series(words_freq)\n", + "s" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "利用list来初始化Series对象。Series是pandas内置的处理一维数据的数据类型。\n", + "- 第一列为Series对象的索引(index)，默认为数字索引；\n", + "- 第二列为Series对象的值(value)\n", + "- 最后一行说明，Series对象的值为int64类型" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2006 200\n", + "2007 300\n", + "2008 400\n", + "2009 350\n", + "2010 390\n", + "2011 600\n", + "2012 900\n", + "2013 400\n", + "2014 300\n", + "2015 120\n", + "dtype: int64" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s = Series(freq_dict['天长地久'], index = years)\n", + "s" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "可以在初始化Series对象时，同时指定索引(index)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "year\n", + "2006 200\n", + "2007 300\n", + "2008 400\n", + "2009 350\n", + "2010 390\n", + "2011 600\n", + "2012 900\n", + "2013 400\n", + "2014 300\n", + "2015 120\n", + "Name: 2006-2015, dtype: int64" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.name = '2006-2015'\n", + "s.index.name = 'year'\n", + "s" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "还可以指定Series对象的名称(name)，指定其索引(index)的名称(name)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + 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vAQBtbW0wmUxYsmQJSkpKAABFRUVIT09HamoqSktLYbFYYDQaYTAYkJCQ4JAi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\nD+5dkYgFc6Y5tEEiIqLxaHx98NPVqfi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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "s.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Series对象可利用pandas内置的plot()直接绘图，默认为折线图。\n", + "\n", + "**折线图**多用于展示**数值型数据**，特别是用于展示历时数据。\n", + "\n", + "统计学中，依据计量尺度，一般可将数据分为三种类型：\n", + "- 分类数据\n", + "- 顺序数据(定序数据)\n", + "- 数值型数据(定量数据)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "s.plot(kind='bar')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "plot()函数的参数`kind`，可以指定plot()函数所绘制图形的种类，`'bar'`为柱状图。也可以采用这种形式：`s.plot.bar()`，效果与指定kind参数为bar类似。\n", + "\n", + "**柱状图**用宽度相同的柱的高度或长短来表示数据多少。\n", + "\n", + "柱状图一般用于展示**分类数据**，展示不同类别数据的多少(可将以上数据的每个年份理解为不同类别)。" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "s.plot('pie',figsize=(6,6))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "'pie'为饼图。\n", + "**饼图**使用圆形及圆内扇形的角度表示数值大小的图形。\n", + "\n", + "一般也用于展示**分类数据**，用来表示各分类部分数据占全部数据的比例(频率分布)。" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "s.plot('pie',figsize=(6,6))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "可利用plot()函数的`figsize`参数，设定图形的大小(长与宽)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. 数据基本分析、度量与展示\n", + "\n", + "**2.1 归一化**\n", + "\n", + "**归一化**一般要使数值的绝对值变成**相对值**，将数值映射到[0,1]区间上。\n", + "\n", + "在本例中，由于每个年度的语料大小不同，单纯观察每个年度某个词汇出现的频次(绝对值)，无法确定其出现的相对高低(相对值)。\n", + "\n", + "因此可将频次归一化，即某词汇每年出现的次数/每年语料的总字数。这样，该词汇就可以利用频次归一化的数值进行横向比较了。\n", + "\n", + "在实际中，也可以将频次归一化的结果乘以一个较大的整数。在本例中，可乘以10000000，表示语料中每10000000字出现该词汇的频次。" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "#python实现\n", + "def norm_freq(freqs, total_freqs, per = 1):\n", + " return [per * freq/total_freq for freq, total_freq in zip(freqs, total_freqs)]\n", + "\n", + "per = 10000000\n", + "s_norm = norm_freq(freq_dict['天长地久'], total_words_freq, per = 10000000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "上面的代码用python实现了将每年的频次归一化为每年每一千万字该词汇出现的频次。" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2006 162.008910\n", + "2007 127.899045\n", + "2008 179.107151\n", + "2009 76.700561\n", + "2010 349.964106\n", + "2011 91.696850\n", + "2012 202.502025\n", + "2013 72.000720\n", + "2014 86.901107\n", + "2015 21.595940\n", + "dtype: float64" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s_norm = Series(s_norm, index = years)\n", + "s_norm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "利用归一化处理后的结果初始化一个Series对象s_norm。注意，此时value的数据类型自动转换为float。" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "year\n", + "2006 162.008910\n", + "2007 127.899045\n", + "2008 179.107151\n", + "2009 76.700561\n", + "2010 349.964106\n", + "2011 91.696850\n", + "2012 202.502025\n", + "2013 72.000720\n", + "2014 86.901107\n", + "2015 21.595940\n", + "Name: 2006-2015, dtype: float64" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#pandas的Series运算\n", + "s_norm = per * s / total_words_freq\n", + "s_norm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "也可以直接利用pandas的序列运算进行归一化。运算结果与python编写代码一直，但是更简洁。\n", + "- `s_norm = per * s / total_words_freq`，其中`s`为Series类型，`total_words_freq`为list类型，直接相除运算的结果是将s中每个对应的值除以list中每个值。这种向量/矩阵运算是numpy及pandas中非常常见的情况，一般被称为**矢量化操作**。" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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LX/98M7+atra+cUZ3Hkajv0vVMWQdYkfNHj4+/ykKCnMjZnNPzmqO1xZxtq2E\n8u4qqrrqeOXsOxi9Q8k2ZpAdlkFiYLzT33N1tWNxJVVDFbxXvpcIn3DWTb+d9nbXfE7XHY4FuH4d\nc4PnEnmjkT8e38pb53ZzsOY496beSXpoqtrRxszVjwU47o+Pqzbvl1566fJ/b9q0iZ/97Gc89dRT\nHD16lPnz57N//34WLFhAdnY2v/71rxkeHsZsNlNVVUVKSopDAgrHOtNWzLbyt+ge7sHoHcqG1HWk\nhSRjDPbH1xrITdMWMmAZoKijlMK2Yoo7y9hbv5+99fvx8/AlOyydbGMGacHJeOg81C7HbdgVOw2m\nRiq6qnm/7kMMWg8eyrwfL72n2tGEG5gdncVP5kezs+YDPjp/gN+deY688BzuSr5N1gxwUWPeiuhH\nP/oRTzzxBE8//TSJiYmsXLkSnU7Hpk2byM/PR1EUHn30UTw95UPHmXQNdbOt/G0K24vRaXSsnr6U\nlfE3X7EB+3j4MC9yNvMiZ2OxWSjrquRMWzFn20s41HScQ03HMegMpIekkmPMIDM0DR+ZTDUmdsVO\nQ18j5d1VVHZXU9ldw6B1CACNRsPmmRuI9otUOaVwJ546A3fOuIW5EbPYWradk61nKOks4/akNSyM\nnuf0o2riizSKoihqBnD1IRBw7qEcm93GJxcO8W71bsw2MzOCEtiYuo5I34gvfN9oarArdmp66ils\nL+ZMWxFtgx0AaDVaZgQlkhOWQbYxnRAv9Xa5ctZj8flmXdFVTVXP35o1XHwmNyUokeTgJBYkZWPv\nd/0tPp31WIyVO9Tx5Rrsip2DF47ydtX7DNmGSAiIZ2PaOmL8olRMOTJ3ORaOIM3bAZz1hKrrPc/W\nsu2c77uAr96HO2fcwoKoOVec/DTWGhRFoXmglTNtxRS2FVPXd/7yv8X6x1xq5BlE+0ZO6mQrZzkW\nX27Wld01DNn+1qyN3qEkX2rWyUGJBHsF/e3fnKSGayV1OI+vq6FnuJfXK97hVGshWo2WpbGLWJOw\nDIPOOSe0ucuxcARp3g7gbCfUoHWIHdW7+aThEAoK8yPzuHPGLfgb/L72Z661hu7hHgrbSihsL6as\nqxK7cnHxnlCvEHIuTXhLCpo+4UNzah0Lm9128Z51dzUVXVVUdtdeoVknkRyc+JVm/WXOdj6Nl9Th\nPEaqobijlFfL3qRjqItQr2DuTb2TjNC0SUw4Ou5yLBxBmrcDOMsJpSgKZ9qK2Fb+Nj3mXsJ9wtiY\nuo6U4BmpW1jyAAAgAElEQVQj/qwjaxiwDFLSUcqZ9mKKO0oZtl1csMfPw5fMsJnkhGWQFpKCYQIm\nvE3WsRipWYd7hzEjKHFUzfrLnOV8ulZSh/MYTQ1mm5mdNR+y9/x+7Iqd2eHZ3J18G4GeAZOUcmTu\nciwcwfVvrAkAOoe62Fb+Fmfbz6HX6FgzfRkr4peoMiPcx8ObOZGzmBM5C4vdSnlXJYVtxRS2l3Ck\n6QRHmk5g0HowMySFbGMGmWEz8fPwnfScY/FZsy7vqqKiu5qq7hqGbMOX/z3cO4y84OzLV9dBnoEq\nphVi7Aw6A3fMWMPcyFlsLd3OqdZCSjrKuT1pNTfEzJcJbU5GrrwdQM2/Bm12G/saDvJezQeYbWaS\ngxLZmLqOCN/wMb3OZNRgV+zU9Z6/eJ+8vZiWgTbg4oS3pMDp5BgzyQ5LJ9Q7ZNzv4ag6RmzWPmEX\n71lPQLN2h6sLkDqcyVhrsCt2Pm08xttVOxm0DjE9II6NqeuY5h89gSlH5i7HwhGkeTuAWidUbW89\nW0u302BqxNfDh7tmrGVe5OxxTRBTo4bm/lYK2y9OeKvprb/89Ri/qEsT3jKZ5je2Fd7GW4fNbuO8\n6QIVXdWUd1dR3V17hWadREpQIjMm+MraHT6gQOpwJuOtoWe4jzcq3uFk6xm0Gi03x97ImoTleKo0\noc1djoUjSPN2gMk+oQatg7xbvZv9DYdRUFgQNYc7k27BzzD+oWe1fym6h3s4236OwraLE95sig2A\nEK/gyzPXkwKno9Pqrvo6o63jy826qrvm8r15gAgfIzOCEielWX+Z2sfCUaQO53GtNZR0lPFK2Zt0\nDHUS4hXMvSl3kBk204EJR8ddjoUjSPN2gMk6oRRF4XTbWV4vf5secx8RPuFsTL2T5OCka35tZ/ql\nGLQOUdJRSmF7CUXtpZcngvnqfcgMm0m2MYOZISlX/Ov/6+qw2W3U912g4nPPWX+5WX/+0S01J+k4\n07G4FlKH83BEDWabmfdr9/Jh/SfYFTuzjFncnXKb/GE7RjJhbYrpGOxkW/lbFHWUotfquTVhBcvi\nb8JD636H0FvvRV5ELnkRuVjtViq6qjlzaXj9aPNJjjafxEOrJy0khZywixPevvwY3MjNOpzkoASn\naNZCuAKDzsDtSasvr9B2uu0s5zrLWZu0ikUx18mEtkkmV94OMJF/DdrsNj46f4CdNR9gtltICZ7B\nhtQ7ifAxOvR9XOEvWrtip76v4dKEtxKa+1sA0KAhKWg62WEZePnoOd1QQlVPLeYvN+vgS8PgQUlO\nu56zoiiYLAp+HhqX3UnsM65wTo2GO9Th6Brsip3Djcd5s2ong9ZB4v1j2Zh2F7ETPKHNXY6FI0jz\ndoCJOqFqeurYWradC6Ym/Dx8uSt5LXMjZk3Ih7or/lK0DLRdegStmJqeehT+dipH+oRfuqpOcOpm\n/WU7DtWyfX81G5cms3yua+/h7Yrn1JW4Qx0TVUOvuY/tFTs43nIarUbLTdMWckvCignbUMddjoUj\nuN+YqxsYsAzyTvUuDl44goLC9VFzuX3GGqd/FnqyRfgYWR5/E8vjb6JnuI+SzjLCggKI0EURYHCN\nZv15nb1D7DhUC8D2A9XkpRoJCfBSN5QQVxFg8OcbGRuZH5XHK2Vv8tH5A5xuPcu9qXeQFZaudjy3\nJjcpnIiiKJxsOcPPj/4nBy4cJsLHyKOzv8d9M++Rxj2CQE9/rouaw/VxeS7ZuAG27avEbLUzZ2YE\nw2YbL39YoXYkIUZlZkgK/zzvH1k1fSm95j6eKXyeP559ga6hbrWjuS258nYS7YOdvFr2JiWdZei1\netYmrmRZ3GL0bjghTXxVWX0Xx861khAVwE++NZ9/+s0nnCpvo6CindzkMLXjCTEig86DtYkrmROR\ny9bSNzjTVkRpZzlrE1exeNr1MqHNweT/pspsdht76vbxr0d/RUlnGWnByZf/gpXGPTXY7HZe+uDi\nVfZ9y1PQaTVsWpWGTqvhpQ/KGDbbVE4oxOhF+UbwyOzvcl/a3eg0Ol6veIenTvyW+t4GtaO5FekO\nKqruqWNr6Rs09jfj7+HH/Wl3kxeR6/KzjMXYfFLQSEObiRuyokiMvvjIWkyYL6vmx/He4TrePljD\n+ptH3lxGCGeh1Wi5PnoeWWHpbK/cwbHmU/zHid9yU+xCbk1YgZde5nJcK2neKhiwDPB21fscbDwK\nwMLo+dyRtBofDx+Vk4nJ1jdg5s391Xh76rjrpi8utrP2+ukcO9fCnuPnWZARQVyEa97LF1OXv8GP\nzekbmB+Zxytl29l3/iCnW8+yPuV2coyZasdzaTJsPokUReFE82n+5eh/crDxKFG+Efzj7L8nP+0u\nadxT1JsHaugfsnL7wgQCfb+4YpzBQ8emFanYFYUtu8uwq/tUpxDjlhZy8Xbg6unL6DOb+OPZF/hD\n4V9lQts1kCvvSdI20MGr5W9yrrMcD62e2xNXc3PcjXJfewqra+7jk9MXiAr14ea8aVf8nszEUObN\nDOfYuVb2FzRy06yYSU4phGN46Dy4NXEFcyJy2Fq2ncL2Ykq7KlibuJLFMdePuG+B+CLpHBPMarfy\nYf1+dtV+iMVuZWZIChtS7yTMO1TtaEJFiqLw8oflKED+shT0uq8fBNuwNJmz1R28/nEVs1KMX7lC\nF8KVRPpG8Mis73Kk+SRvVuzgjYp3OdZ0ko1pdxEf4NoLE00mGTafQJXdNfzi+G94t3oXXnovvpmR\nz8M5D0rjFhw910JFQw+zksPISLj6/uVBfp6sW5TEwLCVV/fKs9/C9Wk0Gq6LmsMTC37I/Mg8zpsa\neerE//Ba+dsMWofUjucS5Mp7AvRbBni7aiefNh4D4IaYBdyeuBofD2+VkwlnMGS28tq+KvQ6Lfcu\nTR7VzyyZFcOhoiaOlLSwMCtqxIYvhCvwN/jxQPq9LIjKY2vZdj5u+PQLE9rkyZuvJ1feDqQoCsea\nT/EvR57i08ZjRPtG8ljew2xMXSeNW1z23uE6uvqGWT0/jvCg0Z0XWq2GB1amodHAlj1lWKzy7Ldw\nHynBM/jxvH9kTcJy+i39PFu0hT+cfZ6OwS61ozktufJ2kNaBdl4te5PSrgo8tB7ckbSGm2NvlEkY\n4gtaugbYfayekABP1lwXP6afjY/0Z/mcWPYcP8+OQ3XcuShxglIKMfk8tHpuSVjOnPCLE9rOtp+j\nrLOSWxJXsGTaDfJZ+iXSvK+R1W7ljeKdvFHyPla7lfTQVO5NuZMwbxnWFF/16t5KrDaF9Utm4Okx\n9g+jO25M4HhpKzuP1LEgI4KoUFnzXriXCN9w/mHW33Gs+RRvVL7Lm5Xvcbz5NBvT1mE0Zqgdz2nI\nsPk1qOiq5hfHfs2rRe/io/fmwcz7+fvsb0njFldUWNVBQWU7qbFBzE0LH9dreBn03Lc8BZtd4YVd\nZai8o68QE0Kj0TA/Ko8n5z/Ogqg5NJga+c8Tv+O1oh1qR3MacuU9DiZLP29V7uRw03E0aFgxYxEr\nopfirZf72uLKrDY7W/dWoNFA/vKUa5qIMzvFSO6MMAoq2zlU1MzCrCgHJhXCefgZfNk0cz0LIvN4\n8dxrvFb8HopZx5LYG9SOpjq58h4DRVE42nSSnx/5Tw43HSfGL4rH8h7mobyN0rjFVX14ooGWzgGW\nzIohNtzvml/vvuUpeHroePWjSkyDFgckFMJ5JQcn8YNZ3yHIK4A3Kt7lVGuh2pFUJ817lFoG2vjv\ngmd54dyrmG1m7pxxCz+a8wMSAuPUjiacXLdpmLc/rcHP24M7bnTMJLPQQC9uvyEB06CF1/ZVOuQ1\nhXBmod4h/HjR9/HUGfhr8VYquqrUjqQqad4jsNit7Kz5gH87+jTlXZVkhqbxk/k/ZFncYpn9KEbl\n9Y+rGDbbWLcoET9vD4e97rI505hm9ONAYRPl52WNaOH+pgfH8u2sB7Cj8Iezf6XR1Kx2JNVI876K\n8q4qfnHsv3iv5gN8PXx5KHMT383+JqHewWpHEy6i8kIPh4qaiQv3Y1FOtENfW6/TsnlVKhrghd1l\nWG12h76+EM4oLSSZTTPXM2gd4ndnnpuym5tI874Ck7mfLSXb+M3pP9A60M7iaQt5YsEPmRWeJSv+\niFGzKwovfVAOXJykptU6/txJiglk8awYGtv72X2s3uGvL4Qzmhc5mzuS1tA93MPvz/yZAcug2pEm\nnTTvz1EUhSNNJ/iXo09xpPkEsX7RPD7n+6xPuR1v2TxejNHBwibqmvtYkB5BSmzQhL3P3YsTCfA1\n8M6ntbR2T70PMTE1LYtbzOJpC2nsb+aPZ/+KxW5VO9KkkuZ9SXN/K785/Qe2nNuGxW7lrhm38vic\n/yO73IhxGRiy8MYnVXh66LhnyYwJfS8fLw82LJ2BxWrnxT3y7LeYGjQaDXcnryXXmEVFdzUvlLyC\nXZk6t46m/HPeFpuF3XX7+KBuH1bFRlZYOutTbifES+5ri/F7+2AtfQMW7lqcSLC/54S/3/yZEXxa\n2ERRdSfHS1uZNzNiwt9TCLVpNVq+kb6B3xaYONVaSJBnIHclr1U71qSY0lfeZZ2V/Nux/+L92g/x\nM/jxnawH+G72N6Rxi2tyob2fvScbCA/yZsXcyXmUUKPRcP/KVPQ6LVs/rGBgaGoNIYqpy0PnwXez\nNxPpG8FH5w+wt36/2pEmxZRs3n1mE38teYX/LvgjbYMdLJl2A0/Mf4wcY6ba0YSLUxSFlz8ox64o\nbFiWjId+8n7FIoJ9WHt9PD39Zt7cXz1p7yuE2nw8fHg451sEGgLYXrmDEy0FakeacFOqeSuKwqHG\n4/z8yH9yrPkUsf4x/NOc/8PdKbfhJRPShAOcKm/jXF0XWYmh5CSFTvr7r5ofT1SoDx+daqCmqXfS\n318ItYR4BfNw7oN46bx4oeRVyjrde/GiKdO8m/tb+PXpZ3ip9DWsipW7k2/j8bzvExcwTe1owk2Y\nLTZe2VuJTqth47JkVR4r9NBr2bQiFQX4665SbPapM4FHiBi/KP4u+wEA/nj2BS6YmlRONHHcvnmb\nbRberd7Nvx37NZXdNeQYM3li/g9ZEiv7wwrH2nW0no7eIVbMjSUyxEe1HGnxwSzMjKS+xcTekxdU\nyyGEGlKCZ/BA+r0M2Yb4XcFzdA51qR1pQrh18y7trODfjj3Nrtq9BBj8+buszXwn6wGCvSbumVsx\nNbX3DPLekToCfQ3cev10teOw/uYZ+HrpefNANZ29Q2rHEWJSzYnIZd2MW+kx9/K7gufotwyoHcnh\n3LJ595lNPF+8ld8WPEv7YCc3x97IT+Y/RrZs5C4myLZ9VVisdu5ZkoS3p/pPYPr7GFi/ZAbDZhsv\nf1ihdhwhJt3SuEXcHHsjzQOt/KHweSw299p9T/1PGQeyK3YONx3nrcqdDFgHifOfxsa0dcT5y31t\nMXHO1XZyorSVpJgAFmREqh3nshuyo/j0bBOnytsoqGgnNzlM7UhCTKo7Z9xC93APp1oLeb7kFR7M\nvA+txj2uWd2jCqDR1Mx/nXqGl0vfwK7YuSfldh6f831p3GJC2ex2Xv6wAg2QvywFrROtfa/RaNi0\nKg2dVsNLH5QxbLapHUmISaXVaHkgfQPJQYkUtJ3l9Yp33GYFQpdv3mabhXeqdvGL47+muqeWXGMW\nTyz4ITdNW+g2f2EJ57Xv1AUutPdzY04UCVEBasf5ipgwX1bNj6Ojd5i3D9aoHUeISeeh1fOdrM1E\n+0byScMhPqz/RO1IDuHS3a2ko4z/d/RX7K77iEBDAN/N/gbfztpEkGeg2tHEFNA7YOatAzV4e+pZ\ntzhJ7Thfa+310zEGebHn+HnOt5rUjiPEpPPx8Obvc75FkGcgb1Xt5FjzKbUjXTOXbN49w338pfhl\nfnfmOTqHu1kat4gnFvyQrLB0taOJKeTN/dUMDFu548YEAnwMasf5WgYPHfevSMWuKLywqxS7mwwb\nCjEWwV5BPJzzIN56b7ac28a5znK1I10Tl2redsXOgQtH+PnRpzjRUsD0gDh+NOcHrJtxK5465/3w\nFO6nrrmP/QWNxIT5smRWjNpxRpSVGMrctHCqGnvZX9CodhwhVBHtF8nfZW1Gi4Znz77A+T7XXQfB\nZZr3BVMTT5/8X14p246iwL0pd/BY3t8zzT9a7WhiilEUhZc+KEcBNi5LRq9zjV+jjcuS8fbU8frH\nVfT0m9WOI4QqkoMT2ZyxEbPNwu/P/JmOwU61I42L03/qmG1m3qrcyb8f/w01vXXMDs/miQWPsWja\n9TIhTajiSHELlRd6yEs1kj49RO04oxbk58m6RUkMDFt5da88+y2mrtnh2dyVvJZecx+/O/McJku/\n2pHGzKm7X3FHKf969Fd8UP8xwZ6BfC/7mzyYeb9MSBOqGRy2su3jSjz0Wu5dMkPtOGO2ZFYMCVH+\nHClpobjGNa84hHCEJbE3sCxuMS0DbTxz5nnMLraIi1M2757hXp4repHfn/kzXcM9LI+7iZ/Mf4zM\nsJlqRxNT3I7DtfSYzKxZEE9YkLfaccZMq9XwwMo0NBrYsqcMi1We/RZT1+1Jq5kTkUtNbx1/KX4Z\nu+I6G/k41QprdsXOwQtHebvqfYZsQyQExLEx7S5i/KLUjiYELZ0D7Dl2ntAAL1bPj1M7zrjFR/qz\nfE4se46fZ8ehOu5clKh2JCFUodVo2TRzPX1mE4XtxWwrf5t7U+5QZUfAsXKaK++GvkZ+dfL3vFr+\nJhoNbEi9k3/M+3tp3MJpbN1bgc2ucO/NMzB4uPaOdHfcmECwvyc7j9TR1OF69/uEcBS9Vs+3sx4g\nxi+KAxcOs7tun9qRRuWqzdtisfD444+Tn5/P3Xffzd69e6mrq2Pjxo3k5+fz05/+FPul/YK3bdvG\nunXrWL9+Pfv2jb74YZuZNyvf45cn/pva3nrywnN4Yv7j3BhznUxIE07jTGU7hVUdzIwPJi/VqHac\na+Zl0HPf8hRsdoUtu8vcZslIIcbDW+/F3+d8i2DPIN6t3sXhphNqRxrRVYfN33nnHYKCgnjqqafo\n7u7mjjvuIC0tjUceeYT58+fz5JNPsnfvXnJzc9myZQtvvPEGw8PD5Ofns3DhQgyGqz97farxLH88\nvpXOoS5CvUK4N/VOMkJTHVqgENfKYrWzdW8FWo2GjcuSXWJIbTRmpxjJnRFGQWU7h4qaWZglo1yf\n120aZvexepbMjSfcX9aRcHdBnoF8P/dBfnXy97xc+joBBn+n7kdXvbRdtWoV//AP/wBcfLZVp9NR\nXFzMvHnzAFi0aBGHDh2isLCQWbNmYTAY8Pf3Jy4ujtLS0hHf/N8P/J7u4R5WxC/hJ/P/0an/R4mp\n64MT52ntGuTm2TFMM/qpHceh8pcnY/DQ8upHlZgGXWu27UQ6dq6FJ/50lN3HzvPP//sp+041qB1J\nTIJI3wi+m/1NdBotfyraQn2v8x73q155+/r6AmAymfjBD37AI488wi9/+cvLVx6+vr709fVhMpnw\n9/f/ws+ZTCOvoTwrKoP7su8kLsj5V6gaidHoP/I3OTl3qAEcW0dHzyA7DtUS4GvgwTuz8fP2cNhr\nX81kHQuj0Z/7Vs7kLzuKefdwHT+4d5bDX9+VmAbMPLP9LJ+cbsDgoeOepcnsPlLHlj3ltPWZ+c4d\nWXjoXfN2nqsdi68z0XUYjVlovR/kV5/+kWfO/oV/XfY4EX7Od6tsxNnmTU1NPPzww+Tn57N27Vqe\neuqpy//W399PQEAAfn5+9Pf3f+Hrn2/mX+f/Lvo+bW19tLX1jTO+czAa/aUGJ+HoOp59t5ghs40N\nS5MZNA0xaBpy2Gt/nck+FtfNNPLBUT8+OFZPXnIYKbFBDnldVzunims7+fN75+jqGyYhKoCHbp1J\nVKgvqxZM52fPHmbX4Vqqznfx8J1ZBPi61jC6qx2LrzNZdSR4JnFPyu1sK3+Ln3/03zyW9zB+Bl+H\nvLaj/vi46p+Q7e3tfOtb3+Lxxx/n7rvvBiA9PZ2jR48CsH//fubMmUN2djYnT55keHiYvr4+qqqq\nSElJcUhAIdRS0dDN4eIW4iP9ucGN7wfrdVo2r0pFA7ywuwyrzXWedXWEYYuNlz4o51evFNBjMnPH\nDQn8eNNsokIvfliHh/jw4/vzmJsWTkVDD//y1+PUNbt+IxRXt3ja9ayIX0LrYDv/W/gXzDbnWlL4\nqs37mWeeobe3l9///vds2rSJTZs28cgjj/Db3/6We++9F4vFwsqVKzEajWzatIn8/Hw2b97Mo48+\niqen52TVIITD2e0KL39wcQnR+5aloNW6xyS1r5MUE8jiWTE0tvez+1i92nEmTU1TL//fX46z92QD\nUaE+/PMDedx2QwI67Rc/Gj0NOr57ewbrFiXS1TvML148ydGSFpVSi8lyW+Iq5kfmUdtbz5+LX8Jm\nd55FjTSKys+IyFCOc3CHGsBxdXxccIEXdpVxXUYk3147uVvNqnUsBoYs/PjZowwOW/n5Q/MJv8YV\n5Jz5nLLa7Lx3uI53P63Frigsy5vG3TclXfH5/S/XUVDRzh8v3U5ZsyCedYsSnf6PO2c+FmOhRh02\nu43/LfwL5zrLWRg9j42pd13TEyeTMmwu3J+iKNQ09XLiXAt2uzzrC9A/ZGH7J9V4GnTcsyRJ7TiT\nxsfLgw1LZ2Cx2nlxj/s++93U0c8vXjzJ2wdrCPQz8NiGXPKXp4x64Z3c5DD++YE5hAd7s/NIHf/9\nRiEDQ9YJTi3UotPqeCjzfmL9ovm08Ri7aveqHQlwsuVRxeSw2xUqGro5WdbGqYo2OnuHAUiKDmDz\n6jS3exxqrN46UINp0MI9S5II8ptat3/mz4zg08Imiqo7OVHWxty0cLUjOYxdUdh36gKv7avEbLVz\nXUYE9y1Pwcdr7E8QxIT58sTmOTzzdjGFVR38vy0n+D93ZRMZ4jMByYXavPRefC/nQX518n/YUbOH\nQM9Aro+eq2om3c9+9rOfqRlgYMC5JgGMh6+vp9PXYbHaKa7p5P2jdTy/q5SPTl2guqkX0DA3zUh8\nVCBnKtvZf6YRm01hRkwgOicfCrySaz0WDa0mnn+/lPAQHx66NV2V4VA1zyeNRkNSTCCfFDRSdr6L\nRdnR4340ypl+L7r6hvnfN8+y99QFvAw6vn1rOmsXJuChH/lq++vqMOh1zE8Px2yxUVDZwaGiZuIi\n/IgIdr4G7kzH4lqoWYeX3pP0kFROtBZwuu0scf4xhPuM/REyX1/HXBBI83YAZ/3FGDJbKaho591D\ntTz/fikHzzZR12LCy6DnuowI1i1OZNOKVOamRbDy+gSMAZ6U1XdzpqqDk2WtxIb7ERropXYZY3It\nx0JRFP7wTjFtPUN8+9b0y7ONJ5va55OftwcKCmcqOxg228hOCh3X66hdx2eOlDTzm9cKaewYICsx\nlH+8N5fE6NFvK3y1OrQaDZkJoYQFenGqvJ3Dxc0YPLTMiAl0qpX4nOVYXCu16/Az+JIUlMDx5lOc\nbj3LzJCUMW9R7ajmLcPmbsY0aOFMZTunytsoqunEYr342E9YoBeLcqLJSzWSFB14xSvK3BlhpMYG\nsX1/NR+dbODfXzrFTbNiuHtxEj5e7n+qnCxro7S+m5yk0HE3LHexen48R4pb+OhUA9dnRZIQFaB2\npDEzDVp4cU8Zx861YvDQ8sDKVBbnRk9IU12YFUVUqC//s72Q1/ZV0dBqYvOqNJffwEZ8VWJgPN/M\nuI9nz77A78/8mcfyHibcJ2zSc6g623zIbKWvZ1Ctt3cYtWdydpuGOV3exsnyNkrrurFfOqQxYb7M\nSjGSl2IkLsLvqh9aX66h8kIPf32/lAvt/QT5GbhveapLbMgx3mMxbLHxk2eP0NNv5ucPzVd16FPt\n8+kzpXVd/MfW08RF+PHE5jlfeXxqJGrWUVTdwZ93nqPbZCYpJoCHbk0f9zEdSx3dpmF+t/0sVY29\nTI/05/vrsggJUH/0ylnOqWvlTHUcuHCEV8q2E+Ydyg/zHsbfMLq5Qo6aba7qsPm6H73L8dJWapv6\n6O43o9Vo8PP2cPrHLr5MjaGc1q4BDhQ28eq+Cl7ZW0lhVQdt3UNMj/Jnad407l+Rwm03JDAzPpgg\nP88Rrza+XENIwMUrdZ1OQ1FNJ0dLWmhoNZE8LQhvT+e9Ch/vsdhxqJaCyg5WL4hnblrEBCQbPbWH\nBj8TFuRNe/cgRTWd+Hp5kBQz9uHBya5j2Gxj694Ktn5YgcVq584bE/nmmjT8fca/ItpY6vjsllRX\n3zBnqzs5UtLCjJhA1Ru4s5xT18qZ6ogPmIZdsVPYXkx5VxVzInLRa0f+bHSLYfPMxDDKz3fR2N7P\nwbNNAHjotcRH+JMYHUBCVACJ0QGEBXo51f0jNSiKwoW2fk6Wt3GqvI3zrRfXjtdoIC0uiFkpRmYn\nGx16j1qv03LbwgTmpoXz/PulnCxvo6Sui3uWJLEoJxqtmxyTtu5Bdh6pJ8jPwC3Xxasdx6msv3kG\nBZXtvHmgmjmpRtWb0NVUNfbwp3dLaOkaJDrMl2/fmk585OSv5+2h1/GtNTOJC/fn1Y8q+Y+tp9i0\nIpUbc6InPYuYWLcmrKB7qIcjzSd4rvhFvpv1DXTayblVovoiLS0tvTR29FPT2Et1Uy81jb00tPVf\nHvoF8PfxuNjILzXz6VEBk7ZBxGhM1FCO/dIz2KfKLg6Jt3ZdvMWg12lInx5ycUvH5DACruGq4jMj\n1WBXFD4paOT1jysZHLaREhvE5lWpqk3q+jrjORa/236Wk+VtfGdtOgsyIico2eg509AgwIEzjfzl\n/VJmpxj5/rqsUf/cZNVhtdl599Na3jtch6IoLJ8by12LE0c1k3w0rqWO4tpOnnmriP4hK0vzpnHv\nzTPQ6yZ/eQ1nO6fGyxnrsNltPFP4PCWdZVwXNZf70u4e8RalI6jevK90IIbNNupa+qhu7KWmqZfq\nxl46er+4IUREsDcJ0RcbekJ0AHHh/qrt9uPIE8pmt1Ne383J8jZOV7TT1XfxGWxPDx1ZSaHkpRjJ\nTlo151IAAB0gSURBVAp1+ND1aGvo6hvmxT1lnK5oR6/TsnbhdFbPj1PlA+lKxnosims7+dUrBcyY\nFsj/vW+2U4zwONsHlKIo/PKlU5Q39PCDu7LJTR7d5JzJqKOxvZ9nd5RQ19xHaIAnD96STlp8sEPf\n41rraO0a4LdvnOVCez8z44P53h2Zk37x4Wzn1Hg5ax1D1mF+c/oZ6vsusHr6Mm5NXPG13+vWzftK\nevrNn7s676G6qY/B4b+taqTTaoiL8CMxKpCEaH8SowMJD/aelKHdaz2hLFYbxTVdnCpvo6Cy/fK+\nyr5eenKTw5idYiRjesiEzlwdaw0ny1p5cU85Pf1mphl92bw6jaQxPH4zUcZSh9Vm52d/OU5Tez9P\nfmOuKkOsV+KMH1AX2vv52Z+PEeRn4F8fWoCnYeRzcSLrsCsKe0808PonVVisdhZmRrJxWcqEPBXh\niDoGh638aUcJpyvaCQv04gd3ZTMtfPIWQ3LGc2o8nLmOXnMfvzrxO9qHOtmYuo4bYhZc8fumXPP+\nMrui0NI5cPnKvKapl/oWE7bPLfHp46kn4XP3zhOjAiZkK7/xnFCDw1bOVndwsqyNwuqLz9MCBPoZ\nmH1phnhKbNCkXdGOp4aBIQvb9lWx/0wjGmDpnGmsW5SIl0G9qRRjqeOD4+fZureCm3KjeWBV2gQn\nGz1n/YB645Mq3jtcx6r5caxfMmPE75+oOjp6hvjzznOcq+vCz9uDzavSJvRJCEfVYVcU3jlYwzuf\n1uLpoeOhW2eSlzo5K9g56zk1Vs5eR+tAG786+Xv6LQP8XfZmssK+ui/ClG/eV2Kx2qhvNV1u5jWN\nvbR0ffFRtLBAr8vNPCEqgPhIfzyv8Yp2tCdU34CZgor2ixO/ajux2i7+rzcGeZGXGk5eipGE6ABV\nJoJdyy9FWX0Xz+8qo6VzgP+/vXuPi6re9z/+GgYG5DKggNy84QUVTQXMbHu/ZXnMS3bRPLrNUo+1\ntdIfu8u2tOJkPnr46JSeU7ZTt5lttdyVqWmWmQZJiVc0UVC8IHIRBYbbcPn+/kBI3InYLGZm0ef5\njzgOa33frrXms75r1vp+/c3uTBnZmR4d7P/cIzQ8R0GRlRfe34eLAV6f2demu5G15qwfUGXllbz0\nQSJ5BWUsfOxOWt+i56h1DqUU+45l8dHOk5SUVdCzgz/T7uuCbyMPYat1jv0nsvlg63Gs5VWM6deO\nMf3DG/2Yd9Z96nbpIUd6wTnePrACBTwdNZNw37o3wUrxbiBLSTnp13rnp6/9WXNZGqpHSGoV6FVb\nzMNDzYT6e93W42r17VB5BaUcPJVLUko2KeevUvO/3SrQm+iIAGI6t6RVoJfDv2vV4tL/lwnpfLXv\nHJVVirsig5g0rFOjXOmoT0NzrN72C3uPZDJ5RATDYlrZoWUN58wfUEdPX+atjYfpEGrmhSkx9RYd\nLXMUFltZuyOF/Sk5uJuMTBrWiQE9Quxy3DTG9jifbWHZpiPk5pcSHRHIE6O7NuoVK2fep26HXnIk\n5/7CiqNraObqwfyYpwi6bhhVKd6/k1KK3PzSOjfDnc0qrB2JDKrn7g0P9qm9Ia59qC/NfW5+dn/j\nDnUpr5gDJ3NISsnhTGZB7esdQs1Edw4kOiLQ6cY/1uqguJBtYfVXJziTWYCXhysTh3XiT92D7XZy\n0pAcZzILiFuzn7BALxY+dudtDz7S2Jz9A+rdz5P5+UQ2U0d2ZnBU2E3fp1WOI2m5rN52gvwiKx1b\n+fLE6Eibpyu9HY21PQqLrbz7eTInzl0lLNCLORN6NFouZ9+nGkpPOeIvJvLxiU34e7RgfsxT+LpX\nF20p3hqqqKwiI6foWs88nzOZhWTmFnH9f4yft4n2ob6Eh1TfDNcu2Kf2ju+AAG8OHMusnqXrZA4Z\nuUVAda++cxs/YjoHEtUpsN4TAEfT8qCoqlJ8m3SBf+05TVl5JZHtmjP13i52+cBtyCNvi9cmkXax\ngL9OitL8zmQtOPsH1FVLGX/7+z4MGPjvmX3xvcnVFVtzlFor2Lgrld2HLmJ0MTB+YHvu7dPG7oM4\nNeb2qKisYsO3qXx74AJeHq48Oa47Xdu10Hw9zr5PNZTecmw9s5NtZ3bS2ieMZ6Jm4eHqIcW7sZWU\nVVRfbr/uknu+5deRfQxAaIAXYYFenM2ykJVXDFQPbNI9/NdnsJ3pefT6NMZBkZtfwtodJzl6+jIm\nVxfGDWjPiDtbNWpP91Y54o9msnLrL9zZpSWzx3VvtHbYQg8fUN8mXWDdzpP0jQxi5phuv/keW3Kk\nXsjngy3Hyb5aQqtAL54YHUmbIMc8DWCP7bHn8EXW7khBKZg4rCPDYlpperVKD/tUQ+gth1KKj09s\nIiHzJ7q2iGB2j8cIDvLTZNnOO86lgzVzd6VruxZ1zoLzCkrr3N1+JrOQjNwimrkb6dO1JTGdW9I9\nvIVTDx9qTwG+zXjmoR4k/pLFxztPsfG7VBKPZzHtvi4OeSyrpKyCT3enYXJ1adDd0uLmhkSFkZCc\nyb7jWfTrEUI3jXqLFZVVfPHDGbbtOwsK7r2rDeMHtHfYGA72MrBnKCH+nvzvZ8l8/M0pzmVbmHJP\n5yafu6kzGAxM7DyeAmsByZdPsO7Ep8wPekKbZUvP+/erqlLk5JcQER5A/tViRzfHJo19RltYbGXD\nrlQSki/hYjAwsk9rxvQPt/lO/xvVl2Pjd6lsTzzH+AHh3N8vXNP1akkvvYuzlwp5dc3PtPRrxquP\n9/m3Ec1uN8eFHAsffHmcc9kWAnw9eGJ0JBGtteml2MKe2yOvoJRl/zrK2UuFdAgz89T4O/DT4G56\nvexTt6LXHGWVVt4+uIKzBefZ+Mi7mixTTuts4OJiIKi5p0z71wA+niaeGB3J/Ed60cLszleJ53h5\nZSLH0/Pssv7My0Xs/Pk8Ab4e3HtXG7uss6lrG+zDiN6tybpSwtYfz/7u5VQpxfbEc7z6j/2cy7Yw\noEcIr0zv4xSF295amD14YXI0fSODSMso4LU1++vc9Cr0yd1oYnaPxwhspt1Uww6dVQxwmhlibOFM\nM938XvbK0LJ5Mwb2DKWyUnH09GUSki+Rm19CRGs/TU6CfiuHUoq/X5uwYvp/dKVVoP1Gtvo99LQ/\ndQjz5cdjlzh2Jo/eXVrWeV6+ITly80tYvukoew5fxKuZK7PGdOO+vm2d6nKxvbeH0ehCdEQg7iYj\nB1JyiE++RICvxy2fq6+Pnvap+ug5h7vRxB0BkQT4anNS6jxHiPjDcDcZeXhoR176c2/atPQm/ugl\nFvx9H4nHs2iMb3EOp14m+Uwe3do1J6qB43KLhmnm7sqjwyOoqFTXbrhq2PZTSvHDkUxeXvkTKeev\nEtUpgNcev4uoTs4/Z7w9GAwG7rurLU8/1BM3Vxf+vuU4G3elUlXl0G85hY38m2n3JIEUb+Ew7YLN\nLPhzbx4a3IESayUrNh/j7U+PcDm/9Na/3EDlFZX889uTGF0MTBoe4fDBcJqi6IgAenUM4MS5qyQk\nX7rl+wuKrfzvZ8ms2vYLANNHdeUvD9xh9wF99KBHB38WTI0huIUn2386x/98cpii0vJb/6Jo8qR4\nC4dyNbpwX9+2vPp4H7q2bc6RtMssWJnIN/vPa9LL+Prn8+RcLWVYTCtCA5xr+tKmwmAw8OiITpjc\nXNiwK7XOCIY3OnQql5c/SOTAyRwiWvvx6vQ+9LfTSGl6FeLvxYKpvenRwZ/kM3nErdnPxWtjSYg/\nLinewikENffk/03sxWOjuuDqYuDjb06x+KMkLuRYfvcy8wpK+TIhHbOnG2Oc+O7ypiDAtxnj+rfH\nUlLOJ9+l/tu/l5RVsHrbL7yz6QjFZRU8PKQjf300igA7jpSmZ54ersyd0INRfduSdaWEuA/3cyg1\n19HNEg4kxVs4DYPBwIAeocTN6Eufri1Ju1jAK6t/5rM9pymvqLzt5X2yOw1reRUTBndolKkiRV3D\ne7eiVaA3e49kcvL81drXT56/ysJVP7H3SCatW3rz8rQ7ufeuNg6ZgEfPXFwMPDi4AzPHRFJZpVj2\n6RG2/pjeKPeJCOcnxVs4HV8vE/81tjtzH+yB2cvElwnpLFz1c52CcCsnz18l8XgW4SE+9LsjpBFb\nK2q4Gl34872dMQBrd6RQUlbBJ9+lsmTdAS4XlPIfd7flpT/3dvq7/Z1d38hgXvjPaPx83Nn0/WlW\nbD5GWfntn9wKfZNHxTSg58cXajhjhuAWngzsGUqZtZLk05f54Wgm+ZYyOrXyu+mjRF5e7lgsZSz7\n11EKiqw89cAd+Js97Nxy2zjjtmioFmYP8ousHD19me0/nuVYeh6Bfh48/WBP+vcIsfu45Fpwxu3h\n5+1O327BpF3M5+jpPI6evswd7f1veoXJGTP8Hk0hh5eXNnNcSM9bOLVm7q5MvieCF6bEEBbgxe5D\nF1nwwT6SUnJu+jvfH77I+WwL/e4IpkOorx1bKwAeHNQes5eJwmIrg3qF8sr0PnRsJdtBa75eJv46\nKYqBPUM5l2Xh1TW3d3VK6JsMj6oBvQ7Zdz09ZKiorGLbvrNsSUinolIRExHIoyMi6szW5uHlzoz/\n3klllWLxzL74ajC0pL3pYVvcStaVYkweJpo30/+9Bs6+PZRSfHcwg39+cwqAySMi/m2qVmfPUFlV\nRWFxOfkWK/lFVvKLyigoqv65oMhKvsVKQbGVNsFm7o4Monv7Frq9Z0KrWcX0f2SJPwxXowtj+oXT\nu3NL1mw/QdLJHI6fvcJDQzowsGcoLgYD67afoKi0gkeGdtRl4W4qgpp7On3BaCoMBgNDo1sR6u/F\n/32ezIc7UjiXbeHR4Z1wNTru4mqVUhSXVpBvKbtWkH8twvkWKwVFv75uKS7nVr1IT3dXEo9dIvFY\n9Yhzg3qFMqBH6B92fADpeWugKXxI6S1DlVJ8f+gin+5OpaSskojWfgyPacV7XyQT1MKTV6b3cegH\nly30ti1uRnLYX+7VEt7ZdJQLORYiWvvx5LjumL1MmmVQSlFqraztFVcX5LJfe8jX9ZYLiqxU3mKs\nhmburvh6mfD1MmG+9qevd83P7rWv+3i64Wp04WppBZ/tOkXi8SysFVUYXQzEdA5kSFQYEa39dDFe\ngMzn7UT0dHDfjF4zXCks46OvUzh46tdnXuc90pPu4dpNAGBvet0WN5IcjlFmrWTl1uPsT8nB3+zO\nnAk9iOkeWm8Ga/m1glxspcByXWGuLcpl13rLVqwVVfWu3+TqUl18vasLcG1RrinS3iZ8PauL8u3O\nZ1CzLYpLy0lIvsTuQxdrB6wJ8fdkcFQYf+oejJeH220t156keDsRvR3cv0XvGZJSsln/7Sl6RrTk\nP4d3cnRzbKL3bVFDcjiOUootCel8tvcMJlcXpo3uRrm1vPb74xt7yiVlFfUuz+hiwHx977imh+xp\nwtfbvU7v2cNkbLQe8I3bQinFqQv5fHcwg/0nsqmsUphcXejTNYjBUWGEh/g4XW9circT0ePBfaOm\nkAEgIMCb3NzfPyqbM2gq20JyON7Bkzm8v+U4ZdZ/fw7cAPh4utUWZLOX+7Xe8o1F2h1PD1enuEGs\nvm1RUGQl/mgmuw9lkHO1en6EtkE+DI4K5a7IIDxMznGLlxRvJ6Lng7tGU8gATSNHU8gAksNZXMor\nJu1SIaqiqs73yj6ebhhd9HVfSEO2RZVSHD+Tx3cHMziUmotS0MzdyN3dghncK4xWNkytqgW521wI\nIcQtBbfw5I7OQbo+AbkdLgYD3dv70729P3kFpew5fJE9hy+y60AGuw5k0LGVL0N6hdG7SyBurrf3\nnbszkeIthBCiSWph9mDcgPbc368dh1Mvs/tgBsln8ki9kM8/v3Wj/x0hDIoKJai5p6ObetukeAsh\nhGjSjC4uREcEEh0RSPaVYr4/dJG9RzLZ/tM5tv90jm7tmjM4KoyeHQN084ipFG8hhBB/GC2be/LQ\nkI6MG9CepJRsdh/M4Fj6FY6lX8HX28SgnqEM7BlKCyefE0GKtxBCiD8cN1cX+nYLpm+3YDJyLOw+\neJGEY5lsjk/ny4R0enYIYEh0GN3CnXMoVineQggh/tDCAr2ZfE8EDw7uQOIvWbV3qh9KzXXaoVil\neAshhBCAu8nIwGuXzc9kFrD7YAaJx7PY9P1pPt97xqmGYpXiLYQQQtwgPMRMeIiZR4Z2rB2K9adf\nsvnpl2ynGIpVircQQghxE54ebgzv3ZphMa04dSGf3Qcz2J+SzT+/OcWm3WkOG4pVircQQghxCwaD\ngYjWfkS09mNicSfij1QPxfrD0Ux+OJpp96FYpXgLIYQQt8HsaeK+vm0ZeVebOkOxrtmewsbvUu0y\nFKsUbyGEEOJ3uHEo1r1HMu02FKsUbyGEEMJGLcwejO0fzug/ta13KFaZmEQIIYRwMrcaivXLpWM1\nWY8UbyGEEKIR1BmK9WQ2uw9kaLZsKd5CCCFEI3JzdaFvZDB9I4M1W6Y+pk8RQgghRC0p3kIIIYTO\naHrZvKqqikWLFpGSkoLJZCIuLo62bdtquQohhBDiD0/Tnvc333yD1Wplw4YNzJ8/nzfeeEPLxQsh\nhBACjYt3UlISAwYMAKBXr14kJydruXghhBBCoPFlc4vFgrf3r8PBGY1GKioqcHW9+Wq0emDd0ZpC\njqaQAZpGjqaQASSHM2kKGaDp5LCVpsXb29uboqKi2r9XVVXVW7gBcnIKtWyCQwQG+ug+R1PIAE0j\nR1PIAJLDmTSFDNA0cmh18qHpZfPo6Gj27NkDwKFDh4iIiNBy8UIIIYRA4573iBEjiI+PZ+LEiSil\neP3117VcvBBCCCHQuHi7uLjw6quvarlIIYQQQtxABmkRQgghdEaKtxBCCKEzBqWUcnQjhBBCCNFw\n0vMWQgghdEaKtxBCCKEzUryFEEIInZHiLYQQQuiMFG8hhBBCZ6R4CyGEEDqj6QhrAOXl5bz44otk\nZGRgtVqZPXs2HTt25Pnnn8dgMNCpUycWLlyIi4sLGzduZP369bi6ujJ79myGDBlCZWUlixcvJjk5\nGavVypw5cxgyZIjWzWz0HO+//z579+4FoKCggNzcXOLj43WVobCwkGeffZbi4mJMJhNvvvkmgYGB\nds2gRY6rV68SGxuLxWLBz8+PuLg4/P39nToHQF5eHpMmTWLz5s24u7tTWlpKbGwsly9fxsvLiyVL\nltCiRQtdZaixc+dOtm/fztKlS+3afq1yFBYW1u5T5eXlPP/880RFRekqQ3FxMfPnz6egoAA3NzeW\nLFlCUFCQXTNokaNGWloaDz/8MAkJCXVe10sOpRQDBw6kXbt2QPW02vPnz7/5CpXGPv30UxUXF6eU\nUurKlStq0KBBatasWWrfvn1KKaVeeukl9fXXX6vs7Gw1evRoVVZWpgoKCmp/3rRpk1q4cKFSSqlL\nly6p1atXa91Eu+S43syZM9XevXt1l+Ef//iHWrJkiVJKqQ0bNqjFixfbPYMWOd544w317rvvKqWU\nio+PVy+++KJT51BKqT179qixY8eqqKgoVVpaqpRSatWqVeqdd95RSim1ZcsW9dprr+kug1JKvfba\na2rkyJHqmWeesXv7a9ia4+233679bEpLS1Pjxo3TXYbVq1erZcuWKaWU2rRpk0P2J6W02acKCwvV\njBkzVN++feu8bk+25khPT1ezZs1q8Po0v2x+77338vTTT9ecGGA0Gjl27Bh9+vQBYODAgSQkJHDk\nyBGioqIwmUz4+PjQpk0bTpw4wQ8//EBQUBAzZ85kwYIFDB06VOsm2iVHja+//hqz2Uz//v11lyEi\nIqJ2ileLxXLL6V2dNUdqaioDBw4Eqme+S0pKcuocUD1PwOrVq/Hz86v9/aSkJAYMGFD73h9//NHO\nCWzPANXbYNGiRXZt941szTFt2jQmTpwIQGVlpUN6elpkmD17NgAXL17EbDbbOUE1W3MopXjppZeY\nN28ezZo1s3+Aa2zNcezYMbKyspgyZQozZszg9OnT9a5P8+Lt5eWFt7c3FouFuXPn8swzz6CUwmAw\n1P57YWEhFosFHx+fOr9nsVi4cuUK586dY8WKFcyYMYMXXnhB6ybaJUeNFStW8Je//MXu7a9piy0Z\nmjdvTnx8PKNGjWLlypU8+OCDuszRtWtXdu3aBcCuXbsoLS116hwA/fr1o3nz5nV+//p817/XnmzN\nADBq1Kja9zuKrTnMZjMeHh7k5OQQGxvLvHnzdJcBwGg0MnXqVD766CNGjBhh1/bXsDXH8uXLGTRo\nEF26dLF7269na47AwEBmzpzJ2rVrmTVrFrGxsfWur1FuWMvMzGTq1KmMHTuW+++/v/YaP0BRURFm\nsxlvb+/aXl3N6z4+Pvj5+TF48GAMBgN9+vQhPT29MZrYILbkAEhNTcVsNtO2bVu7t72GLRmWL1/O\nE088wbZt21i5ciVz5sxxRATAthwzZ84kIyODyZMnc+HCBYKDgx0RAWhYjpu5Pt+t3tuYbMngTGzN\nkZKSwrRp03j22Wdre1f2psW2+PDDD1m3bp3TH983s3nzZjZt2sSUKVPIyclh+vTp9mjyb7IlR/fu\n3Rk2bBgAvXv3Jjs7G1XP6OWaF+/c3FymT59ObGxsbU8tMjKSxMREAPbs2UPv3r3p0aMHSUlJlJWV\nUVhYSFpaGhEREcTExPD9998DcOLECUJCQrRuol1yACQkJNRertVjBrPZXHsi4u/vX6cw6inH/v37\neeihh1i3bh1t27YlOjraqXPcTHR0dO2xsWfPHmJiYhq/0TewNYOzsDVHamoqTz/9NEuXLmXQoEF2\nafONbM2wYsUKPv/8c6C6V2g0Ghu/0b/B1hw7d+5k7dq1rF27lsDAQFatWmWXdt/I1hzLly9nzZo1\nwK+1r74rVJpPTBIXF8dXX31F+/bta1/729/+RlxcHOXl5bRv3564uDiMRiMbN25kw4YNKKWYNWsW\nI0eOxGq1snDhQtLS0lBKsWjRIrp166ZlE+2SA+CVV16hX79+DB8+3O7t1yJDVlYWCxYsoLi4mIqK\nCubOnUu/fv10l+Ps2bM899xzALRs2ZLXX38db29vp85RY+jQoXz11Ve4u7tTUlLCc889R05ODm5u\nbixdutTud//bmqFGYmIi69ev56233rJr+2vYmmP27NmkpKQQFhYGVF8Veffdd3WVITc3l+eeew6r\n1UplZSXz5893yAmhVvtUfa/bg6058vPziY2Npbi4GKPRyMsvv0yHDh1uuj6ZVUwIIYTQGRmkRQgh\nhNAZKd5CCCGEzkjxFkIIIXRGircQQgihM1K8hRBCCJ2R4i2EEELojBRvIYQQQmekeAvRhMTGxrJh\nw4bav0+ZMoXDhw/z2GOPMX78eCZNmsTx48cBOHnyJFOmTGHChAkMGTKEDz/8EIBly5bx+OOPM2rU\nKNatW+eQHEKI+jlmmighRKOYMGECy5Yt45FHHiEjI4O8vDwWL17Myy+/TGRkJKmpqTz11FPs2LGD\nTz75hCeffJK7776b8+fPM2bMGKZOnQqA1Wpl27ZtDk4jhLgZGWFNiCZEKcU999zD6tWr+eKLL1BK\n8d5779UZZjEvL4/NmzdjNpvZu3cvKSkppKSksHXrVlJSUli2bBmlpaW3nNVICOE40vMWogkxGAyM\nGzeOrVu3sn37dt577z1WrVrFF198UfueS5cu4efnx9y5czGbzQwZMoRRo0axdevW2vd4eHg4ovlC\niAaS77yFaGIeeOAB1q9fT3BwMGFhYbRr1662eMfHxzN58uTan+fOncvw4cP5+eefAaisrHRYu4UQ\nDSc9byGamJCQEIKDgxk/fjwAb775JosWLeKDDz7Azc2Nt956C4PBwJw5c3j00Ucxm82Eh4cTFhbG\nhQsXHNx6IURDyHfeQjQhSimys7OZMmUKW7ZswWQyObpJQohGIJfNhWhCduzYwdixY5k3b54UbiGa\nMOl5CyGEEDojPW8hhBBCZ6R4CyGEEDojxVsIIYTQGSneQgghhM5I8RZCCCF0Roq3EEIIoTP/H0Ty\nthPBZlZmAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "s_norm.plot()\n", + "s.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "再次绘图，并将两根折线展示在一个图上。\n", + "\n", + "可以看出，归一化前，峰值出现在2012年；归一化后，峰值出现在2010年。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**1.2 求平均值**" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "396.0" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#python实现\n", + "freq_mean = sum(freq_dict['天长地久'])/len(freq_dict['天长地久'])\n", + "freq_mean" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "计算均值的python代码。\n", + "\n", + "除非特别指出，一般平均值指的就是**算数平均值**。" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "396.0" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#pandas内置求均值函数\n", + "s.mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "利用内置均值函数mean()更为简洁。" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2006 396.0\n", + "2007 396.0\n", + "2008 396.0\n", + "2009 396.0\n", + "2010 396.0\n", + "2011 396.0\n", + "2012 396.0\n", + "2013 396.0\n", + "2014 396.0\n", + "2015 396.0\n", + "dtype: float64" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s_mean = Series([freq_mean]*10, index = years)\n", + "s_mean" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "声明一个每年数值均为均值的Series对象s_mean。" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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SxIz5E1RWVoZTp07hzjvvREVFBbKysgAAOTk5KCoqQmlpKTIyMqBSqaDT6RAbG4vq6mq3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\nKF56rxIb7s7EtDCN2GV9o0te8166dCl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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "s.plot()\n", + "s_mean.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "将频次均值与各年频次画在一个图中" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**1.3 样本方差、标准差、标准分及变异系数**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "**方差**（variance)是在概率论和统计方差衡量随机变量或一组数据时离散程度的度量。\n", + "- 统计中的方差（样本方差）是每个样本值与全体样本值的平均数之差的平方值的平均数。\n", + "\n", + "**总体**：包含所研究的全部个体(数据)的集合。\n", + "\n", + "**样本**：从总体中抽取的一部分元素的集合。\n", + "\n", + "**样本容量**：构成样本的元素的数量称为样本容量(size)。\n", + "\n", + "样本方差计算公式为：\n", + "$$s^2 = \\frac{1}{n-1}\\sum_{i=1}^{n}(x_i-\\widetilde x)^2$$\n", + "\n", + "注意样本方差的公式中，分母为`n-1`。" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "47871.11111111111" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#python实现方差\n", + "def variance(freqs):\n", + " mean = sum(freqs)/len(freqs)\n", + " return sum([(freq-mean)**2 for freq in freqs])/(len(freqs)-1)\n", + "\n", + "freq_var = variance(freq_dict['天长地久'])\n", + "freq_var" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "上述代码用python实现了求样本方差。" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "47871.11111111111" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#pandas内置方差\n", + "s.var()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "而pandas内置求方差的函数var()更为简洁。· " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "**标准差**：标准差是方差的算术平方根。" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "218.7946779771188" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#pandas内置标准差\n", + "s.std()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pandas内置std()函数,可得到序列s的标准差。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**标准分**：变量值与平均数的差除以标准差后的值，也称为**z分数**(z score)。\n", + "\n", + "标准分的计算公式为：\n", + "$$z_i=\\frac{x_i-\\widetilde x}{s}$$" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "year\n", + "2006 -0.895817\n", + "2007 -0.438768\n", + "2008 0.018282\n", + "2009 -0.210243\n", + "2010 -0.027423\n", + "2011 0.932381\n", + "2012 2.303530\n", + "2013 0.018282\n", + "2014 -0.438768\n", + "2015 -1.261457\n", + "Name: 2006-2015, dtype: float64" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(s-s.mean())/s.std()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "上述代码可得到s中所有数值的标准分，存放在一个Series对象中。\n", + "标准差的**经验法则**(当一组数据对称分布时)：\n", + "- 约68%的数据在均值附近正负1个标准差内\n", + "- 约95%的数据在均值附近正负2个标准差内\n", + "- 约99%的数据在均值附近正负3个标准差内\n", + "在正负3个标准差以外的数据，称为**离群点(outlier)**。\n", + "\n", + "**切比雪夫不等式**：对任意分布的一组数据，至少有(1-1/k^2)的数据落在正负k个标准差以内(k>1)。" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "**变异系数**(离散系数)：一组数据的标准差与该组数据均值之比。公式为：\n", + "$$v_i=\\frac{s}{\\widetilde x}$$\n", + "\n", + "该系数是测度离散程度的相对统计量，主要用于比较不同演变数据的离散程度(特别对均值差异较大的数据组)。" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.5525118130735324" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#变异系数\n", + "s.std()/s.mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "变异系数可由标准差/均值直接得到。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**1.4 中位数、分位数、轴距与箱型图**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**中位数**(median)：中数是按顺序排列的一组数据中居于中间位置的数，即在这组数据中，有一半的数据比他大，有一半的数据比他小。如果观察值有偶数个，通常取最中间的两个数值的平均数作为中位数。" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "109.7979476168295" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s_norm.median()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pandas内置了median()函数，即求中位数函数。" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "**分位数**是将总体的全部数据按大小顺序排列后，处于各等分位置的变量值。\n", + "- 将全部数据分成相等的两部分，它就是中位数；如果分成四等分，就是四分位数；八等分就是八分位数。\n", + "- 四分位数也称为四分位点，它是将全部数据分成相等的四部分，其中每部分包括25%的数据，处在各分位点的数值就是四分位数。\n", + "- 四分位数有三个，第一个四分位数就是通常所说的四分位数，称为下四分位数，第二个四分位数就是中位数，第三个四分位数称为上四分位数，分别用Q1、Q2、Q3表示。" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "79.25069739255241" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s_norm.quantile(0.25)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "174.83259076226759" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s_norm.quantile(0.75)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pandas内置的quantile()函数可以求任意分位数，参数为0.25即Q1，参数为0.75即Q3。" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "s_norm.plot(kind='box')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "参数kind为`box`时，对应的就是**箱型图**。\n", + "\n", + "箱型图中间的箱型一般由三根线组成，由下至上分别对应Q1，Q2，Q3；而箱型以外的上下两根线，分别对应最大值与最小值(不含离群点)\n", + "\n", + "箱型图一般用于展示数值型数据，有助于对数据的整体分布有只管了解，特别有利于对离群点/异常点的观察。" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Series箱型图离群点/异常点显示，利用seaborn\n", + "sns.boxplot(s_norm)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "由于pandas内置的箱型图默认不含离群点，可直接利用seaborn中的boxplot函数，为Series对象绘制带离群点的箱型图。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**1.5 样本的累积和**（累积频数与累积频率）\n", + "\n", + "**累积频数**(cumulative frequencies)，将各有序类别或组的频数逐级累加起来的频数。\n", + "\n", + "**累积频率**(cumulative percentages)，将各有序类别或组的百分比逐级累加起来。" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "year\n", + "2006 200\n", + "2007 500\n", + "2008 900\n", + "2009 1250\n", + "2010 1640\n", + "2011 2240\n", + "2012 3140\n", + "2013 3540\n", + "2014 3840\n", + "2015 3960\n", + "Name: 2006-2015, dtype: int64" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.cumsum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pandas内置了cumsum()函数，可以直接得到累积频数。" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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VT797PB7mzJnDoUOHsFgs/PKXvyQkJIQnn3wSi8VCr169mDt3LlarlaVLl5KZmUlgYCDp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\nmwj1zXHR9u3byczM5KWXXmrS+aH+GdL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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "s.cumsum().plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "利用cumsum()得到的仍然是Series对象，可以直接绘图。" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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4Z1Ao5k9IQJCfp8STERHdOYY2ObXWNjN2nbyGzDNlMFsEEiL8sGhKIhL6+Es9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\nAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "s_ratio = s/s.sum()\n", + "s_ratio.cumsum().plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "累积频率(累积百分比)可以类似的得到。\n", + "\n", + "`sum()`为内置的求Series对象中value和的函数。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**1.6 最大值、最小值、众数及总体描述**" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "900" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.max()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "内置最大值函数max()" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "120" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.min()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "内置最小值函数min()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 300\n", + "1 400\n", + "dtype: int64" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.mode()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**众数**(mode)：一组数据中出现次数最多的数据值。一般用于测度分类数据的集中趋势，在数据量较大的时候有意义。\n", + "Pandas内置mode()函数，可以直接得到众数，注意众数可能不唯一，如此时s的众数有两个。" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "count 10.000000\n", + "mean 396.000000\n", + "std 218.794678\n", + "min 120.000000\n", + "25% 300.000000\n", + "50% 370.000000\n", + "75% 400.000000\n", + "max 900.000000\n", + "Name: 2006-2015, dtype: float64" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "Pandas的descibe()函数，可以给出汇总与描述统计的常用值。其中count为数据个数。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**1.7 随机数、直方图、峰度与偏度**(公式以后有时间补充)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "np.random.seed(66)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "调用np中random模块的seed()方法，生成一个随机数种子，在这个种子下，每次第一次生成的随机数均相同。" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1.41561436, -1.0852864 , -0.61630804, ..., 0.08479998,\n", + " -1.16755255, 1.13461007])" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "arr1 = np.random.randn(10000)\n", + "arr1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "利用random的randn()函数，生成一个numpy的array类型的对象，大小为10000，可理解为长度为10000的数组(所有数据元素类型相同)。\n", + "\n", + "数组内数据分布为正态分布。randn即random normal。" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 2.28112862, 0.51138372, 0.68988585, 0.67582702, 0.63808164,\n", + " -1.0410448 , -0.91601431, -1.64294384, 2.16465592, -0.67004449,\n", + " 0.46737616, 0.90362682, -0.34551556, -0.05367347, 0.46465691,\n", + " 1.37674635, -0.21249989, -2.14939938, -0.25040615, 1.43040674,\n", + " 0.27824394, 0.20567202, -0.68650085, 0.75210394, -1.12895709,\n", + " 0.68402948, -0.28490141, 0.29969392, 0.14210905, -0.11570231,\n", + " -0.48913203, -0.74924755, -1.52762847, -0.99353886, 1.81620157,\n", + " -1.20129803, -0.93263586, -0.36954506, 0.08467705, 0.61107937,\n", + " 0.50382233, -1.12408078, 0.398812 , 1.497698 , 0.78490215,\n", + " 0.55041055, -0.25888733, 0.39002582, -1.41102818, -2.09786379])" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "arr2 = np.random.randn(50)\n", + "arr2" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "与前类似，生成了一个长度为50的array，数据为正态分布。" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "s_1 = Series(arr1)\n", + "s_2 = Series(arr2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Numpy的array类型可以直接初始化为Series类型的对象。" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "s_1.plot(kind='hist')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**直方图**(histogram)：又称质量分布图，是数值数据分布的精确图形表示。\n", + "- 将值的范围分段，即将整个值的范围分成一系列间隔(一般要相等)\n", + "- 计算每个间隔中有多少值\n", + "\n", + "当plot()函数的kind参数为hist时，即代表直方图。\n", + "\n", + "此外，Pandas内置hist()函数，可对Series对象直接绘制直方图：`s_norm.hist()`。" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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/HMuXLw9zaUQUSJtvklyMh7pep0aKSc9QJ8I1Qr2npwePPfYYzp49i/z8fGg0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\nls6fhfeOtcDullCQnSh3OUG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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "s_1.plot(kind='kde')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**核密度估计图**(Kernel Density Estimation, KDE)：采用平滑的峰值函数(“核函数”)来拟合观察到的数据点，从而对真实的概率分布曲线进行模拟。\n", + "Pandas的plot()函数中，参数kind的值为kde时，即可绘制KDE图。" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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jR44AAOx2O6qrqy85vmPHDvj9fjz//POz3dvRziFKVOd6J+Dzh1FsS4Nw4Y2d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\noqN/Em5fUOlySMUYzpSQzna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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.distplot(s_2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "类似地，可以为s_2绘制histogram及kde图。" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-0.0063696106929140347,\n", + " 0.027355468589977068,\n", + " -0.020933532783578457,\n", + " -0.20411177773807099)" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s_1.skew(),s_2.skew(),s_1.kurt(),s_2.kurt()," + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**偏度**(Skewness)、偏态、偏态系数，是统计数据分布偏斜方向和程度的度量，是统计数据分布非对称程度的数字特征。正态分布的偏度为零。\n", + "- Skewness=0 分布形态与正态分布偏度相同\n", + "- Skewness>0 正偏差数值较大，为正偏或右偏。长尾巴拖在右边。\n", + "- Skewness<0 负偏差数值较大，为负偏或左偏。长尾巴拖在左边。\n", + "\n", + "**峰度**(Kurtosis)、峰态、峰态系数，是描述总体中所有取值分布形态陡缓程度的统计量。正态分布的峰度为3(也有很多将计算得到的峰度-3，而使正态分布峰度为零的做法)。\n", + "- Kurtosis=3(或0) 分布形态与正态分布峰度相同\n", + "- Kurtosis>3(或0) 正偏差数值较大，尖峰，厚尾。\n", + "- Kurtosis<3(或0) 负偏差数值较大，扁峰，薄尾。\n", + "\n", + "本例中，s_1比s_2更接近于正态分布。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3. Series类型基本操作整理\n", + "**3.1 创建与查看**\n", + "\n", + "创建一个Series的基本方法是：s = Series(data, index=index)。其中，data指代许多不同的数据类型：\n", + "- a Python dict\n", + "- an ndarray\n", + "- a scalar value (like 6)\n", + "\n", + "index指一个标签序列，一般用list类型。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3.1.1 利用list创建Series**" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 0\n", + "1 1\n", + "2 4\n", + "3 9\n", + "4 16\n", + "5 25\n", + "6 36\n", + "7 49\n", + "8 64\n", + "9 81\n", + "10 100\n", + "11 121\n", + "12 144\n", + "13 169\n", + "14 196\n", + "15 225\n", + "16 256\n", + "17 289\n", + "18 324\n", + "19 361\n", + "20 400\n", + "21 441\n", + "22 484\n", + "23 529\n", + "24 576\n", + "25 625\n", + "26 676\n", + "27 729\n", + "28 784\n", + "29 841\n", + " ... \n", + "70 4900\n", + "71 5041\n", + "72 5184\n", + "73 5329\n", + "74 5476\n", + "75 5625\n", + "76 5776\n", + "77 5929\n", + "78 6084\n", + "79 6241\n", + "80 6400\n", + "81 6561\n", + "82 6724\n", + "83 6889\n", + "84 7056\n", + "85 7225\n", + "86 7396\n", + "87 7569\n", + "88 7744\n", + "89 7921\n", + "90 8100\n", + "91 8281\n", + "92 8464\n", + "93 8649\n", + "94 8836\n", + "95 9025\n", + "96 9216\n", + "97 9409\n", + "98 9604\n", + "99 9801\n", + "dtype: int64" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s = Series([x*x for x in range(100)])\n", + "s" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 第一行利用一个list，创建了一个Series对象s\n", + "- 第二行查看该对象。\n", + "- 该对象由两部分组成，第一列为index，第二列为values。" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 0\n", + "1 1\n", + "2 4\n", + "3 9\n", + "4 16\n", + "dtype: int64" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "数据太长，可用head()函数查看前几个数据。" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "90 8100\n", + "91 8281\n", + "92 8464\n", + "93 8649\n", + "94 8836\n", + "95 9025\n", + "96 9216\n", + "97 9409\n", + "98 9604\n", + "99 9801\n", + "dtype: int64" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.tail(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "也可用tail()函数查看最后几个数据，head()及tail()函数内可放整型参数，代表列出的数据个数。" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0, 1, 4, 9, 16, 25, 36, 49, 64, 81, 100,\n", + " 121, 144, 169, 196, 225, 256, 289, 324, 361, 400, 441,\n", + " 484, 529, 576, 625, 676, 729, 784, 841, 900, 961, 1024,\n", + " 1089, 1156, 1225, 1296, 1369, 1444, 1521, 1600, 1681, 1764, 1849,\n", + " 1936, 2025, 2116, 2209, 2304, 2401, 2500, 2601, 2704, 2809, 2916,\n", + " 3025, 3136, 3249, 3364, 3481, 3600, 3721, 3844, 3969, 4096, 4225,\n", + " 4356, 4489, 4624, 4761, 4900, 5041, 5184, 5329, 5476, 5625, 5776,\n", + " 5929, 6084, 6241, 6400, 6561, 6724, 6889, 7056, 7225, 7396, 7569,\n", + " 7744, 7921, 8100, 8281, 8464, 8649, 8836, 9025, 9216, 9409, 9604,\n", + " 9801], dtype=int64)" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "查看序列s的值" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "numpy.ndarray" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(s.values)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 查看s.values的类型\n", + "- Series对象的values的类型确实为numpy的array（数组）类型，即ndarray(n维数组)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "RangeIndex(start=0, stop=100, step=1)" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.index" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 查看s的index(索引)的值" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "pandas.indexes.range.RangeIndex" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(s.index)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 查看s.index的类型\n", + "- Series对象的索引在未指定时，为pandas内置的RangeIndex类型，类似于python的range类型。" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "n 0\n", + "o 1\n", + "w 4\n", + "s 9\n", + "g 16\n", + "dtype: int64" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s2 = Series([x*x for x in range(26)], index=set('abcdefghijklmnopqrstuvwxyz'))\n", + "s2.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 利用list创建Series对象时，可用index参数指定一个list或set对应索引。" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(pandas.indexes.base.Index,\n", + " Index(['n', 'o', 'w', 's', 'g', 'p', 'm', 'i', 'a', 'y', 'j', 'v', 'k', 'e',\n", + " 'u', 'x', 'r', 'q', 'l', 'c', 'b', 't', 'd', 'z', 'f', 'h'],\n", + " dtype='object'))" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(s2.index), s2.index" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 指定索引后，Series对象的索引是pandas内置的Index类型" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3.1.2 利用dict创建Series**" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "喜欢 3000\n", + "恨 500\n", + "爱 7000\n", + "讨厌 1000\n", + "dtype: int64" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = {'喜欢':3000,'爱':7000,'讨厌':1000,'恨':500}\n", + "s3 = Series(data)\n", + "s3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 可用dict创建Series对象，则字典的键自动成为index" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "a NaN\n", + "b NaN\n", + "c NaN\n", + "爱 7000.0\n", + "dtype: float64" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s3 = Series(data, index = ['a','b','c','爱'])\n", + "s3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 初始化时，也可以额外指定index。但是如果指定的index找不到对应的值，则该index对应值设为NaN（Not a Number）" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3.1.3 利用标量创建Series对象**" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "a 5\n", + "b 5\n", + "c 5\n", + "d 5\n", + "e 5\n", + "dtype: int64" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s0 = Series(5, index = ['a','b','c','d','e'])\n", + "s0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 利用标量创建Series对象时，对象的值将均为标量的值" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3.1.4 利用Series对象创建Series对象**" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "a NaN\n", + "b NaN\n", + "c NaN\n", + "爱 7000.0\n", + "dtype: float64" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s4 = Series(s3)\n", + "s4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 这是最简单的利用Series对象创建Series对象的代码" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "喜欢 NaN\n", + "爱 7000.0\n", + "讨厌 NaN\n", + "中 NaN\n", + "dtype: float64" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s5 = Series(s4, index = ['喜欢','爱','讨厌','中'])\n", + "s5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 与利用dict初始化类似，也可以额外指定index。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3.2 访问Series对象的元素**" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(nan, 7000.0, 7000.0)" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s4[0], s4['爱'], s4.爱" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 利用位置索引或者index均可以访问对应元素\n", + "- 还可以利用index的值作为属性来访问对应元素" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "喜欢 NaN\n", + "爱 4078.0\n", + "dtype: float64" + ] + }, + "execution_count": 103, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s4[0:2]" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "爱 7000.0\n", + "dtype: float64" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s4['喜欢':'爱']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 位置索引及index的切片访问也适用，但利用index切片是包含末端的" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(4078.0, 爱 4078.0\n", + " 讨厌 NaN\n", + " dtype: float64)" + ] + }, + "execution_count": 105, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s4.iloc[1], s4.iloc[1:3]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 有时会遇到Index是整数的情况，为避免歧义，可以用iloc来利用位置进行元素选取" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['a', 'b', 'c', '爱'], dtype='object')" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s4.index" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 可以利用index属性访问Series对象的索引" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ nan, nan, nan, 7000.])" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s4.values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 可以利用values属性访问Series对象的值" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a nan\n", + "b nan\n", + "c nan\n", + "爱 7000.0\n" + ] + } + ], + "source": [ + "for k, v in s4.items():\n", + " print(k, v)" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a\n", + "b\n", + "c\n", + "爱\n" + ] + } + ], + "source": [ + "for k in s4.keys():\n", + " print(k)" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "nan\n", + "nan\n", + "nan\n", + "7000.0\n" + ] + } + ], + "source": [ + "for v in s4:\n", + " print(v)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 可以类似dict一样迭代访问Series对象的各个元素" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a\n", + "b\n", + "c\n", + "爱\n" + ] + } + ], + "source": [ + "for k in s4.index:\n", + " print(k)" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "nan\n", + "nan\n", + "nan\n", + "7000.0\n" + ] + } + ], + "source": [ + "for v in s4.values:\n", + " print(v)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 也可以利用index与values属性迭代访问" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3.3 修改、删除、排序等基本运算**" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "a 10000.0\n", + "b NaN\n", + "c NaN\n", + "爱 7000.0\n", + "dtype: float64" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s4[0] = 10000\n", + "s4" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "a 10000.0\n", + "b NaN\n", + "c NaN\n", + "爱 4078.0\n", + "dtype: float64" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s4['爱'] = 4078\n", + "s4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Series对象内的值可以直接根据位置或index被赋值（修改）" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "a 666.0\n", + "b 666.0\n", + "c 666.0\n", + "爱 4078.0\n", + "dtype: float64" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s4[0:3] = 666\n", + "s4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 可以切片式赋值" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "a 666.0\n", + "b 666.0\n", + "c 666.0\n", + "d 4078.0\n", + "dtype: float64" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s4.index = ['a', 'b', 'c', 'd']\n", + "s4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Series对象的index可以直接被赋值（修改），注意新的index的长度要与Series对象中index的长度一致。" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "喜欢 NaN\n", + "爱 4078.0\n", + "讨厌 NaN\n", + "中 NaN\n", + "e NaN\n", + "dtype: float64" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s4 = s3.reindex(['喜欢','爱','讨厌','中', 'e'])\n", + "s4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 可用reindex方法来更改index，找不到对应值的index，则其值为NaN。" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "喜欢 NaN\n", + "爱 4078.0\n", + "讨厌 NaN\n", + "中 NaN\n", + "e NaN\n", + "dtype: float64" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s5 = s4.drop('e')\n", + "s4" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "喜欢 NaN\n", + "爱 4078.0\n", + "讨厌 NaN\n", + "中 NaN\n", + "dtype: float64" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 可利用drop函数删除指定索引及值，生成新的Series对象，但原series对象不变。" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "e NaN\n", + "中 NaN\n", + "喜欢 NaN\n", + "爱 8156.0\n", + "讨厌 NaN\n", + "dtype: float64" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s4+s5" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "e NaN\n", + "中 NaN\n", + "喜欢 NaN\n", + "爱 0.0\n", + "讨厌 NaN\n", + "dtype: float64" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s4-s5" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "e NaN\n", + "中 NaN\n", + "喜欢 NaN\n", + "爱 16630084.0\n", + "讨厌 NaN\n", + "dtype: float64" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s4*s5" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "e NaN\n", + "中 NaN\n", + "喜欢 NaN\n", + "爱 1.0\n", + "讨厌 NaN\n", + "dtype: float64" + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s4/s5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Serires对象可以直接进行四则运算，运算规则是对应索引的值进行运算，整体运算结果仍然是Series对象。\n", + "- 如果索引值无法对应，则结果为NaN。" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "爱 4078.0\n", + "喜欢 NaN\n", + "讨厌 NaN\n", + "中 NaN\n", + "e NaN\n", + "dtype: float64" + ] + }, + "execution_count": 87, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s4.sort_values()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 按照values（值）进行排序，按值排序时，NaN将排在尾端" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "e NaN\n", + "中 NaN\n", + "喜欢 NaN\n", + "爱 4078.0\n", + "讨厌 NaN\n", + "dtype: float64" + ] + }, + "execution_count": 88, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s4.sort_index()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 按照index（索引）进行排序" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "爱 4078.0\n", + "喜欢 NaN\n", + "讨厌 NaN\n", + "中 NaN\n", + "e NaN\n", + "dtype: float64" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s4.sort_values(ascending=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 将ascending参数指定为False，则变为倒序排序" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3.4 处理NaN值**" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "喜欢 1.0\n", + "爱 4078.0\n", + "讨厌 1.0\n", + "中 1.0\n", + "e 1.0\n", + "dtype: float64" + ] + }, + "execution_count": 90, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s5 = s4.fillna(1)\n", + "s5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 可以利用内置fillna()函数，将NaN值替换为指定数值" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "喜欢 NaN\n", + "爱 4078.0\n", + "不讨厌 0.0\n", + "中 NaN\n", + "dtype: float64" + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s6 = s4.reindex(['喜欢','爱','不讨厌','中'], fill_value = 0)\n", + "s6" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 可在reindex方法内利用fill_value参数，指定值来替换在重新索引时新出现的NaN值，\n", + "- 注意，fill_value参数与不会对原有NaN值进行替换。" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "e NaN\n", + "不讨厌 NaN\n", + "中 NaN\n", + "喜欢 NaN\n", + "爱 8156.0\n", + "讨厌 NaN\n", + "dtype: float64" + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s4.add(s6)" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "e NaN\n", + "不讨厌 0.0\n", + "中 NaN\n", + "喜欢 NaN\n", + "爱 8156.0\n", + "讨厌 NaN\n", + "dtype: float64" + ] + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s4.add(s6, fill_value=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 两个Series对象相加可用add方法，设置fill_value参数的值，替换非对齐index部分的NaN值。fill_value参数不会对运算前的NaN进行填充。\n", + "- 类似还有sub，div，mul方法，分别对应减法、除法及乘法。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3.5 过滤**" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 False\n", + "1 False\n", + "2 False\n", + "3 False\n", + "4 False\n", + "5 False\n", + "6 False\n", + "7 False\n", + "8 False\n", + "9 False\n", + "10 False\n", + "11 False\n", + "12 False\n", + "13 False\n", + "14 False\n", + "15 False\n", + "16 False\n", + "17 False\n", + "18 False\n", + "19 False\n", + "20 False\n", + "21 False\n", + "22 False\n", + "23 False\n", + "24 False\n", + "25 False\n", + "26 False\n", + "27 False\n", + "28 False\n", + "29 False\n", + " ... \n", + "70 True\n", + "71 True\n", + "72 True\n", + "73 True\n", + "74 True\n", + "75 True\n", + "76 True\n", + "77 True\n", + "78 True\n", + "79 True\n", + "80 True\n", + "81 True\n", + "82 True\n", + "83 True\n", + "84 True\n", + "85 True\n", + "86 True\n", + "87 True\n", + "88 True\n", + "89 True\n", + "90 True\n", + "91 True\n", + "92 True\n", + "93 True\n", + "94 True\n", + "95 True\n", + "96 True\n", + "97 True\n", + "98 True\n", + "99 True\n", + "dtype: bool" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s > 1000" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Series对象进行比较等逻辑运算，结果仍然是一个Series对象，每一维度的值根据运算结果为True或False" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "32 1024\n", + "33 1089\n", + "34 1156\n", + "35 1225\n", + "36 1296\n", + "37 1369\n", + "38 1444\n", + "39 1521\n", + "40 1600\n", + "41 1681\n", + "42 1764\n", + "43 1849\n", + "44 1936\n", + "45 2025\n", + "46 2116\n", + "47 2209\n", + "48 2304\n", + "49 2401\n", + "50 2500\n", + "51 2601\n", + "52 2704\n", + "53 2809\n", + "54 2916\n", + "55 3025\n", + "56 3136\n", + "57 3249\n", + "58 3364\n", + "59 3481\n", + "60 3600\n", + "61 3721\n", + " ... \n", + "70 4900\n", + "71 5041\n", + "72 5184\n", + "73 5329\n", + "74 5476\n", + "75 5625\n", + "76 5776\n", + "77 5929\n", + "78 6084\n", + "79 6241\n", + "80 6400\n", + "81 6561\n", + "82 6724\n", + "83 6889\n", + "84 7056\n", + "85 7225\n", + "86 7396\n", + "87 7569\n", + "88 7744\n", + "89 7921\n", + "90 8100\n", + "91 8281\n", + "92 8464\n", + "93 8649\n", + "94 8836\n", + "95 9025\n", + "96 9216\n", + "97 9409\n", + "98 9604\n", + "99 9801\n", + "dtype: int64" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s[s>1000]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 可以利用逻辑运算的结果Series，对Series对象进行过滤，得到满足条件的Series" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3.6 时间序列(Time Series)**示例" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "DatetimeIndex(['2012-01-01 00:00:00', '2012-01-01 00:00:01',\n", + " '2012-01-01 00:00:02', '2012-01-01 00:00:03',\n", + " '2012-01-01 00:00:04', '2012-01-01 00:00:05',\n", + " '2012-01-01 00:00:06', '2012-01-01 00:00:07',\n", + " '2012-01-01 00:00:08', '2012-01-01 00:00:09',\n", + " '2012-01-01 00:00:10', '2012-01-01 00:00:11',\n", + " '2012-01-01 00:00:12', '2012-01-01 00:00:13',\n", + " '2012-01-01 00:00:14', '2012-01-01 00:00:15',\n", + " '2012-01-01 00:00:16', '2012-01-01 00:00:17',\n", + " '2012-01-01 00:00:18', '2012-01-01 00:00:19',\n", + " '2012-01-01 00:00:20', '2012-01-01 00:00:21',\n", + " '2012-01-01 00:00:22', '2012-01-01 00:00:23',\n", + " '2012-01-01 00:00:24', '2012-01-01 00:00:25',\n", + " '2012-01-01 00:00:26', '2012-01-01 00:00:27',\n", + " '2012-01-01 00:00:28', '2012-01-01 00:00:29',\n", + " '2012-01-01 00:00:30', '2012-01-01 00:00:31',\n", + " '2012-01-01 00:00:32', '2012-01-01 00:00:33',\n", + " '2012-01-01 00:00:34', '2012-01-01 00:00:35',\n", + " '2012-01-01 00:00:36', '2012-01-01 00:00:37',\n", + " '2012-01-01 00:00:38', '2012-01-01 00:00:39',\n", + " '2012-01-01 00:00:40', '2012-01-01 00:00:41',\n", + " '2012-01-01 00:00:42', '2012-01-01 00:00:43',\n", + " '2012-01-01 00:00:44', '2012-01-01 00:00:45',\n", + " '2012-01-01 00:00:46', '2012-01-01 00:00:47',\n", + " '2012-01-01 00:00:48', '2012-01-01 00:00:49',\n", + " '2012-01-01 00:00:50', '2012-01-01 00:00:51',\n", + " '2012-01-01 00:00:52', '2012-01-01 00:00:53',\n", + " '2012-01-01 00:00:54', '2012-01-01 00:00:55',\n", + " '2012-01-01 00:00:56', '2012-01-01 00:00:57',\n", + " '2012-01-01 00:00:58', '2012-01-01 00:00:59',\n", + " '2012-01-01 00:01:00', '2012-01-01 00:01:01',\n", + " '2012-01-01 00:01:02', '2012-01-01 00:01:03',\n", + " '2012-01-01 00:01:04', '2012-01-01 00:01:05',\n", + " '2012-01-01 00:01:06', '2012-01-01 00:01:07',\n", + " '2012-01-01 00:01:08', '2012-01-01 00:01:09',\n", + " '2012-01-01 00:01:10', '2012-01-01 00:01:11',\n", + " '2012-01-01 00:01:12', '2012-01-01 00:01:13',\n", + " '2012-01-01 00:01:14', '2012-01-01 00:01:15',\n", + " '2012-01-01 00:01:16', '2012-01-01 00:01:17',\n", + " '2012-01-01 00:01:18', '2012-01-01 00:01:19',\n", + " '2012-01-01 00:01:20', '2012-01-01 00:01:21',\n", + " '2012-01-01 00:01:22', '2012-01-01 00:01:23',\n", + " '2012-01-01 00:01:24', '2012-01-01 00:01:25',\n", + " '2012-01-01 00:01:26', '2012-01-01 00:01:27',\n", + " '2012-01-01 00:01:28', '2012-01-01 00:01:29',\n", + " '2012-01-01 00:01:30', '2012-01-01 00:01:31',\n", + " '2012-01-01 00:01:32', '2012-01-01 00:01:33',\n", + " '2012-01-01 00:01:34', '2012-01-01 00:01:35',\n", + " '2012-01-01 00:01:36', '2012-01-01 00:01:37',\n", + " '2012-01-01 00:01:38', '2012-01-01 00:01:39'],\n", + " dtype='datetime64[ns]', freq='S')" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rng = pd.date_range('1/1/2012', periods=100, freq='S')\n", + "rng" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Pandas内置的date_range()函数，可以很方便的得到时间标签\n", + "- 第一个参数'1/1/2012'为时间字符串，表示开始时间\n", + "- 第二个参数periods为时间标签个数，此处设为100\n", + "- 第三个参数freq为时间频率间隔，此处的'S'表示间隔为秒，常用的为S,Min,H,D,M分别为秒、分、小时、天，月。" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2012-01-01 00:00:00 18\n", + "2012-01-01 00:00:01 98\n", + "2012-01-01 00:00:02 182\n", + "2012-01-01 00:00:03 371\n", + "2012-01-01 00:00:04 193\n", + "Freq: S, dtype: int32" + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ts = Series(np.random.randint(0, 500, len(rng)), index=rng)\n", + "ts.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 将时间标签作为Series对象的index，随机生成0-500间100个整数，作为Series对象的值。" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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JcPda1JUOkxdlyFQrEsFoyamVIX/pO+fwZ196HS+/fbXiWEZGWe91QE5GANKe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6txz9jO1RO3WG+jPBJcTJAGRUbfrG4Xt2TjKUxUTINMS7SlMXYuKmTABMEvZF1QlZ6IAwnJjo2o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\npVJBvV63/63reqAxB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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ts.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 利用plot()函数绘图" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2012-01-01 00:00:00 244.950\n", + "2012-01-01 00:01:00 218.425\n", + "Freq: T, dtype: float64" + ] + }, + "execution_count": 99, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ts1 = ts.resample('Min').mean()\n", + "ts1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 利用resample()函数，可将时间序列重新抽样。\n", + "- 样本时间间隔根据resample()函数参数决定，本例中，样本时间间隔设为分钟(Min)\n", + "- 重新设定样本间隔后，可利用mean()函数，求得每个时间间隔内值的均值，作为新的index对应的值" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3.7 输出**" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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LCjsKbxERCZqTtY08vWIbFTWNTB3fjxvGZphdUlhSeIuISFBU1TXx9IptnKhu4KbLM7jp\n8n5mlxS2FN4iItLuql1N/HLFNo5XNXDD2L58Z5yC+2IovEVEpF3VuE4dcR8/Wc/kS9OZNiGTiIgI\ns8sKawpvERFpNzVuD79csY2yynom5fVh+hX9FdxtQOEtIiLtosbt4ZfLt1JWWc+3v9WH2yYOUHC3\nEYW3iIi0uX8N7tuvUnC3JYW3iIi0qRq3h6dPnyq/doyCuz3YzC5AREQ6jprTV5WXVdZzzZg07rha\nwd0eFN4iItImqk9fVa5T5e1P4S0iIhetsqaBRctPDQe7Li+dWyfqqvL2pPAWEZGLcrK2kcWrdzSP\n49ZwsPan8BYRkVarrGlsnvL0hrF9NQFLkCi8RUSkVSqqG/jl6UVGbr8mi2/n9lZwB4mGiomIyAU7\nUVXPL5ZvbV4d7LuTsxXcQaQjbxERuSBllW6eXrGNapeHW67I1LKeJlB4i4hIi5VWuPmfFduocXu4\n/aoBTMpLN7ukTknhLSIiLXLkeB3/s3I7rgYvM68ZyDVj+phdUqel8BYRkfM6WFbLklXbqW/0cdd1\ng7hiZG+zS+rUFN4iInJOX5ZU88wrO2j0+PnBjdlcNqyn2SV1egpvERE5q6LDVfz61UK8vgD33jSU\nvOzuZpckXER4/+EPf+DDDz/E6/UyY8YM8vLymD9/PhEREQwcOJAFCxZgsVhYvXo1K1euxGazMXv2\nbCZOnEhjYyOPPvoolZWV2O12Fi1aRHJyclvul4iIXKTC4kr+3+s7CQQM/s/NwxidlWp2SXJaq8Z5\nb9myhW3btrFixQqWLVtGeXk5Tz31FHPnzmX58uUYhsG6detwOp0sW7aMlStX8vzzz7NkyRI8Hg8r\nVqwgKyuL5cuXM3XqVJYuXdrW+yUiIhfhi6IT/HZNIQAPTs9RcIeYVoX3hg0byMrKYs6cOdx3331c\neeWV7N69m7y8PAAmTJjAxo0bKSwsZNSoUURFReFwOEhPT6eoqIiCggLGjx/fvO2mTZvabo9EROSi\nbNxVxrN/3YXNZuG/bhvB8MwUs0uSf9Gq0+ZVVVUcO3aM3//+95SUlDB79mwMw2ieXcdut1NXV4fL\n5cLhcDQ/zm6343K5zrj9621bIjXVcf6NpE2o18GhPgeH+txyazce5E9v78UeG8l//+dYstKTWvxY\n9Tl4WhXeXbp0ITMzk6ioKDIzM4mOjqa8vLz5frfbTUJCAvHx8bjd7jNudzgcZ9z+9bYt4XS2LOTl\n4qSmOtTrIFCfg0N9brn3thxh9UcHcMRF8sjtI0mKtbW4d+pz+zjbB6JWnTbPzc3lk08+wTAMjh8/\nTkNDA2PHjmXLli0A5OfnM2bMGHJycigoKKCpqYm6ujqKi4vJyspi9OjRrF+/vnnb3NzcVu6WiIhc\nLMMweC2/mNUfHSDJEc38/xhNencdRYeyVh15T5w4kc8//5zp06djGAZPPPEEaWlpPP744yxZsoTM\nzEwmTZqE1Wpl1qxZzJw5E8MwePjhh4mOjmbGjBnMmzePGTNmEBkZyeLFi9t6v0REpAUChsGKD75k\nXUEJ3brE8qMZI+maGGt2WXIeEYZhGGYX0VI6JRMcOv0VHOpzcKjPZ+cPBHjx3SI+3VVO71Q7j9w+\nki7x0a36Xepz+zjbaXNN0iIi0gl5fQH++OZuCvY76dczgYdvG0F8bKTZZUkLKbxFRDqZhiYfv3tt\nJ3sPVzE4vQsP3JJDbLTiIJzo/y0RkU7E1eDlV6t3cLCslpEDujJ76lAibVazy5ILpPAWEekkquqa\nWLxqO8cq3Fw2rAffv34wVkurBh2JyRTeIiKdwPGqehav3E5FTSPXjEnjjqsHYjk9sZaEH4W3iEgH\nd7i8jl+t3k5tvZep4/ox5fKM5hkxJTwpvEVEOrCiw1X8Zk0hTR4/3/12FleNTjO7JGkDCm8RkQ5q\n634nv//rbgzD4N7vaC3ujkThLSLSAeXvOMaf3ysiymbl/mk5DO2XbHZJ0oYU3iIiHYhhGLy96TCv\n539FfGwkD982gn49W7b4k4QPhbeISAcRCJyep3xrCSkJMfzX7SPomWI3uyxpBwpvEZEOwOsL8Nzb\ne/ii6ARpqXYevm0kSY7WzVMuoU/hLSIS5v55utOstEQenJ5DXIzmKe/IFN4iImGsqq6JZ17ZwdET\nLkYN7Mq9Nw0lKlLTnXZ0Cm8RkTB1rMLNr1Zvp7K2iStH9ea712ZhsWjylc5A4S0iEoa+LKnmN68W\n4m70MW1CJjeM7atZ0zoRhbeISJjZut/JH97cjd9vcPf12YzL6Wl2SRJkCm8RkTCyrqCE5e/vJyrS\nyv23Dmd4ZorZJYkJFN4iImEgYBi8+lEx7312hAR7FHNvzSGjhyZf6awU3iIiIc7r8/P8O3v5bO8J\neiTH8fBtI0jtEmt2WWIihbeISAhzN3r57Zqd7D9azYC0RB68JYf4WI3h7uwU3iIiIcpZ3cAzr+yg\nrLKeMYNS+eGUIUTaNIZbFN4iIiHpq2O1/ObVHdTWe5mU14dbJw7AoqFgcprCW0QkxBTsc/LcW7vx\n+gN899tZXDU6zeySJMQovEVEQoRhGLz/RQmr1n1JVKSVB27JYeSArmaXJSFI4S0iEgL8gQDLP/iS\nj7aWkhgfxdzpI+jbw2F2WRKiFN4iIiZraPLx7F93seurk6Sl2nlo+ghSEmPMLktCmMJbRMRElTWN\n/PrVHZQ43QzPTOG+7wwlNlpvzXJueoWIiJjkYFktv3m1kBq3h6tG92bGNQOxWixmlyVhQOEtImKC\nL4pO8Nzbe/D5Atxx9UCuHZOmVcGkxRTeIiJBZBgGb286zOv5XxEdZeWB6bqiXC6cwltEJEi8vgAv\nrt3Lpt3HSUmI5sHpI+jTLd7ssiQMKbxFRIKg1u3hd6/v5EBJDf17JXD/LTkk2qPMLkvClMJbRKSd\nHT3h4jev7qCytolLhnTn7usHa45yuSgKbxGRdrR1v5Pn3tpDk9fPzRMyuXFsX12YJhdN4S0i0g4M\nw+DdzYdZs/4roiItzLl5GLmDupldlnQQCm8RkTbm8fp5cW0Rm/ccJzkhmgdvySG9u6Y6lbaj8BYR\naUMnaxv57Ws7OVxeR//eCdx/83AS46PNLks6GIW3iEgbOVBaw+9e20mt28O44T2ZNWkQkTbNmCZt\nT+EtItIGNhSW8Ze/FeEPGMy4eiDXaMY0aUcKbxGRi+DzB1j94QE+KCjBHmPjvu8MY2i/ZLPLkg5O\n4S0i0kq19R5+/8Yuio5U07urnftvGU73pDizy5JOQOEtItIKh8vr+N1rhVTWNpGblcrdN2RrKU8J\nGr3SREQu0Obd5by4tgivL8DN4/txw2UZWPT9tgSRwltEpIV8/gCvfFTM+18cJTbayn3fyWHkQK0I\nJsGn8BYRaYFat4dn39jFvqPV9EyJ4/5pw+mZYje7LOmkFN4iIudxsKyW3722k6o6fb8toeGiXn2V\nlZVMmzaNF154AZvNxvz584mIiGDgwIEsWLAAi8XC6tWrWblyJTabjdmzZzNx4kQaGxt59NFHqays\nxG63s2jRIpKTNbRCREJP/o5jvPT3ffj9Brdckcn1l2phETFfq6f+8Xq9PPHEE8TExADw1FNPMXfu\nXJYvX45hGKxbtw6n08myZctYuXIlzz//PEuWLMHj8bBixQqysrJYvnw5U6dOZenSpW22QyIibcHj\n9fPCu3t5cW0R0ZFWHr5tBDeMzVBwS0hodXgvWrSIO+64g27dTq2Ss3v3bvLy8gCYMGECGzdupLCw\nkFGjRhEVFYXD4SA9PZ2ioiIKCgoYP35887abNm1qg10REWkbFdUNPPXSVjYUltG3u4MF3/sWwzJT\nzC5LpFmrTpu/9tprJCcnM378eP74xz8Cp5a/+/oTqd1up66uDpfLhcPxj5V07HY7LpfrjNu/3rYl\nUlO1Kk+wqNfBoT4Hx4X0uaDoOItfLqCu3su1eencNy2HqEhrO1bXcej1HDytCu81a9YQERHBpk2b\n2Lt3L/PmzePkyZPN97vdbhISEoiPj8ftdp9xu8PhOOP2r7dtCaezZSEvFyc11aFeB4H6HBwt7XMg\nYPDXDQd5e+MhrFYL35s8mAkjelFTXR+EKsOfXs/t42wfiFp12vzll1/mpZdeYtmyZWRnZ7No0SIm\nTJjAli1bAMjPz2fMmDHk5ORQUFBAU1MTdXV1FBcXk5WVxejRo1m/fn3ztrm5ua3cLRGRi1db7+FX\nq7fz1sZDpCTG8Nis0UwY0cvsskTOqs3GOsybN4/HH3+cJUuWkJmZyaRJk7BarcyaNYuZM2diGAYP\nP/ww0dHRzJgxg3nz5jFjxgwiIyNZvHhxW5UhInJBDpTW8Owbu6iqa2JE/xTumTIEe0yk2WWJnFOE\nYRiG2UW0lE7JBIdOfwWH+hwcZ+uzYRi8//lRXvm4mIBhMG1CJpMv7atpTltJr+f2cbbT5pplQEQ6\nnfpGLy+8W8TW/U4S7FHce9NQsvsmmV2WSIspvEWkUzlUXsvS13dRUdPI4PQu3HvTUBLjo80uS+SC\nKLxFpFMwDIMPt5ay6sMv8fsNbrwsg6nj+mGx6DS5hB+Ft4h0ePWNXv53bREF+5zEx0byn1OGaNIV\nCWsKbxHp0PYfqeKpFz+noqaRrD6nTpMnOXSaXMKbwltEOqSvryZ/dX0xfr/BTZdnMOXyDKyWVs8K\nLRIyFN4i0uHU1nt44Z29FBZX0sURzT03ZDMkQysXSseh8BaRDmXv4Sr++NZualwehmYkMe+uPHxN\nXrPLEmlTCm8R6RD8gQB/3XCIdzYewmKJ4NYr+zPpknSSEmJwOhXe0rEovEUk7DmrG/jjW7spLq2l\na2IM935nKP17JZp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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "s.plot()\n", + "plt.savefig('test.jpg')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 利用内置的plot()函数展示图片后，可利用plt的savefig()函数将图片保存为文件输出。\n", + "- 可以查看当前目录下的test.jpg文件，与当前显示一致。" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "s.to_csv('test.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 利用to_csv()函数，可将Series对象输出为.csv文件\n", + "- 可在当前目录下，找到test.csv，双击打开查看" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/chapter4/pandas_Tutorial_2(DataFrame).ipynb b/chapter4/pandas_Tutorial_2(DataFrame).ipynb new file mode 100644 index 000000000..23a227fc0 --- /dev/null +++ b/chapter4/pandas_Tutorial_2(DataFrame).ipynb @@ -0,0 +1,4830 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Pandas统计分析入门（2）\n", + "- 转载注明转自：https://github.com/liupengyuan/\n", + "- ## 二维数据统计分析(DataFrame基础)\n", + "\n", + "---" + ] + }, + { + "cell_type": "code", + "execution_count": 181, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "from pandas import Series, DataFrame\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 二、二维数据统计分析(DataFrame基础)\n", + "\n", + "- 数据的描述、分析、可视化展示、概括性度量、输入与输出\n", + "df：多维条形图，多维折线图，多层图" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### 1. DataFrame对象及数据展示\n", + "\n", + "- DataFrame是pandas最重要最基础的数据对象之一，可用来表示数据表\n", + "- 如果将Series视为表格中的一列，则DataFrame可以视为表中多列，或者是具有相同index的多个Series\n", + "- 如果将Series视为向量(带索引)，则DataFrame即可视为矩阵(带索引)。\n", + "- 本小节仍将以一个词频统计结果作为实例，进行介绍。\n", + "- 教程中的各个代码段，请自行建立新的python程序，依次键入并顺序执行，观察执行结果。" + ] + }, + { + "cell_type": "code", + "execution_count": 182, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "words_freq = np.array([200,300,400,350,390,600,900,400,300,120])\n", + "freq_dict = {'boy':words_freq,'girl':words_freq*2,'children':words_freq+100, 'child': words_freq+300}\n", + "total_words_freq = [12345000,23456000,22333000,45632000,11144000,65433000,44444000,55555000,34522000,55566000]\n", + "years = pd.date_range('2006', periods=10, freq='A')" + ] + }, + { + "cell_type": "code", + "execution_count": 183, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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      " + ], + "text/plain": [ + " boy child children girl\n", + "2006-12-31 200 500 300 400\n", + "2007-12-31 300 600 400 600\n", + "2008-12-31 400 700 500 800\n", + "2009-12-31 350 650 450 700\n", + "2010-12-31 390 690 490 780\n", + "2011-12-31 600 900 700 1200\n", + "2012-12-31 900 1200 1000 1800\n", + "2013-12-31 400 700 500 800\n", + "2014-12-31 300 600 400 600\n", + "2015-12-31 120 420 220 240" + ] + }, + "execution_count": 184, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = DataFrame(freq_dict, index = years)\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 与Series对象类似，可以在初始化DataFrame的时候，指定index\n", + "- 数据可视为3个有相同index的Series" + ] + }, + { + "cell_type": "code", + "execution_count": 185, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 185, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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BEoEDxVunJS4xBGPt2Cwx+OGH7xMTE8OTTz7PunU/pbGxodvniYnJ/Pe/z6PTebaal6cR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\n9D5V5e9/d4TzrQ7+6tFiFs27NKXZvm8vLb/bRkx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Tjnod2b523H6eXu1YPH10p7ZXbX9Fm9n7V9fW1lb6KOs2Tpt2j39heWo9CTre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\npSN4lzvOXUhDQ32lwzggjrd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k6IF83ZBgyCCTtksiWqqWr+8ymYGiqgptK2pYvaGRVesaaGwJl3W2n4xn6e/J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\nlSPIq3k2AvC7aa7tf57h0Aqs5tvJGNF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Attempting to adjust your arguments for the new API (which might not work). Please update your code. See the version 0.6 release notes for more info.\n", + " warnings.warn(msg, UserWarning)\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 190, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df.plot(kind='scatter', x='boy',y='girl')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 当plot()函数kind为scatter时，可绘制散点图\n", + "- 散点图展现的是2组数据(2个变量)之间的关系\n", + "- 可见当前`boy`与`girl`两组数据之间是一种线性关系" + ] + }, + { + "cell_type": "code", + "execution_count": 192, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 192, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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DataFrame汇总与描述统计" + ] + }, + { + "cell_type": "code", + "execution_count": 193, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "boy 3960\n", + "child 6960\n", + "children 4960\n", + "girl 7920\n", + "dtype: int64" + ] + }, + "execution_count": 193, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Series对象的各类求值及描述统计函数，DataFrame对象均可以使用\n", + "- 默认参数是axis=0，表示以行方向为数据轴（基准），取每列做为一个Series对象对其进行统计" + ] + }, + { + "cell_type": "code", + "execution_count": 194, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2006-12-31 1400\n", + "2007-12-31 1900\n", + "2008-12-31 2400\n", + "2009-12-31 2150\n", + "2010-12-31 2350\n", + "2011-12-31 3400\n", + "2012-12-31 4900\n", + "2013-12-31 2400\n", + "2014-12-31 1900\n", + "2015-12-31 1000\n", + "Freq: A-DEC, dtype: int64" + ] + }, + "execution_count": 194, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.sum(axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 参数如果指定axis=1，表示以列方向为数据轴（基准），取每行做为一个Series对象对其进行统计" + ] + }, + { + "cell_type": "code", + "execution_count": 195, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "boy 120\n", + "child 420\n", + "children 220\n", + "girl 240\n", + "dtype: int32" + ] + }, + "execution_count": 195, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.min()" + ] + }, + { + "cell_type": "code", + "execution_count": 196, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "boy 396.0\n", + "child 696.0\n", + "children 496.0\n", + "girl 792.0\n", + "dtype: float64" + ] + }, + "execution_count": 196, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 197, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "boy 47871.111111\n", + "child 47871.111111\n", + "children 47871.111111\n", + "girl 191484.444444\n", + "dtype: float64" + ] + }, + "execution_count": 197, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.var()" + ] + }, + { + "cell_type": "code", + "execution_count": 198, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "boy 218.794678\n", + "child 218.794678\n", + "children 218.794678\n", + "girl 437.589356\n", + "dtype: float64" + ] + }, + "execution_count": 198, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.std()" + ] + }, + { + "cell_type": "code", + "execution_count": 199, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "boy 0.552512\n", + "child 0.314360\n", + "children 0.441118\n", + "girl 0.552512\n", + "dtype: float64" + ] + }, + "execution_count": 199, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.std()/df.mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 200, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "boy 370.0\n", + "child 670.0\n", + "children 470.0\n", + "girl 740.0\n", + "dtype: float64" + ] + }, + "execution_count": 200, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.median()" + ] + }, + { + "cell_type": "code", + "execution_count": 201, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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      " + ], + "text/plain": [ + " girl children child boy\n", + "2006-12-31 400 300 500 200\n", + "2007-12-31 600 400 600 300\n", + "2008-12-31 800 500 700 400\n", + "2009-12-31 700 450 650 350\n", + "2010-12-31 780 490 690 390\n", + "2011-12-31 1200 700 900 600\n", + "2012-12-31 1800 1000 1200 900\n", + "2013-12-31 800 500 700 400\n", + "2014-12-31 600 400 600 300\n", + "2015-12-31 240 220 420 120" + ] + }, + "execution_count": 214, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.sort_index(axis = 1, ascending = False) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 将参数axis设为1，排序即按照列索引进行排序" + ] + }, + { + "cell_type": "code", + "execution_count": 215, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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      boychildchildrengirl
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      " + ], + "text/plain": [ + " boy child children girl\n", + "2015-12-31 120 420 220 240\n", + "2006-12-31 200 500 300 400\n", + "2007-12-31 300 600 400 600\n", + "2014-12-31 300 600 400 600\n", + "2009-12-31 350 650 450 700\n", + "2010-12-31 390 690 490 780\n", + "2008-12-31 400 700 500 800\n", + "2013-12-31 400 700 500 800\n", + "2011-12-31 600 900 700 1200\n", + "2012-12-31 900 1200 1000 1800" + ] + }, + "execution_count": 215, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.sort_values(by='boy')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 根据值进行排序，如是根据某对应列的值进行排序，需要在参数by中指定数据列索引" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4. 选择DataFrame数据\n", + "**4.1 列选择**" + ] + }, + { + "cell_type": "code", + "execution_count": 216, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2006-12-31 200\n", + "2007-12-31 300\n", + "2008-12-31 400\n", + "2009-12-31 350\n", + "2010-12-31 390\n", + "2011-12-31 600\n", + "2012-12-31 900\n", + "2013-12-31 400\n", + "2014-12-31 300\n", + "2015-12-31 120\n", + "Freq: A-DEC, Name: boy, dtype: int32" + ] + }, + "execution_count": 216, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['boy']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 利用列索引选择一列，将返回该列，且为Series对象。也可以用`df.boy`来访问，效果相同。" + ] + }, + { + "cell_type": "code", + "execution_count": 217, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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      boychildren
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      " + ], + "text/plain": [ + " boy children\n", + "2006-12-31 200 300\n", + "2007-12-31 300 400\n", + "2008-12-31 400 500\n", + "2009-12-31 350 450\n", + "2010-12-31 390 490\n", + "2011-12-31 600 700\n", + "2012-12-31 900 1000\n", + "2013-12-31 400 500\n", + "2014-12-31 300 400\n", + "2015-12-31 120 220" + ] + }, + "execution_count": 218, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[['boy','children']]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 利用指定多个列标签colums的list，可以获取对应colums标签的列，返回DataFrame对象。" + ] + }, + { + "cell_type": "code", + "execution_count": 219, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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      boychildren
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      " + ], + "text/plain": [ + " boy children\n", + "2006-12-31 200 300\n", + "2007-12-31 300 400\n", + "2008-12-31 400 500\n", + "2009-12-31 350 450\n", + "2010-12-31 390 490\n", + "2011-12-31 600 700\n", + "2012-12-31 900 1000\n", + "2013-12-31 400 500\n", + "2014-12-31 300 400\n", + "2015-12-31 120 220" + ] + }, + "execution_count": 219, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[[0,2]]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 利用代表列整数索引的list，可以获取对应colums标签的列，返回DataFrame对象。" + ] + }, + { + "cell_type": "code", + "execution_count": 220, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2006-12-31 200\n", + "2007-12-31 300\n", + "2008-12-31 400\n", + "2009-12-31 350\n", + "2010-12-31 390\n", + "2011-12-31 600\n", + "2012-12-31 900\n", + "2013-12-31 400\n", + "2014-12-31 300\n", + "2015-12-31 120\n", + "Freq: A-DEC, Name: boy, dtype: int32" + ] + }, + "execution_count": 220, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + " df.loc[:,'boy']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 也可以利用DataFrame的loc属性进行列选取\n", + "- loc属性主要通过标签进行数据选取\n", + "- 其中第一个位置参数表示行方向(即axis=0)元素，为`:`即为全部选取\n", + "- 第二个位置参数表示列方向(即axis=1)元素，选取当前指定列标签的数据\n", + "- 返回一个Series对象" + ] + }, + { + "cell_type": "code", + "execution_count": 221, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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      boygirl
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      " + ], + "text/plain": [ + " boy girl\n", + "2006-12-31 200 400\n", + "2007-12-31 300 600\n", + "2008-12-31 400 800\n", + "2009-12-31 350 700\n", + "2010-12-31 390 780\n", + "2011-12-31 600 1200\n", + "2012-12-31 900 1800\n", + "2013-12-31 400 800\n", + "2014-12-31 300 600\n", + "2015-12-31 120 240" + ] + }, + "execution_count": 222, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.loc[:,['boy', 'girl']]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 与前类似，可以选择多个列，返回一个DataFrame对象" + ] + }, + { + "cell_type": "code", + "execution_count": 223, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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      boychildchildrengirl
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      " + ], + "text/plain": [ + " boy child children girl\n", + "2006-12-31 200 500 300 400\n", + "2007-12-31 300 600 400 600\n", + "2008-12-31 400 700 500 800\n", + "2009-12-31 350 650 450 700\n", + "2010-12-31 390 690 490 780\n", + "2011-12-31 600 900 700 1200\n", + "2012-12-31 900 1200 1000 1800\n", + "2013-12-31 400 700 500 800\n", + "2014-12-31 300 600 400 600\n", + "2015-12-31 120 420 220 240" + ] + }, + "execution_count": 223, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.loc[:,'boy': 'girl']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 可以利用列索引进行切片选取\n", + "- 注意切片是两端包含\n", + "- 注意切片不需要在list中" + ] + }, + { + "cell_type": "code", + "execution_count": 224, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2006-12-31 200\n", + "2007-12-31 300\n", + "2008-12-31 400\n", + "2009-12-31 350\n", + "2010-12-31 390\n", + "2011-12-31 600\n", + "2012-12-31 900\n", + "2013-12-31 400\n", + "2014-12-31 300\n", + "2015-12-31 120\n", + "Freq: A-DEC, Name: boy, dtype: int32" + ] + }, + "execution_count": 224, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.iloc[:,0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 还可以利用DataFrame的iloc属性进行列选取\n", + "- iloc属性也称为位置(position)属性，主要通过位置(即对应整数索引)进行数据选取\n", + "- 其中第一个位置参数仍然表示行方向(即axis=0)元素，为`:`即为全部选取\n", + "- 第二个位置参数仍然表示列方向(即axis=1)元素，选取当前指定列标签的数据\n", + "- 返回一个Series对象" + ] + }, + { + "cell_type": "code", + "execution_count": 225, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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      " + ], + "text/plain": [ + " boy child children\n", + "2006-12-31 200 500 300\n", + "2007-12-31 300 600 400\n", + "2008-12-31 400 700 500\n", + "2009-12-31 350 650 450\n", + "2010-12-31 390 690 490" + ] + }, + "execution_count": 249, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.iloc[0:5,0:3]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**4.3.3 利用iat选择单个元素**" + ] + }, + { + "cell_type": "code", + "execution_count": 250, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "600" + ] + }, + "execution_count": 250, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.iat[1,1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 在以上进行行、列选取的各类方法中，推荐使用loc及iloc方法，虽然稍显繁琐，但是逻辑较为严密，且性能更优。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. 条件选择DataFrame数据(布尔索引)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 对DataFrame按照条件来选择数据较为方便记忆的方式是：`对象[布尔索引]`，其中对象可以是整个DataFrame对象，也可以是DataFrame经过前一部分选择出的列、行或者区块。注意布尔索引要与选择出的数据对齐。\n", + "### 达到数据选择结果的方法较为多样，但为避免造成记忆及使用的混乱，其他条件选择数据的方式本教程不予介绍。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5.1 列" + ] + }, + { + "cell_type": "code", + "execution_count": 251, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2006-12-31 False\n", + "2007-12-31 False\n", + "2008-12-31 False\n", + "2009-12-31 False\n", + "2010-12-31 False\n", + "2011-12-31 True\n", + "2012-12-31 True\n", + "2013-12-31 False\n", + "2014-12-31 False\n", + "2015-12-31 False\n", + "Freq: A-DEC, Name: boy, dtype: bool" + ] + }, + "execution_count": 251, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.boy > 400" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 与Series中的类似，是每个元素比较后的布尔值\n", + "- 返回一个Series" + ] + }, + { + "cell_type": "code", + "execution_count": 252, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
      \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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      " + ], + "text/plain": [ + " boy child children girl\n", + "2011-12-31 600 900 700 1200\n", + "2012-12-31 900 1200 1000 1800" + ] + }, + "execution_count": 252, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + " df[df.boy > 400]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 可利用DataFrame对象的某一列数据，进行布尔运算，根据布尔运算的结果过滤数据\n", + "- 返回一个DataFrame对象" + ] + }, + { + "cell_type": "code", + "execution_count": 253, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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      " + ], + "text/plain": [ + " boy child children girl\n", + "2011-12-31 600 900 700 1200\n", + "2012-12-31 900 1200 1000 1800" + ] + }, + "execution_count": 253, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[(df.boy >300) & (df.girl > 900)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 可利用DataFrame对象的多列数据，进行多个布尔运算，注意用括号括起每个部分，表示`and`操作用`&`，'or'用`|`，`not`用`~`。\n", + "- 返回一个DataFrame对象" + ] + }, + { + "cell_type": "code", + "execution_count": 254, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2008-12-31 800\n", + "2009-12-31 700\n", + "2010-12-31 780\n", + "2011-12-31 1200\n", + "2012-12-31 1800\n", + "2013-12-31 800\n", + "Freq: A-DEC, Name: girl, dtype: int32" + ] + }, + "execution_count": 254, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['girl'][df.boy >300]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 选择df中的一列，再根据布尔索引进行过滤选择\n", + "- 返回一个Series" + ] + }, + { + "cell_type": "code", + "execution_count": 255, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
      \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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      \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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      \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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数据分组技术\n", + "\n", + "---" + ] + }, + { + "cell_type": "code", + "execution_count": 161, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "from pandas import Series, DataFrame\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 数据分组技术\n", + "\n", + "- 多层索引、数据分组、计算、合并、处理缺失数据、输入输出" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. 多层索引" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**1.1 Series多层索引**" + ] + }, + { + "cell_type": "code", + "execution_count": 162, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "a 0 -0.849212\n", + " 1 -1.117469\n", + " 2 0.472500\n", + "b 0 -1.272573\n", + " 1 0.252942\n", + " 2 0.360228\n", + "c 0 0.425634\n", + " 1 -0.674811\n", + "d 0 1.335875\n", + " 1 -0.228149\n", + "dtype: float64" + ] + }, + "execution_count": 162, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = Series(np.random.randn(10),index=[['a','a','a','b','b','b','c','c','d','d'],[0,1,2,0,1,2,0,1,0,1]])\n", + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- `index`参数中有两个list，其中第一个为外层索引，第二个为内层索引" + ] + }, + { + "cell_type": "code", + "execution_count": 163, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "MultiIndex(levels=[['a', 'b', 'c', 'd'], [0, 1, 2]],\n", + " labels=[[0, 0, 0, 1, 1, 1, 2, 2, 3, 3], [0, 1, 2, 0, 1, 2, 0, 1, 0, 1]])" + ] + }, + "execution_count": 163, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.index" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 查看index发现，有两个层次的索引，索引list分别为['a', 'b', 'c', 'd'], [0, 1, 2]，各位置元素对应的索引标签为：[0, 0, 0, 1, 1, 1, 2, 2, 3, 3], [0, 1, 2, 0, 1, 2, 0, 1, 0, 1]，以索引标签在索引list中的位置来记录。" + ] + }, + { + "cell_type": "code", + "execution_count": 164, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 -1.272573\n", + "1 0.252942\n", + "2 0.360228\n", + "dtype: float64" + ] + }, + "execution_count": 164, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['b']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 外层索引进行元素选取" + ] + }, + { + "cell_type": "code", + "execution_count": 165, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "a 0 -0.849212\n", + " 1 -1.117469\n", + " 2 0.472500\n", + "b 0 -1.272573\n", + " 1 0.252942\n", + " 2 0.360228\n", + "c 0 0.425634\n", + " 1 -0.674811\n", + "dtype: float64" + ] + }, + "execution_count": 165, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['a':'c']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 外层标签切片" + ] + }, + { + "cell_type": "code", + "execution_count": 166, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "a 0 -0.849212\n", + " 1 -1.117469\n", + " 2 0.472500\n", + "c 0 0.425634\n", + " 1 -0.674811\n", + "dtype: float64" + ] + }, + "execution_count": 166, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data[['a','c']]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 选取外层多个标签" + ] + }, + { + "cell_type": "code", + "execution_count": 167, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "a -1.117469\n", + "b 0.252942\n", + "c -0.674811\n", + "d -0.228149\n", + "dtype: float64" + ] + }, + "execution_count": 167, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data[:,1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 选取全部外层索引，且内层索引为1的元素\n", + "- 返回一个Series" + ] + }, + { + "cell_type": "code", + "execution_count": 168, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "-0.84921151400187134" + ] + }, + "execution_count": 168, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['a',0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 选取某外层索引，且内存索引为0的元素" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 注意，目前多层索引并不支持形如data['a',:]这种符合逻辑的形式，个人认为可能也是设计缺陷之一" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** 2.DataFrame多层索引** " + ] + }, + { + "cell_type": "code", + "execution_count": 169, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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      " + ], + "text/plain": [ + "la beijing shanghai\n", + "one two \n", + "a 1 -1.085143 -1.849417\n", + " 2 -0.443240 -1.113718\n", + "b 1 -4.036908 -2.057308\n", + " 2 -1.924626 -0.464505\n", + "c 1 0.373841 -1.126064\n", + " 2 -0.202254 -1.241607" + ] + }, + "execution_count": 194, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.sum(level = 'la',axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 利用axis参数，可以指定列索引名称进行相应计算\n", + "- 其余指定columns值的计算均类似" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### 2. 数据分组(Groupby)计算\n", + "\n", + "- 数据分组是Pandas常用操作\n", + "- 1.将数据按照某个规律(如标签、数据的规律)分组\n", + "- 2.对每个分组施加(Apply)运算\n", + "- 3.将运算结果合并(Combine)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 对Series进行分组计算" + ] + }, + { + "cell_type": "code", + "execution_count": 195, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "one two three\n", + "a 0 aa -0.849212\n", + " 1 aa -1.117469\n", + " 2 aa 0.472500\n", + "b 0 aa -1.272573\n", + " 1 aa 0.252942\n", + " 2 bb 0.360228\n", + "c 0 bb 0.425634\n", + " 1 bb -0.674811\n", + "d 0 bb 1.335875\n", + " 1 bb -0.228149\n", + "dtype: float64" + ] + }, + "execution_count": 195, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 仍然是以之前的Series作为例子" + ] + }, + { + "cell_type": "code", + "execution_count": 196, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 196, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "grouped = data.groupby(level='one')\n", + "grouped" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 利用groupby()函数，可以将数根据据指定level对应的index，进行分组。\n", + "- grouped是groupby类型，可视为含有一些分组信息的视图(类似dict)" + ] + }, + { + "cell_type": "code", + "execution_count": 197, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a\n", + "---------------------------\n", + "one two three\n", + "a 0 aa -0.849212\n", + " 1 aa -1.117469\n", + " 2 aa 0.472500\n", + "dtype: float64\n", + "b\n", + "---------------------------\n", + "one two three\n", + "b 0 aa -1.272573\n", + " 1 aa 0.252942\n", + " 2 bb 0.360228\n", + "dtype: float64\n", + "c\n", + "---------------------------\n", + "one two three\n", + "c 0 bb 0.425634\n", + " 1 bb -0.674811\n", + "dtype: float64\n", + "d\n", + "---------------------------\n", + "one two three\n", + "d 0 bb 1.335875\n", + " 1 bb -0.228149\n", + "dtype: float64\n" + ] + } + ], + "source": [ + "for name, group in grouped:\n", + " print(name)\n", + " print('---------------------------')\n", + " print(group)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 利用类似dict一样对名字和对应的组进行迭代" + ] + }, + { + "cell_type": "code", + "execution_count": 198, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "one\n", + "a 3\n", + "b 3\n", + "c 2\n", + "d 2\n", + "dtype: int64" + ] + }, + "execution_count": 198, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "grouped.size()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 可以利用groupby对象的size()方法，来查看分组对象的信息\n", + "- 在本例中，对应索引a,b,c,d的数据个数分别为：3,3,2,2" + ] + }, + { + "cell_type": "code", + "execution_count": 199, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "one\n", + "a -1.494180\n", + "b -0.659403\n", + "c -0.249177\n", + "d 1.107726\n", + "dtype: float64" + ] + }, + "execution_count": 199, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "grouped.sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 对分好的组进行分组计算，之前的各种计算均可以直接应用" + ] + }, + { + "cell_type": "code", + "execution_count": 200, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "one\n", + "a -1.494180\n", + "b -0.659403\n", + "c -0.249177\n", + "d 1.107726\n", + "dtype: float64" + ] + }, + "execution_count": 200, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.groupby(level='one').sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 可以合并为一条语句来写，其功能与data.sum(level = 'one')一样" + ] + }, + { + "cell_type": "code", + "execution_count": 201, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n", + "-------------------\n", + "one two three\n", + "a 0 aa -0.849212\n", + " 1 aa -1.117469\n", + " 2 aa 0.472500\n", + "b 0 aa -1.272573\n", + " 1 aa 0.252942\n", + "dtype: float64\n", + "2\n", + "-------------------\n", + "one two three\n", + "b 2 bb 0.360228\n", + "c 0 bb 0.425634\n", + " 1 bb -0.674811\n", + "d 0 bb 1.335875\n", + " 1 bb -0.228149\n", + "dtype: float64\n" + ] + } + ], + "source": [ + "key = [1,1,1,1,1,2,2,2,2,2]\n", + "for name, group in data.groupby(key):\n", + " print(name)\n", + " print('-------------------')\n", + " print(group)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- groupby()强大还在于可以利用任意序列或数组对数据进行分组，不需要该数据一定在数据表中，前提是要两者等长\n", + "- 本例中用[1,1,1,1,1,2,2,2,2,2]将data进行分组" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2 对DataFrame进行分组计算" + ] + }, + { + "cell_type": "code", + "execution_count": 202, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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      " - ], - "text/plain": [ - " boy child children girl\n", - "2006-12-31 200 500 300 400\n", - "2007-12-31 300 600 400 600\n", - "2008-12-31 400 700 500 800\n", - "2009-12-31 350 650 450 700\n", - "2010-12-31 390 690 490 780\n", - "2011-12-31 600 900 700 1200\n", - "2012-12-31 900 1200 1000 1800\n", - "2013-12-31 400 700 500 800\n", - "2014-12-31 300 600 400 600\n", - "2015-12-31 120 420 220 240" - ] - }, - "execution_count": 184, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = DataFrame(freq_dict, index = years)\n", - "df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 与Series对象类似，可以在初始化DataFrame的时候，指定index\n", - "- 数据可视为3个有相同index的Series" - ] - }, - { - "cell_type": "code", - "execution_count": 185, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 185, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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BEoEDxVunJS4xBGPt2Cwx+OGH7xMTE8OTTz7PunU/pbGxodvniYnJ/Pe/z6PTebaal6cR\nHr1AMArIOlCOxdzOrEUJ+Pp5D7c53UhOD6e0sIGiPCPT53m+shXAopWpvXrfg0FJSRHz57uyVyYm\nJhEc3H3NQlcmypGO8OgFghGOzdrBoT1l6PRaZgySkA6EpAmuOP1YzFGfkpJKdvYRACoqymlu7r64\nX632bJbJwUIIvUAwwjm0t4x2m52ZCxIGpUTgQPH18yYqLoiq8mYs5rFVYvDSS1dTXV3Fz352K88/\n/xTe3iPrbspdROhGIBjBWFptZO0vx9ffmymzY4fbnLOSkh5OdXkzxQVGJk8fXTNSeiIvT+bSS1cz\nb94CyspKyco60ln5KYBbbvnpiXbR0TE8/fSLw2doLwiPXiAYwRzYXeoqEbg4ES+vwSsROFC6plmO\ntfBNTEwsr7zyAuvXr+Ovf72L3/zmD8NtUr8QHr1AMEIxNbWRc7iSwGA9E6cNXSHu/hAY7EOYwY/y\n4sYhLTE42ISFhfP4408NtxkDRnj0AsEI5btvinE6FeYuTR4VxT2S08NxOhRKCxt6bywYUkb+2SMQ\njEMa6szkZdcQavBjwuTRUdijq8JVUZ4oCz3SEEIvEIxA9u0oAlyJyzxVOWqwCYvwIyBIT8nxBhx2\n53CbIzgJIfQCwQijptJEUb6RyNhAEtPChtsct1GpVKSkh9PR7qC8pHG4zRGchBB6gWCEsbezqMiC\n5SmjxpvvYqzOvhntCKEXCEYQ5cUNVJQ0EZ8cQkxC8HCb02ciY4PwESUGRxxC6AWCEYKiKOzd3hmb\nX54yzNb0D7VaRdKEcKyWDqormofbHEEnQugFghFCUZ6R2qoWUicaMEQFDLc5/UaEb0YeQugFghGA\n06mwb6erRODcpcNbInCgxCWGiBKDIwwh9ALBCCDvaA2NRgvS1ChCwnyH25wBodGqSUwNo6XZSn3t\n+CkxOJJxa52yJEnzgQdkWV4hSdIM4HHAAdiAG2VZrpEk6VFgCdDSudtqoB14FYjo3L5WlmWxmkIg\nOAmH3cn+nUVoNCOnROBASU4PpyC3lqK8OsIj/YfbnHFPrx69JEm/B54FumqXPQr8XJblFcB7QFeW\nn9nAhbIsr+j81wysB7JkWV4KvAzc5WH7BYJRT87hSlpMNjJmxeIfODJKBA6UhJRQNBqViNOPENwJ\n3RwHrjrp/RpZlg93vtYCVkmS1MAE4GlJknZJkrSu8/MlwObO158B53nAZoFgzNDRbufA7hK8vDXM\nWpgw3OZ4DG+dlrikEOrrzGOyxOBoo9fQjSzL70qSlHTS+yoASZIWAXcAywA/XOGchwAN8JUkSfuB\nQKBrjlULEOSuYQbD6J11MJSIcXKPkTpOO7fm0WbpYNn56SQkjoxVsJ4aq6mz4ig53kBtRQupE0ZH\nvp6+MFLPqTPRr1yikiRdD/wJuESW5TpJkjTAo7IsWzo/3wZMB0xA12gEAE1n6u9M1NW19N5onGMw\nBIhxcoOROk7Wtg52bStA76NlwpSIEWGjJ8cqLMoflQqyDpWTljG2hH6knlNnu/j0edaNJEk/wuXJ\nr5BlubBzczqwS5IkjSRJXrhCNgeBXcDFnW0uAnb29XgCwVjl0J5S2m0OZi5IHDP520/G18+bqNgg\nqstNY67E4GijT0Lf6bk/hss7f0+SpK8lSbpHluVc4BVgD7AdeFmW5aPAk0CGJEnfALcB93jUeoFg\nlGJusZF1oAK/AG+mzBo7pfdOpWvxVHG+eCg7nLjlRsiyXAws6HwbepY2G4ANp2yzANcOwD6BYExy\nYHcJDruTOYuT0I6QEoGKonh8gVNyeji7tx2nMM/I5Blj94I20hl794sCwQinvraV3MwqgkJ8mDgt\narjNAaDWYuSpIy+SEhbPDWnXeSxrZmCwD+ER/lQUN2Kz2tHpheQMB2JlrEAwhLTb7Hz+wVGcToXF\n56ahVg//T7CytZqHDz5JtaWW3WUH2F9zuPed+kByejhOp0JpYb1H+xW4z/CfZQLBOEFRFLZ/nkdz\nQxvT58WNiKIiJaYyHjn4X0ztLVyYuBJvjRfv5H+EucPisWOIJGfDjxB6gWCIyDlcRUFOLZGxgSMi\nDXFBUxGPHXoai72NH068lstTV3HdlMto7TDzfsEnHjtOqMGPwGA9pYUN2O0Oj/UrcB8h9ALBEGCs\naWHX1nx0ei0XrJ6MRjO8P73c+jz+ffhZ2p0d3JxxA4ti5gJwSfpK4vxj+LbqO/IaCzxyLJVKRXK6\nwVVisFiUGBwOhNALBINMu83Olg9ycDgUzr100rDns8msy+a/R15AQeGnU9cyO3L6ic80ag03TLwa\nFSreOPYeHY4OjxwzRYRvhhUh9ALBIKIoCl9/JtPc2MaM+fHDHpffV32QZ7NfRa3W8LPp65gSPum0\nNomB8ayIX0xtm5HNJds8ctzI2EB8/bwpzq/H6XR6pE+B+wihFwgGkaOHKjl+rI6ouEDmLRvegiI7\nK/bwcs6b6DQ6fjHjVtJD0s7a9tLkCwnRBbOl5CsqW6sHfGyVSkXShDCsbR1Ul5sG3J+gbwihFwgG\nibrqFnZ96cplc/7lwxuX31q6nY3ye/h5+fLLmT8lOSixx/Z6rY410pU4FSevH3sXpzJwLzw53QBA\nYZ4oSTHUCKEXCAYBm9XOlg+O4nQorBzGuLyiKGwq3ML7BZ8QrAvi17PWEx/g3grVKeGTmBUxjSJT\nCd9U7B2wLbGJwXjrNBSLEoNDjhB6gcDDdMXlTU1WZi5MIDF1eOLyiqLwXsEmPiveSrg+lF/PWk+U\nX9+ySF4zYTU+Wj0fHv+MJltz7zv0gEajJiE1jBaTDWNN64D6EvQNIfQCgYfJPlhBoVxHdFwQ85Ym\nDYsNTsXJG/K7bCvbSZRfJL+evZ5wnzOmqeqRIF0AV6ZegtVh5e28jwZsl5h9MzwIoRcIPEhtlYnd\nXx5H7+vFeasnD0uKA4fTwUs5G9lVuY94/xh+PfN2gnVu1/w5jYUxc0kNSuJwXRaZdUcHZNuJEoMi\nm+WQIoReIPAQNmsHWz7IwelUOO+ySfgH6Ibchg5HB89mv8r+msOkBCXyi5k/xd/bb0B9qlVqbph4\nNRqVhrfyPqDNbu13X17eWuKSQmmoM9Pc6Lk0C4KeEUIvEHgARVH46lOZlmYrsxclEp/c9zDJQLE5\n2vnvkRc5YjyKFJLGHTNuxdfLxyN9R/lFcmHiOTTZmvm48PMB9dWV+6ZQhG+GDCH0AoEHyNpfQVGe\nkZiEYOYsSRry47fZ2/j34Wc51pjP1PDJrJ92MzqNt0ePcUHSSiJ9Dewo301Rc2m/+0maEIZKJeL0\nQ4kQeoFggNRUmvj2q+P4+Hpx3uWTUKs9k8vdXVrbzTx66GkKm4uZEzmDW6f8GC+Nl8eP46XW8gPp\nahQUXj/2Dg5n/xKU+fh6Ex0XRE2FCXOrzcNWCs6EEHqBYADYrB180Zlf/rzLJ+PnP7Rx+SZbMw8f\n+i9lLRUsip7H2slr0KgHr2LVhJAUFsfMo9JczZelO/rdT9fiKVFicGgQQi8Q9BNFUdj2yTFaTDbm\nLE4kLilkSI9f39bAwweepNpcw8r4pdww8WrUqsH/SV+RejEB3v58WvwFdZb+FRMROeqHFiH0AkE/\nOfJdOcX59cQmBjN7cdKQHrvGXMtDB5/EaG3goqTzuCrtUo+V/+sNXy9frp1wOR1OOxvl9/q1yjUg\nSE94pD8VJU3YrJ7JkCk4O0LoBYJ+UF3RzJ6vC/Hx8+K8y4Y2Ll/eUsnDB/9Lk62ZK9Mu4dKUC4ZM\n5LuYFTGdjLCJHGvMZ1/1wX71kdJZYrDkeIOHrROcihB6gaCPWNs6+OLDHBRF4fzLJ+M7hHH5ouYS\nHjn0FK0dZtZIV3FewvIhO/bJqFQqrk+/Em+1F+8WfExru7nPfXTF6YtEkrNBx62S7JIkzQcekGV5\nhSRJacCLgAJkAz+TZdkpSdKtwE8BO/B3WZY3SZLkA7wKRAAtwFpZlsVfVTBqURSFbZuO0WqyMXdp\nErGJQxeXz2ss4MkjL2J32rlx8vXMi5o1ZMc+E2E+IVyWciHvFmzivYJN3Dj5+j7tHxLuS1CIj6vE\nYIcDrdfgPUQe7/Tq0UuS9HvgWaAr/d5DwF2yLC8FVMBqSZKigF8Ai4ELgfskSdIB64GszrYvA3d5\n/isIBENH5r4ySo7XE5cUwqyFPaf69STZxlz+k/k8DqeDW6b8aFBEvvXwIVqPF/Zpn+Vxi0kIiGVv\n9QGONeT3aV9XicFw7B1OykSJwUHFHY/+OHAV8Ern+9nA9s7XnwEXAA5glyzLNsAmSVIBMA1YAvzz\npLZ/dtcwgyHA3abjGjFO7uGJcSoramDP9iL8A3Vcf9Nc/IYoxcG3ZQd4OuslNGoNf1iynhnRkz3a\nv6IolL6+kcq33qHW35+ZTzyKd3Cw2/v/bOFa/vjF/byV/z4PrvozOq37C7VmzU/k8N4yqkqbmbdo\neAuz9JXR9NvrVehlWX5XkqSkkzapZFnueszeAgQBgcDJOUzPtL1rm1vU1bW423TcYjAEiHFyA0+M\nU5ulnbdfOgCKq+6rxdqOxdruIQvPzrdV+3kt9210Gm/WT19HrDbeo39zRVGoe/N1mrZ+gVqvx97a\nyrEnniH6ttvd7sOfYM6JX8KXpTt4Zf8HrE69yO19vX00+Pp7I2dXUVOTPCxJ4PrDSP3tne3i059R\nPbnUTADQBJg6X/e0vWubQDCq6Jovb26xMXdpMjEJ7nu7A+Hr8l28mvsWvloffjHzNtKCPevxKk4n\nNS+9QNPWL/COiSHxr/fiP2ECLfv2YM4+0qe+Lkm+gFB9CFtLt1PRWuX2fq4Sg+FY2+xUlQ0s373g\n7PRH6A9JkrSi8/VFwE5gH7BUkiS9JElBwCRcD2p3ARef0lYgGFUc3ltG6fEG4pNDmLUwYUiO+Xnx\nNt7O+5BA7wB+Net2EgPjPdq/YrdT/exTmL7ZgS4hkfjf/RGv0FDS7rgdNBpqXn0Zp8399AQ6jXe/\nSw+KHPWDT3+E/k7gHkmSvgW8gXdkWa4GHsMl5NuAP8mybAWeBDIkSfoGuA24xzNmCwRDQ1VZE3u3\nF+Ln7825l00a9PnqiqLw4fHP+KhwMyG6YH4963Zi/KM8egxnRzuVT/6bln170adNIO63f0AT4Lrx\n9ktKIuSCVdiNRuo/er9P/WaETWRO5AyKTaXsqPjW7f1iElwlBovyRYnBwcKt6ZWyLBcDCzpf5wGn\nTd6VZfkZ4JlTtlmAawdspUAwDLRZ2vnioxwAzls9GR9fz2aDPBWn4uSd/I/YXr6bCJ9wfj7zVkL1\nnp2+6bRaqXziMSy5OfhOyiDmjl+g1nV/qBx22Wpa939H45bPCZi3AH1iktv9Xz3hMnLqZT46/hnT\nwzMI0fce5tJo1CSmhZF/tBZjTSuGqNHzkHO0MDqefAgEQ4yiKHz5cS7mlnbmLUsmJn5w4/JOxclr\nue+wvXw3MX5R/GrWeo+LvMNipvzhB7Hk5uA3YyYxv/jlaSIPoPb2JuLHa0FRqHn5RRSH+1kqA70D\nuDLtUmyOdt7M+8BtDz15gmvxVKFYPDUoCKEXCM7AwW9LKStqJCE1lJkLBjcub3faef7o6+yp3k9i\nYDy/mnU7QTrPerX2FhPlGx7AeryAgPkLiLn9Z6i9zn6H4jc5g4CFi7CVFNP05dY+HWth9BwmBKeQ\nZcwhsy7brX0SUkLRaNUiTj9ICKEXCE6hsrSJ73YW4RegY+UlEwc1Lt/u6ODprJc5VHuEtOBkfjHj\nVvy8fD16jI7GRsr/eT+2slKClq0g6pbbUGl7j9oarluD2t8f4wfv0lHvvgCrVCp+MPFqtGptZ+nB\ntl738fLWEJ8UQqPRQlODKDHoaYTQCwQnYTF/H5c/f5Dj8la7lf9kPsfR+mNMDpX42fRb0Gv1ve/Y\nBzrq6ih/4F7aqyoJOf9CIn68FpWbc9W1AYFEXPcDlPZ2al97pU8PSiN9DaxKXElzewsfHt/s1j4i\ndfHgIYReIOjE6XTF5S2t7cxfkUJ0nNvr+/qMpcPC44efJb+pkBmGqdw2bS3eHi79Z6uspPSBf9Bh\nrCP0stWEX7emz3cnAQsX4TtpMuYjmbTu/65P+56fuIIov0i+qdhDYXNxr+2TJoSjUok4/WAghF4g\n6OTgtyVciTMUAAAgAElEQVSUFzeSmBrGjHmenbd+MjXmWh459BTFplLmR81mXcYNeKndmgDnNtbS\nEsr/eR+OpibCr72e8NVX9ijyHXYnb31VwNZ93WvBqlQqIn60FpWXF7VvvIrD7H6WSq1ayw0nSg++\ni91p77G93seL6PhgaitbaG0RJQY9iRB6gQCoKGlk/zfF+AfqWHnp4MTlnYqTraXbue+7R6horWJ5\n3CJ+NOlaj5f+azteQPmG+3GYW4n48U2EXthzSgJbh4PH3z3C5r2lPP72YUprui/t946MJOyy1ThM\nJozvvtUnW1KDk1gSu4Aqcw1bS7f32r5r8ZQoMehZhNALxj0WcztbP8pFpVJx/urJ6H08X1i72lzL\nQwf+w/sFn6DX6Ll1yo+5Lv0Kj5f+s+TmUP7QBpw2G1G33Erw8hU9tm+z2Xn4zcNkFzWQGBmA06nw\n0mYZp7N7PD7kglV4x8bRvGM7ljy5TzatTrmIIO8APiv+khpLz2EZEacfHITQC8Y1TqfC1o9ysJjb\nWbAihahYz8blnYqTL0q+5r7vHqHIVMqcyBncNf9OZkRM9ehxAFozD1Px6EPgcBCz/mcELljUc/u2\nDja8cYi88mbmTIzgTzfOZvnMOIqqTGw7WN6trUqrJXLtzaBSUfPyCzg73C//5+vlw7XpV2B32nnj\n2Ls9PtT1D9RjiAqgslSUGPQkQugF45oDu0uoKGkiKS2MaXPjPNp3lbmGBw88wQfHP8VHq+fWqTdy\nc8YN+Hv7efQ4AC379lL5n8dBrSbm57/Cf+bsHts3t9p44PWDFFe3sGRqNLdfnoFWo+Ynq6fgp9fy\n7o5CGkzWbvv4pKQSfM5KOqqrafh0U5/sm2GYwtTwyeQ3FbKnan+PbZM7SwwWF/Sv8LjgdITQC8Yt\n5cWuuHyAh+PyDqeDLcVfcf++RygxlTE3cqbLizdM8Uj/p9K8cztVz/wXtbc3cb/+LX4ZPR/H2NzG\nfa8dpKLOzLmz47jp4oknat4GB+i4bmUatnYHr27JO837DrvyGrQhITR+9gm2ykq3bXSVHrwCncab\n9wo20dLeeta2InzjeYTQC8Yl5lYbWz/KQa1Wcf4VGej0nonLV7ZW868D/+HDws/w9fLltqlruSnj\nB/h7ed6LB2jcuoWal15A7edH3J1/wGdCeo/taxos3P/aQWob27hkYSI3nDcB9SkXuCVTo5mYEMzh\nAiMHTqn8qfHxIeKGH6PY7dS+8iKK0/0slSH6YC5LWYXF3sY7+R+dvV2YL0GhPpQVNtDR4X76BcHZ\nEUIvGHe44vK5tFk6WHhOKpExgQPu0+F0sLl4Gw989yglLWXMi5rFXfPvZLohwwMWn46iKNRv+oi6\nja+jCQoi/nd/RJ+U1OM+5bWt3PfaQRpMNq5ensLVy1PPeBejUqm4cdVEtBo1r23Nw3JKrNx/5iz8\nZ82mLT+P5m929Mnu5XGLSAyIZ3/NYXLqz/xQV6VSkZIejt3upLyooU/9C86MEHrBuGP/N8VUljaR\nnB7O1DmxA+6vsrWaBw/8m48LN+Pn5cvt025i7eQ1Hk9l0IWiKBjffZv6D95DGxpG/O//F11sz9+j\nsNLEA68fxGRu54fnp3PJwqQe20eF+nLZokSaW9t5Z/vpdWQNP/gRah8fjG+/ib3J/XpCapWaGyZe\njVqlZqP8HjbHmat0Jae7kpyJ8I1nEEIvGFeUFTVwYHcJAUF6zrlYGlBc3uXFf8n93z1KaUsF86Nm\nc9f8O5ka7tmariejOJ3Uvv4KjZs/xSsyivj/9794R0b2uI9c2siGjYew2Oysu3gS585276HzRQsS\niQn34+tDFeSXdxdzr5AQwq+6FmdbG7UbX+/Td4gLiOHc+GXUWxv5tOiLM7aJiA7Az9+b4oJ6nH0I\nDwnOjBB6wbjB3GJj68e5qNUqLrhi8oDi8hWtVWw48G8+Lvwcfy8/1k+7mRsnX4/vIHnxAIrDQc0L\nz9H81Ta8Y+OI//0f8QoN63GfrMJ6Hn4rE7vdyfrVU1gyLdrt42k1am5aNRGAlzbL2B3dBTdo+Qr0\nqWm07t9Ha+bhPn2Xi5PPI1wfyraynZS1VJz2uUqlIik9HJvVTmWpKDE4UITQC8YFTqeTLz7KwWrp\nYNHKVCKi+xeXdzgdfFa0lQe+e4yylgoWRM3hrvl3MiV8koct7o5it1P19JOYvt2FPjmF+N/9P7RB\nPc/533+slsfeOYIC/PzqqcyZGNHn46bFBXHOzFgqjWY+21PS7TOVWk3kjTeBRkPta6/gtFrP3MkZ\n8NZ4s2biVZ2lB985Y+lBUWLQcwihH4U0NVjY9GYmj9/7JTu35FNe3Chub3vhu53FVJU1kyIZmDK7\nf3H58pZKNux/nE1FWwjw9ud/pq/jx5Ovw9fLx8PWdsdps1Hx70dpPbAfH2kicXf+Do2/f4/77Mqq\n4skPs9Fq1fz62ulMSw3v9/GvXp5KkL83H+8upqq+e64bXWwcoasuxt5Qj/HDvpUenBSaztzIWZS2\nVPB1+a7TPo+OD0an11KQW0NFSWO/7ReA5u677x5uG87E3RbLmR/SjGccDieHvi1h60c5NDW04XA4\nqS5vJi+7huwDFTQazYCKgEAdao24hndRVdbMlg9zCAzWc/G109B69S23jN1p57PiL3kpZyPN7SYW\nRs/l9mlrifF3PwzSXxxtbVQ+9jBtuTn4TplG7M9/iVrXcyrjbQfLeWmzjK9Oy51rZpDeh+pYfn46\nTv3teWnVGIL07M2ppaLOzOKpUd2ebejT0mjZvw9L1hH8pk1HG+x+ZazU4CS+rfyO3MZ85kbO7HbR\nVKtVaDRqivONyFk1tJqsRMcF9fnvNxicaZxGAn5+ujPW5RZCP0qoKm/m03eyOH6sDh8/b1ZeMpFr\n184hKNQHL28NzU1tVJebKMit5cj+cuqqW3A6nPgH6tBqh/+HMdQoioKpqY3CvDq2bTqGw+nk0uun\nExjcN++7rKWSJ488z8HaTIJ0gayb8iPOS1iGl9rz+XBOxdHaSvnDD2I9XoD/7DnErP8Zaq+ej/vZ\nnhI2fllAoK8Xv7thFklRfQtRnU3AosN8KattJbuogbBAPYkn1XVVaTToYuMw7f4Ga1ERQUuXuZ3z\nXqfxJsDbn0O1R6hrMzIncka3i0hkbCAJqaHUVpkoK2zkWFY1fv46Qg1+g16ovSdGm9CrRmjVdaWu\nrqX3VuMAm9XOnu2F5BxyrULMmBXD/GUp6PRaDIYAusZJURRqKk0U5xspzDPS3OCq6qNWq4hJCCY5\nPZzkCeH4BZxeI3SsYGpqo7K0iYqSJipKmzCflOp22YXpZMyMcbsvu9PO5uJtfF6yDafiZFH0PK6a\ncAk+2sEN05w4fnMz5Q9toL2inMBFi4lcuw6V5uwXbEVReH9nEZt2FxMSoOO3a2YQHdb3RVonn1On\n0mCy8qdn96JVq/j7rQsI8uueP7/6hecw7dpJ+LXX95ox81TbHzv8DHmNBdwy5UfMiph2WhuHw8mR\n/eXs31mM3e4kPjmEZRem9/nC7Sl6GqfhxGAIOOPVr19CL0nSTcBNnW/1wAxgIbAJyO/c/qQsy29K\nknQr8FPADvxdlmV3kmSMe6FXFIVC2cg3W/OxtLYTEu7LilUSUScVwzjbyaYoCo31ForyjBTl1VFX\n/f1y88iYQJfop4cTHDp4M0SGglaT9YSoV5Y00mL6Xtj1Pl7EJAQTmxjM1JlxKCr3z/OylgpeyX2L\nitYqQnTB/HDiNUwK63nFqSfpqK+n/KF/0lFTQ9A55xLxgx/26CErisIbX+azdX85EcE+/HbNDML7\nKYC9CdjW/WW8vjWf+ZMj+enl3ReDOVpbKf7zH3HabCTd8w+8DAa3j1trMXLvvofQa/X8Zf5vzzp7\nydTUxvbNeZQXN6L1UjN3SRLT5sahdvMOwlOMC6E/GUmSngAyAScQJMvyv076LAr4ApiD64LwDTBH\nluXeqgqMa6FvNVnZuSWf4oJ6NBoVsxcnMWN+PJpT4u7unmwtzVaK8o0U5RmpKmui608eEu5LSrqB\n5PRwwiP9h/VW2B3MLTYqSpuoKGmksrQJU9P3szx0eq1L2BOCiUkI7nZr7+44dTjtbC7+ki0lX+FU\nnCyOmc+VaZfg4+Hyfj3RXlND+b/+ib2hnpCLLiH8qmt6/Lu40gofY+eRKmLC/bjz+hmEDOCurbex\ncjoV/vHKAYqqTPz6uulMTek+vdO091uqn3kK34wpxP7qzj6dU58Xb+Ojws0sjpnPDROvPms7RVHI\nz6ll19YCrG0dhEf6s+IiCUOUZwuq98S4EnpJkuYAD8qyvEKSpCcBCdDi8up/BZwDXCzL8u2d7d8H\n7pVlubeaZONS6J1OhaMHK9i7o4iOdgcxCcEsX5V+Vs+7Pydbm6WdkoJ6CvOMlBc14HC4/v4BgTqS\n0sNJSTcQFRd0IsnVcGJpdQl7VzimufH7ItPeOg0x8cHEJLrEPSzi7Bcqd8ap1FTOK7lvUWmuJkQX\nzI8mXcvE0Ake/T69Yasop/yhDTiamwm78mrCLrmsx/Z2h5NnN+WwL7eWxMgAfnP9dAIGWOPWnbEq\nq23lry9+R0iAjr/dMh+d9/chJUVRqHjkX1iOZhN1608JnL/Q7WM7nA7u/+5RKs3V/HrWetKCk3ts\nb23rYPe248hZ1ahUMHVOHPOWJuHl7dlqXWdivAn9e8Djsix/JUnSzcARWZYPSJL0JyAEOAxMlWX5\nD53tXwZelmV5ay9dj8gHB4NJTaWJj9/OpLK0Cb2PFxdcPpnpc+MH1ctut9kpOFbLsaxq8nNrsFld\npd58/bxJz4hk4tRoUiaED9ksB3OLjZLCeooLjBQX1GOs/T7k5K3TkpgSSlJaOImpYUTFeuZi1OHo\n4J2jn/LhsS04FSfnpy7lR9Ovwsdr6Lx4gJb8AnLu+Rv2llaSf7KOmMsu6bF9e4eDB17ez76caiYl\nhfJ/P1mA3yAUTDkbL32Swzvb8rlyRRrrLusewrHW1HDojl+h8dEz84nH8Apw39POMxby5y8fJCYg\nkn9e+L94aXr/TkX5Rj555wgNRjNBIT5cfPVUJkzqebXwGMazQi9JUjCwS5bljK73siw3db6eDDwO\nPAqskmX5fzq3vw/8Q5blnhNSjyOP3t7hYP+uEjL3leF0KkyYHMGic9Pw9evdM/OkV+FwOKkoaaIo\nr47i/HosZteMAq2XmoSUMJLTXQKr03vOW7K2dZz08LSRRqPlxGdaLzXR8d+HYgxR/v2Ow55tnEpM\nZbyS+xZV5hpC9SH8cOI1Q+7FA1jyZCofexinzUbk2psJWrKsx/bWdjuPv5tFbkkjGUkh3HHVtG5e\n9UBw95xq73Dwl+f2Udfcxl/Wzu02CwegYfOnGN95i8AlS4m66ZY+2fCm/AE7KnZzcfL5XJJ8vlv7\n2DscHNhdwuG9rt9R2iQDi89Nw9d/cCYfjDaPfiC/2mXAlye9/1ySpJ/LsrwPOBc4AOwD/iFJkh7Q\nAZOA7AEcc0xRXtzA9s15mJqsBATpWXbhBBJSel7SPlhoNGoSUkJJSAll2YUKNRUmCjsf5hbKrn9q\ntYrYxGCS0w0kTwjr84/IZu2gsrSZitJGKkuaqK/7fvGNVqsmLinkxANUQ1TAac8kPEWH086nRV+w\ntXQ7TsXJstiFrE69CP0QxuK7MB/NpvKJx1AcDqJvW0/A3Hk9trdYO3j47UyOV5iYOSGc21dPwUs7\n9GsmvL00/HiVxL82HubFzce468bZaE66EIecfyEte/dg+mYngQsW4TvR/ZXDl6eu4ojxKFuKtzE7\nYhpRfr1751ovDfOXp5A2KYLtm/MoyK2jtLCRhStTmDQtesQ/fxpsBuLR/w7okGX5kc73s3B58R1A\nNXCbLMumzlk3t+FahXuvLMvvutH9mPbo2yzt7N52nLzsGlQqmDY3nrlLkvDqo1c2FF6Foig01Jk7\nZ/AYu4VTomK7ZvAYCAo5fZaHzWqnqryJys6ZMcaa7/fVaNVExQaeeIAaERM4aMJ+8jgVm0p5Jfdt\nqs01hOlD+dGka0gPSRuU4/ZGy8EDVD/9JADR/3MH/tNm9NjeZGnnoY2HKa1tZcHkSNZdMgmth8es\nr+fUs5ty2J1dzZqVaVwwL6HbZ9aiQkrv/RteEREk3v031F7uPz/IrMvm6ayXSQ1K5lezftqn2rpO\np0LO4Ur2fF1IR7uD6Pgglq9KJ6Qf003Pxmjz6MU8+iFEURTysmvYva0Aa5sdQ5Q/y1f1b7aAucOC\ndwCoLTo06qFbEGVqajsh+lXl3yebCjX4uWbvRPhTU2mioqQJY03LiRk+ao2KqJhOYU8MISImYMgW\nchkMAVRWN/BJpxevoLA8bhGXp1yEXjs86wpM3+6m+oVnUXl5EfvzX/Xq8Ta22Hhw4yGq6i0snxHD\njy+QBuWBeV8FrMXSzp+e2Uu73cHffzKf8KDuF/zaja/TtHULoZdeRvgVZ59JcyaeznqZzLpsfiBd\nxZLYBX3aF6C1xcY3W/Ipyjei1qiYvTCRmQsS0HjgDkgIvWcYc0Lf3Ghh++Y8Kkqa0Hqpmbc0malz\nYvsUd260NpFZd5TMumwKmotwKk40Kg0GnzAi/SKI8o0g0tdAlJ/r/8EORVjM7RQXuES/vLgRp+P7\nc0mtVhERE3gixh4VGzhsS9eb1EYe2/0iNZZawvWh/HDStaSHpA6LLU5rG3XvvE3z19tQ+/oS+8vf\n4JPa8x1FXVMbG944hLHZyoXz4rnunLRBC0X0R8B2ZVXx3Ce5TEsN45fXTOtmm9Nqpfgvf8Le3ETi\nX+5BF+t+Xd4mWzN/2/MgVoeNeP8YphumMN0whWi/yD59/0K5jm++yMfc2k5ImC/LV6UT3Ye0EGdC\nCL1nGDNC73A4ydxXxv5dJTjsThJSQ1l2QToBQe6JcLW5hsOd4l7aUn5ie1JgAgmh0ZQ2VFFtrsXq\nOD1zYLAuyCX+fhFEnbgARBDoHeBxoWi32SktbKCpwUJkTCBRsUF9DkX1lw5HBw22JurbGqi3NtJg\nbTzxut7acKI+6fK4xaxOvQidZmBTEPuLJTeH6hefw15fj3dMDNG3rkcXH9/jPpVGMw9uPERTazur\nlyRz+eKkQY0390fAFEXhwY2HyS1p5PbVGcw7ZcZL65HDVD72CPrUNOL/8L9up0cAyGs8zpaSr8hr\nPI5DcZUVNPiEdYp+BkmBCW6FdWxWO/t2FJJ90LXCfPKMaBasSOl3qmoh9J5hTAh9TaWJ7Z/J1NeZ\n8fHzYsl5E0idaOh5AYzipLSl/ITnXmNx1exUq9SkB6cy3ZDBNEMGwbqgEyeboiiY2luosdRSba6l\n2lJHjbmWakstTbbTc3n7aPVE+nbeAfgZTlwMwvWhQxoGche7006jtZl6awP11gYa2hoxWhs6Bb2R\n5nbTGffTqDSE6IOJC4pkRfQyJoSkDLHlLhxtbRjfeZPm7V+DWk3oqosJvWx1r3lrSqpb+Nebh2lt\n6+C6c9JYNT+hx/aeoL8CVtNo4S/P7cNHp+Uft87H7xQBrfzvf2jdv4+IH95I8Dkr+9x/m72NbOMx\nMuuyOdog095ZmSrQO4Bp4ZOZbphCekgqWnXP80uqK5r5+jOZRqMFXz9vlpyfRorU82/yTAih9wyj\nWujbbXb27Sgi64CroMKk6dEsPOfs3oPD6SC/qZDMuqMcMR49Ic5eai8mh0lMD89gavik05aFu3Oy\nWe1Waix11FjqqDbXnrgY1LYZT8sBrlFpMPiGE+XrugPoCgdF+BoGNZbtcDposnUKeVvjCU+8vs3l\nnTfZmlHOsLRCrVITogsmTB9CqE8I4fpQQvUhhPmEEqYPIUgXiFqlHtYfpfloNjUvvYC9oR7v2Dii\nbv5Jr7VdAQoqmnn4rUysNjs/XiWxYsbASx66w0DG6pNvi3l3eyHLpsdw00UTu31mb26i+K4/ApD0\nt3v7lOHyVNodHciN+RyuyybLmIO5wzUt10erJyNsItMNU5gcKp31nHU4nBzeW8aBXcU4HAqJaWEs\nu2AC/oHuhzqF0HuGUSv0RflGdm7Jx9xiIzjUh+WrJGISTo8HtjvayW3II7PuKFnGHCx216pPX60P\nU8MnM92QwaTQdLx7CDMM5GRzOB0Y2+q7ef/VllpqzHVnDAOF6IJPxP67QkCuMFDvqROcipNmmwlj\nW6cXfkLQXe8bbc1nLDyhQkWwLogwnxDC9KGdgu76P0wfSrAu0K07kOH4UTra2jC+vZHmHdtdXvzF\nlxB6yeW9evEAucUNPPZuFh12J7dcOomFGVFDYLGLgYyV3eHkry9+R3mdmT/cMBMpobuYN23/mtpX\nXuzMxHmHJ8zF4XRwvLmYzLpsMuuO0mhzlTz0UmuZGDqB6eFTmBo+GX/v02fcNDW4nptVljbh5a1h\n3tJkpsyOdeshtxB6zzDqhN7cYuObrfkUykbUahWzFiYwa2Fityf8lg4LWcZcMo1HyamX6XB2AK5Y\n+nRDBtPDp5AWnOx2+GQwTrauMNAJ779T/M8eBvLp5v0HegfQeFK8vN7aSKO16UR89WRUqAjSBbq8\n8JM88TB9KGE+IQTrgnq9FXeHof5Rurz457E3NOAdF0/UzbegT0xya9/DBUb+8342oHD76inMSnc/\nMZgnGOhYHa9s5t6XDxAZ6ss96+Z1m+OvOJ2Ub7iftvw8Yn72C/xnzvKEyd/3ryiUtVSQWZfNYeNR\nqs01gOvOLzUoiemGKcwwTCFEH9xtHzmrmt3bjmOz2omIDmD5KonwyJ4Luwih9wyjRugV5fs5u+02\nB1Fxrjm7oeEuD6LJ1uwKydQdJa/p+AnPNdI3gumGDGYYppAQENevB2xDfbJ1hYGqO+8AajrvBs4U\nBuoi0DvA5YmfLOSd/4foQ/DygJD3xlCNk8Nioe6tjZi+2QEaDaEXX0rYJZeh0rr3Hffl1vDMxzlo\nNCp+ftU0MpJDB9ni0/HEWL22JY8vD5Zz+eIkrlja/bmIrbKSknv+jDYwkMS/3ovGZ/DSDNdY6jjS\n+ayryFR6YntCQOyJGTxRvhGoVCos5nZ2bysg/2gtKhXMmB/P7MVJeJ1lppgQes8wKoS+oc7M9s0y\n1RUmvHUaFp6TyqTp0dRa6sisO8phYzYlprIT7RMD4l2eu2EKUX59r995KiPlZPs+DFRLS3srIZ0e\neqg+BG83cpUMNkMxTubsI9S89CL2xgZ08fFE3vwT9AmJbu+/I7OSlz47hl6n4ZfXTO9TVShP4omx\narPZuevZvbRY2rn75nnEhHcPmxg/fJ+Gjz8keOV5RNzwowEdy12abM0cqcshsy67m8MV4RvO9HCX\n6CcGxlFe1MSOz/NoaXatVl++Kp34M1xwR8pv71SE0HsQu93Bwd2lHNpTitOpkCIZSFrgi2yRyazL\nptpSC7huGdOCUzrDMhndbhk9wUg92UYagzlODouZujc3Ytq1EzQawi65jNCLL3Xbiwf44rsy3vgy\nH38fL35z/fQ+V4XyJJ4aq0N5dTz+XhbpcUH8/oezUJ88t76jg5J7/kxHTQ3xf7wLn5ShXdNg6bCQ\nXe+awZNTL9N+Ugh1WvhkpgRPpumohqzvylEUSM+IZNG5qficlBl0pP72hNB7iIqSRrZ/nkdzQxs6\nfw3eU00c02Z2ewg0KVRiuiGDKeGT8Pfy3LLrUxmpJ9tIY7DGqfVIJrWvvIi9sRFdfAJR636CLt79\nKZCKorDp2xLe31FIkJ83v10zg1hDz7HhwcaTY/XEe1kcyKtj7SqJ5afMGrLkyZT/8z68Y+NI/PPd\nfbowehLXpIh8MuuyyTbmYra7ZvD4an2Y7DUVzdEozEY7eh8tC1emIU1xLdYaqb+9wUhqNq6wtnWw\na1s+eVm1gEJzdAUVMUdxOhz4qHyYGzmLGYYMJoVJw7YgRzA0OMxm6t58A9Pub1xe/OorCb3okj6J\nlaIovLP9OJ/tKSUsUM9vfzCDyJDRXfHrVG44P52ckgbe+uo409PCCT4pCZ5vukTg0mWYdu6g8YvP\nCb2o57TMg4W3xrsznJqBw+mgoKmITKNrBs/+tn2QDAa/FCLL0/nqk2PkZlWy8qKJGAxDV+TEEwiP\nvhfM7RZ27s+iaE8rtGto8zVRmZSFd6iTaZ2r89KDU4dlodFI9SpGGp4cp9Yjh6l5+UUcTU3oEhKJ\nuvknva5uPRW7w8kbW/P56lAFkaG+/G7NDEL7MId7MPH0OfXVwXJe2ZLH3IkRrL9iSrfPHGazq/Rg\nWxuJ9/wD74iBP7fyFIqiUNpSzuHOaZsNjSaii6cQ2ByBonaSONeX85fNHhHPoE5GhG76QLPNxBHj\nUTJLZNoy/fBvNuBUO7AkVpA2M5wZkRkkBsb3KaPeYCCE3j08MU4Os5m6ja9j+naXy4u/bDWhqy7u\nc8ihrLaV5z7JobSmlTiDP3eumXFake3hxNPnlFNRuP/VgxRUNPOLa6YxIy282+ct+/ZS9fST+E7K\nIPY3vx2x6YSrzbUcrs0m52gZWjkKrw4drfEVnH/BdKaHZ4wYu4XQ90KtxXhi0UVxcxmh1YlEVkxA\n7dSij1RYckEqaTGDW/Gprwihd4+BjlPr4UPUvPISjuYmdIlJrlh8H5JzgcuL//TbEj7eXYzDqbB4\nahQ/OHcCvv3MtTJYDMY5VVHXyt0vfEewvzd/+8l89CeV+lMUhcrHHsacdYSoW24lcOFijx57MKhq\nMLLpjSPYW9RUJGURnu7F1RMuJz4gZrhNE0J/2gEUhfLWyhPiXmmuBsDHHERy6WzULXp0PhqWnDuB\nCRl9y5Y3VAihd4/+jpOjtZXaja/RsudbVFotYZdfQciFF6HS9C1MV1rTwvOf5FJa20pIgI61qyYy\nLXV4Csz0xmCdU+/tOM6m3SVcMDeeNed2r+LVUW+k+M//i8rbm+S/3YemD6UHhwuNSs3TD2+n3Wqn\nOH0/5mAjC6PncGnKKoJ0w2e/EHpcS/GPNxWRaTzqirtZGwHQqrVMDEzHUJ6GMdeOooA0JZKFK7tP\nqYZ7HCYAACAASURBVBppCKF3j/6MU+uhg9S8+hKO5mZ0ScmuWHxs3/LN2B1ONu0u5pNvS3A4FZZM\ni2bNygn4erAco6cZrHOqw+4qPVjb1MZdN84hObr7FNLGLZ9T99YbBC5cTNQtt3r8+J7GYAggO7OC\nj14/jBOFxum5lGkK0Wm8WZV4LufEL3Gr3u0g2DU+hb7D0cGxxvwTOWVaO1zl6/QaPVPCXQmQApoi\n2Lu1iBaTjaAQH5ZdmE5cUv+TLg0VQujdoy/j5GhtpfaNV2nZu8flxa++kpALVvXZiy+pbuH5T3Mp\n6/Tib7poIlOHqUxkXxjMcyq3pJENbxwiIcKfP980p1vpQcXhoPTev2ErKSb2N7/Db3JGDz0NP13j\nVJRXx+b3juLj50X8+Sq21H1Ba4eZMH0IV6RdwkzD1CGNBowroW+zt3HUeIzDxqPk1B/DdpaUpu1t\nTnZtLaAgtxa1WuVa9rwocdgKZPQVIfTu4e44tR464IrFm0zok1OIvPkWdDF99+I/3lXMp3tcXvyy\n6dFcd87I9uJPZrDPqec/yeWbrKozpl22lpZQ+vd78AoLJ/Gev6P2Hh1301kHyvnmiwKCQ31YtWYy\nX9Vs5+vyXTgUB6lBSVwz4XISAvv2TGcAdo3tefSm9pbOvBZHkRsLTiTRCvcJY0lnTpmuIgWKonDs\niCuRUbvNTkRMACtWSYRFDO9iFcHw4GhpofaN12jZ5/Liw6+5jpDzL+yXF//cJzmU15kJDXR58VOS\nR74XP5RctzKNzONGPthZyGzJgCH4+1w3+oT/396ZR7dxX/f+A4IA9x2QSIrUwm20UeIiy/ImWbZk\ny7JlybIdx67dxEntLmny0r7Xk/fcpOlpnbTpeUlP0r6mjVsltePdsmVZtuTdliVbK0nt+nERSVEk\nRRJcQAIEQQAz748BQUoiJXADCer3OUcH0HAwHFzO7zt37u/+7p1Hyrq76PxgDx27dmLZ+tAUnmnw\nFJZm0WN3c+xQA5/trOb+r9/DrXNWsaP6XY7ZTvGzI7/ixvRS7s/dQHJU0pScY1h79DZXeyDPtdZe\nH6hZnhWfGagpkxmXfsmjU2d7L3v3CJoa7JjMRlatyWFxceak9N+cbKRHHxxXs1PP0SO0/v55fD3d\nROfkkv7ktzFnjC57wuNVeefLWt776jyqprGmKJOvrc0jJir8/KhQXFMHTl3kN++cZumCVP7ia8sv\nbT3odlP3N8/g7epi3g//dtRrFELF5XbSNI2Pdp6m+kwbuQutrN+8GIPBQGVnNW9UvUOjoxlzhIm7\n5t3BnXNXT1r+/YwI3WiaRqOjWc+UsZ2i0dEM6OVuc5LmU2RdwjLrUiwxVxYh8vlUyg+c5+iX9ag+\njfn5ady2fnTNBqYbUuiDYzg7eXu6aXvp9/QcPoTBZCJty1bdix9FmzuA2uZutr13hsY2J2mJUXxz\n4yKWzA991cmJIhTXlKZp/PNrxzhZ28HTmxaz6rJ6+84Tx2n85S+Izskh+3//cNR/k1Aw7DXl9bHr\nleM0X7CzfGUWN9+h9wJWNZWvmg/zTs379HgcpEQlsyX3HkpnF014/D5shV7VVM7Z6wNpkO19HQBE\nGowoqfl6az3LEhLMI4ddmi/Y+XyP3j4sLt7MrevzyVFCW+d7MpBCHxyX26nnyGFaX3weX08P0bl5\nuhefnjGqY3q8Kjv317L7gO7F3148h4dvzw1LL34oobqm2rpc/Og/DxJlNvKTp1YRH3Oph9v8m3+n\n59ABrI89Tsod6yb9fEbLSHbqc3l46/fldLX3cuu6PApXDMbmXd4+3q/7hE8bvsCr+ViQOI8H8zex\nIGniWkROuNArilIGDDTrrAV+AvwO0ICTwHeEEKqiKE8Bfwx4gWeFELuudWyPz6PtqywPZMr0ePTm\nztHGKH+rsCUsTltITOTVvXF3n4cDn53jdIXu+S8pyeTG1TlEhcnE2LWQQh8cA3bydnfT+tILOI4c\nxmAyYXngIZLXrR+TF/9f756hyeYkLTGaJzcuZHEYe/FDCeU1tftgPa9/WsOtyzL41sZFl/zMa7dT\n96Nn0Hw+5v/9TzClTq+5jqvZqbvLxZsvlOFyetiwdQkLLmseY3O1s6P6PcrbTgBww+xiNufeMyHV\nbSdU6BVFiQa+EkIUD9m2E/iFEOIzRVH+HXgf+Ar4EFgBRAP7gBVCCPfVjv+N7X+hubx6O7sEUzzL\nrAOZMnlBNarQNI1zoo19H1bT6+wnxRLL7RsU0rOmZiJkItFUlb66OpwVZUS6e1GT0zBnZGJOz8Bk\ntY56AvF6wGKJp3b3J7S++AI+Rw/Refmkf/PbmNNH16LP4/Xx9r46dh+sR9NgbckcHloT/l78UEIp\n9D5V5e9/d4TzrQ7+6tFiFs27NKXZvm8vLb/bRkx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Tjnod2b523H6eXu1YPH10p7ZXbX9Fm9n7V9fW1lb6KOs2Tpt2j39heWo9CTre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\npSN4lzvOXUhDQ32lwzggjrd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k6IF83ZBgyCCTtksiWqqWr+8ymYGiqgptK2pYvaGRVesaaGwJl3W2n4xn6e/J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\nlSPIq3k2AvC7aa7tf57h0Aqs5tvJGNF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Attempting to adjust your arguments for the new API (which might not work). Please update your code. See the version 0.6 release notes for more info.\n", - " warnings.warn(msg, UserWarning)\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 190, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "df.plot(kind='scatter', x='boy',y='girl')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 当plot()函数kind为scatter时，可绘制散点图\n", - "- 散点图展现的是2组数据(2个变量)之间的关系\n", - "- 可见当前`boy`与`girl`两组数据之间是一种线性关系" - ] - }, - { - "cell_type": "code", - "execution_count": 192, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 192, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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DataFrame汇总与描述统计" - ] - }, - { - "cell_type": "code", - "execution_count": 193, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "boy 3960\n", - "child 6960\n", - "children 4960\n", - "girl 7920\n", - "dtype: int64" - ] - }, - "execution_count": 193, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.sum()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- Series对象的各类求值及描述统计函数，DataFrame对象均可以使用\n", - "- 默认参数是axis=0，表示以行方向为数据轴（基准），取每列做为一个Series对象对其进行统计" - ] - }, - { - "cell_type": "code", - "execution_count": 194, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "2006-12-31 1400\n", - "2007-12-31 1900\n", - "2008-12-31 2400\n", - "2009-12-31 2150\n", - "2010-12-31 2350\n", - "2011-12-31 3400\n", - "2012-12-31 4900\n", - "2013-12-31 2400\n", - "2014-12-31 1900\n", - "2015-12-31 1000\n", - "Freq: A-DEC, dtype: int64" - ] - }, - "execution_count": 194, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.sum(axis=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 参数如果指定axis=1，表示以列方向为数据轴（基准），取每行做为一个Series对象对其进行统计" - ] - }, - { - "cell_type": "code", - "execution_count": 195, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "boy 120\n", - "child 420\n", - "children 220\n", - "girl 240\n", - "dtype: int32" - ] - }, - "execution_count": 195, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.min()" - ] - }, - { - "cell_type": "code", - "execution_count": 196, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "boy 396.0\n", - "child 696.0\n", - "children 496.0\n", - "girl 792.0\n", - "dtype: float64" - ] - }, - "execution_count": 196, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.mean()" - ] - }, - { - "cell_type": "code", - "execution_count": 197, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "boy 47871.111111\n", - "child 47871.111111\n", - "children 47871.111111\n", - "girl 191484.444444\n", - "dtype: float64" - ] - }, - "execution_count": 197, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.var()" - ] - }, - { - "cell_type": "code", - "execution_count": 198, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "boy 218.794678\n", - "child 218.794678\n", - "children 218.794678\n", - "girl 437.589356\n", - "dtype: float64" - ] - }, - "execution_count": 198, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.std()" - ] - }, - { - "cell_type": "code", - "execution_count": 199, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "boy 0.552512\n", - "child 0.314360\n", - "children 0.441118\n", - "girl 0.552512\n", - "dtype: float64" - ] - }, - "execution_count": 199, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.std()/df.mean()" - ] - }, - { - "cell_type": "code", - "execution_count": 200, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "boy 370.0\n", - "child 670.0\n", - "children 470.0\n", - "girl 740.0\n", - "dtype: float64" - ] - }, - "execution_count": 200, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.median()" - ] - }, - { - "cell_type": "code", - "execution_count": 201, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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      " - ], - "text/plain": [ - " girl children child boy\n", - "2006-12-31 400 300 500 200\n", - "2007-12-31 600 400 600 300\n", - "2008-12-31 800 500 700 400\n", - "2009-12-31 700 450 650 350\n", - "2010-12-31 780 490 690 390\n", - "2011-12-31 1200 700 900 600\n", - "2012-12-31 1800 1000 1200 900\n", - "2013-12-31 800 500 700 400\n", - "2014-12-31 600 400 600 300\n", - "2015-12-31 240 220 420 120" - ] - }, - "execution_count": 214, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.sort_index(axis = 1, ascending = False) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 将参数axis设为1，排序即按照列索引进行排序" - ] - }, - { - "cell_type": "code", - "execution_count": 215, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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      boychildchildrengirl
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      " - ], - "text/plain": [ - " boy child children girl\n", - "2015-12-31 120 420 220 240\n", - "2006-12-31 200 500 300 400\n", - "2007-12-31 300 600 400 600\n", - "2014-12-31 300 600 400 600\n", - "2009-12-31 350 650 450 700\n", - "2010-12-31 390 690 490 780\n", - "2008-12-31 400 700 500 800\n", - "2013-12-31 400 700 500 800\n", - "2011-12-31 600 900 700 1200\n", - "2012-12-31 900 1200 1000 1800" - ] - }, - "execution_count": 215, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.sort_values(by='boy')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 根据值进行排序，如是根据某对应列的值进行排序，需要在参数by中指定数据列索引" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 4. 选择DataFrame数据\n", - "**4.1 列选择**" - ] - }, - { - "cell_type": "code", - "execution_count": 216, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "2006-12-31 200\n", - "2007-12-31 300\n", - "2008-12-31 400\n", - "2009-12-31 350\n", - "2010-12-31 390\n", - "2011-12-31 600\n", - "2012-12-31 900\n", - "2013-12-31 400\n", - "2014-12-31 300\n", - "2015-12-31 120\n", - "Freq: A-DEC, Name: boy, dtype: int32" - ] - }, - "execution_count": 216, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df['boy']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 利用列索引选择一列，将返回该列，且为Series对象。也可以用`df.boy`来访问，效果相同。" - ] - }, - { - "cell_type": "code", - "execution_count": 217, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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      boy
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      " - ], - "text/plain": [ - " boy\n", - "2006-12-31 200\n", - "2007-12-31 300\n", - "2008-12-31 400\n", - "2009-12-31 350\n", - "2010-12-31 390\n", - "2011-12-31 600\n", - "2012-12-31 900\n", - "2013-12-31 400\n", - "2014-12-31 300\n", - "2015-12-31 120" - ] - }, - "execution_count": 217, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df[['boy']]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 指定一个列标签，放入list中，可以获取对应colums标签的列，返回DataFrame对象。" - ] - }, - { - "cell_type": "code", - "execution_count": 218, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
      \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
      boychildren
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      " - ], - "text/plain": [ - " boy children\n", - "2006-12-31 200 300\n", - "2007-12-31 300 400\n", - "2008-12-31 400 500\n", - "2009-12-31 350 450\n", - "2010-12-31 390 490\n", - "2011-12-31 600 700\n", - "2012-12-31 900 1000\n", - "2013-12-31 400 500\n", - "2014-12-31 300 400\n", - "2015-12-31 120 220" - ] - }, - "execution_count": 218, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df[['boy','children']]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 利用指定多个列标签colums的list，可以获取对应colums标签的列，返回DataFrame对象。" - ] - }, - { - "cell_type": "code", - "execution_count": 219, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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      boychildren
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      " - ], - "text/plain": [ - " boy children\n", - "2006-12-31 200 300\n", - "2007-12-31 300 400\n", - "2008-12-31 400 500\n", - "2009-12-31 350 450\n", - "2010-12-31 390 490\n", - "2011-12-31 600 700\n", - "2012-12-31 900 1000\n", - "2013-12-31 400 500\n", - "2014-12-31 300 400\n", - "2015-12-31 120 220" - ] - }, - "execution_count": 219, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df[[0,2]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 利用代表列整数索引的list，可以获取对应colums标签的列，返回DataFrame对象。" - ] - }, - { - "cell_type": "code", - "execution_count": 220, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "2006-12-31 200\n", - "2007-12-31 300\n", - "2008-12-31 400\n", - "2009-12-31 350\n", - "2010-12-31 390\n", - "2011-12-31 600\n", - "2012-12-31 900\n", - "2013-12-31 400\n", - "2014-12-31 300\n", - "2015-12-31 120\n", - "Freq: A-DEC, Name: boy, dtype: int32" - ] - }, - "execution_count": 220, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - " df.loc[:,'boy']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 也可以利用DataFrame的loc属性进行列选取\n", - "- loc属性主要通过标签进行数据选取\n", - "- 其中第一个位置参数表示行方向(即axis=0)元素，为`:`即为全部选取\n", - "- 第二个位置参数表示列方向(即axis=1)元素，选取当前指定列标签的数据\n", - "- 返回一个Series对象" - ] - }, - { - "cell_type": "code", - "execution_count": 221, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
      \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
      boy
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      " - ], - "text/plain": [ - " boy\n", - "2006-12-31 200\n", - "2007-12-31 300\n", - "2008-12-31 400\n", - "2009-12-31 350\n", - "2010-12-31 390\n", - "2011-12-31 600\n", - "2012-12-31 900\n", - "2013-12-31 400\n", - "2014-12-31 300\n", - "2015-12-31 120" - ] - }, - "execution_count": 221, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - " df.loc[:,['boy']]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 与前类似，如在list中指定列标签，则返回一个DataFrame对象" - ] - }, - { - "cell_type": "code", - "execution_count": 222, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
      \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
      boygirl
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      \n", - "
      " - ], - "text/plain": [ - " boy girl\n", - "2006-12-31 200 400\n", - "2007-12-31 300 600\n", - "2008-12-31 400 800\n", - "2009-12-31 350 700\n", - "2010-12-31 390 780\n", - "2011-12-31 600 1200\n", - "2012-12-31 900 1800\n", - "2013-12-31 400 800\n", - "2014-12-31 300 600\n", - "2015-12-31 120 240" - ] - }, - "execution_count": 222, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.loc[:,['boy', 'girl']]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 与前类似，可以选择多个列，返回一个DataFrame对象" - ] - }, - { - "cell_type": "code", - "execution_count": 223, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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      boychildchildrengirl
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      " - ], - "text/plain": [ - " boy child children girl\n", - "2006-12-31 200 500 300 400\n", - "2007-12-31 300 600 400 600\n", - "2008-12-31 400 700 500 800\n", - "2009-12-31 350 650 450 700\n", - "2010-12-31 390 690 490 780\n", - "2011-12-31 600 900 700 1200\n", - "2012-12-31 900 1200 1000 1800\n", - "2013-12-31 400 700 500 800\n", - "2014-12-31 300 600 400 600\n", - "2015-12-31 120 420 220 240" - ] - }, - "execution_count": 223, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.loc[:,'boy': 'girl']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 可以利用列索引进行切片选取\n", - "- 注意切片是两端包含\n", - "- 注意切片不需要在list中" - ] - }, - { - "cell_type": "code", - "execution_count": 224, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "2006-12-31 200\n", - "2007-12-31 300\n", - "2008-12-31 400\n", - "2009-12-31 350\n", - "2010-12-31 390\n", - "2011-12-31 600\n", - "2012-12-31 900\n", - "2013-12-31 400\n", - "2014-12-31 300\n", - "2015-12-31 120\n", - "Freq: A-DEC, Name: boy, dtype: int32" - ] - }, - "execution_count": 224, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.iloc[:,0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 还可以利用DataFrame的iloc属性进行列选取\n", - "- iloc属性也称为位置(position)属性，主要通过位置(即对应整数索引)进行数据选取\n", - "- 其中第一个位置参数仍然表示行方向(即axis=0)元素，为`:`即为全部选取\n", - "- 第二个位置参数仍然表示列方向(即axis=1)元素，选取当前指定列标签的数据\n", - "- 返回一个Series对象" - ] - }, - { - "cell_type": "code", - "execution_count": 225, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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      child
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      " - ], - "text/plain": [ - " child\n", - "2006-12-31 500\n", - "2007-12-31 600\n", - "2008-12-31 700\n", - "2009-12-31 650\n", - "2010-12-31 690\n", - "2011-12-31 900\n", - "2012-12-31 1200\n", - "2013-12-31 700\n", - "2014-12-31 600\n", - "2015-12-31 420" - ] - }, - "execution_count": 225, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.iloc[:,[1]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 与前类似，如在list中指定列位置，则返回一个DataFrame对象" - ] - }, - { - "cell_type": "code", - "execution_count": 226, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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      childchildren
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      " - ], - "text/plain": [ - " boy child children\n", - "2006-12-31 200 500 300\n", - "2007-12-31 300 600 400\n", - "2008-12-31 400 700 500\n", - "2009-12-31 350 650 450\n", - "2010-12-31 390 690 490" - ] - }, - "execution_count": 249, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.iloc[0:5,0:3]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**4.3.3 利用iat选择单个元素**" - ] - }, - { - "cell_type": "code", - "execution_count": 250, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "600" - ] - }, - "execution_count": 250, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.iat[1,1]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 在以上进行行、列选取的各类方法中，推荐使用loc及iloc方法，虽然稍显繁琐，但是逻辑较为严密，且性能更优。" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5. 条件选择DataFrame数据(布尔索引)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 对DataFrame按照条件来选择数据较为方便记忆的方式是：`对象[布尔索引]`，其中对象可以是整个DataFrame对象，也可以是DataFrame经过前一部分选择出的列、行或者区块。注意布尔索引要与选择出的数据对齐。\n", - "### 达到数据选择结果的方法较为多样，但为避免造成记忆及使用的混乱，其他条件选择数据的方式本教程不予介绍。" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 5.1 列" - ] - }, - { - "cell_type": "code", - "execution_count": 251, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "2006-12-31 False\n", - "2007-12-31 False\n", - "2008-12-31 False\n", - "2009-12-31 False\n", - "2010-12-31 False\n", - "2011-12-31 True\n", - "2012-12-31 True\n", - "2013-12-31 False\n", - "2014-12-31 False\n", - "2015-12-31 False\n", - "Freq: A-DEC, Name: boy, dtype: bool" - ] - }, - "execution_count": 251, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.boy > 400" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 与Series中的类似，是每个元素比较后的布尔值\n", - "- 返回一个Series" - ] - }, - { - "cell_type": "code", - "execution_count": 252, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
      \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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      \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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      " - ], - "text/plain": [ - " boy child children girl\n", - "2011-12-31 600 900 700 1200\n", - "2012-12-31 900 1200 1000 1800" - ] - }, - "execution_count": 253, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df[(df.boy >300) & (df.girl > 900)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 可利用DataFrame对象的多列数据，进行多个布尔运算，注意用括号括起每个部分，表示`and`操作用`&`，'or'用`|`，`not`用`~`。\n", - "- 返回一个DataFrame对象" - ] - }, - { - "cell_type": "code", - "execution_count": 254, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "2008-12-31 800\n", - "2009-12-31 700\n", - "2010-12-31 780\n", - "2011-12-31 1200\n", - "2012-12-31 1800\n", - "2013-12-31 800\n", - "Freq: A-DEC, Name: girl, dtype: int32" - ] - }, - "execution_count": 254, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df['girl'][df.boy >300]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 选择df中的一列，再根据布尔索引进行过滤选择\n", - "- 返回一个Series" - ] - }, - { - "cell_type": "code", - "execution_count": 255, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
      \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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      \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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      " - ], - "text/plain": [ - " girl child\n", - "2011-12-31 1200 900\n", - "2012-12-31 1800 1200" - ] - }, - "execution_count": 256, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df[['girl', 'child']][(df.boy >300) & (df.girl > 900)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 选择多列，并用组合的布尔索引进行数据选择" - ] - }, - { - "cell_type": "code", - "execution_count": 257, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "2006-12-31 False\n", - "2007-12-31 False\n", - "2008-12-31 True\n", - "2009-12-31 True\n", - "2010-12-31 False\n", - "2011-12-31 False\n", - "2012-12-31 False\n", - "2013-12-31 True\n", - "2014-12-31 False\n", - "2015-12-31 False\n", - "Freq: A-DEC, Name: girl, dtype: bool" - ] - }, - "execution_count": 257, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df['girl'].isin([700,800])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 对Series对象应用`isin()`函数，是判断该Series中的values是否在给定的数据表中\n", - "- 返回一个都是布尔值的Series" - ] - }, - { - "cell_type": "code", - "execution_count": 258, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
      \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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      " - ], - "text/plain": [ - " boy child children girl\n", - "2008-12-31 400 700 500 800\n", - "2009-12-31 350 650 450 700\n", - "2013-12-31 400 700 500 800" - ] - }, - "execution_count": 258, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df[df['girl'].isin([700,800])]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 利用isin()函数，选择某列数据进行数据过滤" - ] - }, - { - "cell_type": "code", - "execution_count": 259, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
      \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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      " - ], - "text/plain": [ - " girl children\n", - "2008-12-31 800 500\n", - "2009-12-31 700 450\n", - "2013-12-31 800 500" - ] - }, - "execution_count": 259, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df[['girl', 'children']][df['girl'].isin([700,800])]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 选择后利用isin生成的布尔索引进行数据选择" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 5.2 行\n", - "\n", - "- pandas以布尔索引选择行数据的功能至今较弱" - ] - }, - { - "cell_type": "code", - "execution_count": 260, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "boy False\n", - "child True\n", - "children False\n", - "girl True\n", - "Name: 2007-12-31 00:00:00, dtype: bool" - ] - }, - "execution_count": 260, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.loc[years[1]]>500" - ] - }, - { - "cell_type": "code", - "execution_count": 261, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "child 500\n", - "girl 400\n", - "Name: 2006-12-31 00:00:00, dtype: int32" - ] - }, - "execution_count": 261, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.loc[years[0]][df.loc[years[1]]>500]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 但是暂时还无法将以往行对象选择后，应用`对象[行条件]`的方式进行条件数据选取，个人认为是pandas设计没有考虑周全的地方。虽然布尔索引选择行数据的应用场景较少。" - ] - }, - { - "cell_type": "code", - "execution_count": 262, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
      \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
      childgirl
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      " - ], - "text/plain": [ - " child girl\n", - "2006-12-31 500 400\n", - "2007-12-31 600 600" - ] - }, - "execution_count": 262, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.T[[years[0],years[1]]][df.loc[years[2]]>500].T" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 如确有需求，可以对DataFrame对象进行转置，然后利用列布尔选择，再转置" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From c5d8ca56ece8213f10e947eabe9c7b5484e0ed02 Mon Sep 17 00:00:00 2001 From: liupengyuan Date: Sun, 22 Oct 2017 01:04:03 +0800 Subject: [PATCH 38/44] Delete pandas+Tutorial+Series.ipynb --- chapter4/pandas+Tutorial+Series.ipynb | 3726 ------------------------- 1 file changed, 3726 deletions(-) delete mode 100644 chapter4/pandas+Tutorial+Series.ipynb diff --git a/chapter4/pandas+Tutorial+Series.ipynb b/chapter4/pandas+Tutorial+Series.ipynb deleted file mode 100644 index e97ad8ad7..000000000 --- a/chapter4/pandas+Tutorial+Series.ipynb +++ /dev/null @@ -1,3726 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Pandas统计分析入门（1）\n", - "- 转载注明转自：https://github.com/liupengyuan/\n", - "- ## Pandas及相关python库简介\n", - "- ## 一维数据统计分析(Series基础)\n", - "- ## 二维数据统计分析(DataFrame基础)\n", - "- ## Visualization基础\n", - "---\n", - "## Pandas 简介\n", - "\n", - " pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. It is already well on its way toward this goal.\n", - "\n", - "## Numpy 简介\n", - "\n", - " NumPy is the fundamental package for scientific computing with Python. It contains among other things:\n", - "- a powerful N-dimensional array object\n", - "- sophisticated (broadcasting) functions\n", - "- tools for integrating C/C++ and Fortran code\n", - "- useful linear algebra, Fourier transform, and random number capabilities\n", - "\n", - "## Matplotlib 简介\n", - "\n", - "Matplotlib是一个Python的2D绘图库，它以各种硬拷贝格式和跨平台的交互式环境生成出版质量级别的图形。\n", - "通过Matplotlib，开发者可以仅需要几行代码，便可以生成绘图，直方图，功率谱，条形图，错误图，散点图等。\n", - "\n", - "## Seaborn 简介\n", - "\n", - "Seaborn is a library for making attractive and informative statistical graphics in Python. It is built on top of matplotlib and tightly integrated with the PyData stack, including support for numpy and pandas data structures and statistical routines from scipy and statsmodels.\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- ## 程序头部引入" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "from pandas import Series, DataFrame\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "以上引入各类利用pandas做数据分析可视化的模块，一般可作为每次数据分析项目/任务的起始代码，其中：\n", - "- `%matplotlib inline`是jupyter notebook的一个魔法命令，能够使基于matplotlib的绘图直接显示在网页中\n", - "- `from pandas import Series, DataFrame`，从pandas中引入最为常用的两个对象：Series及DataFrame\n", - "\n", - "**注意**，在以上的引入中，类似`pd`、`np`、`plt`及`sns`均为约定俗成的习惯性名称，**不建议**更改。\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 一、一维数据统计分析(Series基础)\n", - "\n", - "- 数据的描述、分析、可视化展示、概括性度量、输入与输出" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1. Series对象及数据最基本展示\n", - "\n", - "- 将以一个词频统计结果的实例，进行介绍。\n", - "- 教程中的各个代码段，请自行建立新的python程序，依次键入并顺序执行，观察执行结果。\n", - "- Series是pandas最重要最基础的数据对象，可用来表示数据表中的一列或一行。" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "words_freq = [200,300,400,350,390,600,900,400,300,120]\n", - "freq_dict = {'天长地久':words_freq}\n", - "total_words_freq = [12345000,23456000,22333000,45632000,11144000,65433000,44444000,55555000,34522000,55566000]\n", - "years = [2006,2007,2008,2009,2010,2011,2012,2013,2014,2015]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "以上代码中：\n", - "- `words_freq`，是“天长地久”在2006-2015年10年之间的频次list\n", - "- `freq_dict`，键为“天长地久”，值为其历时频次的list\n", - "- `total_words_freq`，是每年统计文本语料的总字数list\n", - "- `years`，是年份list" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0 200\n", - "1 300\n", - "2 400\n", - "3 350\n", - "4 390\n", - "5 600\n", - "6 900\n", - "7 400\n", - "8 300\n", - "9 120\n", - "dtype: int64" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s = Series(words_freq)\n", - "s" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "利用list来初始化Series对象。Series是pandas内置的处理一维数据的数据类型。\n", - "- 第一列为Series对象的索引(index)，默认为数字索引；\n", - "- 第二列为Series对象的值(value)\n", - "- 最后一行说明，Series对象的值为int64类型" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "2006 200\n", - "2007 300\n", - "2008 400\n", - "2009 350\n", - "2010 390\n", - "2011 600\n", - "2012 900\n", - "2013 400\n", - "2014 300\n", - "2015 120\n", - "dtype: int64" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s = Series(freq_dict['天长地久'], index = years)\n", - "s" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "可以在初始化Series对象时，同时指定索引(index)" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "year\n", - "2006 200\n", - "2007 300\n", - "2008 400\n", - "2009 350\n", - "2010 390\n", - "2011 600\n", - "2012 900\n", - "2013 400\n", - "2014 300\n", - "2015 120\n", - "Name: 2006-2015, dtype: int64" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s.name = '2006-2015'\n", - "s.index.name = 'year'\n", - "s" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "还可以指定Series对象的名称(name)，指定其索引(index)的名称(name)" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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ZzNObSmnp6CV7XBwPrizy6bq0EeEhzC3MYG5hBt29Dg6cNK4mOlrZzPpt5azf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\n3H2T4nyPg1++eJjuXoff3ls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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "s.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Series对象可利用pandas内置的plot()直接绘图，默认为折线图。\n", - "\n", - "**折线图**多用于展示**数值型数据**，特别是用于展示历时数据。\n", - "\n", - "统计学中，依据计量尺度，一般可将数据分为三种类型：\n", - "- 分类数据\n", - "- 顺序数据(定序数据)\n", - "- 数值型数据(定量数据)" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "s.plot(kind='bar')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "plot()函数的参数`kind`，可以指定plot()函数所绘制图形的种类，`'bar'`为柱状图。也可以采用这种形式：`s.plot.bar()`，效果与指定kind参数为bar类似。\n", - "\n", - "**柱状图**用宽度相同的柱的高度或长短来表示数据多少。\n", - "\n", - "柱状图一般用于展示**分类数据**，展示不同类别数据的多少(可将以上数据的每个年份理解为不同类别)。" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "s.plot(kind='barh')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "'barh'为条形图，与柱状图类似。" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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7DTrJ33sBiFjBtqB56235DReKkuO+WFsW9VZXrhxBj3Q09a9j/051kKFIiIIN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\nUZ/eNxL7pqff8NTdBa19bpTeXh9jHlU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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "s.plot('pie',figsize=(6,6))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "'pie'为饼图。\n", - "**饼图**使用圆形及圆内扇形的角度表示数值大小的图形。\n", - "\n", - "一般也用于展示**分类数据**，用来表示各分类部分数据占全部数据的比例(频率分布)。" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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7DTrJ33sBiFjBtqB56235DReKkuO+WFsW9VZXrhxBj3Q09a9j/051kKFIiIIN\nxPIPjmNYuMa/FmgDvCsG2gANg+JpU07vPnJs0tLhHiPN19kWzkwDWdC05bb8potFySJS76HaFXqk\nR6vONAQr0AU7Jun5DGHktdsAzWKadt7h3jk33fAWqMoyferp0UeODf/aY7a3OSJ7Y1rQ+Ovb81vq\nC5Njvrd9qCy/66i/teiM78KUMf7OfEF4e6SjaeVwXyiESAY2A5OBVOC7wAHgccAC9gFfklL6hRDr\nsPeV9AHflVK+IIRwAz8CFgVe/20p5QvB3jdR+rBjrtPB+Qz3RYqWP2Heb7xmXq9sU2C32z9jbHFD\n7XBfn+NpDPu4TQsafnVHfmt9YfI14T62Kh/rkT6yYEWgWDvZtSMYt/og0CSlXIZ95+RPsQvwQ4HH\nDKBMCDEG+DL2p/zbge8JIVKBPweSpZSlQBkwPZQ3jfsz7HXb6wxA3/obMZe0AXZOcZ29PtoJZs86\nap2/OLxl6BxvY1g/Hfih/ld35nc05ieH9B9grFLYIx1NbmAW8M4wXvsc8JvA1wb22fNCoDrw2IvY\nd1abQI2U0gt4hRBHgPnYxXufEOL3gdf/r1DeNOSCLYRIwv7m+qSUER0mH2bTAaWjMxPBgDbA1+92\n75gQzTbAtNTeRbk5HYfb2rOHdkZrWVZmb2tJuHL44eKzdxV0N+UlTQvXMaMphnqko2k+wyjYUspO\nACFENnbhfgj4oZSyv+2uA8jFnlEz8DpJ/+NF2LXpHuyLn48RwraFgy6JCCFeDPzvXOAg8CSwRQjx\nfuAxJ9DLIVHUTvaNvzLvKXwpypsCz5tz6OJQX2NgnXNbZliWRPwGF56xi/WUcBwvWiyLLr8n/fW+\nU+I1T90tHs/eTyzynZ2xjL5UJdMLFRj2eAAhRAnwKvCUlPJZ7FbBftnYo1zbufSEsf/xJuAFKaUl\npawGQrrWEewMuzjwv/8B/C8pZX8BXwH8ErghlDdRLOof0TUj/bg1ceUvo9gGmJPdtSQ11Vvv9aaG\n3KWQbHovAiO+acZvcP6Zuwq8zbnOKNaWRavVk7V/QI90eMfVOsuwhm8JIYqBKuBvpZR/Cjz8jhBi\npZRyB3AndjF/E/gXIUQa9sXFWdgXJHcDdwHbhBDXAqdCed9Ql0Ry+4s1gJSyWgiREeJrVdNn2IpE\nsw3QMEiZO+vIgdq9c0Iu2Ol97Z0jfV+/wbmn7yroa8lNmjzSY0VSoEf6oO/C5CwH9EhH03DX5r+B\nvRP7N4UKZ/WGAAAgAElEQVQQ3ww89hXgJ0KIFOAD4DdSSlMI8RNgF/aKxj9JKT1CiEeBTUKI17HX\nsP9HKG866J2OQoh24Bnsjw1PSCkfFULkA18AbpNSxvy40nXb644CU1Xn0CyzmKaaSLYBWhYtL/2x\nNNX0u0M6mZjQ+sFO0fhG0HXDq/EbnH3q7gKzNScpJoeKWX7jtL+j4JjvwuT+HulE6QobquzK8rIR\n//KOhmBn2HOAxdhrLmMDj30OeynkLyMXKzzWba9zAWG7qKSNxIdtgBGbBmgY5M+YfnLnB4emhlSE\nc7wNw/6U6Dc489TdBVasFesr9Ejrn//gZgB1qkOEIq5niazbXjcROKk6h/ZxbnwRaQP0+42TL768\ntCSUs8kbTz5/KrOvbcgF1zQ489Q9hbRlu2PihhHLdB8wm4sbfBemTLR6sh2xjh5jPlNZXvZr1SFC\nEe992JNVB9CuLFJtgC6XNalkwoU3Tp8ZO/gFccvqy+hrH/IFR9Pg1JOrCt3tWe6IT/i7GsvCDPRI\nt/vOT55u9cZlj3Q0OeYGoEELthDiMezbLK9ISvlXYU8UXvpsI8YF2gB7phind9wSpmmAM2ccTz99\nZuygzzHwnzGwhvTzYRqcfHJVYXJ7ljvqg8Q+6pGe4PFdnDgTn960N4wcc40r2Bn2LuyWvo2AJ/Jx\nwm6y6gBaKMLbBpiS7JtfWNC6v6k576obBaT4PI0M4Re66eLk46sKUzoz3YP/JgijAXOk/TE0Rzoe\nOabnfNCCLaXcLISYAUyRUn49SpnCSZ9hO8ilbYDV6cVG07A/qs6dfbi9evfiq/59Rl9bd6jHMl0c\nf3xVYXpnpnvMcPOEakCPdJLZOG5+gvdIR0uR6gChCmUN+58J4ZZJGNoEq8DzRwE1wHwppWfAce4H\n/kxK+dlQv5GriKkr+Fpo7GmAnzKLadp1h3vnnOG0AWZm9CzJyOg5092dfsW18RxvY0gtbj4Xxx+/\ntzCjK8NdHPzZw3PJHOnm4vmge6SjzDFn2EF/aKWUXinlyyEeL6QJVgBCiNux7xS65KxFCPFj4Huh\nZAuBk2bzapcw3BcpWvaEeb+rxry+eqjTAA0D97zZh45e7e9zPY1B58v4XBx9/N7CzEgUa8tvnDbb\nCqu9cuH7nrduL/IeuGmZ2Tx2AbjivREgFjmmYAe76JgBfAv4M+xbeP3AOexJVA9JKS8f/h7qBKvn\nA8e6Fbh8NOZrwH9hz48dKcd81NGuxsh735q54sAwpgEWFrQtSErytfl8SbmX/122t2nQIuxzc+Sx\newtzu9PdYfuPWfdIx6zMVRsq0ivLy6I2+2a4gp3FPgN0Yu/MkIk9uGQlcB741eVPllJ2Sik7Lptg\nZVxhghVSypellE1XOMYWBulMGSJlg/W18Aq0AV7/rO+e19usrDOhvMYwyJ4ljn18EptldaX6uq66\nHu1zczhcxTowR3pHnM2RjkeOOMsO9vFLSCnvv+yxM8C/CiH2XfEF9gSr54GfSSmfFUL8YMBf90+q\nirh12+uygeRovJcWPYE2QM8U40z1J117Ficb5qB3K04Yf0HsOzC9z7JcH/4suCzzrHGV6Wh9bg49\ndm9RQU+6a1ifzgI90vvMpnFtvguT+nukdZ907BtFiAOYVApWsBuEEH8GbBtwodAA1gINlz95CBOs\nouFjH4O1eGGkHbdKVmw2x5+7yfXOu4O1AboMxk6ZdLbm2ImSDy/kpfq6P/bJDqDPjdxcVjTKk+Ya\n0iezK/RIJ8Ic6XjjiOXTYAX7QeBnwC+EEP3r1TnY/dmfv8LzQ5pgNeLUocmJ0vtoili4xr3mXzgu\nWBvgNdNOFh478dFycWZvq/fy5/S5Obj5vqJiT6orP6T3tuiyvBnvm/Ulukc6PjhiSSSkWSKB3WaK\nsC8kNkgpfZEONlLrttfdhH0BU0sIlllM02tXawN8+53Z71ysL7oeYGpTXc2Ulvc+POPuTTI+2FxW\nONab6sob9B0unSM9H8sd9v0gNWX+rrK87MeqQwQTrEskF/g2dj/z81LKpwf83SNSyi9GNt6IOGVe\ntxYWH7YBts4zZPVNrr1LB04DnDPriO9ivf2pN8fT8OFyWW+ScWDzfYXjvSmuKy6h6R7phJGqOkAo\ngi2JPAa8DzwLfF0IsXxAkY71j4B69m9CunIbYFpq76LsrM5jHZ1ZU7O9zeMBvEnG/s33FU7ovaxY\nX2GOtO7nj39hHfUbKcEK9hQp5QMAQojtwO+FEOVSyg3YyyOxLNbzaRF0pWmA8+ceOluz5/r8FL83\n35ts7NtcVljSX6wtv+uIv3XU2b7zU8ZYXXm6RzrxOOKGpaAhhRBjpJQXpJQ9gVvGdwohvkH4eqUj\nRRds7ZI2wE9k75mXldwuvclG5uaywoled/JZs2HMXt+FyROtnuzp2LtYa4kpLs6wvw3UCiHWSyl/\nJ6VsE0LcAbzAMDevjCJdsLUAuw3whDX+3NTRu4/+Pu3azOT9MxrdfWk5QBbQHPijJSg/dKnOEIpg\n0/oqhBCvMOAGFCnleSHEYuDeSIcbIV2wNQAs09/TeaLj7e5THaMvpBcsuaW39sLd8oNr2tLHnG/O\nGNfemjY6qSc5e7TfcE/BMBzx0VgLu9+rDhCKoD+cUsqOgf9fCPGClPIe7HkfsUwX7ATn6/GdbZct\nR3qbPPOBZQB05bJ7cVbW8fG9LQ/86eSo0V0nP5xN4jdc3vbUoiPNGeMaW9LH+DtT8vN8rpQpGEbQ\nQVGa48V8qzIMb6E96rttaNpQeOq73+k43Oo1PeZi7KFll7C6c46cG92+7NEHilo/92Lzm9nd/iUA\nLsufmuepn5nnqf/ouWB1J+eeas4Ye645fay3I7Uow5uUXoLhivhsbC2q4rZgO+XMtU91AC16LNPf\n3Xm8/e2u051j8VuDTvTzNUxITsk8gDfVlbe5rHDxp17v2DHruGeZcYULTwYYmX1tEzPb2iaWtB38\n8PFed1pTS1rxqeaMce1taaOTA0sqk/WSimPFbcG+M+wpIqNddQAt8nxdfafaZcvx3hbvdYS40YbZ\nNHaWNemAzzBIwjCMl2/KWXm0JHXv3TvbxrtCvEU5xfQUFnedLCzuOvnRcQ23pz216HBzxrimlvQx\n/q6UvDyfK2UqhpE1vO9OiyJHnOAFu9NxEvB97DGpvcCTwGIhRC3wV1LKqw6IjwGXz+rW4kjPxe7a\njsOtpt9rLmKoOwuZybmYye+S1PfhkKZjE1Kv23xf4cXPbW9+N73XGtbwJrdlpuV7Ls7K91z88DEL\nrK7k3JMtGWPPN6eP83akFWZ43XpJJQbFxRn208BTwGnszQmeBu7C7hB5Alga0XQjo8+w44zf5+/s\nPNZW132mcwIWC0dyLLN1VGtS0blLHuvKcBc/+kBR0b3VbTsmne9dYYRh+c8AI6uvbVJWW9uky5ZU\nGlvSx5xqTh/X0Zo+OtmTlFUcWFJxRD9wHHJEvQhWsDOllI+AfbYtpfxl4PEtQoiHIhttxPQZdpzw\ndfadaJctJ3tbvdcT4rJHMGZ9SfHlBRvAchnuik/krZxztOfNW97oEEaExvSmmJ6i4s4TRcWdJz7K\nZLh72lOLjttLKmOtrpS8Ap8reQqGkRmJDNolPjYuOhYFK9jnhBDrpJSPAq8KIe6UUr4Y2I+xMQr5\nhu3RuxZ0rdteZ+KQO5i0S1mWZXkudNd2HGnD32suxN7YOWz8nXnCsqg3jCvv+7l/WvqSs6OST3/m\nDy3nUnzWrHC+99W4LTM933Nxtr2kYm+UY4G/KyXvREv62PPNGWN7O1ILMwNLKhHbFDhBxXQ96xes\nYP818JQQ4rvYyyJfFkK0A2eBy3eiiUXt2PO5NYfw+/ztnUfb3uk+2zkJK5IDxgzD6sk+bGR0XHWw\nU2tOUskjq4u8n/5jy64xTb5lkctydQa4snpbJ2f1tk4uafvgw8e97rSG1vQxp5syxnW0pY1O9iRl\nF/sNl15SGb5hnWELIZKBzdgnFKnAd4EDwOPY4zv2AV8asAHMKKAGmC+l9AQ2hDkDHA4cco+U8h+v\n9n7B7nS8AHxKCFEITAs8/4KU8thwvjkF2tAF2xH6OnqPtcuWM31tvQuw9xCNOF/DeCNl0sFBn2O6\njdQttxcsW7yva/dN73UtMGJkbG+q6RlV3Hli1OVLKm1po441Z4xrbkkfQ3dyXr5eUgnZcJdEHgSa\npJR/LoQoAPYG/jwkpdwhhPhPoAx4PrAy8X1g4AXnaUCdlHJVKG8WrEvEBazD3jV9AoFd0wOT+/6v\nlDLWW2EuEOaP0lr4WJbl7znf9Xbn0Ta3v9e/AJgazfc3G8fNtiYeNA0j+LLZW3Mzl54cm3J4zcst\nSW4/U6KRb6jclple0HNhTkHPhQ8fCyypHG9OH3ehf0ml1502EcOlR8Z+pOdb5auGO0vkOT7aRcvA\n7jZZCFQHHnsRuA17n1s/cCtQO+D1C4HxQohXgR7gf0sp5dXeLNiSyH9iz5X+NvZO6QBjgb/AnpX9\nYCjfkUIngRtVh9Au5e/zt3Ucad3bc75rChZLlAUxU/Iwk/aR5JsbytPrC5Ov+fnqoo7PvtiyJ6/T\nvOo+krEksKQyJau3dcrEtgMfPu51pze0pI851ZwxrrMtbVRyT1L2GMteUknEOfIXgj/lyqSUnQBC\niGzswv0Q8EMpZf800w4CF66llC8HnjvwEOeB70kpnxNCLMXuxFt8tfcLVrCXSylnXvbYUWC3EGJ/\nSN+RWjG/C3Ii6WvvPdIuW871tfcuJErLHsGYbaMakwrPB39iQF+yK/uJewtvWvF2x85rD/XcZAwY\njOYkqWbPqDGdx0eN6Tz+4WOm4e5uSxt9vH9JpSs5t8C0l1RiYhkogk6P5MVCiBLsM+ifSSmfFUL8\nYMBfZwOtg7z8bQI94FLK3UKIcUIIY0DBv0Swgt0uhFgspXzrsoA3AZ3BvpEYoAu2YpZlmT1nu97q\nONaWavX5ryfGZk6b9SWjh1Kw+1Uvyl5+bELqvvtebS1wWfExX8dtmRkFPefnFPR89M/DAn9nSv7x\nlvSx/UsqWb3u9IkYhiM2rQ3RsOuEEKIYqAL+Vkr5p8DD7wghVkopd2DfGf7qIIf4Z6AJ+IEQ4lrg\n9NWKNQQv2F/E7hJJ49IlkR7gc8G+mRhwMvhTtEjw95otHUfa3uu50DUNK3aXpfwd+bMsi0bDoGio\nrz09JmXuo/cXNT+4vfntTI8/1rfMGxYDXNm9LVOye1sGWVIZndyTnDXWwjXJoUsqIznD/gZ2Y8M3\nhRDfDDz2FeAnQogU4AM+WuO+ku8DTwsh7sY+0/7Lwd4s1F3TJ2JP6TOAs1JKR5y5rttedy32FVst\nSnrbvLJdttT7OvoWAemq84Qide7uGldG5/A317Us/5017buuOeVdZiTwXqI+I6mrLW3U8eaMcS2t\n6WPoSsktMI3kqRhGrP8c/M9vla/apDpEKELZIux2rtAlIqX8baTDhYE+w44Cy2/5us92vtV5rD3D\n8vmvBUTQF8UQs3E8rolXvTAfnGG4Xlyau+LISU/tnTXtkw0oDF8650iyfJmFPefnFl6ypGKYnSl5\nx5ozxl1oSR/b15FaEItLKo44AYUgZ9hCiIeBJdhXLgcuiXwWOCCl/IeIJxyhddvrWonQ7cWJzuw1\nGzsOte7zXOwW2D8XzpTkbUq7/tV8wxj52XF2l3n+c9ubG1P7rHnhiBavPO6M+sCSSldb2ugUT3Jm\n/5KKivHNc75VvupA8KepF+wMey0wq/8unX5CiF9h38ET8wUbO+fwP+5qH9Pb6v2gXbY0+zr7FgEr\nVecZMV9qIX73ftzmnJEeqiPTPfaR1UVF973aWl1ysS8mOmFiUZrZPXps57HRYzs/ugfPZyR1tqWN\nPmF3qRQb3Sm5haaRPCXCSype4FAEjx9WwQq2B3sp5PKPDJOwv1EneA9dsEfM8lt93ac73uo80Z5t\n+eLv7NHfVtTgLrgY/ImhHMtlJP/2lvwV82X3npW1nXMNu7VLCyLJ8mUV9pybW9jz0VAue0kl/2hz\nxtiLzelj+zpTC7PtG3+MIV8kvooD3ypf5YjRqhC8YG8AdgkhDnHpksgMglzNjCHvqQ7gZKbXrO84\n1HLAU98zC7hZdZ5I8dVPHBWugt3vPZFx05nilOP/7aXmC8km14T14AnCwHJn9zZPy+5tnjap9aNb\nPzxJmRda0secbs4Y192WOio1sKQycRhLKo6qD8FmifxRCPE17AJtAsewBz+9AXyewfsLY8W7qgM4\nkbfZs7/9UEur2eVbTDwsewThby+YZVk0GwYF4Txuc17SlEdWj+pZU9Wye1SrL5bnxztKmq9rzNiO\no2PGdny0h4rPSOpsSx99vCl9fEvrh0sqSVMxjLRBDhU/BVsI8X3se90PYq9n/72Ucmfg7/4H8EjE\nE47c+9hTs5yyF6Uylt/q7TrV8VbXifZ8y7RGvJ7rLIbL8mQeNNK7wv4pwpdkpD97V8HSG9/t3L1k\nf/ciAwYrINowJVm+rMLuc/MKuy9dUulIzT/akj7uYrPdpZLd506bhGH0d/I46oQu2JLI3cD1Ukqf\nEOInQJUQwiulfA6HFMBH71rQuW573THsqVjaFZge34X2Q63S29AzmwRe7zcbx/ldJYeDP3GYXr82\na+mJ8any0y+3pLktJkXsjbQPGVjuHG/ztBxv87RJrfs+fLwnKfNCS/rYU/VZkx11n0awNiYD++wU\nKeVh4B7gx0KIlf2PO4SjPvZEi7ep572GPedfa6g5X+ht6FlBiBvQxitf4wRhWZH9ub5QlCweWV2U\n15bpeiOS76MNLt3XNWZcx5HRX/rP9U2qswxFsIL9HLBDCLEEQEq5H/smmq0464z1bdUBYoVlWp7O\nY227L7565mDL3sb5ZrfvZhw6wCjs+lJH4XeP4A6a0PSmuHIfLyu6Yd+0tGrLIZu/xqk3VQcYqkEL\ntpTyO9ijVTsGPFaDva79WESThddO1QFU8/X4zrXsbdhxcceZrs7j7Ustv3X5FEYN8LcXDnvU5lD9\n6YacFb9bkbvfD+FtT9FC9Vbwp8SWkGaJON267XUp2CMOY32mQdh5Gnr2dhxu8Zg95mL0/pZBuXIb\n3ksVtfOj+Z4ZPWbD57Y3n8nwWtdH8301Sksrtr2mOsRQJETBBli3ve5VEqA9DcAy/T2dJ9rf7jrV\nWYzfmqE6j7NYZtrilzoNI7rjDAy/Zd69q2331LO9yw2HXNB3uA6goLRim6OWpBJpslh18Kc4m6+7\n70zzOw07Lu446+k60bFMF+vhMNyWNyPqcyUsl+F+YUXeiqqbst+2oCXa75+AdjqtWEMI0/riSNyu\nY3vqu+vaD7f2+T3mYuxRAtoImI3jTNeEI0re++CU9MXni1LOfPbF5vMpPmu2khCJ4U/BnxJ7EukM\new/QqzpEuPh9/q72w607L7x6+mjr+00L/B7zBhLr32fE+BomKL2NvC3bPeHnq4umnytKituTjBig\nC3Yse/SuBT048Krw5XxdfSeb6+qr66vP+rpPdSzH76j2SmfoSyu2TFfE2/sG43cbKc/dVrB813WZ\nNRYMd0dv7coasO+AdpxEWhIB+D0OvJPPsizLc7G7tuNIm+X3mgtB3yUXaf6OwvPuvAblGzHUzc4s\nPT0m5eiaqhaS9C/ncHmltGKbI7stEuYMO6BCdYCh8Pv8He2HWqovvnrmRNv+5kV+r7mYxPt3poSv\nviRfdYZ+DQXJ036+etSY5hy3o1rQYpgjl0Mggdr6+q3bXncIYnvUZV9n7/H2gy2n+tp6F6BnKSvi\n96Utruo2DHJUJxloaV3HzgUHe240IEV1FofyA+NLK7ZF7QapcErEs7WYPMu2LMvqOd/1Vv2us283\nvXFxcl9b7wp0sVbIlWR50z9QneJyuxdkL992S94Rv8EZ1VkcardTizUkZsH+L9UBBvL3+dvaDjZX\nX3z1zKm2A82L/b3+RegbJ2KC2Tw2JruKzhanzH70gaLMznSX4y+iK/Ab1QFGIhEL9h6gXnWIvo7e\no01vX9xZv/NsUs/ZrhXocZsxx6wvma46w9V4Ul35v7yvcNHByanVlr25iBacBWxTHWIkEm4NG2Dd\n9rpfAF+I9vtaluXvOdf1VufRtmR/n39BtN9fG7q0RVVHDJc/Zgs3wLTTnnfu3tU+wUjw8bgheK20\nYpvjusQGSsQzbLDHw0aNv89sbfugecfFV8+caz/YcoMu1s7h78g/qzpDMEdL0q7fXFZoelIMPfd9\ncI5eDoHELdh/hMhftOlr7z3c+OaFXfU7z6X2nOtaiaVvG3caX31JTHWJXE1npnvMIw8UzT45JiXu\nZ+YMk0UcFOyEXBIBWLe97l+Ab4T7uJZlmT1nO9/qONaeZvX5rwv38bUoM/y9aYuqeg2DLNVRQjX3\nSM8bn3yzY6ZBdCcOxrhdpRXblqsOMVKJeoYN8Hg4D2b2mk2t+5t2XHzlzMV22XqjLtZxwnKlWL1p\nUZ/eNxL7pqff8NTdBa19bpTeXh9jHlU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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "s.plot('pie',figsize=(6,6))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "可利用plot()函数的`figsize`参数，设定图形的大小(长与宽)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2. 数据基本分析、度量与展示\n", - "\n", - "**2.1 归一化**\n", - "\n", - "**归一化**一般要使数值的绝对值变成**相对值**，将数值映射到[0,1]区间上。\n", - "\n", - "在本例中，由于每个年度的语料大小不同，单纯观察每个年度某个词汇出现的频次(绝对值)，无法确定其出现的相对高低(相对值)。\n", - "\n", - "因此可将频次归一化，即某词汇每年出现的次数/每年语料的总字数。这样，该词汇就可以利用频次归一化的数值进行横向比较了。\n", - "\n", - "在实际中，也可以将频次归一化的结果乘以一个较大的整数。在本例中，可乘以10000000，表示语料中每10000000字出现该词汇的频次。" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "#python实现\n", - "def norm_freq(freqs, total_freqs, per = 1):\n", - " return [per * freq/total_freq for freq, total_freq in zip(freqs, total_freqs)]\n", - "\n", - "per = 10000000\n", - "s_norm = norm_freq(freq_dict['天长地久'], total_words_freq, per = 10000000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "上面的代码用python实现了将每年的频次归一化为每年每一千万字该词汇出现的频次。" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "2006 162.008910\n", - "2007 127.899045\n", - "2008 179.107151\n", - "2009 76.700561\n", - "2010 349.964106\n", - "2011 91.696850\n", - "2012 202.502025\n", - "2013 72.000720\n", - "2014 86.901107\n", - "2015 21.595940\n", - "dtype: float64" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s_norm = Series(s_norm, index = years)\n", - "s_norm" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "利用归一化处理后的结果初始化一个Series对象s_norm。注意，此时value的数据类型自动转换为float。" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "year\n", - "2006 162.008910\n", - "2007 127.899045\n", - "2008 179.107151\n", - "2009 76.700561\n", - "2010 349.964106\n", - "2011 91.696850\n", - "2012 202.502025\n", - "2013 72.000720\n", - "2014 86.901107\n", - "2015 21.595940\n", - "Name: 2006-2015, dtype: float64" - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#pandas的Series运算\n", - "s_norm = per * s / total_words_freq\n", - "s_norm" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "也可以直接利用pandas的序列运算进行归一化。运算结果与python编写代码一直，但是更简洁。\n", - "- `s_norm = per * s / total_words_freq`，其中`s`为Series类型，`total_words_freq`为list类型，直接相除运算的结果是将s中每个对应的值除以list中每个值。这种向量/矩阵运算是numpy及pandas中非常常见的情况，一般被称为**矢量化操作**。" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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137skjYLKZt7dWca01DBCAqQXiLCNlXVrim34iPXFH3C8qYif7S9lacINbnkw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\n3ibmZUYzLTWMjKQQPE0GwsP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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "s_norm.plot()\n", - "s.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "再次绘图，并将两根折线展示在一个图上。\n", - "\n", - "可以看出，归一化前，峰值出现在2012年；归一化后，峰值出现在2010年。" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**1.2 求平均值**" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "396.0" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#python实现\n", - "freq_mean = sum(freq_dict['天长地久'])/len(freq_dict['天长地久'])\n", - "freq_mean" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "计算均值的python代码。\n", - "\n", - "除非特别指出，一般平均值指的就是**算数平均值**。" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "396.0" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#pandas内置求均值函数\n", - "s.mean()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "利用内置均值函数mean()更为简洁。" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "2006 396.0\n", - "2007 396.0\n", - "2008 396.0\n", - "2009 396.0\n", - "2010 396.0\n", - "2011 396.0\n", - "2012 396.0\n", - "2013 396.0\n", - "2014 396.0\n", - "2015 396.0\n", - "dtype: float64" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s_mean = Series([freq_mean]*10, index = years)\n", - "s_mean" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "声明一个每年数值均为均值的Series对象s_mean。" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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UWg08BDiAx7XWG/wRWoixoqO7n827q0lJiGLBzGyz4/hNZnIM2WmxHDvVwoDDSUR4mNmR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\nvKIsevocHDjZxN6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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "s.plot()\n", - "s_mean.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "将频次均值与各年频次画在一个图中" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**1.3 样本方差、标准差、标准分及变异系数**" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "**方差**（variance)是在概率论和统计方差衡量随机变量或一组数据时离散程度的度量。\n", - "- 统计中的方差（样本方差）是每个样本值与全体样本值的平均数之差的平方值的平均数。\n", - "\n", - "**总体**：包含所研究的全部个体(数据)的集合。\n", - "\n", - "**样本**：从总体中抽取的一部分元素的集合。\n", - "\n", - "**样本容量**：构成样本的元素的数量称为样本容量(size)。\n", - "\n", - "样本方差计算公式为：\n", - "$$s^2 = \\frac{1}{n-1}\\sum_{i=1}^{n}(x_i-\\widetilde x)^2$$\n", - "\n", - "注意样本方差的公式中，分母为`n-1`。" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "47871.11111111111" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#python实现方差\n", - "def variance(freqs):\n", - " mean = sum(freqs)/len(freqs)\n", - " return sum([(freq-mean)**2 for freq in freqs])/(len(freqs)-1)\n", - "\n", - "freq_var = variance(freq_dict['天长地久'])\n", - "freq_var" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "上述代码用python实现了求样本方差。" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "47871.11111111111" - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#pandas内置方差\n", - "s.var()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "而pandas内置求方差的函数var()更为简洁。· " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "**标准差**：标准差是方差的算术平方根。" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "218.7946779771188" - ] - }, - "execution_count": 60, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#pandas内置标准差\n", - "s.std()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Pandas内置std()函数,可得到序列s的标准差。" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**标准分**：变量值与平均数的差除以标准差后的值，也称为**z分数**(z score)。\n", - "\n", - "标准分的计算公式为：\n", - "$$z_i=\\frac{x_i-\\widetilde x}{s}$$" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "year\n", - "2006 -0.895817\n", - "2007 -0.438768\n", - "2008 0.018282\n", - "2009 -0.210243\n", - "2010 -0.027423\n", - "2011 0.932381\n", - "2012 2.303530\n", - "2013 0.018282\n", - "2014 -0.438768\n", - "2015 -1.261457\n", - "Name: 2006-2015, dtype: float64" - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(s-s.mean())/s.std()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "上述代码可得到s中所有数值的标准分，存放在一个Series对象中。\n", - "标准差的**经验法则**(当一组数据对称分布时)：\n", - "- 约68%的数据在均值附近正负1个标准差内\n", - "- 约95%的数据在均值附近正负2个标准差内\n", - "- 约99%的数据在均值附近正负3个标准差内\n", - "在正负3个标准差以外的数据，称为**离群点(outlier)**。\n", - "\n", - "**切比雪夫不等式**：对任意分布的一组数据，至少有(1-1/k^2)的数据落在正负k个标准差以内(k>1)。" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "**变异系数**(离散系数)：一组数据的标准差与该组数据均值之比。公式为：\n", - "$$v_i=\\frac{s}{\\widetilde x}$$\n", - "\n", - "该系数是测度离散程度的相对统计量，主要用于比较不同演变数据的离散程度(特别对均值差异较大的数据组)。" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.5525118130735324" - ] - }, - "execution_count": 62, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#变异系数\n", - "s.std()/s.mean()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "变异系数可由标准差/均值直接得到。" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**1.4 中位数、分位数、轴距与箱型图**" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**中位数**(median)：中数是按顺序排列的一组数据中居于中间位置的数，即在这组数据中，有一半的数据比他大，有一半的数据比他小。如果观察值有偶数个，通常取最中间的两个数值的平均数作为中位数。" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "109.7979476168295" - ] - }, - "execution_count": 63, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s_norm.median()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Pandas内置了median()函数，即求中位数函数。" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": false - }, - "source": [ - "**分位数**是将总体的全部数据按大小顺序排列后，处于各等分位置的变量值。\n", - "- 将全部数据分成相等的两部分，它就是中位数；如果分成四等分，就是四分位数；八等分就是八分位数。\n", - "- 四分位数也称为四分位点，它是将全部数据分成相等的四部分，其中每部分包括25%的数据，处在各分位点的数值就是四分位数。\n", - "- 四分位数有三个，第一个四分位数就是通常所说的四分位数，称为下四分位数，第二个四分位数就是中位数，第三个四分位数称为上四分位数，分别用Q1、Q2、Q3表示。" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "79.25069739255241" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s_norm.quantile(0.25)" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "174.83259076226759" - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s_norm.quantile(0.75)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Pandas内置的quantile()函数可以求任意分位数，参数为0.25即Q1，参数为0.75即Q3。" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "s_norm.plot(kind='box')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "参数kind为`box`时，对应的就是**箱型图**。\n", - "\n", - "箱型图中间的箱型一般由三根线组成，由下至上分别对应Q1，Q2，Q3；而箱型以外的上下两根线，分别对应最大值与最小值(不含离群点)\n", - "\n", - "箱型图一般用于展示数值型数据，有助于对数据的整体分布有只管了解，特别有利于对离群点/异常点的观察。" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 67, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#Series箱型图离群点/异常点显示，利用seaborn\n", - "sns.boxplot(s_norm)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "由于pandas内置的箱型图默认不含离群点，可直接利用seaborn中的boxplot函数，为Series对象绘制带离群点的箱型图。" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**1.5 样本的累积和**（累积频数与累积频率）\n", - "\n", - "**累积频数**(cumulative frequencies)，将各有序类别或组的频数逐级累加起来的频数。\n", - "\n", - "**累积频率**(cumulative percentages)，将各有序类别或组的百分比逐级累加起来。" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "year\n", - "2006 200\n", - "2007 500\n", - "2008 900\n", - "2009 1250\n", - "2010 1640\n", - "2011 2240\n", - "2012 3140\n", - "2013 3540\n", - "2014 3840\n", - "2015 3960\n", - "Name: 2006-2015, dtype: int64" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s.cumsum()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Pandas内置了cumsum()函数，可以直接得到累积频数。" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "s.cumsum().plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "利用cumsum()得到的仍然是Series对象，可以直接绘图。" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 70, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "s_ratio = s/s.sum()\n", - "s_ratio.cumsum().plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "累积频率(累积百分比)可以类似的得到。\n", - "\n", - "`sum()`为内置的求Series对象中value和的函数。" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**1.6 最大值、最小值、众数及总体描述**" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "900" - ] - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s.max()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "内置最大值函数max()" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "120" - ] - }, - "execution_count": 72, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s.min()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "内置最小值函数min()" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0 300\n", - "1 400\n", - "dtype: int64" - ] - }, - "execution_count": 73, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s.mode()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**众数**(mode)：一组数据中出现次数最多的数据值。一般用于测度分类数据的集中趋势，在数据量较大的时候有意义。\n", - "Pandas内置mode()函数，可以直接得到众数，注意众数可能不唯一，如此时s的众数有两个。" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "count 10.000000\n", - "mean 396.000000\n", - "std 218.794678\n", - "min 120.000000\n", - "25% 300.000000\n", - "50% 370.000000\n", - "75% 400.000000\n", - "max 900.000000\n", - "Name: 2006-2015, dtype: float64" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s.describe()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "Pandas的descibe()函数，可以给出汇总与描述统计的常用值。其中count为数据个数。" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**1.7 随机数、直方图、峰度与偏度**(公式以后有时间补充)" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "np.random.seed(66)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "调用np中random模块的seed()方法，生成一个随机数种子，在这个种子下，每次第一次生成的随机数均相同。" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 1.41561436, -1.0852864 , -0.61630804, ..., 0.08479998,\n", - " -1.16755255, 1.13461007])" - ] - }, - "execution_count": 76, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "arr1 = np.random.randn(10000)\n", - "arr1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "利用random的randn()函数，生成一个numpy的array类型的对象，大小为10000，可理解为长度为10000的数组(所有数据元素类型相同)。\n", - "\n", - "数组内数据分布为正态分布。randn即random normal。" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 2.28112862, 0.51138372, 0.68988585, 0.67582702, 0.63808164,\n", - " -1.0410448 , -0.91601431, -1.64294384, 2.16465592, -0.67004449,\n", - " 0.46737616, 0.90362682, -0.34551556, -0.05367347, 0.46465691,\n", - " 1.37674635, -0.21249989, -2.14939938, -0.25040615, 1.43040674,\n", - " 0.27824394, 0.20567202, -0.68650085, 0.75210394, -1.12895709,\n", - " 0.68402948, -0.28490141, 0.29969392, 0.14210905, -0.11570231,\n", - " -0.48913203, -0.74924755, -1.52762847, -0.99353886, 1.81620157,\n", - " -1.20129803, -0.93263586, -0.36954506, 0.08467705, 0.61107937,\n", - " 0.50382233, -1.12408078, 0.398812 , 1.497698 , 0.78490215,\n", - " 0.55041055, -0.25888733, 0.39002582, -1.41102818, -2.09786379])" - ] - }, - "execution_count": 77, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "arr2 = np.random.randn(50)\n", - "arr2" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "与前类似，生成了一个长度为50的array，数据为正态分布。" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "s_1 = Series(arr1)\n", - "s_2 = Series(arr2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Numpy的array类型可以直接初始化为Series类型的对象。" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 79, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "s_1.plot(kind='hist')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**直方图**(histogram)：又称质量分布图，是数值数据分布的精确图形表示。\n", - "- 将值的范围分段，即将整个值的范围分成一系列间隔(一般要相等)\n", - "- 计算每个间隔中有多少值\n", - "\n", - "当plot()函数的kind参数为hist时，即代表直方图。\n", - "\n", - "此外，Pandas内置hist()函数，可对Series对象直接绘制直方图：`s_norm.hist()`。" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 80, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "s_1.plot(kind='hist',bins=50)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot()函数有一个重要参数`bins`，指定了间隔个数。此处将间隔数设为50." - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 81, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sns.distplot(s_1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Seaborn中内置的distplot()函数，可以直接对Series对象绘制直方图及KDE图的合一图。" - ] - }, - { - "cell_type": "code", - "execution_count": 83, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 83, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sns.distplot(s_2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "类似地，可以为s_2绘制histogram及kde图。" - ] - }, - { - "cell_type": "code", - "execution_count": 84, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(-0.0063696106929140347,\n", - " 0.027355468589977068,\n", - " -0.020933532783578457,\n", - " -0.20411177773807099)" - ] - }, - "execution_count": 84, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s_1.skew(),s_2.skew(),s_1.kurt(),s_2.kurt()," - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**偏度**(Skewness)、偏态、偏态系数，是统计数据分布偏斜方向和程度的度量，是统计数据分布非对称程度的数字特征。正态分布的偏度为零。\n", - "- Skewness=0 分布形态与正态分布偏度相同\n", - "- Skewness>0 正偏差数值较大，为正偏或右偏。长尾巴拖在右边。\n", - "- Skewness<0 负偏差数值较大，为负偏或左偏。长尾巴拖在左边。\n", - "\n", - "**峰度**(Kurtosis)、峰态、峰态系数，是描述总体中所有取值分布形态陡缓程度的统计量。正态分布的峰度为3(也有很多将计算得到的峰度-3，而使正态分布峰度为零的做法)。\n", - "- Kurtosis=3(或0) 分布形态与正态分布峰度相同\n", - "- Kurtosis>3(或0) 正偏差数值较大，尖峰，厚尾。\n", - "- Kurtosis<3(或0) 负偏差数值较大，扁峰，薄尾。\n", - "\n", - "本例中，s_1比s_2更接近于正态分布。" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3. Series类型基本操作整理\n", - "**3.1 创建与查看**\n", - "\n", - "创建一个Series的基本方法是：s = Series(data, index=index)。其中，data指代许多不同的数据类型：\n", - "- a Python dict\n", - "- an ndarray\n", - "- a scalar value (like 6)\n", - "\n", - "index指一个标签序列，一般用list类型。" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**3.1.1 利用list创建Series**" - ] - }, - { - "cell_type": "code", - "execution_count": 85, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0 0\n", - "1 1\n", - "2 4\n", - "3 9\n", - "4 16\n", - "5 25\n", - "6 36\n", - "7 49\n", - "8 64\n", - "9 81\n", - "10 100\n", - "11 121\n", - "12 144\n", - "13 169\n", - "14 196\n", - "15 225\n", - "16 256\n", - "17 289\n", - "18 324\n", - "19 361\n", - "20 400\n", - "21 441\n", - "22 484\n", - "23 529\n", - "24 576\n", - "25 625\n", - "26 676\n", - "27 729\n", - "28 784\n", - "29 841\n", - " ... \n", - "70 4900\n", - "71 5041\n", - "72 5184\n", - "73 5329\n", - "74 5476\n", - "75 5625\n", - "76 5776\n", - "77 5929\n", - "78 6084\n", - "79 6241\n", - "80 6400\n", - "81 6561\n", - "82 6724\n", - "83 6889\n", - "84 7056\n", - "85 7225\n", - "86 7396\n", - "87 7569\n", - "88 7744\n", - "89 7921\n", - "90 8100\n", - "91 8281\n", - "92 8464\n", - "93 8649\n", - "94 8836\n", - "95 9025\n", - "96 9216\n", - "97 9409\n", - "98 9604\n", - "99 9801\n", - "dtype: int64" - ] - }, - "execution_count": 85, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s = Series([x*x for x in range(100)])\n", - "s" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 第一行利用一个list，创建了一个Series对象s\n", - "- 第二行查看该对象。\n", - "- 该对象由两部分组成，第一列为index，第二列为values。" - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0 0\n", - "1 1\n", - "2 4\n", - "3 9\n", - "4 16\n", - "dtype: int64" - ] - }, - "execution_count": 86, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "数据太长，可用head()函数查看前几个数据。" - ] - }, - { - "cell_type": "code", - "execution_count": 87, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "90 8100\n", - "91 8281\n", - "92 8464\n", - "93 8649\n", - "94 8836\n", - "95 9025\n", - "96 9216\n", - "97 9409\n", - "98 9604\n", - "99 9801\n", - "dtype: int64" - ] - }, - "execution_count": 87, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s.tail(10)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "也可用tail()函数查看最后几个数据，head()及tail()函数内可放整型参数，代表列出的数据个数。" - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0, 1, 4, 9, 16, 25, 36, 49, 64, 81, 100,\n", - " 121, 144, 169, 196, 225, 256, 289, 324, 361, 400, 441,\n", - " 484, 529, 576, 625, 676, 729, 784, 841, 900, 961, 1024,\n", - " 1089, 1156, 1225, 1296, 1369, 1444, 1521, 1600, 1681, 1764, 1849,\n", - " 1936, 2025, 2116, 2209, 2304, 2401, 2500, 2601, 2704, 2809, 2916,\n", - " 3025, 3136, 3249, 3364, 3481, 3600, 3721, 3844, 3969, 4096, 4225,\n", - " 4356, 4489, 4624, 4761, 4900, 5041, 5184, 5329, 5476, 5625, 5776,\n", - " 5929, 6084, 6241, 6400, 6561, 6724, 6889, 7056, 7225, 7396, 7569,\n", - " 7744, 7921, 8100, 8281, 8464, 8649, 8836, 9025, 9216, 9409, 9604,\n", - " 9801], dtype=int64)" - ] - }, - "execution_count": 88, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s.values" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "查看序列s的值" - ] - }, - { - "cell_type": "code", - "execution_count": 89, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "numpy.ndarray" - ] - }, - "execution_count": 89, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "type(s.values)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 查看s.values的类型\n", - "- Series对象的values的类型确实为numpy的array（数组）类型，即ndarray(n维数组)" - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "RangeIndex(start=0, stop=100, step=1)" - ] - }, - "execution_count": 90, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s.index" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 查看s的index(索引)的值" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "pandas.indexes.range.RangeIndex" - ] - }, - "execution_count": 91, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "type(s.index)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 查看s.index的类型\n", - "- Series对象的索引在未指定时，为pandas内置的RangeIndex类型，类似于python的range类型。" - ] - }, - { - "cell_type": "code", - "execution_count": 92, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "e 0\n", - "d 1\n", - "n 4\n", - "b 9\n", - "w 16\n", - "dtype: int64" - ] - }, - "execution_count": 92, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s2 = Series([x*x for x in range(26)], index=set('abcdefghijklmnopqrstuvwxyz'))\n", - "s2.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 利用list创建Series对象时，可用index参数指定一个list或set对应索引。" - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(pandas.indexes.base.Index,\n", - " Index(['e', 'd', 'n', 'b', 'w', 'o', 'm', 't', 'i', 'p', 'a', 'l', 'z', 'y',\n", - " 'u', 'x', 'k', 'j', 'f', 'v', 'c', 's', 'q', 'r', 'g', 'h'],\n", - " dtype='object'))" - ] - }, - "execution_count": 93, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "type(s2.index), s2.index" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 指定索引后，Series对象的索引是pandas内置的Index类型" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**3.1.2 利用dict创建Series**" - ] - }, - { - "cell_type": "code", - "execution_count": 94, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "喜欢 3000\n", - "恨 500\n", - "爱 7000\n", - "讨厌 1000\n", - "dtype: int64" - ] - }, - "execution_count": 94, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data = {'喜欢':3000,'爱':7000,'讨厌':1000,'恨':500}\n", - "s3 = Series(data)\n", - "s3" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 可用dict创建Series对象，则字典的键自动成为index" - ] - }, - { - "cell_type": "code", - "execution_count": 95, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "a NaN\n", - "b NaN\n", - "c NaN\n", - "爱 7000.0\n", - "dtype: float64" - ] - }, - "execution_count": 95, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s3 = Series(data, index = ['a','b','c','爱'])\n", - "s3" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 初始化时，也可以额外指定index。但是如果指定的index找不到对应的值，则该index对应值设为NaN（Not a Number）" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**3.1.3 利用标量创建Series对象**" - ] - }, - { - "cell_type": "code", - "execution_count": 96, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "a 5\n", - "b 5\n", - "c 5\n", - "d 5\n", - "e 5\n", - "dtype: int64" - ] - }, - "execution_count": 96, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s0 = Series(5, index = ['a','b','c','d','e'])\n", - "s0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 利用标量创建Series对象时，对象的值将均为标量的值" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**3.1.4 利用Series对象创建Series对象**" - ] - }, - { - "cell_type": "code", - "execution_count": 97, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "a NaN\n", - "b NaN\n", - "c NaN\n", - "爱 7000.0\n", - "dtype: float64" - ] - }, - "execution_count": 97, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s4 = Series(s3)\n", - "s4" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 这是最简单的利用Series对象创建Series对象的代码" - ] - }, - { - "cell_type": "code", - "execution_count": 98, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "喜欢 NaN\n", - "爱 7000.0\n", - "讨厌 NaN\n", - "中 NaN\n", - "dtype: float64" - ] - }, - "execution_count": 98, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s5 = Series(s4, index = ['喜欢','爱','讨厌','中'])\n", - "s5" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 与利用dict初始化类似，也可以额外指定index。" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**3.2 访问Series对象的元素**" - ] - }, - { - "cell_type": "code", - "execution_count": 99, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(nan, 7000.0, 7000.0)" - ] - }, - "execution_count": 99, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s4[0], s4['爱'], s4.爱" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 利用位置索引或者index均可以访问对应元素\n", - "- 还可以利用index的值作为属性来访问对应元素" - ] - }, - { - "cell_type": "code", - "execution_count": 100, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "a NaN\n", - "b NaN\n", - "dtype: float64" - ] - }, - "execution_count": 100, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s4[0:2]" - ] - }, - { - "cell_type": "code", - "execution_count": 101, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "爱 7000.0\n", - "dtype: float64" - ] - }, - "execution_count": 101, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s4['喜欢':'爱']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 位置索引及index的切片访问也适用，但利用index切片是包含末端的" - ] - }, - { - "cell_type": "code", - "execution_count": 102, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['a', 'b', 'c', '爱'], dtype='object')" - ] - }, - "execution_count": 102, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s4.index" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 可以利用index属性访问Series对象的索引" - ] - }, - { - "cell_type": "code", - "execution_count": 103, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ nan, nan, nan, 7000.])" - ] - }, - "execution_count": 103, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s4.values" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 可以利用values属性访问Series对象的值" - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "a nan\n", - "b nan\n", - "c nan\n", - "爱 7000.0\n" - ] - } - ], - "source": [ - "for k, v in s4.items():\n", - " print(k, v)" - ] - }, - { - "cell_type": "code", - "execution_count": 105, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "a\n", - "b\n", - "c\n", - "爱\n" - ] - } - ], - "source": [ - "for k in s4.keys():\n", - " print(k)" - ] - }, - { - "cell_type": "code", - "execution_count": 106, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "nan\n", - "nan\n", - "nan\n", - "7000.0\n" - ] - } - ], - "source": [ - "for v in s4:\n", - " print(v)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 可以类似dict一样迭代访问Series对象的各个元素" - ] - }, - { - "cell_type": "code", - "execution_count": 107, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "a\n", - "b\n", - "c\n", - "爱\n" - ] - } - ], - "source": [ - "for k in s4.index:\n", - " print(k)" - ] - }, - { - "cell_type": "code", - "execution_count": 108, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "nan\n", - "nan\n", - "nan\n", - "7000.0\n" - ] - } - ], - "source": [ - "for v in s4.values:\n", - " print(v)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 也可以利用index与values属性迭代访问" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**3.3 修改、删除、排序等基本运算**" - ] - }, - { - "cell_type": "code", - "execution_count": 109, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "a 10000.0\n", - "b NaN\n", - "c NaN\n", - "爱 7000.0\n", - "dtype: float64" - ] - }, - "execution_count": 109, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s4[0] = 10000\n", - "s4" - ] - }, - { - "cell_type": "code", - "execution_count": 110, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "a 10000.0\n", - "b NaN\n", - "c NaN\n", - "爱 4078.0\n", - "dtype: float64" - ] - }, - "execution_count": 110, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s4['爱'] = 4078\n", - "s4" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- Series对象内的值可以直接根据位置或index被赋值（修改）" - ] - }, - { - "cell_type": "code", - "execution_count": 111, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "a 666.0\n", - "b 666.0\n", - "c 666.0\n", - "爱 4078.0\n", - "dtype: float64" - ] - }, - "execution_count": 111, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s4[0:3] = 666\n", - "s4" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 可以切片式赋值" - ] - }, - { - "cell_type": "code", - "execution_count": 112, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "a 666.0\n", - "b 666.0\n", - "c 666.0\n", - "d 4078.0\n", - "dtype: float64" - ] - }, - "execution_count": 112, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s4.index = ['a', 'b', 'c', 'd']\n", - "s4" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- Series对象的index可以直接被赋值（修改），注意新的index的长度要与Series对象中index的长度一致。" - ] - }, - { - "cell_type": "code", - "execution_count": 113, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "喜欢 NaN\n", - "爱 4078.0\n", - "讨厌 NaN\n", - "中 NaN\n", - "e NaN\n", - "dtype: float64" - ] - }, - "execution_count": 113, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s4 = s3.reindex(['喜欢','爱','讨厌','中', 'e'])\n", - "s4" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 可用reindex方法来更改index，找不到对应值的index，则其值为NaN。" - ] - }, - { - "cell_type": "code", - "execution_count": 114, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "喜欢 NaN\n", - "爱 4078.0\n", - "讨厌 NaN\n", - "中 NaN\n", - "e NaN\n", - "dtype: float64" - ] - }, - "execution_count": 114, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s5 = s4.drop('e')\n", - "s4" - ] - }, - { - "cell_type": "code", - "execution_count": 115, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "喜欢 NaN\n", - "爱 4078.0\n", - "讨厌 NaN\n", - "中 NaN\n", - "dtype: float64" - ] - }, - "execution_count": 115, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s5" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 可利用drop函数删除指定索引及值，生成新的Series对象，但原series对象不变。" - ] - }, - { - "cell_type": "code", - "execution_count": 116, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "e NaN\n", - "中 NaN\n", - "喜欢 NaN\n", - "爱 8156.0\n", - "讨厌 NaN\n", - "dtype: float64" - ] - }, - "execution_count": 116, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s4+s5" - ] - }, - { - "cell_type": "code", - "execution_count": 117, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "e NaN\n", - "中 NaN\n", - "喜欢 NaN\n", - "爱 0.0\n", - "讨厌 NaN\n", - "dtype: float64" - ] - }, - "execution_count": 117, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s4-s5" - ] - }, - { - "cell_type": "code", - "execution_count": 118, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "e NaN\n", - "中 NaN\n", - "喜欢 NaN\n", - "爱 16630084.0\n", - "讨厌 NaN\n", - "dtype: float64" - ] - }, - "execution_count": 118, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s4*s5" - ] - }, - { - "cell_type": "code", - "execution_count": 119, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "e NaN\n", - "中 NaN\n", - "喜欢 NaN\n", - "爱 1.0\n", - "讨厌 NaN\n", - "dtype: float64" - ] - }, - "execution_count": 119, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s4/s5" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- Serires对象可以直接进行四则运算，运算规则是对应索引的值进行运算，整体运算结果仍然是Series对象。\n", - "- 如果索引值无法对应，则结果为NaN。" - ] - }, - { - "cell_type": "code", - "execution_count": 120, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "爱 4078.0\n", - "喜欢 NaN\n", - "讨厌 NaN\n", - "中 NaN\n", - "e NaN\n", - "dtype: float64" - ] - }, - "execution_count": 120, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s4.sort_values()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 按照values（值）进行排序，按值排序时，NaN将排在尾端" - ] - }, - { - "cell_type": "code", - "execution_count": 121, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "e NaN\n", - "中 NaN\n", - "喜欢 NaN\n", - "爱 4078.0\n", - "讨厌 NaN\n", - "dtype: float64" - ] - }, - "execution_count": 121, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s4.sort_index()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 按照index（索引）进行排序" - ] - }, - { - "cell_type": "code", - "execution_count": 122, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "爱 4078.0\n", - "喜欢 NaN\n", - "讨厌 NaN\n", - "中 NaN\n", - "e NaN\n", - "dtype: float64" - ] - }, - "execution_count": 122, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s4.sort_values(ascending=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 将ascending参数指定为False，则变为倒序排序" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**3.4 处理NaN值**" - ] - }, - { - "cell_type": "code", - "execution_count": 123, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "喜欢 1.0\n", - "爱 4078.0\n", - "讨厌 1.0\n", - "中 1.0\n", - "e 1.0\n", - "dtype: float64" - ] - }, - "execution_count": 123, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s5 = s4.fillna(1)\n", - "s5" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 可以利用内置fillna()函数，将NaN值替换为指定数值" - ] - }, - { - "cell_type": "code", - "execution_count": 124, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "喜欢 NaN\n", - "爱 4078.0\n", - "不讨厌 0.0\n", - "中 NaN\n", - "dtype: float64" - ] - }, - "execution_count": 124, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s6 = s4.reindex(['喜欢','爱','不讨厌','中'], fill_value = 0)\n", - "s6" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 可在reindex方法内利用fill_value参数，指定值来替换在重新索引时新出现的NaN值，\n", - "- 注意，fill_value参数与不会对原有NaN值进行替换。" - ] - }, - { - "cell_type": "code", - "execution_count": 125, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "e NaN\n", - "不讨厌 NaN\n", - "中 NaN\n", - "喜欢 NaN\n", - "爱 8156.0\n", - "讨厌 NaN\n", - "dtype: float64" - ] - }, - "execution_count": 125, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s4.add(s6)" - ] - }, - { - "cell_type": "code", - "execution_count": 126, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "e NaN\n", - "不讨厌 0.0\n", - "中 NaN\n", - "喜欢 NaN\n", - "爱 8156.0\n", - "讨厌 NaN\n", - "dtype: float64" - ] - }, - "execution_count": 126, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s4.add(s6, fill_value=0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 两个Series对象相加可用add方法，设置fill_value参数的值，替换非对齐index部分的NaN值。fill_value参数不会对运算前的NaN进行填充。\n", - "- 类似还有sub，div，mul方法，分别对应减法、除法及乘法。" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**3.5 过滤**" - ] - }, - { - "cell_type": "code", - "execution_count": 127, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0 False\n", - "1 False\n", - "2 False\n", - "3 False\n", - "4 False\n", - "5 False\n", - "6 False\n", - "7 False\n", - "8 False\n", - "9 False\n", - "10 False\n", - "11 False\n", - "12 False\n", - "13 False\n", - "14 False\n", - "15 False\n", - "16 False\n", - "17 False\n", - "18 False\n", - "19 False\n", - "20 False\n", - "21 False\n", - "22 False\n", - "23 False\n", - "24 False\n", - "25 False\n", - "26 False\n", - "27 False\n", - "28 False\n", - "29 False\n", - " ... \n", - "70 True\n", - "71 True\n", - "72 True\n", - "73 True\n", - "74 True\n", - "75 True\n", - "76 True\n", - "77 True\n", - "78 True\n", - "79 True\n", - "80 True\n", - "81 True\n", - "82 True\n", - "83 True\n", - "84 True\n", - "85 True\n", - "86 True\n", - "87 True\n", - "88 True\n", - "89 True\n", - "90 True\n", - "91 True\n", - "92 True\n", - "93 True\n", - "94 True\n", - "95 True\n", - "96 True\n", - "97 True\n", - "98 True\n", - "99 True\n", - "dtype: bool" - ] - }, - "execution_count": 127, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s > 1000" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- Series对象进行比较等逻辑运算，结果仍然是一个Series对象，每一维度的值根据运算结果为True或False" - ] - }, - { - "cell_type": "code", - "execution_count": 128, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "32 1024\n", - "33 1089\n", - "34 1156\n", - "35 1225\n", - "36 1296\n", - "37 1369\n", - "38 1444\n", - "39 1521\n", - "40 1600\n", - "41 1681\n", - "42 1764\n", - "43 1849\n", - "44 1936\n", - "45 2025\n", - "46 2116\n", - "47 2209\n", - "48 2304\n", - "49 2401\n", - "50 2500\n", - "51 2601\n", - "52 2704\n", - "53 2809\n", - "54 2916\n", - "55 3025\n", - "56 3136\n", - "57 3249\n", - "58 3364\n", - "59 3481\n", - "60 3600\n", - "61 3721\n", - " ... \n", - "70 4900\n", - "71 5041\n", - "72 5184\n", - "73 5329\n", - "74 5476\n", - "75 5625\n", - "76 5776\n", - "77 5929\n", - "78 6084\n", - "79 6241\n", - "80 6400\n", - "81 6561\n", - "82 6724\n", - "83 6889\n", - "84 7056\n", - "85 7225\n", - "86 7396\n", - "87 7569\n", - "88 7744\n", - "89 7921\n", - "90 8100\n", - "91 8281\n", - "92 8464\n", - "93 8649\n", - "94 8836\n", - "95 9025\n", - "96 9216\n", - "97 9409\n", - "98 9604\n", - "99 9801\n", - "dtype: int64" - ] - }, - "execution_count": 128, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s[s>1000]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 可以利用逻辑运算的结果Series，对Series对象进行过滤，得到满足条件的Series" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**3.6 时间序列(Time Series)**示例" - ] - }, - { - "cell_type": "code", - "execution_count": 129, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "DatetimeIndex(['2012-01-01 00:00:00', '2012-01-01 00:00:01',\n", - " '2012-01-01 00:00:02', '2012-01-01 00:00:03',\n", - " '2012-01-01 00:00:04', '2012-01-01 00:00:05',\n", - " '2012-01-01 00:00:06', '2012-01-01 00:00:07',\n", - " '2012-01-01 00:00:08', '2012-01-01 00:00:09',\n", - " '2012-01-01 00:00:10', '2012-01-01 00:00:11',\n", - " '2012-01-01 00:00:12', '2012-01-01 00:00:13',\n", - " '2012-01-01 00:00:14', '2012-01-01 00:00:15',\n", - " '2012-01-01 00:00:16', '2012-01-01 00:00:17',\n", - " '2012-01-01 00:00:18', '2012-01-01 00:00:19',\n", - " '2012-01-01 00:00:20', '2012-01-01 00:00:21',\n", - " '2012-01-01 00:00:22', '2012-01-01 00:00:23',\n", - " '2012-01-01 00:00:24', '2012-01-01 00:00:25',\n", - " '2012-01-01 00:00:26', '2012-01-01 00:00:27',\n", - " '2012-01-01 00:00:28', '2012-01-01 00:00:29',\n", - " '2012-01-01 00:00:30', '2012-01-01 00:00:31',\n", - " '2012-01-01 00:00:32', '2012-01-01 00:00:33',\n", - " '2012-01-01 00:00:34', '2012-01-01 00:00:35',\n", - " '2012-01-01 00:00:36', '2012-01-01 00:00:37',\n", - " '2012-01-01 00:00:38', '2012-01-01 00:00:39',\n", - " '2012-01-01 00:00:40', '2012-01-01 00:00:41',\n", - " '2012-01-01 00:00:42', '2012-01-01 00:00:43',\n", - " '2012-01-01 00:00:44', '2012-01-01 00:00:45',\n", - " '2012-01-01 00:00:46', '2012-01-01 00:00:47',\n", - " '2012-01-01 00:00:48', '2012-01-01 00:00:49',\n", - " '2012-01-01 00:00:50', '2012-01-01 00:00:51',\n", - " '2012-01-01 00:00:52', '2012-01-01 00:00:53',\n", - " '2012-01-01 00:00:54', '2012-01-01 00:00:55',\n", - " '2012-01-01 00:00:56', '2012-01-01 00:00:57',\n", - " '2012-01-01 00:00:58', '2012-01-01 00:00:59',\n", - " '2012-01-01 00:01:00', '2012-01-01 00:01:01',\n", - " '2012-01-01 00:01:02', '2012-01-01 00:01:03',\n", - " '2012-01-01 00:01:04', '2012-01-01 00:01:05',\n", - " '2012-01-01 00:01:06', '2012-01-01 00:01:07',\n", - " '2012-01-01 00:01:08', '2012-01-01 00:01:09',\n", - " '2012-01-01 00:01:10', '2012-01-01 00:01:11',\n", - " '2012-01-01 00:01:12', '2012-01-01 00:01:13',\n", - " '2012-01-01 00:01:14', '2012-01-01 00:01:15',\n", - " '2012-01-01 00:01:16', '2012-01-01 00:01:17',\n", - " '2012-01-01 00:01:18', '2012-01-01 00:01:19',\n", - " '2012-01-01 00:01:20', '2012-01-01 00:01:21',\n", - " '2012-01-01 00:01:22', '2012-01-01 00:01:23',\n", - " '2012-01-01 00:01:24', '2012-01-01 00:01:25',\n", - " '2012-01-01 00:01:26', '2012-01-01 00:01:27',\n", - " '2012-01-01 00:01:28', '2012-01-01 00:01:29',\n", - " '2012-01-01 00:01:30', '2012-01-01 00:01:31',\n", - " '2012-01-01 00:01:32', '2012-01-01 00:01:33',\n", - " '2012-01-01 00:01:34', '2012-01-01 00:01:35',\n", - " '2012-01-01 00:01:36', '2012-01-01 00:01:37',\n", - " '2012-01-01 00:01:38', '2012-01-01 00:01:39'],\n", - " dtype='datetime64[ns]', freq='S')" - ] - }, - "execution_count": 129, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "rng = pd.date_range('1/1/2012', periods=100, freq='S')\n", - "rng" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- Pandas内置的date_range()函数，可以很方便的得到时间标签\n", - "- 第一个参数'1/1/2012'为时间字符串，表示开始时间\n", - "- 第二个参数periods为时间标签个数，此处设为100\n", - "- 第三个参数freq为时间频率间隔，此处的'S'表示间隔为秒，常用的为S,Min,H,D,M分别为秒、分、小时、天，月。" - ] - }, - { - "cell_type": "code", - "execution_count": 130, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "2012-01-01 00:00:00 18\n", - "2012-01-01 00:00:01 98\n", - "2012-01-01 00:00:02 182\n", - "2012-01-01 00:00:03 371\n", - "2012-01-01 00:00:04 193\n", - "Freq: S, dtype: int32" - ] - }, - "execution_count": 130, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ts = Series(np.random.randint(0, 500, len(rng)), index=rng)\n", - "ts.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 将时间标签作为Series对象的index，随机生成0-500间100个整数，作为Series对象的值。" - ] - }, - { - "cell_type": "code", - "execution_count": 131, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 131, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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q6kpNfcbrGUQJPC+f9FP14KKiqSaaehOpr9hPvWWilJh6y/tMbENh4ljb3p/C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\nDtIUXE559uou4iTF1s648F3i39Y00TkNWBLQlto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vf/0lPHZlFZc2yklvFWj9UgEZY+r6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\n84LoXqkCW5Nq6UxMlO5lxG9NsZtfXW4gvwhBvZX8om3oJcovakcZ+12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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ts.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 利用plot()函数绘图" - ] - }, - { - "cell_type": "code", - "execution_count": 132, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "2012-01-01 00:00:00 244.950\n", - "2012-01-01 00:01:00 218.425\n", - "Freq: T, dtype: float64" - ] - }, - "execution_count": 132, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ts1 = ts.resample('Min').mean()\n", - "ts1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 利用resample()函数，可将时间序列重新抽样。\n", - "- 样本时间间隔根据resample()函数参数决定，本例中，样本时间间隔设为分钟(Min)\n", - "- 重新设定样本间隔后，可利用mean()函数，求得每个时间间隔内值的均值，作为新的index对应的值" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**3.7 输出**" - ] - }, - { - "cell_type": "code", - "execution_count": 133, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "s.plot()\n", - "plt.savefig('test.jpg')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 利用内置的plot()函数展示图片后，可利用plt的savefig()函数将图片保存为文件输出。\n", - "- 可以查看当前目录下的test.jpg文件，与当前显示一致。" - ] - }, - { - "cell_type": "code", - "execution_count": 134, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "s.to_csv('test.csv')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 利用to_csv()函数，可将Series对象输出为.csv文件\n", - "- 可在当前目录下，找到test.csv，双击打开查看" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.0" - } 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2017 22:05:32 +0800 Subject: [PATCH 40/44] Update 4.ipynb --- chapter2/4.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/chapter2/4.ipynb b/chapter2/4.ipynb index a809b9a0d..51eccdc38 100644 --- a/chapter2/4.ipynb +++ b/chapter2/4.ipynb @@ -236,7 +236,7 @@ "# 主程序\n", "n=100\n", "print('0:',example_0(n))\n", - "print('1:',example_0(n))\n", + "print('1:',example_1(n))\n", "```\n", "\n", "没有`return`语句或者`return`语句后面没有参数，调用函数后函数的值都是`None`。因此，在程序不需要返回值的时候，可以不用return语句。 \n", From c15bc822bdd065cec125bf8a6cb0e83596208fa4 Mon Sep 17 00:00:00 2001 From: liupengyuan Date: Sun, 29 Oct 2017 16:40:19 +0800 Subject: [PATCH 41/44] Update 5.md --- chapter2/5.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/chapter2/5.md b/chapter2/5.md index 7a09a7437..a423eeb29 100644 --- a/chapter2/5.md +++ b/chapter2/5.md @@ -399,7 +399,7 @@ def guess_game(): number = random.randint(1, n) max_times = math.ceil(math.log2(n)) guess_times = 0 - while guess_times <= max_times: + while guess_times < max_times: guess = int(input('请输入你猜测的整数，回车结束。')) guess_times += 1 print('一共可以猜', max_times, '次') @@ -625,7 +625,7 @@ def guess_game(): max_times = math.ceil(math.log(n, 2)) guess_times = 0 - while guess_times <= max_times: + while guess_times < max_times: guess = int(input('请输入你猜测的整数，回车结束。')) guess_times += 1 print('一共可以猜', max_times, '次') From 5e4111859baedfdc69ed38c32c3cb5741c745f1e Mon Sep 17 00:00:00 2001 From: liupengyuan Date: Mon, 6 Nov 2017 00:19:44 +0800 Subject: [PATCH 42/44] Add files via upload --- ...350\241\250\350\276\276\345\274\217.ipynb" | 1044 +++++++++++++++++ 1 file changed, 1044 insertions(+) create mode 100644 "chapter3/python\346\255\243\345\210\231\350\241\250\350\276\276\345\274\217.ipynb" diff --git "a/chapter3/python\346\255\243\345\210\231\350\241\250\350\276\276\345\274\217.ipynb" "b/chapter3/python\346\255\243\345\210\231\350\241\250\350\276\276\345\274\217.ipynb" new file mode 100644 index 000000000..61098b9bb --- /dev/null +++ "b/chapter3/python\346\255\243\345\210\231\350\241\250\350\276\276\345\274\217.ipynb" @@ -0,0 +1,1044 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# python正则表达式快速基础教程" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "正则表达式，这个术语不太容易望文生义(没有去考证是如何被翻译为正则表达式的)，其实其英文为Regular Expression，直接翻译就是：有规律的表达式。这个表达式其实就是一个字符序列，反映某种字符规律，用(字符串模式匹配)来处理字符串。很多高级语言均支持利用正则表达式对字符串进行处理的操作。\n", + "\n", + "python提供的正则表达式文档可参见：https://docs.python.org/3/library/re.html" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true, + "scrolled": true + }, + "outputs": [], + "source": [ + "import re" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 首先引入python正则表达式库re" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**1. 入门**" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true, + "scrolled": true + }, + "outputs": [], + "source": [ + "s = 'Blow low, follow in of which low. lower, lmoww oow aow bow cow 23742937 dow kdiieur998.'\n", + "p = 'low'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 假设要在字符串s中查找单词`low`，由于该单词的规律就是`low`，因此可将`low`作为一个正则表达式，可命名为`p`。" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['low', 'low', 'low', 'low', 'low']" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m = re.findall(p, s)\n", + "m" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- `findall(pattern, string)`是re模块中的函数，会在字符串`string`中将所有匹配正则表达式`pattern`模式的字符串提取出来，并以一个`list`的形式返回。该方法是从左到右进行扫描，所返回的`list`中的每个匹配按照从左到右匹配的顺序进行存放。\n", + "- 正则表达式`low`能够将所有单词`low`匹配出来，但是也会将`lower`，`Blow`等含有`low`字符串中的`low`也匹配出来。" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['low', 'low']" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p = r'\\blow\\b'\n", + "m = re.findall(p, s)\n", + "m" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- `\\b`，即`boundary`，是正则表达式中的一种特殊字符，表示单词的边界。正则表达式`r'\\blow\\b'`就是要单独匹配`low`，该字符串两侧为单词的边界(边界为空格等，但是并不是要匹配之)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['low', 'low', 'low', 'low', 'low', 'mow', 'oow']" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p = r'[lmo]ow'\n", + "m = re.findall(p, s)\n", + "m" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- `[lmo]`，匹配`lmo`字母中的任何一个" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['aow', 'bow', 'cow', 'dow']" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p = r'[a-d]ow'\n", + "m = re.findall(p, s)\n", + "m" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- `[a-d]`，匹配`abcd`字母中的任何一个" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['2', '3', '7', '4', '2', '9', '3', '7', '9', '9', '8']" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p = r'\\d'\n", + "m = re.findall(p, s)\n", + "m" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- `\\d`，即digit，表示数字" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['23742937', '998']" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p = r'\\d+'\n", + "m = re.findall(p, s)\n", + "m" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- `+`，元字符，表示一个或者重复多个对象，对象为`+`前面指定的模式\n", + "- 因此`\\d+`可以匹配长度至少为1的任意正整数。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**2. 基本匹配与实例**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "字符模式|匹配模式内容|等价于\n", + "----|---|--\n", + "[a-d]|One character of: a, b, c, d|[abcd]\n", + "[^a-d]|One character except: a, b, c, d|[^abcd]\n", + "abc丨def|abc or def|\n", + "\\d|One digit|[0-9]\n", + "\\D|One non-digit|[^0-9]\n", + "\\s|One whitespace|[ \\t\\n\\r\\f\\v]\n", + "\\S|One non-whitespace|[^ \\t\\n\\r\\f\\v]\n", + "\\w|One word character|[a-zA-Z0-9_]\n", + "\\W|One non-word character|[^a-zA-Z0-9_]\n", + ".|Any character (except newline)|[^\\n]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "固定点标记|匹配模式内容\n", + "----|---\n", + "^|Start of the string\n", + "$|End of the string\n", + "\\b|Boundary between word and non-word characters" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "数量词|匹配模式内容\n", + "----|---\n", + "{5}|Match expression exactly 5 times\n", + "{2,5}|Match expression 2 to 5 times\n", + "{2,}|Match expression 2 or more times\n", + "{,5}|Match expression 0 to 5 times\n", + "*|Match expression 0 or more times\n", + "{,}|Match expression 0 or more times\n", + "?|Match expression 0 or 1 times\n", + "{0,1}|Match expression 0 or 1 times\n", + "+|Match expression 1 or more times\n", + "{1,}|Match expression 1 or more times" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "字符转义|转义匹配内容\n", + "----|---\n", + "\\\\.|. character\n", + "\\\\\\|\\ character\n", + "\\\\*|* character\n", + "\\\\+|+ character\n", + "\\\\?|? character\n", + "\\\\{|{ character\n", + "\\\\)|) character\n", + "\\\\[|[ character" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['010-66677788', '02166697788', '0451-22882828']" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m = re.findall(r'\\d{3,4}-?\\d{8}', '010-66677788,02166697788, 0451-22882828')\n", + "m" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 匹配电话号码，区号可以是3或者4位，号码为8位，中间可以有`-`或者没有。" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['测', '试', '汉', '字', '测', '试', '可', '以']" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m = re.findall(r'[\\u4e00-\\u9fa5]', '测试 汉 字，abc，测试xia，可以')\n", + "m" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 匹配汉字" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 几个实例\n", + "\n", + "正则表达式|匹配内容\n", + "----|---\n", + "[A-Za-z0-9]|匹配英文和数字\n", + "[\\u4E00-\\u9FA5A-Za-z0-9_]|中文英文和数字及下划线\n", + "^[a-zA-Z][a-zA-Z0-9_]{4,15}$`|合法账号，长度在5-16个字符之间，只能用字母数字下划线，且第一个位置必须为字母" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3. 进阶**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3.1 python正则表达式几个函数**\n", + "\n", + "函数|功能|用法\n", + "----|---|---\n", + "re.search|Return a match object if pattern found in string|re.search(r'[pat]tern', 'string')\n", + "re.finditer|Return an iterable of match objects (one for each match)|re.finditer(r'[pat]tern', 'string')\n", + "re.findall|Return a list of all matched strings (different when capture groups)|re.findall(r'[pat]tern', 'string')\n", + "re.split|Split string by regex delimeter & return string list|re.split(r'[ -]', 'st-ri ng')\n", + "re.compile|Compile a regular expression pattern for later use|re.compile(r'[pat]tern')" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "<_sre.SRE_Match object; span=(0, 12), match='010-66677788'>" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m = re.search(r'\\d{3,4}-?\\d{8}', '010-66677788,02166697788, 0451-22882828')\n", + "m" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'010-66677788'" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m.group()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 利用group()函数，取出match对象中的内容" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "010-66677788\n", + "02166697788\n", + "0451-22882828\n" + ] + } + ], + "source": [ + "ms = re.finditer(r'\\d{3,4}-?\\d{8}', '010-66677788,02166697788, 0451-22882828')\n", + "for m in ms:\n", + " print(m.group())" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['010', '66677788', '02166697788', '0451', '22882828']" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "words = re.split(r'[,-]', '010-66677788,02166697788,0451-22882828')\n", + "words" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['010', '66677788', '02166697788', '0451', '22882828']" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p = re.compile(r'[,-]')\n", + "p.split('010-66677788,02166697788,0451-22882828')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 利用compile()函数将正则表达式编译，如以后多次运行，可加快程序运行速度" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3.2 分组与引用**\n", + "\n", + "Group Type|Expression\n", + "----|---\n", + "Capturing|**(** ... **)**\n", + "Non-capturing|**(?:** ... **)**\n", + "Capturing group named Y|**(?P<Y>** ... **)**\n", + "Match the Y'th captured group|\\Y\n", + "Match the named group Y|(?P=Y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- (...) 将括号中的部分，放在一起，视为一组，即group。以该group来匹配符合条件的字符串。\n", + "- group，可被同一正则表达式的后续，所引用，引用可以利用其位置，或者利用其名称，可称为反向引用。" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'ababababab'" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p = re.compile('(ab)+')\n", + "p.search('ababababab').group()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "('ab',)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p.search('ababababab').groups()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 有分组的情况，用groups()函数取出匹配的所有分组" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'1-2-3'" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p=re.compile('(\\d)-(\\d)-(\\d)')\n", + "p.search('1-2-3').group()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "('1', '2', '3')" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p.search('1-2-3').groups()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['眼睛', '深情']" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s = '喜欢/v 你/x 的/u 眼睛/n 和/u 深情/n 。/w'\n", + "p = re.compile(r'(\\S+)/n')\n", + "m = p.findall(s)\n", + "m" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 按出现顺序捕获名词(/n)。" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'1-2-3'" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p=re.compile('(?P\\d)-(\\d)-(\\d)')\n", + "p.search('1-2-3').group()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 在分组内，可通过`?P`的形式，给该分组命名，其中name是给该分组的命名" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'1'" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p.search('1-2-3').group('first')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 可利用`group('name')`，直接通过组名来获取匹配的该分组" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[('13', 'Tom'), ('18', 'John')]" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s = 'age:13,name:Tom;age:18,name:John'\n", + "p = re.compile(r'age:(\\d+),name:(\\w+)')\n", + "m = p.findall(s)\n", + "m" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['Tom', 'John']" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p = re.compile(r'age:(?:\\d+),name:(\\w+)')\n", + "m = p.findall(s)\n", + "m" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- `(?:\\d+)`，匹配该模式，但不捕获该分组。因此没有捕获该分组的数字" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The match is bb,the capture group is ('b',)\n" + ] + } + ], + "source": [ + "s = 'abcdebbcde'\n", + "p = re.compile(r'([ab])\\1')\n", + "m = p.search(s)\n", + "print('The match is {},the capture group is {}'.format(m.group(), m.groups()))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "- 此即为反向引用\n", + "- 当分组`([ab])`内的`a`或`b`匹配成功后，将开始匹配`\\1`，`\\1`将匹配前面分组成功的字符。因此该正则表达式将匹配`aa`或`bb`。\n", + "- 类似地，r'([a-z])\\1{3}'，该正则将匹配连续的4个英文小写字母。" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'12'" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s = '12,56,89,123,56,98, 12'\n", + "p = re.compile(r'\\b(\\d+)\\b.*\\b\\1\\b')\n", + "m = p.search(s)\n", + "m.group(1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 利用反向引用来判断是否含有重复数字，可提取第一个重复的数字。\n", + "- 其中`\\1`是引用前一个分组的匹配。" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'12'" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s = '12,56,89,123,56,98, 12'\n", + "p = re.compile(r'\\b(?P\\d+)\\b.*\\b(?P=name)\\b')\n", + "m = p.search(s)\n", + "m.group(1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 与前一个类似，但是利用了带分组名称的反向引用。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3.3 贪婪与懒惰**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "数量词|匹配模式内容\n", + "----|---\n", + "{2,5}?|Match 2 to 5 times (less preferred)\n", + "{2,}?|Match 2 or more times (less preferred)\n", + "{,5}?|Match 0 to 5 times (less preferred)\n", + "*?|Match 0 or more times (less preferred)\n", + "{,}?|Match 0 or more times (less preferred)\n", + "??|Match 0 or 1 times (less preferred)\n", + "{0,1}?|Match 0 or 1 times (less preferred)\n", + "+?|Match 1 or more times (less preferred)\n", + "{1,}?|Match 1 or more times (less preferred)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 当正则表达式中包含能接受重复的限定符时，通常的行为是（在使整个表达式能得到匹配的前提下）匹配尽可能多的字符。\n", + "- 而懒惰匹配，是匹配尽可能少的字符。方法是在重复的后面加一个`?`。" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'ababababab'" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p = re.compile('(ab)+')\n", + "p.search('ababababab').group()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'ab'" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p = re.compile('(ab)+?')\n", + "p.search('ababababab').group()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**进一步学习可参考官方文档以及《精通正则表达式(第3版)》**" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 28fa6d054555733f9d5de4deb834dd82d8c085cd Mon Sep 17 00:00:00 2001 From: liupengyuan Date: Mon, 6 Nov 2017 00:31:43 +0800 Subject: [PATCH 43/44] =?UTF-8?q?Update=20and=20rename=20python=E6=AD=A3?= =?UTF-8?q?=E5=88=99=E8=A1=A8=E8=BE=BE=E5=BC=8F.ipynb=20to=20python?= =?UTF-8?q?=E6=AD=A3=E5=88=99=E8=A1=A8=E8=BE=BE=E5=BC=8F=E5=9F=BA=E7=A1=80?= =?UTF-8?q?=E5=BF=AB=E9=80=9F=E6=95=99=E7=A8=8B.ipynb?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...\200\345\277\253\351\200\237\346\225\231\347\250\213.ipynb" | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) rename "chapter3/python\346\255\243\345\210\231\350\241\250\350\276\276\345\274\217.ipynb" => "chapter3/python\346\255\243\345\210\231\350\241\250\350\276\276\345\274\217\345\237\272\347\241\200\345\277\253\351\200\237\346\225\231\347\250\213.ipynb" (99%) diff --git "a/chapter3/python\346\255\243\345\210\231\350\241\250\350\276\276\345\274\217.ipynb" "b/chapter3/python\346\255\243\345\210\231\350\241\250\350\276\276\345\274\217\345\237\272\347\241\200\345\277\253\351\200\237\346\225\231\347\250\213.ipynb" similarity index 99% rename from "chapter3/python\346\255\243\345\210\231\350\241\250\350\276\276\345\274\217.ipynb" rename to "chapter3/python\346\255\243\345\210\231\350\241\250\350\276\276\345\274\217\345\237\272\347\241\200\345\277\253\351\200\237\346\225\231\347\250\213.ipynb" index 61098b9bb..165e7b9c3 100644 --- "a/chapter3/python\346\255\243\345\210\231\350\241\250\350\276\276\345\274\217.ipynb" +++ "b/chapter3/python\346\255\243\345\210\231\350\241\250\350\276\276\345\274\217\345\237\272\347\241\200\345\277\253\351\200\237\346\225\231\347\250\213.ipynb" @@ -4,7 +4,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# python正则表达式快速基础教程" + "# python正则表达式基础快速教程", + "### By liupengyuan@pku.edu.cn" ] }, { From 954c2e7cff96b785d768506acac93efaec7588d4 Mon Sep 17 00:00:00 2001 From: liupengyuan Date: Mon, 6 Nov 2017 00:35:10 +0800 Subject: [PATCH 44/44] =?UTF-8?q?Update=20python=E6=AD=A3=E5=88=99?= =?UTF-8?q?=E8=A1=A8=E8=BE=BE=E5=BC=8F=E5=9F=BA=E7=A1=80=E5=BF=AB=E9=80=9F?= =?UTF-8?q?=E6=95=99=E7=A8=8B.ipynb?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...200\345\277\253\351\200\237\346\225\231\347\250\213.ipynb" | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git "a/chapter3/python\346\255\243\345\210\231\350\241\250\350\276\276\345\274\217\345\237\272\347\241\200\345\277\253\351\200\237\346\225\231\347\250\213.ipynb" "b/chapter3/python\346\255\243\345\210\231\350\241\250\350\276\276\345\274\217\345\237\272\347\241\200\345\277\253\351\200\237\346\225\231\347\250\213.ipynb" index 165e7b9c3..ad37fbf0f 100644 --- "a/chapter3/python\346\255\243\345\210\231\350\241\250\350\276\276\345\274\217\345\237\272\347\241\200\345\277\253\351\200\237\346\225\231\347\250\213.ipynb" +++ "b/chapter3/python\346\255\243\345\210\231\350\241\250\350\276\276\345\274\217\345\237\272\347\241\200\345\277\253\351\200\237\346\225\231\347\250\213.ipynb" @@ -4,8 +4,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# python正则表达式基础快速教程", - "### By liupengyuan@pku.edu.cn" + "# python正则表达式基础快速教程\n", + "### By liupengyuan@pku.edu.cn\n" ] }, {