diff --git a/chapter1/2.md b/chapter1/2.md index f54f9aa7c..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。 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", 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') diff --git a/chapter2/9.md b/chapter2/9.md index b378ef156..a72801614 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='|') @@ -1032,7 +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 @@ -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(): 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。

- -
-
-
-
-
- - - - - - 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" new file mode 100644 index 000000000..ad37fbf0f --- /dev/null +++ "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" @@ -0,0 +1,1045 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# python正则表达式基础快速教程\n", + "### By liupengyuan@pku.edu.cn\n" + ] + }, + { + "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 +} diff --git a/chapter3/task10.md b/chapter3/task10.md index d645c27bc..0d08e6e07 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** @@ -490,7 +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 ``` diff --git a/chapter4/countrys_freq.xlsx b/chapter4/countrys_freq.xlsx new file mode 100644 index 000000000..ea96440ef Binary files /dev/null and b/chapter4/countrys_freq.xlsx differ diff --git a/chapter4/novel_site_list.md b/chapter4/novel_site_list.md new file mode 100644 index 000000000..da2db893d --- /dev/null +++ b/chapter4/novel_site_list.md @@ -0,0 +1,6 @@ +- 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/` +- `http://www.jjxsw.cc/category/wenxuejingdian.html` +- 待补充 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" + }, + { + "data": { + "image/png": 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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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\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": [ + " boy child children girl\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": 206, + "metadata": {}, + "output_type": "execute_result" + } + ], + "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": 207, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "DatetimeIndex(['2006-12-31', '2007-12-31', '2008-12-31', '2009-12-31',\n", + " '2010-12-31', '2011-12-31', '2012-12-31', '2013-12-31',\n", + " '2014-12-31', '2015-12-31'],\n", + " dtype='datetime64[ns]', freq='A-DEC')" + ] + }, + "execution_count": 207, + "metadata": {}, + "output_type": "execute_result" + } + ], + "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": 208, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 200, 500, 300, 400],\n", + " [ 300, 600, 400, 600],\n", + " [ 400, 700, 500, 800],\n", + " [ 350, 650, 450, 700],\n", + " [ 390, 690, 490, 780],\n", + " [ 600, 900, 700, 1200],\n", + " [ 900, 1200, 1000, 1800],\n", + " [ 400, 700, 500, 800],\n", + " [ 300, 600, 400, 600],\n", + " [ 120, 420, 220, 240]])" + ] + }, + "execution_count": 208, + "metadata": {}, + "output_type": "execute_result" + } + ], + "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": 209, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['boy', 'child', 'children', 'girl'], dtype='object')" + ] + }, + "execution_count": 209, + "metadata": {}, + "output_type": "execute_result" + } + ], + "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": 210, + "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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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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boy
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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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child
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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": [ + "
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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": [ + "
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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": [ + "
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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": [ + "three aa bb\n", + "one \n", + "a -0.849212 NaN\n", + "b -1.272573 NaN\n", + "c NaN 0.425634\n", + "d NaN 1.335875" + ] + }, + "execution_count": 185, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "frame['0']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 选择列数据" + ] + }, + { + "cell_type": "code", + "execution_count": 186, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "-0.84921151400187134" + ] + }, + "execution_count": 186, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "frame['0']['aa'].loc['a']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 仿照之前的方法，选择区域数据" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.4 利用层级择、汇总计算数据" + ] + }, + { + "cell_type": "code", + "execution_count": 187, + "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": 187, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 还是用先前的Series做例子" + ] + }, + { + "cell_type": "code", + "execution_count": 188, + "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": 188, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.sum(level = 'one')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 利用sum()求和，用参数level指明以哪层的index为基准进行求和" + ] + }, + { + "cell_type": "code", + "execution_count": 189, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "two\n", + "0 1.193577\n", + "1 0.588540\n", + "2 0.079388\n", + "dtype: float64" + ] + }, + "execution_count": 189, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.std(level='two')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 其他函数也可以类似进行对特定层级的计算" + ] + }, + { + "cell_type": "code", + "execution_count": 190, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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labeijingshanghai
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labeijingshanghai
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" + ], + "text/plain": [ + "la beijing shanghai\n", + "lb boy girl boy\n", + "one \n", + "a 0.427021 -1.955404 -2.963135\n", + "b -1.954603 -4.006931 -2.521813\n", + "c -0.193305 0.364892 -2.367671" + ] + }, + "execution_count": 192, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.sum(level = 'one')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 可以利用各类计算的level参数，根据索引的name，来对指定层次的索引进行求值\n", + "- level参数默认为指定行索引的name" + ] + }, + { + "cell_type": "code", + "execution_count": 193, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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labeijingshanghai
onetwo
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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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labeijingshanghai
lbboygirlboy
onetwo
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ABCDE
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" + ], + "text/plain": [ + " A B C D E\n", + "0 one A foo 0.252884 0.336282\n", + "1 one B foo -0.119435 -0.222245\n", + "2 two C foo 0.130536 2.150678\n", + "3 three A bar -1.801965 0.866635\n", + "4 one B bar 0.724717 2.154735\n", + "5 one C bar 0.713373 -0.538733\n", + "6 two A foo -0.063114 0.026495\n", + "7 three B foo -0.505313 0.779623\n", + "8 one C foo -0.053516 0.256542\n", + "9 one A bar 0.630396 0.424185\n", + "10 two B bar 0.195592 0.971294\n", + "11 three C bar -0.209705 -1.513072" + ] + }, + "execution_count": 234, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.DataFrame({'A' : ['one', 'one', 'two', 'three'] * 3,\n", + " 'B' : ['A', 'B', 'C'] * 4,\n", + " 'C' : ['foo', 'foo', 'foo', 'bar', 'bar', 'bar'] * 2,\n", + " 'D' : np.random.randn(12),\n", + " 'E' : np.random.randn(12)})\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 利用dict新建立一个DataFrame对象" + ] + }, + { + "cell_type": "code", + "execution_count": 239, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "C bar foo\n", + "A B \n", + "one A 0.630396 0.252884\n", + " B 0.724717 -0.119435\n", + " C 0.713373 -0.053516\n", + "three A -1.801965 NaN\n", + " B NaN -0.505313\n", + " C -0.209705 NaN\n", + "two A NaN -0.063114\n", + " B 0.195592 NaN\n", + " C NaN 0.130536" + ] + }, + "execution_count": 239, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pivot_df = df.pivot_table(values='D', index=['A', 'B'], columns=['C'])\n", + "pivot_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 利用pivot_table()函数，选择'D'列的数据，以['A', 'B']为index索引，以['C']为columns索引来列出数据\n", + "- pivot_table()函数中的各个参数，均为可选参数，但index或columns必须至少有一个，其值作为透视分组的依据" + ] + }, + { + "cell_type": "code", + "execution_count": 242, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "A B\n", + "one A 0.630396\n", + " B 0.724717\n", + " C 0.713373\n", + "three A -1.801965\n", + " B NaN\n", + " C -0.209705\n", + "two A NaN\n", + " B 0.195592\n", + " C NaN\n", + "Name: bar, dtype: float64" + ] + }, + "execution_count": 242, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pivot_df['bar']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 本例中pivot_table()函数透视的结果，仍然是DataFrame对象，因此可以对该DataFrame对象施加前面介绍的各类操作与计算" + ] + } + ], + "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/\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..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" @@ -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 @@ -31,13 +32,15 @@ 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：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。 +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。 -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。 @@ -46,6 +49,7 @@ 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 @@ -62,7 +66,7 @@ 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。 @@ -71,9 +75,10 @@ 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 +102,129 @@ 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。 -4. 正则表达式 -5. 用beautifulsoap解析数据 -6. scrapy爬虫框架 +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(): + 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() +``` + +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 + +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','')+'\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: + 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. 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 diff --git a/chapter5/Latex Equation Tutorial .ipynb b/chapter5/Latex Equation Tutorial .ipynb new file mode 100644 index 000000000..9114c1054 --- /dev/null +++ b/chapter5/Latex Equation Tutorial .ipynb @@ -0,0 +1,254 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "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", + "- 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 +} 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。 +``