DOC: Fix dsintro.rst flake8 issues (#24187)

Author: addisonlynch

doc/source/dsintro.rst

@@ -71,7 +71,7 @@ Series can be instantiated from dicts:
 
 .. ipython:: python
 
-   d = {'b' : 1, 'a' : 0, 'c' : 2}
+   d = {'b': 1, 'a': 0, 'c': 2}
    pd.Series(d)
 
 .. note::
@@ -92,7 +92,7 @@ index will be pulled out.
 
 .. ipython:: python
 
-   d = {'a' : 0., 'b' : 1., 'c' : 2.}
+   d = {'a': 0., 'b': 1., 'c': 2.}
    pd.Series(d)
    pd.Series(d, index=['b', 'c', 'd', 'a'])
 
@@ -304,8 +304,8 @@ keys.
 
 .. ipython:: python
 
-    d = {'one' : pd.Series([1., 2., 3.], index=['a', 'b', 'c']),
-         'two' : pd.Series([1., 2., 3., 4.], index=['a', 'b', 'c', 'd'])}
+    d = {'one': pd.Series([1., 2., 3.], index=['a', 'b', 'c']),
+         'two': pd.Series([1., 2., 3., 4.], index=['a', 'b', 'c', 'd'])}
     df = pd.DataFrame(d)
     df
 
@@ -334,8 +334,8 @@ result will be ``range(n)``, where ``n`` is the array length.
 
 .. ipython:: python
 
-   d = {'one' : [1., 2., 3., 4.],
-        'two' : [4., 3., 2., 1.]}
+   d = {'one': [1., 2., 3., 4.],
+        'two': [4., 3., 2., 1.]}
    pd.DataFrame(d)
    pd.DataFrame(d, index=['a', 'b', 'c', 'd'])
 
@@ -346,8 +346,8 @@ This case is handled identically to a dict of arrays.
 
 .. ipython:: python
 
-   data = np.zeros((2,), dtype=[('A', 'i4'),('B', 'f4'),('C', 'a10')])
-   data[:] = [(1,2.,'Hello'), (2,3.,"World")]
+   data = np.zeros((2, ), dtype=[('A', 'i4'), ('B', 'f4'), ('C', 'a10')])
+   data[:] = [(1, 2., 'Hello'), (2, 3., "World")]
 
    pd.DataFrame(data)
    pd.DataFrame(data, index=['first', 'second'])
@@ -507,17 +507,15 @@ derived from existing columns.
 
    iris = pd.read_csv('data/iris.data')
    iris.head()
-
-   (iris.assign(sepal_ratio = iris['SepalWidth'] / iris['SepalLength'])
+   (iris.assign(sepal_ratio=iris['SepalWidth'] / iris['SepalLength'])
         .head())
 
 In the example above, we inserted a precomputed value. We can also pass in
 a function of one argument to be evaluated on the DataFrame being assigned to.
 
 .. ipython:: python
 
-   iris.assign(sepal_ratio = lambda x: (x['SepalWidth'] /
-                                        x['SepalLength'])).head()
+   iris.assign(sepal_ratio=lambda x: (x['SepalWidth'] / x['SepalLength'])).head()
 
 ``assign`` **always** returns a copy of the data, leaving the original
 DataFrame untouched.
@@ -532,8 +530,8 @@ greater than 5, calculate the ratio, and plot:
 
    @savefig basics_assign.png
    (iris.query('SepalLength > 5')
-        .assign(SepalRatio = lambda x: x.SepalWidth / x.SepalLength,
-                PetalRatio = lambda x: x.PetalWidth / x.PetalLength)
+        .assign(SepalRatio=lambda x: x.SepalWidth / x.SepalLength,
+                PetalRatio=lambda x: x.PetalWidth / x.PetalLength)
         .plot(kind='scatter', x='SepalRatio', y='PetalRatio'))
 
 Since a function is passed in, the function is computed on the DataFrame
@@ -705,8 +703,8 @@ Boolean operators work as well:
 
 .. ipython:: python
 
-   df1 = pd.DataFrame({'a' : [1, 0, 1], 'b' : [0, 1, 1] }, dtype=bool)
-   df2 = pd.DataFrame({'a' : [0, 1, 1], 'b' : [1, 1, 0] }, dtype=bool)
+   df1 = pd.DataFrame({'a': [1, 0, 1], 'b': [0, 1, 1]}, dtype=bool)
+   df2 = pd.DataFrame({'a': [0, 1, 1], 'b': [1, 1, 0]}, dtype=bool)
    df1 & df2
    df1 | df2
    df1 ^ df2
@@ -746,7 +744,7 @@ Similarly, the dot method on Series implements dot product:
 
 .. ipython:: python
 
-   s1 = pd.Series(np.arange(5,10))
+   s1 = pd.Series(np.arange(5, 10))
    s1.dot(s1)
 
 DataFrame is not intended to be a drop-in replacement for ndarray as its
@@ -777,7 +775,7 @@ R package):
    :okwarning:
 
    # restore GlobalPrintConfig
-   pd.reset_option('^display\.')
+   pd.reset_option(r'^display\.')
 
 However, using ``to_string`` will return a string representation of the
 DataFrame in tabular form, though it won't always fit the console width:
@@ -798,22 +796,22 @@ option:
 
 .. ipython:: python
 
-   pd.set_option('display.width', 40) # default is 80
+   pd.set_option('display.width', 40)  # default is 80
 
    pd.DataFrame(np.random.randn(3, 12))
 
 You can adjust the max width of the individual columns by setting ``display.max_colwidth``
 
 .. ipython:: python
 
-   datafile={'filename': ['filename_01','filename_02'],
-             'path': ["media/user_name/storage/folder_01/filename_01",
-                      "media/user_name/storage/folder_02/filename_02"]}
+   datafile = {'filename': ['filename_01', 'filename_02'],
+               'path': ["media/user_name/storage/folder_01/filename_01",
+                        "media/user_name/storage/folder_02/filename_02"]}
 
-   pd.set_option('display.max_colwidth',30)
+   pd.set_option('display.max_colwidth', 30)
    pd.DataFrame(datafile)
 
-   pd.set_option('display.max_colwidth',100)
+   pd.set_option('display.max_colwidth', 100)
    pd.DataFrame(datafile)
 
 .. ipython:: python
@@ -833,8 +831,8 @@ accessed like an attribute:
 
 .. ipython:: python
 
-   df = pd.DataFrame({'foo1' : np.random.randn(5),
-                      'foo2' : np.random.randn(5)})
+   df = pd.DataFrame({'foo1': np.random.randn(5),
+                      'foo2': np.random.randn(5)})
    df
    df.foo1
 
@@ -843,7 +841,7 @@ completion mechanism so they can be tab-completed:
 
 .. code-block:: ipython
 
-    In [5]: df.fo<TAB>
+    In [5]: df.fo<TAB>  # noqa: E225, E999
     df.foo1  df.foo2
 
 .. _basics.panel:
@@ -890,8 +888,8 @@ From dict of DataFrame objects
 .. ipython:: python
    :okwarning:
 
-   data = {'Item1' : pd.DataFrame(np.random.randn(4, 3)),
-           'Item2' : pd.DataFrame(np.random.randn(4, 2))}
+   data = {'Item1': pd.DataFrame(np.random.randn(4, 3)),
+           'Item2': pd.DataFrame(np.random.randn(4, 2))}
    pd.Panel(data)
 
 Note that the values in the dict need only be **convertible to
@@ -947,8 +945,9 @@ From DataFrame using ``to_panel`` method
 .. ipython:: python
    :okwarning:
 
-   midx = pd.MultiIndex(levels=[['one', 'two'], ['x','y']], codes=[[1,1,0,0],[1,0,1,0]])
-   df = pd.DataFrame({'A' : [1, 2, 3, 4], 'B': [5, 6, 7, 8]}, index=midx)
+   midx = pd.MultiIndex(levels=[['one', 'two'], ['x', 'y']],
+                        codes=[[1, 1, 0, 0], [1, 0, 1, 0]])
+   df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [5, 6, 7, 8]}, index=midx)
    df.to_panel()
 
 .. _dsintro.panel_item_selection:

setup.cfg

@@ -78,7 +78,6 @@ exclude =
     doc/source/whatsnew/v0.24.0.rst
     doc/source/basics.rst
     doc/source/contributing_docstring.rst
-    doc/source/dsintro.rst
     doc/source/enhancingperf.rst
     doc/source/groupby.rst
     doc/source/indexing.rst