diff --git a/doc/source/whatsnew/v0.10.0.rst b/doc/source/whatsnew/v0.10.0.rst
index 443250592a4a7..aa2749c85a232 100644
--- a/doc/source/whatsnew/v0.10.0.rst
+++ b/doc/source/whatsnew/v0.10.0.rst
@@ -49,8 +49,8 @@ talking about:
    :okwarning:
 
    import pandas as pd
-   df = pd.DataFrame(np.random.randn(6, 4),
-                     index=pd.date_range('1/1/2000', periods=6))
+
+   df = pd.DataFrame(np.random.randn(6, 4), index=pd.date_range("1/1/2000", periods=6))
    df
    # deprecated now
    df - df[0]
@@ -184,12 +184,14 @@ labeled the aggregated group with the end of the interval: the next day).
 
     import io
 
-    data = ('a,b,c\n'
-            '1,Yes,2\n'
-            '3,No,4')
+    data = """
+    a,b,c
+    1,Yes,2
+    3,No,4
+    """
     print(data)
     pd.read_csv(io.StringIO(data), header=None)
-    pd.read_csv(io.StringIO(data), header=None, prefix='X')
+    pd.read_csv(io.StringIO(data), header=None, prefix="X")
 
 - Values like ``'Yes'`` and ``'No'`` are not interpreted as boolean by default,
   though this can be controlled by new ``true_values`` and ``false_values``
@@ -199,7 +201,7 @@ labeled the aggregated group with the end of the interval: the next day).
 
     print(data)
     pd.read_csv(io.StringIO(data))
-    pd.read_csv(io.StringIO(data), true_values=['Yes'], false_values=['No'])
+    pd.read_csv(io.StringIO(data), true_values=["Yes"], false_values=["No"])
 
 - The file parsers will not recognize non-string values arising from a
   converter function as NA if passed in the ``na_values`` argument. It's better
@@ -210,10 +212,10 @@ labeled the aggregated group with the end of the interval: the next day).
 
 .. ipython:: python
 
-   s = pd.Series([np.nan, 1., 2., np.nan, 4])
+   s = pd.Series([np.nan, 1.0, 2.0, np.nan, 4])
    s
    s.fillna(0)
-   s.fillna(method='pad')
+   s.fillna(method="pad")
 
 Convenience methods ``ffill`` and  ``bfill`` have been added:
 
@@ -229,7 +231,8 @@ Convenience methods ``ffill`` and  ``bfill`` have been added:
   .. ipython:: python
 
       def f(x):
-          return pd.Series([x, x**2], index=['x', 'x^2'])
+          return pd.Series([x, x ** 2], index=["x", "x^2"])
+
 
       s = pd.Series(np.random.rand(5))
       s
@@ -272,20 +275,20 @@ The old behavior of printing out summary information can be achieved via the
 
 .. ipython:: python
 
-   pd.set_option('expand_frame_repr', False)
+   pd.set_option("expand_frame_repr", False)
 
    wide_frame
 
 .. ipython:: python
    :suppress:
 
-   pd.reset_option('expand_frame_repr')
+   pd.reset_option("expand_frame_repr")
 
 The width of each line can be changed via 'line_width' (80 by default):
 
 .. code-block:: python
 
-   pd.set_option('line_width', 40)
+   pd.set_option("line_width", 40)
 
    wide_frame
 
diff --git a/doc/source/whatsnew/v0.10.1.rst b/doc/source/whatsnew/v0.10.1.rst
index 3dc680c46a4d9..d71a0d5ca68cd 100644
--- a/doc/source/whatsnew/v0.10.1.rst
+++ b/doc/source/whatsnew/v0.10.1.rst
@@ -45,29 +45,31 @@ You may need to upgrade your existing data files. Please visit the
 
    import os
 
-   os.remove('store.h5')
+   os.remove("store.h5")
 
 You can designate (and index) certain columns that you want to be able to
 perform queries on a table, by passing a list to ``data_columns``
 
 .. ipython:: python
 
-   store = pd.HDFStore('store.h5')
-   df = pd.DataFrame(np.random.randn(8, 3),
-                     index=pd.date_range('1/1/2000', periods=8),
-                     columns=['A', 'B', 'C'])
-   df['string'] = 'foo'
-   df.loc[df.index[4:6], 'string'] = np.nan
-   df.loc[df.index[7:9], 'string'] = 'bar'
-   df['string2'] = 'cool'
+   store = pd.HDFStore("store.h5")
+   df = pd.DataFrame(
+       np.random.randn(8, 3),
+       index=pd.date_range("1/1/2000", periods=8),
+       columns=["A", "B", "C"],
+   )
+   df["string"] = "foo"
+   df.loc[df.index[4:6], "string"] = np.nan
+   df.loc[df.index[7:9], "string"] = "bar"
+   df["string2"] = "cool"
    df
 
    # on-disk operations
-   store.append('df', df, data_columns=['B', 'C', 'string', 'string2'])
-   store.select('df', "B>0 and string=='foo'")
+   store.append("df", df, data_columns=["B", "C", "string", "string2"])
+   store.select("df", "B>0 and string=='foo'")
 
    # this is in-memory version of this type of selection
-   df[(df.B > 0) & (df.string == 'foo')]
+   df[(df.B > 0) & (df.string == "foo")]
 
 Retrieving unique values in an indexable or data column.
 
@@ -75,19 +77,19 @@ Retrieving unique values in an indexable or data column.
 
    # note that this is deprecated as of 0.14.0
    # can be replicated by: store.select_column('df','index').unique()
-   store.unique('df', 'index')
-   store.unique('df', 'string')
+   store.unique("df", "index")
+   store.unique("df", "string")
 
 You can now store ``datetime64`` in data columns
 
 .. ipython:: python
 
     df_mixed = df.copy()
-    df_mixed['datetime64'] = pd.Timestamp('20010102')
-    df_mixed.loc[df_mixed.index[3:4], ['A', 'B']] = np.nan
+    df_mixed["datetime64"] = pd.Timestamp("20010102")
+    df_mixed.loc[df_mixed.index[3:4], ["A", "B"]] = np.nan
 
-    store.append('df_mixed', df_mixed)
-    df_mixed1 = store.select('df_mixed')
+    store.append("df_mixed", df_mixed)
+    df_mixed1 = store.select("df_mixed")
     df_mixed1
     df_mixed1.dtypes.value_counts()
 
@@ -97,7 +99,7 @@ columns, this is equivalent to passing a
 
 .. ipython:: python
 
-   store.select('df', columns=['A', 'B'])
+   store.select("df", columns=["A", "B"])
 
 ``HDFStore`` now serializes MultiIndex dataframes when appending tables.
 
@@ -160,29 +162,31 @@ combined result, by using ``where`` on a selector table.
 
 .. ipython:: python
 
-   df_mt = pd.DataFrame(np.random.randn(8, 6),
-                        index=pd.date_range('1/1/2000', periods=8),
-                        columns=['A', 'B', 'C', 'D', 'E', 'F'])
-   df_mt['foo'] = 'bar'
+   df_mt = pd.DataFrame(
+       np.random.randn(8, 6),
+       index=pd.date_range("1/1/2000", periods=8),
+       columns=["A", "B", "C", "D", "E", "F"],
+   )
+   df_mt["foo"] = "bar"
 
    # you can also create the tables individually
-   store.append_to_multiple({'df1_mt': ['A', 'B'], 'df2_mt': None},
-                            df_mt, selector='df1_mt')
+   store.append_to_multiple(
+       {"df1_mt": ["A", "B"], "df2_mt": None}, df_mt, selector="df1_mt"
+   )
    store
 
    # individual tables were created
-   store.select('df1_mt')
-   store.select('df2_mt')
+   store.select("df1_mt")
+   store.select("df2_mt")
 
    # as a multiple
-   store.select_as_multiple(['df1_mt', 'df2_mt'], where=['A>0', 'B>0'],
-                            selector='df1_mt')
+   store.select_as_multiple(["df1_mt", "df2_mt"], where=["A>0", "B>0"], selector="df1_mt")
 
 .. ipython:: python
    :suppress:
 
    store.close()
-   os.remove('store.h5')
+   os.remove("store.h5")
 
 **Enhancements**
 
diff --git a/doc/source/whatsnew/v0.12.0.rst b/doc/source/whatsnew/v0.12.0.rst
index 9971ae22822f6..4de76510c6bc1 100644
--- a/doc/source/whatsnew/v0.12.0.rst
+++ b/doc/source/whatsnew/v0.12.0.rst
@@ -47,7 +47,7 @@ API changes
 
     .. ipython:: python
 
-        p = pd.DataFrame({'first': [4, 5, 8], 'second': [0, 0, 3]})
+        p = pd.DataFrame({"first": [4, 5, 8], "second": [0, 0, 3]})
         p % 0
         p % p
         p / p
@@ -95,8 +95,8 @@ API changes
 
     .. ipython:: python
 
-       df = pd.DataFrame(range(5), index=list('ABCDE'), columns=['a'])
-       mask = (df.a % 2 == 0)
+       df = pd.DataFrame(range(5), index=list("ABCDE"), columns=["a"])
+       mask = df.a % 2 == 0
        mask
 
        # this is what you should use
@@ -141,21 +141,24 @@ API changes
       .. code-block:: python
 
          from pandas.io.parsers import ExcelFile
-         xls = ExcelFile('path_to_file.xls')
-         xls.parse('Sheet1', index_col=None, na_values=['NA'])
+
+         xls = ExcelFile("path_to_file.xls")
+         xls.parse("Sheet1", index_col=None, na_values=["NA"])
 
       With
 
       .. code-block:: python
 
          import pandas as pd
-         pd.read_excel('path_to_file.xls', 'Sheet1', index_col=None, na_values=['NA'])
+
+         pd.read_excel("path_to_file.xls", "Sheet1", index_col=None, na_values=["NA"])
 
     - added top-level function ``read_sql`` that is equivalent to the following
 
       .. code-block:: python
 
          from pandas.io.sql import read_frame
+
          read_frame(...)
 
   - ``DataFrame.to_html`` and ``DataFrame.to_latex`` now accept a path for
@@ -200,7 +203,7 @@ IO enhancements
     .. ipython:: python
        :okwarning:
 
-        df = pd.DataFrame({'a': range(3), 'b': list('abc')})
+        df = pd.DataFrame({"a": range(3), "b": list("abc")})
         print(df)
         html = df.to_html()
         alist = pd.read_html(html, index_col=0)
@@ -248,16 +251,18 @@ IO enhancements
       .. ipython:: python
 
          from pandas._testing import makeCustomDataframe as mkdf
+
          df = mkdf(5, 3, r_idx_nlevels=2, c_idx_nlevels=4)
-         df.to_csv('mi.csv')
-         print(open('mi.csv').read())
-         pd.read_csv('mi.csv', header=[0, 1, 2, 3], index_col=[0, 1])
+         df.to_csv("mi.csv")
+         print(open("mi.csv").read())
+         pd.read_csv("mi.csv", header=[0, 1, 2, 3], index_col=[0, 1])
 
       .. ipython:: python
          :suppress:
 
          import os
-         os.remove('mi.csv')
+
+         os.remove("mi.csv")
 
   - Support for ``HDFStore`` (via ``PyTables 3.0.0``) on Python3
 
@@ -304,8 +309,8 @@ Other enhancements
 
     .. ipython:: python
 
-        df = pd.DataFrame({'a': list('ab..'), 'b': [1, 2, 3, 4]})
-        df.replace(regex=r'\s*\.\s*', value=np.nan)
+        df = pd.DataFrame({"a": list("ab.."), "b": [1, 2, 3, 4]})
+        df.replace(regex=r"\s*\.\s*", value=np.nan)
 
     to replace all occurrences of the string ``'.'`` with zero or more
     instances of surrounding white space with ``NaN``.
@@ -314,7 +319,7 @@ Other enhancements
 
     .. ipython:: python
 
-        df.replace('.', np.nan)
+        df.replace(".", np.nan)
 
     to replace all occurrences of the string ``'.'`` with ``NaN``.
 
@@ -359,8 +364,8 @@ Other enhancements
 
     .. ipython:: python
 
-       dff = pd.DataFrame({'A': np.arange(8), 'B': list('aabbbbcc')})
-       dff.groupby('B').filter(lambda x: len(x) > 2)
+       dff = pd.DataFrame({"A": np.arange(8), "B": list("aabbbbcc")})
+       dff.groupby("B").filter(lambda x: len(x) > 2)
 
     Alternatively, instead of dropping the offending groups, we can return a
     like-indexed objects where the groups that do not pass the filter are
@@ -368,7 +373,7 @@ Other enhancements
 
     .. ipython:: python
 
-       dff.groupby('B').filter(lambda x: len(x) > 2, dropna=False)
+       dff.groupby("B").filter(lambda x: len(x) > 2, dropna=False)
 
   - Series and DataFrame hist methods now take a ``figsize`` argument (:issue:`3834`)
 
@@ -397,17 +402,18 @@ Experimental features
 
       from pandas.tseries.offsets import CustomBusinessDay
       from datetime import datetime
+
       # As an interesting example, let's look at Egypt where
       # a Friday-Saturday weekend is observed.
-      weekmask_egypt = 'Sun Mon Tue Wed Thu'
+      weekmask_egypt = "Sun Mon Tue Wed Thu"
       # They also observe International Workers' Day so let's
       # add that for a couple of years
-      holidays = ['2012-05-01', datetime(2013, 5, 1), np.datetime64('2014-05-01')]
+      holidays = ["2012-05-01", datetime(2013, 5, 1), np.datetime64("2014-05-01")]
       bday_egypt = CustomBusinessDay(holidays=holidays, weekmask=weekmask_egypt)
       dt = datetime(2013, 4, 30)
       print(dt + 2 * bday_egypt)
       dts = pd.date_range(dt, periods=5, freq=bday_egypt)
-      print(pd.Series(dts.weekday, dts).map(pd.Series('Mon Tue Wed Thu Fri Sat Sun'.split())))
+      print(pd.Series(dts.weekday, dts).map(pd.Series("Mon Tue Wed Thu Fri Sat Sun".split())))
 
 Bug fixes
 ~~~~~~~~~
@@ -430,14 +436,14 @@ Bug fixes
     .. ipython:: python
         :okwarning:
 
-        strs = 'go', 'bow', 'joe', 'slow'
+        strs = "go", "bow", "joe", "slow"
         ds = pd.Series(strs)
 
         for s in ds.str:
             print(s)
 
         s
-        s.dropna().values.item() == 'w'
+        s.dropna().values.item() == "w"
 
     The last element yielded by the iterator will be a ``Series`` containing
     the last element of the longest string in the ``Series`` with all other
diff --git a/doc/source/whatsnew/v0.13.1.rst b/doc/source/whatsnew/v0.13.1.rst
index 9e416f8eeb3f1..1215786b4cccc 100644
--- a/doc/source/whatsnew/v0.13.1.rst
+++ b/doc/source/whatsnew/v0.13.1.rst
@@ -31,16 +31,16 @@ Highlights include:
 
    .. ipython:: python
 
-      df = pd.DataFrame({'A': np.array(['foo', 'bar', 'bah', 'foo', 'bar'])})
-      df['A'].iloc[0] = np.nan
+      df = pd.DataFrame({"A": np.array(["foo", "bar", "bah", "foo", "bar"])})
+      df["A"].iloc[0] = np.nan
       df
 
    The recommended way to do this type of assignment is:
 
    .. ipython:: python
 
-      df = pd.DataFrame({'A': np.array(['foo', 'bar', 'bah', 'foo', 'bar'])})
-      df.loc[0, 'A'] = np.nan
+      df = pd.DataFrame({"A": np.array(["foo", "bar", "bah", "foo", "bar"])})
+      df.loc[0, "A"] = np.nan
       df
 
 Output formatting enhancements
@@ -52,24 +52,27 @@ Output formatting enhancements
 
   .. ipython:: python
 
-     max_info_rows = pd.get_option('max_info_rows')
+     max_info_rows = pd.get_option("max_info_rows")
 
-     df = pd.DataFrame({'A': np.random.randn(10),
-                        'B': np.random.randn(10),
-                        'C': pd.date_range('20130101', periods=10)
-                        })
+     df = pd.DataFrame(
+         {
+             "A": np.random.randn(10),
+             "B": np.random.randn(10),
+             "C": pd.date_range("20130101", periods=10),
+         }
+     )
      df.iloc[3:6, [0, 2]] = np.nan
 
   .. ipython:: python
 
      # set to not display the null counts
-     pd.set_option('max_info_rows', 0)
+     pd.set_option("max_info_rows", 0)
      df.info()
 
   .. ipython:: python
 
      # this is the default (same as in 0.13.0)
-     pd.set_option('max_info_rows', max_info_rows)
+     pd.set_option("max_info_rows", max_info_rows)
      df.info()
 
 - Add ``show_dimensions`` display option for the new DataFrame repr to control whether the dimensions print.
@@ -77,10 +80,10 @@ Output formatting enhancements
   .. ipython:: python
 
       df = pd.DataFrame([[1, 2], [3, 4]])
-      pd.set_option('show_dimensions', False)
+      pd.set_option("show_dimensions", False)
       df
 
-      pd.set_option('show_dimensions', True)
+      pd.set_option("show_dimensions", True)
       df
 
 - The ``ArrayFormatter`` for ``datetime`` and ``timedelta64`` now intelligently
@@ -98,10 +101,9 @@ Output formatting enhancements
 
   .. ipython:: python
 
-     df = pd.DataFrame([pd.Timestamp('20010101'),
-                        pd.Timestamp('20040601')], columns=['age'])
-     df['today'] = pd.Timestamp('20130419')
-     df['diff'] = df['today'] - df['age']
+     df = pd.DataFrame([pd.Timestamp("20010101"), pd.Timestamp("20040601")], columns=["age"])
+     df["today"] = pd.Timestamp("20130419")
+     df["diff"] = df["today"] - df["age"]
      df
 
 API changes
@@ -115,8 +117,8 @@ API changes
 
   .. ipython:: python
 
-      s = pd.Series(['a', 'a|b', np.nan, 'a|c'])
-      s.str.get_dummies(sep='|')
+      s = pd.Series(["a", "a|b", np.nan, "a|c"])
+      s.str.get_dummies(sep="|")
 
 - Added the ``NDFrame.equals()`` method to compare if two NDFrames are
   equal have equal axes, dtypes, and values. Added the
@@ -126,8 +128,8 @@ API changes
 
   .. code-block:: python
 
-      df = pd.DataFrame({'col': ['foo', 0, np.nan]})
-      df2 = pd.DataFrame({'col': [np.nan, 0, 'foo']}, index=[2, 1, 0])
+      df = pd.DataFrame({"col": ["foo", 0, np.nan]})
+      df2 = pd.DataFrame({"col": [np.nan, 0, "foo"]}, index=[2, 1, 0])
       df.equals(df2)
       df.equals(df2.sort_index())
 
@@ -204,8 +206,7 @@ Enhancements
   .. code-block:: python
 
       # Try to infer the format for the index column
-      df = pd.read_csv('foo.csv', index_col=0, parse_dates=True,
-                       infer_datetime_format=True)
+      df = pd.read_csv("foo.csv", index_col=0, parse_dates=True, infer_datetime_format=True)
 
 - ``date_format`` and ``datetime_format`` keywords can now be specified when writing to ``excel``
   files (:issue:`4133`)
@@ -215,10 +216,10 @@ Enhancements
 
   .. ipython:: python
 
-      shades = ['light', 'dark']
-      colors = ['red', 'green', 'blue']
+      shades = ["light", "dark"]
+      colors = ["red", "green", "blue"]
 
-      pd.MultiIndex.from_product([shades, colors], names=['shade', 'color'])
+      pd.MultiIndex.from_product([shades, colors], names=["shade", "color"])
 
 - Panel :meth:`~pandas.Panel.apply` will work on non-ufuncs. See :ref:`the docs<basics.apply>`.
 
diff --git a/doc/source/whatsnew/v0.14.1.rst b/doc/source/whatsnew/v0.14.1.rst
index 354d67a525d0e..78fd182ea86c3 100644
--- a/doc/source/whatsnew/v0.14.1.rst
+++ b/doc/source/whatsnew/v0.14.1.rst
@@ -68,7 +68,8 @@ API changes
      :suppress:
 
      import pandas.tseries.offsets as offsets
-     d = pd.Timestamp('2014-01-01 09:00')
+
+     d = pd.Timestamp("2014-01-01 09:00")
 
   .. ipython:: python
 
@@ -100,10 +101,10 @@ Enhancements
      import pandas.tseries.offsets as offsets
 
      day = offsets.Day()
-     day.apply(pd.Timestamp('2014-01-01 09:00'))
+     day.apply(pd.Timestamp("2014-01-01 09:00"))
 
      day = offsets.Day(normalize=True)
-     day.apply(pd.Timestamp('2014-01-01 09:00'))
+     day.apply(pd.Timestamp("2014-01-01 09:00"))
 
 - ``PeriodIndex`` is represented as the same format as ``DatetimeIndex`` (:issue:`7601`)
 - ``StringMethods`` now work on empty Series (:issue:`7242`)
@@ -123,8 +124,7 @@ Enhancements
 
   .. ipython:: python
 
-     rng = pd.date_range('3/6/2012 00:00', periods=10, freq='D',
-                         tz='dateutil/Europe/London')
+     rng = pd.date_range("3/6/2012 00:00", periods=10, freq="D", tz="dateutil/Europe/London")
      rng.tz
 
   See :ref:`the docs <timeseries.timezone>`.
diff --git a/doc/source/whatsnew/v0.15.1.rst b/doc/source/whatsnew/v0.15.1.rst
index da56f07e84d9f..a1d4f9d14a905 100644
--- a/doc/source/whatsnew/v0.15.1.rst
+++ b/doc/source/whatsnew/v0.15.1.rst
@@ -23,7 +23,7 @@ API changes
 
   .. ipython:: python
 
-     s = pd.Series(pd.date_range('20130101', periods=5, freq='D'))
+     s = pd.Series(pd.date_range("20130101", periods=5, freq="D"))
      s.iloc[2] = np.nan
      s
 
@@ -52,8 +52,7 @@ API changes
   .. ipython:: python
 
      np.random.seed(2718281)
-     df = pd.DataFrame(np.random.randint(0, 100, (10, 2)),
-                       columns=['jim', 'joe'])
+     df = pd.DataFrame(np.random.randint(0, 100, (10, 2)), columns=["jim", "joe"])
      df.head()
 
      ts = pd.Series(5 * np.random.randint(0, 3, 10))
@@ -80,9 +79,9 @@ API changes
 
   .. ipython:: python
 
-     df = pd.DataFrame({'jim': range(5), 'joe': range(5, 10)})
+     df = pd.DataFrame({"jim": range(5), "joe": range(5, 10)})
      df
-     gr = df.groupby(df['jim'] < 2)
+     gr = df.groupby(df["jim"] < 2)
 
   previous behavior (excludes 1st column from output):
 
@@ -106,7 +105,7 @@ API changes
 
   .. ipython:: python
 
-    s = pd.Series(['a', 'b', 'c', 'd'], [4, 3, 2, 1])
+    s = pd.Series(["a", "b", "c", "d"], [4, 3, 2, 1])
     s
 
   previous behavior:
@@ -208,6 +207,7 @@ Enhancements
   .. ipython:: python
 
      from collections import deque
+
      df1 = pd.DataFrame([1, 2, 3])
      df2 = pd.DataFrame([4, 5, 6])
 
@@ -228,8 +228,9 @@ Enhancements
 
   .. ipython:: python
 
-     dfi = pd.DataFrame(1, index=pd.MultiIndex.from_product([['a'],
-                        range(1000)]), columns=['A'])
+     dfi = pd.DataFrame(
+         1, index=pd.MultiIndex.from_product([["a"], range(1000)]), columns=["A"]
+     )
 
   previous behavior:
 
diff --git a/doc/source/whatsnew/v0.16.1.rst b/doc/source/whatsnew/v0.16.1.rst
index a89ede8f024a0..39767684c01d0 100644
--- a/doc/source/whatsnew/v0.16.1.rst
+++ b/doc/source/whatsnew/v0.16.1.rst
@@ -209,9 +209,8 @@ when sampling from rows.
 
 .. ipython:: python
 
-   df = pd.DataFrame({'col1': [9, 8, 7, 6],
-                      'weight_column': [0.5, 0.4, 0.1, 0]})
-   df.sample(n=3, weights='weight_column')
+   df = pd.DataFrame({"col1": [9, 8, 7, 6], "weight_column": [0.5, 0.4, 0.1, 0]})
+   df.sample(n=3, weights="weight_column")
 
 
 .. _whatsnew_0161.enhancements.string:
@@ -229,7 +228,7 @@ enhancements make string operations easier and more consistent with standard pyt
 
   .. ipython:: python
 
-     idx = pd.Index([' jack', 'jill ', ' jesse ', 'frank'])
+     idx = pd.Index([" jack", "jill ", " jesse ", "frank"])
      idx.str.strip()
 
   One special case for the ``.str`` accessor on ``Index`` is that if a string method returns ``bool``, the ``.str`` accessor
@@ -238,11 +237,11 @@ enhancements make string operations easier and more consistent with standard pyt
 
   .. ipython:: python
 
-     idx = pd.Index(['a1', 'a2', 'b1', 'b2'])
+     idx = pd.Index(["a1", "a2", "b1", "b2"])
      s = pd.Series(range(4), index=idx)
      s
-     idx.str.startswith('a')
-     s[s.index.str.startswith('a')]
+     idx.str.startswith("a")
+     s[s.index.str.startswith("a")]
 
 - The following new methods are accessible via ``.str`` accessor to apply the function to each values. (:issue:`9766`, :issue:`9773`, :issue:`10031`, :issue:`10045`, :issue:`10052`)
 
@@ -257,21 +256,21 @@ enhancements make string operations easier and more consistent with standard pyt
 
   .. ipython:: python
 
-     s = pd.Series(['a,b', 'a,c', 'b,c'])
+     s = pd.Series(["a,b", "a,c", "b,c"])
 
      # return Series
-     s.str.split(',')
+     s.str.split(",")
 
      # return DataFrame
-     s.str.split(',', expand=True)
+     s.str.split(",", expand=True)
 
-     idx = pd.Index(['a,b', 'a,c', 'b,c'])
+     idx = pd.Index(["a,b", "a,c", "b,c"])
 
      # return Index
-     idx.str.split(',')
+     idx.str.split(",")
 
      # return MultiIndex
-     idx.str.split(',', expand=True)
+     idx.str.split(",", expand=True)
 
 
 - Improved ``extract`` and ``get_dummies`` methods for ``Index.str`` (:issue:`9980`)
@@ -286,9 +285,9 @@ Other enhancements
 
   .. ipython:: python
 
-     pd.Timestamp('2014-08-01 09:00') + pd.tseries.offsets.BusinessHour()
-     pd.Timestamp('2014-08-01 07:00') + pd.tseries.offsets.BusinessHour()
-     pd.Timestamp('2014-08-01 16:30') + pd.tseries.offsets.BusinessHour()
+     pd.Timestamp("2014-08-01 09:00") + pd.tseries.offsets.BusinessHour()
+     pd.Timestamp("2014-08-01 07:00") + pd.tseries.offsets.BusinessHour()
+     pd.Timestamp("2014-08-01 16:30") + pd.tseries.offsets.BusinessHour()
 
 - ``DataFrame.diff`` now takes an ``axis`` parameter that determines the direction of differencing (:issue:`9727`)
 
@@ -300,8 +299,8 @@ Other enhancements
 
   .. ipython:: python
 
-    df = pd.DataFrame(np.random.randn(3, 3), columns=['A', 'B', 'C'])
-    df.drop(['A', 'X'], axis=1, errors='ignore')
+    df = pd.DataFrame(np.random.randn(3, 3), columns=["A", "B", "C"])
+    df.drop(["A", "X"], axis=1, errors="ignore")
 
 - Add support for separating years and quarters using dashes, for
   example 2014-Q1.  (:issue:`9688`)
@@ -382,19 +381,16 @@ New behavior
 
 .. ipython:: python
 
-   pd.set_option('display.width', 80)
-   pd.Index(range(4), name='foo')
-   pd.Index(range(30), name='foo')
-   pd.Index(range(104), name='foo')
-   pd.CategoricalIndex(['a', 'bb', 'ccc', 'dddd'],
-                       ordered=True, name='foobar')
-   pd.CategoricalIndex(['a', 'bb', 'ccc', 'dddd'] * 10,
-                       ordered=True, name='foobar')
-   pd.CategoricalIndex(['a', 'bb', 'ccc', 'dddd'] * 100,
-                       ordered=True, name='foobar')
-   pd.date_range('20130101', periods=4, name='foo', tz='US/Eastern')
-   pd.date_range('20130101', periods=25, freq='D')
-   pd.date_range('20130101', periods=104, name='foo', tz='US/Eastern')
+   pd.set_option("display.width", 80)
+   pd.Index(range(4), name="foo")
+   pd.Index(range(30), name="foo")
+   pd.Index(range(104), name="foo")
+   pd.CategoricalIndex(["a", "bb", "ccc", "dddd"], ordered=True, name="foobar")
+   pd.CategoricalIndex(["a", "bb", "ccc", "dddd"] * 10, ordered=True, name="foobar")
+   pd.CategoricalIndex(["a", "bb", "ccc", "dddd"] * 100, ordered=True, name="foobar")
+   pd.date_range("20130101", periods=4, name="foo", tz="US/Eastern")
+   pd.date_range("20130101", periods=25, freq="D")
+   pd.date_range("20130101", periods=104, name="foo", tz="US/Eastern")
 
 
 .. _whatsnew_0161.performance:
diff --git a/doc/source/whatsnew/v0.16.2.rst b/doc/source/whatsnew/v0.16.2.rst
index 2cb0cbec68eff..bb2aa166419b4 100644
--- a/doc/source/whatsnew/v0.16.2.rst
+++ b/doc/source/whatsnew/v0.16.2.rst
@@ -48,9 +48,10 @@ This can be rewritten as
 
 .. code-block:: python
 
-   (df.pipe(h)                   # noqa F821
-      .pipe(g, arg1=1)           # noqa F821
-      .pipe(f, arg2=2, arg3=3)   # noqa F821
+   (
+       df.pipe(h)  # noqa F821
+       .pipe(g, arg1=1)  # noqa F821
+       .pipe(f, arg2=2, arg3=3)  # noqa F821
    )
 
 Now both the code and the logic flow from top to bottom. Keyword arguments are next to
@@ -64,15 +65,16 @@ of ``(function, keyword)`` indicating where the DataFrame should flow. For examp
 
    import statsmodels.formula.api as sm
 
-   bb = pd.read_csv('data/baseball.csv', index_col='id')
+   bb = pd.read_csv("data/baseball.csv", index_col="id")
 
    # sm.ols takes (formula, data)
-   (bb.query('h > 0')
-      .assign(ln_h=lambda df: np.log(df.h))
-      .pipe((sm.ols, 'data'), 'hr ~ ln_h + year + g + C(lg)')
-      .fit()
-      .summary()
-    )
+   (
+       bb.query("h > 0")
+       .assign(ln_h=lambda df: np.log(df.h))
+       .pipe((sm.ols, "data"), "hr ~ ln_h + year + g + C(lg)")
+       .fit()
+       .summary()
+   )
 
 The pipe method is inspired by unix pipes, which stream text through
 processes. More recently dplyr_ and magrittr_ have introduced the
diff --git a/doc/source/whatsnew/v0.17.0.rst b/doc/source/whatsnew/v0.17.0.rst
index e8f37a72f6417..9f700dacf38c7 100644
--- a/doc/source/whatsnew/v0.17.0.rst
+++ b/doc/source/whatsnew/v0.17.0.rst
@@ -80,9 +80,13 @@ The new implementation allows for having a single-timezone across all rows, with
 
 .. ipython:: python
 
-   df = pd.DataFrame({'A': pd.date_range('20130101', periods=3),
-                      'B': pd.date_range('20130101', periods=3, tz='US/Eastern'),
-                      'C': pd.date_range('20130101', periods=3, tz='CET')})
+   df = pd.DataFrame(
+       {
+           "A": pd.date_range("20130101", periods=3),
+           "B": pd.date_range("20130101", periods=3, tz="US/Eastern"),
+           "C": pd.date_range("20130101", periods=3, tz="CET"),
+       }
+   )
    df
    df.dtypes
 
@@ -95,8 +99,8 @@ This uses a new-dtype representation as well, that is very similar in look-and-f
 
 .. ipython:: python
 
-   df['B'].dtype
-   type(df['B'].dtype)
+   df["B"].dtype
+   type(df["B"].dtype)
 
 .. note::
 
@@ -119,8 +123,8 @@ This uses a new-dtype representation as well, that is very similar in look-and-f
 
    .. ipython:: python
 
-      pd.date_range('20130101', periods=3, tz='US/Eastern')
-      pd.date_range('20130101', periods=3, tz='US/Eastern').dtype
+      pd.date_range("20130101", periods=3, tz="US/Eastern")
+      pd.date_range("20130101", periods=3, tz="US/Eastern").dtype
 
 .. _whatsnew_0170.gil:
 
@@ -138,9 +142,10 @@ as well as the ``.sum()`` operation.
 
    N = 1000000
    ngroups = 10
-   df = DataFrame({'key': np.random.randint(0, ngroups, size=N),
-                   'data': np.random.randn(N)})
-   df.groupby('key')['data'].sum()
+   df = DataFrame(
+       {"key": np.random.randint(0, ngroups, size=N), "data": np.random.randn(N)}
+   )
+   df.groupby("key")["data"].sum()
 
 Releasing of the GIL could benefit an application that uses threads for user interactions (e.g. QT_), or performing multi-threaded computations. A nice example of a library that can handle these types of computation-in-parallel is the dask_ library.
 
@@ -189,16 +194,16 @@ We are now supporting a ``Series.dt.strftime`` method for datetime-likes to gene
 .. ipython:: python
 
    # DatetimeIndex
-   s = pd.Series(pd.date_range('20130101', periods=4))
+   s = pd.Series(pd.date_range("20130101", periods=4))
    s
-   s.dt.strftime('%Y/%m/%d')
+   s.dt.strftime("%Y/%m/%d")
 
 .. ipython:: python
 
    # PeriodIndex
-   s = pd.Series(pd.period_range('20130101', periods=4))
+   s = pd.Series(pd.period_range("20130101", periods=4))
    s
-   s.dt.strftime('%Y/%m/%d')
+   s.dt.strftime("%Y/%m/%d")
 
 The string format is as the python standard library and details can be found `here <https://docs.python.org/2/library/datetime.html#strftime-and-strptime-behavior>`_
 
@@ -210,7 +215,7 @@ Series.dt.total_seconds
 .. ipython:: python
 
    # TimedeltaIndex
-   s = pd.Series(pd.timedelta_range('1 minutes', periods=4))
+   s = pd.Series(pd.timedelta_range("1 minutes", periods=4))
    s
    s.dt.total_seconds()
 
@@ -225,18 +230,18 @@ A multiplied freq represents a span of corresponding length. The example below c
 
 .. ipython:: python
 
-   p = pd.Period('2015-08-01', freq='3D')
+   p = pd.Period("2015-08-01", freq="3D")
    p
    p + 1
    p - 2
    p.to_timestamp()
-   p.to_timestamp(how='E')
+   p.to_timestamp(how="E")
 
 You can use the multiplied freq in ``PeriodIndex`` and ``period_range``.
 
 .. ipython:: python
 
-   idx = pd.period_range('2015-08-01', periods=4, freq='2D')
+   idx = pd.period_range("2015-08-01", periods=4, freq="2D")
    idx
    idx + 1
 
@@ -249,14 +254,14 @@ Support for SAS XPORT files
 
 .. code-block:: python
 
-    df = pd.read_sas('sas_xport.xpt')
+    df = pd.read_sas("sas_xport.xpt")
 
 It is also possible to obtain an iterator and read an XPORT file
 incrementally.
 
 .. code-block:: python
 
-    for df in pd.read_sas('sas_xport.xpt', chunksize=10000):
+    for df in pd.read_sas("sas_xport.xpt", chunksize=10000):
         do_something(df)
 
 See the :ref:`docs <io.sas>` for more details.
@@ -270,7 +275,7 @@ Support for math functions in .eval()
 
 .. code-block:: python
 
-    df = pd.DataFrame({'a': np.random.randn(10)})
+    df = pd.DataFrame({"a": np.random.randn(10)})
     df.eval("b = sin(a)")
 
 The support math functions are ``sin``, ``cos``, ``exp``, ``log``, ``expm1``, ``log1p``,
@@ -292,23 +297,26 @@ See the :ref:`documentation <io.excel>` for more details.
 
 .. ipython:: python
 
-   df = pd.DataFrame([[1, 2, 3, 4], [5, 6, 7, 8]],
-                     columns=pd.MultiIndex.from_product(
-                     [['foo', 'bar'], ['a', 'b']], names=['col1', 'col2']),
-                     index=pd.MultiIndex.from_product([['j'], ['l', 'k']],
-                                                      names=['i1', 'i2']))
+   df = pd.DataFrame(
+       [[1, 2, 3, 4], [5, 6, 7, 8]],
+       columns=pd.MultiIndex.from_product(
+           [["foo", "bar"], ["a", "b"]], names=["col1", "col2"]
+       ),
+       index=pd.MultiIndex.from_product([["j"], ["l", "k"]], names=["i1", "i2"]),
+   )
 
    df
-   df.to_excel('test.xlsx')
+   df.to_excel("test.xlsx")
 
-   df = pd.read_excel('test.xlsx', header=[0, 1], index_col=[0, 1])
+   df = pd.read_excel("test.xlsx", header=[0, 1], index_col=[0, 1])
    df
 
 .. ipython:: python
    :suppress:
 
    import os
-   os.remove('test.xlsx')
+
+   os.remove("test.xlsx")
 
 Previously, it was necessary to specify the ``has_index_names`` argument in ``read_excel``,
 if the serialized data had index names.  For version 0.17.0 the output format of ``to_excel``
@@ -354,14 +362,14 @@ Some East Asian countries use Unicode characters its width is corresponding to 2
 
 .. ipython:: python
 
-   df = pd.DataFrame({u'国籍': ['UK', u'日本'], u'名前': ['Alice', u'しのぶ']})
+   df = pd.DataFrame({u"国籍": ["UK", u"日本"], u"名前": ["Alice", u"しのぶ"]})
    df;
 
 .. image:: ../_static/option_unicode01.png
 
 .. ipython:: python
 
-   pd.set_option('display.unicode.east_asian_width', True)
+   pd.set_option("display.unicode.east_asian_width", True)
    df;
 
 .. image:: ../_static/option_unicode02.png
@@ -371,7 +379,7 @@ For further details, see :ref:`here <options.east_asian_width>`
 .. ipython:: python
    :suppress:
 
-   pd.set_option('display.unicode.east_asian_width', False)
+   pd.set_option("display.unicode.east_asian_width", False)
 
 .. _whatsnew_0170.enhancements.other:
 
@@ -391,9 +399,9 @@ Other enhancements
 
   .. ipython:: python
 
-    df1 = pd.DataFrame({'col1':[0,1], 'col_left':['a','b']})
-    df2 = pd.DataFrame({'col1':[1,2,2],'col_right':[2,2,2]})
-    pd.merge(df1, df2, on='col1', how='outer', indicator=True)
+    df1 = pd.DataFrame({"col1": [0, 1], "col_left": ["a", "b"]})
+    df2 = pd.DataFrame({"col1": [1, 2, 2], "col_right": [2, 2, 2]})
+    pd.merge(df1, df2, on="col1", how="outer", indicator=True)
 
   For more, see the :ref:`updated docs <merging.indicator>`
 
@@ -407,7 +415,7 @@ Other enhancements
 
   .. ipython:: python
 
-     foo = pd.Series([1, 2], name='foo')
+     foo = pd.Series([1, 2], name="foo")
      bar = pd.Series([1, 2])
      baz = pd.Series([4, 5])
 
@@ -434,46 +442,43 @@ Other enhancements
   .. ipython:: python
 
      ser = pd.Series([np.nan, np.nan, 5, np.nan, np.nan, np.nan, 13])
-     ser.interpolate(limit=1, limit_direction='both')
+     ser.interpolate(limit=1, limit_direction="both")
 
 - Added a ``DataFrame.round`` method to round the values to a variable number of decimal places (:issue:`10568`).
 
   .. ipython:: python
 
-     df = pd.DataFrame(np.random.random([3, 3]),
-                       columns=['A', 'B', 'C'],
-                       index=['first', 'second', 'third'])
+     df = pd.DataFrame(
+         np.random.random([3, 3]),
+         columns=["A", "B", "C"],
+         index=["first", "second", "third"],
+     )
      df
      df.round(2)
-     df.round({'A': 0, 'C': 2})
+     df.round({"A": 0, "C": 2})
 
 - ``drop_duplicates`` and ``duplicated`` now accept a ``keep`` keyword to target first, last, and all duplicates. The ``take_last`` keyword is deprecated, see :ref:`here <whatsnew_0170.deprecations>` (:issue:`6511`, :issue:`8505`)
 
   .. ipython:: python
 
-     s = pd.Series(['A', 'B', 'C', 'A', 'B', 'D'])
+     s = pd.Series(["A", "B", "C", "A", "B", "D"])
      s.drop_duplicates()
-     s.drop_duplicates(keep='last')
+     s.drop_duplicates(keep="last")
      s.drop_duplicates(keep=False)
 
 - Reindex now has a ``tolerance`` argument that allows for finer control of :ref:`basics.limits_on_reindex_fill` (:issue:`10411`):
 
   .. ipython:: python
 
-     df = pd.DataFrame({'x': range(5),
-                        't': pd.date_range('2000-01-01', periods=5)})
-     df.reindex([0.1, 1.9, 3.5],
-                method='nearest',
-                tolerance=0.2)
+     df = pd.DataFrame({"x": range(5), "t": pd.date_range("2000-01-01", periods=5)})
+     df.reindex([0.1, 1.9, 3.5], method="nearest", tolerance=0.2)
 
   When used on a ``DatetimeIndex``, ``TimedeltaIndex`` or ``PeriodIndex``, ``tolerance`` will coerced into a ``Timedelta`` if possible. This allows you to specify tolerance with a string:
 
   .. ipython:: python
 
-     df = df.set_index('t')
-     df.reindex(pd.to_datetime(['1999-12-31']),
-                method='nearest',
-                tolerance='1 day')
+     df = df.set_index("t")
+     df.reindex(pd.to_datetime(["1999-12-31"]), method="nearest", tolerance="1 day")
 
   ``tolerance`` is also exposed by the lower level ``Index.get_indexer`` and ``Index.get_loc`` methods.
 
@@ -627,13 +632,13 @@ Of course you can coerce this as well.
 
 .. ipython:: python
 
-   pd.to_datetime(['2009-07-31', 'asd'], errors='coerce')
+   pd.to_datetime(["2009-07-31", "asd"], errors="coerce")
 
 To keep the previous behavior, you can use ``errors='ignore'``:
 
 .. ipython:: python
 
-   pd.to_datetime(['2009-07-31', 'asd'], errors='ignore')
+   pd.to_datetime(["2009-07-31", "asd"], errors="ignore")
 
 Furthermore, ``pd.to_timedelta`` has gained a similar API, of ``errors='raise'|'ignore'|'coerce'``, and the ``coerce`` keyword
 has been deprecated in favor of ``errors='coerce'``.
@@ -667,9 +672,9 @@ New behavior:
 
 .. ipython:: python
 
-   pd.Timestamp('2012Q2')
-   pd.Timestamp('2014')
-   pd.DatetimeIndex(['2012Q2', '2014'])
+   pd.Timestamp("2012Q2")
+   pd.Timestamp("2014")
+   pd.DatetimeIndex(["2012Q2", "2014"])
 
 .. note::
 
@@ -678,6 +683,7 @@ New behavior:
    .. ipython:: python
 
       import pandas.tseries.offsets as offsets
+
       pd.Timestamp.now()
       pd.Timestamp.now() + offsets.DateOffset(years=1)
 
@@ -780,8 +786,7 @@ Previous behavior:
 
 .. ipython:: python
 
-   df_with_missing = pd.DataFrame({'col1': [0, np.nan, 2],
-                                   'col2': [1, np.nan, np.nan]})
+   df_with_missing = pd.DataFrame({"col1": [0, np.nan, 2], "col2": [1, np.nan, np.nan]})
 
    df_with_missing
 
@@ -806,18 +811,16 @@ New behavior:
 
 .. ipython:: python
 
-   df_with_missing.to_hdf('file.h5',
-                          'df_with_missing',
-                          format='table',
-                          mode='w')
+   df_with_missing.to_hdf("file.h5", "df_with_missing", format="table", mode="w")
 
-   pd.read_hdf('file.h5', 'df_with_missing')
+   pd.read_hdf("file.h5", "df_with_missing")
 
 .. ipython:: python
    :suppress:
 
    import os
-   os.remove('file.h5')
+
+   os.remove("file.h5")
 
 See the :ref:`docs <io.hdf5>` for more details.
 
@@ -848,8 +851,8 @@ regular formatting as well as scientific notation, similar to how numpy's ``prec
 
 .. ipython:: python
 
-  pd.set_option('display.precision', 2)
-  pd.DataFrame({'x': [123.456789]})
+  pd.set_option("display.precision", 2)
+  pd.DataFrame({"x": [123.456789]})
 
 To preserve output behavior with prior versions the default value of ``display.precision`` has been reduced to ``6``
 from ``7``.
@@ -857,7 +860,7 @@ from ``7``.
 .. ipython:: python
   :suppress:
 
-  pd.set_option('display.precision', 6)
+  pd.set_option("display.precision", 6)
 
 .. _whatsnew_0170.api_breaking.categorical_unique:
 
@@ -871,14 +874,11 @@ Changes to ``Categorical.unique``
 
 .. ipython:: python
 
-   cat = pd.Categorical(['C', 'A', 'B', 'C'],
-                        categories=['A', 'B', 'C'],
-                        ordered=True)
+   cat = pd.Categorical(["C", "A", "B", "C"], categories=["A", "B", "C"], ordered=True)
    cat
    cat.unique()
 
-   cat = pd.Categorical(['C', 'A', 'B', 'C'],
-                        categories=['A', 'B', 'C'])
+   cat = pd.Categorical(["C", "A", "B", "C"], categories=["A", "B", "C"])
    cat
    cat.unique()
 
@@ -980,9 +980,11 @@ Removal of prior version deprecations/changes
   .. ipython:: python
 
      np.random.seed(1234)
-     df = pd.DataFrame(np.random.randn(5, 2),
-                       columns=list('AB'),
-                       index=pd.date_range('2013-01-01', periods=5))
+     df = pd.DataFrame(
+         np.random.randn(5, 2),
+         columns=list("AB"),
+         index=pd.date_range("2013-01-01", periods=5),
+     )
      df
 
   Previously
@@ -1005,7 +1007,7 @@ Removal of prior version deprecations/changes
 
   .. ipython:: python
 
-     df.add(df.A, axis='index')
+     df.add(df.A, axis="index")
 
 
 - Remove ``table`` keyword in ``HDFStore.put/append``, in favor of using ``format=`` (:issue:`4645`)
diff --git a/doc/source/whatsnew/v0.17.1.rst b/doc/source/whatsnew/v0.17.1.rst
index 5d15a01aee5a0..6b0a28ec47568 100644
--- a/doc/source/whatsnew/v0.17.1.rst
+++ b/doc/source/whatsnew/v0.17.1.rst
@@ -52,8 +52,8 @@ Here's a quick example:
   .. ipython:: python
 
     np.random.seed(123)
-    df = pd.DataFrame(np.random.randn(10, 5), columns=list('abcde'))
-    html = df.style.background_gradient(cmap='viridis', low=.5)
+    df = pd.DataFrame(np.random.randn(10, 5), columns=list("abcde"))
+    html = df.style.background_gradient(cmap="viridis", low=0.5)
 
 We can render the HTML to get the following table.
 
@@ -80,14 +80,14 @@ Enhancements
 
   .. ipython:: python
 
-     df = pd.DataFrame({'A': ['foo'] * 1000})  # noqa: F821
-     df['B'] = df['A'].astype('category')
+     df = pd.DataFrame({"A": ["foo"] * 1000})  # noqa: F821
+     df["B"] = df["A"].astype("category")
 
      # shows the '+' as we have object dtypes
      df.info()
 
      # we have an accurate memory assessment (but can be expensive to compute this)
-     df.info(memory_usage='deep')
+     df.info(memory_usage="deep")
 
 - ``Index`` now has a ``fillna`` method (:issue:`10089`)
 
@@ -99,11 +99,11 @@ Enhancements
 
   .. ipython:: python
 
-     s = pd.Series(list('aabb')).astype('category')
+     s = pd.Series(list("aabb")).astype("category")
      s
      s.str.contains("a")
 
-     date = pd.Series(pd.date_range('1/1/2015', periods=5)).astype('category')
+     date = pd.Series(pd.date_range("1/1/2015", periods=5)).astype("category")
      date
      date.dt.day
 
diff --git a/doc/source/whatsnew/v0.18.1.rst b/doc/source/whatsnew/v0.18.1.rst
index 13ed6bc38163b..3db00f686d62c 100644
--- a/doc/source/whatsnew/v0.18.1.rst
+++ b/doc/source/whatsnew/v0.18.1.rst
@@ -42,6 +42,7 @@ see :ref:`Custom Business Hour <timeseries.custombusinesshour>` (:issue:`11514`)
 
     from pandas.tseries.offsets import CustomBusinessHour
     from pandas.tseries.holiday import USFederalHolidayCalendar
+
     bhour_us = CustomBusinessHour(calendar=USFederalHolidayCalendar())
 
 Friday before MLK Day
@@ -49,6 +50,7 @@ Friday before MLK Day
 .. ipython:: python
 
     import datetime
+
     dt = datetime.datetime(2014, 1, 17, 15)
 
     dt + bhour_us
@@ -72,41 +74,42 @@ Previously you would have to do this to get a rolling window mean per-group:
 
 .. ipython:: python
 
-   df = pd.DataFrame({'A': [1] * 20 + [2] * 12 + [3] * 8,
-                      'B': np.arange(40)})
+   df = pd.DataFrame({"A": [1] * 20 + [2] * 12 + [3] * 8, "B": np.arange(40)})
    df
 
 .. ipython:: python
 
-   df.groupby('A').apply(lambda x: x.rolling(4).B.mean())
+   df.groupby("A").apply(lambda x: x.rolling(4).B.mean())
 
 Now you can do:
 
 .. ipython:: python
 
-   df.groupby('A').rolling(4).B.mean()
+   df.groupby("A").rolling(4).B.mean()
 
 For ``.resample(..)`` type of operations, previously you would have to:
 
 .. ipython:: python
 
-   df = pd.DataFrame({'date': pd.date_range(start='2016-01-01',
-                                            periods=4,
-                                            freq='W'),
-                      'group': [1, 1, 2, 2],
-                      'val': [5, 6, 7, 8]}).set_index('date')
+   df = pd.DataFrame(
+       {
+           "date": pd.date_range(start="2016-01-01", periods=4, freq="W"),
+           "group": [1, 1, 2, 2],
+           "val": [5, 6, 7, 8],
+       }
+   ).set_index("date")
 
    df
 
 .. ipython:: python
 
-   df.groupby('group').apply(lambda x: x.resample('1D').ffill())
+   df.groupby("group").apply(lambda x: x.resample("1D").ffill())
 
 Now you can do:
 
 .. ipython:: python
 
-   df.groupby('group').resample('1D').ffill()
+   df.groupby("group").resample("1D").ffill()
 
 .. _whatsnew_0181.enhancements.method_chain:
 
@@ -129,9 +132,7 @@ arguments.
 
 .. ipython:: python
 
-   df = pd.DataFrame({'A': [1, 2, 3],
-                      'B': [4, 5, 6],
-                      'C': [7, 8, 9]})
+   df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]})
    df.where(lambda x: x > 4, lambda x: x + 10)
 
 Methods ``.loc[]``, ``.iloc[]``, ``.ix[]``
@@ -146,7 +147,7 @@ can return a valid boolean indexer or anything which is valid for these indexer'
    df.loc[lambda x: x.A >= 2, lambda x: x.sum() > 10]
 
    # callable returns list of labels
-   df.loc[lambda x: [1, 2], lambda x: ['A', 'B']]
+   df.loc[lambda x: [1, 2], lambda x: ["A", "B"]]
 
 Indexing with``[]``
 """""""""""""""""""
@@ -157,17 +158,15 @@ class and index type.
 
 .. ipython:: python
 
-   df[lambda x: 'A']
+   df[lambda x: "A"]
 
 Using these methods / indexers, you can chain data selection operations
 without using temporary variable.
 
 .. ipython:: python
 
-   bb = pd.read_csv('data/baseball.csv', index_col='id')
-   (bb.groupby(['year', 'team'])
-      .sum()
-      .loc[lambda df: df.r > 100])
+   bb = pd.read_csv("data/baseball.csv", index_col="id")
+   (bb.groupby(["year", "team"]).sum().loc[lambda df: df.r > 100])
 
 .. _whatsnew_0181.partial_string_indexing:
 
@@ -180,13 +179,13 @@ Partial string indexing now matches on ``DateTimeIndex`` when part of a ``MultiI
 
    dft2 = pd.DataFrame(
        np.random.randn(20, 1),
-       columns=['A'],
-       index=pd.MultiIndex.from_product([pd.date_range('20130101',
-                                                       periods=10,
-                                                       freq='12H'),
-                                         ['a', 'b']]))
+       columns=["A"],
+       index=pd.MultiIndex.from_product(
+           [pd.date_range("20130101", periods=10, freq="12H"), ["a", "b"]]
+       ),
+   )
    dft2
-   dft2.loc['2013-01-05']
+   dft2.loc["2013-01-05"]
 
 On other levels
 
@@ -195,7 +194,7 @@ On other levels
    idx = pd.IndexSlice
    dft2 = dft2.swaplevel(0, 1).sort_index()
    dft2
-   dft2.loc[idx[:, '2013-01-05'], :]
+   dft2.loc[idx[:, "2013-01-05"], :]
 
 .. _whatsnew_0181.enhancements.assembling:
 
@@ -206,10 +205,9 @@ Assembling datetimes
 
 .. ipython:: python
 
-   df = pd.DataFrame({'year': [2015, 2016],
-                      'month': [2, 3],
-                      'day': [4, 5],
-                      'hour': [2, 3]})
+   df = pd.DataFrame(
+       {"year": [2015, 2016], "month": [2, 3], "day": [4, 5], "hour": [2, 3]}
+   )
    df
 
 Assembling using the passed frame.
@@ -222,7 +220,7 @@ You can pass only the columns that you need to assemble.
 
 .. ipython:: python
 
-   pd.to_datetime(df[['year', 'month', 'day']])
+   pd.to_datetime(df[["year", "month", "day"]])
 
 .. _whatsnew_0181.other:
 
@@ -243,7 +241,7 @@ Other enhancements
 
   .. ipython:: python
 
-     idx = pd.Index([1., 2., 3., 4.], dtype='float')
+     idx = pd.Index([1.0, 2.0, 3.0, 4.0], dtype="float")
 
      # default, allow_fill=True, fill_value=None
      idx.take([2, -1])
@@ -253,8 +251,8 @@ Other enhancements
 
   .. ipython:: python
 
-     idx = pd.Index(['a|b', 'a|c', 'b|c'])
-     idx.str.get_dummies('|')
+     idx = pd.Index(["a|b", "a|c", "b|c"])
+     idx.str.get_dummies("|")
 
 
 - ``pd.crosstab()`` has gained a ``normalize`` argument for normalizing frequency tables (:issue:`12569`). Examples in the updated docs :ref:`here <reshaping.crosstabulations>`.
@@ -313,8 +311,7 @@ The index in ``.groupby(..).nth()`` output is now more consistent when the ``as_
 
 .. ipython:: python
 
-   df = pd.DataFrame({'A': ['a', 'b', 'a'],
-                      'B': [1, 2, 3]})
+   df = pd.DataFrame({"A": ["a", "b", "a"], "B": [1, 2, 3]})
    df
 
 Previous behavior:
@@ -337,16 +334,16 @@ New behavior:
 
 .. ipython:: python
 
-    df.groupby('A', as_index=True)['B'].nth(0)
-    df.groupby('A', as_index=False)['B'].nth(0)
+    df.groupby("A", as_index=True)["B"].nth(0)
+    df.groupby("A", as_index=False)["B"].nth(0)
 
 Furthermore, previously, a ``.groupby`` would always sort, regardless if ``sort=False`` was passed with ``.nth()``.
 
 .. ipython:: python
 
    np.random.seed(1234)
-   df = pd.DataFrame(np.random.randn(100, 2), columns=['a', 'b'])
-   df['c'] = np.random.randint(0, 4, 100)
+   df = pd.DataFrame(np.random.randn(100, 2), columns=["a", "b"])
+   df["c"] = np.random.randint(0, 4, 100)
 
 Previous behavior:
 
@@ -374,8 +371,8 @@ New behavior:
 
 .. ipython:: python
 
-   df.groupby('c', sort=True).nth(1)
-   df.groupby('c', sort=False).nth(1)
+   df.groupby("c", sort=True).nth(1)
+   df.groupby("c", sort=False).nth(1)
 
 
 .. _whatsnew_0181.numpy_compatibility:
@@ -421,8 +418,9 @@ Using ``apply`` on resampling groupby operations (using a ``pd.TimeGrouper``) no
 
 .. ipython:: python
 
-    df = pd.DataFrame({'date': pd.to_datetime(['10/10/2000', '11/10/2000']),
-                      'value': [10, 13]})
+    df = pd.DataFrame(
+        {"date": pd.to_datetime(["10/10/2000", "11/10/2000"]), "value": [10, 13]}
+    )
     df
 
 Previous behavior:
diff --git a/doc/source/whatsnew/v0.19.0.rst b/doc/source/whatsnew/v0.19.0.rst
index 6e8c4273a0550..4acf9d7181781 100644
--- a/doc/source/whatsnew/v0.19.0.rst
+++ b/doc/source/whatsnew/v0.19.0.rst
@@ -49,10 +49,8 @@ except that we match on nearest key rather than equal keys.
 
 .. ipython:: python
 
-   left = pd.DataFrame({'a': [1, 5, 10],
-                        'left_val': ['a', 'b', 'c']})
-   right = pd.DataFrame({'a': [1, 2, 3, 6, 7],
-                        'right_val': [1, 2, 3, 6, 7]})
+   left = pd.DataFrame({"a": [1, 5, 10], "left_val": ["a", "b", "c"]})
+   right = pd.DataFrame({"a": [1, 2, 3, 6, 7], "right_val": [1, 2, 3, 6, 7]})
 
    left
    right
@@ -62,13 +60,13 @@ recent value otherwise.
 
 .. ipython:: python
 
-   pd.merge_asof(left, right, on='a')
+   pd.merge_asof(left, right, on="a")
 
 We can also match rows ONLY with prior data, and not an exact match.
 
 .. ipython:: python
 
-   pd.merge_asof(left, right, on='a', allow_exact_matches=False)
+   pd.merge_asof(left, right, on="a", allow_exact_matches=False)
 
 
 In a typical time-series example, we have ``trades`` and ``quotes`` and we want to ``asof-join`` them.
@@ -76,36 +74,44 @@ This also illustrates using the ``by`` parameter to group data before merging.
 
 .. ipython:: python
 
-   trades = pd.DataFrame({
-       'time': pd.to_datetime(['20160525 13:30:00.023',
-                               '20160525 13:30:00.038',
-                               '20160525 13:30:00.048',
-                               '20160525 13:30:00.048',
-                               '20160525 13:30:00.048']),
-       'ticker': ['MSFT', 'MSFT',
-                  'GOOG', 'GOOG', 'AAPL'],
-       'price': [51.95, 51.95,
-                 720.77, 720.92, 98.00],
-       'quantity': [75, 155,
-                    100, 100, 100]},
-       columns=['time', 'ticker', 'price', 'quantity'])
-
-   quotes = pd.DataFrame({
-       'time': pd.to_datetime(['20160525 13:30:00.023',
-                               '20160525 13:30:00.023',
-                               '20160525 13:30:00.030',
-                               '20160525 13:30:00.041',
-                               '20160525 13:30:00.048',
-                               '20160525 13:30:00.049',
-                               '20160525 13:30:00.072',
-                               '20160525 13:30:00.075']),
-       'ticker': ['GOOG', 'MSFT', 'MSFT', 'MSFT',
-                  'GOOG', 'AAPL', 'GOOG', 'MSFT'],
-       'bid': [720.50, 51.95, 51.97, 51.99,
-               720.50, 97.99, 720.50, 52.01],
-       'ask': [720.93, 51.96, 51.98, 52.00,
-               720.93, 98.01, 720.88, 52.03]},
-       columns=['time', 'ticker', 'bid', 'ask'])
+   trades = pd.DataFrame(
+       {
+           "time": pd.to_datetime(
+               [
+                   "20160525 13:30:00.023",
+                   "20160525 13:30:00.038",
+                   "20160525 13:30:00.048",
+                   "20160525 13:30:00.048",
+                   "20160525 13:30:00.048",
+               ]
+           ),
+           "ticker": ["MSFT", "MSFT", "GOOG", "GOOG", "AAPL"],
+           "price": [51.95, 51.95, 720.77, 720.92, 98.00],
+           "quantity": [75, 155, 100, 100, 100],
+       },
+       columns=["time", "ticker", "price", "quantity"],
+   )
+
+   quotes = pd.DataFrame(
+       {
+           "time": pd.to_datetime(
+               [
+                   "20160525 13:30:00.023",
+                   "20160525 13:30:00.023",
+                   "20160525 13:30:00.030",
+                   "20160525 13:30:00.041",
+                   "20160525 13:30:00.048",
+                   "20160525 13:30:00.049",
+                   "20160525 13:30:00.072",
+                   "20160525 13:30:00.075",
+               ]
+           ),
+           "ticker": ["GOOG", "MSFT", "MSFT", "MSFT", "GOOG", "AAPL", "GOOG", "MSFT"],
+           "bid": [720.50, 51.95, 51.97, 51.99, 720.50, 97.99, 720.50, 52.01],
+           "ask": [720.93, 51.96, 51.98, 52.00, 720.93, 98.01, 720.88, 52.03],
+       },
+       columns=["time", "ticker", "bid", "ask"],
+   )
 
 .. ipython:: python
 
@@ -118,9 +124,7 @@ that forward filling happens automatically taking the most recent non-NaN value.
 
 .. ipython:: python
 
-   pd.merge_asof(trades, quotes,
-                 on='time',
-                 by='ticker')
+   pd.merge_asof(trades, quotes, on="time", by="ticker")
 
 This returns a merged DataFrame with the entries in the same order as the original left
 passed DataFrame (``trades`` in this case), with the fields of the ``quotes`` merged.
@@ -135,9 +139,10 @@ See the full documentation :ref:`here <stats.moments.ts>`.
 
 .. ipython:: python
 
-   dft = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]},
-                      index=pd.date_range('20130101 09:00:00',
-                                          periods=5, freq='s'))
+   dft = pd.DataFrame(
+       {"B": [0, 1, 2, np.nan, 4]},
+       index=pd.date_range("20130101 09:00:00", periods=5, freq="s"),
+   )
    dft
 
 This is a regular frequency index. Using an integer window parameter works to roll along the window frequency.
@@ -151,20 +156,26 @@ Specifying an offset allows a more intuitive specification of the rolling freque
 
 .. ipython:: python
 
-   dft.rolling('2s').sum()
+   dft.rolling("2s").sum()
 
 Using a non-regular, but still monotonic index, rolling with an integer window does not impart any special calculation.
 
 .. ipython:: python
 
 
-   dft = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]},
-                      index=pd.Index([pd.Timestamp('20130101 09:00:00'),
-                                      pd.Timestamp('20130101 09:00:02'),
-                                      pd.Timestamp('20130101 09:00:03'),
-                                      pd.Timestamp('20130101 09:00:05'),
-                                      pd.Timestamp('20130101 09:00:06')],
-                                     name='foo'))
+   dft = pd.DataFrame(
+       {"B": [0, 1, 2, np.nan, 4]},
+       index=pd.Index(
+           [
+               pd.Timestamp("20130101 09:00:00"),
+               pd.Timestamp("20130101 09:00:02"),
+               pd.Timestamp("20130101 09:00:03"),
+               pd.Timestamp("20130101 09:00:05"),
+               pd.Timestamp("20130101 09:00:06"),
+           ],
+           name="foo",
+       ),
+   )
 
    dft
    dft.rolling(2).sum()
@@ -173,7 +184,7 @@ Using the time-specification generates variable windows for this sparse data.
 
 .. ipython:: python
 
-   dft.rolling('2s').sum()
+   dft.rolling("2s").sum()
 
 Furthermore, we now allow an optional ``on`` parameter to specify a column (rather than the
 default of the index) in a DataFrame.
@@ -182,7 +193,7 @@ default of the index) in a DataFrame.
 
    dft = dft.reset_index()
    dft
-   dft.rolling('2s', on='foo').sum()
+   dft.rolling("2s", on="foo").sum()
 
 .. _whatsnew_0190.enhancements.read_csv_dupe_col_names_support:
 
@@ -199,8 +210,8 @@ they are in the file or passed in as the ``names`` parameter (:issue:`7160`, :is
 
 .. ipython:: python
 
-   data = '0,1,2\n3,4,5'
-   names = ['a', 'b', 'a']
+   data = "0,1,2\n3,4,5"
+   names = ["a", "b", "a"]
 
 **Previous behavior**:
 
@@ -235,17 +246,22 @@ converting to ``Categorical`` after parsing.  See the io :ref:`docs here <io.cat
 
 .. ipython:: python
 
-   data = 'col1,col2,col3\na,b,1\na,b,2\nc,d,3'
+   data = """
+   col1,col2,col3
+   a,b,1
+   a,b,2
+   c,d,3
+   """
 
    pd.read_csv(StringIO(data))
    pd.read_csv(StringIO(data)).dtypes
-   pd.read_csv(StringIO(data), dtype='category').dtypes
+   pd.read_csv(StringIO(data), dtype="category").dtypes
 
 Individual columns can be parsed as a ``Categorical`` using a dict specification
 
 .. ipython:: python
 
-   pd.read_csv(StringIO(data), dtype={'col1': 'category'}).dtypes
+   pd.read_csv(StringIO(data), dtype={"col1": "category"}).dtypes
 
 .. note::
 
@@ -256,11 +272,11 @@ Individual columns can be parsed as a ``Categorical`` using a dict specification
 
    .. ipython:: python
 
-      df = pd.read_csv(StringIO(data), dtype='category')
+      df = pd.read_csv(StringIO(data), dtype="category")
       df.dtypes
-      df['col3']
-      df['col3'].cat.categories = pd.to_numeric(df['col3'].cat.categories)
-      df['col3']
+      df["col3"]
+      df["col3"].cat.categories = pd.to_numeric(df["col3"].cat.categories)
+      df["col3"]
 
 .. _whatsnew_0190.enhancements.union_categoricals:
 
@@ -272,6 +288,7 @@ Categorical concatenation
   .. ipython:: python
 
      from pandas.api.types import union_categoricals
+
      a = pd.Categorical(["b", "c"])
      b = pd.Categorical(["a", "b"])
      union_categoricals([a, b])
@@ -280,8 +297,8 @@ Categorical concatenation
 
   .. ipython:: python
 
-     s1 = pd.Series(['a', 'b'], dtype='category')
-     s2 = pd.Series(['b', 'c'], dtype='category')
+     s1 = pd.Series(["a", "b"], dtype="category")
+     s2 = pd.Series(["b", "c"], dtype="category")
 
 **Previous behavior**:
 
@@ -313,25 +330,25 @@ These provide date offsets anchored (by default) to the 15th and end of month, a
 
 .. ipython:: python
 
-   pd.Timestamp('2016-01-01') + SemiMonthEnd()
+   pd.Timestamp("2016-01-01") + SemiMonthEnd()
 
-   pd.date_range('2015-01-01', freq='SM', periods=4)
+   pd.date_range("2015-01-01", freq="SM", periods=4)
 
 **SemiMonthBegin**:
 
 .. ipython:: python
 
-   pd.Timestamp('2016-01-01') + SemiMonthBegin()
+   pd.Timestamp("2016-01-01") + SemiMonthBegin()
 
-   pd.date_range('2015-01-01', freq='SMS', periods=4)
+   pd.date_range("2015-01-01", freq="SMS", periods=4)
 
 Using the anchoring suffix, you can also specify the day of month to use instead of the 15th.
 
 .. ipython:: python
 
-   pd.date_range('2015-01-01', freq='SMS-16', periods=4)
+   pd.date_range("2015-01-01", freq="SMS-16", periods=4)
 
-   pd.date_range('2015-01-01', freq='SM-14', periods=4)
+   pd.date_range("2015-01-01", freq="SM-14", periods=4)
 
 .. _whatsnew_0190.enhancements.index:
 
@@ -344,7 +361,7 @@ The following methods and options are added to ``Index``, to be more consistent
 
 .. ipython:: python
 
-   idx = pd.Index(['a', 'b', 'c'])
+   idx = pd.Index(["a", "b", "c"])
    idx.where([True, False, True])
 
 
@@ -360,11 +377,10 @@ For ``MultiIndex``, values are dropped if any level is missing by default. Speci
 
 .. ipython:: python
 
-   midx = pd.MultiIndex.from_arrays([[1, 2, np.nan, 4],
-                                     [1, 2, np.nan, np.nan]])
+   midx = pd.MultiIndex.from_arrays([[1, 2, np.nan, 4], [1, 2, np.nan, np.nan]])
    midx
    midx.dropna()
-   midx.dropna(how='all')
+   midx.dropna(how="all")
 
 ``Index`` now supports ``.str.extractall()`` which returns a ``DataFrame``, see the :ref:`docs here <text.extractall>` (:issue:`10008`, :issue:`13156`)
 
@@ -415,7 +431,7 @@ The ``pd.get_dummies`` function now returns dummy-encoded columns as small integ
 
 .. ipython:: python
 
-   pd.get_dummies(['a', 'b', 'a', 'c']).dtypes
+   pd.get_dummies(["a", "b", "a", "c"]).dtypes
 
 
 .. _whatsnew_0190.enhancements.to_numeric_downcast:
@@ -427,9 +443,9 @@ Downcast values to smallest possible dtype in ``to_numeric``
 
 .. ipython:: python
 
-   s = ['1', 2, 3]
-   pd.to_numeric(s, downcast='unsigned')
-   pd.to_numeric(s, downcast='integer')
+   s = ["1", 2, 3]
+   pd.to_numeric(s, downcast="unsigned")
+   pd.to_numeric(s, downcast="integer")
 
 .. _whatsnew_0190.dev_api:
 
@@ -447,7 +463,8 @@ The following are now part of this API:
 
    import pprint
    from pandas.api import types
-   funcs = [f for f in dir(types) if not f.startswith('_')]
+
+   funcs = [f for f in dir(types) if not f.startswith("_")]
    pprint.pprint(funcs)
 
 .. note::
@@ -472,16 +489,16 @@ Other enhancements
 
   .. ipython:: python
 
-     df = pd.DataFrame({'date': pd.date_range('2015-01-01', freq='W', periods=5),
-                        'a': np.arange(5)},
-                       index=pd.MultiIndex.from_arrays([[1, 2, 3, 4, 5],
-                                                        pd.date_range('2015-01-01',
-                                                                      freq='W',
-                                                                      periods=5)
-                                                        ], names=['v', 'd']))
+     df = pd.DataFrame(
+         {"date": pd.date_range("2015-01-01", freq="W", periods=5), "a": np.arange(5)},
+         index=pd.MultiIndex.from_arrays(
+             [[1, 2, 3, 4, 5], pd.date_range("2015-01-01", freq="W", periods=5)],
+             names=["v", "d"],
+         ),
+     )
      df
-     df.resample('M', on='date').sum()
-     df.resample('M', level='d').sum()
+     df.resample("M", on="date").sum()
+     df.resample("M", level="d").sum()
 
 - The ``.get_credentials()`` method of ``GbqConnector`` can now first try to fetch `the application default credentials <https://developers.google.com/identity/protocols/application-default-credentials>`__. See the docs for more details (:issue:`13577`).
 - The ``.tz_localize()`` method of ``DatetimeIndex`` and ``Timestamp`` has gained the ``errors`` keyword, so you can potentially coerce nonexistent timestamps to ``NaT``. The default behavior remains to raising a ``NonExistentTimeError`` (:issue:`13057`)
@@ -507,10 +524,9 @@ Other enhancements
 
   .. ipython:: python
 
-     df = pd.DataFrame({'A': [2, 7], 'B': [3, 5], 'C': [4, 8]},
-                       index=['row1', 'row2'])
+     df = pd.DataFrame({"A": [2, 7], "B": [3, 5], "C": [4, 8]}, index=["row1", "row2"])
      df
-     df.sort_values(by='row2', axis=1)
+     df.sort_values(by="row2", axis=1)
 
 - Added documentation to :ref:`I/O<io.dtypes>` regarding the perils of reading in columns with mixed dtypes and how to handle it (:issue:`13746`)
 - :meth:`~DataFrame.to_html` now has a ``border`` argument to control the value in the opening ``<table>`` tag. The default is the value of the ``html.border`` option, which defaults to 1. This also affects the notebook HTML repr, but since Jupyter's CSS includes a border-width attribute, the visual effect is the same. (:issue:`11563`).
@@ -583,12 +599,12 @@ Arithmetic operators align both ``index`` (no changes).
 
 .. ipython:: python
 
-   s1 = pd.Series([1, 2, 3], index=list('ABC'))
-   s2 = pd.Series([2, 2, 2], index=list('ABD'))
+   s1 = pd.Series([1, 2, 3], index=list("ABC"))
+   s2 = pd.Series([2, 2, 2], index=list("ABD"))
    s1 + s2
 
-   df1 = pd.DataFrame([1, 2, 3], index=list('ABC'))
-   df2 = pd.DataFrame([2, 2, 2], index=list('ABD'))
+   df1 = pd.DataFrame([1, 2, 3], index=list("ABC"))
+   df2 = pd.DataFrame([2, 2, 2], index=list("ABD"))
    df1 + df2
 
 Comparison operators
@@ -661,8 +677,8 @@ Logical operators align both ``.index`` of left and right hand side.
 
 .. ipython:: python
 
-   s1 = pd.Series([True, False, True], index=list('ABC'))
-   s2 = pd.Series([True, True, True], index=list('ABD'))
+   s1 = pd.Series([True, False, True], index=list("ABC"))
+   s2 = pd.Series([True, True, True], index=list("ABD"))
    s1 & s2
 
 .. note::
@@ -679,8 +695,8 @@ Logical operators align both ``.index`` of left and right hand side.
 
 .. ipython:: python
 
-   df1 = pd.DataFrame([True, False, True], index=list('ABC'))
-   df2 = pd.DataFrame([True, True, True], index=list('ABD'))
+   df1 = pd.DataFrame([True, False, True], index=list("ABC"))
+   df2 = pd.DataFrame([True, True, True], index=list("ABD"))
    df1 & df2
 
 Flexible comparison methods
@@ -691,8 +707,8 @@ which has the different ``index``.
 
 .. ipython:: python
 
-   s1 = pd.Series([1, 2, 3], index=['a', 'b', 'c'])
-   s2 = pd.Series([2, 2, 2], index=['b', 'c', 'd'])
+   s1 = pd.Series([1, 2, 3], index=["a", "b", "c"])
+   s2 = pd.Series([2, 2, 2], index=["b", "c", "d"])
    s1.eq(s2)
    s1.ge(s2)
 
@@ -749,7 +765,7 @@ This will now convert integers/floats with the default unit of ``ns``.
 
 .. ipython:: python
 
-   pd.to_datetime([1, 'foo'], errors='coerce')
+   pd.to_datetime([1, "foo"], errors="coerce")
 
 Bug fixes related to ``.to_datetime()``:
 
@@ -768,9 +784,9 @@ Merging will now preserve the dtype of the join keys (:issue:`8596`)
 
 .. ipython:: python
 
-   df1 = pd.DataFrame({'key': [1], 'v1': [10]})
+   df1 = pd.DataFrame({"key": [1], "v1": [10]})
    df1
-   df2 = pd.DataFrame({'key': [1, 2], 'v1': [20, 30]})
+   df2 = pd.DataFrame({"key": [1, 2], "v1": [20, 30]})
    df2
 
 **Previous behavior**:
@@ -796,16 +812,16 @@ We are able to preserve the join keys
 
 .. ipython:: python
 
-   pd.merge(df1, df2, how='outer')
-   pd.merge(df1, df2, how='outer').dtypes
+   pd.merge(df1, df2, how="outer")
+   pd.merge(df1, df2, how="outer").dtypes
 
 Of course if you have missing values that are introduced, then the
 resulting dtype will be upcast, which is unchanged from previous.
 
 .. ipython:: python
 
-   pd.merge(df1, df2, how='outer', on='key')
-   pd.merge(df1, df2, how='outer', on='key').dtypes
+   pd.merge(df1, df2, how="outer", on="key")
+   pd.merge(df1, df2, how="outer", on="key").dtypes
 
 .. _whatsnew_0190.api.describe:
 
@@ -889,7 +905,7 @@ As a consequence of this change, ``PeriodIndex`` no longer has an integer dtype:
 
 .. ipython:: python
 
-   pi = pd.PeriodIndex(['2016-08-01'], freq='D')
+   pi = pd.PeriodIndex(["2016-08-01"], freq="D")
    pi
    pd.api.types.is_integer_dtype(pi)
    pd.api.types.is_period_dtype(pi)
@@ -916,7 +932,7 @@ These result in ``pd.NaT`` without providing ``freq`` option.
 
 .. ipython:: python
 
-   pd.Period('NaT')
+   pd.Period("NaT")
    pd.Period(None)
 
 
@@ -955,7 +971,7 @@ of integers (:issue:`13988`).
 
 .. ipython:: python
 
-   pi = pd.PeriodIndex(['2011-01', '2011-02'], freq='M')
+   pi = pd.PeriodIndex(["2011-01", "2011-02"], freq="M")
    pi.values
 
 
@@ -985,7 +1001,7 @@ Previous behavior:
 
 .. ipython:: python
 
-   pd.Index(['a', 'b']) + pd.Index(['a', 'c'])
+   pd.Index(["a", "b"]) + pd.Index(["a", "c"])
 
 Note that numeric Index objects already performed element-wise operations.
 For example, the behavior of adding two integer Indexes is unchanged.
@@ -1011,8 +1027,10 @@ DatetimeIndex objects resulting in a TimedeltaIndex:
 
 .. ipython:: python
 
-    (pd.DatetimeIndex(['2016-01-01', '2016-01-02'])
-     - pd.DatetimeIndex(['2016-01-02', '2016-01-03']))
+    (
+        pd.DatetimeIndex(["2016-01-01", "2016-01-02"])
+        - pd.DatetimeIndex(["2016-01-02", "2016-01-03"])
+    )
 
 
 .. _whatsnew_0190.api.difference:
@@ -1073,8 +1091,7 @@ Previously, most ``Index`` classes returned ``np.ndarray``, and ``DatetimeIndex`
 .. ipython:: python
 
    pd.Index([1, 2, 3]).unique()
-   pd.DatetimeIndex(['2011-01-01', '2011-01-02', '2011-01-03'],
-                    tz='Asia/Tokyo').unique()
+   pd.DatetimeIndex(["2011-01-01", "2011-01-02", "2011-01-03"], tz="Asia/Tokyo").unique()
 
 .. _whatsnew_0190.api.multiindex:
 
@@ -1086,8 +1103,8 @@ in ``MultiIndex`` levels (:issue:`13743`, :issue:`13854`).
 
 .. ipython:: python
 
-   cat = pd.Categorical(['a', 'b'], categories=list("bac"))
-   lvl1 = ['foo', 'bar']
+   cat = pd.Categorical(["a", "b"], categories=list("bac"))
+   lvl1 = ["foo", "bar"]
    midx = pd.MultiIndex.from_arrays([cat, lvl1])
    midx
 
@@ -1113,9 +1130,9 @@ As a consequence, ``groupby`` and ``set_index`` also preserve categorical dtypes
 
 .. ipython:: python
 
-   df = pd.DataFrame({'A': [0, 1], 'B': [10, 11], 'C': cat})
-   df_grouped = df.groupby(by=['A', 'C']).first()
-   df_set_idx = df.set_index(['A', 'C'])
+   df = pd.DataFrame({"A": [0, 1], "B": [10, 11], "C": cat})
+   df_grouped = df.groupby(by=["A", "C"]).first()
+   df_set_idx = df.set_index(["A", "C"])
 
 **Previous behavior**:
 
@@ -1163,7 +1180,7 @@ the result of calling :func:`read_csv` without the ``chunksize=`` argument
 
 .. ipython:: python
 
-   data = 'A,B\n0,1\n2,3\n4,5\n6,7'
+   data = "A,B\n0,1\n2,3\n4,5\n6,7"
 
 **Previous behavior**:
 
@@ -1248,7 +1265,7 @@ Operators now preserve dtypes
 
 .. code-block:: python
 
-   s = pd.SparseSeries([1., 0., 2., 0.], fill_value=0)
+   s = pd.SparseSeries([1.0, 0.0, 2.0, 0.0], fill_value=0)
    s
    s.astype(np.int64)
 
diff --git a/doc/source/whatsnew/v0.19.1.rst b/doc/source/whatsnew/v0.19.1.rst
index f8b60f457b33f..6ff3fb6900a99 100644
--- a/doc/source/whatsnew/v0.19.1.rst
+++ b/doc/source/whatsnew/v0.19.1.rst
@@ -8,7 +8,7 @@ Version 0.19.1 (November 3, 2016)
 .. ipython:: python
    :suppress:
 
-   from pandas import * # noqa F401, F403
+   from pandas import *  # noqa F401, F403
 
 
 This is a minor bug-fix release from 0.19.0 and includes some small regression fixes,
diff --git a/doc/source/whatsnew/v0.19.2.rst b/doc/source/whatsnew/v0.19.2.rst
index 924c95f21ceff..bba89d78be869 100644
--- a/doc/source/whatsnew/v0.19.2.rst
+++ b/doc/source/whatsnew/v0.19.2.rst
@@ -8,7 +8,7 @@ Version 0.19.2 (December 24, 2016)
 .. ipython:: python
    :suppress:
 
-   from pandas import * # noqa F401, F403
+   from pandas import *  # noqa F401, F403
 
 
 This is a minor bug-fix release in the 0.19.x series and includes some small regression fixes,
diff --git a/doc/source/whatsnew/v0.20.2.rst b/doc/source/whatsnew/v0.20.2.rst
index 7f84c6b3f17bd..430a39d2d2e97 100644
--- a/doc/source/whatsnew/v0.20.2.rst
+++ b/doc/source/whatsnew/v0.20.2.rst
@@ -8,7 +8,7 @@ Version 0.20.2 (June 4, 2017)
 .. ipython:: python
    :suppress:
 
-   from pandas import * # noqa F401, F403
+   from pandas import *  # noqa F401, F403
 
 
 This is a minor bug-fix release in the 0.20.x series and includes some small regression fixes,
diff --git a/doc/source/whatsnew/v0.20.3.rst b/doc/source/whatsnew/v0.20.3.rst
index 888d0048ca9f3..ff28f6830783e 100644
--- a/doc/source/whatsnew/v0.20.3.rst
+++ b/doc/source/whatsnew/v0.20.3.rst
@@ -8,7 +8,7 @@ Version 0.20.3 (July 7, 2017)
 .. ipython:: python
    :suppress:
 
-   from pandas import * # noqa F401, F403
+   from pandas import *  # noqa F401, F403
 
 
 This is a minor bug-fix release in the 0.20.x series and includes some small regression fixes
diff --git a/doc/source/whatsnew/v0.21.1.rst b/doc/source/whatsnew/v0.21.1.rst
index f930dfac869cd..fc4391bdba975 100644
--- a/doc/source/whatsnew/v0.21.1.rst
+++ b/doc/source/whatsnew/v0.21.1.rst
@@ -8,7 +8,7 @@ Version 0.21.1 (December 12, 2017)
 .. ipython:: python
    :suppress:
 
-   from pandas import * # noqa F401, F403
+   from pandas import *  # noqa F401, F403
 
 
 This is a minor bug-fix release in the 0.21.x series and includes some small regression fixes,
diff --git a/doc/source/whatsnew/v0.22.0.rst b/doc/source/whatsnew/v0.22.0.rst
index 66d3ab3305565..1ee6db47a4d8e 100644
--- a/doc/source/whatsnew/v0.22.0.rst
+++ b/doc/source/whatsnew/v0.22.0.rst
@@ -8,7 +8,7 @@ Version 0.22.0 (December 29, 2017)
 .. ipython:: python
    :suppress:
 
-   from pandas import * # noqa F401, F403
+   from pandas import *  # noqa F401, F403
 
 
 This is a major release from 0.21.1 and includes a single, API-breaking change.
@@ -119,7 +119,7 @@ instead of ``NaN``.
 
 .. ipython:: python
 
-   grouper = pd.Categorical(['a', 'a'], categories=['a', 'b'])
+   grouper = pd.Categorical(["a", "a"], categories=["a", "b"])
    pd.Series([1, 2]).groupby(grouper).sum()
 
 To restore the 0.21 behavior of returning ``NaN`` for unobserved groups,
@@ -159,15 +159,14 @@ sum and ``1`` for product.
 
 .. ipython:: python
 
-   s = pd.Series([1, 1, np.nan, np.nan],
-                 index=pd.date_range('2017', periods=4))
-   s.resample('2d').sum()
+   s = pd.Series([1, 1, np.nan, np.nan], index=pd.date_range("2017", periods=4))
+   s.resample("2d").sum()
 
 To restore the 0.21 behavior of returning ``NaN``, use ``min_count>=1``.
 
 .. ipython:: python
 
-   s.resample('2d').sum(min_count=1)
+   s.resample("2d").sum(min_count=1)
 
 In particular, upsampling and taking the sum or product is affected, as
 upsampling introduces missing values even if the original series was
@@ -190,7 +189,7 @@ entirely valid.
 
 .. ipython:: python
 
-   idx = pd.DatetimeIndex(['2017-01-01', '2017-01-02'])
+   idx = pd.DatetimeIndex(["2017-01-01", "2017-01-02"])
    pd.Series([1, 2], index=idx).resample("12H").sum()
 
 Once again, the ``min_count`` keyword is available to restore the 0.21 behavior.
diff --git a/doc/source/whatsnew/v0.5.0.rst b/doc/source/whatsnew/v0.5.0.rst
index 7ccb141260f18..7447a10fa1d6b 100644
--- a/doc/source/whatsnew/v0.5.0.rst
+++ b/doc/source/whatsnew/v0.5.0.rst
@@ -9,7 +9,7 @@ Version 0.5.0 (October 24, 2011)
 .. ipython:: python
    :suppress:
 
-   from pandas import * # noqa F401, F403
+   from pandas import *  # noqa F401, F403
 
 
 New features
diff --git a/doc/source/whatsnew/v0.6.0.rst b/doc/source/whatsnew/v0.6.0.rst
index 1cb9dcbe159aa..8ff688eaa91e7 100644
--- a/doc/source/whatsnew/v0.6.0.rst
+++ b/doc/source/whatsnew/v0.6.0.rst
@@ -8,7 +8,7 @@ Version 0.6.0 (November 25, 2011)
 .. ipython:: python
    :suppress:
 
-   from pandas import * # noqa F401, F403
+   from pandas import *  # noqa F401, F403
 
 
 New features
diff --git a/doc/source/whatsnew/v0.7.3.rst b/doc/source/whatsnew/v0.7.3.rst
index 5ed48c0d8d6d9..4ca31baf560bb 100644
--- a/doc/source/whatsnew/v0.7.3.rst
+++ b/doc/source/whatsnew/v0.7.3.rst
@@ -23,7 +23,8 @@ New features
 .. code-block:: python
 
    from pandas.tools.plotting import scatter_matrix
-   scatter_matrix(df, alpha=0.2)        # noqa F821
+
+   scatter_matrix(df, alpha=0.2)  # noqa F821
 
 
 - Add ``stacked`` argument to Series and DataFrame's ``plot`` method for
@@ -31,12 +32,12 @@ New features
 
 .. code-block:: python
 
-   df.plot(kind='bar', stacked=True)    # noqa F821
+   df.plot(kind="bar", stacked=True)  # noqa F821
 
 
 .. code-block:: python
 
-   df.plot(kind='barh', stacked=True)   # noqa F821
+   df.plot(kind="barh", stacked=True)  # noqa F821
 
 
 - Add log x and y :ref:`scaling options <visualization.basic>` to
@@ -52,9 +53,9 @@ Reverted some changes to how NA values (represented typically as ``NaN`` or
 
 .. ipython:: python
 
-   series = pd.Series(['Steve', np.nan, 'Joe'])
-   series == 'Steve'
-   series != 'Steve'
+   series = pd.Series(["Steve", np.nan, "Joe"])
+   series == "Steve"
+   series != "Steve"
 
 In comparisons, NA / NaN will always come through as ``False`` except with
 ``!=`` which is ``True``. *Be very careful* with boolean arithmetic, especially
@@ -63,7 +64,7 @@ filter into boolean array operations if you are worried about this:
 
 .. ipython:: python
 
-   mask = series == 'Steve'
+   mask = series == "Steve"
    series[mask & series.notnull()]
 
 While propagating NA in comparisons may seem like the right behavior to some
@@ -82,15 +83,18 @@ Series, to be more consistent with the ``groupby`` behavior with DataFrame:
 .. ipython:: python
     :okwarning:
 
-    df = pd.DataFrame({'A': ['foo', 'bar', 'foo', 'bar',
-                             'foo', 'bar', 'foo', 'foo'],
-                       'B': ['one', 'one', 'two', 'three',
-                             'two', 'two', 'one', 'three'],
-                       'C': np.random.randn(8), 'D': np.random.randn(8)})
+    df = pd.DataFrame(
+        {
+            "A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
+            "B": ["one", "one", "two", "three", "two", "two", "one", "three"],
+            "C": np.random.randn(8),
+            "D": np.random.randn(8),
+        }
+    )
     df
-    grouped = df.groupby('A')['C']
+    grouped = df.groupby("A")["C"]
     grouped.describe()
-    grouped.apply(lambda x: x.sort_values()[-2:])    # top 2 values
+    grouped.apply(lambda x: x.sort_values()[-2:])  # top 2 values
 
 
 .. _whatsnew_0.7.3.contributors:
diff --git a/doc/source/whatsnew/v0.8.0.rst b/doc/source/whatsnew/v0.8.0.rst
index 9bba68d8c331d..8a84630a28b34 100644
--- a/doc/source/whatsnew/v0.8.0.rst
+++ b/doc/source/whatsnew/v0.8.0.rst
@@ -159,7 +159,8 @@ New plotting methods
 .. code-block:: python
 
    import pandas as pd
-   fx = pd.read_pickle('data/fx_prices')
+
+   fx = pd.read_pickle("data/fx_prices")
    import matplotlib.pyplot as plt
 
 ``Series.plot`` now supports a ``secondary_y`` option:
@@ -168,20 +169,19 @@ New plotting methods
 
    plt.figure()
 
-   fx['FR'].plot(style='g')
+   fx["FR"].plot(style="g")
 
-   fx['IT'].plot(style='k--', secondary_y=True)
+   fx["IT"].plot(style="k--", secondary_y=True)
 
 Vytautas Jancauskas, the 2012 GSOC participant, has added many new plot
 types. For example, ``'kde'`` is a new option:
 
 .. ipython:: python
 
-   s = pd.Series(np.concatenate((np.random.randn(1000),
-                                 np.random.randn(1000) * 0.5 + 3)))
+   s = pd.Series(np.concatenate((np.random.randn(1000), np.random.randn(1000) * 0.5 + 3)))
    plt.figure()
    s.hist(density=True, alpha=0.2)
-   s.plot(kind='kde')
+   s.plot(kind="kde")
 
 See :ref:`the plotting page <visualization.other>` for much more.
 
@@ -205,7 +205,8 @@ with code using scalar values because you are handing control over to NumPy:
 .. ipython:: python
 
    import datetime
-   rng = pd.date_range('1/1/2000', periods=10)
+
+   rng = pd.date_range("1/1/2000", periods=10)
    rng[5]
    isinstance(rng[5], datetime.datetime)
    rng_asarray = np.asarray(rng)
@@ -251,7 +252,7 @@ type. See `matplotlib documentation
 
     .. ipython:: python
 
-       rng = pd.date_range('1/1/2000', periods=10)
+       rng = pd.date_range("1/1/2000", periods=10)
        rng
        np.asarray(rng)
        converted = np.asarray(rng, dtype=object)
diff --git a/doc/source/whatsnew/v0.9.0.rst b/doc/source/whatsnew/v0.9.0.rst
index 5172b1989765d..44ded51e31fda 100644
--- a/doc/source/whatsnew/v0.9.0.rst
+++ b/doc/source/whatsnew/v0.9.0.rst
@@ -41,9 +41,11 @@ API changes
 
    import io
 
-   data = ('0,0,1\n'
-           '1,1,0\n'
-           '0,1,0')
+   data = """
+   0,0,1
+   1,1,0
+   0,1,0
+   """
    df = pd.read_csv(io.StringIO(data), header=None)
    df
 
@@ -59,7 +61,7 @@ API changes
    s1 = pd.Series([1, 2, 3])
    s1
 
-   s2 = pd.Series(s1, index=['foo', 'bar', 'baz'])
+   s2 = pd.Series(s1, index=["foo", "bar", "baz"])
    s2
 
 - Deprecated ``day_of_year`` API removed from PeriodIndex, use ``dayofyear``