Fix

Author: dsaxton

doc/source/user_guide/advanced.rst

@@ -304,7 +304,8 @@ whereas a tuple of lists refer to several values within a level:
 .. ipython:: python
 
    s = pd.Series(
-       [1, 2, 3, 4, 5, 6], index=pd.MultiIndex.from_product([["A", "B"], ["c", "d", "e"]])
+       [1, 2, 3, 4, 5, 6],
+       index=pd.MultiIndex.from_product([["A", "B"], ["c", "d", "e"]]),
    )
    s.loc[[("A", "c"), ("B", "d")]]  # list of tuples
    s.loc[(["A", "B"], ["c", "d"])]  # tuple of lists
@@ -819,7 +820,9 @@ values **not** in the categories, similarly to how you can reindex **any** panda
 
 .. ipython:: python
 
-   df3 = pd.DataFrame({"A": np.arange(3), "B": pd.Series(list("abc")).astype("category")})
+   df3 = pd.DataFrame(
+       {"A": np.arange(3), "B": pd.Series(list("abc")).astype("category")}
+   )
    df3 = df3.set_index("B")
    df3
 
@@ -934,7 +937,9 @@ example, be millisecond offsets.
                np.random.randn(5, 2), index=np.arange(5) * 250.0, columns=list("AB")
            ),
            pd.DataFrame(
-               np.random.randn(6, 2), index=np.arange(4, 10) * 250.1, columns=list("AB")
+               np.random.randn(6, 2),
+               index=np.arange(4, 10) * 250.1,
+               columns=list("AB"),
            ),
        ]
    )

doc/source/user_guide/basics.rst

@@ -464,7 +464,10 @@ which we illustrate:
        {"A": [1.0, np.nan, 3.0, 5.0, np.nan], "B": [np.nan, 2.0, 3.0, np.nan, 6.0]}
    )
    df2 = pd.DataFrame(
-       {"A": [5.0, 2.0, 4.0, np.nan, 3.0, 7.0], "B": [np.nan, np.nan, 3.0, 4.0, 6.0, 8.0]}
+       {
+           "A": [5.0, 2.0, 4.0, np.nan, 3.0, 7.0],
+           "B": [np.nan, np.nan, 3.0, 4.0, 6.0, 8.0],
+       }
    )
    df1
    df2
@@ -712,7 +715,10 @@ Similarly, you can get the most frequently occurring value(s), i.e. the mode, of
     s5 = pd.Series([1, 1, 3, 3, 3, 5, 5, 7, 7, 7])
     s5.mode()
     df5 = pd.DataFrame(
-        {"A": np.random.randint(0, 7, size=50), "B": np.random.randint(-10, 15, size=50)}
+        {
+            "A": np.random.randint(0, 7, size=50),
+            "B": np.random.randint(-10, 15, size=50),
+        }
     )
     df5.mode()
 
@@ -1192,7 +1198,9 @@ to :ref:`merging/joining functionality <merging>`:
 
 .. ipython:: python
 
-   s = pd.Series(["six", "seven", "six", "seven", "six"], index=["a", "b", "c", "d", "e"])
+   s = pd.Series(
+       ["six", "seven", "six", "seven", "six"], index=["a", "b", "c", "d", "e"]
+   )
    t = pd.Series({"six": 6.0, "seven": 7.0})
    s
    s.map(t)
@@ -1494,7 +1502,9 @@ labels).
 
    df = pd.DataFrame(
        {"x": [1, 2, 3, 4, 5, 6], "y": [10, 20, 30, 40, 50, 60]},
-       index=pd.MultiIndex.from_product([["a", "b", "c"], [1, 2]], names=["let", "num"]),
+       index=pd.MultiIndex.from_product(
+           [["a", "b", "c"], [1, 2]], names=["let", "num"]
+       ),
    )
    df
    df.rename_axis(index={"let": "abc"})
@@ -1803,7 +1813,9 @@ used to sort a pandas object by its index levels.
        }
    )
 
-   unsorted_df = df.reindex(index=["a", "d", "c", "b"], columns=["three", "two", "one"])
+   unsorted_df = df.reindex(
+       index=["a", "d", "c", "b"], columns=["three", "two", "one"]
+   )
    unsorted_df
 
    # DataFrame
@@ -1849,7 +1861,9 @@ to use to determine the sorted order.
 
 .. ipython:: python
 
-   df1 = pd.DataFrame({"one": [2, 1, 1, 1], "two": [1, 3, 2, 4], "three": [5, 4, 3, 2]})
+   df1 = pd.DataFrame(
+       {"one": [2, 1, 1, 1], "two": [1, 3, 2, 4], "three": [5, 4, 3, 2]}
+   )
    df1.sort_values(by="two")
 
 The ``by`` parameter can take a list of column names, e.g.:
@@ -1994,7 +2008,9 @@ all levels to ``by``.
 
 .. ipython:: python
 
-   df1.columns = pd.MultiIndex.from_tuples([("a", "one"), ("a", "two"), ("b", "three")])
+   df1.columns = pd.MultiIndex.from_tuples(
+       [("a", "one"), ("a", "two"), ("b", "three")]
+   )
    df1.sort_values(by=("a", "two"))
 
 
@@ -2245,7 +2261,11 @@ to the correct type.
      import datetime
 
      df = pd.DataFrame(
-         [[1, 2], ["a", "b"], [datetime.datetime(2016, 3, 2), datetime.datetime(2016, 3, 2)]]
+         [
+             [1, 2],
+             ["a", "b"],
+             [datetime.datetime(2016, 3, 2), datetime.datetime(2016, 3, 2)],
+         ]
      )
      df = df.T
      df

doc/source/whatsnew/v0.10.1.rst

@@ -180,7 +180,9 @@ combined result, by using ``where`` on a selector table.
    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:

doc/source/whatsnew/v0.13.1.rst

@@ -101,7 +101,9 @@ Output formatting enhancements
 
   .. ipython:: python
 
-     df = pd.DataFrame([pd.Timestamp("20010101"), pd.Timestamp("20040601")], columns=["age"])
+     df = pd.DataFrame(
+         [pd.Timestamp("20010101"), pd.Timestamp("20040601")], columns=["age"]
+     )
      df["today"] = pd.Timestamp("20130419")
      df["diff"] = df["today"] - df["age"]
      df
@@ -206,7 +208,9 @@ 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`)

doc/source/whatsnew/v0.14.1.rst

@@ -124,7 +124,9 @@ 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>`.

doc/source/whatsnew/v0.17.0.rst

@@ -786,7 +786,9 @@ 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
 

doc/source/whatsnew/v0.19.0.rst

@@ -1091,7 +1091,9 @@ 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:
 

doc/source/whatsnew/v0.8.0.rst

@@ -178,7 +178,9 @@ 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")