DOC: Fix code style in documentation

doc/source/getting_started/intro_tutorials/06_calculate_statistics.rst

@@ -122,8 +122,9 @@ aggregating statistics for given columns can be defined using the
 
 .. ipython:: python
 
-    titanic.agg({'Age': ['min', 'max', 'median', 'skew'],
-                 'Fare': ['min', 'max', 'median', 'mean']})
+    titanic.agg(
+        {"Age": ["min", "max", "median", "skew"], "Fare": ["min", "max", "median", "mean"]}
+    )
 
 .. raw:: html
 

doc/source/getting_started/intro_tutorials/07_reshape_table_layout.rst

@@ -101,8 +101,9 @@ measurement.
 
 .. ipython:: python
 
-    air_quality = pd.read_csv("data/air_quality_long.csv",
-                              index_col="date.utc", parse_dates=True)
+    air_quality = pd.read_csv(
+        "data/air_quality_long.csv", index_col="date.utc", parse_dates=True
+    )
     air_quality.head()
 
 .. raw:: html
@@ -247,8 +248,9 @@ I want the mean concentrations for :math:`NO_2` and :math:`PM_{2.5}` in each of
 
 .. ipython:: python
 
-    air_quality.pivot_table(values="value", index="location",
-                            columns="parameter", aggfunc="mean")
+    air_quality.pivot_table(
+        values="value", index="location", columns="parameter", aggfunc="mean"
+    )
 
 In the case of :meth:`~DataFrame.pivot`, the data is only rearranged. When multiple
 values need to be aggregated (in this specific case, the values on
@@ -266,9 +268,13 @@ the ``margin`` parameter to ``True``:
 
 .. ipython:: python
 
-    air_quality.pivot_table(values="value", index="location",
-                            columns="parameter", aggfunc="mean",
-                            margins=True)
+    air_quality.pivot_table(
+        values="value",
+        index="location",
+        columns="parameter",
+        aggfunc="mean",
+        margins=True,
+    )
 
 .. raw:: html
 
@@ -345,12 +351,12 @@ The :func:`pandas.melt` method can be defined in more detail:
 
 .. ipython:: python
 
-    no_2 = no2_pivoted.melt(id_vars="date.utc",
-                            value_vars=["BETR801",
-                                        "FR04014",
-                                        "London Westminster"],
-                            value_name="NO_2",
-                            var_name="id_location")
+    no_2 = no2_pivoted.melt(
+        id_vars="date.utc",
+        value_vars=["BETR801", "FR04014", "London Westminster"],
+        value_name="NO_2",
+        var_name="id_location",
+    )
     no_2.head()
 
 The result in the same, but in more detail defined:

doc/source/getting_started/intro_tutorials/08_combine_dataframes.rst

@@ -155,8 +155,7 @@ index. For example:
 
 .. ipython:: python
 
-    air_quality_ = pd.concat([air_quality_pm25, air_quality_no2],
-                             keys=["PM25", "NO2"])
+    air_quality_ = pd.concat([air_quality_pm25, air_quality_no2], keys=["PM25", "NO2"])
 
 .. ipython:: python
 
@@ -233,8 +232,7 @@ Add the station coordinates, provided by the stations metadata table, to the cor
 
 .. ipython:: python
 
-    air_quality = pd.merge(air_quality, stations_coord,
-                           how='left', on='location')
+    air_quality = pd.merge(air_quality, stations_coord, how="left", on="location")
     air_quality.head()
 
 Using the :meth:`~pandas.merge` function, for each of the rows in the

doc/source/getting_started/intro_tutorials/09_timeseries.rst

@@ -204,10 +204,9 @@ Plot the typical :math:`NO_2` pattern during the day of our time series of all s
 .. ipython:: python
 
     fig, axs = plt.subplots(figsize=(12, 4))
-    air_quality.groupby(
-        air_quality["datetime"].dt.hour)["value"].mean().plot(kind='bar',
-                                                              rot=0,
-                                                              ax=axs)
+    air_quality.groupby(air_quality["datetime"].dt.hour)["value"].mean().plot(
+        kind='bar', rot=0, ax=axs
+    )
     plt.xlabel("Hour of the day");  # custom x label using matplotlib
     @savefig 09_bar_chart.png
     plt.ylabel("$NO_2 (µg/m^3)$");

doc/source/getting_started/intro_tutorials/10_text_data.rst

@@ -224,8 +224,7 @@ In the "Sex" column, replace values of "male" by "M" and values of "female" by "
 
 .. ipython:: python
 
-    titanic["Sex_short"] = titanic["Sex"].replace({"male": "M",
-                                                   "female": "F"})
+    titanic["Sex_short"] = titanic["Sex"].replace({"male": "M", "female": "F"})
     titanic["Sex_short"]
 
 Whereas :meth:`~Series.replace` is not a string method, it provides a convenient way

doc/source/user_guide/10min.rst

@@ -43,12 +43,16 @@ Creating a :class:`DataFrame` by passing a dict of objects that can be converted
 
 .. ipython:: python
 
-   df2 = pd.DataFrame({'A': 1.,
-                       'B': pd.Timestamp('20130102'),
-                       'C': pd.Series(1, index=list(range(4)), dtype='float32'),
-                       'D': np.array([3] * 4, dtype='int32'),
-                       'E': pd.Categorical(["test", "train", "test", "train"]),
-                       'F': 'foo'})
+   df2 = pd.DataFrame(
+       {
+           "A": 1.0,
+           "B": pd.Timestamp("20130102"),
+           "C": pd.Series(1, index=list(range(4)), dtype="float32"),
+           "D": np.array([3] * 4, dtype="int32"),
+           "E": pd.Categorical(["test", "train", "test", "train"]),
+           "F": "foo",
+       }
+   )
    df2
 
 The columns of the resulting :class:`DataFrame` have different
@@ -512,12 +516,14 @@ See the :ref:`Grouping section <groupby>`.
 
 .. ipython:: python
 
-   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
 
 Grouping and then applying the :meth:`~pandas.core.groupby.GroupBy.sum` function to the resulting
@@ -545,10 +551,14 @@ Stack
 
 .. ipython:: python
 
-   tuples = list(zip(*[['bar', 'bar', 'baz', 'baz',
-                        'foo', 'foo', 'qux', 'qux'],
-                       ['one', 'two', 'one', 'two',
-                        'one', 'two', 'one', 'two']]))
+   tuples = list(
+       zip(
+           *[
+               ["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"],
+               ["one", "two", "one", "two", "one", "two", "one", "two"],
+           ]
+       )
+   )
    index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])
    df = pd.DataFrame(np.random.randn(8, 2), index=index, columns=['A', 'B'])
    df2 = df[:4]
@@ -578,11 +588,15 @@ See the section on :ref:`Pivot Tables <reshaping.pivot>`.
 
 .. ipython:: python
 
-   df = pd.DataFrame({'A': ['one', 'one', 'two', 'three'] * 3,
-                      'B': ['A', 'B', 'C'] * 4,
-                      'C': ['foo', 'foo', 'foo', 'bar', 'bar', 'bar'] * 2,
-                      'D': np.random.randn(12),
-                      'E': np.random.randn(12)})
+   df = pd.DataFrame(
+       {
+           "A": ["one", "one", "two", "three"] * 3,
+           "B": ["A", "B", "C"] * 4,
+           "C": ["foo", "foo", "foo", "bar", "bar", "bar"] * 2,
+           "D": np.random.randn(12),
+           "E": np.random.randn(12),
+       }
+   )
    df
 
 We can produce pivot tables from this data very easily:
@@ -653,8 +667,10 @@ pandas can include categorical data in a :class:`DataFrame`. For full docs, see
 
 .. ipython:: python
 
-    df = pd.DataFrame({"id": [1, 2, 3, 4, 5, 6],
-                       "raw_grade": ['a', 'b', 'b', 'a', 'a', 'e']})
+   df = pd.DataFrame(
+       {"id": [1, 2, 3, 4, 5, 6], "raw_grade": ["a", "b", "b", "a", "a", "e"]}
+   )
+
 
 Convert the raw grades to a categorical data type.
 
@@ -674,8 +690,9 @@ Reorder the categories and simultaneously add the missing categories (methods un
 
 .. ipython:: python
 
-    df["grade"] = df["grade"].cat.set_categories(["very bad", "bad", "medium",
-                                                  "good", "very good"])
+    df["grade"] = df["grade"].cat.set_categories(
+        ["very bad", "bad", "medium", "good", "very good"]
+    )
     df["grade"]
 
 Sorting is per order in the categories, not lexical order.
@@ -705,8 +722,7 @@ We use the standard convention for referencing the matplotlib API:
 
 .. ipython:: python
 
-   ts = pd.Series(np.random.randn(1000),
-                  index=pd.date_range('1/1/2000', periods=1000))
+   ts = pd.Series(np.random.randn(1000), index=pd.date_range("1/1/2000", periods=1000))
    ts = ts.cumsum()
 
    @savefig series_plot_basic.png
@@ -717,8 +733,10 @@ of the columns with labels:
 
 .. ipython:: python
 
-   df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index,
-                     columns=['A', 'B', 'C', 'D'])
+   df = pd.DataFrame(
+       np.random.randn(1000, 4), index=ts.index, columns=["A", "B", "C", "D"]
+   )
+
    df = df.cumsum()
 
    plt.figure()

doc/source/user_guide/sparse.rst

@@ -303,24 +303,28 @@ The method requires a ``MultiIndex`` with two or more levels.
 .. ipython:: python
 
    s = pd.Series([3.0, np.nan, 1.0, 3.0, np.nan, np.nan])
-   s.index = pd.MultiIndex.from_tuples([(1, 2, 'a', 0),
-                                        (1, 2, 'a', 1),
-                                        (1, 1, 'b', 0),
-                                        (1, 1, 'b', 1),
-                                        (2, 1, 'b', 0),
-                                        (2, 1, 'b', 1)],
-                                       names=['A', 'B', 'C', 'D'])
-   s
+   s.index = pd.MultiIndex.from_tuples(
+       [
+           (1, 2, "a", 0),
+           (1, 2, "a", 1),
+           (1, 1, "b", 0),
+           (1, 1, "b", 1),
+           (2, 1, "b", 0),
+           (2, 1, "b", 1),
+       ],
+       names=["A", "B", "C", "D"],
+   )
    ss = s.astype('Sparse')
    ss
 
 In the example below, we transform the ``Series`` to a sparse representation of a 2-d array by specifying that the first and second ``MultiIndex`` levels define labels for the rows and the third and fourth levels define labels for the columns. We also specify that the column and row labels should be sorted in the final sparse representation.
 
 .. ipython:: python
 
-   A, rows, columns = ss.sparse.to_coo(row_levels=['A', 'B'],
-                                       column_levels=['C', 'D'],
-                                       sort_labels=True)
+   A, rows, columns = ss.sparse.to_coo(
+       row_levels=["A", "B"], column_levels=["C", "D"], sort_labels=True
+   )
+
 
    A
    A.todense()
@@ -331,9 +335,9 @@ Specifying different row and column labels (and not sorting them) yields a diffe
 
 .. ipython:: python
 
-   A, rows, columns = ss.sparse.to_coo(row_levels=['A', 'B', 'C'],
-                                       column_levels=['D'],
-                                       sort_labels=False)
+   A, rows, columns = ss.sparse.to_coo(
+       row_levels=["A", "B", "C"], column_levels=["D"], sort_labels=False
+   )
 
    A
    A.todense()
@@ -345,8 +349,7 @@ A convenience method :meth:`Series.sparse.from_coo` is implemented for creating
 .. ipython:: python
 
    from scipy import sparse
-   A = sparse.coo_matrix(([3.0, 1.0, 2.0], ([1, 0, 0], [0, 2, 3])),
-                         shape=(3, 4))
+   A = sparse.coo_matrix(([3.0, 1.0, 2.0], ([1, 0, 0], [0, 2, 3])), shape=(3, 4))
    A
    A.todense()