Format all rst files

Author: tqa236

doc/source/development/contributing_codebase.rst

@@ -118,7 +118,7 @@ the ``pandas.util._decorators.deprecate``:
 
     from pandas.util._decorators import deprecate
 
-    deprecate('old_func', 'new_func', '1.1.0')
+    deprecate("old_func", "new_func", "1.1.0")
 
 Otherwise, you need to do it manually:
 
@@ -135,7 +135,7 @@ Otherwise, you need to do it manually:
            Use new_func instead.
         """
         warnings.warn(
-            'Use new_func instead.',
+            "Use new_func instead.",
             FutureWarning,
             stacklevel=find_stack_level(),
         )
@@ -179,6 +179,7 @@ The appropriate way to annotate this would be as follows
 
    str_type = str
 
+
    class SomeClass2:
        str: str_type = None
 
@@ -190,6 +191,7 @@ In some cases you may be tempted to use ``cast`` from the typing module when you
 
    from pandas.core.dtypes.common import is_number
 
+
    def cannot_infer_bad(obj: Union[str, int, float]):
 
        if is_number(obj):
@@ -222,8 +224,8 @@ For example, quite a few functions in pandas accept a ``dtype`` argument. This c
 
    from pandas._typing import Dtype
 
-   def as_type(dtype: Dtype) -> ...:
-       ...
+
+   def as_type(dtype: Dtype) -> ...: ...
 
 This module will ultimately house types for repeatedly used concepts like "path-like", "array-like", "numeric", etc... and can also hold aliases for commonly appearing parameters like ``axis``. Development of this module is active so be sure to refer to the source for the most up to date list of available types.
 
@@ -428,6 +430,7 @@ be located.
            import pandas as pd
            import pandas._testing as tm
 
+
            def test_getitem_listlike_of_ints():
                ser = pd.Series(range(5))
 
@@ -634,25 +637,29 @@ as a comment to a new test.
    import pandas as pd
 
 
-   @pytest.mark.parametrize('dtype', ['int8', 'int16', 'int32', 'int64'])
+   @pytest.mark.parametrize("dtype", ["int8", "int16", "int32", "int64"])
    def test_dtypes(dtype):
        assert str(np.dtype(dtype)) == dtype
 
 
    @pytest.mark.parametrize(
-       'dtype', ['float32', pytest.param('int16', marks=pytest.mark.skip),
-                 pytest.param('int32', marks=pytest.mark.xfail(
-                     reason='to show how it works'))])
+       "dtype",
+       [
+           "float32",
+           pytest.param("int16", marks=pytest.mark.skip),
+           pytest.param("int32", marks=pytest.mark.xfail(reason="to show how it works")),
+       ],
+   )
    def test_mark(dtype):
-       assert str(np.dtype(dtype)) == 'float32'
+       assert str(np.dtype(dtype)) == "float32"
 
 
    @pytest.fixture
    def series():
        return pd.Series([1, 2, 3])
 
 
-   @pytest.fixture(params=['int8', 'int16', 'int32', 'int64'])
+   @pytest.fixture(params=["int8", "int16", "int32", "int64"])
    def dtype(request):
        return request.param
 
@@ -721,10 +728,16 @@ for details <https://hypothesis.readthedocs.io/en/latest/index.html>`_.
     import json
     from hypothesis import given, strategies as st
 
-    any_json_value = st.deferred(lambda: st.one_of(
-        st.none(), st.booleans(), st.floats(allow_nan=False), st.text(),
-        st.lists(any_json_value), st.dictionaries(st.text(), any_json_value)
-    ))
+    any_json_value = st.deferred(
+        lambda: st.one_of(
+            st.none(),
+            st.booleans(),
+            st.floats(allow_nan=False),
+            st.text(),
+            st.lists(any_json_value),
+            st.dictionaries(st.text(), any_json_value),
+        )
+    )
 
 
     @given(value=any_json_value)

doc/source/development/contributing_docstring.rst

@@ -137,7 +137,6 @@ backticks. The following are considered inline code:
 .. code-block:: python
 
     def func():
-
         """Some function.
 
         With several mistakes in the docstring.
@@ -297,7 +296,7 @@ would be used, then we will specify "str, int or None, default None".
 .. code-block:: python
 
     class Series:
-        def plot(self, kind, color='blue', **kwargs):
+        def plot(self, kind, color="blue", **kwargs):
             """
             Generate a plot.
 
@@ -463,6 +462,7 @@ With more than one value:
 
     import string
 
+
     def random_letters():
         """
         Generate and return a sequence of random letters.
@@ -478,8 +478,7 @@ With more than one value:
             String of random letters.
         """
         length = np.random.randint(1, 10)
-        letters = ''.join(np.random.choice(string.ascii_lowercase)
-                          for i in range(length))
+        letters = "".join(np.random.choice(string.ascii_lowercase) for i in range(length))
         return length, letters
 
 If the method yields its value:
@@ -737,7 +736,6 @@ positional arguments ``head(3)``.
             """
             pass
 
-
         def fillna(self, value):
             """
             Replace missing values by ``value``.
@@ -954,14 +952,12 @@ substitute the class names in this docstring.
 
    class ChildA(Parent):
        @doc(Parent.my_function, klass="ChildA")
-       def my_function(self):
-           ...
+       def my_function(self): ...
 
 
    class ChildB(Parent):
        @doc(Parent.my_function, klass="ChildB")
-       def my_function(self):
-           ...
+       def my_function(self): ...
 
 The resulting docstrings are
 
@@ -987,8 +983,7 @@ You can substitute and append in one shot with something like
 .. code-block:: python
 
    @doc(template, **_shared_doc_kwargs)
-   def my_function(self):
-       ...
+   def my_function(self): ...
 
 where ``template`` may come from a module-level ``_shared_docs`` dictionary
 mapping function names to docstrings. Wherever possible, we prefer using

doc/source/development/extending.rst

@@ -526,6 +526,7 @@ The ``__pandas_priority__`` of :class:`DataFrame`, :class:`Series`, and :class:`
             # return `self` and not the addition for simplicity
             return self
 
+
     custom = CustomList()
     series = pd.Series([1, 2, 3])
 

doc/source/development/maintaining.rst

@@ -144,6 +144,7 @@ create a file ``t.py`` in your pandas directory, which contains
 .. code-block:: python
 
     import pandas as pd
+
     assert pd.Series([1, 1]).sum() == 2
 
 and then run::

doc/source/getting_started/comparison/comparison_with_spreadsheets.rst

@@ -427,9 +427,7 @@ The equivalent in pandas:
 
 .. ipython:: python
 
-    pd.pivot_table(
-        tips, values="tip", index=["size"], columns=["sex"], aggfunc=np.average
-    )
+    pd.pivot_table(tips, values="tip", index=["size"], columns=["sex"], aggfunc=np.average)
 
 
 Adding a row
@@ -440,8 +438,9 @@ Assuming we are using a :class:`~pandas.RangeIndex` (numbered ``0``, ``1``, etc.
 .. ipython:: python
 
     df
-    new_row = pd.DataFrame([["E", 51, True]],
-                           columns=["class", "student_count", "all_pass"])
+    new_row = pd.DataFrame(
+        [["E", 51, True]], columns=["class", "student_count", "all_pass"]
+    )
     pd.concat([df, new_row])
 
 

doc/source/getting_started/comparison/comparison_with_sql.rst

@@ -331,9 +331,7 @@ UNION
     df1 = pd.DataFrame(
         {"city": ["Chicago", "San Francisco", "New York City"], "rank": range(1, 4)}
     )
-    df2 = pd.DataFrame(
-        {"city": ["Chicago", "Boston", "Los Angeles"], "rank": [1, 4, 5]}
-    )
+    df2 = pd.DataFrame({"city": ["Chicago", "Boston", "Los Angeles"], "rank": [1, 4, 5]})
 
 .. code-block:: sql
 
@@ -433,9 +431,7 @@ Top n rows per group
 
     (
         tips.assign(
-            rn=tips.sort_values(["total_bill"], ascending=False)
-            .groupby(["day"])
-            .cumcount()
+            rn=tips.sort_values(["total_bill"], ascending=False).groupby(["day"]).cumcount()
             + 1
         )
         .query("rn < 3")
@@ -448,9 +444,7 @@ the same using ``rank(method='first')`` function
 
     (
         tips.assign(
-            rnk=tips.groupby(["day"])["total_bill"].rank(
-                method="first", ascending=False
-            )
+            rnk=tips.groupby(["day"])["total_bill"].rank(method="first", ascending=False)
         )
         .query("rnk < 3")
         .sort_values(["day", "rnk"])

doc/source/getting_started/comparison/includes/time_date.rst

@@ -5,13 +5,11 @@
    tips["date1_year"] = tips["date1"].dt.year
    tips["date2_month"] = tips["date2"].dt.month
    tips["date1_next"] = tips["date1"] + pd.offsets.MonthBegin()
-   tips["months_between"] = tips["date2"].dt.to_period("M") - tips[
-       "date1"
-   ].dt.to_period("M")
+   tips["months_between"] = tips["date2"].dt.to_period("M") - tips["date1"].dt.to_period(
+       "M"
+   )
 
-   tips[
-       ["date1", "date2", "date1_year", "date2_month", "date1_next", "months_between"]
-   ]
+   tips[["date1", "date2", "date1_year", "date2_month", "date1_next", "months_between"]]
 
 .. ipython:: python
    :suppress:

doc/source/getting_started/install.rst

@@ -108,6 +108,7 @@ obtain these directories with.
 .. code-block:: python
 
     import sys
+
     sys.path
 
 One way you could be encountering this error is if you have multiple Python installations on your system

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

@@ -115,7 +115,7 @@ I want to sort the Titanic data according to the cabin class and age in descendi
 
 .. ipython:: python
 
-    titanic.sort_values(by=['Pclass', 'Age'], ascending=False).head()
+    titanic.sort_values(by=["Pclass", "Age"], ascending=False).head()
 
 With :meth:`DataFrame.sort_values`, the rows in the table are sorted according to the
 defined column(s). The index will follow the row order.

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

@@ -40,10 +40,8 @@ Westminster* in respectively Paris, Antwerp and London.
 
 .. ipython:: python
 
-    air_quality_no2 = pd.read_csv("data/air_quality_no2_long.csv",
-                                  parse_dates=True)
-    air_quality_no2 = air_quality_no2[["date.utc", "location",
-                                       "parameter", "value"]]
+    air_quality_no2 = pd.read_csv("data/air_quality_no2_long.csv", parse_dates=True)
+    air_quality_no2 = air_quality_no2[["date.utc", "location", "parameter", "value"]]
     air_quality_no2.head()
 
 .. raw:: html
@@ -75,10 +73,8 @@ Westminster* in respectively Paris, Antwerp and London.
 
 .. ipython:: python
 
-    air_quality_pm25 = pd.read_csv("data/air_quality_pm25_long.csv",
-                                   parse_dates=True)
-    air_quality_pm25 = air_quality_pm25[["date.utc", "location",
-                                         "parameter", "value"]]
+    air_quality_pm25 = pd.read_csv("data/air_quality_pm25_long.csv", parse_dates=True)
+    air_quality_pm25 = air_quality_pm25[["date.utc", "location", "parameter", "value"]]
     air_quality_pm25.head()
 
 .. raw:: html
@@ -123,9 +119,9 @@ concatenated tables to verify the operation:
 
 .. ipython:: python
 
-    print('Shape of the ``air_quality_pm25`` table: ', air_quality_pm25.shape)
-    print('Shape of the ``air_quality_no2`` table: ', air_quality_no2.shape)
-    print('Shape of the resulting ``air_quality`` table: ', air_quality.shape)
+    print("Shape of the ``air_quality_pm25`` table: ", air_quality_pm25.shape)
+    print("Shape of the ``air_quality_no2`` table: ", air_quality_no2.shape)
+    print("Shape of the resulting ``air_quality`` table: ", air_quality.shape)
 
 Hence, the resulting table has 3178 = 1110 + 2068 rows.
 
@@ -265,8 +261,9 @@ Add the parameters' full description and name, provided by the parameters metada
 
 .. ipython:: python
 
-    air_quality = pd.merge(air_quality, air_quality_parameters,
-                           how='left', left_on='parameter', right_on='id')
+    air_quality = pd.merge(
+        air_quality, air_quality_parameters, how="left", left_on="parameter", right_on="id"
+    )
     air_quality.head()
 
 Compared to the previous example, there is no common column name.

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

@@ -174,8 +174,7 @@ What is the average :math:`NO_2` concentration for each day of the week for each
 
 .. ipython:: python
 
-    air_quality.groupby(
-        [air_quality["datetime"].dt.weekday, "location"])["value"].mean()
+    air_quality.groupby([air_quality["datetime"].dt.weekday, "location"])["value"].mean()
 
 Remember the split-apply-combine pattern provided by ``groupby`` from the
 :ref:`tutorial on statistics calculation <10min_tut_06_stats>`?

doc/source/user_guide/10min.rst

@@ -550,8 +550,8 @@ Stack
 .. ipython:: python
 
    arrays = [
-      ["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"],
-      ["one", "two", "one", "two", "one", "two", "one", "two"],
+       ["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"],
+       ["one", "two", "one", "two", "one", "two", "one", "two"],
    ]
    index = pd.MultiIndex.from_arrays(arrays, names=["first", "second"])
    df = pd.DataFrame(np.random.randn(8, 2), index=index, columns=["A", "B"])

doc/source/user_guide/advanced.rst

@@ -621,7 +621,7 @@ return a copy of the data rather than a view:
    )
    dfm = dfm.set_index(["jim", "joe"])
    dfm
-   dfm.loc[(1, 'z')]
+   dfm.loc[(1, "z")]
 
 .. _advanced.unsorted:
 
@@ -630,7 +630,7 @@ Furthermore, if you try to index something that is not fully lexsorted, this can
 .. ipython:: python
    :okexcept:
 
-   dfm.loc[(0, 'y'):(1, 'z')]
+   dfm.loc[(0, "y"):(1, "z")]
 
 The :meth:`~MultiIndex.is_monotonic_increasing` method on a ``MultiIndex`` shows if the
 index is sorted:
@@ -792,9 +792,7 @@ 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
 
@@ -1096,7 +1094,7 @@ index can be somewhat complicated. For example, the following does not work:
 .. ipython:: python
    :okexcept:
 
-    s.loc['c':'e' + 1]
+    s.loc["c" : "e" + 1]
 
 A very common use case is to limit a time series to start and end at two
 specific dates. To enable this, we made the design choice to make label-based