DOC: fix line too long errors throughout file

Author: acrossler

pandas/core/groupby/generic.py

@@ -68,7 +68,6 @@
 from pandas.core.groupby.groupby import (
     GroupBy,
     GroupByPlot,
-    _transform_template,
 )
 from pandas.core.indexes.api import (
     Index,
@@ -683,7 +682,8 @@ def transform(self, func, *args, engine=None, engine_kwargs=None, **kwargs):
         Parameters
         ----------
         func : function, str
-            Function to apply to each group. See the Notes section below for requirements.
+            Function to apply to each group. See the Notes section below for 
+            requirements.
 
             Accepted inputs are:
 
@@ -705,7 +705,7 @@ def transform(self, func, *args, engine=None, engine_kwargs=None, **kwargs):
         engine : str, default None
             * ``'cython'`` : Runs the function through C-extensions from cython.
             * ``'numba'`` : Runs the function through JIT compiled code from numba.
-            * ``None`` : Defaults to ``'cython'`` or the global setting ``compute.use_numba``
+            * ``None`` : Defaults to ``cython`` or global setting ``compute.use_numba``
 
         engine_kwargs : dict, default None
             * For ``'cython'`` engine, there are no accepted ``engine_kwargs``
@@ -760,17 +760,19 @@ def transform(self, func, *args, engine=None, engine_kwargs=None, **kwargs):
 
         .. versionchanged:: 2.0.0
 
-            When using ``.transform`` on a grouped DataFrame and the transformation function
-            returns a DataFrame, pandas now aligns the result's index
+            When using ``.transform`` on a grouped DataFrame and the transformation 
+            function returns a DataFrame, pandas now aligns the result's index
             with the input's index. You can call ``.to_numpy()`` on the
             result of the transformation function to avoid alignment.
 
         Examples
         --------
 
-        >>> ser = pd.Series([390.0, 350.0, 30.0, 20.0],
-        ...                 index=["Falcon", "Falcon", "Parrot", "Parrot"],
-        ...                 name="Max Speed")
+        >>> ser = pd.Series(
+        ...     [390.0, 350.0, 30.0, 20.0],
+        ...     index=["Falcon", "Falcon", "Parrot", "Parrot"],
+        ...     name="Max Speed",
+        ... )
         >>> grouped = ser.groupby([1, 1, 2, 2])
         >>> grouped.transform(lambda x: (x - x.mean()) / x.std())
             Falcon    0.707107
@@ -1776,8 +1778,9 @@ def plot(self) -> GroupByPlot:
             .. versionadded:: 1.5.0
 
         sharex : bool, default True if ax is None else False
-            In case ``subplots=True``, share x axis and set some x axis labels
-            to invisible; defaults to True if ax is None otherwise False if
+            In case ``subplots=True``, share x axis and set some x axis 
+            labels to invisible; 
+            defaults to True if ax is None otherwise False if
             an ax is passed in; Be aware, that passing in both an ax and
             ``sharex=True`` will alter all x axis labels for all axis in a figure.
         sharey : bool, default False
@@ -1816,8 +1819,8 @@ def plot(self) -> GroupByPlot:
         ylim : 2-tuple/list
             Set the y limits of the current axes.
         xlabel : label, optional
-            Name to use for the xlabel on x-axis. Default uses index name as xlabel, or the
-            x-column name for planar plots.
+            Name to use for the xlabel on x-axis. Default uses index name as xlabel, 
+            or the x-column name for planar plots.
 
             .. versionchanged:: 2.0.0
 
@@ -1917,7 +1920,6 @@ def plot(self) -> GroupByPlot:
 
         .. plot::
             :context: close-figs
-
             >>> df = pd.DataFrame(
             ...     {
             ...         "length": [1.5, 0.5, 1.2, 0.9, 3],
@@ -1931,18 +1933,18 @@ def plot(self) -> GroupByPlot:
 
         .. plot::
             :context: close-figs
-
             >>> lst = [-1, -2, -3, 1, 2, 3]
             >>> ser = pd.Series([1, 2, 2, 4, 6, 6], index=lst)
             >>> plot = ser.groupby(lambda x: x > 0).plot(title="SeriesGroupBy Plot")
 
         For DataFrameGroupBy:
 
-        .. plot::  
+        .. plot::
             :context: close-figs
-
             >>> df = pd.DataFrame({"col1": [1, 2, 3, 4], "col2": ["A", "B", "A", "B"]})
-            >>> plot = df.groupby("col2").plot(kind="bar", title="DataFrameGroupBy Plot")
+            >>> plot = df.groupby("col2").plot(
+            ...  kind="bar", title="DataFrameGroupBy Plot"
+            ... )
         """
         result = GroupByPlot(self)
         return result
@@ -2336,13 +2338,15 @@ def corr(
 
         Notes
         -----
-        Pearson, Kendall and Spearman correlation are currently computed using pairwise complete observations.
+        Pearson, Kendall and Spearman correlation are currently computed using 
+        pairwise complete observations.
 
         * `Pearson correlation coefficient <https://en.wikipedia.org/wiki/Pearson_correlation_coefficient>`_
         * `Kendall rank correlation coefficient <https://en.wikipedia.org/wiki/Kendall_rank_correlation_coefficient>`_
         * `Spearman's rank correlation coefficient <https://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient>`_
 
-        Automatic data alignment: as with all pandas operations, automatic data alignment is performed for this method.
+        Automatic data alignment: as with all pandas operations, automatic data 
+        alignment is performed for this method.
         ``corr()`` automatically considers values with matching indices.
 
         Examples
@@ -3241,7 +3245,7 @@ def transform(self, func, *args, engine=None, engine_kwargs=None, **kwargs):
         engine : str, default None
             * ``'cython'`` : Runs the function through C-extensions from cython.
             * ``'numba'`` : Runs the function through JIT compiled code from numba.
-            * ``None`` : Defaults to ``'cython'`` or the global setting ``compute.use_numba``
+            * ``None`` : Defaults to ``cython`` or global setting ``compute.use_numba``
 
         engine_kwargs : dict, default None
             * For ``'cython'`` engine, there are no accepted ``engine_kwargs``
@@ -3296,17 +3300,19 @@ def transform(self, func, *args, engine=None, engine_kwargs=None, **kwargs):
 
         .. versionchanged:: 2.0.0
 
-            When using ``.transform`` on a grouped DataFrame and the transformation function
-            returns a DataFrame, pandas now aligns the result's index
+            When using ``.transform`` on a grouped DataFrame and the transformation
+            function returns a DataFrame, pandas now aligns the result's index
             with the input's index. You can call ``.to_numpy()`` on the
             result of the transformation function to avoid alignment.
 
         Examples
         --------
 
-        >>> ser = pd.Series([390.0, 350.0, 30.0, 20.0],
-        ...                 index=["Falcon", "Falcon", "Parrot", "Parrot"],
-        ...                 name="Max Speed")
+        >>> ser = pd.Series(
+        ...     [390.0, 350.0, 30.0, 20.0],
+        ...     index=["Falcon", "Falcon", "Parrot", "Parrot"],
+        ...     name="Max Speed",
+        ... )
         >>> grouped = ser.groupby([1, 1, 2, 2])
         >>> grouped.transform(lambda x: (x - x.mean()) / x.std())
             Falcon    0.707107
@@ -4233,7 +4239,8 @@ def plot(self) -> GroupByPlot:
             an ax is passed in; Be aware, that passing in both an ax and
             ``sharex=True`` will alter all x axis labels for all axis in a figure.
         sharey : bool, default False
-            In case ``subplots=True``, share y axis and set some y axis labels to invisible.
+            In case ``subplots=True``, share y axis and set some y axis 
+            labels to invisible.
         layout : tuple, optional
             (rows, columns) for the layout of subplots.
         figsize : a tuple (width, height) in inches
@@ -4268,8 +4275,8 @@ def plot(self) -> GroupByPlot:
         ylim : 2-tuple/list
             Set the y limits of the current axes.
         xlabel : label, optional
-            Name to use for the xlabel on x-axis. Default uses index name as xlabel, or the
-            x-column name for planar plots.
+            Name to use for the xlabel on x-axis. Default uses index name as xlabel,
+            or the x-column name for planar plots.
 
             .. versionchanged:: 2.0.0
 
@@ -4394,7 +4401,9 @@ def plot(self) -> GroupByPlot:
             :context: close-figs
 
             >>> df = pd.DataFrame({"col1": [1, 2, 3, 4], "col2": ["A", "B", "A", "B"]})
-            >>> plot = df.groupby("col2").plot(kind="bar", title="DataFrameGroupBy Plot")
+            >>> plot = df.groupby("col2").plot(
+            ... kind="bar", title="DataFrameGroupBy Plot"
+            ... )
         """
         result = GroupByPlot(self)
         return result
@@ -4443,13 +4452,15 @@ def corr(
 
         Notes
         -----
-        Pearson, Kendall and Spearman correlation are currently computed using pairwise complete observations.
+        Pearson, Kendall and Spearman correlation are currently computed using 
+        pairwise complete observations.
 
         * `Pearson correlation coefficient <https://en.wikipedia.org/wiki/Pearson_correlation_coefficient>`_
         * `Kendall rank correlation coefficient <https://en.wikipedia.org/wiki/Kendall_rank_correlation_coefficient>`_
         * `Spearman's rank correlation coefficient <https://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient>`_
 
-        Automatic data alignment: as with all pandas operations, automatic data alignment is performed for this method.
+        Automatic data alignment: as with all pandas operations, 
+        automatic data alignment is performed for this method.
         ``corr()`` automatically considers values with matching indices.
 
         Examples