CLN: D412: No blank lines allowed between a section header and its content

pandas/_config/config.py

@@ -395,7 +395,6 @@ class option_context:
 
     Examples
     --------
-
     >>> with option_context('display.max_rows', 10, 'display.max_columns', 5):
     ...     ...
     """
@@ -716,8 +715,8 @@ def config_prefix(prefix):
     Warning: This is not thread - safe, and won't work properly if you import
     the API functions into your module using the "from x import y" construct.
 
-    Example:
-
+    Example
+    -------
     import pandas._config.config as cf
     with cf.config_prefix("display.font"):
         cf.register_option("color", "red")

pandas/_testing.py

@@ -1970,35 +1970,39 @@ def makeCustomDataframe(
     r_idx_type=None,
 ):
     """
-   nrows,  ncols - number of data rows/cols
-   c_idx_names, idx_names  - False/True/list of strings,  yields No names ,
-        default names or uses the provided names for the levels of the
-        corresponding index. You can provide a single string when
-        c_idx_nlevels ==1.
-   c_idx_nlevels - number of levels in columns index. > 1 will yield MultiIndex
-   r_idx_nlevels - number of levels in rows index. > 1 will yield MultiIndex
-   data_gen_f - a function f(row,col) which return the data value
-        at that position, the default generator used yields values of the form
-        "RxCy" based on position.
-   c_ndupe_l, r_ndupe_l - list of integers, determines the number
-        of duplicates for each label at a given level of the corresponding
-        index. The default `None` value produces a multiplicity of 1 across
-        all levels, i.e. a unique index. Will accept a partial list of length
-        N < idx_nlevels, for just the first N levels. If ndupe doesn't divide
-        nrows/ncol, the last label might have lower multiplicity.
-   dtype - passed to the DataFrame constructor as is, in case you wish to
-        have more control in conjunction with a custom `data_gen_f`
-   r_idx_type, c_idx_type -  "i"/"f"/"s"/"u"/"dt"/"td".
-       If idx_type is not None, `idx_nlevels` must be 1.
-       "i"/"f" creates an integer/float index,
-       "s"/"u" creates a string/unicode index
-       "dt" create a datetime index.
-       "td" create a timedelta index.
-
-        if unspecified, string labels will be generated.
+    Create a DataFrame using supplied parameters.
 
-    Examples:
+    Parameters
+    ----------
+    nrows,  ncols - number of data rows/cols
+    c_idx_names, idx_names  - False/True/list of strings,  yields No names ,
+            default names or uses the provided names for the levels of the
+            corresponding index. You can provide a single string when
+            c_idx_nlevels ==1.
+    c_idx_nlevels - number of levels in columns index. > 1 will yield MultiIndex
+    r_idx_nlevels - number of levels in rows index. > 1 will yield MultiIndex
+    data_gen_f - a function f(row,col) which return the data value
+            at that position, the default generator used yields values of the form
+            "RxCy" based on position.
+    c_ndupe_l, r_ndupe_l - list of integers, determines the number
+            of duplicates for each label at a given level of the corresponding
+            index. The default `None` value produces a multiplicity of 1 across
+            all levels, i.e. a unique index. Will accept a partial list of length
+            N < idx_nlevels, for just the first N levels. If ndupe doesn't divide
+            nrows/ncol, the last label might have lower multiplicity.
+    dtype - passed to the DataFrame constructor as is, in case you wish to
+            have more control in conjunction with a custom `data_gen_f`
+    r_idx_type, c_idx_type -  "i"/"f"/"s"/"u"/"dt"/"td".
+        If idx_type is not None, `idx_nlevels` must be 1.
+        "i"/"f" creates an integer/float index,
+        "s"/"u" creates a string/unicode index
+        "dt" create a datetime index.
+        "td" create a timedelta index.
+
+            if unspecified, string labels will be generated.
 
+    Examples
+    --------
     # 5 row, 3 columns, default names on both, single index on both axis
     >> makeCustomDataframe(5,3)
 
@@ -2514,7 +2518,6 @@ class RNGContext:
 
     Examples
     --------
-
     with RNGContext(42):
         np.random.randn()
     """
@@ -2669,7 +2672,6 @@ def set_timezone(tz: str):
 
     Examples
     --------
-
     >>> from datetime import datetime
     >>> from dateutil.tz import tzlocal
     >>> tzlocal().tzname(datetime.now())

pandas/core/accessor.py

@@ -233,7 +233,6 @@ def __init__(self, pandas_object):  # noqa: E999
 
     Examples
     --------
-
     In your library code::
 
         import pandas as pd

pandas/core/arrays/boolean.py

@@ -488,7 +488,6 @@ def any(self, skipna: bool = True, **kwargs):
 
         Examples
         --------
-
         The result indicates whether any element is True (and by default
         skips NAs):
 
@@ -557,7 +556,6 @@ def all(self, skipna: bool = True, **kwargs):
 
         Examples
         --------
-
         The result indicates whether any element is True (and by default
         skips NAs):
 

pandas/core/arrays/categorical.py

@@ -2388,7 +2388,6 @@ def isin(self, values):
 
         Examples
         --------
-
         >>> s = pd.Categorical(['lama', 'cow', 'lama', 'beetle', 'lama',
         ...                'hippo'])
         >>> s.isin(['cow', 'lama'])

pandas/core/arrays/sparse/dtype.py

@@ -336,7 +336,6 @@ def _subtype_with_str(self):
 
         Returns
         -------
-
         >>> SparseDtype(int, 1)._subtype_with_str
         dtype('int64')
 

pandas/core/computation/pytables.py

@@ -501,7 +501,6 @@ class PyTablesExpr(expr.Expr):
 
     Examples
     --------
-
     'index>=date'
     "columns=['A', 'D']"
     'columns=A'

pandas/core/dtypes/concat.py

@@ -215,13 +215,11 @@ def union_categoricals(
 
     Notes
     -----
-
     To learn more about categories, see `link
     <https://pandas.pydata.org/pandas-docs/stable/user_guide/categorical.html#unioning>`__
 
     Examples
     --------
-
     >>> from pandas.api.types import union_categoricals
 
     If you want to combine categoricals that do not necessarily have

pandas/core/frame.py

@@ -927,7 +927,6 @@ def iterrows(self) -> Iterable[Tuple[Optional[Hashable], Series]]:
 
         Notes
         -----
-
         1. Because ``iterrows`` returns a Series for each row,
            it does **not** preserve dtypes across the rows (dtypes are
            preserved across columns for DataFrames). For example,

pandas/core/generic.py

@@ -899,7 +899,6 @@ def rename(
 
         Examples
         --------
-
         >>> s = pd.Series([1, 2, 3])
         >>> s
         0    1
@@ -2208,7 +2207,6 @@ def to_json(
 
         Examples
         --------
-
         >>> df = pd.DataFrame([['a', 'b'], ['c', 'd']],
         ...                   index=['row 1', 'row 2'],
         ...                   columns=['col 1', 'col 2'])
@@ -2507,7 +2505,6 @@ def to_sql(
 
         Examples
         --------
-
         Create an in-memory SQLite database.
 
         >>> from sqlalchemy import create_engine
@@ -4185,7 +4182,6 @@ def reindex(self: FrameOrSeries, *args, **kwargs) -> FrameOrSeries:
 
         Examples
         --------
-
         ``DataFrame.reindex`` supports two calling conventions
 
         * ``(index=index_labels, columns=column_labels, ...)``
@@ -5768,7 +5764,6 @@ def convert_dtypes(
 
         Notes
         -----
-
         By default, ``convert_dtypes`` will attempt to convert a Series (or each
         Series in a DataFrame) to dtypes that support ``pd.NA``. By using the options
         ``convert_string``, ``convert_integer``, and ``convert_boolean``, it is
@@ -7434,7 +7429,6 @@ def asfreq(
 
         Examples
         --------
-
         Start by creating a series with 4 one minute timestamps.
 
         >>> index = pd.date_range('1/1/2000', periods=4, freq='T')
@@ -7713,7 +7707,6 @@ def resample(
 
         Examples
         --------
-
         Start by creating a series with 9 one minute timestamps.
 
         >>> index = pd.date_range('1/1/2000', periods=9, freq='T')
@@ -8100,7 +8093,6 @@ def rank(
 
         Examples
         --------
-
         >>> df = pd.DataFrame(data={'Animal': ['cat', 'penguin', 'dog',
         ...                                    'spider', 'snake'],
         ...                         'Number_legs': [4, 2, 4, 8, np.nan]})
@@ -9235,7 +9227,6 @@ def tz_localize(
 
         Examples
         --------
-
         Localize local times:
 
         >>> s = pd.Series([1],

pandas/core/groupby/groupby.py

@@ -1995,7 +1995,6 @@ def ngroup(self, ascending: bool = True):
 
         Examples
         --------
-
         >>> df = pd.DataFrame({"A": list("aaabba")})
         >>> df
            A
@@ -2062,7 +2061,6 @@ def cumcount(self, ascending: bool = True):
 
         Examples
         --------
-
         >>> df = pd.DataFrame([['a'], ['a'], ['a'], ['b'], ['b'], ['a']],
         ...                   columns=['A'])
         >>> df

pandas/core/groupby/grouper.py

@@ -77,7 +77,6 @@ class Grouper:
 
     Examples
     --------
-
     Syntactic sugar for ``df.groupby('A')``
 
     >>> df.groupby(Grouper(key='A'))

pandas/core/indexes/base.py

@@ -1452,7 +1452,6 @@ def _get_level_values(self, level):
 
         Examples
         --------
-
         >>> idx = pd.Index(list('abc'))
         >>> idx
         Index(['a', 'b', 'c'], dtype='object')
@@ -2501,7 +2500,6 @@ def union(self, other, sort=None):
 
         Examples
         --------
-
         Union matching dtypes
 
         >>> idx1 = pd.Index([1, 2, 3, 4])
@@ -2632,7 +2630,6 @@ def intersection(self, other, sort=False):
 
         Examples
         --------
-
         >>> idx1 = pd.Index([1, 2, 3, 4])
         >>> idx2 = pd.Index([3, 4, 5, 6])
         >>> idx1.intersection(idx2)
@@ -2713,7 +2710,6 @@ def difference(self, other, sort=None):
 
         Examples
         --------
-
         >>> idx1 = pd.Index([2, 1, 3, 4])
         >>> idx2 = pd.Index([3, 4, 5, 6])
         >>> idx1.difference(idx2)

pandas/core/indexes/multi.py

@@ -1549,7 +1549,6 @@ def get_level_values(self, level):
 
         Examples
         --------
-
         Create a MultiIndex:
 
         >>> mi = pd.MultiIndex.from_arrays((list('abc'), list('def')))
@@ -1713,7 +1712,6 @@ def _sort_levels_monotonic(self):
 
         Examples
         --------
-
         >>> mi = pd.MultiIndex(levels=[['a', 'b'], ['bb', 'aa']],
         ...                    codes=[[0, 0, 1, 1], [0, 1, 0, 1]])
         >>> mi

pandas/core/indexes/period.py

@@ -788,7 +788,6 @@ def period_range(
 
     Examples
     --------
-
     >>> pd.period_range(start='2017-01-01', end='2018-01-01', freq='M')
     PeriodIndex(['2017-01', '2017-02', '2017-03', '2017-04', '2017-05',
                  '2017-06', '2017-06', '2017-07', '2017-08', '2017-09',

pandas/core/indexes/timedeltas.py

@@ -321,7 +321,6 @@ def timedelta_range(
 
     Examples
     --------
-
     >>> pd.timedelta_range(start='1 day', periods=4)
     TimedeltaIndex(['1 days', '2 days', '3 days', '4 days'],
                    dtype='timedelta64[ns]', freq='D')

pandas/core/indexing.py

@@ -48,7 +48,6 @@ class _IndexSlice:
 
     Examples
     --------
-
     >>> midx = pd.MultiIndex.from_product([['A0','A1'], ['B0','B1','B2','B3']])
     >>> columns = ['foo', 'bar']
     >>> dfmi = pd.DataFrame(np.arange(16).reshape((len(midx), len(columns))),
@@ -124,7 +123,6 @@ def iloc(self) -> "_iLocIndexer":
 
         Examples
         --------
-
         >>> mydict = [{'a': 1, 'b': 2, 'c': 3, 'd': 4},
         ...           {'a': 100, 'b': 200, 'c': 300, 'd': 400},
         ...           {'a': 1000, 'b': 2000, 'c': 3000, 'd': 4000 }]

pandas/core/resample.py

@@ -550,7 +550,6 @@ def backfill(self, limit=None):
 
         Examples
         --------
-
         Resampling a Series:
 
         >>> s = pd.Series([1, 2, 3],

pandas/core/strings.py

@@ -349,7 +349,6 @@ def str_contains(arr, pat, case=True, flags=0, na=np.nan, regex=True):
 
     Examples
     --------
-
     Returning a Series of booleans using only a literal pattern.
 
     >>> s1 = pd.Series(['Mouse', 'dog', 'house and parrot', '23', np.NaN])
@@ -1274,7 +1273,6 @@ def str_findall(arr, pat, flags=0):
 
     Examples
     --------
-
     >>> s = pd.Series(['Lion', 'Monkey', 'Rabbit'])
 
     The search for the pattern 'Monkey' returns one match:
@@ -1743,7 +1741,6 @@ def str_wrap(arr, width, **kwargs):
 
     Examples
     --------
-
     >>> s = pd.Series(['line to be wrapped', 'another line to be wrapped'])
     >>> s.str.wrap(12)
     0             line to be\nwrapped

pandas/core/tools/timedeltas.py

@@ -53,7 +53,6 @@ def to_timedelta(arg, unit="ns", errors="raise"):
 
     Examples
     --------
-
     Parsing a single string to a Timedelta:
 
     >>> pd.to_timedelta('1 days 06:05:01.00003')

pandas/core/window/ewm.py

@@ -98,7 +98,6 @@ class EWM(_Rolling):
 
     Examples
     --------
-
     >>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]})
     >>> df
          B