Add missing spacing before or after a param name in docstrings (#23738)

Author: srinivasreddy

pandas/_libs/tslibs/nattype.pyx

@@ -648,7 +648,7 @@ class NaTType(_NaT):
         minute : int, optional
         second : int, optional
         microsecond : int, optional
-        nanosecond: int, optional
+        nanosecond : int, optional
         tzinfo : tz-convertible, optional
         fold : int, optional, default is 0
             added in 3.6, NotImplemented

pandas/_libs/tslibs/period.pyx

@@ -1777,7 +1777,7 @@ cdef class _Period(object):
         freq : string or DateOffset
             Target frequency. Default is 'D' if self.freq is week or
             longer and 'S' otherwise
-        how: str, default 'S' (start)
+        how : str, default 'S' (start)
             'S', 'E'. Can be aliased as case insensitive
             'Start', 'Finish', 'Begin', 'End'
 

pandas/_libs/tslibs/timestamps.pyx

@@ -1155,7 +1155,7 @@ class Timestamp(_Timestamp):
         minute : int, optional
         second : int, optional
         microsecond : int, optional
-        nanosecond: int, optional
+        nanosecond : int, optional
         tzinfo : tz-convertible, optional
         fold : int, optional, default is 0
             added in 3.6, NotImplemented

pandas/compat/pickle_compat.py

@@ -210,10 +210,10 @@ def load(fh, encoding=None, compat=False, is_verbose=False):
 
     Parameters
     ----------
-    fh: a filelike object
-    encoding: an optional encoding
-    compat: provide Series compatibility mode, boolean, default False
-    is_verbose: show exception output
+    fh : a filelike object
+    encoding : an optional encoding
+    compat : provide Series compatibility mode, boolean, default False
+    is_verbose : show exception output
     """
 
     try:

pandas/core/algorithms.py

@@ -387,8 +387,8 @@ def isin(comps, values):
 
     Parameters
     ----------
-    comps: array-like
-    values: array-like
+    comps : array-like
+    values : array-like
 
     Returns
     -------
@@ -856,8 +856,8 @@ def rank(values, axis=0, method='average', na_option='keep',
         The method by which tiebreaks are broken during the ranking.
     na_option : {'keep', 'top'}, default 'keep'
         The method by which NaNs are placed in the ranking.
-        - ``keep``: rank each NaN value with a NaN ranking
-        - ``top``: replace each NaN with either +/- inf so that they
+        - ``keep`` : rank each NaN value with a NaN ranking
+        - ``top`` : replace each NaN with either +/- inf so that they
                    there are ranked at the top
     ascending : boolean, default True
         Whether or not the elements should be ranked in ascending order.
@@ -1473,10 +1473,10 @@ def take(arr, indices, axis=0, allow_fill=False, fill_value=None):
     allow_fill : bool, default False
         How to handle negative values in `indices`.
 
-        * False: negative values in `indices` indicate positional indices
+        * False : negative values in `indices` indicate positional indices
           from the right (the default). This is similar to :func:`numpy.take`.
 
-        * True: negative values in `indices` indicate
+        * True : negative values in `indices` indicate
           missing values. These values are set to `fill_value`. Any other
           other negative values raise a ``ValueError``.
 

pandas/core/arrays/datetimes.py

@@ -925,7 +925,7 @@ def to_perioddelta(self, freq):
 
         Parameters
         ----------
-        freq: Period frequency
+        freq : Period frequency
 
         Returns
         -------

pandas/core/dtypes/concat.py

@@ -231,7 +231,7 @@ def union_categoricals(to_union, sort_categories=False, ignore_order=False):
     sort_categories : boolean, default False
         If true, resulting categories will be lexsorted, otherwise
         they will be ordered as they appear in the data.
-    ignore_order: boolean, default False
+    ignore_order : boolean, default False
         If true, the ordered attribute of the Categoricals will be ignored.
         Results in an unordered categorical.
 

pandas/core/frame.py

@@ -1689,7 +1689,7 @@ def from_csv(cls, path, header=0, sep=',', index_col=0, parse_dates=True,
         tupleize_cols : boolean, default False
             write multi_index columns as a list of tuples (if True)
             or new (expanded format) if False)
-        infer_datetime_format: boolean, default False
+        infer_datetime_format : boolean, default False
             If True and `parse_dates` is True for a column, try to infer the
             datetime format based on the first datetime string. If the format
             can be inferred, there often will be a large parsing speed-up.

pandas/core/generic.py

@@ -1482,14 +1482,14 @@ def _is_level_reference(self, key, axis=0):
 
         Parameters
         ----------
-        key: str
+        key : str
             Potential level name for the given axis
-        axis: int, default 0
+        axis : int, default 0
             Axis that levels are associated with (0 for index, 1 for columns)
 
         Returns
         -------
-        is_level: bool
+        is_level : bool
         """
         axis = self._get_axis_number(axis)
 
@@ -4871,7 +4871,7 @@ def as_matrix(self, columns=None):
 
         Parameters
         ----------
-        columns: list, optional, default:None
+        columns : list, optional, default:None
             If None, return all columns, otherwise, returns specified columns.
 
         Returns
@@ -7307,7 +7307,7 @@ def asfreq(self, freq, method=None, how=None, normalize=False,
             For PeriodIndex only, see PeriodIndex.asfreq
         normalize : bool, default False
             Whether to reset output index to midnight
-        fill_value: scalar, optional
+        fill_value : scalar, optional
             Value to use for missing values, applied during upsampling (note
             this does not fill NaNs that already were present).
 

pandas/core/indexes/timedeltas.py

@@ -41,7 +41,7 @@ class TimedeltaIndex(TimedeltaArray, DatetimeIndexOpsMixin,
     ----------
     data  : array-like (1-dimensional), optional
         Optional timedelta-like data to construct index with
-    unit: unit of the arg (D,h,m,s,ms,us,ns) denote the unit, optional
+    unit : unit of the arg (D,h,m,s,ms,us,ns) denote the unit, optional
         which is an integer/float number
     freq : string or pandas offset object, optional
         One of pandas date offset strings or corresponding objects. The string

pandas/core/resample.py

@@ -725,7 +725,7 @@ def asfreq(self, fill_value=None):
 
         Parameters
         ----------
-        fill_value: scalar, optional
+        fill_value : scalar, optional
             Value to use for missing values, applied during upsampling (note
             this does not fill NaNs that already were present).
 

pandas/core/series.py

@@ -77,7 +77,7 @@
 def remove_na(arr):
     """Remove null values from array like structure.
 
-    .. deprecated:: 0.21.0
+    .. deprecated :: 0.21.0
         Use s[s.notnull()] instead.
     """
 
@@ -1235,7 +1235,7 @@ def to_string(self, buf=None, na_rep='NaN', float_format=None, header=True,
         float_format : one-parameter function, optional
             formatter function to apply to columns' elements if they are floats
             default None
-        header: boolean, default True
+        header : boolean, default True
             Add the Series header (index name)
         index : bool, optional
             Add index (row) labels, default True
@@ -3876,7 +3876,7 @@ def from_csv(cls, path, sep=',', parse_dates=True, header=None,
         encoding : string, optional
             a string representing the encoding to use if the contents are
             non-ascii, for python versions prior to 3
-        infer_datetime_format: boolean, default False
+        infer_datetime_format : boolean, default False
             If True and `parse_dates` is True for a column, try to infer the
             datetime format based on the first datetime string. If the format
             can be inferred, there often will be a large parsing speed-up.

pandas/io/excel.py

@@ -122,7 +122,7 @@
 
     .. versionadded:: 0.20.0
 
-engine: string, default None
+engine : string, default None
     If io is not a buffer or path, this must be set to identify io.
     Acceptable values are None or xlrd
 converters : dict, default None
@@ -368,7 +368,7 @@ class ExcelFile(object):
     io : string, path object (pathlib.Path or py._path.local.LocalPath),
         file-like object or xlrd workbook
         If a string or path object, expected to be a path to xls or xlsx file
-    engine: string, default None
+    engine : string, default None
         If io is not a buffer or path, this must be set to identify io.
         Acceptable values are None or xlrd
     """
@@ -1011,8 +1011,8 @@ def write_cells(self, cells, sheet_name=None, startrow=0, startcol=0,
             cell of formatted data to save to Excel sheet
         sheet_name : string, default None
             Name of Excel sheet, if None, then use self.cur_sheet
-        startrow: upper left cell row to dump data frame
-        startcol: upper left cell column to dump data frame
+        startrow : upper left cell row to dump data frame
+        startcol : upper left cell column to dump data frame
         freeze_panes: integer tuple of length 2
             contains the bottom-most row and right-most column to freeze
         """
@@ -1159,7 +1159,7 @@ def _convert_to_style(cls, style_dict):
         converts a style_dict to an openpyxl style object
         Parameters
         ----------
-        style_dict: style dictionary to convert
+        style_dict : style dictionary to convert
         """
 
         from openpyxl.style import Style
@@ -1652,15 +1652,15 @@ def _style_to_xlwt(cls, item, firstlevel=True, field_sep=',',
         for example:
 
             hstyle = {"font": {"bold": True},
-            "border": {"top": "thin",
-                    "right": "thin",
-                    "bottom": "thin",
-                    "left": "thin"},
-            "align": {"horiz": "center"}}
+            "border" : {"top": "thin",
+                        "right": "thin",
+                        "bottom": "thin",
+                        "left": "thin"},
+            "align" : {"horiz": "center"}}
             will be converted to
-            font: bold on; \
-                    border: top thin, right thin, bottom thin, left thin; \
-                    align: horiz center;
+            font : bold on; \
+                    border : top thin, right thin, bottom thin, left thin; \
+                    align : horiz center;
         """
         if hasattr(item, 'items'):
             if firstlevel:
@@ -1687,8 +1687,8 @@ def _convert_to_style(cls, style_dict, num_format_str=None):
         converts a style_dict to an xlwt style object
         Parameters
         ----------
-        style_dict: style dictionary to convert
-        num_format_str: optional number format string
+        style_dict : style dictionary to convert
+        num_format_str : optional number format string
         """
         import xlwt
 
@@ -1790,8 +1790,8 @@ def convert(cls, style_dict, num_format_str=None):
 
         Parameters
         ----------
-        style_dict: style dictionary to convert
-        num_format_str: optional number format string
+        style_dict : style dictionary to convert
+        num_format_str : optional number format string
         """
 
         # Create a XlsxWriter format object.

pandas/io/feather_format.py

@@ -98,7 +98,7 @@ def read_feather(path, use_threads=True):
 
        .. versionadded 0.21.0
        .. deprecated 0.24.0
-    use_threads: bool, default True
+    use_threads : bool, default True
         Whether to parallelize reading using multiple threads
 
        .. versionadded 0.24.0