Add missing spacing before or after a param name in docstrings (pandas-dev#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