DOC: Fix Order of parameters in docstrings (#23611)

Author: thoo

pandas/core/reshape/pivot.py

@@ -407,12 +407,12 @@ def crosstab(index, columns, values=None, rownames=None, colnames=None,
     values : array-like, optional
         Array of values to aggregate according to the factors.
         Requires `aggfunc` be specified.
-    aggfunc : function, optional
-        If specified, requires `values` be specified as well
     rownames : sequence, default None
         If passed, must match number of row arrays passed
     colnames : sequence, default None
         If passed, must match number of column arrays passed
+    aggfunc : function, optional
+        If specified, requires `values` be specified as well
     margins : boolean, default False
         Add row/column margins (subtotals)
     margins_name : string, default 'All'

pandas/core/window.py

@@ -136,7 +136,7 @@ def _gotitem(self, key, ndim, subset=None):
 
         Parameters
         ----------
-        key : string / list of selections
+        key : str / list of selections
         ndim : 1,2
             requested ndim of result
         subset : object, default None
@@ -464,15 +464,16 @@ class Window(_Window):
         (otherwise result is NA). For a window that is specified by an offset,
         `min_periods` will default to 1. Otherwise, `min_periods` will default
         to the size of the window.
-    center : boolean, default False
+    center : bool, default False
         Set the labels at the center of the window.
-    win_type : string, default None
+    win_type : str, default None
         Provide a window type. If ``None``, all points are evenly weighted.
         See the notes below for further information.
-    on : string, optional
+    on : str, optional
         For a DataFrame, column on which to calculate
         the rolling window, rather than the index
-    closed : string, default None
+    axis : int or str, default 0
+    closed : str, default None
         Make the interval closed on the 'right', 'left', 'both' or
         'neither' endpoints.
         For offset-based windows, it defaults to 'right'.
@@ -481,8 +482,6 @@ class Window(_Window):
 
         .. versionadded:: 0.20.0
 
-    axis : int or string, default 0
-
     Returns
     -------
     a Window or Rolling sub-classed for the particular operation
@@ -661,7 +660,7 @@ def _apply_window(self, mean=True, **kwargs):
 
         Parameters
         ----------
-        mean : boolean, default True
+        mean : bool, default True
             If True computes weighted mean, else weighted sum
 
         Returns
@@ -819,11 +818,11 @@ def _apply(self, func, name=None, window=None, center=None,
 
         Parameters
         ----------
-        func : string/callable to apply
-        name : string, optional
+        func : str/callable to apply
+        name : str, optional
            name of this function
         window : int/array, default to _get_window()
-        center : boolean, default to self.center
+        center : bool, default to self.center
         check_minp : function, default to _use_window
 
         Returns
@@ -1816,9 +1815,9 @@ class Expanding(_Rolling_and_Expanding):
     min_periods : int, default 1
         Minimum number of observations in window required to have a value
         (otherwise result is NA).
-    center : boolean, default False
+    center : bool, default False
         Set the labels at the center of the window.
-    axis : int or string, default 0
+    axis : int or str, default 0
 
     Returns
     -------
@@ -2062,7 +2061,7 @@ def _constructor(self):
 
 Parameters
 ----------
-bias : boolean, default False
+bias : bool, default False
     Use a standard estimation bias correction
 """
 
@@ -2079,7 +2078,7 @@ def _constructor(self):
     will be a MultiIndex DataFrame in the case of DataFrame inputs.
     In the case of missing elements, only complete pairwise observations will
     be used.
-bias : boolean, default False
+bias : bool, default False
    Use a standard estimation bias correction
 """
 
@@ -2110,10 +2109,10 @@ class EWM(_Rolling):
     min_periods : int, default 0
         Minimum number of observations in window required to have a value
         (otherwise result is NA).
-    adjust : boolean, default True
+    adjust : bool, default True
         Divide by decaying adjustment factor in beginning periods to account
         for imbalance in relative weightings (viewing EWMA as a moving average)
-    ignore_na : boolean, default False
+    ignore_na : bool, default False
         Ignore missing values when calculating weights;
         specify True to reproduce pre-0.15.0 behavior
 
@@ -2242,7 +2241,7 @@ def _apply(self, func, **kwargs):
 
         Parameters
         ----------
-        func : string/callable to apply
+        func : str/callable to apply
 
         Returns
         -------

pandas/io/json/normalize.py

@@ -110,10 +110,10 @@ def json_normalize(data, record_path=None, meta=None,
         assumed to be an array of records
     meta : list of paths (string or list of strings), default None
         Fields to use as metadata for each record in resulting table
+    meta_prefix : string, default None
     record_prefix : string, default None
         If True, prefix records with dotted (?) path, e.g. foo.bar.field if
         path to records is ['foo', 'bar']
-    meta_prefix : string, default None
     errors : {'raise', 'ignore'}, default 'raise'
 
         * 'ignore' : will ignore KeyError if keys listed in meta are not

pandas/tseries/offsets.py

@@ -807,7 +807,6 @@ class CustomBusinessDay(_CustomMixin, BusinessDay):
     Parameters
     ----------
     n : int, default 1
-    offset : timedelta, default timedelta(0)
     normalize : bool, default False
         Normalize start/end dates to midnight before generating date range
     weekmask : str, Default 'Mon Tue Wed Thu Fri'
@@ -816,6 +815,7 @@ class CustomBusinessDay(_CustomMixin, BusinessDay):
         list/array of dates to exclude from the set of valid business days,
         passed to ``numpy.busdaycalendar``
     calendar : pd.HolidayCalendar or np.busdaycalendar
+    offset : timedelta, default timedelta(0)
     """
     _prefix = 'C'
     _attributes = frozenset(['n', 'normalize',
@@ -958,7 +958,6 @@ class _CustomBusinessMonth(_CustomMixin, BusinessMixin, MonthOffset):
     Parameters
     ----------
     n : int, default 1
-    offset : timedelta, default timedelta(0)
     normalize : bool, default False
         Normalize start/end dates to midnight before generating date range
     weekmask : str, Default 'Mon Tue Wed Thu Fri'
@@ -967,6 +966,7 @@ class _CustomBusinessMonth(_CustomMixin, BusinessMixin, MonthOffset):
         list/array of dates to exclude from the set of valid business days,
         passed to ``numpy.busdaycalendar``
     calendar : pd.HolidayCalendar or np.busdaycalendar
+    offset : timedelta, default timedelta(0)
     """
     _attributes = frozenset(['n', 'normalize',
                              'weekmask', 'holidays', 'calendar', 'offset'])

pandas/util/testing.py

@@ -1242,18 +1242,18 @@ def assert_series_equal(left, right, check_dtype=True,
     check_less_precise : bool or int, default False
         Specify comparison precision. Only used when check_exact is False.
         5 digits (False) or 3 digits (True) after decimal points are compared.
-        If int, then specify the digits to compare
-    check_exact : bool, default False
-        Whether to compare number exactly.
+        If int, then specify the digits to compare.
     check_names : bool, default True
         Whether to check the Series and Index names attribute.
+    check_exact : bool, default False
+        Whether to compare number exactly.
     check_datetimelike_compat : bool, default False
         Compare datetime-like which is comparable ignoring dtype.
     check_categorical : bool, default True
         Whether to compare internal Categorical exactly.
     obj : str, default 'Series'
         Specify object name being compared, internally used to show appropriate
-        assertion message
+        assertion message.
     """
     __tracebackhide__ = True