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