diff --git a/pandas/core/reshape/pivot.py b/pandas/core/reshape/pivot.py index ec4cdffc56435..d12dbb81765d8 100644 --- a/pandas/core/reshape/pivot.py +++ b/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' diff --git a/pandas/core/window.py b/pandas/core/window.py index 5256532a31870..be28a3bcccec6 100644 --- a/pandas/core/window.py +++ b/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 ------- diff --git a/pandas/io/json/normalize.py b/pandas/io/json/normalize.py index ce07a795017e5..af046d9f309e7 100644 --- a/pandas/io/json/normalize.py +++ b/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 diff --git a/pandas/tseries/offsets.py b/pandas/tseries/offsets.py index 6fb562e301ac2..53719b71d1180 100644 --- a/pandas/tseries/offsets.py +++ b/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']) diff --git a/pandas/util/testing.py b/pandas/util/testing.py index c6457545038e0..d0486b9d8fd3a 100644 --- a/pandas/util/testing.py +++ b/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