pandas.isnull(obj) [source] Detect missing values (NaN in numeric arrays, None/NaN in object arrays) Parameters: arr : ndarray or object value Object to check for null-ness Returns: isnulled : array-like of bool or bool Array or bool indicating whether an object is null or if an array is given which of the element is null. See also pandas.notnull boolean inverse of pandas.isnull

DataFrame.reindex_like(other, method=None, copy=True, limit=None, tolerance=None) [source] Return an object with matching indices to myself. Parameters: other : Object method : string or None copy : boolean, default True limit : int, default None Maximum number of consecutive labels to fill for inexact matches. tolerance : optional Maximum distance between labels of the other object and this object for inexact matches. New in version 0.17.0. Returns: reindexed : same as input Note

CategoricalIndex.max(*args, **kwargs) [source] The maximum value of the object. Only ordered Categoricals have a maximum! Returns: max : the maximum of this Categorical Raises: TypeError If the Categorical is not ordered.

Series.str.isupper() [source] Check whether all characters in each string in the Series/Index are uppercase. Equivalent to str.isupper(). Returns: is : Series/array of boolean values

Series.cat.remove_unused_categories(*args, **kwargs) [source] Removes categories which are not used. Parameters: inplace : boolean (default: False) Whether or not to drop unused categories inplace or return a copy of this categorical with unused categories dropped. Returns: cat : Categorical with unused categories dropped or None if inplace. See also rename_categories, reorder_categories, add_categories, remove_categories, set_categories

Series.dropna(axis=0, inplace=False, **kwargs) [source] Return Series without null values Returns: valid : Series inplace : boolean, default False Do operation in place.

DataFrame.pct_change(periods=1, fill_method='pad', limit=None, freq=None, **kwargs) [source] Percent change over given number of periods. Parameters: periods : int, default 1 Periods to shift for forming percent change fill_method : str, default ?pad? How to handle NAs before computing percent changes limit : int, default None The number of consecutive NAs to fill before stopping freq : DateOffset, timedelta, or offset alias string, optional Increment to use from time series API (e.

Expanding.kurt(**kwargs) [source] Unbiased expanding kurtosis Returns: same type as input See also pandas.Series.expanding, pandas.DataFrame.expanding

Rolling.std(ddof=1, *args, **kwargs) [source] rolling standard deviation Parameters: ddof : int, default 1 Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements. Returns: same type as input See also pandas.Series.rolling, pandas.DataFrame.rolling

CategoricalIndex.copy(name=None, deep=False, dtype=None, **kwargs) [source] Make a copy of this object. Name and dtype sets those attributes on the new object. Parameters: name : string, optional deep : boolean, default False dtype : numpy dtype or pandas type Returns: copy : Index Notes In most cases, there should be no functional difference from using deep, but if deep is passed it will attempt to deepcopy.