pandas.pivot(index, columns, values) [source] Produce ?pivot? table based on 3 columns of this DataFrame. Uses unique values from index / columns and fills with values. Parameters: index : ndarray Labels to use to make new frame?s index columns : ndarray Labels to use to make new frame?s columns values : ndarray Values to use for populating new frame?s values Returns: DataFrame Notes Obviously, all 3 of the input arguments must have the same length

Panel.sample(n=None, frac=None, replace=False, weights=None, random_state=None, axis=None) [source] Returns a random sample of items from an axis of object. New in version 0.16.1. Parameters: n : int, optional Number of items from axis to return. Cannot be used with frac. Default = 1 if frac = None. frac : float, optional Fraction of axis items to return. Cannot be used with n. replace : boolean, optional Sample with or without replacement. Default = False. weights : str or ndarray

Panel4D.rmod(other, axis=0) [source] Modulo of series and other, element-wise (binary operator rmod). Equivalent to other % panel. Parameters: other : Panel or Panel4D axis : {labels, items, major_axis, minor_axis} Axis to broadcast over Returns: Panel4D See also Panel4D.mod

Series.nsmallest(*args, **kwargs) [source] Return the smallest n elements. Parameters: n : int Return this many ascending sorted values keep : {?first?, ?last?, False}, default ?first? Where there are duplicate values: - first : take the first occurrence. - last : take the last occurrence. take_last : deprecated Returns: bottom_n : Series The n smallest values in the Series, in sorted order See also Series.nlargest Notes Faster than .sort_values().head(n) for small n relative to

Series.cat.rename_categories(*args, **kwargs) [source] Renames categories. The new categories has to be a list-like object. All items must be unique and the number of items in the new categories must be the same as the number of items in the old categories. Parameters: new_categories : Index-like The renamed categories. inplace : boolean (default: False) Whether or not to rename the categories inplace or return a copy of this categorical with renamed categories. Returns: cat : Catego

DatetimeIndex.date Returns numpy array of python datetime.date objects (namely, the date part of Timestamps without timezone information).

Rolling.sum(*args, **kwargs) [source] rolling sum Parameters: how : string, default None (DEPRECATED) Method for down- or re-sampling Returns: same type as input See also pandas.Series.rolling, pandas.DataFrame.rolling

Rolling.var(ddof=1, *args, **kwargs) [source] rolling variance 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

classmethod MultiIndex.from_product(iterables, sortorder=None, names=None) [source] Make a MultiIndex from the cartesian product of multiple iterables Parameters: iterables : list / sequence of iterables Each iterable has unique labels for each level of the index. sortorder : int or None Level of sortedness (must be lexicographically sorted by that level). names : list / sequence of strings or None Names for the levels in the index. Returns: index : MultiIndex See also MultiInd

Series.sub(other, level=None, fill_value=None, axis=0) [source] Subtraction of series and other, element-wise (binary operator sub). Equivalent to series - other, but with support to substitute a fill_value for missing data in one of the inputs. Parameters: other: Series or scalar value fill_value : None or float value, default None (NaN) Fill missing (NaN) values with this value. If both Series are missing, the result will be missing level : int or name Broadcast across a level, matchi