Series.tail(n=5) [source] Returns last n rows

DataFrame.add_suffix(suffix) [source] Concatenate suffix string with panel items names. Parameters: suffix : string Returns: with_suffix : type of caller

DatetimeIndex.putmask(mask, value) [source] return a new Index of the values set with the mask See also numpy.ndarray.putmask

MultiIndex.unique() [source] Return Index of unique values in the object. Significantly faster than numpy.unique. Includes NA values. The order of the original is preserved. Returns: uniques : Index

Panel4D.prod(axis=None, skipna=None, level=None, numeric_only=None, **kwargs) [source] Return the product of the values for the requested axis Parameters: axis : {labels (0), items (1), major_axis (2), minor_axis (3)} skipna : boolean, default True Exclude NA/null values. If an entire row/column is NA, the result will be NA level : int or level name, default None If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Panel numeric_only : boolean,

Index.sym_diff(*args, **kwargs) [source]

Panel4D.ftypes Return the ftypes (indication of sparse/dense and dtype) in this object.

Panel4D.set_value(*args, **kwargs) [source] Quickly set single value at (item, major, minor) location Parameters: item : item label (panel item) major : major axis label (panel item row) minor : minor axis label (panel item column) value : scalar takeable : interpret the passed labels as indexers, default False Returns: panel : Panel If label combo is contained, will be reference to calling Panel, otherwise a new object

Index.identical(other) [source] Similar to equals, but check that other comparable attributes are also equal

GroupBy.cumcount(ascending=True) [source] Number each item in each group from 0 to the length of that group - 1. Essentially this is equivalent to >>> self.apply(lambda x: Series(np.arange(len(x)), x.index)) Parameters: ascending : bool, default True If False, number in reverse, from length of group - 1 to 0. See also pandas.Series.groupby, pandas.DataFrame.groupby, pandas.Panel.groupby Examples >>> df = pd.DataFrame([['a'], ['a'], ['a'], ['b'], ['b'], ['a']], ...