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']], ...

GroupBy.prod() [source] Compute prod of group values See also pandas.Series.groupby, pandas.DataFrame.groupby, pandas.Panel.groupby

DatetimeIndex.is_boolean() [source]

CategoricalIndex.tolist() [source] return a list of the Index values

DatetimeIndex.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.

DatetimeIndex.get_slice_bound(label, side, kind) [source] Calculate slice bound that corresponds to given label. Returns leftmost (one-past-the-rightmost if side=='right') position of given label. Parameters: label : object side : {?left?, ?right?} kind : {?ix?, ?loc?, ?getitem?}

CategoricalIndex.to_datetime(dayfirst=False) [source] DEPRECATED: use pandas.to_datetime() instead. For an Index containing strings or datetime.datetime objects, attempt conversion to DatetimeIndex