Panel4D.get_ftype_counts() [source] Return the counts of ftypes in this object.

DataFrameGroupBy.corrwith(other, axis=0, drop=False) Compute pairwise correlation between rows or columns of two DataFrame objects. Parameters: other : DataFrame axis : {0 or ?index?, 1 or ?columns?}, default 0 0 or ?index? to compute column-wise, 1 or ?columns? for row-wise drop : boolean, default False Drop missing indices from result, default returns union of all Returns: correls : Series

Resampler.prod(_method='prod', *args, **kwargs) [source] Compute prod of group values See also pandas.Series.groupby, pandas.DataFrame.groupby, pandas.Panel.groupby

DataFrame.icol(i) [source] DEPRECATED. Use .iloc[:, i] instead

TimedeltaIndex.repeat(repeats, *args, **kwargs) [source] Analogous to ndarray.repeat

Panel4D.rank(axis=0, method='average', numeric_only=None, na_option='keep', ascending=True, pct=False) [source] Compute numerical data ranks (1 through n) along axis. Equal values are assigned a rank that is the average of the ranks of those values Parameters: axis: {0 or ?index?, 1 or ?columns?}, default 0 index to direct ranking method : {?average?, ?min?, ?max?, ?first?, ?dense?} average: average rank of group min: lowest rank in group max: highest rank in group first: ranks assigned

Index.any(*args, **kwargs) [source] Return whether any element is True Parameters: All arguments to numpy.any are accepted. Returns: any : bool or array_like (if axis is specified) A single element array_like may be converted to bool.

TimedeltaIndex.inferred_type

MultiIndex.asi8 = None

Series.tolist() [source] Convert Series to a nested list