Index.equals(other) [source] Determines if two Index objects contain the same elements.

Panel4D.to_sparse(*args, **kwargs) [source]

DataFrameGroupBy.mad(axis=None, skipna=None, level=None) Return the mean absolute deviation of the values for the requested axis Parameters: axis : {index (0), columns (1)} 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 Series numeric_only : boolean, default None Include only float, int, boole

TimedeltaIndex.where(cond, other=None) [source] New in version 0.19.0. Return an Index of same shape as self and whose corresponding entries are from self where cond is True and otherwise are from other. Parameters: cond : boolean same length as self other : scalar, or array-like

Index.argmax(axis=None) [source] return a ndarray of the maximum argument indexer See also numpy.ndarray.argmax

Series.idxmin(axis=None, skipna=True, *args, **kwargs) [source] Index of first occurrence of minimum of values. Parameters: skipna : boolean, default True Exclude NA/null values Returns: idxmin : Index of minimum of values See also DataFrame.idxmin, numpy.ndarray.argmin Notes This method is the Series version of ndarray.argmin.

Series.cumsum(axis=None, skipna=True, *args, **kwargs) [source] Return cumulative sum over requested axis. Parameters: axis : {index (0)} skipna : boolean, default True Exclude NA/null values. If an entire row/column is NA, the result will be NA Returns: cumsum : scalar

Series.asfreq(freq, method=None, how=None, normalize=False) [source] Convert TimeSeries to specified frequency. Optionally provide filling method to pad/backfill missing values. Parameters: freq : DateOffset object, or string method : {?backfill?/?bfill?, ?pad?/?ffill?}, default None Method to use for filling holes in reindexed Series (note this does not fill NaNs that already were present): ?pad? / ?ffill?: propagate last valid observation forward to next valid ?backfill? / ?bfill?: use

Expanding.cov(other=None, pairwise=None, ddof=1, **kwargs) [source] expanding sample covariance Parameters: other : Series, DataFrame, or ndarray, optional if not supplied then will default to self and produce pairwise output pairwise : bool, default None If False then only matching columns between self and other will be used and the output will be a DataFrame. If True then all pairwise combinations will be calculated and the output will be a Panel in the case of DataFrame inputs. In th

Index.asof_locs(where, mask) [source] where : array of timestamps mask : array of booleans where data is not NA