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

Expanding.count(**kwargs) [source] expanding count of number of non-NaN observations inside provided window. Returns: same type as input See also pandas.Series.expanding, pandas.DataFrame.expanding

DataFrame.to_sparse(fill_value=None, kind='block') [source] Convert to SparseDataFrame Parameters: fill_value : float, default NaN kind : {?block?, ?integer?} Returns: y : SparseDataFrame

Expanding.min(*args, **kwargs) [source] expanding minimum Parameters: how : string, default ?min? (DEPRECATED) Method for down- or re-sampling Returns: same type as input See also pandas.Series.expanding, pandas.DataFrame.expanding

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

Expanding.var(ddof=1, *args, **kwargs) [source] expanding 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.expanding, pandas.DataFrame.expanding

DataFrame.add(other, axis='columns', level=None, fill_value=None) [source] Addition of dataframe and other, element-wise (binary operator add). Equivalent to dataframe + other, but with support to substitute a fill_value for missing data in one of the inputs. Parameters: other : Series, DataFrame, or constant axis : {0, 1, ?index?, ?columns?} For Series input, axis to match Series index on fill_value : None or float value, default None Fill missing (NaN) values with this value. If both

DataFrameGroupBy.tshift(periods=1, freq=None, axis=0) Shift the time index, using the index?s frequency if available. Parameters: periods : int Number of periods to move, can be positive or negative freq : DateOffset, timedelta, or time rule string, default None Increment to use from the tseries module or time rule (e.g. ?EOM?) axis : int or basestring Corresponds to the axis that contains the Index Returns: shifted : NDFrame Notes If freq is not specified then tries to use the fr

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

Panel4D.ne(other, axis=None) [source] Wrapper for comparison method ne