Resampler.var(ddof=1, *args, **kwargs) [source] Compute variance of groups, excluding missing values Parameters: ddof : integer, default 1 degrees of freedom

Resampler.__iter__() [source] Groupby iterator Returns: Generator yielding sequence of (name, subsetted object) for each group

Resampler.std(ddof=1, *args, **kwargs) [source] Compute standard deviation of groups, excluding missing values Parameters: ddof : integer, default 1 degrees of freedom

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

Resampler.transform(arg, *args, **kwargs) [source] Call function producing a like-indexed Series on each group and return a Series with the transformed values Parameters: func : function To apply to each group. Should return a Series with the same index Returns: transformed : Series Examples >>> resampled.transform(lambda x: (x - x.mean()) / x.std())

Resampler.sem(_method='sem', *args, **kwargs) [source] Compute standard error of the mean of groups, excluding missing values For multiple groupings, the result index will be a MultiIndex Parameters: ddof : integer, default 1 degrees of freedom See also pandas.Series.groupby, pandas.DataFrame.groupby, pandas.Panel.groupby

Resampler.size(_method='size') [source] Compute group sizes See also pandas.Series.groupby, pandas.DataFrame.groupby, pandas.Panel.groupby

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

Resampler.pad(limit=None) [source] Forward fill the values Parameters: limit : integer, optional limit of how many values to fill See also Series.fillna, DataFrame.fillna

Resampler.ohlc(_method='ohlc', *args, **kwargs) [source] Compute sum of values, excluding missing values For multiple groupings, the result index will be a MultiIndex See also pandas.Series.groupby, pandas.DataFrame.groupby, pandas.Panel.groupby