DataFrameGroupBy.resample(rule, *args, **kwargs) [source] Provide resampling when using a TimeGrouper Return a new grouper with our resampler appended See also pandas.Series.groupby, pandas.DataFrame.groupby, pandas.Panel.groupby

DataFrameGroupBy.shift(periods=1, freq=None, axis=0) [source] Shift each group by periods observations Parameters: periods : integer, default 1 number of periods to shift freq : frequency string axis : axis to shift, default 0 See also pandas.Series.groupby, pandas.DataFrame.groupby, pandas.Panel.groupby

DataFrameGroupBy.size() [source] Compute group sizes See also pandas.Series.groupby, pandas.DataFrame.groupby, pandas.Panel.groupby

DataFrameGroupBy.rank(axis=0, method='average', numeric_only=None, na_option='keep', ascending=True, pct=False) 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

DataFrameGroupBy.quantile(q=0.5, axis=0, numeric_only=True, interpolation='linear') Return values at the given quantile over requested axis, a la numpy.percentile. Parameters: q : float or array-like, default 0.5 (50% quantile) 0 <= q <= 1, the quantile(s) to compute axis : {0, 1, ?index?, ?columns?} (default 0) 0 or ?index? for row-wise, 1 or ?columns? for column-wise interpolation : {?linear?, ?lower?, ?higher?, ?midpoint?, ?nearest?} New in version 0.18.0. This optional par

DataFrameGroupBy.plot Class implementing the .plot attribute for groupby objects

DataFrameGroupBy.pct_change(periods=1, fill_method='pad', limit=None, freq=None, **kwargs) Percent change over given number of periods. Parameters: periods : int, default 1 Periods to shift for forming percent change fill_method : str, default ?pad? How to handle NAs before computing percent changes limit : int, default None The number of consecutive NAs to fill before stopping freq : DateOffset, timedelta, or offset alias string, optional Increment to use from time series API (e.g.

DataFrameGroupBy.idxmax(axis=0, skipna=True) Return index of first occurrence of maximum over requested axis. NA/null values are excluded. Parameters: axis : {0 or ?index?, 1 or ?columns?}, default 0 0 or ?index? for row-wise, 1 or ?columns? for column-wise skipna : boolean, default True Exclude NA/null values. If an entire row/column is NA, the result will be first index. Returns: idxmax : Series See also Series.idxmax Notes This method is the DataFrame version of ndarray.argmax.

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

DataFrameGroupBy.idxmin(axis=0, skipna=True) Return index of first occurrence of minimum over requested axis. NA/null values are excluded. Parameters: axis : {0 or ?index?, 1 or ?columns?}, default 0 0 or ?index? for row-wise, 1 or ?columns? for column-wise skipna : boolean, default True Exclude NA/null values. If an entire row/column is NA, the result will be NA Returns: idxmin : Series See also Series.idxmin Notes This method is the DataFrame version of ndarray.argmin.