DataFrameGroupBy.count() [source] Compute count of group, excluding missing values

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

DataFrameGroupBy.corr(method='pearson', min_periods=1) Compute pairwise correlation of columns, excluding NA/null values Parameters: method : {?pearson?, ?kendall?, ?spearman?} pearson : standard correlation coefficient kendall : Kendall Tau correlation coefficient spearman : Spearman rank correlation min_periods : int, optional Minimum number of observations required per pair of columns to have a valid result. Currently only available for pearson and spearman correlation Returns: y :

DataFrameGroupBy.boxplot(grouped, subplots=True, column=None, fontsize=None, rot=0, grid=True, ax=None, figsize=None, layout=None, **kwds) [source] Make box plots from DataFrameGroupBy data. Parameters: grouped : Grouped DataFrame subplots : False - no subplots will be used True - create a subplot for each group column : column name or list of names, or vector Can be any valid input to groupby fontsize : int or string rot : label rotation angle grid : Setting this to True will show th

DataFrameGroupBy.bfill(limit=None) [source] Backward fill the values Parameters: limit : integer, optional limit of how many values to fill See also pandas.Series.groupby, pandas.DataFrame.groupby, pandas.Panel.groupby

DataFrameGroupBy.any(axis=None, bool_only=None, skipna=None, level=None, **kwargs) Return whether any element is True over 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 bool_only : boolean, default None Include only boolean col

DataFrameGroupBy.all(axis=None, bool_only=None, skipna=None, level=None, **kwargs) Return whether all elements are True over 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 bool_only : boolean, default None Include only boolean c

DataFrameGroupBy.agg(arg, *args, **kwargs) [source] Aggregate using input function or dict of {column -> function} Parameters: arg : function or dict Function to use for aggregating groups. If a function, must either work when passed a DataFrame or when passed to DataFrame.apply. If passed a dict, the keys must be DataFrame column names. Accepted Combinations are: string cythonized function name function list of functions dict of columns -> functions nested dict of names -> dict

DataFrame.__iter__() [source] Iterate over infor axis

DataFrame.xs(key, axis=0, level=None, drop_level=True) [source] Returns a cross-section (row(s) or column(s)) from the Series/DataFrame. Defaults to cross-section on the rows (axis=0). Parameters: key : object Some label contained in the index, or partially in a MultiIndex axis : int, default 0 Axis to retrieve cross-section on level : object, defaults to first n levels (n=1 or len(key)) In case of a key partially contained in a MultiIndex, indicate which levels are used. Levels can b