pandas.tools.pivot.pivot_table¶
- pandas.tools.pivot.pivot_table(data, values=None, rows=None, cols=None, aggfunc='mean', fill_value=None, margins=False)¶
Create a spreadsheet-style pivot table as a DataFrame. The levels in the pivot table will be stored in MultiIndex objects (hierarchical indexes) on the index and columns of the result DataFrame
data : DataFrame values : column to aggregate, optional rows : list of column names or arrays to group on
Keys to group on the x-axis of the pivot table- cols : list of column names or arrays to group on
- Keys to group on the y-axis of the pivot table
- aggfunc : function, default numpy.mean, or list of functions
- If list of functions passed, the resulting pivot table will have hierarchical columns whose top level are the function names (inferred from the function objects themselves)
- fill_value : scalar, default None
- Value to replace missing values with
- margins : boolean, default False
- Add all row / columns (e.g. for subtotal / grand totals)
>>> df A B C D 0 foo one small 1 1 foo one large 2 2 foo one large 2 3 foo two small 3 4 foo two small 3 5 bar one large 4 6 bar one small 5 7 bar two small 6 8 bar two large 7
>>> table = pivot_table(df, values='D', rows=['A', 'B'], ... cols=['C'], aggfunc=np.sum) >>> table small large foo one 1 4 two 6 NaN bar one 5 4 two 6 7
table : DataFrame