pandas.pivot_table¶
- pandas.pivot_table(data, values=None, rows=None, cols=None, aggfunc='mean', fill_value=None, margins=False, dropna=True)¶
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
Parameters : 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)
dropna : boolean, default True
Do not include columns whose entries are all NaN
Returns : table : DataFrame
Examples
>>> 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