pyspark.pandas.DataFrame.agg#
- DataFrame.agg(func)#
- Aggregate using one or more operations over the specified axis. - Parameters
- funcdict or a list
- a dict mapping from column name (string) to aggregate functions (list of strings). If a list is given, the aggregation is performed against all columns. 
 
- Returns
- DataFrame
 
 - See also - DataFrame.apply
- Invoke function on DataFrame. 
- DataFrame.transform
- Only perform transforming type operations. 
- DataFrame.groupby
- Perform operations over groups. 
- Series.aggregate
- The equivalent function for Series. 
 - Notes - agg is an alias for aggregate. Use the alias. - Examples - >>> df = ps.DataFrame([[1, 2, 3], ... [4, 5, 6], ... [7, 8, 9], ... [np.nan, np.nan, np.nan]], ... columns=['A', 'B', 'C']) - >>> df A B C 0 1.0 2.0 3.0 1 4.0 5.0 6.0 2 7.0 8.0 9.0 3 NaN NaN NaN - Aggregate these functions over the rows. - >>> df.agg(['sum', 'min'])[['A', 'B', 'C']].sort_index() A B C min 1.0 2.0 3.0 sum 12.0 15.0 18.0 - Different aggregations per column. - >>> df.agg({'A' : ['sum', 'min'], 'B' : ['min', 'max']})[['A', 'B']].sort_index() A B max NaN 8.0 min 1.0 2.0 sum 12.0 NaN - For multi-index columns: - >>> df.columns = pd.MultiIndex.from_tuples([("X", "A"), ("X", "B"), ("Y", "C")]) >>> df.agg(['sum', 'min'])[[("X", "A"), ("X", "B"), ("Y", "C")]].sort_index() X Y A B C min 1.0 2.0 3.0 sum 12.0 15.0 18.0 - >>> aggregated = df.agg({("X", "A") : ['sum', 'min'], ("X", "B") : ['min', 'max']}) >>> aggregated[[("X", "A"), ("X", "B")]].sort_index() X A B max NaN 8.0 min 1.0 2.0 sum 12.0 NaN