pyspark.pandas.DataFrame.aggregate#

DataFrame.aggregate(func)[source]#

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