pandas.DataFrame.aggregate

DataFrame.aggregate(func, axis=0, *args, **kwargs) [source]

Aggregate using one or more operations over the specified axis.

New in version 0.20.0.

Parameters:

func : function, string, dictionary, or list of string/functions Function to use for aggregating the data. If a function, must either work when passed a DataFrame or when passed to DataFrame.apply. For a DataFrame, can pass a dict, if the keys are DataFrame column names. Accepted combinations are: string function name. function. list of functions. dict of column names -> functions (or list of functions). axis : {0 or ‘index’, 1 or ‘columns’}, default 0 0 or ‘index’: apply function to each column. 1 or ‘columns’: apply function to each row. *args Positional arguments to pass to func. **kwargs Keyword arguments to pass to func.

Returns:

aggregated : DataFrame

See also

DataFrame.apply

Perform any type of operations.

DataFrame.transform

Perform transformation type operations.

pandas.core.groupby.GroupBy

Perform operations over groups.

pandas.core.resample.Resampler

Perform operations over resampled bins.

pandas.core.window.Rolling

Perform operations over rolling window.

pandas.core.window.Expanding

Perform operations over expanding window.

pandas.core.window.EWM

Perform operation over exponential weighted window.

Notes

agg is an alias for aggregate. Use the alias.

A passed user-defined-function will be passed a Series for evaluation.

The aggregation operations are always performed over an axis, either the index (default) or the column axis. This behavior is different from numpy aggregation functions (mean, median, prod, sum, std, var), where the default is to compute the aggregation of the flattened array, e.g., numpy.mean(arr_2d) as opposed to numpy.mean(arr_2d, axis=0).

agg is an alias for aggregate. Use the alias.

Examples

>>> df = pd.DataFrame([[1, 2, 3],
...                    [4, 5, 6],
...                    [7, 8, 9],
...                    [np.nan, np.nan, np.nan]],
...                   columns=['A', 'B', 'C'])

Aggregate these functions over the rows.

>>> df.agg(['sum', 'min'])
        A     B     C
sum  12.0  15.0  18.0
min   1.0   2.0   3.0

Different aggregations per column.

>>> df.agg({'A' : ['sum', 'min'], 'B' : ['min', 'max']})
        A    B
max   NaN  8.0
min   1.0  2.0
sum  12.0  NaN

Aggregate over the columns.

>>> df.agg("mean", axis="columns")
0    2.0
1    5.0
2    8.0
3    NaN
dtype: float64