pandas.DataFrame.agg — pandas 2.2.3 documentation (original) (raw)

DataFrame.agg(func=None, axis=0, *args, **kwargs)[source]#

Aggregate using one or more operations over the specified axis.

Parameters:

funcfunction, str, list or dict

Function to use for aggregating the data. If a function, must either work when passed a DataFrame or when passed to DataFrame.apply.

Accepted combinations are:

• function
• string function name
• list of functions and/or function names, e.g. [np.sum, 'mean']
• dict of axis labels -> functions, function names or list of such.

axis{0 or ‘index’, 1 or ‘columns’}, default 0

If 0 or ‘index’: apply function to each column. If 1 or ‘columns’: apply function to each row.

*args

Positional arguments to pass to func.

**kwargs

Keyword arguments to pass to func.

Returns:

scalar, Series or DataFrame

The return can be:

• scalar : when Series.agg is called with single function
• Series : when DataFrame.agg is called with a single function
• DataFrame : when DataFrame.agg is called with several functions

Notes

The aggregation operations are always performed over an axis, either the index (default) or the column axis. This behavior is different fromnumpy 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 tonumpy.mean(arr_2d, axis=0).

agg is an alias for aggregate. Use the alias.

Functions that mutate the passed object can produce unexpected behavior or errors and are not supported. See Mutating with User Defined Function (UDF) methodsfor more details.

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

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 sum 12.0 NaN min 1.0 2.0 max NaN 8.0

Aggregate different functions over the columns and rename the index of the resulting DataFrame.

df.agg(x=('A', 'max'), y=('B', 'min'), z=('C', 'mean')) A B C x 7.0 NaN NaN y NaN 2.0 NaN z NaN NaN 6.0

Aggregate over the columns.

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