pandas.DataFrame.agg#

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 eitherwork 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 tofunc.

**kwargs

Keyword arguments to pass tofunc.

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

See also

DataFrame.apply

Perform any type of operations.

DataFrame.transform

Perform transformation type operations.

pandas.DataFrame.groupby

Perform operations over groups.

pandas.DataFrame.resample

Perform operations over resampled bins.

pandas.DataFrame.rolling

Perform operations over rolling window.

pandas.DataFrame.expanding

Perform operations over expanding window.

pandas.core.window.ewm.ExponentialMovingWindow

Perform operation over exponential weighted window.

Notes

The aggregation operations are always performed over an axis, either theindex (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 flattenedarray, e.g.,numpy.mean(arr_2d) as opposed tonumpy.mean(arr_2d,axis=0).

agg is an alias foraggregate. Use the alias.

Functions that mutate the passed object can produce unexpectedbehavior or errors and are not supported. SeeMutating 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     Csum  12.0  15.0  18.0min   1.0   2.0   3.0

Different aggregations per column.

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

Aggregate different functions over the columns and rename the index of the resultingDataFrame.

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

Aggregate over the columns.

>>>df.agg("mean",axis="columns")0    2.01    5.02    8.03    NaNdtype: float64