Pandas DataFrame.aggregate()

The main task of DataFrame.aggregate() function is to apply some aggregation to one or more column. Most frequently used aggregations are:

sum: It is used to return the sum of the values for the requested axis.

min: It is used to return the minimum of the values for the requested axis.

max: It is used to return the maximum values for the requested axis.

Syntax:

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

Parameters:

func: It refers callable, string, dictionary, or list of string/callables.

It is used for aggregating the data. For a function, it must either work when passed to a DataFrame or DataFrame.apply(). For a DataFrame, it can pass a dict, if the keys are the column names.

axis: (default 0): It refers to 0 or 'index', 1 or 'columns'

0 or 'index': It is an apply function for each column.

1 or 'columns': It is an apply function for each row.

*args: It is a positional argument that is to be passed to func.

**kwargs: It is a keyword argument that is to be passed to the func.

Returns:

It returns the scalar, Series or DataFrame.

scalar: It is being used when Series.agg is called with the single function.

Series: It is being used when DataFrame.agg is called for the single function.

DataFrame: It is being used when DataFrame.agg is called for the several functions.

Example:

import pandas as pd
import numpy as np
info=pd.DataFrame([[1,5,7],[10,12,15],[18,21,24],[np.nan,np.nan,np.nan]],columns=['X','Y','Z'])
info.agg(['sum','min'])

Output:

X     Y     Z
sum  29.0  38.0  46.0
min   1.0   5.0   7.0

Example2:

import pandas as pd
import numpy as np
info=pd.DataFrame([[1,5,7],[10,12,15],[18,21,24],[np.nan,np.nan,np.nan]],columns=['X','Y','Z'])
df.agg({'A' : ['sum', 'min'], 'B' : ['min', 'max']})

Output:

      X       Y  
max   NaN  21.0
min   1.0  12.0
sum  29.0  NaN