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Pandas DataFrame Aggregate: agg() function

Dataframe.aggregate() function is used to  perform aggregation using one or more operation along the desired axis.
It uses callable, string, dict, or list of string/callables. It can take a string, a function, or a list thereof, and compute all the aggregates at once. 

Syntax

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

Parameter

  • func: It is a callable function, string, dict or list. It is used for aggression of data.
  • axis:By Default 0.{0 or ‘index’, 1 or ‘columns’}
  • *args: It is a Positional arguments to pass to function.
  • **kwargs: It is a keyword arguments to pass to a function.

Return

It returns aggregated DataFrame.

Example

import pandas as pd
import numpy as np

Physics_marks=[44,np.NaN,47,28,39]
Chemistry_marks=[45,46,np.NaN,40,30]
Maths_marks=[35,38,29,30,np.NaN]

Students_marks=pd.DataFrame({'Physics':Physics_marks,
                             'Chemistry':Chemistry_marks,
                             'Maths':Maths_marks})
Students_marks

Output

  Physics Chemistry Maths
0 44.0 45.0 35.0
1 NaN 46.0 38.0
2 47.0 NaN 29.0
3 28.0 40.0 30.0
4 39.0 30.0 NaN

1. Along Rows

Students_marks.aggregate(['sum','max'], axis=0)

Output

  Physics Chemistry Maths
sum 158.0 161.0 132.0
max 47.0 46.0 38.0

2. Along Columns

Students_marks.aggregate(['sum','max'], axis=1)

Output

  sum max
0 124.0 45.0
1 84.0 46.0
2 76.0 47.0
3 98.0 40.0
4 69.0 39.0
Submitted by devanshi.srivastava on February 16, 2021

Devanshi, is working as a Data Scientist with iVagus. She has expertise in Python, NumPy, Pandas and other data science technologies.

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