
The agg()
method in pandas allows you to apply a function or a list of function names to be executed along one of the axis of the DataFrame.
Syntax –
DataFrame.agg(func=None, axis=0, *args, **kwargs)
func – Function to use for aggregating the data.
axis – 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.
Examples –
1 . Create a DataFrame
Let’s create a dataframe to work with.
import pandas as pd
df = pd.DataFrame({'A':[1, 2, 3],
'B':[4, 5, 6],
'C':[7, 8, 9]})
df

2 . Apply a Function to the columns –
Now, Let’s say you want to calculate the sum of all the columns. For that you can use the agg() method in pandas.
df.agg(['sum'])

3 . Apply multiple Functions to columns –
You can also apply multiple functions together. Let’s say along with the sum you also want to calculate the mean of the columns.
df.agg(['sum','mean'])

4 . Apply different Functions to different columns –
You can also apply different functions to different columns using a dictionary.
df.agg({'A':['sum','mean'],
'B':['mean','max']})

5 . Apply functions to Rows –
To apply a function to each rows of the dataframe you need to set the axis parameter to axis=1 or columns.
Let’s calculate the sum of each rows.
df.agg([sum], axis=1)
