Pandas DataFrame.aggregate()

Pandas DataFrame.aggregate() is a method used for aggregating data. It allows us to compute a set of summary statistics for the entire DataFrame or a single column.

Syntax

DataFrame.aggregate(func=None, axis=0, *args, **kwargs)

Parameters

• func: A single function or a list of functions to apply to the data. The default is to compute the average.
• axis: 0 for column-wise or 1 for row-wise.
• *args, **kwargs: Additional arguments passed to the function.

Example

Consider a DataFrame df with four columns A, B, C, and D.

import pandas as pd
data = {'A': [1, 1, 2, 2],
        'B': [3, 4, 5, 6],
        'C': [7, 8, 9, 10],
        'D': [11, 12, 13, 14]}
df = pd.DataFrame(data)

We can compute the average of all the columns using the following command:

df.aggregate('mean')

This will return the average of all the columns:

A    1.5
B    4.5
C    8.5
D    12.5
dtype: float64

Similarly, we can compute multiple summary statistics using a list of functions:

df.aggregate(['sum', 'min', 'max'])

This will return the sum, minimum, and maximum values of all the columns:

       A   B   C   D
sum    6  18  34  50
min    1   3   7  11
max    2   6  10  14

We can also apply different functions to different columns using a dictionary:

df.aggregate({'A': 'sum', 'B': 'mean', 'C': 'max', 'D': 'min'})

This will return the sum of column A, the mean of column B, the maximum value of column C, and the minimum value of column D:

A     6
B     4.5
C    10.0
D    11.0
dtype: float64

Conclusion

Pandas DataFrame.aggregate() is a useful method for computing summary statistics for a DataFrame or a single column. We can compute a variety of summary statistics by providing a list of functions, and we can apply different functions to different columns using a dictionary.