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DataFrame.aggregate() - Pandas DataFrame Basics

The aggregate() function in Pandas is used to perform aggregation operations on a dataframe. It is a powerful tool that can be used to generate summary statistics for a set of data. In this tutorial, we will explore the aggregate() function and how it can be used in a dataframe.

Syntax

The basic syntax for aggregate() function is as follows:

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

Example

Consider the following example that demonstrates the use of aggregate() function:

import pandas as pd

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

# Aggregate the data and calculate the sum, mean and max
result = df.aggregate(['sum', 'mean', 'max'])

print(result)

In this example, we have created a dataframe df that contains some sample data. We have then used the aggregate() function to calculate the sum, mean and maximum value of the data in the dataframe. Finally, we have printed the result.

Output

The output of the above program will be:

         A     B     C
max    4.0   8.0  12.0
mean   2.5   6.5  10.5
sum   10.0  26.0  42.0

In this output, we can see that the aggregate() function has generated summary statistics for each column of the dataframe.

Explanation

The aggregate() function in Pandas is used to perform aggregation operations on a dataframe. These operations include calculating the sum, mean, count, minimum, maximum, and many others.

In the example above, we have passed a list of aggregation functions to be applied on the dataframe. We could have also passed a dictionary where the keys denote the columns and the values are the aggregation functions to be applied on them.

Use

The aggregate() function can be used to calculate summary statistics for a set of data. It is particularly useful when you want to compute multiple aggregation functions on a dataframe simultaneously.

Important Points

  • The aggregate() function in Pandas can be used to perform aggregation operations on a dataframe.
  • It can be used to calculate summary statistics such as mean, sum, count, minimum, maximum, and more.
  • It is useful when you want to compute multiple aggregation functions on a dataframe simultaneously.

Summary

In this tutorial, we have explored the aggregate() function in Pandas. We have seen how it can be used to perform aggregation operations on a dataframe and calculate summary statistics for a set of data. The aggregate() function is a powerful tool in data analysis and can be used to calculate multiple aggregation functions on a dataframe simultaneously.

DataFrame.apply()DataFrame.assign()
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