Pandas DataFrame Agg Method

Introduction

In this lab, you will learn how to use the agg() method in the pandas library for aggregating data in a DataFrame. This method allows you to perform one or more operations on a specified axis, such as rows or columns, and returns a scalar, Series, or DataFrame based on the chosen function.

VM Tips

After the VM startup is done, click the top left corner to switch to the Notebook tab to access Jupyter Notebook for practice.

Sometimes, you may need to wait a few seconds for Jupyter Notebook to finish loading. The validation of operations cannot be automated because of limitations in Jupyter Notebook.

If you face issues during learning, feel free to ask Labby. Provide feedback after the session, and we will promptly resolve the problem for you.

Skills Graph

Import the pandas library

First, you need to import the pandas library using the import statement:

import pandas as pd

Create a DataFrame

Next, create a DataFrame object to work with. You can use the pd.DataFrame() function to create a DataFrame from a list or array. Here's an example:

df = pd.DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]], columns=['A', 'B', 'C'])

Aggregating DataFrame with a single function over the rows

To aggregate the DataFrame using a single function, you can use the agg() method. Specify the function you want to apply to the rows using a string function name or a function object. Here's an example:

print("Printing the sum of values in DataFrame")
print(df.agg(["sum"]))

Aggregating DataFrame with a single function over the columns

To aggregate the DataFrame using a single function over the columns, set the axis parameter to 'columns'. This will apply the function to each column instead of each row. Here's an example:

print("Printing the minimum value in DataFrame")
print(df.agg(["min"], axis='columns'))

Aggregating DataFrame with a list of functions over the rows and columns

You can also aggregate the DataFrame using a list of functions. This allows you to perform multiple operations on the rows or columns. Here's an example:

print("Printing sum and min of the DataFrame with default axis")
print(df.agg(["sum", "min"]))

Aggregating DataFrame with different functions over the columns

For more flexibility, you can pass a dictionary of column names and corresponding functions to the agg() method. This allows you to apply different functions to different columns. Here's an example:

print("Printing different aggregation functions over the columns")
print(df.agg({'A': ["sum"], 'B': ["min", "max"], 'C': ["count"]}))

Summary

In this lab, you learned how to use the agg() method in pandas to aggregate data in a DataFrame. You now know how to apply single and multiple functions over rows and columns of the DataFrame. This method is useful for performing various aggregation operations on your data. Experiment with different functions and axes to analyze and summarize your DataFrame. Happy analyzing!