Data Wrangling with Pandas

In this lesson, we are going to explore techniques to permit advanced data exploration and analysis using Python. We will focus on the Pandas library, focusing on real-life case scenarios to help you to better understand how data can be processed using Pandas.

In particular, we will explore the concept of tidy datasets, the concept of multi-index, and its impact on real datasets, and the concept of concatenating and merging different Pandas objects, with a focus on DataFrames. We’ll look at how to transform a DataFrame, and how to plot results with Pandas.

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Learning Objectives

• Learn what a tidy dataset is and how to tidy data
• Merge and concatenate tidy data
• Learn about multi-level indexing and how to use it
• Transform datasets using advanced data reshape operations with Pandas
• Plot results with Pandas

Intended Audience

This lesson is intended for data scientists, data engineers, or anyone looking to perform data exploration and analysis with Pandas.

Prerequisites

To get the most out of this lesson, you should have some knowledge of Python and the Pandas library. If you want to brush up on Pandas, we recommend taking our Working with Pandas lesson.

Resources

The GitHub repo for this lesson can be found here.