500 Exercises to Master Python Pandas

A complete and thorough guide for learning Pandas

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I’ve been using Pandas for over 3 years both at my jobs working as a data scientist and for content creation.

Pandas is one of most frequently used tools in the data science ecosystem and continues to be a top choice when working with tabular data. It provides numerous functions and methods to clean, manipulate, preprocess, and visualize data.

Based on my experience working on real-life datasets to solve real issues and using the library for personal projects, I created 500 exercises with the goal of teaching you how to handle data with Pandas.

The exercises cover simple operations like creating DataFrames by reading data from different sources as well as complex ones such as finding the top 3 items based on an aggregated metric for each group.

You’ll also get to learn how to use Pandas for data visualization and style Pandas DataFrames to integrate visual components when displaying them.

I also created two lectures on how to take a raw dataset that is not very usable in its current format and then clean it and preprocess it to create a ready-to-use dataset for the downstream processes.

These are just a small part of the entire set of 500 exercises. Whether you are currently working in a data-related role or planning to start your data science journey, you’ll find many things to learn from these exercises.

The exercises are grouped into 22 lectures created as a Jupyter notebook. You can access them all in this Github repository.

Online video course version

I also created a course on Udemy where I go over the notebooks explaining each exercise. The course includes more learning material than the notebooks because as I over the exercises, I give tips and show different variations of some of them. Hence, to get the most out of these notebooks, I recommend downloading them and following up when watching the video lectures.

If you download the notebooks, make sure you also download the data and assets folders. The required datasets available in the data folder. The assets folder includes images and drawings I used…