The Best Python Books for Data Analysts

What role do books play in acquiring data analysis skills? Discover how you can benefit from the best Python books for data analysts – and how to use them as a supplementary learning material.

Data is a highly valuable asset. In fact, we might even argue that it’s the most valuable asset in the 21st century. The success and performance of products depends on the quality of input data. However, data in its raw form does not give us its full potential. Just like crude oil is distilled into more valuable and useful products, we need to process raw data to really benefit from it.

Python is the most popular programming language in the data science ecosystem; it offers numerous tools to clean, process, analyze, and visualize data. It’s also one of the most common tools in the data analyst’s repertoire.

What Does a Data Analyst Do?

Before we discuss the importance of Python for data analysts, let’s go over the typical tasks data analysts perform. The range of tasks expected from a data analyst varies depending on their position and organization, but we can group their tasks in three main categories.

First, data analysts usually clean data to get it into a more manageable form. They also perform exploratory data analysis to understand the data in terms of underlying distribution, trends, and correlations. This step requires some statistical knowledge.

Last but not least, data analysts extract insights from the data and report their findings. In this final step, they might be doing some deeper data analysis. They’ll also use data visualization for building their reports.

Why might you want to be a data analyst? Well, it is one of the most in-demand positions in the data science ecosystem. It’s also well paid; according to Glassdoor,, the average salary for a data analyst in the US is $86,000 per year.  And pretty much every organization – from small businesses to top tech companies like Meta, Google, Apple, Stripe, and Uber – hire them.

Why Should Data Analysts Learn Python?

Having Python in your skillset will make it much easier to handle tasks in the aforementioned three categories of data cleaning, data analysis, and data visualization.

For instance, Python has pandas, one of the most widely used data analysis and manipulation libraries. This library has numerous functions and methods to clean and handle data (both textual and numerical). On the statistics side, Python has SciPy , NumPy, and many other extremely useful libraries. And data visualization is well represented, too, with Matplotlib, Seaborn, Plotly, and many others.

Long story short, if you are or planning to be a data analyst, you should definitely learn Python. If you are a beginner-level programmer or have never done programming before, you can start with our Python Basics track. Its three courses and over 200 interactive exercises will teach you the foundations of Python as well as how to think like a programmer. As of the writing of this article, the first course of this track, Python Basics: Part I, is free of charge.

Of course, most people choose to supplement their online courses with videos, webinars, articles and books about Python. Let’s focus on the books. Which Python data analysis books are worth your time?

The Best Python Books for Data Analysis

It is important to emphasize that while books are great for theoretical knowledge, you really need hands-on practice when you’re learning to code. I strongly recommend reading these books especially if you’re more into traditional learning methods, but you should also do lots of practice. Writing your own code is essential to mastering Python for data analysis (or for any other application).

The selection of Python books for data analysts in this article is based on my own experience and what I have learned from the data science community. Some of these books are introductory level; others are advanced. Each book title is linked to its Amazon page so that you can find it easily, but we’re not earning any commissions on this and Amazon has nothing to do with my selections.

Data Wrangling with Python: Tips and Tools to Make Your Life Easier

To benefit from this book, you don’t need prior Python or programming experience; the book covers basic Python syntax, data types, and language concepts. This is important, as requiring Python knowledge might be a motivation breaker. But you don’t need to worry about it here.

It contains lots of exercises on collecting, cleaning, analyzing, and presenting data. All these steps align with what we mentioned before about typical tasks for data analysts. The book also covers more specific but frequently needed tasks on raw data, such as removing duplicates.

Last but not least, you’ll get to learn scraping websites and APIs to obtain data and potentially curate your own datasets. The first and foremost requirement for improving data analyst skills is data so being able to create your own dataset is very beneficial.

Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter

We mentioned that pandas is a popular Python data analysis and manipulation library. If there was only one person to teach us about this tool, it’d be Wes McKinney – the creator of pandas.

This book is not only good for learning pandas, it also teaches the basic and advanced features of NumPy (another important Python library for data analysis). You may need this when exploring and understanding data.

Python for Data Analysis also covers creating informative visualizations with Matplotlib. You’ll also learn how to solve real-world data analysis problems with the detailed examples covered in the book.

Data Analysis with Python and PySpark

When it comes to working with very large datasets (i.e. billions of rows), your best option is to distribute both data and computations. Spark is an analytics engine that does this distribution very efficiently. PySpark is a Python API for Spark; it offers a simple and intuitive Python syntax so you can easily use Spark in analytics workflows. Thus, you can apply your Python skills to work with large-scale data using Spark engines.

In a sense, PySpark can be considered a large-scale equivalent to pandas; this book teaches you how to use PySpark to read, write, analyze, and manipulate data.

An Introduction to Statistical Learning with Applications in Python

Statistics help us understand and make inferences from data. Compared to a machine learning researcher, data analysts don’t actually need a deep theoretical statistical knowledge. But it’s good to have a decent level of understanding of statistical concepts. The more you learn about statistics as a data analyst, the better you understand and analyze data.

This book is suitable for statisticians as well as non-statisticians; you don’t need to be a statistician to benefit from it. Either way, the topics covered in this book will definitely help you analyze data.

It also contains many labs implemented in Python. This means you’ll obtain theoretical knowledge and learn how to use Python to apply it. Being able to put theoretical concepts into practice right after you learn them is extremely important. That’s why I strongly believe this is a must-read Python book for data analysts.

Effective Pandas: Patterns for Data Manipulation

I’ve been in the data science domain for about 5 years now, and it is safe for me to say that pandas is by far my most useful Python library. One of its biggest advantages – aside from having an easy-to-understand syntax – is its large and active developer community. The pandas library has been around for a long time, but it’s always up to date with recent developments.

This book by Matt Harrison teaches how to use pandas efficiently. I also follow the author on social media and learn a lot from the tips and tricks he shares. By completing this book and the exercises it contains, you’ll learn how to write efficient pandas code. This is very important; how you write the pandas code plays a crucial role in the performance of your scripts. You can get things done in many different ways, but it’s vital to do so efficiently.

Read Python Data Analyst Books. Then Go Practice!

These are my picks for the best Python books for data analysts. However, neither programming nor data analysis can properly be learned just by reading. The learning process needs to be supported by hands-on practice. For actual learning to take place in your brain, connections between neurons must be formed. You can form and strengthen these connections by continuous practice. Although the books listed in this article contain numerous exercises, you need to write lots of your own code. It’s not enough to just read the exercises. Make sure to have an IDE so you can code and test your solutions.

The best way to practice is consistently. At the very least, try to solve exercises and coding challenges after each chapter; the more frequently you can do it, the better. LearnPython.com offers numerous courses full of interactive exercises. If you’re a little familiar with Python, our Python Basics: Practice course will help you strengthen your core skills. As you progress into more advanced topics, you can start completing other courses and tracks. Happy learning!