Essential Books for Learning Pandas

May 26, 2020
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When it comes to data analysis and Python, you can’t escape running into the Pandas library. Since 2012, Pandas usage has grown to be the most popular library in the Python environment by data analysis, scientists, and engineers the world over. Without Pandas, Python simply wouldn’t be as useful as it is today. As such, mastering Pandas is the most important skill that an analyst can pick up when they’re on their journey to becoming a full-fledged Python master. The best Pandas books are easy to read and filled with useful examples.

I’ve read all of the books on this list over the years and recommend each of them for their specific coverage of the library. Pandas is a huge and ever-growing library, so you can’t really get everything you need to know from a single resource.

There is always the official documentation, to get you started, but for concrete and practical examples, the books below take you through all the truly actionable examples of what to do with your data when it comes to:

  • Importing data
  • Cleaning messy data
  • Formatting data for analysis
  • Presentation and visualization of analysis results
  • Critical statistical components of Pandas

Python For Data Analysis

This is the best book on the market for Pandas. Not only is it a holistic overview of the Pandas library and how it interacts with core Python libraries, but it is written by the creator of Pandas, Wes McKinney.

We obviously put this first on this list as not only does the author of this post use this book as a regular reference, but it contains the first holistic coverage of all the critical Pandas material.

The essence of the book is captured in the breakdown of each topic that we cover below:

  • How to setup Pandas and import other critical libraries
  • How to use Jupyter Notebooks and Python basics
  • Fundamental Python data structures and functions – Lists, Dictionaries, Tuples
  • Numpy Basics for array manipulation
  • Pandas Data Structure basics – DataFrames & Series
  • Essential Panddas Functionality
  • Loading data and file formats – text files, JSON data, web api data, and database connectivity
  • Data janitorial tasks dealing with many common data quality problems – NaN values, Replacing values
  • Joining, Concatenating, & Merging data – similar to SQL functions
  • Visualisations & Plotting data – using Pandas and matplotlib
  • Data Aggregation and grouping – Pivot functions, etc.
  • Managing Time series data
  • Advanced Pandas
  • Introducing modeling libraries in Python
  • Advanced functionality in NumPy for managing data with Pandas

One detractor of this book is that since Pandas has taken off, the book has had relatively few updates despite the libraries drastic growth and more information being available about what the most common types of problems end users have with the library are. For instance, renaming columns and converting string datatypes to integers are two of the most common issues end-users, according to SEMRush and StackOverflow data, have with Pandas and there is very little coverage of these topics in the book to the level of details useful for intermediate and advanced Pandas users.

We obviously recommend this book highly and it’s available on Amazon here.

Hands-On Data Analysis with Pandas

Stefanie Molin wrote this book to tackle some of the core use cases for Pandas when crunching large datasets. It ramps up nicely going from basic topics into in-depth tutorials on exploratory data analysis, statistics, and machine learning problems.

The book is highly rated on Amazon largely due to its practical approach to solving business problems with the library.

Contents covered in the book include:

  • Fundamentals of data analysis
  • Statistical fundamentals
  • Setting up your analysis environment
  • Working with Pandas DataFrames and how to understand the basic Pandas data structures such as Series and index
  • Data importing and exporting
  • Adding and removing data
  • Data transformation and cleaning exercises
  • Aggregation functions – Pivot, Groupby functions
  • Visualizations and plotting with Pandas and Matplotlib
  • Plotting with Seaborn
  • Some real-world exercises on stock market data
  • Exploratory Data Analysis of real data
  • Basics of machine learning with scikit-learn and its integration with Pandas

We think the advanced techniques provided at the end of the book are extremely useful. The GitHub repository provided in the book help you to understand Git a little better as a beginner as well as provide the actual source code for the book in a compatible way to reproduce the examples yourself.

The book is available on Amazon.

Pandas Cookbook: Recipes for Scientific Computing, Time Series Analysis and Data Visualization using Python

The Pandas Cookbook is likely the most up to date edition of any book on this list covering the Pandas library. This helps readers quickly catch up with the newest functions and features available in the library as well as in depth reading on the fundamentals of Pandas objects and functions and how they’re used.

Contents Include:

  • Pandas fundamentals – DataFrames and Series
  • Datatypes in Pandas
  • Utilizing Series methods
  • Renaming row and column contents
  • Essential DataFrame operations such as – selecting columns, selecting column methods, ordering columns, comparing missing values
  • Data analysis basics in Pandas such as – sorting, grouping, and changing data types
  • Selecting subsets of data – Series, DataFrame rows
  • Optimization of selection functions
  • Grouping, Selection, and SQL equivalent functions
  • Grouping data and transforming datasets
  • Stacking and unstacking data
  • Merge concatenate and join functions
  • managing time-series data
  • Visualizations with Seaborn, Matplotlib, and Pandas

We recommend the book as a reference for all levels of user as it provides detailed uses of the most used functions within Pandas.

Learning Pandas

The second edition of Learning Pandas is by an effective approach to teaching beginners and intermediate Pandas users the practical parts of the Pandas library. It focuses on using Pandas as an end-to-end analysis library and covers several advanced data collection techniques that can be used along with Pandas including web-scraping.

There are additional chapters at the end of the book which are useful for those interested in finance as well.

The overall topics in the book include:

  • Basic overview of pandas and its object structure
  • Getting started with Pandas and its install setup
  • NumPy for Pandas
  • Working with Pandas Series objects – creation, importing, indexation, slicing data
  • Pandas DataFrame objects – selecting data from rows and columns,
  • Modifying DataFrame structures
  • Accessing data through CSV, text, JSON, web-based APIs, and databases
  • Cleaning up messy data – NaN values
  • Combining and reshaping data – concatenating, joining, merging data
  • Grouping and aggregating data
  • Time series data – dates, time, interval objects
  • Visualization – pandas and statistical analysis with basic EDA plots
  • Applications with practical examples for Finance

The book is available on Amazon here.

Summary

The best pandas books out there are well, the only pandas books out there. You can also look for resources on Pandas education online through many tutorials and courses.

These four books are the best-ranked books on Amazon and while they are for sale, there are many free resources, including the ones on this site for free. Almost all the books have a common theme across the material you’ll learn that are the most critical functions to understand in Pandas: