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Best Pandas Books You Must Read

In this post, we have prepared a curated top list of reading recommendations for beginners and experienced. This hand-picked list of the best Pandas books and tutorials can help fill your brain this April and ensure you’re getting smarter. We have also mentioned the brief introduction of each book based on the relevant Amazon or Reddit descriptions.

1. Python for Data Analysis (2012)

Python for Data Analysis is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. It is also a practical, modern introduction to scientific computing in Python, tailored for data-intensive applications. This is a book about the parts of the Python language and libraries you’ll need to effectively solve a broad set of data analysis problems. This book is not an exposition on analytical methods using Python as the implementation language. Written by Wes McKinney, the main author…

Author(s): Wes McKinney

2. Pandas Cookbook (2017)

Pandas is one of the most powerful, flexible, and efficient scientific computing packages in Python. With this book, you will explore data in pandas through dozens of practice problems with detailed solutions in iPython notebooks. This book will provide you with clean, clear recipes, and solutions that explain how to handle common data manipulation and scientific computing tasks with pandas. You will work with different types of datasets, and perform data manipulation…

Author(s): Theodore Petrou

3. Learning Pandas – Python Data Discovery and Analysis Made Easy (2015)

This learner’s guide will help you understand how to use the features of pandas for interactive data manipulation and analysis. This book is your ideal guide to learning about pandas, all the way from installing it to creating one- and two-dimensional indexed data structures, indexing and slicing-and-dicing that data to derive results, loading data from local and Internet-based resources, and finally creating effective visualizations to form quick insights. You start with an overview of pandas…

Author(s): Michael Heydt

4. Pandas for Everyone: Python Data Analysis (Addison-Wesley Data & Analytics Series) (2018)

The Hands-On, Example-Rich Introduction to Pandas Data Analysis in Python. Today, analysts must manage data characterized by extraordinary variety, velocity, and volume. Using the open source Pandas library, you can use Python to rapidly automate and perform virtually any data analysis task, no matter how large or complex. Pandas can help you ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. Pandas for Everyone brings together practical…
Author(s): Daniel Y. Chen

5. Python Data Science Handbook: Essential Tools for Working with Data (2016)

For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming…

Author(s): Jake VanderPlas

6. Python for Data Science For Dummies (For Dummies (Computer/Tech)) (2015)

Unleash the power of Python for your data analysis projects with For Dummies! Python is the preferred programming language for data scientists and combines the best features of Matlab, Mathematica, and R into libraries specific to data analysis and visualization. Python for Data Science For Dummies shows you how to take advantage of Python programming to acquire, organize, process, and analyze large amounts of information and use basic statistics concepts to identify trends and patterns.

Author(s): John Paul Mueller, Luca Massaron

7. Programming ASP.NET MVC 4: Developing Real-World Web Applications with ASP.NET MVC (2012)

Get up and running with ASP.NET MVC 4, and learn how to build modern server-side web applications. This guide helps you understand how the framework performs, and shows you how to use various features to solve many real-world development scenarios you’re likely to face. In the process, you’ll learn how to work with HTML, JavaScript, the Entity Framework, and other web technologies. You’ll start by learning core concepts such as the Model-View-Controller architectural pattern, and then work your way toward…

Author(s): Jess Chadwick, Todd Snyder

8. Data Visualization with Python and JavaScript: Scrape, Clean, Explore & Transform (2016)

Learn how to turn raw data into rich, interactive web visualizations with the powerful combination of Python and JavaScript. With this hands-on guide, author Kyran Dale teaches you how build a basic dataviz toolchain with best-of-breed Python and JavaScript libraries—including Scrapy, Matplotlib, Pandas, Flask, and D3—for crafting engaging, browser-based visualizations. As a working example, throughout the book Dale walks you through transforming Wikipedia’s table-based list of Nobel Prize winners into an interactive visualization. You’ll examine steps along the entire toolchain…

Author(s): Kyran Dale

9. EJB 3 in Action (2014)

Building on the bestselling first edition, EJB 3 in Action, Second Edition tackles EJB 3.2 head-on, through numerous code samples, real-life scenarios, and illustrations. This book is a fast-paced tutorial for Java EE 6 business component development using EJB 3.2, JPA 2, and CDI. Besides covering the basics of EJB 3.2, this book includes in-depth EJB 3.2 internal implementation details, best practices, design patterns, and performance tuning tips. The EJB 3 framework provides a standard way…

Author(s): Debu Panda, Reza Rahman

10. Cython (2015)

Build software that combines Python’s expressivity with the performance and control of C (and C++). It’s possible with Cython, the compiler and hybrid programming language used by foundational packages such as NumPy, and prominent in projects including Pandas, h5py, and scikits-learn. In this practical guide, you’ll learn how to use Cython to improve Python’s performance—up to 3000x— and to wrap C and C++ libraries in Python with ease. Author Kurt Smith takes you through Cython’s capabilities…

Author(s): Kurt W. Smith

You might also be interested in: Ruby, Hadoop, Dojo, Slack, Express.js, Matlab, ASP.NET MVC, SAAS, Zend, Jenkins Books.

We highly recommend you to buy all paper or e-books in a legal way, for example, on Amazon. But sometimes it might be a need to dig deeper beyond the shiny book cover. Before making a purchase, you can visit resources like Genesis and download some Pandas books mentioned below at your own risk. Once again, we do not host any illegal or copyrighted files, but simply give our visitors a choice and hope they will make a wise decision.

Personal Finance With Python: using pandas, requests, and recurrent.

Author(s): Humber, Max
Publisher: APRESS, Year: 2018, Size: 8 Mb, Ext: epub
ID: 2242313

Python Data Analytics: With Pandas, NumPy, and Matplotlib

Author(s): Fabio Nelli
Publisher: Apress, Year: 2018, Size: 14 Mb, Ext: pdf
ID: 2268922

Python Data Analytics with Pandas, NumPy and Matplotlib [2nd ed.]

Author(s): Fabio Nelli
Publisher: Apress, Year: 2018, Size: 10 Mb, Ext: pdf
ID: 2268961

Python Data Analytics With Pandas, NumPy, and Matplotlib

Author(s): Fabio Nelli
Publisher: Apress, Year: 2018, Size: 15 Mb, Ext: epub
ID: 2269641

Personal Finance with Python: Using pandas, Requests, and Recurrent

Author(s): Max Humber
Publisher: Apress, Year: 2018, Size: 4 Mb, Ext: pdf
ID: 2270495

Hands-On Data Analysis with NumPy and pandas

Author(s): Curtis Miller [Curtis Miller]
Publisher: Packt Publishing, Year: 2018, Size: 7 Mb, Ext: epub
ID: 2289648

Mastering Exploratory Analysis with Pandas

Author(s): Harish Garg
Publisher: Packt Publishing, Year: 2018, Size: 3 Mb, Ext: epub
ID: 2319074

Pandas for Everyone. Python Data Analysis

Author(s): Daniel Y. Chen
Publisher: Addison-Wesley Professional, Year: 2017, Size: 6 Mb, Ext: pdf
ID: 2069343

Python for Data Analysis. Data Wrangling with Pandas, NumPy, and IPython

Author(s): Wes McKinney
Publisher: O’Reilly, Year: 2017, Size: 5 Mb, Ext: pdf
ID: 2123824

Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

Author(s): Wes McKinney
Publisher: O’Reilly Media, Year: 2017, Size: 2 Mb, Ext: epub
ID: 2125247

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