Book Image

Mastering pandas - Second Edition

By : Ashish Kumar
Book Image

Mastering pandas - Second Edition

By: Ashish Kumar

Overview of this book

pandas is a popular Python library used by data scientists and analysts worldwide to manipulate and analyze their data. This book presents useful data manipulation techniques in pandas to perform complex data analysis in various domains. An update to our highly successful previous edition with new features, examples, updated code, and more, this book is an in-depth guide to get the most out of pandas for data analysis. Designed for both intermediate users as well as seasoned practitioners, you will learn advanced data manipulation techniques, such as multi-indexing, modifying data structures, and sampling your data, which allow for powerful analysis and help you gain accurate insights from it. With the help of this book, you will apply pandas to different domains, such as Bayesian statistics, predictive analytics, and time series analysis using an example-based approach. And not just that; you will also learn how to prepare powerful, interactive business reports in pandas using the Jupyter notebook. By the end of this book, you will learn how to perform efficient data analysis using pandas on complex data, and become an expert data analyst or data scientist in the process.
Table of Contents (21 chapters)
Free Chapter
1
Section 1: Overview of Data Analysis and pandas
4
Section 2: Data Structures and I/O in pandas
7
Section 3: Mastering Different Data Operations in pandas
12
Section 4: Going a Step Beyond with pandas

What is pandas?

The pandas we are going to obsess over in this book are not the cute and lazy animals that also do kung fu when needed.

pandas is a high-performance open source library for data analysis in Python developed by Wes McKinney in 2008. pandas stands for panel data, a reference to the tabular format in which it processes the data. It is available for free and is distributed with a 3-Clause BSD License under the open source initiative.

Over the years, it has become the de-facto standard library for data analysis using Python. There's been great adoption of the tool, and there's a large community behind it, (1,200+ contributors, 17,000+ commits, 23 versions, and 15,000+ stars) rapid iteration, features, and enhancements are continuously made.

Some key features of pandas include the following:

  • It can process a variety of datasets in different formats: time series...