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

Data Case Studies Using pandas

So far, we have covered the extensive functionalities of pandas. We'll try to implement these functionalities in some case studies. These case studies will give us an overview of the use of each functionality and help us determine the pivotal points in handling a DataFrame. Moreover, the step-by-step approach of the case studies helps us to deepen our understanding of the pandas functions. This chapter is equipped with practical examples along with code snippets to ensure that, by the end, you understand the pandas approach to solving the DataFrame problems.

We will cover the following case studies:

  • End-to-end exploratory data analysis
  • Web scraping with Python
  • Data validation