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

pandas Compared with Other Tools

This chapter focuses on comparing pandas with R, the statistical package on which much of the pandas functionality is modeled, and other tools such as SQL and SAS, with which it has a significant degree of overlap. It is intended as a guide for R, SQL, and SAS users who wish to use pandas, and for users who wish to replicate functionality that they have seen in their code in pandas. It focuses on a number of key features available to R, SQL, and SAS users, and demonstrates how to achieve similar functionality in pandas by using some illustrative examples. This chapter assumes that you have the R statistical package installed. If not, it can be downloaded and installed from here: http://www.r-project.org/.

By the end of the chapter, data analysis users should have a good grasp of the data analysis capabilities of these tools as compared to pandas...