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

Special Data Operations in pandas

pandas has an array of special operators for generating, aggregating, transforming, reading, and writing data from and to a variety of data types, such as number, string, date, timestamp, and time series. The basic operators in pandas were introduced in the previous chapter. In this chapter, we will continue that discussion and elaborate on the methods, syntax, and usage of some of these operators.

Reading this chapter will allow you to perform the following tasks with confidence:

  • Writing custom functions and applying them on a column or an entire DataFrame
  • Understanding the nature of missing values and handling them
  • Transforming and performing calculations on series using functions
  • Miscellaneous numeric operations on data

Let's delve into it right away. For the most part, we will generate our own data to demonstrate the methods.

The following...