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

A Brief Tour of Machine Learning

This chapter will take you on a whirlwind tour of machine learning, focusing on using the pandas library as a tool to preprocess the data used by machine learning programs. It will also introduce you to the scikit-learn library, which is the most popular machine learning toolkit in Python.

In this chapter, we will illustrate machine learning techniques by applying them to a well-known problem about classifying which passengers survived the Titanic disaster at the turn of the last century. The various topics addressed in this chapter include the following:

  • The role of pandas in machine learning
  • Installing scikit-learn
  • Introduction to machine learning concepts
  • Applying machine learning—Kaggle Titanic competition
  • Data analysis and preprocessing using pandas
  • A naïve approach to the Titanic problem
  • The scikit-learn ML classifier interface...