Mastering Pandas

Master the features and capabilities of pandas, a data analysis toolkit for Python

About This Book

• Master and optimally utilize the capabilities of Pandas for data analysis using IPython a rich interactive environment for Python
• Understand data visualization by plotting data with matplotlib
• Learn predictive analytics and machine learning using pandas and scikit-learn in a pragmatic manner

Who This Book Is For

This book is intended for Python programmers, mathematicians, and analysts who already have a basic understanding of Python and wish to learn about its data analysis capabilities in depth.

In Detail

Python is a ground breaking language for its simplicity and succinctness, allowing the user to achieve a great deal with a few lines of code, especially compared to other programming languages. The pandas brings these features of Python into the data analysis realm, by providing expressiveness, simplicity, and powerful capabilities for the task of data analysis. By mastering pandas, users will be able to do complex data analysis in a short period of time, as well as illustrate their findings using the rich visualization capabilities of related tools such as IPython and matplotlib.

This book is an in-depth guide to the use of pandas for data analysis, for either the seasoned data analysis practitioner or the novice user. It provides a basic introduction to the pandas framework, and takes users through the installation of the library and the IPython interactive environment. Thereafter, you will learn basic as well as advanced features, such as MultiIndexing, modifying data structures, and sampling data, which provide powerful capabilities for data analysis.

Table of Contents

Chapter 1. Introduction to pandas and Data Analysis
Chapter 2. Installation of pandas and the Supporting Software
Chapter 3. The pandas Data Structures
Chapter 4 V’s of big data
Chapter 4. Operations in pandas, Part I – Indexing and Selecting
Chapter 5. Operations in pandas, Part II – Grouping, Merging, and Reshaping of Data
Chapter 6. Missing Data, Time Series, and Plotting Using Matplotlib
Chapter 7. A Tour of Statistics – The Classical Approach
Chapter 8. A Brief Tour of Bayesian Statistics
Chapter 9. The pandas Library Architecture
Chapter 10. R and pandas Compared
Chapter 11. Brief Tour of Machine Learning