Key Features

This is the first book on pandas 1.x

Practical, easy to implement recipes for quick solutions to common problems in data using pandas

Master the fundamentals of pandas to quickly begin exploring any dataset

Book Description

The pandas library is massive, and it's common for frequent users to be unaware of many of its more impressive features. The official pandas documentation, while thorough, does not contain many useful examples of how to piece together multiple commands as one would do during an actual analysis. This book guides you, as if you were looking over the shoulder of an expert, through situations that you are highly likely to encounter.

This new updated and revised edition provides you with unique, idiomatic, and fun recipes for both fundamental and advanced data manipulation tasks with pandas. Some recipes focus on achieving a deeper understanding of basic principles, or comparing and contrasting two similar operations. Other recipes will dive deep into a particular dataset, uncovering new and unexpected insights along the way. Many advanced recipes combine several different features across the pandas library to generate results.

What you will learn

Master data exploration in pandas through dozens of practice problems

Group, aggregate, transform, reshape, and filter data

Merge data from different sources through pandas SQL-like operations

Create visualizations via pandas hooks to matplotlib and seaborn

Use pandas, time series functionality to perform powerful analyses

Import, clean, and prepare real-world datasets for machine learning

Create workflows for processing big data that doesn't fit in memory

Who this book is for

This book is for Python developers, data scientists, engineers, and analysts. Pandas is the ideal tool for manipulating structured data with Python and this book provides ample instruction and examples. Not only does it cover the basics required to be proficient, but it goes into the details of idiomatic pandas.

Table of Contents

Pandas Foundations

Essential DataFrame Operations

Creating and Persisting DataFrames

Beginning Data Analysis

Exploratory Data Analysis

Selecting Subsets of Data

Filtering Rows

Index Alignment

Grouping for Aggregation, Filtration and Transformation

Restructuring Data into a Tidy Form

Combining Pandas Objects

Time Series Analysis

Visualization with Matplotlib, Pandas, and Seaborn

Debugging and Testing Pandas