Pandas 1.x Cookbook: Practical recipes for scientific computing, time series analysis, and exploratory data analysis using Python

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