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

Use the power of pandas to solve most complex scientific computing problems with ease. Revised for pandas 1.x.

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

1. Pandas Foundations
2. Essential DataFrame Operations
3. Creating and Persisting DataFrames
4. Beginning Data Analysis
5. Exploratory Data Analysis
6. Selecting Subsets of Data
7. Filtering Rows
8. Index Alignment
9. Grouping for Aggregation, Filtration and Transformation
10. Restructuring Data into a Tidy Form
11. Combining Pandas Objects
12. Time Series Analysis
13. Visualization with Matplotlib, Pandas, and Seaborn
14. Debugging and Testing Pandas

Matt Harrison has been using Python since 2000. He runs MetaSnake, which provides corporate training for Python and Data Science. He is the author of Machine Learning Pocket Reference, the bestselling Illustrated Guide to Python 3, and Learning the Pandas Library, among other books.

Theodore Petrou is the founder of Dunder Data, a training company dedicated to helping teach the Python data science ecosystem effectively to individuals and corporations. Read his tutorials and attempt his data science challenges at the Dunder Data website.