Language
  • Python 3
Reading time
  • Approximately 53 days
What you will learn
  • Numerical Programming and Data Mining
Author
  • Theodore Petrou
Published
  • 1¬†year, 1¬†month ago
Packages you will be introduced to
  • seaborn
  • matplotlib
  • pandas
Book cover of Pandas Cookbook: Recipes for Scientific Computing, Time Series Analysis and Data Visualization using Python by Theodore Petrou

Official description

Key Features

  • Use the power of pandas to solve most complex scientific computing problems with ease
  • Leverage fast, robust data structures in pandas to gain useful insights from your data
  • Practical, easy to implement recipes for quick solutions to common problems in data using pandas

Book Description

This book will provide 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.

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 like one would do during an actual analysis. This book guides you, as if you were looking over the shoulder of an expert, through practical situations that you are highly likely to encounter.

Many advanced recipes combine several different features across the pandas library to generate results.

What you will learn

  • Master the fundamentals of pandas to quickly begin exploring any dataset
  • Isolate any subset of data by properly selecting and querying the data
  • Split data into independent groups before applying aggregations and transformations to each group
  • Restructure data into tidy form to make data analysis and visualization easier
  • Prepare real-world messy datasets for machine learning
  • Combine and merge data from different sources through pandas SQL-like operations
  • Utilize pandas unparalleled time series functionality
  • Create beautiful and insightful visualizations through pandas direct hooks to Matplotlib and Seaborn

About the Author

Theodore Petrou is a data scientist and the founder of Dunder Data, a professional educational company focusing on exploratory data analysis. He is also the head of Houston Data Science, a meetup group with more than 2,000 members that has the primary goal of getting local data enthusiasts together in the same room to practice data science. Before founding Dunder Data, Ted was a data scientist at Schlumberger, a large oil services company, where he spent the vast majority of his time exploring data.

Some of his projects included using targeted sentiment analysis to discover the root cause of part failure from engineer text, developing customized client/server dashboarding applications, and real-time web services to avoid the mispricing of sales items. Ted received his masters degree in statistics from Rice University, and used his analytical skills to play poker professionally and teach math before becoming a data scientist. Ted is a strong supporter of learning through practice and can often be found answering questions about pandas on Stack Overflow.

Table of Contents

  1. Pandas Foundations
  2. Essential DataFrame Operations
  3. Beginning Data Analysis
  4. Selecting Subsets of Data
  5. Boolean Indexing
  6. Index Alignment
  7. Grouping for Aggregation, Filtration and Transformation
  8. Restructuring Data into Tidy Form
  9. Joining multiple pandas objects
  10. Time Series
  11. Visualization

Reviews

Write a review

Read this book? Comment on this book's GitHub issue page and share what you liked and what you didn't like about it. Your GitHub comment will show up as a review here. See an example.