Pandas Cookbook: Recipes for Scientific Computing, Time Series Analysis and Data Visualization using Python

Pandas Cookbook: Recipes for Scientific Computing, Time Series Analysis and Data Visualization using Python

Posted by jack_miller | Published a year ago

With 37 ratings

By: Theodore Petrou

Purchased At: $39.99

Publisher's Note: A new second edition, updated completely for pandas 1.x with additional chapters, has now been published. This edition from 2017 is outdated and is based on pandas 0.20.

Key Features

  • Use the power of pandas 0.20 to solve most complex scientific computing problems with ease
  • Leverage fast, robust data structures in pandas 0.20 to gain useful insights from your data
  • Practical, easy to implement recipes for quick solutions to common problems in data using pandas 0.20

Book Description

This book will provide you with unique, idiomatic, and fun recipes for both fundamental and advanced data manipulation tasks with pandas 0.20. 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 0.20 library to generate results.

What you will learn

  • Master the fundamentals of pandas 0.20 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 0.20 direct hooks to Matplotlib and Seaborn

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