Pandas 1.x Cookbook: Practical recipes for scientific computing, time series analysis, and exploratory data analysis using Python, 2nd Edition

Pandas 1.x Cookbook: Practical recipes for scientific computing, time series analysis, and exploratory data analysis using Python, 2nd Edition
Pandas 1.x Cookbook: Practical recipes for scientific computing, time series analysis, and exploratory data analysis using Python, 2nd Edition by Matt Harrison
English | 2020 | ISBN: 1839213106 | 626 Pages | True PDF, EPUB | 70 MB

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