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

Posted By: IrGens
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 by Theodore Petrou
English | October 23, 2017 | ISBN: 1784393878 | PDF | 538 pages | 21.9 MB

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

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