Practical data wrangling

Around 80% of time in data analysis is spent on cleaning and preparing data for analysis.

This is, however, an important task, and is a prerequisite to the rest of the data analysis workflow, including visualisation, analysis and reporting.

Python and R are considered a popular choice of tool for data analysis, and have packages that can be best used to manipulate different kinds of data, as per your requirements.

This book will show you the different data wrangling techniques, and how you can leverage the power of Python and R packages to implement them.