Data is everywhere and it is all around us. In these nine chapters, we have learned about how data from different types and sources can be cleaned, corrected, and combined. Using the power of Python and the knowledge of data wrangling and applying the tricks and tips that you have studied in this book, you are ready to be a data wrangler.
Data Wrangling with Python
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Data Wrangling with Python
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Overview of this book
For data to be useful and meaningful, it must be curated and refined. Data Wrangling with Python teaches you the core ideas behind these processes and equips you with knowledge of the most popular tools and techniques in the domain.
The book starts with the absolute basics of Python, focusing mainly on data structures. It then delves into the fundamental tools of data wrangling like NumPy and Pandas libraries. You'll explore useful insights into why you should stay away from traditional ways of data cleaning, as done in other languages, and take advantage of the specialized pre-built routines in Python. This combination of Python tips and tricks will also demonstrate how to use the same Python backend and extract/transform data from an array of sources including the Internet, large database vaults, and Excel financial tables. To help you prepare for more challenging scenarios, you'll cover how to handle missing or wrong data, and reformat it based on the requirements from the downstream analytics tool. The book will further help you grasp concepts through real-world examples and datasets.
By the end of this book, you will be confident in using a diverse array of sources to extract, clean, transform, and format your data efficiently.
Table of Contents (12 chapters)
Data Wrangling with Python
Preface
Free Chapter
Introduction to Data Wrangling with Python
Advanced Data Structures and File Handling
Introduction to NumPy, Pandas, and Matplotlib
A Deep Dive into Data Wrangling with Python
Getting Comfortable with Different Kinds of Data Sources
Learning the Hidden Secrets of Data Wrangling
Advanced Web Scraping and Data Gathering
RDBMS and SQL
Application of Data Wrangling in Real Life
Appendix
Customer Reviews