Book Image

Data Wrangling with Python

By : Dr. Tirthajyoti Sarkar, Shubhadeep Roychowdhury
Book Image

Data Wrangling with Python

By: Dr. Tirthajyoti Sarkar, Shubhadeep Roychowdhury

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
Appendix

Applying Your Knowledge to a Real-life Data Wrangling Task


Suppose you are asked this question: In India, did the enrollment in primary/secondary/tertiary education increase with the improvement of per capita GDP in the past 15 years? The actual modeling and analysis will be done by some senior data scientist, who will use machine learning and data visualization for analysis. As a data wrangling expert, your job will be to acquire and provide a clean dataset that contains educational enrollment and GDP data side by side.

Suppose you have a link for a dataset from the United Nations and you can download the dataset of education (for all the nations around the world). But this dataset has some missing values and moreover it does not have any GDP information. Someone has also given you another separate CSV file (downloaded from the World Bank site) which contains GDP data but in a messy format.

In this activity, we will examine how to handle these two separate sources and clean the data to prepare...