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

Activity 12: Data Wrangling Task – Fixing UN Data


Suppose the agenda of the data analysis is to find out whether the enrolment in primary, secondary, or tertiary education has increased with the improvement of per capita GDP in the past 15 years. For this task, we will first need to clean or wrangle the two datasets, that is, the Education Enrolment and GDP data.

The UN data is available on https://github.com/TrainingByPackt/Data-Wrangling-with-Python/blob/master/Chapter09/Activity12-15/SYB61_T07_Education.csv.

Note

If you download the CSV file and open it using Excel, then you will see that the Footnotes column sometimes contains useful notes. We may not want to drop it in the beginning. If we are interested in a particular country's data (like we are in this task), then it may well turn out that Footnotes will be NaN, that is, blank. In that case, we can drop it at the end. But for some countries or regions, it may contain information.

These steps will guide you to find the solution:

  1. Download...