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Data Wrangling with Python

You're reading from   Data Wrangling with Python Creating actionable data from raw sources

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Product type Paperback
Published in Feb 2019
Publisher Packt
ISBN-13 9781789800111
Length 452 pages
Edition 1st Edition
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Authors (2):
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Shubhadeep Roychowdhury Shubhadeep Roychowdhury
Author Profile Icon Shubhadeep Roychowdhury
Shubhadeep Roychowdhury
Dr. Tirthajyoti Sarkar Dr. Tirthajyoti Sarkar
Author Profile Icon Dr. Tirthajyoti Sarkar
Dr. Tirthajyoti Sarkar
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Table of Contents (12) Chapters Close

Data Wrangling with Python
Preface
1. Introduction to Data Wrangling with Python FREE CHAPTER 2. Advanced Data Structures and File Handling 3. Introduction to NumPy, Pandas, and Matplotlib 4. A Deep Dive into Data Wrangling with Python 5. Getting Comfortable with Different Kinds of Data Sources 6. Learning the Hidden Secrets of Data Wrangling 7. Advanced Web Scraping and Data Gathering 8. RDBMS and SQL 9. Application of Data Wrangling in Real Life 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...

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