Book Image

The Data Wrangling Workshop - Second Edition

By : Brian Lipp, Shubhadeep Roychowdhury, Dr. Tirthajyoti Sarkar
Book Image

The Data Wrangling Workshop - Second Edition

By: Brian Lipp, Shubhadeep Roychowdhury, Dr. Tirthajyoti Sarkar

Overview of this book

While a huge amount of data is readily available to us, it is not useful in its raw form. For data to be meaningful, it must be curated and refined. If you’re a beginner, then The Data Wrangling Workshop will help to break down the process for you. You’ll start with the basics and build your knowledge, progressing from the core aspects behind data wrangling, to using the most popular tools and techniques. This book starts by showing you how to work with data structures using Python. Through examples and activities, you’ll understand why you should stay away from traditional methods of data cleaning used in other languages and take advantage of the specialized pre-built routines in Python. Later, you’ll learn how to use the same Python backend to extract and 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, the book teaches you how to handle missing or incorrect data, and reformat it based on the requirements from your downstream analytics tool. By the end of this book, you will have developed a solid understanding of how to perform data wrangling with Python, and learned several techniques and best practices to extract, clean, transform, and format your data efficiently, from a diverse array of sources.
Table of Contents (11 chapters)

9. Applications in Business Use Cases and Conclusion of the Course

Activity 9.01: Data Wrangling Task – Fixing UN Data


These are the steps to complete this activity:

  1. Import the required libraries:
    import numpy as np
    import pandas as pd
    import matplotlib.pyplot as plt
    import warnings
  2. Save the URL of the dataset (highlighted) and use the pandas read_csv method to directly pass this link and create a DataFrame:
    df1 = pd.read_csv(education_data_link)
  3. Print the data in the DataFrame:

    The output (partially shown) is as follows:

    Figure 9.7: Partial DataFrame from the UN data

  4. As the first row does not contain useful information, use the skiprows parameter to remove the first row...