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The Data Wrangling Workshop

You're reading from   The Data Wrangling Workshop Create your own actionable insights using data from multiple raw sources

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Product type Paperback
Published in Jul 2020
Publisher Packt
ISBN-13 9781839215001
Length 576 pages
Edition 2nd Edition
Languages
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Authors (3):
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Dr. Tirthajyoti Sarkar Dr. Tirthajyoti Sarkar
Author Profile Icon Dr. Tirthajyoti Sarkar
Dr. Tirthajyoti Sarkar
Shubhadeep Roychowdhury Shubhadeep Roychowdhury
Author Profile Icon Shubhadeep Roychowdhury
Shubhadeep Roychowdhury
Brian Lipp Brian Lipp
Author Profile Icon Brian Lipp
Brian Lipp
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Toc

Table of Contents (11) Chapters Close

Preface
1. Introduction to Data Wrangling with Python 2. Advanced Operations on Built-In Data Structures FREE CHAPTER 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. Applications in Business Use Cases and Conclusion of the Course Appendix

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

Activity 9.01: Data Wrangling Task – Fixing UN Data

Solution:

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
    warnings.filterwarnings('ignore')
  2. Save the URL of the dataset (highlighted) and use the pandas read_csv method to directly pass this link and create a DataFrame:
    education_data_link="http://data.un.org/_Docs/SYB/CSV/"\
                        "SYB61_T07_Education.csv"
    df1 = pd.read_csv(education_data_link)
  3. Print the data in the DataFrame:
    df1.head()

    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...
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