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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

4. A Deep Dive into Data Wrangling with Python

Activity 4.01: Working with the Adult Income Dataset (UCI)

Solution:

These are the steps to complete this activity:

  1. Load the necessary libraries:
    import numpy as np
    import pandas as pd
    import matplotlib.pyplot as plt
  2. Read in the Adult Income Dataset (given as a .csv file) from the local directory and check the first five records:
    df = pd.read_csv("../datasets/adult_income_data.csv")
    df.head()

    Note

    The highlighted path must be changed based on the location of the file on your system.

    The output is as follows:

    Figure 4.76: DataFrame displaying the first five records from the .csv file

  3. Create a script that will read a text file line by line and extract the first line, which is the header of the .csv file:
    names = []
    with open('../datasets/adult_income_names.txt','r') as f:
        for line in f:
            f.readline()
        ...
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