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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

1. Introduction to Data Wrangling with Python

Activity 1.01: Handling Lists

Solution:

These are the steps to complete this activity:

  1. Import the random library:
    import random
  2. Use the randint method from the random library to create 100 random numbers:
    random_number_list = [random.randint(0, 100) \
                          for x in range(0, 100)]
  3. Print random_number_list:
    random_number_list

    The sample output is as follows:

    Figure 1.20: Section of output for random_number_list

    Note

    The output is susceptible to change since we are generating random numbers.

  4. Create a list_with_divisible_by_3 list from random_number_list, which will contain only numbers that are divisible by 3:
    list_with_divisible_by_3 = [a for a in \
                            ...
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