Learn Pandas Basics in Weekend
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Learn Pandas Basics in Weekend

Learn Pandas in Weekend Part I.

About the Book

The Goal.

The goal this weekend is to introduce a foundation of pandas by thoroughly inspecting the Series and DataFrame data structures. It is vital for pandas users to know each component of the Series and the DataFrame, and to understand that each column of data in pandas holds precisely one data type.

Also, you will learn how to select a single column of data from a DataFrame, which is returned as a Series. Working with this one-dimensional object makes it easy to show how different methods and operators work. Many Series methods return another Series as output. This leads to the possibility of calling further methods in succession, which is known as method chaining.

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    • Data Science
    • Artificial Intelligence
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About the Author

Hisham El-Amir
Hisham El-Amir

Hisham Elamir is a data scientist with expertise in machine learning, deep learning, and statistics. He currently lives and works in Cairo, Egypt. In his work projects, he faces challenges ranging from natural language processing (NLP), behavioral analysis, and machine learning to distributed processing. He is very passionate about his job and always tries to stay updated about the latest developments in data science technologies, attending meet-ups, conferences, and other events.

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Table of Contents

  • 1. Dissecting the anatomy of a DataFrame (2 steps)
  • 2. Accessing the main DataFrame components (4 steps)
  • 3. Understanding data types (2 steps)
  • 4. Selecting a single column of data as a Series (4 steps)
  • 5. Calling Series methods (12 steps)
  • 6. Working with operators on a Series (5 steps)
  • 7. Chaining Series methods together (4 steps)
  • 8. Making the index meaningful (2 steps)
  • 9. Renaming row and column names (3 steps)
  • 10. Creating and deleting columns (11 steps)
  • 11. Selecting multiple DataFrame columns (2 steps)
  • 12. Selecting columns with methods (5 steps)
  • 13. Ordering column names sensibly (5 steps)
  • 14. Operating on the entire DataFrame (5 steps)
  • 15. Chaining DataFrame methods together (4 steps)
  • 16. Working with operators on a DataFrame (5 steps)
  • 17. Comparing missing values (6 steps)
  • 18. Transposing the direction of a DataFrame operation (5 steps)
  • 19. Determining college campus diversity (9 steps)
  • 20. Summary.

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