MISSION 314

Dictionaries and Frequency Tables

Building upon what was taught in the Conditional Statements mission, you will continue to build and refine your Python programming skills by learning about dictionaries and how to take advantage of them to build frequency tables to check how many times a certain event occurs in a dataset.

In this mission, you'll learn concepts such as what dictionaries are and how they're a useful data structure in Python, terminology related to dictionaries such as keys and values, how to update dictionary values using dictionary keys, and more. You'll also combine your knowledge of for loops to loop over dictionaries to create a frequency table for key-value pairs within the dictionary.

Upon completion of this mission, you'll be more confident in your programming ability when using Python for data science. If you feel like you need practice, think of something you can create using what you've learned so far; it doesn't have to be big, it can be something small, like a calculator. Or if you feel like you need more practice with dictionaries in Python — or just want to learn more concepts as it relates to dictionaries — check out this tutorial that goes through the basics of Python dictionaries

Objectives

  • Learn how to create dictionaries.
  • Learn how to update dictionary values.
  • Learn how to check for dictionary membership.
  • Learn how to count with dictionaries and build frequency tables.

Mission Outline

1. Storing Data
2. Dictionaries
3. Indexing
4. Alternative Way of Creating a Dictionary
5. Key-Value Pairs
6. Checking for Membership
7. Counting with Dictionaries
8. Finding the Unique Values
9. Proportions and Percentages
10. Looping over Dictionaries
11. Keeping the Dictionaries Separate
12. Frequency Tables for Numerical Columns
13. Filtering for the Intervals
14. Next Steps
15. Takeaways

python-for-data-science-fundamentals

Course Info:

Beginner

The median completion time for this course is 5.8 hours. View Details

This course is free and includes 7 missions, 1 guided project, and 1 tutorial.  It is the first course in the Data Analyst in Python path and Data Scientist in Python path.

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