Review of Python Courses (Part 2)

In Part 1, I summarized the first two Datacamp courses I took. Today I will continue with the next three.

As a reminder, I introduced you to my recent work learning Python here.

Course #3 was Data Manipulation with pandas. This course started out with:

• Aspects of dataframes
• Sorting and subsetting
• Adding new columns
• Calculating summary statistics
• Counting (categorical data)

The course continues on with:

• Grouped Summary Statistics
• Pivot tables
• Explicit indexes
• Slicing and subsetting with .loc and .iloc accessors
• Data visualization
• Missing values
• Reading and writing .csv files

The next course I took was Data Types for Data Science in Python. Concepts covered in this class include:

• Lists (and iteration)
• Tuples (and iteration)
• Sets for unordered and unique data
• Using dictionaries
• Altering dictionaries
• Working with .csv files
• Counting with Collections module
• Dictionaries of unknown structure (defaultdict)
• Maintaining dictionary order with orderedDict
• Named tuples
• Working with datetime objects
• Adding and subtracting time
• Pendulum library
• Counting crimes case study

The next course I took was Python Data Science Toolbox, Part 1. Concepts covered in this class include:

• User-defined functions
• Multiple parameters and return values
• Object scope
• Nested functions
• Default and flexible arguments
• Lambda functions

I will continue summarizing classes later.