Python Data Science Toolbox (Part 2)
by datacamp
Overview:
- Continue to build your modern Data Science skills by learning about iterators and list comprehensions.
Chapter 1 : Using iterators in PythonLand
- Learn all about iterators and iterables, and some handy functions that allow you to effectively work with iterators.
| Section | Content |
|---|---|
| 1 | Introduction to iterators |
| 2 | Playing with iterators |
| 3 | Using iterators to load large files into memory |
Chapter 2 : List comprehensions and generators
- List comprehensions allow you to create complicated lists, and can dramatically simplify your code and make it more efficient. Generators are extremely helpful when working with large sequences of data that you may not want to store in memory, but instead generate on the fly.
| Section | Content |
|---|---|
| 1 | List comprehensions |
| 2 | Advanced comprehensions |
| 3 | Introduction to generator expressions |
| 4 | Wrapping up comprehensions and generators |
Chapter 3 : Bringing it all together
- Apply your newly acquired skills toward wrangling and extracting meaningful information from a real-world dataset -- the World Bank's World Development Indicators.
| Section | Content |
|---|---|
| 1 | Welcome to the case study! |
| 2 | Using Python generators for streaming data |
| 3 | Using pandas' read_csv iterator for streaming data |