Interactive Course

Python Data Science Toolbox (Part 2)

Continue to build your modern Data Science skills by learning about iterators and list comprehensions.

4 hours
12 Videos
46 Exercises
116,502 Participants
3,800 XP

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Course Description

In this second Python Data Science Toolbox course, you'll continue to build your Python data science skills. First, you'll learn about iterators, objects you have already encountered in the context of for loops. You'll then learn about list comprehensions, which are extremely handy tools for all data scientists working in Python. You'll end the course by working through a case study in which you'll apply all the techniques you learned in both parts of this course.

You'll learn all about iterators and iterables, which you have already worked with when writing for loops. You'll learn some handy functions that will allow you to effectively work with iterators. And you’ll finish the chapter with a use case that is pertinent to the world of data science and dealing with large amounts of data—in this case, data from Twitter that you will load in chunks using iterators.

This chapter will allow you to apply your newly acquired skills toward wrangling and extracting meaningful information from a real-world dataset—the World Bank's World Development Indicators. You'll have the chance to write your own functions and list comprehensions as you work with iterators and generators to solidify your Python data science chops.

In this chapter, you'll build on your knowledge of iterators and be introduced to list comprehensions, which allow you to create complicated lists—and lists of lists—in one line of code! List comprehensions can dramatically simplify your code and make it more efficient, and will become a vital part of your Python data science toolbox. You'll then learn about generators, which 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.

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Using iterators in PythonLand
Free

You'll learn all about iterators and iterables, which you have already worked with when writing for loops. You'll learn some handy functions that will allow you to effectively work with iterators. And you’ll finish the chapter with a use case that is pertinent to the world of data science and dealing with large amounts of data—in this case, data from Twitter that you will load in chunks using iterators.
View Chapter Details Play Chapter Now
List comprehensions and generators

In this chapter, you'll build on your knowledge of iterators and be introduced to list comprehensions, which allow you to create complicated lists—and lists of lists—in one line of code! List comprehensions can dramatically simplify your code and make it more efficient, and will become a vital part of your Python data science toolbox. You'll then learn about generators, which 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.
View Chapter Details Play Chapter Now
Bringing it all together!

This chapter will allow you to apply your newly acquired skills toward wrangling and extracting meaningful information from a real-world dataset—the World Bank's World Development Indicators. You'll have the chance to write your own functions and list comprehensions as you work with iterators and generators to solidify your Python data science chops.
View Chapter Details Play Chapter Now

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Hugo Bowne-Anderson

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Hugo is a data scientist, educator, writer and podcaster at DataCamp. His main interests are promoting data & AI literacy, helping to spread data skills through organizations and society and doing amateur stand up comedy in NYC. If you want to know what he likes to talk about, definitely check out DataFramed, the DataCamp podcast, which he hosts and produces: https://www.datacamp.com/community/podcast

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Python Data Science Toolbox (Part 2)

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Loved by learners at thousands of top companies:

Course Description

Using iterators in PythonLand

Introduction to iterators

Iterators vs Iterables

Iterating over iterables (1)

Iterating over iterables (2)

Iterators as function arguments

Playing with iterators

Using enumerate

Using zip

Using * and zip to 'unzip'

Using iterators to load large files into memory

Processing large amounts of Twitter data

Extracting information for large amounts of Twitter data

Congratulations!

Bringing it all together!

Welcome to the case study!

Dictionaries for data science

Writing a function to help you

Using a list comprehension

Turning this all into a DataFrame

Using Python generators for streaming data

Processing data in chunks (1)

Writing a generator to load data in chunks (2)

Writing a generator to load data in chunks (3)

Using pandas' read_csv iterator for streaming data

Writing an iterator to load data in chunks (1)

Writing an iterator to load data in chunks (2)

Writing an iterator to load data in chunks (3)

Writing an iterator to load data in chunks (4)

Writing an iterator to load data in chunks (5)

Final thoughts

List comprehensions and generators

List comprehensions

Write a basic list comprehension

List comprehension over iterables

Writing list comprehensions

Nested list comprehensions

Advanced comprehensions

Using conditionals in comprehensions (1)

Using conditionals in comprehensions (2)

Dict comprehensions

Introduction to generator expressions

List comprehensions vs generators

Write your own generator expressions

Changing the output in generator expressions

Build a generator

Wrapping up comprehensions and generators.

List comprehensions for time-stamped data

Conditional list comprehensions for time-stamped data