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Python Data Science Toolbox (Part 2)

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

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4 Hours12 Videos46 Exercises206,262 Learners3800 XPData Scientist TrackPython Fundamentals TrackPython Programmer Track

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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.

  1. 1

    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.

    Play Chapter Now
    Introduction to iterators
    50 xp
    Iterators vs. Iterables
    50 xp
    Iterating over iterables (1)
    100 xp
    Iterating over iterables (2)
    100 xp
    Iterators as function arguments
    100 xp
    Playing with iterators
    50 xp
    Using enumerate
    100 xp
    Using zip
    100 xp
    Using * and zip to 'unzip'
    100 xp
    Using iterators to load large files into memory
    50 xp
    Processing large amounts of Twitter data
    100 xp
    Extracting information for large amounts of Twitter data
    100 xp
    Congratulations!
    50 xp
  2. 2

    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.

    Play Chapter Now
    List comprehensions
    50 xp
    Write a basic list comprehension
    50 xp
    List comprehension over iterables
    50 xp
    Writing list comprehensions
    100 xp
    Nested list comprehensions
    100 xp
    Advanced comprehensions
    50 xp
    Using conditionals in comprehensions (1)
    100 xp
    Using conditionals in comprehensions (2)
    100 xp
    Dict comprehensions
    100 xp
    Introduction to generator expressions
    50 xp
    List comprehensions vs. generators
    50 xp
    Write your own generator expressions
    100 xp
    Changing the output in generator expressions
    100 xp
    Build a generator
    100 xp
    Wrapping up comprehensions and generators.
    50 xp
    List comprehensions for time-stamped data
    100 xp
    Conditional list comprehensions for time-stamped data
    100 xp
  3. 3

    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.

    Play Chapter Now
    Welcome to the case study!
    50 xp
    Dictionaries for data science
    100 xp
    Writing a function to help you
    100 xp
    Using a list comprehension
    100 xp
    Turning this all into a DataFrame
    100 xp
    Using Python generators for streaming data
    50 xp
    Processing data in chunks (1)
    100 xp
    Writing a generator to load data in chunks (2)
    100 xp
    Writing a generator to load data in chunks (3)
    100 xp
    Using pandas' read_csv iterator for streaming data
    50 xp
    Writing an iterator to load data in chunks (1)
    100 xp
    Writing an iterator to load data in chunks (2)
    100 xp
    Writing an iterator to load data in chunks (3)
    100 xp
    Writing an iterator to load data in chunks (4)
    100 xp
    Writing an iterator to load data in chunks (5)
    100 xp
    Final thoughts
    50 xp

In the following tracks

Data Scientist Python FundamentalsPython Programmer

Datasets

TweetsWorld Bank World Development Indicators

Collaborators

fgcastroFrancisco CastroyashasYashas Roy

Prerequisites

Python Data Science Toolbox (Part 1)
Hugo Bowne-Anderson Headshot

Hugo Bowne-Anderson

Data Scientist at DataCamp

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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What do other learners have to say?

I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.

Devon Edwards Joseph
Lloyds Banking Group

DataCamp is the top resource I recommend for learning data science.

Louis Maiden
Harvard Business School

DataCamp is by far my favorite website to learn from.

Ronald Bowers
Decision Science Analytics, USAA