Python Data Science Toolbox (Part 2)

Course Description

In this second course in the Python Data Science Toolbox, you'll continue to build your Python Data Science skills. First you'll enter the wonderful world of iterators, objects that you have already encountered in the context of for loops without having necessarily known it. You'll then learn about list comprehensions, which are extremely handy tools that form a basic component in the toolbox of all modern Data Scientists working in Python. You'll end the course by working through a case study in which you'll apply all of the techniques you learned both in this course as well as the prequel. If you're looking to make it as a Pythonista Data Science ninja, you have come to the right place.

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

Course Outline

1. 1

   Using iterators in PythonLand

   Free

   Here, you'll learn all about iterators and iterables, which you have already worked with before when writing for loops! You'll learn about some very useful functions that will allow you to effectively work with iterators and finish the chapter with a use case that is pertinent to the world of Data Science - dealing with large amounts of data - in this case, data from Twitter that you will load in chunks using iterators!

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     Introduction to iterators

     50 xp
   • 

     Iterators vs Iterables

     50 xp
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     Iterating over iterables (1)

     100 xp
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     Iterating over iterables (2)

     100 xp
   • 

     Iterators as function arguments

     100 xp
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     Playing with iterators

     50 xp
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     Using enumerate

     100 xp
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     Using zip

     100 xp
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     Using * and zip to 'unzip'

     100 xp
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     Using iterators to load large files into memory

     50 xp
   • 

     Processing large amounts of Twitter data

     100 xp
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     Extracting information for large amounts of Twitter data

     100 xp
   • 

     Congratulations!!

     50 xp
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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.

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     List comprehensions

     50 xp
   • 

     Write a basic list comprehension

     50 xp
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     List comprehension over iterables

     50 xp
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     Writing list comprehensions

     100 xp
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     Nested list comprehensions

     100 xp
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     Advanced comprehensions

     50 xp
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     Using conditionals in comprehensions (1)

     100 xp
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     Using conditionals in comprehensions (2)

     100 xp
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     Dict comprehensions

     100 xp
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     Introduction to generator expressions

     50 xp
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     List comprehensions vs generators

     50 xp
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     Write your own generator expressions

     100 xp
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     Changing the output in generator expressions

     100 xp
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     Build a generator

     100 xp
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     Wrapping up comprehensions and generators.

     50 xp
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     List comprehensions for time-stamped data

     100 xp
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     Conditional list comprehensions for time-stamped data

     100 xp
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3. Bringing it all together!

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

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     Welcome to the case study!

     50 xp
   • 

     Dictionaries for data science

     100 xp
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     Writing a function to help you

     100 xp
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     Using a list comprehension

     100 xp
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     Turning this all into a DataFrame

     100 xp
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     Using Python generators for streaming data

     50 xp
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     Processing data in chunks (1)

     100 xp
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     Writing a generator to load data in chunks (2)

     100 xp
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     Writing a generator to load data in chunks (3)

     100 xp
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     Using pandas` read_csv iterator for streaming data

     50 xp
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     Writing an iterator to load data in chunks (1)

     100 xp
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     Writing an iterator to load data in chunks (2)

     100 xp
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     Writing an iterator to load data in chunks (3)

     100 xp
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     Writing an iterator to load data in chunks (4)

     100 xp
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     Writing an iterator to load data in chunks (5)

     100 xp
   • 

     Final thoughts

     50 xp
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