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.
Using iterators in PythonLandFree
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.Introduction to iterators50 xpIterators vs. Iterables50 xpIterating over iterables (1)100 xpIterating over iterables (2)100 xpIterators as function arguments100 xpPlaying with iterators50 xpUsing enumerate100 xpUsing zip100 xpUsing * and zip to 'unzip'100 xpUsing iterators to load large files into memory50 xpProcessing large amounts of Twitter data100 xpExtracting information for large amounts of Twitter data100 xpCongratulations!50 xp
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.List comprehensions50 xpWrite a basic list comprehension50 xpList comprehension over iterables50 xpWriting list comprehensions100 xpNested list comprehensions100 xpAdvanced comprehensions50 xpUsing conditionals in comprehensions (1)100 xpUsing conditionals in comprehensions (2)100 xpDict comprehensions100 xpIntroduction to generator expressions50 xpList comprehensions vs. generators50 xpWrite your own generator expressions100 xpChanging the output in generator expressions100 xpBuild a generator100 xpWrapping up comprehensions and generators.50 xpList comprehensions for time-stamped data100 xpConditional list comprehensions for time-stamped data100 xp
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.Welcome to the case study!50 xpDictionaries for data science100 xpWriting a function to help you100 xpUsing a list comprehension100 xpTurning this all into a DataFrame100 xpUsing Python generators for streaming data50 xpProcessing data in chunks (1)100 xpWriting a generator to load data in chunks (2)100 xpWriting a generator to load data in chunks (3)100 xpUsing pandas' read_csv iterator for streaming data50 xpWriting an iterator to load data in chunks (1)100 xpWriting an iterator to load data in chunks (2)100 xpWriting an iterator to load data in chunks (3)100 xpWriting an iterator to load data in chunks (4)100 xpWriting an iterator to load data in chunks (5)100 xpFinal thoughts50 xp
In the following tracksData Scientist with PythonData Scientist Professional with PythonPython FundamentalsPython Programmer
PrerequisitesPython Data Science Toolbox (Part 1)
Hugo Bowne-AndersonSee More
Hugo is a data scientist, educator, writer and podcaster formerly 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 hosted and produced.