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Writing Efficient Python Code

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4 hours
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Course Description

As a Data Scientist, the majority of your time should be spent gleaning actionable insights from data -- not waiting for your code to finish running. Writing efficient Python code can help reduce runtime and save computational resources, ultimately freeing you up to do the things you love as a Data Scientist. In this course, you'll learn how to use Python's built-in data structures, functions, and modules to write cleaner, faster, and more efficient code. We'll explore how to time and profile code in order to find bottlenecks. Then, you'll practice eliminating these bottlenecks, and other bad design patterns, using Python's Standard Library, NumPy, and pandas. After completing this course, you'll have the necessary tools to start writing efficient Python code!
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  1. 1

    Foundations for efficiencies

    Free

    In this chapter, you'll learn what it means to write efficient Python code. You'll explore Python's Standard Library, learn about NumPy arrays, and practice using some of Python's built-in tools. This chapter builds a foundation for the concepts covered ahead.

    Play Chapter Now
    Welcome!
    50 xp
    Pop quiz: what is efficient
    50 xp
    A taste of things to come
    100 xp
    Zen of Python
    50 xp
    Building with built-ins
    50 xp
    Built-in practice: range()
    100 xp
    Built-in practice: enumerate()
    100 xp
    Built-in practice: map()
    100 xp
    The power of NumPy arrays
    50 xp
    Practice with NumPy arrays
    100 xp
    Bringing it all together: Festivus!
    100 xp
  2. 2

    Timing and profiling code

    In this chapter, you will learn how to gather and compare runtimes between different coding approaches. You'll practice using the line_profiler and memory_profiler packages to profile your code base and spot bottlenecks. Then, you'll put your learnings to practice by replacing these bottlenecks with efficient Python code.

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    Examining runtime
    50 xp
    Using %timeit: your turn!
    100 xp
    Using %timeit: specifying number of runs and loops
    50 xp
    Using %timeit: formal name or literal syntax
    100 xp
    Using cell magic mode (%%timeit)
    50 xp
    Code profiling for runtime
    50 xp
    Pop quiz: steps for using %lprun
    50 xp
    Using %lprun: spot bottlenecks
    50 xp
    Using %lprun: fix the bottleneck
    50 xp
    Code profiling for memory usage
    50 xp
    Pop quiz: steps for using %mprun
    50 xp
    Using %mprun: Hero BMI
    50 xp
    Using %mprun: Hero BMI 2.0
    50 xp
    Bringing it all together: Star Wars profiling
    100 xp
  3. 3

    Gaining efficiencies

    This chapter covers more complex efficiency tips and tricks. You'll learn a few useful built-in modules for writing efficient code and practice using set theory. You'll then learn about looping patterns in Python and how to make them more efficient.

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    Efficiently combining, counting, and iterating
    50 xp
    Combining Pokémon names and types
    100 xp
    Counting Pokémon from a sample
    100 xp
    Combinations of Pokémon
    100 xp
    Set theory
    50 xp
    Comparing Pokédexes
    100 xp
    Searching for Pokémon
    100 xp
    Gathering unique Pokémon
    100 xp
    Eliminating loops
    50 xp
    Gathering Pokémon without a loop
    100 xp
    Pokémon totals and averages without a loop
    100 xp
    Writing better loops
    50 xp
    One-time calculation loop
    100 xp
    Holistic conversion loop
    100 xp
    Bringing it all together: Pokémon z-scores
    100 xp
  4. 4

    Basic pandas optimizations

    This chapter offers a brief introduction on how to efficiently work with pandas DataFrames. You'll learn the various options you have for iterating over a DataFrame. Then, you'll learn how to efficiently apply functions to data stored in a DataFrame.

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    Intro to pandas DataFrame iteration
    50 xp
    Iterating with .iterrows()
    100 xp
    Run differentials with .iterrows()
    100 xp
    Another iterator method: .itertuples()
    50 xp
    Iterating with .itertuples()
    100 xp
    Run differentials with .itertuples()
    100 xp
    pandas alternative to looping
    50 xp
    Analyzing baseball stats with .apply()
    100 xp
    Settle a debate with .apply()
    100 xp
    Optimal pandas iterating
    50 xp
    Replacing .iloc with underlying arrays
    100 xp
    Bringing it all together: Predict win percentage
    100 xp
    Congratulations!
    50 xp

In the following tracks

Data EngineerPython DeveloperPython ProgrammingPython Toolbox

Datasets

Baseball statistics

Collaborators

Collaborator's avatar
Chester Ismay
Collaborator's avatar
Becca Robins

Prerequisites

Data Types for Data Science in PythonPython Data Science Toolbox (Part 2)
Logan Thomas HeadshotLogan Thomas

Scientific Software Technical Trainer, Enthought

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