Interactive Course

Writing Efficient Python Code

Learn to write efficient code that executes quickly and allocates resources skillfully to avoid unnecessary overhead.

4 hours
15 Videos
53 Exercises
4,102 Participants
4,050 XP

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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!

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.

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.

In this chapter, you will learn how to gather and compare runtimes between different coding approaches. You'll practice using the line_profiler and memroy_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.

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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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.
View Chapter Details Play Chapter Now
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 memroy_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.
View Chapter Details Play Chapter Now
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.
View Chapter Details Play Chapter Now
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.
View Chapter Details Play Chapter Now

What do other learners have to say?

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Lloyd's Banking Group

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Harvard Business School

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Decision Science Analytics @ USAA

Logan Thomas

Lead Data Scientist at Nielsen

Logan is a member of the Algorithms and Machine Learning team at Nielsen – a global measurement and data analytics company that provides a complete and trusted view of consumers and markets worldwide. As a Lead Data Scientist, he focuses on writing robust, scalable, and efficient Python code to enable measurement and empower decision making. As a self-proclaimed Python enthusiast, Logan enjoys attending meetups and conferences in the Austin, Texas area to share his knowledge with others.

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Writing Efficient Python Code

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Course Description

Foundations for efficiencies

Welcome!

Pop quiz: what is efficient

A taste of things to come

Zen of Python

Building with built-ins

Built-in practice: range()

Built-in practice: enumerate()

Built-in practice: map()

The power of NumPy arrays

Practice with NumPy arrays

Bringing it all together: Festivus!

Gaining efficiencies

Efficiently combining, counting, and iterating

Combining Pokémon names and types

Counting Pokémon from a sample

Combinations of Pokémon

Set theory

Comparing Pokédexes

Searching for Pokémon

Gathering unique Pokémon

Eliminating loops

Gathering Pokémon without a loop

Pokémon totals and averages without a loop

Writing better loops

One-time calculation loop

Holistic conversion loop

Bringing it all together: Pokémon z-scores

Timing and profiling code

Examining runtime

Using %timeit: your turn!

Using %timeit: specifying number of runs and loops

Using %timeit: formal name or literal syntax

Using cell magic mode (%%timeit)

Code profiling for runtime

Pop quiz: steps for using %lprun

Using %lprun: spot bottlenecks

Using %lprun: fix the bottleneck

Code profiling for memory usage

Pop quiz: steps for using %mprun

Using %mprun: Hero BMI

Using %mprun: Hero BMI 2.0

Bringing it all together: Star Wars profiling

Basic pandas optimizations

Intro to pandas DataFrame iteration

Iterating with .iterrows()

Run differentials with .iterrows()

Another iterator method: .itertuples()

Iterating with .itertuples()

Run differentials with .itertuples()

pandas alternative to looping

Analyzing baseball stats with .apply()

Settle a debate with .apply()

Optimal pandas iterating

Replacing .iloc with underlying arrays

Bringing it all together: Predict win percentage

Congratulations!