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.
Welcome! 50 xp
Pop quiz: what is efficient 50 xp
A taste of things to come 100 xp
Zen of Python 35 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
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.
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
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.
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
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.
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