Writing Efficient Python Code
Learn to write efficient code that executes quickly and allocates resources skillfully to avoid unnecessary overhead.
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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!
- 1
Foundations for efficiencies
FreeIn 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. - 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. - 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. - 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.
Datasets
Baseball statistics
Chester Ismay
Becca Robins
Logan Thomas
Senior Data Scientist at Protection Engineering Consultants
Logan is a member of the Engineering Analytics team at Protection Engineering Consultants – an organization that provides engineering services and products for mitigation and protection against hazards, extraordinary events and attacks for hundreds of industry clients and dozens of government and military customers worldwide. As a Senior Associate Data Scientist, he focuses on writing robust, scalable, and efficient Python code to 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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Lloyds Banking Group
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Harvard Business School
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Decision Science Analytics, USAA
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