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  3. Writing Efficient Python Code
  • 1

    Foundations for efficiencies

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    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.

  • 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.

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