Writing Efficient Python Code Data Camp

Overview

Context

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 Writing Efficient Python Code course offered by Data Camp, 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!

What you'll learn

• Assess when and how to replace explicit loops with vectorized NumPy array or pandas DataFrame operations for faster computation
• Differentiate between pandas row-iteration methods (iloc, iterrows, itertuples, apply) to select the most performant approach for a given task
• Evaluate code execution time and memory usage by applying %timeit, line_profiler, and memory_profiler outputs
• Identify built-in Python functions, data structures, and modules that provide efficient alternatives to manual implementations
• Recognize scenarios where combinatoric generators, Counter objects, and set operations reduce runtime relative to traditional looping constructs