Python Tutorial : Writing Efficient Python Code

Want to learn more? Take the full course at https://learn.datacamp.com/courses/writing-efficient-python-code at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Hello and welcome! My name is Logan Thomas, and I'll be your guide through this course about writing efficient code using Python. As a data scientist, the majority of your time should be spent gleaning actionable insights from data. Whether you're cleaning and curating a messy dataset, deploying a machine learning model, or creating a sleek data visualization, the code you write should be a helpful tool to quickly get you where you need to go - not something that leaves you waiting around. In this course, you'll learn how to write cleaner, faster, and more efficient Python code. We'll explore how to time and profile your code in order to find potential 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 everything you need to start writing elegant and efficient Python code! But first, let's explore what is meant by efficient Python code. In the context of this course, efficient refers to code that satisfies two key concepts. First, efficient code is fast and has a small latency between execution and returning a result. Second, efficient code allocates resources skillfully and isn't subjected to unnecessary overhead. Although your definition of fast runtime and small memory usage may depend on the task at hand, the goal of writing efficient code is still to reduce both latency and overhead. For the remainder of this course, we'll be exploring how to write Python code that runs quickly and has little memory overhead. We've defined what is meant by efficient code, but it is also important to note that this course focuses on writing efficient code using Python. Python is a language that prides itself on