realivanivani/writing_efficient_pycode

Notes and code from the DataCamp course: Writing Efficient Python Code

Efficient Python Code for Data Scientists

Welcome to the comprehensive guide on writing efficient Python code tailored for data scientists! Whether you're an experienced practitioner or just starting in the field, optimizing your code is crucial for achieving actionable insights and making data-driven decisions.

Introduction

In the world of data science, the ability to process and analyze data efficiently is paramount. This guide is designed to equip data scientists with the foundational knowledge and practical skills needed to write elegant and high-performance Python code. From leveraging built-in functions to harnessing the power of libraries like NumPy and pandas, we'll explore techniques that enhance the efficiency of your data analysis and predictive modeling workflows.

Table of Contents

1. Foundations for Efficiencies

• Understand the concept of efficient code and embrace Pythonic practices.
• Harness the capabilities of Python's Standard Library for streamlined development.
• Explore the power of NumPy arrays for optimized numerical operations.

2. Timing and Profiling Code

• Learn how to examine runtime and identify performance bottlenecks.
• Dive into code profiling using Python's built-in tools to gain insights into both runtime and memory usage.
• Gaining Efficiencies

3. Efficiently combine, count, and iterate through data.

• Apply set theory to handle data operations more efficiently.
• Explore methods to eliminate loops or write them more effectively.

4. Basic Pandas Optimizations

• Gain an introduction to iterating through pandas DataFrames.
• Explore alternative iterator methods like .itertuples() and understand when to use them.
• Discover pandas alternatives to traditional looping with the powerful .apply() function.
• Learn advanced techniques for optimal pandas iteration with large datasets.

Usage

The guide is presented in Jupyter notebook format, providing detailed code examples and explanations for each concept. Whether you're working on customer segmentation or predictive maintenance models, implementing these practices will enhance the efficiency of your Python code, enabling you to derive more meaningful insights from your data.

Contributing

Have suggestions or improvements? Feel free to contribute by opening an issue or submitting a pull request. Let's collectively build a resource that empowers data scientists to write efficient and elegant Python code.

Happy coding!

Note: This guide is structured in a style similar to a GitHub README file, making it easy for data scientists to navigate and quickly grasp the key concepts.