Why efficient Python? Because using Python more efficiently will make your code more readable and run more efficiently.
Why for data scientist? Because Python has a wide application. The Python tools used in the data science field are not necessarily useful for other fields such as web development.
The goal of this book is to spread the awareness of efficient ways to do Python. They include:
- efficient methods and libraries to work with iterator, dictionary, function, and class
- efficient methods to work with popular data science libraries such as pandas and NumPy
- efficient tools to incorporate in a data science project
- efficient tools to incorporate in any project
- efficient tools to work with Jupyter Notebook.
This book expects you to have some basic knowledge of Python and data science.
You should also expect bite-size code snippets for each section. This will allow you to obtain multiple pieces of knowledge in fewer than one minute. I included the link to the resources for every tools introduced in case you want to explore them further.
For a printer-friendly version of the tools mentioned in this book, sign up for CodeCut's free PDF guide. This comprehensive 264-page document covers over 100 essential data science tools, providing you with a valuable reference that you can print and keep at your desk.
Khuyen Tran wrote over 150 data science articles with 100k+ views per month on Towards Data Science. She also wrote 800+ daily data science tips at CodeCut. Her current mission is to make open-source more accessible to the data science community.
