Python 3 Best Practices
At the end of this course, you will be able to write clearer, more readable, and more maintainable code, with better documentation.
Prerequisites: Be familiar with the basics of Python programming.
Sign in to read this course
A free account unlocks all 514 courses. 20 are readable without one.
What's inside
6 sections- 1 Table of Contents
- 2 Course Overview
- 3 Follow Python style conventions: PEP 8
- 4 Document your project
- 5 Improve your code with type checking
- 6 Conclusion
More Python Foundations & Data Analysis courses
View all 26Python Data Essentials: Python Introduction
Get started with Python for data science — the basics, packages and core data structures.
Python Data Essentials: Programming Fundamentals
Conditionals, loops, functions and object-oriented basics to prepare for pandas and NumPy.
Up and Running with Pandas
Pandas fundamentals — representing, exploring and evaluating data programmatically.
Cleaning Data with Pandas
Practical data cleaning, correlation analysis and data preparation with pandas.
Normalize Data for Analysis with Pandas
Why and how to normalize data, from simple techniques to Gaussian normalization with pandas and scikit-learn.
Pandas Functions
Advanced pandas — merge, join, concatenate, binning, pivot, melt and crosstab.
Interested in this course?
Contact us to book it or get a custom training plan for your team.