Top 15 Python Tips for Data Cleaning - Understanding

Discover 15 essential Python tips for effective data cleaning and understanding in this 25-minute EuroPython 2020 conference talk. Learn how to tackle common data cleaning tasks such as merging, appending, checking data completeness, validating values, de-duplication, handling missing values, and recoding. Explore techniques using primarily Pandas and Numpy to improve the accuracy of your data analysis. Gain valuable insights for newcomers to data analytics and data science, as well as Excel users looking to transition to Python. Familiarize yourself with basic Python programming concepts to make the most of this informative presentation on enhancing your data cleaning skills.

Intro
Welcome
High Level Overview
Complex Transformations
Context
Materials
Libraries
Mock Data
Preview Data
Viewability Matrix
Fake Data
Jupyter