Master Missing Data with Pandas: Essential Techniques for Data Pros

In this video, we delve into the world of Pandas interpolation for data analysis. Join me as I demonstrate how to clean data effectively using linear, quadratic, and cubic spline interpolation techniques in Pandas. Whether you're a data scientist or analyst, understanding interpolation is crucial for accurate data manipulation. Stay tuned as I break down the theory behind interpolation and provide practical examples for real-world applications. Let's master the art of data cleaning with Pandas together! 📖 CHAPTERS 00:00 - Intro 00:49 - Setup 01:18 - Data Preparation 03:43 - Initial Plotting of the Data 07:23 - Linear Interpolation Intro 08:30 - Linear Interpolation Theory 10:16 - Linear Interpolation Using Pandas 12:43 - Quadratic Interpolation Intro 13:15 - Quadratic Interpolation Theory 15:18 - Quadratic Interpolation Using Pandas 18:14 - Ow My Spline! 18:44 - Spline Interpolation Theory 20:42 - Spline Interpolation in Pandas 22:07 - Error Estimation 31:26 - Regression vs. Interpolation 32:15 - Summary 32:32 - Outro and Thanks! 🔗REFERENCES - Data Cleaning Intro: https://www.youtube.com/watch?v=ycQbwBqB8wY - Github: https://github.com/trentpark8800/python-bites/blob/main/advanced-data-cleaning-with-pandas/data/data_to_be_cleaned.csv - MIT OCW Interpolation Chapter - https://ocw.mit.edu/courses/18-330-introduction-to-numerical-analysis-spring-2012/fd3b0e48e1babfcc36db7c012078318d_MIT18_330S12_Chapter3.pdf 💵SUPPORT THE CHANNEL - Become a channel member: https://www.youtube.com/channel/UCY5ZuZAWVUBzkDXQr5b84sQ/join - Direct, once off transfers: https://www.paypal.com/paypalme/trentdoesmath