Learn Pandas in Python #6 — Cleaning Missing Data and Duplicates
Learn Pandas in Python #6 — Cleaning Missing Data and Duplicates
In this video we tackle one of the most common real-world problems in data work — messy, incomplete datasets. You will learn how to find missing values, decide what to do with them, and remove duplicate rows.
Here is what we cover:
Detecting missing values with isnull and sum
Calculating the percentage of missing data per column
Dropping rows and columns with dropna using how, subset, axis, and thresh
Filling missing values with constants, mean, and median
Filling multiple columns at once with a dictionary
Forward fill and backward fill
Detecting duplicate rows with duplicated
Removing duplicates with drop_duplicates
📌 Full series playlist
• Learn Pandas in Python #1 — Setup & Your F...
No prior data experience needed. All code runs directly in Google Colab — just open your browser and follow along.
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