Pandas Intermediate Part 3 | Handling Missing Values & Data Quality (Real-World)
Автор: PYAI HUB
Загружено: 2026-01-10
Просмотров: 19
Welcome to Pandas Intermediate – Part 3 🚀
In real-world datasets, data is never perfect. Missing values, incorrect data types, and poor data quality can completely break your analysis.
In this video, you’ll learn:
How to detect missing values using Pandas
When to drop vs fill missing data
How to make data-quality decisions, not just run functions
Why data types matter in real analysis
How analysts handle imperfect datasets professionally
📌 This video is part of the Pandas Intermediate series on PyAI Hub.
If you already know Pandas basics, this series helps you think like a data analyst.
👉 Next video: GroupBy Fundamentals (Part 4)
🔔 Subscribe to follow the full series step by step
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