17/05/2026
Outlier Detection & Handling | Part 2: Implementation
4 lines of Pandas. That's all it takes to detect outliers and save your model. Here's exactly how. 🔧
IQR method + Z-Score + reusable clean pipeline — all in real Python with a real dataset. Copy, paste, run.
⏱ CHAPTERS
00:00 The dataset — spotting the problems
00:10 Setting up imports (pandas, numpy, scipy)
00:20 IQR detection in 4 lines of code
00:34 Capping outliers with .clip()
00:46 Z-Score detection with scipy.stats
01:00 Drop vs Cap — applying both strategies
01:12 Full reusable pipeline function
📌 CODE COVERED
• col.quantile(0.25/0.75) → IQR fences
• np.abs(stats.zscore(col)) > 3 → Z-Score filter
• df[col].clip(lower, upper) → capping in one line
• clean_outliers(df, col, method='iqr', strategy='cap') → reusable function
💾 Want the full code? Drop "CODE" in the comments and I'll share the GitHub link.
📂 DATA CLEANING SERIES PLAYLIST → [link]
→ Ep 1 Pt 1: Outlier Theory (watch this first if you haven't!)
→ Ep 2: Missing Values Implementation
→ Ep 3: Feature Scaling Code
💬 IQR or Z-Score — which do you default to in your projects? Drop your answer below 👇
