Paths/MLOps Engineer/Python / Data Wrangling with Pandas
Clean a Frame Before Training
Nulls, duplicates and wrong dtypes quietly destroy a model. Write the cleaning step that handles all three.
Validator
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01Requirements
live checks- File ~/ml/data/clean.py exists○
- The script drops rows with missing values○
- The script fills the remaining gaps with a sensible default○
- The script removes duplicate rows○
02Walkthrough
1 of 4 revealed- 01
Write the cleaning step
dropna removes the rows that are missing something essential. Fill the gaps only after you know which ones matter.
echo 'import pandas as pd\n\ndef clean(df):\n df = df.dropna(subset=["user_id", "event_time"])\n df = df.fillna({"country": "unknown"})' > ~/ml/data/clean.py - 3 locked · each failed submit reveals one
03Stuck?
0 of 3 revealedTake a hint without losing the lab — each one points at the thing to look at, not at the answer.
04AI Tutor
guides, never solvesTell me what you're stuck on — I'll nudge, not solve. Which requirement are you looking at?
04Notebook
saved locally in this browserlearner@lab — ubuntu:24.04
Session is disposable
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