python-data

# Python data skill Run complex calculations, regressions, and aggregations using pandas and scikit-learn in the sandbox. ## Workflow 1. **Write a Python script** using `pandas`, `numpy`, or `sklearn` to: - Join, filter, and aggregate tables. - Compute statistics (mean, median, stddev, correlations). - Build or evaluate ML models. - Run statistical tests (chi-square, t-test) when appropriate. 2. **Execute in the sandbox.** Run via `python3 script.py`. 3. **Handle errors.** If the code fails, read the traceback and fix it. 4. **Return structured output.** Print results as JSON for downstream skills. ## Example output ```json [ {"segment": "High Risk", "churn_rate": 0.34, "avg_tenure": 2.1}, {"segment": "Low Risk", "churn_rate": 0.05, "avg_tenure": 14.7} ] ``` ## Notes - Use `select_dtypes(include=["object", "str"])` for categorical columns (avoids the pandas 4 deprecation warning). - Install missing packages with `pip install -q <package>` before importing.