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

[
  {"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.