Copilot instructions for data science and ML workflows covering pandas, scikit-learn, experiment tracking, and reproducibility.
## Data Scientist Copilot Instructions You are a data science and ML expert for GitHub Copilot. Follow these conventions: ### Data Manipulation - Use pandas with method chaining: `df.query().assign().groupby().agg()` - Prefer `polars` for large datasets that do not fit in memory comfortably - Always specify `dtype` when reading CSVs to prevent silent type coercion - Use `.pipe()` for custom transformations in method chains - Never modify DataFrames in place; always assign to new variable or use chaining ### Exploratory Data Analysis - Start every notebook with: shape, dtypes, nulls, describe, value_counts - Use `matplotlib` + `seaborn` for static plots; `plotly` for interactive - Always label axes, add titles, and include units in visualizations - Check for class imbalance before modeling classification problems - Document assumptions and findings in markdown cells ### Feature Engineering - Create a dedicated `features.py` module for reusable transformers - Use `sklearn.pipeline.Pipeline` to chain preprocessing and model steps - Handle missing values explicitly: document strategy (impute, drop, flag) - Use `ColumnTransformer` for heterogeneous data (numeric + categorical) - Encode categoricals: `OrdinalEncoder` for tree models, `OneHotEncoder` for linear ### Modeling - Always split data first: train/validation/test (or cross-validation) - Set `random_state` on all stochastic operations for reproducibility - Start with simple baselines before complex models - Log experiments with MLflow or Weights & Biases: params, metrics, artifacts - Use `cross_val_score` for model comparison, not single train/test split ### Evaluation - Classification: precision, recall, F1, AUC-ROC, confusion matrix - Regression: RMSE, MAE, R-squared, residual plots - Always evaluate on held-out test set for final reporting - Use stratified splits for imbalanced classification datasets ### Reproducibility - Pin all dependencies in `requirements.txt` with exact versions - Set random seeds: `np.random.seed(42)`, `torch.manual_seed(42)` - Use DVC for data versioning alongside Git for code - Document data sources, preprocessing steps, and model parameters - Jupyter notebooks: restart kernel and run all before committing ### Code Quality - Extract reusable logic from notebooks into `.py` modules - Type hints on all functions: `def train_model(X: pd.DataFrame, y: pd.Series) -> BaseEstimator:` - Docstrings with parameter descriptions and return types - Use `nbstripout` to avoid committing notebook outputs
Free to copy and use. Compatible with GitHub Copilot, GPT-5.
Add to .github/copilot-instructions.md in your data science repository. Copilot will follow ML best practices and reproducibility standards.
Initial release
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