From Kaggle Notebook to Reusable Python Package 2026

From Kaggle Notebook to Reusable Python Package 2026

You just finished a great Kaggle competition. Your notebook works well and got a solid rank. But now it’s just a messy collection of cells with hard-coded paths, no tests, and no structure. In 2026, professional data scientists turn that winning notebook into a clean, reusable, installable Python package that can be used across projects, shared with teammates, or even published. This guide shows you the exact step-by-step process.

TL;DR — The Complete Transformation

• Extract logic into functions and classes
• Create a proper package structure with pyproject.toml + uv
• Add type hints, docstrings, and tests
• Use DVC for data and model versioning
• Publish to PyPI or your private index

1. From Notebook to Clean Code

# Kaggle-style (messy)
df = pd.read_csv("/kaggle/input/train.csv")
df["feature"] = df["col1"] * df["col2"]
model.fit(...)

Refactored into clean, reusable code:

# src/my_package/feature_engineering.py
import polars as pl
from pydantic import BaseModel

class FeatureConfig(BaseModel):
    target_column: str

def engineer_features(df: pl.DataFrame, config: FeatureConfig) -> pl.DataFrame:
    """Apply reusable feature engineering steps."""
    return df.with_columns((pl.col("col1") * pl.col("col2")).alias("feature"))

2. Professional Package Structure (2026 Standard)

my_kaggle_utils/
├── pyproject.toml
├── README.md
├── src/
│   └── my_kaggle_utils/
│       ├── __init__.py
│       ├── data_loader.py
│       ├── feature_engineering.py
│       └── model_utils.py
├── tests/
├── dvc.yaml
└── models/

3. Modern pyproject.toml Setup

[project]
name = "my-kaggle-utils"
version = "1.0.0"
description = "Reusable utilities from Kaggle competitions"
requires-python = ">=3.11"
dependencies = ["polars", "pydantic", "scikit-learn"]

4. Testing & Documentation

# tests/test_feature_engineering.py
def test_engineer_features():
    df = pl.DataFrame({"col1": [1, 2], "col2": [3, 4]})
    config = FeatureConfig(target_column="target")
    result = engineer_features(df, config)
    assert "feature" in result.columns

Best Practices in 2026

• Use uv for fast dependency management
• Always include type hints and Pydantic models
• Write tests for every public function
• Use DVC to version models and data
• Publish to PyPI or your company’s private index

Conclusion

Turning a Kaggle notebook into a reusable Python package is one of the highest-leverage skills for data scientists in 2026. Instead of copying code between projects, you install your own package with one command and get consistent, tested, documented utilities everywhere. This is how you move from competition winner to production-ready professional.

Next steps:

• Take your best Kaggle notebook and convert it into a proper package using the structure above
• Install it in your other projects with uv add ./my-kaggle-utils
• Continue the “Software Engineering For Data Scientists” series on pyinns.com