---
id: papermill
version: "2.7.0"
license: BSD
license_treatment: permissive
maintenance: active
---

# papermill — Parameterize and run Jupyter and nteract Notebooks

License: permissive · Maintenance: active · Downloads: 15.4M/mo

## What it is and what it does

Papermill is a tool for turning Jupyter Notebooks into parameterized, reusable components in data pipelines. It lets you inject parameters into a notebook before execution, then run it and capture the output—all without manual intervention. This is useful when you have a notebook-based analysis or report that needs to run with different inputs (e.g., different dates, thresholds, or datasets) or when you want to chain multiple notebooks together in a workflow.

The package works by looking for a cell tagged 'parameters' in your notebook, then injecting a new cell with your supplied parameter values before execution. You can pass parameters via the Python API, command-line flags, YAML files, or base64-encoded strings. It supports reading from and writing to local files, HTTP URLs, AWS S3, Azure storage, and Google Cloud Storage, making it suitable for both local development and cloud-based data pipelines.

Use it for:

- Run a financial or business report notebook on different dates (month-end, year-end) with parameters controlling the date range.
- Build a data pipeline where one notebook's output feeds into another notebook's parameters, automating multi-step analyses.
- Batch-execute the same analysis notebook across many datasets or cohorts, storing results in cloud storage.
- Integrate notebook-based models or analyses into scheduled jobs or CI/CD workflows without rewriting the logic.
- Generate parameterized reports on demand, injecting user-supplied values (thresholds, filters, model hyperparameters) at runtime.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Papermill parameterizes, executes, and analyzes Jupyter Notebooks programmatically, allowing you to inject parameters into notebooks and run them with different inputs via Python API or CLI.

Yes. Papermill is actively maintained, has low install friction, carries no known vulnerabilities, and solves a real problem for anyone using Jupyter Notebooks in production pipelines or reporting workflows. The permissive BSD license poses no restrictions. Install it if you need to automate notebook execution with varying inputs or integrate notebooks into larger workflows.

## Install

pip install papermill
uv add papermill
poetry add papermill

## Installing papermill

Before you install: Low friction installation with a pure-Python wheel. Actively maintained with recent releases; last commit 2026-07-06 and 6469 repository stars indicate sustained community use.

License in practice: BSD license is permissive; you can use, modify, and distribute papermill with minimal restrictions in both open-source and commercial projects.

Quickstart:

pip install papermill

import papermill as pm

pm.execute_notebook(
    'path/to/input.ipynb',
    'path/to/output.ipynb',
    parameters=dict(alpha=0.6, ratio=0.1)
)

Requires Python 3.10 or later; notebook must contain a cell tagged 'parameters' for parameter injection to work as intended.

Verify before relying:

- Whether optional I/O bundles (s3, azure, all) are required for your cloud storage use case or if local/HTTP execution alone suffices.
- Performance characteristics when executing large notebooks or running many notebooks in parallel workflows.

## Package facts

- License: BSD (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 15.4M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

jupyter notebook automation, parameterized notebook execution, notebook workflow orchestration, batch notebook runner, jupyter parameter injection, notebook pipeline tool, automated notebook execution, notebook-automation, data-pipeline, jupyter-tools

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