apache-airflow-providers-papermill
Provider package apache-airflow-providers-papermill for Apache Airflow
Decision gist · record as of 2026-08-14
Yes, if you run Apache Airflow and want to execute Jupyter notebooks as orchestrated tasks. The package is actively maintained, has no known vulnerabilities, and low install friction. It's the standard way to integrate Papermill into Airflow. Install it only if you have an Airflow deployment already running and a genuine need to parameterize and schedule notebook execution; it adds dependencies (pandas, nbconvert, ipykernel) that are only useful in that context.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Apache Airflow >=2.11.0 and Python >=3.10; Jupyter notebook infrastructure must be available in your Airflow environment.
- Low friction installation as a pure-Python wheel.
- Actively maintained with a release 6 days old; Airflow ecosystem packages are typically well-supported.
License · maintenance · safety
Apache-2.0 (permissive) — Apache License 2.0 (permissive). You can use, modify, and distribute this package freely in commercial and private projects with minimal restrictions.
last release 2026-08-08 (6 days) · last repo commit 2026-08-14 · 46,490 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 751,398 downloads/mo, #5,156 on PyPI
Alternatives
Verify before relying
pip install apache-airflow-providers-papermill
from airflow.providers.papermill.operators.papermill import PapermillOperator
task = PapermillOperator(
task_id='run_notebook',
input_nb='path/to/notebook.ipynb',
output_nb='path/to/output.ipynb',
parameters={'param1': 'value1'}
)- Whether the package supports execution in containerized or remote Airflow environments (e.g., Kubernetes executor).
- Performance characteristics when running large or long-running notebooks as Airflow tasks.
- How scrapbook integration works for capturing notebook outputs and metrics in Airflow context.
What it is and what it does
This is an Apache Airflow provider package that bridges Jupyter notebooks and Airflow orchestration by wrapping the Papermill library. It lets you define notebook execution as native Airflow tasks, passing parameters into notebooks and capturing their outputs within your DAG. The package depends on Papermill for parameterized notebook execution, Scrapbook for output collection, and standard data-science libraries (pandas, ipykernel, nbconvert) to handle notebook rendering and kernel management.
You install it alongside an existing Airflow deployment (>=2.11.0) and use its PapermillOperator to run .ipynb files with injected parameters, making notebooks first-class citizens in Airflow workflows. It's designed for teams that want to orchestrate notebook-based analytics, ML experiments, or data processing pipelines without rewriting them as Python scripts.
Use it for
- Schedule parameterized Jupyter notebooks as recurring Airflow tasks, injecting date ranges or configuration values at runtime.
- Orchestrate multi-step ML pipelines where each step is a notebook, capturing metrics and artifacts between steps.
- Run exploratory data analysis notebooks on a schedule with different input datasets, storing outputs for review.
- Integrate notebook-based reporting into Airflow DAGs, generating reports with dynamic parameters.
- Chain notebook execution with other Airflow operators to build hybrid workflows mixing notebooks and Python tasks.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you run Apache Airflow and want to execute Jupyter notebooks as orchestrated tasks.
The package is actively maintained, has no known vulnerabilities, and low install friction. It's the standard way to integrate Papermill into Airflow. Install it only if you have an Airflow deployment already running and a genuine need to parameterize and schedule notebook execution; it adds dependencies (pandas, nbconvert, ipykernel) that are only useful in that context.
Install
apache-airflow-providers-papermill on PyPI
Before you install
Low friction installation as a pure-Python wheel. Actively maintained with a release 6 days old; Airflow ecosystem packages are typically well-supported. Requires Apache Airflow >=2.11.0 and seven runtime dependencies including papermill, scrapbook, pandas, and nbconvert.
Requires Apache Airflow >=2.11.0 and Python >=3.10; Jupyter notebook infrastructure must be available in your Airflow environment.
License in practice
Apache License 2.0 (permissive). You can use, modify, and distribute this package freely in commercial and private projects with minimal restrictions.
Quickstart
pip install apache-airflow-providers-papermill
from airflow.providers.papermill.operators.papermill import PapermillOperator
task = PapermillOperator(
task_id='run_notebook',
input_nb='path/to/notebook.ipynb',
output_nb='path/to/output.ipynb',
parameters={'param1': 'value1'}
)Verify before relying
- Whether the package supports execution in containerized or remote Airflow environments (e.g., Kubernetes executor).
- Performance characteristics when running large or long-running notebooks as Airflow tasks.
- How scrapbook integration works for capturing notebook outputs and metrics in Airflow context.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 7 packagesapache-airflowapache-airflow-providers-common-compatpapermillscrapbookipykernelpandasnbconvert |
| Maintenance | Actively maintained 6 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 751,398 / month, #5,156 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableEnvironment :: ConsoleEnvironment :: Web EnvironmentFramework :: Apache AirflowFramework :: Apache Airflow :: ProviderIntended Audience :: DevelopersIntended Audience :: System AdministratorsProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: System :: Monitoring |
Evidence: apache_airflow_providers_papermill-3.13.2-py3-none-any.whl
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See also apache-airflow-providers-asana · papermill · apache-airflow-providers-salesforce · apache-airflow-providers-openfaas · apache-airflow-providers-apache-hdfs · apache-airflow-providers-tableau · apache-airflow-providers-zendesk · apache-airflow-providers-cohere · apache-airflow-providers-exasol · apache-airflow-providers-apache-hive