ChartBook
A developer platform for data science teams.
Discover, document, and share data science work across your organization. ChartBook provides a centralized catalog for data pipelines, charts, and documentation—making it easy to find, understand, and reuse analytics work.
Installation
pip install chartbook
One install, everything included: data loading, plotting utilities, and the CLI for building documentation.
Quick Start
Load data from a pipeline
from chartbook import data
df = data.load(pipeline="fred_charts", dataframe="fred")
Build documentation
chartbook docs generate
chartbook docs generate -f --watch # rebuild as you edit; refresh the browser
Check a project in CI
chartbook check # fails on broken paths, expiring licenses, and fields a [policy] requires
Ask the manifest where a file belongs
chartbook path repo_public # the path chartbook.toml declares for that dataframe
chartbook path chart/repo_rates # ...and for that chart
from chartbook import assets, data
df.to_parquet(data.path("repo_public")) # a dataframe
fig.write_html(assets.path("chart/repo_rates")) # anything else says its kind
Declare a trained model
[models.rate_forecaster]
path = "./_models/rate_forecaster.joblib"
trained_on = ["repo_public"]
metrics_path = "./_output/rate_forecaster_metrics.json"
from chartbook import models
joblib.dump(model, models.path("rate_forecaster")) # the model gets a page: scores, lineage
Browse your catalog
# List all pipelines, dataframes, and charts
chartbook ls
# List dataframes only
chartbook ls dataframes
# Get path to a dataframe's parquet file
chartbook data get-path --pipeline fred_charts --dataframe fred
See the documentation for configuration options and detailed guides.
Terminology
ChartBook supports two project types:
- Pipeline — A single analytics pipeline with its own charts, dataframes, and documentation
- Catalog — A collection of multiple pipelines aggregated into a unified documentation site
See Documenting a Pipeline and Catalogs and Data Access for how the two fit together, and Vocabulary for the words ChartBook uses: a manifest asks, a lockfile records what was got, a snapshot records what was made, and an index records what a site shows.
Features
- Pipeline Catalog — Organize and discover data pipelines across your team
- Documentation Generation — Build searchable documentation websites from your analytics work
- Data Governance — Track data sources, licenses, and access permissions
- Programmatic Data Access — Load pipeline outputs directly into pandas or polars
- Multi-Pipeline Catalogs — Aggregate multiple pipelines into a single documentation site
Documentation
Full documentation is available at backofficedev.github.io/chartbook.
Contributing
Contributions are welcome. See CONTRIBUTING for guidelines.
To work on ChartBook itself you need uv and nothing else. From a clone:
uv sync # the project, editable, and its dev toolchain, from uv.lock
uv run doit # notebooks, docs, examples, the package, then the test matrix
uv run doit build_package is the same without the tests, and uv run doit test is the suite on one Python.
License
Modified BSD License
1 maintainer
Metadata
1 maintainer
Release files for chartbook 0.2.0
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| chartbook-0.2.0.tar.gz | 11.9 MB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| chartbook-0.2.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 23.9 MB
Release files / chartbook-0.2.0.tar.gz
| Download URL | chartbook-0.2.0.tar.gz |
|---|---|
| Size | 11.9 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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| Download URL | chartbook-0.2.0-py3-none-any.whl |
|---|---|
| Size | 12.0 MB |
| Tags | Python 3 |
|
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No |
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python-httpx/0.28.1
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