CartoBoost

PyPI Python CI Docs Release License: MIT

CartoBoost is a Python toolkit for regression, classification, grouped ranking, and forecasting problems where place, time, route structure, or repeated identifiers matter. It is aimed at scientific and applied modeling workflows such as mobility, logistics, demand forecasting, route ranking, and other structured prediction problems.

Choose CartoBoost when a standard tabular booster is a serious baseline, but the study also needs model structure for:

  • cyclic time such as hour-of-day, weekday, or seasonal demand;
  • 2D spatial patterns such as corridors, neighborhoods, hotspots, and service boundaries;
  • list-valued memberships such as zones, route cells, H3 cells, or S2 cells;
  • directed movement such as source to target flow;
  • high-cardinality place or route ids that may benefit from learned embeddings;
  • leakage-aware validation and reproducible benchmark comparisons.

CartoBoost keeps a familiar estimator workflow, but the main goal is not to hide the modeling choices. It helps you state them clearly, test them against simpler baselines, and preserve the fitted artifacts that produced the result.

When It Fits

CartoBoost is most useful when the scientific question is about structured temporal-spatial signal:

  • Does hour-of-day interact with location context when estimating duration?
  • Do zone memberships change fare estimates after distance and calendar features are included?
  • Does preserving route direction change source-target predictions compared with unordered identifiers?
  • How do rolling-origin demand forecasts compare with naive, seasonal naive, theta, ETS, or supervised lag baselines on the same split?
  • Do spatial splitters recover zone or corridor signal that an axis-only model approximates poorly?

It is less useful when place/time structure is irrelevant, the dataset is too small to support structured validation, or a simple interpretable model already answers the study question.

Modeling Primitives

CartoBoost supports:

  • L2 and quantile regression objectives.
  • Constant and linear residual leaves.
  • Axis, histogram-axis, diagonal 2D, Gaussian/radial 2D, periodic, sparse-set, and fuzzy split behavior.
  • Dense numeric arrays plus list-valued sparse-set features.
  • Feature schemas for numeric, periodic, sparse-set, and model-contract validation.
  • JSON model artifacts and portable weights artifacts.
  • Optional SHAP explanations, Optuna tuning, Polars input support, and ONNX export for the supported dense axis-tree subset.
  • Standalone neural embedding regressors and optional neural feature-generation workflows for high-cardinality IDs.
  • node2vec, GraphSAGE, heterogeneous GraphSAGE, and typed-schema HinSAGE graph regressors, link predictors, and graph feature encoders.
  • Forecasting APIs for geographic and temporal single-series or panel demand, including rolling-origin backtests, naive/seasonal naive/theta/optimized-theta/ETS/AutoARIMA models, supervised CartoBoost lag forecasting, weighted ensembles, CLI runs, and portable forecast artifacts.
  • General utilities outside the forecasting API, including single-series forecast helpers, local-level/local-linear Kalman filters, Croston/SBA/TSB intermittent demand, and ordinary kriging.

Install

Install the released package from PyPI:

uv add cartoboost

CartoBoost 0.3 keeps the root import surface intentionally small. Read the 0.3 migration guide when upgrading imports from 0.2; forecasting and validation use named modules, while advanced model families are explicitly under cartoboost.preview.

Optional integrations stay optional:

uv add "cartoboost[explain]"  # SHAP support
uv add "cartoboost[h3]"       # H3 point and decoded-route encoder
uv add "cartoboost[s2]"       # S2 point and decoded-route encoder
uv add "cartoboost[duckdb]"   # DuckDB relation inputs
uv add "cartoboost[optuna]"   # Optuna tuning
uv add "cartoboost[polars]"   # Polars inputs
uv add "cartoboost[onnx]"     # ONNX export subset

Verify the install:

python -c "import cartoboost; print(cartoboost.__version__)"
python examples/quickstart.py

The five-minute NumPy quickstart creates a complete place/time dataset, performs an out-of-time split, compares axis-only and structured models, and checks save/load prediction parity. The optional pandas extra enables ForecastFrame.from_pandas and other pandas adapters:

uv add "cartoboost[pandas]"

Structured Regression Workflow

Start with the scientific design:

  1. Define the target, such as transformed duration, fare amount, or demand.
  2. Hold out data in a way that matches deployment, usually out-of-time for tabular rows or rolling-origin for demand forecasts.
  3. Compare against serious baselines on the same rows, such as LightGBM or XGBoost for tabular regression.
  4. Add CartoBoost structure only when it maps to a real place, time, or relationship hypothesis.

Then fit the estimator:

from cartoboost import CartoBoostRegressor

model = CartoBoostRegressor(
    n_estimators=200,
    learning_rate=0.04,
    max_depth=5,
    min_samples_leaf=30,
    split_policy="structured",
)

model.fit(X_train, y_train)
predictions = model.predict(X_validation)

For structured mobility or operations data, dense columns might include trip distance, hour, weekday, coordinates, route context, or category flags. Add sparse-set columns when each row has route-cell, zone, or similar memberships. Decoded OSRM or Valhalla routes can be converted into H3/S2 sparse rows with build_h3_route_sparse_sets or build_s2_route_sparse_sets.

schema = {
    "dense": [
        {"name": "trip_distance", "kind": "numeric"},
        {"name": "pickup_hour", "kind": "periodic", "period": 24},
        {"name": "pickup_x", "kind": "numeric"},
        {"name": "pickup_y", "kind": "numeric"},
    ],
    "sparse_sets": [
        {"name": "zone_ids", "kind": "sparse_set"},
    ],
}

model = CartoBoostRegressor(
    n_estimators=200,
    learning_rate=0.04,
    max_depth=5,
    min_samples_leaf=30,
    split_policy="structured",
)

model.fit(
    X_train_dense,
    y_train,
    sparse_sets={"zone_ids": zone_ids_train},
    feature_schema=schema,
)

Why these choices can matter:

  • periodic:24 treats midnight-adjacent pickup hours as neighbors.
  • diagonal_2d can represent oblique spatial boundaries more directly than axis-only trees.
  • gaussian_2d can isolate radial neighborhoods around hotspots or airports.
  • sparse_set splits on list-valued route or cell membership without a wide one-hot matrix.
  • fuzzy routing can reduce hard jumps near spatial or temporal boundaries.

Forecast Regular Series

Use forecasting APIs when the target is future demand, counts, or other regular series.

from cartoboost.preview.forecasting import ForecastFrame, ThetaForecaster

frame = ForecastFrame.from_pandas(
    lane_demand,
    timestamp_col="timestamp",
    target_col="demand",
    series_id_col="series_id",
    freq="D",
)

model = ThetaForecaster(season_length=7)
model.fit(frame)
forecast = model.predict(horizon=14)

Forecast outputs use deterministic columns: series_id, timestamp, horizon, model, and mean. Use rolling-origin backtests before making quality claims, and compare against naive, seasonal, local, or external forecasting baselines on the same series and cutoff dates.

Graph And Learned-ID Structure

Use graph models when relationships are part of the observation process: directed flows, zone hierarchies, route networks, or metapaths. Direction is explicit, so A -> B and B -> A can be different facts, features, and embeddings.

Use neural embedding models when high-cardinality ids, such as locations or route ids, carry stable residual signal. Treat these as hypotheses to validate, not automatic upgrades.

from cartoboost.preview import NeuralEmbeddingRegressor

model = NeuralEmbeddingRegressor(
    dim=16,
    base_model_kwargs={"n_estimators": 80, "split_policy": "axis_only"},
    final_model_kwargs={"n_estimators": 120, "split_policy": "structured"},
)

model.fit(X_train, y_train, ids=location_ids_train)
predictions = model.predict(X_validation, ids=location_ids_validation)

Benchmarks And Claims

Benchmark reports should identify the dataset, target, feature set, split design, comparison models, metrics, and meaning of the result. In this repo, benchmarks track structured regression and forecasting tasks over real data families.

Quality claims should come from real runs with fixed comparable settings. Record RMSE, MAE, R2, training time, prediction time, model settings, sample size, task names, and split names.

Do not publish a benchmark claim unless the CartoBoost row satisfies the primary metric threshold under the same split, comparable feature access, comparable tuning budget, and complete baseline set. If a required baseline fails or interval coverage is not actually computed, the benchmark is incomplete for that claim.

Save, Load, And Explain

model.save("duration.cartoboost.json")
loaded = CartoBoostRegressor.load("duration.cartoboost.json")

explanation = loaded.explain_shap(
    X_validation_dense,
    background=X_train_dense,
    sparse_sets={"zone_ids": zone_ids_validation},
    background_sparse_sets={"zone_ids": zone_ids_train},
)

Model artifacts are versioned JSON and include optional metadata, feature schema, and training configuration fields. Graph and neural standalone artifacts are complete model artifacts. Feature-generation artifacts should be persisted with whichever downstream model consumes their generated columns.

Source-checkout CLI

The Rust CLI is a source-checkout tool while the Python wheel remains focused on the estimator and forecasting APIs. Run it from this repository with cargo run -p cartoboost-cli -- --help; it is not installed by uv add cartoboost.

Documentation

Project links

Data verified by PyPI on Jul 14, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (0.3.11)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for theculliganman from gravatar.com theculliganman

Credits

Author: Ryan Culligan

GitHub Statistics

Data verified by PyPI on Jul 14, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

MIT License

Requires

Python >=3.10

Provides Extra

chronos duckdb explain foundation h3 holidays moirai onnx optuna pandas polars s2 sklearn tabpfn tensorboard timegpt timesfm visualization

Tags

boosting gradient-boosting machine-learning python rust

Classifiers

Report project as malware

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

cartoboost-0.3.6.tar.gz (804.1 kB view details)

Uploaded Source

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

cartoboost-0.3.6-cp313-cp313-win_arm64.whl (4.6 MB view details)

Uploaded CPython 3.13Windows ARM64

cartoboost-0.3.6-cp313-cp313-win_amd64.whl (5.0 MB view details)

Uploaded CPython 3.13Windows x86-64

cartoboost-0.3.6-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.8 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.17+ x86-64

cartoboost-0.3.6-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (4.4 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.17+ ARM64

cartoboost-0.3.6-cp313-cp313-macosx_11_0_arm64.whl (4.4 MB view details)

Uploaded CPython 3.13macOS 11.0+ ARM64

cartoboost-0.3.6-cp313-cp313-macosx_10_12_x86_64.whl (4.7 MB view details)

Uploaded CPython 3.13macOS 10.12+ x86-64

cartoboost-0.3.6-cp312-cp312-win_arm64.whl (4.6 MB view details)

Uploaded CPython 3.12Windows ARM64

cartoboost-0.3.6-cp312-cp312-win_amd64.whl (5.0 MB view details)

Uploaded CPython 3.12Windows x86-64

cartoboost-0.3.6-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.8 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.17+ x86-64

cartoboost-0.3.6-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (4.4 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.17+ ARM64

cartoboost-0.3.6-cp312-cp312-macosx_11_0_arm64.whl (4.4 MB view details)

Uploaded CPython 3.12macOS 11.0+ ARM64

cartoboost-0.3.6-cp312-cp312-macosx_10_12_x86_64.whl (4.7 MB view details)

Uploaded CPython 3.12macOS 10.12+ x86-64

cartoboost-0.3.6-cp311-cp311-win_arm64.whl (4.6 MB view details)

Uploaded CPython 3.11Windows ARM64

cartoboost-0.3.6-cp311-cp311-win_amd64.whl (5.1 MB view details)

Uploaded CPython 3.11Windows x86-64

cartoboost-0.3.6-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.8 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.17+ x86-64

cartoboost-0.3.6-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (4.4 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.17+ ARM64

cartoboost-0.3.6-cp311-cp311-macosx_11_0_arm64.whl (4.4 MB view details)

Uploaded CPython 3.11macOS 11.0+ ARM64

cartoboost-0.3.6-cp311-cp311-macosx_10_12_x86_64.whl (4.7 MB view details)

Uploaded CPython 3.11macOS 10.12+ x86-64

cartoboost-0.3.6-cp310-cp310-win_amd64.whl (5.1 MB view details)

Uploaded CPython 3.10Windows x86-64

cartoboost-0.3.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.8 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.17+ x86-64

cartoboost-0.3.6-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (4.4 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.17+ ARM64

cartoboost-0.3.6-cp310-cp310-macosx_11_0_arm64.whl (4.4 MB view details)

Uploaded CPython 3.10macOS 11.0+ ARM64

cartoboost-0.3.6-cp310-cp310-macosx_10_12_x86_64.whl (4.7 MB view details)

Uploaded CPython 3.10macOS 10.12+ x86-64

File details

Details for the file cartoboost-0.3.6.tar.gz.

File metadata

  • Download URL: cartoboost-0.3.6.tar.gz
  • Upload date:
  • Size: 804.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for cartoboost-0.3.6.tar.gz
Algorithm Hash digest
SHA256 6cb2aeb8dc16076913daf0c8842c14ae1444a55777c616eaf54b01088b580e3b
MD5 73e4d36e8a8f5f97a27097ff96063e3c
BLAKE2b-256 89c6b0c161b7ada5d1b27f31cf83e872d8afe59497a6a837256c3ee98ddb41cc

See more details on using hashes here.

Provenance

The following attestation bundles were made for cartoboost-0.3.6.tar.gz:

Publisher: publish-pypi.yml on TheCulliganMan/CartoBoost

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file cartoboost-0.3.6-cp313-cp313-win_arm64.whl.

File metadata

  • Download URL: cartoboost-0.3.6-cp313-cp313-win_arm64.whl
  • Upload date:
  • Size: 4.6 MB
  • Tags: CPython 3.13, Windows ARM64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for cartoboost-0.3.6-cp313-cp313-win_arm64.whl
Algorithm Hash digest
SHA256 9080ffd56b2b6f406ed640c039f288d88a1654fdb353e097a8075a7d4f12d9ee
MD5 3d942cc89e3eb2106f161c5179afe391
BLAKE2b-256 8185afcf90202aac2e76dbcdd9e4d2b8fbcf1bbdb2e16f2d685d1f11081546e5

See more details on using hashes here.

Provenance

The following attestation bundles were made for cartoboost-0.3.6-cp313-cp313-win_arm64.whl:

Publisher: publish-pypi.yml on TheCulliganMan/CartoBoost

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file cartoboost-0.3.6-cp313-cp313-win_amd64.whl.

File metadata

  • Download URL: cartoboost-0.3.6-cp313-cp313-win_amd64.whl
  • Upload date:
  • Size: 5.0 MB
  • Tags: CPython 3.13, Windows x86-64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for cartoboost-0.3.6-cp313-cp313-win_amd64.whl
Algorithm Hash digest
SHA256 bac2e7e107124a258ce09e87aeca0aa2201e47ecf676e41182c729c1a4f82f71
MD5 50282324ad689eff6c920a9f1d199c58
BLAKE2b-256 033d8bda444793d026b30e808690e0ec4988dbfc4bfb5fc1d8b606ecb1dac56d

See more details on using hashes here.

Provenance

The following attestation bundles were made for cartoboost-0.3.6-cp313-cp313-win_amd64.whl:

Publisher: publish-pypi.yml on TheCulliganMan/CartoBoost

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file cartoboost-0.3.6-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for cartoboost-0.3.6-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 f5d73ed8a61868ab17eb7c59d5b005cf0875841bece4b8af9086f4740784fde2
MD5 037bc5cc9b4d67f63f6b01847d6f11e8
BLAKE2b-256 5a0b1f7ac5a318081842c22c69cce6636743da63b484ebc4a38267d469dab909

See more details on using hashes here.

Provenance

The following attestation bundles were made for cartoboost-0.3.6-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl:

Publisher: publish-pypi.yml on TheCulliganMan/CartoBoost

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file cartoboost-0.3.6-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for cartoboost-0.3.6-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 cad21a89809805140bc9def803e2eb935c4566dc1713759263a079e3ba81fb88
MD5 aca5ee02a0b59e920fc246c3090e7104
BLAKE2b-256 35b1e3d625d2d55027e2385b95a696bb4eea7aff750023531eb8c9fa968c4018

See more details on using hashes here.

Provenance

The following attestation bundles were made for cartoboost-0.3.6-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl:

Publisher: publish-pypi.yml on TheCulliganMan/CartoBoost

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file cartoboost-0.3.6-cp313-cp313-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for cartoboost-0.3.6-cp313-cp313-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 0b432f05dfe5711e7fce2204b2e487463a11a4c53e7bbeddb4a3054e583823f1
MD5 e478a2e59ee4e472631795b018601866
BLAKE2b-256 cf8c4b7ffb42c515103d793b442085eb4b517c457facc319d2390b3d6dd92525

See more details on using hashes here.

Provenance

The following attestation bundles were made for cartoboost-0.3.6-cp313-cp313-macosx_11_0_arm64.whl:

Publisher: publish-pypi.yml on TheCulliganMan/CartoBoost

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file cartoboost-0.3.6-cp313-cp313-macosx_10_12_x86_64.whl.

File metadata

File hashes

Hashes for cartoboost-0.3.6-cp313-cp313-macosx_10_12_x86_64.whl
Algorithm Hash digest
SHA256 6b8b5f1a51d50c361aa0b9852a724b874349d0fdc7aec913cf35ba534ce4d91d
MD5 f85f148f44f767473805af0f4c19cafe
BLAKE2b-256 318045e617fcd689b2f7099438978d041d04aa94e0e0fcbf2918db265fd98710

See more details on using hashes here.

Provenance

The following attestation bundles were made for cartoboost-0.3.6-cp313-cp313-macosx_10_12_x86_64.whl:

Publisher: publish-pypi.yml on TheCulliganMan/CartoBoost

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file cartoboost-0.3.6-cp312-cp312-win_arm64.whl.

File metadata

  • Download URL: cartoboost-0.3.6-cp312-cp312-win_arm64.whl
  • Upload date:
  • Size: 4.6 MB
  • Tags: CPython 3.12, Windows ARM64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for cartoboost-0.3.6-cp312-cp312-win_arm64.whl
Algorithm Hash digest
SHA256 e6f50004cdcebc9c409b17e7468f944cc04bc3f75fa4f265dc74df480c58ebfe
MD5 7e9c5e3bfcf87e93b65e491c8390a23c
BLAKE2b-256 fc3efa1ef23079587821e928cd41b399b887eee686cbae78b00235f4dff02e84

See more details on using hashes here.

Provenance

The following attestation bundles were made for cartoboost-0.3.6-cp312-cp312-win_arm64.whl:

Publisher: publish-pypi.yml on TheCulliganMan/CartoBoost

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file cartoboost-0.3.6-cp312-cp312-win_amd64.whl.

File metadata

  • Download URL: cartoboost-0.3.6-cp312-cp312-win_amd64.whl
  • Upload date:
  • Size: 5.0 MB
  • Tags: CPython 3.12, Windows x86-64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for cartoboost-0.3.6-cp312-cp312-win_amd64.whl
Algorithm Hash digest
SHA256 991d5bb6c29f9d2c2ba947706e14107c8c779f060f324fdffda980b29c8528e0
MD5 a9a75613420aef1e348a54b44bd348bb
BLAKE2b-256 dfbb66a52e714619c3af254b8fe2f7da0b2f6d11bf40aa8f79b9b01202755b32

See more details on using hashes here.

Provenance

The following attestation bundles were made for cartoboost-0.3.6-cp312-cp312-win_amd64.whl:

Publisher: publish-pypi.yml on TheCulliganMan/CartoBoost

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file cartoboost-0.3.6-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for cartoboost-0.3.6-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 dc2d7a3c49304bff5e31c228488d4b5ab02672c6f7a33ffffdf56806f095a7e1
MD5 28f9c8b13fb8603aace2705edb7ed83a
BLAKE2b-256 1b45f50b6cc9adbb43fc2266438dae15c3c934c0d8c4a7a2d4f0e0c54b8ec1ba

See more details on using hashes here.

Provenance

The following attestation bundles were made for cartoboost-0.3.6-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl:

Publisher: publish-pypi.yml on TheCulliganMan/CartoBoost

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file cartoboost-0.3.6-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for cartoboost-0.3.6-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 f13484029b49c129dcdf46d2302d71028b9f210bbf47ada94fe1b1a7e26168ce
MD5 eeb9c8541eb8989a748ca07deea349df
BLAKE2b-256 d2c1d94b8df7d05ce44f2fcd437283203b02494aa268d60c78751c8f3558e2fc

See more details on using hashes here.

Provenance

The following attestation bundles were made for cartoboost-0.3.6-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl:

Publisher: publish-pypi.yml on TheCulliganMan/CartoBoost

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file cartoboost-0.3.6-cp312-cp312-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for cartoboost-0.3.6-cp312-cp312-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 deefc0aab9be32a5c62ed90000fa5bf1075473cbdabccee080f0574fca9ea50c
MD5 205a57d28906ae07f1f0cba8a7ed36ad
BLAKE2b-256 dd3e968fac1e291ee80c5284ebf8b7ad5edcab4b5b7a032be1ac92c44161bfcb

See more details on using hashes here.

Provenance

The following attestation bundles were made for cartoboost-0.3.6-cp312-cp312-macosx_11_0_arm64.whl:

Publisher: publish-pypi.yml on TheCulliganMan/CartoBoost

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file cartoboost-0.3.6-cp312-cp312-macosx_10_12_x86_64.whl.

File metadata

File hashes

Hashes for cartoboost-0.3.6-cp312-cp312-macosx_10_12_x86_64.whl
Algorithm Hash digest
SHA256 98652c2706a44a7e72e03facf351542e4d678e8305bde8b490caff3372e6b007
MD5 15360f977d07a6862b1d5596f8be57ce
BLAKE2b-256 ec8806a2827f0967e10225055d5a95b1e71a90a0ae31ed65be7ad4dbdce2c713

See more details on using hashes here.

Provenance

The following attestation bundles were made for cartoboost-0.3.6-cp312-cp312-macosx_10_12_x86_64.whl:

Publisher: publish-pypi.yml on TheCulliganMan/CartoBoost

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file cartoboost-0.3.6-cp311-cp311-win_arm64.whl.

File metadata

  • Download URL: cartoboost-0.3.6-cp311-cp311-win_arm64.whl
  • Upload date:
  • Size: 4.6 MB
  • Tags: CPython 3.11, Windows ARM64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for cartoboost-0.3.6-cp311-cp311-win_arm64.whl
Algorithm Hash digest
SHA256 1c596eb73e79f99ceff92617f9a0cf88775ccab98a3b2dee10aa2b02fae95242
MD5 725e2d943a8d5ad1e1a0e7d638dddac6
BLAKE2b-256 43d0ac57d7857ceee9928fdf0c4b9f56984b5988b0f4d6d441a2ce24462b648f

See more details on using hashes here.

Provenance

The following attestation bundles were made for cartoboost-0.3.6-cp311-cp311-win_arm64.whl:

Publisher: publish-pypi.yml on TheCulliganMan/CartoBoost

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file cartoboost-0.3.6-cp311-cp311-win_amd64.whl.

File metadata

  • Download URL: cartoboost-0.3.6-cp311-cp311-win_amd64.whl
  • Upload date:
  • Size: 5.1 MB
  • Tags: CPython 3.11, Windows x86-64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for cartoboost-0.3.6-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 4799919540b26f10f9e22d0ac07b384c6dd40b7dd0de3265ef93f1e3b4244360
MD5 4844f91b0d120c16a73539959071b5cf
BLAKE2b-256 177b1a7e5c24db1e97484a3ee15e7a2f250d37be599fdb1bde788c191e36c81c

See more details on using hashes here.

Provenance

The following attestation bundles were made for cartoboost-0.3.6-cp311-cp311-win_amd64.whl:

Publisher: publish-pypi.yml on TheCulliganMan/CartoBoost

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file cartoboost-0.3.6-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for cartoboost-0.3.6-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 6d6191db6f57fa910a6ed26bd32f2eb2088d37c2a18ec40fbfdca450990e92c2
MD5 917e0963f6a6886a7c2d2a0a39d2d02d
BLAKE2b-256 abbea960cd3611c369da358338c406f2dbe093f4499f707173c92bc6d02e3043

See more details on using hashes here.

Provenance

The following attestation bundles were made for cartoboost-0.3.6-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl:

Publisher: publish-pypi.yml on TheCulliganMan/CartoBoost

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file cartoboost-0.3.6-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for cartoboost-0.3.6-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 2015c767483c5a39592d87d020eee390e190bb56445ec5eb626a27c4bd3cf4b9
MD5 f96e28d13d2919ff30dee23eca93812e
BLAKE2b-256 95f3a7e23898e96afa1ca5e179e73f516190c4ba4da173400b2f4204cd98a2b7

See more details on using hashes here.

Provenance

The following attestation bundles were made for cartoboost-0.3.6-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl:

Publisher: publish-pypi.yml on TheCulliganMan/CartoBoost

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file cartoboost-0.3.6-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for cartoboost-0.3.6-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 a0dfa9e4cbcbd78d595c0fe91383edd227a82e8178a0ec23a2a16247c02dcdb6
MD5 c81f9bd79c8e219262ee0f62894ca415
BLAKE2b-256 ded895b56a973134dcff1cdbf3062ef021b32b3e48590ed65bf162936f96fc93

See more details on using hashes here.

Provenance

The following attestation bundles were made for cartoboost-0.3.6-cp311-cp311-macosx_11_0_arm64.whl:

Publisher: publish-pypi.yml on TheCulliganMan/CartoBoost

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file cartoboost-0.3.6-cp311-cp311-macosx_10_12_x86_64.whl.

File metadata

File hashes

Hashes for cartoboost-0.3.6-cp311-cp311-macosx_10_12_x86_64.whl
Algorithm Hash digest
SHA256 7f44f3835fad2ff48c6ff61f4b368b1c3ebc1c58e55cb3fac253e684bb9e5b70
MD5 c964a7b43911552c7bb802b423865562
BLAKE2b-256 d89fa3886c4c0a7a5ee3de5b8cdb158c02bea0499c4c49f4c3d2c98fcb088966

See more details on using hashes here.

Provenance

The following attestation bundles were made for cartoboost-0.3.6-cp311-cp311-macosx_10_12_x86_64.whl:

Publisher: publish-pypi.yml on TheCulliganMan/CartoBoost

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file cartoboost-0.3.6-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: cartoboost-0.3.6-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 5.1 MB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for cartoboost-0.3.6-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 6a309102d2f5165aa1b60458b83d32a49734d5457fb41d7d2ae7baa2b214bd2d
MD5 ca04e5f3a198dc96b795674ab100afe8
BLAKE2b-256 5fee7b7319dc9b2f09a9d67b40394b3c6c3cdeaf2f4f1f8ac0393f846301e94e

See more details on using hashes here.

Provenance

The following attestation bundles were made for cartoboost-0.3.6-cp310-cp310-win_amd64.whl:

Publisher: publish-pypi.yml on TheCulliganMan/CartoBoost

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file cartoboost-0.3.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for cartoboost-0.3.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 5d215a6cd167886df2d7e055539d204491fa8ce75d81addd2842e4afa02d0c0d
MD5 dc4cd715de462887e221e1429243437f
BLAKE2b-256 b50a625e175c161cb92459c8cd9ceef3ceb57b502c5f2a33a2a4842ff73651d9

See more details on using hashes here.

Provenance

The following attestation bundles were made for cartoboost-0.3.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl:

Publisher: publish-pypi.yml on TheCulliganMan/CartoBoost

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file cartoboost-0.3.6-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for cartoboost-0.3.6-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 e2d3cc5973ea966d1989abc47f5a9244decb8ed71095b41ff78e640979552842
MD5 431b8fe13d5b8d6c4017e0d7ab0d44a2
BLAKE2b-256 977b606c47ce3d614c769992327f6b7322f19eed650dbc8610d93f56cf07247e

See more details on using hashes here.

Provenance

The following attestation bundles were made for cartoboost-0.3.6-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl:

Publisher: publish-pypi.yml on TheCulliganMan/CartoBoost

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file cartoboost-0.3.6-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for cartoboost-0.3.6-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 331c4b83899c0b4163d4e13edfecbffb5615712f71ec0b1a13546d6f74988908
MD5 5aa178239409ad54d5c18be20b32def0
BLAKE2b-256 c2ca868f47341fb9ef8ca5176d482a8a29189a7e472bb221da58f51bef1e72da

See more details on using hashes here.

Provenance

The following attestation bundles were made for cartoboost-0.3.6-cp310-cp310-macosx_11_0_arm64.whl:

Publisher: publish-pypi.yml on TheCulliganMan/CartoBoost

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file cartoboost-0.3.6-cp310-cp310-macosx_10_12_x86_64.whl.

File metadata

File hashes

Hashes for cartoboost-0.3.6-cp310-cp310-macosx_10_12_x86_64.whl
Algorithm Hash digest
SHA256 8b6afe161c5d4b4bd7f9173ebc98677669b7829711cc56047aaab651e3f71d1b
MD5 77bc6b8d8a78083af27304b102a1afd7
BLAKE2b-256 3bfebb44fa85612f8785e2aec8d84fc64d6f9fba76015c9821892dcb56b3438f

See more details on using hashes here.

Provenance

The following attestation bundles were made for cartoboost-0.3.6-cp310-cp310-macosx_10_12_x86_64.whl:

Publisher: publish-pypi.yml on TheCulliganMan/CartoBoost

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Release history Release notifications | RSS feed

0.3.11

36 files

0.3.10

36 files

0.3.9

36 files

0.3.8

36 files

0.3.7

36 files

This release

0.3.6 This release

24 files

0.3.5

24 files

0.3.0

24 files

0.2.45

24 files

0.2.44

24 files

0.2.43

24 files

0.2.41

24 files

0.2.40

24 files

0.2.39

24 files

0.2.38

24 files

0.2.37

24 files

0.2.36

24 files

0.2.35

24 files

0.2.34

24 files

0.2.33

24 files

0.2.32

24 files

0.2.31

24 files

0.2.30

24 files

0.2.28

24 files

0.2.26

24 files

0.2.25

24 files

0.2.24

24 files

0.2.22

24 files

0.2.21

24 files

0.2.20

24 files

0.2.19

24 files

0.2.18

24 files

0.2.17

24 files

0.2.16

24 files

0.2.15

24 files

0.2.14

24 files

0.2.13

24 files

0.2.12

24 files

0.2.11

24 files

0.2.10

24 files

0.2.9

24 files

0.2.8

24 files

0.2.7

24 files

0.2.6

24 files

0.2.4

24 files

0.2.3

24 files

0.1.115

24 files

0.1.114

24 files

0.1.87

24 files

0.1.86

24 files

0.1.84

24 files

0.1.83

24 files

0.1.82

24 files

0.1.81

24 files

0.1.80

24 files

0.1.79

24 files

0.1.78

24 files

0.1.77

24 files

0.1.76

24 files

0.1.75

24 files

0.1.74

24 files

0.1.73

24 files

0.1.72

24 files

0.1.70

24 files

0.1.69

24 files

0.1.68

24 files

0.1.66

24 files

0.1.65

24 files

0.1.64

24 files

0.1.63

24 files

0.1.62

24 files

0.1.61

24 files

0.1.60

24 files

0.1.59

24 files

0.1.57

24 files

0.1.56

24 files

0.1.55

24 files

0.1.54

24 files

0.1.53

24 files

0.1.52

24 files

0.1.50

24 files

0.1.49

24 files

0.1.48

24 files

0.1.47

24 files

0.1.46

24 files

0.1.45

24 files

0.1.44

24 files

0.1.43

24 files

0.1.42

24 files

0.1.41

24 files

0.1.40

24 files

0.1.39

24 files

0.1.38

24 files

0.1.37

24 files

0.1.35

24 files

0.1.34

24 files

0.1.33

24 files

0.1.32

24 files

0.1.31

24 files

0.1.30

24 files

0.1.29

24 files

0.1.28

24 files

0.1.27

24 files

0.1.26

24 files

0.1.25

24 files

0.1.24

24 files

0.1.23

24 files

0.1.22

24 files

0.1.21

24 files

0.1.20

24 files

0.1.19

24 files

0.1.0

24 files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page