View dask on Snyk Open Source Advisor

Package Health Score

91 / 100

security
No known security issues
popularity
Key ecosystem project
maintenance
Sustainable
community
Active

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Popularity

Key ecosystem project

GitHub Stars: 12.61K
Forks: 1.71K
Contributors: 420

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package dask, we found that it has been starred 12,613 times.

Security

No known security issues

Security and license risk for latest version

Release Date: Nov 13, 2024
Direct Vulnerabilities: 0
C
0
H
0
M
0
L
Indirect Vulnerabilities: 0
C
0
H
0
M
0
L
License Risk: 0
H
0
M
0
L

All security vulnerabilities belong to production dependencies of direct and indirect packages.

License

BSD-3-Clause

Security Policy

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Maintenance

Sustainable

Commit Frequency

Open Issues: 909
Open PR: 187
Last Release: 9 days ago
Last Commit: 1 day ago

Further analysis of the maintenance status of dask based on released PyPI versions cadence, the repository activity, and other data points determined that its maintenance is Sustainable.

We found that dask demonstrates a positive version release cadence with at least one new version released in the past 3 months.

As a healthy sign for on-going project maintenance, we found that the GitHub repository had at least 1 pull request or issue interacted with by the community.

Community

Active

Readme: Yes
Contributing.md: Yes
Code of Conduct: No
Contributors: 420
Funding: Yes

A good and healthy external contribution signal for dask project, which invites more than one hundred open source maintainers to collaborate on the repository.

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Package

Python Versions Compatibility: >=3.10

Age: 10 years
Latest Release: 9 days ago
Dependencies: 0 Direct
Versions: 212
Maintainers: 8
Wheels: OS Independent

Not sure how to use dask?

To help you get started, we've collected the most common ways that dask is being used within popular public projects.

dask / dask-ml / tests / test_pca.py View on Github

def test_pca_inverse():
    # Test that the projection of data can be inverted
    rng = np.random.RandomState(0)
    n, p = 50, 3
    X = rng.randn(n, p)  # spherical data
    X[:, 1] *= 0.00001  # make middle component relatively small
    X += [5, 4, 3]  # make a large mean
    dX = da.from_array(X, chunks=(n // 2, p))

    # same check that we can find the original data from the transformed
    # signal (since the data is almost of rank n_components)
    pca = dd.PCA(n_components=2, svd_solver="full").fit(dX)
    Y = pca.transform(dX)
    Y_inverse = pca.inverse_transform(Y)
    assert_almost_equal(X, Y_inverse, decimal=3)

    # same as above with whitening (approximate reconstruction)
    for solver in solver_list:
        pca = dd.PCA(n_components=2, whiten=True, svd_solver=solver)
        pca.fit(dX)
        Y = pca.transform(dX)
        Y_inverse = pca.inverse_transform(Y)
        assert_eq(dX, Y_inverse, atol=1e-3)

View more ways to use dask

FAQs

What is dask?

Parallel PyData with Task Scheduling. Visit Snyk Advisor to see a full health score report for dask, including popularity, security, maintenance & community analysis.

Is dask popular?

The python package dask receives a total of 2,654,714 weekly downloads. As such, dask popularity was classified as a key ecosystem project. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is dask well maintained?

We found indications that dask maintenance is sustainable demonstrating some project activity. We saw a total of 420 open source contributors collaborating on the project. See the full package health analysis to learn more about the package maintenance status.

Is dask safe to use?

The python package dask was scanned for known vulnerabilities and missing license, and no issues were found. Thus the package was deemed as safe to use. See the full health analysis review.

Last updated on 22 November-2024, at 12:40 (UTC).

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dask

Parallel PyData with Task Scheduling For more information about how to use this package see README

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