View bokeh on Snyk Open Source Advisor

Package Health Score

94 / 100

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

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Popularity

Key ecosystem project

GitHub Stars: 18.81K
Forks: 4.16K
Contributors: -

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package bokeh, we found that it has been starred 18,807 times.

Security

No known security issues

Security and license risk for latest version

Release Date: Apr 11, 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

Yes

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Maintenance

Healthy

Commit Frequency

Open Issues: 729
Open PR: 32
Last Release: 1 month ago
Last Commit: 1 month ago

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

We found that bokeh 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: Yes
Contributors: 0
Funding: Yes

This project has seen only 10 or less contributors.

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Package

Python Versions Compatibility: >=3.9

Age: 11 years
Latest Release: 1 month ago
Dependencies: 0 Direct
Versions: 197
Maintainers: 3
Wheels: OS Independent

Readme

Bokeh is an interactive visualization library for modern web browsers. It provides elegant, concise construction of versatile graphics and affords high-performance interactivity across large or streaming datasets. Bokeh can help anyone who wants to create interactive plots, dashboards, and data applications quickly and easily.

Package
Project
Downloads
Build
Community

Consider making a donation if you enjoy using Bokeh and want to support its development.

4x9 image grid of Bokeh plots

Installation

To install Bokeh and its required dependencies using pip, enter the following command at a Bash or Windows command prompt:

pip install bokeh

To install conda, enter the following command at a Bash or Windows command prompt:

conda install bokeh

Refer to the installation documentation for more details.

Resources

Once Bokeh is installed, check out the first steps guides.

Visit the full documentation site to view the User's Guide or launch the Bokeh tutorial to learn about Bokeh in live Jupyter Notebooks.

Community support is available on the Project Discourse.

If you would like to contribute to Bokeh, please review the Contributor Guide and request an invitation to the Bokeh Dev Slack workspace.

Note: Everyone who engages in the Bokeh project's discussion forums, codebases, and issue trackers is expected to follow the Code of Conduct.

Support

Fiscal Support

The Bokeh project is grateful for individual contributions, as well as for monetary support from the organizations and companies listed below:

If your company uses Bokeh and is able to sponsor the project, please contact info@bokeh.org

Bokeh is a Sponsored Project of NumFOCUS, a 501(c)(3) nonprofit charity in the United States. NumFOCUS provides Bokeh with fiscal, legal, and administrative support to help ensure the health and sustainability of the project. Visit numfocus.org for more information.

Donations to Bokeh are managed by NumFOCUS. For donors in the United States, your gift is tax-deductible to the extent provided by law. As with any donation, you should consult with your tax adviser about your particular tax situation.

In-kind Support

Non-monetary support can help with development, collaboration, infrastructure, security, and vulnerability management. The Bokeh project is grateful to the following companies for their donation of services:

FAQs

What is bokeh?

Interactive plots and applications in the browser from Python. Visit Snyk Advisor to see a full health score report for bokeh, including popularity, security, maintenance & community analysis.

Is bokeh popular?

The python package bokeh receives a total of 1,451,294 weekly downloads. As such, bokeh popularity was classified as a key ecosystem project. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is bokeh well maintained?

We found that bokeh demonstrated a healthy version release cadence and project activity. It has a community of 0 open source contributors collaborating on the project. See the full package health analysis to learn more about the package maintenance status.

Is bokeh safe to use?

The python package bokeh 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 18 April-2024, at 02:03 (UTC).

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bokeh

Interactive plots and applications in the browser from Python For more information about how to use this package see README

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FAQs

What is bokeh?

Is bokeh popular?

Is bokeh well maintained?

Is bokeh safe to use?

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Fix identified vulnerabilities

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Package Health Score

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Direct Usage Popularity

Security and license risk for latest version

Commit Frequency

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Snyk Vulnerability Scanner

Readme

Installation

Resources

Support

Fiscal Support

In-kind Support

FAQs

What is bokeh?

Is bokeh popular?

Is bokeh well maintained?

Is bokeh safe to use?

Build a secure application checklist

Select a recommended open source package

Scan your app for vulnerabilities

Source Code

Open Source

Container

Config

Fix identified vulnerabilities

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