Package Health Score

57 / 100

security
Security review needed
popularity
Recognized
maintenance
Inactive
community
Active

Explore Similar Packages

Popularity

Recognized

GitHub Stars: 8.31K
Forks: 591
Contributors: 70

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package vaex, we found that it has been starred 8,315 times.

Security

Security review needed

Security and license risk for latest version

Release Date: Jul 21, 2023
Direct Vulnerabilities: 0
C
0
H
0
M
0
L
Indirect Vulnerabilities: 0
C
0
H
0
M
0
L
License Risk: 0
H
17
M
0
L

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

License

MIT

Security Policy

Yes

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Maintenance

Inactive

Commit Frequency

Open Issues: 437
Open PR: 98
Last Release: 1 year ago
Last Commit: 2 months ago

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

An important project maintenance signal to consider for vaex is that it hasn't seen any new versions released to PyPI in the past 12 months, and could be considered as a discontinued project, or that which receives low attention from its maintainers.

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: 70
Funding: No

With more than 10 contributors for the vaex repository, this is possibly a sign for a growing and inviting community.

We found a way for you to contribute to the project! Looks like vaex is missing a Code of Conduct.

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Package

Python Versions Compatibility: Unspecified

Age: 10 years
Latest Release: 1 year ago
Dependencies: 7 Direct / 98 Total
Versions: 58
Maintainers: 1
Wheels: OS Independent

Not sure how to use vaex?

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

vaexio / vaex / test / ui.py View on Github

def test_select_by_expression(self):
		self.window.xlabel = "x"
		self.window.ylabel = "y"
		##self.window._wait() # TODO: is this a bug? if we don't wait and directly do the selection, the ThreadPoolIndex
		## is entered twice, not sure this can happen from the gui
		vaex.ui.qt.set_choose("x &lt; 0", True)
		logger.debug("click mouse")
		QtTest.QTest.mouseClick(self.layer.button_selection_expression, QtCore.Qt.LeftButton)
		logger.debug("clicked mouse")
		return
		self.window._wait()
		self.assertTrue(self.no_exceptions)

		filename = self.window.plot_to_png()
		self.compare(filename, get_comparison_image("example_xy_selection_on_x"))

View more ways to use vaex

What is Vaex?

Vaex is a high performance Python library for lazy Out-of-Core DataFrames (similar to Pandas), to visualize and explore big tabular datasets. It calculates statistics such as mean, sum, count, standard deviation etc, on an N-dimensional grid for more than a billion (10^9) samples/rows per second. Visualization is done using histograms, density plots and 3d volume rendering, allowing interactive exploration of big data. Vaex uses memory mapping, zero memory copy policy and lazy computations for best performance (no memory wasted).

Installing

With pip:

$ pip install vaex

Or conda:

$ conda install -c conda-forge vaex

For more details, see the documentation

Key features

Instant opening of Huge data files (memory mapping)

HDF5 and Apache Arrow supported.

opening1a

opening1b

Read the documentation on how to efficiently convert your data from CSV files, Pandas DataFrames, or other sources.

Lazy streaming from S3 supported in combination with memory mapping.

opening1c

Expression system

Don't waste memory or time with feature engineering, we (lazily) transform your data when needed.

expression

Out-of-core DataFrame

Filtering and evaluating expressions will not waste memory by making copies; the data is kept untouched on disk, and will be streamed only when needed. Delay the time before you need a cluster.

occ-animated

Fast groupby / aggregations

Vaex implements parallelized, highly performant groupby operations, especially when using categories (>1 billion/second).

groupby

Fast and efficient join

Vaex doesn't copy/materialize the 'right' table when joining, saving gigabytes of memory. With subsecond joining on a billion rows, it's pretty fast!

join

More features

Remote DataFrames (documentation coming soon)
Integration into Jupyter and Voila for interactive notebooks and dashboards
Machine Learning without (explicit) pipelines

Contributing

See contributing page.

Slack

Join the discussion in our Slack channel!

Learn more about Vaex

Articles
Follow our tutorials
Watch our more recent talks:
- PyData London 2019
- SciPy 2019
Contact us for data science solutions, training, or enterprise support at https://vaex.io/

vaex dependencies

vaex-astro vaex-core vaex-hdf5 vaex-jupyter vaex-ml vaex-server vaex-viz

FAQs

What is vaex?

Out-of-Core DataFrames to visualize and explore big tabular datasets. Visit Snyk Advisor to see a full health score report for vaex, including popularity, security, maintenance & community analysis.

Is vaex popular?

The python package vaex receives a total of 6,023 weekly downloads. As such, vaex popularity was classified as a recognized. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is vaex well maintained?

We found indications that vaex is an Inactive project. See the full package health analysis to learn more about the package maintenance status.

Is vaex safe to use?

While scanning the latest version of vaex, we found that a security review is needed. A total of 0 vulnerabilities or license issues were detected. See the full security scan results.

Last updated on 20 December-2024, at 23:45 (UTC).

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