Skip to main content

Out-of-Core DataFrames to visualize and explore big tabular datasets

Project description

Documentation

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

Learn more about Vaex

Project details


Download files

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

Source Distribution

vaex-4.2.0.tar.gz (4.6 kB view details)

Uploaded Source

Built Distribution

vaex-4.2.0-py3-none-any.whl (4.6 kB view details)

Uploaded Python 3

File details

Details for the file vaex-4.2.0.tar.gz.

File metadata

  • Download URL: vaex-4.2.0.tar.gz
  • Upload date:
  • Size: 4.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.5.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.0 CPython/3.7.10

File hashes

Hashes for vaex-4.2.0.tar.gz
Algorithm Hash digest
SHA256 04246ae48b6f21f211ba75f59a3a8a2c502b3f87ff68352d3c59e6cefec9fe58
MD5 1f33aa43556164f842730b602415361a
BLAKE2b-256 ab3121fa592379eddff6aeb0f2771d924d1b57743434124c879cddd249897078

See more details on using hashes here.

File details

Details for the file vaex-4.2.0-py3-none-any.whl.

File metadata

  • Download URL: vaex-4.2.0-py3-none-any.whl
  • Upload date:
  • Size: 4.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.5.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.0 CPython/3.7.10

File hashes

Hashes for vaex-4.2.0-py3-none-any.whl
Algorithm Hash digest
SHA256 0a0053a6e941e9a20d274ba4c44a28ec4cd8a46b5b602cd3909fd18ecd5a3476
MD5 d8e4a28fa5b814b5d60dcb72a8c0903f
BLAKE2b-256 f80ba248e6f7d7b88cc0d58087867812c487c6711ca893212b833ca000731fa6

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page