Skip to main content

Data science toolkit support for OmniSciDB

Project description

Jenkins Build Status

This package enables using common Python data science toolkits with OmniSciDB. It brings data frame support on CPU and GPU as well as support for arrow. See the documentation for more.

Quick Install (CPU)

Packages are available on conda-forge and PyPI:

conda install -c conda-forge pyomnisci

pip install pyomnisci

Quick Install (GPU)

We recommend creating a fresh conda 3.7 or 3.8 environment when installing pymapd with GPU capabilities.

To install pymapd and cudf for GPU Dataframe support (conda-only):

conda create -n omnisci-gpu -c rapidsai -c nvidia -c conda-forge \
 -c defaults cudf=0.18 python=3.7 cudatoolkit=11.0 pyomnisci

Documentation

Further documentation for pyomnisci usage is available at: http://pyomnisci.readthedocs.io/

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

pyomnisci-0.28.2.tar.gz (6.4 MB view details)

Uploaded Source

Built Distribution

pyomnisci-0.28.2-py3-none-any.whl (21.3 kB view details)

Uploaded Python 3

File details

Details for the file pyomnisci-0.28.2.tar.gz.

File metadata

  • Download URL: pyomnisci-0.28.2.tar.gz
  • Upload date:
  • Size: 6.4 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.6.0 importlib_metadata/4.8.2 pkginfo/1.8.0 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.9

File hashes

Hashes for pyomnisci-0.28.2.tar.gz
Algorithm Hash digest
SHA256 adc518d0caab317da34a150c77722073c732a5a7b351f75629e9df69436f6a98
MD5 c3f43c13b30849c6b1f73942d2e5e5da
BLAKE2b-256 62490cd31c194758ff3fa72d5952dafdbbb44891357bb264473f8ea97691ed74

See more details on using hashes here.

File details

Details for the file pyomnisci-0.28.2-py3-none-any.whl.

File metadata

  • Download URL: pyomnisci-0.28.2-py3-none-any.whl
  • Upload date:
  • Size: 21.3 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.6.0 importlib_metadata/4.8.2 pkginfo/1.8.0 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.9

File hashes

Hashes for pyomnisci-0.28.2-py3-none-any.whl
Algorithm Hash digest
SHA256 1249c50ec9bdbaa0581a5b25167c15be02e63a3ab5a0d8e9f2d0eb9f34c323d5
MD5 083c007cc5bd3413f9b574505570bc09
BLAKE2b-256 bc0414a7ab82d48cf14d5863a08b943dd4bb617bb9ccfdb5a39e1858c58e5eee

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