Skip to main content

Utilities for fast timeseries analysis

Project description

pyg-timeseries

pandas is great but pyg-timeseries introduces a few improvements.

  • pyg is designed so that for dataframes/series without nans, it matches pandas exactly
  • consistent treatments of nan's: unlike pandas, pyg ignores nans everywhere in its calculations.
  • np.ndarray and pandas dataframes are treated the same and pyg operates on np.arrays seemlessly
  • state-management: pyg introduces a framework for returning not just the timeseries, but also the state of the calculation. This can be fed into the next calculation batch, allowing us not to have to 're-run' everything from the distant past.

pip install from https://pypi.org/project/pyg-timeseries/

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

pyg-timeseries-0.0.6.tar.gz (38.0 kB view details)

Uploaded Source

Built Distribution

pyg_timeseries-0.0.6-py3-none-any.whl (47.2 kB view details)

Uploaded Python 3

File details

Details for the file pyg-timeseries-0.0.6.tar.gz.

File metadata

  • Download URL: pyg-timeseries-0.0.6.tar.gz
  • Upload date:
  • Size: 38.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.8.0 pkginfo/1.8.2 readme-renderer/34.0 requests/2.27.1 requests-toolbelt/0.9.1 urllib3/1.26.8 tqdm/4.63.0 importlib-metadata/4.11.3 keyring/23.5.0 rfc3986/2.0.0 colorama/0.4.4 CPython/3.9.10

File hashes

Hashes for pyg-timeseries-0.0.6.tar.gz
Algorithm Hash digest
SHA256 87edabf21d8311a9e2ef3b9611eb5909899b3a2e876397adab8cfaf588b3e947
MD5 a14ee7c08c0e7d52b7bca5c4a3c2edae
BLAKE2b-256 64c79814f26e068f8e75b0990a5c45c842c82a92967de87beaf86dbb0857dcbe

See more details on using hashes here.

File details

Details for the file pyg_timeseries-0.0.6-py3-none-any.whl.

File metadata

  • Download URL: pyg_timeseries-0.0.6-py3-none-any.whl
  • Upload date:
  • Size: 47.2 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.8.0 pkginfo/1.8.2 readme-renderer/34.0 requests/2.27.1 requests-toolbelt/0.9.1 urllib3/1.26.8 tqdm/4.63.0 importlib-metadata/4.11.3 keyring/23.5.0 rfc3986/2.0.0 colorama/0.4.4 CPython/3.9.10

File hashes

Hashes for pyg_timeseries-0.0.6-py3-none-any.whl
Algorithm Hash digest
SHA256 59853b596455cfe265767b4ad550f161a9e8a4b54f97fe232a71c0fb0bfc7e0f
MD5 e001de8518fb5e8cff65484b964a5103
BLAKE2b-256 8452d821d359f976ac0758b4421519e8c5425e0e3bba6280630fd602a0de321e

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