Skip to main content

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.
  • performance-wise, pyg is implemented via numba with performance times comparable to pandas

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

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-0.0.61.tar.gz (82.8 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

pyg_timeseries-0.0.61-py3-none-any.whl (87.5 kB view details)

Uploaded Python 3

File details

Details for the file pyg_timeseries-0.0.61.tar.gz.

File metadata

  • Download URL: pyg_timeseries-0.0.61.tar.gz
  • Upload date:
  • Size: 82.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.10.8

File hashes

Hashes for pyg_timeseries-0.0.61.tar.gz
Algorithm Hash digest
SHA256 96a1ea932014bc47a776f60891baa80debc8f22ba6f41977ac6558759b0b38b8
MD5 609478e80568864e81438ea80035173e
BLAKE2b-256 6052e15d95f1e0b22aa28238524288d57b124f627f850c53a38bbfcbe3543cbe

See more details on using hashes here.

File details

Details for the file pyg_timeseries-0.0.61-py3-none-any.whl.

File metadata

File hashes

Hashes for pyg_timeseries-0.0.61-py3-none-any.whl
Algorithm Hash digest
SHA256 64c76e00f0287b778a5bc6b37127d3b7522a032fc1c49d453aa9f2316244fa9e
MD5 2845ff722a4ec87695abc6e9ee196c52
BLAKE2b-256 0a81cb4f5f26953cadac93482d923f4430a4fddeea82e9176b1a389a10cef8ba

See more details on using hashes here.

Release history Release notifications | RSS feed

0.0.72

2 files

0.0.71

2 files

0.0.70

2 files

0.0.69

2 files

0.0.68

2 files

0.0.67

2 files

0.0.66

2 files

0.0.65

2 files

0.0.64

2 files

0.0.63

2 files

0.0.62

2 files

This release

0.0.61 This release

2 files

0.0.60

2 files

0.0.59

2 files

0.0.58

2 files

0.0.57

2 files

0.0.56

2 files

0.0.55

2 files

0.0.54

2 files

0.0.53

2 files

0.0.52

2 files

0.0.51

2 files

0.0.50

2 files

0.0.49

2 files

0.0.48

2 files

0.0.47

2 files

0.0.46

2 files

0.0.45

2 files

0.0.44

2 files

0.0.43

2 files

0.0.42

2 files

0.0.41

2 files

0.0.40

2 files

0.0.39

2 files

0.0.38

2 files

0.0.37

2 files

0.0.36

2 files

0.0.35

2 files

0.0.34

2 files

0.0.33

2 files

0.0.32

2 files

0.0.31

2 files

0.0.30

2 files

0.0.29

2 files

0.0.28

2 files

0.0.27

2 files

0.0.26

2 files

0.0.25

2 files

0.0.24

2 files

0.0.23

2 files

0.0.22

2 files

0.0.21

2 files

0.0.20

2 files

0.0.19

2 files

0.0.18

2 files

0.0.17

2 files

0.0.16

2 files

0.0.15

2 files

0.0.14

2 files

0.0.13

2 files

0.0.12

2 files

0.0.11

2 files

0.0.10

2 files

0.0.8

2 files

0.0.7

2 files

0.0.6

2 files

0.0.5

2 files

0.0.4

2 files

0.0.3

2 files

0.0.2

2 files

0.0.1

2 files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page