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-0.0.58.tar.gz (78.8 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-0.0.58-py3-none-any.whl (84.6 kB view details)

Uploaded Python 3

File details

Details for the file pyg_timeseries-0.0.58.tar.gz.

File metadata

  • Download URL: pyg_timeseries-0.0.58.tar.gz
  • Upload date:
  • Size: 78.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.10.8

File hashes

Hashes for pyg_timeseries-0.0.58.tar.gz
Algorithm Hash digest
SHA256 55b2ed8d861cf8f48400cad18d35a7da9cf80fc71e10c64cc0d7b62462b3b1e0
MD5 dcfdda55590c4b7cef0bd1212f905e51
BLAKE2b-256 ecc293629084abbfbfe14f857594afcffec56c61a4081abf7b5217950b7ff101

See more details on using hashes here.

File details

Details for the file pyg_timeseries-0.0.58-py3-none-any.whl.

File metadata

File hashes

Hashes for pyg_timeseries-0.0.58-py3-none-any.whl
Algorithm Hash digest
SHA256 2a189b52031b25f6ba7e3a35ebe6fc01f410d80ba18fdaac1e703e93491d38b8
MD5 4d6fd23f4bb195776c31020f490d0bb4
BLAKE2b-256 3ca65076999da7f9051f9e80c5e325e0af2a3bac2e3e7bd2d418a8229f9f1cea

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

0.0.61

2 files

0.0.60

2 files

0.0.59

2 files

This release

0.0.58 This release

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