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-0.0.50.tar.gz (77.1 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-0.0.50-py3-none-any.whl (83.4 kB view details)

Uploaded Python 3

File details

Details for the file pyg_timeseries-0.0.50.tar.gz.

File metadata

  • Download URL: pyg_timeseries-0.0.50.tar.gz
  • Upload date:
  • Size: 77.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.2

File hashes

Hashes for pyg_timeseries-0.0.50.tar.gz
Algorithm Hash digest
SHA256 aeb398dce15f382efcb582418bb0e4a93c20f9f3b5953acfe26065a9645836d8
MD5 f2021896d35e278afa8bd7bae7ead0e6
BLAKE2b-256 6f69a700d78ee7d76fecc0d52e12c4ce9993a343ad9a2805c791b9318ad4ef7c

See more details on using hashes here.

File details

Details for the file pyg_timeseries-0.0.50-py3-none-any.whl.

File metadata

File hashes

Hashes for pyg_timeseries-0.0.50-py3-none-any.whl
Algorithm Hash digest
SHA256 9539470cdabc2adfcf566347a62533ccfd0e565f4fa777de3b5dd1f2fea05caf
MD5 e5c0a4a4a40260f6e2d901316a634c52
BLAKE2b-256 77d802bd98c125f4d54efa01ab1c1ed2d9aebd3e4a20bccbb8f53c0877ed7d1a

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

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

This release

0.0.50 This release

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