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.
- performance-wise, pyg is implemented via numba with performance times comparable to pandas
pip install from https://pypi.org/project/pyg-timeseries/
Project details
Release history Release notifications | RSS feed
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-0.0.29.tar.gz
(69.9 kB
view details)
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file pyg_timeseries-0.0.29.tar.gz.
File metadata
- Download URL: pyg_timeseries-0.0.29.tar.gz
- Upload date:
- Size: 69.9 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.1.0 CPython/3.13.1
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
3261e0240c2b971d093f3d3095c88f4c856b9135ba1f01cd4bde25cc16ebaccb
|
|
| MD5 |
b73e545cbd5f73806400e2b4b3df2f6a
|
|
| BLAKE2b-256 |
2e76efdcf9f1b0d954a6fcdf23f593a965f20b3f240257d440d4a26766c7254e
|
File details
Details for the file pyg_timeseries-0.0.29-py3-none-any.whl.
File metadata
- Download URL: pyg_timeseries-0.0.29-py3-none-any.whl
- Upload date:
- Size: 76.6 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.1.0 CPython/3.13.1
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
2af714a952f248c2f1116948554bd3558de077ce8f708b53f5820e66c4b45f56
|
|
| MD5 |
79552c0f1dd96202ce8f7a89f458ae84
|
|
| BLAKE2b-256 |
cf022a3cd645545d03400a99d8b411063feb56d1251a9b5ffab2ed525c1e62e1
|