This project has been archived by its maintainers, and is no longer receiving any updates.
pdLSR by Michelle L. Gill
pdLSR is a library for performing least squares regression. It attempts to seamlessly incorporate this task in a Pandas-focused workflow. Input data are expected in dataframes, and multiple regressions can be performed using functionality similar to Pandas groupby. Results are returned as grouped dataframes and include best-fit parameters, statistics, residuals, and more.
pdLSR has been tested on python 2.7, 3.4, and 3.5. It requires Numpy, Pandas, multiprocess (https://github.com/uqfoundation/multiprocess), and lmfit (https://github.com/lmfit/lmfit-py). All dependencies are installable via pip or conda (see README.md).
A demonstration notebook is provided in the demo directory or the demo can be run via GitHub (see README.md).
1 maintainer
Metadata
1 maintainer
Release files for pdLSR 0.3.6
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| pdLSR-0.3.6.tar.gz | 336.2 kB | Details |
Release files / pdLSR-0.3.6.tar.gz
| Download URL | pdLSR-0.3.6.tar.gz |
|---|---|
| Size | 336.2 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
df7599ae5bbfabd0edd9d4c1a5bf484c6394d4e6947ee109146352a7882aa646
|
|
BLAKE2b-256 checksum How to use checksums |
4dbeeab707be4c321b2bca8b53d9031e24d7a58fff2bf3df425b4b79faad72e5
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
