pdLSR: Pandas-aware least squares regression.
Project description
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).
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
File details
Details for the file pdLSR-0.3.5.tar.gz.
File metadata
- Download URL: pdLSR-0.3.5.tar.gz
- Upload date:
- Size: 336.2 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | c3e5394b0421115d2cf5bd3c45b0e2d089f168f6f9155df23c04a372409b9576 |
|
| MD5 | f858995febdb6eed52a09ec0b43a26ab |
|
| BLAKE2b-256 | ed72e62f260c8d7d5e693ef2ad2c9933e05da63093093e42b6b318fc155bfd73 |
