Tools for machine learning, automation, statistics, graphics
Project description
In brevi
dawgdad is a Python library for data scientists, machine learning engineers, and statisticians. It contains statistical and graphical functions for making sense of data to create information and understanding.
- Supervised machine learning
- Six Sigma methodology
- Regular expressions
- Process capability
- Process variation
- Excel file edits
- Taguchi Methods
- Data Science
- Automation
- Analytics
Why dawgdad?
- Equivalent Python functions that are available in R, SAS, JMP, Minitab
- Other packages have limited process control analysis features
- Other packages are abandoned or inadequately supported
- Functions to support measurement system analysis
- Functions to simplify statistics, graphs, etc.
- Functions to support process control charts
- Functions to support SQL functionality
- Develop a free open source package
Documentation
Full documentation is available at <replace this https://dawgdad.readthedocs.io/en/latest/.
References
To cite this repository, please use:
@software{dawgdad, author = {Gilles Pilon}, title = {dawgdad}, url = {https://github.com/gillespilon/dawgdad}, version = {1.0.0}, date = {2024-07-25} }
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file dawgdad-1.0.0.tar.gz
.
File metadata
- Download URL: dawgdad-1.0.0.tar.gz
- Upload date:
- Size: 94.2 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: python-requests/2.32.3
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | cf7d47ed743ebc2ae5329c704a66124195d38e86285e77e06f5070de25a8cc2e |
|
MD5 | 468503ef8b10d784be32a9bcc772f940 |
|
BLAKE2b-256 | f7e02aac5548cb17a5488147c6a0f9b70fc68d4d60e00c2112f2c2181bf557b0 |
File details
Details for the file dawgdad-1.0.0-py3-none-any.whl
.
File metadata
- Download URL: dawgdad-1.0.0-py3-none-any.whl
- Upload date:
- Size: 70.1 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: python-requests/2.32.3
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | b5cbe061d956ac3a5031eec28f53a121b2cf29d2ca7a5f57738d7b2387f8c981 |
|
MD5 | 686a531387362a582a8d67d10634af55 |
|
BLAKE2b-256 | 45ea7a4657d3bdd6b8c0590089e47b6f3eef8bcaa877f2b67f902fa6015f4ccc |