PandaShifu is a user interface for descriptive and predictive analytics.
Project description
Panda Shifu 
PandaShifu is an open-source Python package that provides friendly graphical user interfaces for descriptive and predictive analytics on a given dataset. Specifically, the package is capable of processing and visualizing the given dataset, and building pipelines for econometrical and machine learning models. The software is developed to facilitate the teaching of the following courses offered by NUS Business School:
- DAO2702/DAO2702X Programming for Business Analytics
- BMK5202 Python Programming for Business Analytics
- BMH5104 Artificial Intelligence for HR
Installation
The PandaShifu package can be installed from the PyPI platform via the command:
pip install pandashifu
Author
The Panda Shifu package is developed and maintained by Dr. Peng Xiong, who is currently a senior lecturer at the NUS Business School.
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
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 pandashifu-1.2.1.tar.gz.
File metadata
- Download URL: pandashifu-1.2.1.tar.gz
- Upload date:
- Size: 1.4 MB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.1.0 CPython/3.12.2
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
131b698062e27e141e07ca5a749dca205039fb36d59dc45734cf8ce1e3827ff5
|
|
| MD5 |
ca2ce0f8dcf0b0538b977621b8808efe
|
|
| BLAKE2b-256 |
dc915c294fb7ebe13a2d27964997d5063f533d1ffd95ac23ef6a8970b11c1f97
|
File details
Details for the file pandashifu-1.2.1-py3-none-any.whl.
File metadata
- Download URL: pandashifu-1.2.1-py3-none-any.whl
- Upload date:
- Size: 1.4 MB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.1.0 CPython/3.12.2
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
1cecfd9878f31a913b884ca20711e778581dcfab0bc1d7b8f3b7b0e3f7e16b58
|
|
| MD5 |
1a3e05fcd54bd3b6b9cfcfb79afd57d1
|
|
| BLAKE2b-256 |
199b8e70d664e61d8d0665d5c31a7a618ea18a75b933082ac47c82837ffe160e
|
