semvec/pypi-packages huggingface dataset:
pyG2 1.0b1
Grammar of graphics for python

PyG2
========

A grammar of graphics python library for jupyter notebooks on top of G2

Author
========
Thamalu Maliththa Piyadigama

Based on 
==========

AntV/G2 javascript library

Description
=============

A python wrapped version of G2 charting library. Works on **Jupyter notebooks**. Provide Grammar of Graphics support to jupyter notebooks. 

Syntax is closer to original G2.  

What is new!
=============
+ Support for configuration of guides, legends, interaction and other graph elements. 
+ Offline chart generation

Future developments
=======================


+ Add support to colab and other IPython environments
+ Add support to different rendering methods
+ Provide javascript code snippets to embed graphs into web pages.
randomForestRules-lukassykora 1.1.2
Random forest classification rules mining package
Requires Python: >=3.6

# Random Forest Rules
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

Get the representation of all rules found by sklearn RandomForestClassifier. It works in following way:

- On each feature, it applies one-hot encoding that makes each column binary.
- Random Forest runs on the features and a target attribute.
- All trees are extracted from the Random Forest Regressor.
- Decision Trees are split to classification rules.


## GIT repository

https://github.com/lukassykora/randomForestRules

## Installation

pip install randomForestRules-lukassykora

## Jupyter Notebook

- [Audiology](https://github.com/lukassykora/randomForestRules/blob/master/notebooks/AudiologyRandomForest.ipynb) 

## Example
```python
from randomForestRules import RandomForestRules
import pandas as pd

df = pd.read_csv("data/audiology.csv")
df.columns = df.columns.str.replace("_", "-") # underscore not allowed
# All feature columns
cols=[]
for col in df.columns:
    if col != 'binaryClass':
        cols.append(col)
# Initialize
randomForest = RandomForestRules()
# Load data
randomForest.load_pandas(df)
# Fit
randomForest.fit(antecedent = cols, consequent = 'binaryClass', supp=0.005, conf=50)
# Get result
frame = randomForest.get_frame()
```
lura 1.1.0
a bag of tricks
Requires Python: >= 3.6

`lura` is a collection of devops-oriented utility modules.

| Module           | Description                                                                |
| ---------------- | -------------------------------------------------------------------------- |
| `assets`         | syntactic sugar for `pkg_resources`                                        |
| `at`             | api for periodic task scheduling using `schedule`                          |
| `attrs`          | `dict`s with keys accessible as attributes                                 |
| `click`          | click helpers                                                              |
| `expanders`      | api for expanding templates with jinja2, etc.                              |
| `formats`        | api for dealing with json, yaml, csv, etc.                                 |
| `fs`             | filesystem helpers                                                         |
| `hash`           | syntactic sugar for hashlib                                                |
| `logutils`       | extensions for logging and an easy package-level configurator              |
| `messaging`      | api for sending messages to discord, teams, etc.                           |
| `rpc`            | syntactic sugar for `rpyc`                                                 |
| `run`            | popen front-end with sudo support                                          |
| `threads`        | threads which capture their results and exceptions                         |
| `time`           | time utilities                                                             |