semvec/pypi-packages huggingface dataset:
mhpp 0.3.0
A median house price prediction project to demonstrate packaging.

# Introduction

A median house price prediction (mhpp) project is dealt to demonstrate packaging.
This package helps to fetch, preprocess and split the data set into train and test and also
train models using Linear Regression, Decision Tree and Random Forest algorithms and also
evaluate them using MSE (Mean Squarred Error), RMSE (Root mean square error) and MAS (Mean 
Absolute Error).


# Installation

Create a virtual environment using conda before installation.
```
  conda env create -f deploy/conda/env.yaml
```

#### Using pip
```
  pip install mhpp
```
#### From source

1. Clone the repo using git clone command.
   **Note**: Requires colab access as of now.
  ```
      git clone git@github.com:varun-mle/mle-training.git
  ```
2. Execute the below command.
  ```
      pip install . 
  ```

# Usage 
1. For fetching data **fetch-data** command is used.  Below command helps to know its usage
    ```
        fetch-data -h
    ```
 2. For preprocessing and splitting the data into train & test datasets **train-test-data** is used. Below command helps to know its usage.
    ```
        train-test-data -h 
    ```
 3. For training the model **train** command is used. Below command helps to know its usage
    ```
        train -h 
    ```
 4. For evaluating the model **evaluate** command is used. Below command helps to know its usage
    ```
        evaluate -h
    ```
  Refer **docs** folder for the detailed documentation.
netbox-devicetype-importer 0.0.6
Import DeviceTypes from github repo

# Netbox DeviceType Import Plugin
[NetBox](https://github.com/netbox-community/netbox) plugin for easy import DeviceType from [NetBox Device Type Library](https://github.com/netbox-community/devicetype-library)

## Description
The plugin uses [GitHub GraphQL API](https://docs.github.com/en/graphql) to load DeviceType from [NetBox Device Type Library](https://github.com/netbox-community/devicetype-library). The plugin loads only file tree representation from github repo and shows it as a table with vendor and model columns. DeviceType definitions files are loaded when you try to import selected models.
To use GraphQL API you need to set GitHub personal access token in plugin settings.  You don't need to grant any permissions to the token.    
How to create the token, see ["Creating a personal access token."](https://docs.github.com/en/github/authenticating-to-github/creating-a-personal-access-token)

## Compatibility

This plugin in compatible with [NetBox](https://netbox.readthedocs.org/) 3.0 and later

## Installation

The plugin is available as a Python package in pypi and can be installed with pip

```
pip install netbox-devicetype-importer
```
Enable the plugin in [NetBox Configuration](https://netbox.readthedocs.io/en/stable/configuration/)
```
PLUGINS = ['netbox_devicetype_importer']
```

## Configuration
Put your GitHub personal access token to [NetBox plugins config](https://netbox.readthedocs.io/en/stable/configuration/optional-settings/#plugins_config)  
```
PLUGINS_CONFIG = {
    'netbox_devicetype_importer': {
        'github_token': '<YOUR-GITHUB-TOKEN>'
    }
}
```
## Screenshots

![](docs/img/import.gif) 

## Future 
* Import device images from GitHub repo
* Add a GitHub REST API client that allows this plugin to be used without the GitHub token
smplstack 0.1.0
Simple stack implementation
Requires Python: >=3.10,<4.0

Simple stack implementation.

Use:
 .push method to add a new element into stack
 .pop to delete and return last element from stack
 .peek to return a last element from stack
 .delete to delete a last element from stack
 .show to print elements from stack 
 .clear to clear a stack
 .pop_all to delete and return all elements from stack
 .peek_all to return all elements from stack
Also, this stack is iterable. The iteration starts from the last element of the stack.
airflow-providers-oraclethick-hook 0.0.2
Thick driver initialization for oracledb Airflow hook
Requires Python: >=3.8

# airflow-providers-oraclethick

Provider for Oracle DB for Airflow 2.X. Just initializes thick client.
Creates connection type OracleThick.


Build and install locally:

```
python3 -m build

pip install airflow-providers-oraclethick-hook --no-index --find-links file:///<path>/git/airflow_provider_oracle_thick/dist/
```

Start airflow:

```
airflow webserver
```

Check if provider is registered and if new connection type has appeared.

Install:

```
pip install airflow-providers-oraclethick-hook
```
mgjson 0.1.0b1
A nifty library for encoding mgjson documents from python.
Requires Python: >=3.7

# python-mgjson
A nifty library for encoding mgjson documents from python.

Made possibly by the the works of [JuanIrache](https://github.com/JuanIrache/mgjson) . This library is a new partial implementation in python for generating Motion Graphics Json documents.

Static data properties of type int, boolean and ascii-string are supported.
Dynamic data as time series are supported. These will be translated to timezone encoded time series.

## installation

```python
pip install mgjson
```

## Usage

```python
from mgjson import MgJSON

mgjson = MgJSON()

# static properties
mgjson.add_property("numberOfCats", 3)
mgjson.add_property("isItTrue", False)
mgjson.add_property("title", "A new adventure awaits!")

# dynamic data (time series)
mgjson.add_stream("temperature", [[0.0, 1.000], [2.59292, 0.777]])

print(mgjson.json)
```



## License

MIT