semvec/pypi-packages huggingface dataset:
tui-executor 0.4.1
Execute Python code in an automatically generated TUI App.
License: MIT
Requires Python: >=3.8
Keywords: TUI, execute, task


# TUI Executor

You ever wanted to execute your Python code from a simple TUI without the need to use a REPL or commandline? Look no further, use TUI Executor.


## Installation

Install this package in your virtual environment:

```
$ python3 -m pip install [--upgrade] tui-executor 
```

## Documentation

You can find the documentation at https://rhuygen.github.io/tui-executor/.


## Running the Demo

You can run a demo of the app with the following command:

```shell
$ python3 -m tui_executor.demo
```

This will start the app with a number of tasks that demonstrate the capabilities of the `tui-executor` app.

## Running the app with test tasks

This assumes you have cloned the git repository and navigated to the project folder. There you should do a `pip install -e .` to have a working editable installation.

From the project folder, run the following command to test the app's capabilities:

```shell
$ PYTHONPATH=tests tui-executor --module-path tasks.docs
```

You can add more module paths as argument to the above command. This will create additional TABs for each (sub-)package in that module path.

```shell
$ PYTHONPATH=tests tui-executor --module-path tasks.docs --module-path tasks.specific --module-path tasks.shared
```
solathon 1.0.7
A high-performance, easy-to-use, and feature-rich Solana SDK for Python.
License: MIT
Requires Python: <4.0,>=3.9
Keywords: solana, web3, sdk, blockchain, crypto

<p align="center">
  <a href="#">
    <img
      alt="Solathon logo"
      src="https://solathon.vercel.app/solathon.svg"
      width="140"
    />
  </a>
</p>


<p align="center">
  <a href="https://pypi.org/project/solathon/" target="_blank"><img src="https://badge.fury.io/py/solathon.svg" alt="PyPI version"></a>
  <a href="https://github.com/GitBolt/solathon/blob/master/LICENSE" target="_blank"><img src="https://img.shields.io/badge/License-MIT-yellow.svg" alt="MIT License"></a>
  <br>
</p>

<h1 align="center">Solathon</h1>

Solathon is a high performance, easy to use and feature-rich Solana SDK for Python. Easy for beginners, powerful for real world applications.

# ✨ Getting started
## Installation
```
pip install solathon
```
## Client example
```python
from solathon import Client

client = Client("https://api.devnet.solana.com")
```
## Basic usage example
```python
# Basic example of fetching a public key's balance
from solathon import Client, PublicKey

client = Client("https://api.devnet.solana.com")
public_key = PublicKey("B3BhJ1nvPvEhx3hq3nfK8hx4WYcKZdbhavSobZEA44ai")

balance = client.get_balance(public_key)
print(balance)
```

# 🗃️ Contribution
Drop a pull request for anything which seems wrong or can be improved, could be a small typo or an entirely new feature! Checkout [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines on how to proceed.
Pratik-model 0.1.6
This package directly gives you output performance on 12 different algorithms
License: MIT
Keywords: Pratik_model

Pratik_model
- The best thing about this package is that you do not have to train and predict every classification or regression algorithm to check performance.
- This package directly gives you output performance on 13 different algorithms.

How to use it  - 
For Classification
x= Independent variables
y= Dependent variables

* From Pratik_model import smart_classifier
* model = smart_classifier(x,y)
* model.accuracy_score()
* model.classification_report()
* model.confusion_matrix()
* model.cross_validation()
* model.mean_absolute_error()
* model.precision_score()
* model.recall_score()
* model.mean_absolute_error()
* model.mean_absolute_error()
* model.mean_squared_error()
* model.cross_validation()

For Regression -

* From Pratik_model import smart_regressor
* model=smart_regressor(x,y)
* model.r2_score()
* model.mean_absolute_error()
* model.mean_absolute_error()
* model.mean_squared_error()
* model.cross_validation()
* model.overfitting()

Check Pratik_Model_Package.ipynb file on Github for practical code.

Pratik_model for Classification: 
It will check the performance on this Classification models:
- Passive Aggressive Classifier
- Decision Tree Classifier
- Random Forest Classifier
- Extra Trees Classifier
- Logistic Regression
- Ridge Classifier
- K Neighbors Classifier
- Support Vector Classification
- Naive Bayes Classifier
- LGBM Classifier
- CatBoost Classifier
- XGB Classifier

And for classification problems Pratik_model can give the output of:
- Accuracy Score.
- Classification Report
- Confusion Matrix
- Cross validation (Cross validation score)
- Mean Absolute Error
- Mean Squared Error
- Overfitting (will give accuracy of training and testing data.)
- Precision Score
- Recall Score

Pratik_model for Regression: 
Similarly, It will check performance on this Regression model:
- Passive Aggressive Regressor
- Gradient Boosting Regressor
- Decision Tree Regressor
- Random Forest Regressor
- Extra Trees Regressor
- Lasso Regression
- K Neighbors Regressor
- Linear Regression
- Support Vector Regression
- LGBM Regressor
- CatBoost Regressor
- XGB Regressor

And for Regression problem Pratik_model
can give an output of:
- R2 Score.
- Cross validation (Cross validation score)
- Mean Absolute Error
- Mean Squared Error
- Overfitting (will give accuracy of training and testing data.)


First Release
0.0.7 (29/3/2022)

Thank You!!.