semvec/pypi-packages huggingface dataset:
poetry-to-uv 0.0.4
Help make the migration from using a Poetry pyproject.toml file to a UV compatible pyproject.toml file.
Requires Python: >=3.7

poetry-to-uv
-----------------------------

[![Code style: Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)

Helps convert a Poetry formatted pyproject.toml file to a UV formatted pyproject.toml file (also 
compatible with pip and pip-compile)

The rationale behind this library is to make it easier to unpin the dependencies specified in your 
pyproject.toml so when you use `uv pip install -r pyproject.toml` it will ensure you keep the same
versions installed as did Poetry. Although you can manually export the resolved dependencies from 
Poetry, these will include ever single transient dependency, so it's really difficult to separate 
all of these out to ensure you only keep the existing deps.

# Installation
```
uv pip install poetry_to_uv
```

# Usage
```
usage: poetry_to_uv [-h] [--output-file OUTPUT_FILE] pyproject_path

Helps convert a Poetry formatted pyproject.toml file to a UV formatted pyproject.toml file (also compatible with pip and pip-compile). It will unpin all of your dependencies for you to ensure UV uses the exact same requirements, for your specified dependencies.

positional arguments:
  pyproject_path        Path to the Poetry formatted pyproject.toml file.

options:
  -h, --help            show this help message and exit
  --output-file OUTPUT_FILE
                        The output file to write the UV formatted pyproject.toml file. If not specified then it will print the results to the terminal.

Only meant as a helper to reduce some horrible BAU, especially if you've left all your dependencies unpinned!
```

# Known Issues
1) The new `[project]` settings are placed at the bottom of the file. So if you hate it, just as much as I do, then you'll have to manually move it to the top!
2) We rely on `poetry export` still existing, but it's currently deprecated in v1.7 of Poetry. So if you have issues, make sure to install the [Export Poetry Plugin](https://github.com/python-poetry/poetry-plugin-export).
vizutils 0.1.1
A library for creating visualizations for data analysis.
Requires Python: >=3.6

# VizUtils

**VizUtils** is a Python library that simplifies the creation of various types of visualizations, ranging from basic plots to advanced and interactive visualizations. Whether you're performing exploratory data analysis or presenting insights, VizUtils provides an easy-to-use interface for generating insightful plots.

## Features

- **Basic Plots**: Create common visualizations like bar plots, histograms, line plots, and scatter plots with minimal code.

Below are examples of how to use different features of VizUtils.

from vizutils.basic_plots import bar_plot, histogram, line_plot, scatter_plot
import pandas as pd

# Load your dataset
df = pd.read_csv("data.csv")

# Bar Plot
bar_plot(df, x='category', y='values', title='Category vs Values')

# Histogram
histogram(df['values'], bins=15, title='Value Distribution')

# Line Plot
line_plot(df, x='date', y='values', title='Values Over Time')

# Scatter Plot
scatter_plot(df, x='feature1', y='feature2', title='Feature1 vs Feature2', hue='category')



- **Advanced Plots**: Generate more complex visualizations such as correlation heatmaps, pair plots, and box plots.

Below is the code example

from vizutils.advanced_plots import correlation_heatmap, pair_plot, box_plot

# Correlation Heatmap
correlation_heatmap(df, title='Correlation Matrix')

# Pair Plot
pair_plot(df, hue='category', title='Pair Plot of Features')

# Box Plot
box_plot(df, x='category', y='values', title='Box Plot of Values by Category')

- **Interactive Plots**: Use `Plotly` to create interactive plots that can be embedded in notebooks and web pages.

Below is the code example:

from vizutils.interactive_plots import interactive_scatter, interactive_line, interactive_histogram

# Interactive Scatter Plot
interactive_scatter(df, x='feature1', y='feature2', color='category', title='Interactive Scatter Plot')

# Interactive Line Plot
interactive_line(df, x='date', y='values', color='category', title='Interactive Line Plot')

# Interactive Histogram
interactive_histogram(df['values'], nbins=20, title='Interactive Histogram')

## Requirements
Python 3.6+
pandas
matplotlib
seaborn
plotly


## Installation

You can install VizUtils using `pip`:

```bash
pip install vizutils
w3ai-sdk 0.0.7
W3AI Flatform API

# w3ai-sdk

## Description
Using w3ai-sdk package to querry some of data from W3AI Flatform using APIKey. For example , Getting user balance, creating a task, getting task result, getting statistics and history of models, checking API permission ...
## Installation
``` 
pip install w3ai-python-sdk
```
## Setup
To start, simply require the W3AI-SDK and set up an instance with your W3AI API Keys.Please checking out your W3AI Page. In the example below show you how to using the SDK.

```
from 'w3ai-python-sdk' import W3AIClient
client = W3AIClient(API_KEY, SERVER_ENDPOINT)
```

## Usage


### API Key Permission
```
client.get_api_key_permission(API_KEY)

```
Response :

```
{
  status: 'success',
  data: {
    limit_models: true,
    models: [ '7da922fe-02b9-4534-b218-c5b7c79d2195' ]
  }
}

```

### Models infomation

```
client.get_model_info(MODEL_ID)
```

Response :
```
{
  status: 'success',
  data: {
    model_id: '7da922fe-02b9-4534-b218-c5b7c79d2195',
    api_price: 0.12342134,
    model_description: 'this is best model',
    commit_hash: 'd1c366e0c6bbfdd28cb31301852030c113013576',
    input_format: { input: [Object] },
    output_format: { output: [Object] }
  }
}
```


### Model serving info
```
client.check_model_is_serving(MODEL_ID)
```

Response :
```
{ status: 'success', data: { consumers: 1, serving: true } }
```

### Task executing cost info
```
client.get_model_task_cost(MODEL_ID)
```

Response :
```
{
  status: 'success',
  message: 'Success',
  data: { cost: 0.011000000000000001, symbol: '$', unit: 'USD' }
}
```

### Creating a task

Argument exmaple:
```
FILE_LIST = [
    {
      "key": "input",
      "data": "file_stotage_url",
      "name": "image.jpg"
    },
    {
      "key": "input2",
      "data": "file_stotage_url2",
      "name": "image2.jpg"
    }
  ]

INPUT_PARAMS = {
  "input": {
    "type": "file",
    "samples": [],
    "required": true,
    "mime_type": [
      "image/*"
    ]
  }
}

MODEL_ID = "7da922fe-02b9-4534-b218-c5b7c79d2195"
```


```
client.create_task(files= FILE_LIST, input_params=INPUT_PARAMS, model_id= MODEL_ID)
```

Response :
```
{
  status: 'success',
  message: 'Success',
  data: "e52fa152-351d-4a40-a4ce-83a4ff11d87e"
}
```

### Getting task result
```
client.get_task_result(task_id = TASK_ID)
```

Response :
```
{
  "result": {
    "additionalProp1": {}
  },
  "status": "sucess",
  "success": true
}
```
datasu 0.1.1
Essential utilities for data scientists
Keywords: python, data science, machine learning, deep learning, artificial intelligence


# 🛠️ Data Scientist Utilities

## 🚀 Overview

Welcome to the **Data Scientist Utilities** repository! This repository is a carefully curated collection of tools and utilities that I've developed throughout my journey as a Data Scientist. Whether you're aiming to streamline data manipulation, elevate your data augmentation strategies, or craft stunning visualizations, you'll find valuable resources here to boost your productivity.

## 🎨 Repository Contents

Explore the core components of this repository:

- **`data_augmentation/`**: Scripts and methods for augmenting datasets, designed to improve model robustness and performance across various scenarios.
- **`data_manipulation/`**: A comprehensive set of tools for efficient data manipulation, including cleaning, transforming, and reshaping datasets to fit your needs.
- **`data_visualization/`**: A collection of visualization tools to help you explore, analyze, and present your data in insightful and compelling ways.

*Note: This repository is continuously evolving, with new tools and updates being added regularly. Be sure to check back often for the latest enhancements!*

## ✨ Getting Started

To start using these utilities, follow these steps:

1. Install the package via pip:

   ```bash
   pip install datasu
   ```

2. Explore the contents and integrate the tools into your projects to enhance your data science workflows.

## 🌟 Contributing

Your contributions are invaluable in making this toolbox more robust and versatile! Whether you've developed new tools, discovered bugs, or have ideas for enhancements, feel free to open an issue or submit a pull request. Let's work together to expand this collection and support the data science community!

## 🤖 License

This project is licensed under the [MIT License](LICENSE). You're free to use, modify, and share the code. Happy coding, experimenting, and data exploration!