semvec/pypi-packages huggingface dataset:
BitForger 2.3.5
A brief description of your package
Requires Python: >=3.6

# DataForge

DataForge is a powerful tool designed for data processing, transformation, and analysis. It provides a user-friendly interface and robust functionalities to help users manage and manipulate their data efficiently.

## Table of Contents

- [Features](#features)
- [Installation](#installation)
- [Usage](#usage)
- [Contributing](#contributing)
- [License](#license)
- [Contact](#contact)

## Features

- **Data Processing:** Easily process large datasets with various built-in functions.
- **Data Transformation:** Transform data formats to suit your analysis needs.
- **Visualization Tools:** Visualize data trends and insights using built-in charts and graphs.
- **Custom Scripts:** Support for custom scripts to extend functionalities.
- **User-Friendly Interface:** Intuitive design for seamless user experience.

## Installation

To install DataForge, follow these steps:

1. Clone the repository:
   ```bash
   git clone https://github.com/yourusername/dataforge.git
text-data-visualization2 0.1.0
This program offers 10 different types of data visualizations, specifically designed to work with textual data.
Requires Python: >=3.12.2

\# Data Visualization Tool - README 

Description: 
This program offers 10 different types of data visualizations, specifically designed to work with textual data. Whether you\'re analyzing word frequency, identifying trends in text, or evaluating data quality, this tool provides an intuitive way to visualize text-based datasets. The visualizations were chosen with textual data in mind, making it ideal for tasks such as sentiment analysis, topic modeling, or general text exploration.

List of Visualizations: 
1. Word Cloud (TXT File): Visualizes text data by displaying the most frequent words in varying sizes. 
2. Bar Plot: Displays categorical data with rectangular bars. 
3. Pie Chart: Represents data as proportional slices of a circle. 
4. Bubble Chart: Visualizes three dimensions of data using circles (x, y coordinates, and bubble size). 
5. Line Chart: Shows data points connected by straight lines, ideal for tracking trends over time. 
6. Multiple Lines Chart: Displays multiple data series on the same axes for comparison. 
7. Grouped Bar Plot: Compares multiple categories across different groups.
8. Stacked Bar Plot: Displays cumulative totals for each category in stacked bars. 
9. Scatter Plot: Shows data points plotted along two axes, used to identify relationships or correlations. 
10. Data Quality: Provides insights into missing values, duplicates, or overall data cleanliness.

File Requirements: 
â€¢ TXT File: Required for generating a Word Cloud. The file should contain plain text, and the most frequent words will be visualized. 
â€¢ CSV File: Required for all other visualizations. The CSV file should contain well-structured tabular data with columns corresponding to the type of plot you wish to create. 

Once a file is loaded, it can be reused across multiple visualizations. If needed, you can load a new file using the \'Load New File\' option.

Saving Images: 
After generating a visualization, you can save the image by selecting the \"Save Image\" option on each library toolbar. Note that generating a new visualization will overwrite the current one, so be sure to save any visualizations you need beforehand.
hygen-add 0.1.0
Python version of the JS `hygen-add` project, which currently seems abandoned.
Requires Python: >=3.0

# Hygen Add

**Disclaimer** this is a spiritual fork of https://github.com/jondot/hygen-add which currently
seems to not be working. 

Usage example:

`hygen-add https://github.com/italomaia/hygen-axum-podman-template`

Basically this package installs a executable that will download the hygen `_templates` folder 
from a github repository of your choice into the current folder. `npx hygen` should work fine
with this setup and you can keep your templates together.
text-data-visualization 0.1.0
This program offers 10 different types of data visualizations, specifically designed to work with textual data.
Requires Python: >=3.12.2

\# Data Visualization Tool - README 

Description: 
This program offers 10 different types of data visualizations, specifically designed to work with textual data. Whether you\'re analyzing word frequency, identifying trends in text, or evaluating data quality, this tool provides an intuitive way to visualize text-based datasets. The visualizations were chosen with textual data in mind, making it ideal for tasks such as sentiment analysis, topic modeling, or general text exploration.

List of Visualizations: 
1. Word Cloud (TXT File): Visualizes text data by displaying the most frequent words in varying sizes. 
2. Bar Plot: Displays categorical data with rectangular bars. 
3. Pie Chart: Represents data as proportional slices of a circle. 
4. Bubble Chart: Visualizes three dimensions of data using circles (x, y coordinates, and bubble size). 
5. Line Chart: Shows data points connected by straight lines, ideal for tracking trends over time. 
6. Multiple Lines Chart: Displays multiple data series on the same axes for comparison. 
7. Grouped Bar Plot: Compares multiple categories across different groups.
8. Stacked Bar Plot: Displays cumulative totals for each category in stacked bars. 
9. Scatter Plot: Shows data points plotted along two axes, used to identify relationships or correlations. 
10. Data Quality: Provides insights into missing values, duplicates, or overall data cleanliness.

File Requirements: 
â€¢ TXT File: Required for generating a Word Cloud. The file should contain plain text, and the most frequent words will be visualized. 
â€¢ CSV File: Required for all other visualizations. The CSV file should contain well-structured tabular data with columns corresponding to the type of plot you wish to create. 

Once a file is loaded, it can be reused across multiple visualizations. If needed, you can load a new file using the \'Load New File\' option.

Saving Images: 
After generating a visualization, you can save the image by selecting the \"Save Image\" option on each library toolbar. Note that generating a new visualization will overwrite the current one, so be sure to save any visualizations you need beforehand.
mantlebio 2.1.1
MantleBio Python SDK

# Mantle SDK

Python SDK for running pipelines and analysis within Mantle


## Requirements
- Python 3.6+
- Mantle Account with username and password

### Installation 

to install using pip use the following command

```bash
pip install mantlebio
```

when including the package as a requirement in a requirements.txt file use the following

```
mantlebio
```

USAGE:

```python
import mantlebio

mantle = MantleClient()

```
wc-scrape 0.2.0
A simple Twitter scraper
Requires Python: <4.0,>=3.12

# Will Chen's Twitter Scraper

A simple command-line tool to fetch and process tweets.

## Features

- Fetch tweets from a specific user (most recent -- up to 3200)
- Save tweets to JSON
- Convert JSON tweets to a readable text format
- Retrieve a specific tweet by ID
- Retry on rate limit with 15-minute backoff

## Installation

Install from PyPI:

```sh
$ pip install wc-scrape
```

or

```sh
$ pip3 install wc-scrape
```

(you may need to use `sudo` or `--break-system-packages` on some systems)

## Usage

First, set up your Twitter Bearer Token:

```sh
$ wc-scrape setup
```

### Fetching Tweets

Fetch the latest tweets from a user:

```sh
$ wc-scrape fetch-tweets <username> <count>
```

This will save the tweets to a JSON file.

To generate a text file output, use the `--to-txt` flag when fetching tweets:

```sh
$ wc-scrape fetch-tweets <username> <count> --to-txt
```

To retry on rate limit with a 15-minute backoff, use the `--retry` flag:

```sh
$ wc-scrape fetch-tweets <username> <count> --retry
```
firebase-cart 0.1.1
A Firebase-based cart package for Python applications
Requires Python: >=3.7

# Firebase Cart

A Firebase-based cart package for Python applications.

## Installation

```
pip install firebase_cart
```

## Usage

```python
from firebase_cart import CartHandler, CartItem, FirebaseConfig

# Initialize with your Firebase credentials
firebase_config = FirebaseConfig(
    credentials={
        "type": "service_account",
        "project_id": "your-project-id",
        "private_key_id": "your-private-key-id",
        "private_key": "your-private-key",
        # ... other credential fields ...
    },
    database_url="https://your-project-id.firebaseio.com"  # Optional
)

cart_handler = CartHandler(firebase_config)

# Add item to cart
item = CartItem(product_id="123", quantity=2)
cart_handler.add_to_cart("user123", item)

# Get cart
cart = cart_handler.get_cart("user123")

# Update cart
cart.items.append(CartItem(product_id="456", quantity=1))
cart_handler.update_cart(cart)

# Clear cart
cart_handler.clear_cart("user123")
```

## API Reference

### CartHandler

* `__init__(config: FirebaseConfig)`: Initialize the CartHandler with Firebase configuration.
* `add_to_cart(user_id: str, item: CartItem, context: Optional[CartContext] = None)`: Add an item to the cart.
* `get_cart(user_id: str) -> Optional[Cart]`: Retrieve a user's cart.
* `update_cart(cart: Cart)`: Update an entire cart.
* `clear_cart(user_id: str)`: Clear a user's cart.

### CartItem

* `product_id: str`: The ID of the product.
* `quantity: int`: The quantity of the product.


### CartContext

* `ip: Optional[str]`: The IP address of the user.
* `user_agent: Optional[str]`: The user agent of the client.
* `additional_info: Optional[Dict[str, Any]]`: Any additional context information.

## Error Handling

The package raises `FirebaseError` for any Firebase-related errors. Handle these appropriately in your application.

## Logging

The package logs important events and errors. Configure logging in your application to capture these logs.