semvec/pypi-packages huggingface dataset:
xml-to-pandas 0.3
Converts all kinds of XML files to a pandas dataframe
License: MIT

# XML to DataFrame Converter

## Overview

This Python utility provides a robust and flexible solution for converting XML files into pandas DataFrames. The library offers advanced parsing capabilities that can handle complex XML structures, including nested elements, attributes, and varying node types.

## Features

- 🔄 Recursive XML parsing
- 🌳 Handles nested and complex XML structures
- 🏷️ Optional namespace removal
- 📊 Converts XML to pandas DataFrame
- 🔧 Configurable root tag preservation
- 🚨 Comprehensive error handling and logging

## Requirements

- Python 3.7+
- pandas
- xml.etree.ElementTree (Standard Library)

## Installation

1. Clone the repository:
```bash
git clone https://github.com/Pi2003/xml_to_pandas.git
cd xml_to_pandas
```

2. Install dependencies:
```bash
pip install pandas
pip install xml_to_pandas
```

## Usage

### Basic Example

```python
from xml_to_pandas import xml_to_pandas

# Convert XML to DataFrame
df = xml_to_pandas.convert('your_file.xml')
print(df)
```

### Advanced Usage

```python
# Ignore XML namespaces
df = xml_to_pandas.convert('your_file.xml', ignore_namespaces=True)

# Control root tag preservation
df = xml_to_pandas.convert('your_file.xml', preserve_root_tag=False)
```

## Parsing Behavior

The converter handles different XML structures intelligently:

- 📍 **Leaf Nodes**: Converted to single-row DataFrames
- 🌿 **Parent Nodes with Leaf Children**: Concatenated side by side
- 🌳 **Parent Nodes with Parent Children**: Concatenated vertically
- 🔀 **Mixed Node Types**: Intelligently merged

## Logging

The library uses Python's standard logging module to provide informative runtime messages:
- File not found errors
- XML parsing errors
- Unexpected exceptions

## Error Handling

- Returns `None` for parsing failures
- Logs detailed error messages
- Handles various XML parsing scenarios

## Contributing

1. Fork the repository
2. Create your feature branch (`git checkout -b feature/AmazingFeature`)
3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request

## License

Distributed under the MIT License. See `LICENSE` for more information.

## Contact

Pinak Tendulkar - pdtendulkar140203@gmail.com

Project Link: [https://github.com/Pi2003/xml_to_pandas](https://github.com/Pi2003/xml_to_pandas)
english2kana 0.1.5
A lightweight tool to convert English and romanized Japanese words into Katakana.
License: MIT
Requires Python: <4.0,>=3.11

# english2kana

english2kana is a Python library designed to convert English words and romanized Japanese text into their Katakana representations. It leverages a sequence-to-sequence model with an attention mechanism, trained on data derived from English company names and their corresponding Katakana forms found on the Japan Corporate Number Publication Site.

Key Features:
- Converts English words to Katakana.
- Employs a seq2seq model architecture with attention.
- Achieves high accuracy with the given dataset.

Model Performance:
- Test Loss: 0.1440
- Test Accuracy: 0.9552

### Requirements
Python 3.11 or higher

### Installation
```bash
pip install english2kana
```

### Usage
```python
from english2kana import english2kana

# Initialize the translator
e2k = english2kana()
# Load the pretrained model
e2k.load_model()

# Translate an English word into Katakana
e2k.translate('simple')
print(output)  # シンプル
```

### Data
The training data is sourced from the Japan Corporate Number Publication Site. The dataset includes a wide array of English corporate names along with their correct Katakana representations, ensuring the model is exposed to various letter combinations and phonetic patterns.

### Contributing
Contributions are welcome! Feel free to open issues or submit pull requests to help improve this library.

### License
The MIT License (MIT)