semvec/pypi-packages huggingface dataset:
pyany2json 0.1.3
Python binding to Any2Json
Requires Python: <4.0,>=3.11

# PyAny2Json

![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)
![Servier Inspired](https://raw.githubusercontent.com/servierhub/.github/main/badges/inspired.svg)

This repository is a binging of Any2Json for Python language.

## Description

In today's data-driven landscape, navigating the complexities of semi-structured documents poses a significant challenge
for organizations. These documents, characterized by diverse formats and a lack of standardization, often require
specialized skills for effective manipulation and analysis. However, we propose a novel framework to address this
challenge. By leveraging innovative algorithms and machine learning techniques, [Any2Json](https://github.com/RomualdRousseau/Any2Json)
offers a solution that gives you control over the data extraction process with tweakable and repeatable settings.
Moreover, by automating the extraction process, it not only saves time but also minimizes errors, particularly beneficial
for industries dealing with large volumes of such documents. Crucially, this framework integrates with machine learning workflows,
unlocking new possibilities for data enrichment and predictive modeling. By leveraging determinist algorithms, this framework is perfect
to prepare your data for training processes in a predictive and reproductible manner. Aligned with the paradigm of data as a service,
it offers a scalable and efficient means of managing semi-structured data, thereby expanding the toolkit of data services available
to organizations.

Visit our [full documentation](https://romualdrousseau.github.io/PyAny2Json/) and learn more about how it works, try our
tutorials and find a full list of plugins and models.

## Getting Started

### Dependencies

* The Java Developer Kit, version 17.
* Python 3.8.2 or above.
* Pip 20.0.2 or above.
* Poetry 1.7.1 or above.
* Just 1.24.0 or above.

### Install and setup locally

Run the following command line:

```bash
pip install pyany2json
```

Before to use the package, you need to download the jars in your project. Run the following command line:

```bash
python pyany2json/setup.py
```

### Run the examples

To run the tutorial1:

```bash
python examples/tutorial1.py
```

### Documentation

The following links will give you documentation about some background information, takes you through some implementation details,
and then focuses on step-by-step instructions for getting the most out of Any2Json:

* Using PyAny2Json: [here](https://romualdrousseau.github.io/PyAny2Json/).

## Contribute

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

## Authors

* Romuald Rousseau, romuald.rousseau@servier.com

## Version History

* 0.1.1
* 0.1.0
* Initial Release
dpone-datagenie 0.1.1
 Synthetic DataGen is a powerful online library designed to generate synthetic data sets for various use cases. It leverages advanced algorithms and customizable parameters to create realistic data that closely mimics real-world data while ensuring privacy and security.
Requires Python: <4.0,>=3.12

# Synthetic: A Python Library for Synthetic Data Generation

Synthetic is a Python library designed to facilitate the generation of synthetic datasets for machine learning and artificial intelligence applications. It provides functions to create synthetic dataframes with various features, including numerical and categorical variables, and offers utilities for visualization and data augmentation. With Synthetic, users can quickly generate synthetic datasets tailored to their specific needs, whether for model training, testing, or exploration.

## Features

- **Supervised Data Generation**: Generate synthetic datasets with labeled data for supervised learning tasks.
- **Numerical and Categorical Features**: Create synthetic dataframes with both numerical and categorical features.
- **Visualization Tools**: Visualize correlations and distributions of features within the synthetic datasets.
- **Data Augmentation**: Apply techniques such as SMOTE (Synthetic Minority Over-sampling Technique) to address class imbalance in the datasets.

## Installation

You can install Synthetic using pip:

```bash
pip install synthetic
```

## Usage

### 1. Generating Synthetic Dataframes

```python
import synthetic.generate as synthetic

# Generate a supervised numerical dataframe
numerical_df = synthetic.generate_supervised_numerical_dataframe(num_samples=1000, num_features=5)

# Generate a supervised dataframe with mixed numerical and categorical features
mixed_df = synthetic.generate_supervised_mixed_dataframe(num_samples=1000, num_numerical_features=3, num_categorical_features=2)

# Display the first few rows of the generated dataframes
print("Numerical DataFrame:")
print(numerical_df.head())

print("\nMixed DataFrame:")
print(mixed_df.head())
```

### 2. Visualization

```python
import synthetic

# Plot correlation heatmap
synthetic.plot_correlation(mixed_df)

# Plot distribution of features
synthetic.plot_distribution(mixed_df)
```

### 3. Data Augmentation

```python
import synthetic

# Apply SMOTE for data augmentation
augmented_df = synthetic.apply_smote(mixed_df, target_column='label')

# Display the first few rows of the augmented dataframe
print("Augmented DataFrame:")
print(augmented_df.head())
```

## Examples

For more examples and detailed usage instructions, please refer to the [Examples](./examples) directory in the repository.

## Contributing

We welcome contributions from the community. If you encounter any issues, have suggestions for improvements, or would like to contribute code, please feel free to open an issue or pull request on our [GitHub repository](https://github.com/your_username/synthetic).

## License

This project is licensed under the MIT License - see the [LICENSE](./LICENSE) file for details.