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https://github.com/surajwate/datalab
DataLab is a versatile toolkit designed to simplify data exploration, analysis, and visualization for data scientists.
https://github.com/surajwate/datalab
data-analysis data-science python visualization
Last synced: 10 days ago
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DataLab is a versatile toolkit designed to simplify data exploration, analysis, and visualization for data scientists.
- Host: GitHub
- URL: https://github.com/surajwate/datalab
- Owner: surajwate
- License: mit
- Created: 2024-08-26T17:11:21.000Z (3 months ago)
- Default Branch: master
- Last Pushed: 2024-11-04T11:23:43.000Z (15 days ago)
- Last Synced: 2024-11-09T22:48:53.109Z (10 days ago)
- Topics: data-analysis, data-science, python, visualization
- Language: Python
- Homepage: http://surajwate.com/DataLab/
- Size: 1000 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# suraj_datalab
[](https://pypi.org/project/suraj_datalab/)
[](https://opensource.org/licenses/MIT)
[](https://github.com/surajwate/DataLab/actions)
`suraj_datalab` is a Python package designed to streamline the process of analyzing and visualizing both categorical and numerical data. It also includes utilities for data cleaning and preparing datasets for machine learning models, like creating K-Folds for cross-validation.
## Table of Contents
- [Features](#features)
- [Installation](#installation)
- [Quickstart](#quickstart)
- [Usage](#usage)
- [Examples](#examples)
- [API Reference](#api-reference)
- [Contributing](#contributing)
- [License](#license)
- [Acknowledgments](#acknowledgments)
- [Contact](#contact)
## Features
- **Categorical Analysis**: Effortlessly analyze and visualize categorical data in relation to target variables.
- **Numerical Analysis**: Detect, analyze, and visualize outliers in numerical data.
- **Data Cleaning**: Automatically handle rare categories in your datasets.
- **Cross-Validation Preparation**: Create K-Folds for both classification and regression tasks, including stratified K-Folds.
- **Visualization**: Built-in support for generating insightful plots with minimal code.
- **Extensible**: Designed with flexibility in mind, allowing easy extension and integration with other data processing workflows.
## Installation
### Requirements
- Python 3.12 or higher
- [pandas](https://pandas.pydata.org/)
- [seaborn](https://seaborn.pydata.org/)
- [matplotlib](https://matplotlib.org/)
### Install via pip
```bash
pip install suraj_datalab
```
## Quickstart
Here’s how you can quickly get started with `suraj_datalab`:
```python
import pandas as pd
from suraj_datalab.analysis import analyze_categorical_feature, analyze_numerical_feature
# Sample DataFrame
data = {'Feature': ['A', 'B', 'A', 'B'], 'Transported': [True, False, True, False]}
df = pd.DataFrame(data)
# Analyze categorical feature
result = analyze_categorical_feature(df, 'Feature', 'Transported')
print(result)
```
## Usage
For detailed usage instructions, please refer to the [Usage Guide](https://surajwate.com/DataLab/usage/).
## Examples
Check out the [Examples](https://surajwate.com/DataLab/examples/) section for practical examples of how to use the functions and classes provided by `suraj_datalab`.
## API Reference
For a detailed reference of all available functions and classes, see the [API Reference](https://surajwate.com/DataLab/api_reference/).
## Contributing
Contributions are welcome! Please read the [Contributing Guidelines](CONTRIBUTING.md) for more details.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Acknowledgments
Thanks to all contributors who have helped with this project.
## Contact
For any questions or suggestions, please contact [Suraj Wate](mailto:[email protected]).