DataGlide

About DataGlide

DataGlide is a streamlined web application built with Streamlit, allowing users to manipulate datasets easily using MitoSheet and visualize them interactively with PyGWalker. Whether you're exploring, cleaning, or visualizing your data, DataGlide provides an intuitive interface for seamless data analysis.

Features

Data Upload: Upload CSV or Excel files directly into the app.
Data Manipulation: Utilize MitoSheet's Excel-like interface to clean, modify, and explore your dataset.
Interactive Visualizations: Create dynamic charts and graphs using PyGWalker for better insight and exploration of your data.
Real-time Updates: Any changes made to the data in MitoSheet will reflect instantly in the visualizations.

How It Works

Upload Your Data: Load your dataset by uploading a CSV or Excel file.
Manipulate Data with MitoSheet: Once the data is uploaded, use MitoSheet’s interface to filter, sort, and clean the data without writing a single line of code.
Visualize with PyGWalker: After the data manipulation, seamlessly generate visualizations using PyGWalker, exploring patterns, trends, and insights in your data.

Installation

To run the app locally, follow these steps:

Clone the Repository:

git clone https://github.com/your-username/DataGlide.git
cd DataGlide

Install Dependencies:

Make sure you have Python installed. Then install the required libraries:
```
pip install streamlit mitosheet pygwalker pandas
```
Run the App:
```
streamlit run app.py
```

Usage

Launch the app locally or deploy it on a cloud platform.
Use the file uploader to select your dataset.
Modify the dataset with MitoSheet.
Generate visualizations with PyGWalker.
Export your modified data and visualizations for further analysis or reporting.

Technologies Used

Streamlit: For building the interactive user interface.
MitoSheet: A code-free tool for spreadsheet-like data manipulation.
PyGWalker: For creating interactive, draggable charts and visualizations.
Pandas: Backend data manipulation library.

Future Improvements

Add support for more data formats.
Include more customizable visualization options.
Enable real-time collaboration and sharing of data projects.
Integrate machine learning models for data prediction.

Contributing

Contributions are welcome! Feel free to open an issue or submit a pull request to improve the app.

License

This project is licensed under the MIT License – see the LICENSE file for details.

Let me know if you need to make any adjustments or add specific details!

MarkPhamm/DataGlide