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https://github.com/asifdotexe/quickvu
Quick VU: No-code, data cleaning analysis and visualization tool built on Streamlit. Quickly clean, visualize, explore, and understand data relationships and correlations with ease. Perfect for analysts, business users, and anyone looking to gain data insights—without writing a single line of code.
https://github.com/asifdotexe/quickvu
automation data-analysis data-cleaning data-visualization python3 streamlit-application toolkit
Last synced: about 2 months ago
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Quick VU: No-code, data cleaning analysis and visualization tool built on Streamlit. Quickly clean, visualize, explore, and understand data relationships and correlations with ease. Perfect for analysts, business users, and anyone looking to gain data insights—without writing a single line of code.
- Host: GitHub
- URL: https://github.com/asifdotexe/quickvu
- Owner: Asifdotexe
- Created: 2024-10-16T15:45:19.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2024-11-10T11:04:26.000Z (about 2 months ago)
- Last Synced: 2024-11-10T11:19:44.279Z (about 2 months ago)
- Topics: automation, data-analysis, data-cleaning, data-visualization, python3, streamlit-application, toolkit
- Language: Python
- Homepage: https://quickvu.streamlit.app/
- Size: 4.08 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
# Quick VU
**Quick VU** (Quick Visual Understanding) is an easy-to-use tool for data preparation, analysis, and visualization. It helps you clean, explore, and visualize your data without coding.
Just upload your data, choose your analysis, and Quick VU will guide you through the process.
## 📌 Project Objective
**Quick VU** _(Quick Visual Understanding)_ is a user-friendly tool for data preparation, analysis, and visualization. It allows you to clean, explore, and visualize your data without any coding. Simply upload your dataset, choose your analysis, and let Quick VU guide you through the process.
* Quickly clean your data and export it for further analysis.
* Efficiently analyze your data and gain insights.
## 📃 Features
* QuickPrep: A data cleaning tool that helps with tasks such as column name standardization, handling missing values, detecting and removing outliers, changing data types, filtering rows, and scaling data.
* QuickGlance: A data analysis tool that provides summary statistics, visualizes correlations, and generates quick plots to give you a better understanding of your data.
## 📦 Installation
1. **Clone the Repository**
```bash
git clone https://github.com/yourusername/quick-vu.git
cd quick-vu
```
2. **Create a conda envirioment**
Assuming you have ananconda installed on your system
```bash
conda create -name quickvu python=3.12
conda activate quickvu
```
2. **Install Dependencies**
Ensure you have Python installed. Then, install the required packages:
```bash
pip install -r requirements.txt
```
3. **Install Google API SDK**
The Python SDK for the Gemini API is contained in the google-generativeai package. Install the dependency using pip:
```bash
pip install -q -U google-generativeai
```
4. **Run the Application**
Start the Streamlit app:
```bash
streamlit run app.py
```
5. **Use Quick VU**
- Open the provided local URL in your browser.
- Upload your CSV dataset and start exploring your data!
OR
- Visit: https://quickvu.streamlit.app/
## 🤝 Contributions
Contributions are welcome! If you have suggestions, bug reports, or want to contribute code, please open an issue or submit a pull request.
## 📄 License
This project is licensed under the MIT License.
## 📧 Contact
For any inquiries or feedback, please contact [Asif Sayyed](mailto:[email protected]).
---
With **Quick VU**, you can explore and understand your data efficiently without the need for coding. Simplify your data analysis process and gain valuable insights effortlessly.