A professional, interactive dataset visualization and analysis tool built with Streamlit and Plotly.
Designed for data professionals, analysts, and researchers to explore, analyze, and summarize CSV datasets efficiently.
Try the interactive dataset visualizer online:
- Minimalistic, professional dashboard layout
- Dark mode only for a clean and focus-friendly interface
- Total entries and fields
- Duplicate detection
- Field type distribution
- Missing values overview and heatmap
- Mean, Standard Deviation, Quartiles
- Skewness & Kurtosis
- Outlier detection
- Histograms, Boxplots
- Correlation heatmaps
- Unique value counts
- Most frequent values
- Bar charts for top categories
- Range and missing values
- Trend line plots
- Side-by-side numeric and categorical summaries
- Comparison plots
- Grouped analysis by categorical fields
- Download professional summary CSV
- Clone the repository:
git clone https://github.com/YOUR_USERNAME/dataset-visualizer.git
cd dataset-visualizer- Create a virtual environment:
python3 -m venv .venv
source .venv/bin/activate # Linux/macOS
.venv\Scripts\activate # Windows- Install dependencies:
pip install -r requirements.txt- Run the app:
streamlit run app.py- Upload your CSV dataset using the sidebar.
- Navigate through the tabs:
- Overview
- Numeric Analysis
- Categorical Analysis
- Datetime Analysis
- Multi-Field Comparison
- Generate interactive charts and professional summaries.
- Export the summary CSV for reporting or presentations.
- Streamlit - Interactive web interface
- Pandas – Data manipulation and analysis
- Plotly – Interactive charts
- Python 3.12
- Fork the repository
- Create a branch: git checkout -b feature-name
- Commit your changes: git commit -m 'Add new feature'
- Push to the branch: git push origin feature-name
- Open a Pull Request
This project is open-source under the MIT License.
Designed to help data professionals explore datasets efficiently with a sleek, interactive, and highly functional UI.