{"id":26937735,"url":"https://github.com/srinibas-masanta/streamlit-dataprofile","last_synced_at":"2026-04-15T07:39:49.238Z","repository":{"id":252663450,"uuid":"841073626","full_name":"srinibas-masanta/Streamlit-Dataprofile","owner":"srinibas-masanta","description":"Data Profiler is a Streamlit app designed to provide insightful data analysis and visualization. Users can upload their datasets in '.csv' or '.xlsx' format, and the app generates a comprehensive profiling report using the YData Profiling library. 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Simply upload your data in `.csv` or `.xlsx` format, and generate comprehensive profiling reports that help you detect anomalies, patterns, and trends within your data.\n\n## **Features**\n\n- **Automated Data Analysis:** Quickly generate detailed profiling reports by uploading your dataset.\n- **Customizable Reports:** Choose between different display modes, including `Primary`, `Dark`, and `Orange`.\n- **Support for Multiple Formats:** Upload `.csv` or `.xlsx` files (up to 10 MB) for analysis.\n- **Interactive UI:** Easy-to-use interface with options to select specific sheets for `.xlsx` files.\n- **Downloadable Reports:** Save the profiling report as an HTML file for offline analysis.\n\n## **Installation**\n\nTo run the Data Profiler application on your local machine, follow the steps below:\n\n### **1. Clone the Repository**\n\n```bash\ngit clone https://github.com/srinibas-masanta/data-profiler.git\ncd data-profiler\n```\n\n### **2. Set Up a Virtual Environment**\n\nCreate and activate a virtual environment to manage dependencies.\n\n```bash\npython -m venv dataprofile\n.\\dataprofile\\Scripts\\activate  # On Windows\nsource dataprofile/bin/activate  # On macOS/Linux\n```\n\n### **3. Install Dependencies**\n\nInstall the required Python packages listed in the `requirements.txt` file.\n\n```bash\npip install -r requirements.txt\n```\n\nAlternatively, manually install the necessary packages:\n\n```bash\npip install numpy pandas scipy matplotlib streamlit ydata-profiling streamlit-pandas-profiling openpyxl xlrd\n```\n\n### **4. Run the Application**\n\nStart the Streamlit application by running the following command:\n\n```bash\nstreamlit run app.py\n```\n\n## **Usage**\n\nOnce the application is running, follow these steps:\n\n1. **Upload Your Data:** Use the sidebar to upload a `.csv` or `.xlsx` file (up to 10 MB).\n2. **Select Options:** Choose the report mode (`Primary`, `Dark`, `Orange`), and decide if you want a minimal report or a full report.\n3. **Generate Report:** Click to generate the report, which will be displayed within the app.\n4. **Download Report (Optional):** If desired, save the report as an HTML file using the download button.\n\n## **Project Structure**\n\n- **app.py:** Main script containing the Streamlit application logic.\n- **media/DP Logo.jpg:** Logo used in the welcome page of the application.\n- **requirements.txt:** List of all the Python dependencies required to run the application.\n\n## **License**\n\nThis project is licensed under the MIT License - see the [LICENSE.txt](LICENSE.txt) file for details.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsrinibas-masanta%2Fstreamlit-dataprofile","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsrinibas-masanta%2Fstreamlit-dataprofile","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsrinibas-masanta%2Fstreamlit-dataprofile/lists"}