Global Statistics Dashboard

The Global Statistics Dashboard is a comprehensive tool designed to provide insights into various global statistics. This project includes data processing scripts, a web application interface, and modular components for loading and visualizing data.

Table of Contents

• Installation
• Usage
• Project Structure
• Libraries Used
• License
• Contributing

Installation

1. Clone the repository:

   git clone https://github.com/UznetDev/Global-Internet-users.git

2. Navigate to the project directory:

   cd Global-Internet-users

3. Create a virtual environment:

   python -m venv env

4. Activate the virtual environment:
   • On Windows:

     env\Scripts\activate

   • On macOS and Linux:

     source env/bin/activate

5. Install the necessary libraries:

   pip install -r requirements.txt

Usage

To run the dashboard, use the following command:

streamlit run 🏠_Home.py

This will start the application, and you can view it in your web browser by navigating to http://localhost:5000 (or the specified port).

Project Structure

• .gitignore: Specifies files and directories to be ignored by git.
• build_data.ipynb: Jupyter Notebook for building, cleaning and processing the dataset.
• dataset/: Directory containing the dataset files used for the dashboard.
• function.py: Contains functions used across the project.
• 🏠_Home.py: The main script to run the web application.
• loader.py: Script responsible for loading data into the application.
• pages/: Directory containing the different pages of the web application.
  • 1_🗺️Map.py: This module contains the code for displaying a global map with various statistical overlays. It visualizes geographical data and provides interactive map features.
  • 2_ 📊Statistics_by_country.py: This module provides statistical data breakdowns by country. Users can select a country and view detailed statistics relevant to that country.
  • 3_🎯_Future_Product.py: This module is designed for future product implementations. It serves as a placeholder for features that are planned for future releases.
  • init.py: This file indicates that the pages directory is a Python package.

Detailed Breakdown

• .gitignore: Configuration file to specify untracked files that Git should ignore.
• build_data.ipynb: Jupyter Notebook for data preprocessing. This includes steps to clean, transform, and prepare data for visualization.
• dataset/: This folder holds raw and processed data files necessary for generating statistics.
• function.py: This module contains reusable functions that are utilized in various parts of the project to ensure modularity and code reuse.
• 🏠_Home.py: The entry point for the web application. Running this script launches the dashboard.
• loader.py: Handles data loading operations. This script ensures that the data from the dataset directory is correctly loaded and ready for use in the application.
• pages/: Contains different page modules for the web application. Each page is a separate component/module that can be accessed through the dashboard interface.

Libraries Used

The following libraries are used in this project:

• pandas: For data manipulation and analysis.
• numpy: For numerical operations.
• plotly: For creating interactive visualizations.
• streamlit: For building the web application.
• Matplotlib: For making Dashboard.
• jupyter: For interactive computing and developing the build_data.ipynb notebook.

These libraries are listed in the requirements.txt file and can be installed using the installation instructions provided above.

License

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

Contributing

Contributions are welcome! Please fork the repository and submit a pull request for any changes.

1. Fork the Repository: Click on the Fork button at the top right corner of this page to create a copy of this repository under your GitHub account.

2. Clone the Forked Repository:

   git clone https://github.com/YOUR_USERNAME/Global-Statistics-Dashboard.git
   cd Global-Statistics-Dashboard

3. Create a New Branch:

   git checkout -b feature/YourFeatureName

4. Commit Your Changes:

   git add .
   git commit -m 'Add some feature'

5. Push to the Branch:

   git push origin feature/YourFeatureName

6. Create a Pull Request: Open a pull request to the original repository.

Contact

If you have any questions or suggestions, please contact:

• Email: uznetdev@example.com
• GitHub Issues: Issues section
• GitHub Profile: UznetDev
• Telegram: UZNet_Dev
• Linkedin: Abdurahmon Niyozaliev

Thank you for your interest in the project!