Economic Events Dashboard

This project is a web scraping application built with Streamlit, designed to demonstrate how to fetch, store, manipulate, and email economic events data. The application integrates web scraping, data storage, data visualization, and email functionalities into a simple and user-friendly interface.

Features

• Fetch and Store Data: Fetch economic events data from the web and store it in a SQLite database.
• View Stored Data: View and filter stored economic events data in a tabular format.
• Send Email: Send an email with today's economic events, including the option to customize the recipient, subject, and message. Note: This feature is only supported on Windows.
• Automated Testing: Unit, integration, and performance tests to ensure the reliability and correctness of the application.
• Benchmarking: Performance benchmarking to measure and optimize the performance of various application components.

Requirements

• Python 3.7+
• Required Python packages are listed in the requirements.txt file.

Installation

1. Clone the repository:

   git clone https://github.com/pedrosfaria2/economic_events.git
   cd economic_events

2. Create and activate a virtual environment:

   On Windows

   python -m venv venv
   venv\Scripts\activate

   Install the required packages:

   pip install -r requirements.txt

   Install additional packages for Windows:

   pip install -r requirements-dev.txt

   On macOS and Linux

   python3 -m venv venv
   source venv/bin/activate

   Install the required packages:

   pip install -r requirements.txt

Usage

1. Run the Streamlit application:

   streamlit run app.py

2. Open your web browser and go to http://localhost:8501 to view the application.

Running Tests

1. To run the tests, simply use:

   pytest

   This will execute all the tests located in the tests directory. Note: Tests involving email sending are only supported on Windows.

2. To run the benchmark tests, use:

   pytest tests/test_benchmark.py --benchmark-only

   This will execute the benchmark tests to measure the performance of the application components.

Benchmark Results

The benchmark tests measure the performance of key functions in the application. Here are the results:

send_email

• Time: Approximately 567.1 microseconds
• Operations per Second (OPS): ~1763 operations per second
• Consistency: Very consistent with no outliers detected

store_events

• Time: Mean of 180.1895 microseconds
• Operations per Second (OPS): ~5549 operations per second
• Consistency: Some variability with outliers detected

get_events

• Time: Mean of 725.5073 microseconds
• Operations per Second (OPS): ~1378 operations per second
• Consistency: Some variability with outliers detected

fetch_economic_events

• Time: Mean of 1.648 seconds
• Operations per Second (OPS): ~0.6067 operations per second
• Consistency: Higher variability due to network latency and response times

These results show that while fetch_economic_events is the most time-consuming operation due to its reliance on network requests, other operations like send_email, store_events, and get_events perform efficiently. The presence of outliers in store_events and get_events suggests potential areas for optimization.

Project Structure

• app.py: Main file to run the Streamlit application.
• data_fetcher.py: Contains functions to fetch economic events data from the web.
• data_storer.py: Contains functions to store and retrieve data from the SQLite database.
• email_sender.py: Contains functions to send emails with economic events data.
• requirements.txt: Lists all the required Python packages.
• requirements-dev.txt: Lists additional packages required for Windows.
• README.md: Provides an overview of the project and instructions for setup and usage.
• tests/: Directory containing unit, integration, performance, and benchmark tests for the application.

Overview

This project serves as an example of how to combine various Python functionalities into a cohesive application. It covers:

• Web Scraping: Using httpx and BeautifulSoup to fetch and parse data from websites.
• Data Storage: Using sqlite3 to store the fetched data in a SQLite database.
• Data Manipulation: Using pandas to manipulate and filter the data.
• Email Sending: Using pywin32 to send emails through Outlook (Windows only).
• User Interface: Using Streamlit to create a simple and interactive web interface.
• Automated Testing: Using pytest for unit tests, integration tests, and performance tests to ensure the application works as expected.
• Benchmarking: Using pytest-benchmark to measure and optimize the performance of the application components.

Automated Testing

• Unit Tests: Ensure individual functions work correctly.
• Integration Tests: Verify that different parts of the application work together as expected.
• Performance Tests: Check that key operations perform within acceptable time limits.
• Benchmarking Tests: Measure the performance of various components to identify potential bottlenecks and areas for optimization.

Note: Some functionalities and tests (such as email sending) are only supported on Windows. To test these features on other platforms, consider using a Windows environment or testing framework that supports Windows-specific features.

The goal is to provide a comprehensive example of how to build a full-stack Python application that handles data from collection to presentation, with a focus on performance and reliability.

License

This project is licensed under the MIT License.