Unparalleled suite of productivity-boosting Web APIs & cloud-based micro-service applications for developers and companies of any size.


Unveiling the Skies: A Deep Dive into Airline Analysis with Python and Flask, Aviationstack and Zenserp


Air travel is a mesmerizing world. The convergence of engineering marvels, logistic masterpieces, and tales of human experiences all come together at 30,000 feet above ground. But have you ever wondered about the intricate networks airlines weave across the skies? Or the stories each route tells? In this blog, we’ll explore how to analyse airline routes, fetch associated news, display relevant images, and visualize these routes on a map using Python, Flask, and a sprinkle of AI.

At the end of this blog we will supply you with the code to: 

  • Analyse the expansive networks of airline routes using robust data sources. Gather and interpret associated news about airlines to stay updated on the latest industry happenings. 
  • Seamlessly integrate and display relevant images to enhance the user’s understanding and experience. 
  • Visualize and interact with these routes on a map, providing a comprehensive view of global airline operations. 
  • Harness the power of AI, particularly OpenAI’s GPT-4, to generate informative and relevant narratives about selected airline routes. Integrate the various tools and libraries such as requests, Plotly Express, Pandas, and Geopy to facilitate the smooth functioning of the application. 
  • Construct a user-friendly interface using Flask, ensuring the tool is accessible to both tech-savvy and novice users. 
  • By the culmination of this blog, readers will be equipped with the knowledge and codebase to create a holistic platform that not only demystifies the airline route networks but also narrates the tales behind them. Whether you’re a data enthusiast, developer, or someone simply intrigued by the aviation industry, this blog aims to provide a comprehensive insight into the convergence of data, visualization, and AI in the realm of airline analysis.

Giving users control from a web page to analyse an airline of their choice from the dropdown menu.

Python; dropdown menu

Retrieving flight route data from Aviationstack API and passing it to Mapbox. Providing a commentary about the flight routes and destinations using GPT-4.

aviationstack API

Retrieving news and image data from Google using Zenserp API.

zenserp API; Google; scraping

Our solution integrates a suite of specialized tools and APIs:

  • requests: Facilitates data retrieval from various APIs.
  • Plotly Express: Employs advanced graphics capabilities to visually represent airline routes on interactive maps.
  • Pandas: Provides robust capabilities for data manipulation and analysis.
  • Geopy: Converts city or airport names into precise geographical coordinates.
  • OpenAI: Uses the GPT-4 model to generate descriptive content related to airline routes and their narratives.


Flask as the Web Framework

Flask is our chosen lightweight web framework for Python. It supports the rapid development of this application, with a primary interface where users can view an airline’s routes, read about its history, and access the latest news and images.


Data Processing and Visualization

  • Data Retrieval: We utilize the aviationstack API, a rich source of information on airline routes, associated airports, and the types of aircraft in use.
  • AI-Powered Narratives: After processing the data, we employ OpenAI’s GPT-4 model. This offers users a detailed description of the airline’s history, its primary routes, and unique experiences.
  • Visualization: Plotly Express enables us to represent these routes on a map, with the Nominatim service from Geopy converting airport names to geographical coordinates for precision.


Updates on News and Images

We integrate the Zenserp API to continually update the application with:

  • News: The latest developments related to an airline, including new route launches or mergers.
  • Images: Visuals that showcase the branding and aesthetics of the airline’s fleet.


Interactivity and UI

The Flask-powered user interface boasts:

  • Dropdown Menu: Allows users to select from an extensive list of airlines.
  • Integrated Results: Displays visualized routes, narratives generated by GPT-4, current news, and related images upon selection.


Optimization Strategies

We have implemented certain best practices for efficiency:

  • Caching: We’ve incorporated a caching mechanism to reduce redundancy in geolocation queries.
  • Environment Variables for API Keys: API keys are sourced from environment variables for enhanced security and adaptability.


Deployment Overview with PythonAnywhere

Deploying our application is crucial to reach a global audience. To achieve this, we’ve chosen PythonAnywhere, an ideal platform for Python web applications.

Our application’s core lies in the union of flask_app.py and templates/airlines.html. The former manages data processes, API interactions, and routing, while the latter visualizes this data for users. This combination ensures the application is not only functional but also globally accessible. With this structure, we seamlessly bridge backend operations and frontend visuals, immersing users in the airline domain.


Key Aspects of the Deployment:

    • Deployment Platform: We’ve adopted PythonAnywhere, tailoring our application for global accessibility.
  • WSGI Configuration:
      • Adjusts system path to pinpoint project files.
      • Configures environment variables, enhancing security by preventing hardcoded sensitive data.
      • Prepares the Flask app to be the main touchpoint for web requests.
  • Flask Application Components:
    • flask_app.py: The Flask application’s nucleus. It oversees component management, routing, and user interfacing.
      • Sets up initial configurations, including necessary imports.
      • Carves out navigation paths, pinpointing the primary route.
    • templates/airlines.html: Steers the user interface and data portrayal.
      • Leverages Jinja2 for fluid data representation.
      • Incorporates interactive elements like maps and charts.
      • Facilitates user interactions through various UI elements

In essence, by harnessing Python, Flask, and PythonAnywhere, we’ve crafted a comprehensive tool for airline insights, enabling users to delve deeply into the world of aviation.


How to Find and Edit Files on PythonAnywhere:

  1. Login to PythonAnywhere:

   Begin by logging in to your PythonAnywhere account.

  1. Access the Dashboard:

   Once logged in, you’ll be directed to your dashboard. This dashboard displays an array of options, such as ‘Files’, ‘Consoles’, ‘Web’, and more.


For templates/airlines.html:

  1. Navigate to ‘Files’:

   On the dashboard, click on the ‘Files’ tab.

  1. Locate the ‘templates’ Directory:

   Browse through the list of directories and files to find the ‘templates’ directory.

  1. Open / create airlines.html:

   Inside the ‘templates’ directory, find and click on the airlines.html file to open it.

  1. Input Your Code:

   Here, you can paste or write the code intended for airlines.html. Once done, save the changes.


For flask_app.py:

  1. **Back to ‘Files’ Directory**:

   If not already there, click on the ‘Files’ tab on the dashboard.

  1. **Find flask_app.py**:

   Browse the directory to locate the flask_app.py file.

  1. **Edit the File**:

   Click on flask_app.py to open it. Paste or write your desired code and remember to save the changes afterward.

For WSGI Configuration:

  1. Head to the ‘Web’ Tab:

   Go back to your dashboard and select the ‘Web’ tab.

  1. WSGI Configuration File:

   In the web application settings, locate the section labeled ‘WSGI Configuration File’. You’ll see a link to the file path, which typically looks like /var/www/your-user-name_pythonanywhere_com_wsgi.py.

  1. Open and Edit:

   Click on the link to open the file. Here, input the necessary WSGI configuration code. Ensure you save the file after making your edits.

Remember: Always ensure that your code is correctly formatted and free of errors before saving to avoid potential issues when running your application.


This project underscores the potential of integrating data analysis, visualization, and AI in a streamlined web interface. It offers value not just to travelers and aviation enthusiasts, but also to industry professionals and data analysts. As the skies become busier, understanding the networks and narratives that shape air travel becomes increasingly vital. We believe this tool serves as a significant step in that direction

Related posts

API Development: Difference Between GraphQL and REST API


API Key vs. Token: What Is the Difference?


What Is API Orchestration, Why, and How? (With Examples)


The Best IP Locations API With Enterprise Plans

Leave a Reply

Your email address will not be published. Required fields are marked *