Airport schedule data is crucial for flight tracking, travel planning, airline operations, and aviation analytics. In this guide, we will demonstrate how to extract real-time airport schedule data using Python.

By the end of this tutorial, you’ll be able to fetch flight arrival and departure schedules using a simple API request.

Table of Contents

What is Airport Schedule Data?

Arrival & departure times
Flight numbers & airline details
Terminal & gate information
Flight status (on-time, delayed, canceled, etc.)
Origin & destination details

Requirements

I hope you have already installed Python on your machine, if not then you can download it from here. Then create a folder where we will keep all the files.

mkdir flightdata

Then inside this folder install requests and pandas library.

requests for making an HTTP connection with the data host.
pandas for saving the data to a CSV file.

pip install requests

Now, create a Python file using any name you like. I am naming the file as schedule.py. The final step would be to create a trial account on FlightAPI. You will get 30 credits for testing the API.

Extracting Airport Schedule Data

Once you sign up for the trial pack you will be redirected to the dashboard which will look like this.

Before proceeding with the coding it would be great to read the documentation to get the full idea of how the API works.

import requests

API_KEY = "your-api-key"
AIRPORT_CODE = "JFK"
mode="arrivals"
URL = f"https://api.flightapi.io/schedule/62677fded20b140b78fa40bc?mode={mode}&iata={AIRPORT_CODE}&day=1"

response = requests.get(URL)

if response.status_code == 200:
    airport_data = response.json()
    print(airport_data)  
else:
    print(f"Error: {response.status_code}")

Let me briefly explain the code.

First, we imported the requests library.
Then we declared a few parameters like AIRPORT_CODE which will be the airport code of the target Airport, the mode tells whether you need data of arriving flights or departing flights, and the API_KEY. Also, data parameter acts as the page number here. So, you can check it’s value to get the data of the next page.
Then we made the GET request using requests.
Finally, we are collecting the data by printing it on the console.

Let’s run the code and see what appears.

You got a beautiful JSON response for all the arriving flights at JFK. Let’s parse the schedule data out of this huge JSON data.

For your simplification, the data looks like this.

From this, we need schedule data. But if you need weather or aircraft image data then you can parse that too.

import requests
o={}
l=[]
API_KEY = "your-api-key"
AIRPORT_CODE = "JFK"  # Replace with your airport code
mode="arrivals"
URL = f"https://api.flightapi.io/schedule/62677fded20b140b78fa40bc?mode={mode}&iata={AIRPORT_CODE}&day=1"

response = requests.get(URL)

if response.status_code == 200:
    airport_data = response.json()
    completeData=airport_data['airport']['pluginData']['schedule']['arrivals']['data']

    for i in range(0,len(completeData)):
        try:
            o["flight"]=completeData[i]['flight']['aircraft']['model']["text"]
        except:
            o["flight"]=None
        try:
            o["departure"]=completeData[i]['flight']['time']['scheduled']['departure']
        except:
            o["departure"]=None

        try:
            o["arrival"]=completeData[i]['flight']['time']['scheduled']['arrival']
        except:
            o["arrival"]=None
        l.append(o)
        o={}
    print(l)
else:
    print(response.text)
    print(f"Error: {response.status_code}")

After running the code you will get this parsed JSON response.

Saving the data to a CSV file

Our parsing is done now let’s save the data directly to a CSV file. We will use pandas for this process.

import requests
import pandas as pd


o={}
l=[]
API_KEY = "your-api-key"
AIRPORT_CODE = "JFK"  # Replace with your airport code
mode="arrivals"
URL = f"https://api.flightapi.io/schedule/62677fded20b140b78fa40bc?mode={mode}&iata={AIRPORT_CODE}&day=1"

response = requests.get(URL)

if response.status_code == 200:
    airport_data = response.json()
    completeData=airport_data['airport']['pluginData']['schedule']['arrivals']['data']
    print(len(completeData))
    for i in range(0,len(completeData)):
        try:
            o["flight"]=completeData[i]['flight']['aircraft']['model']["text"]
        except:
            o["flight"]=None
        try:
            o["departure"]=completeData[i]['flight']['time']['scheduled']['departure']
        except:
            o["departure"]=None

        try:
            o["arrival"]=completeData[i]['flight']['time']['scheduled']['arrival']
        except:
            o["arrival"]=None
        l.append(o)
        o={}



    df = pd.DataFrame(l)
    df.to_csv('schedule.csv', index=False, encoding='utf-8')

    print("done")


else:
    print(response.text)
    print(f"Error: {response.status_code}")

Once you run this code you will see a CSV file by the name schedule.csv inside your folder.

Conclusion

Extracting airport schedule data using Python and FlightAPI.io is a powerful way to access real-time flight arrivals, departures, and status updates. By leveraging APIs, we can efficiently fetch, process, and analyze flight data without relying on manual tracking or unreliable sources.

Apart from airport schedule data, FlightAPI.io offers a range of other services, including flight price comparison data from different vendors through its Flight Price API. You can explore the documentation for different trip types:

Additionally, it offers a Flight Status API that allows you to retrieve real-time flight tracking data using Python, including flight number, departure airport, scheduled departure time, arrival airport, and scheduled arrival time. If you prefer a no-code solution, check out our step-by-step tutorial on automating flight status data extraction using Google Sheets.