Can't send json reponse in requests mock

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29-09-2024

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Sending JSON Responses with Requests Mock: A Guide to Simulating API Behavior

When testing your Python applications, it's often crucial to mock external API calls to isolate your code and ensure reliable testing. The requests_mock library is a popular choice for this purpose. However, you might encounter challenges when attempting to return a JSON response from your mocked endpoint. This article will explore common issues and provide solutions for effectively sending JSON responses with requests_mock.

The Problem

Imagine you have a function that makes a GET request to a weather API endpoint:

import requests

def get_weather_data(city):
  url = f"https://api.example.com/weather?city={city}"
  response = requests.get(url)
  if response.status_code == 200:
    return response.json()
  else:
    return None

Now, let's say you want to test this function without actually making a real API call. You might try using requests_mock like this:

import requests_mock

def test_get_weather_data():
  with requests_mock.Mocker() as m:
    m.get("https://api.example.com/weather", json={'temperature': 25, 'city': 'London'})
    result = get_weather_data("London")
    assert result['temperature'] == 25

However, this code might throw an error, indicating that the response is not JSON-serializable. This is because requests_mock returns a requests.Response object by default, which isn't directly convertible to a JSON format.

The Solution

To return a JSON response, you need to explicitly tell requests_mock to do so. This can be achieved by using the content argument and encoding your JSON data as a string:

import requests_mock
import json

def test_get_weather_data():
  with requests_mock.Mocker() as m:
    response_data = {'temperature': 25, 'city': 'London'}
    m.get("https://api.example.com/weather", content=json.dumps(response_data))
    result = get_weather_data("London")
    assert result['temperature'] == 25

Here, we use the json.dumps() function to convert the dictionary response_data into a JSON string. The content argument of m.get() then ensures that this JSON string is returned as the response body.

Additional Notes:

• You can also use the text argument in m.get() to directly provide a string that will be treated as the response body. However, this option might not be suitable for all situations as it requires you to format the response in JSON manually.

• requests_mock offers various methods for mocking different HTTP requests, such as post, put, delete, etc. The content argument remains applicable for all of these methods.

• For more complex scenarios where you need to customize the mock response further, consider using the json argument in conjunction with status_code and headers arguments to control the response status code and headers.

• By utilizing requests_mock, you can effectively simulate API behavior, ensuring your application logic is robust and resilient to external dependencies.

Conclusion

By understanding how to correctly send JSON responses with requests_mock, you can significantly enhance the testing process for your Python applications. Remember to utilize the content argument and encode your JSON data as a string to achieve this. Employing proper mocking strategies will make your testing process more efficient and reliable, leading to higher quality code.