How to handle API errors and status codes in Python
Jul 09, 2025 am 01:44 AMHandling API errors and status codes can be achieved in Python by understanding common status codes, using the requests library to handle exceptions, catch network errors, and record logs. First, you should be familiar with status codes such as 200 OK, 400 Bad Request, 401 Unauthorized, 403 Forbidden, 404 Not Found, and 500 Internal Server Error to quickly locate problems. Secondly, you can use the status_code attribute or raise_for_status() method of the requests library to determine whether the request is successful. 1. Use try-except to catch exception types such as HTTPError, Timeout and ConnectionError and handle them separately. 2. Set timeout to prevent the program from being stuck and consider introducing a retry mechanism to improve reliability. 3. Use the logging module to record error information and preserve the context for debugging. Through these methods, the robustness of the program can be effectively improved and the investigation process can be simplified.
Handling API errors and status codes is not actually complicated in Python, but it is easy to ignore details. Especially when calling third-party services or building your own web applications, a good error handling mechanism can not only improve the robustness of the program, but also help you quickly locate problems.

Below are some commonly used practices and suggestions in actual development.
Understand common HTTP status codes
Before writing code, it is necessary to have a basic understanding of common status codes. for example:

-
200 OK
: The request was successful -
400 Bad Request
: The request sent by the client is incorrect -
401 Unauthorized
: Authentication is required -
403 Forbidden
: The server refuses to execute the request -
404 Not Found
: The resource does not exist -
500 Internal Server Error
: An error occurred inside the server
After knowing this, you can quickly determine which layer the problem lies when processing the response.
Basic error handling methods using requests library
The most commonly used library in Python for initiating HTTP requests is requests
. It provides some simple but practical ways to check if the request is successful.

import requests response = requests.get('https://api.example.com/data') if response.status_code == 200: data = response.json() else: print(f"Request failed, status code: {response.status_code}")
In addition to checking status_code
, you can also use the response.raise_for_status()
method to automatically throw exceptions:
try: response = requests.get('https://api.example.com/data') response.raise_for_status() # If not 2xx, HTTPError will be thrown except requests.exceptions.HTTPError as err: print(f"HTTP error occurred: {err}")
This can avoid writing many if judgments manually.
Handle connection and timeout errors
In addition to the status code returned by the server, some errors occur at the network layer, such as connection failure, timeout, etc. These situations cannot be handled by checking status_code only.
You can use try-except to catch a wider range of exception types:
try: response = requests.get('https://api.example.com/data', timeout=5) response.raise_for_status() except requests.exceptions.Timeout: print("Request timed out, please check the network or try again") except requests.exceptions.ConnectionError: print("Cannot connect to the server, please check whether the network or URL is correct") except requests.exceptions.HTTPError as err: print(f"HTTP error: {err}")
Here are a few key points to pay attention to:
- Set a reasonable
timeout
time to prevent the program from getting stuck - Differentiate different exception types and provide corresponding feedback
- In production environments, it is recommended to add a retry mechanism (such as using the
tenacity
library)
Logging and debugging information
If you are working on a long-term service or need to troubleshoot online problems, it is recommended to record the error message. You can save logs with the logging
module:
import logging logging.basicConfig(filename='api_errors.log', level=logging.ERROR) try: response = requests.get('https://api.example.com/data', timeout=5) response.raise_for_status() except Exception as e: logging.error(f"API request failed: {e}, response content: {response.text if 'response' in locals() else 'no response'}")
Doing so allows for the detailed error context to be preserved without affecting the user.
Basically that's it. The core of API error handling is "predicting in advance", "reasonable capture", and "clear feedback". Although it seems to be common operations, if one or two details are missed, the program may lapse or data loss.
The above is the detailed content of How to handle API errors and status codes in Python. For more information, please follow other related articles on the PHP Chinese website!

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