Supprimer Rendre public Rendre privé Add tags Delete tags
  Ajouter un tag   Annuler
  Supprimer le tag   Annuler
  • • DevOps notes •
  •  
  • Tags
  • Connexion

Handling Errors and Status Codes/shaare/3pMTRg

  • python
  • python

Handling Errors and Status Codes

  • HTTP status codes communicate the outcome of an API request, and handling them correctly is key to robust automation.
  • A simple 200 OK means success, while codes like 404 Not Found or 500 Internal Server Error indicate different failure modes.
  • In this lecture, we’ll learn how to check status codes, use response.ok, raise errors automatically, and inspect error details for troubleshooting.

Understanding HTTP Status Codes

  • Status codes are grouped by their first digit: 1xx (informational), 2xx (success), 3xx (redirection), 4xx (client error), 5xx (server error).
  • Examples include 200 OK, 201 Created, 301 Moved Permanently, 404 Not Found, and 500 Internal Server Error.
  • Knowing these categories helps you decide how to handle each response in your scripts.

Checking response.status_code

  • After a requests call, the integer response.status_code tells you the exact HTTP code returned.
  • You can compare it directly (e.g., if resp.status_code == 404:) to implement custom logic based on the code.
  • This explicit check is useful when you need fine-grained control over specific status codes.
GITHUB_ENDPOINT = "https://api.github.com"
HTTPBIN_ENDPOINT = "https://httpbin.org"
import requests

urls = {
    "ok": f"{GITHUB_ENDPOINT}/zen",
    "not_found": f"{GITHUB_ENDPOINT}/nonexistentendpoint"
}

for description, url in urls.items():
    response = requests.get(url, timeout=5)
    print(f"{description}: status {response.status_code}")

Using response.ok

  • The boolean response.ok is True for any status code below 400 (1xx, 2xx, 3xx) and False for 4xx/5xx errors.
  • This provides a quick success/failure check without examining the numeric code directly.
  • It’s a handy shorthand when you only need to know if the request broadly succeeded.
import requests

urls = {
    "ok": f"{GITHUB_ENDPOINT}/zen",
    "not_found": f"{GITHUB_ENDPOINT}/nonexistentendpoint"
}

for description, url in urls.items():
    response = requests.get(url, timeout=5)
    print(f"{description}: ok? {"Yes" if response.ok else f"No. Failed with status {response.status_code}"}")

Automatic Error Raising with raise_for_status()

  • Calling response.raise_for_status() will do nothing on 1xx, 2xx and 3xx codes but raise an HTTPError on 4xx/5xx.
  • This follows the EAFP (“Easier to Ask Forgiveness than Permission”) style: try the request, and catch errors if they occur.
  • The caught exception carries the original response in its response attribute, letting you inspect headers and body.
import requests
import json

urls = {
    "ok": f"{GITHUB_ENDPOINT}/zen",
    "not_found": f"{GITHUB_ENDPOINT}/nonexistentendpoint"
}

for url in urls.values():
    print(f"Requesting: {url}")
    try:
        res = requests.get(url, timeout=5)
        res.raise_for_status()
        print("  Success!")
    except requests.exceptions.HTTPError as err:
        print(f"  HTTPError: {err} (status {err.response.status_code})")
        try:
            details = err.response.json()
            print("  Error details:")
            print(json.dumps(details, indent=2))
        except ValueError:
            print(f"  Non-JSON response body: {err.response.text[:100]}")

Common Pitfalls & How to Avoid Them

  • Not checking errors: Treating any response as success can mask failures. Always use ok or raise_for_status().
  • Catching too broadly: A generic except Exception: hides HTTP errors. Catch HTTPError specifically.
  • Ignoring error bodies: APIs often return JSON error messages; inspect response.text or response.json().
4 months ago Permalien
cluster icon
  • Working with CSV files : Working with CSV files CSV (Comma Separated Values) is a plain-text tabular format where each line is a row and fields are delimited (commonly by com...
  • Concise Iteration: List Comprehensions : Concise Iteration: List Comprehensions Simple for loops to create lists can be verbose. We can leverage list comprehensions to define the list content...
  • Editable Installs with pyproject.toml : Editable Installs with pyproject.toml The Python interpreter doesn't automatically know about our project's structure. The modern and most robust solu...
  • Typing : Introduction Python is a dynamically typed language, meaning you can assign values to variables without declaring their types, and type checking happ...
  • Running External Commands with subprocess.run : Running External Commands with subprocess.run DevOps automation often requires invoking existing CLI tools or scripts to leverage their functionality...


(110)
Filtrer par liens sans tag
Replier Replier tout Déplier Déplier tout Êtes-vous sûr de vouloir supprimer ce lien ? Êtes-vous sûr de vouloir supprimer ce tag ? Le gestionnaire de marque-pages personnel, minimaliste, et sans base de données par la communauté Shaarli