Mastering the Powerful Python Requests Patch Method

Sending HTTP requests is integral for accessing today‘s web APIs and services. Popular Python frameworks like Django and Flask make it easy to build full web applications using simple GET and POST requests. However, additional HTTP verbs enable more flexible manipulation of resources. The HTTP PATCH method offers a powerful way to partially update resources without replacing them entirely.

Let‘s dive into everything you need to know to master patch requests in Python.

Why Patching Matters

Before jumping into syntax specifics, it‘s worth stepping back to understand why patching can be valuable:

• Lightweight updates: No need to send full resources when only changing a few fields. Avoid overfetching.
• Atomic changes: Apply independent modifications without affecting other parts of a document.
• Fine-grained control: Only update specific nested fields instead of entire objects.
• Versioning: Maintain history by adding updates instead of direct edits.

Consider a blog post resource with tons of metadata – title, text, images, view count, categories, etc. Updating the whole thing for a tiny change like fixing a typo seems wasteful.

With patch semantics we can surgically apply our edits.

This drives efficiency, scales better, and enables richer applications.

An Idempotent Update

Patch requests involve sending modification instructions to selectively update resource properties. Like PUT, PATCH is considered idempotent – calling it multiple times yields same result.

It‘s important to distinguish key differences from other methods:

Request Method	Description	Idempotent
POST	Create new resource	No
GET	Read/retrieve resource	Yes
PUT	Update by replacing resource	Yes
PATCH	Selective update of resource	Yes
DELETE	Delete resource	Yes

Note how PUT swaps the entire resource document while PATCH applies a partial change. We don‘t simply override properties but gently modify specific elements.

Let‘s visualize editing a sample blog post:

Instead of wholesale replacement, we surgically update fields.

Now let‘s call the right Python methods.

Patching in Python Requests

The Requests module simplifies all HTTP requests thanks to a beautifully intuitive interface.

To send a patch:

import requests

response = requests.patch(url, data=doc)  

We import Requests, call .patch() instead of .get() or .post(), pass the resource URL and patch document payload, and get back the response to check our status.

Let‘s try a simple example:

import requests, json 

endpoint = "http://httpbin.org/patch"

data = [
    {
        "op": "replace",
        "path": "/job",
        "value": "Developer"  
    }
]

resp = requests.patch(endpoint, json=data)
json_resp = json.loads(resp.text) # decode JSON

print(resp.status_code)
# 200

print(json_resp["json"]["job"])
# Developer 

By calling patch on the httpbin testing endpoint, passing a JSON Patch document, we successfully update the job property!

HTTP status codes indicate whether resources get modified properly.

Now let‘s explore common patch formats.

JSON Patch vs Merge Patch

Two popular standards exist for specifying request payloads:

JSON Patch (RFC 6902): Format with "ops" objects like replace, add, remove, containing instructions for editing a target document.

JSON Merge Patch (RFC 7396): Format that directly maps desired properties onto a document (add/update fields, delete missing ones).

Let‘s see both in action.

Given original doc:

{
  "title": "My Document",
  "language": "English", 
  "translations": [
     "French"
  ] 
}

JSON Patch would be:

[
    { "op": "replace", "path": "/title", "value": "Our Document" },
    { "op": "add", "path": "/translations/1", "value": "Spanish" } 
]

While JSON Merge Patch would be:

{
   "title": "Our Document",
   "translations": [
     "French",
     "Spanish"
   ]
}

So JSON Patch is more imperative/granular while JSON Merge directly maps desired state.

Let‘s visualize editing:

Choose whichever patch format you or your API prefer!

Now onto advanced usage…

Multi-Operation Patching

You can apply several patch operations in a single request. Building atop our simple example:

data = [
    { "op": "replace", "path": "/job", "value": "Developer"},
    { "op": "add", "path": "/languages", "value": ["Python", "JavaScript"] } 
]

response = requests.patch(url, json=data)  
print(response.text)

This concurrently updates job and appends our language skills.

The response echoes all changes together:

{
  "json": {
    "job": "Developer",
    "languages": ["Python", "JavaScript"] 
  }
}

We could continue adding discrete modification instructions to incrementally modify documents.

Change tracking helps illuminate exactly what differs between revisions.

Visualizing Changes

How exactly does our document transform on successive patches? Print responses before/after won‘t clearly show granular edits within nested properties.

JSON diff utilities help solve comparing states, visualizing underlying changes:

import jsonpatch, jsondiff

before_json = json.loads(response1.text)  
after_json = json.loads(response2.text)

patch = jsonpatch.JsonPatch.from_diff(before_json, after_json)  

print(jsondiff.diff(before_json, after_json))

Now we pretty print structural differences!

Output:

{
   "bio": "Python developer and writer",
-  "role": "Engineer"
+  "role": "Engineering Manager", 
   "reports": [
-    "Alice", "Bob"
+    "Wendy", "Sarah", "Viola"
   ]
}

This helps ensure our intended changes get applied properly.

Next up, securing access…

Securing Patch Requests

Since patching enables updating production data, securing authorization is paramount for avoiding data corruption.

Protect endpoints using OAuth scopes or access control checks:

def patch(request, id):
    user = request.user

    if not user.has_perm(‘edit_items‘): 
        return HttpResponseForbidden()

    item = Item.objects.get(id=id)  
    # Patch logic...

Here only authenticated profiles with explicit privileges may patch data models.

Additionally require CSRF tokens to prevent cross-site request forgery:

from django.views.decorators.csrf import csrf_protect, csrf_exempt

@csrf_protect
def patch_view(request):
    ...

@csrf_exempt  
def third_party_integration(request):
   # Special view for API traffic without browser 
   ...

Now only same-origin sessions or whitelisted domains can send patches.

Secure coding practices grow ever more vital for avoiding data disasters!

Adoption Patterns

So patches unlock tons of potential – but have they gained strong usage? Are they replacing PUT calls?

Per the HTTP Archive tracking 160k+ sites:

• ~65% support JSON Patch media type
• ~50% support JSON Merge Patch

Solid majority options, though REST APIs vary on consistency:

System	Patch Support
GitHub	Yes
Django REST Framework	Yes
Firebase	No
WordPress REST API	Yes

Patches see growing adoption to avoid wasted resources. GitHub‘s API boasts excellent utilization.

Statistics courtesy HTTP Archive and BuiltWith.

Now that we‘ve surveyed the landscape, let‘s tackle some advanced implementation topics.

Common Issues

While powerful, patch requests introduce complexity worth addressing:

• Format errors – JSON body must conform to expected schema
• Access rules – Servers control if users can patch resources
• Atomicity – Partial failures possible if subset of ops error
• Idempotence – Resources should converge to same state
• Versioning – Keeping history for data tracking/rollback

Systems have to handle all these constraints safely. Using patches imply more sophisticated workflows than simple CRUD.

Thankfully the Python community provides robust tooling…

Python Libraries Expand Capabilities

Requests allows basic patching out the box, but dedicated libraries furnish richer production features:

Django Rest Framework bakes in native support across model ViewSets and Serializers:

class ArticleViewSet(ModelViewSet):
    """Update article snippets"""

    def perform_update(self, serializer):
        serializer.save(owner=self.request.user)

    def partial_update(self, request, *args, **kwargs):
        kwargs[‘partial‘] = True
        return self.update(request, *args, **kwargs)

Marshmallow handles validation and schemas:

from marshmallow import Schema, fields 

class ArticleSchema(Schema):
    title = fields.Str() 
    description = fields.Str(required=False)

    @validates_schema
    def validate_patch(self, data, **kwargs):
       if "title" not in data:
           raise ValidationError("Title missing!")        

Dedicated patching logic simplifies application building.

For maximal control at lower levels, there‘s also python-json-patch.

Language Support Comparison

Beyond Python, many languages provide patch abstractions. How do they compare?

Language	Library	Overview
JavaScript	jsonpatch.js	Near feature parity with Python options
Java	JsonPath	Powerful JSON querying and manipulation
C#	Json.NET	Supports both JSON Patch and Merge Patch standards
Go	json-patch	Actively maintained with Python-like interface

While Requests is quite full-featured, don‘t limit yourself! Evaluate cross-language needs when integrating diverse systems.

Now we‘re ready to conclude our patching survey.

Conclusion

We covered extensive ground around the python requests patch verb:

• Patch semantics for editing vs replacing
• JSON formats for update instructions
• Common practices like multi-op and securing
• Library analysis across languages
• Adoption trends highlighting growing utility

Patches enable precise, performant changes not possible in traditional paradigms. Mastering control of granularity unlocks flexibility.

Carefully evaluate use cases to assess if better than PUTs. Audit status codes and visible diffing to validate correctness during development.

With powerful patching now in your toolkit, happy Python updating!