An Introduction to REST API with Python

A REST API (Representational State Transfer Application Programming Interface) is a web service that uses HTTP requests to allow applications to communicate over the internet. REST APIs are stateless: each request from a client contains all the information needed to process it, with no session data stored on the server. Python is one of the most popular languages for both consuming and building REST APIs, thanks to libraries like requests for calling APIs and frameworks like Flask and FastAPI for building them.

What you'll learn in this guide:

• What REST APIs are and the 6 architectural constraints that define them
• How to call external REST APIs from Python using the "requests" library
• How to build a REST API in Python using Flask, FastAPI, or Django REST Framework
• What HTTP methods (GET, POST, PUT, PATCH, DELETE) and status codes mean
• How to choose the right Python framework for your REST API project

What Is an API?

An API (Application Programming Interface) is a set of rules and protocols for building and interacting with software applications. It acts as a bridge between different software programs, enabling them to communicate with each other.

A REST API is the most widely adopted style of API for web services today. It uses standard HTTP methods, returns data in lightweight formats like JSON, and works across any platform or programming language.

REST Architecture: The 6 Core Constraints

REST is not a protocol or a standard; it is an architectural style defined by six constraints. A web service that satisfies these constraints is considered "RESTful."

• Stateless: Each request from a client must contain all the information needed to process it. The server stores no session state between requests.
• Client-server: The client and server are separate concerns. The client handles the user interface; the server handles data storage and business logic.
• Cacheable: Responses must define whether they can be cached. Caching reduces server load and improves performance.
• Uniform interface: All REST APIs use a consistent interface, standard HTTP methods, resource-based URLs, and self-descriptive messages.
• Layered system: A client cannot tell whether it is connected directly to the server or to an intermediary (such as a load balancer or cache).
• Code on demand (optional): Servers can optionally send executable code (such as JavaScript) to clients.

For a deeper look at how these principles shape API design, see REST API design principles and REST API design standards.

Why Use a REST API?

Why REST APIs are the standard for web services comes down to a few practical advantages:

• URL-addressable resources: Every resource has a predictable URL, making requests easy to construct and remember.
• Platform independence: REST APIs work over standard HTTP (Hypertext Transfer Protocol), so any client on any platform can consume them.
• Scalability: Because requests are stateless, REST APIs scale horizontally without session management overhead.
• Speed: No waiting for a full data payload before making additional calls. Multiple requests can run in parallel, and responses arrive as soon as each is ready.

One practical example: transferring learner data from a Learning Management System (LMS) to Salesforce via a REST API, tracking course completions and surfacing learner demographics directly inside your CRM.

What Is Python?

Python is an open-source, object-oriented programming language first released in 1991. It has grown into one of the most widely used languages in the world, particularly for data engineering, automation, and web development.

Python's appeal for REST API work comes from its excellent documentation, active community, and a rich ecosystem of libraries that handle HTTP, serialization, and web framework concerns out of the box.

Why Python Is a Good Language for REST API Development

Python's syntax is clear and concise, reducing development time and making it accessible for both beginners and experienced engineers. Its extensive libraries, including Flask, FastAPI, and Django REST Framework, handle the low-level HTTP plumbing so you can focus on business logic. Python also has strong built-in support for serializing and deserializing JSON and XML, the two most common data formats in REST APIs.

Python is a strong choice for Python ETL tools and data pipeline work for the same reasons: readable code, broad library support, and easy integration with cloud services and databases.

HTTP Methods: GET, POST, PUT, PATCH, and DELETE

Every REST API call uses one of five standard HTTP methods. Each method signals a different intent to the server.

GET retrieves an existing resource without modifying it. POST creates a new resource. PUT replaces an existing resource entirely with new data. PATCH is a fifth method that partially updates a resource, changing only the fields included in the request. DELETE removes a resource.

HTTP Status Codes Reference

Every REST API response includes an HTTP status code. Status codes tell the client whether the request succeeded, failed, or requires further action.

Status codes fall into five ranges: 1xx (informational), 2xx (success), 3xx (redirection), 4xx (client error), and 5xx (server error). In practice, most REST APIs work primarily with 2xx and 4xx codes.

Consuming REST APIs in Python with the "requests" Library

The requests library is Python's standard tool for sending HTTP requests to REST APIs. This covers the most common use case for "python rest api": calling an external API from your Python code, not building one.

Install requests via pip:

pip install requests

The examples below use JSONPlaceholder, a free public test API that requires no authentication.

GET Request

Retrieve a list of posts:

import requests

response = requests.get("https://jsonplaceholder.typicode.com/posts")
print(response.status_code) # 200
posts = response.json # Parse JSON response body
print(posts[0]) # {'userId': 1, 'id': 1, 'title': '...', 'body': '...'}

POST Request

Create a new resource by sending JSON in the request body:

import requests

new_post = {
 "title": "Python REST API Guide",
 "body": "A practical introduction to REST APIs in Python.",
 "userId": 1
}

response = requests.post(
 "https://jsonplaceholder.typicode.com/posts",
 json=new_post
)
print(response.status_code) # 201
print(response.json) # {'id': 101, 'title': '...', ...}

PUT Request

Replace an existing resource entirely:

import requests

updated_post = {
 "id": 1,
 "title": "Updated Title",
 "body": "Updated body content.",
 "userId": 1
}

response = requests.put(
 "https://jsonplaceholder.typicode.com/posts/1",
 json=updated_post
)
print(response.status_code) # 200

PATCH Request

Update only specific fields of an existing resource:

import requests

response = requests.patch(
 "https://jsonplaceholder.typicode.com/posts/1",
 json={"title": "Patched Title Only"}
)
print(response.status_code) # 200
print(response.json)

DELETE Request

Remove a resource:

import requests

response = requests.delete("https://jsonplaceholder.typicode.com/posts/1")
print(response.status_code) # 200

Reading Response Metadata

Beyond the response body, requests gives you access to headers and the status code:

response = requests.get("https://jsonplaceholder.typicode.com/posts/1")
print(response.status_code) # 200
print(response.headers["Content-Type"]) # application/json; charset=utf-8
print(response.json)

Key takeaway: For most Python developers, the requests library covers 90% of REST API consumption needs. Install it once, and the same pattern (method, URL, optional json= body, .status_code, .json) applies to every API you call.

Python REST API Frameworks: Flask vs FastAPI vs Django REST Framework

Choosing the right framework shapes how fast you can build, how your API performs, and how easy it is to maintain. As of 2026, three frameworks dominate Python REST API development.

Flask

Flask is a lightweight micro-framework. It gives you routing and request handling with minimal boilerplate, and you add only what you need. Flask is the right choice when you want full control over your application structure or are building a small, focused API.

FastAPI

FastAPI is built on Starlette and uses Python type hints for automatic request validation via Pydantic. It generates OpenAPI documentation automatically and supports async endpoints natively. As of 2026, FastAPI has become the most-adopted framework for new Python REST API projects where performance and developer experience are priorities.

Django REST Framework

Django REST Framework (DRF) is the right choice when you already have a Django project and need to expose an API layer. It provides serializers, viewsets, and a browsable API interface out of the box. The learning curve is steeper than Flask or FastAPI, but it integrates tightly with Django's ORM and authentication system.

Which should you choose? For a new standalone API, start with FastAPI. For a small prototype or a project where you want minimal dependencies, use Flask. If you are already running Django, use Django REST Framework.

For a broader view of tooling, see API management tools and REST API best practices for data integration.

Building a REST API with Python and Flask: Step-by-Step

This section walks through how to build a REST API using Flask, from installation to a working endpoint you can test with curl.

Step 1: Install Flask

pip install flask

Step 2: Create app.py

Create a new file called app.py in your project directory.

Step 3: Define a data model

For this example, use an in-memory list of books. In a production app, this would be a database.

from flask import Flask, jsonify, request, abort

app = Flask(__name__)

books = [
 {"id": 1, "title": "Python Essentials", "author": "Jane Doe"},
 {"id": 2, "title": "REST API Design", "author": "John Smith"}
]

Step 4: Add a GET endpoint

@app.route("/books", methods=["GET"])
def get_books:
 return jsonify({"books": books})

Step 5: Add a POST endpoint with basic validation

@app.route("/books", methods=["POST"])
def add_book:
 if not request.json or "title" not in request.json:
 abort(400)
 new_book = {
 "id": books[-1]["id"] + 1 if books else 1,
 "title": request.json["title"],
 "author": request.json.get("author", "Unknown")
 }
 books.append(new_book)
 return jsonify(new_book), 201

Step 6: Add error handling

@app.errorhandler(404)
def not_found(error):
 return jsonify({"error": "Resource not found"}), 404

@app.errorhandler(400)
def bad_request(error):
 return jsonify({"error": "Bad request"}), 400

Step 7: Run the server

if __name__ == "__main__":
 app.run(debug=True)

Run with:

flask run

Step 8: Test with curl

# GET all books
curl http://127.0.0.1:5000/books

# POST a new book
curl -X POST http://127.0.0.1:5000/books \
 -H "Content-Type: application/json" \
 -d '{"title": "Flask in Action", "author": "Alice Brown"}'

Common mistakes to avoid

• Forgetting Content-Type: application/json in POST requests: Flask will not parse the body correctly without this header, and request.json will return None.
• Using debug=True in production: Debug mode exposes an interactive debugger. Always set debug=False or use an environment variable in production deployments.
• No error handling on missing fields: If a required field is absent and you try to access it directly, Flask raises an unhandled exception. Use .get with a default or check for the key explicitly before accessing it.
• Mutable default arguments in route functions: Avoid using mutable objects as default parameter values in route handlers; this is a common Python gotcha that causes unexpected state sharing between requests.

Key takeaway: A working Flask REST API requires fewer than 30 lines of code. Add validation, error handlers, and a proper data layer as your project grows.

Benefits of Using REST APIs

• Faster development time: REST APIs build on existing HTTP protocols and JSON formats, reducing the amount of custom infrastructure you need to write.
• Platform independence: Any client on any platform can consume a REST API, from mobile apps to desktop programs to other web services. This also makes it straightforward to reuse API logic across projects.
• Scalability: Stateless design means you can add server capacity without managing shared session state. REST APIs built on TCP/IP work across any network topology.
• Speed: Because requests are independent, multiple calls can run in parallel. There is no need to wait for a full payload before making additional requests, which reduces latency and processing overhead.

What Is a REST API Call?

A REST API call is a single HTTP request from a client to a server. Every call includes a method (GET, POST, PUT, PATCH, or DELETE), a URL that identifies the resource, optional headers, and an optional request body.

The three components that make up a REST API call:

• URL-addressable resources: All URLs follow a consistent pattern identifying the resource type and path (for example, GET /books/1).
• Uniform Resource Identifiers (URI): URIs identify specific endpoints and the actions available on them.
• HTTP (Hypertext Transfer Protocol): The transport layer that carries requests between client and server.

When making a call, each request must use one of the five HTTP methods described above. The server processes the request and returns a response with a status code and, in most cases, a JSON body.

For a practical guide to the Universal REST API connector and how to pull data from any REST endpoint into a pipeline, see the linked resource.

How to Make a Simple REST API with Python

A how to make a REST API project in Python follows this pattern regardless of the framework you choose:

REST APIs use web services to communicate between client applications and server-side software. The core structure involves:

• URL-addressable resources: Each resource type has its own URL pattern (for example, /books, /users, /orders).
• HTTP methods: The method signals what action to perform on the resource.
• Response format: Most modern REST APIs return JSON.

The Flask step-by-step walkthrough above covers the full implementation. For a FastAPI equivalent, the structure is similar but uses Python type hints for automatic validation:

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI

class Book(BaseModel):
 title: str
 author: str

books = []

@app.get("/books")
def get_books:
 return {"books": books}

@app.post("/books", status_code=201)
def add_book(book: Book):
 books.append(book.dict)
 return book

FastAPI automatically generates interactive API documentation at /docs (Swagger UI) and /redoc when you run the server.

How Integrate.io Can Help You Connect REST APIs to Your Data Stack

Integrate.io's Universal REST API connector lets you connect to any RESTful API and pull data directly into your pipelines without writing custom ingestion code. A common use case: pulling order data from a REST API, transforming it, and loading it into Snowflake or pushing enriched records back to Salesforce via reverse ETL, all in a single low-code pipeline.

Beyond consuming APIs, Integrate.io's API generation platform lets you instantly generate secure REST APIs for over 20 native database connectors, including Snowflake, BigQuery, and Redshift, in under five minutes. No custom server code required.

If you are building data pipelines that interact with REST APIs, schedule a demo to see how Integrate.io handles the connection layer so your team can focus on the data itself.

FAQ

What is a REST API in Python?

A REST API in Python is a web service built using Python that follows REST (Representational State Transfer) architectural principles. It uses HTTP methods like GET, POST, PUT, PATCH, and DELETE to allow client applications to create, read, update, and delete resources. Python frameworks like Flask, FastAPI, and Django REST Framework make it straightforward to build REST APIs without writing low-level HTTP handling code.

How do I call a REST API from Python?

Use the requests library. Install it with pip install requests, then call requests.get(url) to retrieve data or requests.post(url, json=data) to send data. The response object includes .json to parse the response body and .status_code to check the result.

What is the difference between GET, POST, PUT, PATCH, and DELETE in a REST API?

GET retrieves an existing resource without modifying it. POST creates a new resource. PUT replaces an existing resource entirely with new data. PATCH partially updates a resource, changing only the fields included in the request. DELETE removes a resource.

Which Python framework is best for building a REST API?

Flask is best for small APIs and developers who want full control over structure. FastAPI is best for high-performance APIs and teams using modern Python features like async and type hints. Django REST Framework is best when you already have a Django project and need to add an API layer quickly.

What is the difference between REST and SOAP APIs?

REST APIs use standard HTTP methods and typically exchange data in JSON format, making them lightweight and easy to consume from any platform. SOAP APIs use XML and a strict message format, which adds overhead but provides built-in error handling and security standards. REST is the dominant choice for modern web and mobile applications.

Tags: big data, python, Rest API