This article demonstrates using Python's requests library to make HTTP requests. It covers GET, POST, PUT, DELETE, and other methods, explaining how to handle status codes and send data (including JSON and files). Error handling using response.rai
How to Use Requests to Make HTTP Requests in Python?
The requests
library in Python simplifies making HTTP requests. It provides a clean, intuitive API that abstracts away much of the complexity involved in handling HTTP connections, headers, and responses. To use it, you first need to install it. You can do this using pip:
pip install requests
Once installed, you can start making requests. The most common function is requests.get()
, used for retrieving data from a URL. Here's a basic example:
import requests response = requests.get("https://www.example.com") # Check the status code print(response.status_code) # Access the content print(response.text)
This code fetches the HTML content of example.com
. The response
object contains various attributes, including status_code
(HTTP status code like 200 OK) and text
(the response body). Other useful attributes include headers
(response headers), json()
(for parsing JSON responses), and content
(raw response bytes). Error handling is crucial; we'll cover that in a later section. For other HTTP methods (like POST, PUT, DELETE), you use corresponding functions like requests.post()
, requests.put()
, and requests.delete()
.
What are the common HTTP methods supported by the Requests library in Python?
The requests
library supports all the common HTTP methods, including:
- GET: Retrieves data from a specified resource. This is the most frequently used method.
- POST: Submits data to be processed to the specified resource. Often used to create new resources.
- PUT: Replaces all current representations of the target resource with the uploaded content.
- PATCH: Applies partial modifications to a resource.
- DELETE: Deletes the specified resource.
- HEAD: Similar to GET, but only retrieves the headers, not the body.
- OPTIONS: Describes the communication options for the target resource.
Each method is represented by a corresponding function in the requests
library (e.g., requests.get()
, requests.post()
, etc.). The specific usage might vary depending on the method and the API you're interacting with, but the basic structure remains similar. For instance, requests.post()
requires specifying the data to be sent in the request body.
How can I handle different HTTP status codes using the Requests library?
HTTP status codes indicate the outcome of an HTTP request. The requests
library makes it easy to check and handle these codes. The response.status_code
attribute provides the status code (e.g., 200 for success, 404 for Not Found, 500 for Internal Server Error). You should always check the status code to ensure the request was successful. Here's an example:
import requests try: response = requests.get("https://www.example.com") response.raise_for_status() # Raises an exception for bad status codes (4xx or 5xx) print("Request successful!") print(response.text) except requests.exceptions.RequestException as e: print(f"An error occurred: {e}")
response.raise_for_status()
is a convenient method that automatically raises an exception if the status code indicates an error (4xx or 5xx client/server errors). This simplifies error handling. You can also manually check the status code and handle different cases using if
statements:
if response.status_code == 200: print("Success!") elif response.status_code == 404: print("Not Found") elif response.status_code == 500: print("Server Error") else: print(f"Unknown status code: {response.status_code}")
How do I send POST requests with data using the Requests library in Python?
Sending POST requests with data involves using the requests.post()
function and specifying the data to be sent in the request body. The data can be in various formats, such as dictionaries, lists, or files.
Here's how to send a POST request with data as a dictionary:
import requests data = {'key1': 'value1', 'key2': 'value2'} response = requests.post("https://httpbin.org/post", data=data) # httpbin.org is a useful testing site print(response.status_code) print(response.json()) # httpbin.org returns the POST data as JSON
This example sends a POST request to httpbin.org/post
with the provided dictionary as the request body. httpbin.org
is a useful service for testing HTTP requests. For sending JSON data, use the json
parameter:
import requests import json data = {'key1': 'value1', 'key2': 'value2'} response = requests.post("https://httpbin.org/post", json=data) print(response.status_code) print(response.json())
Remember to handle potential errors using try...except
blocks and response.raise_for_status()
as shown in the previous section. For sending files, use the files
parameter with a dictionary mapping filenames to file objects. The requests
library offers great flexibility in handling different data types for POST requests.
The above is the detailed content of How to Use Requests to Make HTTP Requests in Python?. For more information, please follow other related articles on the PHP Chinese website!

Hot AI Tools

Undress AI Tool
Undress images for free

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Clothoff.io
AI clothes remover

Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Article

Hot Tools

Notepad++7.3.1
Easy-to-use and free code editor

SublimeText3 Chinese version
Chinese version, very easy to use

Zend Studio 13.0.1
Powerful PHP integrated development environment

Dreamweaver CS6
Visual web development tools

SublimeText3 Mac version
God-level code editing software (SublimeText3)

The key to dealing with API authentication is to understand and use the authentication method correctly. 1. APIKey is the simplest authentication method, usually placed in the request header or URL parameters; 2. BasicAuth uses username and password for Base64 encoding transmission, which is suitable for internal systems; 3. OAuth2 needs to obtain the token first through client_id and client_secret, and then bring the BearerToken in the request header; 4. In order to deal with the token expiration, the token management class can be encapsulated and automatically refreshed the token; in short, selecting the appropriate method according to the document and safely storing the key information is the key.

Assert is an assertion tool used in Python for debugging, and throws an AssertionError when the condition is not met. Its syntax is assert condition plus optional error information, which is suitable for internal logic verification such as parameter checking, status confirmation, etc., but cannot be used for security or user input checking, and should be used in conjunction with clear prompt information. It is only available for auxiliary debugging in the development stage rather than substituting exception handling.

InPython,iteratorsareobjectsthatallowloopingthroughcollectionsbyimplementing__iter__()and__next__().1)Iteratorsworkviatheiteratorprotocol,using__iter__()toreturntheiteratorand__next__()toretrievethenextitemuntilStopIterationisraised.2)Aniterable(like

TypehintsinPythonsolvetheproblemofambiguityandpotentialbugsindynamicallytypedcodebyallowingdeveloperstospecifyexpectedtypes.Theyenhancereadability,enableearlybugdetection,andimprovetoolingsupport.Typehintsareaddedusingacolon(:)forvariablesandparamete

A common method to traverse two lists simultaneously in Python is to use the zip() function, which will pair multiple lists in order and be the shortest; if the list length is inconsistent, you can use itertools.zip_longest() to be the longest and fill in the missing values; combined with enumerate(), you can get the index at the same time. 1.zip() is concise and practical, suitable for paired data iteration; 2.zip_longest() can fill in the default value when dealing with inconsistent lengths; 3.enumerate(zip()) can obtain indexes during traversal, meeting the needs of a variety of complex scenarios.

To create modern and efficient APIs using Python, FastAPI is recommended; it is based on standard Python type prompts and can automatically generate documents, with excellent performance. After installing FastAPI and ASGI server uvicorn, you can write interface code. By defining routes, writing processing functions, and returning data, APIs can be quickly built. FastAPI supports a variety of HTTP methods and provides automatically generated SwaggerUI and ReDoc documentation systems. URL parameters can be captured through path definition, while query parameters can be implemented by setting default values ??for function parameters. The rational use of Pydantic models can help improve development efficiency and accuracy.

To test the API, you need to use Python's Requests library. The steps are to install the library, send requests, verify responses, set timeouts and retry. First, install the library through pipinstallrequests; then use requests.get() or requests.post() and other methods to send GET or POST requests; then check response.status_code and response.json() to ensure that the return result is in compliance with expectations; finally, add timeout parameters to set the timeout time, and combine the retrying library to achieve automatic retry to enhance stability.

A virtual environment can isolate the dependencies of different projects. Created using Python's own venv module, the command is python-mvenvenv; activation method: Windows uses env\Scripts\activate, macOS/Linux uses sourceenv/bin/activate; installation package uses pipinstall, use pipfreeze>requirements.txt to generate requirements files, and use pipinstall-rrequirements.txt to restore the environment; precautions include not submitting to Git, reactivate each time the new terminal is opened, and automatic identification and switching can be used by IDE.
