为什么使用 requests 模块?
在深入研究细节之前,重要的是要了解为什么 Requests 模块比 urllib 等替代方案更受欢迎:
- 单纯:Requests 模块具有更直接的 API,需要更少的代码行来有效执行 HTTP 请求。
- 会话功能:它支持请求之间的持久会话,这对于涉及同一服务器多个事务的任务至关重要。
- 表单处理:通过其直观的方法,提交表单数据变得简单。
- JSON 响应处理:该库允许轻松检索和管理 JSON 数据,JSON 数据是 Web 数据交换的常用格式。
- 异常处理:它会自动为错误状态代码引发异常,从而促进更好的错误管理和更顺畅的调试过程。
安装request模块
如果你还没有安装 requests 模块,你可以使用 pip 轻松完成:
pip install requests
基本用法
要开始使用 requests 模块,您需要导入它,然后您可以使用其函数发送各种类型的 HTTP 请求。
import requests
发送 GET 请求
以下是发送简单的 GET 请求以从服务器获取数据的方法:
import requests
# The URL to which the GET request will be sent.
url = 'https://api.example.com/data'
# Sending a GET request to the specified URL.
# The 'requests.get' function makes an HTTP GET request to the provided URL and stores the response in the 'response' variable.
response = requests.get(url)
# Printing the text content of the response.
# 'response.text' contains the body of the response from the server, typically in string format.
# This is useful for checking what data the server returned in response to the GET request.
print(response.text)
检查响应
发送请求后,检查响应至关重要:
# Check the HTTP status code of the response.
if response.status_code == 200:
# If the status code is 200, it indicates that the request was successful.
print('The request was successful!')
else:
# If the status code is not 200, the request failed.
# Print the status code to help diagnose the issue.
print('The request failed with status code:', response.status_code)
GET 请求中的查询参数
如果需要使用 GET 请求发送查询参数:
# A dictionary containing query parameters for an HTTP GET request.
# These parameters will be appended to the URL as query strings.
params = {
'key1': 'value1',
'key2': 'value2'
}
# Sending a GET request to the specified URL. The 'params' dictionary is converted into query parameters.
# The Requests library automatically encodes these parameters and appends them to the URL.
response = requests.get(url, params=params)
# Printing the final URL after the query parameters have been appended.
# This is useful for debugging to verify that the URL has been constructed correctly.
print(response.url) # This will show the URL with the appended query parameters.
POST 请求
向服务器发送数据通常是使用 POST 请求完成的。以下是对请求执行此操作的方法:
import requests
# A dictionary containing the login credentials, typically a username and password.
data = {
'username': 'john',
'password': 'secret'
}
# Sending a POST request to the login endpoint of the API.
# The 'data' dictionary is passed as form-encoded data to the server.
response = requests.post('https://api.example.com/login', data=data)
# Printing the text of the response from the server, which might include details like login status or tokens.
print(response.text)
# A dictionary representing the data of a new article, including its title, content and associated tags.
json_data = {
'title': 'New Article',
'content': 'This is a new article',
'tags': ['python', 'requests']
}
# Sending a POST request to create a new article on the API.
# The 'json_data' dictionary is passed as JSON. The Requests library automatically sets the appropriate headers.
response = requests.post('https://api.example.com/articles', json=json_data)
# Printing the text of the response from the server, which could be details of the newly created article or an error message.
print(response.text)
处理响应内容
了解如何处理不同类型的响应内容至关重要:
# Printing the text content of the response.
# 'response.text' contains the raw string response received from the server.
print(response.text)
# Parsing the JSON response.
# 'response.json()' converts the JSON formatted string in the response to a Python dictionary.
# This method is convenient for handling JSON data, which is common in REST APIs.
data = response.json()
# Printing the converted Python dictionary.
# This displays the JSON data structured as a dictionary, making it easier to access and manipulate specific data fields.
print(data)
结论
Python 的 Requests 库简化了 HTTP 请求,使开发人员能够更轻松地高效处理网络通信。其简单的 API 支持各种任务,从会话管理到 JSON 数据处理,为经常与 Web API 交互的数据工程师和开发人员展示了它的实用性。
本指南仅触及了 Requests 库的皮毛。除了基础知识之外,该库还支持高级功能,例如流式上传、用于跨请求保留设置的会话对象,以及用于对请求处理进行精细控制的自定义适配器实现。
了解和使用请求库可以增强数据管道和 Web 服务交互,确保它们健壮且易于管理。提供的示例突出了它的多功能性和易用性,巩固了它作为任何 Python 开发人员工具包中必不可少的工具的作用。