


Retrieving Redirected URL using Python Requests Library
In the realm of web scraping and automation, it often becomes necessary to track the redirects that occur when accessing a web page. The Python Requests library provides comprehensive functionality for managing HTTP requests, including the ability to navigate redirects. This article aims to clarify how to retrieve the redirected URL using the Requests library, addressing a query raised in the community.
Problem:
Within a script, the allow_redirects=True flag is set to automatically follow redirects. However, the user wishes to determine the final URL to which the request is redirected. The question arises: how can one obtain this information programmatically?
Solution:
The solution lies in utilizing the response.history attribute provided by Requests. This attribute stores a list of responses that encompass the entire journey of redirects leading to the final destination. To access the final URL, the response.url property can be employed.
Here's a code snippet that demonstrates the process:
response = requests.get(someurl) if response.history: print("Request was redirected") for resp in response.history: print(resp.status_code, resp.url) print("Final destination:") print(response.status_code, response.url) else: print("Request was not redirected")
Demo:
Consider the following example where a request is made to a website configured to perform multiple redirects:
import requests response = requests.get('http://httpbin.org/redirect/3') for resp in response.history: print(resp.status_code, resp.url) print(response.status_code, response.url)
Output:
302 http://httpbin.org/redirect/3 302 http://httpbin.org/redirect/2 302 http://httpbin.org/redirect/1 200 http://httpbin.org/get
As evident from the output, the code successfully logs the status codes and URLs of each redirect, as well as the final destination.
The above is the detailed content of How to retrieve redirected URLs using the Python Requests library?. For more information, please follow other related articles on the PHP Chinese website!

ToappendelementstoaPythonlist,usetheappend()methodforsingleelements,extend()formultipleelements,andinsert()forspecificpositions.1)Useappend()foraddingoneelementattheend.2)Useextend()toaddmultipleelementsefficiently.3)Useinsert()toaddanelementataspeci

TocreateaPythonlist,usesquarebrackets[]andseparateitemswithcommas.1)Listsaredynamicandcanholdmixeddatatypes.2)Useappend(),remove(),andslicingformanipulation.3)Listcomprehensionsareefficientforcreatinglists.4)Becautiouswithlistreferences;usecopy()orsl

In the fields of finance, scientific research, medical care and AI, it is crucial to efficiently store and process numerical data. 1) In finance, using memory mapped files and NumPy libraries can significantly improve data processing speed. 2) In the field of scientific research, HDF5 files are optimized for data storage and retrieval. 3) In medical care, database optimization technologies such as indexing and partitioning improve data query performance. 4) In AI, data sharding and distributed training accelerate model training. System performance and scalability can be significantly improved by choosing the right tools and technologies and weighing trade-offs between storage and processing speeds.

Pythonarraysarecreatedusingthearraymodule,notbuilt-inlikelists.1)Importthearraymodule.2)Specifythetypecode,e.g.,'i'forintegers.3)Initializewithvalues.Arraysofferbettermemoryefficiencyforhomogeneousdatabutlessflexibilitythanlists.

In addition to the shebang line, there are many ways to specify a Python interpreter: 1. Use python commands directly from the command line; 2. Use batch files or shell scripts; 3. Use build tools such as Make or CMake; 4. Use task runners such as Invoke. Each method has its advantages and disadvantages, and it is important to choose the method that suits the needs of the project.

ForhandlinglargedatasetsinPython,useNumPyarraysforbetterperformance.1)NumPyarraysarememory-efficientandfasterfornumericaloperations.2)Avoidunnecessarytypeconversions.3)Leveragevectorizationforreducedtimecomplexity.4)Managememoryusagewithefficientdata

InPython,listsusedynamicmemoryallocationwithover-allocation,whileNumPyarraysallocatefixedmemory.1)Listsallocatemorememorythanneededinitially,resizingwhennecessary.2)NumPyarraysallocateexactmemoryforelements,offeringpredictableusagebutlessflexibility.

InPython, YouCansSpectHedatatYPeyFeLeMeReModelerErnSpAnT.1) UsenPyNeRnRump.1) UsenPyNeRp.DLOATP.PLOATM64, Formor PrecisconTrolatatypes.


Hot AI Tools

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

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

Undress AI Tool
Undress images for free

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

DVWA
Damn Vulnerable Web App (DVWA) is a PHP/MySQL web application that is very vulnerable. Its main goals are to be an aid for security professionals to test their skills and tools in a legal environment, to help web developers better understand the process of securing web applications, and to help teachers/students teach/learn in a classroom environment Web application security. The goal of DVWA is to practice some of the most common web vulnerabilities through a simple and straightforward interface, with varying degrees of difficulty. Please note that this software

SAP NetWeaver Server Adapter for Eclipse
Integrate Eclipse with SAP NetWeaver application server.

Dreamweaver Mac version
Visual web development tools

Atom editor mac version download
The most popular open source editor

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