


Tuning Selenium to Bypass Slow Script Loading
Selenium's default behavior is to wait until a page fully loads before proceeding, which can become problematic when pages contain slow or unreliable scripts. To mitigate this issue, consider adjusting Selenium's page loading strategy.
The pageLoadStrategy property allows you to manipulate how Selenium handles page load events. By specifying the appropriate strategy, you can limit the time Selenium waits, block AJAX requests, and even disable script loading entirely.
Configure pageLoadStrategy for Different Browsers
Firefox:
from selenium import webdriver from selenium.webdriver.common.desired_capabilities import DesiredCapabilities caps = DesiredCapabilities().FIREFOX caps["pageLoadStrategy"] = "normal" # full page load # caps["pageLoadStrategy"] = "eager" # interactive # caps["pageLoadStrategy"] = "none" driver = webdriver.Firefox(desired_capabilities=caps, executable_path=r'C:\path\to\geckodriver.exe') driver.get("http://google.com")
Chrome:
from selenium import webdriver from selenium.webdriver.common.desired_capabilities import DesiredCapabilities caps = DesiredCapabilities().CHROME caps["pageLoadStrategy"] = "normal" # full page load # caps["pageLoadStrategy"] = "eager" # interactive # caps["pageLoadStrategy"] = "none" driver = webdriver.Chrome(desired_capabilities=caps, executable_path=r'C:\path\to\chromedriver.exe') driver.get("http://google.com")
pageLoadStrategy Options
- normal: Wait for the full page load, including scripts and AJAX requests.
- eager: Wait until the page is interactive, allowing Selenium to continue execution while scripts and AJAX still load asynchronously.
- none: Disable all script loading and AJAX requests, allowing Selenium to immediately proceed.
Note: The "eager" strategy is still under development for ChromeDriver implementations, so it may not be fully supported across all browsers.
The above is the detailed content of How to Speed Up Selenium Tests by Tuning Page Loading Strategy?. For more information, please follow other related articles on the PHP Chinese website!

Pythonlistsareimplementedasdynamicarrays,notlinkedlists.1)Theyarestoredincontiguousmemoryblocks,whichmayrequirereallocationwhenappendingitems,impactingperformance.2)Linkedlistswouldofferefficientinsertions/deletionsbutslowerindexedaccess,leadingPytho

Pythonoffersfourmainmethodstoremoveelementsfromalist:1)remove(value)removesthefirstoccurrenceofavalue,2)pop(index)removesandreturnsanelementataspecifiedindex,3)delstatementremoveselementsbyindexorslice,and4)clear()removesallitemsfromthelist.Eachmetho

Toresolvea"Permissiondenied"errorwhenrunningascript,followthesesteps:1)Checkandadjustthescript'spermissionsusingchmod xmyscript.shtomakeitexecutable.2)Ensurethescriptislocatedinadirectorywhereyouhavewritepermissions,suchasyourhomedirectory.

ArraysarecrucialinPythonimageprocessingastheyenableefficientmanipulationandanalysisofimagedata.1)ImagesareconvertedtoNumPyarrays,withgrayscaleimagesas2Darraysandcolorimagesas3Darrays.2)Arraysallowforvectorizedoperations,enablingfastadjustmentslikebri

Arraysaresignificantlyfasterthanlistsforoperationsbenefitingfromdirectmemoryaccessandfixed-sizestructures.1)Accessingelements:Arraysprovideconstant-timeaccessduetocontiguousmemorystorage.2)Iteration:Arraysleveragecachelocalityforfasteriteration.3)Mem

Arraysarebetterforelement-wiseoperationsduetofasteraccessandoptimizedimplementations.1)Arrayshavecontiguousmemoryfordirectaccess,enhancingperformance.2)Listsareflexiblebutslowerduetopotentialdynamicresizing.3)Forlargedatasets,arrays,especiallywithlib

Mathematical operations of the entire array in NumPy can be efficiently implemented through vectorized operations. 1) Use simple operators such as addition (arr 2) to perform operations on arrays. 2) NumPy uses the underlying C language library, which improves the computing speed. 3) You can perform complex operations such as multiplication, division, and exponents. 4) Pay attention to broadcast operations to ensure that the array shape is compatible. 5) Using NumPy functions such as np.sum() can significantly improve performance.

In Python, there are two main methods for inserting elements into a list: 1) Using the insert(index, value) method, you can insert elements at the specified index, but inserting at the beginning of a large list is inefficient; 2) Using the append(value) method, add elements at the end of the list, which is highly efficient. For large lists, it is recommended to use append() or consider using deque or NumPy arrays to optimize performance.


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

VSCode Windows 64-bit Download
A free and powerful IDE editor launched by Microsoft

Atom editor mac version download
The most popular open source editor

Dreamweaver Mac version
Visual web development tools

ZendStudio 13.5.1 Mac
Powerful PHP integrated development environment

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