Through continuous testing, we found that python can efficiently and quickly compare the differences between two lists. It can operate with the help of the set operation provided by python set(). This method is very efficient.
In the Java language, method 1 of the following methods is more efficient and faster than method 2 (set operation). This can be regarded as a little difference between the similarities between the two languages.
The code is as follows:
#-*- coding:utf-8 -*- import time #方法1: def getDiff1(arr1,arr2): start_time = time.time() print('1_start:',start_time) arr_more1 = [] arr_more2 = [] dic_result = {} for str_1 in arr1: dic_result[str(str_1)] = 1 for str_2 in arr2: if dic_result.get(str(str_2)): dic_result[str(str_2)] = 2 else: arr_more2.append(str_2) for key,val in dic_result.items(): if val == 1: arr_more1.append(key) print('arr1比arr2多的内容为:',len(arr_more1)) print('arr2比arr1多的内容为:',len(arr_more2)) end_time = time.time() print('1_end:',end_time) print('方法1_比对用时为',end_time-start_time) #方法2:使用集合运算: def getDiff2(arr1,arr2): start_time = time.time() print('2_start:',start_time) set_1 = () set_2 = () #将列表转换为集合set() set_1 = set(arr1) set_2 = set(arr2) set_more1 = () set_more2 = () #集合运算 set_1_2 = set_1 & set_2 set_more1 = set_1 -set_1_2 set_more2 = set_2 -set_1_2 print('arr1比arr2多的内容为:',len(set_more1)) print('arr2比arr1多的内容为:',len(set_more2)) end_time = time.time() print('2_end:',end_time) print('方法2_比对用时为',end_time-start_time) #测试 # 初始化500w条数据数据 arr1 = [] arr2 = [] i = 0 while(True): arr1.append(i * 2) arr2.append(i * 3) i += 1 if i > 5000000: break print('arr1的长度为:',len(arr1)) print('arr2的长度为:',len(arr2)) print('+'*30) getDiff1(arr1,arr2) print('-'*30) getDiff2(arr1,arr2)
Running results:
Learn in the Python tutorial column!
The above is the detailed content of How to compare two lists in python. For more information, please follow other related articles on the PHP Chinese website!

Pythonarrayssupportvariousoperations:1)Slicingextractssubsets,2)Appending/Extendingaddselements,3)Insertingplaceselementsatspecificpositions,4)Removingdeleteselements,5)Sorting/Reversingchangesorder,and6)Listcomprehensionscreatenewlistsbasedonexistin

NumPyarraysareessentialforapplicationsrequiringefficientnumericalcomputationsanddatamanipulation.Theyarecrucialindatascience,machinelearning,physics,engineering,andfinanceduetotheirabilitytohandlelarge-scaledataefficiently.Forexample,infinancialanaly

Useanarray.arrayoveralistinPythonwhendealingwithhomogeneousdata,performance-criticalcode,orinterfacingwithCcode.1)HomogeneousData:Arrayssavememorywithtypedelements.2)Performance-CriticalCode:Arraysofferbetterperformancefornumericaloperations.3)Interf

No,notalllistoperationsaresupportedbyarrays,andviceversa.1)Arraysdonotsupportdynamicoperationslikeappendorinsertwithoutresizing,whichimpactsperformance.2)Listsdonotguaranteeconstanttimecomplexityfordirectaccesslikearraysdo.

ToaccesselementsinaPythonlist,useindexing,negativeindexing,slicing,oriteration.1)Indexingstartsat0.2)Negativeindexingaccessesfromtheend.3)Slicingextractsportions.4)Iterationusesforloopsorenumerate.AlwayschecklistlengthtoavoidIndexError.

ArraysinPython,especiallyviaNumPy,arecrucialinscientificcomputingfortheirefficiencyandversatility.1)Theyareusedfornumericaloperations,dataanalysis,andmachinelearning.2)NumPy'simplementationinCensuresfasteroperationsthanPythonlists.3)Arraysenablequick

You can manage different Python versions by using pyenv, venv and Anaconda. 1) Use pyenv to manage multiple Python versions: install pyenv, set global and local versions. 2) Use venv to create a virtual environment to isolate project dependencies. 3) Use Anaconda to manage Python versions in your data science project. 4) Keep the system Python for system-level tasks. Through these tools and strategies, you can effectively manage different versions of Python to ensure the smooth running of the project.

NumPyarrayshaveseveraladvantagesoverstandardPythonarrays:1)TheyaremuchfasterduetoC-basedimplementation,2)Theyaremorememory-efficient,especiallywithlargedatasets,and3)Theyofferoptimized,vectorizedfunctionsformathematicalandstatisticaloperations,making


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

MinGW - Minimalist GNU for Windows
This project is in the process of being migrated to osdn.net/projects/mingw, you can continue to follow us there. MinGW: A native Windows port of the GNU Compiler Collection (GCC), freely distributable import libraries and header files for building native Windows applications; includes extensions to the MSVC runtime to support C99 functionality. All MinGW software can run on 64-bit Windows platforms.

EditPlus Chinese cracked version
Small size, syntax highlighting, does not support code prompt function

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

Dreamweaver Mac version
Visual web development tools
