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- Understanding Lists in Python
- Detailed explanation of Python lists In Python, lists are a widely used and commonly used data structure. They are ordered sequences capable of storing elements of different data types, including integers, floating point numbers, strings, and even other lists. This flexibility, coupled with its intuitive structure, makes it a cornerstone of Python programming. Key attribute definitions and syntax for lists Lists are defined using square brackets [] and elements are separated by commas. Example: my_list=[1,2,3,4,5] A heterogeneous element list can store elements of different data types. Example: mixed_list=[42,"hello",3.14,True] Indexing and slicing lists support indexing (accessing specific elements
- Python Tutorial 817 2025-01-13 22:46:44
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- Getting Started with Python: Installing Python and Writing Your First Program (Day f Days of Python)
- Lasttime,wediscussedtheprogrammer'srole:craftingstep-by-stepinstructions(aprogram)forcomputers.Today,we'llexploresoftwaretypesandbuildourfirstPythonprogram.SoftwareandPrograms:AClarificationBeforedelvingintosoftwarecategories,let'sdefinesoftware.Esse
- Python Tutorial 443 2025-01-13 22:37:44
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- Data Engineering Foundations: A Hands-On Guide
- ApracticalguidetobuildingadataengineeringETLpipeline.Thisguideprovidesahands-onapproachtounderstandingandimplementingdataengineeringfundamentals,coveringstorage,processing,automation,andmonitoring.WhatisDataEngineering?Dataengineeringfocusesonorganiz
- Python Tutorial 457 2025-01-13 22:33:44
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- How do I measure the execution time of Celery tasks?
- A new member has been added to the collection of duplicate code: tracking the execution time of Celery tasks. Each Celery task actually has two different "execution" times: Actual execution time: the time the code runs. "Completion time": Includes time spent waiting in the queue for available worker processes. Both are important because our ultimate goal is to know when the task is complete. After triggering a task, we need to know when the task is completed and when the results can be expected. It's like project estimating. What managers really want to know is when the project will be completed, not that it will be completed in a week but no one will have time to do it in the next six months. Using Celery signals We can use Celery signals to time tasks. Tip 1: Celery Letter
- Python Tutorial 946 2025-01-13 22:28:44
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- A Developer&#s Guide to Odoo CRM Customization
- OdooCRM:ADeveloper'sPlaygroundforBespokeBusinessSolutionsOdooCRMtranscendsasimplecustomerrelationshipmanagementtool;it'sahighlyadaptableplatformdesignedtomeetdiversebusinessneeds.Fordevelopers,itsopen-sourcenatureandextensivecustomizationoptionsunloc
- Python Tutorial 639 2025-01-13 20:49:45
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- Scraping Infinite Scroll Pages with a &#Load More&# Button: A Step-by-Step Guide
- Areyourscrapersstuckwhentryingtoloaddatafromdynamicwebpages?Areyoufrustratedwithinfinitescrollsorthosepesky"Loadmore"buttons?You'renotalone.Manywebsitestodayimplementthesedesignstoimproveuserexperience—buttheycanbechallengingforwebscrapers.
- Python Tutorial 851 2025-01-13 18:09:43
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- Intelligent PDF Data Extraction and database creation
- ProjectGoal:Developasystemforextractingstructuredandunstructureddatafromvendor-suppliedPDFs,storingitinadatabaseforefficientsearchandretrieval,andintegratingachatbotfornaturallanguagequeryingoftheextractedinformation.ProjectScope:Input:Diverselystruc
- Python Tutorial 1013 2025-01-13 16:20:47
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- Climbing a depth-first search hill, Advent of Code day 10
- Today'schallengetacklesDay10'spuzzle,a2DgridsimilartoDay6,butrequiringexplorationofmultiplepaths.Thispuzzleelegantlyshowcasesthepowerofdepth-firstsearch(DFS).AnAI-generatedillustrationofthepuzzleThemapisrepresentedasadictionary;keysare(x,y)coordinate
- Python Tutorial 392 2025-01-13 14:09:43
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- dvanced Python Techniques for Efficient Text Processing and Analysis
- Asaprolificauthor,IinviteyoutoexploremybooksonAmazon.RemembertofollowmeonMediumforcontinuedsupportandupdates.Thankyouforyourinvaluablebacking!YearsofPythondevelopmentfocusedontextprocessingandanalysishavetaughtmetheimportanceofefficienttechniques.Thi
- Python Tutorial 177 2025-01-13 11:48:43
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- Using proxy IP for data cleaning and preprocessing
- Bigdatanecessitatesrobustdatacleaningandpreprocessing.Toensuredataaccuracyandefficiency,datascientistsemployvarioustechniques.UsingproxyIPssignificantlyenhancesdataacquisitionefficiencyandsecurity.ThisarticledetailshowproxyIPsaiddatacleaningandprepro
- Python Tutorial 297 2025-01-13 11:05:41
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- Building a Real-Time Weather Data Collection System with Python and AWS
- HarnessingthePowerofWeatherData:APython-BasedCollectionSystemwithAWSS3Intoday'sdata-drivenworld,real-timeweatherinformationisacriticalassetforbusinessesandindividualsalike.Fromoptimizinglogisticsandagriculturalpracticestoenhancingtravelplanning,acces
- Python Tutorial 687 2025-01-13 08:51:43
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- CREATING A ROCK, PAPER, & SCISSORS GAME IN PYTHON
- BuildaClassicRock,Paper,ScissorsGameinPythonPython'sversatilitymakesitperfectforcreatingsimpleyetengaginggames.Thistutorial,bySeyiLufadeju,guidesyouthroughbuildingaRock,Paper,Scissorsgame,idealforbeginnersandthoselookingforafuncodingproject.Step1:Und
- Python Tutorial 409 2025-01-13 07:07:42
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- Building an Open-Source AI Newsletter Engine
- TheChallenge:TrackingAIAdvancementsKeepingupwithAIbreakthroughsacrossarXiv,GitHub,andvariousnewssourcesisamonumentaltask.Manuallyjuggling40 browsertabsisn'tjustinefficient;it'sarecipeforalaptopmeltdown.TheSolution:AiLert–AnOpen-SourceAnswerToaddresst
- Python Tutorial 1089 2025-01-13 06:58:11
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- Designing Context for New Modules in HyperGraph
- Modularsystemdesignrequiresadelicatebalancebetweenflexibilityandconsistency.ThisarticlesharesinsightsfromdesigningthecontextstructurefornewmoduledevelopmentwithinHyperGraph,anopen-sourceframeworkforLargeLanguageModel(LLM)systems.TheContextChallengeHy
- Python Tutorial 847 2025-01-13 06:41:45
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- Modernizing HyperGraph&#s CLI: A Journey Towards Better Architecture
- HyperGraph, my personal project, aims to be an innovative knowledge management system that integrates peer-to-peer networks, category theory, and high-level language models into a unified architecture. Currently still in the early stages of a proof-of-concept, HyperGraph's vision is to revolutionize the way we organize, share and develop collective knowledge, enabling truly decentralized collaboration while protecting individual autonomy and privacy. Although not yet operational, the system is being designed with a sophisticated server layer that will integrate distributed state management, event processing, and P2P infrastructure. During the development of HyperGraph, I have recently encountered some challenges with the architecture of the CLI module. Although the initial implementation was fully functional, it had some limitations
- Python Tutorial 723 2025-01-13 06:39:46
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