Full text download link: http://tecdat.cn/?p=23681 What is the difference between the linear mixed effects model and the linear model we already know(Click “Read the original text” at the end of the article to get the completeCode data)? Related videos A linear mixed model (sometimes called a “multilevel model” or a “hierarchical model”, depending on the […]
Tag: relation
On the love-hate relationship with kafka
Previous article We used kafka in the project, but a series of things that happened later made me understand kafka more deeply. Some time ago, there was a problem with online kafka. Messages accumulated and were not consumed. Will consumption start after restarting the service? Kafka in the production environment stops consuming after a period […]
[SQL] 1. The relationship between Flink dynamic tables and streams and DDL syntax
Article directory 1. Start the SQL client 2. Commonly used configurations of SQL clients 3. Dynamic tables and continuous queries 4. Convert the flow into a dynamic table 5. Use SQL to query continuously 6. Convert dynamic table to stream 7. Time attribute 8. DDL-database related 9. DDL-table related 1. Start SQL client Start Flink […]
R language uses a linear mixed effects (multi-level/layered/nested) model to analyze the relationship between pitch and polite attitude…
Full text download link: http://tecdat.cn/?p=23681 What is the difference between the linear mixed effects model and the linear model we already know(Click “Read the original text” at the end of the article to get the completeCode data)? Related videos A linear mixed model (sometimes called a “multilevel model” or “hierarchical model”, depending on the context) […]
The Broken Ethical Relationship: The Inheritance of JS
In JavaScript, inheritance is an important concept that allows us to create an object and inherit properties and methods from other objects. There are many ways to implement inheritance, each with its own advantages, disadvantages, and applicable scenarios. Below we will explain in detail how each inheritance method works and how to use it. 6 […]
echarts force guide diagram_relationship diagram_knowledge diagram
Echarts commonly used various chart template configurations Note: This is mainly based on various charts and uses more various configuration items of Echarts; The following codes can be copied to the Echarts official website and previewed directly; Icon template directory Echarts commonly used various chart template configurations 1. Force guide chart 2. Donut chart 3. […]
Use Python to analyze the distribution relationship between Luckin and Starbucks nationwide stores
WillLuckinshakeStarbucks’industrystatus? Lastmonth,LuckinCoffee’ssaucelattebecamepopular,puttingLuckininthespotlightagain.Thelasttimewaswhenitcommittedfinancialfraud. Thedomesticcoffeemarkethasbeenboominginrecentyears,drivingtherapiddevelopmentofmanycoffeebrandsincludingLuckin.From2013to2023,China’spercapitacoffeeconsumptionisexpectedtoincreaseby238%.Thetotalnumberofcoffeestoresinthecountrynowexceeds100,000.Andthenumberisgrowingbytensofthousandseveryyear. TheriseofLuckinCoffeeremindsusofStarbucks,thebenchmarkinthecoffeeindustry.Starbuckshasbeenalmostsynonymouswithcoffeeinthepasttenyearsandisalsothelifestyleofurbanwhite-collarworkers. WhatishappeningnowisthatwhereverthereisaStarbucksstore,thereisalmostaLuckinstorewithinafewhundredmeters,andsomeareevensurroundedbytwoorthree. ThefollowingusesvisualdashboardsandPythondataanalysistocomparethedifferencesandcorrelationsbetweenStarbucksandLuckinstoresintermsofquantity,regionaldistribution. Therearetwomainfindings: 1.StarbucksismoreconcentratedineconomicallydevelopedcoastalareassuchastheYangtzeRiverDelta,PearlRiverDelta,andBeijing-Tianjin-Hebei,especiallyfirst-andsecond-tiercities.LuckinismoredispersedthanStarbucksandhasstoresinmanythird-andfourth-tiercitiesandbelow. 2.LuckinstorelocationsareconcentratedaroundStarbucks.Datashowsthatwithinaradiusof500meters,thereareanaverageof0.6LuckinstoresaroundeachStarbucksstorenationwide. Preparationphase ThetoolsusedinthisanalysistaskincludeNextDataRobot,Python,andshapely. XiamiaoDataRobotisaclouddataplatformthatintegratesdatasets,datacleaning,dataanalysis,datavisualizationandbillboardconstruction.TheStarbucksandLuckinstoredatasetsusedinthisanalysisareallstoredinXiamiaoDataRobot. Wewilldevelopdatadashboardsbasedondatasets,andalsouseAPIinterfacestodirectlycallPythonfordataanalysisandvisualization.Pythonisusedtoconnecttothedatainterfaceoftherobotandprocessandanalyzethedata. Shapelyisathird-partylibraryforPython,usedtoprocesslatitudeandlongitudedata,andcandeterminethedistanceandinclusionrelationshipbetweendifferentgeographicalcoordinates. Dataset BecauseweneedtocompareandanalyzethenumberandlocationofStarbucksandLuckinstores,themainfieldsofthedatasetincludestorename,longitude,latitude,andcity. ? Note:Thedatasetisin2022,andthereisaquantitativeerrorofabout20%. ? NationalStarbuckscoffeestoredataset:NationalLuckinCoffeeStoredataset: Bothdatasetsarestoredonthenextdatarobotplatform.Youcandirectlyviewandusethedatasetsthroughthedataview,whichwewillusetobuildadatadashboardlater. BecausePythonisneededtoprocessthedatalater,thedataneedstobeobtainedthroughtheAPIdatainterface.Itisveryconvenienttooperateandcanbesavedforlateruse. importrequests headers={“x-token”:”yourauthenticationtoken”} response=requests.get(“http://app.chafer.nexadata.cn/openapi/v1/sheet/sht22nId5uouP2/records?size=1&page=1”,headers=headers) print(response.json()) Buildananalysisdashboard ItisrelativelysimpletobuildadashboardontheNextDataRobot.First,createaprocesstaskandselecttwodataviews:StarbucksandLuckin. ThencreateaKanbanboardandeditthedesignchart,whichissimilartowhatweusuallydoonBIsoftware. Therearemorethanadozenchartformshere,whichcanbasicallymeetmostvisualizationscenarios. ComparisonofthenumberofStarbucksandLuckinstoresnationwide Asofthedatasettime(2022),thenumberofStarbucksstoresnationwideisexpectedtobe4,442,andthenumberofLuckinCoffeestoresnationwideisexpectedtobe3,904.Starbuckshas14%morestoresthanLuckin. Judgingfromthemagnitude,thetwoareveryclose,andLuckinisexpandingitsstoresataterrifyinggrowthrate.Takingthebusinessdistrictnearmyhomeasanexample,therewasonlyoneLuckinlastyear,andtherearethreethisyear. StarbucksismoredemandingthanRuixingintermsoflocation,storeopeningcost,storearea,andnumberofstoreemployees.Ruixingspecializesintakeout+takeout.ThisisalsothereasonwhyRuixingcanexpandrapidlyinadditiontomarketdemandfactors. Starbucksisdistributedinthetop20citiesacrossthecountry ThetopfivecitieswiththenumberofStarbucksstoresare:Shanghai,Beijing,Hangzhou,Shenzhen,andGuangzhou.Amongthetop20cities,thereare6intheYangtzeRiverDelta,5inthePearlRiverDelta,and2intheBeijing-Tianjin-Hebeiregion. ThenumberofStarbucksstoresinShanghaiis668,whichistwiceasmanyasthesecond-placeBeijing.Atthesametime,ShanghaiisalsothecitywiththelargestnumberofStarbucksstoresintheworld.ItseemsthatthepeopleofShanghaihaveawell-deservedloveforcoffee. ThenumberofStarbucksinHangzhouissecondonlytoShanghaiandBeijing,andhigherthanShenzhenandGuangzhou.Internetande-commercepractitionersinHangzhoualsopreferStarbucks. LuckinisdistributedinTop20citiesacrossthecountry ThetopfivecitieswiththenumberofLuckinstoresare:Shanghai,Beijing,Guangzhou,Shenzhen,andHangzhou.TheyarethesameasthetopfivecitiesforStarbucks,buttheorderingisslightlydifferent. Amongthetop20cities,thereare6intheYangtzeRiverDelta,2inthePearlRiverDelta,and2intheBeijing-Tianjin-Hebeiregion. Starbucksismainlyconcentratedinfirst-andsecond-tiercoastalcities,whileLuckinisrapidlyoccupyingthemarketininlandcities.Luckin’stop20citiesalreadyincludeHefei,Kunming,andZhengzhou,butthesethreeprovincialcapitalcitiesarenotinStarbucks’top20. Therefore,thedistributionofLuckinstoresismoredispersedandnotoverlyconcentratedinfirst-tiercities. Starbucksnationwidedistributionheatmap ItcanalsobeseenfromtheheatmapofStarbucksstoresthattheredhigh-densityareasaremainlyconcentratedincoastalareas,whiletheinlandareasaredistributedinapoint-likemannerandarerelativelysparse. Ruixing’snationwidedistributionheatmap ThedistributionofLuckinstoresismoreeven.Inadditiontocoastalareas,therearealsomanystoresincentralChinasuchasHunan,Anhui,Hubei,andHunan. StarbucksShanghaidistributionheatmap Shanghaiisthecitywiththelargestcoffeeconsumptiondemandinthecountry.Let’stakealookatthedistributionofStarbucksstoresinShanghai. Generallyspeaking,StarbucksstoresareconcentratedintheinnerringofShanghai,andarescatteredoutwardsindottedlines.Thefivemajornewcitiesinthesuburbs,PudongAirport,andHongqiaohubarealsorelativelyconcentratedareas. LuckinShanghaidistributionheatmap TheconcentrationofLuckinintheinnerringofShanghaiisnotasobviousasthatofStarbucks,andtheoverallnumberisalsomuchsmaller. Pythondataanalysis Previously,weanalyzedthedistributionofStarbucksandLuckinstoresacrossthecountrybybuildingavisualsignageontheNextMiaoRobot.Theregionaldifferencesarestillquiteobvious. Let’sfurtheranalyzetherelationshipbetweenStarbucksandLuckinstores.WeknowthatLuckinCoffeeisarisingstar.ItissaidthatthelocationofmanystoresmainlydependsonwhetherthereisaStarbucksnearby. […]
oracle (9)Storage & Relationship Strut
Table of Contents 1. Basic knowledge 1. Database logical structure diagram 2. Types of Segments 3. Storage Clause Precedence The priority of storage clauses 4. Range allocation and deallocation of Extent Alloc & Dealloc area 5. Used and Free Extents Used and Free Extents 6. Database Block database block 7. Multiple Block Size Rules 8. […]
An in-depth exploration of C++ polymorphism ② – Inheritance relationships
Foreword The previous chapter briefly described the calling link of virtual functions. This chapter mainly explores the polymorphic characteristics of class objects with various inheritance relationships in C++. In-depth exploration of C++ polymorphism ① – Virtual function call link A Deeper Exploration of C++ Polymorphism ② – Inheritance A Deeper Exploration of C++ Polymorphism ③ […]
[K8S] External access request principle process (relationship between service, kube-proxy, pod)
Article directory Simple process 1. Ingress 2. Service 1. Key concepts 2.Service types and use cases: 1)ClusterIP: 2) NodePort 3) LoadBalancer 3. Kube-proxy 1 Introduction 2. Introduction to the three agency modes 1) userspace mode: 2) IPtables mode: 3) ipvs mode: 4. The relationship between service, kube-proxy and pod Simple process The user initiates a […]