Cov kab lus no tau mob siab rau kev kawm txog kev tsim kho hauv nroog loj ntawm Silicon Valley - San Francisco. San Francisco yog lub thev naus laus zis "Moscow" ntawm peb lub ntiaj teb, siv nws qhov piv txwv (nrog kev pab ntawm cov ntaub ntawv qhib) los saib xyuas kev tsim kho kev lag luam hauv cov nroog loj thiab cov peev.
Kev tsim kho cov duab kos thiab suav tau ua tiav hauv (ntawm Kaggle.com platform).
Cov ntaub ntawv ntawm ntau tshaj li ib lab daim ntawv tso cai hauv tsev (cov ntaub ntawv hauv ob daim ntawv teev npe) los ntawm San Francisco Lub Tsev Haujlwm Saib Xyuas - tso cai txheeb xyuas tsis tsuas yog kev tsim kho kev ua haujlwm hauv nroog, tab sis kuj tseem xav txog qhov tseeb tiam sis thiab keeb kwm ntawm txoj kev loj hlob ntawm kev tsim kho kev lag luam hauv 40 xyoo dhau los, ntawm 1980 thiab 2019.
Qhib cov ntaub ntawv ua rau nws tuaj yeem tshawb nrhiav yam tseem ceeb uas tau cuam tshuam thiab yuav cuam tshuam rau kev tsim kho kev lag luam nyob rau hauv lub nroog, faib lawv mus rau hauv "sab nraud" (kev lag luam booms thiab crises) thiab "sab hauv" (kev cuam tshuam ntawm hnub so thiab caij nyoog-ib xyoos ib zaug).
Txheem
Qhib cov ntaub ntawv thiab tshuaj xyuas cov txheej txheem hauv paus.
Qhov no tsis yog kev txhais lus ntawm tsab xov xwm. Kuv sau rau ntawm LinkedIn thiab thiaj li tsis tsim cov duab hauv ntau hom lus, txhua daim duab yog lus Askiv.
Txuas mus rau qhov thib ob:
City of San Francisco Building Permit Data - Los ntawm Qhib Cov Ntaub Ntawv Portal - . Lub portal muaj ob peb datasets ntawm lub ncauj lus ntawm kev tsim kho. Ob lub datasets khaws thiab hloov kho cov ntaub ntawv ntawm daim ntawv tso cai tawm rau kev tsim kho lossis kho cov khoom hauv nroog:
- (850 cov ntaub ntawv)
- (280 txhiab cov ntaub ntawv, cov ntaub ntawv raug rub tawm thiab hloov kho txhua lub lim tiam)
Cov ntaub ntawv no muaj cov ntaub ntawv hais txog cov ntawv tso cai tsim kho, nrog rau ntau yam yam ntxwv ntawm cov khoom uas tau tso cai tawm. Tag nrho cov naj npawb nkag (kev tso cai) tau txais nyob rau lub sijhawm 1980-2019 - 1 tso cai.
Cov tsis tseem ceeb ntawm cov ntaub ntawv no uas tau siv los tshuaj xyuas:
- permit_creation_date - hnub tsim daim ntawv thov (qhov tseeb, hnub uas pib ua haujlwm)
- piav qhia - piav qhia ntawm daim ntawv thov (ob lossis peb lo lus tseem ceeb piav qhia txog kev tsim kho (ua haujlwm) uas daim ntawv tso cai tau tsim)
- kwv yees_ nqi - kwv yees (kwv yees) tus nqi ntawm kev tsim kho
- kho_cost - Tus nqi kho dua tshiab (tus nqi ntawm kev ua haujlwm tom qab rov ntsuas dua, nce lossis txo qis ntawm qhov pib ntim ntawm daim ntawv thov)
- uas twb muaj lawm_ siv - hom vaj tse (ib-, ob tsev neeg lub tsev, chav tsev, chaw ua haujlwm, ntau lawm, thiab lwm yam)
- zipcode, qhov chaw - Xa ntawv code thiab cov khoom tswj xyuas
Kev tsim kho txhua xyoo hauv San Francisco
Daim duab hauv qab no qhia cov kev txwv kwv yees_ nqi ΠΈ kho_cost nthuav tawm raws li kev faib tawm ntawm tag nrho cov nqi ntawm kev ua haujlwm los ntawm lub hli.
data_cost_m = data_cost.groupby(pd.Grouper(freq='M')).sum()Txhawm rau txo cov "cov nyiaj tau los" txhua hli, cov ntaub ntawv txhua hli yog pab pawg los ntawm xyoo. Daim duab ntawm tus nqi ntawm cov nyiaj tau los ntawm lub xyoo tau txais ntau qhov laj thawj thiab cov ntaub ntawv txheeb xyuas.
data_cost_y = data_cost.groupby(pd.Grouper(freq='Y')).sum()
Raws li kev txav txhua xyoo ntawm cov nqi ntawm cov nqi (tag nrho cov ntawv tso cai rau lub xyoo) rau cov chaw hauv nroog Kev lag luam uas cuam tshuam los ntawm 1980 txog 2019 tau pom meej meej ntawm tus naj npawb thiab tus nqi ntawm kev tsim kho, lossis lwm yam ntawm kev nqis peev hauv San Francisco vaj tsev.
Tus naj npawb ntawm cov ntawv tso cai hauv tsev (tus naj npawb ntawm cov haujlwm tsim kho lossis tus lej ntawm kev nqis peev) dhau 40 xyoo dhau los tau cuam tshuam nrog kev lag luam hauv Silicon Valley.
Thawj lub ncov ntawm kev tsim kho kev ua haujlwm tau cuam tshuam nrog kev siv hluav taws xob hype ntawm nruab nrab-80s hauv hav. Kev lag luam hluav taws xob thiab kev lag luam nyiaj poob qis hauv xyoo 1985 tau xa cov khoom lag luam hauv cheeb tsam mus rau qhov poob qis uas nws tsis tau rov qab los ze li kaum xyoo.
Tom qab ntawd, ob zaug ntxiv (hauv 1993-2000 thiab 2009-2016) ua ntej kev tawg ntawm Dotcom npuas thiab thev naus laus zis ntawm xyoo tas los no. San Francisco kev tsim kho kev lag luam tau ntsib kev loj hlob ntawm ntau txhiab feem pua..
Los ntawm kev tshem tawm qhov nruab nrab nruab nrab peaks thiab troughs thiab tawm qhov tsawg kawg nkaus thiab siab tshaj qhov tseem ceeb rau txhua lub voj voog kev lag luam, nws pom tseeb tias qhov kev lag luam loj npaum li cas tau ua rau kev lag luam hauv 40 xyoo dhau los.
Qhov loj tshaj plaws hauv kev nqis peev hauv kev tsim kho tau tshwm sim thaum lub sijhawm dot-com boom, thaum nruab nrab ntawm 1993 thiab 2001 $ 10 billion tau nqis peev hauv kev kho dua tshiab thiab kev tsim kho, lossis kwv yees li $ 1 nphom hauv ib xyoos. Yog tias peb suav hauv square metres (tus nqi ntawm 1 mΒ² hauv 1995 yog $ 3000), qhov no yog kwv yees li 350 m000 ib xyoos rau 2 xyoo, pib xyoo 10.
Kev loj hlob ntawm txhua xyoo kev nqis peev hauv lub sijhawm no yog 1215%.
Cov tuam txhab uas xauj cov cuab yeej siv hauv lub sijhawm no zoo ib yam li cov tuam txhab muag shovels thaum lub caij kub kub (hauv tib cheeb tsam hauv nruab nrab-19th caug xyoo). Tsuas yog siv shovels, nyob rau xyoo 2000s twb muaj cranes thiab pob zeb twj tso kua mis rau cov tuam txhab tsim kho tshiab uas xav kom tau nyiaj ntawm kev tsim kho.
Tom qab txhua qhov teeb meem ntau yam uas kev tsim kho kev lag luam tau ntsib ntau xyoo, nyob rau ob lub xyoos tom qab kev kub ntxhov, kev nqis peev (ntau npaum li cas ntawm daim ntawv tso cai) rau kev tsim kho poob los ntawm tsawg kawg yog 50% txhua lub sijhawm.
Qhov teeb meem loj tshaj plaws hauv San Francisco kev tsim kho kev lag luam tau tshwm sim hauv 90s. Qhov twg, nrog rau lub sijhawm ntawm 5 xyoos, kev lag luam poob qis (-85% hauv lub sijhawm 1983-1986), tom qab ntawd tau nce ntxiv (+ 895% nyob rau lub sijhawm 1988-1992), tseem tshuav nyob rau xyoo 1981, 1986, 1988 , 1993 - nyob rau tib theem.
Tom qab xyoo 1993, tag nrho cov kev poob qis tom qab hauv kev tsim kho kev lag luam tsis pub ntau tshaj 50%. Tab sis mus txog qhov teebmeem nyiaj txiag (vim COVID-19) tuaj yeem tsim kom muaj kev kub ntxhov hauv kev tsim kho kev lag luam nyob rau hauv lub sij hawm 2017-2021, qhov poob ntawm uas twb nyob rau hauv lub sij hawm 2017-2019 yog tag nrho ntawm ntau tshaj 60%.
Kev loj hlob ntawm pej xeem San Francisco dynamics nyob rau lub sij hawm 1980-1993 kuj pom yuav luag exponential kev loj hlob. Lub zog ntawm kev lag luam thiab lub zog tshiab ntawm Silicon Valley yog lub hauv paus ruaj khov uas yog lub hauv paus tseem ceeb ntawm Kev Lag Luam Tshiab, Asmeskas Renaissance, thiab dot-coms tau tsim. Nws yog qhov tseem ceeb ntawm kev lag luam tshiab. Tab sis tsis zoo li qhov nce hauv kev nqis peev hauv vaj tse, tom qab dot-com ncov, cov pej xeem tau nce siab.
Ua ntej dot-com ncov hauv 2001, txhua xyoo cov pej xeem kev loj hlob txij li xyoo 1950 tau kwv yees li 1% hauv ib xyoos. Tom qab ntawd, tom qab lub npuas tawg, qhov kev nkag ntawm cov neeg tshiab tau qeeb thiab txij li xyoo 2001 tsuas yog 0.2 feem pua ββββntawm ib xyoos.
Hauv xyoo 2019 (thawj zaug txij li xyoo 1950), qhov kev loj hlob zoo tau pom tias muaj kev tawm ntawm cov pejxeem (-0.21% lossis 7000 tus neeg) los ntawm lub nroog San Francisco.
Kev cia siab thiab kev muaj tiag thaum npaj cov nqi kwv yees
Hauv cov ntaub ntawv siv, cov ntaub ntawv ntawm tus nqi ntawm daim ntawv tso cai rau kev tsim kho yog muab faib ua:
- thawj kwv yees tus nqi (kwv yees_ nqi)
- tus nqi ua haujlwm tom qab rov ntsuas dua (kho_cost)
Thaum lub sij hawm boom lub sij hawm, lub hom phiaj tseem ceeb ntawm kev ntsuam xyuas yog nce tus nqi pib, thaum tus neeg ua lag luam (tus neeg siv khoom tsim kho) qhia qhov qab los noj mov tom qab pib tsim kho.
Thaum lub sijhawm muaj kev kub ntxhov, lawv sim tsis txhob tshaj tus nqi kwv yees, thiab qhov kev kwv yees thawj zaug tsis muaj kev hloov pauv (tshwj tsis yog av qeeg xyoo 1989).
Raws li daim duab tsim los ntawm qhov sib txawv ntawm qhov ntsuas thiab kwv yees tus nqi (revised_cost - kwv yees_cost), nws tuaj yeem pom tias:
Tus nqi ntawm tus nqi nce thaum rov ntsuas qhov ntim ntawm kev tsim kho kev ua haujlwm ncaj qha nyob ntawm kev lag luam boom cycles
data_spread = data_cost.assign(spread = (data_cost.revised_cost-data_cost.estimated_cost))
Thaum lub sijhawm ntawm kev loj hlob sai, cov neeg siv khoom ntawm kev ua haujlwm (cov tub ua lag luam) siv lawv cov nyiaj tau zoo heev, nce lawv qhov kev thov tom qab pib ua haujlwm.
Tus neeg siv khoom (tus neeg ua lag luam), muaj kev ntseeg siab txog nyiaj txiag, nug tus neeg cog lus tsim kho lossis tus kws tsim vaj tsev kom txuas ntxiv daim ntawv tso cai tsim tsev. Qhov no tej zaum yuav yog ib qho kev txiav txim siab los nce qhov ntev ntawm lub pas dej ua ke los yog nce thaj tsam ntawm lub tsev (tom qab pib ua hauj lwm thiab muab daim ntawv tso cai rau lub tsev).
Thaum lub ncov ntawm dot-com era, xws li "ntxiv" cov nuj nqis mus txog "ntxiv" 1 billion ib xyoos.
Yog tias koj saib ntawm cov lus no twb nyob rau hauv feem pua ββββntawm cov kev hloov pauv, ces qhov nce siab tshaj plaws hauv kev kwv yees (100% lossis 2 npaug ntawm tus nqi kwv yees) tau tshwm sim nyob rau xyoo ua ntej av qeeg uas tshwm sim xyoo 1989 nyob ze lub nroog. Kuv xav tias tom qab av qeeg, kev tsim kho uas pib xyoo 1988 xav tau, tom qab av qeeg xyoo 1989, ntau lub sijhawm thiab nyiaj txiag rau kev siv.
Hloov pauv, kev hloov kho qis ntawm tus nqi kwv yees (uas tau tshwm sim tsuas yog ib zaug thaum lub sijhawm xyoo 1980 txog 2019) ob peb xyoos ua ntej av qeeg yog qhov tseeb vim tias qee qhov haujlwm pib xyoo 1986-1987 tau khov lossis kev nqis peev hauv cov haujlwm no raug txiav. nqes. Raws sij hawm Qhov nruab nrab rau txhua qhov haujlwm tau pib xyoo 1987 - qhov txo qis ntawm tus nqi kwv yees yog -20% ntawm cov phiaj xwm qub.
data_spred_percent = data_cost_y.assign(spred = ((data_cost_y.revised_cost-data_cost_y.estimated_cost)/data_cost_y.estimated_cost*100))
Ib qho kev nce hauv thawj zaug kwv yees tus nqi ntau dua 40% qhia los yog tejzaum nws yog qhov tshwm sim los ntawm kev nce npuas hauv cov nyiaj txiag thiab tom qab kev tsim kho kev lag luam.
Dab tsi yog vim li cas rau qhov txo qis ntawm kev sib kis (qhov txawv) ntawm qhov kwv yees thiab cov nqi kho dua tom qab xyoo 2007?
Tej zaum cov tub ua lag luam pib ua tib zoo saib cov lej (qhov nruab nrab ntau tshaj 20 xyoo tau nce los ntawm $ 100 txhiab mus rau $ 2 lab) lossis tej zaum lub tuam tsev tsim kho, tiv thaiv thiab cuam tshuam cov npuas uas tshwm sim hauv kev ua lag luam vaj tsev, qhia txog cov cai tshiab thiab kev txwv kom txo tau cov kev tsim kho. thiab muaj peev xwm txaus ntshai uas yuav tshwm sim thaum muaj kev kub ntxhov xyoo.
Kev tsim kho kev ua haujlwm nyob ntawm lub caij nyoog ntawm lub xyoo
Los ntawm kev faib cov ntaub ntawv los ntawm lub lis piam ntawm lub xyoo (54 lub lis piam), koj tuaj yeem saib xyuas kev tsim kho hauv nroog San Francisco nyob ntawm lub caij nyoog thiab lub sijhawm xyoo.
Thaum Christmas, txhua lub koom haum tsim kho tau sim kom tau txais kev tso cai rau cov haujlwm tshiab "loj" nyob rau lub sijhawm. (nyob rau tib lub sijhawm! tus lej! ntawm cov ntawv tso cai hauv tib lub hlis no yog nyob rau tib theem thoob plaws hauv lub xyoo). Cov neeg lag luam, npaj kom tau txais lawv cov cuab yeej nyob rau hauv lub xyoo tom ntej, nkag mus rau hauv cov ntawv cog lus nyob rau lub caij ntuj no, suav nrog cov nyiaj cheb loj (txij li thaum lub caij ntuj sov cog lus, feem ntau, yuav los txog rau thaum xaus ntawm lub xyoo thiab cov tuam txhab tsim kho tau txaus siab. hauv tau txais cov ntawv thov tshiab).
Ua ntej Christmas, cov ntawv thov ntau tshaj plaws raug xa mus (nce los ntawm qhov nruab nrab ntawm 1-1,5 billion ib hlis mus rau 5 billion nyob rau lub Kaum Ob Hlis ib leeg). Nyob rau tib lub sijhawm, tag nrho cov ntawv thov los ntawm lub hli tseem nyob rau tib theem (saib nqe lus hauv qab no: txheeb cais ntawm tag nrho cov ntawv thov los ntawm lub hli thiab hnub)
Tom qab lub caij ntuj no lub caij ntuj no, kev tsim kho kev lag luam tseem tab tom npaj thiab ua raws li "Christmas" xaj (nrog yuav luag tsis muaj cov ntawv tso cai nce ntxiv) txhawm rau txhawm rau tso cov peev txheej los ntawm nruab nrab xyoo (ua ntej hnub so ywj pheej) ua ntej lub tshiab. nthwv dej ntawm cov ntawv cog lus caij ntuj sov pib tam sim tom qab lub Rau Hli hnub so.
data_month_year = data_month_year.assign(week_year = data_month_year.permit_creation_date.dt.week)
data_month_year = data_month_year.groupby(['week_year'])['estimated_cost'].sum()
Tib cov ntaub ntawv feem pua ββββ(kab txiv kab ntxwv) kuj qhia tau hais tias kev lag luam ua haujlwm "ntu" thoob plaws hauv lub xyoo, tab sis ua ntej thiab tom qab hnub so, kev ua si ntawm kev tso cai nce mus rau 150% nyob rau lub sij hawm nruab nrab ntawm lub lim tiam 20-24 (ua ntej Hnub Kev ywj pheej), thiab txo tam sim ntawd tom qab hnub so mus txog -70%.
Ua ntej Halloween thiab Christmas, kev ua si hauv San Francisco kev tsim kho kev lag luam nce 43% thaum lub lim tiam 44-150 (los ntawm hauv qab mus rau lub ncov) thiab tom qab ntawd txo qis rau xoom thaum hnub so.
Yog li, kev lag luam nyob rau hauv lub voj voog rau lub hlis, uas yog cais los ntawm cov hnub so "US Independence Day" (lub lim tiam 20) ββthiab "Christmas" (lub lim tiam 52).
Tag nrho cov vaj tse peev hauv San Francisco
Raws li cov ntaub ntawv ntawm kev tso cai tsim tsev hauv nroog:
Tag nrho cov peev hauv kev tsim kho hauv San Francisco los ntawm 1980 txog 2019 yog $ 91,5 billion.
sf_worth = data_location_lang_long.cost.sum()
Tag nrho cov nqi lag luam ntawm tag nrho cov vaj tsev nyob hauv San Francisco raug soj ntsuam los ntawm cov se vaj tse (uas yog tus nqi ntsuas ntawm tag nrho cov vaj tse thiab tag nrho cov khoom ntiag tug los ntawm San Francisco) .
Cov cheeb tsam twg hauv San Francisco tau nqis peev hauv 40 xyoo dhau los?
Siv lub tsev qiv ntawv Folium, cia saib qhov twg qhov no $ 91,5 billion tau nqis peev los ntawm cheeb tsam. Txhawm rau ua qhov no, tau muab cov ntaub ntawv los ntawm zip code, peb yuav sawv cev rau qhov txiaj ntsig uas siv cov voj voog (lub voj voog ua haujlwm los ntawm lub tsev qiv ntawv Folium).
import folium
from folium import Circle
from folium import Marker
from folium.features import DivIcon
# map folium display
lat = data_location_lang_long.lat.mean()
long = data_location_lang_long.long.mean()
map1 = folium.Map(location = [lat, long], zoom_start = 12)
for i in range(0,len(data_location_lang_long)):
Circle(
location = [data_location_lang_long.iloc[i]['lat'], data_location_lang_long.iloc[i]['long']],
radius= [data_location_lang_long.iloc[i]['cost']/20000000],
fill = True, fill_color='#cc0000',color='#cc0000').add_to(map1)
Marker(
[data_location_mean.iloc[i]['lat'], data_location_mean.iloc[i]['long']],
icon=DivIcon(
icon_size=(6000,3336),
icon_anchor=(0,0),
html='<div style="font-size: 14pt; text-shadow: 0 0 10px #fff, 0 0 10px #fff;; color: #000";"">%s</div>'
%("$ "+ str((data_location_lang_long.iloc[i]['cost']/1000000000).round()) + ' mlrd.'))).add_to(map1)
map1
Nws yog tseeb los ntawm cov cheeb tsam uas Feem ntau ntawm cov ncuav qab zib tau mus rau DownTown. Tau sim ua kom yooj yim ntawm kev sib koom ua ke ntawm txhua yam khoom los ntawm kev ncua deb ntawm lub nroog thiab lub sijhawm nws yuav siv sij hawm mus rau hauv lub nroog nruab nrab (qhov tseeb, cov tsev kim kuj tau tsim rau ntawm ntug dej hiav txwv), tag nrho cov ntawv tso cai tau muab faib ua 4 pawg: 'Downtown' , '<0.5H Hauv Nroog', '< 1H Hauv Nroog', 'Sab nraum SF'.
from geopy.distance import vincenty
def distance_calc (row):
start = (row['lat'], row['long'])
stop = (37.7945742, -122.3999445)
return vincenty(start, stop).meters/1000
df_pr['distance'] = df_pr.apply (lambda row: distance_calc (row),axis=1)
def downtown_proximity(dist):
'''
< 2 -> Near Downtown, >= 2, <4 -> <0.5H Downtown
>= 4, <6 -> <1H Downtown, >= 8 -> Outside SF
'''
if dist < 2:
return 'Downtown'
elif dist < 4:
return '<0.5H Downtown'
elif dist < 6:
return '<1H Downtown'
elif dist >= 6:
return 'Outside SF'
df_pr['downtown_proximity'] = df_pr.distance.apply(downtown_proximity)Ntawm 91,5 billion tau nqis peev hauv nroog, yuav luag 70 billion (75% ntawm tag nrho cov kev nqis peev) tau nqis peev hauv kev kho thiab kev tsim kho yog nyob hauv lub nroog. (thaj tsam ntsuab) thiab mus rau lub nroog cheeb tsam hauv ib lub vojvoog ntawm 2 km. los ntawm qhov chaw (xiav cheeb tsam).
Qhov nruab nrab kwv yees tus nqi ntawm daim ntawv thov kev tsim kho los ntawm lub nroog hauv nroog
Tag nrho cov ntaub ntawv, ib yam li tag nrho cov peev txheej, tau muab faib ua tus zip code. Tsuas yog nyob rau hauv rooj plaub no nrog qhov nruab nrab (.mean()) kwv yees tus nqi ntawm daim ntawv thov los ntawm zip code.
data_location_mean = data_location.groupby(['zipcode'])['lat','long','estimated_cost'].mean()
Hauv thaj chaw zoo tib yam ntawm lub nroog (ntau tshaj 2 km ntawm lub nroog nruab nrab) - qhov nruab nrab kwv yees tus nqi ntawm daim ntawv thov kev tsim kho yog $ 50 txhiab.
Qhov nruab nrab kwv yees tus nqi hauv cheeb tsam hauv nroog yog kwv yees li peb zaug siab dua ($ 150 txhiab txog $ 400 txhiab) dua li lwm thaj chaw ($ 30-50 txhiab).
Ntxiv nrog rau cov nqi av, peb yam tseem ceeb txiav txim siab tag nrho cov nqi ntawm kev tsim vaj tsev: kev ua haujlwm, khoom siv, thiab cov nqi tseem ceeb. Peb lub ntsiab lus no siab dua hauv California dua li lwm lub tebchaws. California cov cai hauv tsev raug suav hais tias yog qee qhov kev qhia dav dav thiab nruj tshaj plaws hauv lub tebchaws (vim av qeeg thiab ib puag ncig cov cai), feem ntau xav tau cov khoom kim dua thiab kev ua haujlwm.
Piv txwv li, tsoomfwv xav kom cov neeg tsim khoom siv cov khoom siv hauv tsev zoo dua (qhov rais, rwb thaiv tsev, cua sov thiab cua txias) kom ua tiav cov qauv siv hluav taws xob zoo.
Los ntawm cov txheeb cais dav dav ntawm tus nqi nruab nrab ntawm daim ntawv tso cai, ob qhov chaw sawv tawm:
- Pov Hwm Tsab - ib lub kob dag nyob hauv San Francisco Bay. Qhov nruab nrab kwv yees tus nqi ntawm daim ntawv tso cai tsim tsev yog $ 6,5 lab.
- Lub Hom Phiaj Bay β (cov pej xeem 2926) Qhov nruab nrab kwv yees tus nqi ntawm daim ntawv tso cai tsim tsev yog $ 1,5 lab.
Qhov tseeb, daim ntawv thov siab nruab nrab hauv ob qhov chaw no muaj feem cuam tshuam nrog tsawg kawg ntawm cov ntawv thov rau cov chaw xa ntawv no (145 thiab 3064 raws li, kev tsim kho ntawm cov kob yog txwv tsis pub dhau), thaum lwm tus lej xa ntawv - XNUMXthiab lub sijhawm 1980-2019 tau txais kwv yees li 1300 daim ntawv thov ib xyoos (tag nrho ntawm qhov nruab nrab 30 -50 txhiab daim ntawv thov rau tag nrho lub sijhawm).
Raws li "tus naj npawb ntawm cov ntawv thov", qhov zoo kawg nkaus txawm tias faib cov naj npawb ntawm daim ntawv thov ib tus lej xa ntawv thoob plaws hauv nroog yog pom tau.
Kev txheeb cais ntawm tag nrho cov ntawv thov los ntawm lub hli thiab hnub
Tag nrho cov txheeb cais ntawm tag nrho cov ntawv thov los ntawm lub hli thiab hnub ntawm lub lim tiam ntawm 1980 thiab 2019 qhia tias Lub hlis uas ntsiag to tshaj plaws rau kev tsim kho yog lub caij nplooj ntoos hlav thiab lub caij ntuj no. Nyob rau tib lub sijhawm, cov peev txheej tau teev tseg hauv cov ntawv thov sib txawv heev thiab txawv ntawm lub hli mus rau hli los ntawm ntau zaus (saib ntxiv βKev tsim ua haujlwm nyob ntawm lub caijβ). Ntawm cov hnub ntawm lub lim tiam, hnub Monday cov khoom thauj ntawm lub tuam tsev yog kwv yees li 20% tsawg dua li lwm hnub ntawm lub lim tiam.
months = [ 'January', 'February', 'March', 'April', 'May','June', 'July', 'August', 'September', 'October', 'November', 'December' ]
data_month_count = data_month.groupby(['permit_creation_date']).count().reindex(months)
Thaum Lub Rau Hli thiab Lub Xya Hli yog qhov ua tau zoo ib yam ntawm cov ntawv thov, hais txog tag nrho cov nqi kwv yees qhov sib txawv nce mus txog 100% (4,3 billion hauv lub Tsib Hlis thiab Lub Xya Hli thiab 8,2 billion hauv Lub Rau Hli).
data_month_sum = data_month.groupby(['permit_creation_date']).sum().reindex(months)
Lub neej yav tom ntej ntawm San Francisco kev tsim kho kev lag luam, kwv yees kev ua si los ntawm cov qauv.
Thaum kawg, cia peb sib piv cov qauv kev tsim kho hauv San Francisco nrog rau daim ntawv qhia tus nqi Bitcoin (2015-2018) thiab daim ntawv qhia tus nqi kub (1940 - 1980).
Qauv (los ntawm cov qauv lus Askiv - qauv, qauv) - nyob rau hauv kev tsom xam ruaj khov rov qab ua ke ntawm cov nqi, ntim lossis cov ntaub ntawv qhia tau hu ua. Cov qauv kev tsom xam yog raws li ib qho ntawm cov kev tshawb fawb kev tshawb fawb: "keeb kwm rov ua dua nws tus kheej" - nws ntseeg tias kev sib txuas ntawm cov ntaub ntawv rov qab ua rau muaj txiaj ntsig zoo sib xws.
Cov qauv tseem ceeb uas tuaj yeem pom ntawm daim ntawv qhia ua haujlwm txhua xyoo yog Qhov no yog "Lub taub hau thiab lub xub pwg nyom" kev hloov pauv hloov pauv. Yog li lub npe vim tias daim duab zoo li tib neeg lub taub hau (ncej) thiab lub xub pwg nyom ntawm ob sab (tsawg dua peaks). Thaum tus nqi cuam tshuam cov kab txuas ntawm cov troughs, tus qauv txiav txim siab ua tiav thiab qhov kev txav mus los yuav yog qhov qis dua.
Kev txav mus los hauv kev ua si hauv San Francisco kev tsim kho kev lag luam yuav luag tag nrog kev nce hauv tus nqi kub thiab bitcoin. Cov keeb kwm kev ua tau zoo ntawm peb tus nqi thiab cov kab kos ua haujlwm no qhia pom qhov zoo sib xws.
Yuav kom muaj peev xwm kwv yees tus cwj pwm ntawm kev tsim kho kev lag luam yav tom ntej, Nws yog ib qho tsim nyog los suav cov coefficient ntawm kev sib raug zoo nrog rau txhua qhov ntawm ob txoj kev sib txawv no.
Ob qhov sib txawv ntawm qhov sib txawv yog hu ua correlated yog tias lawv lub sij hawm sib raug zoo (lossis sib txheeb coefficient) txawv ntawm xoom; thiab hu ua uncorrelated quantities yog tias lawv correlation moment is zero.
Yog tias qhov txiaj ntsig tshwm sim ze dua rau 0 dua li rau 1, ces tsis muaj qhov taw qhia hauv kev hais txog tus qauv meej. Qhov no yog ib tug complex lej, uas tej zaum yuav raug coj los ntawm cov laus comrades uas tej zaum yuav txaus siab rau lub ntsiab lus no.
Yog! tsis paub! saib lub ntsiab lus ntawm kev txhim kho ntxiv ntawm kev tsim kho kev lag luam hauv San Francisco: yog tias tus qauv txuas ntxiv ua ke nrog tus nqi ntawm Bitcoin, ces raws li qhov kev xaiv pessimistic no - Kev tawm ntawm kev kub ntxhov hauv kev tsim kho kev lag luam hauv San Francisco yuav tsis yooj yim rau lub sijhawm tam sim tom qab muaj teeb meem.
Nrog rau qhov kev xaiv ntau dua "optimistic". txoj kev loj hlob, rov nthuav dav nthuav dav hauv kev tsim kho kev lag luam yog ua tau yog tias kev ua haujlwm ntawm no ua raws li "tus nqi kub" scenario. Nyob rau hauv cov ntaub ntawv no, nyob rau hauv 20-30 xyoo (tej zaum nyob rau hauv 10), kev tsim kho sector yuav ntsib ib tug tshiab surge ntawm kev ua hauj lwm thiab kev loj hlob.
Kuv yuav ua tib zoo saib ntawm tus kheej cov haujlwm ntawm kev tsim kho (kho lub ru tsev, chav ua noj, kev tsim cov ntaiv, chav dej, yog tias koj muaj lus pom zoo ntawm kev lag luam lossis lwm cov ntaub ntawv - thov sau rau hauv cov lus) thiab sib piv kev nce nqi rau tus kheej hom kev ua haujlwm nrog Cov nqi tas li ntawm cov nyiaj txais qiv thiab cov txiaj ntsig ntawm tsoomfwv Meskas cov ntawv cog lus (Fixed Mortgage Rates & US Treasury Yield).
Txuas mus rau qhov thib ob:
Txuas rau Jupyter Notebook:
Thov, rau cov uas nrog Kaggle, muab Phau Ntawv Ntxiv ntxiv (Ua tsaug!).
(Cov lus pom thiab piav qhia ntawm cov cai yuav muab ntxiv tom qab hauv Phau Ntawv)
Txuas mus rau Askiv version:
Yog tias koj txaus siab rau kuv cov ntsiab lus, thov txiav txim siab yuav kuv kas fes.
ua tsaug rau koj txoj kev txhawb nqa!
Tsuas yog cov neeg siv sau npe tuaj yeem koom nrog hauv daim ntawv ntsuam xyuas. thov.
Lub neej yav tom ntej yog dab tsi rau San Francisco kev tsim kho kev lag luam?
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66,7%Kev tsim kho kev lag luam feem ntau yuav ua raws li txoj hauv kev ntawm Bitcoin2
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0,0%Kev tsim kho kev lag luam tuaj yeem ua raws li tus nqi kub0
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0,0%Lub sector cia siab tias hype tshaj 10 xyoo tom ntej0
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33,3%Txoj kev loj hlob ntawm cov sector tsis mus raws li cov qauv 1
3 cov neeg siv pov npav. 6 cov neeg siv txwv tsis pub siv.
Tau qhov twg los: www.hab.com