def CalculatePriceChanges():
distribution = DiscountDistribution()
priceMap = {}
# data = ds.ZooplaMatchedDaily(2000000) # during rising housing market
data = ds.ZooplaMatchedDaily() # at bottom of housing market
chunk = data.read(500000)
chunk.rename(columns={'\xef\xbb\xbfLISTING ID': 'LISTING ID'},
inplace=True)
filteredchunk = chunk[chunk["MARKET"] == "SALE"][[
'LISTING ID', 'DAY', 'PRICE'
]][chunk['PRICE'] > 0]
for row in filteredchunk.values:
# row: LISTING ID DAY PRICE
listingid = row[0]
if listingid in priceMap:
lastRecord = priceMap[listingid]
oldPrice = lastRecord.currentprice
startDay, endDay, percent = lastRecord.add(row[1], row[2])
if (oldPrice == row[2]): # no price change
distribution.noChange(startDay / 30, endDay / 30)
else: # price has changed
distribution.addChange(startDay / 30, endDay / 30, percent)
else:
priceMap[listingid] = PriceCalc(row[1], row[2])
return (distribution)
def ZooplaPriceChanges():
total = 0
pSame = 0
priceMap = {}
# distribution = DiscountDistribution()
data = ds.ZooplaMatchedDaily()
# store = pd.HDFStore('rawDaily.hd5',mode='w')
# for chunk in data.parser:
chunk = data.read(1000)
chunk.rename(columns={'\xef\xbb\xbfLISTING ID': 'LISTING ID'},
inplace=True)
filteredchunk = chunk[chunk["MARKET"] == "SALE"][[
'LISTING ID', 'DAY', 'PRICE'
]][chunk['PRICE'] > 0]
for row in filteredchunk.values:
currentState = priceMap.get(row[0])
if currentState == None:
priceMap[row[0]] = PriceCalc(row[1], row[2])
else:
startDay, endDay, percent = currentState.add(row[1], row[2])
distribution.add(startDay, endDay, percent)
# now get deletion dates
delData = ds.ZooplaMatchedCollated()
# for chunk in delData.parser:
chunk = delData.read(1000)
chunk.rename(columns={'\xef\xbb\xbfLISTING ID': 'LISTING ID'},
inplace=True)
filteredchunk = chunk[chunk["MARKET"] == "SALE"][['LISTING ID', 'DELETED']]
for row in filteredchunk.values:
currentState = priceMap.get(row[0])
if currentState != None:
if (currentState.currentprice == currentState.initialmarketprice):
pSame += 1
total += 1
startDay, endDay, percent = currentState.add(row[1], 0)
distribution.add(startDay, endDay, percent)
priceMap.pop(row[0])
print len(priceMap)
print pSame, total, pSame * 1.0 / total
plotProbability(distribution.dist)
def ZooplaPriceChanges():
total = 0
pSame = 0
priceMap = {}
# distribution = DiscountDistribution()
data = ds.ZooplaMatchedDaily()
# store = pd.HDFStore('rawDaily.hd5',mode='w')
# for chunk in data.parser:
chunk = data.read(10000000)
chunk.rename(columns={'\xef\xbb\xbfLISTING ID':'LISTING ID'},inplace=True)
filteredchunk = chunk[chunk["MARKET"]=="SALE"][['LISTING ID','DAY','PRICE']][chunk['PRICE']>0]
change = []
changeprice = []
nochange = []
for row in filteredchunk.values:
if row[0] in priceMap:
if(priceMap[row[0]].currentprice == row[2]):
# no change
nochange.append(priceMap[row[0]].daysonmarket/30)
else:'
change.append(priceMap[row[0]].daysonmarket/30)
changeprice.append([priceMap[row[0]].daysonmarket/30, -(priceMap[row[0]].currentprice-row[2])/row[2]*100])
startDay, endDay, percent = priceMap[row[0]].add(row[1],row[2])
distribution.add(startDay, endDay, percent)
else:
priceMap[row[0]] = PriceCalc(row[1],row[2])
# now get deletion dates
delData = ds.ZooplaMatchedCollated()
# for chunk in delData.parser:
chunk = delData.read(10000000)
chunk.rename(columns={'\xef\xbb\xbfLISTING ID':'LISTING ID'},inplace=True)
filteredchunk = chunk[chunk["MARKET"]=="SALE"][['LISTING ID','DELETED']]
for row in filteredchunk.values:
if row[0] in priceMap:
if(priceMap[row[0]].currentprice == priceMap[row[0]].initialmarketprice):
pSame += 1
total += 1
print pSame, total, pSame*1.0/total
for row in filteredchunk.values:
if row[0] in priceMap:
startDay, endDay, percent = priceMap[row[0]].add(row[1],0)
distribution.add(startDay, endDay, percent)
priceMap.pop(row[0])
print len(priceMap)
global savedOutput1
global savedOutput2
global savedOutput3
savedOutput1 = nochange
savedOutput2 = change
savedOutput3 = changeprice
plotProbability(distribution.dist)
global hist
global n, n1, n2, nprice, df
# hist = np.histogram(savedOutput1)
n1, bins1, patches1 = pyl.hist(savedOutput1,bins=range(min(savedOutput1), max(savedOutput1) + 1, 1))
n2, bins2, patches2 = pyl.hist(savedOutput2,bins=range(min(savedOutput2), max(savedOutput2) + 1, 1))
dist, binsa, binsb = np.histogram2d([x[0] for x in savedOutput3], [x[1] for x in savedOutput3], range=[[0,30],[-30,0]], bins=[30,20])
# plt.imshow(dist)
n = n2/(n1+n2)
return(n, n1, n2)
