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91 lines
4.0 KiB
91 lines
4.0 KiB
import pandas as pd
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import pyecharts.options as opts
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from pyecharts.charts import Pie
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import re
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with open('data/房源信息.txt','rb') as file:
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house_list = []
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while True:
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line = file.readline()
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if not line:
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break
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line = eval(line.decode('utf-8'))
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line['面积'] = int(re.findall('\d+',line['面积'])[0])
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line['价格'] = int(re.findall('\d+',line['价格'])[0])
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house_list.append(line)
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house_list_DF = pd.DataFrame(house_list)
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xingzhengqu = [item for item in set(house_list_DF.get(key='行政区')) if item]
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# 租房面积统计
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bins = [-1,30,60,90,120,200,300,400,10000]
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attr = ['0-30平方米','30-60平方米','60-90平方米','90-120平方米','120-200平方米','200-300平方米','300-400平方米','400+平方米']
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tmpDF = house_list_DF.groupby(pd.cut(house_list_DF['面积'],bins = bins,labels=attr)).size().reset_index(name = 'count')
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value = list(map(int,tmpDF['count'].values))
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pie = Pie(init_opts=opts.InitOpts(width='800px',height='800px'))
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pie.add('',zip(attr,value)).set_global_opts(title_opts=opts.TitleOpts(title='租房面积统计'))
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pie.render('images/house/广州租房面积统计.html')
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# 求每个区的每平方米的租房单价
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from pyecharts.charts import TreeMap
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def getAvgPrice(xingzhengqu):
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totalPrice = 0
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totalArea = 0
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for item in house_list:
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if item['行政区'] == xingzhengqu:
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totalArea = totalArea + item['面积']
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totalPrice = totalPrice + item['价格']
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return totalPrice / totalArea if totalArea >0 else 1
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# 获取每个区 单月每平方米的价格
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def getTotalAvgPrice():
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totalAvgPriceList = []
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totalAvgPriceDirList = []
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for index, item in enumerate(xingzhengqu):
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avg_price = getAvgPrice(item)
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totalAvgPriceList.append(round(avg_price,3))
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totalAvgPriceDirList.append({'value':round(avg_price,3),'name':item + " ￥" + str(round(avg_price,3))})
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return totalAvgPriceDirList
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# 获取每月每平方米的价格
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data = getTotalAvgPrice()
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treemap = TreeMap(init_opts=opts.InitOpts(width='900px',height='800px'))
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treemap.add('广州各区房租单价:平方米/月',data,label_opts=opts.LabelOpts(is_show=True, position='inside',font_size=13))
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treemap.render('images/house/广州各区房租单价.html')
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# 获取每个区 单日每平方米的价格
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from pyecharts.charts import Bar
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totalAvgPriceList = []
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for index,item in enumerate(xingzhengqu):
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avg_price = getAvgPrice(item)
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totalAvgPriceList.append(round(avg_price/30,3))
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attr, value = (xingzhengqu,totalAvgPriceList)
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bar = Bar(init_opts=opts.InitOpts(width='900px',height='800px'))
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bar.add_xaxis(attr)
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bar.add_yaxis("广州",value)
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bar.set_global_opts(title_opts=opts.TitleOpts(title='广州各区房租单价:平方米/日'))
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bar.render('images/house/广州每日每平方米的价格.html')
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# 获取户型数据
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from pyecharts.charts import WordCloud
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def getRooms():
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results = house_list_DF.groupby('房间').size().reset_index(name='count')
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room_list = list(results.房间.values)
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weight_list = list(map(int,results['count'].values))
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return (room_list, weight_list)
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attr, value = getRooms()
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wordcloud = WordCloud(init_opts=opts.InitOpts(width='900px',height='400px'))
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wordcloud.add('',zip(attr,value),word_size_range=[2,100])
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wordcloud.render('images/house/广州户型数据.html')
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# 获取各个区的房源比重
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from pyecharts.charts import Pie
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def getAreaWeight():
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result = house_list_DF.groupby('行政区').size().reset_index(name='count')
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areaName = list(result.行政区.values)
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areaWeight = list(map(int,result['count'].values))
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areaName_tmp = []
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for index,item in enumerate(areaName):
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areaName_tmp.append(item + str(round(areaWeight[index]/sum(areaWeight)*100,2))+'%')
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return zip(areaName_tmp,areaWeight)
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pie = Pie(init_opts=opts.InitOpts(width='600px',height='400px'))
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pie.add('',getAreaWeight()).set_global_opts(title_opts=opts.TitleOpts(title='广州房源分布'))
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pie.render('images/house/广州房源分布.html') |