房价数据转换和清洗2-白红宇

1.下载厦门房价信息源文件

下载链接：密码：e1fg

2.编写代码

1.原来的数据总共有15列：分别为：标题title、价格price、首付downPayment、户型sizeType、面积size、单价unitPrice、朝向orientation、楼层floor、装修decoration、社区community、区域region、学校school、房屋详情houseDetail、核心卖点keySellingPoint、配套设施equipment

2.进行简单的房价预测不需要用到文本识别和语义分析，因此不需要用到title、

keySellingPoint、equipment,根据现实的情况来说因为先有单价才有总房价，

而进行预测的正是单价，所以用不到price、downPayment。观察房屋详情，发现

其中的数据有错误，有的20多层的楼房却显示没有电梯，这不符合高层住房电梯

规定，7层及以上住房必须安装电梯，不符合实际，所有房产有无电梯根据总楼层数判断

3.根据房屋的层数区分低层住宅、多层住宅、小高层住宅、高层住宅等类别

4.根据房屋的建造年代区分5年以内建造、6-10年前建造、11-15年前建造等类别

5.根据房屋所在行政区划分思明、集美、湖里、海沧、翔安、同安6个类别

6.根据房屋所在区域划分思北、莲花、厦大、中山路等类别

import pandas as pdimport reimport timeimport jsondef getSizeType(df):    def findNumber(reStr,sourceStr):        result_list = re.findall(reStr,sourceStr)        if len(result_list):            return result_list[0]        else:            return 0    sizeType_list = []    for i in range(len(df)):        sizeType = df['sizeType'].iloc[i]        sizeType_dict = dict(            room = findNumber('([0-9]*)室',sizeType),            hall = findNumber('([0-9]*)厅',sizeType),            restroom =findNumber('([0-9]*)卫',sizeType)        )         sizeType_list.append(sizeType_dict)    return pd.DataFrame(sizeType_list,columns=sizeType_list[0].keys())def getPrice(df):    df1 = df['price'].copy()    for i in range(len(df1)):        df1.iloc[i] = df1.iloc[i].strip("万")    return df1def getSize(df):    df1 = df['size'].copy()    for i in range(len(df1)):        df1.iloc[i] = df1.iloc[i].strip("平米")    return df1def getElevator(df):    ele_list = []    for i in range(len(df)):        str1 = df['floor'].iloc[i].split(' ')[1]        allFloor = int(re.findall("共(.*)层",str1)[0])        elevator = 1 if allFloor >= 8 else 0        ele_dict = {'elevator':elevator}        ele_list.append(ele_dict)    df1 = pd.DataFrame(ele_list)    return df1def getSchool(df):    df1 = df['school'].copy()    for i in range(len(df1)):        df1.iloc[i] = 1 if df['school'].iloc[i] == \        df['school'].iloc[i] else 0    return df1def getHeightType(df):    df1 = df['floor'].copy()    heightType = ["低层住宅(1-3层)","多层住宅(4-7层)","小高层住宅(8-16层)",\                  "中高层住宅(17-25层)","高层住宅(26-40层)","超高层住宅(40层以上)"]    for i in range(len(df1)):        str1 = df1.iloc[i].split(' ')[1]        allFloor = int(re.findall("共(.*)层",str1)[0])        if allFloor < 4:            df1.iloc[i] = heightType[0]        elif allFloor < 8:            df1.iloc[i] = heightType[1]        elif allFloor < 17:            df1.iloc[i] = heightType[2]        elif allFloor < 26:            df1.iloc[i] = heightType[3]        elif allFloor < 41:            df1.iloc[i] = heightType[4]        else:            df1.iloc[i] = heightType[5]    return pd.get_dummies(df1)def getBuildTime(df):    df1 = df['houseDetail'].copy()    year_now = 2018    for i in range(len(df1)):        details = json.loads(df1.iloc[i])        if '建筑年代' in details:            year_build = int(details['建筑年代'].strip('年'))        else:            year_build = 2010        year_diff = year_now - year_build        if year_diff < 5:            df1.iloc[i] = '5年以内建造'        elif year_diff < 10:            df1.iloc[i] = '6-10年前建造'        elif year_diff < 15:            df1.iloc[i] = '11-15年前建造'        elif year_diff < 20:            df1.iloc[i] = '16-20年前建造'        else:            df1.iloc[i] = '超过20年前建造'    return pd.get_dummies(df1)            def getOrientation(df):    return pd.get_dummies(df['orientation'])def getHeight(df):    df1 = df['floor'].copy()    for i in range(len(df)):        df1.iloc[i] = df['floor'].iloc[i].split(' ')[0][0]    return pd.get_dummies(df1)def getDecoration(df):    df1 = df['decoration'].copy()    for i in range(len(df)):        df1.iloc[i] = df['decoration'].iloc[i].strip('修')    return pd.get_dummies(df1)def getDistrict(df):    df1 = df['region'].copy()    for i in range(len(df)):        df1.iloc[i] = df['region'].iloc[i].split('-')[0]    return pd.get_dummies(df1)def getRegion(df):    df1 = df['region'].copy()    for i in range(len(df)):        region = df['region'].iloc[i].split('-')[1]        df1.iloc[i] = region.strip('(').strip(')')    return pd.get_dummies(df1)    def cleanFloor(df):    for i in range(len(df)):        if '共' not in df['floor'].loc[i]:            df = df.drop([i])    df = df.reset_index(drop=True)    return dfdef cleanSizeType(df):    for i in range(len(df)):        if '室' not in df['sizeType'].loc[i]:            df = df.drop([i])    df = df.reset_index(drop=True)    return dfdef cleanCommunity(df):    df = df[df['community'] == df['community']]    df = df.reset_index(drop=True)    return dfdef cleanDecoration(df):    for i in range(len(df)):        if df['decoration'].loc[i].strip() == '暂无':            df = df.drop([i])    df = df.reset_index(drop=True)    return dfdef cleanOrientation(df):    for i in range(len(df)):        if df['orientation'].loc[i].strip() == '暂无':            df = df.drop([i])    df = df.reset_index(drop=True)    return df if __name__ == "__main__":    startTime = time.time()    df = pd.read_excel("厦门房价数据(房天下版).xlsx")    df = cleanCommunity(df)    df = cleanFloor(df)    df = cleanSizeType(df)    df = cleanDecoration(df)    df = cleanOrientation(df)    df = df.drop_duplicates().reset_index(drop=True)    #下面几个字段是列数较少的字段    price = getPrice(df)    size = getSize(df)    sizeType = getSizeType(df)    elevator = getElevator(df)    school = getSchool(df)    #下面的字段是通过get_dummies方法产生的9-1矩阵，列数较多    heightType = getHeightType(df)    buildTime = getBuildTime(df)    orientaion = getOrientation(df)    height = getHeight(df)    decoration = getDecoration(df)    district = getDistrict(df)    region = getRegion(df)    df_new = pd.concat([price,size,sizeType,elevator,school,heightType,\                        buildTime,orientaion,height,decoration,\                        district,region],axis=1)    df_new.to_excel("厦门房价数据处理结果2.xlsx",columns = df_new.iloc[0].keys())    print("数据处理共花费%.2f秒" %(time.time()-startTime))

3.数据处理结果截图

处理结果截图.png

从上图中可以看出房屋分类可以分为81个区域，10个房屋朝向，5个装修程度。产生的新DataFrame为df_new变量，共有21502行，123列。123列中有1列为房价，为需要预测的数据，有122列为输入变量。