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数据竞赛（二）-数据清洗

由 隐身守侯 提交于 2020-01-26 06:20:04

缺失值分析及处理

  • 缺失值出现的原因分析
  • 采取合适的方式对缺失值进行填充

异常值分析及处理

  • 根据测试集数据的分布处理训练集的数据分布
  • 使用合适的方法找出异常值
  • 对异常值进行处理

深度清洗

  • 分析每一个communityName、city、region、plate的数据分布并对其进行数据清洗

参考资料：
一文带你探索性数据分析(EDA)

分割线正文

主要思路分析

虽然这步骤是缺失值处理，但还会涉及到一些最最基础的数据处理。

  1. 缺失值处理
    缺失值的处理手段大体可以分为：删除、填充、映射到高维(当做类别处理)。详细的请自行查找相关资料学习。
    根据任务一，直接找到的缺失值情况是pu和uv；但是，根据特征nunique分布的分析，可以发现rentType存在"–"的情况，这也算是一种缺失值。

    此外，诸如rentType的"未知方式"；houseToward的"暂无数据"等，本质上也算是一种缺失值，但是对于这些缺失方式，我们可以把它当做是特殊的一类处理，而不需要去主动修改或填充值。

    将rentType的"–“转换成"未知方式"类别；
    pv/uv的缺失值用均值填充；
    buildYear存在"暂无信息”，将其用众数填充。

  2. 转换object类型数据
    这里直接采用LabelEncoder的方式编码，详细的编码方式请自行查阅相关资料学习。

  3. 时间字段的处理
    buildYear由于存在"暂无信息",所以需要主动将其转换int类型；
    tradeTime，将其分割成月和日。

  4. 删除无关字段
    ID是唯一码，建模无用，所以直接删除；
    city只有一个SH值，也直接删除；
    tradeTime已经分割成月和日，删除原来字段

1.缺失值处理

处理特征的缺失值，同时去掉一些无用的特征（主观经验判断）

from sklearn.preprocessing import LabelEncoder
def preprocessingData(data):
    #填充缺失值
    data['rentType'][data['rentType'] == '--'] = '未知方式'
    
    #转换object类型数据
    columns = ['rentType', 'communityName', 'houseType', 'houseFloor', 'houseToward', 'houseDecoration', 'region', 'plate']
    
    for feature in columns:
        data[feature] = LabelEncoder().fit_transform(data[feature])
        
    #将buildYear列转换为整形数据处理特殊值
    buildYearmean = pd.DataFrame(data[data['buildYear'] != '暂无信息']['buildYear'].mode())#找出众数，来更换暂无信息
    data.loc[data[data['buildYear'] == '暂无信息'].index, 'buildYear'] = buildYearmean.iloc[0,0]
    data['buildYear'] = data['buildYear'].astype('int')
    
    # 处理pu和uv的空值
    data['pv'].fillna(data['pv'].mean(), inplace = True)
    data['uv'].fillna(data['uv'].mean(), inplace = True)
    data['pv'] = data['pv'].astype('int')
    data['uv'] = data['uv'].astype('int')
    
    #分割交易时间
    def month(x):
        month = int(x.split('/')[1])
        return month
    def day(x):
        day = int(x.split('/')[2])
        return day
    data['month'] = data['tradeTime'].apply(lambda x: month(x))
    data['day'] = data['tradeTime'].apply(lambda x: day(x))
    
    #去掉无效特征来减少干扰
    data.drop('city', axis = 1, inplace = True)
    data.drop('tradeTime', axis =1, inplace = True)
    data.drop('ID', axis =1, inplace = True)
    return data

异常值处理

首先使用孤立森林来处理异常值

from sklearn.ensemble import IsolationForest

# clean data孤立森林异常检测
def IF_drop(train):
    IForest = IsolationForest(contamination=0.01)
    IForest.fit(train["tradeMoney"].values.reshape(-1,1))
    y_pred = IForest.predict(train["tradeMoney"].values.reshape(-1,1))
    drop_index = train.loc[y_pred==-1].index
    print(drop_index)
    train.drop(drop_index,inplace=True)
    return train

data_train = IF_drop(data_train)

def dropData(train):
    #丢弃部分异常值
    train = train[train.area <= 200]
    train = train[(train.tradeMoney <= 16000) & (train.tradeMoney >= 700)]
    train.drop(train[(train['totalFloor'] == 0)].index, inplace = True)
    return train
# 数据集异常值处理
data_train = dropData(data_train)

# 处理完异常值后再次查看面积和租金分布图
sns.set(style="whitegrid")
plt.figure(figsize = (15,5))
sns.boxplot(x = data_train['area'] )
plt.show()
plt.figure(figsize = (15,5))
sns.boxplot(x = data_train['tradeMoney'])
plt.show()

分布图

3.深度清洗

根据常识来利用某些特征的异常值来对数据进行清洗，对训练数据进行删除。单人住的，卫生间大于个数1就没有意义了。比如租房面积很大，结果费用很低，就不合理了。等等通过一些常识特征来处理。
可以先看一下连续数值类型的特征分布情况：

numerical_col = [col for col in data_train.columns if data_train[col].dtypes != 'object' and col not in ['tradeMoney']]
for col_1 in numerical_col:
    plt.figure(figsize = (7,3))
    sns.distplot(data_train[col_1])
    plt.show()

如图
根据分布再结合主观来进行深度清洗：

def cleanData(data):
    data.drop(data[(data['region']=='RG00001') & (data['tradeMoney']<1000)&(data['area']>50)].index,inplace=True)
    data.drop(data[(data['region']=='RG00001') & (data['tradeMoney']>25000)].index,inplace=True)
    data.drop(data[(data['region']=='RG00001') & (data['area']>250)&(data['tradeMoney']<20000)].index,inplace=True)
    data.drop(data[(data['region']=='RG00001') & (data['area']>400)&(data['tradeMoney']>50000)].index,inplace=True)
    data.drop(data[(data['region']=='RG00001') & (data['area']>100)&(data['tradeMoney']<2000)].index,inplace=True)
    data.drop(data[(data['region']=='RG00002') & (data['area']<100)&(data['tradeMoney']>60000)].index,inplace=True)
    data.drop(data[(data['region']=='RG00003') & (data['area']<300)&(data['tradeMoney']>30000)].index,inplace=True)
    data.drop(data[(data['region']=='RG00003') & (data['tradeMoney']<500)&(data['area']<50)].index,inplace=True)
    data.drop(data[(data['region']=='RG00003') & (data['tradeMoney']<1500)&(data['area']>100)].index,inplace=True)
    data.drop(data[(data['region']=='RG00003') & (data['tradeMoney']<2000)&(data['area']>300)].index,inplace=True)
    data.drop(data[(data['region']=='RG00003') & (data['tradeMoney']>5000)&(data['area']<20)].index,inplace=True)
    data.drop(data[(data['region']=='RG00003') & (data['area']>600)&(data['tradeMoney']>40000)].index,inplace=True)
    data.drop(data[(data['region']=='RG00004') & (data['tradeMoney']<1000)&(data['area']>80)].index,inplace=True)
    data.drop(data[(data['region']=='RG00006') & (data['tradeMoney']<200)].index,inplace=True)
    data.drop(data[(data['region']=='RG00005') & (data['tradeMoney']<2000)&(data['area']>180)].index,inplace=True)
    data.drop(data[(data['region']=='RG00005') & (data['tradeMoney']>50000)&(data['area']<200)].index,inplace=True)
    data.drop(data[(data['region']=='RG00006') & (data['area']>200)&(data['tradeMoney']<2000)].index,inplace=True)
    data.drop(data[(data['region']=='RG00007') & (data['area']>100)&(data['tradeMoney']<2500)].index,inplace=True)
    data.drop(data[(data['region']=='RG00010') & (data['area']>200)&(data['tradeMoney']>25000)].index,inplace=True)
    data.drop(data[(data['region']=='RG00010') & (data['area']>400)&(data['tradeMoney']<15000)].index,inplace=True)
    data.drop(data[(data['region']=='RG00010') & (data['tradeMoney']<3000)&(data['area']>200)].index,inplace=True)
    data.drop(data[(data['region']=='RG00010') & (data['tradeMoney']>7000)&(data['area']<75)].index,inplace=True)
    data.drop(data[(data['region']=='RG00010') & (data['tradeMoney']>12500)&(data['area']<100)].index,inplace=True)
    data.drop(data[(data['region']=='RG00004') & (data['area']>400)&(data['tradeMoney']>20000)].index,inplace=True)
    data.drop(data[(data['region']=='RG00008') & (data['tradeMoney']<2000)&(data['area']>80)].index,inplace=True)
    data.drop(data[(data['region']=='RG00009') & (data['tradeMoney']>40000)].index,inplace=True)
    data.drop(data[(data['region']=='RG00009') & (data['area']>300)].index,inplace=True)
    data.drop(data[(data['region']=='RG00009') & (data['area']>100)&(data['tradeMoney']<2000)].index,inplace=True)
    data.drop(data[(data['region']=='RG00011') & (data['tradeMoney']<10000)&(data['area']>390)].index,inplace=True)
    data.drop(data[(data['region']=='RG00012') & (data['area']>120)&(data['tradeMoney']<5000)].index,inplace=True)
    data.drop(data[(data['region']=='RG00013') & (data['area']<100)&(data['tradeMoney']>40000)].index,inplace=True)
    data.drop(data[(data['region']=='RG00013') & (data['area']>400)&(data['tradeMoney']>50000)].index,inplace=True)
    data.drop(data[(data['region']=='RG00013') & (data['area']>80)&(data['tradeMoney']<2000)].index,inplace=True)
    data.drop(data[(data['region']=='RG00014') & (data['area']>300)&(data['tradeMoney']>40000)].index,inplace=True)
    data.drop(data[(data['region']=='RG00014') & (data['tradeMoney']<1300)&(data['area']>80)].index,inplace=True)
    data.drop(data[(data['region']=='RG00014') & (data['tradeMoney']<8000)&(data['area']>200)].index,inplace=True)
    data.drop(data[(data['region']=='RG00014') & (data['tradeMoney']<1000)&(data['area']>20)].index,inplace=True)
    data.drop(data[(data['region']=='RG00014') & (data['tradeMoney']>25000)&(data['area']>200)].index,inplace=True)
    data.drop(data[(data['region']=='RG00014') & (data['tradeMoney']<20000)&(data['area']>250)].index,inplace=True)
    data.drop(data[(data['region']=='RG00005') & (data['tradeMoney']>30000)&(data['area']<100)].index,inplace=True)
    data.drop(data[(data['region']=='RG00005') & (data['tradeMoney']<50000)&(data['area']>600)].index,inplace=True)
    data.drop(data[(data['region']=='RG00005') & (data['tradeMoney']>50000)&(data['area']>350)].index,inplace=True)
    data.drop(data[(data['region']=='RG00006') & (data['tradeMoney']>4000)&(data['area']<100)].index,inplace=True)
    data.drop(data[(data['region']=='RG00006') & (data['tradeMoney']<600)&(data['area']>100)].index,inplace=True)
    data.drop(data[(data['region']=='RG00006') & (data['area']>165)].index,inplace=True)
    data.drop(data[(data['region']=='RG00012') & (data['tradeMoney']<800)&(data['area']<30)].index,inplace=True)
    data.drop(data[(data['region']=='RG00007') & (data['tradeMoney']<1100)&(data['area']>50)].index,inplace=True)
    data.drop(data[(data['region']=='RG00004') & (data['tradeMoney']>8000)&(data['area']<80)].index,inplace=True)
    data.loc[(data['region']=='RG00002')&(data['area']>50)&(data['rentType']=='合租'),'rentType']='整租'
    data.loc[(data['region']=='RG00014')&(data['rentType']=='合租')&(data['area']>60),'rentType']='整租'
    data.drop(data[(data['region']=='RG00008')&(data['tradeMoney']>15000)&(data['area']<110)].index,inplace=True)
    data.drop(data[(data['region']=='RG00008')&(data['tradeMoney']>20000)&(data['area']>110)].index,inplace=True)
    data.drop(data[(data['region']=='RG00008')&(data['tradeMoney']<1500)&(data['area']<50)].index,inplace=True)
    data.drop(data[(data['region']=='RG00008')&(data['rentType']=='合租')&(data['area']>50)].index,inplace=True)
    data.drop(data[(data['region']=='RG00015') ].index,inplace=True)
    data.reset_index(drop=True, inplace=True)
    return data

data_train = cleanData(data_train)
标签
数据清洗
大数据
异常值
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