上一篇文章：《数据质量检查-理论篇》主要介绍了数据质量检查的基本思路与方法，本文作为补充，从实战角度出发，总结一套基于Python的数据质量检查模板。
承接上文，仍然从重复值检查、缺失值检查、数据倾斜检查、异常值检查四方面进行描述。

1.环境介绍

版本：python2.7
工具：Spyder
开发人：hbsygfz

2.数据集介绍

数据集：dataset.xlsx

3.代码实现

3.1 导入相关库

import pandas as pd

###3.2 读取数据集

dataset = pd.read_excel("/labcenter/python/dataset.xlsx")
discColList = [\'col4\',\'col7\']
contColList = [\'col1\',\'col2\',\'col3\',\'col5\',\'col6\']

###3.3 重复值检查
主要统计指标：重复记录数、字段唯一值数。

### (1)重复记录数
def dupRowsCheck(df):
    dupRows = df.duplicated().sum()
    return dupRows

### (2)字段唯一值数
def uiqColValCheck(df):
    # 记录数,变量数
    m,n = df.shape
    uiqDf = pd.DataFrame(index=df.columns,columns=[\'rows\',\'uiqCnt\'])
    uiqDf[\'rows\'] = m
    for j in range(n):
        ser = df.iloc[:,j]
        name = df.columns[j]
        uiqCnt = len(ser.unique())
        uiqDf.loc[name,\'uiqCnt\'] = uiqCnt
    return uiqDf

执行与结果：

dupRowsCheck(dataset)
Out[95]: 0

uiqColValCheck(dataset)
Out[96]: 
      rows uiqCnt
col1    10     10
col2    10      9
col3    10     10
col4    10      3
col5    10      9
col6    10      5
col7    10      2

###3.4 缺失值检查
主要统计指标：字段空值记录数。

def missingCheck(df):
    # 记录数,变量数
    m,n = df.shape
    rowsSer = pd.Series(index=df.columns)
    rowsSer.name = \'rows\'
    # 空值记录数
    nullCntSer = df.isnull().sum()
    nullCntSer.name = \'nullCnt\'
    # 合并结果
    missDf = pd.concat([rowsSer,nullCntSer],axis=1)
    missDf[\'rows\'] = m
    return missDf

执行与结果：

missingCheck(dataset)
Out[97]: 
      rows  nullCnt
col1    10        0
col2    10        1
col3    10        0
col4    10        0
col5    10        1
col6    10        0
col7    10        0

###3.5 数据倾斜检查
主要统计指标：记录数、类别个数、最大类别记录数、最大类别记录数占比。

def skewCheck(df,discList,contList,bins):
    # 离散型变量类别统计
    new_df1 = df[discList]
    skewDf1 = pd.DataFrame(index=discList,columns=[\'rows\',\'classCnt\',\'mostClassCnt\',\'mostClassRio\'])
    m1,n1 = new_df1.shape
    for j in range(n1):
        ser = new_df1.iloc[:,j]
        name = new_df1.columns[j]
        freqSer = pd.value_counts(ser)
        skewDf1.loc[name,\'rows\'] = m1
        skewDf1.loc[name,\'classCnt\'] = len(freqSer)
        skewDf1.loc[name,\'mostClassCnt\'] = freqSer[0] 
        skewDf1.loc[name,\'mostClassRio\'] = freqSer[0] * 1.00 / m1
    # 连续型变量分箱统计
    new_df2 = df[contList]
    skewDf2 = pd.DataFrame(index=contList,columns=[\'rows\',\'classCnt\',\'mostClassCnt\',\'mostClassRio\'])
    m2,n2 = new_df2.shape
    for j in range(n2):
        ser = new_df2.iloc[:,j]
        name = new_df2.columns[j]
        freqSer = pd.value_counts(pd.cut(ser,bins))
        skewDf2.loc[name,\'rows\'] = m2
        skewDf2.loc[name,\'classCnt\'] = len(freqSer)
        skewDf2.loc[name,\'mostClassCnt\'] = freqSer[0] 
        skewDf2.loc[name,\'mostClassRio\'] = freqSer[0] * 1.00 / m2
    # 合并结果
    skewDf = pd.concat([skewDf1,skewDf2],axis=0)
    return skewDf

执行与结果：

skewCheck(dataset,discColList,contColList,4)
Out[98]: 
     rows classCnt mostClassCnt mostClassRio
col4   10        3            5          0.5
col7   10        2            6          0.6
col1   10        4            3          0.3
col2   10        4            3          0.3
col3   10        4            4          0.4
col5   10        4            3          0.3
col6   10        4            1          0.1

###3.6 异常值检查
主要统计指标：最大值、最小值、平均值、标准差、变异系数、大于平均值+3倍标准差的记录数、小于平均值-3倍标准差记录数、大于上四分位+1.5倍的四分位间距记录数、小于下四分位-1.5倍的四分位间距记录数、正值记录数、零值记录数、负值记录数。

### (1)异常值统计
def outCheck(df,contList):
    new_df = df[contList]
    resDf = new_df.describe()
    resDf.loc[\'cov\'] = resDf.loc[\'std\'] / resDf.loc[\'mean\']     #计算变异系数
    resDf.loc[\'mean+3std\'] = resDf.loc[\'mean\'] + 3 * resDf.loc[\'std\']  #计算平均值+3倍标准差
    resDf.loc[\'mean-3std\'] = resDf.loc[\'mean\'] - 3 * resDf.loc[\'std\']  #计算平均值-3倍标准差
    resDf.loc[\'75%+1.5dist\'] = resDf.loc[\'75%\'] + 1.5 * (resDf.loc[\'75%\'] - resDf.loc[\'25%\'])  #计算上四分位+1.5倍的四分位间距
    resDf.loc[\'25%-1.5dist\'] = resDf.loc[\'25%\'] - 1.5 * (resDf.loc[\'75%\'] - resDf.loc[\'25%\'])  #计算下四分位-1.5倍的四分位间距
    # 3segma检查
    segmaSer1 = new_df[new_df > resDf.loc[\'mean+3std\']].count()    #平均值+3倍标准差
    segmaSer1.name = \'above3SegmaCnt\'
    segmaSer2 = new_df[new_df < resDf.loc[\'mean-3std\']].count()    #平均值-3倍标准差
    segmaSer2.name = \'below3SegmaCnt\'
    # 箱线图检查
    boxSer1 = new_df[new_df > resDf.loc[\'75%+1.5dist\']].count()    #上四分位+1.5倍的四分位间距 
    boxSer1.name = \'aboveBoxCnt\'
    boxSer2 = new_df[new_df < resDf.loc[\'25%-1.5dist\']].count()    #下四分位-1.5倍的四分位间距
    boxSer2.name = \'belowBoxCnt\'
    # 合并结果
    outTmpDf1 = pd.concat([segmaSer1,segmaSer2,boxSer1,boxSer2],axis=1)
    outTmpDf2 = resDf.loc[[\'max\',\'min\',\'mean\',\'std\',\'cov\']]
    outDf = pd.concat([outTmpDf2.T,outTmpDf1],axis=1)
    return outDf
    
### (2)正负分布检查
def distCheck(df,contList):
    new_df = df[contList]
    distDf = pd.DataFrame(index=contList,columns=[\'rows\',\'posCnt\',\'zeroCnt\',\'negCnt\'])
    m,n = new_df.shape
    for j in range(n):
        ser = new_df.iloc[:,j]
        name = new_df.columns[j]
        posCnt = ser[ser>0].count()
        zeroCnt = ser[ser==0].count()
        negCnt = ser[ser<0].count()
        distDf.loc[name,\'rows\'] = m
        distDf.loc[name,\'posCnt\'] = posCnt
        distDf.loc[name,\'zeroCnt\'] = zeroCnt
        distDf.loc[name,\'negCnt\'] = negCnt
    return distDf

执行与结果：

outCheck(dataset,contColList)
Out[101]: 
           max    min        mean         std       cov  above3SegmaCnt  below3SegmaCnt  aboveBoxCnt  belowBoxCnt
col1  110.0000  101.0  105.500000    3.027650  0.028698               0               0            0            0
col2   58.0000   20.0   34.444444   11.959422  0.347209               0               0            1            0
col3  221.0000   10.0   87.700000   71.030588  0.809927               0               0            0            0
col5  598.0000    0.0  246.333333  235.303647  0.955225               0               0            0            0
col6    0.0115   -0.3   -0.027740    0.095759 -3.452026               0               0            2            1

distCheck(dataset,contColList)
Out[102]: 
     rows posCnt zeroCnt negCnt
col1   10     10       0      0
col2   10      9       0      0
col3   10     10       0      0
col5   10      7       2      0
col6   10      3       6      1