【Python】处理城市空气质量数据（异常值处理，interpolate()线性插值）

拼搏现实的明天。 2023-03-13 13:01 7阅读 0赞

### 一、内容来源 ###

课程来源：[大数据分析师（第一期）（学堂在线 北邮 杨亚）][Link 1]

数据集分享：链接：https://pan.baidu.com/s/1nU29LEfrILve3-ERqccUTQ  
提取码：6ptf

## 二、学习笔记（广州） ##

3σ原则为

数值分布在（μ-σ,μ+σ)中的概率为0.6827

数值分布在（μ-2σ,μ+2σ)中的概率为0.9545

数值分布在（μ-3σ,μ+3σ)中的概率为0.9973

**数据处理代码1：找出异常值，并通过线性插值的方式处理掉**

import numpy as np
    import pandas as pd
    
    #1 读取数据
    filename = 'GuangzhouPM20100101_20151231.csv'
    #df = pd.read_csv(filename,encoding='utf-8',dtype=str)
    #df = pd.read_csv(filename,encoding='utf-8')
    df = pd.read_csv(filename,encoding='utf-8',usecols=[0,1,2,3,4,5,10])
    
    #2 查看数据基本情况
    print('head--------------------------------\n',df.head())
    print('deacribe----------------------------\n',df.describe())
    print('info--------------------------------\n',df.info())
    
    # 查找HUMI中小于0的值
    temp_list = df[df.HUMI < 0].index.tolist()
    print(temp_list)
    
    df["HUMI_new1"] = df["HUMI"]
    for i in temp_list:
        df["HUMI_new1"][i] = np.nan
        #df.loc['HUMI_new1',i] = np.nan
    df["HUMI_new2"]=df["HUMI_new1"].interpolate()
    
    
    # 保存文件
    df.to_csv('gz1.csv')

**数据处理代码2：找出小于3σ的异常数据并处理掉**

import numpy as np
    import pandas as pd
    
    #读取数据
    filename = 'gz1.csv'
    df = pd.read_csv(filename,encoding='utf-8',usecols=[1,2,3,4,5,6,9])
    print('-------------------------head--------------------------\n',df.head())
    print('------------------------deacribe------------------------\n',df.describe())
    print('-------------------------info---------------------------\n',df.info())
    
    HUMI_mean = df.HUMI_new2.mean()
    HUMI_std = df.HUMI_new2.std()
    print(HUMI_mean-3 * HUMI_std, HUMI_mean + 3 *HUMI_std)
    
    #求出HUMI_new2列中数据小于3倍标准差的数据
    index_list = df[df.HUMI_new2 < HUMI_mean-3 * HUMI_std].index.tolist()
    value_list = df[df.HUMI_new2 < HUMI_mean-3 * HUMI_std]
    print("there are {} item:".format(len(index_list)))
    print(index_list)
    print(value_list)
    
    #将这些数改为3倍标准差（下边界）
    df["HUMI_new3"] = df["HUMI_new2"]
    for i in index_list:
        df["HUMI_new3"][i] = int(HUMI_mean-3 * HUMI_std)
    
    #保存文件
    df.to_csv("gz2.csv")

## 三、作业（北京） ##

![在这里插入图片描述][20200513202816944.png_x-oss-process_image]

import numpy as np
    import pandas as pd
    
    #读取数据
    filename = 'BeijingPM20100101_20151231.csv'
    df = pd.read_csv(filename,encoding='utf-8')
    
    #查看数据基本情况
    print('-------------------------head--------------------------\n',df.head())
    print('------------------------deacribe------------------------\n',
          df["HUMI"].describe(),df["PRES"].describe(),df["TEMP"].describe())
    print('-----------------------缺失值---------------------------\n',
          df.isnull().sum().sort_values(ascending=False))
    
    #对缺失值进行线性插值
    df["HUMI"]=df["HUMI"].interpolate()
    df["PRES"]=df["PRES"].interpolate()
    df["TEMP"]=df["TEMP"].interpolate()
    print('-----------------------缺失值---------------------------\n',
          df.isnull().sum().sort_values(ascending=False))
    #对超过3倍标准差的高度异常数据，修改为3倍标准差的数值
    #这里只写一个，其他两个同理，其实观察describe结果可知没有数据超过3倍标准差
    HUMI_mean = df.HUMI.mean()
    HUMI_std = df.HUMI.std()
    index_list = df[df.HUMI > HUMI_mean+3 * HUMI_std].index.tolist()
    for i in index_list:
        df["HUMI"][i] = int(HUMI_mean+3 * HUMI_std)
    
    
    #对PM_Dongsi、PM_Dongsihuan、PM_Nongzhanguan三列中超过500的数据，修改为500
    print('------------------------deacribe(before)------------------------\n',
          df["PM_Dongsi"].describe(),df["PM_Dongsihuan"].describe(),df["PM_Dongsihuan"].describe())
    
    for i in df[df.PM_Dongsi > 500 ].index.tolist():
        df["PM_Dongsi"][i] = 500
    for i in df[df.PM_Dongsihuan > 500 ].index.tolist():
        df["PM_Dongsihuan"][i] = 500
    for i in df[df.PM_Nongzhanguan > 500 ].index.tolist():
        df["PM_Nongzhanguan"][i] = 500
    
    print('------------------------deacribe(after)------------------------\n',
          df["PM_Dongsi"].describe(),df["PM_Dongsihuan"].describe(),df["PM_Dongsihuan"].describe())
    
    #修改cbwd列中值为“cv"的单元格，其值用后项数据填充。
    temp_index = df[df.cbwd =='cv' ].index.tolist()
    for i in reversed(temp_index):  #倒叙遍历
        df["cbwd"][i] = df["cbwd"][i+1]
    print("after",len(df[df.cbwd =='cv' ].index.tolist()))
    
    '''#方法2：全部数据倒序遍历
    print("before",len(df[df.cbwd =='cv' ].index.tolist()))
    for i in reversed(range(len(df))):
        if df["cbwd"][i] =='cv':
            df["cbwd"][i] = df["cbwd"][i+1]
    print("after",len(df[df.cbwd =='cv' ].index.tolist()))
    '''
    
    #保存文件
    df.to_csv("bj.csv")

[Link 1]: http://www.xuetangx.com/courses/course-v1:xuetangx+BigDataBA01+2019_T2/about
[20200513202816944.png_x-oss-process_image]: /images/20230312/dfd62b1137a041d59ba020fa40dc1669.png