pandas DataFrame 删除 / 选取含有特定数值的行或列

男娘i 2023-02-27 12:07 42阅读 0赞

**1. 删除 / 选取某列含有特殊数值的行**

import pandas as pd
    import numpy as np
     
    a=np.array([[1,2,3],[4,5,6],[7,8,9]])
    df1=pd.DataFrame(a,index=['row0','row1','row2'],columns=list('ABC'))
    print(df1)
    df2=df1.copy()
     
    #删除/选取某列含有特定数值的行
    #df1=df1[df1['A'].isin([1])]
    #df1[df1['A'].isin([1])]  选取df1中A列包含数字1的行
     
    df1=df1[~df1['A'].isin([1])]
    #通过~取反，选取不包含数字1的行
    print(df1)

运行结果：

![70][]

# 2. 删除 / 选取某行含有特殊数值的列 #

#删除/选取某行含有特定数值的列
    cols=[x for i,x in enumerate(df2.columns) if df2.iat[0,i]==3]
    #利用enumerate对row0进行遍历，将含有数字3的列放入cols中
    print(cols)
     
    #df2=df2[cols]   选取含有特定数值的列
    df2=df2.drop(cols,axis=1) #利用drop方法将含有特定数值的列删除
    print(df2)

运行结果：

![70 1][]

# 3. 删除含有空值的行或列 #

实现思路：利用 [pandas.DateFrame.fillna][] 对空值赋予特定值，再利用上文介绍的方法找到这些含有特定值的行或列去除即可。

import pandas as pd
    import numpy as np
     
    df1 = pd.DataFrame(
        [
            [np.nan, 2, np.nan, 0],
            [3, 4, np.nan, 1],
            [np.nan, np.nan, np.nan, 5],
            [np.nan, 3, np.nan, 4]
        ],columns=list('ABCD'))
    print(df1)
    df2=df1.copy()
     
    df1['A']=df1['A'].fillna('null') #将df中A列所有空值赋值为'null'
    print(df1)
    df1=df1[~df1['A'].isin(['null'])]
    print(df1)
     
    #删除某行空值所在列
     
    df2[0:1]=df2[0:1].fillna('null')
    print(df2)
    cols=[x for i,x in enumerate(df2.columns) if df2.iat[0,i]=='null']
    print(cols)
    df2=df2.drop(cols,axis=1)
    print(df2)

运行结果：

![70 2][]

[70]: /images/20230209/f9786816e33347c1b4c2b364fd659e3d.png
[70 1]: https://img-blog.csdn.net/20180620090722142?watermark/2/text/aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2x1b2NoZW5nNzQzMA==/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70
[pandas.DateFrame.fillna]: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.fillna.html
[70 2]: https://img-blog.csdn.net/20180620091318788?watermark/2/text/aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2x1b2NoZW5nNzQzMA==/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70