Python 跨多个列应用贴图

我试图在pandas中的多个列上应用一个映射，以反映数据无效的情况。当数据在我的df['Count']列中无效时，我想将我的df['Value']、df['Lower Confidence Interval']、df['Upper Confidence Interval']和df['Denominator']列设置为-1

这是数据帧的一个示例：

Count   Value       Lower Confidence Interval  Upper Confidence Interval  Denominator
121743  54.15758428 53.95153779                54.36348867                224794
280     91.80327869 88.18009411                94.38654088                305
430     56.95364238 53.39535553                60.44152684                755
970     70.54545455 68.0815009                 72.89492873                1375
nan             
70      28.57142857 23.27957213                34.52488678                245
125     62.5        55.6143037                 68.91456314                200

目前，我正在尝试：

set_minus_1s = {np.nan: -1, '*': -1, -1: -1}

然后：

以及获取错误：

ValueError: Must have equal len keys and value when setting with an iterable

有没有办法链接列引用以调用一次映射，而不是为每个列单独设置一行来调用

set\u减号\u 1s

字典作为映射？

我认为您可以在应用

map

之后使用或替换所有未应用的行：

val = df['Count'].map(set_minus_1s)
print (val)
0    NaN
1    NaN
2    NaN
3    NaN
4   -1.0
5    NaN
6    NaN
Name: Count, dtype: float64

cols =['Value','Count','Lower Confidence Interval','Upper Confidence Interval','Denominator']
df[cols] = df[cols].where(val.isnull(), val, axis=0)
print (df)
      Count      Value  Lower Confidence Interval  Upper Confidence Interval  \
0  121743.0  54.157584                  53.951538                  54.363489   
1     280.0  91.803279                  88.180094                  94.386541   
2     430.0  56.953642                  53.395356                  60.441527   
3     970.0  70.545455                  68.081501                  72.894929   
4      -1.0  -1.000000                  -1.000000                  -1.000000   
5      70.0  28.571429                  23.279572                  34.524887   
6     125.0  62.500000                  55.614304                  68.914563   

   Denominator  
0     224794.0  
1        305.0  
2        755.0  
3       1375.0  
4         -1.0  
5        245.0  
6        200.0  

---

---

对不起，我的代表太低了-现在不是！向上投票。

val = df['Count'].map(set_minus_1s)
print (val)
0    NaN
1    NaN
2    NaN
3    NaN
4   -1.0
5    NaN
6    NaN
Name: Count, dtype: float64

cols =['Value','Count','Lower Confidence Interval','Upper Confidence Interval','Denominator']
df[cols] = df[cols].where(val.isnull(), val, axis=0)
print (df)
      Count      Value  Lower Confidence Interval  Upper Confidence Interval  \
0  121743.0  54.157584                  53.951538                  54.363489   
1     280.0  91.803279                  88.180094                  94.386541   
2     430.0  56.953642                  53.395356                  60.441527   
3     970.0  70.545455                  68.081501                  72.894929   
4      -1.0  -1.000000                  -1.000000                  -1.000000   
5      70.0  28.571429                  23.279572                  34.524887   
6     125.0  62.500000                  55.614304                  68.914563   

   Denominator  
0     224794.0  
1        305.0  
2        755.0  
3       1375.0  
4         -1.0  
5        245.0  
6        200.0  

cols = ['Value', 'Count', 'Lower Confidence Interval', 'Upper Confidence Interval', 'Denominator']
df[cols] = df[cols].mask(val.notnull(), val, axis=0)
print (df)
      Count      Value  Lower Confidence Interval  Upper Confidence Interval  \
0  121743.0  54.157584                  53.951538                  54.363489   
1     280.0  91.803279                  88.180094                  94.386541   
2     430.0  56.953642                  53.395356                  60.441527   
3     970.0  70.545455                  68.081501                  72.894929   
4      -1.0  -1.000000                  -1.000000                  -1.000000   
5      70.0  28.571429                  23.279572                  34.524887   
6     125.0  62.500000                  55.614304                  68.914563   

   Denominator  
0     224794.0  
1        305.0  
2        755.0  
3       1375.0  
4         -1.0  
5        245.0  
6        200.0