np.where多个变量

Question

我有一个数据帧：

customer_id [1,2,3,4,5,6,7,8,9,10] 
feature1 [0,0,1,1,0,0,1,1,0,0] 
feature2 [1,0,1,0,1,0,1,0,1,0] 
feature3 [0,0,1,0,0,0,1,0,0,0] 

使用此我想创建一个新的变量（比如说new_var）的说法，当特征1是1，则new_var = 1，如果feature_2 = 1 then new_var = 2，feature3 = 1然后new_var = 3 else 4.我正在尝试np.where，但虽然它不会给我一个错误，但它没有做正确的事情 - 所以我想嵌套的np .where仅适用于单个变量。在这种情况下，在熊猫中执行嵌套if/case的最佳方法是什么？

我np.where代码是这样的：

df[new_var]=np.where(df['feature1']==1,'1', np.where(df['feature2']==1,'2', np.where(df[feature3']==1,'3','4'))) 

Answer 1

我认为你需要numpy.select - 它首先选择True值和所有其他都不重要：

m1 = df['feature1']==1 
m2 = df['feature2']==1  
m3 = df['feature3']==1 
df['new_var'] = np.select([m1, m2, m3], ['1', '2', '3'], default='4') 

样品：

customer_id = [1,2,3,4,5,6,7,8,9,10] 
feature1 = [0,0,1,1,0,0,1,1,0,0] 
feature2 = [1,0,1,0,1,0,1,0,1,0] 
feature3 = [0,0,1,0,0,0,1,0,0,0] 

df = pd.DataFrame({'customer_id':customer_id, 
        'feature1':feature1, 
        'feature2':feature2, 
        'feature3':feature3}) 

m1 = df['feature1']==1 
m2 = df['feature2']==1  
m3 = df['feature3']==1 
df['new_var'] = np.select([m1, m2, m3], ['1', '2', '3'], default='4') 
print (df) 
    customer_id feature1 feature2 feature3 new_var 
0   1   0   1   0  2 
1   2   0   0   0  4 
2   3   1   1   1  1 
3   4   1   0   0  1 
4   5   0   1   0  2 
5   6   0   0   0  4 
6   7   1   1   1  1 
7   8   1   0   0  1 
8   9   0   1   0  2 
9   10   0   0   0  4 

如果features仅1和0可转换0到False和1到True：

m1 = df['feature1'].astype(bool) 
m2 = df['feature2'].astype(bool) 
m3 = df['feature3'].astype(bool) 
df['new_var'] = np.select([m1, m2, m3], ['1', '2', '3'], default='4') 
print (df) 
    customer_id feature1 feature2 feature3 new_var 
0   1   0   1   0  2 
1   2   0   0   0  4 
2   3   1   1   1  1 
3   4   1   0   0  1 
4   5   0   1   0  2 
5   6   0   0   0  4 
6   7   1   1   1  1 
7   8   1   0   0  1 
8   9   0   1   0  2 
9   10   0   0   0  4