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I have a dataframe that looks like this:

listing_id  price_4 price_5 price_6 price_7 price_8 price_9 min_price
0   42729   173.0   170.0   158.0   140.0   150.0   150.0   140.0
1   23135   184.0   180.0   173.0   150.0   160.0   160.0   150.0
2   138216  141.0   141.0   141.0   148.0   101.0   100.0   100.0
3   164989  146.0   121.0   121.0   121.0   121.0   121.0   121.0
4   301034  144.0   141.0   72.0    53.0    53.0    100.0   53.0

As you can understand, the min_price is populated by the minimum value from each row. I want to create another column that will be populated with the column name, where the value is minimum. For example, for row 0 the value should be price_7, for row 1 the value should be also price_7, and so on. If it's possible, I would love to do it without the min_price column (I have created it just for that).

Thanks!

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1 Answer 1

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Use DataFrame.filter with DataFrame.idxmin:

df['new'] = df.filter(like='price_').idxmin(axis=1)
print (df)
   listing_id  price_4  price_5  price_6  price_7  price_8  price_9  \
0       42729    173.0    170.0    158.0    140.0    150.0    150.0   
1       23135    184.0    180.0    173.0    150.0    160.0    160.0   
2      138216    141.0    141.0    141.0    148.0    101.0    100.0   
3      164989    146.0    121.0    121.0    121.0    121.0    121.0   
4      301034    144.0    141.0     72.0     53.0     53.0    100.0   

   min_price      new  
0      140.0  price_7  
1      150.0  price_7  
2      100.0  price_9  
3      121.0  price_5  
4       53.0  price_7

Or if want all columns without first by DataFrame.iloc with slicing:

df['new'] = df.iloc[:, 1:].idxmin(axis=1)

Or if want all columns without first and last:

df['new'] = df.iloc[:, 1:-1].idxmin(axis=1)
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