2

Assuming that I have a dataframe with the following values:

id    product1sold   product2sold   product3sold
1     2              3              3
2     0              0              5
3     3              2              1

How do I add a 'most_sold' and 'least_sold' column containing all most and least sold products in a list per id? It should look like this.

id    product1   product2   product3    most_sold                least_sold
1        2          3          3        [product2, product3]      [product1]     
2        0          0          5        [product3]                [product1, product2]
3        3          2          1        [product1]                [product3]
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2 Answers 2

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2

Use list comprehension with test minimal and maximal values for list of products:

#select all columns without first
df1 = df.iloc[:, 1:]
cols = df1.columns.to_numpy()

df['most_sold'] = [cols[x].tolist() for x in df1.eq(df1.max(axis=1), axis=0).to_numpy()]
df['least_sold'] = [cols[x].tolist() for x in df1.eq(df1.min(axis=1), axis=0).to_numpy()]
print (df)
   id  product1sold  product2sold  product3sold                     most_sold  \
0   1             2             3             3  [product2sold, product3sold]   
1   2             0             0             5                [product3sold]   
2   3             3             2             1                [product1sold]   

                     least_sold  
0                [product1sold]  
1  [product1sold, product2sold]  
2                [product3sold]

If performance is not important is possible use DataFrame.apply:

df1 = df.iloc[:, 1:]

f = lambda x: x.index[x].tolist()
df['most_sold'] = df1.eq(df1.max(axis=1), axis=0).apply(f, axis=1)
df['least_sold'] = df1.eq(df1.min(axis=1), axis=0).apply(f, axis=1)
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Comments

-1

You can do something like this.

minValueCol = yourDataFrame.idxmin(axis=1) maxValueCol = yourDataFrame.idxmax(axis=1)

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