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I have a dataframe (small sample shown below, it has more columns), and I want to find the column names with the minimum values.

enter image description here

Right now, I have the following code to deal with it:

finaldf['min_pillar_score'] = finaldf.iloc[:, 2:9].idxmin(axis="columns")

This works fine, but does not return multiple values of column names in case there is more than one instance of minimum values. How can I change this to return multiple column names in case there is more than one instance of the minimum value?

Please note, I want row wise results, i.e. minimum column names for each row.

Thanks!

Ray92
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2 Answers2

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Assuming this input as df:

   A  B  C  D
0  5  8  9  5
1  0  0  1  7
2  6  9  2  4
3  5  2  4  2
4  4  7  7  9

You can use the underlying numpy array to get the min value, then compare the values to the min and get the columns that have a match:

s = df.eq(df.to_numpy().min()).any()
list(s[s].index)

output: ['A', 'B']

mozway
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1

try the code below and see if it's in the output format you'd anticipated. it produces the intended result at least.

result will be stored in mins.

mins = df.idxmin(axis="columns")

for i, r in df.iterrows():
    mins[i] = list(r[r == r[mins[i]]].index)

Get column name where value is something in pandas dataframe might be helpful also.

EDIT: adding an image of the output and the full code context.enter image description here

oerdal
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