pandas dataframe - how to find multiple column names with minimum values-CodePudding

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

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!

CodePudding user response：

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)

CodePudding user response：

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']