Get Row and Column with Minimum value in Entire Pandas DataFrame

Question

The problem is simple and so must be solution but I am not able to find it.

I want to find which row and column in Pandas DataFrame has minimum value and how much is it.

I have tried following code (in addition to various combinations):

df = pd.DataFrame(data=[[4,5,6],[2,1,3],[7,0,5],[2,5,3]], 
                 index = ['R1','R2','R3','R4'], 
                 columns=['C1','C2','C3'])

print(df)

print(df.loc[df.idxmin(axis=0), df.idxmin(axis=1)])

The dataframe (df) being searched is:

    C1  C2  C3
R1   4   5   6
R2   2   1   3
R3   7   0   5
R4   2   5   3

Output for the loc command:

    C1  C2  C2  C1
R2   2   1   1   2
R3   7   0   0   7
R2   2   1   1   2

What I need is:

    C2
R3   0

How can I get this simple result?

Answer 1

Use:

a, b = df.stack().idxmin()
print(df.loc[[a], [b]])
    C2
R3   0

Another @John Zwinck solution working with missing values - use numpy.nanargmin :

df = pd.DataFrame(data=[[4,5,6],[2,np.nan,3],[7,0,5],[2,5,3]], 
    index = ['R1','R2','R3','R4'], 
    columns=['C1','C2','C3'])

print(df)
    C1   C2  C3
R1   4  5.0   6
R2   2  NaN   3
R3   7  0.0   5
R4   2  5.0   3

#https://stackoverflow.com/a/3230123
ri, ci = np.unravel_index(np.nanargmin(df.values), df.shape)
print(df.iloc[[ri], [ci]])
     C2
R3  0.0

Answer 2

I'd get the index this way:

np.unravel_index(np.argmin(df.values), df.shape)

This is much faster than df.stack().idxmin() .

It gives you a tuple such as (2, 1) in your example. Pass that to df.iloc[] to get the value.

Answer 3

Or min + min + dropna + T + dropna + T :

>>> df[df==df.min(axis=1).min()].dropna(how='all').T.dropna().T
     C2
R3  0.0
>>>