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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?

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

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.

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

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