Conditional Selection Of Data In A Pandas DataFrame

I have two columns in my pandas DataFrame. A B 0 1 5 1 2 3 2 3 2 3 4 0 4 5 1 I need the value in A where the value of B is minimum. In the ab

Solution 1:

To find the minimum in column B, you can use df.B.min(). For your DataFrame this returns 0.

To find values at particular locations in a DataFrame, you can use loc:

>>> df.loc[(df.B == df.B.min()), 'A']
3    4
Name: A, dtype: int64

So here, loc picks out all of the rows where column B is equal to its minimum value (df.B == df.B.min()) and selects the corresponding values in column A.

This method returns all values in A corresponding to the minimum value in B. If you only need to find one of the values, the better way is to use idxmin as @aus_lacy suggests.

Solution 2:

Here's one way:

b_min = df.B.idxmin()
a_val = df.A[b_min]

idxmin() returns the index of the minimum value within column B. You then locate the value at that same index in column A.

or if you want a single, albeit less readable, line you can do it like:

a_val = df.A[df.B.idxmin()]

Also, as a disclaimer this solution assumes that the minimum value in column B is unique. For example if you were to have a data set that looked like this:

A  B
1  2
2  5
3  0
4  3
5  0

My solution would return the first instance where B's minimum value is located which in this case is in the third row and has a corresponding A value of 3. If you believe that the minimum value of B is not unique then you should go with @ajcr's solution.