Extracting minimum values per row using numpy

Question

I have a question and I could not find the answer on the internet nor on this website. I am sure it is very easy though. Let's say I have a set of 20 numbers and I have them in a 5x4 matrix:

numbers = np.arange(20).reshape(5,4) 

This yields the following matrix:

[ 0,  1,  2,  3]
[ 4,  5,  6,  7]
[ 8,  9, 10, 11]
[12, 13, 14, 15]
[16, 17, 18, 19]

Now I would like to have the minimum value of each row, in this case amounting to 0,4,8,12,16. However, I would like to add that for my problem the minimum value is NOT always in the first column, it can be at a random place in the matrix (i.e. first, second, third or fourth column for each row). If someone could shed some light on this it would be greatly appreciated.

Bally

Answer 1

You just need to specify the axis across which you want to take the minimum. To find the minimum value in each row, you need to specify axis 1:

>>> numbers.min(axis=1)
array([ 0,  4,  8, 12, 16])

For a 2D array, numbers.min() finds the single minimum value in the array, numbers.min(axis=0) returns the minimum value for each column and numbers.min(axis=1) returns the minimum value for each row.

Scansorial

while doing as you say I am getting an error " 'list' object has no attribute 'min' " that is min is not a part of a matrix should i need to run any operation for getting min? – Icterus

\\\n import numpy \\\n numpy.miin(numbers,axis=1) worked – Icterus