Minimum value on a 2d array python

Question

I have an array of the following structure which is simplified for this question:

8 2 3 4 5 6
3 6 6 7 2 6
3 8 5 1 2 9
6 4 2 7 8 3

I wish to find the minimum value in this 2D array however using the inbuilt min function returns a value error:

ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

I have looked into the alternative of using np.argmin:

https://docs.scipy.org/doc/numpy/reference/generated/numpy.argmin.html

However it only evaluates along a single axis and returns the index of the minimum value along a single row/column whereas I wish to evaluate the whole array and return the lowest value not the indices.

If it is possible to return the index values of the lowest item in the array then that would be preferable also as from that the lowest value can easily be found.

EDIT: Thanks to the comments below np.min is the solution I was looking for and I was not aware of it existing so my answer is solved.

Answer 1

You can use np.min()

>>> arr = np.array([[8,2,3,4,5,6],
                    [3,6,6,7,2,6],
                    [3,8,5,1,2,9],
                    [6,4,2,7,8,3]])

>>> arr.min()
1

Answer 2

Alternatively for a non-numpy solution:

>>> a = [[8,2,3,4,5,6],
... [3,6,6,7,2,6],
... [3,8,5,1,2,9],
... [6,4,2,7,8,3]]
>>> mymin = min([min(r) for r in a])
>>> mymin
1

Answer 3

However it only evaluates along a single axis and returns the index of the minimum value along a single row/column whereas I wish to evaluate the whole array and return the lowest value not the indices.

numpy.argmin does not by default evaluate along a single axis, the default is to evaluate along the flattened matrix and it returns the linear index in the flattened array; from the numpy docs that you linked:

By default, the index is into the flattened array, otherwise along the specified axis.

Either way, use numpy.amin or numpy.min to return the minimum value, or equivalently for an array arrname use arrname.min(). As you mentioned, numpy.argmin returns the index of the minimum value (of course, you can then use this index to return the minimum value by indexing your array with it). You could also flatten into a single dimension array with arrname.flatten() and pass that into the built-in min function.

The four following methods produce what you want.

import numpy as np

values = np.array([
    [8,2,3,4,5,6],
    [3,6,6,7,2,6],
    [3,8,5,1,2,9],
    [6,4,2,7,8,3]])

values.min()          # = 1
np.min(values)        # = 1
np.amin(values)       # = 1
min(values.flatten()) # = 1