Find the min/max excluding zeros in a numpy array

Question

I have an array. The valid values are not zero (either positive or negetive). I want to find the minimum and maximum within the array which should not take zeros into account. For example if the numbers are only negative. Zeros will be problematic.

Answer 1

A simple way would be to use a list comprehension to exclude zeros.

>>> tup = (0, 1, 2, 5, 2)
>>> min([x for x in tup if x !=0])
1

Answer 2

You could use a generator expression to filter out the zeros:

array = [-2, 0, -4, 0, -3, -2]
max(x for x in array if x != 0)

Answer 3

Here's another way of masking which I think is easier to remember (although it does copy the array). For the case in point, it goes like this:

>>> import numpy
>>> a = numpy.array([1.0, 0.0, 2.0])
>>> ma = a[a != 0]
>>> ma.max()
2.0
>>> ma.min()
1.0
>>> 

It generalizes to other expressions such as a > 0, numpy.isnan(a), ... And you can combine masks with standard operators (+ means OR, * means AND, - means NOT) e.g:

# Identify elements that are outside interpolation domain or NaN
outside = (xi < x[0]) + (eta < y[0]) + (xi > x[-1]) + (eta > y[-1])
outside += numpy.isnan(xi) + numpy.isnan(eta)
inside = -outside
xi = xi[inside]
eta = eta[inside]

Answer 4

How about:

import numpy as np
minval = np.min(a[np.nonzero(a)])
maxval = np.max(a[np.nonzero(a)])

where a is your array.

Answer 5

If you can choose the "invalid" value in your array, it is better to use nan instead of 0:

>>> a = numpy.array([1.0, numpy.nan, 2.0])
>>> numpy.nanmax(a)
2.0
>>> numpy.nanmin(a)
1.0

If this is not possible, you can use an array mask:

>>> a = numpy.array([1.0, 0.0, 2.0])
>>> ma = numpy.ma.masked_equal(a, 0.0, copy=False)
>>> ma.max()
2.0
>>> ma.min()
1.0

Compared to Josh's answer using advanced indexing, this has the advantage of avoiding to create a copy of the array.