Find the min/max excluding zeros in a numpy array (or a tuple) in python

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.

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.

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

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

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]