[FIXED] Numpy find number of occurrences in a 2D array

Issue

Is there a numpy function to count the number of occurrences of a certain value in a 2D numpy array. E.g.

np.random.random((3,3))

array([[ 0.68878371,  0.2511641 ,  0.05677177],
       [ 0.97784099,  0.96051717,  0.83723156],
       [ 0.49460617,  0.24623311,  0.86396798]])

How do I find the number of times 0.83723156 occurs in this array?

Solution

To count the number of times x appears in any array, you can simply sum the boolean array that results from a == x:

>>> col = numpy.arange(3)
>>> cols = numpy.tile(col, 3)
>>> (cols == 1).sum()
3

It should go without saying, but I'll say it anyway: this is not very useful with floating point numbers unless you specify a range, like so:

>>> a = numpy.random.random((3, 3))
>>> ((a > 0.5) & (a < 0.75)).sum()
2

This general principle works for all sorts of tests. For example, if you want to count the number of floating point values that are integral:

>>> a = numpy.random.random((3, 3)) * 10
>>> a
array([[ 7.33955747,  0.89195947,  4.70725211],
       [ 6.63686955,  5.98693505,  4.47567936],
       [ 1.36965745,  5.01869306,  5.89245242]])
>>> a.astype(int)
array([[7, 0, 4],
       [6, 5, 4],
       [1, 5, 5]])
>>> (a == a.astype(int)).sum()
0
>>> a[1, 1] = 8
>>> (a == a.astype(int)).sum()
1

You can also use np.isclose() as described by Imanol Luengo, depending on what your goal is. But often, it's more useful to know whether values are in a range than to know whether they are arbitrarily close to some arbitrary value.

The problem with isclose is that its default tolerance values (rtol and atol) are arbitrary, and the results it generates are not always obvious or easy to predict. To deal with complex floating point arithmetic, it does even more floating point arithmetic! A simple range is much easier to reason about precisely. (This is an expression of a more general principle: first, do the simplest thing that could possibly work.)

Still, isclose and its cousin allclose have their uses. I usually use them to see if a whole array is very similar to another whole array, which doesn't seem to be your question.

Answered By - senderle