[FIXED] How to test if all rows are equal in a numpy

Issue

In numpy, is there a nice idiomatic way of testing if all rows are equal in a 2d array?

I can do something like

np.all([np.array_equal(M[0], M[i]) for i in xrange(1,len(M))])

This seems to mix python lists with numpy arrays which is ugly and presumably also slow.

Is there a nicer/neater way?

Solution

One way is to check that every row of the array arr is equal to its first row arr[0]:

(arr == arr[0]).all()

Using equality == is fine for integer values, but if arr contains floating point values you could use np.isclose instead to check for equality within a given tolerance:

np.isclose(a, a[0]).all()

If your array contains NaN and you want to avoid the tricky NaN != NaN issue, you could combine this approach with np.isnan:

(np.isclose(a, a[0]) | np.isnan(a)).all()

Answered By - Alex Riley