How do I find out if a numpy array contains integers?

Question

I know there is a simple solution to this but can\'t seem to find it at the moment.

Given a numpy array, I need to know if the array contains integers.

Checking

Answer 1

While the accepted answer from 2009 is still valid, there is a new and enhanced solution as of Numpy v0.19, released in September 2014:

All numerical numpy types are now registered with the type hierarchy in the python numbers module.

This allows for checking the dtype against Python's Numeric abstract base classes.

isinstance(np.dtype('int8'), numbers.Integral)
issubclass(np.dtype('int32').type, numbers.Integral)

You can test against numbers.Complex, numbers.Real and numbers.Integral.

P.S. As you don't need to access .type anymore you can shorten your line by a few characters now. ;)

Answer 2

Found it in the numpy book! Page 23:

The other types in the hierarchy define particular categories of types. These categories can be useful for testing whether or not the object returned by self.dtype.type is of a particular class (using issubclass).

issubclass(n.dtype('int8').type, n.integer)
>>> True
issubclass(n.dtype('int16').type, n.integer)
>>> True

Answer 3

Checking for an integer type does not work for floats that are integers, e.g. 4. Better solution is np.equal(np.mod(x, 1), 0), as in:

>>> import numpy as np
>>> def isinteger(x):
...     return np.equal(np.mod(x, 1), 0)
... 
>>> foo = np.array([0., 1.5, 1.])
>>> bar = np.array([-5,  1,  2,  3, -4, -2,  0,  1,  0,  0, -1,  1])
>>> isinteger(foo)
array([ True, False,  True], dtype=bool)
>>> isinteger(bar)
array([ True,  True,  True,  True,  True,  True,  True,  True,  True,
    True,  True,  True], dtype=bool)
>>> isinteger(1.5)
False
>>> isinteger(1.)
True
>>> isinteger(1)
True

Answer 4

This also works:

  n.dtype('int8').kind == 'i'

Answer 5

Numpy's issubdtype() function can be used as follows:

import numpy as np

size=(3,3)
A = np.random.randint(0, 255, size)
B = np.random.random(size)

print 'Array A:\n',  A
print 'Integers:', np.issubdtype(A[0,0], int)
print 'Floats:', np.issubdtype(A[0,0], float)

print '\nArray B:\n',  B
print 'Integers:', np.issubdtype(B[0,0], int)
print 'Floats:', np.issubdtype(B[0,0], float)

Results:

Array A:
[[  9 224  33]
 [210 117  83]
 [206 139  60]]
Integers: True
Floats: False

Array B:
[[ 0.54221849  0.96021118  0.72322367]
 [ 0.02207826  0.55162813  0.52167972]
 [ 0.74106348  0.72457807  0.9705301 ]]
Integers: False
Floats: True

PS. Keep in mind that the elements of an array are always of the same datatype.