Convert 2D float array to 2D int array in NumPy

Question

How do I convert a float NumPy array into an int NumPy array?

score 476 · Accepted Answer · edited Jul 30 '22 at 08:39

476

Use the astype method.

>>> x = np.array([[1.0, 2.3], [1.3, 2.9]])
>>> x
array([[ 1. ,  2.3],
       [ 1.3,  2.9]])
>>> x.astype(int)
array([[1, 2],
       [1, 2]])

edited Jul 30 '22 at 08:39

Mateen Ulhaq

24,552
19
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135

answered Jun 03 '12 at 20:48

BrenBarn

242,874
37
412
384

35

Just make sure you don't have `np.inf`or `np.nan` in your array, since they have surprising results. For example, `np.array([np.inf]).astype(int)` outputs `array([-9223372036854775808])`. – Garrett Jan 22 '15 at 08:42
On my machine, `np.array([np.inf]).astype(int)`, `np.array([-np.inf]).astype(int)`, and `np.array([np.nan]).astype(int)` all return the same thing. Why? – BallpointBen May 14 '18 at 20:47
1

@BallpointBen: `nan` and `inf` are floating-point values and can't be meaningfully converted to int. As the comment before yours notes, there will be surprising behavior, and I don't think the precise behavior is well-defined. If you want to map `nan` and `inf` to certain values, you need to do that yourself. – BrenBarn May 15 '18 at 18:21
1

Note that x.astype(int)[0][0] is not of type `int`. It's `numpy.int32`. – chris Jun 06 '18 at 19:34
1

Note that although this does convert the array to ints, @fhtuft's answer that may result in less surprises – Nathan Musoke Apr 15 '20 at 18:07

fhtuft · Answer 2 · 2012-06-04T11:27:12.397

78

Some numpy functions for how to control the rounding: rint, floor,trunc, ceil. depending how u wish to round the floats, up, down, or to the nearest int.

>>> x = np.array([[1.0,2.3],[1.3,2.9]])
>>> x
array([[ 1. ,  2.3],
       [ 1.3,  2.9]])
>>> y = np.trunc(x)
>>> y
array([[ 1.,  2.],
       [ 1.,  2.]])
>>> z = np.ceil(x)
>>> z
array([[ 1.,  3.],
       [ 2.,  3.]])
>>> t = np.floor(x)
>>> t
array([[ 1.,  2.],
       [ 1.,  2.]])
>>> a = np.rint(x)
>>> a
array([[ 1.,  2.],
       [ 1.,  3.]])

To make one of this in to int, or one of the other types in numpy, astype (as answered by BrenBern):

a.astype(int)
array([[1, 2],
       [1, 3]])

>>> y.astype(int)
array([[1, 2],
       [1, 2]])

edited Jun 04 '12 at 11:27

answered Jun 03 '12 at 21:09

fhtuft

966
5
8

2

Exactly what I was looking for. `astype` is often too generic, and I think it probably is more useful when doing intx - inty conversions. When I want to do float - int conversion being able to choose the kind of rounding is a nice feature. – Bakuriu Sep 11 '12 at 07:03
15

So the simplest way to safely convert almost-ints like `7.99999` to ints like `8`, is `np.rint(arr).astype(int)`? – endolith Oct 12 '12 at 18:53
any way in numpy to make it uint8? – Ryan Feb 06 '18 at 12:47
2

@Ryan `astype(np.uint8)` – chris Jun 06 '18 at 19:28
Can you also comment on the runtime of these methods ? – Divyanshu Srivastava Mar 26 '22 at 21:59

score 19 · Answer 3 · answered Feb 01 '16 at 07:33

19

you can use np.int_:

>>> x = np.array([[1.0, 2.3], [1.3, 2.9]])
>>> x
array([[ 1. ,  2.3],
       [ 1.3,  2.9]])
>>> np.int_(x)
array([[1, 2],
       [1, 2]])

answered Feb 01 '16 at 07:33

Hackaholic

19,069
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score 17 · Answer 4 · answered Apr 06 '16 at 08:04

If you're not sure your input is going to be a Numpy array, you can use asarray with dtype=int instead of astype:

>>> np.asarray([1,2,3,4], dtype=int)
array([1, 2, 3, 4])

If the input array already has the correct dtype, asarray avoids the array copy while astype does not (unless you specify copy=False):

>>> a = np.array([1,2,3,4])
>>> a is np.asarray(a)  # no copy :)
True
>>> a is a.astype(int)  # copy :(
False
>>> a is a.astype(int, copy=False)  # no copy :)
True

Convert 2D float array to 2D int array in NumPy

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