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up vote 24 down vote favorite
4

How to convert real numpy array to int numpy array? Tried using map directly to array but it did not work.

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2 Answers

up vote 28 down vote accepted

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]])
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up vote 13 down vote

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]])
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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 7:03
2  
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

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