Convert ndarray from float64 to integer
Question:
I’ve got an ndarray in python with a dtype of float64. I’d like to convert the array to be an array of integers. How should I do this?
int() won’t work, as it says it can’t convert it to a scalar. Changing the dtype field itself obviously doesn’t work, as the actual bytes haven’t changed. I can’t seem to find anything on Google or in the documentation – what’s the best way to do this?
Answers:
Use .astype.
>>> a = numpy.array([1, 2, 3, 4], dtype=numpy.float64)
>>> a
array([ 1., 2., 3., 4.])
>>> a.astype(numpy.int64)
array([1, 2, 3, 4])
See the documentation for more options.
There’s also a really useful discussion about converting the array in place, In-place type conversion of a NumPy array. If you’re concerned about copying your array (which is whatastype() does) definitely check out the link.
While astype is probably the “best” option there are several other ways to convert it to an integer array. I’m using this arr in the following examples:
>>> import numpy as np
>>> arr = np.array([1,2,3,4], dtype=float)
>>> arr
array([ 1., 2., 3., 4.])
The int* functions from NumPy
>>> np.int64(arr)
array([1, 2, 3, 4])
>>> np.int_(arr)
array([1, 2, 3, 4])
The NumPy *array functions themselves:
>>> np.array(arr, dtype=int)
array([1, 2, 3, 4])
>>> np.asarray(arr, dtype=int)
array([1, 2, 3, 4])
>>> np.asanyarray(arr, dtype=int)
array([1, 2, 3, 4])
The astype method (that was already mentioned but for completeness sake):
>>> arr.astype(int)
array([1, 2, 3, 4])
Note that passing int as dtype to astype or array will default to a default integer type that depends on your platform. For example on Windows it will be int32, on 64bit Linux with 64bit Python it’s int64. If you need a specific integer type and want to avoid the platform “ambiguity” you should use the corresponding NumPy types like np.int32 or np.int64.
All I used is
numpyfloat = (1.0, 2.0, 4.0)
a = numpy.array(numpyfloat, dtype=numpy.int)
That’s just it
I’ve got an ndarray in python with a dtype of float64. I’d like to convert the array to be an array of integers. How should I do this?
int() won’t work, as it says it can’t convert it to a scalar. Changing the dtype field itself obviously doesn’t work, as the actual bytes haven’t changed. I can’t seem to find anything on Google or in the documentation – what’s the best way to do this?
Use .astype.
>>> a = numpy.array([1, 2, 3, 4], dtype=numpy.float64)
>>> a
array([ 1., 2., 3., 4.])
>>> a.astype(numpy.int64)
array([1, 2, 3, 4])
See the documentation for more options.
There’s also a really useful discussion about converting the array in place, In-place type conversion of a NumPy array. If you’re concerned about copying your array (which is whatastype() does) definitely check out the link.
While astype is probably the “best” option there are several other ways to convert it to an integer array. I’m using this arr in the following examples:
>>> import numpy as np
>>> arr = np.array([1,2,3,4], dtype=float)
>>> arr
array([ 1., 2., 3., 4.])
The int* functions from NumPy
>>> np.int64(arr)
array([1, 2, 3, 4])
>>> np.int_(arr)
array([1, 2, 3, 4])
The NumPy *array functions themselves:
>>> np.array(arr, dtype=int)
array([1, 2, 3, 4])
>>> np.asarray(arr, dtype=int)
array([1, 2, 3, 4])
>>> np.asanyarray(arr, dtype=int)
array([1, 2, 3, 4])
The astype method (that was already mentioned but for completeness sake):
>>> arr.astype(int)
array([1, 2, 3, 4])
Note that passing int as dtype to astype or array will default to a default integer type that depends on your platform. For example on Windows it will be int32, on 64bit Linux with 64bit Python it’s int64. If you need a specific integer type and want to avoid the platform “ambiguity” you should use the corresponding NumPy types like np.int32 or np.int64.
All I used is
numpyfloat = (1.0, 2.0, 4.0)
a = numpy.array(numpyfloat, dtype=numpy.int)
That’s just it