We have a method called astype(data_type) to change the data type of a numpy array. If we have a numpy array of type float64, then we can change it to int32 by giving the data type to the astype() method of numpy array.
To convert numpy float to int array in Python, use the np. astype() function. The np. astype() function takes an array of float values and converts it into an integer array.
Note that append does not occur in-place: a new array is allocated and filled. If axis is None, out is a flattened array. Insert elements into an array. Delete elements from an array.
Update: This function only avoids copy if it can, hence this is not the correct answer for this question. unutbu's answer is the right one.
a = a.astype(numpy.float32, copy=False)
numpy astype has a copy flag. Why shouldn't we use it ?
You can make a view with a different dtype, and then copy in-place into the view:
import numpy as np
x = np.arange(10, dtype='int32')
y = x.view('float32')
y[:] = x
print(y)
yields
array([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9.], dtype=float32)
To show the conversion was in-place, note that copying from x
to y
altered x
:
print(x)
prints
array([ 0, 1065353216, 1073741824, 1077936128, 1082130432,
1084227584, 1086324736, 1088421888, 1090519040, 1091567616])
You can change the array type without converting like this:
a.dtype = numpy.float32
but first you have to change all the integers to something that will be interpreted as the corresponding float. A very slow way to do this would be to use python's struct
module like this:
def toi(i):
return struct.unpack('i',struct.pack('f',float(i)))[0]
...applied to each member of your array.
But perhaps a faster way would be to utilize numpy's ctypeslib tools (which I am unfamiliar with)
- edit -
Since ctypeslib doesnt seem to work, then I would proceed with the conversion with the typical numpy.astype
method, but proceed in block sizes that are within your memory limits:
a[0:10000] = a[0:10000].astype('float32').view('int32')
...then change the dtype when done.
Here is a function that accomplishes the task for any compatible dtypes (only works for dtypes with same-sized items) and handles arbitrarily-shaped arrays with user-control over block size:
import numpy
def astype_inplace(a, dtype, blocksize=10000):
oldtype = a.dtype
newtype = numpy.dtype(dtype)
assert oldtype.itemsize is newtype.itemsize
for idx in xrange(0, a.size, blocksize):
a.flat[idx:idx + blocksize] = \
a.flat[idx:idx + blocksize].astype(newtype).view(oldtype)
a.dtype = newtype
a = numpy.random.randint(100,size=100).reshape((10,10))
print a
astype_inplace(a, 'float32')
print a
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