Menu
When I use column_stack to concatenate NumPy arrays, the dtype gets converted:
a = numpy.array([1., 2., 3.], dtype=numpy.float64)
b = numpy.array([1, 2, 3], dtype=numpy.int64)
print numpy.column_stack((a, b)).dtype
>>> float64
Is there a way to preserve the dtype of the individual columns?
693
column_stack() function is used to stack 1-D arrays as columns into a 2-D array.It takes a sequence of 1-D arrays and stack them as columns to make a single 2-D array. 2-D arrays are stacked as-is, just like with hstack function.
Data Types in NumPyNumPy has some extra data types, and refer to data types with one character, like i for integers, u for unsigned integers etc. Below is a list of all data types in NumPy and the characters used to represent them.
NumPy uses much less memory to store data The NumPy arrays takes significantly less amount of memory as compared to python lists. It also provides a mechanism of specifying the data types of the contents, which allows further optimisation of the code.
You can stack two arrays with numpy.lib.recfunctions method and preserve the type with it:
>>> from numpy.lib.recfunctions import append_fields
>>> a = numpy.rec.array(a, dtype=[('a', numpy.float64)])
>>> new_a = append_fields(a, 'b', b, usemask=False, dtypes=[numpy.int64])
>>> new_a
array([(1.0, 1), (2.0, 2), (3.0, 3)],
dtype=[('a', '<f8'), ('b', '<i8')])
>>> new_a['a']
array([ 1., 2., 3.])
>>> new_a['b']
array([1, 2, 3])
My arrays got converted into string (S18) when I column_stack'ed em.
I used astype(desired dtype) to convert them back to what they were after the stacking.
http://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.astype.html
example:
new_array= old_array.astype(float64)
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With
