Numpy has some very useful string operations, which vectorize the usual Python string operations.
Compared to these operation and to pandas.str, the numpy strings module seems to be missing a very important one: the ability to slice each string in the array. For example,
a = numpy.array(['hello', 'how', 'are', 'you'])
numpy.char.sliceStr(a, slice(1, 3))
>>> numpy.array(['el', 'ow', 're' 'ou'])
Am I missing some obvious method in the module with this functionality? Otherwise, is there a fast vectorized way to achieve this?
Here's a vectorized approach -
def slicer_vectorized(a,start,end):
b = a.view((str,1)).reshape(len(a),-1)[:,start:end]
return np.fromstring(b.tostring(),dtype=(str,end-start))
Sample run -
In [68]: a = np.array(['hello', 'how', 'are', 'you'])
In [69]: slicer_vectorized(a,1,3)
Out[69]:
array(['el', 'ow', 're', 'ou'],
dtype='|S2')
In [70]: slicer_vectorized(a,0,3)
Out[70]:
array(['hel', 'how', 'are', 'you'],
dtype='|S3')
Runtime test -
Testing out all the approaches posted by other authors that I could run at my end and also including the vectorized approach from earlier in this post.
Here's the timings -
In [53]: # Setup input array
...: a = np.array(['hello', 'how', 'are', 'you'])
...: a = np.repeat(a,10000)
...:
# @Alberto Garcia-Raboso's answer
In [54]: %timeit slicer(1, 3)(a)
10 loops, best of 3: 23.5 ms per loop
# @hapaulj's answer
In [55]: %timeit np.frompyfunc(lambda x:x[1:3],1,1)(a)
100 loops, best of 3: 11.6 ms per loop
# Using loop-comprehension
In [56]: %timeit np.array([i[1:3] for i in a])
100 loops, best of 3: 12.1 ms per loop
# From this post
In [57]: %timeit slicer_vectorized(a,1,3)
1000 loops, best of 3: 787 µs per loop