python - Element-wise string concatenation in numpy

39
votes

Is this a bug?

import numpy as np
a1=np.array(['a','b'])
a2=np.array(['E','F'])

In [20]: add(a1,a2)
Out[20]: NotImplemented

I am trying to do element-wise string concatenation. I thought Add() was the way to do it in numpy but obviously it is not working as expected.

5
pythonarraysstringnumpyelementwise-operations
As the name implies, number is for numbers. Python itself has pretty good string operations. Why not just use that? "".join(["a", "b"]) works fine. - Keith
I was looking at this docs.scipy.org/doc/numpy/reference/routines.char.html - Dave31415
That's cool. But: "All of them are based on the string methods in the Python standard library.". So if you just use the standard library you can write code that doesn't depend on numpy. - Keith
The add operation does not do the same thing as join. numpy's add can be useful for multidimensional arrays or nested lists. - gypaetus
Where did add come from? - Eric

5 Answers

64
votes

This can be done using numpy.core.defchararray.add. Here is an example:

>>> import numpy as np
>>> a1 = np.array(['a', 'b'])
>>> a2 = np.array(['E', 'F'])
>>> np.core.defchararray.add(a1, a2)
array(['aE', 'bF'], 
      dtype='<U2')

There are other useful string operations available for NumPy data types.

12
votes

You can use the chararray subclass to perform array operations with strings:

a1 = np.char.array(['a', 'b'])
a2 = np.char.array(['E', 'F'])

a1 + a2
#chararray(['aE', 'bF'], dtype='|S2')

another nice example:

b = np.array([2, 4])
a1*b
#chararray(['aa', 'bbbb'], dtype='|S4')
6
votes

This can (and should) be done in pure Python, as numpy also uses the Python string manipulation functions internally:

>>> a1 = ['a','b']
>>> a2 = ['E','F']
>>> map(''.join, zip(a1, a2))
['aE', 'bF']
2
votes

Another solution is to convert string arrays into arrays of python of objects so that str.add is called:

>>> import numpy as np
>>> a = np.array(['a', 'b', 'c', 'd'], dtype=np.object)   
>>> print a+a
array(['aa', 'bb', 'cc', 'dd'], dtype=object)

This is not that slow (less than twice as slow as adding integer arrays).

2
votes

One more basic, elegant and fast solution:

In [11]: np.array([x1 + x2 for x1,x2 in zip(a1,a2)])
Out[11]: array(['aE', 'bF'], dtype='<U2')

It is very fast for smaller arrays. 

In [12]: %timeit np.array([x1 + x2 for x1,x2 in zip(a1,a2)])
3.67 µs ± 136 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

In [13]: %timeit np.core.defchararray.add(a1, a2)
6.27 µs ± 28.3 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

In [14]: %timeit np.char.array(a1) + np.char.array(a2)
22.1 µs ± 319 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)

For larger arrays, time difference is not much.

In [15]: b1 = np.full(10000,'a')    
In [16]: b2 = np.full(10000,'b')    

In [189]: %timeit np.array([x1 + x2 for x1,x2 in zip(b1,b2)])
6.74 ms ± 66.9 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

In [188]: %timeit np.core.defchararray.add(b1, b2)
7.03 ms ± 419 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

In [187]: %timeit np.char.array(b1) + np.char.array(b2)
6.97 ms ± 284 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)