Returning a NumPy array (or list) of strings of words repeated N times

Question

I have a list of \$n\$ words, and a corresponding \$m \space x \space n\$ frequency matrix (as a NumPy array). I would like to return a list/array of strings of length \$m\$ where the \$m\$th string is comprised of each word repeated according to the frequencies in the \$m\$th row of the frequency matrix. I have managed to achieve the desired result (help from here), but the code is not particularly easy to understand at a glance. Is there a cleaner and more efficient way to perform the following operation?

import numpy as np
x = ['yugoslavia', 'zealand', 'zimbabwe', 'zip', 'zone']
y = np.array([[2,1,0,0,5], [0,0,1,3,0]])
z = np.apply_along_axis(lambda b: ' '.join([ item for sublist in [[x[i]]*b[i] for i in range(len(x))] for item in sublist]),1,y)

>>> z
array(['yugoslavia yugoslavia zealand zone zone zone zone zone',
   'zimbabwe zip zip zip'],
  dtype='<U54')

I am looking for solutions compatible with Python 3.5.

Answer 1

It would seem you're doing it the right way. One thing though: you might want to replace the following piece of code:

[[x[i]]*b[i] for i in range(len(x))]

A few points as to how you could improve this:

• I suggest you use zip to iterate over two arrays simultaneously.
• Also, prefer using () over [], since it creates a generator expression, rather than a list.
• A similar argument holds with the construct join([ ... ]). Simply use join( ... ) instead, which would avoid creating the list in memory.
• Better variable names will also help with clarity.

([s] * count for s, count in zip(strings, counts))

Finally, formatting can make loads of difference:

import numpy as np

strings = ['yugoslavia', 'zealand', 'zimbabwe', 'zip', 'zone']
counts_array = np.array([[2,1,0,0,5], [0,0,1,3,0]])
result = np.apply_along_axis(
    lambda counts: ' '.join(item for sublist in
                                ([s] * count for s, count in zip(strings, counts))
                            for item in sublist),
    1, counts_array)

An equally ugly alternative might involve using two join statements:

result = np.apply_along_axis(
    lambda counts: ' '.join(filter(None,
                  (' '.join([s] * count) for (s, count) in zip(strings, counts)))),
    1, counts_array)

Note how I've had to use filter, as per this question, in order to remove the extra spaces emanating from the empty strings.

Answer 2

np.array([' '.join( np.repeat(x, z)) for z in y ])

repeat handles the repetition part for row of y nicely. The rest is just iteration on the rows. We don't need the generality of apply_along_axis here.

The repeat could be applied to all of y without loop with

n=y.shape[0]
X = np.repeat([x]*n, y.flat).reshape(n, -1)

But the join still has to be done iteratively.

[' '.join(I) for I in X]