Converting int arrays to string arrays in numpy without truncation

Question

Trying to convert int arrays to string arrays in numpy

In [66]: a=array([0,33,4444522])
In [67]: a.astype(str)
Out[67]: 
array(['0', '3', '4'], 
      dtype='|S1')

Not what I intended

In [68]: a.astype('S10')
Out[68]: 
array(['0', '33', '4444522'], 
      dtype='|S10')

This works but I had to know 10 was big enough to hold my longest string. Is there a way of doing this easily without knowing ahead of time what size string you need? It seems a little dangerous that it just quietly truncates your string without throwing an error.

Answer 1

Again, this can be solved in pure Python:

>>> map(str, [0,33,4444522])
['0', '33', '4444522']

Or if you need to convert back and forth:

>>> a = np.array([0,33,4444522])
>>> np.array(map(str, a))
array(['0', '33', '4444522'], 
      dtype='|S7')

Answer 2

You can stay in numpy, doing

np.char.mod('%d', a)

This is twice faster than map or list comprehensions for 10 elements, four times faster for 100. This and other string operations are documented here.

Answer 3

Use arr.astype(str), as int to str conversion is now supported by numpy with the desired outcome:

import numpy as np

a = np.array([0,33,4444522])

res = a.astype(str)

print(res)

array(['0', '33', '4444522'], 
      dtype='<U11')

Answer 4

You can find the smallest sufficient width like so:

In [3]: max(len(str(x)) for x in [0,33,4444522])
Out[3]: 7

Alternatively, just construct the ndarray from a list of strings:

In [7]: np.array([str(x) for x in [0,33,4444522]])
Out[7]: 
array(['0', '33', '4444522'], 
      dtype='|S7')

or, using map():

In [8]: np.array(map(str, [0,33,4444522]))
Out[8]: 
array(['0', '33', '4444522'], 
      dtype='|S7')