replace an empty numpy string with 0

user_6315533 0
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I have to numpy string array which look like this:

[['0', '', '12.12', '140.65', '', ''],
['3', '', '10.45', '154.45', '', ''],
['5', '', '15.65', '184.74', '', '']]

What I need to do is to replace the empty cells with a number in order to convert it into a float array. I can't just delete the columns because in some cases the empty cells are filled. I tried this:

data = np.char.replace(data, '','0').astype(np.float64)

But this will just put a 0 everywhere between all characters which ends up in this:

[[0, 0, 1020.0102, 104000.0605, 0, 0],
[30, 0, 1000.0405, 105040.0405, 0, 0],
[50, 0, 1050.0605, 108040.0704, 0, 0]]

I can't figure out why python does that? I searched via google but couldn't find a good explanation for numpy.char.replace. Can anyone explain to me how it works?

numpy arrays python replace string
3 Answers
>>> a = np.array([['0', '', '12.12', '140.65', '', ''],
... ['3', '', '10.45', '154.45', '', ''],
... ['5', '', '15.65', '184.74', '', '']])
>>> a[a == ''] = 0
>>> a.astype(np.float64)
array([[   0.  ,    0.  ,   12.12,  140.65,    0.  ,    0.  ],
       [   3.  ,    0.  ,   10.45,  154.45,    0.  ,    0.  ],
       [   5.  ,    0.  ,   15.65,  184.74,    0.  ,    0.  ]])
user_36200030

data = np.char.replace(data, '','0')

It seems to replace all empty places, like '' has one place , and '0' has two places, '12.12' has 6 places. The result is 

[['000' '0' '01020.01020' '0104000.06050' '0' '0']
 ['030' '0' '01000.04050' '0105040.04050' '0' '0']
 ['050' '0' '01050.06050' '0108040.07040' '0' '0']]

Try this :

import numpy as np

a = np.array([['0', '', '12.12', '140.65', '', ''],
              ['3', '', '10.45', '154.45', '', ''],
              ['5', '', '15.65', '184.74', '', '']])

#a[np.where(a == '')] = '0'
a[a == ''] = '0'

a = a.astype(np.float64)

print(a)
user_40133370

I know that this is an old question, but unfortunately, the accepted answer does not work properly today. If you do the [a == ''] comparison you will get a FutureWarning:

FutureWarning: elementwise comparison failed; returning scalar
instead, but in the future will perform elementwise comparison

one method that will do the trick with no waring is to use the numpy.where()

   import numpy as np
   a = np.array([['0', '', '12.12', '140.65', '', ''],
               ['3', '', '10.45', '154.45', '', ''],
               ['5', '', '15.65', '184.74', '', '']])

   result = np.where(a=='', '0', a)
   print(result)

The result is

[['0' '0' '12.12' '140.65' '0' '0']  
 ['3' '0' '10.45' '154.45' '0' '0']  
 ['5' '0' '15.65' '184.74' '0' '0']]
user_19915500
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