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up vote 3 down vote favorite

Now I have an numpy array X with certain column names, format and length. How can I set all the values to 0 (or empty) in this array, without deleting the format/names etc.?

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up vote 5 down vote accepted

Use numpy.ndarray.fill:

>>> import numpy as np
>>> a = np.arange(10)
>>> a
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> a.fill(0)
>>> a
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
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1  
What about Np.Zeroslike – agconti Jan 16 '14 at 18:57
1  
That would create an entirely new array – Nick T Jan 16 '14 at 18:58
    
@NickT, add @agconti to notify him/her. – falsetru Jan 16 '14 at 19:02
    
@NickT you can use myArray = np.zeros_like(myArray), it works the same I think. – heltonbiker Jan 16 '14 at 19:13
2  
@heltonbiker you're still creating an entirely new array then releasing the old one to get GC'd. Will use twice as much memory and probably take longer. – Nick T Jan 16 '14 at 20:05
up vote 4 down vote

You can use slicing:

>>> a = np.array([[1,2],[3,4]])
>>> a[:] = 0
>>> a
array([[0, 0],
       [0, 0]])
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up vote 2 down vote

Use numpy.zeroes_like to create a new array, filled with zeroes but retaining type information from your existing array:

zeroed_X = numpy.zeroes_like(X)

If you want, you can save that type information from your structured array for future use too. It's all in the dtype:

my_dtype = X.dtype
share|improve this answer
    
Useful perhaps, but if someone wants to simply "empty" the array, it's better to .fill() it with something--you don't double the memory usage. – Nick T Jan 16 '14 at 20:07

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