Replace negative values in an numpy array

Is there a simple way of replacing all negative values in an array with 0?

I'm having a complete block on how to do it using a NumPy array.

E.g.

a = array([1, 2, 3, -4, 5])

I need to return

[1, 2, 3, 0, 5]

a < 0 gives:

[False, False, False, True, False]

This is where I'm stuck - how to use this array to modify the original array.

You are halfway there. Try:

In [4]: a[a < 0] = 0

In [5]: a
Out[5]: array([1, 2, 3, 0, 5])

Try numpy.clip:

>>> import numpy
>>> a = numpy.arange(-10, 10)
>>> a
array([-10,  -9,  -8,  -7,  -6,  -5,  -4,  -3,  -2,  -1,   0,   1,   2,
         3,   4,   5,   6,   7,   8,   9])
>>> a.clip(0, 10)
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

You can clip only the bottom half with clip(0).

>>> a = numpy.array([1, 2, 3, -4, 5])
>>> a.clip(0)
array([1, 2, 3, 0, 5])

You can clip only the top half with clip(max=n). (This is much better than my previous suggestion, which involved passing NaN to the first parameter and using out to coerce the type.):

>>> a.clip(max=2)
array([ 1,  2,  2, -4,  2])

Another interesting approach is to use where:

>>> numpy.where(a <= 2, a, 2)
array([ 1,  2,  2, -4,  2])

Finally, consider aix's answer. I prefer clip for simple operations because it's self-documenting, but his answer is preferable for more complex operations.

Another minimalist Python solution without using numpy:

[0 if i < 0 else i for i in a]

No need to define any extra functions.

a = [1, 2, 3, -4, -5.23, 6]
[0 if i < 0 else i for i in a]

yields:

[1, 2, 3, 0, 0, 6]

And yet another possibility:

In [2]: a = array([1, 2, 3, -4, 5])

In [3]: where(a<0, 0, a)
Out[3]: array([1, 2, 3, 0, 5])