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Getting Started with Python Machine Learning
The funny thing starts when we realize just how much the NumPy package is
optimized. For example, it avoids copies wherever possible.
>>> b[1][0]=77
>>> b
array([[ 0, 1],
[77, 3],
[ 4, 5]])
>>> a
array([ 0, 1, 77, 3, 4, 5])
In this case, we have modified the value 2 to 77 in b, and we can immediately see
the same change reflected in a as well. Keep that in mind whenever you need a
true copy.
>>> c = a.reshape((3,2)).copy()
>>> c
array([[ 0, 1],
[77, 3],
[ 4, 5]])
>>> c[0][0] = -99
>>> a
array([ 0, 1, 77, 3, 4, 5])
>>> c
array([[-99, 1],
[ 77, 3],
[ 4, 5]])
Here, c and a are totally independent copies.
Another big advantage of NumPy arrays is that the operations are propagated
to the individual elements.
>>> a*2
array([ 2, 4, 6, 8, 10])
>>> a**2
array([ 1, 4, 9, 16, 25])
Contrast that to ordinary Python lists:
>>> [1,2,3,4,5]*2
[1, 2, 3, 4, 5, 1, 2, 3, 4, 5]
>>> [1,2,3,4,5]**2
Traceback (most recent call last):
File "", line 1, in
TypeError: unsupported operand type(s) for ** or pow(): 'list' and
'int'
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