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Plus equal is giving a different answer than assigning to the explicit sum (which is the answer one expects), when slicing is involved. Is there a reason for this? Should plus equal be avoided?
a = np.arange(10)
b = np.arange(10)
a[3:] += a[:-3]
b[3:] = b[3:] + b[:-3]
print a
#[ 0 1 2 3 5 7 9 12 15 18]
print b
#[ 0 1 2 3 5 7 9 11 13 15]
231
NumPy does not support jagged arrays natively.
Numpy with Python Basic slicing is an extension of Python's basic concept of slicing to n dimensions. A Python slice object is constructed by giving start, stop, and step parameters to the built-in slice function. This slice object is passed to the array to extract a part of array.
NumPy Arrays are faster than Python Lists because of the following reasons: An array is a collection of homogeneous data-types that are stored in contiguous memory locations. On the other hand, a list in Python is a collection of heterogeneous data types stored in non-contiguous memory locations.
As predicted, we can see that NumPy arrays are significantly faster than lists.
As JBernardo commented, += change the array in place.
a[3:] += [a:-3] is similar to following:
>>> import numpy as np
>>> a = np.arange(10)
>>>
>>> for i in range(3, 10):
... print('a[{}] ({}) += a[{}] ({})'.format(i, a[i], i-3, a[i-3]))
... a[i] += a[i-3]
... print(' a[{}] -> {}'.format(i, a[i]))
...
a[3] (3) += a[0] (0)
a[3] -> 3
a[4] (4) += a[1] (1)
a[4] -> 5
a[5] (5) += a[2] (2)
a[5] -> 7
a[6] (6) += a[3] (3)
a[6] -> 9
a[7] (7) += a[4] (5) # NOTE: not (4)
a[7] -> 12
a[8] (8) += a[5] (7)
a[8] -> 15
a[9] (9) += a[6] (9)
a[9] -> 18
To avoid that, use copy of the array:
>>> a = np.arange(10)
>>> a[3:] += np.copy(a[:-3]) # OR np.array(a[:-3])
>>> a
array([ 0, 1, 2, 3, 5, 7, 9, 11, 13, 15])
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