#numpy

NumPy

Broadcasting


Broadcasting describes how NumPy automatically brings two arrays with different shapes to a compatible shape during arithmetic operations. Generally, the smaller array is “repeated” multiple times until both arrays have the same shape. Broadcasting is memory-efficient as it doesn’t actually copy the smaller array multiple times.

Code:

import numpy as np

A = np.array([1, 2, 3])
res = A * 3 # scalar is broadcasted to [3 3 3]
print(res)

Output:
# [3 6 9]

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#numpy

NumPy

Smart use of ‘:’ to extract the right shape


Sometimes you encounter a 3-dim array that is of shape (N, T, D), while your function requires a shape of (N, D). At a time like this, reshape() will do more harm than good, so you are left with one simple solution:

Example:

for t in xrange(T):
x[:, t, :] = # ...


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