NumPy - Broadcasting
Thomas J. Kennedy
1 Getting Started
The next couple snippets are extracted from broadcasting.py.
2 Loops & Comprehensions
In Python… each element of a list must be updated one at a time. If a list of prices needed to be reduced by 10%, each one would need to be multiplied by 0.9 within a loop…
prices = [1.00, 2.95, 8.40, 3.50, 3.30, 16.91]
for idx in range(len(prices)):
price[idx] *= 0.9
or using a list comprehension…
prices = [1.00, 2.95, 8.40, 3.50, 3.30, 16.91]
prices = [0.9 * price for price in prices]
print(prices)
3 NumPy Broadcasting
NumPy’s broadcasting mechanic allows us to write a simple prices *= 0.9.
prices = np.array([1.00, 2.95, 8.40, 3.50, 3.30, 16.91])
prices *= 0.9
print(prices)
print()
print("*" * 80)
print()
The obvious benefit is less typing. The more important one is optimization. NumPy’s core is implemented in C. The official NumPy Documentation provides a succinct overview in its Why is NumPy Fast? section.
4 Benchmarks & Performance
How much faster is NumPy? Let us run a quick using benchmark_broadcasting.py.
num_values = 1000000
num_runs = 100
def op_wrapper_py():
prices = range(1, num_values, 1)
prices = [0.9 * price for price in prices]
py_list = timeit.timeit(op_wrapper_py, number=num_runs)
def op_wrapper_np():
prices = np.arange(0, num_values, 1, dtype=np.float64)
prices[:] *= 0.9
np_array = timeit.timeit(op_wrapper_np, number=num_runs)
print(f"Python Time: {py_list:.4f}")
print(f"NumPy Time : {np_array:.4f}")
On a Core i7-6700k… For 1 million numbers, run 100 times… The NumPy code is a little over 10 times faster than the pure Python code.
| Time (sec) | |
|---|---|
| Python | 5.1248 |
| NumPy | 0.3168 |