Math function
- The function applies to each element without a loop
import math as mnumbers = [0,1,4,9,16,25]square_roots = []
for a_number in numbers:
# Append to new list
square_roots.append(m.sqrt(a_number))print(square_roots)
# print listimport NumPy as np square_roots = np.sqrt(numbers)
print(square_roots)
- Unary function: Take the single argument
- Binary universal function
- ex) Max: Compare two arrays → result has the max of two elements in each location
a = np.random.rand(10)
b = np.random.rand(10)
c = np.maximum(a,b)- faster than writing a loop
read official documentation: Give an idea of what library types are available.
Vectorized operation is faster than loop
Example
import math as m
def using_loops():
coordinates = range(-99,100) matrix = list() for x in coordinates:
# hold the result of our calculation
row = list()
for y in coordinates:
row.append(m.sqrt(x ** 2 + y**2)) matrix.append(row)
return matrixprint(matrix)
