#Numpy tips and tricks 1
import numpy as np
a = np.array([5,3,7])
b = 2
print(a+b)
"""Here this is called broadcasting, it allow the operator
addition to be applied between b and a element-wise, even
though b is just a numbver and a is an array it will add
b to each element of a"""
#Now let's run it
Structured Arrays
#Numpy tips and tricks 2
import numpy as np
#We have to define data types for an array
dt = np.dtype([('name', np.str_, 16),
('age', np.int32),
('weight', np.float64)])
#Now we can create a structured array
data = np.array([('Jake', 19, 74.5),
('Sally', 23, 64.3),
('Jack', 16, 78.9)], dtype=dt)
#print(data)
#Now let's run it
#There are also more useful fetaures to this
print("Names:",data['name'])
print("Ages:",data['age'])
print("Weights:",data['weight'])
#You can also change a field directly
data['weight'][0] = 75.7
print("New weights:",data['weight'])
#You can also print out a record like this
print(data[0]['name'],"has info:",data[0])
Fancy Indexing
#Numpy tips and ticks 3
import numpy as np
a = np.array([1,4,8,6,9,2])
#You can select elements by index
print(a[[1, 5, 2]])
#You can also modify elements like this
a[[1,2,3]] = 0
print(a)
Vectorisation
#Numpy tips and tricks 4
import numpy as np
import timeit
#First we will create two random number arrays
a = np.random.rand(100000)
b = np.random.rand(100000)
def dot_product1():
dot_product = 0
for num in range(len(a)):
dot_product += a[num] * b[num]
print(dot_product)
def dot_product2():
dot_product = np.dot(a, b)
print(dot_product)
time1 = timeit.timeit(lambda: dot_product1, number=1)
time2 = timeit.timeit(lambda: dot_product2, number=1)
"""Here we have put the two dot_product codes in their
own functions and we're going to time how long they each
take to run only 1 time"""
print("Function 1 took",time1,"seconds to run")
print("Function 2 took",time2,"seconds to run")
#We can also use vectorisation with angles
angles_deg = np.array([0,30,45,60,90])
#Now we will use the vectorisation to convert the angles
angles_rad = np.radians(angles_deg)
print(angles_rad)
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