Create a double for loop that goes through a randomly generated matrix and counts (and prints) the number of threes that appear in the matrix. This code samples from a uniform distribution so think about how many threes you would expect to see. Optional (not for marks): Another way to count these (as long as your matrix is a NumPy matrix) would be (randMat == 3).sum(). If you're curious about how that works, try generating a smaller matrix like smallMat = np.random.randint(1, 11, size = (10, 10)) and then smallIMat == 3 to see what you get. You might be surprised that summing the result matrix works, but in Python True is equivalent to 1 in many cases and False equivalent to 0. There are often these sorts of 'terse' codes which can greatly reduce the typing required to achieve a result. In some cases, they can also be faster. However, for now we mostly want to emphasize how to use the for loop to loop through the matrix. n[ ]: H randMat = np.random.randint(1, 101, size = (100, 100)) for loop(for loop ) n [139]: import numpy as np randMat = np.random.randint(1, 101, size = (100, 100)) for i in range(len(randMat)): print (i)
Create a double for loop that goes through a randomly generated matrix and counts (and prints) the number of threes that appear in the matrix. This code samples from a uniform distribution so think about how many threes you would expect to see. Optional (not for marks): Another way to count these (as long as your matrix is a NumPy matrix) would be (randMat == 3).sum(). If you're curious about how that works, try generating a smaller matrix like smallMat = np.random.randint(1, 11, size = (10, 10)) and then smallIMat == 3 to see what you get. You might be surprised that summing the result matrix works, but in Python True is equivalent to 1 in many cases and False equivalent to 0. There are often these sorts of 'terse' codes which can greatly reduce the typing required to achieve a result. In some cases, they can also be faster. However, for now we mostly want to emphasize how to use the for loop to loop through the matrix. n[ ]: H randMat = np.random.randint(1, 101, size = (100, 100)) for loop(for loop ) n [139]: import numpy as np randMat = np.random.randint(1, 101, size = (100, 100)) for i in range(len(randMat)): print (i)
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Computing in python plz help with matrices
![Q6 - Matrices 3
Create a double for loop that goes through a randomly generated matrix and counts (and prints) the number of threes that appear in the matrix. This code
samples from a uniform distribution so think about how many threes you would expect to see.
Optional (not for marks): Another way to count these (as long as your matrix is a NumPy matrix) would be (randMat == 3).sum(). If you're curious about
how that works, try generating a smaller matrix like smallMat = np.random.randint(1, 11, size = (10, 10)) and then smallMat == 3 to see what
you get. You might be surprised that summing the result matrix works, but in Python True is equivalent to 1 in many cases and False equivalent to 0.
There are often these sorts of 'terse' codes which can greatly reduce the typing required to achieve a result. In some cases, they can also be faster. However,
for now we mostly want to emphasize how to use the for loop to loop through the matrix.
H randMat = np.random.randint(1, 101, size = (100, 100))
for loop(for loop)
n[]:
n [139]:
import numpy as np
randMat = np.random.randint(1, 101, size = (100, 100))
for i in range(len(randMat)):
print (i)
1.
2
4
5:38 PM
ENG
-18°C Mostly cloudy
2022-01-2
search](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F44e7a3ce-803b-45aa-8658-f230b13e4980%2F06c05fc0-c49d-49ac-9d5c-3b69911e95fa%2Fn0cnba_processed.jpeg&w=3840&q=75)
Transcribed Image Text:Q6 - Matrices 3
Create a double for loop that goes through a randomly generated matrix and counts (and prints) the number of threes that appear in the matrix. This code
samples from a uniform distribution so think about how many threes you would expect to see.
Optional (not for marks): Another way to count these (as long as your matrix is a NumPy matrix) would be (randMat == 3).sum(). If you're curious about
how that works, try generating a smaller matrix like smallMat = np.random.randint(1, 11, size = (10, 10)) and then smallMat == 3 to see what
you get. You might be surprised that summing the result matrix works, but in Python True is equivalent to 1 in many cases and False equivalent to 0.
There are often these sorts of 'terse' codes which can greatly reduce the typing required to achieve a result. In some cases, they can also be faster. However,
for now we mostly want to emphasize how to use the for loop to loop through the matrix.
H randMat = np.random.randint(1, 101, size = (100, 100))
for loop(for loop)
n[]:
n [139]:
import numpy as np
randMat = np.random.randint(1, 101, size = (100, 100))
for i in range(len(randMat)):
print (i)
1.
2
4
5:38 PM
ENG
-18°C Mostly cloudy
2022-01-2
search
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