exam_review_in_class.ipynb

Kernel: Python 3 (system-wide)

import numpy as np

print(np.arange(0, 30))

arr = np.arange(0, 30).reshape(5,6)
arr

[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
 24 25 26 27 28 29]

array([[ 0,  1,  2,  3,  4,  5],
       [ 6,  7,  8,  9, 10, 11],
       [12, 13, 14, 15, 16, 17],
       [18, 19, 20, 21, 22, 23],
       [24, 25, 26, 27, 28, 29]])

#  Make all of the first column values be 0
arr[:, 0] = 0
arr

array([[ 0,  1,  2,  3,  4,  5],
       [ 0,  7,  8,  9, 10, 11],
       [ 0, 13, 14, 15, 16, 17],
       [ 0, 19, 20, 21, 22, 23],
       [ 0, 25, 26, 27, 28, 29]])

#  Make the last entry in the array be 100
arr[-1, -1] = 100
print(arr)

#  Another way
arr[arr.shape[0]-1, arr.shape[1]-1] = 29
print(arr)

[[  0   1   2   3   4   5]
 [  0   7   8   9  10  11]
 [  0  13  14  15  16  17]
 [  0  19  20  21  22  23]
 [  0  25  26  27  28 100]]
[[ 0  1  2  3  4  5]
 [ 0  7  8  9 10 11]
 [ 0 13 14 15 16 17]
 [ 0 19 20 21 22 23]
 [ 0 25 26 27 28 29]]

#  Make the last row be twice the first row
arr[-1,:] = 2*arr[0,:]
print(arr)

twos = np.full((1, 6),2)
print(twos)
arr[-1, :] = twos * arr[0,:]
print(arr)

[[ 0  1  2  3  4  5]
 [ 0  7  8  9 10 11]
 [ 0 13 14 15 16 17]
 [ 0 19 20 21 22 23]
 [ 0  2  4  6  8 10]]
[[2 2 2 2 2 2]]
[[ 0  1  2  3  4  5]
 [ 0  7  8  9 10 11]
 [ 0 13 14 15 16 17]
 [ 0 19 20 21 22 23]
 [ 0  2  4  6  8 10]]

#  Allocate space for a 5x6 array without initializing the values
random_values = np.empty((5, 6))
print(random_values)
print(type(random_values[0,0]))

[[2.55288620e-316 0.00000000e+000 0.00000000e+000 0.00000000e+000
  6.92258687e-310 5.02034658e+175]
 [1.77103950e-051 1.39407471e+165 6.53316657e-042 1.22014882e+165
  6.79378448e-144 1.12855837e+277]
 [6.32672840e+180 2.66371964e+233 5.04621361e+180 8.37170571e-144
  3.97062405e+246 1.16318408e-028]
 [7.11426094e-038 3.30399831e+179 6.12975327e-062 3.14190039e+179
  3.98450697e+252 6.74640243e-067]
 [4.57134226e-071 5.06636302e-086 3.35809595e-143 4.82337433e+228
  6.14415221e-144 6.92278571e-310]]
<class 'numpy.float64'>

numbers = np.random.randint(80, 100, 8)
print(numbers)

[98 89 89 85 89 91 93 95]

sorted_indices = np.argsort(numbers)
print(sorted_indices)

sorted_list = numbers[sorted_indices]
print(sorted_list)

[3 1 2 4 5 6 7 0]
[85 89 89 89 91 93 95 98]

numbers = np.random.randint(70, 100, (8, 3))
print(numbers)

[[82 93 80]
 [88 85 79]
 [99 83 75]
 [85 73 98]
 [85 70 98]
 [99 78 96]
 [81 85 72]
 [89 95 71]]

sorted_by_last_exam_indexes = np.argsort(numbers[:, 2])
sorted_by_last_exam = numbers[sorted_by_last_exam_indexes]
print(sorted_by_last_exam)

[[89 95 71]
 [81 85 72]
 [99 83 75]
 [88 85 79]
 [82 93 80]
 [99 78 96]
 [85 73 98]
 [85 70 98]]

#  What were the scores for people who got a score in the 70s on the last exam?
less_than_80_as_boolean = numbers[:, 2] < 80
at_least_70_as_boolean = numbers[:, 2] >= 70
print(less_than_80_as_boolean)
print(at_least_70_as_boolean)

in_the_70s = np.logical_and(less_than_80_as_boolean, at_least_70_as_boolean)
in_the_70s = less_than_80_as_boolean & at_least_70_as_boolean

print(numbers[in_the_70s][:, 2])

[False  True  True False False False  True  True]
[ True  True  True  True  True  True  True  True]
[79 75 72 71]