Advanced Numpy
Mon 30 June 2025
import numpy as np
import pandas as pd
import time
from numpy.linalg import inv, det, eig, norm
arr_1 = np.arange(6).reshape(2, 3) # 6 is divisible by 2
print(arr_1)
[[0 1 2]
[3 4 5]]
# Cell 2 – Array Creation
arr_2 = np.arange(2*3).reshape(3, -1)
print(arr_2)
[[0 1]
[2 3]
[4 5]]
# Cell 3 – Array Creation
arr_3 = np.arange(3*3).reshape(1, -1)
print(arr_3)
[[0 1 2 3 4 5 6 7 8]]
# Cell 4 – Array Creation
arr_4 = np.arange(4*3).reshape(2, -1)
print(arr_4)
[[ 0 1 2 3 4 5]
[ 6 7 8 9 10 11]]
# Cell 5 – Array Creation
arr_5 = np.arange(5*3).reshape(3, -1)
print(arr_5)
[[ 0 1 2 3 4]
[ 5 6 7 8 9]
[10 11 12 13 14]]
# Cell 6 – Array Creation
arr_6 = np.arange(6*3).reshape(1, -1)
print(arr_6)
[[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17]]
# Cell 7 – Array Creation
arr_7 = np.arange(24).reshape(2, -1) # 24 ÷ 2 = 12 columns
print(arr_7)
[[ 0 1 2 3 4 5 6 7 8 9 10 11]
[12 13 14 15 16 17 18 19 20 21 22 23]]
# Cell 8 – Array Creation
arr_8 = np.arange(8*3).reshape(3, -1)
print(arr_8)
[[ 0 1 2 3 4 5 6 7]
[ 8 9 10 11 12 13 14 15]
[16 17 18 19 20 21 22 23]]
# Cell 9 – Array Creation
arr_9 = np.arange(9*3).reshape(1, -1)
print(arr_9)
[[ 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]]
# Cell 10 – Array Creation
arr_10 = np.arange(10*3).reshape(2, -1)
print(arr_10)
[[ 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]]
# Cell 11 – Slicing & Indexing
print(arr_1[:1])
[[0 1 2]]
# Cell 12 – Slicing & Indexing
print(arr_2[:1])
[[0 1]]
# Cell 13 – Slicing & Indexing
print(arr_3[:1])
[[0 1 2 3 4 5 6 7 8]]
# Cell 14 – Slicing & Indexing
print(arr_4[:1])
[[0 1 2 3 4 5]]
# Cell 15 – Slicing & Indexing
print(arr_5[:1])
[[0 1 2 3 4]]
# Cell 16 – Slicing & Indexing
print(arr_6[:1])
[[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17]]
# Cell 17 – Slicing & Indexing
print(arr_7[:1])
[[ 0 1 2 3 4 5 6 7 8 9 10 11]]
# Cell 18 – Slicing & Indexing
print(arr_8[:1])
[[0 1 2 3 4 5 6 7]]
# Cell 19 – Slicing & Indexing
print(arr_9[:1])
[[ 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]]
# Cell 20 – Slicing & Indexing
print(arr_10[:1])
[[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14]]
# Cell 21 – Stats
print('mean:', arr_1.mean(), 'std:', arr_1.std())
mean: 2.5 std: 1.707825127659933
# Cell 22 – Stats
print('mean:', arr_2.mean(), 'std:', arr_2.std())
mean: 2.5 std: 1.707825127659933
# Cell 23 – Stats
print('mean:', arr_3.mean(), 'std:', arr_3.std())
mean: 4.0 std: 2.581988897471611
# Cell 24 – Stats
print('mean:', arr_4.mean(), 'std:', arr_4.std())
mean: 5.5 std: 3.452052529534663
# Cell 25 – Stats
print('mean:', arr_5.mean(), 'std:', arr_5.std())
mean: 7.0 std: 4.320493798938574
# Cell 26 – Stats
print('mean:', arr_6.mean(), 'std:', arr_6.std())
mean: 8.5 std: 5.188127472091127
# Cell 27 – Stats
print('mean:', arr_7.mean(), 'std:', arr_7.std())
mean: 11.5 std: 6.922186552431729
# Cell 28 – Stats
print('mean:', arr_8.mean(), 'std:', arr_8.std())
mean: 11.5 std: 6.922186552431729
# Cell 29 – Stats
print('mean:', arr_9.mean(), 'std:', arr_9.std())
mean: 13.0 std: 7.788880963698615
# Cell 30 – Stats
print('mean:', arr_10.mean(), 'std:', arr_10.std())
mean: 14.5 std: 8.65544144839919
# Cell 31 – Matrix Ops
m = np.random.randint(1, 10, (3, 3))
print('Matrix:', m)
print('Det:', det(m))
Matrix: [[8 2 2]
[4 3 9]
[8 9 6]]
Det: -384.0
# Cell 32 – Matrix Ops
m = np.random.randint(1, 10, (3, 3))
print('Matrix:', m)
print('Det:', det(m))
Matrix: [[6 3 6]
[9 5 7]
[6 5 8]]
Det: 30.000000000000014
# Cell 33 – Matrix Ops
m = np.random.randint(1, 10, (3, 3))
print('Matrix:', m)
print('Det:', det(m))
Matrix: [[9 2 4]
[3 5 5]
[6 4 1]]
Det: -152.99999999999997
# Cell 34 – Matrix Ops
m = np.random.randint(1, 10, (3, 3))
print('Matrix:', m)
print('Det:', det(m))
Matrix: [[3 5 1]
[8 8 5]
[6 3 3]]
Det: 32.999999999999986
# Cell 35 – Matrix Ops
m = np.random.randint(1, 10, (3, 3))
print('Matrix:', m)
print('Det:', det(m))
Matrix: [[4 6 8]
[5 5 7]
[9 7 7]]
Det: 32.000000000000014
# Cell 36 – Matrix Ops
m = np.random.randint(1, 10, (3, 3))
print('Matrix:', m)
print('Det:', det(m))
Matrix: [[8 4 6]
[9 5 5]
[8 4 5]]
Det: -4.000000000000003
# Cell 37 – Matrix Ops
m = np.random.randint(1, 10, (3, 3))
print('Matrix:', m)
print('Det:', det(m))
Matrix: [[8 9 2]
[7 2 8]
[5 7 3]]
Det: -151.0
# Cell 38 – Matrix Ops
m = np.random.randint(1, 10, (3, 3))
print('Matrix:', m)
print('Det:', det(m))
Matrix: [[1 3 3]
[8 2 9]
[9 2 4]]
Det: 131.00000000000006
# Cell 39 – Matrix Ops
m = np.random.randint(1, 10, (3, 3))
print('Matrix:', m)
print('Det:', det(m))
Matrix: [[4 8 8]
[2 8 3]
[3 6 1]]
Det: -79.99999999999997
# Cell 40 – Matrix Ops
m = np.random.randint(1, 10, (3, 3))
print('Matrix:', m)
print('Det:', det(m))
Matrix: [[1 9 7]
[3 4 3]
[7 3 6]]
Det: -90.99999999999997
# Cell 41 – Matrix Ops
m = np.random.randint(1, 10, (3, 3))
print('Matrix:', m)
print('Det:', det(m))
Matrix: [[2 2 5]
[9 1 5]
[6 5 2]]
Det: 172.99999999999991
# Cell 42 – Matrix Ops
m = np.random.randint(1, 10, (3, 3))
print('Matrix:', m)
print('Det:', det(m))
Matrix: [[6 5 1]
[5 8 1]
[1 4 7]]
Det: 154.00000000000006
# Cell 43 – Matrix Ops
m = np.random.randint(1, 10, (3, 3))
print('Matrix:', m)
print('Det:', det(m))
Matrix: [[8 3 3]
[6 2 7]
[1 6 9]]
Det: -230.99999999999994
# Cell 44 – Matrix Ops
m = np.random.randint(1, 10, (3, 3))
print('Matrix:', m)
print('Det:', det(m))
Matrix: [[6 6 3]
[7 5 6]
[8 3 4]]
Det: 74.99999999999997
# Cell 45 – Matrix Ops
m = np.random.randint(1, 10, (3, 3))
print('Matrix:', m)
print('Det:', det(m))
Matrix: [[1 5 7]
[5 3 7]
[6 1 5]]
Det: 1.9999999999999905
# Cell 46 – Random
np.random.seed(46)
print(np.random.rand(3, 3))
[[0.78383235 0.63483371 0.24904309]
[0.75807586 0.31307694 0.93723736]
[0.04286545 0.4408672 0.91272229]]
# Cell 47 – Random
np.random.seed(47)
print(np.random.rand(3, 3))
[[0.11348847 0.97448309 0.72873463]
[0.35146781 0.70760514 0.7996046 ]
[0.64556185 0.41459961 0.70603101]]
# Cell 48 – Random
np.random.seed(48)
print(np.random.rand(3, 3))
[[0.01749027 0.89157327 0.28486117]
[0.29897638 0.79203426 0.3244706 ]
[0.86471039 0.44751263 0.54822991]]
# Cell 49 – Random
np.random.seed(49)
print(np.random.rand(3, 3))
[[0.30096446 0.24706183 0.92633514]
[0.89160344 0.68327676 0.56688466]
[0.54696588 0.21042384 0.76975447]]
# Cell 50 – Random
np.random.seed(50)
print(np.random.rand(3, 3))
[[0.49460165 0.2280831 0.25547392]
[0.39632991 0.3773151 0.99657423]
[0.4081972 0.77189399 0.76053669]]
# Cell 51 – Random
np.random.seed(51)
print(np.random.rand(3, 3))
[[0.67573142 0.04471218 0.34330367]
[0.64401973 0.284213 0.94933781]
[0.15767017 0.38797296 0.58999368]]
# Cell 52 – Random
np.random.seed(52)
print(np.random.rand(3, 3))
[[0.82311034 0.02611798 0.21077064]
[0.61842177 0.09828447 0.62013131]
[0.05389022 0.96065406 0.98042937]]
# Cell 53 – Random
np.random.seed(53)
print(np.random.rand(3, 3))
[[0.84666241 0.56116554 0.4548754 ]
[0.35217491 0.58585138 0.53574974]
[0.82745628 0.2645422 0.47837906]]
# Cell 54 – Random
np.random.seed(54)
print(np.random.rand(3, 3))
[[0.42018297 0.3632395 0.18487669]
[0.51828273 0.00860545 0.96893621]
[0.80138126 0.75731199 0.6714843 ]]
# Cell 55 – Random
np.random.seed(55)
print(np.random.rand(3, 3))
[[0.09310829 0.97165592 0.48385998]
[0.2425227 0.53112383 0.28554424]
[0.86263038 0.04110015 0.10834773]]
# Cell 56 – Logical Ops
print(arr_7 > 1)
[[False False True True True True True True True True True True]
[ True True True True True True True True True True True True]]
# Cell 57 – Logical Ops
print(arr_8 > 2)
[[False False False True True True True True]
[ True True True True True True True True]
[ True True True True True True True True]]
# Cell 58 – Logical Ops
print(arr_9 > 3)
[[False False False False True True True True True True True True
True True True True True True True True True True True True
True True True]]
# Cell 59 – Logical Ops
print(arr_10 > 4)
[[False False False False False True True True True True True True
True True True]
[ True True True True True True True True True True True True
True True True]]
# Cell 60 – Logical Ops
print(arr_1 > 0)
[[False True True]
[ True True True]]
# Cell 61 – Logical Ops
print(arr_2 > 1)
[[False False]
[ True True]
[ True True]]
# Cell 62 – Logical Ops
print(arr_3 > 2)
[[False False False True True True True True True]]
# Cell 63 – Logical Ops
print(arr_4 > 3)
[[False False False False True True]
[ True True True True True True]]
# Cell 64 – Logical Ops
print(arr_5 > 4)
[[False False False False False]
[ True True True True True]
[ True True True True True]]
# Cell 65 – Logical Ops
print(arr_6 > 0)
[[False True True True True True True True True True True True
True True True True True True]]
# Cell 66 – Sort & Argsort
rand = np.random.randint(1, 100, 5)
print('Sorted:', np.sort(rand))
Sorted: [13 25 72 78 95]
# Cell 67 – Sort & Argsort
rand = np.random.randint(1, 100, 5)
print('Sorted:', np.sort(rand))
Sorted: [32 52 73 75 98]
# Cell 68 – Sort & Argsort
rand = np.random.randint(1, 100, 5)
print('Sorted:', np.sort(rand))
Sorted: [ 1 56 92 94 94]
# Cell 69 – Sort & Argsort
rand = np.random.randint(1, 100, 5)
print('Sorted:', np.sort(rand))
Sorted: [44 50 63 83 92]
# Cell 70 – Sort & Argsort
rand = np.random.randint(1, 100, 5)
print('Sorted:', np.sort(rand))
Sorted: [11 49 61 79 89]
# Cell 71 – Sort & Argsort
rand = np.random.randint(1, 100, 5)
print('Sorted:', np.sort(rand))
Sorted: [45 60 60 89 93]
# Cell 72 – Sort & Argsort
rand = np.random.randint(1, 100, 5)
print('Sorted:', np.sort(rand))
Sorted: [22 53 63 78 94]
# Cell 73 – Sort & Argsort
rand = np.random.randint(1, 100, 5)
print('Sorted:', np.sort(rand))
Sorted: [34 46 53 59 62]
# Cell 74 – Sort & Argsort
rand = np.random.randint(1, 100, 5)
print('Sorted:', np.sort(rand))
Sorted: [19 25 28 56 81]
# Cell 75 – Sort & Argsort
rand = np.random.randint(1, 100, 5)
print('Sorted:', np.sort(rand))
Sorted: [19 43 57 63 63]
# Cell 76 – Stack/Split
a = np.ones(2)
b = np.zeros(2)
print(np.vstack((a,b)))
[[1. 1.]
[0. 0.]]
# Cell 77 – Stack/Split
a = np.ones(3)
b = np.zeros(3)
print(np.vstack((a,b)))
[[1. 1. 1.]
[0. 0. 0.]]
# Cell 78 – Stack/Split
a = np.ones(4)
b = np.zeros(4)
print(np.vstack((a,b)))
[[1. 1. 1. 1.]
[0. 0. 0. 0.]]
# Cell 79 – Stack/Split
a = np.ones(5)
b = np.zeros(5)
print(np.vstack((a,b)))
[[1. 1. 1. 1. 1.]
[0. 0. 0. 0. 0.]]
# Cell 80 – Stack/Split
a = np.ones(2)
b = np.zeros(2)
print(np.vstack((a,b)))
[[1. 1.]
[0. 0.]]
# Cell 81 – Stack/Split
a = np.ones(3)
b = np.zeros(3)
print(np.vstack((a,b)))
[[1. 1. 1.]
[0. 0. 0.]]
# Cell 82 – Stack/Split
a = np.ones(4)
b = np.zeros(4)
print(np.vstack((a,b)))
[[1. 1. 1. 1.]
[0. 0. 0. 0.]]
# Cell 83 – Stack/Split
a = np.ones(5)
b = np.zeros(5)
print(np.vstack((a,b)))
[[1. 1. 1. 1. 1.]
[0. 0. 0. 0. 0.]]
# Cell 84 – Stack/Split
a = np.ones(2)
b = np.zeros(2)
print(np.vstack((a,b)))
[[1. 1.]
[0. 0.]]
# Cell 85 – Stack/Split
a = np.ones(3)
b = np.zeros(3)
print(np.vstack((a,b)))
[[1. 1. 1.]
[0. 0. 0.]]
# Cell 86 – Set Ops
a = np.array([1,2,3,4])
b = np.array([3,4,5])
print(np.union1d(a,b))
[1 2 3 4 5]
# Cell 87 – Set Ops
a = np.array([1,2,3,4])
b = np.array([3,4,5])
print(np.union1d(a,b))
[1 2 3 4 5]
# Cell 88 – Set Ops
a = np.array([1,2,3,4])
b = np.array([3,4,5])
print(np.union1d(a,b))
[1 2 3 4 5]
# Cell 89 – Set Ops
a = np.array([1,2,3,4])
b = np.array([3,4,5])
print(np.union1d(a,b))
[1 2 3 4 5]
# Cell 90 – Set Ops
a = np.array([1,2,3,4])
b = np.array([3,4,5])
print(np.union1d(a,b))
[1 2 3 4 5]
# Cell 91 – Set Ops
a = np.array([1,2,3,4])
b = np.array([3,4,5])
print(np.union1d(a,b))
[1 2 3 4 5]
# Cell 92 – Set Ops
a = np.array([1,2,3,4])
b = np.array([3,4,5])
print(np.union1d(a,b))
[1 2 3 4 5]
# Cell 93 – Set Ops
a = np.array([1,2,3,4])
b = np.array([3,4,5])
print(np.union1d(a,b))
[1 2 3 4 5]
# Cell 94 – Set Ops
a = np.array([1,2,3,4])
b = np.array([3,4,5])
print(np.union1d(a,b))
[1 2 3 4 5]
# Cell 95 – Set Ops
a = np.array([1,2,3,4])
b = np.array([3,4,5])
print(np.union1d(a,b))
[1 2 3 4 5]
# Cell 96 – Flatten
x = np.array([[1,2],[3,4]])
print(x.flatten())
[1 2 3 4]
# Cell 97 – Flatten
x = np.array([[1,2],[3,4]])
print(x.flatten())
[1 2 3 4]
# Cell 98 – Flatten
x = np.array([[1,2],[3,4]])
print(x.flatten())
[1 2 3 4]
# Cell 99 – Flatten
x = np.array([[1,2],[3,4]])
print(x.flatten())
[1 2 3 4]
# Cell 100 – Flatten
x = np.array([[1,2],[3,4]])
print(x.flatten())
[1 2 3 4]
# Cell 101 – Save/Load
np.save('temp_101.npy', arr_2)
print(np.load('temp_101.npy'))
[[0 1]
[2 3]
[4 5]]
# Cell 102 – Save/Load
np.save('temp_102.npy', arr_3)
print(np.load('temp_102.npy'))
[[0 1 2 3 4 5 6 7 8]]
# Cell 103 – Save/Load
np.save('temp_103.npy', arr_4)
print(np.load('temp_103.npy'))
[[ 0 1 2 3 4 5]
[ 6 7 8 9 10 11]]
# Cell 104 – Save/Load
np.save('temp_104.npy', arr_5)
print(np.load('temp_104.npy'))
[[ 0 1 2 3 4]
[ 5 6 7 8 9]
[10 11 12 13 14]]
# Cell 105 – Save/Load
np.save('temp_105.npy', arr_6)
print(np.load('temp_105.npy'))
[[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17]]
# Cell 106 – Save/Load
np.save('temp_106.npy', arr_7)
print(np.load('temp_106.npy'))
[[ 0 1 2 3 4 5 6 7 8 9 10 11]
[12 13 14 15 16 17 18 19 20 21 22 23]]
# Cell 107 – Save/Load
np.save('temp_107.npy', arr_8)
print(np.load('temp_107.npy'))
[[ 0 1 2 3 4 5 6 7]
[ 8 9 10 11 12 13 14 15]
[16 17 18 19 20 21 22 23]]
# Cell 108 – Save/Load
np.save('temp_108.npy', arr_9)
print(np.load('temp_108.npy'))
[[ 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]]
# Cell 109 – Save/Load
np.save('temp_109.npy', arr_10)
print(np.load('temp_109.npy'))
[[ 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]]
# Cell 110 – Save/Load
np.save('temp_110.npy', arr_1)
print(np.load('temp_110.npy'))
[[0 1 2]
[3 4 5]]
# Cell 111 – Bonus Trick
x = np.random.rand(1000)
start = time.time()
s = np.sum(x)
end = time.time()
print('Time:', end - start)
Time: 0.0
# Cell 112 – Bonus Trick
x = np.random.rand(1000)
start = time.time()
s = np.sum(x)
end = time.time()
print('Time:', end - start)
Time: 0.0
# Cell 113 – Bonus Trick
x = np.random.rand(1000)
start = time.time()
s = np.sum(x)
end = time.time()
print('Time:', end - start)
Time: 0.0010080337524414062
# Cell 114 – Bonus Trick
x = np.random.rand(1000)
start = time.time()
s = np.sum(x)
end = time.time()
print('Time:', end - start)
Time: 0.0
# Cell 115 – Bonus Trick
x = np.random.rand(1000)
start = time.time()
s = np.sum(x)
end = time.time()
print('Time:', end - start)
Time: 0.0
# Cell 116 – Bonus Trick
x = np.random.rand(1000)
start = time.time()
s = np.sum(x)
end = time.time()
print('Time:', end - start)
Time: 0.0
# Cell 117 – Bonus Trick
x = np.random.rand(1000)
start = time.time()
s = np.sum(x)
end = time.time()
print('Time:', end - start)
Time: 0.0
# Cell 118 – Bonus Trick
x = np.random.rand(1000)
start = time.time()
s = np.sum(x)
end = time.time()
print('Time:', end - start)
Time: 0.0
x = np.random.rand(1000)
start = time.time()
s = np.sum(x)
end = time.time()
print('Time:', end - start)
Time: 0.0
x = np.random.rand(1000)
start = time.time()
s = np.sum(x)
end = time.time()
print('Time:', end - start)
Time: 0.0
Score: 125
Category: basics