NumPy: Create a two-dimensional array of specified format
NumPy: Basic Exercise-46 with Solution
Write a NumPy program to create a two-dimensional array of a specified format.
Sample format:
[[ 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 30] [ 31 32 33 34 35 36 37 38 39 40] [ 41 42 43 44 45 46 47 48 49 50] [ 51 52 53 54 55 56 57 58 59 60] [ 61 62 63 64 65 66 67 68 69 70] [ 71 72 73 74 75 76 77 78 79 80] [ 81 82 83 84 85 86 87 88 89 90] [ 91 92 93 94 95 96 97 98 99 100] [101 102 103 104 105 106 107 108 109 110] [111 112 113 114 115 116 117 118 119 120] [121 122 123 124 125 126 127 128 129 130] [131 132 133 134 135 136 137 138 139 140] [141 142 143 144 145 146 147 148 149 150]]
Sample Solution :
Python Code :
# Importing the NumPy library with an alias 'np'
import numpy as np
# Printing a message indicating the creation of an array of shape (15,10)
print("Create an array of shape (15,10):")
# Printing a message indicating Command-1 and displaying the result
print("Command-1")
print(np.arange(1, 151).reshape(15, 10))
# Printing a message indicating Command-2 and displaying the result
print("\nCommand-2")
print(np.arange(1, 151).reshape(-1, 10))
# Printing a message indicating Command-3 and displaying the result
print("\nCommand-3")
print(np.arange(1, 151).reshape(15, -1))
Sample Output:
Create an array of shape (15,10): Command-1 [[ 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 30] [ 31 32 33 34 35 36 37 38 39 40] [ 41 42 43 44 45 46 47 48 49 50] [ 51 52 53 54 55 56 57 58 59 60] [ 61 62 63 64 65 66 67 68 69 70] [ 71 72 73 74 75 76 77 78 79 80] [ 81 82 83 84 85 86 87 88 89 90] [ 91 92 93 94 95 96 97 98 99 100] [101 102 103 104 105 106 107 108 109 110] [111 112 113 114 115 116 117 118 119 120] [121 122 123 124 125 126 127 128 129 130] [131 132 133 134 135 136 137 138 139 140] [141 142 143 144 145 146 147 148 149 150]] Command-2 [[ 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 30] [ 31 32 33 34 35 36 37 38 39 40] [ 41 42 43 44 45 46 47 48 49 50] [ 51 52 53 54 55 56 57 58 59 60] [ 61 62 63 64 65 66 67 68 69 70] [ 71 72 73 74 75 76 77 78 79 80] [ 81 82 83 84 85 86 87 88 89 90] [ 91 92 93 94 95 96 97 98 99 100] [101 102 103 104 105 106 107 108 109 110] [111 112 113 114 115 116 117 118 119 120] [121 122 123 124 125 126 127 128 129 130] [131 132 133 134 135 136 137 138 139 140] [141 142 143 144 145 146 147 148 149 150]] Command-3 [[ 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 30] [ 31 32 33 34 35 36 37 38 39 40] [ 41 42 43 44 45 46 47 48 49 50] [ 51 52 53 54 55 56 57 58 59 60] [ 61 62 63 64 65 66 67 68 69 70] [ 71 72 73 74 75 76 77 78 79 80] [ 81 82 83 84 85 86 87 88 89 90] [ 91 92 93 94 95 96 97 98 99 100] [101 102 103 104 105 106 107 108 109 110] [111 112 113 114 115 116 117 118 119 120] [121 122 123 124 125 126 127 128 129 130] [131 132 133 134 135 136 137 138 139 140] [141 142 143 144 145 146 147 148 149 150]]
Explanation:
In the above code –
print(np.arange(1, 151).reshape(15, 10)): This statement creates a NumPy array with integers from 1 (inclusive) to 151 (exclusive), i.e., integers from 1 to 150, and reshapes it into a 15x10 matrix (15 rows and 10 columns). A copy of the results is printed.
print(np.arange(1, 151).reshape(-1, 10)): This statement creates a NumPy array with integers from 1 to 150, similar to the first example. It then reshapes it into a matrix with 10 columns, and the number of rows is inferred automatically (indicated by -1). In this case, the inferred number of rows is 15. A copy of the results is printed.
print(np.arange(1, 151).reshape(15, -1)): This statement creates a NumPy array with integers from 1 to 150. It then reshapes it into a matrix with 15 rows, and the number of columns is inferred automatically (indicated by -1). In this case, the inferred number of columns is 10. A copy of the results is printed.
Python-Numpy Code Editor:
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