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Interpret the input as a matrix and display a different type for the output in Numpy
To Interpret the input as a matrix, use the numpy.asmatrix() method in Python Numpy. The dtype parameter is used to set the type of the output array. Unlike matrix, asmatrix does not make a copy if the input is already a matrix or an ndarray. Equivalent to matrix(data, copy=False).
NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. It supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries.
Steps
At first, import the required library −
import numpy as np
Create a 2d array −
arr = np.array([[36, 36, 78, 88], [92, 81, 98, 45], [22, 67, 54, 69 ], [69, 80, 80, 99]])
Displaying our array −
print("Array...
",arr)Get the datatype −
print("
Array datatype...
",arr.dtype)Get the dimensions of the Array −
print("
Array Dimensions...
",arr.ndim)Get the shape of the Array −
print("
Our Array Shape...
",arr.shape)Get the number of elements of the Array −
print("
Elements in the Array...
",arr.size)To Interpret the input as a matrix, use the numpy.asmatrix() method. The dtype parameter is used to set the type of the output array −
res = np.asmatrix(arr, dtype = float)
print("
Result...
",res)Example
import numpy as np
# Create a 2d array
arr = np.array([[36, 36, 78, 88], [92, 81, 98, 45], [22, 67, 54, 69], [69, 80, 80, 99]])
# Displaying our array
print("Array...
",arr)
# Get the datatype
print("
Array datatype...
",arr.dtype)
# Get the dimensions of the Array
print("
Array Dimensions...
",arr.ndim)
# Get the shape of the Array
print("
Our Array Shape...
",arr.shape)
# Get the number of elements of the Array
print("
Elements in the Array...
",arr.size)
# To Interpret the input as a matrix, use the numpy.asmatrix() method in Python Numpy
# The dtype parameter is used to set the type of the output array
res = np.asmatrix(arr, dtype = float)
print("
Result...
",res)Output
Array... [[36 36 78 88] [92 81 98 45] [22 67 54 69] [69 80 80 99]] Array datatype... int64 Array Dimensions... 2 Our Array Shape... (4, 4) Elements in the Array... 16 Result... [[36. 36. 78. 88.] [92. 81. 98. 45.] [22. 67. 54. 69.] [69. 80. 80. 99.]]
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