# 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.]]