How to create an empty matrix with NumPy in Python ? - GeeksforGeeks

11 Dec, 2020
The term empty matrix has no rows and no columns. A matrix that contains missing values has at least one row and column, as does a matrix that contains zeros. Numerical Python (NumPy)  provides an abundance of useful features and functions for operations on numeric arrays and matrices in Python. If you want to create an empty matrix with the help of NumPy. We can use a function:
numpy.emptynumpy.zeros
numpy.empty
numpy.zeros
1. numpy.empty : It Returns a new array of given shape and type, without initializing entries.
Syntax : numpy.empty(shape, dtype=float, order=’C’)
Parameters:
shape :int or tuple of int i.e shape of the array (5,6) or 5.
dtype data-type, optional i.e desired output data-type for the array, e.g, numpy.int8. Default isnumpy.float64.
order{‘C’, ‘F’}, optional, default: ‘C’ i.e whether to store multi-dimensional data in row-major (C-style) or column-major (Fortran-style) order in memory.
Let’s get started with empty function in NumPy considering an example that you want to create a empty matrix 5 x 5
Example 1: To create an empty matrix of 5 columns and 0 row :
Python3
import numpy as np    x = np.empty((0, 5))print('The value is :', x)  # if we check the matrix dimensions # using shape:print('The shape of matrix is :', x.shape)  # by default the matrix type is float64print('The type of matrix is :', x.dtype)
Output:
The value is : []
The shape of matrix is : (0, 5)
The type of matrix is : float64
Here, the matrix consists of 0 rows and 5 columns that’s why the result is ‘[ ]’. Let’s take another example of empty function in NumPy considering a example that you want to create a empty matrix 4 x 2 with some random numbers.
Example 2: Initializing an empty array, using the expected dimensions/size :
Python3
# import the libraryimport numpy as np  # Here 4 is the number of rows and 2 # is the number of columnsy = np.empty((4, 2))  # print the matrixprint('The matrix is : \n', y)  # print the matrix consist of 25 random numbersz = np.empty(25)  # print the matrixprint('The matrix with 25 random values:', z)
Output :
The matrix is :[[1.41200958e-316 3.99539825e-306][3.38460865e+125 1.06264595e+248][1.33360465e+241 6.76067859e-311][1.80734135e+185 6.47273003e+170]]
The matrix with 25 random values: [1.28430744e-316 8.00386346e-322 0.00000000e+000 0.00000000e+0000.00000000e+000 1.16095484e-028 5.28595592e-085 1.04316726e-0761.75300433e+243 3.15476290e+180 2.45128397e+198 9.25608172e+1354.73517493e-120 2.16209963e+233 3.99255547e+252 1.03819288e-0282.16209973e+233 7.35874688e+223 2.34783498e+251 4.52287158e+2178.78424170e+247 4.62381317e+252 1.47278596e+179 9.08367237e+2231.16466228e-028]
Here, we define the number of rows and columns so the matrix is filled with random numbers.
2. numpy.zeros : It returns a new array of given shape and type, filled with zeros.
Syntax : numpy.zeros(shape, dtype=float, order=’C’)
Parameters:
shape : int or tuple of int i.e shape of the array (5,6) or 5.
dtype data-type, optional i.e desired output data-type for the array, e.g, numpy.int8. Default is numpy.float64.
order{‘C’, ‘F’}, optional, default: ‘C’ i.e whether to store multi-dimensional data in row-major (C-style) or column-major (Fortran-style) order in memory.
Let’s get started with zeros function in NumPy considering an example that you want to create a matrix with zeros.
Example: To create an zeros matrix of 7 columns and 5 rows :
Python3
import numpy as npx = np.zeros((7, 5))  # print the matrixprint('The matrix is : \n', x)  # check the type of matrixx.dtype
Output :
The matrix is : 
 [[0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0.]]
dtype('float64')
Python numpy-Matrix Function
Python-numpy
Python
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