How do you add elements to an empty matrix in python?

Question

I can start with a 2 element object dtype array:

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How do you add an element to a matrix in Python?
How do I add elements to an empty NumPy array in Python?
How do you add data to an empty array?
How do you create an empty array and append value in Python?

In [351]: M = np.array((None,None))
In [352]: M.shape
Out[352]: (2,)
In [353]: M
Out[353]: array([None, None], dtype=object)
In [354]: M[0]=(5,)
In [355]: M[1]=()
In [356]: M
Out[356]: array([(5,), ()], dtype=object)
In [357]: print(M)
[(5,) ()]

Or more directly (from a list of tuples) (beware, sometimes this produces a error rather than object array).

In [362]: np.array([(55,),()])
Out[362]: array([(55,), ()], dtype=object)

But I don't see what it's good for. It would easier to construct a list of tuples:

In [359]: [(5,), ()]
Out[359]: [(5,), ()]

Do not try to use np.append like the list append. It is just a clumsy front end to np.concatenate.

M as you create it is:

In [360]: M = np.array(((),()))
In [361]: M
Out[361]: array([], shape=(2, 0), dtype=float64)

It can't hold any elements. And you can't change the shape of the slots as you can with a list. In numpy shape and dtype are significant.

You can specify object dtype:

In [367]: M = np.array([(),()], object)
In [368]: M
Out[368]: array([], shape=(2, 0), dtype=object)

but it's still impossible to reference and change one of those 0 elements.

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.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)

print('The shape of matrix is :', x.shape)

print('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 numpy as np

y = np.empty((4, 2))

print('The matrix is : \n', y)

z = np.empty(25)

print('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+000
0.00000000e+000 1.16095484e-028 5.28595592e-085 1.04316726e-076
1.75300433e+243 3.15476290e+180 2.45128397e+198 9.25608172e+135
4.73517493e-120 2.16209963e+233 3.99255547e+252 1.03819288e-028
2.16209973e+233 7.35874688e+223 2.34783498e+251 4.52287158e+217
8.78424170e+247 4.62381317e+252 1.47278596e+179 9.08367237e+223
1.16466228e-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 np

x = np.zeros((7, 5))

print('The matrix is : \n', x)

x.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')

How do you add an element to a matrix in Python?

If you are using array module, you can use the concatenation using the + operator, append(), insert(), and extend() functions to add elements to the array. If you are using NumPy arrays, use the append() and insert() function.