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Matrices are used as a mathematical tool for a variety of purposes in the real world. In this article, we will discuss everything there is about Matrices in Python using the famous NumPy library in the following order:

- What is NumPy and when to use it?
- Creating a Matrix in NumPy
- Matrix operations and examples
- Slicing of Matrices

**BONUS**: Putting It All Together – Python Code to Solve a System of Linear Equations

Let’s get started with Matrices in Python.

NumPy is a Python library allowing easy numerical calculations involving single and multidimensional arrays and matrices. As the name suggests, NumPy excels in performing numerical calculations. Many data science libraries like **Pandas**, **Scikit-learn**, **SciPy**, **matplotlib, **etc. depend on NumPy. It forms an integral part of today’s data science applications written in Python.

Many **linear algebra** calculations become easy to solve using NumPy. **Linear algebra** is the core mathematical tool used in many **Machine Learning** algorithms. Hence knowing NumPy in detail helps you in creating libraries or extend existing Machine Learning libraries.

NumPy provides:

- a powerful N-dimensional array object called as ndarray
- Broadcasting functions
- Tools for integrating C/C++ and Fortran code
- Useful linear algebra, Fourier transform, and random number capabilities

Now let’s move on with our matrices in Python and see how to create a matrix.

## Import numpy import numpy as np ## Create a 2D numpy array using python lists arr = np.array([[ 1, 2, 3],[ 4, 5, 6]]) print(arr)

np.array is used to create NumPy array from a list. NumPy arrays are of type ndarray.

The output of the above program is:

It represents a 2D matrix where input to np.array() is a list of lists [[ 1, 2, 3],[ 4, 5, 6]] . Each list in the parent list forms a row in the matrix.

np.arange() can generate a sequence of numbers given the start and end.

## Generate numbers from (start) to (end-1) ## Here start = 0 ## end = 5 ## Generated a NumPy array from 0 to 4 print(np.arange(0,5))

Above statement outputs the following 1D array:

To generate 2D matrix we can use np.arange() inside a list. We pass this list into np.array() which makes it a 2D NumPy array.

print(np.array([np.arange(0,5), np.arange(5,10)]))

Above statement outputs the following 2D array:

We refer to any NumPy object as an array of N-dimensions. In mathematics it is referred to as matrix of N-dimensions. Every NumPy ndarray object can be queried for its shape. A shape is a tuple of the format (n_rows, n_cols)

Following snippet prints shape of a matrix

## Using example from above section to generate a matrix using ranges arr_2d = np.array([np.arange(0,5), np.arange(5,10)]) print(arr_2d.shape)

**Output:**

(2, 5) means that the matrix has 2 rows and 5 columns

## Create a matrix of shape (3, 4) filled wit zeros ## By default float64 type of numbers are generated if not specified print(np.zeros((3, 4)))

**Output:**

By default type of numbers are generated in the array is of float64 if not specified.

## Create a matrix of shape (2, 2) filled with ones ## Here we have specified dtype = np.int16 which asks NumPy to generate integers print(np.ones((2, 2), dtype=np.int16))

**Output:**

There is a twist while generating a matrix with ones, we have passed an additional parameter dtype=np.int16. This forces the np.ones function to generate integers rather than the default float. This additional parameter can also be passed into np.zeros

The example below explains two types of addition:

- Scalar addition
- Matrix addition

import numpy as np ## Generate two matrices mat_2d_1 = np.array([np.arange(0,3), np.arange(3,6)]) mat_2d_2 = np.array([np.arange(6,9), np.arange(9,12)])</pre> print("Matrix1: n ", mat_2d_1) print("Matrix2: n ", mat_2d_2) ## Add 1 to each element in mat_2d_1 and print it print("Scalar addition: n ", mat_2d_1 + 1) ## Add two matrices above elementwise print("Element wise addition of two matrices of same size: n ", mat_2d_1 + mat_2d_2)

**Output:**

Subtraction is similar to addition. We just need to change the operation from addition to subtraction.

import numpy as np ## Generate two matrices mat_2d_1 = np.array([np.arange(0,3), np.arange(3,6)]) mat_2d_2 = np.array([np.arange(6,9), np.arange(9,12)]) print("Matrix1: n ", mat_2d_1) print("Matrix2: n ", mat_2d_2) ## Subtract 1 from each element in mat_2d_1 and print it print("Scalar addition: n ", mat_2d_1 - 1) ## Subtract two matrices above elementwise print("Element wise subtraction of two matrices of same size: n ", mat_2d_1 - mat_2d_2)

**Output:**

Two types of multiplication or product operation can be done on NumPy matrices

- Scalar product: A scalar value is multiplied with all elements of a matrix
- Dot product: This is the product of two matrices as per the rules of matrix multiplication. Refer Matrix Multiplication for rules of matrix multiplication.

import numpy as np ## Generate two matrices of shape (2,3) and (3,2) so that we can find ## dot product mat_2d_1 = np.array([np.arange(0,3), np.arange(3,6)]) mat_2d_2 = np.array([np.arange(0,2), np.arange(2,4), np.arange(4,6)]) ## Print shapes and matrices print("Matrix1: n ", mat_2d_1) print("Matrix1 shape: n", mat_2d_1.shape) print("Matrix2: n ", mat_2d_2) print("Matrix2 shape: n", mat_2d_2.shape) ## Multiply each element by 2 in mat_2d_1 and print it print("Scalar Product: n ", mat_2d_1 * 2) ## Find product of two matrices above using dot product print("Dot Product: n ", np.dot(mat_2d_1, mat_2d_2))

**IMPORTANT**: Notice carefully that * operator is used for scalar multiplication only. However, for matrix multiplication we use a function np.dot() which takes two NumPy 2D arrays as argument.

**Output:**

Element wise scalar division can be done using division operator /

import numpy as np ## Generate a matrix of shape (2,3) mat_2d = np.array([np.arange(0,3), np.arange(3,6)]) ## Print the matrix print("Matrix: n ", mat_2d) ## Element wise division by scalar print("Scalar Division: n ", mat_2d / 2)

**Output:**

Element wise exponent can be found out using operator **

import numpy as np ## Generate a matrix of shape (2,3) mat_2d = np.array([np.arange(0,3), np.arange(3,6)]) ## Print the matrix print("Matrix: n ", mat_2d) ## Find exponent element wise i.e. raise each element in matrix to power 2 print("Matrix raised to power of 2: n ", mat_2d ** 2)

**Output:**

- The
*transpose*of a*matrix*is a new*matrix*whose rows are the columns of the original - A (2, 3) matrix becomes (3, 2) matrix in shape
- Numpy has a property on every ndarray object that stores transpose of a matrix. We need not use any special operator to find transpose of a matrix.
- matrix.T provides transpose of a matrix in NumPy
- Below snippet demonstrates transpose operation

import numpy as np ## Generate a matrix of shape (2,3) mat_2d = np.array([np.arange(0,3), np.arange(3,6)]) ## Print the matrix print("Matrix: n ", mat_2d) ## Matrix Transpose print("Transpose n ", mat_2d.T)

**Output:**

- A matrix slice is selecting a sub-matrix. Python provides a wonderful syntax to index and slice matrices.
- Slicing uses below syntax:

- matrix[row index range, column index range, step number]
- Row and column index ranges follow standard python syntax begin index: end index
- The range selected is always from begin index to (end index – 1) when code is run

import numpy as np # Create a matrix mat_2d = np.array([np.arange(0,3), np.arange(3,6)]) print("Matrix: n", mat_2d) # Slice to get second row in matrix print("Sliced: n ", mat_2d[1:, :])

**Output:**

- IMPORTANT:

- The row range selection 1: means select row index 1 until the last row
- The column range index : means select all columns in selected row range

import numpy as np # Create a matrix mat_2d = np.array([np.arange(0,3), np.arange(3,6)]) print("Matrix: n", mat_2d) # Slice to get last column in matrix print("Sliced: n ", mat_2d[:, 2:])

**Output:**

- IMPORTANT:

- The row range selection : means select all rows
- The column range index 2: means select all columns beginning from index 2 till the end

import numpy as np # Create a matrix mat_2d = np.array([np.arange(0,4), np.arange(4,8), np.arange(8,12), np.arange(12,16)]) print("Matrix: n", mat_2d) # Slice to get (2, 2) submatrix in the centre of mat_2d # i.e. # [ [ 5 6] # [ 9 10] ] print("Sliced: n ", mat_2d[1:3, 1:3])

**Output:**

- IMPORTANT:

- The row range selection 1:3 selects rows with index 1 to 2 inclusive
- The column range selection 1:3 selects columns with index 1 to 2 inclusive

*System of Equations*

- The matrix notation to solve a system of equations is:

- X = np.dot( (Inverse of A), B)
- Where:

- X = vector of unknows
- A = Coefficients on LHS
- B = Values on RHS

- For example, consider below linear system of equations:

- x + y + z = 1

2x + 4y + z = -2

x – y + z = 0 - can be represented in matrices as:
- X = [ x y z] the vector of unknows

A = [ [1 1 1]

[2 4 1]

[1 -1 1] ]

B = [ 1 -2 0 ]

- Using matrix formula mentioned above, we can solve the system of equations as follows:

import numpy as np ## A = (3,3) matrix A = np.array([[1,1,1], [2,4,1], [1,-1,1]]) ## B = (3,1) matrix B = np.array([1,-2, 0]).T ## X = Inv(A).B = (3, 1) in shape X = np.dot(np.linalg.inv(A), B) print("Solution: n ", X)

**Output:**

Where, x = -4.5, y = 0.5 and z = 5.0

With this, we have come to the end of our article. I hope you understood what are matrices in Python.

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*Got a question for us? Please mention it in the comments section of the ” Matrices in Python” blog and we will get back to you as soon as possible.*

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