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Nick McCullum

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NumPy Indexing and Assignment

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In this lesson, we will explore indexing and assignment in NumPy arrays.

The Array I'll Be Using In This Lesson

As before, I will be using a specific array through this lesson. This time it will be generated using the np.random.rand method. Here's how I generated the array:

Here is the actual array:

To make this array easier to look at, I will round every element of the array to 2 decimal places using NumPy's round method:

Here's the new array:

How To Return A Specific Element From A NumPy Array

We can select (and return) a specific element from a NumPy array in the same way that we could using a normal Python list: using square brackets.

An example is below:

We can also reference multiple elements of a NumPy array using the colon operator. For example, the index [2:] selects every element from index 2 onwards. The index [:3] selects every element up to and excluding index 3. The index [2:4] returns every element from index 2 to index 4, excluding index 4. The higher endpoint is always excluded.

A few example of indexing using the colon operator are below.

Element Assignment in NumPy Arrays

We can assign new values to an element of a NumPy array using the = operator, just like regular python lists. A few examples are below (note that this is all one code block, which means that the element assignments are carried forward from step to step).

arr[2:5] = 0.5

Returns array([0. , 0. , 0.5, 0.5, 0.5])

As you can see, modifying second_new_array also changed the value of new_array .

Why is this?

By default, NumPy does not create a copy of an array when you reference the original array variable using the = assignment operator. Instead, it simply points the new variable to the old variable, which allows the second variable to make modification to the original variable - even if this is not your intention.

This may seem bizarre, but it does have a logical explanation. The purpose of array referencing is to conserve computing power. When working with large data sets, you would quickly run out of RAM if you created a new array every time you wanted to work with a slice of the array.

Fortunately, there is a workaround to array referencing. You can use the copy method to explicitly copy a NumPy array.

An example of this is below.

As you can see below, making modifications to the copied array does not alter the original.

So far in the lesson, we have only explored how to reference one-dimensional NumPy arrays. We will now explore the indexing of two-dimensional arrays.

Indexing Two-Dimensional NumPy Arrays

To start, let's create a two-dimensional NumPy array named mat :

There are two ways to index a two-dimensional NumPy array:

  • mat[row, col]
  • mat[row][col]

I personally prefer to index using the mat[row][col] nomenclature because it is easier to visualize in a step-by-step fashion. For example:

You can also generate sub-matrices from a two-dimensional NumPy array using this notation:

Array referencing also applies to two-dimensional arrays in NumPy, so be sure to use the copy method if you want to avoid inadvertently modifying an original array after saving a slice of it into a new variable name.

Conditional Selection Using NumPy Arrays

NumPy arrays support a feature called conditional selection , which allows you to generate a new array of boolean values that state whether each element within the array satisfies a particular if statement.

An example of this is below (I also re-created our original arr variable since its been awhile since we've seen it):

You can also generate a new array of values that satisfy this condition by passing the condition into the square brackets (just like we do for indexing).

An example of this is below:

Conditional selection can become significantly more complex than this. We will explore more examples in this section's associated practice problems.

In this lesson, we explored NumPy array indexing and assignment in thorough detail. We will solidify your knowledge of these concepts further by working through a batch of practice problems in the next section.

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  • Introduction
  • Introduction to NumPy
  • NumPy Array Creation
  • NumPy N-d Array Creation
  • NumPy Data Types
  • NumPy Array Attributes
  • NumPy Input Output
  • NumPy Array Indexing

NumPy Array Slicing

NumPy Array Reshaping

Array Operations

  • NumPy Arithmetic Array Operations

NumPy Array Functions

  • NumPy Comparison/Logical Operations
  • NumPy Math Functions
  • NumPy Constants
  • NumPy Statistical Functions
  • NumPy String Functions

Advance NumPy Operations

  • NumPy Broadcasting
  • NumPy Matrix Operations
  • NumPy Set Operations
  • NumPy Vectorization
  • NumPy Boolean Indexing
  • NumPy Fancy Indexing

Additional Topics

  • NumPy Random
  • NumPy Linear Algebra
  • NumPy Histogram
  • NumPy Interpolation
  • NumPy Files
  • NumPy Error Handling
  • NumPy Date and Time
  • NumPy Data Visualization
  • NumPy Universal Function

NumPy Tutorials

  • NumPy det()
  • NumPy matmul()
  • NumPy matrix()
  • NumPy norm()
  • NumPy trace()

Numpy Linear Algebra

A matrix is a two-dimensional data structure where numbers are arranged into rows and columns. For example,

NumPy Matrix

The above matrix is a 3x3 (pronounced "three by three") matrix because it has 3 rows and 3 columns.

Here are some of the basic matrix operations provided by NumPy.

  • Create Matrix in NumPy

In NumPy, we use the np.array() function to create a matrix. For example,

Here, we have created two matrices: 2x2 matrix and 3x3 matrix by passing a list of lists to the np.array() function respectively.

  • Perform Matrix Multiplication in NumPy

We use the np.dot() function to perform multiplication between two matrices.

Let's see an example.

In this example, we have used the np.dot(matrix1, matrix2) function to perform matrix multiplication between two matrices: matrix1 and matrix2 .

To learn more about Matrix multiplication, please visit NumPy Matrix Multiplication .

Note : We can only take a dot product of matrices when they have a common dimension size. For example, For A = (M x N) and B = (N x K) when we take a dot product of C = A . B the resulting matrix is of size C = (M x K) .

  • Transpose NumPy Matrix

The transpose of a matrix is a new matrix that is obtained by exchanging the rows and columns. For 2x2 matrix,

In NumPy, we can obtain the transpose of a matrix using the np.transpose() function. For example,

Here, we have used the np.transpose(matrix1) function to obtain the transpose of matrix1 .

Note : Alternatively, we can use the .T attribute to get the transpose of a matrix. For example, if we used matrix1.T in our previous example, the result would be the same.

  • Calculate Inverse of a Matrix in NumPy

In NumPy, we use the np.linalg.inv() function to calculate the inverse of the given matrix.

However, it is important to note that not all matrices have an inverse. Only square matrices that have a non-zero determinant have an inverse.

Now, let's use np.linalg.inv() to calculate the inverse of a square matrix.

Note : If we try to find the inverse of a non-square matrix, we will get an error message: numpy.linalg.linalgerror: Last 2 dimensions of the array must be square

Find Determinant of a Matrix in NumPy

We can find the determinant of a square matrix using the np.linalg.det() function to calculate the determinant of the given matrix.

Suppose we have a 2x2 matrix A :

So, the determinant of a 2x2 matrix will be:

where a, b, c, and d are the elements of the matrix.

Here, we have used the np.linalg.det(matrix1) function to find the determinant of the square matrix matrix1 .

  • Flatten Matrix in NumPy

Flattening a matrix simply means converting a matrix into a 1D array.

To flatten a matrix into a 1-D array we use the array.flatten() function. Let's see an example.

Here, we have used the matrix1.flatten() function to flatten matrix1 into a 1D array, without compromising any of its elements

Table of Contents

Related tutorials.

Programming

5 Best Ways to Explain Python Matrix with Examples

💡 Problem Formulation: Matrices are fundamental for a multitude of operations in software development, data analysis, and scientific computing. In Python, a matrix can be represented and manipulated in various ways. This article solves the problem of how to create, modify, and perform operations on Python matrices with practical examples. Imagine you want to represent 2D data like pixel values in an image or distances between cities; such tasks require creating and managing a matrix efficiently.

Method 1: Using Nested Lists

In Python, one of the most straightforward ways to represent a matrix is using nested lists. Each inner list represents a row in the matrix, and the elements of these lists are the matrix elements. Accessing, updating, and iterating over these matrices is intuitive and requires no additional libraries.

Here’s an example:

The code snippet creates a 3×3 matrix with nested lists. It then accesses and prints the element on the second row and third column, which is the number 6. This method is easy to understand and works well without additional dependencies, but can become inefficient for large matrices or complex operations.

Method 2: Using NumPy Arrays

NumPy is a powerful library for numerical computing in Python. Its array object is more efficient and convenient for large matrices and supports a wide range of mathematical operations. NumPy arrays are homogeneous, which can lead to better performance compared to nested lists.

This example demonstrates creating a NumPy array to represent a matrix and performing an element-wise addition with another matrix. NumPy arrays offer more efficient storage and better functionality for large-scale operations than lists.

Method 3: Using pandas DataFrame

pandas is another library that’s extremely useful for data analysis, and it provides the DataFrame object which can be thought of as a matrix with more functionality like labeled rows and columns. DataFrames are great for handling tabular data and can be created from lists, dicts, or even reading from files like CSV.

This code snippet creates a 3×3 matrix as a pandas DataFrame with labeled rows and columns. It selects and prints the element from row labeled ‘Y’ and column labeled ‘B’. pandas DataFrames are ideal for complex data manipulation but may be an overkill for simple matrix operations.

Method 4: Using List Comprehensions

List comprehensions provide a concise way to create lists including matrices. They are elegant and can be used to initialize, transform, and even transpose matrices with readable and compact code. List comprehensions are a Pythonic way to operate with matrices represented by lists.

The given code uses a list comprehension to create a 3×3 identity matrix, a matrix with 1s on the diagonal and 0s elsewhere. List comprehensions are a compact and readable method to create matrices, but can become less readable for very complex operations.

Bonus One-Liner Method 5: Using zip() and * Operator

Transposing a matrix, which is flipping it over its diagonal, can be elegantly achieved in Python by using the zip() function in conjunction with the star operator * . This one-liner is very readable and takes advantage of Python’s unpacking feature.

The code takes a 3×2 matrix and transposes it to a 2×3 matrix using the zip() function. The star operator unpacks the rows of the original matrix such that they are passed as separate arguments to zip() , which pairs elements of the same index from these arguments. This is a short and efficient one-liner for transposing matrices but may be less obvious to beginners.

Summary/Discussion

  • Method 1: Nested Lists. Easily understandable. Not as efficient for large or complex matrices.
  • Method 2: Using NumPy Arrays. Highly efficient and versatile. Requires additional knowledge of NumPy.
  • Method 3: Using pandas DataFrames. Great for complex data manipulations with labeled axes. Overly powerful for simple tasks.
  • Method 4: List Comprehensions. Elegant and Pythonic. Can become unwieldy for complex operations.
  • Bonus Method 5: Using zip() and * Operator. Succinct for transposing. Less intuitive for those unfamiliar with argument unpacking.

Emily Rosemary Collins is a tech enthusiast with a strong background in computer science, always staying up-to-date with the latest trends and innovations. Apart from her love for technology, Emily enjoys exploring the great outdoors, participating in local community events, and dedicating her free time to painting and photography. Her interests and passion for personal growth make her an engaging conversationalist and a reliable source of knowledge in the ever-evolving world of technology.

How Matrix Work in Python and How to Use Them

December 29, 2021, table of contents, quick access, python: popular features of this language, how to automate the download folder with python, how to connect mongodb database with django.

python

Before getting into the topic of how to create an NXNXM Matrix in Python , we must define what a Matrix consists of within this popular programming language.

When we talk about Matrix in Python, we are referring to a specialized two-dimensional rectangular array of data, which is stored in rows and columns. Within this matrix, there can be data in the form of numbers, strings, symbols, expressions, etc. The matrix is ​​one of the important data structures that can be used in mathematical and scientific calculations.

How does Matrix work in Python?

The data inside the matrix looks like the one shown in this graph:

python

First step:

Here a 2x2 matrix is ​​shown, where two rows and two columns are observed. The data within this matrix are presented in the form of numbers, where the values ​​2, 3 are observed in row one and the values ​​4, 5 in row two. In the columns, there is column one with the values ​​3 and 4 and column two with the values ​​3 and 5.

Second step:

A slightly different 2 x 3 matrix is ​​shown with two rows and three columns. The data within the first row has values ​​2, 3, 4 and in the second row, it has values ​​5, 6, 7. The first column has values ​​2.5, the second column has values ​​3.6 and the third column has values 4.7.

Similarly, you can have your data stored inside the nxn Matrix in Python. Many operations can be performed in addition, subtraction, multiplication, etc.

Create a Python Matrix using a nested list data type

In Python, Matrix is represented by the list data type. We are going to teach you how to create a 3x3 matrix using the list.

The matrix is ​​made up of three rows and three columns.

  • Row number one within the list format will have the following data: [8,14, -6]
  • Row number two will be: [12,7,4]
  • Row number three will be: [-11,3,21]

The matrix within a list with all rows and columns can look like this:

List = [[Row1],            [Row2],            [Row3]            ...            [RowN]]

So, according to the above array, the Matrix's direct data list type is as follows:

M1 = [[8, 14, -6], [12,7,4], [-11,3,21]]

How we can read data into a Python Matrix when using a list

We will use the previous matrix. The example will read the data, print the Matrix, display the last element in each row.

Example: to print the matrix

M1 = [[8, 14, -6],            [12,7,4],            [-11.3.21]]

#To print the matrix print (M1)

Production:

The Matrix M1 = [[8, 14, -6], [12, 7, 4], [-11, 3, 21]]

Example 2: Read the last element of each row.

matrix_length = len (M1)

#To read the last element from each row. for i in range (matrix_length):     print (M1 [i] [- 1])

-6 4 twenty-one

Example 3: to print the rows in the matrix

#To print the rows in the Matrix for i in range (matrix_length):     print (M1 [i])

[8, 14, -6] [12, 7, 4] [-11, 3, 21]

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Python Tutorial

File handling, python modules, python numpy, python pandas, python matplotlib, python scipy, machine learning, python mysql, python mongodb, python reference, module reference, python how to, python examples, python arrays.

Note: Python does not have built-in support for Arrays, but Python Lists can be used instead.

Note: This page shows you how to use LISTS as ARRAYS, however, to work with arrays in Python you will have to import a library, like the NumPy library .

Arrays are used to store multiple values in one single variable:

Create an array containing car names:

What is an Array?

An array is a special variable, which can hold more than one value at a time.

If you have a list of items (a list of car names, for example), storing the cars in single variables could look like this:

However, what if you want to loop through the cars and find a specific one? And what if you had not 3 cars, but 300?

The solution is an array!

An array can hold many values under a single name, and you can access the values by referring to an index number.

Access the Elements of an Array

You refer to an array element by referring to the index number .

Get the value of the first array item:

Modify the value of the first array item:

The Length of an Array

Use the len() method to return the length of an array (the number of elements in an array).

Return the number of elements in the cars array:

Note: The length of an array is always one more than the highest array index.

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Looping Array Elements

You can use the for in loop to loop through all the elements of an array.

Print each item in the cars array:

Adding Array Elements

You can use the append() method to add an element to an array.

Add one more element to the cars array:

Removing Array Elements

You can use the pop() method to remove an element from the array.

Delete the second element of the cars array:

You can also use the remove() method to remove an element from the array.

Delete the element that has the value "Volvo":

Note: The list's remove() method only removes the first occurrence of the specified value.

Array Methods

Python has a set of built-in methods that you can use on lists/arrays.

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Solving Assignment Problem using Linear Programming in Python

Learn how to use Python PuLP to solve Assignment problems using Linear Programming.

In earlier articles, we have seen various applications of Linear programming such as transportation, transshipment problem, Cargo Loading problem, and shift-scheduling problem. Now In this tutorial, we will focus on another model that comes under the class of linear programming model known as the Assignment problem. Its objective function is similar to transportation problems. Here we minimize the objective function time or cost of manufacturing the products by allocating one job to one machine.

If we want to solve the maximization problem assignment problem then we subtract all the elements of the matrix from the highest element in the matrix or multiply the entire matrix by –1 and continue with the procedure. For solving the assignment problem, we use the Assignment technique or Hungarian method, or Flood’s technique.

The transportation problem is a special case of the linear programming model and the assignment problem is a special case of transportation problem, therefore it is also a special case of the linear programming problem.

In this tutorial, we are going to cover the following topics:

Assignment Problem

A problem that requires pairing two sets of items given a set of paired costs or profit in such a way that the total cost of the pairings is minimized or maximized. The assignment problem is a special case of linear programming.

For example, an operation manager needs to assign four jobs to four machines. The project manager needs to assign four projects to four staff members. Similarly, the marketing manager needs to assign the 4 salespersons to 4 territories. The manager’s goal is to minimize the total time or cost.

Problem Formulation

A manager has prepared a table that shows the cost of performing each of four jobs by each of four employees. The manager has stated his goal is to develop a set of job assignments that will minimize the total cost of getting all 4 jobs.  

Assignment Problem

Initialize LP Model

In this step, we will import all the classes and functions of pulp module and create a Minimization LP problem using LpProblem class.

Define Decision Variable

In this step, we will define the decision variables. In our problem, we have two variable lists: workers and jobs. Let’s create them using  LpVariable.dicts()  class.  LpVariable.dicts()  used with Python’s list comprehension.  LpVariable.dicts()  will take the following four values:

  • First, prefix name of what this variable represents.
  • Second is the list of all the variables.
  • Third is the lower bound on this variable.
  • Fourth variable is the upper bound.
  • Fourth is essentially the type of data (discrete or continuous). The options for the fourth parameter are  LpContinuous  or  LpInteger .

Let’s first create a list route for the route between warehouse and project site and create the decision variables using LpVariable.dicts() the method.

Define Objective Function

In this step, we will define the minimum objective function by adding it to the LpProblem  object. lpSum(vector)is used here to define multiple linear expressions. It also used list comprehension to add multiple variables.

Define the Constraints

Here, we are adding two types of constraints: Each job can be assigned to only one employee constraint and Each employee can be assigned to only one job. We have added the 2 constraints defined in the problem by adding them to the LpProblem  object.

Solve Model

In this step, we will solve the LP problem by calling solve() method. We can print the final value by using the following for loop.

From the above results, we can infer that Worker-1 will be assigned to Job-1, Worker-2 will be assigned to job-3, Worker-3 will be assigned to Job-2, and Worker-4 will assign with job-4.

In this article, we have learned about Assignment problems, Problem Formulation, and implementation using the python PuLp library. We have solved the Assignment problem using a Linear programming problem in Python. Of course, this is just a simple case study, we can add more constraints to it and make it more complicated. You can also run other case studies on Cargo Loading problems , Staff scheduling problems . In upcoming articles, we will write more on different optimization problems such as transshipment problem, balanced diet problem. You can revise the basics of mathematical concepts in  this article  and learn about Linear Programming  in this article .

  • Solving Blending Problem in Python using Gurobi
  • Transshipment Problem in Python Using PuLP

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scipy.optimize.linear_sum_assignment ¶

Solve the linear sum assignment problem.

The linear sum assignment problem is also known as minimum weight matching in bipartite graphs. A problem instance is described by a matrix C, where each C[i,j] is the cost of matching vertex i of the first partite set (a “worker”) and vertex j of the second set (a “job”). The goal is to find a complete assignment of workers to jobs of minimal cost.

Formally, let X be a boolean matrix where \(X[i,j] = 1\) iff row i is assigned to column j. Then the optimal assignment has cost

s.t. each row is assignment to at most one column, and each column to at most one row.

This function can also solve a generalization of the classic assignment problem where the cost matrix is rectangular. If it has more rows than columns, then not every row needs to be assigned to a column, and vice versa.

The method used is the Hungarian algorithm, also known as the Munkres or Kuhn-Munkres algorithm.

New in version 0.17.0.

  • http://csclab.murraystate.edu/bob.pilgrim/445/munkres.html
  • Harold W. Kuhn. The Hungarian Method for the assignment problem. Naval Research Logistics Quarterly , 2:83-97, 1955.
  • Harold W. Kuhn. Variants of the Hungarian method for assignment problems. Naval Research Logistics Quarterly , 3: 253-258, 1956.
  • Munkres, J. Algorithms for the Assignment and Transportation Problems. J. SIAM , 5(1):32-38, March, 1957.
  • https://en.wikipedia.org/wiki/Hungarian_algorithm

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Python Operators

Precedence and associativity of operators in python.

  • Python Arithmetic Operators
  • Difference between / vs. // operator in Python
  • Python - Star or Asterisk operator ( * )
  • What does the Double Star operator mean in Python?
  • Division Operators in Python
  • Modulo operator (%) in Python
  • Python Logical Operators
  • Python OR Operator
  • Difference between 'and' and '&' in Python
  • not Operator in Python | Boolean Logic

Ternary Operator in Python

  • Python Bitwise Operators

Python Assignment Operators

Assignment operators in python.

  • Walrus Operator in Python 3.8
  • Increment += and Decrement -= Assignment Operators in Python
  • Merging and Updating Dictionary Operators in Python 3.9
  • New '=' Operator in Python3.8 f-string

Python Relational Operators

  • Comparison Operators in Python
  • Python NOT EQUAL operator
  • Difference between == and is operator in Python
  • Chaining comparison operators in Python
  • Python Membership and Identity Operators
  • Difference between != and is not operator in Python

In Python programming, Operators in general are used to perform operations on values and variables. These are standard symbols used for logical and arithmetic operations. In this article, we will look into different types of Python operators. 

  • OPERATORS: These are the special symbols. Eg- + , * , /, etc.
  • OPERAND: It is the value on which the operator is applied.

Types of Operators in Python

  • Arithmetic Operators
  • Comparison Operators
  • Logical Operators
  • Bitwise Operators
  • Assignment Operators
  • Identity Operators and Membership Operators

Python Operators

Arithmetic Operators in Python

Python Arithmetic operators are used to perform basic mathematical operations like addition, subtraction, multiplication , and division .

In Python 3.x the result of division is a floating-point while in Python 2.x division of 2 integers was an integer. To obtain an integer result in Python 3.x floored (// integer) is used.

Example of Arithmetic Operators in Python

Division operators.

In Python programming language Division Operators allow you to divide two numbers and return a quotient, i.e., the first number or number at the left is divided by the second number or number at the right and returns the quotient. 

There are two types of division operators: 

Float division

  • Floor division

The quotient returned by this operator is always a float number, no matter if two numbers are integers. For example:

Example: The code performs division operations and prints the results. It demonstrates that both integer and floating-point divisions return accurate results. For example, ’10/2′ results in ‘5.0’ , and ‘-10/2’ results in ‘-5.0’ .

Integer division( Floor division)

The quotient returned by this operator is dependent on the argument being passed. If any of the numbers is float, it returns output in float. It is also known as Floor division because, if any number is negative, then the output will be floored. For example:

Example: The code demonstrates integer (floor) division operations using the // in Python operators . It provides results as follows: ’10//3′ equals ‘3’ , ‘-5//2’ equals ‘-3’ , ‘ 5.0//2′ equals ‘2.0’ , and ‘-5.0//2’ equals ‘-3.0’ . Integer division returns the largest integer less than or equal to the division result.

Precedence of Arithmetic Operators in Python

The precedence of Arithmetic Operators in Python is as follows:

  • P – Parentheses
  • E – Exponentiation
  • M – Multiplication (Multiplication and division have the same precedence)
  • D – Division
  • A – Addition (Addition and subtraction have the same precedence)
  • S – Subtraction

The modulus of Python operators helps us extract the last digit/s of a number. For example:

  • x % 10 -> yields the last digit
  • x % 100 -> yield last two digits

Arithmetic Operators With Addition, Subtraction, Multiplication, Modulo and Power

Here is an example showing how different Arithmetic Operators in Python work:

Example: The code performs basic arithmetic operations with the values of ‘a’ and ‘b’ . It adds (‘+’) , subtracts (‘-‘) , multiplies (‘*’) , computes the remainder (‘%’) , and raises a to the power of ‘b (**)’ . The results of these operations are printed.

Note: Refer to Differences between / and // for some interesting facts about these two Python operators.

Comparison of Python Operators

In Python Comparison of Relational operators compares the values. It either returns True or False according to the condition.

= is an assignment operator and == comparison operator.

Precedence of Comparison Operators in Python

In Python, the comparison operators have lower precedence than the arithmetic operators. All the operators within comparison operators have the same precedence order.

Example of Comparison Operators in Python

Let’s see an example of Comparison Operators in Python.

Example: The code compares the values of ‘a’ and ‘b’ using various comparison Python operators and prints the results. It checks if ‘a’ is greater than, less than, equal to, not equal to, greater than, or equal to, and less than or equal to ‘b’ .

Logical Operators in Python

Python Logical operators perform Logical AND , Logical OR , and Logical NOT operations. It is used to combine conditional statements.

Precedence of Logical Operators in Python

The precedence of Logical Operators in Python is as follows:

  • Logical not
  • logical and

Example of Logical Operators in Python

The following code shows how to implement Logical Operators in Python:

Example: The code performs logical operations with Boolean values. It checks if both ‘a’ and ‘b’ are true ( ‘and’ ), if at least one of them is true ( ‘or’ ), and negates the value of ‘a’ using ‘not’ . The results are printed accordingly.

Bitwise Operators in Python

Python Bitwise operators act on bits and perform bit-by-bit operations. These are used to operate on binary numbers.

Precedence of Bitwise Operators in Python

The precedence of Bitwise Operators in Python is as follows:

  • Bitwise NOT
  • Bitwise Shift
  • Bitwise AND
  • Bitwise XOR

Here is an example showing how Bitwise Operators in Python work:

Example: The code demonstrates various bitwise operations with the values of ‘a’ and ‘b’ . It performs bitwise AND (&) , OR (|) , NOT (~) , XOR (^) , right shift (>>) , and left shift (<<) operations and prints the results. These operations manipulate the binary representations of the numbers.

Python Assignment operators are used to assign values to the variables.

Let’s see an example of Assignment Operators in Python.

Example: The code starts with ‘a’ and ‘b’ both having the value 10. It then performs a series of operations: addition, subtraction, multiplication, and a left shift operation on ‘b’ . The results of each operation are printed, showing the impact of these operations on the value of ‘b’ .

Identity Operators in Python

In Python, is and is not are the identity operators both are used to check if two values are located on the same part of the memory. Two variables that are equal do not imply that they are identical. 

Example Identity Operators in Python

Let’s see an example of Identity Operators in Python.

Example: The code uses identity operators to compare variables in Python. It checks if ‘a’ is not the same object as ‘b’ (which is true because they have different values) and if ‘a’ is the same object as ‘c’ (which is true because ‘c’ was assigned the value of ‘a’ ).

Membership Operators in Python

In Python, in and not in are the membership operators that are used to test whether a value or variable is in a sequence.

Examples of Membership Operators in Python

The following code shows how to implement Membership Operators in Python:

Example: The code checks for the presence of values ‘x’ and ‘y’ in the list. It prints whether or not each value is present in the list. ‘x’ is not in the list, and ‘y’ is present, as indicated by the printed messages. The code uses the ‘in’ and ‘not in’ Python operators to perform these checks.

in Python, Ternary operators also known as conditional expressions are operators that evaluate something based on a condition being true or false. It was added to Python in version 2.5. 

It simply allows testing a condition in a single line replacing the multiline if-else making the code compact.

Syntax :   [on_true] if [expression] else [on_false] 

Examples of Ternary Operator in Python

The code assigns values to variables ‘a’ and ‘b’ (10 and 20, respectively). It then uses a conditional assignment to determine the smaller of the two values and assigns it to the variable ‘min’ . Finally, it prints the value of ‘min’ , which is 10 in this case.

In Python, Operator precedence and associativity determine the priorities of the operator.

Operator Precedence in Python

This is used in an expression with more than one operator with different precedence to determine which operation to perform first.

Let’s see an example of how Operator Precedence in Python works:

Example: The code first calculates and prints the value of the expression 10 + 20 * 30 , which is 610. Then, it checks a condition based on the values of the ‘name’ and ‘age’ variables. Since the name is “ Alex” and the condition is satisfied using the or operator, it prints “Hello! Welcome.”

Operator Associativity in Python

If an expression contains two or more operators with the same precedence then Operator Associativity is used to determine. It can either be Left to Right or from Right to Left.

The following code shows how Operator Associativity in Python works:

Example: The code showcases various mathematical operations. It calculates and prints the results of division and multiplication, addition and subtraction, subtraction within parentheses, and exponentiation. The code illustrates different mathematical calculations and their outcomes.

To try your knowledge of Python Operators, you can take out the quiz on Operators in Python . 

Python Operator Exercise Questions

Below are two Exercise Questions on Python Operators. We have covered arithmetic operators and comparison operators in these exercise questions. For more exercises on Python Operators visit the page mentioned below.

Q1. Code to implement basic arithmetic operations on integers

Q2. Code to implement Comparison operations on integers

Explore more Exercises: Practice Exercise on Operators in Python

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