Python Programming

Practice Python Exercises and Challenges with Solutions

Free Coding Exercises for Python Developers. Exercises cover Python Basics , Data structure , to Data analytics . As of now, this page contains 18 Exercises.

What included in these Python Exercises?

Each exercise contains specific Python topic questions you need to practice and solve. These free exercises are nothing but Python assignments for the practice where you need to solve different programs and challenges.

  • All exercises are tested on Python 3.
  • Each exercise has 10-20 Questions.
  • The solution is provided for every question.
  • Practice each Exercise in Online Code Editor

These Python programming exercises are suitable for all Python developers. If you are a beginner, you will have a better understanding of Python after solving these exercises. Below is the list of exercises.

Select the exercise you want to solve .

Basic Exercise for Beginners

Practice and Quickly learn Python’s necessary skills by solving simple questions and problems.

Topics : Variables, Operators, Loops, String, Numbers, List

Python Input and Output Exercise

Solve input and output operations in Python. Also, we practice file handling.

Topics : print() and input() , File I/O

Python Loop Exercise

This Python loop exercise aims to help developers to practice branching and Looping techniques in Python.

Topics : If-else statements, loop, and while loop.

Python Functions Exercise

Practice how to create a function, nested functions, and use the function arguments effectively in Python by solving different questions.

Topics : Functions arguments, built-in functions.

Python String Exercise

Solve Python String exercise to learn and practice String operations and manipulations.

Python Data Structure Exercise

Practice widely used Python types such as List, Set, Dictionary, and Tuple operations in Python

Python List Exercise

This Python list exercise aims to help Python developers to learn and practice list operations.

Python Dictionary Exercise

This Python dictionary exercise aims to help Python developers to learn and practice dictionary operations.

Python Set Exercise

This exercise aims to help Python developers to learn and practice set operations.

Python Tuple Exercise

This exercise aims to help Python developers to learn and practice tuple operations.

Python Date and Time Exercise

This exercise aims to help Python developers to learn and practice DateTime and timestamp questions and problems.

Topics : Date, time, DateTime, Calendar.

Python OOP Exercise

This Python Object-oriented programming (OOP) exercise aims to help Python developers to learn and practice OOP concepts.

Topics : Object, Classes, Inheritance

Python JSON Exercise

Practice and Learn JSON creation, manipulation, Encoding, Decoding, and parsing using Python

Python NumPy Exercise

Practice NumPy questions such as Array manipulations, numeric ranges, Slicing, indexing, Searching, Sorting, and splitting, and more.

Python Pandas Exercise

Practice Data Analysis using Python Pandas. Practice Data-frame, Data selection, group-by, Series, sorting, searching, and statistics.

Python Matplotlib Exercise

Practice Data visualization using Python Matplotlib. Line plot, Style properties, multi-line plot, scatter plot, bar chart, histogram, Pie chart, Subplot, stack plot.

Random Data Generation Exercise

Practice and Learn the various techniques to generate random data in Python.

Topics : random module, secrets module, UUID module

Python Database Exercise

Practice Python database programming skills by solving the questions step by step.

Use any of the MySQL, PostgreSQL, SQLite to solve the exercise

Exercises for Intermediate developers

The following practice questions are for intermediate Python developers.

If you have not solved the above exercises, please complete them to understand and practice each topic in detail. After that, you can solve the below questions quickly.

Exercise 1: Reverse each word of a string

Expected Output

  • Use the split() method to split a string into a list of words.
  • Reverse each word from a list
  • finally, use the join() function to convert a list into a string

Steps to solve this question :

  • Split the given string into a list of words using the split() method
  • Use a list comprehension to create a new list by reversing each word from a list.
  • Use the join() function to convert the new list into a string
  • Display the resultant string

Exercise 2: Read text file into a variable and replace all newlines with space

Given : Assume you have a following text file (sample.txt).

Expected Output :

  • First, read a text file.
  • Next, use string replace() function to replace all newlines ( \n ) with space ( ' ' ).

Steps to solve this question : -

  • First, open the file in a read mode
  • Next, read all content from a file using the read() function and assign it to a variable.
  • Display final string

Exercise 3: Remove items from a list while iterating

Description :

In this question, You need to remove items from a list while iterating but without creating a different copy of a list.

Remove numbers greater than 50

Expected Output : -

  • Get the list's size
  • Iterate list using while loop
  • Check if the number is greater than 50
  • If yes, delete the item using a del keyword
  • Reduce the list size

Solution 1: Using while loop

Solution 2: Using for loop and range()

Exercise 4: Reverse Dictionary mapping

Exercise 5: display all duplicate items from a list.

  • Use the counter() method of the collection module.
  • Create a dictionary that will maintain the count of each item of a list. Next, Fetch all keys whose value is greater than 2

Solution 1 : - Using collections.Counter()

Solution 2 : -

Exercise 6: Filter dictionary to contain keys present in the given list

Exercise 7: print the following number pattern.

Refer to Print patterns in Python to solve this question.

  • Use two for loops
  • The outer loop is reverse for loop from 5 to 0
  • Increment value of x by 1 in each iteration of an outer loop
  • The inner loop will iterate from 0 to the value of i of the outer loop
  • Print value of x in each iteration of an inner loop
  • Print newline at the end of each outer loop

Exercise 8: Create an inner function

Question description : -

  • Create an outer function that will accept two strings, x and y . ( x= 'Emma' and y = 'Kelly' .
  • Create an inner function inside an outer function that will concatenate x and y.
  • At last, an outer function will join the word 'developer' to it.

Exercise 9: Modify the element of a nested list inside the following list

Change the element 35 to 3500

Exercise 10: Access the nested key increment from the following dictionary

Under Exercises: -

Python Object-Oriented Programming (OOP) Exercise: Classes and Objects Exercises

Updated on:  December 8, 2021 | 51 Comments

Python Date and Time Exercise with Solutions

Updated on:  December 8, 2021 | 11 Comments

Python Dictionary Exercise with Solutions

Updated on:  May 6, 2023 | 57 Comments

Python Tuple Exercise with Solutions

Updated on:  December 8, 2021 | 95 Comments

Python Set Exercise with Solutions

Updated on:  October 20, 2022 | 27 Comments

Python if else, for loop, and range() Exercises with Solutions

Updated on:  September 6, 2021 | 293 Comments

Updated on:  August 2, 2022 | 153 Comments

Updated on:  September 6, 2021 | 108 Comments

Python List Exercise with Solutions

Updated on:  December 8, 2021 | 197 Comments

Updated on:  December 8, 2021 | 7 Comments

Python Data Structure Exercise for Beginners

Updated on:  December 8, 2021 | 116 Comments

Python String Exercise with Solutions

Updated on:  October 6, 2021 | 221 Comments

Updated on:  March 9, 2021 | 23 Comments

Updated on:  March 9, 2021 | 51 Comments

Updated on:  July 20, 2021 | 29 Comments

Python Basic Exercise for Beginners

Updated on:  August 31, 2023 | 492 Comments

Useful Python Tips and Tricks Every Programmer Should Know

Updated on:  May 17, 2021 | 23 Comments

Python random Data generation Exercise

Updated on:  December 8, 2021 | 13 Comments

Python Database Programming Exercise

Updated on:  March 9, 2021 | 17 Comments

  • Online Python Code Editor

Updated on:  June 1, 2022 |

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10 Python Practice Exercises for Beginners with Solutions

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A great way to improve quickly at programming with Python is to practice with a wide range of exercises and programming challenges. In this article, we give you 10 Python practice exercises to boost your skills.

Practice exercises are a great way to learn Python. Well-designed exercises expose you to new concepts, such as writing different types of loops, working with different data structures like lists, arrays, and tuples, and reading in different file types. Good exercises should be at a level that is approachable for beginners but also hard enough to challenge you, pushing your knowledge and skills to the next level.

If you’re new to Python and looking for a structured way to improve your programming, consider taking the Python Basics Practice course. It includes 17 interactive exercises designed to improve all aspects of your programming and get you into good programming habits early. Read about the course in the March 2023 episode of our series Python Course of the Month .

Take the course Python Practice: Word Games , and you gain experience working with string functions and text files through its 27 interactive exercises.  Its release announcement gives you more information and a feel for how it works.

Each course has enough material to keep you busy for about 10 hours. To give you a little taste of what these courses teach you, we have selected 10 Python practice exercises straight from these courses. We’ll give you the exercises and solutions with detailed explanations about how they work.

To get the most out of this article, have a go at solving the problems before reading the solutions. Some of these practice exercises have a few possible solutions, so also try to come up with an alternative solution after you’ve gone through each exercise.

Let’s get started!

Exercise 1: User Input and Conditional Statements

Write a program that asks the user for a number then prints the following sentence that number of times: ‘I am back to check on my skills!’ If the number is greater than 10, print this sentence instead: ‘Python conditions and loops are a piece of cake.’ Assume you can only pass positive integers.

Here, we start by using the built-in function input() , which accepts user input from the keyboard. The first argument is the prompt displayed on the screen; the input is converted into an integer with int() and saved as the variable number. If the variable number is greater than 10, the first message is printed once on the screen. If not, the second message is printed in a loop number times.

Exercise 2: Lowercase and Uppercase Characters

Below is a string, text . It contains a long string of characters. Your task is to iterate over the characters of the string, count uppercase letters and lowercase letters, and print the result:

We start this one by initializing the two counters for uppercase and lowercase characters. Then, we loop through every letter in text and check if it is lowercase. If so, we increment the lowercase counter by one. If not, we check if it is uppercase and if so, we increment the uppercase counter by one. Finally, we print the results in the required format.

Exercise 3: Building Triangles

Create a function named is_triangle_possible() that accepts three positive numbers. It should return True if it is possible to create a triangle from line segments of given lengths and False otherwise. With 3 numbers, it is sometimes, but not always, possible to create a triangle: You cannot create a triangle from a = 13, b = 2, and c = 3, but you can from a = 13, b = 9, and c = 10.

The key to solving this problem is to determine when three lines make a triangle regardless of the type of triangle. It may be helpful to start drawing triangles before you start coding anything.

Python Practice Exercises for Beginners

Notice that the sum of any two sides must be larger than the third side to form a triangle. That means we need a + b > c, c + b > a, and a + c > b. All three conditions must be met to form a triangle; hence we need the and condition in the solution. Once you have this insight, the solution is easy!

Exercise 4: Call a Function From Another Function

Create two functions: print_five_times() and speak() . The function print_five_times() should accept one parameter (called sentence) and print it five times. The function speak(sentence, repeat) should have two parameters: sentence (a string of letters), and repeat (a Boolean with a default value set to False ). If the repeat parameter is set to False , the function should just print a sentence once. If the repeat parameter is set to True, the function should call the print_five_times() function.

This is a good example of calling a function in another function. It is something you’ll do often in your programming career. It is also a nice demonstration of how to use a Boolean flag to control the flow of your program.

If the repeat parameter is True, the print_five_times() function is called, which prints the sentence parameter 5 times in a loop. Otherwise, the sentence parameter is just printed once. Note that in Python, writing if repeat is equivalent to if repeat == True .

Exercise 5: Looping and Conditional Statements

Write a function called find_greater_than() that takes two parameters: a list of numbers and an integer threshold. The function should create a new list containing all numbers in the input list greater than the given threshold. The order of numbers in the result list should be the same as in the input list. For example:

Here, we start by defining an empty list to store our results. Then, we loop through all elements in the input list and test if the element is greater than the threshold. If so, we append the element to the new list.

Notice that we do not explicitly need an else and pass to do nothing when integer is not greater than threshold . You may include this if you like.

Exercise 6: Nested Loops and Conditional Statements

Write a function called find_censored_words() that accepts a list of strings and a list of special characters as its arguments, and prints all censored words from it one by one in separate lines. A word is considered censored if it has at least one character from the special_chars list. Use the word_list variable to test your function. We've prepared the two lists for you:

This is another nice example of looping through a list and testing a condition. We start by looping through every word in word_list . Then, we loop through every character in the current word and check if the current character is in the special_chars list.

This time, however, we have a break statement. This exits the inner loop as soon as we detect one special character since it does not matter if we have one or several special characters in the word.

Exercise 7: Lists and Tuples

Create a function find_short_long_word(words_list) . The function should return a tuple of the shortest word in the list and the longest word in the list (in that order). If there are multiple words that qualify as the shortest word, return the first shortest word in the list. And if there are multiple words that qualify as the longest word, return the last longest word in the list. For example, for the following list:

the function should return

Assume the input list is non-empty.

The key to this problem is to start with a “guess” for the shortest and longest words. We do this by creating variables shortest_word and longest_word and setting both to be the first word in the input list.

We loop through the words in the input list and check if the current word is shorter than our initial “guess.” If so, we update the shortest_word variable. If not, we check to see if it is longer than or equal to our initial “guess” for the longest word, and if so, we update the longest_word variable. Having the >= condition ensures the longest word is the last longest word. Finally, we return the shortest and longest words in a tuple.

Exercise 8: Dictionaries

As you see, we've prepared the test_results variable for you. Your task is to iterate over the values of the dictionary and print all names of people who received less than 45 points.

Here, we have an example of how to iterate through a dictionary. Dictionaries are useful data structures that allow you to create a key (the names of the students) and attach a value to it (their test results). Dictionaries have the dictionary.items() method, which returns an object with each key:value pair in a tuple.

The solution shows how to loop through this object and assign a key and a value to two variables. Then, we test whether the value variable is greater than 45. If so, we print the key variable.

Exercise 9: More Dictionaries

Write a function called consonant_vowels_count(frequencies_dictionary, vowels) that takes a dictionary and a list of vowels as arguments. The keys of the dictionary are letters and the values are their frequencies. The function should print the total number of consonants and the total number of vowels in the following format:

For example, for input:

the output should be:

Working with dictionaries is an important skill. So, here’s another exercise that requires you to iterate through dictionary items.

We start by defining a list of vowels. Next, we need to define two counters, one for vowels and one for consonants, both set to zero. Then, we iterate through the input dictionary items and test whether the key is in the vowels list. If so, we increase the vowels counter by one, if not, we increase the consonants counter by one. Finally, we print out the results in the required format.

Exercise 10: String Encryption

Implement the Caesar cipher . This is a simple encryption technique that substitutes every letter in a word with another letter from some fixed number of positions down the alphabet.

For example, consider the string 'word' . If we shift every letter down one position in the alphabet, we have 'xpse' . Shifting by 2 positions gives the string 'yqtf' . Start by defining a string with every letter in the alphabet:

Name your function cipher(word, shift) , which accepts a string to encrypt, and an integer number of positions in the alphabet by which to shift every letter.

This exercise is taken from the Word Games course. We have our string containing all lowercase letters, from which we create a shifted alphabet using a clever little string-slicing technique. Next, we create an empty string to store our encrypted word. Then, we loop through every letter in the word and find its index, or position, in the alphabet. Using this index, we get the corresponding shifted letter from the shifted alphabet string. This letter is added to the end of the new_word string.

This is just one approach to solving this problem, and it only works for lowercase words. Try inputting a word with an uppercase letter; you’ll get a ValueError . When you take the Word Games course, you slowly work up to a better solution step-by-step. This better solution takes advantage of two built-in functions chr() and ord() to make it simpler and more robust. The course contains three similar games, with each game comprising several practice exercises to build up your knowledge.

Do You Want More Python Practice Exercises?

We have given you a taste of the Python practice exercises available in two of our courses, Python Basics Practice and Python Practice: Word Games . These courses are designed to develop skills important to a successful Python programmer, and the exercises above were taken directly from the courses. Sign up for our platform (it’s free!) to find more exercises like these.

We’ve discussed Different Ways to Practice Python in the past, and doing interactive exercises is just one way. Our other tips include reading books, watching videos, and taking on projects. For tips on good books for Python, check out “ The 5 Best Python Books for Beginners .” It’s important to get the basics down first and make sure your practice exercises are fun, as we discuss in “ What’s the Best Way to Practice Python? ” If you keep up with your practice exercises, you’ll become a Python master in no time!

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Python Exercises

python exercises for beginner programmers. If you are looking for a python challenge and are a beginner programmer, this might be for you. These exercises will help you with Python training.

Python for dummies? No, challenging exercises to become a good developer!

You can download the answers here .

Related course: Complete Python Programming Course & Exercises

Python Practice

Beginner exercises.

Run Python programs

  • Make a Python program that prints your name.
  • Make a program that displays the lyrics of a song.
  • Make a program that displays several numbers.
  • Make a program that solves and shows the summation of 64 + 32.
  • Do the same as in 2, but make it sum x + y.
  • Make a program that displays your favourite actor/actress.
  • Try to print the word ‘lucky’ inside s.
  • Try to print the day, month, year in the form “Today is 2/2/2016”.

String replace

  • Try the replace program
  • Can a string be replaced twice?
  • Does replace only work with words or also phrases?

String find

  • Find out if string find is case sensitive
  • What if a query string appers twice in the string?
  • Write a program that asks console input and searches for a query.

String join

  • Create a list of words and join them, like the example above.
  • Try changing the seperator string from a space to an underscore.

String split

  • Can a string be split on multiple characters?
  • Can you split a string this string?: World,Earth,America,Canada
  • Given an article, can you split it based on phrases?

Random numbers

  • Make a program that creates a random number and stores it into x.
  • Make a program that prints 3 random numbers.
  • Create a program that generates 100 random numbers and find the frequency of each number.

Keyboard input

  • Make a program that asks a phone number.
  • Make a program that asks the users preferred programming language.

If statements

  • Make a program that asks the number between 1 and 10. If the number is out of range the program should display “invalid number”.
  • Make a program that asks a password.

,'USA','Mexico','Australia']
  • Create a loop that counts from 0 to 100
  • Make a multiplication table using a loop
  • Output the numbers 1 to 10 backwards using a loop
  • Create a loop that counts all even numbers to 10
  • Create a loop that sums the numbers from 100 to 200

While loops

Make a program that lists the countries in the set below using a while loop.


,"USA","Mexico"]

What’s the difference between a while loop and a for loop?

  • Can you sum numbers in a while loop?
  • Can a for loop be used inside a while loop?
  • Make a function that sums the list mylist = [1,2,3,4,5]
  • Can functions be called inside a function?
  • Can a function call itself? (hint: recursion)
  • Can variables defined in a function be used in another function? (hint: scope)

, .. ,'Wyoming' ]
  • Display all states starting with the letter M

List operations

  • Given the list y = [6,4,2] add the items 12, 8 and 4.
  • Change the 2nd item of the list to 3.

Sorting list

  • Given a list with pairs, sort on the first element x = [ (3,6),(4,7),(5,9),(8,4),(3,1)]
  • Now sort on the second element

Range function

  • Create a list of one thousand numbers
  • Get the largest and smallest number from that list
  • Create two lists, an even and odd one.
  • Make a mapping from countries to country short codes
  • Print each item (key and value)
  • Read a file and number every line
  • Find out what the program does if the file doesn’t exist.
  • What happens if you create a file with another user and try to open it?
  • Write the text “Take it easy” to a file
  • Write the line open(“text.txt”) to a file

Nested loops

  • Given a tic-tac-toe board of 3x3, print every position
  • Create a program where every person meets the other persons = [ “John”, “Marissa”, “Pete”, “Dayton” ]
  • If a normal for loop finishes in n steps O(n), how many steps has a nested loop?

Take a slice of the list below:



Given the text “Hello World”, take the slice “World”

Multiple return

  • Create a function that returns a,b and a+b
  • Create a function that returns 5 variables
  • Add a function reduce amount that changes the variable balance
  • Create a function with a local variable

Time and date

  • Print the date in format year-month-day
  • Can try-except be used to catch invalid keyboard input?
  • Can try-except catch the error if a file can’t be opened?
  • When would you not use try-except?

OOP exercises

  • Can you have more than one class in a file?
  • Can multiple objects be created from the same class?
  • Can objects create classes?
  • Using the code above, create another object
  • Add a method to the class: location()

Getter and setter

  • Add a variable age and create a getter and setter
  • Why would you use getter and setter methods?
  • Import the math module and call the sin function
  • Create your own module with the function snake()

Inheritance

  • Create a new class that inherits from the class App
  • Try to create a class that inherits from two super classes (multiple inheritance)

Static method

  • Can a method inside a class be called without creating an object?
  • Why does not everybody like static methods?
  • What is an iterable?
  • Which types of data can be used with an iterable?

Classmethod

  • What is a classmethod?
  • How does a classmethod differ from a staticmethod?

Multiple inheritance

  • Do all programming languages support multiple inheritance?
  • Why would you not use multiple inheritance?
  • Is there a limit to the number of classes you can inherit from?

Holy Python

Beginner python exercises.

Here are some enjoyable Python Exercises for you to solve! We strive to offer a huge selection of Python Exercises so you can internalize the Python Concepts through these exercises. 

Among these Python Exercises you can find the most basic Python Concepts about Python Syntax and built-in functions and methods .

Holy Python is reader-supported. When you buy through links on our site, we may earn an affiliate commission.

Choose the topics you'd like to practice from our extensive exercise list.

Python Beginner Exercises consist of some 125+ exercises that can be solved by beginner coders and newcomers to the Python world.

First batch of the exercises starts with print function as a warm up but quickly, it becomes all about data. 

You can start exercising with making variable assignments and move on with the most fundamental data types in Python:

Exercise 2: Variables    |   (2) : Practice assigning data to variables with these Python exercises.

Exercise 4: Type Conversion    |   (8) : Practice converting between basic data types of Python when applicable.

Exercise 5: Data Structures    |   (6) : Next stop is exercises of most commonly used Python Data Structures. Namely;

Exercise 7: Tuples    |   (8) : Python Tuples Exercises with basic applications as well as common tuples methods

Exercise 8: Dictionaries    |   (11) : Practice with Python Dictionaries and some common dictionary methods.

Next batch consist of some builtin Python functions and Python methods. The difference between function and method in Python is that functions are more like standalone blocks of codes that take arguments while methods apply directly on a class of objects.

Exercise 10: len() function     |   (5) : Python’s len function tells the length of an object. It’s definitely a must know and very useful in endless scenarios. Whether it’s manipulating strings or counting elements in a list, len function is constantly used in computer programming.

Exercise 11: .sort() method     |   (7) : Practice sort method in Beginner Python Exercises and later on you’ll have the opportunity practice sorted function in Intermediate Python Exercises.

Exercise 14: range() function     |   (5) : You might want to take a real close look at range function because it can be very useful in numerous scenarios.

Exercise 16: Defining Functions    |   (9) : Practicing user defined Python functions will take your programming skills to the next step. Writing functions are super useful and fun. It makes code reusable too.

Exercise 17: Python Slicing Notation     |   (8) : Python’s slicing notations are very interesting but can be confusing for beginner coders. These exercises will help you master them.

If you struggle with any of the exercises, you can always refer to the Beginner Python Lessons .

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Python lessons, beginner lessons.

Simple builtin Python functions and fundamental concepts.

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Advanced lessons, python exercises, beginner exercises.

Basic Python exercises that are simple and straightforward.

Intermediate Exercises

Slightly more complex Python exercises and more Python functions to practice.

Advanced Exercises

Thank you for checking out our Python programming content. All of the Python lessons and exercises we provide are free of charge. Also, majority of the Python exercises have an online and interactive interface which can be helpful in the early stages of learning computer programming.

Python's Assignment Operator: Write Robust Assignments

Python's Assignment Operator: Write Robust Assignments

Table of Contents

The Assignment Statement Syntax

The assignment operator, assignments and variables, other assignment syntax, initializing and updating variables, making multiple variables refer to the same object, updating lists through indices and slices, adding and updating dictionary keys, doing parallel assignments, unpacking iterables, providing default argument values, augmented mathematical assignment operators, augmented assignments for concatenation and repetition, augmented bitwise assignment operators, annotated assignment statements, assignment expressions with the walrus operator, managed attribute assignments, define or call a function, work with classes, import modules and objects, use a decorator, access the control variable in a for loop or a comprehension, use the as keyword, access the _ special variable in an interactive session, built-in objects, named constants.

Python’s assignment operators allow you to define assignment statements . This type of statement lets you create, initialize, and update variables throughout your code. Variables are a fundamental cornerstone in every piece of code, and assignment statements give you complete control over variable creation and mutation.

Learning about the Python assignment operator and its use for writing assignment statements will arm you with powerful tools for writing better and more robust Python code.

In this tutorial, you’ll:

  • Use Python’s assignment operator to write assignment statements
  • Take advantage of augmented assignments in Python
  • Explore assignment variants, like assignment expressions and managed attributes
  • Become aware of illegal and dangerous assignments in Python

You’ll dive deep into Python’s assignment statements. To get the most out of this tutorial, you should be comfortable with several basic topics, including variables , built-in data types , comprehensions , functions , and Python keywords . Before diving into some of the later sections, you should also be familiar with intermediate topics, such as object-oriented programming , constants , imports , type hints , properties , descriptors , and decorators .

Free Source Code: Click here to download the free assignment operator source code that you’ll use to write assignment statements that allow you to create, initialize, and update variables in your code.

Assignment Statements and the Assignment Operator

One of the most powerful programming language features is the ability to create, access, and mutate variables . In Python, a variable is a name that refers to a concrete value or object, allowing you to reuse that value or object throughout your code.

To create a new variable or to update the value of an existing one in Python, you’ll use an assignment statement . This statement has the following three components:

  • A left operand, which must be a variable
  • The assignment operator ( = )
  • A right operand, which can be a concrete value , an object , or an expression

Here’s how an assignment statement will generally look in Python:

Here, variable represents a generic Python variable, while expression represents any Python object that you can provide as a concrete value—also known as a literal —or an expression that evaluates to a value.

To execute an assignment statement like the above, Python runs the following steps:

  • Evaluate the right-hand expression to produce a concrete value or object . This value will live at a specific memory address in your computer.
  • Store the object’s memory address in the left-hand variable . This step creates a new variable if the current one doesn’t already exist or updates the value of an existing variable.

The second step shows that variables work differently in Python than in other programming languages. In Python, variables aren’t containers for objects. Python variables point to a value or object through its memory address. They store memory addresses rather than objects.

This behavior difference directly impacts how data moves around in Python, which is always by reference . In most cases, this difference is irrelevant in your day-to-day coding, but it’s still good to know.

The central component of an assignment statement is the assignment operator . This operator is represented by the = symbol, which separates two operands:

  • A value or an expression that evaluates to a concrete value

Operators are special symbols that perform mathematical , logical , and bitwise operations in a programming language. The objects (or object) on which an operator operates are called operands .

Unary operators, like the not Boolean operator, operate on a single object or operand, while binary operators act on two. That means the assignment operator is a binary operator.

Note: Like C , Python uses == for equality comparisons and = for assignments. Unlike C, Python doesn’t allow you to accidentally use the assignment operator ( = ) in an equality comparison.

Equality is a symmetrical relationship, and assignment is not. For example, the expression a == 42 is equivalent to 42 == a . In contrast, the statement a = 42 is correct and legal, while 42 = a isn’t allowed. You’ll learn more about illegal assignments later on.

The right-hand operand in an assignment statement can be any Python object, such as a number , list , string , dictionary , or even a user-defined object. It can also be an expression. In the end, expressions always evaluate to concrete objects, which is their return value.

Here are a few examples of assignments in Python:

The first two sample assignments in this code snippet use concrete values, also known as literals , to create and initialize number and greeting . The third example assigns the result of a math expression to the total variable, while the last example uses a Boolean expression.

Note: You can use the built-in id() function to inspect the memory address stored in a given variable.

Here’s a short example of how this function works:

The number in your output represents the memory address stored in number . Through this address, Python can access the content of number , which is the integer 42 in this example.

If you run this code on your computer, then you’ll get a different memory address because this value varies from execution to execution and computer to computer.

Unlike expressions, assignment statements don’t have a return value because their purpose is to make the association between the variable and its value. That’s why the Python interpreter doesn’t issue any output in the above examples.

Now that you know the basics of how to write an assignment statement, it’s time to tackle why you would want to use one.

The assignment statement is the explicit way for you to associate a name with an object in Python. You can use this statement for two main purposes:

  • Creating and initializing new variables
  • Updating the values of existing variables

When you use a variable name as the left operand in an assignment statement for the first time, you’re creating a new variable. At the same time, you’re initializing the variable to point to the value of the right operand.

On the other hand, when you use an existing variable in a new assignment, you’re updating or mutating the variable’s value. Strictly speaking, every new assignment will make the variable refer to a new value and stop referring to the old one. Python will garbage-collect all the values that are no longer referenced by any existing variable.

Assignment statements not only assign a value to a variable but also determine the data type of the variable at hand. This additional behavior is another important detail to consider in this kind of statement.

Because Python is a dynamically typed language, successive assignments to a given variable can change the variable’s data type. Changing the data type of a variable during a program’s execution is considered bad practice and highly discouraged. It can lead to subtle bugs that can be difficult to track down.

Unlike in math equations, in Python assignments, the left operand must be a variable rather than an expression or a value. For example, the following construct is illegal, and Python flags it as invalid syntax:

In this example, you have expressions on both sides of the = sign, and this isn’t allowed in Python code. The error message suggests that you may be confusing the equality operator with the assignment one, but that’s not the case. You’re really running an invalid assignment.

To correct this construct and convert it into a valid assignment, you’ll have to do something like the following:

In this code snippet, you first import the sqrt() function from the math module. Then you isolate the hypotenuse variable in the original equation by using the sqrt() function. Now your code works correctly.

Now you know what kind of syntax is invalid. But don’t get the idea that assignment statements are rigid and inflexible. In fact, they offer lots of room for customization, as you’ll learn next.

Python’s assignment statements are pretty flexible and versatile. You can write them in several ways, depending on your specific needs and preferences. Here’s a quick summary of the main ways to write assignments in Python:

Up to this point, you’ve mostly learned about the base assignment syntax in the above code snippet. In the following sections, you’ll learn about multiple, parallel, and augmented assignments. You’ll also learn about assignments with iterable unpacking.

Read on to see the assignment statements in action!

Assignment Statements in Action

You’ll find and use assignment statements everywhere in your Python code. They’re a fundamental part of the language, providing an explicit way to create, initialize, and mutate variables.

You can use assignment statements with plain names, like number or counter . You can also use assignments in more complicated scenarios, such as with:

  • Qualified attribute names , like user.name
  • Indices and slices of mutable sequences, like a_list[i] and a_list[i:j]
  • Dictionary keys , like a_dict[key]

This list isn’t exhaustive. However, it gives you some idea of how flexible these statements are. You can even assign multiple values to an equal number of variables in a single line, commonly known as parallel assignment . Additionally, you can simultaneously assign the values in an iterable to a comma-separated group of variables in what’s known as an iterable unpacking operation.

In the following sections, you’ll dive deeper into all these topics and a few other exciting things that you can do with assignment statements in Python.

The most elementary use case of an assignment statement is to create a new variable and initialize it using a particular value or expression:

All these statements create new variables, assigning them initial values or expressions. For an initial value, you should always use the most sensible and least surprising value that you can think of. For example, initializing a counter to something different from 0 may be confusing and unexpected because counters almost always start having counted no objects.

Updating a variable’s current value or state is another common use case of assignment statements. In Python, assigning a new value to an existing variable doesn’t modify the variable’s current value. Instead, it causes the variable to refer to a different value. The previous value will be garbage-collected if no other variable refers to it.

Consider the following examples:

These examples run two consecutive assignments on the same variable. The first one assigns the string "Hello, World!" to a new variable named greeting .

The second assignment updates the value of greeting by reassigning it the "Hi, Pythonistas!" string. In this example, the original value of greeting —the "Hello, World!" string— is lost and garbage-collected. From this point on, you can’t access the old "Hello, World!" string.

Even though running multiple assignments on the same variable during a program’s execution is common practice, you should use this feature with caution. Changing the value of a variable can make your code difficult to read, understand, and debug. To comprehend the code fully, you’ll have to remember all the places where the variable was changed and the sequential order of those changes.

Because assignments also define the data type of their target variables, it’s also possible for your code to accidentally change the type of a given variable at runtime. A change like this can lead to breaking errors, like AttributeError exceptions. Remember that strings don’t have the same methods and attributes as lists or dictionaries, for example.

In Python, you can make several variables reference the same object in a multiple-assignment line. This can be useful when you want to initialize several similar variables using the same initial value:

In this example, you chain two assignment operators in a single line. This way, your two variables refer to the same initial value of 0 . Note how both variables hold the same memory address, so they point to the same instance of 0 .

When it comes to integer variables, Python exhibits a curious behavior. It provides a numeric interval where multiple assignments behave the same as independent assignments. Consider the following examples:

To create n and m , you use independent assignments. Therefore, they should point to different instances of the number 42 . However, both variables hold the same object, which you confirm by comparing their corresponding memory addresses.

Now check what happens when you use a greater initial value:

Now n and m hold different memory addresses, which means they point to different instances of the integer number 300 . In contrast, when you use multiple assignments, both variables refer to the same object. This tiny difference can save you small bits of memory if you frequently initialize integer variables in your code.

The implicit behavior of making independent assignments point to the same integer number is actually an optimization called interning . It consists of globally caching the most commonly used integer values in day-to-day programming.

Under the hood, Python defines a numeric interval in which interning takes place. That’s the interning interval for integer numbers. You can determine this interval using a small script like the following:

This script helps you determine the interning interval by comparing integer numbers from -10 to 500 . If you run the script from your command line, then you’ll get an output like the following:

This output means that if you use a single number between -5 and 256 to initialize several variables in independent statements, then all these variables will point to the same object, which will help you save small bits of memory in your code.

In contrast, if you use a number that falls outside of the interning interval, then your variables will point to different objects instead. Each of these objects will occupy a different memory spot.

You can use the assignment operator to mutate the value stored at a given index in a Python list. The operator also works with list slices . The syntax to write these types of assignment statements is the following:

In the first construct, expression can return any Python object, including another list. In the second construct, expression must return a series of values as a list, tuple, or any other sequence. You’ll get a TypeError if expression returns a single value.

Note: When creating slice objects, you can use up to three arguments. These arguments are start , stop , and step . They define the number that starts the slice, the number at which the slicing must stop retrieving values, and the step between values.

Here’s an example of updating an individual value in a list:

In this example, you update the value at index 2 using an assignment statement. The original number at that index was 7 , and after the assignment, the number is 3 .

Note: Using indices and the assignment operator to update a value in a tuple or a character in a string isn’t possible because tuples and strings are immutable data types in Python.

Their immutability means that you can’t change their items in place :

You can’t use the assignment operator to change individual items in tuples or strings. These data types are immutable and don’t support item assignments.

It’s important to note that you can’t add new values to a list by using indices that don’t exist in the target list:

In this example, you try to add a new value to the end of numbers by using an index that doesn’t exist. This assignment isn’t allowed because there’s no way to guarantee that new indices will be consecutive. If you ever want to add a single value to the end of a list, then use the .append() method.

If you want to update several consecutive values in a list, then you can use slicing and an assignment statement:

In the first example, you update the letters between indices 1 and 3 without including the letter at 3 . The second example updates the letters from index 3 until the end of the list. Note that this slicing appends a new value to the list because the target slice is shorter than the assigned values.

Also note that the new values were provided through a tuple, which means that this type of assignment allows you to use other types of sequences to update your target list.

The third example updates a single value using a slice where both indices are equal. In this example, the assignment inserts a new item into your target list.

In the final example, you use a step of 2 to replace alternating letters with their lowercase counterparts. This slicing starts at index 1 and runs through the whole list, stepping by two items each time.

Updating the value of an existing key or adding new key-value pairs to a dictionary is another common use case of assignment statements. To do these operations, you can use the following syntax:

The first construct helps you update the current value of an existing key, while the second construct allows you to add a new key-value pair to the dictionary.

For example, to update an existing key, you can do something like this:

In this example, you update the current inventory of oranges in your store using an assignment. The left operand is the existing dictionary key, and the right operand is the desired new value.

While you can’t add new values to a list by assignment, dictionaries do allow you to add new key-value pairs using the assignment operator. In the example below, you add a lemon key to inventory :

In this example, you successfully add a new key-value pair to your inventory with 100 units. This addition is possible because dictionaries don’t have consecutive indices but unique keys, which are safe to add by assignment.

The assignment statement does more than assign the result of a single expression to a single variable. It can also cope nicely with assigning multiple values to multiple variables simultaneously in what’s known as a parallel assignment .

Here’s the general syntax for parallel assignments in Python:

Note that the left side of the statement can be either a tuple or a list of variables. Remember that to create a tuple, you just need a series of comma-separated elements. In this case, these elements must be variables.

The right side of the statement must be a sequence or iterable of values or expressions. In any case, the number of elements in the right operand must match the number of variables on the left. Otherwise, you’ll get a ValueError exception.

In the following example, you compute the two solutions of a quadratic equation using a parallel assignment:

In this example, you first import sqrt() from the math module. Then you initialize the equation’s coefficients in a parallel assignment.

The equation’s solution is computed in another parallel assignment. The left operand contains a tuple of two variables, x1 and x2 . The right operand consists of a tuple of expressions that compute the solutions for the equation. Note how each result is assigned to each variable by position.

A classical use case of parallel assignment is to swap values between variables:

The highlighted line does the magic and swaps the values of previous_value and next_value at the same time. Note that in a programming language that doesn’t support this kind of assignment, you’d have to use a temporary variable to produce the same effect:

In this example, instead of using parallel assignment to swap values between variables, you use a new variable to temporarily store the value of previous_value to avoid losing its reference.

For a concrete example of when you’d need to swap values between variables, say you’re learning how to implement the bubble sort algorithm , and you come up with the following function:

In the highlighted line, you use a parallel assignment to swap values in place if the current value is less than the next value in the input list. To dive deeper into the bubble sort algorithm and into sorting algorithms in general, check out Sorting Algorithms in Python .

You can use assignment statements for iterable unpacking in Python. Unpacking an iterable means assigning its values to a series of variables one by one. The iterable must be the right operand in the assignment, while the variables must be the left operand.

Like in parallel assignments, the variables must come as a tuple or list. The number of variables must match the number of values in the iterable. Alternatively, you can use the unpacking operator ( * ) to grab several values in a variable if the number of variables doesn’t match the iterable length.

Here’s the general syntax for iterable unpacking in Python:

Iterable unpacking is a powerful feature that you can use all around your code. It can help you write more readable and concise code. For example, you may find yourself doing something like this:

Whenever you do something like this in your code, go ahead and replace it with a more readable iterable unpacking using a single and elegant assignment, like in the following code snippet:

The numbers list on the right side contains four values. The assignment operator unpacks these values into the four variables on the left side of the statement. The values in numbers get assigned to variables in the same order that they appear in the iterable. The assignment is done by position.

Note: Because Python sets are also iterables, you can use them in an iterable unpacking operation. However, it won’t be clear which value goes to which variable because sets are unordered data structures.

The above example shows the most common form of iterable unpacking in Python. The main condition for the example to work is that the number of variables matches the number of values in the iterable.

What if you don’t know the iterable length upfront? Will the unpacking work? It’ll work if you use the * operator to pack several values into one of your target variables.

For example, say that you want to unpack the first and second values in numbers into two different variables. Additionally, you would like to pack the rest of the values in a single variable conveniently called rest . In this case, you can use the unpacking operator like in the following code:

In this example, first and second hold the first and second values in numbers , respectively. These values are assigned by position. The * operator packs all the remaining values in the input iterable into rest .

The unpacking operator ( * ) can appear at any position in your series of target variables. However, you can only use one instance of the operator:

The iterable unpacking operator works in any position in your list of variables. Note that you can only use one unpacking operator per assignment. Using more than one unpacking operator isn’t allowed and raises a SyntaxError .

Dropping away unwanted values from the iterable is a common use case for the iterable unpacking operator. Consider the following example:

In Python, if you want to signal that a variable won’t be used, then you use an underscore ( _ ) as the variable’s name. In this example, useful holds the only value that you need to use from the input iterable. The _ variable is a placeholder that guarantees that the unpacking works correctly. You won’t use the values that end up in this disposable variable.

Note: In the example above, if your target iterable is a sequence data type, such as a list or tuple, then it’s best to access its last item directly.

To do this, you can use the -1 index:

Using -1 gives you access to the last item of any sequence data type. In contrast, if you’re dealing with iterators , then you won’t be able to use indices. That’s when the *_ syntax comes to your rescue.

The pattern used in the above example comes in handy when you have a function that returns multiple values, and you only need a few of these values in your code. The os.walk() function may provide a good example of this situation.

This function allows you to iterate over the content of a directory recursively. The function returns a generator object that yields three-item tuples. Each tuple contains the following items:

  • The path to the current directory as a string
  • The names of all the immediate subdirectories as a list of strings
  • The names of all the files in the current directory as a list of strings

Now say that you want to iterate over your home directory and list only the files. You can do something like this:

This code will issue a long output depending on the current content of your home directory. Note that you need to provide a string with the path to your user folder for the example to work. The _ placeholder variable will hold the unwanted data.

In contrast, the filenames variable will hold the list of files in the current directory, which is the data that you need. The code will print the list of filenames. Go ahead and give it a try!

The assignment operator also comes in handy when you need to provide default argument values in your functions and methods. Default argument values allow you to define functions that take arguments with sensible defaults. These defaults allow you to call the function with specific values or to simply rely on the defaults.

As an example, consider the following function:

This function takes one argument, called name . This argument has a sensible default value that’ll be used when you call the function without arguments. To provide this sensible default value, you use an assignment.

Note: According to PEP 8 , the style guide for Python code, you shouldn’t use spaces around the assignment operator when providing default argument values in function definitions.

Here’s how the function works:

If you don’t provide a name during the call to greet() , then the function uses the default value provided in the definition. If you provide a name, then the function uses it instead of the default one.

Up to this point, you’ve learned a lot about the Python assignment operator and how to use it for writing different types of assignment statements. In the following sections, you’ll dive into a great feature of assignment statements in Python. You’ll learn about augmented assignments .

Augmented Assignment Operators in Python

Python supports what are known as augmented assignments . An augmented assignment combines the assignment operator with another operator to make the statement more concise. Most Python math and bitwise operators have an augmented assignment variation that looks something like this:

Note that $ isn’t a valid Python operator. In this example, it’s a placeholder for a generic operator. This statement works as follows:

  • Evaluate expression to produce a value.
  • Run the operation defined by the operator that prefixes the = sign, using the previous value of variable and the return value of expression as operands.
  • Assign the resulting value back to variable .

In practice, an augmented assignment like the above is equivalent to the following statement:

As you can conclude, augmented assignments are syntactic sugar . They provide a shorthand notation for a specific and popular kind of assignment.

For example, say that you need to define a counter variable to count some stuff in your code. You can use the += operator to increment counter by 1 using the following code:

In this example, the += operator, known as augmented addition , adds 1 to the previous value in counter each time you run the statement counter += 1 .

It’s important to note that unlike regular assignments, augmented assignments don’t create new variables. They only allow you to update existing variables. If you use an augmented assignment with an undefined variable, then you get a NameError :

Python evaluates the right side of the statement before assigning the resulting value back to the target variable. In this specific example, when Python tries to compute x + 1 , it finds that x isn’t defined.

Great! You now know that an augmented assignment consists of combining the assignment operator with another operator, like a math or bitwise operator. To continue this discussion, you’ll learn which math operators have an augmented variation in Python.

An equation like x = x + b doesn’t make sense in math. But in programming, a statement like x = x + b is perfectly valid and can be extremely useful. It adds b to x and reassigns the result back to x .

As you already learned, Python provides an operator to shorten x = x + b . Yes, the += operator allows you to write x += b instead. Python also offers augmented assignment operators for most math operators. Here’s a summary:

Operator Description Example Equivalent
Adds the right operand to the left operand and stores the result in the left operand
Subtracts the right operand from the left operand and stores the result in the left operand
Multiplies the right operand with the left operand and stores the result in the left operand
Divides the left operand by the right operand and stores the result in the left operand
Performs of the left operand by the right operand and stores the result in the left operand
Finds the remainder of dividing the left operand by the right operand and stores the result in the left operand
Raises the left operand to the power of the right operand and stores the result in the left operand

The Example column provides generic examples of how to use the operators in actual code. Note that x must be previously defined for the operators to work correctly. On the other hand, y can be either a concrete value or an expression that returns a value.

Note: The matrix multiplication operator ( @ ) doesn’t support augmented assignments yet.

Consider the following example of matrix multiplication using NumPy arrays:

Note that the exception traceback indicates that the operation isn’t supported yet.

To illustrate how augmented assignment operators work, say that you need to create a function that takes an iterable of numeric values and returns their sum. You can write this function like in the code below:

In this function, you first initialize total to 0 . In each iteration, the loop adds a new number to total using the augmented addition operator ( += ). When the loop terminates, total holds the sum of all the input numbers. Variables like total are known as accumulators . The += operator is typically used to update accumulators.

Note: Computing the sum of a series of numeric values is a common operation in programming. Python provides the built-in sum() function for this specific computation.

Another interesting example of using an augmented assignment is when you need to implement a countdown while loop to reverse an iterable. In this case, you can use the -= operator:

In this example, custom_reversed() is a generator function because it uses yield . Calling the function creates an iterator that yields items from the input iterable in reverse order. To decrement the control variable, index , you use an augmented subtraction statement that subtracts 1 from the variable in every iteration.

Note: Similar to summing the values in an iterable, reversing an iterable is also a common requirement. Python provides the built-in reversed() function for this specific computation, so you don’t have to implement your own. The above example only intends to show the -= operator in action.

Finally, counters are a special type of accumulators that allow you to count objects. Here’s an example of a letter counter:

To create this counter, you use a Python dictionary. The keys store the letters. The values store the counts. Again, to increment the counter, you use an augmented addition.

Counters are so common in programming that Python provides a tool specially designed to facilitate the task of counting. Check out Python’s Counter: The Pythonic Way to Count Objects for a complete guide on how to use this tool.

The += and *= augmented assignment operators also work with sequences , such as lists, tuples, and strings. The += operator performs augmented concatenations , while the *= operator performs augmented repetition .

These operators behave differently with mutable and immutable data types:

Operator Description Example
Runs an augmented concatenation operation on the target sequence. Mutable sequences are updated in place. If the sequence is immutable, then a new sequence is created and assigned back to the target name.
Adds to itself times. Mutable sequences are updated in place. If the sequence is immutable, then a new sequence is created and assigned back to the target name.

Note that the augmented concatenation operator operates on two sequences, while the augmented repetition operator works on a sequence and an integer number.

Consider the following examples and pay attention to the result of calling the id() function:

Mutable sequences like lists support the += augmented assignment operator through the .__iadd__() method, which performs an in-place addition. This method mutates the underlying list, appending new values to its end.

Note: If the left operand is mutable, then x += y may not be completely equivalent to x = x + y . For example, if you do list_1 = list_1 + list_2 instead of list_1 += list_2 above, then you’ll create a new list instead of mutating the existing one. This may be important if other variables refer to the same list.

Immutable sequences, such as tuples and strings, don’t provide an .__iadd__() method. Therefore, augmented concatenations fall back to the .__add__() method, which doesn’t modify the sequence in place but returns a new sequence.

There’s another difference between mutable and immutable sequences when you use them in an augmented concatenation. Consider the following examples:

With mutable sequences, the data to be concatenated can come as a list, tuple, string, or any other iterable. In contrast, with immutable sequences, the data can only come as objects of the same type. You can concatenate tuples to tuples and strings to strings, for example.

Again, the augmented repetition operator works with a sequence on the left side of the operator and an integer on the right side. This integer value represents the number of repetitions to get in the resulting sequence:

When the *= operator operates on a mutable sequence, it falls back to the .__imul__() method, which performs the operation in place, modifying the underlying sequence. In contrast, if *= operates on an immutable sequence, then .__mul__() is called, returning a new sequence of the same type.

Note: Values of n less than 0 are treated as 0 , which returns an empty sequence of the same data type as the target sequence on the left side of the *= operand.

Note that a_list[0] is a_list[3] returns True . This is because the *= operator doesn’t make a copy of the repeated data. It only reflects the data. This behavior can be a source of issues when you use the operator with mutable values.

For example, say that you want to create a list of lists to represent a matrix, and you need to initialize the list with n empty lists, like in the following code:

In this example, you use the *= operator to populate matrix with three empty lists. Now check out what happens when you try to populate the first sublist in matrix :

The appended values are reflected in the three sublists. This happens because the *= operator doesn’t make copies of the data that you want to repeat. It only reflects the data. Therefore, every sublist in matrix points to the same object and memory address.

If you ever need to initialize a list with a bunch of empty sublists, then use a list comprehension :

This time, when you populate the first sublist of matrix , your changes aren’t propagated to the other sublists. This is because all the sublists are different objects that live in different memory addresses.

Bitwise operators also have their augmented versions. The logic behind them is similar to that of the math operators. The following table summarizes the augmented bitwise operators that Python provides:

Operator Operation Example Equivalent
Augmented bitwise AND ( )
Augmented bitwise OR ( )
Augmented bitwise XOR ( )
Augmented bitwise right shift
Augmented bitwise left shift

The augmented bitwise assignment operators perform the intended operation by taking the current value of the left operand as a starting point for the computation. Consider the following example, which uses the & and &= operators:

Programmers who work with high-level languages like Python rarely use bitwise operations in day-to-day coding. However, these types of operations can be useful in some situations.

For example, say that you’re implementing a Unix-style permission system for your users to access a given resource. In this case, you can use the characters "r" for reading, "w" for writing, and "x" for execution permissions, respectively. However, using bit-based permissions could be more memory efficient:

You can assign permissions to your users with the OR bitwise operator or the augmented OR bitwise operator. Finally, you can use the bitwise AND operator to check if a user has a certain permission, as you did in the final two examples.

You’ve learned a lot about augmented assignment operators and statements in this and the previous sections. These operators apply to math, concatenation, repetition, and bitwise operations. Now you’re ready to look at other assignment variants that you can use in your code or find in other developers’ code.

Other Assignment Variants

So far, you’ve learned that Python’s assignment statements and the assignment operator are present in many different scenarios and use cases. Those use cases include variable creation and initialization, parallel assignments, iterable unpacking, augmented assignments, and more.

In the following sections, you’ll learn about a few variants of assignment statements that can be useful in your future coding. You can also find these assignment variants in other developers’ code. So, you should be aware of them and know how they work in practice.

In short, you’ll learn about:

  • Annotated assignment statements with type hints
  • Assignment expressions with the walrus operator
  • Managed attribute assignments with properties and descriptors
  • Implicit assignments in Python

These topics will take you through several interesting and useful examples that showcase the power of Python’s assignment statements.

PEP 526 introduced a dedicated syntax for variable annotation back in Python 3.6 . The syntax consists of the variable name followed by a colon ( : ) and the variable type:

Even though these statements declare three variables with their corresponding data types, the variables aren’t actually created or initialized. So, for example, you can’t use any of these variables in an augmented assignment statement:

If you try to use one of the previously declared variables in an augmented assignment, then you get a NameError because the annotation syntax doesn’t define the variable. To actually define it, you need to use an assignment.

The good news is that you can use the variable annotation syntax in an assignment statement with the = operator:

The first statement in this example is what you can call an annotated assignment statement in Python. You may ask yourself why you should use type annotations in this type of assignment if everybody can see that counter holds an integer number. You’re right. In this example, the variable type is unambiguous.

However, imagine what would happen if you found a variable initialization like the following:

What would be the data type of each user in users ? If the initialization of users is far away from the definition of the User class, then there’s no quick way to answer this question. To clarify this ambiguity, you can provide the appropriate type hint for users :

Now you’re clearly communicating that users will hold a list of User instances. Using type hints in assignment statements that initialize variables to empty collection data types—such as lists, tuples, or dictionaries—allows you to provide more context about how your code works. This practice will make your code more explicit and less error-prone.

Up to this point, you’ve learned that regular assignment statements with the = operator don’t have a return value. They just create or update variables. Therefore, you can’t use a regular assignment to assign a value to a variable within the context of an expression.

Python 3.8 changed this by introducing a new type of assignment statement through PEP 572 . This new statement is known as an assignment expression or named expression .

Note: Expressions are a special type of statement in Python. Their distinguishing characteristic is that expressions always have a return value, which isn’t the case with all types of statements.

Unlike regular assignments, assignment expressions have a return value, which is why they’re called expressions in the first place. This return value is automatically assigned to a variable. To write an assignment expression, you must use the walrus operator ( := ), which was named for its resemblance to the eyes and tusks of a walrus lying on its side.

The general syntax of an assignment statement is as follows:

This expression looks like a regular assignment. However, instead of using the assignment operator ( = ), it uses the walrus operator ( := ). For the expression to work correctly, the enclosing parentheses are required in most use cases. However, there are certain situations in which these parentheses are superfluous. Either way, they won’t hurt you.

Assignment expressions come in handy when you want to reuse the result of an expression or part of an expression without using a dedicated assignment to grab this value beforehand.

Note: Assignment expressions with the walrus operator have several practical use cases. They also have a few restrictions. For example, they’re illegal in certain contexts, such as lambda functions, parallel assignments, and augmented assignments.

For a deep dive into this special type of assignment, check out The Walrus Operator: Python 3.8 Assignment Expressions .

A particularly handy use case for assignment expressions is when you need to grab the result of an expression used in the context of a conditional statement. For example, say that you need to write a function to compute the mean of a sample of numeric values. Without the walrus operator, you could do something like this:

In this example, the sample size ( n ) is a value that you need to reuse in two different computations. First, you need to check whether the sample has data points or not. Then you need to use the sample size to compute the mean. To be able to reuse n , you wrote a dedicated assignment statement at the beginning of your function to grab the sample size.

You can avoid this extra step by combining it with the first use of the target value, len(sample) , using an assignment expression like the following:

The assignment expression introduced in the conditional computes the sample size and assigns it to n . This way, you guarantee that you have a reference to the sample size to use in further computations.

Because the assignment expression returns the sample size anyway, the conditional can check whether that size equals 0 or not and then take a certain course of action depending on the result of this check. The return statement computes the sample’s mean and sends the result back to the function caller.

Python provides a few tools that allow you to fine-tune the operations behind the assignment of attributes. The attributes that run implicit operations on assignments are commonly referred to as managed attributes .

Properties are the most commonly used tool for providing managed attributes in your classes. However, you can also use descriptors and, in some cases, the .__setitem__() special method.

To understand what fine-tuning the operation behind an assignment means, say that you need a Point class that only allows numeric values for its coordinates, x and y . To write this class, you must set up a validation mechanism to reject non-numeric values. You can use properties to attach the validation functionality on top of x and y .

Here’s how you can write your class:

In Point , you use properties for the .x and .y coordinates. Each property has a getter and a setter method . The getter method returns the attribute at hand. The setter method runs the input validation using a try … except block and the built-in float() function. Then the method assigns the result to the actual attribute.

Here’s how your class works in practice:

When you use a property-based attribute as the left operand in an assignment statement, Python automatically calls the property’s setter method, running any computation from it.

Because both .x and .y are properties, the input validation runs whenever you assign a value to either attribute. In the first example, the input values are valid numbers and the validation passes. In the final example, "one" isn’t a valid numeric value, so the validation fails.

If you look at your Point class, you’ll note that it follows a repetitive pattern, with the getter and setter methods looking quite similar. To avoid this repetition, you can use a descriptor instead of a property.

A descriptor is a class that implements the descriptor protocol , which consists of four special methods :

  • .__get__() runs when you access the attribute represented by the descriptor.
  • .__set__() runs when you use the attribute in an assignment statement.
  • .__delete__() runs when you use the attribute in a del statement.
  • .__set_name__() sets the attribute’s name, creating a name-aware attribute.

Here’s how your code may look if you use a descriptor to represent the coordinates of your Point class:

You’ve removed repetitive code by defining Coordinate as a descriptor that manages the input validation in a single place. Go ahead and run the following code to try out the new implementation of Point :

Great! The class works as expected. Thanks to the Coordinate descriptor, you now have a more concise and non-repetitive version of your original code.

Another way to fine-tune the operations behind an assignment statement is to provide a custom implementation of .__setitem__() in your class. You’ll use this method in classes representing mutable data collections, such as custom list-like or dictionary-like classes.

As an example, say that you need to create a dictionary-like class that stores its keys in lowercase letters:

In this example, you create a dictionary-like class by subclassing UserDict from collections . Your class implements a .__setitem__() method, which takes key and value as arguments. The method uses str.lower() to convert key into lowercase letters before storing it in the underlying dictionary.

Python implicitly calls .__setitem__() every time you use a key as the left operand in an assignment statement. This behavior allows you to tweak how you process the assignment of keys in your custom dictionary.

Implicit Assignments in Python

Python implicitly runs assignments in many different contexts. In most cases, these implicit assignments are part of the language syntax. In other cases, they support specific behaviors.

Whenever you complete an action in the following list, Python runs an implicit assignment for you:

  • Define or call a function
  • Define or instantiate a class
  • Use the current instance , self
  • Import modules and objects
  • Use a decorator
  • Use the control variable in a for loop or a comprehension
  • Use the as qualifier in with statements , imports, and try … except blocks
  • Access the _ special variable in an interactive session

Behind the scenes, Python performs an assignment in every one of the above situations. In the following subsections, you’ll take a tour of all these situations.

When you define a function, the def keyword implicitly assigns a function object to your function’s name. Here’s an example:

From this point on, the name greet refers to a function object that lives at a given memory address in your computer. You can call the function using its name and a pair of parentheses with appropriate arguments. This way, you can reuse greet() wherever you need it.

If you call your greet() function with fellow as an argument, then Python implicitly assigns the input argument value to the name parameter on the function’s definition. The parameter will hold a reference to the input arguments.

When you define a class with the class keyword, you’re assigning a specific name to a class object . You can later use this name to create instances of that class. Consider the following example:

In this example, the name User holds a reference to a class object, which was defined in __main__.User . Like with a function, when you call the class’s constructor with the appropriate arguments to create an instance, Python assigns the arguments to the parameters defined in the class initializer .

Another example of implicit assignments is the current instance of a class, which in Python is called self by convention. This name implicitly gets a reference to the current object whenever you instantiate a class. Thanks to this implicit assignment, you can access .name and .job from within the class without getting a NameError in your code.

Import statements are another variant of implicit assignments in Python. Through an import statement, you assign a name to a module object, class, function, or any other imported object. This name is then created in your current namespace so that you can access it later in your code:

In this example, you import the sys module object from the standard library and assign it to the sys name, which is now available in your namespace, as you can conclude from the second call to the built-in dir() function.

You also run an implicit assignment when you use a decorator in your code. The decorator syntax is just a shortcut for a formal assignment like the following:

Here, you call decorator() with a function object as an argument. This call will typically add functionality on top of the existing function, func() , and return a function object, which is then reassigned to the func name.

The decorator syntax is syntactic sugar for replacing the previous assignment, which you can now write as follows:

Even though this new code looks pretty different from the above assignment, the code implicitly runs the same steps.

Another situation in which Python automatically runs an implicit assignment is when you use a for loop or a comprehension. In both cases, you can have one or more control variables that you then use in the loop or comprehension body:

The memory address of control_variable changes on each iteration of the loop. This is because Python internally reassigns a new value from the loop iterable to the loop control variable on each cycle.

The same behavior appears in comprehensions:

In the end, comprehensions work like for loops but use a more concise syntax. This comprehension creates a new list of strings that mimic the output from the previous example.

The as keyword in with statements, except clauses, and import statements is another example of an implicit assignment in Python. This time, the assignment isn’t completely implicit because the as keyword provides an explicit way to define the target variable.

In a with statement, the target variable that follows the as keyword will hold a reference to the context manager that you’re working with. As an example, say that you have a hello.txt file with the following content:

You want to open this file and print each of its lines on your screen. In this case, you can use the with statement to open the file using the built-in open() function.

In the example below, you accomplish this. You also add some calls to print() that display information about the target variable defined by the as keyword:

This with statement uses the open() function to open hello.txt . The open() function is a context manager that returns a text file object represented by an io.TextIOWrapper instance.

Since you’ve defined a hello target variable with the as keyword, now that variable holds a reference to the file object itself. You confirm this by printing the object and its memory address. Finally, the for loop iterates over the lines and prints this content to the screen.

When it comes to using the as keyword in the context of an except clause, the target variable will contain an exception object if any exception occurs:

In this example, you run a division that raises a ZeroDivisionError . The as keyword assigns the raised exception to error . Note that when you print the exception object, you get only the message because exceptions have a custom .__str__() method that supports this behavior.

There’s a final detail to remember when using the as specifier in a try … except block like the one in the above example. Once you leave the except block, the target variable goes out of scope , and you can’t use it anymore.

Finally, Python’s import statements also support the as keyword. In this context, you can use as to import objects with a different name:

In these examples, you use the as keyword to import the numpy package with the np name and pandas with the name pd . If you call dir() , then you’ll realize that np and pd are now in your namespace. However, the numpy and pandas names are not.

Using the as keyword in your imports comes in handy when you want to use shorter names for your objects or when you need to use different objects that originally had the same name in your code. It’s also useful when you want to make your imported names non-public using a leading underscore, like in import sys as _sys .

The final implicit assignment that you’ll learn about in this tutorial only occurs when you’re using Python in an interactive session. Every time you run a statement that returns a value, the interpreter stores the result in a special variable denoted by a single underscore character ( _ ).

You can access this special variable as you’d access any other variable:

These examples cover several situations in which Python internally uses the _ variable. The first two examples evaluate expressions. Expressions always have a return value, which is automatically assigned to the _ variable every time.

When it comes to function calls, note that if your function returns a fruitful value, then _ will hold it. In contrast, if your function returns None , then the _ variable will remain untouched.

The next example consists of a regular assignment statement. As you already know, regular assignments don’t return any value, so the _ variable isn’t updated after these statements run. Finally, note that accessing a variable in an interactive session returns the value stored in the target variable. This value is then assigned to the _ variable.

Note that since _ is a regular variable, you can use it in other expressions:

In this example, you first create a list of values. Then you call len() to get the number of values in the list. Python automatically stores this value in the _ variable. Finally, you use _ to compute the mean of your list of values.

Now that you’ve learned about some of the implicit assignments that Python runs under the hood, it’s time to dig into a final assignment-related topic. In the following few sections, you’ll learn about some illegal and dangerous assignments that you should be aware of and avoid in your code.

Illegal and Dangerous Assignments in Python

In Python, you’ll find a few situations in which using assignments is either forbidden or dangerous. You must be aware of these special situations and try to avoid them in your code.

In the following sections, you’ll learn when using assignment statements isn’t allowed in Python. You’ll also learn about some situations in which using assignments should be avoided if you want to keep your code consistent and robust.

You can’t use Python keywords as variable names in assignment statements. This kind of assignment is explicitly forbidden. If you try to use a keyword as a variable name in an assignment, then you get a SyntaxError :

Whenever you try to use a keyword as the left operand in an assignment statement, you get a SyntaxError . Keywords are an intrinsic part of the language and can’t be overridden.

If you ever feel the need to name one of your variables using a Python keyword, then you can append an underscore to the name of your variable:

In this example, you’re using the desired name for your variables. Because you added a final underscore to the names, Python doesn’t recognize them as keywords, so it doesn’t raise an error.

Note: Even though adding an underscore at the end of a name is an officially recommended practice , it can be confusing sometimes. Therefore, try to find an alternative name or use a synonym whenever you find yourself using this convention.

For example, you can write something like this:

In this example, using the name booking_class for your variable is way clearer and more descriptive than using class_ .

You’ll also find that you can use only a few keywords as part of the right operand in an assignment statement. Those keywords will generally define simple statements that return a value or object. These include lambda , and , or , not , True , False , None , in , and is . You can also use the for keyword when it’s part of a comprehension and the if keyword when it’s used as part of a ternary operator .

In an assignment, you can never use a compound statement as the right operand. Compound statements are those that require an indented block, such as for and while loops, conditionals, with statements, try … except blocks, and class or function definitions.

Sometimes, you need to name variables, but the desired or ideal name is already taken and used as a built-in name. If this is your case, think harder and find another name. Don’t shadow the built-in.

Shadowing built-in names can cause hard-to-identify problems in your code. A common example of this issue is using list or dict to name user-defined variables. In this case, you override the corresponding built-in names, which won’t work as expected if you use them later in your code.

Consider the following example:

The exception in this example may sound surprising. How come you can’t use list() to build a list from a call to map() that returns a generator of square numbers?

By using the name list to identify your list of numbers, you shadowed the built-in list name. Now that name points to a list object rather than the built-in class. List objects aren’t callable, so your code no longer works.

In Python, you’ll have nothing that warns against using built-in, standard-library, or even relevant third-party names to identify your own variables. Therefore, you should keep an eye out for this practice. It can be a source of hard-to-debug errors.

In programming, a constant refers to a name associated with a value that never changes during a program’s execution. Unlike other programming languages, Python doesn’t have a dedicated syntax for defining constants. This fact implies that Python doesn’t have constants in the strict sense of the word.

Python only has variables. If you need a constant in Python, then you’ll have to define a variable and guarantee that it won’t change during your code’s execution. To do that, you must avoid using that variable as the left operand in an assignment statement.

To tell other Python programmers that a given variable should be treated as a constant, you must write your variable’s name in capital letters with underscores separating the words. This naming convention has been adopted by the Python community and is a recommendation that you’ll find in the Constants section of PEP 8 .

In the following examples, you define some constants in Python:

The problem with these constants is that they’re actually variables. Nothing prevents you from changing their value during your code’s execution. So, at any time, you can do something like the following:

These assignments modify the value of two of your original constants. Python doesn’t complain about these changes, which can cause issues later in your code. As a Python developer, you must guarantee that named constants in your code remain constant.

The only way to do that is never to use named constants in an assignment statement other than the constant definition.

You’ve learned a lot about Python’s assignment operators and how to use them for writing assignment statements . With this type of statement, you can create, initialize, and update variables according to your needs. Now you have the required skills to fully manage the creation and mutation of variables in your Python code.

In this tutorial, you’ve learned how to:

  • Write assignment statements using Python’s assignment operators
  • Work with augmented assignments in Python
  • Explore assignment variants, like assignment expression and managed attributes
  • Identify illegal and dangerous assignments in Python

Learning about the Python assignment operator and how to use it in assignment statements is a fundamental skill in Python. It empowers you to write reliable and effective Python code.

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Python's Assignment Operator: Write Robust Assignments (Source Code)

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Python for absolute beginners, next episode, variables and assignment.

Overview Teaching: 15 min Exercises: 15 min Questions How can I store data in programs? Objectives Write scripts that assign values to variables and perform calculations with those values. Correctly trace value changes in scripts that use assignment.

Use variables to store values

Variables are one of the fundamental building blocks of Python. A variable is like a tiny container where you store values and data, such as filenames, words, numbers, collections of words and numbers, and more.

The variable name will point to a value that you “assign” it. You might think about variable assignment like putting a value “into” the variable, as if the variable is a little box 🎁

(In fact, a variable is not a container as such but more like an adress label that points to a container with a given value. This difference will become relevant once we start talking about lists and mutable data types.)

You assign variables with an equals sign ( = ). In Python, a single equals sign = is the “assignment operator.” (A double equals sign == is the “real” equals sign.)

  • Variables are names for values.
  • In Python the = symbol assigns the value on the right to the name on the left.
  • The variable is created when a value is assigned to it.
  • Here, Python assigns an age to a variable age and a name in quotation marks to a variable first_name :

Variable names

Variable names can be as long or as short as you want, but there are certain rules you must follow.

  • Cannot start with a digit.
  • Cannot contain spaces, quotation marks, or other punctuation.
  • May contain an underscore (typically used to separate words in long variable names).
  • Having an underscore at the beginning of a variable name like _alistairs_real_age has a special meaning. So we won’t do that until we understand the convention.
  • The standard naming convention for variable names in Python is the so-called “snake case”, where each word is separated by an underscore. For example my_first_variable . You can read more about naming conventions in Python here .

Use meaningful variable names

Python doesn’t care what you call variables as long as they obey the rules (alphanumeric characters and the underscore). As you start to code, you will almost certainly be tempted to use extremely short variables names like f . Your fingers will get tired. Your coffee will wear off. You will see other people using variables like f . You’ll promise yourself that you’ll definitely remember what f means. But you probably won’t.

So, resist the temptation of bad variable names! Clear and precisely-named variables will:

  • Make your code more readable (both to yourself and others).
  • Reinforce your understanding of Python and what’s happening in the code.
  • Clarify and strengthen your thinking.

Use meaningful variable names to help other people understand what the program does. The most important “other person” is your future self!

Python is case-sensitive

Python thinks that upper- and lower-case letters are different, so Name and name are different variables. There are conventions for using upper-case letters at the start of variable names so we will use lower-case letters for now.

Off-Limits Names

The only variable names that are off-limits are names that are reserved by, or built into, the Python programming language itself — such as print , True , and list . Some of these you can overwrite into variable names (not ideal!), but Jupyter Lab (and many other environments and editors) will catch this by colour coding your variable. If your would-be variable is colour-coded green, rethink your name choice. This is not something to worry too much about. You can get the object back by resetting your kernel.

Use print() to display values

We can check to see what’s “inside” variables by running a cell with the variable’s name. This is one of the handiest features of a Jupyter notebook. Outside the Jupyter environment, you would need to use the print() function to display the variable.

You can run the print() function inside the Jupyter environment, too. This is sometimes useful because Jupyter will only display the last variable in a cell, while print() can display multiple variables. Additionally, Jupyter will display text with \n characters (which means “new line”), while print() will display the text appropriately formatted with new lines.

  • Python has a built-in function called print() that prints things as text.
  • Provide values to the function (i.e., the things to print) in parentheses.
  • To add a string to the printout, wrap the string in single or double quotations.
  • The values passed to the function are called ‘arguments’ and are separated by commas.
  • When using the print() function, we can also separate with a ‘+’ sign. However, when using ‘+’ we have to add spaces in between manually.
  • print() automatically puts a single space between items to separate them.
  • And wraps around to a new line at the end.

Variables must be created before they are used

If a variable doesn’t exist yet, or if the name has been misspelled, Python reports an error (unlike some languages, which “guess” a default value).

The last line of an error message is usually the most informative. This message lets us know that there is no variable called eye_color in the script.

Variables Persist Between Cells Variables defined in one cell exist in all other cells once executed, so the relative location of cells in the notebook do not matter (i.e., cells lower down can still affect those above). Notice the number in the square brackets [ ] to the left of the cell. These numbers indicate the order, in which the cells have been executed. Cells with lower numbers will affect cells with higher numbers as Python runs the cells chronologically. As a best practice, we recommend you keep your notebook in chronological order so that it is easier for the human eye to read and make sense of, as well as to avoid any errors if you close and reopen your project, and then rerun what you have done. Remember: Notebook cells are just a way to organize a program! As far as Python is concerned, all of the source code is one long set of instructions.

Variables can be used in calculations

  • We can use variables in calculations just as if they were values. Remember, we assigned 42 to age a few lines ago.

This code works in the following way. We are reassigning the value of the variable age by taking its previous value (42) and adding 3, thus getting our new value of 45.

Use an index to get a single character from a string

  • The characters (individual letters, numbers, and so on) in a string are ordered. For example, the string ‘AB’ is not the same as ‘BA’. Because of this ordering, we can treat the string as a list of characters.
  • Each position in the string (first, second, etc.) is given a number. This number is called an index or sometimes a subscript.
  • Indices are numbered from 0 rather than 1.
  • Use the position’s index in square brackets to get the character at that position.

Use a slice to get a substring

A part of a string is called a substring. A substring can be as short as a single character. A slice is a part of a string (or, more generally, any list-like thing). We take a slice by using [start:stop] , where start is replaced with the index of the first element we want and stop is replaced with the index of the element just after the last element we want. Mathematically, you might say that a slice selects [start:stop] . The difference between stop and start is the slice’s length. Taking a slice does not change the contents of the original string. Instead, the slice is a copy of part of the original string.

Use the built-in function len() to find the length of a string

The built-in function len() is used to find the length of a string (and later, of other data types, too).

Note that the result is 6 and not 7. This is because it is the length of the value of the variable (i.e. 'helium' ) that is being counted and not the name of the variable (i.e. element )

Also note that nested functions are evaluated from the inside out, just like in mathematics. Thus, Python first reads the len() function, then the print() function.

Choosing a Name Which is a better variable name, m , min , or minutes ? Why? Hint: think about which code you would rather inherit from someone who is leaving the library: ts = m * 60 + s tot_sec = min * 60 + sec total_seconds = minutes * 60 + seconds Solution minutes is better because min might mean something like “minimum” (and actually does in Python, but we haven’t seen that yet).
Swapping Values Draw a table showing the values of the variables in this program after each statement is executed. In simple terms, what do the last three lines of this program do? x = 1.0 y = 3.0 swap = x x = y y = swap Solution swap = x # x->1.0 y->3.0 swap->1.0 x = y # x->3.0 y->3.0 swap->1.0 y = swap # x->3.0 y->1.0 swap->1.0 These three lines exchange the values in x and y using the swap variable for temporary storage. This is a fairly common programming idiom.
Predicting Values What is the final value of position in the program below? (Try to predict the value without running the program, then check your prediction.) initial = "left" position = initial initial = "right" Solution initial = "left" # Initial is assigned the string "left" position = initial # Position is assigned the variable initial, currently "left" initial = "right" # Initial is assigned the string "right" print(position) left The last assignment to position was “left”
Can you slice integers? If you assign a = 123 , what happens if you try to get the second digit of a ? Solution Numbers are not stored in the written representation, so they can’t be treated like strings. a = 123 print(a[1]) TypeError: 'int' object is not subscriptable
Slicing What does the following program print? library_name = 'social sciences' print('library_name[1:3] is:', library_name[1:3]) If thing is a variable name, low is a low number, and high is a high number: What does thing[low:high] do? What does thing[low:] (without a value after the colon) do? What does thing[:high] (without a value before the colon) do? What does thing[:] (just a colon) do? What does thing[number:negative-number] do? Solution library_name[1:3] is: oc It will slice the string, starting at the low index and ending an element before the high index It will slice the string, starting at the low index and stopping at the end of the string It will slice the string, starting at the beginning on the string, and ending an element before the high index It will print the entire string It will slice the string, starting the number index, and ending a distance of the absolute value of negative-number elements from the end of the string
Key Points Use variables to store values. Use meaningful variable names. Python is case-sensitive. Use print() to display values. Variables must be created before they are used. Variables persist between cells. Variables can be used in calculations. Use an index to get a single character from a string. Use a slice to get a substring. Use the built-in function len to find the length of a string.
  • Contributors

Basic Statements in Python

Table of contents, what is a statement in python, statement set, multi-line statements, simple statements, expression statements, the assert statement, the try statement.

Statements in Python

In Python, statements are instructions or commands that you write to perform specific actions or tasks. They are the building blocks of a Python program.

A statement is a line of code that performs a specific action. It is the smallest unit of code that can be executed by the Python interpreter.

Assignment Statement

In this example, the value 10 is assigned to the variable x using the assignment statement.

Conditional Statement

In this example, the if-else statement is used to check the value of x and print a corresponding message.

By using statements, programmers can instruct the computer to perform a variety of tasks, from simple arithmetic operations to complex decision-making processes. Proper use of statements is crucial to writing efficient and effective Python code.

Here's a table summarizing various types of statements in Python:

Statement Description
Multi-Line Statements Statements spanning multiple lines using line continuation or braces.
Compound Statements Statements that contain other statements (e.g., , while, for).
Simple Statements Basic standalone statements that perform a single action.
Expression Statements Statements that evaluate and produce a value.
Statement A placeholder statement that does nothing.
Statement Used to delete references to objects.
Statement Terminates a function and returns a value (optional).
Statement Imports modules or specific objects from modules.
and Statements Control flow statements used in loops ( skips to the next iteration, exits the loop).

Please note that this table provides a brief overview of each statement type, and there may be additional details and variations for each statement.

Multi-line statements are a convenient way to write long code in Python without making it cluttered. They allow you to write several lines of code as a single statement, making it easier for developers to read and understand the code. Here are two examples of multi-line statements in Python:

  • Using backslash:
  • Using parentheses:

Simple statements are the smallest unit of execution in Python programming language and they do not contain any logical or conditional expressions. They are usually composed of a single line of code and can perform basic operations such as assigning values to variables , printing out values, or calling functions .

Examples of simple statements in Python:

Simple statements are essential to programming in Python and are often used in combination with more complex statements to create robust programs and applications.

Expression statements in Python are lines of code that evaluate and produce a value. They are used to assign values to variables, call functions, and perform other operations that produce a result.

In this example, we assign the value 5 to the variable x , then add 3 to x and assign the result ( 8 ) to the variable y . Finally, we print the value of y .

In this example, we define a function square that takes one argument ( x ) and returns its square. We then call the function with the argument 5 and assign the result ( 25 ) to the variable result . Finally, we print the value of result .

Overall, expression statements are an essential part of Python programming and allow for the execution of mathematical and computational operations.

The assert statement in Python is used to test conditions and trigger an error if the condition is not met. It is often used for debugging and testing purposes.

Where condition is the expression that is tested, and message is the optional error message that is displayed when the condition is not met.

In this example, the assert statement tests whether x is equal to 5 . If the condition is met, the statement has no effect. If the condition is not met, an error will be raised with the message x should be 5 .

In this example, the assert statement tests whether y is not equal to 0 before performing the division. If the condition is met, the division proceeds as normal. If the condition is not met, an error will be raised with the message Cannot divide by zero .

Overall, assert statements are a useful tool in Python for debugging and testing, as they can help catch errors early on. They are also easily disabled in production code to avoid any unnecessary overhead.

The try statement in Python is used to catch exceptions that may occur during the execution of a block of code. It ensures that even when an error occurs, the code does not stop running.

Examples of Error Processing

Dive deep into the topic.

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7. Simple statements ¶

A simple statement is comprised within a single logical line. Several simple statements may occur on a single line separated by semicolons. The syntax for simple statements is:

7.1. Expression statements ¶

Expression statements are used (mostly interactively) to compute and write a value, or (usually) to call a procedure (a function that returns no meaningful result; in Python, procedures return the value None ). Other uses of expression statements are allowed and occasionally useful. The syntax for an expression statement is:

An expression statement evaluates the expression list (which may be a single expression).

In interactive mode, if the value is not None , it is converted to a string using the built-in repr() function and the resulting string is written to standard output on a line by itself (except if the result is None , so that procedure calls do not cause any output.)

7.2. Assignment statements ¶

Assignment statements are used to (re)bind names to values and to modify attributes or items of mutable objects:

(See section Primaries for the syntax definitions for attributeref , subscription , and slicing .)

An assignment statement evaluates the expression list (remember that this can be a single expression or a comma-separated list, the latter yielding a tuple) and assigns the single resulting object to each of the target lists, from left to right.

Assignment is defined recursively depending on the form of the target (list). When a target is part of a mutable object (an attribute reference, subscription or slicing), the mutable object must ultimately perform the assignment and decide about its validity, and may raise an exception if the assignment is unacceptable. The rules observed by various types and the exceptions raised are given with the definition of the object types (see section The standard type hierarchy ).

Assignment of an object to a target list, optionally enclosed in parentheses or square brackets, is recursively defined as follows.

If the target list is a single target with no trailing comma, optionally in parentheses, the object is assigned to that target.

If the target list contains one target prefixed with an asterisk, called a “starred” target: The object must be an iterable with at least as many items as there are targets in the target list, minus one. The first items of the iterable are assigned, from left to right, to the targets before the starred target. The final items of the iterable are assigned to the targets after the starred target. A list of the remaining items in the iterable is then assigned to the starred target (the list can be empty).

Else: The object must be an iterable with the same number of items as there are targets in the target list, and the items are assigned, from left to right, to the corresponding targets.

Assignment of an object to a single target is recursively defined as follows.

If the target is an identifier (name):

If the name does not occur in a global or nonlocal statement in the current code block: the name is bound to the object in the current local namespace.

Otherwise: the name is bound to the object in the global namespace or the outer namespace determined by nonlocal , respectively.

The name is rebound if it was already bound. This may cause the reference count for the object previously bound to the name to reach zero, causing the object to be deallocated and its destructor (if it has one) to be called.

If the target is an attribute reference: The primary expression in the reference is evaluated. It should yield an object with assignable attributes; if this is not the case, TypeError is raised. That object is then asked to assign the assigned object to the given attribute; if it cannot perform the assignment, it raises an exception (usually but not necessarily AttributeError ).

Note: If the object is a class instance and the attribute reference occurs on both sides of the assignment operator, the right-hand side expression, a.x can access either an instance attribute or (if no instance attribute exists) a class attribute. The left-hand side target a.x is always set as an instance attribute, creating it if necessary. Thus, the two occurrences of a.x do not necessarily refer to the same attribute: if the right-hand side expression refers to a class attribute, the left-hand side creates a new instance attribute as the target of the assignment:

This description does not necessarily apply to descriptor attributes, such as properties created with property() .

If the target is a subscription: The primary expression in the reference is evaluated. It should yield either a mutable sequence object (such as a list) or a mapping object (such as a dictionary). Next, the subscript expression is evaluated.

If the primary is a mutable sequence object (such as a list), the subscript must yield an integer. If it is negative, the sequence’s length is added to it. The resulting value must be a nonnegative integer less than the sequence’s length, and the sequence is asked to assign the assigned object to its item with that index. If the index is out of range, IndexError is raised (assignment to a subscripted sequence cannot add new items to a list).

If the primary is a mapping object (such as a dictionary), the subscript must have a type compatible with the mapping’s key type, and the mapping is then asked to create a key/value pair which maps the subscript to the assigned object. This can either replace an existing key/value pair with the same key value, or insert a new key/value pair (if no key with the same value existed).

For user-defined objects, the __setitem__() method is called with appropriate arguments.

If the target is a slicing: The primary expression in the reference is evaluated. It should yield a mutable sequence object (such as a list). The assigned object should be a sequence object of the same type. Next, the lower and upper bound expressions are evaluated, insofar they are present; defaults are zero and the sequence’s length. The bounds should evaluate to integers. If either bound is negative, the sequence’s length is added to it. The resulting bounds are clipped to lie between zero and the sequence’s length, inclusive. Finally, the sequence object is asked to replace the slice with the items of the assigned sequence. The length of the slice may be different from the length of the assigned sequence, thus changing the length of the target sequence, if the target sequence allows it.

CPython implementation detail: In the current implementation, the syntax for targets is taken to be the same as for expressions, and invalid syntax is rejected during the code generation phase, causing less detailed error messages.

Although the definition of assignment implies that overlaps between the left-hand side and the right-hand side are ‘simultaneous’ (for example a, b = b, a swaps two variables), overlaps within the collection of assigned-to variables occur left-to-right, sometimes resulting in confusion. For instance, the following program prints [0, 2] :

The specification for the *target feature.

7.2.1. Augmented assignment statements ¶

Augmented assignment is the combination, in a single statement, of a binary operation and an assignment statement:

(See section Primaries for the syntax definitions of the last three symbols.)

An augmented assignment evaluates the target (which, unlike normal assignment statements, cannot be an unpacking) and the expression list, performs the binary operation specific to the type of assignment on the two operands, and assigns the result to the original target. The target is only evaluated once.

An augmented assignment expression like x += 1 can be rewritten as x = x + 1 to achieve a similar, but not exactly equal effect. In the augmented version, x is only evaluated once. Also, when possible, the actual operation is performed in-place , meaning that rather than creating a new object and assigning that to the target, the old object is modified instead.

Unlike normal assignments, augmented assignments evaluate the left-hand side before evaluating the right-hand side. For example, a[i] += f(x) first looks-up a[i] , then it evaluates f(x) and performs the addition, and lastly, it writes the result back to a[i] .

With the exception of assigning to tuples and multiple targets in a single statement, the assignment done by augmented assignment statements is handled the same way as normal assignments. Similarly, with the exception of the possible in-place behavior, the binary operation performed by augmented assignment is the same as the normal binary operations.

For targets which are attribute references, the same caveat about class and instance attributes applies as for regular assignments.

7.2.2. Annotated assignment statements ¶

Annotation assignment is the combination, in a single statement, of a variable or attribute annotation and an optional assignment statement:

The difference from normal Assignment statements is that only a single target is allowed.

The assignment target is considered “simple” if it consists of a single name that is not enclosed in parentheses. For simple assignment targets, if in class or module scope, the annotations are evaluated and stored in a special class or module attribute __annotations__ that is a dictionary mapping from variable names (mangled if private) to evaluated annotations. This attribute is writable and is automatically created at the start of class or module body execution, if annotations are found statically.

If the assignment target is not simple (an attribute, subscript node, or parenthesized name), the annotation is evaluated if in class or module scope, but not stored.

If a name is annotated in a function scope, then this name is local for that scope. Annotations are never evaluated and stored in function scopes.

If the right hand side is present, an annotated assignment performs the actual assignment before evaluating annotations (where applicable). If the right hand side is not present for an expression target, then the interpreter evaluates the target except for the last __setitem__() or __setattr__() call.

The proposal that added syntax for annotating the types of variables (including class variables and instance variables), instead of expressing them through comments.

The proposal that added the typing module to provide a standard syntax for type annotations that can be used in static analysis tools and IDEs.

Changed in version 3.8: Now annotated assignments allow the same expressions in the right hand side as regular assignments. Previously, some expressions (like un-parenthesized tuple expressions) caused a syntax error.

7.3. The assert statement ¶

Assert statements are a convenient way to insert debugging assertions into a program:

The simple form, assert expression , is equivalent to

The extended form, assert expression1, expression2 , is equivalent to

These equivalences assume that __debug__ and AssertionError refer to the built-in variables with those names. In the current implementation, the built-in variable __debug__ is True under normal circumstances, False when optimization is requested (command line option -O ). The current code generator emits no code for an assert statement when optimization is requested at compile time. Note that it is unnecessary to include the source code for the expression that failed in the error message; it will be displayed as part of the stack trace.

Assignments to __debug__ are illegal. The value for the built-in variable is determined when the interpreter starts.

7.4. The pass statement ¶

pass is a null operation — when it is executed, nothing happens. It is useful as a placeholder when a statement is required syntactically, but no code needs to be executed, for example:

7.5. The del statement ¶

Deletion is recursively defined very similar to the way assignment is defined. Rather than spelling it out in full details, here are some hints.

Deletion of a target list recursively deletes each target, from left to right.

Deletion of a name removes the binding of that name from the local or global namespace, depending on whether the name occurs in a global statement in the same code block. If the name is unbound, a NameError exception will be raised.

Deletion of attribute references, subscriptions and slicings is passed to the primary object involved; deletion of a slicing is in general equivalent to assignment of an empty slice of the right type (but even this is determined by the sliced object).

Changed in version 3.2: Previously it was illegal to delete a name from the local namespace if it occurs as a free variable in a nested block.

7.6. The return statement ¶

return may only occur syntactically nested in a function definition, not within a nested class definition.

If an expression list is present, it is evaluated, else None is substituted.

return leaves the current function call with the expression list (or None ) as return value.

When return passes control out of a try statement with a finally clause, that finally clause is executed before really leaving the function.

In a generator function, the return statement indicates that the generator is done and will cause StopIteration to be raised. The returned value (if any) is used as an argument to construct StopIteration and becomes the StopIteration.value attribute.

In an asynchronous generator function, an empty return statement indicates that the asynchronous generator is done and will cause StopAsyncIteration to be raised. A non-empty return statement is a syntax error in an asynchronous generator function.

7.7. The yield statement ¶

A yield statement is semantically equivalent to a yield expression . The yield statement can be used to omit the parentheses that would otherwise be required in the equivalent yield expression statement. For example, the yield statements

are equivalent to the yield expression statements

Yield expressions and statements are only used when defining a generator function, and are only used in the body of the generator function. Using yield in a function definition is sufficient to cause that definition to create a generator function instead of a normal function.

For full details of yield semantics, refer to the Yield expressions section.

7.8. The raise statement ¶

If no expressions are present, raise re-raises the exception that is currently being handled, which is also known as the active exception . If there isn’t currently an active exception, a RuntimeError exception is raised indicating that this is an error.

Otherwise, raise evaluates the first expression as the exception object. It must be either a subclass or an instance of BaseException . If it is a class, the exception instance will be obtained when needed by instantiating the class with no arguments.

The type of the exception is the exception instance’s class, the value is the instance itself.

A traceback object is normally created automatically when an exception is raised and attached to it as the __traceback__ attribute. You can create an exception and set your own traceback in one step using the with_traceback() exception method (which returns the same exception instance, with its traceback set to its argument), like so:

The from clause is used for exception chaining: if given, the second expression must be another exception class or instance. If the second expression is an exception instance, it will be attached to the raised exception as the __cause__ attribute (which is writable). If the expression is an exception class, the class will be instantiated and the resulting exception instance will be attached to the raised exception as the __cause__ attribute. If the raised exception is not handled, both exceptions will be printed:

A similar mechanism works implicitly if a new exception is raised when an exception is already being handled. An exception may be handled when an except or finally clause, or a with statement, is used. The previous exception is then attached as the new exception’s __context__ attribute:

Exception chaining can be explicitly suppressed by specifying None in the from clause:

Additional information on exceptions can be found in section Exceptions , and information about handling exceptions is in section The try statement .

Changed in version 3.3: None is now permitted as Y in raise X from Y .

Added the __suppress_context__ attribute to suppress automatic display of the exception context.

Changed in version 3.11: If the traceback of the active exception is modified in an except clause, a subsequent raise statement re-raises the exception with the modified traceback. Previously, the exception was re-raised with the traceback it had when it was caught.

7.9. The break statement ¶

break may only occur syntactically nested in a for or while loop, but not nested in a function or class definition within that loop.

It terminates the nearest enclosing loop, skipping the optional else clause if the loop has one.

If a for loop is terminated by break , the loop control target keeps its current value.

When break passes control out of a try statement with a finally clause, that finally clause is executed before really leaving the loop.

7.10. The continue statement ¶

continue may only occur syntactically nested in a for or while loop, but not nested in a function or class definition within that loop. It continues with the next cycle of the nearest enclosing loop.

When continue passes control out of a try statement with a finally clause, that finally clause is executed before really starting the next loop cycle.

7.11. The import statement ¶

The basic import statement (no from clause) is executed in two steps:

find a module, loading and initializing it if necessary

define a name or names in the local namespace for the scope where the import statement occurs.

When the statement contains multiple clauses (separated by commas) the two steps are carried out separately for each clause, just as though the clauses had been separated out into individual import statements.

The details of the first step, finding and loading modules, are described in greater detail in the section on the import system , which also describes the various types of packages and modules that can be imported, as well as all the hooks that can be used to customize the import system. Note that failures in this step may indicate either that the module could not be located, or that an error occurred while initializing the module, which includes execution of the module’s code.

If the requested module is retrieved successfully, it will be made available in the local namespace in one of three ways:

If the module name is followed by as , then the name following as is bound directly to the imported module.

If no other name is specified, and the module being imported is a top level module, the module’s name is bound in the local namespace as a reference to the imported module

If the module being imported is not a top level module, then the name of the top level package that contains the module is bound in the local namespace as a reference to the top level package. The imported module must be accessed using its full qualified name rather than directly

The from form uses a slightly more complex process:

find the module specified in the from clause, loading and initializing it if necessary;

for each of the identifiers specified in the import clauses:

check if the imported module has an attribute by that name

if not, attempt to import a submodule with that name and then check the imported module again for that attribute

if the attribute is not found, ImportError is raised.

otherwise, a reference to that value is stored in the local namespace, using the name in the as clause if it is present, otherwise using the attribute name

If the list of identifiers is replaced by a star ( '*' ), all public names defined in the module are bound in the local namespace for the scope where the import statement occurs.

The public names defined by a module are determined by checking the module’s namespace for a variable named __all__ ; if defined, it must be a sequence of strings which are names defined or imported by that module. The names given in __all__ are all considered public and are required to exist. If __all__ is not defined, the set of public names includes all names found in the module’s namespace which do not begin with an underscore character ( '_' ). __all__ should contain the entire public API. It is intended to avoid accidentally exporting items that are not part of the API (such as library modules which were imported and used within the module).

The wild card form of import — from module import * — is only allowed at the module level. Attempting to use it in class or function definitions will raise a SyntaxError .

When specifying what module to import you do not have to specify the absolute name of the module. When a module or package is contained within another package it is possible to make a relative import within the same top package without having to mention the package name. By using leading dots in the specified module or package after from you can specify how high to traverse up the current package hierarchy without specifying exact names. One leading dot means the current package where the module making the import exists. Two dots means up one package level. Three dots is up two levels, etc. So if you execute from . import mod from a module in the pkg package then you will end up importing pkg.mod . If you execute from ..subpkg2 import mod from within pkg.subpkg1 you will import pkg.subpkg2.mod . The specification for relative imports is contained in the Package Relative Imports section.

importlib.import_module() is provided to support applications that determine dynamically the modules to be loaded.

Raises an auditing event import with arguments module , filename , sys.path , sys.meta_path , sys.path_hooks .

7.11.1. Future statements ¶

A future statement is a directive to the compiler that a particular module should be compiled using syntax or semantics that will be available in a specified future release of Python where the feature becomes standard.

The future statement is intended to ease migration to future versions of Python that introduce incompatible changes to the language. It allows use of the new features on a per-module basis before the release in which the feature becomes standard.

A future statement must appear near the top of the module. The only lines that can appear before a future statement are:

the module docstring (if any),

blank lines, and

other future statements.

The only feature that requires using the future statement is annotations (see PEP 563 ).

All historical features enabled by the future statement are still recognized by Python 3. The list includes absolute_import , division , generators , generator_stop , unicode_literals , print_function , nested_scopes and with_statement . They are all redundant because they are always enabled, and only kept for backwards compatibility.

A future statement is recognized and treated specially at compile time: Changes to the semantics of core constructs are often implemented by generating different code. It may even be the case that a new feature introduces new incompatible syntax (such as a new reserved word), in which case the compiler may need to parse the module differently. Such decisions cannot be pushed off until runtime.

For any given release, the compiler knows which feature names have been defined, and raises a compile-time error if a future statement contains a feature not known to it.

The direct runtime semantics are the same as for any import statement: there is a standard module __future__ , described later, and it will be imported in the usual way at the time the future statement is executed.

The interesting runtime semantics depend on the specific feature enabled by the future statement.

Note that there is nothing special about the statement:

That is not a future statement; it’s an ordinary import statement with no special semantics or syntax restrictions.

Code compiled by calls to the built-in functions exec() and compile() that occur in a module M containing a future statement will, by default, use the new syntax or semantics associated with the future statement. This can be controlled by optional arguments to compile() — see the documentation of that function for details.

A future statement typed at an interactive interpreter prompt will take effect for the rest of the interpreter session. If an interpreter is started with the -i option, is passed a script name to execute, and the script includes a future statement, it will be in effect in the interactive session started after the script is executed.

The original proposal for the __future__ mechanism.

7.12. The global statement ¶

The global statement is a declaration which holds for the entire current code block. It means that the listed identifiers are to be interpreted as globals. It would be impossible to assign to a global variable without global , although free variables may refer to globals without being declared global.

Names listed in a global statement must not be used in the same code block textually preceding that global statement.

Names listed in a global statement must not be defined as formal parameters, or as targets in with statements or except clauses, or in a for target list, class definition, function definition, import statement, or variable annotation.

CPython implementation detail: The current implementation does not enforce some of these restrictions, but programs should not abuse this freedom, as future implementations may enforce them or silently change the meaning of the program.

Programmer’s note: global is a directive to the parser. It applies only to code parsed at the same time as the global statement. In particular, a global statement contained in a string or code object supplied to the built-in exec() function does not affect the code block containing the function call, and code contained in such a string is unaffected by global statements in the code containing the function call. The same applies to the eval() and compile() functions.

7.13. The nonlocal statement ¶

When the definition of a function or class is nested (enclosed) within the definitions of other functions, its nonlocal scopes are the local scopes of the enclosing functions. The nonlocal statement causes the listed identifiers to refer to names previously bound in nonlocal scopes. It allows encapsulated code to rebind such nonlocal identifiers. If a name is bound in more than one nonlocal scope, the nearest binding is used. If a name is not bound in any nonlocal scope, or if there is no nonlocal scope, a SyntaxError is raised.

The nonlocal statement applies to the entire scope of a function or class body. A SyntaxError is raised if a variable is used or assigned to prior to its nonlocal declaration in the scope.

The specification for the nonlocal statement.

Programmer’s note: nonlocal is a directive to the parser and applies only to code parsed along with it. See the note for the global statement.

7.14. The type statement ¶

The type statement declares a type alias, which is an instance of typing.TypeAliasType .

For example, the following statement creates a type alias:

This code is roughly equivalent to:

annotation-def indicates an annotation scope , which behaves mostly like a function, but with several small differences.

The value of the type alias is evaluated in the annotation scope. It is not evaluated when the type alias is created, but only when the value is accessed through the type alias’s __value__ attribute (see Lazy evaluation ). This allows the type alias to refer to names that are not yet defined.

Type aliases may be made generic by adding a type parameter list after the name. See Generic type aliases for more.

type is a soft keyword .

Added in version 3.12.

Introduced the type statement and syntax for generic classes and functions.

Table of Contents

  • 7.1. Expression statements
  • 7.2.1. Augmented assignment statements
  • 7.2.2. Annotated assignment statements
  • 7.3. The assert statement
  • 7.4. The pass statement
  • 7.5. The del statement
  • 7.6. The return statement
  • 7.7. The yield statement
  • 7.8. The raise statement
  • 7.9. The break statement
  • 7.10. The continue statement
  • 7.11.1. Future statements
  • 7.12. The global statement
  • 7.13. The nonlocal statement
  • 7.14. The type statement

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Python Assignment Operators

Assignment operators in python.

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The Python Operators are used to perform operations on values and variables. These are the special symbols that carry out arithmetic, logical, and bitwise computations. The value the operator operates on is known as the Operand. Here, we will cover Different Assignment operators in Python .

Operators

=

Assign the value of the right side of the expression to the left side operandc = a + b 


+=

Add right side operand with left side operand and then assign the result to left operanda += b   

-=

Subtract right side operand from left side operand and then assign the result to left operanda -= b  


*=

Multiply right operand with left operand and then assign the result to the left operanda *= b     


/=

Divide left operand with right operand and then assign the result to the left operanda /= b


%=

Divides the left operand with the right operand and then assign the remainder to the left operanda %= b  


//=

Divide left operand with right operand and then assign the value(floor) to left operanda //= b   


**=

Calculate exponent(raise power) value using operands and then assign the result to left operanda **= b     


&=

Performs Bitwise AND on operands and assign the result to left operanda &= b   


|=

Performs Bitwise OR on operands and assign the value to left operanda |= b    


^=

Performs Bitwise XOR on operands and assign the value to left operanda ^= b    


>>=

Performs Bitwise right shift on operands and assign the result to left operanda >>= b     


<<=

Performs Bitwise left shift on operands and assign the result to left operanda <<= b 


:=

Assign a value to a variable within an expression

a := exp

Here are the Assignment Operators in Python with examples.

Assignment Operator

Assignment Operators are used to assign values to variables. This operator is used to assign the value of the right side of the expression to the left side operand.

Addition Assignment Operator

The Addition Assignment Operator is used to add the right-hand side operand with the left-hand side operand and then assigning the result to the left operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the addition assignment operator which will first perform the addition operation and then assign the result to the variable on the left-hand side.

S ubtraction Assignment Operator

The Subtraction Assignment Operator is used to subtract the right-hand side operand from the left-hand side operand and then assigning the result to the left-hand side operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the subtraction assignment operator which will first perform the subtraction operation and then assign the result to the variable on the left-hand side.

M ultiplication Assignment Operator

The Multiplication Assignment Operator is used to multiply the right-hand side operand with the left-hand side operand and then assigning the result to the left-hand side operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the multiplication assignment operator which will first perform the multiplication operation and then assign the result to the variable on the left-hand side.

D ivision Assignment Operator

The Division Assignment Operator is used to divide the left-hand side operand with the right-hand side operand and then assigning the result to the left operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the division assignment operator which will first perform the division operation and then assign the result to the variable on the left-hand side.

M odulus Assignment Operator

The Modulus Assignment Operator is used to take the modulus, that is, it first divides the operands and then takes the remainder and assigns it to the left operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the modulus assignment operator which will first perform the modulus operation and then assign the result to the variable on the left-hand side.

F loor Division Assignment Operator

The Floor Division Assignment Operator is used to divide the left operand with the right operand and then assigs the result(floor value) to the left operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the floor division assignment operator which will first perform the floor division operation and then assign the result to the variable on the left-hand side.

Exponentiation Assignment Operator

The Exponentiation Assignment Operator is used to calculate the exponent(raise power) value using operands and then assigning the result to the left operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the exponentiation assignment operator which will first perform exponent operation and then assign the result to the variable on the left-hand side.

Bitwise AND Assignment Operator

The Bitwise AND Assignment Operator is used to perform Bitwise AND operation on both operands and then assigning the result to the left operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the bitwise AND assignment operator which will first perform Bitwise AND operation and then assign the result to the variable on the left-hand side.

Bitwise OR Assignment Operator

The Bitwise OR Assignment Operator is used to perform Bitwise OR operation on the operands and then assigning result to the left operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the bitwise OR assignment operator which will first perform bitwise OR operation and then assign the result to the variable on the left-hand side.

Bitwise XOR Assignment Operator 

The Bitwise XOR Assignment Operator is used to perform Bitwise XOR operation on the operands and then assigning result to the left operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the bitwise XOR assignment operator which will first perform bitwise XOR operation and then assign the result to the variable on the left-hand side.

Bitwise Right Shift Assignment Operator

The Bitwise Right Shift Assignment Operator is used to perform Bitwise Right Shift Operation on the operands and then assign result to the left operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the bitwise right shift assignment operator which will first perform bitwise right shift operation and then assign the result to the variable on the left-hand side.

Bitwise Left Shift Assignment Operator

The Bitwise Left Shift Assignment Operator is used to perform Bitwise Left Shift Opertator on the operands and then assign result to the left operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the bitwise left shift assignment operator which will first perform bitwise left shift operation and then assign the result to the variable on the left-hand side.

Walrus Operator

The Walrus Operator in Python is a new assignment operator which is introduced in Python version 3.8 and higher. This operator is used to assign a value to a variable within an expression.

Example: In this code, we have a Python list of integers. We have used Python Walrus assignment operator within the Python while loop . The operator will solve the expression on the right-hand side and assign the value to the left-hand side operand ‘x’ and then execute the remaining code.

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Latest Articles : Python Interview Questions and Answers      Python PyQt

List of Python Exercises :

  • Python Basic (Part -I) [ 150 Exercises with Solution ]
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  • Python Programming Puzzles [ 100 Exercises with Solution ]
  • Python Advanced [ 15 Exercises with Solution ]
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  • Recursion [ 11 Exercises with Solution ]
  • Python Data Types - String [ 113 Exercises with Solution ]
  • Python JSON [ 9 Exercises with Solution ]
  • Python Data Types - List [ 281 Exercises with Solution ]
  • Python Data Types - List Advanced [ 15 Exercises with Solution ]
  • Python Data Types - Dictionary [ 80 Exercises with Solution ]
  • Python Data Types - Tuple [ 33 Exercises with Solution ]
  • Python Data Types - Sets [ 30 Exercises with Solution ]
  • Python Data Types - Collections [ 36 Exercises with Solution ]
  • Python Array [ 24 Exercises with Solution ]
  • Python Enum [ 5 Exercises with Solution ]
  • Python Class [ 28 Exercises with Solution ]
  • Python Unit test [ 10 Exercises with Solution ]
  • Python Exception Handling [ 10 exercises with solution ]
  • Python Object-Oriented Programming [ 11 Exercises with Solution ]
  • Python Decorator [ 12 Exercises with Solution ]
  • Python functions [ 21 Exercises with Solution ]
  • Python Lambda [ 52 Exercises with Solution ]
  • Python Map [ 17 Exercises with Solution ]
  • Python Itertools [ 44 exercises with solution ]
  • Python - Filter Function [ 11 Exercises with Solution ]
  • Python Date Time [ 63 Exercises with Solution ]
  • Python Pendulum (DATETIMES made easy) Module [34 Exercises with Solution]
  • Python File Input Output [ 21 Exercises with Solution ]
  • Python CSV File Reading and Writing [ 11 exercises with solution ]
  • Python Regular Expression [ 58 Exercises with Solution ]
  • Search and Sorting [ 39 Exercises with Solution ]
  • Linked List [ 14 Exercises with Solution ]
  • Binary Search Tree [ 6 Exercises with Solution ]
  • Python heap queue algorithm [ 29 exercises with solution ]
  • Python Bisect [ 9 Exercises with Solution ]
  • Python Boolean Data Type [ 10 Exercises with Solution ]
  • Python None Data Type [ 10 Exercises with Solution ]
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List of Exercises with Solutions :

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Popularity of Programming Language Worldwide, Nov 2023 compared to a year ago:

`
Rank Change Language Share Trend
1 Python 27.99 % +0.0 %
2 Java 15.91 % -0.8%
3 Javascript 9.18 % -0.3%
4 C/C++ 6.76 % +0.2%
5 C# 6.67 % -0.3 %
6 PHP 4.86 % -0.3 %
7 R 4.45% +0.4%
8 TypeScript 2.95 % +0.1%
9 Swift 2.7 % +0.6%
10 Objective-C 2.32% +0.2%
11 Rust1.98% +0.3%
12 Go 1.98% -0.0%
13 Kotlin 1.76 % -0.1%
14 Matlab 1.6 % +0.0%
15 Ada 1.02% +0.2%
16 Ruby 1.0 % -0.1 %
17 Dart 0.99 % +0.1 %
18 Powershell 0.93 % +0.0 %
19 VBA 0.93 % -0.1 %
20 Scala 0.62 % -0.1 %
21 Lua 0.62 % 0.0 %
22 Abap 0.58 % +0.1 %
23 Visual Basic 0.55 % -0.1 %
24 Julia 0.35 % -0.0 %
25 Groovy 0.31 % -0.1 %
26 Perl 0.31 % -0.1 %
27 Haskell 0.27 % -0.0 %
28 Cobol 0.25 % -0.1 %
29 Delphi/Pascal 0.18 % +0.2 %

Source : https://pypl.github.io/PYPL.html

TIOBE Index for November 2023

Nov 2023 Nov 2022 Change Programming Language Ratings Change
1 1 Python 14.16% -3.02%
2 2 C 11.77% -3.31%
3 4 C++ 10.36% -0.39%
4 3 Java 8.35% -3.63%
5 5 C# 7.65% +3.40%
6 7 JavaScript 3.21% +0.47%
7 10 PHP 2.30% +0.61%
8 6 Visual Basic 2.10% -2.01%
9 9 SQL 1.88% +0.07%
10 8 Assembly language 1.35% -0.83%
11 17 Scratch 1.31% +0.43%
12 24 Fortran 1.30% +0.74%
13 11 Go 1.19% +0.05%
14 15 MATLAB 1.15% +0.14%
15 28 Kotlin 1.15% +0.68%
16 14 Delphi/Object Pascal 1.14% +0.07%
17 18 Swift 1.04% +0.17%
18 19 Ruby 0.99% +0.14%
19 12 R 0.93% -0.20%
20 20 Rust 0.91% +0.16%

Source : https://www.tiobe.com/tiobe-index/

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Python Lab Assignments

You can not learn a programming language by only reading the language construct. It also requires programming - writing your own code and studying those of others. Solve these assignments, then study the solutions presented here.

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Python Operators: Arithmetic, Assignment, Comparison, Logical, Identity, Membership, Bitwise

Operators are special symbols that perform some operation on operands and returns the result. For example, 5 + 6 is an expression where + is an operator that performs arithmetic add operation on numeric left operand 5 and the right side operand 6 and returns a sum of two operands as a result.

Python includes the operator module that includes underlying methods for each operator. For example, the + operator calls the operator.add(a,b) method.

Above, expression 5 + 6 is equivalent to the expression operator.add(5, 6) and operator.__add__(5, 6) . Many function names are those used for special methods, without the double underscores (dunder methods). For backward compatibility, many of these have functions with the double underscores kept.

Python includes the following categories of operators:

Arithmetic Operators

Assignment operators, comparison operators, logical operators, identity operators, membership test operators, bitwise operators.

Arithmetic operators perform the common mathematical operation on the numeric operands.

The arithmetic operators return the type of result depends on the type of operands, as below.

  • If either operand is a complex number, the result is converted to complex;
  • If either operand is a floating point number, the result is converted to floating point;
  • If both operands are integers, then the result is an integer and no conversion is needed.

The following table lists all the arithmetic operators in Python:

Operation Operator Function Example in Python Shell
Sum of two operands + operator.add(a,b)
Left operand minus right operand - operator.sub(a,b)
* operator.mul(a,b)
Left operand raised to the power of right ** operator.pow(a,b)
/ operator.truediv(a,b)
equivilant to // operator.floordiv(a,b)
Reminder of % operator.mod(a, b)

The assignment operators are used to assign values to variables. The following table lists all the arithmetic operators in Python:

Operator Function Example in Python Shell
=
+= operator.iadd(a,b)
-= operator.isub(a,b)
*= operator.imul(a,b)
/= operator.itruediv(a,b)
//= operator.ifloordiv(a,b)
%= operator.imod(a, b)
&= operator.iand(a, b)
|= operator.ior(a, b)
^= operator.ixor(a, b)
>>= operator.irshift(a, b)
<<= operator.ilshift(a, b)

The comparison operators compare two operands and return a boolean either True or False. The following table lists comparison operators in Python.

Operator Function Description Example in Python Shell
> operator.gt(a,b) True if the left operand is higher than the right one
< operator.lt(a,b) True if the left operand is lower than right one
== operator.eq(a,b) True if the operands are equal
!= operator.ne(a,b) True if the operands are not equal
>= operator.ge(a,b) True if the left operand is higher than or equal to the right one
<= operator.le(a,b) True if the left operand is lower than or equal to the right one

The logical operators are used to combine two boolean expressions. The logical operations are generally applicable to all objects, and support truth tests, identity tests, and boolean operations.

Operator Description Example
and True if both are true
or True if at least one is true
not Returns True if an expression evalutes to false and vice-versa

The identity operators check whether the two objects have the same id value e.i. both the objects point to the same memory location.

Operator Function Description Example in Python Shell
is operator.is_(a,b) True if both are true
is not operator.is_not(a,b) True if at least one is true

The membership test operators in and not in test whether the sequence has a given item or not. For the string and bytes types, x in y is True if and only if x is a substring of y .

Operator Function Description Example in Python Shell
in operator.contains(a,b) Returns True if the sequence contains the specified item else returns False.
not in not operator.contains(a,b) Returns True if the sequence does not contains the specified item, else returns False.

Bitwise operators perform operations on binary operands.

Operator Function Description Example in Python Shell
& operator.and_(a,b) Sets each bit to 1 if both bits are 1.
| operator.or_(a,b) Sets each bit to 1 if one of two bits is 1.
^ operator.xor(a,b) Sets each bit to 1 if only one of two bits is 1.
~ operator.invert(a) Inverts all the bits.
<< operator.lshift(a,b) Shift left by pushing zeros in from the right and let the leftmost bits fall off.
>> operator.rshift(a,b) Shift right by pushing copies of the leftmost bit in from the left, and let the rightmost bits fall off.
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Precedence and Associativity of Operators in Python

  • Python Keywords and Identifiers
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Operators are special symbols that perform operations on variables and values. For example,

Here, + is an operator that adds two numbers: 5 and 6 .

  • Types of Python Operators

Here's a list of different types of Python operators that we will learn in this tutorial.

  • Arithmetic Operators
  • Assignment Operators
  • Comparison Operators
  • Logical Operators
  • Bitwise Operators
  • Special Operators

1. Python Arithmetic Operators

Arithmetic operators are used to perform mathematical operations like addition, subtraction, multiplication, etc. For example,

Here, - is an arithmetic operator that subtracts two values or variables.

Operator Operation Example
Addition
Subtraction
Multiplication
Division
Floor Division
Modulo
Power

Example 1: Arithmetic Operators in Python

In the above example, we have used multiple arithmetic operators,

  • + to add a and b
  • - to subtract b from a
  • * to multiply a and b
  • / to divide a by b
  • // to floor divide a by b
  • % to get the remainder
  • ** to get a to the power b

2. Python Assignment Operators

Assignment operators are used to assign values to variables. For example,

Here, = is an assignment operator that assigns 5 to x .

Here's a list of different assignment operators available in Python.

Operator Name Example
Assignment Operator
Addition Assignment
Subtraction Assignment
Multiplication Assignment
Division Assignment
Remainder Assignment
Exponent Assignment

Example 2: Assignment Operators

Here, we have used the += operator to assign the sum of a and b to a .

Similarly, we can use any other assignment operators as per our needs.

3. Python Comparison Operators

Comparison operators compare two values/variables and return a boolean result: True or False . For example,

Here, the > comparison operator is used to compare whether a is greater than b or not.

Operator Meaning Example
Is Equal To gives us
Not Equal To gives us
Greater Than gives us
Less Than gives us
Greater Than or Equal To give us
Less Than or Equal To gives us

Example 3: Comparison Operators

Note: Comparison operators are used in decision-making and loops . We'll discuss more of the comparison operator and decision-making in later tutorials.

4. Python Logical Operators

Logical operators are used to check whether an expression is True or False . They are used in decision-making. For example,

Here, and is the logical operator AND . Since both a > 2 and b >= 6 are True , the result is True .

Operator Example Meaning
a b :
only if both the operands are
a b :
if at least one of the operands is
a :
if the operand is and vice-versa.

Example 4: Logical Operators

Note : Here is the truth table for these logical operators.

5. Python Bitwise operators

Bitwise operators act on operands as if they were strings of binary digits. They operate bit by bit, hence the name.

For example, 2 is 10 in binary, and 7 is 111 .

In the table below: Let x = 10 ( 0000 1010 in binary) and y = 4 ( 0000 0100 in binary)

Operator Meaning Example
Bitwise AND x & y = 0 ( )
Bitwise OR x | y = 14 ( )
Bitwise NOT ~x = -11 ( )
Bitwise XOR x ^ y = 14 ( )
Bitwise right shift x >> 2 = 2 ( )
Bitwise left shift x 0010 1000)

6. Python Special operators

Python language offers some special types of operators like the identity operator and the membership operator. They are described below with examples.

  • Identity operators

In Python, is and is not are used to check if two values are located at the same memory location.

It's important to note that having two variables with equal values doesn't necessarily mean they are identical.

Operator Meaning Example
if the operands are identical (refer to the same object)
if the operands are not identical (do not refer to the same object)

Example 4: Identity operators in Python

Here, we see that x1 and y1 are integers of the same values, so they are equal as well as identical. The same is the case with x2 and y2 (strings).

But x3 and y3 are lists. They are equal but not identical. It is because the interpreter locates them separately in memory, although they are equal.

  • Membership operators

In Python, in and not in are the membership operators. They are used to test whether a value or variable is found in a sequence ( string , list , tuple , set and dictionary ).

In a dictionary, we can only test for the presence of a key, not the value.

Operator Meaning Example
if value/variable is in the sequence
if value/variable is in the sequence

Example 5: Membership operators in Python

Here, 'H' is in message , but 'hello' is not present in message (remember, Python is case-sensitive).

Similarly, 1 is key, and 'a' is the value in dictionary dict1 . Hence, 'a' in y returns False .

  • Precedence and Associativity of operators in Python

Table of Contents

  • Introduction
  • Python Arithmetic Operators
  • Python Assignment Operators
  • Python Comparison Operators
  • Python Logical Operators
  • Python Bitwise operators
  • Python Special operators

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Assignment: Introduction to Python Instructions: Answer the following questions based on your understanding of Python programming. Provide detailed explanations and examples where appropriate.

Python Basics:

  • What is Python, and what are some of its key features that make it popular among developers? Provide examples of use cases where Python is particularly effective.

Installing Python:

  • Describe the steps to install Python on your operating system (Windows, macOS, or Linux). Include how to verify the installation and set up a virtual environment.

Python Syntax and Semantics:

  • Write a simple Python program that prints "Hello, World!" to the console. Explain the basic syntax elements used in the program.

Data Types and Variables:

  • List and describe the basic data types in Python. Write a short script that demonstrates how to create and use variables of different data types.

Control Structures:

  • Explain the use of conditional statements and loops in Python. Provide examples of an if-else statement and a for loop.

Functions in Python:

  • What are functions in Python, and why are they useful? Write a Python function that takes two arguments and returns their sum. Include an example of how to call this function.

Lists and Dictionaries:

  • Describe the differences between lists and dictionaries in Python. Write a script that creates a list of numbers and a dictionary with some key-value pairs, then demonstrates basic operations on both.

Exception Handling:

  • What is exception handling in Python? Provide an example of how to use try , except , and finally blocks to handle errors in a Python script.

Modules and Packages:

  • Explain the concepts of modules and packages in Python. How can you import and use a module in your script? Provide an example using the math module.
  • How do you read from and write to files in Python? Write a script that reads the content of a file and prints it to the console, and another script that writes a list of strings to a file.

Submission Guidelines:

  • Your answers should be well-structured, concise, and to the point.
  • Provide code snippets or complete scripts where applicable.
  • Cite any references or sources you use in your answers.
  • Submit your completed assignment by [due date].

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The 10 Best Python Courses That are Worth Taking in 2024

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Python continues to reign supreme as the world’s most popular programming language , according to the TIOBE Index. This versatile and user-friendly language, named after beloved British comedy troupe Monty Python, has become an essential tool for developers, data scientists and tech enthusiasts alike.

Python’s simplicity and readability make the programming language an ideal choice for beginners, while its robust libraries and frameworks support advanced applications in web development, machine learning and data analysis. TechRepublic takes a look at the top 10 Python courses available in 2024 for developers with different experiences and goals.

When assessing online courses, we examined the reliability and popularity of the provider, the depth and variety of topics offered, the practicality of the information, the cost and the duration. The courses and certification programs vary considerably, so be sure to choose the option that is right for your goals and learning style.

  • Best for beginners on a budget: Python Programming MOOC 2024 - University of Helsinki
  • Best for aspiring data analysts: Python for Everybody Specialization - University of Michigan - Coursera
  • Best for beginners who want daily lessons: 100 Days of Code - The Complete Python Pro Bootcamp - Udemy
  • Best for beginners who want a crash course: The Complete Python Bootcamp From Zero to Hero in Python - Udemy
  • Best for learning on the go: futurecoder
  • Best for automating real-life tasks: Automate the Boring Stuff with Python Programming - Udemy
  • Best for in-depth computer science: MITx Introduction to Computer Science and Programming Using Python - edX
  • Best for machine learning and data science for beginners: Professional Certificate in Learning Python for Data Science - Harvard University - edX
  • Best for advanced data analysts: Google Advanced Data Analytics Professional Certificate - Coursera
  • Best for well-known certification: PCAP- Programming Essentials in Python - Cisco Networking Academy

SEE: Udemy Report: Which IT Skills Are Most in Demand in Q1 2024?

Best Python courses: Comparison table

Course

Python Programming MOOC 2024 - University of Helsinki: Best for beginners on a budget

Python Programming MOOC 2024 screenshot.

Python Programming MOOC 2024 is a highly-regarded Python course suitable for complete beginners and run by the University of Helsinki. MOOC stands for Massive Online Open Course, so there are no limitations on the number of students who can use the resources throughout the year.

Course content is split into 14 sections of notes, each of which come with about 30 exercises to complete in-browser. This is a self-paced course, though there are exams put on by the University of Helsinki at set dates and times. It is free, but to do the exercises, you need to sign up for a MOOC account.

12 hour-long lectures with written notes and exercises. Those who have completed the course say it takes about two months overall.

  • Beginner friendly.
  • There’s an active Discord channel where course participants discuss their progress.
  • Must be self-motivated to complete, as it is self-paced.
  • Less intensive and in-depth than other listed courses.

Pre-requisites

Python for everybody specialization - university of michigan - coursera: best for aspiring data analysts.

Python for Everybody Specialization course screenshot.

Charles Russell Severance, a Clinical Professor at the University of Michigan School of Information — AKA “Dr Chuck” — is the instructor for Python for Everybody Specialization on Coursera. It covers basic programming principles, but in the Python language, setting up participants to be able to move onto other languages. It covers all the fundamentals as well as data structures, web scraping, databases and more. It ends with a Capstone project where participants can build a program that retrieves, processes and visualises data from the web with a JavaScript library.

$59 a month after a 7-day free trial.

10 hours a week for two months, but it can take up to eight months.

  • Experienced instructor.
  • Teaches practical uses of Python for entry-level data analysis role.
  • Course fees.
  • Requires significant time dedication.
  • Career certificate itself is not well-respected.

100 Days of Code - The Complete Python Pro Bootcamp - Udemy: Best for beginners who want daily lessons

100 Days of Code: The Complete Python Pro Bootcamp course screenshot.

Dr Angela Yu’s 100 Days of Code: The Complete Python Pro Bootcamp is designed to turn beginners into proficient Python programmers over a manageable timespan. Dr. Yu is a leading bootcamp instructor and has been invited by companies such as Twitter, Facebook and Google to teach their employees. Her course helps its students to actually understand the code rather than just churn out simple exercises by providing a challenge with every new concept. Topics covered include automation, game, app and web development, data science and machine learning. Reviewers praise the pacing, Dr. Yu’s teaching and the diverse projects.

100 days at one hour a day, however, course content is available on-demand.

  • Encourages you to code every day.
  • Course fee.
  • Some outdated content based on reviewers comments.
  • There are self-guided projects towards the end of the course that some learners may find tricky without an instructor.

The Complete Python Bootcamp From Zero to Hero in Python - Udemy: Best for beginners who want a crash course

The Complete Python Bootcamp From Zero to Hero in Python course screenshot.

Engineering graduate and professional instructor Jose Portilla leads The Complete Python Bootcamp From Zero to Hero in Python, another top-rated Udemy course. Through a series of 100 lectures, the course starts by guiding learners through installing Python on their operating system before getting into the basics and using the language to manipulate emails, PDFs, Excel files, images and more. Along with lectures, learners have access to tests, exercises and three larger projects.

Full price is $189.99, though Udemy has weekly sales that see this reduced significantly.

22 hours of on-demand video lectures plus articles and exercises.

None. It does contain modules about advanced Python features that are suitable for programmers with basic experience.

  • Good explanations of the basics.
  • Limited content on more advanced topics and using Python for real-life tasks.

futurecoder: Best for learning on the go

futurecoder course screenshot.

If you’re not into video lectures and want to learn Python at your own pace through step-by-step exercises, then futurecoder may be the course for you. This has the fewest barriers to entry of all the courses, as no account is required to get started (but you can get one if you want to save your progress), and the whole thing takes place in-browser.

Standout features include the very gradual guidance provided with each exercise, rather than large hints or complete solutions. Answers are automatically graded, error messages are easily understandable and it provides debugging tools for extra understanding of what went wrong. futurecoder is 100% free and has no ads, and feels more like Duolingo for coding than a university-style course.

60 exercises, each of which take between 10-30 minutes to complete.

  • Free with no ads.
  • Slick interface.
  • No video tutorials, but this may be a pro depending on your learning style.
  • It’s quite short, not taking the learner to a very advanced level.

Automate the Boring Stuff with Python Programming - Udemy: Best for automating real-life tasks

Automate the Boring Stuff with Python Programming course screenshot.

Automate the Boring Stuff, taught by software engineer and tech book author Al Sweigart, is best for learners who want to dive right into real-world applications of Python. It is perfect for office and administrative workers looking to improve their productivity by programmatically updating spreadsheets, parsing documents, sending email alerts and more.

While there are a few quizzes, the majority of the course is screen recordings with Sweigart’s voiceover and some downloadable scripts, so those looking for something more interactive may want to choose a different course.

Full price is $119.99; however, Udemy has weekly sales that see this reduced significantly.

  • Focuses on practical uses of Python from the get-go.
  • Based on a well-reviewed book of the same name.
  • Book of theory can be downloaded for free.
  • There is not a lot about computer science theory, so this is not ideal for those looking to go into a career as a developer.
  • Certification is not well-regarded.
  • Some reviewers have found the exercises difficult without prior Python knowledge.

MITx Introduction to Computer Science and Programming Using Python - edX: Best for in-depth computer science

Introduction to Computer Science and Programming Using Python course screenshot.

Introduction to Computer Science and Programming Using Python is the first course on this list to require a basic knowledge of mathematics and programming. Run by MITx, the online learning arm of the Massachusetts Institute of Technology, the lecture series intends to teach students how to think computationally to prepare them for a career in software engineering. It includes lecture videos, exercises and problem sets using Python 3.5 and covers lots of different areas, like algorithms, testing and debugging and data structures.

$149 for full access to course materials and certification upon completion. However, limited access to course materials is provided for free, but you cannot take graded assignments or exams.

9 weeks, 14 – 16 hours a week.

High school algebra and a reasonable aptitude for mathematics are required. Students without a prior programming background will find there is a steep learning curve.

  • Expert tutoring from MIT professors.
  • Course material is free.
  • Not many real-world applications are taught that are based on the computer science theory.
  • Reviewers say it is easier with the accompanying textbook, which comes at an additional cost.
  • Complicated mathematics involved in the latter part of course.

Professional Certificate in Learning Python for Data Science - Harvard University - edX: Best for machine learning and data science for beginners

Professional Certificate in Learning Python for Data Science course screenshot.

If you’re looking to initiate a career in data science, then an education from Harvard University is a great place to start. The Professional Certificate in Learning Python for Data Science, offered via edEx, consists of video lectures, programming exercises and quizzes.

Learners have access to the Python portion of the famous CS50: Introduction to Computer Science module, as well as others covering probability, data science and machine learning. While the material becomes in-depth like the other courses on this list, it does begin with teaching how to read and write Python code. It also includes applications of Python specific to data science, utilising libraries like numPy, matplotlip and Pandas.

6 months, three to six hours a week.

  • Taught by Harvard University experts.
  • Specific to data science.
  • Course duration is long.

Google Advanced Data Analytics Professional Certificate - Coursera: Best for advanced data analysts

Google Advanced Data Analytics Professional Certificate course screenshot.

Google’s Advanced Data Analytics Professional Certificate expects some proficiency with data analytics prior to starting the course, though Python fundamentals are covered as appropriate for beginners. The series of five courses takes learners from language and data analysis basics into more advanced regression analysis and machine learning.

Specifically, it covers the manipulation of large data sets, using machine learning to find patterns in data, data visualisation, analytics tools — like Jupyter Notebook, Tableau and more. Projects are distributed throughout the course that can add to the learner’s portfolio of work, and there is a capstone at the end that combines all the lessons.

Six months at 10 hours a week.

Prior knowledge of foundational analytical principles, skills and tools. Some programming knowledge is also useful.

  • Goes beyond the theoretical to teach how to apply data analytics to real-world scenarios.
  • Includes information about the data analyst job market and how to prepare for applications.
  • Requires experience. Beginners should try the entry-level version .
  • Substantial time commitment required.

PCAP- Programming Essentials in Python - Cisco Networking Academy: Best for well-known certification

PCAP: Programming Essentials in Python course screenshot.

While certifications are somewhat controversial amongst developers, with some believing that experience is more valuable, pursuing a well-regarded accreditation is often a good way of ensuring you have comprehensive knowledge in an area. The Python Institute offers the most well-known set of Python certifications, which are aimed at four progressive levels: entry-level, associate, professional 1 and professional 2.

This free course from Cisco Networking Academy guides learners through the content of the first two levels and prepares them for the Certified Entry-Level Python Programmer Certification and Certified Associate Python Programmer Certification exams. Content is delivered through practice labs, interactive activities, videos and assessments.

Free, though there are fees associated with the exams that lead to formal certification. Currently, it costs $59 to take the PCEP exam once and $295 for the PCAP.

  • Course content is free.
  • The exams are not free.

Is it worth paying for a Python course?

The short answer is it depends on your budget and what motivates you.

There is a wealth of free resources available through some of the courses listed here as well as in library books, on YouTube and elsewhere online that will enable a motivated student to get to grips with Python. Many developers insist it is possible to master programming without paying a penny by working on small projects like those on GitHub and learning on the go, which could be a good option for those on a budget. Alternatively, check out the free courses Python Programming MOOC 2024 and futurecoder.

However, the key to learning any new skill is to be persistent, and it can be difficult to remain motivated without a defined learning programme to follow, coursemates to connect with or a course fee at risk of going to waste. For individuals with a tendency to start projects but not finish them, an initial investment in a structured course may provide the motivation they need. Many paid courses also give direct access to qualified instructors who can provide tailored help that would otherwise not be available. Therefore, a paid course like 100 Days of Code may be worth it in the long run, as the fee provides motivation to continue progressing.

Is Python the best programming language to learn?

Python is widely regarded as one of the best programming languages for beginners for a number of reasons.

  • It is easy to understand, as its syntax is intuitive and closely resembles natural language.
  • It is versatile, used in various relevant domains such as web development, data science, automation, AI and scientific computing.
  • It has extensive libraries and frameworks, like NumPy, Pandas and TensorFlow, enabling developers to build larger programs without starting from scratch.
  • It integrates well with other commonly-used languages like C++ and Java, technologies like Terraform and REST APIs, and platforms like Amazon Web Services and Raspberry Pi.
  • It is used by many major tech companies, including Google, Netflix and Meta.
  • It has a large and active community of users who update tutorials, documents and open-source projects to support learning.

SEE: Top Python AI and Machine Learning Libraries

Do employers look at Python certifications?

Every employer is different, and while some may be looking for specific skills that are proven by well-regarded certifications, others may be more interested in project work, collaboration and other experiences. Before embarking on a Python course to obtain a certificate, look at the requirements of vacancies that interest you.

Certifications are somewhat controversial in the developer community for a number of reasons. Many believe that the availability of free resources for learning means that paid certifications are financially exploitative. Programming languages and technologies also evolve quickly, so certifications can become outdated but remain advertised as worthwhile. Some courses focus more on teaching theoretical knowledge rather than how to solve real-world problems, and therefore the certification is not representative of programming ability.

On the other hand, the job market for junior developers is tight, with many career-changers looking for an entry-level role in tech, and AI tools taking on tasks normally given to less experienced employees. Therefore, it is important to differentiate your CV from others, and certifications in specific areas of software development can be a good way of doing so. A certificate can also provide motivation to push through to the end of a lengthy course of study.

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JavaScript ( JS ) is a lightweight interpreted (or just-in-time compiled) programming language with first-class functions . While it is most well-known as the scripting language for Web pages, many non-browser environments also use it, such as Node.js , Apache CouchDB and Adobe Acrobat . JavaScript is a prototype-based , multi-paradigm, single-threaded , dynamic language, supporting object-oriented, imperative, and declarative (e.g. functional programming) styles.

JavaScript's dynamic capabilities include runtime object construction, variable parameter lists, function variables, dynamic script creation (via eval ), object introspection (via for...in and Object utilities ), and source-code recovery (JavaScript functions store their source text and can be retrieved through toString() ).

This section is dedicated to the JavaScript language itself, and not the parts that are specific to Web pages or other host environments. For information about APIs that are specific to Web pages, please see Web APIs and DOM .

The standards for JavaScript are the ECMAScript Language Specification (ECMA-262) and the ECMAScript Internationalization API specification (ECMA-402). As soon as one browser implements a feature, we try to document it. This means that cases where some proposals for new ECMAScript features have already been implemented in browsers, documentation and examples in MDN articles may use some of those new features. Most of the time, this happens between the stages 3 and 4, and is usually before the spec is officially published.

Do not confuse JavaScript with the Java programming language — JavaScript is not "Interpreted Java" . Both "Java" and "JavaScript" are trademarks or registered trademarks of Oracle in the U.S. and other countries. However, the two programming languages have very different syntax, semantics, and use.

JavaScript documentation of core language features (pure ECMAScript , for the most part) includes the following:

  • The JavaScript guide
  • The JavaScript reference

For more information about JavaScript specifications and related technologies, see JavaScript technologies overview .

Learn how to program in JavaScript with guides and tutorials.

For complete beginners

Head over to our Learning Area JavaScript topic if you want to learn JavaScript but have no previous experience with JavaScript or programming. The complete modules available there are as follows:

Answers some fundamental questions such as "what is JavaScript?", "what does it look like?", and "what can it do?", along with discussing key JavaScript features such as variables, strings, numbers, and arrays.

Continues our coverage of JavaScript's key fundamental features, turning our attention to commonly-encountered types of code blocks such as conditional statements, loops, functions, and events.

The object-oriented nature of JavaScript is important to understand if you want to go further with your knowledge of the language and write more efficient code, therefore we've provided this module to help you.

Discusses asynchronous JavaScript, why it is important, and how it can be used to effectively handle potential blocking operations such as fetching resources from a server.

Explores what APIs are, and how to use some of the most common APIs you'll come across often in your development work.

JavaScript guide

A much more detailed guide to the JavaScript language, aimed at those with previous programming experience either in JavaScript or another language.

Intermediate

JavaScript frameworks are an essential part of modern front-end web development, providing developers with proven tools for building scalable, interactive web applications. This module gives you some fundamental background knowledge about how client-side frameworks work and how they fit into your toolset, before moving on to a series of tutorials covering some of today's most popular ones.

An overview of the basic syntax and semantics of JavaScript for those coming from other programming languages to get up to speed.

Overview of available data structures in JavaScript.

JavaScript provides three different value comparison operations: strict equality using === , loose equality using == , and the Object.is() method.

How different methods that visit a group of object properties one-by-one handle the enumerability and ownership of properties.

A closure is the combination of a function and the lexical environment within which that function was declared.

Explanation of the widely misunderstood and underestimated prototype-based inheritance.

Memory life cycle and garbage collection in JavaScript.

JavaScript has a runtime model based on an "event loop".

Browse the complete JavaScript reference documentation.

Get to know standard built-in objects Array , Boolean , Date , Error , Function , JSON , Math , Number , Object , RegExp , String , Map , Set , WeakMap , WeakSet , and others.

Learn more about the behavior of JavaScript's operators instanceof , typeof , new , this , the operator precedence , and more.

Learn how do-while , for-in , for-of , try-catch , let , var , const , if-else , switch , and more JavaScript statements and keywords work.

Learn how to work with JavaScript's functions to develop your applications.

JavaScript classes are the most appropriate way to do object-oriented programming.

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    4. Write a Python program that calculates the area of a circle based on the radius entered by the user. Sample Output : r = 1.1 Area = 3.8013271108436504 Click me to see the sample solution. 5. Write a Python program that accepts the user's first and last name and prints them in reverse order with a space between them.

  7. Python Exercise with Practice Questions and Solutions

    The best way to learn is by practising it more and more. The best thing about this Python practice exercise is that it helps you learn Python using sets of detailed programming questions from basic to advanced. It covers questions on core Python concepts as well as applications of Python in various domains.

  8. 10 Python Practice Exercises for Beginners with Solutions

    Exercise 1: User Input and Conditional Statements. Write a program that asks the user for a number then prints the following sentence that number of times: 'I am back to check on my skills!'. If the number is greater than 10, print this sentence instead: 'Python conditions and loops are a piece of cake.'.

  9. Python Exercises

    Python Practice. Beginner exercises. Run Python programs. Make a Python program that prints your name. Make a program that displays the lyrics of a song. Variables. Make a program that displays several numbers. Make a program that solves and shows the summation of 64 + 32. Do the same as in 2, but make it sum x + y.

  10. Beginner Python Exercises w/ Solutions

    Exercise 1: print () function | (3) : Get your feet wet and have some fun with the universal first step of programming. Exercise 2: Variables | (2) : Practice assigning data to variables with these Python exercises. Exercise 3: Data Types | (4) : Integer (int), string (str) and float are the most basic and fundamental building blocks of data in ...

  11. Python's Assignment Operator: Write Robust Assignments

    Here, variable represents a generic Python variable, while expression represents any Python object that you can provide as a concrete value—also known as a literal—or an expression that evaluates to a value. To execute an assignment statement like the above, Python runs the following steps: Evaluate the right-hand expression to produce a concrete value or object.

  12. Different Forms of Assignment Statements in Python

    Multiple- target assignment: x = y = 75. print(x, y) In this form, Python assigns a reference to the same object (the object which is rightmost) to all the target on the left. OUTPUT. 75 75. 7. Augmented assignment : The augmented assignment is a shorthand assignment that combines an expression and an assignment.

  13. Variables and Assignment

    In Python, a single equals sign = is the "assignment operator." (A double equals sign == is the "real" equals sign.) Variables are names for values. In Python the = symbol assigns the value on the right to the name on the left. The variable is created when a value is assigned to it. Here, Python assigns an age to a variable age and a ...

  14. Introduction into Python Statements: Assignment, Conditional Examples

    Expression statements in Python are lines of code that evaluate and produce a value. They are used to assign values to variables, call functions, and perform other operations that produce a result. x = 5. y = x + 3. print(y) In this example, we assign the value 5 to the variable x, then add 3 to x and assign the result ( 8) to the variable y.

  15. 7. Simple statements

    Although the definition of assignment implies that overlaps between the left-hand side and the right-hand side are 'simultaneous' (for example a, b = b, a swaps two variables), overlaps within the collection of assigned-to variables occur left-to-right, sometimes resulting in confusion. For instance, the following program prints [0, 2]:

  16. Solve Python

    Easy Python (Basic) Max Score: 20 Success Rate: 97.28%. Solve Challenge. List Comprehensions. Easy Python (Basic) Max Score: 10 Success Rate: 97.68%. Solve Challenge. Find the Runner-Up Score! Easy Python (Basic) Max Score: 10 Success Rate: 94.18%. Solve Challenge. Nested Lists. Easy Python (Basic) Max Score: 10 Success Rate: 91.72%. Solve ...

  17. Python Examples

    The best way to learn Python is by practicing examples. This page contains examples on basic concepts of Python. We encourage you to try these examples on your own before looking at the solution. All the programs on this page are tested and should work on all platforms. Want to learn Python by writing code yourself?

  18. Assignment Operators in Python

    Assignment Operator. Assignment Operators are used to assign values to variables. This operator is used to assign the value of the right side of the expression to the left side operand. Python. # Assigning values using # Assignment Operator a = 3 b = 5 c = a + b # Output print(c) Output. 8.

  19. Python Exercises, Practice, Solution

    Python is a widely used high-level, general-purpose, interpreted, dynamic programming language. Its design philosophy emphasizes code readability, and its syntax allows programmers to express concepts in fewer lines of code than possible in languages such as C++ or Java. Python supports multiple programming paradigms, including object-oriented ...

  20. Python Lab Assignments

    python programs with output for class 12 and 11 students. Simple Assignements are for beginners starting from basics hello world program to game development using class and object concepts. A list of assignment solutions with source code are provided to learn concept by examples in an easy way.

  21. Python Operators: Arithmetic, Assignment, Comparison, Logical, Identity

    Python Operators: Arithmetic, Assignment, Comparison, Logical, Identity, Membership, Bitwise. Operators are special symbols that perform some operation on operands and returns the result. For example, 5 + 6 is an expression where + is an operator that performs arithmetic add operation on numeric left operand 5 and the right side operand 6 and ...

  22. Assignments

    ASSN # ASSIGNMENTS SUPPORTING FILES Homework 1 Handout ()Written exercises ()Code template () Homework 2 Handout ()Written exercises ()Code template ()nims.py ()strings_and_lists.py ()Project 1: Hangman

  23. Python Operators (With Examples)

    6. Python Special operators. Python language offers some special types of operators like the identity operator and the membership operator. They are described below with examples. Identity operators. In Python, is and is not are used to check if two values are located at the same memory location.

  24. Powerlearnproject/se-assignment-6-introduction-to-python ...

    Python Syntax and Semantics: Write a simple Python program that prints "Hello, World!" to the console. Explain the basic syntax elements used in the program. Data Types and Variables: List and describe the basic data types in Python. Write a short script that demonstrates how to create and use variables of different data types. Control Structures:

  25. The 10 Best Python Courses in 2024

    The Complete Python Bootcamp From Zero to Hero in Python - Udemy: Best for beginners who want a crash course This course sees learners create mini games like Tic Tac Toe and Blackjack with Python ...

  26. JavaScript

    JavaScript (JS) is a lightweight interpreted (or just-in-time compiled) programming language with first-class functions. While it is most well-known as the scripting language for Web pages, many non-browser environments also use it, such as Node.js, Apache CouchDB and Adobe Acrobat. JavaScript is a prototype-based, multi-paradigm, single-threaded, dynamic language, supporting object-oriented ...