compound assignment operators in python

• Python Basics
• Interview Questions
• Python Quiz
• Popular Packages
• Python Projects
• Practice Python
• AI With Python
• Learn Python3
• Python Automation
• Python Web Dev
• DSA with Python
• Python OOPs
• Dictionaries

Assignment Operators in Python

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 .

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.

Assignment Operators in Python – FAQs

What are assignment operators in python.

Assignment operators in Python are used to assign values to variables. These operators can also perform additional operations during the assignment. The basic assignment operator is = , which simply assigns the value of the right-hand operand to the left-hand operand. Other common assignment operators include += , -= , *= , /= , %= , and more, which perform an operation on the variable and then assign the result back to the variable.

What is the := Operator in Python?

The := operator, introduced in Python 3.8, is known as the “walrus operator”. It is an assignment expression, which means that it assigns values to variables as part of a larger expression. Its main benefit is that it allows you to assign values to variables within expressions, including within conditions of loops and if statements, thereby reducing the need for additional lines of code. Here’s an example: # Example of using the walrus operator in a while loop while (n := int(input("Enter a number (0 to stop): "))) != 0: print(f"You entered: {n}") This loop continues to prompt the user for input and immediately uses that input in both the condition check and the loop body.

What is the Assignment Operator in Structure?

In programming languages that use structures (like C or C++), the assignment operator = is used to copy values from one structure variable to another. Each member of the structure is copied from the source structure to the destination structure. Python, however, does not have a built-in concept of ‘structures’ as in C or C++; instead, similar functionality is achieved through classes or dictionaries.

What is the Assignment Operator in Python Dictionary?

In Python dictionaries, the assignment operator = is used to assign a new key-value pair to the dictionary or update the value of an existing key. Here’s how you might use it: my_dict = {} # Create an empty dictionary my_dict['key1'] = 'value1' # Assign a new key-value pair my_dict['key1'] = 'updated value' # Update the value of an existing key print(my_dict) # Output: {'key1': 'updated value'}

What is += and -= in Python?

The += and -= operators in Python are compound assignment operators. += adds the right-hand operand to the left-hand operand and assigns the result to the left-hand operand. Conversely, -= subtracts the right-hand operand from the left-hand operand and assigns the result to the left-hand operand. Here are examples of both: # Example of using += a = 5 a += 3 # Equivalent to a = a + 3 print(a) # Output: 8 # Example of using -= b = 10 b -= 4 # Equivalent to b = b - 4 print(b) # Output: 6 These operators make code more concise and are commonly used in loops and iterative data processing.

Similar Reads

• Python-Operators

Please Login to comment...

Improve your coding skills with practice.

What kind of Experience do you want to share?

• Introduction to Python
• Download and Install Python
• The First Python Program in IDLE
• Data Types in Python
• Python Keywords and Identifiers
• Python Variables and Constants
• Python If Statement
• Python If Else Statement
• Python Elif Statement
• Python Nested If Statement
• Python Nested If Else Statement
• Python While Loop
• Python For Loop
• Python Break Statement
• Python Arithmetic Operators
• Python Compound Assignment Operators
• Python Relational Operators
• Python Logical Operators
• Python Conditional Operator
• Python Classes
• Python Constructors
• Python Static Variable
• Python Static Method
• Python Class Method
• Python Inheritance
• Python Super() Function
• Python Method Overriding
• Python Polymorphism
• Python Garbage Collection
• Python Array from Array Module
• Python Array from Numpy Module
• Python two-dimensional Array
• Python Exception Handling
• Python Exception Propagation
• Python Try, Except Block
• Python Multiple Except Block
• Python Try, Except, Else Block
• Python Finally Block
• Python Raise Keyword
• Python User-Defined Exception
• Python Functions
• Python Functions with Arguments
• Python Functions with Default Arguments
• Python Recursive Function
• Python Local Variables
• Python Global Variables
• Python Tuple
• Python List
• Python Dictionary
• Python - String
• Python - String Methods
• Python - String capitalize() Method
• Python - String upper() Method
• Python - String isupper() Method
• Python - String casefold() Method
• Python - String lower() Method
• Python - String islower() Method
• Python - String swapcase() Method
• Python - String title() Method
• Python - String istitle() Method
• Python - String strip() Method
• Python - String lstrip() Method
• Python - String rstrip() Method
• Python - String isnumeric() Method
• Python - String isdigit() Method
• Python - String isalpha() Method
• Python - String isalnum() Method
• Python - String isdecimal() Method
• Python - String isidentifier() Method
• Python - String isprintable() Method
• Python - String startswith() Method
• Python - String endswith() Method
• Python - String index() Method
• Python - String find() Method
• Python - String rfind() Method
• Python - String rindex() Method
• Python - String center() Method
• Python - String rjust() Method
• Python - String ljust() Method
• Python - String replace() Method
• Python - String count() Method
• Python - String split() Method
• Python - String rsplit() Method
• Python - String splitlines() Method
• Python - String partition() Method
• Python - String rpartition() Method
• Python - String zfill() Method
• Python - String Formatting
• Python - Input/Output Functions
• Python - File and File Modes
• Python - Read a File
• Python - Write a File
• Python - Append to a File
• Python - Modify a File

Advertisement

+= operator

• First, an add operation.
• Next, the assignment of the result of an add operation.
• Statement i+=2 is equal to i=i+2 , hence 2 will be added to the value of i, which gives us 4.
• Finally, the result of addition, 4 is assigned back to i, updating its original value from 2 to 4.

A special case scenario for all the compound assigned operators

Example with += operator, -= operator.

• Subtraction operation.
• Assignment of the result of a subtract operation.
• Statement i-=2 is equal to i=i-2 , hence 2 will be subtracted from the value of i, which gives us 0.
• Finally, the result of subtraction i.e. 0 is assigned back to i, updating its value to 0.

Example with -= operator

*= operator.

• Multiplication operation.
• Assignment of the result of a multiplication operation.
• Statement i*=2 is equal to i=i*2 , hence 2 will be multiplied with the value of i, which gives us 4.
• Finally, the result of multiplication, 4 is assigned back to i, updating its value to 4.

Example with *= operator

/= operator.

• floating-point division operation.
• Assignment of the result of floating-point division operation.
• Statement i/=2 is equal to i=i/2 , hence 4 will be divided by the value of i, which gives us 2.0
• Finally, the result of division i.e. 2.0 is assigned back to i, updating its value from 4 to 2.0.

Example with /= operator

//= operator.

• Integer division operation, which gives us an integer quotient value after dividing two integers and it gives a floating-point quotient after dividing a floating-point number with an integer value or vice versa.
• Assignment of the result of an integer division operation.
• Statement i/=2 is equal to i=i//2 , hence 4 will be divided by the value of i, which gives us 2.
• Finally, the result of division i.e. 2 is assigned back to i, updating its value from 4 to 2.

Example with //= operator

Please share this article -.

Please Subscribe

Notifications

Please check our latest addition C#, PYTHON and DJANGO

• Python »
• 3.13.0 Documentation »
• The Python Language Reference »
• 8. Compound statements
• Theme Auto Light Dark |

8. Compound statements ¶

Compound statements contain (groups of) other statements; they affect or control the execution of those other statements in some way. In general, compound statements span multiple lines, although in simple incarnations a whole compound statement may be contained in one line.

The if , while and for statements implement traditional control flow constructs. try specifies exception handlers and/or cleanup code for a group of statements, while the with statement allows the execution of initialization and finalization code around a block of code. Function and class definitions are also syntactically compound statements.

A compound statement consists of one or more ‘clauses.’ A clause consists of a header and a ‘suite.’ The clause headers of a particular compound statement are all at the same indentation level. Each clause header begins with a uniquely identifying keyword and ends with a colon. A suite is a group of statements controlled by a clause. A suite can be one or more semicolon-separated simple statements on the same line as the header, following the header’s colon, or it can be one or more indented statements on subsequent lines. Only the latter form of a suite can contain nested compound statements; the following is illegal, mostly because it wouldn’t be clear to which if clause a following else clause would belong:

Also note that the semicolon binds tighter than the colon in this context, so that in the following example, either all or none of the print() calls are executed:

Summarizing:

Note that statements always end in a NEWLINE possibly followed by a DEDENT . Also note that optional continuation clauses always begin with a keyword that cannot start a statement, thus there are no ambiguities (the ‘dangling else ’ problem is solved in Python by requiring nested if statements to be indented).

The formatting of the grammar rules in the following sections places each clause on a separate line for clarity.

8.1. The if statement ¶

The if statement is used for conditional execution:

It selects exactly one of the suites by evaluating the expressions one by one until one is found to be true (see section Boolean operations for the definition of true and false); then that suite is executed (and no other part of the if statement is executed or evaluated). If all expressions are false, the suite of the else clause, if present, is executed.

8.2. The while statement ¶

The while statement is used for repeated execution as long as an expression is true:

This repeatedly tests the expression and, if it is true, executes the first suite; if the expression is false (which may be the first time it is tested) the suite of the else clause, if present, is executed and the loop terminates.

A break statement executed in the first suite terminates the loop without executing the else clause’s suite. A continue statement executed in the first suite skips the rest of the suite and goes back to testing the expression.

8.3. The for statement ¶

The for statement is used to iterate over the elements of a sequence (such as a string, tuple or list) or other iterable object:

The starred_list expression is evaluated once; it should yield an iterable object. An iterator is created for that iterable. The first item provided by the iterator is then assigned to the target list using the standard rules for assignments (see Assignment statements ), and the suite is executed. This repeats for each item provided by the iterator. When the iterator is exhausted, the suite in the else clause, if present, is executed, and the loop terminates.

A break statement executed in the first suite terminates the loop without executing the else clause’s suite. A continue statement executed in the first suite skips the rest of the suite and continues with the next item, or with the else clause if there is no next item.

The for-loop makes assignments to the variables in the target list. This overwrites all previous assignments to those variables including those made in the suite of the for-loop:

Names in the target list are not deleted when the loop is finished, but if the sequence is empty, they will not have been assigned to at all by the loop. Hint: the built-in type range() represents immutable arithmetic sequences of integers. For instance, iterating range(3) successively yields 0, 1, and then 2.

Changed in version 3.11: Starred elements are now allowed in the expression list.

8.4. The try statement ¶

The try statement specifies exception handlers and/or cleanup code for a group of statements:

Additional information on exceptions can be found in section Exceptions , and information on using the raise statement to generate exceptions may be found in section The raise statement .

8.4.1. except clause ¶

The except clause(s) specify one or more exception handlers. When no exception occurs in the try clause, no exception handler is executed. When an exception occurs in the try suite, a search for an exception handler is started. This search inspects the except clauses in turn until one is found that matches the exception. An expression-less except clause, if present, must be last; it matches any exception.

For an except clause with an expression, the expression must evaluate to an exception type or a tuple of exception types. The raised exception matches an except clause whose expression evaluates to the class or a non-virtual base class of the exception object, or to a tuple that contains such a class.

If no except clause matches the exception, the search for an exception handler continues in the surrounding code and on the invocation stack. [ 1 ]

If the evaluation of an expression in the header of an except clause raises an exception, the original search for a handler is canceled and a search starts for the new exception in the surrounding code and on the call stack (it is treated as if the entire try statement raised the exception).

When a matching except clause is found, the exception is assigned to the target specified after the as keyword in that except clause, if present, and the except clause’s suite is executed. All except clauses must have an executable block. When the end of this block is reached, execution continues normally after the entire try statement. (This means that if two nested handlers exist for the same exception, and the exception occurs in the try clause of the inner handler, the outer handler will not handle the exception.)

When an exception has been assigned using as target , it is cleared at the end of the except clause. This is as if

was translated to

This means the exception must be assigned to a different name to be able to refer to it after the except clause. Exceptions are cleared because with the traceback attached to them, they form a reference cycle with the stack frame, keeping all locals in that frame alive until the next garbage collection occurs.

Before an except clause’s suite is executed, the exception is stored in the sys module, where it can be accessed from within the body of the except clause by calling sys.exception() . When leaving an exception handler, the exception stored in the sys module is reset to its previous value:

8.4.2. except* clause ¶

The except* clause(s) are used for handling ExceptionGroup s. The exception type for matching is interpreted as in the case of except , but in the case of exception groups we can have partial matches when the type matches some of the exceptions in the group. This means that multiple except* clauses can execute, each handling part of the exception group. Each clause executes at most once and handles an exception group of all matching exceptions. Each exception in the group is handled by at most one except* clause, the first that matches it.

Any remaining exceptions that were not handled by any except* clause are re-raised at the end, along with all exceptions that were raised from within the except* clauses. If this list contains more than one exception to reraise, they are combined into an exception group.

If the raised exception is not an exception group and its type matches one of the except* clauses, it is caught and wrapped by an exception group with an empty message string.

An except* clause must have a matching expression; it cannot be except*: . Furthermore, this expression cannot contain exception group types, because that would have ambiguous semantics.

It is not possible to mix except and except* in the same try . break , continue and return cannot appear in an except* clause.

8.4.3. else clause ¶

The optional else clause is executed if the control flow leaves the try suite, no exception was raised, and no return , continue , or break statement was executed. Exceptions in the else clause are not handled by the preceding except clauses.

8.4.4. finally clause ¶

If finally is present, it specifies a ‘cleanup’ handler. The try clause is executed, including any except and else clauses. If an exception occurs in any of the clauses and is not handled, the exception is temporarily saved. The finally clause is executed. If there is a saved exception it is re-raised at the end of the finally clause. If the finally clause raises another exception, the saved exception is set as the context of the new exception. If the finally clause executes a return , break or continue statement, the saved exception is discarded:

The exception information is not available to the program during execution of the finally clause.

When a return , break or continue statement is executed in the try suite of a try … finally statement, the finally clause is also executed ‘on the way out.’

The return value of a function is determined by the last return statement executed. Since the finally clause always executes, a return statement executed in the finally clause will always be the last one executed:

Changed in version 3.8: Prior to Python 3.8, a continue statement was illegal in the finally clause due to a problem with the implementation.

8.5. The with statement ¶

The with statement is used to wrap the execution of a block with methods defined by a context manager (see section With Statement Context Managers ). This allows common try … except … finally usage patterns to be encapsulated for convenient reuse.

The execution of the with statement with one “item” proceeds as follows:

The context expression (the expression given in the with_item ) is evaluated to obtain a context manager.

The context manager’s __enter__() is loaded for later use.

The context manager’s __exit__() is loaded for later use.

The context manager’s __enter__() method is invoked.

If a target was included in the with statement, the return value from __enter__() is assigned to it.

The with statement guarantees that if the __enter__() method returns without an error, then __exit__() will always be called. Thus, if an error occurs during the assignment to the target list, it will be treated the same as an error occurring within the suite would be. See step 7 below.

The suite is executed.

The context manager’s __exit__() method is invoked. If an exception caused the suite to be exited, its type, value, and traceback are passed as arguments to __exit__() . Otherwise, three None arguments are supplied.

If the suite was exited due to an exception, and the return value from the __exit__() method was false, the exception is reraised. If the return value was true, the exception is suppressed, and execution continues with the statement following the with statement.

If the suite was exited for any reason other than an exception, the return value from __exit__() is ignored, and execution proceeds at the normal location for the kind of exit that was taken.

The following code:

is semantically equivalent to:

With more than one item, the context managers are processed as if multiple with statements were nested:

You can also write multi-item context managers in multiple lines if the items are surrounded by parentheses. For example:

Changed in version 3.1: Support for multiple context expressions.

Changed in version 3.10: Support for using grouping parentheses to break the statement in multiple lines.

The specification, background, and examples for the Python with statement.

8.6. The match statement ¶

Added in version 3.10.

The match statement is used for pattern matching. Syntax:

This section uses single quotes to denote soft keywords .

Pattern matching takes a pattern as input (following case ) and a subject value (following match ). The pattern (which may contain subpatterns) is matched against the subject value. The outcomes are:

A match success or failure (also termed a pattern success or failure).

Possible binding of matched values to a name. The prerequisites for this are further discussed below.

The match and case keywords are soft keywords .

PEP 634 – Structural Pattern Matching: Specification

PEP 636 – Structural Pattern Matching: Tutorial

8.6.1. Overview ¶

Here’s an overview of the logical flow of a match statement:

The subject expression subject_expr is evaluated and a resulting subject value obtained. If the subject expression contains a comma, a tuple is constructed using the standard rules .

Each pattern in a case_block is attempted to match with the subject value. The specific rules for success or failure are described below. The match attempt can also bind some or all of the standalone names within the pattern. The precise pattern binding rules vary per pattern type and are specified below. Name bindings made during a successful pattern match outlive the executed block and can be used after the match statement .

During failed pattern matches, some subpatterns may succeed. Do not rely on bindings being made for a failed match. Conversely, do not rely on variables remaining unchanged after a failed match. The exact behavior is dependent on implementation and may vary. This is an intentional decision made to allow different implementations to add optimizations.

If the pattern succeeds, the corresponding guard (if present) is evaluated. In this case all name bindings are guaranteed to have happened.

If the guard evaluates as true or is missing, the block inside case_block is executed.

Otherwise, the next case_block is attempted as described above.

If there are no further case blocks, the match statement is completed.

Users should generally never rely on a pattern being evaluated. Depending on implementation, the interpreter may cache values or use other optimizations which skip repeated evaluations.

A sample match statement:

In this case, if flag is a guard. Read more about that in the next section.

8.6.2. Guards ¶

A guard (which is part of the case ) must succeed for code inside the case block to execute. It takes the form: if followed by an expression.

The logical flow of a case block with a guard follows:

Check that the pattern in the case block succeeded. If the pattern failed, the guard is not evaluated and the next case block is checked.

If the pattern succeeded, evaluate the guard .

If the guard condition evaluates as true, the case block is selected.

If the guard condition evaluates as false, the case block is not selected.

If the guard raises an exception during evaluation, the exception bubbles up.

Guards are allowed to have side effects as they are expressions. Guard evaluation must proceed from the first to the last case block, one at a time, skipping case blocks whose pattern(s) don’t all succeed. (I.e., guard evaluation must happen in order.) Guard evaluation must stop once a case block is selected.

8.6.3. Irrefutable Case Blocks ¶

An irrefutable case block is a match-all case block. A match statement may have at most one irrefutable case block, and it must be last.

A case block is considered irrefutable if it has no guard and its pattern is irrefutable. A pattern is considered irrefutable if we can prove from its syntax alone that it will always succeed. Only the following patterns are irrefutable:

AS Patterns whose left-hand side is irrefutable

OR Patterns containing at least one irrefutable pattern

Capture Patterns

Wildcard Patterns

parenthesized irrefutable patterns

8.6.4. Patterns ¶

This section uses grammar notations beyond standard EBNF:

the notation SEP.RULE+ is shorthand for RULE (SEP RULE)*

the notation !RULE is shorthand for a negative lookahead assertion

The top-level syntax for patterns is:

The descriptions below will include a description “in simple terms” of what a pattern does for illustration purposes (credits to Raymond Hettinger for a document that inspired most of the descriptions). Note that these descriptions are purely for illustration purposes and may not reflect the underlying implementation. Furthermore, they do not cover all valid forms.

8.6.4.1. OR Patterns ¶

An OR pattern is two or more patterns separated by vertical bars | . Syntax:

Only the final subpattern may be irrefutable , and each subpattern must bind the same set of names to avoid ambiguity.

An OR pattern matches each of its subpatterns in turn to the subject value, until one succeeds. The OR pattern is then considered successful. Otherwise, if none of the subpatterns succeed, the OR pattern fails.

In simple terms, P1 | P2 | ... will try to match P1 , if it fails it will try to match P2 , succeeding immediately if any succeeds, failing otherwise.

8.6.4.2. AS Patterns ¶

An AS pattern matches an OR pattern on the left of the as keyword against a subject. Syntax:

If the OR pattern fails, the AS pattern fails. Otherwise, the AS pattern binds the subject to the name on the right of the as keyword and succeeds. capture_pattern cannot be a _ .

In simple terms P as NAME will match with P , and on success it will set NAME = <subject> .

8.6.4.3. Literal Patterns ¶

A literal pattern corresponds to most literals in Python. Syntax:

The rule strings and the token NUMBER are defined in the standard Python grammar . Triple-quoted strings are supported. Raw strings and byte strings are supported. f-strings are not supported.

The forms signed_number '+' NUMBER and signed_number '-' NUMBER are for expressing complex numbers ; they require a real number on the left and an imaginary number on the right. E.g. 3 + 4j .

In simple terms, LITERAL will succeed only if <subject> == LITERAL . For the singletons None , True and False , the is operator is used.

8.6.4.4. Capture Patterns ¶

A capture pattern binds the subject value to a name. Syntax:

A single underscore _ is not a capture pattern (this is what !'_' expresses). It is instead treated as a wildcard_pattern .

In a given pattern, a given name can only be bound once. E.g. case x, x: ... is invalid while case [x] | x: ... is allowed.

Capture patterns always succeed. The binding follows scoping rules established by the assignment expression operator in PEP 572 ; the name becomes a local variable in the closest containing function scope unless there’s an applicable global or nonlocal statement.

In simple terms NAME will always succeed and it will set NAME = <subject> .

8.6.4.5. Wildcard Patterns ¶

A wildcard pattern always succeeds (matches anything) and binds no name. Syntax:

_ is a soft keyword within any pattern, but only within patterns. It is an identifier, as usual, even within match subject expressions, guard s, and case blocks.

In simple terms, _ will always succeed.

8.6.4.6. Value Patterns ¶

A value pattern represents a named value in Python. Syntax:

The dotted name in the pattern is looked up using standard Python name resolution rules . The pattern succeeds if the value found compares equal to the subject value (using the == equality operator).

In simple terms NAME1.NAME2 will succeed only if <subject> == NAME1.NAME2

If the same value occurs multiple times in the same match statement, the interpreter may cache the first value found and reuse it rather than repeat the same lookup. This cache is strictly tied to a given execution of a given match statement.

8.6.4.7. Group Patterns ¶

A group pattern allows users to add parentheses around patterns to emphasize the intended grouping. Otherwise, it has no additional syntax. Syntax:

In simple terms (P) has the same effect as P .

8.6.4.8. Sequence Patterns ¶

A sequence pattern contains several subpatterns to be matched against sequence elements. The syntax is similar to the unpacking of a list or tuple.

There is no difference if parentheses or square brackets are used for sequence patterns (i.e. (...) vs [...] ).

A single pattern enclosed in parentheses without a trailing comma (e.g. (3 | 4) ) is a group pattern . While a single pattern enclosed in square brackets (e.g. [3 | 4] ) is still a sequence pattern.

At most one star subpattern may be in a sequence pattern. The star subpattern may occur in any position. If no star subpattern is present, the sequence pattern is a fixed-length sequence pattern; otherwise it is a variable-length sequence pattern.

The following is the logical flow for matching a sequence pattern against a subject value:

If the subject value is not a sequence [ 2 ] , the sequence pattern fails.

If the subject value is an instance of str , bytes or bytearray the sequence pattern fails.

The subsequent steps depend on whether the sequence pattern is fixed or variable-length.

If the sequence pattern is fixed-length:

If the length of the subject sequence is not equal to the number of subpatterns, the sequence pattern fails

Subpatterns in the sequence pattern are matched to their corresponding items in the subject sequence from left to right. Matching stops as soon as a subpattern fails. If all subpatterns succeed in matching their corresponding item, the sequence pattern succeeds.

Otherwise, if the sequence pattern is variable-length:

If the length of the subject sequence is less than the number of non-star subpatterns, the sequence pattern fails.

The leading non-star subpatterns are matched to their corresponding items as for fixed-length sequences.

If the previous step succeeds, the star subpattern matches a list formed of the remaining subject items, excluding the remaining items corresponding to non-star subpatterns following the star subpattern.

Remaining non-star subpatterns are matched to their corresponding subject items, as for a fixed-length sequence.

The length of the subject sequence is obtained via len() (i.e. via the __len__() protocol). This length may be cached by the interpreter in a similar manner as value patterns .

In simple terms [P1, P2, P3, … , P<N>] matches only if all the following happens:

check <subject> is a sequence

len(subject) == <N>

P1 matches <subject>[0] (note that this match can also bind names)

P2 matches <subject>[1] (note that this match can also bind names)

… and so on for the corresponding pattern/element.

8.6.4.9. Mapping Patterns ¶

A mapping pattern contains one or more key-value patterns. The syntax is similar to the construction of a dictionary. Syntax:

At most one double star pattern may be in a mapping pattern. The double star pattern must be the last subpattern in the mapping pattern.

Duplicate keys in mapping patterns are disallowed. Duplicate literal keys will raise a SyntaxError . Two keys that otherwise have the same value will raise a ValueError at runtime.

The following is the logical flow for matching a mapping pattern against a subject value:

If the subject value is not a mapping [ 3 ] ,the mapping pattern fails.

If every key given in the mapping pattern is present in the subject mapping, and the pattern for each key matches the corresponding item of the subject mapping, the mapping pattern succeeds.

If duplicate keys are detected in the mapping pattern, the pattern is considered invalid. A SyntaxError is raised for duplicate literal values; or a ValueError for named keys of the same value.

Key-value pairs are matched using the two-argument form of the mapping subject’s get() method. Matched key-value pairs must already be present in the mapping, and not created on-the-fly via __missing__() or __getitem__() .

In simple terms {KEY1: P1, KEY2: P2, ... } matches only if all the following happens:

check <subject> is a mapping

KEY1 in <subject>

P1 matches <subject>[KEY1]

… and so on for the corresponding KEY/pattern pair.

8.6.4.10. Class Patterns ¶

A class pattern represents a class and its positional and keyword arguments (if any). Syntax:

The same keyword should not be repeated in class patterns.

The following is the logical flow for matching a class pattern against a subject value:

If name_or_attr is not an instance of the builtin type , raise TypeError .

If the subject value is not an instance of name_or_attr (tested via isinstance() ), the class pattern fails.

If no pattern arguments are present, the pattern succeeds. Otherwise, the subsequent steps depend on whether keyword or positional argument patterns are present.

For a number of built-in types (specified below), a single positional subpattern is accepted which will match the entire subject; for these types keyword patterns also work as for other types.

If only keyword patterns are present, they are processed as follows, one by one:

I. The keyword is looked up as an attribute on the subject.

If this raises an exception other than AttributeError , the exception bubbles up. If this raises AttributeError , the class pattern has failed. Else, the subpattern associated with the keyword pattern is matched against the subject’s attribute value. If this fails, the class pattern fails; if this succeeds, the match proceeds to the next keyword.

II. If all keyword patterns succeed, the class pattern succeeds.

If any positional patterns are present, they are converted to keyword patterns using the __match_args__ attribute on the class name_or_attr before matching:

I. The equivalent of getattr(cls, "__match_args__", ()) is called.

If this raises an exception, the exception bubbles up. If the returned value is not a tuple, the conversion fails and TypeError is raised. If there are more positional patterns than len(cls.__match_args__) , TypeError is raised. Otherwise, positional pattern i is converted to a keyword pattern using __match_args__[i] as the keyword. __match_args__[i] must be a string; if not TypeError is raised. If there are duplicate keywords, TypeError is raised. See also Customizing positional arguments in class pattern matching

the match proceeds as if there were only keyword patterns.

For the following built-in types the handling of positional subpatterns is different:

These classes accept a single positional argument, and the pattern there is matched against the whole object rather than an attribute. For example int(0|1) matches the value 0 , but not the value 0.0 .

In simple terms CLS(P1, attr=P2) matches only if the following happens:

isinstance(<subject>, CLS)

convert P1 to a keyword pattern using CLS.__match_args__

For each keyword argument attr=P2 :

hasattr(<subject>, "attr")

P2 matches <subject>.attr

… and so on for the corresponding keyword argument/pattern pair.

8.7. Function definitions ¶

A function definition defines a user-defined function object (see section The standard type hierarchy ):

A function definition is an executable statement. Its execution binds the function name in the current local namespace to a function object (a wrapper around the executable code for the function). This function object contains a reference to the current global namespace as the global namespace to be used when the function is called.

The function definition does not execute the function body; this gets executed only when the function is called. [ 4 ]

A function definition may be wrapped by one or more decorator expressions. Decorator expressions are evaluated when the function is defined, in the scope that contains the function definition. The result must be a callable, which is invoked with the function object as the only argument. The returned value is bound to the function name instead of the function object. Multiple decorators are applied in nested fashion. For example, the following code

is roughly equivalent to

except that the original function is not temporarily bound to the name func .

Changed in version 3.9: Functions may be decorated with any valid assignment_expression . Previously, the grammar was much more restrictive; see PEP 614 for details.

A list of type parameters may be given in square brackets between the function’s name and the opening parenthesis for its parameter list. This indicates to static type checkers that the function is generic. At runtime, the type parameters can be retrieved from the function’s __type_params__ attribute. See Generic functions for more.

Changed in version 3.12: Type parameter lists are new in Python 3.12.

When one or more parameters have the form parameter = expression , the function is said to have “default parameter values.” For a parameter with a default value, the corresponding argument may be omitted from a call, in which case the parameter’s default value is substituted. If a parameter has a default value, all following parameters up until the “ * ” must also have a default value — this is a syntactic restriction that is not expressed by the grammar.

Default parameter values are evaluated from left to right when the function definition is executed. This means that the expression is evaluated once, when the function is defined, and that the same “pre-computed” value is used for each call. This is especially important to understand when a default parameter value is a mutable object, such as a list or a dictionary: if the function modifies the object (e.g. by appending an item to a list), the default parameter value is in effect modified. This is generally not what was intended. A way around this is to use None as the default, and explicitly test for it in the body of the function, e.g.:

Function call semantics are described in more detail in section Calls . A function call always assigns values to all parameters mentioned in the parameter list, either from positional arguments, from keyword arguments, or from default values. If the form “ *identifier ” is present, it is initialized to a tuple receiving any excess positional parameters, defaulting to the empty tuple. If the form “ **identifier ” is present, it is initialized to a new ordered mapping receiving any excess keyword arguments, defaulting to a new empty mapping of the same type. Parameters after “ * ” or “ *identifier ” are keyword-only parameters and may only be passed by keyword arguments. Parameters before “ / ” are positional-only parameters and may only be passed by positional arguments.

Changed in version 3.8: The / function parameter syntax may be used to indicate positional-only parameters. See PEP 570 for details.

Parameters may have an annotation of the form “ : expression ” following the parameter name. Any parameter may have an annotation, even those of the form *identifier or **identifier . (As a special case, parameters of the form *identifier may have an annotation “ : *expression ”.) Functions may have “return” annotation of the form “ -> expression ” after the parameter list. These annotations can be any valid Python expression. The presence of annotations does not change the semantics of a function. The annotation values are available as values of a dictionary keyed by the parameters’ names in the __annotations__ attribute of the function object. If the annotations import from __future__ is used, annotations are preserved as strings at runtime which enables postponed evaluation. Otherwise, they are evaluated when the function definition is executed. In this case annotations may be evaluated in a different order than they appear in the source code.

Changed in version 3.11: Parameters of the form “ *identifier ” may have an annotation “ : *expression ”. See PEP 646 .

It is also possible to create anonymous functions (functions not bound to a name), for immediate use in expressions. This uses lambda expressions, described in section Lambdas . Note that the lambda expression is merely a shorthand for a simplified function definition; a function defined in a “ def ” statement can be passed around or assigned to another name just like a function defined by a lambda expression. The “ def ” form is actually more powerful since it allows the execution of multiple statements and annotations.

Programmer’s note: Functions are first-class objects. A “ def ” statement executed inside a function definition defines a local function that can be returned or passed around. Free variables used in the nested function can access the local variables of the function containing the def. See section Naming and binding for details.

The original specification for function annotations.

Definition of a standard meaning for annotations: type hints.

Ability to type hint variable declarations, including class variables and instance variables.

Support for forward references within annotations by preserving annotations in a string form at runtime instead of eager evaluation.

Function and method decorators were introduced. Class decorators were introduced in PEP 3129 .

8.8. Class definitions ¶

A class definition defines a class object (see section The standard type hierarchy ):

A class definition is an executable statement. The inheritance list usually gives a list of base classes (see Metaclasses for more advanced uses), so each item in the list should evaluate to a class object which allows subclassing. Classes without an inheritance list inherit, by default, from the base class object ; hence,

is equivalent to

The class’s suite is then executed in a new execution frame (see Naming and binding ), using a newly created local namespace and the original global namespace. (Usually, the suite contains mostly function definitions.) When the class’s suite finishes execution, its execution frame is discarded but its local namespace is saved. [ 5 ] A class object is then created using the inheritance list for the base classes and the saved local namespace for the attribute dictionary. The class name is bound to this class object in the original local namespace.

The order in which attributes are defined in the class body is preserved in the new class’s __dict__ . Note that this is reliable only right after the class is created and only for classes that were defined using the definition syntax.

Class creation can be customized heavily using metaclasses .

Classes can also be decorated: just like when decorating functions,

The evaluation rules for the decorator expressions are the same as for function decorators. The result is then bound to the class name.

Changed in version 3.9: Classes may be decorated with any valid assignment_expression . Previously, the grammar was much more restrictive; see PEP 614 for details.

A list of type parameters may be given in square brackets immediately after the class’s name. This indicates to static type checkers that the class is generic. At runtime, the type parameters can be retrieved from the class’s __type_params__ attribute. See Generic classes for more.

Programmer’s note: Variables defined in the class definition are class attributes; they are shared by instances. Instance attributes can be set in a method with self.name = value . Both class and instance attributes are accessible through the notation “ self.name ”, and an instance attribute hides a class attribute with the same name when accessed in this way. Class attributes can be used as defaults for instance attributes, but using mutable values there can lead to unexpected results. Descriptors can be used to create instance variables with different implementation details.

The proposal that changed the declaration of metaclasses to the current syntax, and the semantics for how classes with metaclasses are constructed.

The proposal that added class decorators. Function and method decorators were introduced in PEP 318 .

8.9. Coroutines ¶

Added in version 3.5.

8.9.1. Coroutine function definition ¶

Execution of Python coroutines can be suspended and resumed at many points (see coroutine ). await expressions, async for and async with can only be used in the body of a coroutine function.

Functions defined with async def syntax are always coroutine functions, even if they do not contain await or async keywords.

It is a SyntaxError to use a yield from expression inside the body of a coroutine function.

An example of a coroutine function:

Changed in version 3.7: await and async are now keywords; previously they were only treated as such inside the body of a coroutine function.

8.9.2. The async for statement ¶

An asynchronous iterable provides an __aiter__ method that directly returns an asynchronous iterator , which can call asynchronous code in its __anext__ method.

The async for statement allows convenient iteration over asynchronous iterables.

Is semantically equivalent to:

See also __aiter__() and __anext__() for details.

It is a SyntaxError to use an async for statement outside the body of a coroutine function.

8.9.3. The async with statement ¶

An asynchronous context manager is a context manager that is able to suspend execution in its enter and exit methods.

See also __aenter__() and __aexit__() for details.

It is a SyntaxError to use an async with statement outside the body of a coroutine function.

The proposal that made coroutines a proper standalone concept in Python, and added supporting syntax.

8.10. Type parameter lists ¶

Added in version 3.12.

Changed in version 3.13: Support for default values was added (see PEP 696 ).

Functions (including coroutines ), classes and type aliases may contain a type parameter list:

Semantically, this indicates that the function, class, or type alias is generic over a type variable. This information is primarily used by static type checkers, and at runtime, generic objects behave much like their non-generic counterparts.

Type parameters are declared in square brackets ( [] ) immediately after the name of the function, class, or type alias. The type parameters are accessible within the scope of the generic object, but not elsewhere. Thus, after a declaration def func[T](): pass , the name T is not available in the module scope. Below, the semantics of generic objects are described with more precision. The scope of type parameters is modeled with a special function (technically, an annotation scope ) that wraps the creation of the generic object.

Generic functions, classes, and type aliases have a __type_params__ attribute listing their type parameters.

Type parameters come in three kinds:

typing.TypeVar , introduced by a plain name (e.g., T ). Semantically, this represents a single type to a type checker.

typing.TypeVarTuple , introduced by a name prefixed with a single asterisk (e.g., *Ts ). Semantically, this stands for a tuple of any number of types.

typing.ParamSpec , introduced by a name prefixed with two asterisks (e.g., **P ). Semantically, this stands for the parameters of a callable.

typing.TypeVar declarations can define bounds and constraints with a colon ( : ) followed by an expression. A single expression after the colon indicates a bound (e.g. T: int ). Semantically, this means that the typing.TypeVar can only represent types that are a subtype of this bound. A parenthesized tuple of expressions after the colon indicates a set of constraints (e.g. T: (str, bytes) ). Each member of the tuple should be a type (again, this is not enforced at runtime). Constrained type variables can only take on one of the types in the list of constraints.

For typing.TypeVar s declared using the type parameter list syntax, the bound and constraints are not evaluated when the generic object is created, but only when the value is explicitly accessed through the attributes __bound__ and __constraints__ . To accomplish this, the bounds or constraints are evaluated in a separate annotation scope .

typing.TypeVarTuple s and typing.ParamSpec s cannot have bounds or constraints.

All three flavors of type parameters can also have a default value , which is used when the type parameter is not explicitly provided. This is added by appending a single equals sign ( = ) followed by an expression. Like the bounds and constraints of type variables, the default value is not evaluated when the object is created, but only when the type parameter’s __default__ attribute is accessed. To this end, the default value is evaluated in a separate annotation scope . If no default value is specified for a type parameter, the __default__ attribute is set to the special sentinel object typing.NoDefault .

The following example indicates the full set of allowed type parameter declarations:

8.10.1. Generic functions ¶

Generic functions are declared as follows:

This syntax is equivalent to:

Here annotation-def indicates an annotation scope , which is not actually bound to any name at runtime. (One other liberty is taken in the translation: the syntax does not go through attribute access on the typing module, but creates an instance of typing.TypeVar directly.)

The annotations of generic functions are evaluated within the annotation scope used for declaring the type parameters, but the function’s defaults and decorators are not.

The following example illustrates the scoping rules for these cases, as well as for additional flavors of type parameters:

Except for the lazy evaluation of the TypeVar bound, this is equivalent to:

The capitalized names like DEFAULT_OF_arg are not actually bound at runtime.

8.10.2. Generic classes ¶

Generic classes are declared as follows:

Here again annotation-def (not a real keyword) indicates an annotation scope , and the name TYPE_PARAMS_OF_Bag is not actually bound at runtime.

Generic classes implicitly inherit from typing.Generic . The base classes and keyword arguments of generic classes are evaluated within the type scope for the type parameters, and decorators are evaluated outside that scope. This is illustrated by this example:

This is equivalent to:

8.10.3. Generic type aliases ¶

The type statement can also be used to create a generic type alias:

Except for the lazy evaluation of the value, this is equivalent to:

Here, annotation-def (not a real keyword) indicates an annotation scope . The capitalized names like TYPE_PARAMS_OF_ListOrSet are not actually bound at runtime.

Table of Contents

• 8.1. The if statement
• 8.2. The while statement
• 8.3. The for statement
• 8.4.1. except clause
• 8.4.2. except* clause
• 8.4.3. else clause
• 8.4.4. finally clause
• 8.5. The with statement
• 8.6.1. Overview
• 8.6.2. Guards
• 8.6.3. Irrefutable Case Blocks
• 8.6.4.1. OR Patterns
• 8.6.4.2. AS Patterns
• 8.6.4.3. Literal Patterns
• 8.6.4.4. Capture Patterns
• 8.6.4.5. Wildcard Patterns
• 8.6.4.6. Value Patterns
• 8.6.4.7. Group Patterns
• 8.6.4.8. Sequence Patterns
• 8.6.4.9. Mapping Patterns
• 8.6.4.10. Class Patterns
• 8.7. Function definitions
• 8.8. Class definitions
• 8.9.1. Coroutine function definition
• 8.9.2. The async for statement
• 8.9.3. The async with statement
• 8.10.1. Generic functions
• 8.10.2. Generic classes
• 8.10.3. Generic type aliases

Previous topic

7. Simple statements

9. Top-level components

• Report a Bug
• Show Source

• The Roadmap

Python Assignment Operators

In Python, assignment operators play a vital role in assigning values to variables and modifying them efficiently. While the basic assignment operator = is used most often, Python offers several compound operators that combine assignment with arithmetic or bitwise operations, allowing for more concise and expressive code. These operators help streamline code by saving you from repeatedly referencing the same variable. Let’s dive into each of these assignment operators and understand how they work.

Basic Assignment: =

The simplest assignment operator in Python is the = operator. It assigns the value on the right-hand side to the variable on the left. For instance, if you write:

You are assigning the value 5 to the variable x . This operator forms the basis for all other assignment operators.

Add and Assign: +=

The += operator is a shorthand for adding a value to a variable and then assigning the result back to that same variable. Essentially, x += y is the same as writing x = x + y . This helps shorten code, especially when you’re updating a variable multiple times.

For example:

This is both readable and efficient, especially in loops or repetitive calculations.

Subtract and Assign: -=

Similar to += , the -= operator subtracts a value from the variable and assigns the result back to the variable. Instead of writing x = x - value , you can simply use x -= value .

It keeps your code neat while performing subtraction updates.

Multiply and Assign: *=

The *= operator multiplies the variable by a value and reassigns the result back to that variable. It’s a more concise form of x = x * value , making it handy when you need to scale a value repeatedly.

For instance:

This operator shines in scenarios where multiplication is repeatedly applied to a variable.

Divide and Assign: /=

The /= operator divides the variable by the specified value and assigns the quotient back to the variable. It’s equivalent to writing x = x / value , but more compact.

It’s worth noting that division always results in a floating-point number, even if the operands are integers.

Floor Division and Assign: //=

If you need to perform floor division, which gives you the quotient rounded down to the nearest whole number, you can use the //= operator. This is equivalent to x = x // value .

Here’s an example:

This operator is particularly useful when you need integer division results without any remainder.

Modulus and Assign: %=

The %= , or modulus assignment operator, computes the remainder when dividing the variable by a value and assigns that remainder back to the variable. It’s a quick way to express x = x % value .

This is often used in scenarios like cycling through a sequence or checking even/odd numbers.

Exponent and Assign: **=

If you need to raise a variable to a power, you can use the **= operator. It simplifies x = x ** value , where the variable is raised to the power of the value on the right-hand side.

This is especially useful in mathematical applications requiring exponential growth or power operations.

Bitwise AND and Assign: &=

The &= operator performs a bitwise AND operation between the variable and the value, then assigns the result back to the variable. It’s a concise way to write x = x & value , and it’s used mainly in low-level bit manipulation.

Bitwise OR and Assign: |=

Similarly, the |= operator applies a bitwise OR operation between the variable and a value, reassigning the result. It’s shorthand for x = x | value .

This operator is often used in situations where you need to set specific bits in a binary value.

Bitwise XOR and Assign: ^=

The ^= operator performs a bitwise XOR (exclusive OR) operation between the variable and a value, and then assigns the result back to the variable. It’s a compact way to express x = x ^ value .

This operation is frequently used in cryptography and checksum algorithms.

Bitwise Shift Left and Assign: <<=

The <<= operator shifts the bits of a variable to the left by a specified number of positions and assigns the result back to the variable. This is equivalent to writing x = x << value .

Shifting left is often used for multiplication by powers of two in bit-level optimization.

Bitwise Shift Right and Assign: >>=

Finally, the >>= operator shifts the bits of the variable to the right by the given number of positions and reassigns the result. This is shorthand for x = x >> value .

This is useful when performing division by powers of two at the bit level.

Python Assignment Operators: Conclusion

Python’s assignment operators offer a powerful way to update variables concisely while combining arithmetic or bitwise operations. Understanding these operators not only helps in writing more efficient code but also enhances its readability. Whether you’re performing simple arithmetic or diving into bit-level manipulations, Python’s assignment operators provide the flexibility needed to streamline your code.

Happy Coding!

If you found this post helpful and want to dive deeper into mastering Python, check out the complete Python Roadmap! It’s packed with all the posts you need, covering everything from basic concepts to advanced topics. Explore the full Python learning path here!

Add comment

Cancel reply.

Save my name, email, and website in this browser for the next time I comment.

Identity Operators in Python

Introduction to Identity Operators Identity operators in Python are used to compare the memory locations of two objects. This is different from checking if their values are equal; identity operators help determine whether two variables reference the exact same object in memory. This distinction is crucial, particularly when dealing with mutable and immutable data types, as it can prevent bugs in...

Membership Operators in Python: An Introduction

When working with Python, you’ll often find yourself needing to check whether a particular element exists within a sequence or container, such as a list, string, set, tuple, or dictionary. This is where membership operators come in handy. Membership operators in Python provide a clean and efficient way to verify the presence or absence of values within these data structures. In this post...

Bitwise Operators in Python: A Beginner’s Guide

When you’re just starting with Python, you might encounter bitwise operators and wonder what they are and why you’d ever need to use them. Bitwise operators work directly on the binary representations of numbers and allow you to perform operations at the bit level. While this might seem like a niche topic, bitwise operations are incredibly useful for optimization, low-level programming, and...

Logical Operators in Python

When programming in Python, one of the core skills you’ll need is understanding how to control the flow of decision-making. This is where logical operators come in. These operators allow you to combine conditional statements, providing the backbone for your code’s logic. Whether you’re checking multiple conditions or refining the behavior of your program, logical operators play...

Hi, I’m Peter, a professional developer with over 25 years of experience. My journey with coding started when I was just a kid, exploring the world of programming and building my first projects out of pure curiosity and passion. Since then, I’ve turned this lifelong passion into a rewarding career, working on a wide range of projects, from small scripts to complex applications.

Now, I’m here to help others get started with coding through this blog. I know that learning to code can feel overwhelming at first, but I believe that with the right guidance, anyone can develop the skills they need to become a proficient programmer. My goal is to simplify the learning process and provide step-by-step resources that make coding accessible, fun, and practical for everyone.

Whether you’re just starting out or looking to sharpen your skills, I hope this blog serves as a valuable resource on your coding journey. Let’s dive into Python together!

Get in touch

Have any questions or feedback? Feel free to reach out—I’m always happy to help you on your coding journey!

• Data Analysis
• Deep Learning
• Large Language Model
• Machine Learning
• Neural Networks
• Rocky Linux

Python Assignment Operators

In Python, an assignment operator is used to assign a value to a variable. The assignment operator is a single equals sign (=). Here is an example of using the assignment operator to assign a value to a variable:

In this example, the variable x is assigned the value 5.

There are also several compound assignment operators in Python, which are used to perform an operation and assign the result to a variable in a single step. These operators include:

• +=: adds the right operand to the left operand and assigns the result to the left operand
• -=: subtracts the right operand from the left operand and assigns the result to the left operand
• *=: multiplies the left operand by the right operand and assigns the result to the left operand
• /=: divides the left operand by the right operand and assigns the result to the left operand
• %=: calculates the remainder of the left operand divided by the right operand and assigns the result to the left operand
• //=: divides the left operand by the right operand and assigns the result as an integer to the left operand
• **=: raises the left operand to the power of the right operand and assigns the result to the left operand

Here are some examples of using compound assignment operators:

Python In-Place Assignment Operators

In-place assignment operators (also called compound assignment operators) perform an operation in-place on a variable provided as first operand. They overwrite the value of the first operand variable with the result of the operation when performing the operator without assignment. For example, x += 3 is the same as x = x + 3 of first calculating the result of x + 3 and then assigning it to the variable x.

You can watch me go over all of these operators in the following video:

We’ll rush over all in-place operators one-by-one next!

Python In-Place Addition

Python provides the operator x += y to add two objects in-place by calculating the sum x + y and assigning the result to the first operands variable name x . You can set up the in-place addition behavior for your own class by overriding the magic “dunder” method __iadd__(self, other) in your class definition.

The expression x += y is syntactical sugar for the longer-form x = x + y :

Related Tutorial: Python In-Place Addition

Python In-Place Subtraction

Python provides the operator x -= y to subtract two objects in-place by calculating the difference x - y and assigning the result to the first operands variable name x . You can set up the in-place subtraction behavior for your own class by overriding the magic “dunder” method __isub__(self, other) in your class definition.

The expression x -= y is syntactical sugar for the longer-form x = x - y :

Related Tutorial: Python In-Place Subtraction

Python In-Place Multiplication

Python provides the operator x *= y to multiply two objects in-place by calculating the product x * y and assigning the result to the first operands variable name x . You can set up the in-place multiplication behavior for your own class by overriding the magic “dunder” method __imul__(self, other) in your class definition.

The expression x *= y is syntactical sugar for the longer-form x = x * y :

Related Tutorial: Python In-Place Multiplication

Python In-Place Division

Python’s in-place division operator x /= y divides two objects in-place by calculating x / y and assigning the result to the first operands variable name x . Set up in-place division for your own class by overriding the magic “dunder” method __truediv__(self, other) in your class definition.

The expression x /= y is syntactical sugar for the longer-form x = x / y :

Related Tutorial: Python In-Place Division

Python In-Place Modulo

Python provides the operator x %= y to calculate the modulo operation x % y , and assign the result in-place to the first operands variable x . You can set up the in-place modulo behavior for your own class by overriding the magic “dunder” method __imod__(self, other) in your class definition.

The expression x %= y is syntactical sugar for the longer-form x = x % y :

Related Tutorial: Python In-Place Modulo

Python In-Place Integer Division

Python’s in-place integer division operator x //= y divides two objects in-place by calculating x // y and assigning the result to the first operands variable name x . Set up in-place integer (or floor) division for your own class by overriding the magic “dunder” method __floordiv__(self, other) in your class definition.

Related Tutorial: Python In-Place Integer Division

Python In-Place Exponentiation

Python provides the in-place exponentiation operator x **= y that raises x to the power of y using x ** y and assigns the result to the first operands variable name x . You can set up the in-place exponentiation behavior for your own class by overriding the magic “dunder” method __ipow__(self, other) in your class definition.

The expression x **= y is syntactical sugar for the longer-form x = x ** y :

Related Tutorial: Python In-Place Exponentiation

Python In-Place Bitwise AND

Python’s in-place bitwise AND operator x &= y calcualtes bitwise-and x & y and assigns the result to the first operand x . To set it up for your own class, override the magic “dunder” method __iand__(self, other) in your class definition.

The expression x &= y is syntactical sugar for the longer-form x = x & y :

Related Tutorial: Python In-Place Bitwise AND

Python In-Place Bitwise OR

Python’s A |= B applies the | operator in place. Thus, it is semantically identical to the longer-form version A = A | B of first performing the operation A | B and then assigning the result to the variable A .

The following minimal example creates two Boolean variables A and B and performs the in-place B |= A operation to perform a logical OR operation B | A and assigning the result to the first operand B that becomes True :

In this example, you’ve seen this in-place operation on Boolean operands. But the | operator is overloaded in Python. The three most frequent use cases for the | and |= operators are the following:

• Python Sets : set union operator
• Python Dictionaries : dictionary update operator
• Python Booleans : logical OR operator

Related Tutorial: Python In-Place Bitwise OR

Python In-Place Bitwise XOR

Python’s in-place bitwise XOR operator x ^= y calcualtes bitwise XOR x ^ y and assigns the result to the first operand x . To set this up for your own class, override the magic “dunder” method __ixor__(self, other) in your class definition.

The expression x ^ = y is syntactical sugar for the longer-form x = x ^ y :

Related Tutorial: Python In-Place Bitwise XOR

Python In-Place Bitwise Right-Shift

Python’s in-place bitwise right-shift operator x >>= y calculates the right-shift operation x >> y , and assigns the result to the first operands variable name x . You can set up the in-place right-shift behavior in your own class by overriding the magic “dunder” method __irshift__(self, other) in your class definition.

The expression x >>= y is syntactical sugar for the longer-form x = x >> y :

Related Tutorial: Python In-Place Bitwise Right-Shift

Python In-Place Bitwise Left-Shift

Python’s in-place bitwise left-shift operator x <<= y calculates the left-shift operation x << y , and assigns the result to the first operands variable name x . You can set up the in-place left-shift behavior in your own class by overriding the magic “dunder” method __ilshift__(self, other) in your class definition.

The expression x <<= y is syntactical sugar for the longer-form x = x << y :

Related Tutorial: Python In-Place Bitwise Left-Shift

Python In-Place Magic Methods

The following table provides the names of the magic methods you need to define to enable in-place operators on your custom class:

In the following code example, we create a custom class Data and define our “magic” double-underscore methods so that we can perform in-place computations on objects of this class.

Let’s try these out!

What do you think about us?

Assignment Operators in Python

Concept map.

Python's assignment operators, including the basic `=` and compound operators like `+=`, `-=`, `*=`, and `/=`, are crucial for efficient variable assignment and data manipulation. These operators allow for concise code by combining arithmetic operations with assignment, and their understanding is key to writing clear and reliable programs. Overloading these operators in custom classes can further enhance code readability and expressiveness.

Basic and Compound Assignment Operators

Basic assignment operator (=).

The basic assignment operator (=) assigns a value to a variable

Compound Assignment Operators (+=, -=, *=, /=, %=, //=, **=, &=, |=, ^=, >>=, <<=)

Arithmetic Compound Assignment Operators (+=, -=, *=, /=, %=)

Compound assignment operators combine arithmetic operations with assignment, allowing for more compact and readable code

Bitwise Compound Assignment Operators (&=, |=, ^=, >>=, <<=)

Bitwise compound assignment operators perform an operation on the variable's value and the right-hand operand before reassigning the result to the variable

Exponentiation Compound Assignment Operator (**=)

The exponentiation compound assignment operator (**=) performs exponentiation on the variable's value and the right-hand operand before reassigning the result to the variable

Overloading Assignment Operators in User-Defined Classes

Overloading assignment operators in user-defined classes allows for custom behaviors and higher levels of abstraction in object interaction

Operator Precedence

Understanding operator precedence.

Operator precedence determines the sequence in which operations are executed within an expression

Impact of Operator Precedence on Expressions

Operator precedence can significantly impact the result of complex expressions and should be carefully considered when writing code

Importance of Parentheses in Expressions

Using parentheses can clarify the intended order of operations and prevent errors in complex expressions

Want to create maps from your material?

Enter text, upload a photo, or audio to Algor. In a few seconds, Algorino will transform it into a conceptual map, summary, and much more!

Learn with Algor Education flashcards

Click on each card to learn more about the topic.

In Python, the single equal sign (`______`) is used to assign the value to the variable's left.

Basic assignment operator in Python

`=` is used to assign a value to a variable.

Purpose of compound assignment operators

Simplify code by combining an operation with assignment, enhancing succinctness and performance.

Examples of bitwise compound assignment operators

`&=`, `|=`, `^=`, `>>=`, `<<=` perform bitwise operations and reassign the result to the variable.

To perform division and assign the result to the same variable in Python, the ______ operator is used.

Python 'magic methods' for operator overloading

Special methods like `__add__()` enable custom behavior for operators in classes.

Purpose of `__add__()` method in Python

Defines class response to `+=` operator, allowing intuitive operations like built-in types.

Abstraction benefit of operator overloading

Hides complex code, offers simple object interaction interface.

To ensure the correct order of operations, programmers may use ______ or simplify complex expressions.

parentheses

Basic Python assignment operator

The `=` operator assigns a value to a variable.

Python compound assignment operators

Operators like `+=`, `-=`, combine assignment with arithmetic operation, modifying variable in place.

Operator overloading in custom classes

Allows custom definition of assignment operators in classes to tailor object behavior.

Here's a list of frequently asked questions on this topic

What is the purpose of python's assignment operators, and can you give an example of a basic and a compound operator, what types of operations can python's compound assignment operators perform, how do python's assignment operators simplify code when performing mathematical operations, how does overloading assignment operators in python enhance user-defined classes, why is it important to understand operator precedence in python programming, what are the overall benefits of using python's assignment operators in programming, similar contents, explore other maps on similar topics.

Understanding Processor Cores

Bitwise Shift Operations in Computer Science

Computer Memory

The Significance of Terabytes in Digital Storage

Secondary Storage in Computer Systems

The Importance of Bits in the Digital World

Karnaugh Maps: A Tool for Simplifying Boolean Algebra Expressions

Can't find what you were looking for?

Search for a topic by entering a phrase or keyword

Exploring Python's Assignment Operators

Comprehensive Overview of Python Assignment Operators

Demonstrating python assignment operators through examples, customizing behavior with overloaded assignment operators in python, the significance of operator precedence in python expressions, concluding insights on python assignment operators.

Edit available

7. Basic Python Operators: Arithmetic, Comparison, and Assignment

Diving into Python, one of the most popular programming languages today, it’s crucial to grasp the basics. Operators in Python are the building blocks for performing calculations, making decisions, and storing values. I’ll walk you through the essentials of arithmetic, comparison, and assignment operators, which are foundational for anyone looking to code in Python.

Whether you’re a beginner just starting out or a seasoned coder brushing up on the basics, understanding these operators is key to writing efficient and effective code. From adding numbers to comparing values and assigning data, I’ve got you covered. Let’s unlock the power of Python operators together and make your coding journey a breeze.

Table of Contents

Arithmetic Operators

Python’s arithmetic operators are the backbone of numerical computations in this versatile language. I’ll break them down for you, so you have a clear understanding of how each operator works and where it’s applicable.

Addition and Subtraction

The Addition ( + ) and Subtraction ( - ) operators are straightforward. You’ll use them for adding or subtracting two numbers:

Subtraction

Multiplication and division.

For Multiplication ( * ) and Division ( / ), Python obeys the typical order of operations to ensure accurate results:

Multiplication

Remember, division always produces a float, even if you’re dividing two integers.

Modulus and Exponentiation

Moving on, the Modulus ( % ) operator finds the remainder after division of one number by another. It’s particularly useful in algorithms that require you to determine if a number is even or odd. Meanwhile, the Exponentiation ( ** ) operator raises one number, the base, to the power of another, the exponent:

Exponentiation

Floor division.

Lastly, Floor Division ( // ) divides and rounds down to the nearest whole number:

Mastering these operations is a must for any task that requires mathematical calculations. Whether you’re building financial models or creating games, arithmetic operators are your fundamental tools. Next, let’s move on to another set of operators that are as critical as arithmetic ones: comparison operators. This will help you make decisions in your code based on the data at hand.

When diving into Python, one of the fundamental tools in your programmer’s kit is the addition operator. Simple to use, it’s represented by the + sign and performs exactly what you’d expect—adds together two values. In practice, here’s how it plays out:

Adding integers

Adding floats.

This operator doesn’t just handle numerical data types like integers and floats, it can also concatenate strings and lists, making it extremely versatile:

Concatenating strings

Combining lists.

Furthermore, the addition operator can be used in an augmented assignment format to both add and assign a value to a variable in one expression:

Augmented assignment

It’s important to note that while addition in Python is fairly straightforward, it’s essential to consider the data type of the items you’re working with to avoid errors. For instance, trying to add together a string and an integer will result in a TypeError, as Python expects homogeneity in addition operations.

As you become more skilled with Python, you’ll find that the addition operator is not just about arithmetic. It’s a powerful tool that allows you to accumulate values, construct strings, merge data structures, and much more. Whether you’re calculating sums or constructing complex data payloads, the addition operator remains a reliable ally in your coding endeavors.

Just as the addition operator plays a crucial role in Python, the subtraction operator is equally fundamental for performing arithmetic calculations. Signified by a minus sign ( - ), subtraction in Python allows you to calculate the difference between numbers, and just like addition, can also be used with various data types.

When working with integers and floats , the subtraction operator operates as expected. If I write 10 - 5 , the output will naturally be 5 . However, subtracting a float from an integer, or vice versa, will result in a float: 7 - 2.0 yields 5.0 . It’s simple arithmetic but forms the basis for many complex operations in Python programming.

Interestingly, in the realm of strings and lists, Python does not directly support the subtraction operator. Trying to subtract one string from another, or one list from another, results in a TypeError . This is because the concept of subtraction doesn’t naturally extend to these data types in the same intuitive way as it does for numbers. However, developers often work around this limitation by using other methods to achieve similar outcomes, such as string methods or list comprehension.

Augmented assignment with subtraction is also available in Python. Using -= allows for a value to be subtracted from a variable and the result assigned back to that variable in a single, concise step. For example, I can write x -= 2 and whatever value x held would be decremented by 2 .

Remember, while Python’s subtraction operator might seem straightforward, its application is broad, acting as one of the pillars of mathematical operations in coding. From calculating simple differences to adjusting values on the fly, subtraction is vital for data manipulation and needs to be understood in depth by any aspiring Python programmer.

When it comes to programming in Python, the multiplication operator is as fundamental as it gets. Denoted by the asterisk (*), this operator allows for the product of two numbers, be they integers or floats. Here’s a quick example: if I write 3 * 4 , Python outputs 12 . This same principle applies to floats: 2.5 * 4.0 returns 10.0 .

But multiplication isn’t limited to numbers alone. Python’s flexibility shines when the multiplication operator is applied to strings. For instance, 'Python!' * 3 gives us 'Python!Python!Python!' , repeating the string thrice. It’s important to remember, this operation doesn’t combine separate strings — it repeats the same string multiple times.

Lists see similar benefits. Multiplying a list by an integer repeats the list’s contents. Therefore, [1, 2, 3] * 2 transforms into [1, 2, 3, 1, 2, 3] . This can be particularly useful when initializing a list with a fixed number of identical elements without manually typing them out.

I should point out that Python enforces type consistency when using the multiplication operator. Attempts to multiply incompatible types, like a string by a list, will result in a TypeError . This ensures that you’re aware of the data types you’re working with and helps maintain clean code.

Moreover, Python supports multiplications using external libraries like NumPy, which offer advanced features for array multiplications. In these scenarios, the multiplication operator can perform operations like matrix multiplications and dot products, taking Python’s capabilities well beyond basic arithmetic.

Using multiplication operator with caution is recommended, particularly when applying it to lists, since it could potentially lead to memory issues. Large list operations can consume significant amounts of memory and slow down performance, especially if you’re repeating a substantial list. Hence, always assess the impact on your code’s efficiency and memory usage before running such operations.

When we dive into the world of Python arithmetic, the division operator emerges as a crucial tool for numerical calculations. Python uses the forward slash (/) for division, providing a straightforward way to divide numbers. This operator can handle both integers and floats with ease. Here’s a quick example: if I write 10 / 5 , Python will give me 2.0 . Notice how the result is a float, not an integer. This is because Python automatically converts integer division results into float for more precision.

But what if you’re interested in integer division? That’s where the floor division operator (//) steps in. If I want to divide 10 by 3 and get an integer result without any decimal places, I’d use 10 // 3 , which yields 3 . It effectively rounds down the result to the nearest whole number. It’s paramount to mention that if there’s a negative number involved, floor division will round towards the negative of infinity. This behavior ensures consistency across different arithmetic operations.

Perhaps less commonly discussed but equally important is the modulus operator (%) , which gives us the remainder of a division operation. If I want to know what’s left over when 10 is divided by 3 , 10 % 3 would return 1 .

Knowing how to handle division in Python is particularly vital in data analysis and scientific computations, where precision and the type of division are key. It’s also important to highlight that Python will raise a ZeroDivisionError if you attempt to divide by zero. Hence, checks for zero are critical before performing division operations to prevent runtime errors.

One might also incorporate libraries like NumPy when dealing with arrays or matrices as these can offer more sophisticated functions and handle division in a way that’s optimized for large datasets. The flexibility of these libraries in handling various numerical operations complements the built-in Python division capabilities.

Remember, just like multiplication, division must be approached with an understanding of the data type involved to avoid type inconsistency errors. By keeping these intricacies in mind, you’ll be able to write more efficient and error-free code.

When I delve into Python’s arithmetic operations, the modulo operator often stands out. Represented by the % symbol, the modulo provides the remainder of a division operation rather than the quotient. It’s especially handy in various programming scenarios, such as determining odd or even numbers or iterating over a sequence with wrap-around.

Understanding the Modulo Operator

To use the modulo operator:

Where dividend is the number you’re dividing and divisor is the number by which you’re dividing. For example, 10 % 3 would return 1 because when dividing 10 by 3, the remainder is 1.

Key Uses of Modulo in Python

• Looping Techniques : When you need to cycle through a list repeatedly, modulo helps to reset the index.
• Even: if number % 2 == 0
• Odd: if number % 2 != 0
• Time Calculations : Modulo is perfect for converting seconds to minutes and hours, ensuring values stay within calendar bounds.

Unlike division operators, the modulo is less prone to runtime errors like ZeroDivisionError , but you still can’t use zero as a divisor. If attempted, Python will raise a ZeroDivisionError , similar to using the division operator.

Integrating modulo requires mindfulness about operand types — a common theme in Python arithmetic. The result of a modulo operation involving floats can be counterintuitive because we’re often taught to think of remainders in the context of integers.

In cases involving negative values:

The operation considers the sign of the divisor, resulting in 2 , because Python’s modulo returns the remainder closest to zero.

For advanced scenarios, external packages might offer specialized functions. NumPy, for instance, provides a variation of the modulo operator that behaves differently with negative values. Choosing the right tool for each job is critical in programming for efficiency and accuracy.

Whenever I’m faced with a problem requiring the determination of a quotient’s remainder, I make sure the modulo operator is top of mind. It’s a small but mighty tool in Python’s operator arsenal and can be the difference between a good and a great solution. With this knowledge, I can navigate numbers with precision and employ effective strategies for commonly faced challenges in programming.

When diving into Python’s arithmetic operators, we quickly come across the power operator , ** , which is used for exponentiation. This operator raises a number to the power of another, making it an invaluable tool when working with powers and roots in Python.

Let’s take a look at how it’s used:

Raising 2 to the power of 3

In this example, 2 is the base and 3 is the exponent. Python beautifully simplifies what could otherwise be a complex mathematical process into a single line of code.

Understanding the rules and behavior of exponentiation in Python is crucial, especially when dealing with large numbers or when performing precision computing. Here are some key points:

• Positive exponents : The base is multiplied by itself as many times as the value of the exponent.
• Negative exponents : Python will return the reciprocal of the base raised to the absolute value of the exponent.
• Zero exponent : Any base raised to the power of zero will always be 1.

The ** operator isn’t just limited to integers. It can also be used with floats to handle scenarios requiring fractional exponents, which is common in scientific computations:

Square root of 9

Remember though, float operations might introduce rounding errors due to the nature of binary representation in computing. It’s always good practice to be aware of this when expecting precise results.

For more complex mathematical operations that go beyond what Python’s built-in functions offer, you might want to look into modules like math or numpy . These libraries have optimized functions for exponentiation and other intricate mathematical computations that ensure accuracy and efficiency.

As we continue to explore operators, it’s essential to practice using them in real-world problems that require exponentiation. There are countless applications, from calculating compound interest to transforming datasets in data science projects. Empowering yourself with the power operator opens up a universe of possibilities in Python programming.

When working with division in Python, you’ll inevitably come across the Floor Division operator, denoted by a double forward slash // . Unlike the standard division operator / that returns a floating-point result, floor division rounds down the result to the nearest whole number.

Let’s take a deeper dive into why floor division is crucial for certain calculations. One of the primary uses of floor division is to get an integer quotient from the division of two numbers. For instance, if you’re dividing 7 by 2 using the standard division operator, the result would be 3.5. However, with floor division, the result is simply 3 .

Here are a few scenarios where floor division is particularly handy:

• Allocating resources evenly and determining leftovers
• Working with time calculations , where decimal parts don’t make sense
• Processing images or graphics by ensuring pixel counts remain whole numbers

Consider this code snippet for a better understanding:

It’s important to note that when dealing with negative numbers, floor division can have surprising results. For example:

Although -11 / 3 would typically yield approximately -3.6667, floor division will round this down to -4 . This behavior keeps the floor division consistent, always rounding towards minus infinity, which can be essential for maintaining predictable results in your code.

Using floor division in Python is an easy way to keep your calculations integer-based when needed. Integrating this operator into your toolbox can save time and additional steps, especially when working with loops or array indices where non-integer values could cause errors. Remember to test your code with a variety of inputs to understand how floor division operates across different scenarios.

Comparison Operators

When coding in Python, Comparison Operators are the bread and butter for making decisions. These operators allow me to compare two values and, based on the comparison, return a Boolean value of either True or False .

The Common Comparison Operators Are:

• == : Equal to
• != : Not equal
• > : Greater than
• < : Less than
• >= : Greater than or equal to
• <= : Less than or equal to

These operators are fundamental for control flow, enabling functions like if statements and while loops to make logical decisions. For example, I’ll compare user input to a set value to determine the next steps in a program.

Practical Uses of Comparison Operators

In real-world scenarios, I find comparison operators particularly useful when sorting items, validating user input , or implementing algorithms that require condition checking. They’re also crucial while filtering data , such as when I need to extract specific information from large datasets.

Avoiding Common Pitfalls

A common mistake I can make when using comparison operators is confusing the ‘equal to’ operator == with the assignment operator = . It’s vital to ensure that I’m using the correct operator to avoid unexpected behavior in my code. Another issue to watch out for is the mix-up between is and == . While is checks if two variables point to the same object, == evaluates if the values they hold are the same. This distinction is significant when working with mutable objects like lists.

Mastery Through Practice

The more I work with comparison operators, the more intuitive they become. I often incorporate various comparison operations in my practice exercises to get a solid grasp of each operator’s nuances. By combining comparison operators with logical ones like and , or , and not , I can craft complex conditions that can handle multifaceted scenarios in my programs.

Understanding the ‘equal to’ operator in Python is crucial for carrying out effective comparisons. The operator is denoted by two equal signs (==) and is used to determine whether two values are identical. When it comes to programming logic, this operator plays a pivotal role.

Consider a situation where I need to validate user input . I’d use the ‘equal to’ operator to compare the input with a predefined correct answer. If the user’s answer matches the correct answer, the operation evaluates to True .

Here’s a simple code snippet to illustrate its use:

In the code above, if the user_input matches secret_code , the message “Access Granted!” is displayed. Otherwise, the user sees “Access Denied!”

It’s also vital to remember that the ‘equal to’ operator checks for value equality, not identity. Object identity, or whether two variables point to the same object in memory, is checked using the is operator .

Here’s how you shouldn’t confuse ‘equal to’ with the assignment operator:

• Assignment operator ( = ): Assigns a value to a variable.
• Equal to operator ( == ): Compares two values for equality.

Moreover, when working with floating-point numbers, programmers need to be cautious of precision issues . Due to the way computers represent floating-point numbers, two values that seem identical may not be considered equal. Always validate such comparisons within a tolerance level or use the math.isclose() function for comparing floating-point numbers.

Using the ‘equal to’ operator effectively requires a firm grasp of the types of data being compared. Always ensure the operands are comparable and understand how Python treats different data types during comparison. For instance, comparing a string with an integer using ‘equal to’ will result in False because their data types are different.

In my next section, I’ll delve into another comparison operator that is often used side by side with ‘equal to’: the ‘not equal to’ operator.

Not Equal To

While the ‘equal to’ operator checks for similarity, the ‘not equal to’ operator serves the opposite function. Represented by an exclamation point followed by an equal sign (!=) , it determines if two values are different. I often use this operator when I need to execute a block of code only if a certain condition is not met. For instance, when dealing with user input, it’s crucial to ensure the input doesn’t match something specific, like a forbidden password or username.

Here’s a simple scenario:

In this example, the != operator saves the day by preventing users from choosing a restricted username. This safeguard is as vital as allowing correct inputs, especially when considering security implications.

Handling Data Types is another area where the ‘not equal to’ operator comes into play. Since Python is dynamically typed, you don’t always know what data types you’ll receive. So, checking for inequality also requires an understanding of how Python compares different data types. When Python compares an integer and a floating-point number, even if their mathematical values are the same, the != operator will consider the different types and may not behave as expected.

It’s especially important to Test Thoroughly when working with != . Situations involving collections like lists and dictionaries might not be straightforward, as the operator checks for inequality between all elements, potentially leading to confusion if not used correctly. Consider how Python treats different container types:

• Lists: Element by element comparison
• Dictionaries: Pair by pair (key and value) comparison

When you master the ‘not equal to’ operator, you enhance your error handling and control flow capabilities dramatically. This improvement leads to more robust and reliable code.

Greater Than

In the universe of Python’s comparison operators, the Greater than symbol > stands tall. It’s straightforward yet potent, enabling me to compare two values and determine if one is larger than the other. This operator is commonly used in control structures like if statements and loops, where decisions hinge on numerical comparisons.

When I apply the > operator, Python does what you’d expect – it checks to see if the value on the left is indeed greater than the value on the right. For instance, if I’ve got two variables, age1 set to 30 and age2 set to 25, writing age1 > age2 would yield True because 30 is, in fact, greater than 25. But the practical applications go far beyond just comparing simple integers.

Imagine working on a piece of code where I need to filter a list of items based on their prices. I’ll loop through each item and use the > operator to select only those whose price exceeds a certain threshold. This kind of operation is crucial in tasks like data analysis, where I’m often sieving through vast quantities of data to find significant figures.

• Syntax: value1 > value2
• Returns: True if value1 is greater than value2 , otherwise False

It’s also important to be aware of how Python handles comparisons between different data types. For example, comparing an integer to a float works seamlessly since Python knows how to interpret these kinds. However, using the > operator between incompatible types, such as an integer and a string, throws a TypeError. Intuitively, it’s comparing apples to oranges, which Python wisely refrains from.

In complex structures like dictionaries, I need to be more careful. Since dictionaries can hold various data types as values, ensuring I’m comparing items of the same type is imperative. Oftentimes, I’d have to iterate over the dictionary’s values and, based on context, apply the > operator to the elements that I can fairly compare.

Leveraging the > operator intelligently can propel condition-based logic to work precisely as I intend. It’s a cornerstone in building robust Python scripts that respond dynamically to varying data.

Just as crucial as the ‘greater than’ operator, the ‘less than’ operator in Python is denoted by the symbol < . This operator assesses whether the left-hand operand is smaller than the right-hand operand. It returns a boolean value – True if the assertion is correct and False otherwise.

Useful in various programming scenarios , the ‘less than’ operator is fundamental when sorting algorithms are in play or when we need to impose a threshold. Here are some common applications:

• Imposing limits within loops
• Conditional expressions in if statements
• Filtering data during analysis

I use the ‘less than’ operator with numbers predominantly, but it’s versatile with other compatible types in Python, such as strings which are compared based on their alphabetical order.

When comparing strings with numbers , however, Python will raise a TypeError . It’s vital to ensure compatibility between the data types to prevent any unintended errors in the code. Keep in mind that specific comparisons involving complex data structures might require casting or additional checks for a smooth experience.

Up next, I’ll tackle the versatile yet simple assignment operators. Thriving in simplicity, these operators play a pivotal role in almost every aspect of a Python script. From variables initialization to the reassignment of values, assignment operators maintain state and control flow within a program, proving their indispensability in the realm of Python coding.

Greater Than or Equal To

When I’m programming in Python, the ‘greater than or equal to’ operator (>=) is equally essential as its counterpart. It’s used to compare two values, checking not only if one value is greater than the other but also if they’re equal. This operator is particularly useful when setting boundary conditions or ranges within my code.

Let’s explore its application with a real-world example . Imagine I’m writing a program that determines if a user is eligible for a senior discount. The eligibility age is 65 or older.

Here, I’m using the >= operator to check if the age entered is 65 or more. If the condition is true, the message ‘Eligible for discount’ is printed.

Moving beyond simple comparisons, the ‘greater than or equal to’ operator is vital in loop structures . For instance, consider processing a list of scores to determine how many are above a certain threshold:

print(f”Scores above threshold: {above_threshold}”)

In this snippet, each score is checked against the threshold, and the counter increases for each score that meets the condition.

Just as with the ‘less than’ operator, ensuring data type compatibility is crucial. Remember, comparing a string and an integer with >= could lead to errors. To maintain the integrity of my programs I ensure the variables in comparison are of the same or coercible types. This prevents unexpected behavior and promotes reliable code execution.

Like the threads of a tapestry, these operators interweave to form the logic of our Python script, making them indispensable tools in my arsenal for crafting efficient and effective code.

As I familiarize myself with these operators I find my scripts growing not only more sophisticated but also more robust. Stepping through each one, the journey through Python’s basic operators continues to unfold, revealing the simplicity and power of the language.

Less Than or Equal To

Just as important as the ‘greater than or equal to’ operator in Python is the ‘less than or equal to’ operator. This operator is denoted by <= and serves a critical role in programming—especially when you need to evaluate whether a value falls below or exactly at a certain threshold. For example, when managing inventory, confirming that stock levels are sufficient before processing a sale is essential. Here’s how it’s used:

In this snippet, Python checks if the order_quantity is less than or equal to stock . If the order quantity is 10 or less, the condition is true, and we proceed to process the order.

Understanding <= in Different Contexts

The <= operator isn’t limited to simple numerical comparisons—it’s also effective in other contexts:

• String Comparison : Strings are compared lexicographically in Python, so you can check if one precedes another alphabetically.
• Date Comparison : When working with date objects, <= ensures that an event occurs before or on a certain date.

Practical Applications of <=

I’ve observed that the use of <= spans a variety of applications:

• Setting thresholds in game development to trigger an event
• Validating user input to ensure it doesn’t exceed a limit
• Analyzing datasets to filter entries based on a condition

It’s crucial to remember that the data types on either side of the <= must be compatible to avoid a TypeError .

Applying the ‘less than or equal to’ operator within loops and conditional statements allows for more complex decision-making processes:

This loop will only print numbers that are 5 or less, demonstrating how <= can control the flow of a program. As you can see, employing <= alongside other Python operators enhances the control and precision we have over our code. With this understanding, you’ll be able to craft more intricate and reliable Python programs that handle a multitude of scenarios effectively.

Assignment Operators

Assignment operators in Python are the foundation of variable management and data storage. These operators are used to assign values to variables . The most common assignment operator is the = sign, which might seem straightforward but is the workhorse of just about any Python program. For instance, when I create a variable to keep track of a score in a game, I use the = to set its initial value: score = 0 .

Beyond the basic assignment, Python also provides a suite of compound assignment operators that combine arithmetic operations with assignment. These are incredibly handy for modifying variable values efficiently. They not only make code cleaner and easier to read but often reduce the chance of typing errors in the process. Here are the main ones I frequently utilize:

• += for adding and assignment: counter += 1 increments the counter by one.
• -= for subtraction and assignment: health -= damage subtracts damage from health.
• *= for multiplication and assignment: price *= discount applies a discount to the price.
• /= for division and assignment: total /= num_items calculates the average price per item.

In Python, these operators do more than just reduce the amount of typing. For example, they can streamline loop operations or increment counters within a loop without having to write out the full assignment. This can make a significant difference in the readability and performance of the code. Let’s say I’m processing a list of numbers to get a total:

Here, the += operator is effortlessly increasing the total with each iteration. Additionally, in scenarios where I’m working with mutable data types like lists, these operators allow for the modification of data in place, which can lead to more memory-efficient code .

Moreover, Python’s assignment operators support chaining , which brings another layer of elegance to variable management. For example, I can initialize multiple variables at once: x = y = z = 0 . It’s also worth noting that Python 3.8 introduced the walrus operator := , which assigns values within an expression, further expanding the realms of assignment possibilities.

Simple Assignment

In mastering Python, Simple assignment is an essential tool in your programming arsenal. It’s the most straightforward form of assigning a value to a variable. You’ve already seen the = operator in action, which serves as the backbone for variable creation and manipulation in the language.

When I use simple assignment, I follow the basic syntax where the variable name comes first, followed by the = sign, and then the value I wish to assign. Here’s an easy-to-understand example:

In this case, my_variable now holds the value 10 . It’s a clear, concise method that underpins virtually every Python program I write. Additionally, Python allows for multiple assignments in a single line, further simplifying the code:

Here, x , y , and z are assigned to 1 , 2 , and 3 , respectively. It’s a handy shortcut that I often use to initialize several variables at once.

But simple assignment isn’t just about initializing; it’s also used for reassigning values . If I decide that my_variable needs a new value, a simple reassignment does the trick:

my_variable holds 30 instead of 10 . It’s crucial to remember that in Python, variables are just labels pointing to objects, and reassignment doesn’t affect the object originally referenced; it merely attaches the label to a new object.

Furthermore, Python uses dynamic typing , which means that I don’t need to declare the data type of a variable beforehand. This contrasts with statically-typed languages, in which variable types must be explicitly stated. Dynamic typing allows for more flexibility and quicker coding—Python figures out the data type on its own:

Initially, dynamic_var starts as an integer with any numerically assigned value but can just as quickly be reassigned to hold a string. This flexibility is one of Python’s strengths, making it an excellent choice for rapid development and iterative coding processes.

Addition Assignment

Shifting gears from simple assignment, let’s delve into addition assignment. In Python, the += operator does more than just add two numbers together; it’s used to add a value to a variable, updating the variable itself in the process. If I have a variable x and I want to increase its value by 10, I’d simply write x += 10 . This is equivalent to x = x + 10 but far more succinct.

What makes addition assignment invaluable is its ability to streamline code. Think about running totals or iterative updates in a loop – that’s where += shines. It’s not just beneficial for numbers; I’ve frequently used addition assignment with strings to concatenate additional text.

Here’s a quick glimpse:

This code snippet would output Hello, World! , demonstrating how += appends text to an existing string.

Common use cases for addition assignment in practical coding scenarios include:

• Accumulating values in counters or sums
• Updating the value of a variable in response to events
• Concatenating strings or lists over multiple iterations

It’s important to note that addition assignment is part of a broader category of compound assignment operators . These operators include others like subtraction assignment ( -= ), multiplication assignment ( *= ), and division assignment ( /= ), each performing a similar update-in-place for their respective operations.

One aspect of addition assignment that’s often overlooked is its atomicity in single-threaded scenarios. When I use x += 1 , it’s a near-guarantee that x will be incremented by exactly one without the risk of interference from other operations, making it safer in certain applications over a separated statement like x = x + 1 .

Embracing addition assignment helps in writing more efficient and readable code . It’s a small piece of syntax that eloquently embodies Python’s philosophy of simplicity and elegance in programming.

Subtraction Assignment

Following the theme of compound assignment operators, let’s delve into subtraction assignment, another handy operator that Python programmers frequently use. Similar to addition assignment, subtraction assignment uses the -= operator to subtract a value from a variable and then update that same variable in one succinct step. Subtraction assignment is particularly useful when you need to decrease the value of a variable incrementally, such as when tracking decrements in a loop or managing a countdown.

Practical applications of subtraction assignment are easy to spot in day-to-day coding scenarios. For instance, imagine you’re developing a basic game where the player’s health decreases with each enemy encounter. Here’s how subtraction assignment simplifies the code:

player_health -= enemy_damage

By employing subtraction assignment, you avoid the more verbose and less intuitive player_health = player_health - enemy_damage , making the code cleaner and more maintainable.

More than just a convenient shortcut, subtraction assignment can knock out a few processor cycles, optimizing performance at a micro level. This might not stand out in smaller scripts, but when you’re dealing with large-scale applications, every bit of efficiency counts.

Subtraction assignment plays well within the bounds of atomicity in certain contexts, contributing to safer coding patterns. However, it’s important to note that like all operators, the atomicity of a -= operation isn’t guaranteed across all environments, especially in multi-threaded applications where race conditions might occur.

To master Python, practicing with these operators is essential. They’re not merely shorthand—they represent a deeper understanding of Python’s design philosophy that favors simplicity over complexity. Incorporating subtraction assignment where appropriate will ensure your code is not only functional but also adheres to Python’s ethos, making it both efficient and elegant.

Moving alongside subtraction assignment, multiplication and division assignment operators offer similar benefits with their unique twists…

Multiplication Assignment

Moving on to multiplication assignment in Python, we encounter the *= operator. This operator functions similarly to subtraction assignment but focuses on multiplying the current value of a variable by another and then updating the variable. Just like subtraction assignment, multiplication assignment streamlines code , making it more readable and efficient. Here’s how you can put it to use:

my_number is now 15

In the above example, my_number originally holds the value of 5. After applying my_number *= 3 , the value of my_number becomes 15. This tool is particularly useful when dealing with iterative multiplication within loops.

One must keep in mind that multiplication assignment can lead to unexpected results when used with mutable data types, such as lists. For instance, using *= on a list will repeat the elements in that list:

my_list is now [1, 2, 3, 1, 2, 3]

This convenience comes with the same caveats as subtraction assignment. Atomicity isn’t assured, especially in multi-threaded applications where race conditions might affect the value of the variable before the operation takes place.

Just as subtraction assignment simplified decrementing a value, multiplication assignment makes incrementing values exponentially a succinct operation. It ensures that code isn’t cluttered with long-winded expressions, adhering to Python’s philosophy that “Readability counts”. Practicing these operators allows programmers to harness their true potential, enabling one to write concise and clearly structured code.

While multiplication assignment is ideal for numerical calculations , it also plays a significant role in creating repeated sequences or extending lists within your programs. Implementing the *= operator pushes the envelope on what can be achieved with a single line of Python code, reminding us that sometimes, powerful solutions are just an operator away.

Division Assignment

In Python programming, Division assignment is another operation that streamlines the process of dividing a variable by a number and then updating that variable with the new value. Just like multiplication assignment, division assignment uses a compound operator, which is /= . This operator takes the variable on its left, divides it by the expression on its right, and then assigns the result back to the originally named variable.

Consider the scenario where I have a variable defining a quantity of items and I’d like to split these items evenly among a certain number of people. Instead of using two lines of code—one to divide and another to assign the result—I can simply employ the /= operator to perform both actions simultaneously. Here’s how it’s done:

After this operation, the items variable would contain the value 24, precisely what you’d expect after dividing 120 by 5. Easy and efficient, isn’t it?

It’s important to highlight that the division assignment operator in Python always performs floating-point division . This means that even when dividing two integers, the result will be a float . If an integer result is needed, you’d have to manually convert the outcome back to an integer using the int() function or use the floor division assignment operator //= .

However, when using division assignment, a key thing to be cautious about is ZeroDivisionError . This error occurs if the right-hand side of the assignment is zero. In real-world applications, it’s always smart to implement error handling to catch and address such potential issues:

While division assignment is extremely useful, it’s also vital to keep in mind that this operator modifies the variable in-place. If the variable is shared across different parts of a program or among multiple threads, one must consider the implications of altering its value through division assignment to avoid unexpected behaviors or conflicts.

Modulo Assignment

When working with Python, I often find the modulo assignment operator as a secret weapon for certain tasks. Modulo assignment `%=“ combines the modulo operation with assignment in one step. This operator takes the current value of a variable, performs a modulo operation with the specified value, and then updates the variable with the result. Here’s how it works in practice:

After executing, my_number becomes 1, because 10 modulo 3 leaves a remainder of 1.

This is particularly useful when I need to:

• Ensure a Value Stays Within a Range : If I’m iterating over a list and want to wrap around when I reach the end, modulo assignment ensures that my index value never exceeds the list length.
• Perform Frequent Remainder Operations : In situations like checking for even or odd numbers, calculating residues in math problems or creating checksums, using %= makes the code cleaner and more efficient.

It’s vital to consider the datatype of the variables involved, as modulo assignment with floating-point numbers can lead to unexpected results due to precision issues.

Common errors such as TypeError may occur if I try to use modulo assignment between incompatible data types like strings and integers. For example, my_string %='world' would raise a TypeError because a string cannot be the left operand for this operator.

The beauty of using modulo assignment lies in its ability to simplify code and reduce the chance of mistakes that might happen if I were to split the modulo operation and the assignment into two separate lines. However, like division assignment, I’m cautious to avoid ZeroDivisionError when the right-hand side is zero, which is crucial to keeping my code exception-proof.

Exponentiation Assignment

When I dive into the concept of Exponentiation assignment in Python, it’s evident that it serves a significant role in mathematical operations. This operator combines exponentiation with assignment, streamlining the process of raising a variable to the power of another number. Exponentiation assignment is represented by **= and is a shorthand way to write the operation x = x ** y , where x is the base variable and y is the exponent.

By using this operator, I can conveniently update the value of a variable without needing to type its name multiple times. This is incredibly efficient when dealing with complex calculations or iterating over large datasets . Here’s an example that illustrates its simplicity:

The result would be 125 , as 5 to the power of 3 is 125 . Using exponentiation assignment, the variable x is now updated to hold the value of 125 .

It’s important to remember that while this operator is powerful, it should be used with precision, particularly with floating-point numbers, where results might not be exact due to the nature of binary floating-point representation.

Moreover, just like with modulo assignment, there’s the need to be aware of and prevent any attempt to raise a number to the power of 0 . Not because it will cause an error, since any number to the power of 0 is 1 , but because it might result in unexpected behavior depending on the use case. Ensuring that the data types are correct before performing operations will help to avoid errors and ensure that the code runs as expected.

In practice, exponentiation assignment can be a very handy tool when coding functions that involve exponential growth , such as compound interest calculations or geometric progression. This operator also highlights Python’s design philosophy of readability, making code more concise and readable at a glance.

Floor Division Assignment

When delving into the realm of arithmetic operators in Python, we often encounter the floor division operator // , which divides two numbers and rounds down to the nearest whole number. Combining this with an assignment operator creates the Floor division assignment operator //= , which is both a time-saver and an enhancer of code legibility. This operator effectively changes the value of a variable to the result of the floor division of its current value by another number.

Imagine working with a dataset that requires the normalization of values by batches. Doing this efficiently requires updating each value without introducing a temporary variable. That’s where //= shines. For example, a //= b translates to a = a // b , hence reducing the lines of code and potential for error.

However, it’s crucial to remember that floor division behaves differently with positive and negative numbers. While 9 // 2 will give you 4 , -9 // 2 will result in -5 , since the result is always rounded down to the nearest whole number. This nuance must be kept in mind to avoid unexpected results, especially when working with datasets that may include negative numbers.

Floor division assignment also plays well with integer and floating-point numbers. Precision may vary with floating-point numbers, so confirming the accuracy of results is always good practice.

In scenarios involving repeated halving or distribution of elements into bins, //= remains a beneficial tool. Whether you’re rounding down timestamps to the nearest minute, or partitioning resources, it provides a straightforward, readable approach to in-place arithmetic operations.

Python’s emphasis on readability and simplicity is echoed throughout its operator suite – and the floor division assignment operator is a testament to that. It streamlines mathematical operations while maintaining clarity, an essential trait for writing clean and maintainable code .

I’ve taken you through the essentials of Python’s arithmetic, comparison, and assignment operators, highlighting the floor division assignment operator’s role in streamlining your code. Remember, while it’s a powerful tool for batch normalization, it’s crucial to be mindful of its behavior with different number types. With these operators in your toolkit, you’re now better equipped to write more concise and readable Python scripts. Embrace these fundamentals, and you’ll see your coding efficiency soar.