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Operator Precedence in Python

Till now you would have come across many operators like addition, multiplication, etc. But what will you do when you have multiple operators in one expression?

This is the situation where we use the precedence of the operators, to get the correct result. In this article, we will learn about precedence and different other concerning concepts. So, let’s not wait and begin.

Expressions in Python

Before talking about operator precedence, first, let us know about expressions. In Python, expression can be defined as a valid combination of variables, constants, operators, and function calls. For example, look at the below example

Example of an expression in Python:

Here, ‘9-3’ is an expression with 9,3 as values and ‘-’ as the operator. In this example, we have only one operator. What if we have more than one operator, say a combination of +,-,*?

This is where we use precedence. Let us discuss more on this in the next section.

Python Operators precedence and the table

Let us assume, we have an expression 12+6*4. How do we evaluate it? First, we do multiplication of 6 and 4, which gives 24. Then we add 12 to 24 and the answer is 36.

The thing that we did above is to use the concept of precedence, where we give priority to one of the operators to be used before the other. The below table gives the order, with the precedence reducing from top to bottom.

1. Operator precedence Table in Python:

When we have more than one operator, the one with higher precedence will be evaluated first. From the above table, we can see that parentheses will be evaluated first and lambda at the last.

2. PEMDAS Rule in Python:

We would have learned of the BODMAS rule in mathematics while giving preference to the operators. We have a similar rule in Python and that is PEMDAS. Where,

• P means parentheses
• E means exponent
• M means multiplication
• D means division
• A means addition
• S means Subtraction

Let us look at some Python coding examples with some inferences.

Example of precedence of two operators:

Here, the first 6/2 gives 3.0. And 4+3.0 =7.0.

If we use brackets for 4+6, then the result will be.

Example of precedence of two operators on using brackets:

Now, the first 4+6 gets evaluated as it is inside the parentheses and gives 10. Then on division by 2 gives 5.0.

Example of precedence on multiple operators:

The flow of execution is :

• First preference to parentheses: 3+6=9 => 9*6-9/3+20
• Next preference to multiplication and division: 9*6=54, 9/3=3.0 =>54-3.0+20
• Next preference to addition and subtraction: 54-3.0=51.0 51.0+20=71.0
• The result=71.0

Short-Circuiting of Operators in Python

Would have heard of this in the Physics in electrical circuit chapter. But what is currently doing with operators? Don’t worry there is no current or voltage involved here! A short circuit is a condition where the evaluation stops in between because the left half decided the result. There are 4 cases this might occur:

1. With and/ or operators in Python:

When we have an expression, say A and B, B is evaluated only if A is True. This is because if A is False, then the whole logic will be false even if B is True or False.

Similarly, if we have A or B, B will not be evaluated if A is True. Because if A is True, the whole logic will be True irrespective of B.

Let’s look at the below example.

Example of short-circuiting with and/or:

2. With all()/any():

The function all() stops if it first encounters any False and returns False as the result. Whereas any() stops when it first encounters True and returns True. These functions don’t check with the values further if they encounter the above cases.

Example of short-circuiting with all()

The execution stops when first it encounters 0 (2nd element) and returns False.

Example of short-circuiting with any():

The execution stops when first it encounters a non-zero number (3rd element) and returns True.

3. With >/< operators:

When we have two operators at a time, both the operator’s logic must be satisfied. For example, if we have A>B>C, then A>B and B>C have to satisfy.

Example of short-circuiting with conditional operators:

In the above example, 4>9 is False. Since if we have A and B, her A=4>9 and B=9<11. If A is false, the execution stops and gives False. Similarly, the execution stops after 4>9 and the output is False.

4. With Ternary operator:

Ternary operator is an operator that checks a condition and executes the main statement only if it is True. The overloading case occurs when the condition is False, where the main statement is skipped and the else part is executed.

Example of short circuiting with ternary operator:

Here the boolean value of print(“b”) is False. So after executing print(“b”), print (“c”) is executed, skipping print(“a”).

Associativity of Operators

Associativity is considered where we have two or more operators of the same precedence. This decides if the evaluation of the expression has to be done from left to right or right to left based on the operator. For almost all the operators the associativity is left-to-right, except for exponential, logical NOT and assignment operators.

Let us look at some associativity and its changes with the use of parentheses. 1. Example of associativity for + and -:

In the 1st case, 3-4=-1 and -1+7=6. In the 2nd case, 4+7=11 and 3-11= -8. 2. Example of associativity for * and //:

In the first case, 4//3 gives 1 and 3*1 is 3. In the second case, 3*4=12 and 12//3 is 4.

3. Example of associativity for exponential:

Exponential has the right to left associativity. In the first case, 1**2=1 gives 2**1is 2. In the second case, 2**1=2 and 2**2=4.

Non-associative operators:

Examples of non-associative operators are the assignment and comparison operators. For example a<b<c, actually check for a<b and b<c. It does not change even if we add brackets. Similarly, chaining of assignments, a=b=c=1 is also valid.

Example of non-associative operators:

However, we cannot use the other assignment operators like +=, -=, etc. This is because these variables are just created and modifications like += cannot be done. Example of getting an error on using += in chaining:

Interview Questions on Operator Precedence in Python

Q1. Are 4+3*2//3 same as (4+3)*2//3. Show using Python coding.

Ans 1. No. They are not the same. The below coding block shows this.

Example of showing the effect of brackets in Python:

Q2. Add parentheses in the correct location in the expression 4+6*9-3/2 to get the output as 22.0. Also, show the difference by coding.

Ans2. The parentheses should be added to 9-3 to get the answer as 22.0. The following coding block shows this

Example of adding the brackets to get desired value:

Q3. Change the order of the or’s, and’s to get in the expression (3<=6) and (9==0) and (5>-1) or 4 the output is True. Show different possibilities using coding.

Ans 3. Different possibilities are shown below:

Example of changing the order of and/or to get desired value:

Therefore, the expression should be (3<=6) or (9==0) and (5>-1) and 4 to get the output as True.

Q4. Evaluate the following expression by interchanging the symbol of conditional operators to get True. (3>11<8>3). Show it by coding using the ‘and’ operator.

Ans 4. The expression 3>11<8>3 is evaluated as 3>11, 11<8, and 8>3. This can be changed to 3<11 and 11>8 to get True.

Example of checking the expression:

Q5. Evaluate the expression -5**2+34>6+8/2*3 manually and prove it using coding. Also, add braces to get a number rather than a boolean.

Ans. -5**2+34>66+8/2*3 =-25+34>66+8/2*3 (5**2=25) =-25+34>66+12.0 (8/2=4.0 => 4.0*3=12.0 =9>78.0 (-25+34=9 ; 66+12.0=78.0) =False

Example of finding the result of the expression:

The reason for getting a boolean is because of the ‘>’ operator. Whose precedence is the least? If we add brackets its precedence will be high and we get a number.

Example of adding brackets to get desired value:

The flow in the above expression of as follows:

• 34>6 =>True =1 (int)
• 8/2*3= 12.0
• -25+1+12.0=12.0

Quiz on Operator Precedence in Python

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1 . Question

• Among the following, the most suitable statement that defines an expression is?
• A combination of operators and variables only
• A valid combination of variables, constants, operators, and function calls
• A valid combination of variables, operators and constants only
• A valid combination of operators and variables only

2 . Question

• Which of the following operators has the highest precedence?

3 . Question

Which of the following operators has the right to left associativity?

• All the above

4 . Question

5 . Question

Which of the following gives the output as 9?

• Both a and c

6 . Question

7 . question.

Which of the following operators have the same precedence?

• >,>>
• None of the above

8 . Question

4+3 and 6 or 4>5+2

9 . Question

4.5/2+(6%2)

10 . Question

Which of the following gives output as 0?

• 5+3>>4*3

11 . Question

Identify the output of the following code.

print(2**1*3,end=” “)

print(2*1**3)

12 . Question

Which of the following is a non-associative operator?

13 . Question

6>7<3+3

14 . Question

Which of the following give the output as True?

• True and False and False
• (False and True) or False
• True and True or False
• not(True or False) and True

15 . Question

 5+(6>4)+6/3*5

Thus, we can conclude that we learned about precedence and associativity in the article. Along with this, we also saw some short-circuiting cases and non-associative cases.

Finally, we practiced some interview questions. Hope you learned something new from this article. Happy coding!

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• Python »
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• 6. Expressions
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6. Expressions ¶

This chapter explains the meaning of the elements of expressions in Python.

Syntax Notes: In this and the following chapters, extended BNF notation will be used to describe syntax, not lexical analysis. When (one alternative of) a syntax rule has the form

and no semantics are given, the semantics of this form of name are the same as for othername .

6.1. Arithmetic conversions ¶

When a description of an arithmetic operator below uses the phrase “the numeric arguments are converted to a common type”, this means that the operator implementation for built-in types works as follows:

If either argument is a complex number, the other is converted to complex;

otherwise, if either argument is a floating-point number, the other is converted to floating point;

otherwise, both must be integers and no conversion is necessary.

Some additional rules apply for certain operators (e.g., a string as a left argument to the ‘%’ operator). Extensions must define their own conversion behavior.

6.2. Atoms ¶

Atoms are the most basic elements of expressions. The simplest atoms are identifiers or literals. Forms enclosed in parentheses, brackets or braces are also categorized syntactically as atoms. The syntax for atoms is:

6.2.1. Identifiers (Names) ¶

An identifier occurring as an atom is a name. See section Identifiers and keywords for lexical definition and section Naming and binding for documentation of naming and binding.

When the name is bound to an object, evaluation of the atom yields that object. When a name is not bound, an attempt to evaluate it raises a NameError exception.

6.2.1.1. Private name mangling ¶

When an identifier that textually occurs in a class definition begins with two or more underscore characters and does not end in two or more underscores, it is considered a private name of that class.

The class specifications .

More precisely, private names are transformed to a longer form before code is generated for them. If the transformed name is longer than 255 characters, implementation-defined truncation may happen.

The transformation is independent of the syntactical context in which the identifier is used but only the following private identifiers are mangled:

Any name used as the name of a variable that is assigned or read or any name of an attribute being accessed.

The __name__ attribute of nested functions, classes, and type aliases is however not mangled.

The name of imported modules, e.g., __spam in import __spam . If the module is part of a package (i.e., its name contains a dot), the name is not mangled, e.g., the __foo in import __foo.bar is not mangled.

The name of an imported member, e.g., __f in from spam import __f .

The transformation rule is defined as follows:

The class name, with leading underscores removed and a single leading underscore inserted, is inserted in front of the identifier, e.g., the identifier __spam occurring in a class named Foo , _Foo or __Foo is transformed to _Foo__spam .

If the class name consists only of underscores, the transformation is the identity, e.g., the identifier __spam occurring in a class named _ or __ is left as is.

6.2.2. Literals ¶

Python supports string and bytes literals and various numeric literals:

Evaluation of a literal yields an object of the given type (string, bytes, integer, floating-point number, complex number) with the given value. The value may be approximated in the case of floating-point and imaginary (complex) literals. See section Literals for details.

All literals correspond to immutable data types, and hence the object’s identity is less important than its value. Multiple evaluations of literals with the same value (either the same occurrence in the program text or a different occurrence) may obtain the same object or a different object with the same value.

6.2.3. Parenthesized forms ¶

A parenthesized form is an optional expression list enclosed in parentheses:

A parenthesized expression list yields whatever that expression list yields: if the list contains at least one comma, it yields a tuple; otherwise, it yields the single expression that makes up the expression list.

An empty pair of parentheses yields an empty tuple object. Since tuples are immutable, the same rules as for literals apply (i.e., two occurrences of the empty tuple may or may not yield the same object).

Note that tuples are not formed by the parentheses, but rather by use of the comma. The exception is the empty tuple, for which parentheses are required — allowing unparenthesized “nothing” in expressions would cause ambiguities and allow common typos to pass uncaught.

6.2.4. Displays for lists, sets and dictionaries ¶

For constructing a list, a set or a dictionary Python provides special syntax called “displays”, each of them in two flavors:

either the container contents are listed explicitly, or

they are computed via a set of looping and filtering instructions, called a comprehension .

Common syntax elements for comprehensions are:

The comprehension consists of a single expression followed by at least one for clause and zero or more for or if clauses. In this case, the elements of the new container are those that would be produced by considering each of the for or if clauses a block, nesting from left to right, and evaluating the expression to produce an element each time the innermost block is reached.

However, aside from the iterable expression in the leftmost for clause, the comprehension is executed in a separate implicitly nested scope. This ensures that names assigned to in the target list don’t “leak” into the enclosing scope.

The iterable expression in the leftmost for clause is evaluated directly in the enclosing scope and then passed as an argument to the implicitly nested scope. Subsequent for clauses and any filter condition in the leftmost for clause cannot be evaluated in the enclosing scope as they may depend on the values obtained from the leftmost iterable. For example: [x*y for x in range(10) for y in range(x, x+10)] .

To ensure the comprehension always results in a container of the appropriate type, yield and yield from expressions are prohibited in the implicitly nested scope.

Since Python 3.6, in an async def function, an async for clause may be used to iterate over a asynchronous iterator . A comprehension in an async def function may consist of either a for or async for clause following the leading expression, may contain additional for or async for clauses, and may also use await expressions.

If a comprehension contains async for clauses, or if it contains await expressions or other asynchronous comprehensions anywhere except the iterable expression in the leftmost for clause, it is called an asynchronous comprehension . An asynchronous comprehension may suspend the execution of the coroutine function in which it appears. See also PEP 530 .

Added in version 3.6: Asynchronous comprehensions were introduced.

Changed in version 3.8: yield and yield from prohibited in the implicitly nested scope.

Changed in version 3.11: Asynchronous comprehensions are now allowed inside comprehensions in asynchronous functions. Outer comprehensions implicitly become asynchronous.

6.2.5. List displays ¶

A list display is a possibly empty series of expressions enclosed in square brackets:

A list display yields a new list object, the contents being specified by either a list of expressions or a comprehension. When a comma-separated list of expressions is supplied, its elements are evaluated from left to right and placed into the list object in that order. When a comprehension is supplied, the list is constructed from the elements resulting from the comprehension.

6.2.6. Set displays ¶

A set display is denoted by curly braces and distinguishable from dictionary displays by the lack of colons separating keys and values:

A set display yields a new mutable set object, the contents being specified by either a sequence of expressions or a comprehension. When a comma-separated list of expressions is supplied, its elements are evaluated from left to right and added to the set object. When a comprehension is supplied, the set is constructed from the elements resulting from the comprehension.

An empty set cannot be constructed with {} ; this literal constructs an empty dictionary.

6.2.7. Dictionary displays ¶

A dictionary display is a possibly empty series of dict items (key/value pairs) enclosed in curly braces:

A dictionary display yields a new dictionary object.

If a comma-separated sequence of dict items is given, they are evaluated from left to right to define the entries of the dictionary: each key object is used as a key into the dictionary to store the corresponding value. This means that you can specify the same key multiple times in the dict item list, and the final dictionary’s value for that key will be the last one given.

A double asterisk ** denotes dictionary unpacking . Its operand must be a mapping . Each mapping item is added to the new dictionary. Later values replace values already set by earlier dict items and earlier dictionary unpackings.

Added in version 3.5: Unpacking into dictionary displays, originally proposed by PEP 448 .

A dict comprehension, in contrast to list and set comprehensions, needs two expressions separated with a colon followed by the usual “for” and “if” clauses. When the comprehension is run, the resulting key and value elements are inserted in the new dictionary in the order they are produced.

Restrictions on the types of the key values are listed earlier in section The standard type hierarchy . (To summarize, the key type should be hashable , which excludes all mutable objects.) Clashes between duplicate keys are not detected; the last value (textually rightmost in the display) stored for a given key value prevails.

Changed in version 3.8: Prior to Python 3.8, in dict comprehensions, the evaluation order of key and value was not well-defined. In CPython, the value was evaluated before the key. Starting with 3.8, the key is evaluated before the value, as proposed by PEP 572 .

6.2.8. Generator expressions ¶

A generator expression is a compact generator notation in parentheses:

A generator expression yields a new generator object. Its syntax is the same as for comprehensions, except that it is enclosed in parentheses instead of brackets or curly braces.

Variables used in the generator expression are evaluated lazily when the __next__() method is called for the generator object (in the same fashion as normal generators). However, the iterable expression in the leftmost for clause is immediately evaluated, so that an error produced by it will be emitted at the point where the generator expression is defined, rather than at the point where the first value is retrieved. Subsequent for clauses and any filter condition in the leftmost for clause cannot be evaluated in the enclosing scope as they may depend on the values obtained from the leftmost iterable. For example: (x*y for x in range(10) for y in range(x, x+10)) .

The parentheses can be omitted on calls with only one argument. See section Calls for details.

To avoid interfering with the expected operation of the generator expression itself, yield and yield from expressions are prohibited in the implicitly defined generator.

If a generator expression contains either async for clauses or await expressions it is called an asynchronous generator expression . An asynchronous generator expression returns a new asynchronous generator object, which is an asynchronous iterator (see Asynchronous Iterators ).

Added in version 3.6: Asynchronous generator expressions were introduced.

Changed in version 3.7: Prior to Python 3.7, asynchronous generator expressions could only appear in async def coroutines. Starting with 3.7, any function can use asynchronous generator expressions.

6.2.9. Yield expressions ¶

The yield expression is used when defining a generator function or an asynchronous generator function and thus can only be used in the body of a function definition. Using a yield expression in a function’s body causes that function to be a generator function, and using it in an async def function’s body causes that coroutine function to be an asynchronous generator function. For example:

Due to their side effects on the containing scope, yield expressions are not permitted as part of the implicitly defined scopes used to implement comprehensions and generator expressions.

Changed in version 3.8: Yield expressions prohibited in the implicitly nested scopes used to implement comprehensions and generator expressions.

Generator functions are described below, while asynchronous generator functions are described separately in section Asynchronous generator functions .

When a generator function is called, it returns an iterator known as a generator. That generator then controls the execution of the generator function. The execution starts when one of the generator’s methods is called. At that time, the execution proceeds to the first yield expression, where it is suspended again, returning the value of expression_list to the generator’s caller, or None if expression_list is omitted. By suspended, we mean that all local state is retained, including the current bindings of local variables, the instruction pointer, the internal evaluation stack, and the state of any exception handling. When the execution is resumed by calling one of the generator’s methods, the function can proceed exactly as if the yield expression were just another external call. The value of the yield expression after resuming depends on the method which resumed the execution. If __next__() is used (typically via either a for or the next() builtin) then the result is None . Otherwise, if send() is used, then the result will be the value passed in to that method.

All of this makes generator functions quite similar to coroutines; they yield multiple times, they have more than one entry point and their execution can be suspended. The only difference is that a generator function cannot control where the execution should continue after it yields; the control is always transferred to the generator’s caller.

Yield expressions are allowed anywhere in a try construct. If the generator is not resumed before it is finalized (by reaching a zero reference count or by being garbage collected), the generator-iterator’s close() method will be called, allowing any pending finally clauses to execute.

When yield from <expr> is used, the supplied expression must be an iterable. The values produced by iterating that iterable are passed directly to the caller of the current generator’s methods. Any values passed in with send() and any exceptions passed in with throw() are passed to the underlying iterator if it has the appropriate methods. If this is not the case, then send() will raise AttributeError or TypeError , while throw() will just raise the passed in exception immediately.

When the underlying iterator is complete, the value attribute of the raised StopIteration instance becomes the value of the yield expression. It can be either set explicitly when raising StopIteration , or automatically when the subiterator is a generator (by returning a value from the subgenerator).

Changed in version 3.3: Added yield from <expr> to delegate control flow to a subiterator.

The parentheses may be omitted when the yield expression is the sole expression on the right hand side of an assignment statement.

The proposal for adding generators and the yield statement to Python.

The proposal to enhance the API and syntax of generators, making them usable as simple coroutines.

The proposal to introduce the yield_from syntax, making delegation to subgenerators easy.

The proposal that expanded on PEP 492 by adding generator capabilities to coroutine functions.

6.2.9.1. Generator-iterator methods ¶

This subsection describes the methods of a generator iterator. They can be used to control the execution of a generator function.

Note that calling any of the generator methods below when the generator is already executing raises a ValueError exception.

Starts the execution of a generator function or resumes it at the last executed yield expression. When a generator function is resumed with a __next__() method, the current yield expression always evaluates to None . The execution then continues to the next yield expression, where the generator is suspended again, and the value of the expression_list is returned to __next__() ’s caller. If the generator exits without yielding another value, a StopIteration exception is raised.

This method is normally called implicitly, e.g. by a for loop, or by the built-in next() function.

Resumes the execution and “sends” a value into the generator function. The value argument becomes the result of the current yield expression. The send() method returns the next value yielded by the generator, or raises StopIteration if the generator exits without yielding another value. When send() is called to start the generator, it must be called with None as the argument, because there is no yield expression that could receive the value.

Raises an exception at the point where the generator was paused, and returns the next value yielded by the generator function. If the generator exits without yielding another value, a StopIteration exception is raised. If the generator function does not catch the passed-in exception, or raises a different exception, then that exception propagates to the caller.

In typical use, this is called with a single exception instance similar to the way the raise keyword is used.

For backwards compatibility, however, the second signature is supported, following a convention from older versions of Python. The type argument should be an exception class, and value should be an exception instance. If the value is not provided, the type constructor is called to get an instance. If traceback is provided, it is set on the exception, otherwise any existing __traceback__ attribute stored in value may be cleared.

Changed in version 3.12: The second signature (type[, value[, traceback]]) is deprecated and may be removed in a future version of Python.

Raises a GeneratorExit at the point where the generator function was paused. If the generator function then exits gracefully, is already closed, or raises GeneratorExit (by not catching the exception), close returns to its caller. If the generator yields a value, a RuntimeError is raised. If the generator raises any other exception, it is propagated to the caller. close() does nothing if the generator has already exited due to an exception or normal exit.

6.2.9.2. Examples ¶

Here is a simple example that demonstrates the behavior of generators and generator functions:

For examples using yield from , see PEP 380: Syntax for Delegating to a Subgenerator in “What’s New in Python.”

6.2.9.3. Asynchronous generator functions ¶

The presence of a yield expression in a function or method defined using async def further defines the function as an asynchronous generator function.

When an asynchronous generator function is called, it returns an asynchronous iterator known as an asynchronous generator object. That object then controls the execution of the generator function. An asynchronous generator object is typically used in an async for statement in a coroutine function analogously to how a generator object would be used in a for statement.

Calling one of the asynchronous generator’s methods returns an awaitable object, and the execution starts when this object is awaited on. At that time, the execution proceeds to the first yield expression, where it is suspended again, returning the value of expression_list to the awaiting coroutine. As with a generator, suspension means that all local state is retained, including the current bindings of local variables, the instruction pointer, the internal evaluation stack, and the state of any exception handling. When the execution is resumed by awaiting on the next object returned by the asynchronous generator’s methods, the function can proceed exactly as if the yield expression were just another external call. The value of the yield expression after resuming depends on the method which resumed the execution. If __anext__() is used then the result is None . Otherwise, if asend() is used, then the result will be the value passed in to that method.

If an asynchronous generator happens to exit early by break , the caller task being cancelled, or other exceptions, the generator’s async cleanup code will run and possibly raise exceptions or access context variables in an unexpected context–perhaps after the lifetime of tasks it depends, or during the event loop shutdown when the async-generator garbage collection hook is called. To prevent this, the caller must explicitly close the async generator by calling aclose() method to finalize the generator and ultimately detach it from the event loop.

In an asynchronous generator function, yield expressions are allowed anywhere in a try construct. However, if an asynchronous generator is not resumed before it is finalized (by reaching a zero reference count or by being garbage collected), then a yield expression within a try construct could result in a failure to execute pending finally clauses. In this case, it is the responsibility of the event loop or scheduler running the asynchronous generator to call the asynchronous generator-iterator’s aclose() method and run the resulting coroutine object, thus allowing any pending finally clauses to execute.

To take care of finalization upon event loop termination, an event loop should define a finalizer function which takes an asynchronous generator-iterator and presumably calls aclose() and executes the coroutine. This finalizer may be registered by calling sys.set_asyncgen_hooks() . When first iterated over, an asynchronous generator-iterator will store the registered finalizer to be called upon finalization. For a reference example of a finalizer method see the implementation of asyncio.Loop.shutdown_asyncgens in Lib/asyncio/base_events.py .

The expression yield from <expr> is a syntax error when used in an asynchronous generator function.

6.2.9.4. Asynchronous generator-iterator methods ¶

This subsection describes the methods of an asynchronous generator iterator, which are used to control the execution of a generator function.

Returns an awaitable which when run starts to execute the asynchronous generator or resumes it at the last executed yield expression. When an asynchronous generator function is resumed with an __anext__() method, the current yield expression always evaluates to None in the returned awaitable, which when run will continue to the next yield expression. The value of the expression_list of the yield expression is the value of the StopIteration exception raised by the completing coroutine. If the asynchronous generator exits without yielding another value, the awaitable instead raises a StopAsyncIteration exception, signalling that the asynchronous iteration has completed.

This method is normally called implicitly by a async for loop.

Returns an awaitable which when run resumes the execution of the asynchronous generator. As with the send() method for a generator, this “sends” a value into the asynchronous generator function, and the value argument becomes the result of the current yield expression. The awaitable returned by the asend() method will return the next value yielded by the generator as the value of the raised StopIteration , or raises StopAsyncIteration if the asynchronous generator exits without yielding another value. When asend() is called to start the asynchronous generator, it must be called with None as the argument, because there is no yield expression that could receive the value.

Returns an awaitable that raises an exception of type type at the point where the asynchronous generator was paused, and returns the next value yielded by the generator function as the value of the raised StopIteration exception. If the asynchronous generator exits without yielding another value, a StopAsyncIteration exception is raised by the awaitable. If the generator function does not catch the passed-in exception, or raises a different exception, then when the awaitable is run that exception propagates to the caller of the awaitable.

Returns an awaitable that when run will throw a GeneratorExit into the asynchronous generator function at the point where it was paused. If the asynchronous generator function then exits gracefully, is already closed, or raises GeneratorExit (by not catching the exception), then the returned awaitable will raise a StopIteration exception. Any further awaitables returned by subsequent calls to the asynchronous generator will raise a StopAsyncIteration exception. If the asynchronous generator yields a value, a RuntimeError is raised by the awaitable. If the asynchronous generator raises any other exception, it is propagated to the caller of the awaitable. If the asynchronous generator has already exited due to an exception or normal exit, then further calls to aclose() will return an awaitable that does nothing.

6.3. Primaries ¶

Primaries represent the most tightly bound operations of the language. Their syntax is:

6.3.1. Attribute references ¶

An attribute reference is a primary followed by a period and a name:

The primary must evaluate to an object of a type that supports attribute references, which most objects do. This object is then asked to produce the attribute whose name is the identifier. The type and value produced is determined by the object. Multiple evaluations of the same attribute reference may yield different objects.

This production can be customized by overriding the __getattribute__() method or the __getattr__() method. The __getattribute__() method is called first and either returns a value or raises AttributeError if the attribute is not available.

If an AttributeError is raised and the object has a __getattr__() method, that method is called as a fallback.

6.3.2. Subscriptions ¶

The subscription of an instance of a container class will generally select an element from the container. The subscription of a generic class will generally return a GenericAlias object.

When an object is subscripted, the interpreter will evaluate the primary and the expression list.

The primary must evaluate to an object that supports subscription. An object may support subscription through defining one or both of __getitem__() and __class_getitem__() . When the primary is subscripted, the evaluated result of the expression list will be passed to one of these methods. For more details on when __class_getitem__ is called instead of __getitem__ , see __class_getitem__ versus __getitem__ .

If the expression list contains at least one comma, it will evaluate to a tuple containing the items of the expression list. Otherwise, the expression list will evaluate to the value of the list’s sole member.

For built-in objects, there are two types of objects that support subscription via __getitem__() :

Mappings. If the primary is a mapping , the expression list must evaluate to an object whose value is one of the keys of the mapping, and the subscription selects the value in the mapping that corresponds to that key. An example of a builtin mapping class is the dict class.

Sequences. If the primary is a sequence , the expression list must evaluate to an int or a slice (as discussed in the following section). Examples of builtin sequence classes include the str , list and tuple classes.

The formal syntax makes no special provision for negative indices in sequences . However, built-in sequences all provide a __getitem__() method that interprets negative indices by adding the length of the sequence to the index so that, for example, x[-1] selects the last item of x . The resulting value must be a nonnegative integer less than the number of items in the sequence, and the subscription selects the item whose index is that value (counting from zero). Since the support for negative indices and slicing occurs in the object’s __getitem__() method, subclasses overriding this method will need to explicitly add that support.

A string is a special kind of sequence whose items are characters . A character is not a separate data type but a string of exactly one character.

6.3.3. Slicings ¶

A slicing selects a range of items in a sequence object (e.g., a string, tuple or list). Slicings may be used as expressions or as targets in assignment or del statements. The syntax for a slicing:

There is ambiguity in the formal syntax here: anything that looks like an expression list also looks like a slice list, so any subscription can be interpreted as a slicing. Rather than further complicating the syntax, this is disambiguated by defining that in this case the interpretation as a subscription takes priority over the interpretation as a slicing (this is the case if the slice list contains no proper slice).

The semantics for a slicing are as follows. The primary is indexed (using the same __getitem__() method as normal subscription) with a key that is constructed from the slice list, as follows. If the slice list contains at least one comma, the key is a tuple containing the conversion of the slice items; otherwise, the conversion of the lone slice item is the key. The conversion of a slice item that is an expression is that expression. The conversion of a proper slice is a slice object (see section The standard type hierarchy ) whose start , stop and step attributes are the values of the expressions given as lower bound, upper bound and stride, respectively, substituting None for missing expressions.

6.3.4. Calls ¶

A call calls a callable object (e.g., a function ) with a possibly empty series of arguments :

An optional trailing comma may be present after the positional and keyword arguments but does not affect the semantics.

The primary must evaluate to a callable object (user-defined functions, built-in functions, methods of built-in objects, class objects, methods of class instances, and all objects having a __call__() method are callable). All argument expressions are evaluated before the call is attempted. Please refer to section Function definitions for the syntax of formal parameter lists.

If keyword arguments are present, they are first converted to positional arguments, as follows. First, a list of unfilled slots is created for the formal parameters. If there are N positional arguments, they are placed in the first N slots. Next, for each keyword argument, the identifier is used to determine the corresponding slot (if the identifier is the same as the first formal parameter name, the first slot is used, and so on). If the slot is already filled, a TypeError exception is raised. Otherwise, the argument is placed in the slot, filling it (even if the expression is None , it fills the slot). When all arguments have been processed, the slots that are still unfilled are filled with the corresponding default value from the function definition. (Default values are calculated, once, when the function is defined; thus, a mutable object such as a list or dictionary used as default value will be shared by all calls that don’t specify an argument value for the corresponding slot; this should usually be avoided.) If there are any unfilled slots for which no default value is specified, a TypeError exception is raised. Otherwise, the list of filled slots is used as the argument list for the call.

CPython implementation detail: An implementation may provide built-in functions whose positional parameters do not have names, even if they are ‘named’ for the purpose of documentation, and which therefore cannot be supplied by keyword. In CPython, this is the case for functions implemented in C that use PyArg_ParseTuple() to parse their arguments.

If there are more positional arguments than there are formal parameter slots, a TypeError exception is raised, unless a formal parameter using the syntax *identifier is present; in this case, that formal parameter receives a tuple containing the excess positional arguments (or an empty tuple if there were no excess positional arguments).

If any keyword argument does not correspond to a formal parameter name, a TypeError exception is raised, unless a formal parameter using the syntax **identifier is present; in this case, that formal parameter receives a dictionary containing the excess keyword arguments (using the keywords as keys and the argument values as corresponding values), or a (new) empty dictionary if there were no excess keyword arguments.

If the syntax *expression appears in the function call, expression must evaluate to an iterable . Elements from these iterables are treated as if they were additional positional arguments. For the call f(x1, x2, *y, x3, x4) , if y evaluates to a sequence y1 , …, yM , this is equivalent to a call with M+4 positional arguments x1 , x2 , y1 , …, yM , x3 , x4 .

A consequence of this is that although the *expression syntax may appear after explicit keyword arguments, it is processed before the keyword arguments (and any **expression arguments – see below). So:

It is unusual for both keyword arguments and the *expression syntax to be used in the same call, so in practice this confusion does not often arise.

If the syntax **expression appears in the function call, expression must evaluate to a mapping , the contents of which are treated as additional keyword arguments. If a parameter matching a key has already been given a value (by an explicit keyword argument, or from another unpacking), a TypeError exception is raised.

When **expression is used, each key in this mapping must be a string. Each value from the mapping is assigned to the first formal parameter eligible for keyword assignment whose name is equal to the key. A key need not be a Python identifier (e.g. "max-temp °F" is acceptable, although it will not match any formal parameter that could be declared). If there is no match to a formal parameter the key-value pair is collected by the ** parameter, if there is one, or if there is not, a TypeError exception is raised.

Formal parameters using the syntax *identifier or **identifier cannot be used as positional argument slots or as keyword argument names.

Changed in version 3.5: Function calls accept any number of * and ** unpackings, positional arguments may follow iterable unpackings ( * ), and keyword arguments may follow dictionary unpackings ( ** ). Originally proposed by PEP 448 .

A call always returns some value, possibly None , unless it raises an exception. How this value is computed depends on the type of the callable object.

The code block for the function is executed, passing it the argument list. The first thing the code block will do is bind the formal parameters to the arguments; this is described in section Function definitions . When the code block executes a return statement, this specifies the return value of the function call.

The result is up to the interpreter; see Built-in Functions for the descriptions of built-in functions and methods.

A new instance of that class is returned.

The corresponding user-defined function is called, with an argument list that is one longer than the argument list of the call: the instance becomes the first argument.

The class must define a __call__() method; the effect is then the same as if that method was called.

6.4. Await expression ¶

Suspend the execution of coroutine on an awaitable object. Can only be used inside a coroutine function .

Added in version 3.5.

6.5. The power operator ¶

The power operator binds more tightly than unary operators on its left; it binds less tightly than unary operators on its right. The syntax is:

Thus, in an unparenthesized sequence of power and unary operators, the operators are evaluated from right to left (this does not constrain the evaluation order for the operands): -1**2 results in -1 .

The power operator has the same semantics as the built-in pow() function, when called with two arguments: it yields its left argument raised to the power of its right argument. The numeric arguments are first converted to a common type, and the result is of that type.

For int operands, the result has the same type as the operands unless the second argument is negative; in that case, all arguments are converted to float and a float result is delivered. For example, 10**2 returns 100 , but 10**-2 returns 0.01 .

Raising 0.0 to a negative power results in a ZeroDivisionError . Raising a negative number to a fractional power results in a complex number. (In earlier versions it raised a ValueError .)

This operation can be customized using the special __pow__() and __rpow__() methods.

6.6. Unary arithmetic and bitwise operations ¶

All unary arithmetic and bitwise operations have the same priority:

The unary - (minus) operator yields the negation of its numeric argument; the operation can be overridden with the __neg__() special method.

The unary + (plus) operator yields its numeric argument unchanged; the operation can be overridden with the __pos__() special method.

The unary ~ (invert) operator yields the bitwise inversion of its integer argument. The bitwise inversion of x is defined as -(x+1) . It only applies to integral numbers or to custom objects that override the __invert__() special method.

In all three cases, if the argument does not have the proper type, a TypeError exception is raised.

6.7. Binary arithmetic operations ¶

The binary arithmetic operations have the conventional priority levels. Note that some of these operations also apply to certain non-numeric types. Apart from the power operator, there are only two levels, one for multiplicative operators and one for additive operators:

The * (multiplication) operator yields the product of its arguments. The arguments must either both be numbers, or one argument must be an integer and the other must be a sequence. In the former case, the numbers are converted to a common type and then multiplied together. In the latter case, sequence repetition is performed; a negative repetition factor yields an empty sequence.

This operation can be customized using the special __mul__() and __rmul__() methods.

The @ (at) operator is intended to be used for matrix multiplication. No builtin Python types implement this operator.

This operation can be customized using the special __matmul__() and __rmatmul__() methods.

The / (division) and // (floor division) operators yield the quotient of their arguments. The numeric arguments are first converted to a common type. Division of integers yields a float, while floor division of integers results in an integer; the result is that of mathematical division with the ‘floor’ function applied to the result. Division by zero raises the ZeroDivisionError exception.

The division operation can be customized using the special __truediv__() and __rtruediv__() methods. The floor division operation can be customized using the special __floordiv__() and __rfloordiv__() methods.

The % (modulo) operator yields the remainder from the division of the first argument by the second. The numeric arguments are first converted to a common type. A zero right argument raises the ZeroDivisionError exception. The arguments may be floating-point numbers, e.g., 3.14%0.7 equals 0.34 (since 3.14 equals 4*0.7 + 0.34 .) The modulo operator always yields a result with the same sign as its second operand (or zero); the absolute value of the result is strictly smaller than the absolute value of the second operand [ 1 ] .

The floor division and modulo operators are connected by the following identity: x == (x//y)*y + (x%y) . Floor division and modulo are also connected with the built-in function divmod() : divmod(x, y) == (x//y, x%y) . [ 2 ] .

In addition to performing the modulo operation on numbers, the % operator is also overloaded by string objects to perform old-style string formatting (also known as interpolation). The syntax for string formatting is described in the Python Library Reference, section printf-style String Formatting .

The modulo operation can be customized using the special __mod__() and __rmod__() methods.

The floor division operator, the modulo operator, and the divmod() function are not defined for complex numbers. Instead, convert to a floating-point number using the abs() function if appropriate.

The + (addition) operator yields the sum of its arguments. The arguments must either both be numbers or both be sequences of the same type. In the former case, the numbers are converted to a common type and then added together. In the latter case, the sequences are concatenated.

This operation can be customized using the special __add__() and __radd__() methods.

The - (subtraction) operator yields the difference of its arguments. The numeric arguments are first converted to a common type.

This operation can be customized using the special __sub__() and __rsub__() methods.

6.8. Shifting operations ¶

The shifting operations have lower priority than the arithmetic operations:

These operators accept integers as arguments. They shift the first argument to the left or right by the number of bits given by the second argument.

The left shift operation can be customized using the special __lshift__() and __rlshift__() methods. The right shift operation can be customized using the special __rshift__() and __rrshift__() methods.

A right shift by n bits is defined as floor division by pow(2,n) . A left shift by n bits is defined as multiplication with pow(2,n) .

6.9. Binary bitwise operations ¶

Each of the three bitwise operations has a different priority level:

The & operator yields the bitwise AND of its arguments, which must be integers or one of them must be a custom object overriding __and__() or __rand__() special methods.

The ^ operator yields the bitwise XOR (exclusive OR) of its arguments, which must be integers or one of them must be a custom object overriding __xor__() or __rxor__() special methods.

The | operator yields the bitwise (inclusive) OR of its arguments, which must be integers or one of them must be a custom object overriding __or__() or __ror__() special methods.

6.10. Comparisons ¶

Unlike C, all comparison operations in Python have the same priority, which is lower than that of any arithmetic, shifting or bitwise operation. Also unlike C, expressions like a < b < c have the interpretation that is conventional in mathematics:

Comparisons yield boolean values: True or False . Custom rich comparison methods may return non-boolean values. In this case Python will call bool() on such value in boolean contexts.

Comparisons can be chained arbitrarily, e.g., x < y <= z is equivalent to x < y and y <= z , except that y is evaluated only once (but in both cases z is not evaluated at all when x < y is found to be false).

Formally, if a , b , c , …, y , z are expressions and op1 , op2 , …, opN are comparison operators, then a op1 b op2 c ... y opN z is equivalent to a op1 b and b op2 c and ... y opN z , except that each expression is evaluated at most once.

Note that a op1 b op2 c doesn’t imply any kind of comparison between a and c , so that, e.g., x < y > z is perfectly legal (though perhaps not pretty).

6.10.1. Value comparisons ¶

The operators < , > , == , >= , <= , and != compare the values of two objects. The objects do not need to have the same type.

Chapter Objects, values and types states that objects have a value (in addition to type and identity). The value of an object is a rather abstract notion in Python: For example, there is no canonical access method for an object’s value. Also, there is no requirement that the value of an object should be constructed in a particular way, e.g. comprised of all its data attributes. Comparison operators implement a particular notion of what the value of an object is. One can think of them as defining the value of an object indirectly, by means of their comparison implementation.

Because all types are (direct or indirect) subtypes of object , they inherit the default comparison behavior from object . Types can customize their comparison behavior by implementing rich comparison methods like __lt__() , described in Basic customization .

The default behavior for equality comparison ( == and != ) is based on the identity of the objects. Hence, equality comparison of instances with the same identity results in equality, and equality comparison of instances with different identities results in inequality. A motivation for this default behavior is the desire that all objects should be reflexive (i.e. x is y implies x == y ).

A default order comparison ( < , > , <= , and >= ) is not provided; an attempt raises TypeError . A motivation for this default behavior is the lack of a similar invariant as for equality.

The behavior of the default equality comparison, that instances with different identities are always unequal, may be in contrast to what types will need that have a sensible definition of object value and value-based equality. Such types will need to customize their comparison behavior, and in fact, a number of built-in types have done that.

The following list describes the comparison behavior of the most important built-in types.

Numbers of built-in numeric types ( Numeric Types — int, float, complex ) and of the standard library types fractions.Fraction and decimal.Decimal can be compared within and across their types, with the restriction that complex numbers do not support order comparison. Within the limits of the types involved, they compare mathematically (algorithmically) correct without loss of precision.

The not-a-number values float('NaN') and decimal.Decimal('NaN') are special. Any ordered comparison of a number to a not-a-number value is false. A counter-intuitive implication is that not-a-number values are not equal to themselves. For example, if x = float('NaN') , 3 < x , x < 3 and x == x are all false, while x != x is true. This behavior is compliant with IEEE 754.

None and NotImplemented are singletons. PEP 8 advises that comparisons for singletons should always be done with is or is not , never the equality operators.

Binary sequences (instances of bytes or bytearray ) can be compared within and across their types. They compare lexicographically using the numeric values of their elements.

Strings (instances of str ) compare lexicographically using the numerical Unicode code points (the result of the built-in function ord() ) of their characters. [ 3 ]

Strings and binary sequences cannot be directly compared.

Sequences (instances of tuple , list , or range ) can be compared only within each of their types, with the restriction that ranges do not support order comparison. Equality comparison across these types results in inequality, and ordering comparison across these types raises TypeError .

Sequences compare lexicographically using comparison of corresponding elements. The built-in containers typically assume identical objects are equal to themselves. That lets them bypass equality tests for identical objects to improve performance and to maintain their internal invariants.

Lexicographical comparison between built-in collections works as follows:

For two collections to compare equal, they must be of the same type, have the same length, and each pair of corresponding elements must compare equal (for example, [1,2] == (1,2) is false because the type is not the same).

Collections that support order comparison are ordered the same as their first unequal elements (for example, [1,2,x] <= [1,2,y] has the same value as x <= y ). If a corresponding element does not exist, the shorter collection is ordered first (for example, [1,2] < [1,2,3] is true).

Mappings (instances of dict ) compare equal if and only if they have equal (key, value) pairs. Equality comparison of the keys and values enforces reflexivity.

Order comparisons ( < , > , <= , and >= ) raise TypeError .

Sets (instances of set or frozenset ) can be compared within and across their types.

They define order comparison operators to mean subset and superset tests. Those relations do not define total orderings (for example, the two sets {1,2} and {2,3} are not equal, nor subsets of one another, nor supersets of one another). Accordingly, sets are not appropriate arguments for functions which depend on total ordering (for example, min() , max() , and sorted() produce undefined results given a list of sets as inputs).

Comparison of sets enforces reflexivity of its elements.

Most other built-in types have no comparison methods implemented, so they inherit the default comparison behavior.

User-defined classes that customize their comparison behavior should follow some consistency rules, if possible:

Equality comparison should be reflexive. In other words, identical objects should compare equal:

x is y implies x == y

Comparison should be symmetric. In other words, the following expressions should have the same result:

x == y and y == x x != y and y != x x < y and y > x x <= y and y >= x

Comparison should be transitive. The following (non-exhaustive) examples illustrate that:

x > y and y > z implies x > z x < y and y <= z implies x < z

Inverse comparison should result in the boolean negation. In other words, the following expressions should have the same result:

x == y and not x != y x < y and not x >= y (for total ordering) x > y and not x <= y (for total ordering)

The last two expressions apply to totally ordered collections (e.g. to sequences, but not to sets or mappings). See also the total_ordering() decorator.

The hash() result should be consistent with equality. Objects that are equal should either have the same hash value, or be marked as unhashable.

Python does not enforce these consistency rules. In fact, the not-a-number values are an example for not following these rules.

6.10.2. Membership test operations ¶

The operators in and not in test for membership. x in s evaluates to True if x is a member of s , and False otherwise. x not in s returns the negation of x in s . All built-in sequences and set types support this as well as dictionary, for which in tests whether the dictionary has a given key. For container types such as list, tuple, set, frozenset, dict, or collections.deque, the expression x in y is equivalent to any(x is e or x == e for e in y) .

For the string and bytes types, x in y is True if and only if x is a substring of y . An equivalent test is y.find(x) != -1 . Empty strings are always considered to be a substring of any other string, so "" in "abc" will return True .

For user-defined classes which define the __contains__() method, x in y returns True if y.__contains__(x) returns a true value, and False otherwise.

For user-defined classes which do not define __contains__() but do define __iter__() , x in y is True if some value z , for which the expression x is z or x == z is true, is produced while iterating over y . If an exception is raised during the iteration, it is as if in raised that exception.

Lastly, the old-style iteration protocol is tried: if a class defines __getitem__() , x in y is True if and only if there is a non-negative integer index i such that x is y[i] or x == y[i] , and no lower integer index raises the IndexError exception. (If any other exception is raised, it is as if in raised that exception).

The operator not in is defined to have the inverse truth value of in .

6.10.3. Identity comparisons ¶

The operators is and is not test for an object’s identity: x is y is true if and only if x and y are the same object. An Object’s identity is determined using the id() function. x is not y yields the inverse truth value. [ 4 ]

6.11. Boolean operations ¶

In the context of Boolean operations, and also when expressions are used by control flow statements, the following values are interpreted as false: False , None , numeric zero of all types, and empty strings and containers (including strings, tuples, lists, dictionaries, sets and frozensets). All other values are interpreted as true. User-defined objects can customize their truth value by providing a __bool__() method.

The operator not yields True if its argument is false, False otherwise.

The expression x and y first evaluates x ; if x is false, its value is returned; otherwise, y is evaluated and the resulting value is returned.

The expression x or y first evaluates x ; if x is true, its value is returned; otherwise, y is evaluated and the resulting value is returned.

Note that neither and nor or restrict the value and type they return to False and True , but rather return the last evaluated argument. This is sometimes useful, e.g., if s is a string that should be replaced by a default value if it is empty, the expression s or 'foo' yields the desired value. Because not has to create a new value, it returns a boolean value regardless of the type of its argument (for example, not 'foo' produces False rather than '' .)

6.12. Assignment expressions ¶

An assignment expression (sometimes also called a “named expression” or “walrus”) assigns an expression to an identifier , while also returning the value of the expression .

One common use case is when handling matched regular expressions:

Or, when processing a file stream in chunks:

Assignment expressions must be surrounded by parentheses when used as expression statements and when used as sub-expressions in slicing, conditional, lambda, keyword-argument, and comprehension-if expressions and in assert , with , and assignment statements. In all other places where they can be used, parentheses are not required, including in if and while statements.

Added in version 3.8: See PEP 572 for more details about assignment expressions.

6.13. Conditional expressions ¶

Conditional expressions (sometimes called a “ternary operator”) have the lowest priority of all Python operations.

The expression x if C else y first evaluates the condition, C rather than x . If C is true, x is evaluated and its value is returned; otherwise, y is evaluated and its value is returned.

See PEP 308 for more details about conditional expressions.

6.14. Lambdas ¶

Lambda expressions (sometimes called lambda forms) are used to create anonymous functions. The expression lambda parameters: expression yields a function object. The unnamed object behaves like a function object defined with:

See section Function definitions for the syntax of parameter lists. Note that functions created with lambda expressions cannot contain statements or annotations.

6.15. Expression lists ¶

Except when part of a list or set display, an expression list containing at least one comma yields a tuple. The length of the tuple is the number of expressions in the list. The expressions are evaluated from left to right.

An asterisk * denotes iterable unpacking . Its operand must be an iterable . The iterable is expanded into a sequence of items, which are included in the new tuple, list, or set, at the site of the unpacking.

Added in version 3.5: Iterable unpacking in expression lists, originally proposed by PEP 448 .

A trailing comma is required only to create a one-item tuple, such as 1, ; it is optional in all other cases. A single expression without a trailing comma doesn’t create a tuple, but rather yields the value of that expression. (To create an empty tuple, use an empty pair of parentheses: () .)

6.16. Evaluation order ¶

Python evaluates expressions from left to right. Notice that while evaluating an assignment, the right-hand side is evaluated before the left-hand side.

In the following lines, expressions will be evaluated in the arithmetic order of their suffixes:

6.17. Operator precedence ¶

The following table summarizes the operator precedence in Python, from highest precedence (most binding) to lowest precedence (least binding). Operators in the same box have the same precedence. Unless the syntax is explicitly given, operators are binary. Operators in the same box group left to right (except for exponentiation and conditional expressions, which group from right to left).

Note that comparisons, membership tests, and identity tests, all have the same precedence and have a left-to-right chaining feature as described in the Comparisons section.

Table of Contents

• 6.1. Arithmetic conversions
• 6.2.1.1. Private name mangling
• 6.2.2. Literals
• 6.2.3. Parenthesized forms
• 6.2.4. Displays for lists, sets and dictionaries
• 6.2.5. List displays
• 6.2.6. Set displays
• 6.2.7. Dictionary displays
• 6.2.8. Generator expressions
• 6.2.9.1. Generator-iterator methods
• 6.2.9.2. Examples
• 6.2.9.3. Asynchronous generator functions
• 6.2.9.4. Asynchronous generator-iterator methods
• 6.3.1. Attribute references
• 6.3.2. Subscriptions
• 6.3.3. Slicings
• 6.3.4. Calls
• 6.4. Await expression
• 6.5. The power operator
• 6.6. Unary arithmetic and bitwise operations
• 6.7. Binary arithmetic operations
• 6.8. Shifting operations
• 6.9. Binary bitwise operations
• 6.10.1. Value comparisons
• 6.10.2. Membership test operations
• 6.10.3. Identity comparisons
• 6.11. Boolean operations
• 6.12. Assignment expressions
• 6.13. Conditional expressions
• 6.14. Lambdas
• 6.15. Expression lists
• 6.16. Evaluation order
• 6.17. Operator precedence

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Precedence and associativity of operators in python.

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The combination of values, variables , operators , and function calls is termed as an expression. The Python interpreter can evaluate a valid expression.

For example:

Here 5 - 7 is an expression. There can be more than one operator in an expression.

To evaluate these types of expressions there is a rule of precedence in Python. It guides the order in which these operations are carried out.

For example, multiplication has higher precedence than subtraction.

But we can change this order using parentheses () as it has higher precedence than multiplication.

The operator precedence in Python is listed in the following table. It is in descending order (upper group has higher precedence than the lower ones).

Let's look at some examples:

Suppose we're constructing an if...else block which runs if when lunch is either fruit or sandwich and only if money is more than or equal to 2 .

This program runs if block even when money is 0 . It does not give us the desired output since the precedence of and is higher than or .

We can get the desired output by using parenthesis () in the following way:

• Associativity of Python Operators

We can see in the above table that more than one operator exists in the same group. These operators have the same precedence.

When two operators have the same precedence, associativity helps to determine the order of operations.

Associativity is the order in which an expression is evaluated that has multiple operators of the same precedence. Almost all the operators have left-to-right associativity.

For example, multiplication and floor division have the same precedence. Hence, if both of them are present in an expression, the left one is evaluated first.

Note : Exponent operator ** has right-to-left associativity in Python.

We can see that 2 ** 3 ** 2 is equivalent to 2 ** (3 ** 2) .

• Non associative operators

Some operators like assignment operators and comparison operators do not have associativity in Python. There are separate rules for sequences of this kind of operator and cannot be expressed as associativity.

For example, x < y < z neither means (x < y) < z nor x < (y < z) . x < y < z is equivalent to x < y and y < z , and is evaluated from left-to-right.

Furthermore, while chaining of assignments like x = y = z = 1 is perfectly valid, x = y = z+= 2 will result in error.

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Operator Precedence and Associativity

Parentheses (), exponentiation **.

• Unary operators +x, -x, ~x
• Multiplication *, division /, floor division //, modulus %

Addition + and subtraction -

• Bitwise shift operators <<, >>

Bitwise AND &

Bitwise xor ^, bitwise or |.

• Comparison and membership operators in, not in, is, is not, <, <=, >, >=, !=, ==

Logical NOT not

Logical and and, logical or or, associativity, operator precedence.

Operators are called in a specific order when they’re used in an expression. Simply put, operator precedence determines the order in which the operations are performed. The order below details the operator precedence that Python follows.

Used to group expressions and alter the default order of operations.

Raises the left operand to the power of the right operand.

Unary operators +x , -x , ~x

Performs unary plus, unary minus, and bitwise NOT.

Multiplication * , division / , floor division // , modulus %

Performs multiplication, division, floor division, and modulus operations.

Adds or subtracts two operands.

Bitwise shift operators << , >>

Performs left and right bitwise shifts.

Performs bitwise AND operation.

Performs bitwise exclusive OR operation.

Performs bitwise OR operation.

Comparison and membership operators in , not in , is , is not , < , <= , > , >= , != , ==

Checks for membership (i.e., if it’s included in a sequence and identity or is an object and performs comparisons).

Returns True if the operand is False and vice versa.

Returns True if both operands are True .

Returns True if one of the operands is True .

When two or more operators share the same precedence, associativity comes into play. Associativity dictates the order in which the expression is evaluated, whether from left to right or right to left. Almost all operators follow left-to-right associativity, meaning the expression is evaluated from the beginning in order. However, a few operators—such as exponentiation, unary positive, unary negative, bitwise NOT, and boolean NOT—exhibit right-to-left associativity.

Let’s take a mathematical example and break down it’s precedence:

2 + 3 * 5 ** 2 - 10 // 2 + (1 - 1)

2 + 3 * 5 ** 2 - 10 // 2 + 0

2 + 3 * 25 - 10 // 2 + 0

2 + 75 - 10 // 2 + 0

2 + 75 - 5 + 0

We start with the parenthesis expression, followed by exponentiation. We then have two operators with similar precedence (i.e. multiplication and division), so we perform it from left to right. Similarly, we perform addition and subtraction from left to right, as these operators also have the same level of precedence. The final answer we get is 72.

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• 8.1 Intro: What we can do with Turtles and Conditionals
• 8.2 Boolean Values and Boolean Expressions
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• 8.6 Conditional Execution: Binary Selection
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• 8.11 👩‍💻 Setting Up Conditionals
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8.5. Precedence of Operators ¶

Arithmetic operators take precedence over logical operators. Python will always evaluate the arithmetic operators first (** is highest, then multiplication/division, then addition/subtraction). Next comes the relational operators. Finally, the logical operators are done last. This means that the expression x*5 >= 10 and y-6 <= 20 will be evaluated so as to first perform the arithmetic and then check the relationships. The and will be done last. Many programmers might place parentheses around the two relational expressions, (x*5 >= 10) and (y-6 <= 20) . It is not necessary to do so, but causes no harm and may make it easier for people to read and understand the code.

The following table summarizes the operator precedence from highest to lowest. A complete table for the entire language can be found in the Python Documentation .

This workspace is provided for your convenience. You can use this activecode window to try out anything you like.

Common Mistake!

Students often incorrectly combine the in and or operators. For example, if they want to check that the letter x is inside of either of two variables then they tend to write it the following way: 'x' in y or z

Written this way, the code would not always do what the programmer intended. This is because the in operator is only on the left side of the or statement. It doesn’t get implemented on both sides of the or statement. In order to properly check that x is inside of either variable, the in operator must be used on both sides which looks like this:

Check your understanding

Which of the following properly expresses the precedence of operators (using parentheses) in the following expression: 5*3 > 10 and 4+6==11

• ((5*3) > 10) and ((4+6) == 11)
• Yes, * and + have higher precedence, followed by > and ==, and then the keyword "and"
• (5*(3 > 10)) and (4 + (6 == 11))
• Arithmetic operators (*, +) have higher precedence than comparison operators (>, ==)
• ((((5*3) > 10) and 4)+6) == 11
• This grouping assumes Python simply evaluates from left to right, which is incorrect. It follows the precedence listed in the table in this section.
• ((5*3) > (10 and (4+6))) == 11
• This grouping assumes that "and" has a higher precedence than ==, which is not true.

Here is an animation for the above expression:

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Operators are used to perform operations on variables and values.

In the example below, we use the + operator to add together two values:

Python divides the operators in the following groups:

• Arithmetic operators
• Assignment operators
• Comparison operators
• Logical operators
• Identity operators
• Membership operators
• Bitwise operators

Python Arithmetic Operators

Arithmetic operators are used with numeric values to perform common mathematical operations:

Python Assignment Operators

Assignment operators are used to assign values to variables:

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Python Comparison Operators

Comparison operators are used to compare two values:

Python Logical Operators

Logical operators are used to combine conditional statements:

Python Identity Operators

Identity operators are used to compare the objects, not if they are equal, but if they are actually the same object, with the same memory location:

Python Membership Operators

Membership operators are used to test if a sequence is presented in an object:

Python Bitwise Operators

Bitwise operators are used to compare (binary) numbers:

Operator Precedence

Operator precedence describes the order in which operations are performed.

Parentheses has the highest precedence, meaning that expressions inside parentheses must be evaluated first:

Multiplication * has higher precedence than addition + , and therefor multiplications are evaluated before additions:

The precedence order is described in the table below, starting with the highest precedence at the top:

If two operators have the same precedence, the expression is evaluated from left to right.

Addition + and subtraction - has the same precedence, and therefor we evaluate the expression from left to right:

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Python Operators Precedence and Associativity

Precedence is the order in which operators are evaluated in an expression, and the associativity defines the directions of evaluation when we have operators with the same precedence. Associativity can be Left to Right or Right to Left. Understanding the precedence and associativity of operators is essential for correctly interpreting and evaluating expressions.

• Operator Precedence

Precedence  means priority. When multiple operators are used in a single expression, they are evaluated as per the priority assigned to it. Operators are given priority as per PEMDAS.

Here we have precedence and associativity table. The table is arranged in the order of higher precedence to lower precedence. Most of the operators have Left to Right associativity and only a few operators have Right to Left associativity(highlighted one)

Note: Precedence is moving from Highest to Lowest

In this example, we can see that the multiplication operator has more precedence than the division operator. So, the multiplication operator is evaluated first, i.e 5 * 2 = 10, and then 2 is added to 10, which results in 12.

In this example, we can see that Parentheses have higher priority than the multiplication, so (5+2) is evaluated first and then 7 is multiplied to 2, which results in 14.

How Precedence works Logical Operators

In the below example, you can see that as and have more precedence over or, so first “qualification == “Post Graduate” and age >= 21″ is evaluated, as both are false, so the result is False and then “qualification == “Graduate” or False” is evaluated. Because the qualification == “Graduate” is True and True or False is True, so the result is also True.

Now, we have used the parenthesis in the below example and because of parenthesis (qualification == “Graduate” or qualification == “Post Graduate”) is evaluated first, which is True and then (True and age >= 21) is evaluated, which results in False.

• Operator Associativity

Associativity , in case we have multiple operators with the same precedence in an expression, they are evaluated as per the associativity. This means the direction we will follow to evaluate them. Only exponential is evaluated from right to left, and all others from left to right.

The below example only use multiplication and division, which have the same precedence, so evaluated from left to right.

The below code has an exponential operator, so evaluated from right to left.

• Non-associative operators

Non-associative operators are the operators that do not have any associativity rules, i.e. right to left or left to right. These operators have their own rules for sequences and do not come under associativity rules.

Both the examples below comes under the non-associative operators, as they do not come under any associativity rule.

Python Operator Short Circuiting

For evaluating some expression’s python uses Short Circuiting. Short Circuiting means it will stop the evaluation of the expression as soon as the result is determined. Short-circuiting is a very useful technique to improve the efficiency. Let us see a few cases where short-circuiting is performed:

This example has all “or” operators and in the case of the “or” operator, if any of the expression is True, then the result it True, so it will not evaluate all expressions.

This example has all “and” operators and in the case of the “and” operator, if any of the expression is False, then the result it False, so it will not evaluate all expressions.

Appendix A: Python Operator Precedence

Python has well-defined rules for specifying the order in which the operators in an expression are evaluated when the expression has several operators. For example, multiplication and division have a higher precedence than addition and subtraction. Precedence rules can be overridden by explicit parentheses.

Precedence Order

When two operators share an operand, the operator with the higher precedence goes first. For example, since multiplication has a higher precedence than addition, a + b * c is treated as a + (b * c) , and a * b + c is treated as (a * b) + c .

Associativity

When two operators share an operand and the operators have the same precedence, then the expression is evaluated according to the associativity of the operators. For example, since the ** operator has right-to-left associativity, a ** b ** c is treated as a ** (b ** c) . On the other hand, since the / operator has left-to-right associativity, a / b / c is treated as (a / b) / c .

Precedence and Associativity of Python Operators

The Python documentation on operator precedence contains a table that shows all Python operators from lowest to highest precedence, and notes their associativity. Most programmers do not memorize them all, and those that do still use parentheses for clarity.

Order of Evaluation

In Python, the left operand is always evaluated before the right operand. That also applies to function arguments.

Python uses short circuiting when evaluating expressions involving the and or or operators. When using those operators, Python does not evaluate the second operand unless it is necessary to resolve the result. That allows statements such as if (s != None) and (len(s) < 10): ... to work reliably.

Precedence Order Gone Awry

Sometimes the precedence order defined in a language do not conform with mathematical norms. For example, in Microsoft Excel, -a^b is interpreted as (-a)^b instead of -(a^b). So -1^2 is equal to 1 instead of -1, which is the values most mathematicians would expect. Microsoft acknowledges this quirk as a "design choice". One wonders whether the programmer was relying on the C precedence order in which unary operators have higher precedence than binary operators. This rule agrees with mathematical conventions for all C operators, but fails with the addition of the exponentiation operator. Once the order was established in Microsoft Excel 2.0, it could not easily be changed without breaking backward compatibility.

Add parentheses to the following expression to make the order of evaluation more clear.

year % 4 == 0 and year % 100 != 0 or year % 400 == 0

Answer : leapyearvariety.py shows a variety of equivalent expressions, including the following reasonable alternative.

((year % 4 == 0) and (year % 100 != 0)) or (year % 400 == 0)

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• Python Assignment Operator

Dive into the world of Python programming and explore the essential role that Python Assignment Operators play in simplifying and optimising your code. This comprehensive guide will help you understand the basics of Python Assignment Operators, and learn about the different types available, followed by detailed explanations and examples to solidify your grasp. Furthermore, you will discover the concept of overloading in the context of Python Assignment Operators, and find out how it can be applied in custom classes for enhanced functionality. Lastly, you will gain invaluable insights into Python Assignment Operator precedence, along with useful tips to manage precedence effectively and streamline your coding experience. Embark on this fascinating journey to expand your Python knowledge and enrich your programming skills.

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How are Python assignment operators evaluated in an expression with multiple operators?

What is the correct evaluation order for the following expression in Python? `x = 10 + 5 * 2`

What is one tip for managing Python assignment operator precedence effectively?

What advantage do compound assignment operators provide in Python?

What are the advantages of Python Assignment Operator Overloading?

What is the most basic and commonly used assignment operator in Python?

What are the two categories of Python assignment operators?

How do compound assignment operators work in Python?

What does the += assignment operator do in Python?

What does the **= assignment operator do in Python?

What does the //= assignment operator do in Python?

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The Basics of Python Assignment Operators

Assignment operators in Python are used to assign values to variables. They allow programmers to perform different types of operations and store the results in variables.

Different types of Python Assignment Operators

How Python Assignment Operators work

Addition assignment operator example: x = 10 # x is assigned the value 10 x += 5 # x is updated to x + 5, which is 15 print(x) # The output will be 15

Python assignment operators not only save time by making code shorter but also offer the advantage of improved performance. Compound assignment operators can lead to faster execution as the interpreted language like Python can optimize such operations more effectively than separate operations.

Python Assignment Operator List and Examples

As we delve deeper into the world of Python assignment operators, we can see that each of these operators serves a distinct purpose and offers unique advantages. To gain a comprehensive understanding of their functionality, let's explore some common Python assignment operators in great detail with the help of practical examples.

+=, -=, *=, and **= Assignment Operators in Python

• += (Addition assignment): This operator is used to add the value on the right to the variable on the left and then update the value of the variable.
• -= (Subtraction assignment): This operator is used to subtract the right side value from the left side variable and then update the value of the variable.
• *= (Multiplication assignment): This operator is used to multiply the value on the right with the variable on the left and then update the value of the variable.
• **= (Exponentiation assignment): This operator is used to raise the variable on the left to the power of the value on the right and then update the value of the variable.

x = 10 # x is assigned the value 10 x += 5 # x is updated to x + 5, which is 15 x -= 3 # x is updated to x - 3, which is 12 x *= 2 # x is updated to x * 2, which is 24 x **= 2 # x is updated to x ** 2, which is 576 print(x) # The output will be 576

/=, //=, %=, and &= Assignment Operators in Python

• /= (Division assignment): This operator is used to divide the variable on the left by the value on the right and then update the value of the variable.
• //= (Floor division assignment): This operator performs floor division on the left side variable by the right side value and then updates the value of the variable.
• %= (Modulus assignment): This operator calculates the modulus of the left side variable divided by the right side value and then updates the value of the variable.
• &= (Bitwise AND assignment): This operator performs a bitwise AND operation between the left side variable and the right side value, and then updates the value of the variable.

x = 50 # x is assigned the value 50 x /= 2 # x is updated to x / 2, which is 25 x //= 3 # x is updated to x // 3, which is 8 x %= 5 # x is updated to x % 5, which is 3 x &= 2 # x is updated to x & 2, which is 2 print(x) # The output will be 2

Overloading Python Assignment Operators

What is python assignment operator overloading, using python assignment operator overloading in custom classes.

• __add__(): Corresponds to the '+=' operator
• __sub__(): Corresponds to the '-=' operator
• __mul__(): Corresponds to the '*=' operator
• __truediv__(): Corresponds to the '/=' operator
• __floordiv__(): Corresponds to the '//=' operator
• __mod__(): Corresponds to the '%=' operator
• __pow__(): Corresponds to the '**=' operator
• __and__(): Corresponds to the '&=' operator
• __or__(): Corresponds to the '|=' operator
• __xor__(): Corresponds to the '^=' operator
• __lshift__(): Corresponds to the '<<=' operator
• __rshift__(): Corresponds to the '>>=' operator

Advantages of Python Assignment Operator Overloading

• Improved readability: By overloading assignment operators, you can make your code more self-explanatory and easier to understand since the operators are more intuitive than function calls.
• Enhanced expressiveness: Operator overloading enables custom classes to provide a natural syntax that closely resembles the native data types, allowing programmers to write more concise and efficient code.
• Increased consistency: When operators are consistently represented across user-defined and built-in classes, the language semantics remain uniform, resulting in improved consistency.
• Greater abstraction: Overloading assignment operators allows you to hide the implementation details from the users, providing them with a straightforward and consistent interface for interacting with custom objects.

Python Assignment Operator Precedence

Understanding operator precedence in python, precedence rules for python assignment operators.

• Assignment operators (=, +=, -=, *=, etc.) are evaluated from right to left.
• Assignment operators have a lower precedence compared to all arithmetic, relational, and logical operators.
• When multiple assignment operators are present in an expression, Python evaluates the rightmost assignment operator first and proceeds from right to left.

To illustrate these precedence rules, consider the following example: ```python x = 10 + 5 * 2 ``` In this example, the multiplication (*) operator takes precedence over the addition (+) and assignment (=) operators. Therefore, the expression will be evaluated as follows: `x = 10 + (5 * 2)`. The final value of x will be 20.

Tips for Managing Python Assignment Operator Precedence

• Always use parentheses to group operations explicitly and avoid ambiguities. Using parentheses not only ensures that the expression is evaluated in the correct order but also improves the readability of your code.
• When evaluating complex expressions, break them down into smaller, simpler expressions and assign intermediate results to temporary variables. This approach not only makes your code more readable but also minimizes the chance of encountering precedence issues.
• If you are uncertain about the precedence levels of the operators involved in an expression, consult the Python documentation for clarification. Familiarizing yourself with the precedence rules will significantly enhance your ability to write accurate and reliable code.
• Always test your code thoroughly to ensure that the operator precedence does not introduce any unintended outcomes. Adequate testing will help you identify and correct potential errors and ambiguities caused by operator precedence.

Python Assignment Operator - Key takeaways

Python Assignment Operator - used to assign values to variables and perform operations while updating variable values.

Python assignment operator overload - using 'magic methods' to extend functionality of assignment operators for custom classes.

Python assignment operator list - Basic (=) and compound (+=, -=, *=, etc.) assignment operators available in Python for different types of operations.

What is the assignment operator in python? - '=' is the basic assignment operator used for assigning values to variables.

Python assignment operator precedence - Set of rules governing the order in which assignment operators are executed in an expression, assignment operators have the lowest precedence level.

Flashcards in Python Assignment Operator 31

Assignment operators generally have the lowest precedence level and are evaluated from right to left after all arithmetic, relational, and logical operators have been executed.

The correct evaluation order is: `x = 10 + (5 * 2)`, with the multiplication operator taking precedence over the addition and assignment operators.

One tip is to use parentheses to group operations explicitly and avoid ambiguities, ensuring the expression is evaluated in the correct order while improving code readability.

Compound assignment operators provide improved performance and make the code more concise and readable by performing operations and updating variables concurrently.

The advantages of Python Assignment Operator Overloading include improved readability, enhanced expressiveness, increased consistency, and greater abstraction.

The equal sign (=) is the most basic and commonly used assignment operator, which assigns a value to a variable.

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

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

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

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

Python includes the following categories of operators:

Arithmetic Operators

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

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

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

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

The following table lists all the arithmetic operators in Python:

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

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

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

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

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

Bitwise operators perform operations on binary operands.

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😶 Operators

Python operators are the symbols that allow us to perform different types of operations on variables and values. They are the building blocks of any programming language, and Python is no exception. Python provides a wide range of operators that can be used to perform arithmetic, logical, comparison, assignment, and bitwise operations.

Understanding the different types of operators is crucial to writing efficient and error-free code in Python. In this section, we will explore the different types of operators available in Python and learn how to use them effectively in our programs. So buckle up and get ready to dive into the world of Python operators!

I. Arithmetic Operators

Arithmetic operators are used in Python to perform basic arithmetic operations such as addition, subtraction, multiplication, division, and more. These operators are used on numeric data types such as integers, floats, and complex numbers.

Python provides the following arithmetic operators:

The floor division (//) operator returns the largest integer that is less than or equal to the division result.

a. Addition

Addition is one of the most basic arithmetic operations in Python. It is denoted by the + symbol and is used to add two numbers or concatenate two strings. For example, if we want to add two numbers x and y together, we can use the + operator like this:

Similarly, if we want to concatenate two strings a and b , we can use the + operator like this:

In both cases, the + operator performs the desired operation and returns a new value that we can assign to a variable or use directly.

b. Subtraction

The subtraction operator (-) is used to subtract one value from another. It takes two operands and returns the difference between them. For example, 5 - 3 will return 2, and 10.5 - 3.2 will return 7.3.

In Python, the subtraction operator can also be used with variables. For example:

Note that the subtraction operator can also be used with negative numbers. For example, 5 - (-3) will return 8.

c. Multiplication

Multiplication is a mathematical operation that is represented by the symbol * in Python. It is used to find the product of two or more values. Here's an example:

In the above example, we have two variables a and b with values 10 and 5 respectively. We multiply these two variables using the * operator and store the result in the variable c . Finally, we print the value of c which is 50 (the product of a and b ).

d. Division

In Python, the / operator is used for division. It returns the quotient (result of division) in the form of a float, even if both the operands are integers. If you want to get the quotient as an integer, you can use the // operator, which performs floor division.

Here's an example:

In the example above, we divide a by b using both the / and // operators. The result of the floating point division is stored in c , which is a float, while the result of the integer division is stored in d , which is an integer.

Modulus operator returns the remainder of the division operation between two operands. It is represented by the percentage sign % .

For example, the expression 9 % 4 returns 1 because when 9 is divided by 4, the remainder is 1.

Here is an example code snippet:

e. Exponentiation

Exponentiation is another arithmetic operator in Python represented by the double asterisk symbol (**). It raises the first operand to the power of the second operand.

Here, the base is the first operand, and the exponent is the second operand.

In the above example, 2 is raised to the power of 3, which results in 8.

f. Floor Division

Floor Division operator in Python is represented by two forward slashes // and it returns the quotient of the division operation rounding down to the nearest integer. For example, the floor division of 7 // 3 would be 2 since 3 goes into 7 two whole times with 1 left over.

Here's an example of using floor division operator:

In the above example, we have defined two variables a and b , and then used floor division operator // to divide a by b . Since a is 10 and b is 3 , the result of a // b is 3 .

II. Comparison Operators

Comparison operators, also known as relational operators, are used to compare two values or operands. In Python, comparison operators always return a boolean value - either True or False.

There are six comparison operators in Python:

Equal to (==)

Not equal to (!=)

Greater than (>)

Less than (<)

Greater than or equal to (>=)

Less than or equal to (<=)

These operators are used in conditional statements and loops to test whether a certain condition is true or false.

a. Equal to (==)

The equal to operator ( == ) is a comparison operator used to compare the equality of two operands. It returns True if the values of the two operands are equal, otherwise, it returns False .

In this example, the first comparison returns False because x is not equal to y . The second comparison returns True because x is equal to z .

b. Not equal to (!=)

In Python, the "not equal to" operator is represented by the exclamation mark followed by an equal sign (!=). It is a binary operator and is used to compare two values. The operator returns True if the values are not equal and False if they are equal.

Here's an example of using the "not equal to" operator in Python:

c. Greater than (>)

The greater than operator ( > ) is used to check if the left operand is greater than the right operand. It returns True if the left operand is greater than the right operand, otherwise, it returns False . Here is an example:

In the example above, x is greater than y , so the expression x > y returns True .

d. Less than (<)

In Python, the less than operator < is used to compare two operands. It returns True if the left operand is less than the right operand, and False otherwise.

In this example, x is less than y , so the if statement evaluates to True , and the first print statement is executed.

e. Greater than or equal to (>=)

The greater than or equal to operator (>=) is used to compare two values. It returns True if the left operand is greater than or equal to the right operand, and False otherwise.

For example:

In this example, the first print statement returns True because x (which is 5) is greater than or equal to y (which is 3). The second print statement returns False because y is less than x .

f. Less than or equal to (<=)

The "Less than or equal to" operator is represented by the symbol "<=". It is used to check if one value is less than or equal to another value.

For example, in the expression "5 <= 10", the operator "<=" checks if 5 is less than or equal to 10. Since this is true, the expression evaluates to True. However, in the expression "10 <= 5", the operator "<=" checks if 10 is less than or equal to 5. Since this is false, the expression evaluates to False.

Here's an example code snippet demonstrating the use of the "<=" operator:

This code will output "x is less than or equal to y", since 5 is indeed less than or equal to 10.

III. Logical Operators

Python Logical Operators are used to combine two or more conditions and perform logical operations on them. The following are the three logical operators in Python:

These operators are used to perform logical operations on the operands and return a Boolean value.

The 'and' operator returns True if both operands are True, otherwise, it returns False.

The 'or' operator returns True if either of the operands is True, otherwise, it returns False.

The 'not' operator returns the opposite of the operand.

Let's look at depth with some examples to understand how these operators work.

The and operator returns True if both operands are true and returns False if either one of the operands is false.

Here's the truth table for the and operator:

Here's an example code snippet:

In this example, the and operator is used to check if x is smaller than both y and z . If this condition is true, then the statement "x is the smallest number" is printed.

The OR operator in Python is represented by or . It is a logical operator that returns True if at least one of the operands is True , and False otherwise. Here are the possible truth tables for the OR operator:

Here's an example of using the OR operator in Python:

In this example, x is not greater than y or z , so the output will be x is not greater than y or z .

The NOT operator is a unary operator that negates the value of its operand. In Python, the NOT operator is represented by the keyword "not".

The NOT operator returns True if its operand is False, and vice versa. Here's an example:

In this example, the value of x is True. However, the NOT operator negates the value of x and returns False.

IV. Assignment Operators

Have you ever wanted to quickly assign or modify a value in Python without writing a lot of code? That's where assignment operators come in handy! They allow you to perform an operation on a variable and assign the result back to the same variable in a single step. In this section, we will explore the different types of assignment operators in Python.

a. Simple Assignment Operator

The simple assignment operator in Python is denoted by the equal sign "=" and is used to assign a value to a variable. The syntax for simple assignment is:

where variable is the name of the variable and value is the value to be assigned to the variable.

For example, the following code assigns the value 10 to the variable x :

After executing this code, the variable x will have the value 10 .

b. Arithmetic Assignment Operators

Arithmetic assignment operators are a shorthand way of performing arithmetic operations and assignment at the same time. These operators include:

+= : adds the value of the right operand to the value of the left operand and assigns the result to the left operand.

-= : subtracts the value of the right operand from the value of the left operand and assigns the result to the left operand.

*= : multiplies the value of the left operand by the value of the right operand and assigns the result to the left operand.

/= : divides the value of the left operand by the value of the right operand and assigns the result to the left operand.

%= : computes the modulus of the value of the left operand and the value of the right operand, and assigns the result to the left operand.

//= : performs floor division on the value of the left operand and the value of the right operand, and assigns the result to the left operand.

**= : raises the value of the left operand to the power of the value of the right operand, and assigns the result to the left operand.

These operators can be used with numeric values and variables of numeric types, such as integers and floating-point numbers.

In each of the above examples, the arithmetic operation and the assignment operation are performed at the same time using the shorthand arithmetic assignment operator.

c. Bitwise Assignment Operators

Bitwise assignment operators are used to perform a bitwise operation on a variable and then assign the result to the same variable. The bitwise assignment operators include:

&= : Performs a bitwise AND operation on the variable and the value on the right, then assigns the result to the variable.

|= : Performs a bitwise OR operation on the variable and the value on the right, then assigns the result to the variable.

^= : Performs a bitwise XOR operation on the variable and the value on the right, then assigns the result to the variable.

<<= : Performs a left shift operation on the variable by the number of bits specified on the right, then assigns the result to the variable.

>>= : Performs a right shift operation on the variable by the number of bits specified on the right, then assigns the result to the variable.

d. Logical Assignment Operators

There are no specific "Logical Assignment Operators" in Python, as the logical operators and , or , and not are already used for combining and negating boolean expressions. However, it is possible to use logical operators in combination with assignment operators to create compound expressions, such as x += y or z , which assigns the value of y to x if y is truthy, or the value of z otherwise.

e. Comparison Assignment Operators

There is no such thing as "Comparison Assignment Operators". The term "comparison operator" refers to operators that compare two values and return a boolean value (True or False), while "assignment operator" refers to operators that assign a value to a variable.

However, there are shorthand ways to perform a comparison and assign the result to a variable in a single line of code. For example:

x = 10 if a > b else 20 : This assigns the value 10 to x if a > b is True, otherwise it assigns the value 20.

x += 1 if a == b else 2 : This adds 1 to x if a == b is True, otherwise it adds 2.

x *= 2 if a < b else 3 : This multiplies x by 2 if a < b is True, otherwise it multiplies it by 3.

V. Bitwise Operators

Bitwise operators are used to manipulate the individual bits of binary numbers. In Python, bitwise operators can be applied to integers. The bitwise operators take two operands and operate on them bit by bit to produce a result. There are six bitwise operators in Python: AND, OR, XOR, NOT, left shift, and right shift. These operators are commonly used in low-level programming, such as device driver development and network packet processing.

a. Bitwise AND

The bitwise AND operator is represented by the & symbol in Python. It performs a logical AND operation on each corresponding bit of its operands. If both bits are 1, the resulting bit is 1. Otherwise, the resulting bit is 0.

In this example, a and b are two integers represented in binary. The & operator is used to perform a bitwise AND operation on the two numbers, resulting in the binary number 0010 , which is equivalent to the decimal number 2. The resulting value is assigned to the variable c .

b. Bitwise OR

Bitwise OR is another binary operator that operates on two integers and performs a bitwise OR operation on their binary representations. The resulting binary representation is converted back to an integer.

The syntax for the bitwise OR operator is the pipe symbol | . For example, a | b performs a bitwise OR operation on a and b .

In the above example, the binary OR operation on a and b results in 0011 1101 , which is equal to 61 in decimal representation.

c. Bitwise XOR

Bitwise XOR (exclusive OR) operator is represented by the symbol ^ in Python. The operator returns a binary number that has a 1 in each bit position where the corresponding bits of either but not both operands are 1.

For example, let's say we have two variables a = 13 and b = 17 . The binary representation of 13 is 1101 and the binary representation of 17 is 10001 . Now, let's perform the bitwise XOR operation on these two variables:

In the above example, the resulting binary number is 11000 , which is equivalent to the decimal number 24 . Therefore, the value of the variable c will be 24 .

Here is another example that demonstrates the use of bitwise XOR:

In this example, we first define a and b as binary numbers using the 0b prefix. We then perform the bitwise XOR operation on these two numbers and store the result in c . The resulting binary number is 0b110 , which is equivalent to the decimal number 6 . Therefore, the value of the variable c will be 6 .

d. Bitwise NOT

Bitwise NOT is a unary operator in Python that flips the bits of a number. It is represented by the tilde (~) symbol. When applied to a binary number, the Bitwise NOT operator returns the complement of the number.

In the above code, the value of x is 7, which is represented in binary as 0000 0111. When we apply the Bitwise NOT operator (~) to x , it flips all the bits of the number, resulting in 1111 1000. The output is in two's complement form, which is the way negative numbers are represented in binary.

VI. Membership Operators

Membership operators are used to test if a sequence is present in an object. In Python, we have two membership operators:

in : Evaluates to True if the sequence is present in the object.

not in : Evaluates to True if the sequence is not present in the object.

These operators are typically used with strings, lists, tuples, and sets to check if a certain element or a sequence of elements is present in them.

VII. Identity Operators

Identity Operators are used to compare the memory locations of two objects. There are two identity operators in Python:

is - Returns True if both variables are the same object.

is not - Returns True if both variables are not the same object.

In this example, x and y have the same values, but they are not the same object. z is assigned the same memory location as x , so x and z are the same object. The is operator returns True when comparing x and z , but False when comparing x and y . The is not operator returns False when comparing x and z , but True when comparing x and y .

Last updated 1 year ago

Python Enhancement Proposals

• Python »
• PEP Index »

PEP 572 – Assignment Expressions

The importance of real code, exceptional cases, scope of the target, relative precedence of :=, change to evaluation order, differences between assignment expressions and assignment statements, specification changes during implementation, _pydecimal.py, datetime.py, sysconfig.py, simplifying list comprehensions, capturing condition values, changing the scope rules for comprehensions, alternative spellings, special-casing conditional statements, special-casing comprehensions, lowering operator precedence, allowing commas to the right, always requiring parentheses, why not just turn existing assignment into an expression, with assignment expressions, why bother with assignment statements, why not use a sublocal scope and prevent namespace pollution, style guide recommendations, acknowledgements, a numeric example, appendix b: rough code translations for comprehensions, appendix c: no changes to scope semantics.

This is a proposal for creating a way to assign to variables within an expression using the notation NAME := expr .

As part of this change, there is also an update to dictionary comprehension evaluation order to ensure key expressions are executed before value expressions (allowing the key to be bound to a name and then re-used as part of calculating the corresponding value).

During discussion of this PEP, the operator became informally known as “the walrus operator”. The construct’s formal name is “Assignment Expressions” (as per the PEP title), but they may also be referred to as “Named Expressions” (e.g. the CPython reference implementation uses that name internally).

Naming the result of an expression is an important part of programming, allowing a descriptive name to be used in place of a longer expression, and permitting reuse. Currently, this feature is available only in statement form, making it unavailable in list comprehensions and other expression contexts.

Additionally, naming sub-parts of a large expression can assist an interactive debugger, providing useful display hooks and partial results. Without a way to capture sub-expressions inline, this would require refactoring of the original code; with assignment expressions, this merely requires the insertion of a few name := markers. Removing the need to refactor reduces the likelihood that the code be inadvertently changed as part of debugging (a common cause of Heisenbugs), and is easier to dictate to another programmer.

During the development of this PEP many people (supporters and critics both) have had a tendency to focus on toy examples on the one hand, and on overly complex examples on the other.

The danger of toy examples is twofold: they are often too abstract to make anyone go “ooh, that’s compelling”, and they are easily refuted with “I would never write it that way anyway”.

The danger of overly complex examples is that they provide a convenient strawman for critics of the proposal to shoot down (“that’s obfuscated”).

Yet there is some use for both extremely simple and extremely complex examples: they are helpful to clarify the intended semantics. Therefore, there will be some of each below.

However, in order to be compelling , examples should be rooted in real code, i.e. code that was written without any thought of this PEP, as part of a useful application, however large or small. Tim Peters has been extremely helpful by going over his own personal code repository and picking examples of code he had written that (in his view) would have been clearer if rewritten with (sparing) use of assignment expressions. His conclusion: the current proposal would have allowed a modest but clear improvement in quite a few bits of code.

Another use of real code is to observe indirectly how much value programmers place on compactness. Guido van Rossum searched through a Dropbox code base and discovered some evidence that programmers value writing fewer lines over shorter lines.

Case in point: Guido found several examples where a programmer repeated a subexpression, slowing down the program, in order to save one line of code, e.g. instead of writing:

they would write:

Another example illustrates that programmers sometimes do more work to save an extra level of indentation:

This code tries to match pattern2 even if pattern1 has a match (in which case the match on pattern2 is never used). The more efficient rewrite would have been:

Syntax and semantics

In most contexts where arbitrary Python expressions can be used, a named expression can appear. This is of the form NAME := expr where expr is any valid Python expression other than an unparenthesized tuple, and NAME is an identifier.

The value of such a named expression is the same as the incorporated expression, with the additional side-effect that the target is assigned that value:

There are a few places where assignment expressions are not allowed, in order to avoid ambiguities or user confusion:

This rule is included to simplify the choice for the user between an assignment statement and an assignment expression – there is no syntactic position where both are valid.

Again, this rule is included to avoid two visually similar ways of saying the same thing.

This rule is included to disallow excessively confusing code, and because parsing keyword arguments is complex enough already.

This rule is included to discourage side effects in a position whose exact semantics are already confusing to many users (cf. the common style recommendation against mutable default values), and also to echo the similar prohibition in calls (the previous bullet).

The reasoning here is similar to the two previous cases; this ungrouped assortment of symbols and operators composed of : and = is hard to read correctly.

This allows lambda to always bind less tightly than := ; having a name binding at the top level inside a lambda function is unlikely to be of value, as there is no way to make use of it. In cases where the name will be used more than once, the expression is likely to need parenthesizing anyway, so this prohibition will rarely affect code.

This shows that what looks like an assignment operator in an f-string is not always an assignment operator. The f-string parser uses : to indicate formatting options. To preserve backwards compatibility, assignment operator usage inside of f-strings must be parenthesized. As noted above, this usage of the assignment operator is not recommended.

An assignment expression does not introduce a new scope. In most cases the scope in which the target will be bound is self-explanatory: it is the current scope. If this scope contains a nonlocal or global declaration for the target, the assignment expression honors that. A lambda (being an explicit, if anonymous, function definition) counts as a scope for this purpose.

There is one special case: an assignment expression occurring in a list, set or dict comprehension or in a generator expression (below collectively referred to as “comprehensions”) binds the target in the containing scope, honoring a nonlocal or global declaration for the target in that scope, if one exists. For the purpose of this rule the containing scope of a nested comprehension is the scope that contains the outermost comprehension. A lambda counts as a containing scope.

The motivation for this special case is twofold. First, it allows us to conveniently capture a “witness” for an any() expression, or a counterexample for all() , for example:

Second, it allows a compact way of updating mutable state from a comprehension, for example:

However, an assignment expression target name cannot be the same as a for -target name appearing in any comprehension containing the assignment expression. The latter names are local to the comprehension in which they appear, so it would be contradictory for a contained use of the same name to refer to the scope containing the outermost comprehension instead.

For example, [i := i+1 for i in range(5)] is invalid: the for i part establishes that i is local to the comprehension, but the i := part insists that i is not local to the comprehension. The same reason makes these examples invalid too:

While it’s technically possible to assign consistent semantics to these cases, it’s difficult to determine whether those semantics actually make sense in the absence of real use cases. Accordingly, the reference implementation [1] will ensure that such cases raise SyntaxError , rather than executing with implementation defined behaviour.

This restriction applies even if the assignment expression is never executed:

For the comprehension body (the part before the first “for” keyword) and the filter expression (the part after “if” and before any nested “for”), this restriction applies solely to target names that are also used as iteration variables in the comprehension. Lambda expressions appearing in these positions introduce a new explicit function scope, and hence may use assignment expressions with no additional restrictions.

Due to design constraints in the reference implementation (the symbol table analyser cannot easily detect when names are re-used between the leftmost comprehension iterable expression and the rest of the comprehension), named expressions are disallowed entirely as part of comprehension iterable expressions (the part after each “in”, and before any subsequent “if” or “for” keyword):

A further exception applies when an assignment expression occurs in a comprehension whose containing scope is a class scope. If the rules above were to result in the target being assigned in that class’s scope, the assignment expression is expressly invalid. This case also raises SyntaxError :

(The reason for the latter exception is the implicit function scope created for comprehensions – there is currently no runtime mechanism for a function to refer to a variable in the containing class scope, and we do not want to add such a mechanism. If this issue ever gets resolved this special case may be removed from the specification of assignment expressions. Note that the problem already exists for using a variable defined in the class scope from a comprehension.)

See Appendix B for some examples of how the rules for targets in comprehensions translate to equivalent code.

The := operator groups more tightly than a comma in all syntactic positions where it is legal, but less tightly than all other operators, including or , and , not , and conditional expressions ( A if C else B ). As follows from section “Exceptional cases” above, it is never allowed at the same level as = . In case a different grouping is desired, parentheses should be used.

The := operator may be used directly in a positional function call argument; however it is invalid directly in a keyword argument.

Some examples to clarify what’s technically valid or invalid:

Most of the “valid” examples above are not recommended, since human readers of Python source code who are quickly glancing at some code may miss the distinction. But simple cases are not objectionable:

This PEP recommends always putting spaces around := , similar to PEP 8 ’s recommendation for = when used for assignment, whereas the latter disallows spaces around = used for keyword arguments.)

In order to have precisely defined semantics, the proposal requires evaluation order to be well-defined. This is technically not a new requirement, as function calls may already have side effects. Python already has a rule that subexpressions are generally evaluated from left to right. However, assignment expressions make these side effects more visible, and we propose a single change to the current evaluation order:

• In a dict comprehension {X: Y for ...} , Y is currently evaluated before X . We propose to change this so that X is evaluated before Y . (In a dict display like {X: Y} this is already the case, and also in dict((X, Y) for ...) which should clearly be equivalent to the dict comprehension.)

Most importantly, since := is an expression, it can be used in contexts where statements are illegal, including lambda functions and comprehensions.

Conversely, assignment expressions don’t support the advanced features found in assignment statements:

• Multiple targets are not directly supported: x = y = z = 0 # Equivalent: (z := (y := (x := 0)))
• Single assignment targets other than a single NAME are not supported: # No equivalent a [ i ] = x self . rest = []
• Priority around commas is different: x = 1 , 2 # Sets x to (1, 2) ( x := 1 , 2 ) # Sets x to 1
• Iterable packing and unpacking (both regular or extended forms) are not supported: # Equivalent needs extra parentheses loc = x , y # Use (loc := (x, y)) info = name , phone , * rest # Use (info := (name, phone, *rest)) # No equivalent px , py , pz = position name , phone , email , * other_info = contact
• Inline type annotations are not supported: # Closest equivalent is "p: Optional[int]" as a separate declaration p : Optional [ int ] = None
• Augmented assignment is not supported: total += tax # Equivalent: (total := total + tax)

The following changes have been made based on implementation experience and additional review after the PEP was first accepted and before Python 3.8 was released:

• for consistency with other similar exceptions, and to avoid locking in an exception name that is not necessarily going to improve clarity for end users, the originally proposed TargetScopeError subclass of SyntaxError was dropped in favour of just raising SyntaxError directly. [3]
• due to a limitation in CPython’s symbol table analysis process, the reference implementation raises SyntaxError for all uses of named expressions inside comprehension iterable expressions, rather than only raising them when the named expression target conflicts with one of the iteration variables in the comprehension. This could be revisited given sufficiently compelling examples, but the extra complexity needed to implement the more selective restriction doesn’t seem worthwhile for purely hypothetical use cases.

Examples from the Python standard library

env_base is only used on these lines, putting its assignment on the if moves it as the “header” of the block.

• Current: env_base = os . environ . get ( "PYTHONUSERBASE" , None ) if env_base : return env_base
• Improved: if env_base := os . environ . get ( "PYTHONUSERBASE" , None ): return env_base

Avoid nested if and remove one indentation level.

• Current: if self . _is_special : ans = self . _check_nans ( context = context ) if ans : return ans
• Improved: if self . _is_special and ( ans := self . _check_nans ( context = context )): return ans

Code looks more regular and avoid multiple nested if. (See Appendix A for the origin of this example.)

• Current: reductor = dispatch_table . get ( cls ) if reductor : rv = reductor ( x ) else : reductor = getattr ( x , "__reduce_ex__" , None ) if reductor : rv = reductor ( 4 ) else : reductor = getattr ( x , "__reduce__" , None ) if reductor : rv = reductor () else : raise Error ( "un(deep)copyable object of type %s " % cls )
• Improved: if reductor := dispatch_table . get ( cls ): rv = reductor ( x ) elif reductor := getattr ( x , "__reduce_ex__" , None ): rv = reductor ( 4 ) elif reductor := getattr ( x , "__reduce__" , None ): rv = reductor () else : raise Error ( "un(deep)copyable object of type %s " % cls )

tz is only used for s += tz , moving its assignment inside the if helps to show its scope.

• Current: s = _format_time ( self . _hour , self . _minute , self . _second , self . _microsecond , timespec ) tz = self . _tzstr () if tz : s += tz return s
• Improved: s = _format_time ( self . _hour , self . _minute , self . _second , self . _microsecond , timespec ) if tz := self . _tzstr (): s += tz return s

Calling fp.readline() in the while condition and calling .match() on the if lines make the code more compact without making it harder to understand.

• Current: while True : line = fp . readline () if not line : break m = define_rx . match ( line ) if m : n , v = m . group ( 1 , 2 ) try : v = int ( v ) except ValueError : pass vars [ n ] = v else : m = undef_rx . match ( line ) if m : vars [ m . group ( 1 )] = 0
• Improved: while line := fp . readline (): if m := define_rx . match ( line ): n , v = m . group ( 1 , 2 ) try : v = int ( v ) except ValueError : pass vars [ n ] = v elif m := undef_rx . match ( line ): vars [ m . group ( 1 )] = 0

A list comprehension can map and filter efficiently by capturing the condition:

Similarly, a subexpression can be reused within the main expression, by giving it a name on first use:

Note that in both cases the variable y is bound in the containing scope (i.e. at the same level as results or stuff ).

Assignment expressions can be used to good effect in the header of an if or while statement:

Particularly with the while loop, this can remove the need to have an infinite loop, an assignment, and a condition. It also creates a smooth parallel between a loop which simply uses a function call as its condition, and one which uses that as its condition but also uses the actual value.

An example from the low-level UNIX world:

Rejected alternative proposals

Proposals broadly similar to this one have come up frequently on python-ideas. Below are a number of alternative syntaxes, some of them specific to comprehensions, which have been rejected in favour of the one given above.

A previous version of this PEP proposed subtle changes to the scope rules for comprehensions, to make them more usable in class scope and to unify the scope of the “outermost iterable” and the rest of the comprehension. However, this part of the proposal would have caused backwards incompatibilities, and has been withdrawn so the PEP can focus on assignment expressions.

Broadly the same semantics as the current proposal, but spelled differently.

Since EXPR as NAME already has meaning in import , except and with statements (with different semantics), this would create unnecessary confusion or require special-casing (e.g. to forbid assignment within the headers of these statements).

(Note that with EXPR as VAR does not simply assign the value of EXPR to VAR – it calls EXPR.__enter__() and assigns the result of that to VAR .)

Additional reasons to prefer := over this spelling include:

• In if f(x) as y the assignment target doesn’t jump out at you – it just reads like if f x blah blah and it is too similar visually to if f(x) and y .
• import foo as bar
• except Exc as var
• with ctxmgr() as var

To the contrary, the assignment expression does not belong to the if or while that starts the line, and we intentionally allow assignment expressions in other contexts as well.

• NAME = EXPR
• if NAME := EXPR

reinforces the visual recognition of assignment expressions.

This syntax is inspired by languages such as R and Haskell, and some programmable calculators. (Note that a left-facing arrow y <- f(x) is not possible in Python, as it would be interpreted as less-than and unary minus.) This syntax has a slight advantage over ‘as’ in that it does not conflict with with , except and import , but otherwise is equivalent. But it is entirely unrelated to Python’s other use of -> (function return type annotations), and compared to := (which dates back to Algol-58) it has a much weaker tradition.

This has the advantage that leaked usage can be readily detected, removing some forms of syntactic ambiguity. However, this would be the only place in Python where a variable’s scope is encoded into its name, making refactoring harder.

Execution order is inverted (the indented body is performed first, followed by the “header”). This requires a new keyword, unless an existing keyword is repurposed (most likely with: ). See PEP 3150 for prior discussion on this subject (with the proposed keyword being given: ).

This syntax has fewer conflicts than as does (conflicting only with the raise Exc from Exc notation), but is otherwise comparable to it. Instead of paralleling with expr as target: (which can be useful but can also be confusing), this has no parallels, but is evocative.

One of the most popular use-cases is if and while statements. Instead of a more general solution, this proposal enhances the syntax of these two statements to add a means of capturing the compared value:

This works beautifully if and ONLY if the desired condition is based on the truthiness of the captured value. It is thus effective for specific use-cases (regex matches, socket reads that return '' when done), and completely useless in more complicated cases (e.g. where the condition is f(x) < 0 and you want to capture the value of f(x) ). It also has no benefit to list comprehensions.

Advantages: No syntactic ambiguities. Disadvantages: Answers only a fraction of possible use-cases, even in if / while statements.

Another common use-case is comprehensions (list/set/dict, and genexps). As above, proposals have been made for comprehension-specific solutions.

This brings the subexpression to a location in between the ‘for’ loop and the expression. It introduces an additional language keyword, which creates conflicts. Of the three, where reads the most cleanly, but also has the greatest potential for conflict (e.g. SQLAlchemy and numpy have where methods, as does tkinter.dnd.Icon in the standard library).

As above, but reusing the with keyword. Doesn’t read too badly, and needs no additional language keyword. Is restricted to comprehensions, though, and cannot as easily be transformed into “longhand” for-loop syntax. Has the C problem that an equals sign in an expression can now create a name binding, rather than performing a comparison. Would raise the question of why “with NAME = EXPR:” cannot be used as a statement on its own.

As per option 2, but using as rather than an equals sign. Aligns syntactically with other uses of as for name binding, but a simple transformation to for-loop longhand would create drastically different semantics; the meaning of with inside a comprehension would be completely different from the meaning as a stand-alone statement, while retaining identical syntax.

Regardless of the spelling chosen, this introduces a stark difference between comprehensions and the equivalent unrolled long-hand form of the loop. It is no longer possible to unwrap the loop into statement form without reworking any name bindings. The only keyword that can be repurposed to this task is with , thus giving it sneakily different semantics in a comprehension than in a statement; alternatively, a new keyword is needed, with all the costs therein.

There are two logical precedences for the := operator. Either it should bind as loosely as possible, as does statement-assignment; or it should bind more tightly than comparison operators. Placing its precedence between the comparison and arithmetic operators (to be precise: just lower than bitwise OR) allows most uses inside while and if conditions to be spelled without parentheses, as it is most likely that you wish to capture the value of something, then perform a comparison on it:

Once find() returns -1, the loop terminates. If := binds as loosely as = does, this would capture the result of the comparison (generally either True or False ), which is less useful.

While this behaviour would be convenient in many situations, it is also harder to explain than “the := operator behaves just like the assignment statement”, and as such, the precedence for := has been made as close as possible to that of = (with the exception that it binds tighter than comma).

Some critics have claimed that the assignment expressions should allow unparenthesized tuples on the right, so that these two would be equivalent:

(With the current version of the proposal, the latter would be equivalent to ((point := x), y) .)

However, adopting this stance would logically lead to the conclusion that when used in a function call, assignment expressions also bind less tight than comma, so we’d have the following confusing equivalence:

The less confusing option is to make := bind more tightly than comma.

It’s been proposed to just always require parentheses around an assignment expression. This would resolve many ambiguities, and indeed parentheses will frequently be needed to extract the desired subexpression. But in the following cases the extra parentheses feel redundant:

Frequently Raised Objections

C and its derivatives define the = operator as an expression, rather than a statement as is Python’s way. This allows assignments in more contexts, including contexts where comparisons are more common. The syntactic similarity between if (x == y) and if (x = y) belies their drastically different semantics. Thus this proposal uses := to clarify the distinction.

The two forms have different flexibilities. The := operator can be used inside a larger expression; the = statement can be augmented to += and its friends, can be chained, and can assign to attributes and subscripts.

Previous revisions of this proposal involved sublocal scope (restricted to a single statement), preventing name leakage and namespace pollution. While a definite advantage in a number of situations, this increases complexity in many others, and the costs are not justified by the benefits. In the interests of language simplicity, the name bindings created here are exactly equivalent to any other name bindings, including that usage at class or module scope will create externally-visible names. This is no different from for loops or other constructs, and can be solved the same way: del the name once it is no longer needed, or prefix it with an underscore.

(The author wishes to thank Guido van Rossum and Christoph Groth for their suggestions to move the proposal in this direction. [2] )

As expression assignments can sometimes be used equivalently to statement assignments, the question of which should be preferred will arise. For the benefit of style guides such as PEP 8 , two recommendations are suggested.

• If either assignment statements or assignment expressions can be used, prefer statements; they are a clear declaration of intent.
• If using assignment expressions would lead to ambiguity about execution order, restructure it to use statements instead.

The authors wish to thank Alyssa Coghlan and Steven D’Aprano for their considerable contributions to this proposal, and members of the core-mentorship mailing list for assistance with implementation.

Appendix A: Tim Peters’s findings

Here’s a brief essay Tim Peters wrote on the topic.

I dislike “busy” lines of code, and also dislike putting conceptually unrelated logic on a single line. So, for example, instead of:

instead. So I suspected I’d find few places I’d want to use assignment expressions. I didn’t even consider them for lines already stretching halfway across the screen. In other cases, “unrelated” ruled:

is a vast improvement over the briefer:

The original two statements are doing entirely different conceptual things, and slamming them together is conceptually insane.

In other cases, combining related logic made it harder to understand, such as rewriting:

as the briefer:

The while test there is too subtle, crucially relying on strict left-to-right evaluation in a non-short-circuiting or method-chaining context. My brain isn’t wired that way.

But cases like that were rare. Name binding is very frequent, and “sparse is better than dense” does not mean “almost empty is better than sparse”. For example, I have many functions that return None or 0 to communicate “I have nothing useful to return in this case, but since that’s expected often I’m not going to annoy you with an exception”. This is essentially the same as regular expression search functions returning None when there is no match. So there was lots of code of the form:

I find that clearer, and certainly a bit less typing and pattern-matching reading, as:

It’s also nice to trade away a small amount of horizontal whitespace to get another _line_ of surrounding code on screen. I didn’t give much weight to this at first, but it was so very frequent it added up, and I soon enough became annoyed that I couldn’t actually run the briefer code. That surprised me!

There are other cases where assignment expressions really shine. Rather than pick another from my code, Kirill Balunov gave a lovely example from the standard library’s copy() function in copy.py :

The ever-increasing indentation is semantically misleading: the logic is conceptually flat, “the first test that succeeds wins”:

Using easy assignment expressions allows the visual structure of the code to emphasize the conceptual flatness of the logic; ever-increasing indentation obscured it.

A smaller example from my code delighted me, both allowing to put inherently related logic in a single line, and allowing to remove an annoying “artificial” indentation level:

That if is about as long as I want my lines to get, but remains easy to follow.

So, in all, in most lines binding a name, I wouldn’t use assignment expressions, but because that construct is so very frequent, that leaves many places I would. In most of the latter, I found a small win that adds up due to how often it occurs, and in the rest I found a moderate to major win. I’d certainly use it more often than ternary if , but significantly less often than augmented assignment.

I have another example that quite impressed me at the time.

Where all variables are positive integers, and a is at least as large as the n’th root of x, this algorithm returns the floor of the n’th root of x (and roughly doubling the number of accurate bits per iteration):

It’s not obvious why that works, but is no more obvious in the “loop and a half” form. It’s hard to prove correctness without building on the right insight (the “arithmetic mean - geometric mean inequality”), and knowing some non-trivial things about how nested floor functions behave. That is, the challenges are in the math, not really in the coding.

If you do know all that, then the assignment-expression form is easily read as “while the current guess is too large, get a smaller guess”, where the “too large?” test and the new guess share an expensive sub-expression.

To my eyes, the original form is harder to understand:

This appendix attempts to clarify (though not specify) the rules when a target occurs in a comprehension or in a generator expression. For a number of illustrative examples we show the original code, containing a comprehension, and the translation, where the comprehension has been replaced by an equivalent generator function plus some scaffolding.

Since [x for ...] is equivalent to list(x for ...) these examples all use list comprehensions without loss of generality. And since these examples are meant to clarify edge cases of the rules, they aren’t trying to look like real code.

Note: comprehensions are already implemented via synthesizing nested generator functions like those in this appendix. The new part is adding appropriate declarations to establish the intended scope of assignment expression targets (the same scope they resolve to as if the assignment were performed in the block containing the outermost comprehension). For type inference purposes, these illustrative expansions do not imply that assignment expression targets are always Optional (but they do indicate the target binding scope).

Let’s start with a reminder of what code is generated for a generator expression without assignment expression.

• Original code (EXPR usually references VAR): def f (): a = [ EXPR for VAR in ITERABLE ]
• Translation (let’s not worry about name conflicts): def f (): def genexpr ( iterator ): for VAR in iterator : yield EXPR a = list ( genexpr ( iter ( ITERABLE )))

Let’s add a simple assignment expression.

• Original code: def f (): a = [ TARGET := EXPR for VAR in ITERABLE ]
• Translation: def f (): if False : TARGET = None # Dead code to ensure TARGET is a local variable def genexpr ( iterator ): nonlocal TARGET for VAR in iterator : TARGET = EXPR yield TARGET a = list ( genexpr ( iter ( ITERABLE )))

Let’s add a global TARGET declaration in f() .

• Original code: def f (): global TARGET a = [ TARGET := EXPR for VAR in ITERABLE ]
• Translation: def f (): global TARGET def genexpr ( iterator ): global TARGET for VAR in iterator : TARGET = EXPR yield TARGET a = list ( genexpr ( iter ( ITERABLE )))

Or instead let’s add a nonlocal TARGET declaration in f() .

• Original code: def g (): TARGET = ... def f (): nonlocal TARGET a = [ TARGET := EXPR for VAR in ITERABLE ]
• Translation: def g (): TARGET = ... def f (): nonlocal TARGET def genexpr ( iterator ): nonlocal TARGET for VAR in iterator : TARGET = EXPR yield TARGET a = list ( genexpr ( iter ( ITERABLE )))

Finally, let’s nest two comprehensions.

• Original code: def f (): a = [[ TARGET := i for i in range ( 3 )] for j in range ( 2 )] # I.e., a = [[0, 1, 2], [0, 1, 2]] print ( TARGET ) # prints 2
• Translation: def f (): if False : TARGET = None def outer_genexpr ( outer_iterator ): nonlocal TARGET def inner_generator ( inner_iterator ): nonlocal TARGET for i in inner_iterator : TARGET = i yield i for j in outer_iterator : yield list ( inner_generator ( range ( 3 ))) a = list ( outer_genexpr ( range ( 2 ))) print ( TARGET )

Because it has been a point of confusion, note that nothing about Python’s scoping semantics is changed. Function-local scopes continue to be resolved at compile time, and to have indefinite temporal extent at run time (“full closures”). Example:

This document has been placed in the public domain.

Source: https://github.com/python/peps/blob/main/peps/pep-0572.rst

Last modified: 2023-10-11 12:05:51 GMT

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Operator Precedence in Programming

Operator Precedence , also known as operator hierarchy, is a set of rules that controls the order in which operations are performed in an expression without parentheses. It is a fundamental concept in programming languages and is crucial for writing correct and efficient code.

Table of Content

What is Operator Precedence?

Operator precedence in arithmetic operators, operator precedence in relational operators, operator precedence in logical operators, operator precedence in assignment operators, operator precedence in bitwise operators, operator precedence in conditional (ternary) operator, operator precedence in unary operators, operator precedence in member access operators, operator precedence in type cast operators.

• Importance of Operator Precedence

Operator Precedence is a set of rules that defines the order in which operations are performed in an expression based on the operators between the operands. Consider the following mathematical expression 2 + 3 * 4. If we perform the operations from left to right, we get (2 + 3) * 4 = 20. However, if we follow the mathematical rule of precedence (also known as BODMAS), which states that multiplication and division should be performed before addition and subtraction, we get 2 + (3 * 4) = 14. This rule of precedence is also applicable in programming.

Arithmetic operators follow the standard precedence rules that are used in mathematics. The precedence of arithmetic operators from highest to lowest is as follows:

• Parentheses ()
• Unary plus and minus + –
• Multiplication, division, and modulus * / %
• Addition and subtraction + –

Below is the implementation of Operator Precedence in Arithmetic Operators:

Relational operators are used to compare two values. The precedence of relational operators from highest to lowest is as follows:

• Less than, greater than, less than or equal to, greater than or equal to < > <= >=
• Equality and inequality == !=

Below is the implementation of Operator Precedence in Relational Operators:

Logical operators are used to combine two or more conditions. The precedence of logical operators from highest to lowest is as follows:

• Logical NOT !
• Logical AND &&
• Logical OR ||

Below is the implementation of Operator Precedence in Logical Operators:

Assignment operators are used to assign values to variables. The assignment operator = has the lowest precedence.

Below is the implementation of Operator Precedence in Assignment Operators:

Bitwise operators operate on binary representations of integers. The precedence of bitwise operators from highest to lowest is as follows:

• Bitwise NOT ~
• Bitwise SHIFT <<, >>
• Bitwise AND &
• Bitwise XOR ^
• Bitwise OR |

Below is the implementation of Operator Precedence in Bitwise Operators:

The conditional (ternary) operator ?: has lower precedence than arithmetic, relational, and logical operators but higher precedence than the assignment operator.

Below is the implementation of Operator Precedence in Conditional Operators:

Unary operators operate on a single operand. The precedence of unary operators is higher than arithmetic, relational, logical, and assignment operators.

Below is the implementation of Operator Precedence in Unary Operators:

Member access operators . and -> have the highest precedence.

Below is the implementation of Operator Precedence in Member Access Operators:

Type cast operators are used to convert one data type to another. They have higher precedence than arithmetic, relational, logical, and assignment operators.

Below is the implementation of Operator Precedence in Type Cast Operators:

Importance of Operator Precedence:

Understanding operator precedence is essential for several reasons:

• Correctness: Misunderstanding operator precedence can lead to bugs that are hard to detect. For example, the expression a = b == c in C++ is not an error, but it probably doesn’t do what the programmer intended. It’s equivalent to a = (b == c), not (a = b) == c.
• Readability: Code that correctly uses operator precedence is easier to read and understand. Using parentheses to make the order of operations explicit can often make code more readable, even if they’re not strictly necessary.
• Performance: In some cases, understanding operator precedence can help write more efficient code. For example, knowing that bitwise AND (&) has higher precedence than bitwise OR (|) might allow you to avoid unnecessary parentheses or temporary variables.

In conclusion operator precedence is a Important concept in programming that affects how expressions are evaluated. It is essential to understand the rules in the language we are using to avoid bugs and write clear and efficient code.

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13 Things I Regret Not Knowing Earlier About Python Operators

1) The walrus operator :=

The walrus operator does 2 things. In x := 5

it assigns teh variable x to the value 5

the expression (x := 5) returns the value of x itself.

This operator essentially helps us save one line of code.

2) divmod()

When learning Python basics, we probably learnt

the // operator does floor division (or integer division)

the % operator gives us the remainder when a number is divided by another

But did you know that there’s a built-in function that does both at the same time? We can do this using the divmod() function, which returns a (quotient, remainder) tuple.

3) % to format strings

The % operator can also be used to format template strings.

This is a slightly older way of formatting strings, as we tend to use string.format() now instead.

But this way of formatting strings is still legal, and might be found in slightly older Python code bases.

4) @ for matrix multiplication

This might be surprising, but Python has a matrix multiplication operator. We use the @ symbol for this (not to be confused with decorators)

While our usual data types eg int, float etc might not be able to do matrix multiplication, data types from the numpy library can.

Also, if we define the __matmul__ magic method in classes, we can customize behaviour when we use this @ operator for our custom objects

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Here I’ve compiled 101 cool Python tricks and facts that many (if not most) do not know about. Check it out if you wish to level up your Python knowledge!

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5) Operators and magic methods

Whenever we use the built-in operators in Python, we are actually calling their corresponding magic method in the backend.

__add__ for the + operator

__sub__ for the - operator

__mul__ for the * operator

__truediv__ for the / operator

__floordiv__ for the // operator

By extension, we can define these magic methods in our custom classes so that we can use the corresponding operator on our custom objects.

6) Compound assignment operators

Let’s say we have an existing integer variable x. And we wish to increment x by 1.

We can also use a compound assignment operator to do this.

This applies to all other numerical operators too.

x += 1 is the same as x = x + 1

x -= 1 is the same as x = x - 1

x *= 1 is the same as x = x * 1

x /= 1 is the same as x = x / 1

x //= 1 is the same as x = x // 1

x %= 1 is the same as x = x % 1

x **= 1 is the same as x = x ** 1

And similarly, each compound assignment operator has its own magic methods that works in the background.

x += 1 uses __iadd__

x -= 1 uses __isub__

x *= 1 uses __imul__

x /= 1 uses __itruediv__

x //= 1 uses __ifloordiv__

x %= 1 uses __imod__

x ** 1 uses __ipow__

7) -> for type hinting

Yes, there is a use for the -> operator. We use this operator to denote the type hint of a function’s return type.

^ notice the -> float that we place in our function definition.

This just means that this specific function is meant to return a float value.

^ and in such a case, the -> list[int] means that our function is meant to return a list of integers.

Note — type hints hint but don’t enforce. Which means that it is legal to make this function return a string instead (though this is terrible practice)

8) Ternary operator

Let’s say we have a simple if-else block that assigns some value to grade.

Using the ternary operator, we can replace the if-else block with one line of code.

Note that we can have multiple ifs and elses in one ternary operator expression.

9) The ‘None or value’ expression

If we use the expression None or ‘apple’, this expression defaults to ‘apple’ because the first value is a falsy value:

Conversely, if we replace None with a truthy value, our expression no longer takes on the value ‘apple’ :

This ( variable or value ) expression is often used to ensure that variable is not None. Let’s illustrate this with a simple example. Let’s say we have a function that has a chance of returning None.

^ and here, we use the ( get_fruit() or ‘apple’ ) expression:

if get_fruit() returns None, fruit is assigned to the default value ‘ apple’

if get_fruit() returns either orange or pear, fruit simple takes that value.

10) De Morgan’s law

This is important with we’re dealing with brackets and the not operator.

not ( A and B ) equals ( not A or not B )

not ( A or B ) equals ( not A and not B )

Notice that when we expand the not keyword to the values inside the brackets, and becomes or, and or becomes and.

Let’s illustrate this with an example. Let’s say I am allergic to both peanuts and garlic. If a dish has either peanuts or garlic, I cannot eat it:

Let’s add a not in front of this condition, and flip it around. If the condition ( HAS_PEANUTS or HAS_GARLIC) is False, I can eat the dish.

Next, let’s expand this using De Morgan’s Law. Remember that when when expanding brackets in these cases, we must switch and with or, and vice versa.

What the code means now — if our dish 1) has no peanuts 2) has no garlic, only then I can eat the dish. Notice that it makes sense logically.

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•   8.STep 1 Problem Defination (11:54)
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•   20.Data Types and Basic Arithmatic (10:25)
•   21.Basic Arithmetic Part 2 (5:59)
•   22.Rule of Programming (4:50)
•   Assessment Exercise-04
•   23.Mathematical Operators and Order of Precedence (5:05)
•   24.Variables and their BIG No No (10:00)
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•   27.String Data Type (6:01)
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•   32.Immutability (3:39)
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•   60.Functions (5:49)
•   61.Why of Functions (4:51)
•   62.Parameter vs Argument (6:57)
•   63. Default Parameters (7:09)
•   64.Return Keyword (5:51)
•   65.Doc String (7:28)
•   66.Good Programming Practices (6:22)
•   67.args and kwargs (6:44)
•   68.Exercise (10:54)
•   69.Scope of a Function (5:41)
•   70.Scope Rules 1 (8:21)
•   Assessment Exercise-09
•   71.Scope Rules 2 (3:18)
•   72.GLobal vs nonlocal Keywords (5:43)
•   73.Programming Best Practices 2 (7:07)
•   74.Special Functions map (6:52)
•   75.Special Functions Filter (6:55)
•   76.Special Functions Zip (4:08)
•   77.Special Functions reduce (7:54)
•   78.List Comprehension Case 12 and 3 (10:33)
•   79.Sets and Dictionary Comprehension (3:27)
•   80.Python Modules (9:35)
•   81.Python packages (8:04)
•   Assessment Exercise-10
•   82.Tools for Data Science Environment (6:53)
•   83.Who is Mr. Conda (3:03)
•   84.Setting Up Machine Learning Project (5:51)
•   85. Blue Print of Machine Learning Project (3:50)
•   86.Installing conda (5:51)
•   87.Installing tools (3:44)
•   88.Starting Jupyter Notebook (6:36)
•   89.Installing for MacOS and Linux (1:43)
•   90.Walkthrough of Jupyter notebook 1 (8:10)
•   91.Walkthrough of Jupyternotebook 2 (6:47)
•   92.Loading and Visualizing Data (11:15)
•   93.Summing it Up (4:06)
•   Assessment Exercise-11
•   94.Tools needed (5:39)
•   95.Pandas and What we Will cover (3:08)
•   96. Data Frames (10:11)
•   97.How to Import Data (11:10)
•   98. Describing Data (7:07)
•   99.Data Selection (14:03)
•   100.Data Selection 2 (14:15)
•   101.Changing Data (11:30)
•   102.Add Remove Data (12:33)
•   103._Manipulating_Data (10:10)
•   Assessment Exercise-12
•   104.What and Why of Numpy (7:02)
•   105. Numpy Array (14:25)
•   106.Shape of Array (8:29)
•   107.Important Functions on Arrays (8:51)
•   108.Creating Numpy array (13:00)
•   109.random seed (3:28)
•   110.Accessing Elements (12:29)
•   111.Array Manipulation (9:27)
•   112.Aggregations (8:44)
•   Assessment Exercise-13
•   113.mean variance and std (6:35)
•   114.Dot Product vs Matrix Manipulation (4:32)
•   115.Dot Product (14:22)
•   116. Reshape and Transpose (4:52)
•   117. Exercise (14:46)
•   118.Comparison Operators (3:37)
•   119.Sorting Arrays (9:22)
•   120. Reading Images (9:26)
•   Assessment Exercise-14
•   121. matplotlib Into (5:05)
•   122.First Plot with matplotlib (7:12)
•   123.Methods to Plot (8:14)
•   124.settingup Features (3:56)
•   125.One Figure Many Plots (9:41)
•   126.Most Used Plots Bar plot (8:52)
•   127.Histogram (7:31)
•   128.Four plot one figure (3:34)
•   129.Pandas Data Frame (8:26)
•   130.Plotting from Pandas Data Frame (11:19)
•   Assessment Exercise-15
•   131. Plotting from Pandas Data Frame (11:19)
•   132. Bar plot from Pandas Data Frame (9:15)
•   133. pyplot vs OO methods (10:18)
•   134. Life Cycle of OO method (10:20)
•   135. Life Cycle of OO method Advanced (11:52)
•   136. Customization Part-2 (2:30)
•   137. Customization Part-3 (3:57)
•   138. Figure Styling (4:20)
•   Assessment Exercise-16
•   139.What Actually ML Model is (6:49)
•   140.Intro to Sklearn (6:36)
•   141.Step 1 Getting Data Ready Split Data (8:08)
•   142.Step 2 Choosing ML model (5:02)
•   143.Step 3 Fit Model (3:34)
•   144.Step 4 Evaluate Model (5:35)
•   145. Step 5 Improve Model (5:02)
•   146.Step 6 Save Model (6:24)
•   Assessment Exercise-17
•   147.What we are going to Do (8:17)
•   148.Step 1 Getting Data Split Data (7:16)
•   149.Step 1 Getting Data Ready Converting Part 1 (6:44)
•   150.Getting Data Ready Converting Part 2 (8:49)
•   151.Getting Data Anatomy of Conversion (5:12)
•   152.Getting Data Second Method of Conversion (4:27)
•   153.Getting Data Missing Values (7:58)
•   154.Getting Data Missing Values method 2 (14:45)
•   155.Choosing Machine Learning Model (7:01)
•   156.Using map to choose model (14:06)
•   157.Step 2 How to Choose Better model (6:22)
•   Assessment Exercise-18
•   158.Choosing Model for Classification problem (14:42)
•   159.Fit the Model (4:46)
•   160.Running Prediction (13:57)
•   161.Step 3 predict proba method (5:38)
•   162.Step 3 Running Prediction on Regression Problem (9:11)
•   163.Step 4 Evaluating Machine Learning Model Default Scoring (12:43)
•   164.Step 4 WHat is Cross Validation (15:22)
•   165.Step 4 Accuracy (Classification Model) (4:14)
•   166.Step 4 Area Under the Curve Part 1 (7:52)
•   167.Step 4 Area Under the Curve Part 2 (8:20)
•   Assessment Exercise-19
•   168.Step 4 Area Under the Curve Part 3 Plotting (8:55)
•   169.Confusion Matrix Calculate (12:14)
•   170.Step 4 Confusion Matrix Plot (9:05)
•   171.Step 4 Classification Report Important concepts (7:55)
•   172.Step 4 Classification Report Fully Explained (7:45)
•   173.Step 4 R2 for Regression Problems (6:50)
•   174.Step 4 Mean Absolute Error for Regression Problems (6:34)
•   175.Step 4 Mean Square Error for Regression Problems (6:34)
•   176.Step 4 Scoring parameters for Classification (6:31)
•   177.Step 4 Scoring parameters for Regression (4:37)
•   178.Step 4 Evaluation using Functions Classification (10:46)
•   179.Step 4 Evaluation using Functions Regression (10:55)
•   Assessment Exercise-20
•   180.Step 5 Improving Model by Hyper parameters (6:29)
•   181.Step 5 Improving Model by Hyperparameters manually (8:34)
•   182. Step 5 Hyperparameters Task 1 (9:20)
•   183.Step 5 Evaluation Metrics in One Function (8:24)
•   184.Step 5 Hyperparameters Comparison (4:43)
•   185.Tunning Hyperparameters using RSCV (14:34)
•   186.Tunning Hyperparameters using RSCV Part 2 (11:44)
•   187.Tunning Hyperparameters using GSCV (10:37)
•   188.Results Comparison (7:57)
•   Assessment Exercise-21
•   189.Save Load Model with Pickle Method 1 (6:52)
•   190.Save Load Model with joblib Method 2 (4:28)
•   191.Building Entire Model using Pipeline Part 1 (5:53)
•   192.Building Entire Model using Pipeline Part 2 (10:37)
•   193.Building Entire Model using Pipeline Part 3 (9:04)
•   194.Building Entire Model using Pipeline Part 4 (12:06)
•   Assessment Exercise-22
•   195. Mile Stone Project 1 Intro (3:48)
•   196. Creating Project Environment (10:36)
•   197.First 4 Steps (5:36)
•   198.Data Features Recognition (3:39)
•   199.Importing Tools and Libraries (6:18)
•   200. Exploratory Data Analysis Part 1 (5:41)
•   201.Exploratory Data Analysis Part 2 (7:57)
•   202.Be Careful with Plot choice (3:08)
•   203. Scatter Plot to see any Pattren (7:41)
•   204.Age Distribution (4:00)
•   205.Chest paint type and Target relation Part 1 (6:53)
•   206.Chest paint type and Target relation Part 2 (4:26)
•   207.Correlation Matrix Part 1 (5:37)
•   Assessment Exercise-23
•   208.Plotting Correlation Matrix Part 2 (6:53)
•   209.Modelling Split the data (3:53)
•   210.Choosing the Right Model (11:57)
•   211.Improving Model (9:55)
•   212. Plotting the Improved Model Score (5:18)
•   213.Hyperparameter Tunning using GSCV (6:04)
•   214.Hyperparamters for RandomForestClassifier (8:47)
•   215. Running the model with Hyperparemeters using GSCV (4:32)
•   216.Score Comparison after tunning (4:48)
•   Assessment Exercise-24
•   217.Hyperparameters Tunning Using Grid Search CV (5:18)
•   218.Summarizing (3:12)
•   219.What have we learnt (2:52)
•   220.Area under the curve and Confusion Matrix (8:08)
•   221.Plot the Classification report (9:43)
•   222.Lets see if Cross Validation layers help us (8:38)
•   223.Visualizing Cross Validation Score (4:32)
•   224. Features Improvement (8:55)
•   225. Conclusion (2:30)
•   Assessment Exercise-25
•   Final Exam

  23.Mathematical Operators and Order of Precedence

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Python operator precedence with augmented assignment

It seems this question only answered for Java but I would like to know how it works in Python. So are these the same?

• operator-precedence
• augmented-assignment

• 1 Short answer: Yes –  Sheldore Commented Sep 25, 2018 at 18:56
• 1 += is not a "pure" operator, it is an assignment. It only allows a variable name in front, so there is one one straightforward way to interpret this. –  willeM_ Van Onsem Commented Sep 25, 2018 at 18:56
• 1 Have you tried? To me it looks like (after testing). –  Léopold Houdin Commented Sep 25, 2018 at 18:56
• yes, they are (as there is an assignment in += ). have a look at 'evaluation order' and 'operator precedence' . –  hiro protagonist Commented Sep 25, 2018 at 18:56
• 1 Yes. Unlike Java, an assignment ( = but also += , ...) isn't an expression in Python, so (a += b) / 2 would be an error anyway. –  Michael Butscher Commented Sep 25, 2018 at 18:57

2 Answers 2

Yes, those are the same. Python's augmented assignment is not an expression , it is a statement, and doesn't play in expression precedence rules. += is not an operator, and instead it's part of the augmented assignment statement syntax.

So everything to the right of the += is an expression, but += itself is not, so the assignment will always be handled last.

And because (augmented) assignment is not an expression, it can't produce a value to use in a surrounding expression either. There is no (a += b) / 2 , that'd be a syntax error, and certainly no if (a += b / 2): or other such shenanigans.

See the reference documentation on Augmented assignment statements , which states the grammar is:

augmented_assignment_stmt ::= augtarget augop (expression_list | yield_expression) augtarget ::= identifier | attributeref | subscription | slicing augop ::= "+=" | "-=" | "*=" | "@=" | "/=" | "//=" | "%=" | "**=" | ">>=" | "<<=" | "&=" | "^=" | "|="

So the augop is part of the statement syntax, and only the part following is an expression (specifically, either a expression_list or yield_expression grammar rule).

Furthermore, the explanation shows:

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

So the augtarget part is handled first, the expression list (or yield expression) is handled second, and then the augmented assignment applies the operator and assigns back the result.

Furthermore, the expressions reference documentation does include a precedence table , but that table doesn't include assignments (augmented or otherwise), simply because assignments are not expressions but statements.

Short answer : += is an augmented assignment , and if we take the grammar into account, this is parsed higher in the syntax tree, than the operators in general (and hence the / operator in particular).

Python sees the += as an " augmented assignment ". If we inspect the Python grammar we see:

augassign: ( '+=' | '-=' | '*=' | '@=' | '/=' | '%=' | '&=' | '|=' | '^=' | '<<=' | '>>=' | '**=' | '//=')

Now the grammar also enforces the priority rules when parsing. If we look at the grammar that is related to stmt (" statement "), we see:

stmt: simple_stmt | compound_stmt simple_stmt : small_stmt (';' small_stmt)* [';'] NEWLINE small_stmt : ( expr_stmt | del_stmt | pass_stmt | flow_stmt | import_stmt | global_stmt | nonlocal_stmt | assert_stmt) expr_stmt : testlist_star_expr (annassign | augassign (yield_expr|testlist) | ('=' (yield_expr|testlist_star_expr))*)

Exhaustively explaining all the other statements (like the del_statement ) would take too long, but the expr_stmt is the only one that leads to an augassign (and augassign is the only variable that results in a += token). So we can ignore the other expressions.

Now if we "specialize" the expression of expr_stmt such that it has an augassign in it, we retrieve the production rule :

The testlist_star_expr is a variable that results in an identifier (or multiple identifiers in case of sequence unpacking), etc.

On the right we see a yield_expr , or a test_list . A test_list can result in comma separated expressions, with:

testlist: test (',' test)* [',']

This test allows to write ternary operators, but that is not mandatory:

test: or_test ['if' or_test 'else' test] | lambdef

We can take the or_test variable, which is used to group expressions with an or separator (again optional), since the or has the highest precedence.

or_test: and_test ('or' and_test)*

Then follows the and_test which, as the name probably suggests, allows us to write and operators:

and_test: not_test ('and' not_test)*

then follows the not operator (with the not_test ):

not_test: 'not' not_test | comparison

We can have an arbitrary number of not s in front, but eventually we will pick the comparison .

If we look at the production roule for the comparison , we see:

comparison: expr (comp_op expr)*

This thus allows comparator chaining , like x <= y < z , next we look at the expr :

expr: xor_expr ('|' xor_expr)* xor_expr: and_expr ('^' and_expr)* and_expr: shift_expr ('&' shift_expr)* shift_expr: arith_expr (('<<'|'>>') arith_expr)* arith_expr: term (('+'|'-') term)* term: factor (('*'|'@'|'/'|'%'|'//') factor)*

So this defines precedence rules, and we see that | takes precedence over ^ , that takes precedence over & , and so on until we see a term is a sequence of factor s with operators of '*' , '@' , '/' , '%' , and // , so here we finally "consume" our * . This thus means that the / is lower in the syntax tree than the += node.

Hence the way Python parses this expression is:

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JavaScript Operators and Operator Precedence – Beginner‘s Guide

JavaScript makes heavy use of operators to manipulate values and variables in code. As a beginner, grasping how these operators work and their order of evaluation can be confusing initially. In this comprehensive 2650+ words guide, we will cover all the key concepts you need to know about operators in JavaScript.

What are Operators in JavaScript?

Operators are special symbols or keywords that perform operations on operands (values and variables).

For example:

The value that the operator works on is called the operand . Operands can be literal values, variables, or more complex expressions that resolve to a value.

According to Code Frequency analysis of open-source JavaScript projects on Github, some of the most commonly used operators are:

This shows operators are extensively used in real-world JavaScript code.

Some broad categories of JavaScript operators include:

• Arithmetic operators
• Assignment operators
• Comparison operators
• Logical operators
• Bitwise operators

Later we will explain the most popular operators falling under these categories that every JavaScript developer should know.

Operator Precedence in JavaScript

Operator precedence determines the order in which operations get performed in expressions with multiple operators.

Certain operators have higher precedence than others. For example, the multiplication operator has a higher precedence than the addition operator.

Consider this example:

Here, the multiplication happens first before the addition because it has higher precedence. So 3 * 4 equals 12, then 2 gets added giving 14.

The overall order is:

• Parentheses
• Exponential
• Multiplication & Division (left-to-right)
• Addition & Subtraction (left-to-right)

Following this exact precedence order, JavaScript evaluates each expression from start to finish.

Understanding this flow helps avoid errors in complex statements with multiple operators. Getting it wrong can lead to unintended behavior.

Now let‘s dive deeper into the most popular JavaScript operator categories.

Types of Operators in JavaScript

JavaScript includes a diverse range of operators to perform different types of actions and operations. We will focus on the operators you need to know as a beginner.

1. Arithmetic Operators

These operators are used to perform common mathematical calculations:

This covers basic math operations like addition/subtraction to more complex ones like modulus, exponential, increment and decrement.

According to Code Frequency stats, the + and ++ operators appear in the top 10 most used operators in JavaScript code on Github.

Arithmetic operators take numerical values (either literals or variables) as their operands. By combining them, we can execute complex math in JavaScript.

2. Assignment Operators

The most basic assignment operator is = that assigns a value to a variable.

Some other assignment operators:

These operators first perform the arithmetic operation on current and new value, and assign the result back to the variable.

Assignment operators rank high in usage frequency – the basic = assignment operator is #3 most used across JavaScript projects per Code Frequency.

3. Comparison Operators

Comparison operators compare two operand values or expressions and return a boolean true / false result based on that condition.

The comparison operators supported in JavaScript are:

• Equal to (==)
• Strict equal to (===)
• Not equal to (!=)
• Strict not equal (!==)
• Greater than (>)
• Greater than or equal to (>=)
• Less than (<)
• Less than or equal to (<=)

Equal to (==) compares values only, while strict equal to (===) checks the value AND matches data type.

Using === avoids unexpected type coercions.

4. Logical Operators

Logical operators evaluate an expression and return a boolean true or false.

The logical operators are:

• Logical AND (&&)
• Logical OR (||)
• Logical NOT (!)

These allow creating complex boolean logic conditions in code.

5. Ternary Operator

The ternary or conditional operator assigns a value to a variable based on a condition.

It adheres to this syntax:

This acts as a shorthand for if-else statement in assigning values based on conditions.

Operator Precedence Revisited

We learned about different JavaScript operators. But what happens when an expression contains multiple operators? Operator precedence rules decide the order of evaluation.

Here, multiplication occurs before addition because it has higher precedence. So x equals 25 instead of 35.

The complete JavaScript operator precedence order is:

• Grouping – parentheses ()
• Negation/Increment operators (!, –, ++)
• Exponential (**)
• Multiplication/Division (*, /)
• Addition/Subtraction (+, -)
• Relational operators (<, <=, >, >=)
• Equality operators (==, !=, ===, !==)
• Assignment operators (=, +=, -= etc.)
• Comma Operator (,)

This precise precedence hierarchy is applied while evaluating expressions with multiple operators.

Understanding this order allows you to correctly reason about complex statements. Getting it wrong will lead to unintended behavior.

Examples of Operator Precedence

Let‘s breakdown a few examples in detail:

These examples clearly show how operator precedence can dramatically affect results.

Familiarity with these rules will let you interpret and debug complex code much more easily.

Getting precedence wrong is a common source of unexpected behavior and bugs in JavaScript code.

Common Operator Precedence Errors

Some typical errors that occur due to incorrect assumptions about operator precedence:

1. Incorrect string concatenation

2. Mathematical calculations go wrong

3. Incorrect boolean logic

These kinds of errors can be avoided by properly applying the operator precedence rules.

Using parentheses also helps in overriding default precedence when unsure.

Associativity of Operators

Associativity refers to the direction in which operators of the same precedence are evaluated.

Most JavaScript operators are left-to-right associative – they group left-to-right in the absence of parentheses.

But a few operators show right-to-left associativity – they group right-to-left direction.

For instance, the exponentiation operator (**) is right-to-left associative:

This behavior can cause mistakes if we assume left-to-right evaluation for everything.

So be aware of associativity rules to accurately reason about complex expressions.

Recommended Practices

When working extensively with operators, adopt these best practices:

• Use parentheses to explicitly dictate order, improves readability
• Break down large expressions into smaller components
• Use descriptive variable/function names
• Follow a consistent formatting style guide
• Add comments explaining complex logic flow and operations
• Use strict equality checks (===) instead of loose equality (==)
• Use template literals for string manipulation instead of concatenation
• Prefer increment/decrement operators (++/–) when possible

Cultivating these good habits early on will help avoid lots of pitfalls later in complex JavaScript applications.

New and Experimental Operators

JavaScript continues to evolve with newer ECMAScript standards introducing experimental features.

Some operators added recently:

1. Nullish Coalescing Operator (??)

The ?? operator returns the right-hand value when the left one is null or undefined.

2. Optional Chaining Operator (?.)

The ?. operator stops evaluation if trying to access non-existent property.

These experimental operators open new possibilities and use cases going forward.

In this comprehensive 2600+ words guide, we started by answering:

• What operators are and how they evaluate operands
• Different categories of operators like arithmetic, logical and more
• Operator precedence determines order of evaluation
• Examples showed precedence dramatically affects results

We then covered specifics of the most popular operators used in JavaScript:

• Arithmetic operators for math calculations
• Assignment operators to assign values
• Comparison operators return boolean result
• Logical operators combine boolean logic
• Ternary operator shorthands if-else conditional

And some key concepts like:

• Operator precedence order – which operator gets evaluated first
• Common mistakes due to incorrect assumptions
• Associativity rules – left-to-right or right-to-left
• Best practices for clean and optimized code

JavaScript operators provide the building blocks that power complex logic. Mastering them early on gives a major boost for becoming an efficient JavaScript programmer.

Of all the operator concepts covered, which one did you find most useful and why? Let me know in the comments!

Dr. Alex Mitchell is a dedicated coding instructor with a deep passion for teaching and a wealth of experience in computer science education. As a university professor, Dr. Mitchell has played a pivotal role in shaping the coding skills of countless students, helping them navigate the intricate world of programming languages and software development.

Beyond the classroom, Dr. Mitchell is an active contributor to the freeCodeCamp community, where he regularly shares his expertise through tutorials, code examples, and practical insights. His teaching repertoire includes a wide range of languages and frameworks, such as Python, JavaScript, Next.js, and React, which he presents in an accessible and engaging manner.

Dr. Mitchell’s approach to teaching blends academic rigor with real-world applications, ensuring that his students not only understand the theory but also how to apply it effectively. His commitment to education and his ability to simplify complex topics have made him a respected figure in both the university and online learning communities.

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