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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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Precedence of Python Operators

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.

Sorry about that.

Python Operators Explained in Detail with Examples

January 9, 2024

When diving into the world of Python programming, understanding Python operators is akin to learning the alphabets of a new language. These operators act as the building blocks for any Python script, playing a critical role in carrying out operations like arithmetic calculations, comparisons, and logical decisions. Not only do they facilitate code execution, but they also make your code more efficient and readable. In this comprehensive guide, we will explore various types of Python operators, how they function, and why they are essential for anyone aspiring to master Python programming.

Different Python Operators

Here are the list of Python Operators:

Arithmetic Operators
Comparison Operators
Logical Operators
Assignment Operators
Augmented Operators
Bitwise Operators
Identity Operators
Membership Operators

We will cover these in detail in next chapters.

1. Arithmetic operators in Python

As the name suggests, these operators perform various arithmetic operations and calculations like addition, multiplications, division, etc. They can be used with numerical data types like integers and floats, and some can even be used with strings and lists for operations like concatenation and repetition.

Below is a table that provides a detailed explanation of arithmetic operators in Python.

In each example, the variable a is used to store the result of the operation, and then print(a) is used to display the output.

2. Comparison operators in Python

Comparison operators are also known as rational operators as well. They are used to compare different values. They evaluate different types of expressions and manipulate the values of variables by performing different operations on them.

In each code example, the variable a is used to store the result of the comparison operation, and then print(a) is used to display the Boolean output ( True or False ).

3. Logical operators in Python

Python logical operators are used to evaluate the one or more than one condition between variables or operands by providing specific results. i.e True and False . Useful for making decisions in code (conditionals) and testing multiple conditions . It can be used with any data types that can be converted to Boolean values.

In each example, the variable a is used to store the result of the logical operation, and print(a) is used to display the resulting Boolean value ( True or False ).

4. Assignment Operators

Assignment operators are primarily used to assign values to variables. They perform a single function of assigning a value from the right-hand side to the variable on the left-hand side. It is useful for initializing variables and updating their values.

Below is a table that explains the assignment operators in Python, along with code examples using a small variable name ( a ) and the print() statement to display the result.

In each example, the variable a is initially assigned a value. Then, an assignment operator modifies this value, and print(a) displays the updated value.

5. Augmented Operators (Compound Assignment Operators)

Python has a set of operators known as "augmented assignment operators," also sometimes called "compound assignment operators." These operators provide a shorthand way to apply an operation and an assignment in a single step. They are similar to assignment operators but perform an additional operation before the assignment.

Unlike normal assignment operators, Augmented Assignment Operators are used to replace those statements where a binary operator takes two operands, variable1 and variable1 and then assigns a final result back to one of the operands.

Below is a table that outlines the augmented assignment operators in Python, complete with code examples using a small variable name ( a ) and the print() function to display the result.

In each example, the variable a is initially assigned a value. Then, an augmented assignment operator modifies this value, and print(a) displays the updated value.

6. Bitwise Operators

Bitwise operators in Python are used to operate at a binary level. This means they look directly at the binary digits or binary bits of an integer. Below is a table that explains each bitwise operator in Python, along with a code example that uses a small variable name ( a ) and the print() statement to display the result.

Below are some examples to help you understand how these operators work in Python:

Bitwise AND ( & ): This operator compares each binary digit of the first operand to the corresponding binary digit of the second operand. If both digits are 1 , the digit in the resulting binary representation is 1 , otherwise 0 .

Bitwise OR ( | ): This operator also compares each binary digit of the first operand to the corresponding binary digit of the second operand. If either of the digits is 1 , the resulting binary digit is 1 .

Bitwise XOR ( ^ ): The XOR operator compares each bit of its first operand to the corresponding bit of its second operand. If one bit is 0 and the other bit is 1 , the corresponding result bit is set to 1 .

Bitwise NOT ( ~ ): This operator is a unary operator, meaning it works with a single operand. Bitwise NOT will invert the number's bit-wise representation. Note that because Python uses signed integers , the result will be the two's complement of the inverted bits.

Left Shift ( << ): The left shift operator shifts the bits of a number to the left by a specified number of positions. Each left shift doubles the number, as it is equivalent to multiplication by 2 to the power of the number of shifted positions.

Right Shift ( >> ): The right shift operator shifts the bits of a number to the right by a specified number of positions. Each right shift halves the number, rounding down.

7. Identity Operators

Identity operators in Python are used to compare the memory locations of two objects. They help to check if two variables refer to the same object in memory. Below is a table that describes the identity operators, accompanied by Python code examples using small variable names ( a and b ) and the print() function to display the result.

Description and Use-cases

is Operator : The is operator checks whether both the operands refer to the same object (i.e., point to the same memory location). It's often used to test if a variable is None , although it can also be used for other objects like lists or dictionaries to ensure that two variables point to the same data structure.

is not Operator : The is not operator is essentially the opposite of the is operator. It checks whether two variables refer to different objects in memory. This is useful for ensuring that two variables don't share the same underlying data.

Remember, is and is not operators should not be confused with == and != operators. The former compare the memory locations, whereas the latter compare the values.

8. Membership Operators

Membership operators are used to test whether a value is a member of a sequence such as a list, tuple, or string, or a collection like a dictionary . Below is a table explaining the membership operators in Python, with code examples using small variable names ( a and b ) and the print() statement to display the result.

in Operator : The in operator is used to check if a value exists in a sequence or a collection. It's a quick way to check for the presence of a specific item.

not in Operator : The not in operator is essentially the opposite of the in operator. It checks if a specified element is not present in a given sequence or collection.

Assignment Vs Augmented Assignment Operators

The fundamental difference between assignment operators and augmented assignment operators lies in their functionality and readability.

Assignment Operators:

Basic Function : Assignment operators are mainly used to assign values to variables.

Single Operation : Assignment operators perform just one function: they assign the value on the right-hand side to the variable on the left-hand side.

Multiple Steps for Operations : If you need to perform an operation (like addition, subtraction, etc.) and then assign the result to the variable, you have to do it in multiple steps or lines.

Augmented Assignment Operators:

Enhanced Function : Augmented assignment operators perform an operation in addition to assignment, effectively combining the two into a single action.

Conciseness : They allow for more concise code. Instead of writing a = a + 2 , you can simply write a += 2 .

Efficiency : Augmented assignment operators may be more efficient in some cases, as they can reduce the amount of typing and potentially perform the operation faster, although the speed difference is generally negligible for basic types.

Multiple Operations : They make it easier to perform complex calculations and reassignments in a single step.

Operators precedence order in Python

Operator precedence in Python determines the order in which operators are evaluated when multiple operators are present in an expression. Operators with higher precedence are evaluated before operators with lower precedence.

Here is a list of operators in Python , sorted from highest precedence to lowest:

Parentheses () : Expressions inside parentheses are evaluated first.
Exponentiation ** : Right-to-left associativity.
Complement, Unary Plus and Minus ~ + - : Unary operators.
Multiply, Divide, Floor Divide, and Modulus * / // % : Left-to-right associativity.
Addition and Subtraction + - : Left-to-right associativity.
Bitwise Right Shift and Left Shift >> << : Left-to-right associativity.
Bitwise AND & : Left-to-right associativity.
Bitwise XOR ^ : Left-to-right associativity.
Bitwise OR | : Left-to-right associativity.
Comparison Operators == != < <= > >= : Left-to-right associativity.
Identity Operators is is not : Left-to-right associativity.
Membership Operators in not in : Left-to-right associativity.
Logical NOT not : Right-to-left associativity.
Logical AND and : Left-to-right associativity.
Logical OR or : Left-to-right associativity.
Conditional Expression if else : Right-to-left associativity.
Assignment = : Right-to-left associativity.
Augmented Assignment += -= *= /= //= %= **= &= ^= |= <<= >>= : Right-to-left associativity.

When multiple operators have the same precedence, Python usually evaluates them from left to right (left-to-right associativity). The exception to this rule is the exponentiation operator ( ** ), which has right-to-left associativity, meaning that operations are evaluated from right to left.

For example:

With the expression 3 + 4 * 2 , the multiplication ( * ) operator has higher precedence than addition ( + ), so 4 * 2 is evaluated first, resulting in 3 + 8 which is 11 .
In the expression 2 ** 3 ** 2 , the ** operator is right associative, so 3 ** 2 is evaluated first, resulting in 2 ** 9 which is 512

Special Cases and Syntax

Python allows some operators to be used in unconventional ways. For example, you can use the * operator to repeat strings and lists.

While using operators, you should follow certain syntax rules, like putting them between operands (in infix notation), or ensuring that they are applied to compatible data types.

Chaining : Python allows you to chain multiple comparisons to perform a more complex test.

Chained comparisons also benefit from short-circuit behavior. This means that if one comparison fails, the remaining ones are not executed, which can improve performance.

Mixing Operators in Expressions: Python allows for mixing certain types of operators, but it's important to use parentheses to make the expression clear.

When mixing operators with different data types, Python will usually automatically convert the types to make them compatible, if possible.

Common Mistakes and Best Practices

Importance of parentheses.

Clarifying Ambiguity : Parentheses help in making the expression unambiguous. This is especially crucial when dealing with operators of different precedences.

Parentheses can also make complex expressions more readable, even if they're not strictly necessary.

Floating-Point Arithmetic Caveats

Precision : Floating-point numbers do not always represent decimal numbers precisely, which can lead to errors in calculations.

Integer Division : When dividing integers, use // for integer division and / for floating-point division to avoid unexpected results.

Common Pitfalls with Assignment Operators

Immutable Reassignment : Be cautious when you are working with immutable types like tuples or strings. Reassignment will produce a new object instead of modifying the original one.

Augmented Assignment : Remember that augmented assignment ( += , *= etc.) can behave differently for mutable and immutable objects.

Chaining Assignments : While Python allows for chained assignments like a = b = 1 , be cautious when using this feature with mutable objects as it can lead to unexpected behavior.

Practical Examples

How to use Arithmetic Operators in Math Calculations

Arithmetic operators come in handy for a wide range of math calculations. Here we are calculating Area and Perimeter:

Using Comparison Operators in Conditional Statements

Comparison operators are frequently used in conditional statements like if , elif , and else . Here we are determining the Largest Number:

Using Logical Operators in Decision Making

Logical operators can be used for making complex decisions. Here we are checking the eligibility for a Loan:

Real-world Scenarios where Bitwise Operators are Useful

Bitwise operators are particularly useful in low-level programming and bit manipulation:

Setting Flags

Checking Flags

How Membership and Identity Operators Work in Data Structures like Lists, Sets, and Dictionaries

Membership and identity operators are quite useful when working with data structures:

Dictionaries

Python has seven different types of operators. Arithmetic operators, logic operators, assignment operators, augmented operators, bitwise operators, and two special kinds of operators; membership operator and identity operator. In this tutorial, we learned about each type of these operators by taking different examples. Moreover, we also covered the precedence of these operators over each other.

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

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:

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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Multiply 10 with 5 , and print the result.

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Assignment operator in python.

Last Updated on June 8, 2023 by Prepbytes

To fully comprehend the assignment operators in Python, it is important to have a basic understanding of what operators are. Operators are utilized to carry out a variety of operations, including mathematical, bitwise, and logical operations, among others, by connecting operands. Operands are the values that are acted upon by operators. In Python, the assignment operator is used to assign a value to a variable. The assignment operator is represented by the equals sign (=), and it is the most commonly used operator in Python. In this article, we will explore the assignment operator in Python, how it works, and its different types.

What is an Assignment Operator in Python?

The assignment operator in Python is used to assign a value to a variable. The assignment operator is represented by the equals sign (=), and it is used to assign a value to a variable. When an assignment operator is used, the value on the right-hand side is assigned to the variable on the left-hand side. This is a fundamental operation in programming, as it allows developers to store data in variables that can be used throughout their code.

For example, consider the following line of code:

Explanation: In this case, the value 10 is assigned to the variable a using the assignment operator. The variable a now holds the value 10, and this value can be used in other parts of the code. This simple example illustrates the basic usage and importance of assignment operators in Python programming.

Types of Assignment Operator in Python

There are several types of assignment operator in Python that are used to perform different operations. Let’s explore each type of assignment operator in Python in detail with the help of some code examples.

1. Simple Assignment Operator (=)

The simple assignment operator is the most commonly used operator in Python. It is used to assign a value to a variable. The syntax for the simple assignment operator is:

Here, the value on the right-hand side of the equals sign is assigned to the variable on the left-hand side. For example

Explanation: In this case, the value 25 is assigned to the variable a using the simple assignment operator. The variable a now holds the value 25.

2. Addition Assignment Operator (+=)

The addition assignment operator is used to add a value to a variable and store the result in the same variable. The syntax for the addition assignment operator is:

Here, the value on the right-hand side is added to the variable on the left-hand side, and the result is stored back in the variable on the left-hand side. For example

Explanation: In this case, the value of a is incremented by 5 using the addition assignment operator. The result, 15, is then printed to the console.

3. Subtraction Assignment Operator (-=)

The subtraction assignment operator is used to subtract a value from a variable and store the result in the same variable. The syntax for the subtraction assignment operator is

Here, the value on the right-hand side is subtracted from the variable on the left-hand side, and the result is stored back in the variable on the left-hand side. For example

Explanation: In this case, the value of a is decremented by 5 using the subtraction assignment operator. The result, 5, is then printed to the console.

4. Multiplication Assignment Operator (*=)

The multiplication assignment operator is used to multiply a variable by a value and store the result in the same variable. The syntax for the multiplication assignment operator is:

Here, the value on the right-hand side is multiplied by the variable on the left-hand side, and the result is stored back in the variable on the left-hand side. For example

Explanation: In this case, the value of a is multiplied by 5 using the multiplication assignment operator. The result, 50, is then printed to the console.

5. Division Assignment Operator (/=)

The division assignment operator is used to divide a variable by a value and store the result in the same variable. The syntax for the division assignment operator is:

Here, the variable on the left-hand side is divided by the value on the right-hand side, and the result is stored back in the variable on the left-hand side. For example

Explanation: In this case, the value of a is divided by 5 using the division assignment operator. The result, 2.0, is then printed to the console.

6. Modulus Assignment Operator (%=)

The modulus assignment operator is used to find the remainder of the division of a variable by a value and store the result in the same variable. The syntax for the modulus assignment operator is

Here, the variable on the left-hand side is divided by the value on the right-hand side, and the remainder is stored back in the variable on the left-hand side. For example

Explanation: In this case, the value of a is divided by 3 using the modulus assignment operator. The remainder, 1, is then printed to the console.

7. Floor Division Assignment Operator (//=)

The floor division assignment operator is used to divide a variable by a value and round the result down to the nearest integer, and store the result in the same variable. The syntax for the floor division assignment operator is:

Here, the variable on the left-hand side is divided by the value on the right-hand side, and the result is rounded down to the nearest integer. The rounded result is then stored back in the variable on the left-hand side. For example

Explanation: In this case, the value of a is divided by 3 using the floor division assignment operator. The result, 3, is then printed to the console.

8. Exponentiation Assignment Operator (**=)

The exponentiation assignment operator is used to raise a variable to the power of a value and store the result in the same variable. The syntax for the exponentiation assignment operator is:

Here, the variable on the left-hand side is raised to the power of the value on the right-hand side, and the result is stored back in the variable on the left-hand side. For example

Explanation: In this case, the value of a is raised to the power of 3 using the exponentiation assignment operator. The result, 8, is then printed to the console.

9. Bitwise AND Assignment Operator (&=)

The bitwise AND assignment operator is used to perform a bitwise AND operation on the binary representation of a variable and a value, and store the result in the same variable. The syntax for the bitwise AND assignment operator is:

Here, the variable on the left-hand side is ANDed with the value on the right-hand side using the bitwise AND operator, and the result is stored back in the variable on the left-hand side. For example,

Explanation: In this case, the value of a is ANDed with 3 using the bitwise AND assignment operator. The result, 2, is then printed to the console.

10. Bitwise OR Assignment Operator (|=)

The bitwise OR assignment operator is used to perform a bitwise OR operation on the binary representation of a variable and a value, and store the result in the same variable. The syntax for the bitwise OR assignment operator is:

Here, the variable on the left-hand side is ORed with the value on the right-hand side using the bitwise OR operator, and the result is stored back in the variable on the left-hand side. For example,

Explanation: In this case, the value of a is ORed with 3 using the bitwise OR assignment operator. The result, 7, is then printed to the console.

11. Bitwise XOR Assignment Operator (^=)

The bitwise XOR assignment operator is used to perform a bitwise XOR operation on the binary representation of a variable and a value, and store the result in the same variable. The syntax for the bitwise XOR assignment operator is:

Here, the variable on the left-hand side is XORed with the value on the right-hand side using the bitwise XOR operator, and the result are stored back in the variable on the left-hand side. For example,

Explanation: In this case, the value of a is XORed with 3 using the bitwise XOR assignment operator. The result, 5, is then printed to the console.

12. Bitwise Right Shift Assignment Operator (>>=)

The bitwise right shift assignment operator is used to shift the bits of a variable to the right by a specified number of positions, and store the result in the same variable. The syntax for the bitwise right shift assignment operator is:

Here, the variable on the left-hand side has its bits shifted to the right by the number of positions specified by the value on the right-hand side, and the result is stored back in the variable on the left-hand side. For example,

Explanation: In this case, the value of a is shifted 2 positions to the right using the bitwise right shift assignment operator. The result, 2, is then printed to the console.

13. Bitwise Left Shift Assignment Operator (<<=)

The bitwise left shift assignment operator is used to shift the bits of a variable to the left by a specified number of positions, and store the result in the same variable. The syntax for the bitwise left shift assignment operator is:

Here, the variable on the left-hand side has its bits shifted to the left by the number of positions specified by the value on the right-hand side, and the result is stored back in the variable on the left-hand side. For example,

Conclusion Assignment operator in Python is used to assign values to variables, and it comes in different types. The simple assignment operator (=) assigns a value to a variable. The augmented assignment operators (+=, -=, *=, /=, %=, &=, |=, ^=, >>=, <<=) perform a specified operation and assign the result to the same variable in one step. The modulus assignment operator (%) calculates the remainder of a division operation and assigns the result to the same variable. The bitwise assignment operators (&=, |=, ^=, >>=, <<=) perform bitwise operations and assign the result to the same variable. The bitwise right shift assignment operator (>>=) shifts the bits of a variable to the right by a specified number of positions and stores the result in the same variable. The bitwise left shift assignment operator (<<=) shifts the bits of a variable to the left by a specified number of positions and stores the result in the same variable. These operators are useful in simplifying and shortening code that involves assigning and manipulating values in a single step.

Here are some Frequently Asked Questions on Assignment Operator in Python:

Q1 – Can I use the assignment operator to assign multiple values to multiple variables at once? Ans – Yes, you can use the assignment operator to assign multiple values to multiple variables at once, separated by commas. For example, "x, y, z = 1, 2, 3" would assign the value 1 to x, 2 to y, and 3 to z.

Q2 – Is it possible to chain assignment operators in Python? Ans – Yes, you can chain assignment operators in Python to perform multiple operations in one line of code. For example, "x = y = z = 1" would assign the value 1 to all three variables.

Q3 – How do I perform a conditional assignment in Python? Ans – To perform a conditional assignment in Python, you can use the ternary operator. For example, "x = a (if a > b) else b" would assign the value of a to x if a is greater than b, otherwise it would assign the value of b to x.

Q4 – What happens if I use an undefined variable in an assignment operation in Python? Ans – If you use an undefined variable in an assignment operation in Python, you will get a NameError. Make sure you have defined the variable before trying to assign a value to it.

Q5 – Can I use assignment operators with non-numeric data types in Python? Ans – Yes, you can use assignment operators with non-numeric data types in Python, such as strings or lists. For example, "my_list += [4, 5, 6]" would append the values 4, 5, and 6 to the end of the list named my_list.

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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9. Classes ¶

Classes provide a means of bundling data and functionality together. Creating a new class creates a new type of object, allowing new instances of that type to be made. Each class instance can have attributes attached to it for maintaining its state. Class instances can also have methods (defined by its class) for modifying its state.

Compared with other programming languages, Python’s class mechanism adds classes with a minimum of new syntax and semantics. It is a mixture of the class mechanisms found in C++ and Modula-3. Python classes provide all the standard features of Object Oriented Programming: the class inheritance mechanism allows multiple base classes, a derived class can override any methods of its base class or classes, and a method can call the method of a base class with the same name. Objects can contain arbitrary amounts and kinds of data. As is true for modules, classes partake of the dynamic nature of Python: they are created at runtime, and can be modified further after creation.

In C++ terminology, normally class members (including the data members) are public (except see below Private Variables ), and all member functions are virtual . As in Modula-3, there are no shorthands for referencing the object’s members from its methods: the method function is declared with an explicit first argument representing the object, which is provided implicitly by the call. As in Smalltalk, classes themselves are objects. This provides semantics for importing and renaming. Unlike C++ and Modula-3, built-in types can be used as base classes for extension by the user. Also, like in C++, most built-in operators with special syntax (arithmetic operators, subscripting etc.) can be redefined for class instances.

(Lacking universally accepted terminology to talk about classes, I will make occasional use of Smalltalk and C++ terms. I would use Modula-3 terms, since its object-oriented semantics are closer to those of Python than C++, but I expect that few readers have heard of it.)

9.1. A Word About Names and Objects ¶

Objects have individuality, and multiple names (in multiple scopes) can be bound to the same object. This is known as aliasing in other languages. This is usually not appreciated on a first glance at Python, and can be safely ignored when dealing with immutable basic types (numbers, strings, tuples). However, aliasing has a possibly surprising effect on the semantics of Python code involving mutable objects such as lists, dictionaries, and most other types. This is usually used to the benefit of the program, since aliases behave like pointers in some respects. For example, passing an object is cheap since only a pointer is passed by the implementation; and if a function modifies an object passed as an argument, the caller will see the change — this eliminates the need for two different argument passing mechanisms as in Pascal.

9.2. Python Scopes and Namespaces ¶

Before introducing classes, I first have to tell you something about Python’s scope rules. Class definitions play some neat tricks with namespaces, and you need to know how scopes and namespaces work to fully understand what’s going on. Incidentally, knowledge about this subject is useful for any advanced Python programmer.

Let’s begin with some definitions.

A namespace is a mapping from names to objects. Most namespaces are currently implemented as Python dictionaries, but that’s normally not noticeable in any way (except for performance), and it may change in the future. Examples of namespaces are: the set of built-in names (containing functions such as abs() , and built-in exception names); the global names in a module; and the local names in a function invocation. In a sense the set of attributes of an object also form a namespace. The important thing to know about namespaces is that there is absolutely no relation between names in different namespaces; for instance, two different modules may both define a function maximize without confusion — users of the modules must prefix it with the module name.

By the way, I use the word attribute for any name following a dot — for example, in the expression z.real , real is an attribute of the object z . Strictly speaking, references to names in modules are attribute references: in the expression modname.funcname , modname is a module object and funcname is an attribute of it. In this case there happens to be a straightforward mapping between the module’s attributes and the global names defined in the module: they share the same namespace! [ 1 ]

Attributes may be read-only or writable. In the latter case, assignment to attributes is possible. Module attributes are writable: you can write modname.the_answer = 42 . Writable attributes may also be deleted with the del statement. For example, del modname.the_answer will remove the attribute the_answer from the object named by modname .

Namespaces are created at different moments and have different lifetimes. The namespace containing the built-in names is created when the Python interpreter starts up, and is never deleted. The global namespace for a module is created when the module definition is read in; normally, module namespaces also last until the interpreter quits. The statements executed by the top-level invocation of the interpreter, either read from a script file or interactively, are considered part of a module called __main__ , so they have their own global namespace. (The built-in names actually also live in a module; this is called builtins .)

The local namespace for a function is created when the function is called, and deleted when the function returns or raises an exception that is not handled within the function. (Actually, forgetting would be a better way to describe what actually happens.) Of course, recursive invocations each have their own local namespace.

A scope is a textual region of a Python program where a namespace is directly accessible. “Directly accessible” here means that an unqualified reference to a name attempts to find the name in the namespace.

Although scopes are determined statically, they are used dynamically. At any time during execution, there are 3 or 4 nested scopes whose namespaces are directly accessible:

the innermost scope, which is searched first, contains the local names

the scopes of any enclosing functions, which are searched starting with the nearest enclosing scope, contain non-local, but also non-global names

the next-to-last scope contains the current module’s global names

the outermost scope (searched last) is the namespace containing built-in names

If a name is declared global, then all references and assignments go directly to the next-to-last scope containing the module’s global names. To rebind variables found outside of the innermost scope, the nonlocal statement can be used; if not declared nonlocal, those variables are read-only (an attempt to write to such a variable will simply create a new local variable in the innermost scope, leaving the identically named outer variable unchanged).

Usually, the local scope references the local names of the (textually) current function. Outside functions, the local scope references the same namespace as the global scope: the module’s namespace. Class definitions place yet another namespace in the local scope.

It is important to realize that scopes are determined textually: the global scope of a function defined in a module is that module’s namespace, no matter from where or by what alias the function is called. On the other hand, the actual search for names is done dynamically, at run time — however, the language definition is evolving towards static name resolution, at “compile” time, so don’t rely on dynamic name resolution! (In fact, local variables are already determined statically.)

A special quirk of Python is that – if no global or nonlocal statement is in effect – assignments to names always go into the innermost scope. Assignments do not copy data — they just bind names to objects. The same is true for deletions: the statement del x removes the binding of x from the namespace referenced by the local scope. In fact, all operations that introduce new names use the local scope: in particular, import statements and function definitions bind the module or function name in the local scope.

The global statement can be used to indicate that particular variables live in the global scope and should be rebound there; the nonlocal statement indicates that particular variables live in an enclosing scope and should be rebound there.

9.2.1. Scopes and Namespaces Example ¶

This is an example demonstrating how to reference the different scopes and namespaces, and how global and nonlocal affect variable binding:

The output of the example code is:

Note how the local assignment (which is default) didn’t change scope_test 's binding of spam . The nonlocal assignment changed scope_test 's binding of spam , and the global assignment changed the module-level binding.

You can also see that there was no previous binding for spam before the global assignment.

9.3. A First Look at Classes ¶

Classes introduce a little bit of new syntax, three new object types, and some new semantics.

9.3.1. Class Definition Syntax ¶

The simplest form of class definition looks like this:

Class definitions, like function definitions ( def statements) must be executed before they have any effect. (You could conceivably place a class definition in a branch of an if statement, or inside a function.)

In practice, the statements inside a class definition will usually be function definitions, but other statements are allowed, and sometimes useful — we’ll come back to this later. The function definitions inside a class normally have a peculiar form of argument list, dictated by the calling conventions for methods — again, this is explained later.

When a class definition is entered, a new namespace is created, and used as the local scope — thus, all assignments to local variables go into this new namespace. In particular, function definitions bind the name of the new function here.

When a class definition is left normally (via the end), a class object is created. This is basically a wrapper around the contents of the namespace created by the class definition; we’ll learn more about class objects in the next section. The original local scope (the one in effect just before the class definition was entered) is reinstated, and the class object is bound here to the class name given in the class definition header ( ClassName in the example).

9.3.2. Class Objects ¶

Class objects support two kinds of operations: attribute references and instantiation.

Attribute references use the standard syntax used for all attribute references in Python: obj.name . Valid attribute names are all the names that were in the class’s namespace when the class object was created. So, if the class definition looked like this:

then MyClass.i and MyClass.f are valid attribute references, returning an integer and a function object, respectively. Class attributes can also be assigned to, so you can change the value of MyClass.i by assignment. __doc__ is also a valid attribute, returning the docstring belonging to the class: "A simple example class" .

Class instantiation uses function notation. Just pretend that the class object is a parameterless function that returns a new instance of the class. For example (assuming the above class):

creates a new instance of the class and assigns this object to the local variable x .

The instantiation operation (“calling” a class object) creates an empty object. Many classes like to create objects with instances customized to a specific initial state. Therefore a class may define a special method named __init__() , like this:

When a class defines an __init__() method, class instantiation automatically invokes __init__() for the newly created class instance. So in this example, a new, initialized instance can be obtained by:

Of course, the __init__() method may have arguments for greater flexibility. In that case, arguments given to the class instantiation operator are passed on to __init__() . For example,

9.3.3. Instance Objects ¶

Now what can we do with instance objects? The only operations understood by instance objects are attribute references. There are two kinds of valid attribute names: data attributes and methods.

data attributes correspond to “instance variables” in Smalltalk, and to “data members” in C++. Data attributes need not be declared; like local variables, they spring into existence when they are first assigned to. For example, if x is the instance of MyClass created above, the following piece of code will print the value 16 , without leaving a trace:

The other kind of instance attribute reference is a method . A method is a function that “belongs to” an object. (In Python, the term method is not unique to class instances: other object types can have methods as well. For example, list objects have methods called append, insert, remove, sort, and so on. However, in the following discussion, we’ll use the term method exclusively to mean methods of class instance objects, unless explicitly stated otherwise.)

Valid method names of an instance object depend on its class. By definition, all attributes of a class that are function objects define corresponding methods of its instances. So in our example, x.f is a valid method reference, since MyClass.f is a function, but x.i is not, since MyClass.i is not. But x.f is not the same thing as MyClass.f — it is a method object , not a function object.

9.3.4. Method Objects ¶

Usually, a method is called right after it is bound:

In the MyClass example, this will return the string 'hello world' . However, it is not necessary to call a method right away: x.f is a method object, and can be stored away and called at a later time. For example:

will continue to print hello world until the end of time.

What exactly happens when a method is called? You may have noticed that x.f() was called without an argument above, even though the function definition for f() specified an argument. What happened to the argument? Surely Python raises an exception when a function that requires an argument is called without any — even if the argument isn’t actually used…

Actually, you may have guessed the answer: the special thing about methods is that the instance object is passed as the first argument of the function. In our example, the call x.f() is exactly equivalent to MyClass.f(x) . In general, calling a method with a list of n arguments is equivalent to calling the corresponding function with an argument list that is created by inserting the method’s instance object before the first argument.

In general, methods work as follows. When a non-data attribute of an instance is referenced, the instance’s class is searched. If the name denotes a valid class attribute that is a function object, references to both the instance object and the function object are packed into a method object. When the method object is called with an argument list, a new argument list is constructed from the instance object and the argument list, and the function object is called with this new argument list.

9.3.5. Class and Instance Variables ¶

Generally speaking, instance variables are for data unique to each instance and class variables are for attributes and methods shared by all instances of the class:

As discussed in A Word About Names and Objects , shared data can have possibly surprising effects with involving mutable objects such as lists and dictionaries. For example, the tricks list in the following code should not be used as a class variable because just a single list would be shared by all Dog instances:

Correct design of the class should use an instance variable instead:

9.4. Random Remarks ¶

If the same attribute name occurs in both an instance and in a class, then attribute lookup prioritizes the instance:

Data attributes may be referenced by methods as well as by ordinary users (“clients”) of an object. In other words, classes are not usable to implement pure abstract data types. In fact, nothing in Python makes it possible to enforce data hiding — it is all based upon convention. (On the other hand, the Python implementation, written in C, can completely hide implementation details and control access to an object if necessary; this can be used by extensions to Python written in C.)

Clients should use data attributes with care — clients may mess up invariants maintained by the methods by stamping on their data attributes. Note that clients may add data attributes of their own to an instance object without affecting the validity of the methods, as long as name conflicts are avoided — again, a naming convention can save a lot of headaches here.

There is no shorthand for referencing data attributes (or other methods!) from within methods. I find that this actually increases the readability of methods: there is no chance of confusing local variables and instance variables when glancing through a method.

Often, the first argument of a method is called self . This is nothing more than a convention: the name self has absolutely no special meaning to Python. Note, however, that by not following the convention your code may be less readable to other Python programmers, and it is also conceivable that a class browser program might be written that relies upon such a convention.

Any function object that is a class attribute defines a method for instances of that class. It is not necessary that the function definition is textually enclosed in the class definition: assigning a function object to a local variable in the class is also ok. For example:

Now f , g and h are all attributes of class C that refer to function objects, and consequently they are all methods of instances of C — h being exactly equivalent to g . Note that this practice usually only serves to confuse the reader of a program.

Methods may call other methods by using method attributes of the self argument:

Methods may reference global names in the same way as ordinary functions. The global scope associated with a method is the module containing its definition. (A class is never used as a global scope.) While one rarely encounters a good reason for using global data in a method, there are many legitimate uses of the global scope: for one thing, functions and modules imported into the global scope can be used by methods, as well as functions and classes defined in it. Usually, the class containing the method is itself defined in this global scope, and in the next section we’ll find some good reasons why a method would want to reference its own class.

Each value is an object, and therefore has a class (also called its type ). It is stored as object.__class__ .

9.5. Inheritance ¶

Of course, a language feature would not be worthy of the name “class” without supporting inheritance. The syntax for a derived class definition looks like this:

The name BaseClassName must be defined in a namespace accessible from the scope containing the derived class definition. In place of a base class name, other arbitrary expressions are also allowed. This can be useful, for example, when the base class is defined in another module:

Execution of a derived class definition proceeds the same as for a base class. When the class object is constructed, the base class is remembered. This is used for resolving attribute references: if a requested attribute is not found in the class, the search proceeds to look in the base class. This rule is applied recursively if the base class itself is derived from some other class.

There’s nothing special about instantiation of derived classes: DerivedClassName() creates a new instance of the class. Method references are resolved as follows: the corresponding class attribute is searched, descending down the chain of base classes if necessary, and the method reference is valid if this yields a function object.

Derived classes may override methods of their base classes. Because methods have no special privileges when calling other methods of the same object, a method of a base class that calls another method defined in the same base class may end up calling a method of a derived class that overrides it. (For C++ programmers: all methods in Python are effectively virtual .)

An overriding method in a derived class may in fact want to extend rather than simply replace the base class method of the same name. There is a simple way to call the base class method directly: just call BaseClassName.methodname(self, arguments) . This is occasionally useful to clients as well. (Note that this only works if the base class is accessible as BaseClassName in the global scope.)

Python has two built-in functions that work with inheritance:

Use isinstance() to check an instance’s type: isinstance(obj, int) will be True only if obj.__class__ is int or some class derived from int .

Use issubclass() to check class inheritance: issubclass(bool, int) is True since bool is a subclass of int . However, issubclass(float, int) is False since float is not a subclass of int .

9.5.1. Multiple Inheritance ¶

Python supports a form of multiple inheritance as well. A class definition with multiple base classes looks like this:

For most purposes, in the simplest cases, you can think of the search for attributes inherited from a parent class as depth-first, left-to-right, not searching twice in the same class where there is an overlap in the hierarchy. Thus, if an attribute is not found in DerivedClassName , it is searched for in Base1 , then (recursively) in the base classes of Base1 , and if it was not found there, it was searched for in Base2 , and so on.

In fact, it is slightly more complex than that; the method resolution order changes dynamically to support cooperative calls to super() . This approach is known in some other multiple-inheritance languages as call-next-method and is more powerful than the super call found in single-inheritance languages.

Dynamic ordering is necessary because all cases of multiple inheritance exhibit one or more diamond relationships (where at least one of the parent classes can be accessed through multiple paths from the bottommost class). For example, all classes inherit from object , so any case of multiple inheritance provides more than one path to reach object . To keep the base classes from being accessed more than once, the dynamic algorithm linearizes the search order in a way that preserves the left-to-right ordering specified in each class, that calls each parent only once, and that is monotonic (meaning that a class can be subclassed without affecting the precedence order of its parents). Taken together, these properties make it possible to design reliable and extensible classes with multiple inheritance. For more detail, see The Python 2.3 Method Resolution Order .

9.6. Private Variables ¶

“Private” instance variables that cannot be accessed except from inside an object don’t exist in Python. However, there is a convention that is followed by most Python code: a name prefixed with an underscore (e.g. _spam ) should be treated as a non-public part of the API (whether it is a function, a method or a data member). It should be considered an implementation detail and subject to change without notice.

Since there is a valid use-case for class-private members (namely to avoid name clashes of names with names defined by subclasses), there is limited support for such a mechanism, called name mangling . Any identifier of the form __spam (at least two leading underscores, at most one trailing underscore) is textually replaced with _classname__spam , where classname is the current class name with leading underscore(s) stripped. This mangling is done without regard to the syntactic position of the identifier, as long as it occurs within the definition of a class.

Name mangling is helpful for letting subclasses override methods without breaking intraclass method calls. For example:

The above example would work even if MappingSubclass were to introduce a __update identifier since it is replaced with _Mapping__update in the Mapping class and _MappingSubclass__update in the MappingSubclass class respectively.

Note that the mangling rules are designed mostly to avoid accidents; it still is possible to access or modify a variable that is considered private. This can even be useful in special circumstances, such as in the debugger.

Notice that code passed to exec() or eval() does not consider the classname of the invoking class to be the current class; this is similar to the effect of the global statement, the effect of which is likewise restricted to code that is byte-compiled together. The same restriction applies to getattr() , setattr() and delattr() , as well as when referencing __dict__ directly.

9.7. Odds and Ends ¶

Sometimes it is useful to have a data type similar to the Pascal “record” or C “struct”, bundling together a few named data items. The idiomatic approach is to use dataclasses for this purpose:

A piece of Python code that expects a particular abstract data type can often be passed a class that emulates the methods of that data type instead. For instance, if you have a function that formats some data from a file object, you can define a class with methods read() and readline() that get the data from a string buffer instead, and pass it as an argument.

Instance method objects have attributes, too: m.__self__ is the instance object with the method m() , and m.__func__ is the function object corresponding to the method.

9.8. Iterators ¶

By now you have probably noticed that most container objects can be looped over using a for statement:

This style of access is clear, concise, and convenient. The use of iterators pervades and unifies Python. Behind the scenes, the for statement calls iter() on the container object. The function returns an iterator object that defines the method __next__() which accesses elements in the container one at a time. When there are no more elements, __next__() raises a StopIteration exception which tells the for loop to terminate. You can call the __next__() method using the next() built-in function; this example shows how it all works:

Having seen the mechanics behind the iterator protocol, it is easy to add iterator behavior to your classes. Define an __iter__() method which returns an object with a __next__() method. If the class defines __next__() , then __iter__() can just return self :

9.9. Generators ¶

Generators are a simple and powerful tool for creating iterators. They are written like regular functions but use the yield statement whenever they want to return data. Each time next() is called on it, the generator resumes where it left off (it remembers all the data values and which statement was last executed). An example shows that generators can be trivially easy to create:

Anything that can be done with generators can also be done with class-based iterators as described in the previous section. What makes generators so compact is that the __iter__() and __next__() methods are created automatically.

Another key feature is that the local variables and execution state are automatically saved between calls. This made the function easier to write and much more clear than an approach using instance variables like self.index and self.data .

In addition to automatic method creation and saving program state, when generators terminate, they automatically raise StopIteration . In combination, these features make it easy to create iterators with no more effort than writing a regular function.

9.10. Generator Expressions ¶

Some simple generators can be coded succinctly as expressions using a syntax similar to list comprehensions but with parentheses instead of square brackets. These expressions are designed for situations where the generator is used right away by an enclosing function. Generator expressions are more compact but less versatile than full generator definitions and tend to be more memory friendly than equivalent list comprehensions.

9.1. A Word About Names and Objects
9.2.1. Scopes and Namespaces Example
9.3.1. Class Definition Syntax
9.3.2. Class Objects
9.3.3. Instance Objects
9.3.4. Method Objects
9.3.5. Class and Instance Variables
9.4. Random Remarks
9.5.1. Multiple Inheritance
9.6. Private Variables
9.7. Odds and Ends
9.8. Iterators
9.9. Generators
9.10. Generator Expressions

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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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COMMENTS

Python's Assignment Operator: Write Robust Assignments
To create a new variable or to update the value of an existing one in Python, you'll use an assignment statement. This statement has the following three components: A left operand, which must be a variable. The assignment operator ( =) A right operand, which can be a concrete value, an object, or an expression.
Python Assignment Order
In the first version, when you write. a, b = b, a + b the expression. b, a + b is evaluated first. After it has been evaluated, it is subsequently assigned to a, b with tuple unpacking. The key point is that the entire right hand side of an assignment statement is evaluated in its entirety before performing the binding of the left-hand side names.
PEP 572
Unparenthesized assignment expressions are prohibited for the value of a keyword argument in a call. Example: foo(x = y := f(x)) # INVALID foo(x=(y := f(x))) # Valid, though probably confusing. This rule is included to disallow excessively confusing code, and because parsing keyword arguments is complex enough already.
6. Expressions
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 ...
Assignment Operators in Python
The Walrus Operator in Python is a new assignment operator which is introduced in Python version 3.8 and higher. This operator is used to assign a value to a variable within an expression. Syntax: a := expression. Example: In this code, we have a Python list of integers. We have used Python Walrus assignment operator within the Python while loop.
Precedence and Associativity of Operators in Python
In Python, operators have different levels of precedence, which determine the order in which they are evaluated. When multiple operators are present in an expression, the ones with higher precedence are evaluated first. In the case of operators with the same precedence, their associativity comes into play, determining the order of evaluation.
Python Assignment Operators
Python Assignment Operators. Assignment operators are used to assign values to variables: Operator. Example. Same As. Try it. =. x = 5. x = 5.
Operator Precedence in Python
If we use brackets for 4+6, then the result will be. Example of precedence of two operators on using brackets: (4+6)/2. Output: 5.0. 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: (3+6)*6-9/3+20.
Precedence and Associativity of Operators in Python
Precedence of Python Operators. The combination of values, variables, operators, and function calls is termed as an expression. The Python interpreter can evaluate a valid expression. For example: >>> 5 - 7 -2. Here 5 - 7 is an expression. There can be more than one operator in an expression.
Python Operators Explained in Detail with Examples
Below is a table that explains the assignment operators in Python, along with code examples using a small variable name (a) and the print() statement to display the result. Operator ... Operators precedence order in Python. Operator precedence in Python determines the order in which operators are evaluated when multiple operators are present in ...
Augmented Assignment Operators in Python
An assignment operator is an operator that is used to assign some value to a variable. Like normally in Python, we write "a = 5" to assign value 5 to variable 'a'. Augmented assignment operators have a special role to play in Python programming.
Python Operators
Python Assignment Operators. Assignment operators are used to assign values to variables: Operator Example Same As Try it = x = 5: x = 5: ... Operator precedence describes the order in which operations are performed. Example. Parentheses has the highest precedence, meaning that expressions inside parentheses must be evaluated first: ...
Assignment Operator in Python
The simple assignment operator is the most commonly used operator in Python. It is used to assign a value to a variable. The syntax for the simple assignment operator is: variable = value. Here, the value on the right-hand side of the equals sign is assigned to the variable on the left-hand side. For example.
Python Operators: Arithmetic, Assignment, Comparison, Logical, Identity
Python Operators: Arithmetic, Assignment, Comparison, Logical, Identity, Membership, Bitwise. Operators are special symbols that perform some operation on operands and returns the result. For example, 5 + 6 is an expression where + is an operator that performs arithmetic add operation on numeric left operand 5 and the right side operand 6 and ...
What is the operator precedence of "=" in Python?
10. = is not an operator. = is an assignment statement. Because it is a statement, it can't be part of an expression (expressions are instead part of certain statements, and never the other way around), so ordering is irrelevant. The expression is always executed to serve a statement.
9. Classes
Note how the local assignment (which is default) didn't change scope_test's binding of spam.The nonlocal assignment changed scope_test's binding of spam, and the global assignment changed the module-level binding.. You can also see that there was no previous binding for spam before the global assignment.. 9.3. A First Look at Classes¶. Classes introduce a little bit of new syntax, three new ...
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.
python
x = temp. y = temp. Note the order. The leftmost target is assigned first. (A similar expression in C may assign in the opposite order .) From the docs on Python assignment: ...assigns the single resulting object to each of the target lists, from left to right. Disassembly shows this: >>> def chained_assignment():