Assignment Expressions
E256161
Assignment Expressions are a Python language feature introduced by PEP 572 that allow assigning values to variables as part of larger expressions using the “walrus” operator (:=).
All labels observed (3)
| Label | Occurrences |
|---|---|
| Assignment Expressions canonical | 1 |
| Python Language Reference – Expressions – Assignment expressions | 1 |
| walrus operator | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T2321019 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Assignment Expressions Context triple: [PEP 572, title, Assignment Expressions]
-
A.
Data Analysis Expressions
Data Analysis Expressions is a formula and query language used in Microsoft Power BI, Excel, and other Microsoft BI tools to create custom calculations and analyze data in tabular models.
-
B.
operator grammar
Operator grammar is a formal linguistic framework developed by Zellig Harris that analyzes sentence structure through algebraic-like operations on strings rather than traditional phrase-structure rules.
-
C.
SETSqx
SETSqx is a hybrid electronic trading service on the London Stock Exchange that combines periodic auctions with continuous quote-driven market making for less liquid securities.
-
D.
ASM
ASM is the three-letter ISO 3166-1 alpha-3 country code assigned to American Samoa, an unincorporated territory of the United States in the South Pacific Ocean.
-
E.
ASM
ASM is a leading professional organization dedicated to advancing the microbial sciences through research, education, and advocacy.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Assignment Expressions Target entity description: Assignment Expressions are a Python language feature introduced by PEP 572 that allow assigning values to variables as part of larger expressions using the “walrus” operator (:=).
-
A.
Data Analysis Expressions
Data Analysis Expressions is a formula and query language used in Microsoft Power BI, Excel, and other Microsoft BI tools to create custom calculations and analyze data in tabular models.
-
B.
operator grammar
Operator grammar is a formal linguistic framework developed by Zellig Harris that analyzes sentence structure through algebraic-like operations on strings rather than traditional phrase-structure rules.
-
C.
SETLX
SETLX is a modern, open-source programming language designed for teaching and experimenting with set theory and mathematical concepts through executable code.
-
D.
SETSqx
SETSqx is a hybrid electronic trading service on the London Stock Exchange that combines periodic auctions with continuous quote-driven market making for less liquid securities.
-
E.
ASM
ASM is the three-letter ISO 3166-1 alpha-3 country code assigned to American Samoa, an unincorporated territory of the United States in the South Pacific Ocean.
- F. None of above. chosen
Statements (43)
| Predicate | Object |
|---|---|
| instanceOf |
Python language feature
ⓘ
expression construct ⓘ |
| alsoKnownAs |
Assignment Expressions
ⓘ
surface form:
walrus operator
PEP 572 ⓘ
surface form:
walrus operator (:=)
|
| author | Chris Angelico ⓘ |
| bdfLDecisionBy | Guido van Rossum ⓘ |
| bindingTime | at runtime during expression evaluation ⓘ |
| canBeUsedIn |
comprehensions
ⓘ
generator expressions ⓘ if statements ⓘ lambda expressions (with restrictions) ⓘ while statements ⓘ |
| cannotTarget | attribute references in some contexts (subject to normal assignment rules) ⓘ |
| category | Python expression syntax ⓘ |
| controversy |
PEP 572
ⓘ
surface form:
PEP 572 sparked significant discussion in the Python community
|
| designGoal |
avoid repeated expensive computations
ⓘ
improve readability in some patterns ⓘ |
| documentationSection |
Assignment Expressions
self-linksurface differs
ⓘ
surface form:
Python Language Reference – Expressions – Assignment expressions
|
| enables |
inline assignment
ⓘ
reusing computed values within an expression ⓘ |
| evaluationOrder | expression on right of := is evaluated then bound to target name ⓘ |
| example |
if (n := len(items)) > 0: print(n)
ⓘ
results = [y for x in data if (y := f(x)) is not None] ⓘ while (line := file.readline()) != "": process(line) ⓘ |
| exampleUseCase |
capturing match results from regular expressions
ⓘ
filtering and binding in list comprehensions ⓘ reading input in a while loop ⓘ |
| introducedBy | PEP 572 ⓘ |
| introducedInVersion | Python 3.8 ⓘ |
| language | Python ⓘ |
| notAllowedIn |
simple assignment target list
ⓘ
top-level of comprehension iterable expression (to avoid scoping confusion) ⓘ |
| pepNumber | 572 ⓘ |
| purpose | assign values as part of larger expressions ⓘ |
| reduces | need for separate assignment statements ⓘ |
| relatedTo |
assignment statements
ⓘ
name binding ⓘ |
| returns | assigned value ⓘ |
| scopeRule | follows normal Python scoping rules for the target name ⓘ |
| standardizedIn | Python 3 language specification ⓘ |
| status | accepted PEP ⓘ |
| syntaxForm | NAME := expression ⓘ |
| usesOperator | := ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Assignment Expressions Description of subject: Assignment Expressions are a Python language feature introduced by PEP 572 that allow assigning values to variables as part of larger expressions using the “walrus” operator (:=).
Referenced by (3)
Full triples — surface form annotated when it differs from this entity's canonical label.
this entity surface form:
walrus operator
this entity surface form:
Python Language Reference – Expressions – Assignment expressions