# 3.4. Assignment Expression¶

## 3.4.1. Rationale¶

• Since Python 3.8: PEP 572 -- Assignment Expressions

• A.K.A. "the walrus operator"

• A.K.A. "Named Expressions"

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). 1

## 3.4.2. Syntax¶

(x := <VALUE>)


It's not substitution for equals:

>>> x = 1
>>>
>>> print(x)
1

>>> x := 1
Traceback (most recent call last):
SyntaxError: invalid syntax

>>> (x := 1)
1
>>>
>>> print(x)
1

>>> x = 1, 2
>>>
>>> print(x)
(1, 2)

>>> (x := 1, 2)
(1, 2)
>>>
>>> print(x)
1

>>> result = (x := 1, 2)
>>>
>>> print(result)
(1, 2)

>>> x = 0
>>> x += 1
>>>
>>> print(x)
1

>>> x = 0
>>> x +:= 1
Traceback (most recent call last):
SyntaxError: invalid syntax

>>> data = {}
>>> data['commander'] = 'Mark Watney'
>>>
>>> data = {}
>>> data['commander'] := 'Mark Watney'
Traceback (most recent call last):
SyntaxError: cannot use assignment expressions with subscript


Figure 3.13. Guido van Rossum stepped down after accepting PEP 572 -- Assignment Expressions

## 3.4.3. Example¶

Reusing Results:

>>> def f(x):
...     return 1
>>>
>>>
>>> result = [f(x), f(x)+1, f(x)+2]
>>>
>>> result = [res := f(x), res+1, res+2]


Processing Steams in Chunks:

>>> # doctest: +SKIP
...
... file = open('_temporary.txt')
... chunk = file.read(8192)
...
... while chunk:
...     print(chunk)
...     chunk = file.read(8192)

>>> # doctest: +SKIP
...
... file = open('_temporary.txt')
...
... while chunk := file.read(8192):
...     print(chunk)


## 3.4.4. Checking Match¶

>>> import re
>>>
>>>
>>> DATA = 'mark.watney@nasa.gov'
>>> result = re.search(r'@nasa.gov', DATA)
>>>
>>> if result:
...     print(result)
<re.Match object; span=(11, 20), match='@nasa.gov'>

>>> import re
>>>
>>>
>>> DATA = 'mark.watney@nasa.gov'
>>>
>>> if (result := re.search(r'@nasa.gov', DATA)):
...     print(result)
<re.Match object; span=(11, 20), match='@nasa.gov'>


## 3.4.5. Patterns¶

>>> import re
>>>
>>>
>>> data = 'mark.watney@nasa.gov'
>>> pattern = r'([a-z]+)\.([a-z]+)@nasa.gov'
>>>
>>> match = re.match(pattern, data)
>>> result = match.groups() if match else None
>>>
>>> print(result)
('mark', 'watney')

>>> import re
>>>
>>>
>>> data = 'mark.watney@nasa.gov'
>>> pattern = r'([a-z]+)\.([a-z]+)@nasa.gov'
>>>
>>> result = re.match(pattern, data).groups() if re.match(pattern, data) else None
>>>
>>> print(result)
('mark', 'watney')

>>> import re
>>>
>>>
>>> data = 'mark.watney@nasa.gov'
>>> pattern = r'([a-z]+)\.([a-z]+)@nasa.gov'
>>>
>>> result = x.groups() if (x := re.match(pattern, data)) else None
>>>
>>> print(result)
('mark', 'watney')


## 3.4.6. Comprehensions¶

>>> result = [x for x in range(0,10)]
>>> result = [x for x in range(0,10) if x%2 == 0]

>>> DATA = ['Jan Twardowski',
...         'Melissa Lewis',
...         'Mark Watney']
>>>
>>>
>>> result = [{'firstname': fullname.split()[0],
...            'lastname': fullname.split()[1]}
...           for fullname in DATA]
>>>
>>> print(result)  # doctest: +NORMALIZE_WHITESPACE
[{'firstname': 'Jan', 'lastname': 'Twardowski'},
{'firstname': 'Melissa', 'lastname': 'Lewis'},
{'firstname': 'Mark', 'lastname': 'Watney'}]
>>>
>>> result = [{'firstname': name[0], 'lastname': name[1]}
...           for fullname in DATA
...           if (name := fullname.split())]
>>>
>>> print(result)  # doctest: +NORMALIZE_WHITESPACE
[{'firstname': 'Jan', 'lastname': 'Twardowski'},
{'firstname': 'Melissa', 'lastname': 'Lewis'},
{'firstname': 'Mark', 'lastname': 'Watney'}]


Syntax:

result = [<RETURN>
for <VARIABLE1> in <ITERABLE>
if (<VARIABLE2> := <EXPR>)]

result = [<RETURN>
for <VARIABLE1> in <ITERABLE>
if (<VARIABLE2> := <EXPR>)
and (<VARIABLE3> := <EXPR>)]

result = [<RETURN>
for <VARIABLE1> in <ITERABLE>
if (<VARIABLE2> := <EXPR>)
and (<VARIABLE3> := <EXPR>)
or (<VARIABLE4> := <EXPR>)]

>>> DATA = ['5.8,2.7,5.1,1.9,virginica',
...         '5.1,3.5,1.4,0.2,setosa',
...         '5.7,2.8,4.1,1.3,versicolor']
>>>
>>> result = []
>>>
>>> for line in DATA:
...     line = line.split(',')
...     result.append(line[0:4])
>>>
>>> print(result)  # doctest: +NORMALIZE_WHITESPACE
[['5.8', '2.7', '5.1', '1.9'],
['5.1', '3.5', '1.4', '0.2'],
['5.7', '2.8', '4.1', '1.3']]
>>>
>>> result = [line.split(',')[0:4] for line in DATA]
>>> print(result)  # doctest: +NORMALIZE_WHITESPACE
[['5.8', '2.7', '5.1', '1.9'],
['5.1', '3.5', '1.4', '0.2'],
['5.7', '2.8', '4.1', '1.3']]

>>> DATA = ['5.8,2.7,5.1,1.9,virginica',
...         '5.1,3.5,1.4,0.2,setosa',
...         '5.7,2.8,4.1,1.3,versicolor']
>>>
>>> result = []
>>>
>>> for line in DATA:
...     X = [float(x) for x in line.split(',')[0:4]]
...     result.append(X)
>>>
>>> print(result)  # doctest: +NORMALIZE_WHITESPACE
[[5.8, 2.7, 5.1, 1.9],
[5.1, 3.5, 1.4, 0.2],
[5.7, 2.8, 4.1, 1.3]]
>>>
>>> result = [[float(x) for x in line.split(',')[0:4]]
...           for line in DATA]
>>> print(result)  # doctest: +NORMALIZE_WHITESPACE
[[5.8, 2.7, 5.1, 1.9],
[5.1, 3.5, 1.4, 0.2],
[5.7, 2.8, 4.1, 1.3]]
>>>
>>> result = [[float(x) for x in X]
...           for line in DATA
...           if (X := line.split(',')[0:4])]
>>> print(result)  # doctest: +NORMALIZE_WHITESPACE
[[5.8, 2.7, 5.1, 1.9],
[5.1, 3.5, 1.4, 0.2],
[5.7, 2.8, 4.1, 1.3]]

>>> DATA = ['5.8,2.7,5.1,1.9,virginica',
...         '5.1,3.5,1.4,0.2,setosa',
...         '5.7,2.8,4.1,1.3,versicolor']
...
>>> result = [[float(x) for x in X] + [y]
...           for line in DATA
...           if (row := line.split(','))
...           and (X := row[0:4])
...           and (y := row[4])]
>>>
>>> print(result)  # doctest: +NORMALIZE_WHITESPACE
[[5.8, 2.7, 5.1, 1.9, 'virginica'],
[5.1, 3.5, 1.4, 0.2, 'setosa'],
[5.7, 2.8, 4.1, 1.3, 'versicolor']]


## 3.4.7. Use Case¶

>>> DATA = [{'is_astronaut': True,  'name': 'JaN TwarDOwski'},
...         {'is_astronaut': True,  'name': 'Mark Jim WaTNey'},
...         {'is_astronaut': False, 'name': 'José Maria Jiménez'},
...         {'is_astronaut': True,  'name': 'Melissa Lewis'},
...         {'is_astronaut': False, 'name': 'Alex Vogel'}]
>>>
>>>
>>> result = [{'firstname': person['name'].title().split()[0],
...            'lastname': person['name'].title().split()[-1]}
...           for person in DATA
...           if person['is_astronaut']]
>>>
>>> result = [{'firstname': name[0],
...            'lastname': name[-1]}
...           for person in DATA
...           if person['is_astronaut']
...           and (name := person['name'].title().split())]
>>>
>>> result = [{'firstname': fname,
...            'lastname': lname}
...           for person in DATA
...           if person['is_astronaut']
...           and (name := person['name'].title().split())
...           and (fname := name[0])
...           and (lname := name[-1])]
>>>
>>> print(result)  # doctest: +NORMALIZE_WHITESPACE
[{'firstname': 'Jan', 'lastname': 'Twardowski'},
{'firstname': 'Mark', 'lastname': 'Watney'},
{'firstname': 'Melissa', 'lastname': 'Lewis'}]

>>> from dataclasses import dataclass
>>>
>>>
>>> @dataclass
... class Iris:
...     sepal_length: float
...     sepal_width: float
...     petal_length: float
...     petal_width: float
>>>
>>>
>>> class Versicolor(Iris):
...     pass
>>>
>>> class Virginica(Iris):
...     pass
>>>
>>> class Setosa(Iris):
...     pass
>>>
>>>
>>> DATA = [('Sepal length', 'Sepal width', 'Petal length', 'Petal width', 'Species'),
...         (5.8, 2.7, 5.1, 1.9, 'virginica'),
...         (5.1, 3.5, 1.4, 0.2, 'setosa'),
...         (5.7, 2.8, 4.1, 1.3, 'versicolor'),
...         (6.3, 2.9, 5.6, 1.8, 'virginica'),
...         (6.4, 3.2, 4.5, 1.5, 'versicolor'),
...         (4.7, 3.2, 1.3, 0.2, 'setosa'),
...         (7.0, 3.2, 4.7, 1.4, 'versicolor')]
>>>
>>>
>>> result = [cls(*features)
...           for *features, species in DATA[1:]
...           if (clsname := species.capitalize())
...           and (cls := globals()[clsname])]
>>>
>>> print(result)  # doctest: +NORMALIZE_WHITESPACE
[Virginica(sepal_length=5.8, sepal_width=2.7, petal_length=5.1, petal_width=1.9),
Setosa(sepal_length=5.1, sepal_width=3.5, petal_length=1.4, petal_width=0.2),
Versicolor(sepal_length=5.7, sepal_width=2.8, petal_length=4.1, petal_width=1.3),
Virginica(sepal_length=6.3, sepal_width=2.9, petal_length=5.6, petal_width=1.8),
Versicolor(sepal_length=6.4, sepal_width=3.2, petal_length=4.5, petal_width=1.5),
Setosa(sepal_length=4.7, sepal_width=3.2, petal_length=1.3, petal_width=0.2),
Versicolor(sepal_length=7.0, sepal_width=3.2, petal_length=4.7, petal_width=1.4)]


## 3.4.8. References¶

1

Angelico, C. and Peters T. and van Rossum, G. PEP 572 -- Assignment Expressions. Python Software Foundation. Year: 2018. Retrieved: 2020-12-04. Url: https://www.python.org/dev/peps/pep-0572/#abstract

## 3.4.9. Assignments¶

"""
* Assignment: Idioms Assignement Expression
* Complexity: medium
* Lines of code: 6 lines
* Time: 13 min

English:
1. Use code from "Given" section (see below)
2. Split DATA by lines and then by colon :
3. Extract system accounts (users with UID [third field] is less than 1000)
4. Return list of system account logins
5. Implement solution using list comprehension and assignment expression
6. Mind the root user who has uid == 0 (whether is not filtered-out in if statement)
7. Compare result with "Tests" section (see below)

Polish:
1. Użyj kodu z sekcji "Given" (patrz poniżej)
2. Podziel DATA po liniach a następnie po dwukropku :
3. Wyciągnij konta systemowe (użytkownicy z UID [trzecie pole] mniejszym niż 1000)
4. Zwróć listę loginów użytkowników systemowych
5. Zaimplementuj rozwiązanie wykorzystując list comprehension i assignment expression
6. Zwróć uwagę na użytkownika root, który ma uid == 0 (czy nie jest odfiltrowany w instrukcji if)
7. Porównaj wyniki z sekcją "Tests" (patrz poniżej)

Hint:
* str.splitlines()
* str.strip()
* str.split()

Tests:
>>> type(result)
<class 'list'>
>>> all(type(x) is str for x in result)
True
>>> result
['root', 'bin', 'daemon', 'adm', 'shutdown', 'halt', 'nobody', 'sshd']
"""

# Given
DATA = """root:x:0:0:root:/root:/bin/bash
bin:x:1:1:bin:/bin:/sbin/nologin
daemon:x:2:2:daemon:/sbin:/sbin/nologin
adm:x:3:4:adm:/var/adm:/sbin/nologin
shutdown:x:6:0:shutdown:/sbin:/sbin/shutdown
halt:x:7:0:halt:/sbin:/sbin/halt
nobody:x:99:99:Nobody:/:/sbin/nologin
sshd:x:74:74:Privilege-separated SSH:/var/empty/sshd:/sbin/nologin
watney:x:1000:1000:Mark Watney:/home/watney:/bin/bash
jimenez:x:1001:1001:José Jiménez:/home/jimenez:/bin/bash
ivanovic:x:1002:1002:Иван Иванович:/home/ivanovic:/bin/bash
lewis:x:1003:1002:Melissa Lewis:/home/ivanovic:/bin/bash"""

result: list