3.3. Unpacking Arguments¶
3.3.1. Recap¶
argument - Value/variable/reference being passed to the function
positional argument - Value passed to function - order is important
keyword arguments - Value passed to function resolved by name - order is not important
keyword arguments must be on the right side
order of keyword arguments doesn't matter
echo(1) # positional argument
echo(a=1) # keyword argument
echo(1, 2) # positional arguments
echo(2, 1) # positional arguments
echo(a=1, b=2) # keyword arguments
echo(b=2, a=1) # keyword arguments, order doesn't matter
echo(1, b=2) # positional and keyword arguments
echo(a=1, 2) # SyntaxError: positional argument follows keyword argument
3.3.3. Positional Arguments¶
*is used for positional argumentsthere is no convention, but you can use any name
*unpacks fromtuple,listorset
def echo(a, b, c=0):
print(f'{a=}, {b=}, {c=}')
echo(1, 2)
# a=1, b=2, c=0
data = (1, 2)
echo(*data)
# a=1, b=2, c=0
data = (1, 2)
echo(data)
# Traceback (most recent call last):
# TypeError: echo() missing 1 required positional argument: 'b'
3.3.4. Keyword Arguments¶
**is used for keyword argumentsthere is no convention, but you can use any name
**unpacks fromdict
Keyword arguments passed directly:
def echo(a, b, c=0):
print(f'{a=}, {b=}, {c=}')
echo(a=1, b=2)
# a=1, b=2, c=0
data = {'a': 1, 'b': 2}
echo(**data)
# a=1, b=2, c=0
3.3.5. Positional and Keyword Arguments¶
def echo(a, b, c=0):
print(f'{a=}, {b=}, {c=}')
echo(1, b=2)
# a=1, b=2, c=0
data1 = (1,)
data2 = {'b': 2}
echo(*data1, **data2)
# a=1, b=2, c=0
data1 = (1, 2)
data2 = {'b': 2}
echo(*data1, **data2)
# Traceback (most recent call last):
# TypeError: echo() got multiple values for argument 'b'
3.3.6. Objects From Sequence¶
DATA = (6.0, 3.4, 4.5, 1.6, 'versicolor')
class Iris:
def __init__(self, sepal_length, sepal_width, petal_length, petal_width, species):
self.sepal_length = sepal_length
self.sepal_width = sepal_width
self.petal_length = petal_length
self.petal_width = petal_width
self.species = species
result = Iris(*DATA)
print(result.species)
# 'versicolor'
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'),
(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')]
class Iris:
def __init__(self, sepal_length, sepal_width, petal_length, petal_width, species):
self.sepal_length = sepal_length
self.sepal_width = sepal_width
self.petal_length = petal_length
self.petal_width = petal_width
self.species = species
def __repr__(self):
return f'{self.species}'
result = [Iris(*row) for row in DATA]
print(result)
# [virginica, setosa, versicolor,
# virginica, versicolor, setosa]
from dataclasses import dataclass
MOVEMENT = [(0, 0),
(1, 0),
(2, 1, 1),
(3, 2),
(3, 3, -1),
(2, 3)]
@dataclass
class Point:
x: int
y: int
z: int = 0
movement = [Point(x,y) for x,y in MOVEMENT]
# Traceback (most recent call last):
# ValueError: too many values to unpack (expected 2)
movement = [Point(*coordinates) for coordinates in MOVEMENT]
movement
# [Point(x=0, y=0, z=0),
# Point(x=1, y=0, z=0),
# Point(x=2, y=1, z=1),
# Point(x=3, y=2, z=0),
# Point(x=3, y=3, z=-1),
# Point(x=2, y=3, z=0)]
3.3.7. Objects From Mappings¶
DATA = {"sepalLength":5.8,"sepalWidth":2.7,"petalLength":5.1,"petalWidth":1.9,"species":"virginica"}
class Iris:
def __init__(self, sepalLength, sepalWidth, petalLength, petalWidth, species):
self.sepal_length = sepalLength
self.sepal_width = sepalWidth
self.petal_length = petalLength
self.petal_width = petalWidth
self.species = species
iris = Iris(**DATA)
iris.species
# 'virginica'
DATA = [{"sepalLength":5.8,"sepalWidth":2.7,"petalLength":5.1,"petalWidth":1.9,"species":"virginica"},
{"sepalLength":5.1,"sepalWidth":3.5,"petalLength":1.4,"petalWidth":0.2,"species":"setosa"},
{"sepalLength":5.7,"sepalWidth":2.8,"petalLength":4.1,"petalWidth":1.3,"species":"versicolor"},
{"sepalLength":6.3,"sepalWidth":2.9,"petalLength":5.6,"petalWidth":1.8,"species":"virginica"},
{"sepalLength":6.4,"sepalWidth":3.2,"petalLength":4.5,"petalWidth":1.5,"species":"versicolor"},
{"sepalLength":4.7,"sepalWidth":3.2,"petalLength":1.3,"petalWidth":0.2,"species":"setosa"}]
class Iris:
def __init__(self, sepalLength, sepalWidth, petalLength, petalWidth, species):
self.sepal_length = sepalLength
self.sepal_width = sepalWidth
self.petal_length = petalLength
self.petal_width = petalWidth
self.species = species
def __repr__(self):
return f'{self.species}'
result = [Iris(**row) for row in DATA]
print(result)
# [virginica, setosa, versicolor,
# virginica, versicolor, setosa]
3.3.8. Examples¶
Defining complex number by passing keyword arguments directly:
complex(real=3, imag=5)
# (3+5j)
number = {'real': 3, 'imag': 5}
complex(**number)
# (3+5j)
Passing vector to the function:
def cartesian_coordinates(x, y, z):
print(x) # 1
print(y) # 0
print(z) # 1
vector = (1, 0, 1)
cartesian_coordinates(*vector)
Passing point to the function:
def cartesian_coordinates(x, y, z):
print(x) # 1
print(y) # 0
print(z) # 1
point = {'x': 1, 'y': 0, 'z': 1}
cartesian_coordinates(**point)
str.format() expects keyword arguments, which keys are used in string. It is cumbersome to pass format(name=name, agency=agency) for every variable in the code. Since Python 3.6 f-string formatting are preferred:
firstname = 'Jan'
lastname = 'Twardowski'
location = 'Moon'
result = 'Astronaut {firstname} {lastname} on the {location}'.format(**locals())
print(result)
# Astronaut Jan Twardowski on the Moon
Calling a function which has similar parameters. Passing configuration to the function, which sets parameters from the config:
def draw_line(x, y, color, type, width, markers):
...
draw_line(x=1, y=2, color='red', type='dashed', width='2px', markers='disc')
draw_line(x=3, y=4, color='red', type='dashed', width='2px', markers='disc')
draw_line(x=5, y=6, color='red', type='dashed', width='2px', markers='disc')
style = {'color': 'red',
'type': 'dashed',
'width': '2px',
'markers': 'disc'}
draw_line(x=1, y=2, **style)
draw_line(x=3, y=4, **style)
draw_line(x=5, y=6, **style)
Database connection configuration read from config file:
config = {
'host': 'example.com',
'port': 5432,
'username': 'myusername',
'password': 'mypassword',
'database': 'mydatabase'}
def database_connect(host, port, username, password, database):
return ...
connection = database_connect(**config)
Calling function with all variables from higher order function. locals() will return a dict with all the variables in local scope of the function:
def template(template, **user_data):
print('Template:', template)
print('Data:', user_data)
def controller(firstname, lastname, uid=0):
groups = ['admins', 'astronauts']
permission = ['all', 'everywhere']
return template('user_details.html', **locals())
# template('user_details.html',
# firstname='Jan',
# lastname='Twardowski',
# uid=0,
# groups=['admins', 'astronauts'],
# permission=['all', 'everywhere'])
controller('Jan', 'Twardowski')
# Template: user_details.html
# Data: {'firstname': 'Jan',
# 'lastname': 'Twardowski',
# 'uid': 0,
# 'groups': ['admins', 'astronauts'],
# 'permission': ['all', 'everywhere']}
Proxy functions. One of the most common use of *args, **kwargs:
def read_csv(filepath_or_buffer, sep=', ', delimiter=None, header='infer',
names=None, index_col=None, usecols=None, squeeze=False, prefix=None,
mangle_dupe_cols=True, dtype=None, engine=None, converters=None,
true_values=None, false_values=None, skipinitialspace=False,
skiprows=None, nrows=None, na_values=None, keep_default_na=True,
na_filter=True, verbose=False, skip_blank_lines=True, parse_dates=False,
infer_datetime_format=False, keep_date_col=False, date_parser=None,
dayfirst=False, iterator=False, chunksize=None, compression='infer',
thousands=None, decimal=b'.', lineterminator=None, quotechar='"',
quoting=0, escapechar=None, comment=None, encoding=None, dialect=None,
tupleize_cols=None, error_bad_lines=True, warn_bad_lines=True,
skipfooter=0, doublequote=True, delim_whitespace=False, low_memory=True,
memory_map=False, float_precision=None):
"""
Definition of pandas.read_csv() function
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html
"""
def mycsv(file, encoding='utf-8', decimal=b',',
lineterminator='\n', *args, **kwargs):
return read_csv(file, encoding=encoding, decimal=decimal,
lineterminator=lineterminator, *args, **kwargs)
mycsv('iris1.csv')
mycsv('iris2.csv', encoding='iso-8859-2')
mycsv('iris3.csv', encoding='cp1250', verbose=True)
mycsv('iris4.csv', verbose=True, usecols=['Sepal Length', 'Species'])
Decorators. Decorators are functions, which get pointer to the decorated function as it's argument, and has closure which gets original function arguments as positional and keyword arguments:
def login_required(func):
def wrapper(request, *args, **kwargs):
if not request.user.is_authenticated():
raise PermissionError
return func(*args, **kwargs)
return wrapper
@login_required
def edit_profile(request):
...
3.3.9. Assignments¶
"""
* Assignment: Unpacking Arguments Define
* Complexity: medium
* Lines of code: 15 lines
* Time: 13 min
English:
1. Use data from "Given" section (see below)
2. Create function `mean(*args)`, which calculates arithmetic mean for `args`
3. Do not import any libraries and modules
4. Define `result: list[tuple[str, float]]`
5. Iterate over `DATA` separating `features` from `label`
6. To `result` append `label` and arithmetic mean of `features`
7. Compare result with "Tests" section (see below)
Polish:
1. Użyj kodu z sekcji "Given" (patrz poniżej)
2. Stwórz funkcję `mean(*args)`, która liczy średnią arytmetyczną dla `args`
3. Nie importuj żadnych bibliotek i modułów
4. Zdefiniuj `result: list[tuple[str, float]]`
5. Iteruj po `DATA` separując `features` od `label`
6. Do `result` dodawaj `label` oraz wynik średniej arytmetycznej `features`
7. Porównaj wyniki z sekcją "Tests" (patrz poniżej)
Tests:
>>> mean(1)
1.0
>>> mean(1, 3)
2.0
>>> mean(1, 2, 3)
2.0
>>> assert type(result) is list
>>> assert all(type(row) is tuple for row in result)
>>> result # doctest: +NORMALIZE_WHITESPACE
[('virginica', 3.875),
('setosa', 2.65),
('versicolor', 3.475),
('virginica', 6.0),
('versicolor', 3.95),
('setosa', 4.7)]
"""
# Given
DATA = [('Sepal length', 'Sepal width', 'Petal length', 'Petal width', 'Species'),
(5.8, 2.7, 5.1, 1.9, 'virginica'),
(5.1, 0.2, 'setosa'),
(5.7, 2.8, 4.1, 1.3, 'versicolor'),
(6.3, 5.7, 'virginica'),
(6.4, 1.5, 'versicolor'),
(4.7, 'setosa')]
def mean(*args):
return sum(args) / len(args)