# 2.2. Arbitrary Number of Arguments

## 2.2.1. Positional arguments

• * in this context, is not multiplication in mathematical sense

• * is used for positional arguments

• args is a convention, but you can use any name

• *args unpacks from tuple, list or set

Listing 436. Positional arguments passed directly
def echo(a, b, c=0):
print(a)    # 1
print(b)    # 2
print(c)    # 0

echo(1, 2)

Listing 437. Positional arguments passed from sequence
def echo(a, b, c=0):
print(a)    # 1
print(b)    # 2
print(c)    # 0

args = (1, 2)
echo(*args)


## 2.2.2. Keyword Arguments

• ** in this context, is not power in mathematical sense

• ** is used for keyword arguments

• kwargs is a convention, but you can use any name

• **kwargs unpacks from dict

Listing 438. Keyword arguments passed directly
def echo(a, b, c=0):
print(a)    # 1
print(b)    # 2
print(c)    # 0

echo(a=1, b=2)

Listing 439. Keyword arguments passed from dict
def echo(a, b, c=0):
print(a)    # 1
print(b)    # 2
print(c)    # 0

kwargs = {'a': 1, 'b': 2}
echo(**kwargs)


## 2.2.3. Positional and Keyword Arguments

Listing 440. Positional and keyword arguments passed directly
def echo(a, b, c=0):
print(a)    # 1
print(b)    # 2
print(c)    # 0

echo(1, b=2)

Listing 441. Positional and keyword arguments passed from sequence and dict
def echo(a, b, c=0):
print(a)    # 1
print(b)    # 2
print(c)    # 0

args = (1,)
kwargs = {'b': 2}

echo(*args, **kwargs)


## 2.2.4. Dynamically create objects

### 2.2.4.1. 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

iris = Iris(*DATA)
iris.species
# 'versicolor'

DATA = [
(6.0, 3.4, 4.5, 1.6, 'versicolor'),
(4.9, 3.1, 1.5, 0.1, "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}'

output = [Iris(*row) for row in DATA]
print(output)
# [versicolor, setosa]


### 2.2.4.2. From mapping

DATA = {"sepal_length": 6.0, "sepal_width": 3.4, "petal_length": 4.5, "petal_width": 1.6, "species": "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

iris = Iris(**DATA)
iris.species
# 'versicolor'

DATA = [
{"sepal_length": 6.0, "sepal_width": 3.4, "petal_length": 4.5, "petal_width": 1.6, "species": "versicolor"},
{"sepal_length": 4.9, "sepal_width": 3.1, "petal_length": 1.5, "petal_width": 0.1, "species": "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}'

output = [Iris(**row) for row in DATA]
print(output)
# ['versicolor', 'setosa']


## 2.2.5. Examples

### 2.2.5.1. Creating complex numbers

Listing 442. Defining complex number by passing keyword arguments directly
complex(real=3, imag=5)
# (3+5j)

Listing 443. Defining complex number by passing keyword arguments in dict
number = {'real': 3, 'imag': 5}

complex(**number)
# (3+5j)


### 2.2.5.2. Vectors

Listing 444. 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)


### 2.2.5.3. Point

Listing 445. Passing vector 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)


### 2.2.5.5. Common configuration

Listing 447. Calling a function which has similar parameters
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')

Listing 448. Passing configuration to the function, which sets parameters from the config
def draw_line(x, y, color, type, width, markers):
...

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)

Listing 449. Database connection configuration read from config file
config = {
'host': 'localhost',
'port': 5432,
'database': 'my_database',
}

return ...

connection = database_connect(**config)


### 2.2.5.6. Calling function with all variables from higher order function

Listing 450. Passing arguments to lower 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):
permission = ['all', 'everywhere']
return template('user_details.html', **locals())

# template('user_details.html',
#    firstname='Jan',
#    lastname='Twardowski',
#    uid=0,
#    permission=['all', 'everywhere'])

controller('Jan', 'Twardowski')
# Template: user_details.html
# Data: {'firstname': 'Jan',
#        'lastname': 'Twardowski',
#        'uid': 0,
#        'permission': ['all', 'everywhere']}


### 2.2.5.7. Proxy functions

Listing 451. One of the most common use of *args, **kwargs is for proxy methods.
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,
skipfooter=0, doublequote=True, delim_whitespace=False, low_memory=True,
memory_map=False, float_precision=None):
"""
"""

def my_csv(file, encoding='utf-8', decimal=b',', lineterminator='\n', *args, **kwargs):
lineterminator=lineterminator, *args, **kwargs)

my_csv('iris1.csv')
my_csv('iris2.csv', encoding='iso-8859-2')
my_csv('iris3.csv', encoding='cp1250', verbose=True)
my_csv('iris4.csv', verbose=True, usecols=['Sepal Length', 'Species'])


### 2.2.5.8. Decorators

Listing 452. 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(original_function):

def wrapper(*args, **kwargs):
user = kwargs['request'].user

if user.is_authenticated():
return original_function(*args, **kwargs)
else:
print('Permission denied')

return wrapper

def edit_profile(request):
...


## 2.2.6. Assignments

### 2.2.6.1. Iris

English
1. Download data/iris.csv and save as iris.csv

2. Remove species column

4. For each line extract values by splitting lines by coma ,

5. Create output: List[dict] by zipping header and measurements:

• key: column name from the header

• value: measurement at the position

6. Create function mean(**kwargs), function

7. Iterate over output and call mean() by passing arguments as keywords

8. Print mean for each row

Polish
1. Pobierz plik data/iris.csv i zapisz jako iris.csv

2. Usuń kolumnę species

3. Odseparuj nagłówek od pomiarów

4. Wyciągnij wartości z każdej linii przez podział jej po przecinku ,

5. Stwórz output: List[dict] poprzez scalenie nagłówka i pomiarów z każdego wiersza

• klucz: nazwa kolumny z nagłówka

• wartość: pomiar z odpowiedniej kolumny

6. Stwórz funkcję mean(**kwargs)

7. Iterując po output wywołuj mean() podając argumenty nazwanie

8. Wypisz średnią dla każdego wiersza

Non-functional requirements
• Use only str.split() method

• Don't use pandas, numpy or csv etc.

Output
header: list
# ['sepal_length', 'sepal_width' ,'petal_length', 'petal_width']

output: List[Dict[str, float]] = [
{'sepal_length': 5.4, 'sepal_width': 3.9, 'petal_length': 1.3, 'petal_width': 0.4},
{'sepal_length': 5.9, 'sepal_width': 3.0, 'petal_length': 5.1, 'petal_width': 1.8},
{'sepal_length': 6.0, 'sepal_width': 3.4, 'petal_length': 4.5, 'petal_width': 1.6},
...
]

Hint
• map(float, measurements)