4.2. Passing many arguments

4.2.1. Arbitrary number of 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 241. Positional arguments passed directly
def echo(a, b, c=0):
    print(a)    # 1
    print(b)    # 2
    print(c)    # 0

echo(1, 2)
Listing 242. 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)

4.2.2. Arbitrary number of 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 243. 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 244. 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)

4.2.3. Arbitrary number of positional and keyword arguments

Listing 245. 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 246. 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)

4.2.4. Examples

4.2.4.1. Creating complex numbers

Listing 247. Defining complex number by passing keyword arguments directly
complex(real=3, imag=5)
# (3+5j)
Listing 248. Defining complex number by passing keyword arguments in dict
kwargs = {'real': 3, 'imag': 5}

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

4.2.4.2. Vectors

Listing 249. 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)

4.2.4.4. Common configuration

Listing 251. 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 252. 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 253. Database connection configuration read from config file
config = {
    'host': 'localhost',
    'port': 5432,
    'username': 'my_username',
    'password': 'my_password',
    'database': 'my_database',
}


def database_connect(host, port, username, password, database):
    return ...


connection = database_connect(**config)

4.2.4.5. Calling function with all variables from higher order function

Listing 254. Passing arguments to lower order function. locals() will return a dict with all the variables in local scope of the function.
def lower(a, b, c, d, e):
    print(a, b, c, d, e)

def higher(a, b, c=0):
    d = 4
    e = 5
    lower(**locals())
    # lower(a=1, b=2, c=0, d=4, e=5)


higher(1, 2)
# 1 2 0 4 5

4.2.4.6. Proxy functions

Listing 255. 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,
             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 my_csv(file, encoding='utf-8', *args, **kwargs):
    return read_csv(file, encoding=encoding, *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'])

4.2.4.7. Decorators

Listing 256. 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


@login_required
def edit_profile(request):
    ...

4.2.5. Assignments

4.2.5.1. Iris

  • Complexity level: medium

  • Lines of code to write: 15 lines

  • Estimated time of completion: 20 min

  • Filename: solution/calling_kwargs.py

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

  2. Remove species column

  3. Separate header from measurements

  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)