Additional modules

🔥

The following modules deal with classes that are not considered as basic, but because of their extreme importance and often usage it would be nice to take a look at them:

Module collections
Module array
Module bytearray
Module enum

`collections`

`namedtuple`

Named tuples assign meaning to each position in a tuple and allow for more readable, self-documenting code. They can be used wherever regular tuples are used, and they add the ability to access fields by name instead of position index.

Usual approach:

🪄 Code:

student = ("John", "Jakeson", 23, "18 B")
print(f'Student {student[1]} from group {student[3]} is {student[2]} year old')

📟 Output:

Student Jakeson from group 18 B is 23 year old

To avoid the mess of various indexes we can add some light "OOP-flavor" to this use-case via namedtuple:

🪄 Code:

from collections import namedtuple

Student = namedtuple("Student", "name surname age group")

student = Student("John", "Jakeson", 23, "18 B")
print(f'Student {student.name} {student.surname} from group {student.group} is {student.age} year old')
print(student)

📟 Output:

Student John Jakeson from group 18 B is 23 year old
Student(name='John', surname='Jakeson', age=23, group='18 B')

To transform into dict:

🪄 Code:

print(student._asdict())

📟 Output:

{'name': 'John', 'surname': 'Jakeson', 'age': 23, 'group': '18 B'}

`deque`

deque stands for "Double Edged Queue"

Returns a new deque object initialized left-to-right (using append()) with data from iterable. If iterable is not specified, the new deque is empty.

While regular list is efficiently performant only when accessing it's "right side" (via append, extend, pop methods) deque (double edged queue) is efficient from left and right ends both by adding appendleft, extendleft and popleft methods.

🪄 Code:

from collections import deque

de = deque("abcde") 

print(de.pop())
print(de.popleft())

📟 Output:

e
a

🪄 Code:

de.appendleft("BEGIN")
print(de)

📟 Output:

deque(['BEGIN', 'b', 'c', 'd'])

`defaultdict`

defaultdict is a sub-class of dict object:

defaultdict(default_factory[, ...]) --> dict with default factory

If default_factory is not None, it is called without arguments to provide a default value for the given key, this value is inserted in the dictionary for the key, and returned.

🪄 Code:

# Groupping stuff:
encountered_animals = [("birds", "eagle"), ("mammals", "hippo"), ("mammals", "panther"), 
                       ("snakes", "python"), ("birds", "hawk"), ("snakes", "anaconda")]

from collections import defaultdict

def f(animals):
    spotted_by_group = defaultdict(list)  # here list is default_factory which will be called when key will not be found
    for type_, animal in animals:
        spotted_by_group[type_].append(animal)
        
    return spotted_by_group

print(f(encountered_animals))

%timeit f(encountered_animals)

📟 Output:

defaultdict(<class 'list'>, {'birds': ['eagle', 'hawk'], 'mammals': ['hippo', 'panther'], 'snakes': ['python', 'anaconda']})
2.65 µs ± 144 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)

The same using setdefault method:

🪄 Code:

encountered_animals = [("birds", "eagle"), ("mammals", "hippo"), ("mammals", "panther"), 
                       ("snakes", "python"), ("birds", "hawk"), ("snakes", "anaconda")]

def f(encountered_animals):
    spotted_by_group = {}
    # spotted_by_group = collection.defaultdict(list) 
    for type_, animal in encountered_animals:
        #spotted_by_group[type_].append(animal)  # defaultdict
        spotted_by_group.setdefault(type_, []).append(animal)
    return spotted_by_group
    
print(f(encountered_animals))
%timeit f(encountered_animals)

📟 Output:

{'birds': ['eagle', 'hawk'], 'mammals': ['hippo', 'panther'], 'snakes': ['python', 'anaconda']}
2.25 µs ± 24.4 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)

If we set default_factory to int we can create a counter of things:

🪄 Code:

s = 'AnnaMadrigal'
d = defaultdict(int)   #  d = {} # if via setdefault
for k in s.lower():
    # d.setdefault(k, 0) # if via setdefault
    d[k] += 1

print(d)

📟 Output:

defaultdict(<class 'int'>, {'a': 4, 'n': 2, 'm': 1, 'd': 1, 'r': 1, 'i': 1, 'g': 1, 'l': 1})

`Counter`

This class is a sub-class of dict and used to measure how often each member is present in a given iterable

This is very similar to previous example of defaultdict and int as default_factory

🪄 Code:

from collections import Counter

print(Counter(['red', 'blue', 'red', 'green', 'blue', 'blue']))

📟 Output:

Counter({'blue': 3, 'red': 2, 'green': 1})

🪄 Code:

cnt = Counter('AnnaMadrigal'.lower())
print(cnt)

📟 Output:

Counter({'a': 4, 'n': 2, 'm': 1, 'd': 1, 'r': 1, 'i': 1, 'g': 1, 'l': 1})

It has many useful methods:

🪄 Code:

print(Counter("Asdasdasdasd").items())

📟 Output:

dict_items([('A', 1), ('s', 4), ('d', 4), ('a', 3)])

🪄 Code:

# elements()
print(list(cnt.elements()))

📟 Output:

['a', 'a', 'a', 'a', 'n', 'n', 'm', 'd', 'r', 'i', 'g', 'l']

🪄 Code:

# most_common(n)
print(cnt.most_common(3))

📟 Output:

[('a', 4), ('n', 2), ('m', 1)]

🪄 Code:

# substract
c = Counter(a=4, b=2, c=0, d=-2)
d = Counter(a=1, b=2, c=3, d=4)
c.subtract(d)
print(c)

📟 Output:

Counter({'a': 3, 'b': 0, 'c': -3, 'd': -6})

`array`

New in version 3.3

This module defines an object type which can compactly represent an array of basic values: characters, integers, floating point numbers.

Arrays are sequence types and behave very much like lists, except that the type of objects stored in them is constrained. The main idea is to give similar API but much less memory footprint if we need a collection of object of the same type. array will consume about sizeof(one object) * len(array) bytes.

Type code

Python Type

Minimum size in bytes

'i'

int

'h'

int

'l'

int

'f'

float

'd'

float

'b'

int

Syntax for creating an array:

array.array(typecode[, initializer])

Array has the similar to list operations like indexing and slicing. Also, array has these methods that are the same as in list:

append
extend
count
pop
remove
reverse
index

Some examples:

🪄 Code:

import array

a = array.array('f', [1.2, 2.1])
print(f'Array now is {a}, item size is {a.itemsize})')

a.append(3.141516925)
print(f'After appending array is {a}')

# Emulating bytearray type:
bytes_array = array.array('b', b"ABC")
print(bytes_array)
bytes_array.frombytes(b"123")
print(bytes_array)

📟 Output:

Array now is array('f', [1.2000000476837158, 2.0999999046325684]), item size is 4)
After appending array is array('f', [1.2000000476837158, 2.0999999046325684, 3.141516923904419])
array('b', [65, 66, 67])
array('b', [65, 66, 67, 49, 50, 51])

`numpy`

a powerful N-dimensional array object
sophisticated (broadcasting) functions
tools for integrating C/C++ and Fortran code
useful linear algebra, Fourier transform, and random number capabilities

NumPy's main object is the homogeneous multidimensional array. It is a table of elements (usually numbers), all of the same type, indexed by a tuple of non-negative integers. In NumPy dimensions are called axes.

🪄 Code:

import numpy as np

a = np.array([2,3,4])
print(f'a is {a}, type is {a.dtype}, len is {len(a)}, shape is {a.shape}')

b = np.array([(1,2,3), (4,5,6)])
print(f'b:\n{b}, shape is {b.shape}')


print(f"Transpose T:\n{b.T}")

📟 Output:

a is [2 3 4], type is int64, len is 3, shape is (3,)
b:
[[1 2 3]
 [4 5 6]], shape is (2, 3)
Transpose T:
[[1 4]
 [2 5]
 [3 6]]

🪄 Code:

print("Transforming, add 0.5:\n", b + .5)
print("Transforming, add 3*b:\n", b + 3*b)
print("Transforming, mult by 3:\n", b * 3)
print("Changing shape:\n", b.reshape(1, 6))

📟 Output:

Transforming, add 0.5:
 [[1.5 2.5 3.5]
 [4.5 5.5 6.5]]
Transforming, add 3*b:
 [[ 4  8 12]
 [16 20 24]]
Transforming, mult by 3:
 [[ 3  6  9]
 [12 15 18]]
Changing shape:
 [[1 2 3 4 5 6]]

`bytearray`

Array of bytes type (length of item is 1B)
Joke: "Mutable string", at last :)

In Python 3 string is a sequence of Unicode characters. If we encode them we will get so-called bytes object suitable for sending over internet or writing it to a socket or file (but usually python hides this from programmer when dealing with files using default encoding). bytes is analogue of the strings used in Python 2. bytes object is immutable, just like regular string and has the same methods.

🪄 Code:

str_ = "Привіт, Світе!"
bytes_ = str_.encode("utf8")

print(bytes_)

print(dir(bytes_))

📟 Output:

b'\xd0\x9f\xd1\x80\xd0\xb8\xd0\xb2\xd1\x96\xd1\x82, \xd0\xa1\xd0\xb2\xd1\x96\xd1\x82\xd0\xb5!'
['__add__', '__class__', '__contains__', '__delattr__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getitem__', '__getnewargs__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__iter__', '__le__', '__len__', '__lt__', '__mod__', '__mul__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__rmod__', '__rmul__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', 'capitalize', 'center', 'count', 'decode', 'endswith', 'expandtabs', 'find', 'fromhex', 'hex', 'index', 'isalnum', 'isalpha', 'isascii', 'isdigit', 'islower', 'isspace', 'istitle', 'isupper', 'join', 'ljust', 'lower', 'lstrip', 'maketrans', 'partition', 'removeprefix', 'removesuffix', 'replace', 'rfind', 'rindex', 'rjust', 'rpartition', 'rsplit', 'rstrip', 'split', 'splitlines', 'startswith', 'strip', 'swapcase', 'title', 'translate', 'upper', 'zfill']

bytearray bultin type is mutable counterpart of bytes type.

There is no dedicated literal syntax for bytearray objects, instead they are always created by calling the constructor:

Creating an empty instance: bytearray()
Creating a zero-filled instance with a given length: bytearray(10)
From an iterable of integers: bytearray(range(20))
Copying existing binary data via the buffer protocol: bytearray(b'Hi!')

🪄 Code:

b = bytearray(b'Hello World')
print(b)
print(b[0], b[3:6], b[:5:-1], sep=", ")

📟 Output:

bytearray(b'Hello World')
72, bytearray(b'lo '), bytearray(b'dlroW')

Good news: due to Python's duck-typing, methods of str, bytes and bytearray are the same, the main difference is that they return the object of the correspondent type. Also, when iterating through bytearray, it will yield bytecode of the character. This can sometimes overcomplicate things.

🪄 Code:

print(b)

print(b.lower().startswith(b'hello'))
for i in b[:5:-1]:
    print(i)
    
print()
print(b''.join([chr(x).encode('utf8') for x in reversed(b[:5:-1])]))

📟 Output:

bytearray(b'Hello World')
True
100
108
114
111
87

b'World'

`enum`

New in version 3.4

An enumeration is a set of symbolic names (members) bound to unique, constant values. Within an enumeration, the members can be compared by identity, and the enumeration itself can be iterated over.

In a simplified sense, this is an iteratable set of constants.

from enum import Enum

class Color(Enum):
    RED = 1
    GREEN = 2
    BLUE = 3

Member values can be anything: int, str, etc.. If the exact value is unimportant you may use auto instances and an appropriate value will be chosen for you. Care must be taken if you mix auto with other values.

🪄 Code:

from enum import Enum, auto

class Page(Enum):
    LOGIN = auto()
    DASHBOARD = auto()
    SEARCH = auto()

class Color(Enum):
    RED = 1
    GREEN = 2
    BLUE = 3
    
# All enum members are hashable:
color_settings_per_page = {
    Page.LOGIN: Color.RED,
    Page.DASHBOARD: Color.GREEN,
    Page.SEARCH: Color.BLUE
}
print(color_settings_per_page)

📟 Output:

{<Page.LOGIN: 1>: <Color.RED: 1>, <Page.DASHBOARD: 2>: <Color.GREEN: 2>, <Page.SEARCH: 3>: <Color.BLUE: 3>}

Enums can be iterated over:

🪄 Code:

print("We have these color options:")
for color in Color:
    print(color)

📟 Output:

We have these color options:
Color.RED
Color.GREEN
Color.BLUE

It behaves like a new type:

🪄 Code:

print(type(Color.RED))
print(isinstance(Color.GREEN, Color))
print(Color.RED.name)

📟 Output:

<enum 'Color'>
True
RED

🪄 Code:

Animal = Enum('Animal', 'ANT BEE CAT DOG')
print(Animal)
print(list(Animal))

📟 Output:

<enum 'Animal'>
[<Animal.ANT: 1>, <Animal.BEE: 2>, <Animal.CAT: 3>, <Animal.DOG: 4>]

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Was this helpful?

from collections import namedtuple Student = namedtuple("Student", "name surname age group") student = Student("John", "Jakeson", 23, "18 B") print(f'Student {student.name} {student.surname} from group {student.group} is {student.age} year old') print(student)

# Groupping stuff: encountered_animals = [("birds", "eagle"), ("mammals", "hippo"), ("mammals", "panther"), ("snakes", "python"), ("birds", "hawk"), ("snakes", "anaconda")] from collections import defaultdict def f(animals): spotted_by_group = defaultdict(list) # here list is default_factory which will be called when key will not be found for type_, animal in animals: spotted_by_group[type_].append(animal) return spotted_by_group print(f(encountered_animals)) %timeit f(encountered_animals)

defaultdict(<class 'list'>, {'birds': ['eagle', 'hawk'], 'mammals': ['hippo', 'panther'], 'snakes': ['python', 'anaconda']}) 2.65 µs ± 144 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)

encountered_animals = [("birds", "eagle"), ("mammals", "hippo"), ("mammals", "panther"), ("snakes", "python"), ("birds", "hawk"), ("snakes", "anaconda")] def f(encountered_animals): spotted_by_group = {} # spotted_by_group = collection.defaultdict(list) for type_, animal in encountered_animals: #spotted_by_group[type_].append(animal) # defaultdict spotted_by_group.setdefault(type_, []).append(animal) return spotted_by_group print(f(encountered_animals)) %timeit f(encountered_animals)

import array a = array.array('f', [1.2, 2.1]) print(f'Array now is {a}, item size is {a.itemsize})') a.append(3.141516925) print(f'After appending array is {a}') # Emulating bytearray type: bytes_array = array.array('b', b"ABC") print(bytes_array) bytes_array.frombytes(b"123") print(bytes_array)

Array now is array('f', [1.2000000476837158, 2.0999999046325684]), item size is 4) After appending array is array('f', [1.2000000476837158, 2.0999999046325684, 3.141516923904419]) array('b', [65, 66, 67]) array('b', [65, 66, 67, 49, 50, 51])

import numpy as np a = np.array([2,3,4]) print(f'a is {a}, type is {a.dtype}, len is {len(a)}, shape is {a.shape}') b = np.array([(1,2,3), (4,5,6)]) print(f'b:\n{b}, shape is {b.shape}') print(f"Transpose T:\n{b.T}")

b'\xd0\x9f\xd1\x80\xd0\xb8\xd0\xb2\xd1\x96\xd1\x82, \xd0\xa1\xd0\xb2\xd1\x96\xd1\x82\xd0\xb5!' ['__add__', '__class__', '__contains__', '__delattr__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getitem__', '__getnewargs__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__iter__', '__le__', '__len__', '__lt__', '__mod__', '__mul__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__rmod__', '__rmul__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', 'capitalize', 'center', 'count', 'decode', 'endswith', 'expandtabs', 'find', 'fromhex', 'hex', 'index', 'isalnum', 'isalpha', 'isascii', 'isdigit', 'islower', 'isspace', 'istitle', 'isupper', 'join', 'ljust', 'lower', 'lstrip', 'maketrans', 'partition', 'removeprefix', 'removesuffix', 'replace', 'rfind', 'rindex', 'rjust', 'rpartition', 'rsplit', 'rstrip', 'split', 'splitlines', 'startswith', 'strip', 'swapcase', 'title', 'translate', 'upper', 'zfill']

from enum import Enum, auto class Page(Enum): LOGIN = auto() DASHBOARD = auto() SEARCH = auto() class Color(Enum): RED = 1 GREEN = 2 BLUE = 3 # All enum members are hashable: color_settings_per_page = { Page.LOGIN: Color.RED, Page.DASHBOARD: Color.GREEN, Page.SEARCH: Color.BLUE } print(color_settings_per_page)