It is a new selection of tips and tricks about Python and programming from my Telegram-channel @pythonetc.

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If you want to iterate over several iterables at once, you can use the zip function (it has nothing to do with ZIP file format):

from datetime import timedelta

names = [
    'Eleven. Return and Revert',
    'Wilderness',
    'The Menagerie Inside',
    'Evaporate',
]

years = [
    2010,
    2013,
    2015,
    2018,
]

durations = [
    timedelta(minutes=57, seconds=38),
    timedelta(minutes=48, seconds=5),
    timedelta(minutes=46, seconds=34),
    timedelta(minutes=43, seconds=25),
]

print('Midas Fall LPs:')
for name, year, duration in zip(
    names, years, durations
):
    print(f'  * {name} ({year}) — {duration}')

Output:

Midas Fall LPs:
  * Eleven. Return and Revert (2010) — 0:57:38
  * Wilderness (2013) — 0:48:05
  * The Menagerie Inside (2015) — 0:46:34
  * Evaporate (2018) — 0:43:25

A generator can be stopped. You can explicitly call g.close() but usually garbage collector does that for you. Once close is called, the GeneratorExit is raised at the point where the generator function was paused:

def gen():
    try:
        yield 1
        yield 2
    finally:
        print('END')


g = gen()
print(next(g))  # prints '1'
g.close()  # prints 'END'

Mind three things. First, you can’t yield values while handling GeneratorExit:

def gen():
    try:
        yield 1
    finally:
        yield 3


g = gen()
next(g)
g.close()  # RuntimeError

Second, the exception is not raised if a generator is not yet started, but the generator still becomes stopped:

def gen():
    try:
        yield 1
    finally:
        print('END')


g = gen()
g.close()  # nothing
print(list(g))  # prints '[]'

Third, close does nothing if a generator is already finished:

def gen():
    try:
        yield 1
        yield 2
    finally:
        print('END')


g = gen()
print(list(g))
print('Closing now')
g.close()

# END
# [1, 2]
# Closing now

f-strings allow you to specify the width for the printed value as well as other format specifiers:

>>> x = 42
>>> f'{x:5}+{x:15f}'
'   42+      42.000000'

They can also contain evaluated expressions which can be useful when width is unknown upfront:

def print_table(matrix):
    cols_width = [
        max(len(str(row[col])) for row in matrix)
        for col in range(len(matrix[0]))
    ]

    for row in matrix:
        for i, cell in enumerate(row):
            print(
                f'{cell:{cols_width[i]}} ',
                end=''
            )
        print()

albums = [
    ['Eleven. Return and Revert', 2010],
    ['Wilderness', 2013],
    ['The Menagerie Inside', 2015],
    ['Evaporate', 2018],
]

print_table(albums)

Output:

Eleven. Return and Revert 2010
Wilderness                2013
The Menagerie Inside      2015
Evaporate                 2018

If your class is derived from another, the metaclass of your class have to be also derived from the metaclass of that class:

from collections import UserDict
from abc import ABCMeta

# ABCMeta is a metaclass of UserDict
class MyDictMeta(ABCMeta):
    def __new__(cls, name, bases, dct):
        return super().__new__(cls, name, bases, dct)

class MyDict(UserDict, metaclass=MyDictMeta):
    pass

It may be a good idea to get the metaclass of that other class automatically:

def create_my_dict_class(parents):
    class MyDictMeta(*[type(c) for c in parents]):
        def __new__(cls, name, bases, dct):
            return super().__new__(cls, name, bases, dct)

    class MyDict(*parents, metaclass=MyDictMeta):
        pass


MyDict = create_my_dict_class((UserDict,))

__init__ allows you to modify an object right after the creation. If you want to control what is created you should use __new__ instead:

from typing import Tuple, Dict
from cached_property import cached_property


class Numbers:
    _LOADED: Dict[Tuple[int, ...], 'Numbers'] = {}

    def __new__(cls, ints: Tuple[int, ...]):
        if ints not in cls._LOADED:
            obj = super().__new__(cls)
            cls._LOADED[ints] = obj

        return cls._LOADED[ints]

    def __init__(self, ints: Tuple[int, ...]):
        self._ints = ints

    @cached_property
    def biggest(self):
        print('calculating...')
        return max(self._ints)


print(Numbers((4, 3, 5)).biggest)
print(Numbers((4, 3, 5)).biggest)
print(Numbers((4, 3, 6)).biggest)