Learn Python Coding
Learn Python through simple, practical examples and real coding ideas. Clear explanations, useful snippets, and hands-on learning for anyone starting or improving their programming skills. Admin: @HusseinSheikho || @Hussein_Sheikho
Больше📈 Аналитический обзор Telegram-канала Learn Python Coding
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📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 40 900 подписчиков.
Согласно последним данным от 26 октября, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 455, а за последние 24 часа — 5, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 3.66%. В первые 24 часа после публикации контент обычно набирает 1.51% реакций от общего числа подписчиков.
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- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 2.
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📝 Описание и контентная политика
Автор описывает ресурс как площадку для выражения субъективного мнения:
“Learn Python through simple, practical examples and real coding ideas. Clear explanations, useful snippets, and hands-on learning for anyone starting or improving their programming skills.
Admin: @HusseinSheikho || @Hussein_Sheikho”
Благодаря высокой частоте обновлений (последние данные получены 27 октября, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Технологии и приложения.
heapq.merge() combines such sources into a single, ordered iterator.
In this guide:
• We will combine multiple sorted sequences;
• We will explore lazy processing of large data sources;
• We will configure comparison using the key argument;
• We will combine data sorted in reverse order.
This is especially useful when working with logs, files, and query results, where each source already provides data in the correct order.
#Python #heapq #DataProcessing #CodingTips #Programming #Algorithms
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⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2Awarnings.warn()was often used for this purpose:
import warnings
def old_api():
warnings.warn(
"Use new_api()",
DeprecationWarning
)
In Python 3.13, a deprecated()decorator has been introduced:
from warnings import deprecated
@deprecated("Use new_api()")
def old_api():
return "old"
Now, the information about deprecation is part of the API itself. It can be recognized not only during runtime, but also by editors and static analysis tools.
This also works for classes:
@deprecated("Use NewClient")
class OldClient:
pass
This is convenient for libraries, SDKs, and large projects where the API is gradually changing.
#Python #Python313 #Deprecated #Coding #Programming #SoftwareDevelopment
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price: float,
quantity: int,
discount_rate: float,
tax_rate: float
) -> float:
# Calculate the subtotal
subtotal = price * quantity
# Apply the discount
discounted_amount = subtotal * (1 - discount_rate)
# Calculate the tax
final_total = discounted_amount * (1 + tax_rate)
return final_total
The problem here is that the calculation of the subtotal, discount, and tax are all combined into one function. Any change to one of these steps can affect the entire calculation.
It's better to break down the logic into smaller, more specialized functions:
def calculate_subtotal(price: float, quantity: int) -> float:
return price * quantity
def apply_discount(subtotal: float, discount: float) -> float:
return subtotal * (1 - discount)
def calculate_tax(amount: float, tax_rate: float) -> float:
return amount * (1 + tax_rate)
This is much better. ✅
Smaller functions with a single task are easier to test with unit tests because they have fewer dependencies and require less mocking.
Furthermore, isolated components are easier to reuse in different parts of the application or pipeline without bringing in unnecessary dependencies.
Therefore, keep your functions simple and focused.
One function – one responsibility. 📝
#Python #Coding #SoftwareDevelopment #CleanCode #Programming #BestPractices
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It was introduced in Python 3.8 and allows you to assign a value to a variable and use it directly within the expression at the same time.
For example:
while (line := input("Say something: ")) != "quit":
print(f"You said: {line}")
Without it, you would have to retrieve the value separately using input(), and then check it.
Have you ever used the := operator in your code?
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("admin", "alex"),
("user", "max"),
("admin", "kate"),
]
groups = {}
for role, name in users:
if role not in groups:
groups[role] = []
groups[role].append(name)
The setdefault() method allows you to perform this operation directly when accessing the dictionary. If the key exists, it returns its current value. If the key is missing, the provided value is written to the dictionary and then returned:
groups = {}
for role, name in users:
groups.setdefault(
role,
[],
).append(name)
The result is the same structure without a separate key existence check:
print(groups)
# {
# 'admin': ['alex', 'kate'],
# 'user': ['max']
# }
It's important to note that the expression of the second argument is evaluated every time setdefault() is called, even if the key already exists. Therefore, you should avoid creating expensive objects or performing functions with side effects there:
value = cache.setdefault(
key,
build_value(),
)
In this code, build_value() will be called before the method itself is executed. If the value creation should only happen when the key is missing, it's better to use an explicit check or a suitable data structure, such as defaultdict.
🔥 setdefault() is well-suited for compactly initializing simple mutable containers when grouping and aggregating data. However, it's important to remember that the provided value is evaluated regardless of whether the key exists.
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