№ 4974297878
Обучающий канал по Python
Ссылка для друга - https://t.me/+I7jrAQKR5xAyYTAy
По всем вопросам @mascarov_valentin
Реклама на бирже - https://telega.in/c/Python_per_month
Created
Between 1 July 2021 and 28 February 2023 — estimated from Telegram’s id allocation, not measured. How this range is calculated.
Education — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-FP8, prompt version 1) read this channel’s own recent posts on 10 September 2026 and assigned it the closest of 31 fixed categories, at 59% confidence. This is a model’s judgement about what the channel is likely to be about, not a fact this register measured the way a subscriber count or a view count is measured — it can be revised on a later pass, and it carries no weight anywhere else on this page. How this classification works, and why it has no browse page of its own yet.
Growth
36 measurements spanning 51 days, net -415. Dots are measurements; the straight line between them is drawn to join them, not to claim we know the path taken in between — snapshots are recorded only when a count changes, so gaps mean “no change observed”, never “interpolated”. The vertical axis spans 28,176–28,715 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 36
Measured (UTC)
Subscribers
Change
26 Sept 2026, 18:01
28,238
-45
19 Sept 2026, 15:19
28,283
-22
17 Sept 2026, 02:18
28,305
-14
15 Sept 2026, 00:36
28,319
-17
13 Sept 2026, 12:37
28,336
-11
11 Sept 2026, 15:55
28,347
-21
9 Sept 2026, 00:39
28,368
-21
5 Sept 2026, 16:56
28,389
-14
3 Sept 2026, 17:37
28,403
-17
2 Sept 2026, 12:34
28,420
-13
1 Sept 2026, 11:35
28,433
-13
31 Aug 2026, 13:35
28,446
-11
30 Aug 2026, 12:55
28,457
-2
29 Aug 2026, 10:43
28,459
-7
28 Aug 2026, 08:13
28,466
-4
27 Aug 2026, 07:24
28,470
-4
26 Aug 2026, 10:04
28,474
-8
25 Aug 2026, 13:05
28,482
-11
24 Aug 2026, 12:15
28,493
-18
22 Aug 2026, 20:48
28,511
first reading
Engagement
23 posts held, back to 23 January 2026 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 70 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
5.91%
avg views ÷ 28,238 subscribers
Avg views / post
1,670
1 post measured
Reaction rate
0.359%
reactions ÷ views · ER floor
Posts in window
1
of 23 held
ERR is average views per post over the last 30 days divided by subscribers, the definition TGStat uses, so this figure is comparable with the one you will see elsewhere. It falls structurally as a channel grows: a high ERR on a small channel and a low one on a large channel describe reach mathematics, not quality. We publish the figure and the sample it came from and pass no verdict on it.
ER is defined industry-wide as (forwards + reactions + comments) ÷ views — note the denominator is views, not subscribers. Telegram’s public web preview carries views and reactions but not forward or comment counts, so the reaction rate above is the reactions term only and is therefore a floor: the true ER for this channel is higher by an amount we have not measured and will not estimate.
What these figures were computed from
Window
Rolling 30 days · latest post in window 14 September 2026
Posts held
23 (23 January 2026 – 14 September 2026)
Views total
1,670
Reactions total
6
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
27 Sept 2026, 19:39 UTC
Views are a single reading per post, taken at the time above. A post published in the last day or two is still accumulating views, which pulls the 30-day average down slightly. That is a property of the standard definition rather than a fault in it, so we keep the definition rather than “correcting” the number into something nobody can reproduce.
Precision. Telegram publishes view counts on its public widget in short form — 8.12K, 3.7M — so any reading at or above 1,000 reaches us rounded to three significant figures, and only counts below 1,000 are exact. Averages and rates derived from them are shown to the same precision rather than to the unit: a figure like 3,701,250 would assert digits nobody measured.
Reaction counts are published per emoji and rounded the same way, so a total below 1,000 is exact and a larger one is a sum that may carry a rounded component from each emoji above 1,000. Because it is a sum, it does not look rounded — read a large reaction total as three significant figures per contributing emoji rather than as the figure it prints.
What this channel posts
Photos
≈2,000
Videos
≈8
Links
≈1,020
Lifetime counters from Telegram’s own channel header, read 27 September 2026 — not the date at the top of this page, which is when the subscriber count was last read. A count marked ≈ was rounded by Telegram before we ever saw it — t.me prints these counters in full below 1,000 and to three significant figures above, so ≈142,000 means somewhere between 141,500 and 142,499.
Reaction mix
173 reactions across 23 posts, in 10 distinct kinds. The most used accounts for 86.7% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
👍
150
86.7%
🔥
5
2.89%
🍾
4
2.31%
🤔
4
2.31%
❤🔥
2
1.16%
🆒
2
1.16%
🎉
2
1.16%
😁
2
1.16%
💔
1
0.578%
🥰
1
0.578%
No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.
Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.
Coverage. Reactions were read on 23 of the 23 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 173 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 23 most recent posts we hold, published 23 January 2026 to 14 September 2026, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
Advertising
Ad load
13.0%
3 of 23 posts carry an ad marker
Regulatory tokens
3
posts carrying an erid · 3 distinct tokens
Median views · ads
3,900
over 3 measured posts
Median views · rest
4,380
over 20 measured posts
An ad marker, not a judgement about a post. A post is counted here because it carries one of two explicit markings: an erid token, which Russian law has required on paid placements since 2022 and which is issued against a specific advertising contract, or a #реклама / #ad hashtag in the body, which is the channel declaring it itself. The first is documentary; the second is a self-declaration and is weaker. No classifier reads the text and decides — nothing on this site guesses that a post is an advertisement.
This is a floor, and it can only ever be a floor. A channel that runs paid placements without marking them produces no marker for us to count, and an unmarked ad is indistinguishable from an ordinary post on the public surface. The ad load above therefore means “the share of posts that declared themselves”, never “the share of posts that were paid for”. A low figure is not evidence of a channel that runs few ads.
Both figures are medians, and no ratio between them is published. Each is a view reading that actually occurred on a post, picked by percentile_disc rather than averaged, so one viral post cannot move it and no interpolated value is invented between two readings. The sample on one side is under five posts, which is too thin to compare. The two figures are shown side by side with the count behind each, and deliberately not divided into a headline like “ads get x% fewer views” — an arithmetic that is easy to print and, at this sample size, means nothing.
Advertising tokens recorded on this entry
erid
Posts
First seen
Last seen
2Vtzqw9sKdb
1
23 June 2026
23 June 2026
2VtzqwRtpJu
1
9 July 2026
9 July 2026
2VtzqwWTgtz
1
13 July 2026
13 July 2026
A token repeated across several posts is one advertising contract placed more than once, which is what the identifier is for. The strings are reproduced exactly as they appeared in the post or in its click-through URL and are not validated against any registry — we record the marker a channel published, and whether it resolves to a real contract is a question for the register that issued it.
Measured over the 23 most recent posts we hold, published 23 January 2026 to 14 September 2026. Views are the latest single reading held for each post, and any reading at or above 1,000 is rounded by Telegram to three significant figures.
➡️ Memray — профилирование памяти для Python
Memray — это современный инструмент для профилирования памяти в Python, разработанный командой Bloomberg. Он позволяет детально отслеживать выделение и использование памяти в ваших приложениях, что помогает выявлять утечки и оптимизировать производительность.
• Глубокий анализ: Memray отслеживает все выделения памяти, включая те, что происходят в нативных расширениях на …
Срезы
Срезы (slices) в Python — это способ получения подстроки (подсписка) из последовательности, такой как строка (str), список (list) или кортеж (tuple).
Python Learning 👩💻
Нейросеть, LLM (большие языковые модели) и ИИ-агент
Эти слова в разговорах часто используют как синонимы. Но за ними стоят разные механизмы, и от того, с чем вы работаете, зависит результат.
LLM генерирует текст на основе того, на чем ее обучили. Запускается только по запросу человека, работает в один цикл «промпт → ответ», между сессиями ничего не помнит и умеет ровно одно — писать текст. Как эрудированный консуль…
👩💻 Задача по Python
Создайте функцию find_longest_substring для поиска самой длинной подстроки в строке, содержащей уникальные символы. Функция должна возвращать длину этой подстроки.
Пример:
print(find_longest_substring("abcabcbb")) # Вернёт 3, т.к. самая длинная уникальная подстрока "abc"
print(find_longest_substring("bbbbb")) # Вернёт 1, т.к. самая длинная уникальная подстрока "b"
print(find_longest_subst…
🔥 Три разных человека. Три разных проекта. Один и тот же подход.
— Юра взял «скучную» нишу с готовым спросом → сначала печальные $100/мес, через год уже ~$10K/мес
— Денис сделал Telegram-игру в одиночку на основе AI → ~ $1500 за 1,5 месяца после запуска
— Аня без кода запустила AI-бота для изучения английского → первые ~$200 уже в 1 месяц
Разные результаты. Разный масштаб. Но общие правила:
1. не придумывать «гени…
⚙️ enumerate()
Когда тебе нужно итерировать по списку с доступом к индексу элемента, используй enumerate(). Эта встроенная функция возвращает и индекс, и сам элемент в одном цикле, что удобно и лаконично.
Python Learning 👩💻
Почему Python — основной язык в offensive security?
Большинство задач в ИБ так или иначе упирается в скрипты: автоматизация, работа с сетью, парсинг, фаззинг, свои утилиты под конкретную инфраструктуру. Готовых инструментов часто недостаточно — нужен код, который можно написать и доработать под себя.
Python для Пентестера от Codeby — курс для тех, кто уже знает Python на базовом уровне и хочет применять его в инфор…
❓ Вопрос на собеседовании
Как в Python работают функции с переменным количеством аргументов (*args и **kwargs), и как это можно использовать для создания гибких функций?
Ответ ⬇️
Функции с *args принимают произвольное количество позиционных аргументов, а с **kwargs — именованных аргументов. Это позволяет передавать любое количество значений и делать интерфейс функций более гибким. *args упаковывает аргументы в корт…
➡️ Использование cachetools для кэширования в Python
cachetools — это небольшая, но мощная библиотека для кэширования, которая предоставляет различные стратегии кэширования, такие как LRU (Least Recently Used), LFU (Least Frequently Used) и другие. Она позволяет оптимизировать производительность, избегая повторных вычислений или запросов.
• cachetools полезна, когда требуется хранить временные результаты или промеж…
🔍Тестовое собеседование на Middle Python с разработчиком из Авито завтра вечером
Уже завтра вечером в 19:00 по мск приходи онлайн на открытое собеседование, чтобы посмотреть на настоящее интервью на Middle Python-разработчика.
Как это будет:
📂 Даня, старший разработчик в Авито, будет задавать реальные вопросы и задачи разработчику-добровольцу
📂 Даня будет комментировать каждый ответ респондента, чтобы дать понять ч…
➡️ Использование функции itertools.tee() для дублирования итераторов
itertools.tee() — это интересная функция из модуля itertools, позволяющая создавать несколько независимых копий одного и того же итератора.
🗣️ Это полезно, когда вам нужно одновременно итерировать по одним и тем же данным в разных частях кода, не повторяя вычисления.
✔️ itertools.tee() делает работу с итераторами гибче и удобнее.
Python Learning…
Библиотека python-decouple
Библиотека python-decouple для Python помогает отделить конфигурационные параметры от вашего исходного кода. Это означает, что вы можете хранить секретные данные, такие как ключи API, пароли и URL-адреса базы данных, вне вашего кода, улучшая безопасность.
Python Learning 👩💻
👍8❤🔥2🆒1
Showing the 12 most recent of 23 posts we hold for @Python_per_month. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.
Forward network
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.
Mentions
Named by 1 registered channel — every channel on the register whose own posts have named this one, by its current username or any other username it currently holds, merged from two separately captured readings of the same fact so a namer caught by only one of them is not missed and a namer both caught is not counted twice. A username this channel has since dropped is not matched — that handle may belong to someone else now, and crediting today’s namer to yesterday’s owner would misattribute it.
Named by
Channels on the register whose posts name this channel's handle.
A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.
Channels Telegram recommends alongside this one
Telegram’s own answer, not this register’s. When this register asks Telegram’s API what is similar to this channel, this is the list it returns, in the exact order Telegram returns it — never re-sorted by subscribers or by anything else this register measures. The relationship, and the order, are Telegram’s; we record them and date them, and make no claim of our own about which of these channels actually resemble this one.
Read from Telegram’s recommendation API, most recently 9 September 2026. Telegram holds a list like this for a small and growing share of the register — how this is measured, and why most channel pages show nothing here.
Appears in Telegram’s recommendations for other channels
The reverse of the list above, and a different kind of signal. This does not require this channel to have ever been asked about directly — each row below is a channel we DID ask Telegram about, whose Telegram-generated list happened to include this one. A channel can appear here with an empty list above it, because being named by someone else’s query is independent of having been queried itself.
Python и 1000 уязвимостей @osint_pythons · 36,535 Telegram ranks this channel #3 of 84 here — alongside 83 others — read 1 September 2026
Senior Python Developer @seniorpy · 39,864 Telegram ranks this channel #3 of 92 here — alongside 91 others — read 30 August 2026
Python обучающий @pythonist24 · 55,872 Telegram ranks this channel #5 of 88 here — alongside 87 others — read 23 August 2026
Однажды в трендах @trendo · 67,788 Telegram ranks this channel #7 of 90 here — alongside 89 others — read 20 August 2026
Python Hacks @python_secrets · 40,594 Telegram ranks this channel #13 of 82 here — alongside 81 others — read 30 August 2026
Python Academy @python_academy · 44,284 Telegram ranks this channel #14 of 87 here — alongside 86 others — read 28 August 2026
[PYTHON:TODAY] @python2day · 63,764 Telegram ranks this channel #16 of 91 here — alongside 90 others — read 21 August 2026
Pythonist.ru - образование по питону @pythonist_ru · 24,046 Telegram ranks this channel #17 of 82 here — alongside 81 others — read 17 September 2026
Python Developer @python_tg · 20,963 Telegram ranks this channel #19 of 85 here — alongside 84 others — read 25 September 2026
Python Portal @PythonPortal · 50,417 Telegram ranks this channel #25 of 80 here — alongside 79 others — read 25 August 2026
Python/ django @pythonl · 58,866 Telegram ranks this channel #29 of 84 here — alongside 83 others — read 22 August 2026
Простой Python | Программирование @python_piton_javascript · 126,249 Telegram ranks this channel #38 of 84 here — alongside 83 others — read 13 August 2026
Библиотека питониста | Python, Django, Flask @pyproglib · 37,384 Telegram ranks this channel #39 of 84 here — alongside 83 others — read 1 September 2026
Базы данных | Access, SQL, Big Data @databases_secrets · 30,002 Telegram ranks this channel #41 of 82 here — alongside 81 others — read 7 September 2026
Python вопросы с собеседований @python_job_interview · 24,881 Telegram ranks this channel #43 of 90 here — alongside 89 others — read 15 September 2026
Python learning @python3learning · 22,563 Telegram ranks this channel #60 of 71 here — alongside 70 others — read 20 September 2026
Полка Разработчика @developer_shelf · 26,815 Telegram ranks this channel #62 of 63 here — alongside 62 others — read 11 September 2026
Python Job | Вакансии | Стажировки @job_python · 23,025 Telegram ranks this channel #68 of 96 here — alongside 95 others — read 19 September 2026
Поколение Python 🐍 @pygen_ru · 50,072 Telegram ranks this channel #69 of 93 here — alongside 92 others — read 25 August 2026
Windows Community @wind_community · 41,313 Telegram ranks this channel #71 of 74 here — alongside 73 others — read 29 August 2026
This channel appears in 20 seed channels' Telegram-generated recommendation lists in total. Each is Telegram’s list for THAT channel, not this one — see how this is measured.
Cite this entry
A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 26 September 2026 — this
entry's latest reading, not the date you are reading this.
“Python Learning” (@Python_per_month), 28,238 subscribers as measured 26 September 2026. Telegram Register, tgregister.com/channel/Python_per_month.
Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.