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Channel
Python challenge️
@pythonchallenge
On this record: Growth · Engagement · Reactions · Posts · Citations · Cite this entry
4,566subscribers
-8 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of 3,162–10,000.
Register entry
| Telegram ID | -1001125417196 |
|---|---|
| Type | Channel |
| Username | @pythonchallenge |
| Created | Between 1 June 2017 and 30 June 2020— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 6 August 2026 |
| Last confirmed live | 12 August 2026 |
| Measurements held | 4 |
| Confirmed unchanged | 1 time, most recently 12 August 2026 |
| On Telegram | t.me/pythonchallenge |
Growth
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 12 Aug 2026, 23:44 | 4,566 | -6 |
| 10 Aug 2026, 07:16 | 4,572 | -2 |
| 6 Aug 2026, 04:02 | 4,574 | no change |
| 6 Aug 2026, 02:24 | 4,574 | first reading |
Engagement
21 posts held, back to 11 September 2025 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 7 pagesof Telegram’s post history, 20 posts per page.
- ERR · 30 days
- 5.88%
- avg views ÷ 4,566 subscribers
- Avg views / post
- 269
- 2 posts measured
- Reaction rate
- —
- this channel exposes no reaction counts
- Posts in window
- 2
- of 21 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.
| Window | Rolling 30 days · latest post in window 9 August 2026 |
|---|---|
| Posts held | 21 (11 September 2025 – 9 August 2026) |
| Views total | 537 |
| Reactions total | — |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 12 Aug 2026, 06:33 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.
Reaction mix
79 reactions across 17 posts, in 4 distinct kinds. The most used accounts for 62.0% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 49 | 62.0% | |
| 👍 | 16 | 20.3% | |
| 👏 | 9 | 11.4% | |
| 🙏 | 5 | 6.33% |
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 17 of the 21 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 79reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 21 most recent posts we hold, published 11 September 2025 to 9 August 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.
Recent posts
ما در کانال آموزش هوش مصنوعی، مباحث آموزشی از پایه که مناسب دانش آموزان هست تا تخصصی که مناسب دانشجویان و علاقه مندان حوزه AI هست رو قرار میدیم. در صورت تمایل میتوانید از طریق آی دی زیر در این کانال هم عضو شوید. https://t.me/studentai نکته : فقط مباحث آموزشی در کانال فوق قرار میگیرد. 📖 مجله هوش مصنوعی ➖➖➖➖➖ 🆔 : @HomeAI
#چالش_پایتون_در_هوش_مصنوعی 🔵 فاز ۳ – قسمت ۴: الگوریتمهای طبقهبندی (Classification) 🔍🤖 طبقهبندی (Classification) یکی از مهمترین شاخههای یادگیری نظارتشده است. هدف: دستهبندی دادهها در گروههای مشخص ✅ ✨ طبقهبندی یعنی چی؟ فرض کن دادههایی داریم از ایمیلها و میخوایم تصمیم بگیریم که هر ایمیل اسپم است یا نه. مدل با یادگیری از دادههای گذشته، به هر ایمیل جدید یک برچسب اختصاص میدهد. 📌 مثالهای رایج * تشخیص…
❤4
Python challenge️ pinned «📚 فازهای آموزش پایتون و هوش مصنوعی 🔹 فاز ۱: مقدمات پایتون آشنایی با پایتون و نصب ابزارها 🖥 متغیرها و انواع دادهها 🔤🔢 شرطها و حلقهها 🔄 توابع و ماژولها ⚙️ کار با کتابخانهها و نصب پکیجها 📦 پروژه کوچک عملی 🎯 (جمعبندی) 🔹 فاز ۲: پردازش دادهها آشنایی با…»
#چالش_پایتون_در_هوش_مصنوعی 🔵 فاز ۳ – قسمت ۳: رگرسیون خطی (Linear Regression) 📈🤖 رگرسیون خطی یکی از سادهترین و مهمترین الگوریتمهای یادگیری ماشینه. مدل تلاش میکنه بین ورودیها و خروجیها یک خط رابطه پیدا کنه. اگه ورودی تغییر کرد، از روی اون خط مقدار جدید رو پیشبینی میکنه. 😎 ✨ رگرسیون خطی یعنی چی؟ فرض کن یک نمودار داری که روی محور افقی «متراژ خانه» و روی محور عمودی «قیمت» قرار گرفته. نقاط مختلف پخش شدن، ولی ی…
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#چالش_پایتون_در_هوش_مصنوعی 🔵 فاز ۳ – قسمت ۲: دادههای آموزشی و تست چیه؟ 📂🤖 برای اینکه یک مدل یادگیری ماشین درست تربیت بشه، باید دادههامون رو درست تقسیم کنیم. این کار باعث میشه مدل هم یاد بگیره و هم آزمون پس بده! 😎 🧩 چرا دادهها رو تقسیم میکنیم؟ وقتی همه دادهها رو بدیم به مدل، فقط حفظ میکنه! ولی وقتی یک بخش رو نگه میداریم برای تست، مدل یاد میگیره عمومی فکر کنه و واقعیتر رفتار کنه. 📌 انواع دادهها: 1️⃣…
❤4👍1
#چالش_پایتون_در_هوش_مصنوعی 🤖 فاز ۳ – مبانی یادگیری ماشین 🔵 قسمت ۱: یادگیری ماشین یعنی چی؟ یادگیری ماشین (Machine Learning) یک شاخه از هوش مصنوعی است که به کامپیوترها یاد میدهد از روی دادهها الگو پیدا کنند و بدون اینکه به آنها مرحلهبهمرحله بگوییم چهکار کنند، خودشان پیشبینی یا تصمیمگیری کنند 😎 ✨ یک مثال ساده فرض کن به کامپیوتر میگی: «اگه قیمت، متراژ و تعداد اتاقهای چندتا خانه رو بهت بدم، یاد بگیر قیمت …
👍5❤1👏1🙏1
#چالش_پایتون_در_هوش_مصنوعی 🧠 قسمت ۱۲: پروژه عملی پردازش داده 📑 سلام دوستان! 👋 تو این قسمت میخوایم همه چیزهایی که تو فاز ۲ یاد گرفتیم (NumPy، Pandas، Matplotlib) رو ترکیب کنیم و یک پروژه کامل انجام بدیم. 🎯 --- 🎓 پروژه: تحلیل نمرات یک کلاس ۱. آمادهسازی دادهها فرض کنید یک فایل CSV به اسم grades.csv داریم: نام,ریاضی,فیزیک,شیمی علی,18,17,16 سارا,20,19,18 نیما,15,14,13 رضا,19,20,18 --- ۲. خواندن دادهها با Pa…
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#چالش_پایتون_در_هوش_مصنوعی 🧠 قسمت ۱۱: تجسم دادهها با Matplotlib 🎨 سلام دوستان! 👋 یکی از بهترین روشها برای درک بهتر دادهها، نقاشی کردنشونه! 🎨 کتابخونهی Matplotlib به ما کمک میکنه دادهها رو به شکل نمودار نشون بدیم. --- ۱. نصب و import pip install matplotlib import matplotlib.pyplot as plt --- ۲. رسم نمودار خطی (Line Plot) x = [1, 2, 3, 4, 5] y = [2, 4, 6, 8, 10] plt.plot(x, y) plt.title("نمودار خطی 📈") …
🙏4
#چالش_پایتون_در_هوش_مصنوعی 🧠 قسمت ۱۰: پاکسازی و آمادهسازی دادهها 🧹 سلام دوستان! 👋 دادههای دنیای واقعی همیشه مرتب نیستن! 😅 گاهی دادهها ناقص، تکراری یا اشتباه هستن. برای همین قبل از تحلیل یا یادگیری ماشین، باید دادهها رو پاکسازی و آمادهسازی کنیم. --- ۱. شناسایی دادههای گمشده (NaN) import pandas as pd import numpy as np data = { "نام": ["علی", "سارا", "نیما", "رضا"], "نمره": [18, np.nan, 15, 20] }…
👍4
#چالش_پایتون_در_هوش_مصنوعی 🧠 قسمت ۹: کار با دادههای جدولی 📊 سلام دوستان! 👋 حالا که با Pandas آشنا شدیم، بیایید یاد بگیریم چطوری روی دادههای جدولی عملیات مختلف انجام بدیم. این بخش خیلی مهمه چون بیشتر دادههای دنیای واقعی به شکل جدول هستن. 🗂 --- ۱. فیلتر کردن دادهها import pandas as pd data = { "نام": ["علی", "سارا", "نیما", "رضا"], "سن": [18, 20, 19, 21], "نمره": [15, 19, 17, 20] } df = pd.DataFr…
❤5
#چالش_پایتون_در_هوش_مصنوعی 🧠 قسمت ۸: آشنایی با Pandas 🗂 سلام دوستان! 👋 امروز میخوایم با Pandas آشنا بشیم. این کتابخونه فوقالعاده برای کار با دادههای جدولی مثل فایلهای Excel یا CSV استفاده میشه. 📊 --- ۱. نصب Pandas pip install pandas --- ۲. وارد کردن Pandas import pandas as pd ✅ معمولاً با اسم کوتاه pd استفاده میشه. --- ۳. ساخت جدول (DataFrame) import pandas as pd data = {"نام": ["علی", "سارا", "نیم…
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Showing the 12 most recent of 21 posts we hold for @pythonchallenge. 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.
Citation-graph rank
Citation-graph rank — 364,913 of 1,169,250entries in the measured graph. A weighted position computed from the forward and mention edges below — republished posts weigh more than named mentions — and recomputed periodically, over the whole graph. Published only as this ordinal position, never as a score: a position is a fact, and a score printed beside one channel’s name would read as a verdict this register does not make. The two counts beneath stay separate for the same reason mentions are never summed with forwards anywhere else on this page — a named-by count costs nothing to manufacture. The top 100 by this measure, or how it is computed.
Forward network
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
Republishes
Channels on the register whose posts this channel has forwarded.
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 3 registered channels — 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.
Names
Channels on the register whose handles appear in this channel's posts.
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.
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 12 August 2026 — this entry's latest reading, not the date you are reading this.
“Python challenge️” (@pythonchallenge), 4,566 subscribers as measured 12 August 2026. Telegram Register, tgregister.com/channel/pythonchallenge.
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.