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Channel

Python4Finance

@python4finance

On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Cite this entry

8,870subscribers

-11 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-1001368167051
TypeChannel
Username@python4finance
Descriptionکانال Python4Finance آموزش پایتون در اقتصاد و مدیریت مالی هر روز چند نکته را در خصوص پایتون برای مالی بیاموزیم *** آپارت: aparat.com/Python4Finance
Created17 August 2019measured — cross-checked against a third-party dataset (TGDataset)
First recorded6 August 2026
Last confirmed live26 August 2026
Measurements held8
Confirmed unchanged1 time, most recently 26 August 2026
On Telegramt.me/python4finance

Growth

8,8708,8828,8766 August 2026 — 8,881 subscribers7 August 2026 — 8,881 subscribers9 August 2026 — 8,882 subscribers12 August 2026 — 8,880 subscribers16 August 2026 — 8,874 subscribers19 August 2026 — 8,876 subscribers23 August 2026 — 8,871 subscribers26 August 2026 — 8,870 subscribers6 August 202626 August 2026
8 measurements spanning 20 days, net -11. 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 8,868–8,884 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
26 Aug 2026, 19:238,870-1
23 Aug 2026, 16:058,871-5
19 Aug 2026, 17:038,876+2
16 Aug 2026, 15:148,874-6
12 Aug 2026, 18:148,880-2
9 Aug 2026, 21:318,882+1
7 Aug 2026, 01:528,881no change
6 Aug 2026, 12:458,881first reading

Engagement

20 posts held, back to 16 April 2026the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 29 pagesof Telegram’s post history, 20 posts per page.

Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 20 posts for this entry, the most recent from 24 July 2026. An engagement rate over an empty window would be a number about nothing.

What this channel posts

Photos
598
Videos
44
Links
810

Lifetime counters from Telegram’s own channel header, read 27 August 2026 — not the date at the top of this page, which is when the subscriber count was last read. Below Telegram’s rounding threshold, so these counts are exact.

Reaction mix

429 reactions across 20 posts, in 5 distinct kinds. The most used accounts for 93.5% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
40193.5%
👌81.86%
😍81.86%
🙏81.86%
👍40.932%

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 20 of the 20 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 429reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 20 most recent posts we hold, published 16 April 2026 to 24 July 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

24 Jul 2026, 06:47 UTC≈2,060 views25 reactionsread 27 August 2026
File

دانلود کتاب «AI In Finance» هوش مصنوعی دیگر فقط یک فناوری جذاب نیست بلکه امروز به یکی از مهم‌ترین موتورهای تحول در صنعت مالی تبدیل شده است. از معاملات الگوریتمی و مدیریت ریسک گرفته تا کشف تقلب و ارائه خدمات مالی شخصی‌سازی‌شده، ردپای این فناوری را می‌توان در همه بخش‌های این صنعت دید. کتاب «AI In Finance» با نگاهی کاربردی، شما را با مفاهیم پایه، نمونه‌های واقعی، کاربردهای عملی و چالش‌های مهم این حوزه آشنا می‌کند. علا

18🙏7

18 Jul 2026, 04:34 UTC≈2,530 views22 reactionsread 27 August 2026
Photo

افزونه Ruff ، یک افزونه عالی و فوق العاده سریع برای linter و code formatter در پایتون در Vscode یکی از ویژگی های اصلی IDE ها ، توانایی Lint (یعنی نمایش خطاها) و قالب بندی استاندارد کدها است. این موضوع علی الخصوص برای پایتون که هدف اصلی آن خوانایی بالاست اهمیت بیشتری پیدا می کند. (ترجمه PEP8 شیوه‌نامه نگارش برنامه در پایتون به زبان فارسی) یکی از ماژول های بسیار جالب در این خصوص Ruff است. ویژگی اصلی Ruff سرعت بسیار با

22

17 Jul 2026, 17:13 UTC≈2,500 views21 reactionsread 27 August 2026

کدام چت‌بات هوش‌مصنوعی (LLM) را انتخاب کنیم؟ یکی از سوالاتی که خیلی از عزیزان مطرح می کنند در میان LLM های متداول، از کدامیک استفاده کنیم؟ اگر چه این موضوع بیشتر به نوع کاربری شما مربوط است و شاید تجربه کاربردی بهترین پاسخ برای هر فرد است. برای مثال هوش مصنوعی که برای تولید عکس مناسب است احتمالا در پاسخ به سوالات تحلیلی، جستجوی متن، حل مسائل ریاضی و ... مناسب نباشد. خیلی از سایت ها هستند که به بررسی تطبیقی LLM ها م

21

9 Jul 2026, 16:24 UTC≈3,060 views104 reactionsread 27 August 2026
Photo

وَسَلَامٌ عَلَيْهِ يَوْمَ وُلِدَ وَيَوْمَ يَمُوتُ وَيَوْمَ يُبْعَثُ حَيًّا خداحافظ آقای شهید ایران

95😍6👌2🙏1

2 Jul 2026, 19:22 UTC≈3,850 views30 reactionsread 27 August 2026
Photo

رفع مشکل بیش برازش مدل‌های یادگیری عمیق تصور کنید یک تیم تحلیل مالی دارید که تمام تصمیماتش وابسته به یک کارشناس است. اگر آن شخص مرخصی برود یا اشتباه کند، کل تیم فلج می‌شود. اما اگر به‌طور تصادفی هر روز یکی از اعضای تیم از جلسات حذف شود بقیه مجبور می‌شوند یاد بگیرند و مستقل تصمیم بگیرند. در یادگیری عمیق، به این تکنیک «Dropout» می‌گویند. دراپ‌اوت با غیرفعال کردن تصادفی برخی از نرون‌ها در طول آموزش مدل، مانع از وابستگی

24👌4👍2

26 Jun 2026, 15:30 UTC≈4,390 views31 reactionsread 27 August 2026
File

دانلود کتاب CFA® 2027 • LEVEL I • VOLUME 1-QUANTITATIVE METHODS کتاب «روش‌های کمی» نسخه 2027 منتشر شد. این نسخه بر کاربردهای دنیای واقعی متمرکز شده و در سراسر آن، پرسش‌وپاسخ‌های بیشتر و مثال‌های هدفمندتری گنجانده شده است. پوشش مباحث برآورد و شبیه‌سازی نیز گسترش یافته و راهنمای گام‌به‌گام اجرای شبیه‌سازی تاریخی، بوت‌استرپینگ و روش‌ مونت‌کارلو با استفاده از توابع اکسل و Google Sheets ارائه شده است. همچنین منابع مربوط ب

31

24 Jun 2026, 16:48 UTC≈3,350 views61 reactionsread 27 August 2026
Photo

فرارسیدن عاشورای حسینی تسلیت باد. التماس دعای ویژه از همه شما عزیزان محمدصادق کریمی مهرآبادی

57👌2😍2

29 May 2026, 18:19 UTC≈5,220 views16 reactionsread 27 August 2026
Photo

یک هوش‌مصنوعی جذاب برای برنامه‌نویسان پایتون قبلا در پست های زیر در خصوص ابزارهای هوش مصنوعی برای کد نویسی پایتون صحبت کرده بودیم. 1️⃣ لینک 1 2️⃣ لینک 2 3️⃣ لینک 3 در این پست می خواهم ابزار جالبی را معرفی کنم که کارهای جالبی را به شرح زیر انجام می دهد: ➖ تبدیل کد از یک زبان برنامه نویسی به زبان دیگر ➖ تولید کد بر اساس پرامپ مورد نظر ➖ توضیح کارکرد یک کد ➖ حذف توضیحات ➖ بررسی کد و رفع خطا جالب اینکه تمام اینکارها برای

16

22 May 2026, 06:31 UTC≈4,220 views10 reactionsread 27 August 2026
File

یادگیری ماشین به زبان خودمانی- قسمت 4 در این قسمت مفهوم رگرسیون را با هم بررسی می کنیم. پایتون برای مالی 🆔 t.me/python4finance 🆔 ble.ir/python4finance

9👍1

3 May 2026, 04:09 UTC≈4,800 views9 reactionsread 27 August 2026
File

یادگیری ماشین به زبان خودمانی- قسمت 3 پایتون برای مالی 🆔 t.me/python4finance 🆔 ble.ir/python4finance

9

3 May 2026, 02:59 UTC≈4,540 views10 reactionsread 27 August 2026
Photo

لایه های توسعه AI - مدل مفهومی پایتون برای مالی 🆔 t.me/python4finance 🆔 ble.ir/python4finance

10

2 May 2026, 17:59 UTC≈3,600 views9 reactionsread 27 August 2026
Photo

مسیر تبدیل شدن به تحلیل گر داده (Data Analyst ) چیست؟ پایتون برای مالی 🆔 t.me/python4finance 🆔 ble.ir/python4finance

9

Showing the 12 most recent of 20 posts we hold for @python4finance. 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 — 843,398 of 1,621,120entries 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.

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.

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 August 2026 — this entry's latest reading, not the date you are reading this.

“Python4Finance” (@python4finance), 8,870 subscribers as measured 26 August 2026. Telegram Register, tgregister.com/channel/python4finance.

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.