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Channel

Python Programming

@pythonpundit

On this record: Topic · Growth · Engagement · What this channel posts · Posts · Telegram's recommendations · Cite this entry

8,719subscribers

-163 since we began measuring on 12 August 2026

Risers and fallers across the register · movement among entries of 3,162–10,000.

Register entry

Telegram ID-1001405035303
TypeChannel
Username@pythonpundit
Description"A Perfect Blend of Free Python Tutorials, Practicals and Projects", that will surely help you in becoming a maestro of the language. P.S. - The Tutorials are arranged with relevant topics next to each other so you can follow them in order.
CreatedBetween 1 January 2019 and 30 November 2021 — estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded12 August 2026
Last confirmed live1 November 2026
Measurements held20
Confirmed unchanged1 time, most recently 1 November 2026
On Telegramt.me/pythonpundit

Topic

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 11 September 2026 and assigned it the closest of 31 fixed categories, at 96% 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 the other topics.

Growth

8,7198,8838,80112 August 2026 — 8,882 subscribers12 August 2026 — 8,883 subscribers16 August 2026 — 8,876 subscribers19 August 2026 — 8,869 subscribers22 August 2026 — 8,875 subscribers25 August 2026 — 8,863 subscribers28 August 2026 — 8,846 subscribers31 August 2026 — 8,849 subscribers3 September 2026 — 8,848 subscribers9 September 2026 — 8,835 subscribers13 September 2026 — 8,828 subscribers2 October 2026 — 8,809 subscribers8 October 2026 — 8,787 subscribers13 October 2026 — 8,779 subscribers17 October 2026 — 8,767 subscribers19 October 2026 — 8,755 subscribers23 October 2026 — 8,751 subscribers26 October 2026 — 8,742 subscribers29 October 2026 — 8,732 subscribers1 November 2026 — 8,719 subscribers12 August 20261 November 2026
20 measurements spanning 81 days, net -163. 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 8,694–8,908 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
1 Nov 2026, 07:158,719-13
29 Oct 2026, 11:088,732-10
26 Oct 2026, 05:558,742-9
23 Oct 2026, 02:368,751-4
19 Oct 2026, 22:508,755-12
17 Oct 2026, 03:208,767-12
13 Oct 2026, 19:298,779-8
8 Oct 2026, 13:188,787-22
2 Oct 2026, 05:358,809-19
13 Sept 2026, 06:368,828-7
9 Sept 2026, 05:018,835-13
3 Sept 2026, 17:598,848-1
31 Aug 2026, 17:448,849+3
28 Aug 2026, 13:328,846-17
25 Aug 2026, 04:228,863-12
22 Aug 2026, 09:468,875+6
19 Aug 2026, 10:258,869-7
16 Aug 2026, 02:228,876-7
12 Aug 2026, 17:458,883+1
12 Aug 2026, 16:318,882first reading

Engagement

37 posts held, back to 3 June 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 19 pages of Telegram’s post history, 20 posts per page.

ERR · 30 days
6.54%
avg views ÷ 8,719 subscribers
Avg views / post
571
7 posts measured
Reaction rate
—
this channel exposes no reaction counts
Posts in window
7
of 37 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
WindowRolling 30 days · latest post in window 27 October 2026
Posts held37 (3 June 2026 – 27 October 2026)
Views total3,994
Reactions total—
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken3 Nov 2026, 21:47 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
120
Links
4

Lifetime counters from Telegram’s own channel header, read 3 November 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.

Recent posts

27 Oct 2026, 14:50 UTC319 viewsread 3 November 2026
Photo

🐼 𝟵 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗣𝗮𝗻𝗱𝗮𝘀 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗣𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻𝗮𝗹𝘀 Mastering Pandas makes data analysis in Python faster and easier! 🚀 📌 𝗞𝗲𝘆 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻: 1️⃣ Data Import — Read CSV, Excel, SQL & JSON 2️⃣ Data Selection — loc, iloc, query() 3️⃣ Data Manipulation — groupby(), merge(), pivot() 4️⃣ Statistics — Summary stats & correlations 5️⃣ Data Cleaning — Missing values & duplicates 6️⃣ Time Series — Dates, resam…

24 Oct 2026, 13:32 UTC420 viewsread 3 November 2026
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🚀 SQL vs Python (Pandas): Which Should You Learn? Starting your journey in Data Analytics or Data Science? The answer is simple: Learn both! 📊 🔹 SQL – Retrieve, filter, join, and aggregate data stored in databases. 🔹 Python (Pandas) – Clean, transform, analyze, and automate data workflows. 💡 Key Takeaway: ✅ Use SQL to extract and manage data. ✅ Use Pandas for advanced analysis and data manipulation. ✅ Combine …

21 Oct 2026, 13:33 UTC510 viewsread 3 November 2026
Photo

🚀 Python: One Language, Endless Career Opportunities! Python opens doors to multiple tech domains, from web development to AI and automation. 🔹 Django & Flask – Web Development 🔹 Pandas & NumPy – Data Analysis 🔹 Scikit-learn – Machine Learning 🔹 TensorFlow & PyTorch – AI & Deep Learning 🔹 Matplotlib & Seaborn – Data Visualization 🔹 Beautiful Soup – Web Scraping 🔹 FastAPI – API Development 🔹 SQL & SQLite – Da…

17 Oct 2026, 13:33 UTC615 viewsread 3 November 2026
Photo

🐍 𝗣𝘆𝘁𝗵𝗼𝗻 𝘃𝘀 𝗥 𝗪𝗵𝗶𝗰𝗵 𝗦𝗵𝗼𝘂𝗹𝗱 𝗬𝗼𝘂 𝗟𝗲𝗮𝗿𝗻? 📊 Choosing between Python and R for Data Analytics & Data Science? Here's a quick guide! 🐍 𝗣𝘆𝘁𝗵𝗼𝗻 ✅ Beginner-friendly and versatile ✅ Data analysis, automation & AI/ML ✅ Libraries: Pandas, NumPy, Matplotlib, Scikit-learn 📊 𝗥 ✅ Statistical computing & analysis ✅ Powerful visualizations with ggplot2 ✅ Ideal for research, hypothesis testing & bioinformatics 🎯 𝗪𝗵𝗶𝗰𝗵 𝗢𝗻𝗲 𝗦𝗵𝗼𝘂𝗹𝗱 𝗬𝗼…

13 Oct 2026, 13:32 UTC637 viewsread 3 November 2026
Photo

🐍 Python Roadmap From Beginner to AI Developer Every Python expert started with basic code. The key is to learn consistently and build real-world projects. 🚀 Your Learning Roadmap: 🔹 1. Python Fundamentals Variables, data types, loops, functions, and error handling. 🔹 2. Core Python Skills OOP, file handling, APIs, and problem-solving. 🔹 3. Essential Libraries 📊 Pandas & NumPy – Data Analysis 📈 Matplotlib & Seab…

10 Oct 2026, 14:14 UTC724 viewsread 3 November 2026
Photo

🚀 Python Roadmap for Data Engineering: Zero to Pipeline-Ready Want to become a Data Engineer? Follow this learning path step by step! 👇 📌 1. Python Basics: Variables, loops, functions, data structures 📌 2. Intermediate Python: Generators, decorators, type hints 📌 3. OOP: Classes, objects, inheritance 📌 4. Data Processing: Pandas, NumPy, PySpark 📌 5. Data Sources: CSV, JSON, APIs, Parquet, cloud storage 📌 6. Da…

6 Oct 2026, 13:34 UTC769 viewsread 3 November 2026
Photo

🚀 100 Python Functions Every Data Analyst Should Know Python productivity isn’t about memorizing functions—it’s about knowing the right tools for real-world data tasks. 📌 This cheat sheet covers: 🔹 Pandas – Cleaning, filtering, grouping & merging 🔹 NumPy – Numerical & statistical operations 🔹 Strings – Text cleaning & transformation 🔹 Datetime – Date parsing & time-based analysis 🔹 Visualization – Matplotlib &…

26 Sept 2026, 13:34 UTC≈1,050 viewsread 3 November 2026
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🚀 18 Important Python Functions Every Beginner Should Know Master these Python fundamentals to write cleaner and more efficient code: 🔹 print() – Display output 🔹 len() – Find length 🔹 input() – Take user input 🔹 range() – Generate sequences 🔹 str(), int(), float() – Convert data types 🔹 list(), dict() – Work with collections 🔹 if...else – Make decisions 🔹 for / while – Handle loops 🔹 append() – Add list elements 🔹…

22 Sept 2026, 13:35 UTC≈1,050 viewsread 3 November 2026
Photo

🚀 10 Python Libraries Every AI Professional Should Know Building AI solutions with Python? Keep these libraries on your radar: ✅ TensorFlow – Deep learning & production ✅ PyTorch – Research & deep learning ✅ Scikit-learn – Classical ML ✅ NumPy – Numerical computing ✅ Pandas – Data analysis & preprocessing ✅ XGBoost – High-performance boosting ✅ LightGBM – Fast gradient boosting ✅ Keras – Neural networks ✅ T…

19 Sept 2026, 13:31 UTC946 viewsread 3 November 2026
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🚀 𝗧𝗼𝗽 𝟮𝟬 𝗣𝘆𝘁𝗵𝗼𝗻/𝗣𝗮𝗻𝗱𝗮𝘀 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 𝗙𝗼𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁𝘀 Master these essential functions to clean, transform, analyze, and visualize data more efficiently: 🔹 𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴: head() info() describe() dropna() fillna() rename() 🔹 𝗙𝗶𝗹𝘁𝗲𝗿𝗶𝗻𝗴: loc[] iloc[] query() isin() 🔹 𝗔𝗴𝗴𝗿𝗲𝗴𝗮𝘁𝗶𝗼𝗻: groupby() agg() sum() mean() count() 🔹 𝗝𝗼𝗶𝗻𝗶𝗻𝗴: merge() concat() join() 🔹 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀: value_counts() pivot_table() plot() 💡 Don’t just memorize …

15 Sept 2026, 13:31 UTC964 viewsread 3 November 2026
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🚀 40+ Python Libraries Every Data Professional Should Know in 2026 Python’s ecosystem makes Data Science, AI, ML, Analytics & Automation faster and more powerful. 🔹 Data: NumPy • Pandas • Polars • Vaex 🔹 Visualization: Matplotlib • Seaborn • Plotly • Altair • Folium 🔹 ML & AI: Scikit-learn • TensorFlow • PyTorch • Keras • XGBoost • JAX 🔹 Big Data: PySpark • Dask • Ray • Hadoop 🔹 Scraping & Automation: Beautiful…

12 Sept 2026, 13:35 UTC920 viewsread 3 November 2026
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🚀 7 Python Features Every Data Analyst Should Know Beyond Pandas and SQL, mastering Python’s built-in tools can make your data work cleaner, faster, and more efficient. 🔹 enumerate() — Iterate with index & value 🔹 zip() — Combine multiple lists easily 🔹 any() & all() — Quick data validation 🔹 Counter — Simple frequency counting 🔹 defaultdict — Easy grouping & organization 🔹 itertools.groupby() — Efficient data…

Showing the 12 most recent of 37 posts we hold for @pythonpundit. 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.

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.

DataFlair
@dataflair · 18,636
Telegram ranks this channel #1 of 83 here — alongside 82 others — read 4 October 2026
UGC NET_JRF PAPER 1
@UGC_NET_SET · 24,006
Telegram ranks this channel #31 of 46 here — alongside 45 others — read 16 September 2026
Certified Free Courses - MindLuster
@mindluster · 229,390
Telegram ranks this channel #31 of 61 here — alongside 60 others — read 12 August 2026
Tech Guide
@techguide · 40,902
Telegram ranks this channel #40 of 76 here — alongside 75 others — read 29 August 2026
Daily HINDI Motivational Thoughts™ VICHAR
@HINDI_MOTIVATIONAL_QUOTES_VICHAR · 140,265
Telegram ranks this channel #62 of 64 here — alongside 63 others — read 21 August 2026
UPSC Civil Services Upsc Prelims Mains Exam Current Affairs GS Gk Tricks SSC CGL IAS IFS IPS EPFO PCS UPPCS MPPCS Bihar PCS
@cseupscnotes · 147,106
Telegram ranks this channel #75 of 89 here — alongside 88 others — read 12 August 2026

This channel appears in 6 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 1 November 2026 — this entry's latest reading, not the date you are reading this.

“Python Programming” (@pythonpundit), 8,719 subscribers as measured 1 November 2026. Telegram Register, tgregister.com/channel/pythonpundit.

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.