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Channel

Python Programming

@pythonpundit

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

8,875subscribers

-7 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 April 2019 and 31 October 2021— estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded12 August 2026
Last confirmed live22 August 2026
Measurements held5
Confirmed unchanged1 time, most recently 22 August 2026
On Telegramt.me/pythonpundit

Growth

8,8698,8838,87612 August 2026 — 8,882 subscribers12 August 2026 — 8,883 subscribers16 August 2026 — 8,876 subscribers19 August 2026 — 8,869 subscribers22 August 2026 — 8,875 subscribers8,87512 August 202622 August 2026
5 measurements spanning 10 days, net -7. 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 8,867–8,885 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
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

22 posts held, back to 3 June 2026the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 8 pagesof Telegram’s post history, 20 posts per page.

ERR · 30 days
7.23%
avg views ÷ 8,875 subscribers
Avg views / post
642
8 posts measured
Reaction rate
this channel exposes no reaction counts
Posts in window
8
of 22 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 22 August 2026
Posts held22 (3 June 202622 August 2026)
Views total5,135
Reactions total
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken22 Aug 2026, 18:37 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
105
Links
4

Lifetime counters from Telegram’s own channel header, read 22 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.

Recent posts

22 Aug 2026, 13:33 UTC106 viewsread 22 August 2026
Photo

🐼 Pandas Tip df. info() vs df.describe() Before analyzing a dataset, understand what’s inside it. 🔹 df. info() → Dataset structure • Columns & data types • Non-null values • Memory usage 👉 Great for spotting missing values and incorrect data types. 🔹 df. describe() → Statistical summary • Count, mean, std • Min, max & quartiles 👉 Useful for understanding distributions and spotting potential outliers. 💡 Pro Tip: U

18 Aug 2026, 13:59 UTC390 viewsread 22 August 2026
Photo

🚀 Python One-Liners Every Data Professional Should Know Boost your productivity with these useful Pandas shortcuts: ✅ df.duplicated() → Find duplicates ✅ df.isna().sum() → Count missing values ✅ df.describe() → Quick statistics ✅ df.drop_duplicates() → Remove duplicates ✅ df.fillna() → Handle missing data ✅ df.value_counts(normalize=True) → Calculate percentages ✅ df.merge() → Combine datasets ✅ df.pivot_tab

11 Aug 2026, 13:16 UTC634 viewsread 22 August 2026
Photo

🐍 Python Roadmap for Beginners Want to start your programming journey with Python? Follow this structured path: 🔹 1. Python Basics • Variables, Data Types, Operators • Input/Output & Comments 🔹 2. Control Flow • if-else • for & while loops • break, continue, pass 🔹 3. Data Structures • Lists, Tuples, Sets, Dictionaries 🔹 4. Functions • Parameters & Return • *args & **kwargs 🔹 5. Modules & File Handling • Import

8 Aug 2026, 13:31 UTC695 viewsread 22 August 2026
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🐍 FREE PYTHON DEMO SESSION Start your Python journey with practical, industry-focused learning. 📅 Date:-10,11.12 Aug ⏰ Time: 6:30PM IST 💻 Zoom:-https://us06web.zoom.us/meeting/register/shA5Kv5qQZezcVbV6HKt1w 📞 Call/WhatsApp:- 84510-97879 Limited Seats — Register Now!

4 Aug 2026, 13:50 UTC852 viewsread 22 August 2026
Photo

🚀 Python Methods & Functions Every Developer Should Know 🐍 Mastering Python isn't just about syntax—it's about knowing the right function for the right task. 📌 Key Areas to Learn: 🔢 Numeric: abs(), round(), min(), max(), sum() 📝 Strings: split(), join(), replace(), upper(), lower() 📋 Lists: append(), extend(), remove(), sort() 📚 Dictionaries: get(), keys(), values(), items() ⚙️ Functions: def, lambda, map(), f

1 Aug 2026, 13:33 UTC804 viewsread 22 August 2026
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🐍 Python Cheat Sheet 🚀 Mastering Python fundamentals is the foundation for careers in: ✅ Data Analysis ✅ Automation ✅ Web Development ✅ AI & Machine Learning ✅ Backend Development 📌 This cheat sheet covers: • Variables & Data Types • Lists & Dictionaries • Conditionals & Loops • Functions • File Handling • OOP Basics • Exception Handling • Modules • List Comprehensions • Built-in Functions & Methods 💡 Learn the fu

28 Jul 2026, 13:49 UTC788 viewsread 22 August 2026
Photo

🚀 Top 10 Python Tricks Every Beginner Should Know 🐍 Boost your Python skills with these time-saving tricks: ✅ Swap variables: a, b = b, a ✅ Reverse a list: my_list[::-1] ✅ Join strings: " ".join(my_list) ✅ Use in for cleaner conditions ✅ List comprehensions ✅ enumerate() for indexing ✅ zip() for parallel iteration ✅ Remove duplicates with set() ✅ Master *args & **kwargs ✅ Use lambda for quick functions 💡

25 Jul 2026, 13:34 UTC866 viewsread 22 August 2026
Photo

🚀 Evolution of Python DSA 🐍 Python makes learning Data Structures & Algorithms simple, practical, and interview-ready. 💡 Master these concepts: ✅ Arrays, Linked Lists, Stacks & Queues ✅ Trees, Graphs & Hash Tables ✅ Sorting, Binary Search, Recursion ✅ Dynamic Programming, BFS & DFS 🎯 Learning Path: 1️⃣ Python Basics 2️⃣ Data Structures 3️⃣ Algorithms 4️⃣ Solve Problems Daily 5️⃣ Build Logic & Consistency DSA isn'

21 Jul 2026, 13:48 UTC917 viewsread 22 August 2026
Photo

🚀 Pandas The Backbone of Data Analysis in Python If you work with data, Pandas is a must-have skill. With Pandas, you can: ✅ Read CSV, Excel, JSON & SQL data ✅ Clean and preprocess datasets ✅ Filter, sort, group & aggregate data ✅ Handle missing values ✅ Transform raw data into meaningful insights 📌 Master these essentials: • DataFrames & Series • head(), info(), describe() • Filtering & grouping • Missing value h

18 Jul 2026, 13:33 UTC923 viewsread 22 August 2026
Photo

🚀 Python Cheat Sheet Every Developer Should Bookmark 🐍 Master the Python fundamentals that power real-world development: ✅ Data Types ✅ Operators ✅ Control Flow ✅ Data Structures ✅ Built-in Functions ✅ Strings ✅ List Comprehensions ✅ Functions ✅ File Handling ✅ Exception Handling ✅ Productivity Tips 💡 Strong Python fundamentals are essential for Data Analytics, AI/ML, Web Development, and Automation. Don't just m

14 Jul 2026, 14:15 UTC966 viewsread 22 August 2026
Photo

📊 Data Cleaning Cheat Sheet (SQL + Python) Clean data is the foundation of accurate analysis. Master these essential techniques: 🔹 Missing Values • SQL: IS NULL, COALESCE() • Python: isnull(), fillna() 🔹 Remove Duplicates • SQL: SELECT DISTINCT • Python: drop_duplicates() 🔹 Data Formatting • Fix data types, standardize dates, trim & clean text 🔹 Outlier Detection • Use the IQR method to identify extreme values

11 Jul 2026, 13:33 UTC994 viewsread 22 August 2026
Photo

📊 Pandas Cheat Sheet Every Data Analyst Should Know Master these essential Pandas operations to analyze data faster and more efficiently: 🔹 Read & Inspect: read_csv(), .shape, .dtypes, .describe() 🔹 Filter Data: Select columns and apply boolean conditions 🔹 Select Rows: Use .loc and .iloc 🔹 Handle Missing Values: .isnull(), .dropna(), .fillna() 🔹 Group & Aggregate: .groupby(), mean(), count(), etc. 🔹 Merge Dat

Showing the 12 most recent of 22 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.

Certified Free Courses - MindLuster
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Telegram ranks this channel #31 of 61 here — alongside 60 others — read 12 August 2026
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@HINDI_MOTIVATIONAL_QUOTES_VICHAR · 148,382
Telegram ranks this channel #62 of 64 here — alongside 63 others — read 21 August 2026
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@cseupscnotes · 151,816
Telegram ranks this channel #75 of 89 here — alongside 88 others — read 12 August 2026

This channel appears in 3 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 22 August 2026 — this entry's latest reading, not the date you are reading this.

“Python Programming” (@pythonpundit), 8,875 subscribers as measured 22 August 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.