11 Dec 2024, 13:56 UTC≈6,340 viewsread 12 August 2026
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Python Codes
@python_codes
On this record: Growth · Engagement · Posts · Citations · Cite this entry
5,300subscribers
-79 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 | -1001438240938 |
|---|---|
| Type | Channel |
| Username | @python_codes |
| Created | Between 1 April 2019 and 31 October 2021 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 6 August 2026 |
| Last confirmed live | 5 September 2026 |
| Measurements held | 11 |
| Confirmed unchanged | 1 time, most recently 5 September 2026 |
| On Telegram | t.me/python_codes |
Growth
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 5 Sept 2026, 02:42 | 5,300 | -11 |
| 1 Sept 2026, 19:04 | 5,311 | -8 |
| 30 Aug 2026, 04:04 | 5,319 | -8 |
| 26 Aug 2026, 22:28 | 5,327 | -4 |
| 24 Aug 2026, 00:37 | 5,331 | -13 |
| 20 Aug 2026, 03:28 | 5,344 | -4 |
| 17 Aug 2026, 11:14 | 5,348 | -14 |
| 13 Aug 2026, 15:58 | 5,362 | -11 |
| 10 Aug 2026, 16:51 | 5,373 | -6 |
| 7 Aug 2026, 08:38 | 5,379 | no change |
| 6 Aug 2026, 12:20 | 5,379 | first reading |
Engagement
20 posts held, back to 13 October 2022 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 8 pages of 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 11 December 2024. An engagement rate over an empty window would be a number about nothing.
Recent posts
24 Dec 2022, 06:30 UTC≈22,700 viewsread 12 August 2026
NumPy tricks for beginners : 👉 Reshaping arrays: NumPy provides the np.reshape() function, which allows you to change the shape of an array while preserving its data. This can be useful for converting between different data formats, such as converting a one-dimensional array into a two-dimensional matrix. For example, the following code reshapes a one-dimensional array into a two-dimensional matrix with two rows and…
22 Dec 2022, 06:29 UTC≈17,200 viewsread 12 August 2026
Basic NumPy for beginners: Creating a NumPy array: To create a NumPy array from a list or tuple, you can use the np.array() function. For example, the following code creates a NumPy array from a list of numbers: import numpy as np # Create a NumPy array from a list of numbers numbers = [1, 2, 3, 4, 5] numbers_array = np.array(numbers) # Print the array print(numbers_array) output: [1 2 3 4 5] Basic mathematical…
20 Dec 2022, 06:30 UTC≈12,900 viewsread 12 August 2026
NumPy is a library for scientific computing in Python. It provides tools for working with arrays of data, including functions for mathematical operations, linear algebra, and random number generation. 👉🏻One of the key features of NumPy is its array data structure, which is similar to a list but allows for more efficient mathematical operations on large datasets. NumPy arrays can be created from existing data, such a…
18 Dec 2022, 06:30 UTC≈11,400 viewsread 12 August 2026
Commonly used Python libraries are: 👉🏻NumPy: This library is used for scientific computing and working with arrays of data. It provides functions for working with arrays of data, including mathematical operations, linear algebra, and random number generation. 👉🏻Pandas: This library is used for data manipulation and analysis. It provides tools for importing, cleaning, and transforming data, as well as tools for work…
16 Dec 2022, 06:31 UTC≈10,300 viewsread 12 August 2026
common techniques for using the type() function in Python: Get the type of an object: my_object = "Hello, world!" my_type = type(my_object) Check if an object is of a given type: my_object = "Hello, world!" if type(my_object) == str: print("my_object is a string.") Create a new object of a given type: my_type = int my_object = my_type("1") Use the type of an object as a dictionary key: my_object = "Hel…
14 Dec 2022, 06:30 UTC≈8,940 viewsread 12 August 2026
Common techniques for using the repr() function in Python: Get the string representation of an object: my_object = "Hello, world!" my_repr = repr(my_object) Print the string representation of an object: my_object = "Hello, world!" print(repr(my_object)) Use the string representation of an object for debugging: my_object = "Hello, world!" print(f"my_object: {repr(my_object)}") Use the string representation o…
12 Dec 2022, 06:30 UTC≈8,190 viewsread 12 August 2026
Common techniques for using the hash() function in Python: Get the hash value of an object: my_object = "Hello, world!" my_hash = hash(my_object) Hash multiple objects: my_object1 = "Hello, world!" my_object2 = (1, 2, 3) my_object3 = {"key": "value"} my_hash1 = hash(my_object1) my_hash2 = hash(my_object2) my_hash3 = hash(my_object3) Use the hash value for an object as a dictionary key: my_object = "Hello, w…
11 Dec 2022, 09:04 UTC≈7,000 viewsread 12 August 2026
If you want to contact us Message to this account Username: @Ping_TG
10 Dec 2022, 06:30 UTC≈7,670 viewsread 12 August 2026
In general, the Python standard library includes many built-in functions that are available to use in your code without needing to import any additional modules. Some common examples of built-in functions include: 👉🏻 abs() : Returns the absolute value of a number. 👉🏻 all() : Returns True if all elements of an iterable are True, and False otherwise. 👉🏻 any() : Returns True if any element of an iter…
8 Dec 2022, 06:30 UTC≈6,650 viewsread 12 August 2026
The union of two sets can be found using the union() method. Here is an example: set1 = {1, 2, 3} set2 = {3, 4, 5} union_set = set1.union(set2) This will create a new set called union_set that contains all of the items from set1 and set2. In this case, union_set will be equal to {1, 2, 3, 4, 5}. You can also use the | operator to find the union of two sets. For example: set1 = {1, 2, 3} set2 = {3, 4, 5} union_se…
6 Dec 2022, 06:30 UTC≈6,750 viewsread 12 August 2026
Common techniques for using the zip() function in Python: 1. Zip two lists together: list1 = [1, 2, 3] list2 = [4, 5, 6] zipped_lists = zip(list1, list2) 2. Unzip a zipped list: zipped_lists = [(1, 4), (2, 5), (3, 6)] list1, list2 = zip(*zipped_lists) 3. Loop through a zipped list: zipped_lists = [(1, 4), (2, 5), (3, 6)] for item1, item2 in zipped_lists: print(item1, item2) 4.Convert a zipped list to a …
Showing the 12 most recent of 20 posts we hold for @python_codes. 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.
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.
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 5 September 2026 — this entry's latest reading, not the date you are reading this.
“Python Codes” (@python_codes), 5,300 subscribers as measured 5 September 2026. Telegram Register, tgregister.com/channel/python_codes.
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.