Learn Python Coding
Learn Python through simple, practical examples and real coding ideas. Clear explanations, useful snippets, and hands-on learning for anyone starting or improving their programming skills. Admin: @HusseinSheikho || @Hussein_Sheikho
Show moreπ Analytical overview of Telegram channel Learn Python Coding
Channel Learn Python Coding (@pythonre) in the English language segment is an active participant. Currently, the community unites 40 878 subscribers, ranking 3 287 in the Technologies & Applications category and 9 164 in the India region.
π Audience metrics and dynamics
Since its creation on Π½Π΅Π²ΡΠ΄ΠΎΠΌΠΎ, the project has demonstrated rapid growth, gathering an audience of 40 878 subscribers.
According to the latest data from 29 October, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 336 over the last 30 days and by 1 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 5.33%. Within the first 24 hours after publication, content typically collects 1.52% reactions from the total number of subscribers.
- Post reach: On average, each post receives 2 179 views. Within the first day, a publication typically gains 622 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 3.
- Thematic interests: Content is focused on key topics such as math, harvard, oxford, supervision, waybienad.
π Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
βLearn Python through simple, practical examples and real coding ideas. Clear explanations, useful snippets, and hands-on learning for anyone starting or improving their programming skills.
Admin: @HusseinSheikho || @Hussein_Sheikhoβ
Thanks to the high frequency of updates (latest data received on 30 October, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.
.format() in everyday code, but their capabilities are not always fully utilized. They support formatting, function calls, working with data structures, and convenient debugging (from 3.8+).
f-strings are convenient for aligning columns without additional tools. This makes the output readable in the CLI and logs:
rows = [
("id", "name", "role"),
(1, "Ivan", "admin"),
(2, "Olga", "editor"),
]
for r in rows:
print(f"{r[0]:<5} {r[1]:<10} {r[2]:<10}")
Debug expressions (Python 3.8+): {x=> displays the name and value of the variable, which speeds up debugging. Supports formatting of calculations:
x = 12
y = 7
print(f"{x=} {y=} {x*y=} x/y={x/y:.3f}")
Specifiers !r, !a: !r - repr(), !a - ascii() for unambiguous logs. Eliminates ambiguities in the output of objects:
path = "/var/data/config.yaml"
print(f"{path!r} {path!a}") # repr and ascii()
Specifiers support width and padding, for example 08d for zeros. This is convenient for reports and IDs:
n = 42
print(f"{n:08d}") # β #00000042
You can access dictionaries and immediately calculate metrics, for example len():
data = {"user": "Ivan", "items": [1, 2, 3]}
print(f"{data['user']}=Β», items={data['items']}")
print(f"len(data['items'])={len(data['items'])}")
π₯ f-strings are a cool tool for formatting, logging, and debugging, if you apply them taking into account the version of Python and the context of the output.
πͺ @DataScience4