پایتون ( Machine Learning | Data Science )
◀️اینجا با تمرین و چالش با هم پایتون رو یاد می گیریم ⏮بانک اطلاعاتی پایتون پروژه / code/ cheat sheet +ویدیوهای آموزشی +کتابهای پایتون تبلیغات: @alloadv 🔁ادمین : @maryam3771
Show more📈 Analytical overview of Telegram channel پایتون ( Machine Learning | Data Science )
Channel پایتون ( Machine Learning | Data Science ) (@python4all_pro) in the Farsi language segment is an active participant. Currently, the community unites 24 681 subscribers, ranking 5 550 in the Technologies & Applications category and 13 713 in the Iran region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 24 681 subscribers.
According to the latest data from 12 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 1 519 over the last 30 days and by 257 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 4.71%. Within the first 24 hours after publication, content typically collects 2.31% reactions from the total number of subscribers.
- Post reach: On average, each post receives 1 160 views. Within the first day, a publication typically gains 570 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 مصنوعی, دنیا, آموزش, پایتون, وبینار.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“◀️اینجا با تمرین و چالش با هم پایتون رو یاد می گیریم
⏮بانک اطلاعاتی پایتون
پروژه / code/ cheat sheet
+ویدیوهای آموزشی
+کتابهای پایتون
تبلیغات:
@alloadv
🔁ادمین :
@maryam3771”
Thanks to the high frequency of updates (latest data received on 13 June, 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.
def TeamCard(name, role, location="Remote"):
icons = ("mail", "linkedin", "github")
return Card(
DivLAligned(
DiceBearAvatar(name, h=24, w=24),
Div(H3(name), P(role))),
footer=DivFullySpaced(
DivHStacked(UkIcon("map-pin", height=16), P(location)),
DivHStacked(*(UkIconLink(icon, height=16) for icon in icons))))
Everything is clean, semantic, without CSS death and classes.
➡️Start
Pip Install Monsterui
pip install MonsterUI
```python
from fasthtml.common import *
from monsterui.all import *
app, rt = fast_app(hdrs=Theme.blue.headers())
@rt
def index():
return Card(H1("Hello MonsterUI"), P("The application is ready!"))
serve()
`
➡️ Advantages:
• Quick start with modern UI
• Clean, readable python code
• Flexibility in customization through Tailwind
• confirmed suitability in production
➡️ Read more https://www.answer.ai/posts/2025/01/15/monsterui.html
#Python #WebDev
📱 @Python4all_proflatten = lambda lst: [x for sub in lst for x in (flatten(sub) if isinstance(sub, list) else [sub])]
2. Decorator for memoization of the results of the function
memoize = lambda f: (lambda *args, _cache={}, **kwargs: _cache.setdefault((args, tuple(kwargs.items())), f(*args, **kwargs)))
3. Missing the list into pieces of length n
chunked = lambda lst, n: [lst[i:i+n] for i in range(0, len(lst), n)]
4. Uniqueization of the sequence with the preservation of order
uniq = lambda seq: list(dict.fromkeys(seq))
5. Deep access to the invested dictionary keys
deep_get = lambda d, *keys: __import__('functools').reduce(lambda a, k: a.get(k) if isinstance(a, dict) else None, keys, d)
6. Transformation of the Python object to the readable json
pretty_json = lambda obj: __import__('json').dumps(obj, ensure_ascii=False, indent=2)
7. Reading the latest n lakes of the file (analogue Tail)
tail = lambda f, n=10: list(__import__('collections').deque(open(f), maxlen=n))
8. Performing shell team and return of the withdrawal
sh = lambda cmd: __import__('subprocess').run(cmd, shell=True, check=True, capture_output=True).stdout.decode().strip()
9. Quick route association
path_join = lambda *p: __import__('os').path.join(*p)
10. Grouping of the list of dictionaries by key value
group_by = lambda seq, key: {k: [d for d in seq if d.get(key) == k] for k in set(d.get(key) for d in seq)}
📱 @Python4all_proIRAN
🔗 راهنمای دریافت آموزش – [کلیک کنید]
✔️ توجه: فقط باید یک آموزش در سبد سفارش باشد.
📚 دسترسی به تمام آموزشها [+]
🧡 به امید روزهای روشن و آرام
🔄 FaraDars - فرادرسIRAN
✔️ توجه: فقط یک آموزش باید در سبد سفارش باشد.
🌱 ما نیز همچون شما امیدوار به روزهای بهتر هستیم و باور داریم که یادگیری، نه فقط ابزار پیشرفت، بلکه راهی برای زنده نگهداشتن امید است.
🔗 راهنمای دریافت آموزش – [کلیک کنید]
🔄 FaraDars - فرادرس
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