پایتون | Data Science | Machine Learning
◀️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
Ko'proq ko'rsatish📈 Telegram kanali پایتون | Data Science | Machine Learning analitikasi
پایتون | Data Science | Machine Learning (@python4all_pro) Forsiy til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 24 469 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 5 278-o'rinni va Eron mintaqasida 13 849-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 24 469 obunachiga ega bo‘ldi.
31 Avgust, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -334 ga, so‘nggi 24 soatda esa -15 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 2.72% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.47% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 666 marta ko‘riladi; birinchi sutkada odatda 361 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 2 ta reaksiya keladi.
- Tematik yo‘nalishlar: Kontent مصنوعی, دنیا, آموزش, پایتون, وبینار kabi asosiy mavzularga jamlangan.
📝 Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“◀️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”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 01 Sentabr, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.
pip install gremllm
🔧 Example:
from gremllm import Gremllm
counter = Gremllm('counter')
counter.value = 5
counter.increment()
print(counter.value) # → 6?
print(counter.to_roman_numerals()) # → VI?
🔸
✔️ Opportunities:
- dynamic behavior: everything is determined "on the fly" using LLM
- Support Openai, Claude, Gemini, and local models
- Wet Mode: You can build challenges of calls (methods return objects)
- Verbose Mode: Bodes which code was generated
- smart processing of errors and setting through inheritance
🖥 Github: https://github.com/ur-whitelab/gremllm
🔸
#پایتون #Python
📱 @Python4all_prodef f(x, y, z=None):
a = x * 2
b = y + a if z else y - a
c = [i for i in range(a) if i % 2]
return sum(c) + b
2. 🧠 Sake maximum logic in one line
Complex thornar expressions and nested List CompreHance - all in one line.
result = [x if x > 0 else (y if y < 0 else z) for x in data if x or y and not z]
3.⚠️ Use Eval () and Exec ()
It is slow, unsafe and stupid - but spectacular.
eval("d['" + key + "']")
4.🔁 Reprint variables with different types
Let one variable be a line, and a number, and a list - a dynamic typification
value = "42"
value = int(value)
value = [value] * value
5.🌍 Use global variables
Change the condition of the application from anywhere. Especially from the inside of the functions.
counter = 0
def increment():
global counter
counter += 1
6.🔮 Use magic numbers and lines
Without explanation. Let colleagues guess why exactly 42 or "XYZ"
if user.role == "xyz" and user.level > 42:
access_granted()
7.📏 Ignore style and indentation
No PEP8, no rules. Write as you want
def foo():print("start")
if True:
print("yes")
else:
print("no")
8.🧱 Copy the code from Stack Overflow without delving
Ctrl+C is also a development
def complex_logic(x):
return (lambda y: (lambda z: z**2)(y + 1))(x)
9.🧩 Invent abstraction unnecessary
Instead of a simple function - classes, factories and strategies
class HandlerFactory:
def get_handler(self):
class Handler:
def handle(self, x): return x
return Handler()
10. 💤 Add dead code
Never remove - suddenly comes in handy. And let it be loaded into every launch
def legacy_feature():
print("This feature is deprecated")
return
# нигде не вызывается
11.🔀 Do not write the documentation
Comments only interfere. Whoever wants to figure it out
def a(x): return x+1def 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_pro