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
Ko'proq ko'rsatish📈 Telegram kanali پایتون ( Machine Learning | Data Science ) analitikasi
پایتون ( Machine Learning | Data Science ) (@python4all_pro) Forsiy til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 24 681 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 5 550-o'rinni va Eron mintaqasida 13 713-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 24 681 obunachiga ega bo‘ldi.
12 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 1 519 ga, so‘nggi 24 soatda esa 257 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 4.71% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 2.31% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 1 160 marta ko‘riladi; birinchi sutkada odatda 570 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 3 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 13 Iyun, 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+1
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