Endi mavjud! Telegram Tadqiqoti 2025 — yilning asosiy insaytlari 
Data Analytics
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Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making. Admin: @HusseinSheikho || @Hussein_Sheikho
Ko'proq ko'rsatish📈 Telegram kanali Data Analytics analitikasi
Data Analytics (@dataanalyticsx) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 28 861 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 4 781-o'rinni va Rossiya mintaqasida 22 925-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 28 861 obunachiga ega bo‘ldi.
03 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 565 ga, so‘nggi 24 soatda esa 25 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 3.46% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.99% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 998 marta ko‘riladi; birinchi sutkada odatda 575 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 sellerflash, buybox, buyer, chaos, effortless kabi asosiy mavzularga jamlangan.
📝 Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making.
Admin: @HusseinSheikho || @Hussein_Sheikho”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 04 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.
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| 20 | Data validation with Pydantic! 🐍✨
In the early stages of development, data validation usually doesn't cause problems. In many Python projects, validation initially looks simple:
if not isinstance(age, int):
raise ValueError("age must be an int")
But then come email, JSON from APIs, query parameters, nested objects, configs, nullable fields, and type conversion. At some point, the code turns into a set of if/else and manual checks.
For such tasks, Pydantic is often used. Installation:
pip install pydantic
pip install "pydantic[email]"
Create a model:
from pydantic import BaseModel
class User(BaseModel):
name: str
age: int
Now the data is validated automatically:
user = User(
name="Alex",
age="30"
)
print(user.age)
print(type(user.age))
The result:
30
<class 'int'>
Pydantic will automatically convert the string "30" to an int. If you pass an incorrect value, you'll get a ValidationError:
User(
name="Alex",
age="test"
)
This is especially convenient when working with APIs, JSON, query parameters, and incoming data from outside.
A common production case is checking email:
from pydantic import BaseModel, EmailStr
class User(BaseModel):
email: EmailStr
User(email="alex@test.com")
If the email is invalid, Pydantic will throw a ValidationError. You can set default values:
from pydantic import BaseModel
class Config(BaseModel):
host: str = "localhost"
port: int = 5432
And allow None:
from pydantic import BaseModel
class User(BaseModel):
nickname: str | None = None
This field becomes optional. A practical example is processing an API response:
from pydantic import BaseModel
class Product(BaseModel):
id: int
title: str
price: float
data = {
"id": "1",
"title": "Keyboard",
"price": "99.5"
}
product = Product(**data)
print(product)
The types will be automatically converted. For nested model structures, you can combine:
from pydantic import BaseModel
class Address(BaseModel):
city: str
zip_code: str
class User(BaseModel):
name: str
address: Address
user = User(
name="Alex",
address={
"city": "Berlin",
"zip_code": "10115"
}
)
print(user)
The nested object will also be validated. Serialization in Pydantic v2:
print(user.model_dump())
print(user.model_dump_json())
Pydantic is actively used in FastAPI, ETL, microservices, data pipelines, and API clients.
For working with environment variables in Pydantic v2, a separate package is usually used:
pip install pydantic-settings
It's important to understand: Pydantic is not an ORM and does not replace business logic. Its task is to validate data, convert types, and describe schemas.
🔥 Pydantic significantly reduces the amount of manual data validation and makes processing incoming structures more predictable.
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