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Har bir Python dasturchi bilishi kerak bo'lgan 50 ta kutubxona

Python ekotizimi juda katta. Lekin har bir kutubxonani bilish shart emas — muhim joyi shundaki, siz ishga, loyiha turiga va maqsadga mos kutubxonalarni tanlay olishingiz kerak. Bu maqolada Python dasturchi uchun eng foydali 50 ta kutubxonani yo'nalishlar bo'yicha jamladik.

Bu ro'yxat boshlovchilar uchun ham, amaliy loyiha qilayotganlar uchun ham foydali. Maqsad — "qaysi kutubxonani qachon o'rganish kerak?" savoliga aniqroq javob berish.

1. Web va API

#	Kutubxona	Vazifasi
1	FastAPI	Zamonaviy va tez API. Type hint, validation, avtomatik documentation
2	Django	Katta web loyihalar. Admin panel, auth, ORM
3	Flask	Yengil web framework. Kichik API va prototiplar
4	Starlette	FastAPI asosidagi ASGI framework
5	httpx	Zamonaviy HTTP client. Sync va async
6	requests	Eng mashhur HTTP client. Boshlovchilar uchun qulay
7	Uvicorn	ASGI server. FastAPI ishga tushirish
8	Gunicorn	Production server. Flask va Django uchun
9	Jinja2	Template engine
10	aiohttp	Asinxron HTTP client va server

# FastAPI — minimal misol
from fastapi import FastAPI
app = FastAPI()

@app.get("/")
def root():
    return {"message": "Salom, dunyo!"}

# requests — API dan ma'lumot olish
import requests
resp = requests.get("https://api.github.com/users/octocat")
print(resp.json()["name"])

2. Data va analiz

#	Kutubxona	Vazifasi
11	NumPy	Matematik hisob-kitob va massivlar
12	Pandas	Jadval ko'rinishidagi data bilan ishlash
13	Polars	Tez data frame kutubxonasi
14	Matplotlib	Grafik chizish — klassik vizualizatsiya
15	Seaborn	Chiroyli statistik vizualizatsiya
16	Plotly	Interaktiv chartlar
17	SciPy	Ilmiy hisob-kitob va statistika
18	SymPy	Symbolic matematik hisoblash
19	openpyxl	Excel fayllari bilan ishlash
20	pyarrow	Columnar formatlar va data exchange

# Pandas — CSV o'qish va tahlil
import pandas as pd

df = pd.read_csv("sotuvlar.csv")
print(df.groupby("shahar")["summa"].sum())
print(df.describe())

3. Database va ORM

#	Kutubxona	Vazifasi
21	SQLAlchemy	Eng mashhur ORM va database toolkit
22	Alembic	SQLAlchemy migration boshqaruvi
23	Django ORM	Django ichidagi kuchli ORM
24	psycopg	PostgreSQL driver
25	asyncpg	PostgreSQL asinxron driver
26	Tortoise ORM	Asinxron ORM. FastAPI uchun
27	sqlite3	Python ichki SQLite moduli
28	redis-py	Redis client
29	pymongo	MongoDB client
30	mongoengine	MongoDB uchun ORM-like yondashuv

# SQLAlchemy — model va so'rov
from sqlalchemy import Column, Integer, String, create_engine
from sqlalchemy.orm import DeclarativeBase, Session

class Base(DeclarativeBase):
    pass

class Foydalanuvchi(Base):
    __tablename__ = "foydalanuvchilar"
    id = Column(Integer, primary_key=True)
    ism = Column(String(100))

engine = create_engine("sqlite:///test.db")
Base.metadata.create_all(engine)

4. AI va ML

#	Kutubxona	Vazifasi
31	OpenAI	AI model API'lari bilan ishlash
32	LangChain	LLM agent va workflow qurish
33	LlamaIndex	Hujjatlar bilan AI ishlatish
34	transformers	Hugging Face NLP va LLM
35	datasets	AI uchun data to'plash
36	sentence-transformers	Embedding va semantic search
37	scikit-learn	Klassik machine learning
38	PyTorch	Deep learning framework
39	TensorFlow	ML va deep learning ekotizimi
40	XGBoost	Gradient boosting. Tabular data

# OpenAI — oddiy chat
from openai import OpenAI

client = OpenAI()
javob = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Python nima?"}]
)
print(javob.choices[0].message.content)

5. Automation va scraping

#	Kutubxona	Vazifasi
41	BeautifulSoup	HTML parsing va scraping
42	Scrapy	Katta web scraping framework
43	Selenium	Browser automation va test
44	Playwright	Zamonaviy browser automation
45	pyautogui	GUI avtomatlashtirish
46	schedule	Oddiy vaqtga bog'langan task'lar
47	APScheduler	Murakkab scheduling
48	watchdog	Fayl tizimi o'zgarishlarini kuzatish
49	paramiko	SSH orqali serverlar bilan ishlash
50	fabric	Remote server avtomatlashtirish

# BeautifulSoup — HTML parsing
from bs4 import BeautifulSoup
import requests

html = requests.get("https://example.com").text
soup = BeautifulSoup(html, "html.parser")
print(soup.title.text)

Qaysi tartibda o'rganish kerak?

Agar endi boshlayotgan bo'lsangiz, quyidagi ketma-ketlik foydali:

BOSHLASH KETMA-KETLIGI:
1. requests, httpx          — HTTP va API tushunchasi
2. FastAPI yoki Flask       — web backend
3. SQLAlchemy + PostgreSQL  — database
4. Pandas + NumPy           — data tahlil
5. BeautifulSoup + Playwright — scraping
6. OpenAI + LangChain       — AI integratsiya

Backend yo'nalishi uchun birinchi navbatda: FastAPI, SQLAlchemy, Alembic, PostgreSQL, Redis, Uvicorn.

Data yo'nalishi uchun: Pandas, NumPy, Polars, pyarrow, Plotly.

Qaysi kutubxona qachon kerak?

Vazifa	Kutubxona
REST API yozish	FastAPI, Flask
Katta web ilova	Django
Ma'lumot tahlili	Pandas, NumPy
AI chatbot	OpenAI, LangChain
Web scraping	BeautifulSoup, Scrapy
Browser test	Playwright, Selenium
Production deploy	Gunicorn, Uvicorn
Migration	Alembic, Django migrations

Xulosa

Python'ning kuchi uning kutubxonalarida. To'g'ri kutubxonani to'g'ri joyda ishlatsangiz, loyiha tezroq, tozaroq va barqarorroq bo'ladi. Hamma narsani bir yo'la o'rganish shart emas — avval asosiylarini chuqur bilish yetarli.

Eng yaxshi strategiya — har bir kutubxonani "bu nima?" deb emas, "qachon ishlataman?" deb o'rganish. Shunda siz faqat kod yozmay, haqiqiy muammolarni hal qiladigan dasturchiga aylanasiz.

Maslahat: 50 tasini birdan o'rganmang — bir yo'nalish tanlang (masalan, backend) va undagi 5–7 ta kutubxonani chuqur o'rganing. Qolganlari vaqt o'tishi bilan o'z-o'zidan qo'shiladi.