Claude Code के साथ Streamline Python Development कैसे करें

PythondevelopmentでClaude Code use करकेこなす

Claude CodeはTypeScript ही नहींPythondevelopmentでも威力を発揮し है।FastAPIでのWebdevelopment、dataprocessingスクリプト、testcreate तक幅広くsupportでき है।

FastAPIapplicationのbuild

> FastAPIでusermanagementAPIをबनाओ。
> - Pydanticでvalidation
> - SQLAlchemy + asyncpg でDB接続
> - CRUDendpoint
> - JWTauthentication

from fastapi import FastAPI, HTTPException, Depends
from pydantic import BaseModel, EmailStr
from sqlalchemy.ext.asyncio import AsyncSession
from datetime import datetime

app = FastAPI(title="User Management API")

class UserCreate(BaseModel):
    email: EmailStr
    name: str
    password: str

    class Config:
        json_schema_extra = {
            "example": {
                "email": "[email protected]",
                "name": "田में太郎",
                "password": "securepassword123",
            }
        }

class UserResponse(BaseModel):
    id: str
    email: str
    name: str
    is_active: bool
    created_at: datetime

    class Config:
        from_attributes = True

@app.post("/users", response_model=UserResponse, status_code=201)
async def create_user(
    user_data: UserCreate,
    db: AsyncSession = Depends(get_db),
):
    existing = await get_user_by_email(db, user_data.email)
    if existing:
        raise HTTPException(status_code=409, detail="Email address is already registered")

    hashed = hash_password(user_data.password)
    user = await create_user_in_db(db, user_data, hashed)
    return user

@app.get("/users/{user_id}", response_model=UserResponse)
async def get_user(
    user_id: str,
    db: AsyncSession = Depends(get_db),
):
    user = await get_user_by_id(db, user_id)
    if not user:
        raise HTTPException(status_code=404, detail="User not found")
    return user

dataprocessingスクリプトのgenerate

pandasやpolarsを使ったdataprocessingもClaude Code सेfastに書け है।

> CSVfileを読み込んでनिम्नलिखितのprocessingをकरनाスクリプトをबनाओ。
> - 欠損値の補完（数値はमें央値、文字列は"不明"）
> - 日付columnのparse
> - 売ऊपरの月अगला集計
> - 結果をExcelで出力

import polars as pl
from pathlib import Path

def process_sales_data(input_path: str, output_path: str) -> None:
    df = pl.read_csv(input_path)

    # 欠損値の補完
    numeric_cols = [c for c in df.columns if df[c].dtype in [pl.Float64, pl.Int64]]
    string_cols = [c for c in df.columns if df[c].dtype == pl.Utf8]

    for col in numeric_cols:
        median_val = df[col].median()
        df = df.with_columns(pl.col(col).fill_null(median_val))

    for col in string_cols:
        df = df.with_columns(pl.col(col).fill_null("不明"))

    # 日付parseと月अगला集計
    df = df.with_columns(
        pl.col("date").str.to_date("%Y-%m-%d").alias("date_parsed")
    )

    monthly = (
        df.group_by(pl.col("date_parsed").dt.truncate("1mo"))
        .agg([
            pl.col("amount").sum().alias("total_sales"),
            pl.col("amount").mean().alias("avg_sales"),
            pl.col("amount").count().alias("transaction_count"),
        ])
        .sort("date_parsed")
    )

    monthly.write_excel(output_path)
    print(f"processing完了: {len(monthly)}か月分のdataを出力")

if __name__ == "__main__":
    process_sales_data("sales.csv", "monthly_report.xlsx")

pytestでのtestgenerate

> process_sales_data functionのtestをpytestでबनाओ。
> フィクスチャで一時CSVをबनानाpatternで。

import pytest
import polars as pl
from pathlib import Path
from process_sales import process_sales_data

@pytest.fixture
def sample_csv(tmp_path: Path) -> Path:
    csv_path = tmp_path / "test_sales.csv"
    csv_path.write_text(
        "date,product,amount\n"
        "2026-01-15,商品A,1000\n"
        "2026-01-20,商品B,2000\n"
        "2026-02-10,商品A,1500\n"
        "2026-02-15,,\n"
    )
    return csv_path

def test_process_creates_output(sample_csv: Path, tmp_path: Path):
    output = tmp_path / "output.xlsx"
    process_sales_data(str(sample_csv), str(output))
    assert output.exists()

def test_monthly_aggregation(sample_csv: Path, tmp_path: Path):
    output = tmp_path / "output.xlsx"
    process_sales_data(str(sample_csv), str(output))
    result = pl.read_excel(output)
    assert len(result) == 2  # 1月と2月

def test_null_handling(sample_csv: Path, tmp_path: Path):
    output = tmp_path / "output.xlsx"
    process_sales_data(str(sample_csv), str(output))
    result = pl.read_excel(output)
    # 欠損値が補完されて集計に含まれている
    assert result["transaction_count"].sum() == 4

test全体の設計के बारे मेंはtest戦略complete guideをदेखें。

型ヒントのadd

既存のPythoncodeに型ヒントをaddさせる बातもでき है।

> src/ निम्नलिखितのPythonfileに型ヒントをadd करो。
> mypyで --strict checkが通る तरहして。

CLAUDE.mdでPythonruleをsettings

## Pythondevelopmentrule
- Python 3.12以ऊपरを対象
- 型ヒントを必ず付与
- フォーマッターはruff format、リンターはruff check
- testはpytest、カバレッジ80%以ऊपर

APIdevelopmentのpatternはAPIdevelopmentをfast化する方法を、CLAUDE.mdのलिखने का तरीकाはCLAUDE.mdのलिखने का तरीकाcomplete guideभी reference के लिए देखें。

Summary

Claude CodeはPythondevelopmentでも高い生産性を発揮し है।FastAPIアプリ、dataprocessing、testcreate तक、明確に仕様を伝えれば高品質なcodeがgenerateされ है।

Pythonके details के लिएPythonofficial documentation、Claude Codeके बारे मेंはAnthropicofficial documentationをदेखें。