How to Streamline Python Development with Claude Code

Mastering Claude Code for Python Development

Claude Code is not just for TypeScript — it’s equally powerful for Python development. It handles everything from FastAPI web development to data processing scripts and test creation.

Building a FastAPI Application

> Create a user management API with FastAPI.
> - Use Pydantic for validation
> - Use SQLAlchemy + asyncpg for DB connections
> - CRUD endpoints
> - JWT authentication

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": "John Doe",
                "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

Generating Data Processing Scripts

You can write data processing scripts using pandas or polars at high speed with Claude Code.

> Create a script that reads a CSV file and performs the following:
> - Fill missing values (median for numbers, "unknown" for strings)
> - Parse date columns
> - Monthly sales aggregation
> - Output results to Excel

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)

    # Fill missing values
    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("unknown"))

    # Date parsing and monthly aggregation
    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 complete: exported {len(monthly)} months of data")

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

Generating Tests with pytest

> Create tests for the process_sales_data function using pytest.
> Use fixtures to create temporary CSV files.

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,Product A,1000\n"
        "2026-01-20,Product B,2000\n"
        "2026-02-10,Product 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  # January and February

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)
    # Missing values are filled and included in aggregation
    assert result["transaction_count"].sum() == 4

For overall test design, see the Complete Testing Strategy Guide.

Adding Type Hints

You can also have Claude Code add type hints to existing Python code.

> Add type hints to all Python files under src/.
> Make sure they pass mypy --strict checks.

Setting Python Rules in CLAUDE.md

## Python Development Rules
- Target Python 3.12+
- Always include type hints
- Use ruff format for formatting, ruff check for linting
- Use pytest for tests, maintain 80%+ coverage

For API development patterns, see How to Accelerate API Development. For CLAUDE.md best practices, see the Complete Guide to Writing CLAUDE.md.

Summary

Claude Code delivers high productivity in Python development as well. From FastAPI apps to data processing and test creation, it generates high-quality code when you communicate your specifications clearly.

For Python details, refer to the official Python documentation. For Claude Code, see the official Anthropic documentation.