أفضل ذكاء اصطناعي لـ Write a Python script for automation
Generate a Python script for automation, scraping, file processing, API calls, or any one-off task — with proper error handling and clear structure.
Claude
In hands-on tests against real Python tasks, Claude generated cleaner, more conventional code with proper type hints, clear naming, and verification test cases. Real users on developer forums consistently note Claude doesn't lose context across iterations the way ChatGPT does. For terminal-first work, Claude Code understands Python packaging and iterates autonomously on errors.
افتح ClaudePython for Everybody Specialization
Charles Severance's classic Python intro. Covers data structures, web scraping, databases, JSON. The depth that lets you write the right prompt to AI when automating real work, instead of getting AI scripts you don't understand.
Write a Python script that does the following: Goal: [WHAT THE SCRIPT SHOULD DO] Inputs: - [INPUT 1 — file path / API key / URL / etc.] - [INPUT 2] Output: - [WHAT THE SCRIPT SHOULD PRODUCE — file / printout / database row / etc.] Requirements: - Python 3.11+ syntax - Type hints on all functions - Error handling for [LIKELY FAILURE POINTS] - Use [SPECIFIC LIBRARIES] if relevant - Include a brief docstring at the top - If it needs dependencies, list them as a pip install line at the top Then: 1. Walk me through how it works in 3-4 sentences 2. Show me how to run it 3. Suggest one test I should run to verify it works
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قبل وبعد استخدام هذا التوجيه
import requests import json url = "https://api.example.com/data" response = requests.get(url) data = response.json() for item in data: print(item['name'])
"""Fetch items from the example API and print their names. Used by the daily ops report. Replaced an old shell script that silently swallowed network errors and falsely showed empty reports. """ from __future__ import annotations import argparse import logging import sys from typing import Any import requests API_URL = "https://api.example.com/data" TIMEOUT_SECONDS = 10 log = logging.getLogger(__name__) def fetch_items(url: str) -> list[dict[str, Any]]: """Return items from the API. Raises on network or HTTP errors.""" resp = requests.get(url, timeout=TIMEOUT_SECONDS) resp.raise_for_status() return resp.json() def main(argv: list[str]) -> int: parser = argparse.ArgumentParser(description=__doc__) parser.add_argument("--url", default=API_URL) parser.add_argument("--verbose", action="store_true") args = parser.parse_args(argv) logging.basicConfig( level=logging.DEBUG if args.verbose else logging.INFO, format="%(asctime)s %(levelname)s %(message)s", ) try: items = fetch_items(args.url) except requests.RequestException as e: log.error("API request failed: %s", e) return 2 if not items: log.warning("API returned 0 items — investigate before trusting this report.") return 1 for item in items: name = item.get("name") if name is None: log.warning("Item missing 'name' field: %s", item) continue print(name) return 0 if __name__ == "__main__": sys.exit(main(sys.argv[1:])) # Run: python fetch_items.py --verbose
ChatGPT (with Advanced Data Analysis)
Best when you need to actually execute the script in the same conversation. Advanced Data Analysis runs Python in a sandbox, so you can verify the output before copying the code. Slightly less polished code than Claude but the run-in-place workflow is unbeatable for quick experiments.
افتح ChatGPT (with Advanced Data Analysis)الأسئلة الشائعة
Should I use Claude Code instead of just chatting with Claude?
For multi-file scripts or projects, yes — Claude Code reads your file system, runs your scripts, and iterates on errors automatically. For single-file scripts, the chat version is faster.
How do I get AI to write production-grade Python, not toy scripts?
Tell it explicitly. "This is for production — include type hints, error handling, logging, and a main guard." Without those instructions, AI defaults to demo-quality code that's brittle in real conditions.
Why does AI sometimes generate Python code that uses outdated libraries?
Training data lag. AI may suggest deprecated libraries (e.g., urllib2 instead of urllib3, pandas methods removed in 2.0). Always check the library docs for the current API before pasting code into production.