Effective ChatGPT Prompts for Writing Python Code

Tested prompts for chatgpt prompts for python coding compared across 5 leading AI models.

BEST BY JUDGE SCORE Claude Opus 4.7 9/10

If you searched for ChatGPT prompts for Python coding, you are probably staring at a blank file or a half-broken script and want an AI to help you move faster. The problem is that vague prompts produce vague code. Typing 'write me a Python script' gets you something generic that needs heavy editing. The right prompt structure gets you code you can actually run.

This page shows you exactly how to prompt ChatGPT for Python tasks: how to specify inputs and outputs, how to communicate constraints, and how to get back clean, documented, production-ready code instead of a rough sketch. The prompts and outputs tested here cover real scenarios from data processing to API calls to automation scripts.

Whether you are a beginner trying to automate a repetitive task or an experienced developer using ChatGPT as a coding accelerator, the difference between a useful result and a useless one comes down to how you write the prompt. The examples below are designed to be copied, adapted, and used immediately.

When to use this

Using ChatGPT to write Python code works best when you have a clearly scoped task, know what inputs and outputs you need, and want a working first draft fast. It fits solo developers, data analysts, and teams that need utility scripts, automation tools, or boilerplate generated without burning hours on setup.

Writing a one-off data processing or file transformation script
Generating boilerplate for API integrations, CLI tools, or database queries
Prototyping a function or class before refining it in your actual codebase
Translating logic you know in plain English into working Python syntax
Getting a second implementation of something you wrote to compare approaches

When this format breaks down

When your codebase has deep custom abstractions or internal libraries ChatGPT has never seen: it will write code that looks right but will not integrate without significant rework.
When correctness is mission-critical and untested: ChatGPT can produce plausible but subtly wrong logic, especially in edge cases involving date handling, floating point arithmetic, or stateful systems.
When you need code optimized for a specific hardware environment or performance budget: the output will be idiomatic but not tuned, and profiling it later adds more time than writing it correctly from the start.
When the task requires real-time knowledge, such as using the latest version of a rapidly changing library: ChatGPT's training cutoff means it may reference deprecated APIs or outdated patterns.

The prompt we tested

You are an expert Python developer and coding mentor. Write clean, production-quality Python code that solves the user's request below.

Follow these instructions carefully:
Use Python 3.10+ with type hints, docstrings, and PEP 8 formatting. Include error handling for edge cases, prefer standard library when possible, and keep the explanation under 150 words so the code remains the focus.

User's coding request:
I need a Python script that reads a CSV file of sales transactions, groups them by product category, and outputs the total revenue and average order value per category to a new CSV. The input file has columns: date, product, category, quantity, unit_price.

Respond with the complete Python solution, including code, inline comments explaining key logic, and a brief usage example.

Model comparison

Model	Quality	Cost	Latency	Tokens
Claude Opus 4.7	9	$0.1199	14.6s	249 / 1549
Claude Haiku 4.5	7	$0.0050	5.8s	179 / 966
GPT-5.4	8	$0.0203	16.8s	164 / 1326
Grok 4.1 Fast Reasoning	8	$0.0005	16.5s	313 / 801

Outputs

Claude Opus 4.7 9/10

# Sales Transaction Aggregator

```python
"""Aggregate sales transactions by product category."""

from __future__ import annotations

import argparse
import csv
import sys
from collections import defaultdict
from dataclasses import dataclass
from pathlib import Path


@dataclass
class CategoryStats:
    """Running totals for a single product category."""
    revenue: float = 0.0
    order_count: int = 0

    @property
    def average_order_value(self) -> float:
        """Mean revenue per order; zero if no orders recorded."""
        return self.revenue / self.order_count if self.order_count else 0.0


REQUIRED_COLUMNS = {"date", "product", "category", "quantity", "unit_price"}


def aggregate_sales(input_path: Path) -> dict[str, CategoryStats]:
    """Read a sales CSV and return per-category aggregated statistics.

    Args:
        input_path: Path to the input CSV file.

    Returns:
        Mapping of category name -> CategoryStats.

    Raises:
        FileNotFoundError: If the input file does not exist.
        ValueError: If required columns are missing.
    """
    if not input_path.is_file():
        raise FileNotFoundError(f"Input file not found: {input_path}")

    stats: dict[str, CategoryStats] = defaultdict(CategoryStats)

    with input_path.open(newline="", encoding="utf-8") as f:
        reader = csv.DictReader(f)

        # Validate headers once up-front to fail fast on malformed input.
        if reader.fieldnames is None or not REQUIRED_COLUMNS.issubset(reader.fieldnames):
            missing = REQUIRED_COLUMNS - set(reader.fieldnames or [])
            raise ValueError(f"Missing required columns: {sorted(missing)}")

        for line_no, row in enumerate(reader, start=2):  # start=2 accounts for header
            try:
                quantity = int(row["quantity"])
                unit_price = float(row["unit_price"])
                category = row["category"].strip()
            except (ValueError, AttributeError) as exc:
                print(f"Skipping malformed row {line_no}: {exc}", file=sys.stderr)
                continue

            if not category:
                print(f"Skipping row {line_no}: empty category", file=sys.stderr)
                continue

            # Each row is one order line; accumulate revenue and count.
            stats[category].revenue += quantity * unit_price
            stats[category].order_count += 1

    return stats


def write_summary(stats: dict[str, CategoryStats], output_path: Path) -> None:
    """Write category statistics to a CSV file, sorted by revenue desc."""
    output_path.parent.mkdir(parents=True, exist_ok=True)

    with output_path.open("w", newline="", encoding="utf-8") as f:
        writer = csv.writer(f)
        writer.writerow(["category", "total_revenue", "average_order_value"])

        for category, s in sorted(stats.items(), key=lambda kv: kv[1].revenue, reverse=True):
            writer.writerow([category, f"{s.revenue:.2f}", f"{s.average_order_value:.2f}"])


def main() -> int:
    parser = argparse.ArgumentParser(description="Summarize sales by category.")
    parser.add_argument("input", type=Path, help="Input CSV of transactions")
    parser.add_argument("output", type=Path, help="Output CSV for summary")
    args = parser.parse_args()

    try:
        stats = aggregate_sales(args.input)
        write_summary(stats, args.output)
    except (FileNotFoundError, ValueError) as exc:
        print(f"Error: {exc}", file=sys.stderr)
        return 1

    print(f"Wrote summary for {len(stats)} categories to {args.output}")
    return 0


if __name__ == "__main__":
    sys.exit(main())
```

## Usage

```bash
python sales_summary.py sales.csv summary.csv
```

**Sample input (`sales.csv`):**
```
date,product,category,quantity,unit_price
2024-01-03,Widget,Hardware,2,9.99
2024-01-04,Book,Media,1,24.50
```

The script validates headers, skips malformed rows with warnings to stderr, uses a `dataclass` for clean stat tracking, and emits categories sorted by revenue. Revenue is computed as `quantity * unit_price`, and average order value divides total revenue by the number of transaction lines per category.

Claude Haiku 4.5 7/10

```python
"""
Sales transaction analyzer: Groups transactions by category and calculates metrics.
"""

import csv
from collections import defaultdict
from pathlib import Path
from dataclasses import dataclass
from typing import Dict


@dataclass
class CategoryMetrics:
    """Stores aggregated metrics per category."""
    total_revenue: float = 0.0
    total_orders: int = 0
    order_count: int = 0

    @property
    def average_order_value(self) -> float:
        """Calculate average revenue per order."""
        return self.total_revenue / self.order_count if self.order_count > 0 else 0.0


def analyze_sales(input_file: str, output_file: str) -> None:
    """
    Read sales CSV, aggregate by category, and write results to output CSV.
    
    Args:
        input_file: Path to input CSV with columns: date, product, category, 
                   quantity, unit_price
        output_file: Path to output CSV with aggregated metrics
        
    Raises:
        FileNotFoundError: If input file doesn't exist
        ValueError: If required columns are missing
    """
    input_path = Path(input_file)
    
    if not input_path.exists():
        raise FileNotFoundError(f"Input file not found: {input_file}")
    
    # Dictionary to store metrics per category
    categories: Dict[str, CategoryMetrics] = defaultdict(CategoryMetrics)
    
    # Read and aggregate transaction data
    try:
        with open(input_path, 'r', encoding='utf-8') as f:
            reader = csv.DictReader(f)
            
            # Validate required columns
            if not reader.fieldnames or not all(
                col in reader.fieldnames 
                for col in ['category', 'quantity', 'unit_price']
            ):
                raise ValueError("Missing required columns: category, quantity, unit_price")
            
            for row in reader:
                category = row['category'].strip()
                quantity = float(row['quantity'])
                unit_price = float(row['unit_price'])
                
                # Calculate revenue for this transaction
                revenue = quantity * unit_price
                
                # Update metrics
                categories[category].total_revenue += revenue
                categories[category].order_count += 1
    
    except (ValueError, KeyError) as e:
        raise ValueError(f"Error parsing CSV data: {e}")
    
    # Write aggregated results
    with open(output_file, 'w', newline='', encoding='utf-8') as f:
        writer = csv.writer(f)
        writer.writerow(['Category', 'Total Revenue', 'Average Order Value'])
        
        # Sort by category for consistent output
        for category in sorted(categories.keys()):
            metrics = categories[category]
            writer.writerow([
                category,
                f"{metrics.total_revenue:.2f}",
                f"{metrics.average_order_value:.2f}"
            ])
    
    print(f"✓ Analysis complete. Results written to: {output_file}")


if __name__ == "__main__":
    # Example usage
    analyze_sales('sales_transactions.csv', 'sales_summary.csv')
```

**Usage Example:**

Create `sales_transactions.csv`:
```csv
date,product,category,quantity,unit_price
2024-01-01,Widget A,Electronics,5,29.99
2024-01-02,Widget B,Electronics,3,49.99
2024-01-03,Gadget X,Home,2,79.99
```

Run:
```bash
python sales_analyzer.py
```

Output `sales_summary.csv`:
```csv
Category,Total Revenue,Average Order Value
Electronics,299.92,149.96
Home,159.98,159.98
```

**Key Features:** Type hints, dataclass for metrics, error handling, clean CSV I/O, and sorted output for reproducibility.

GPT-5.4 8/10

```python
#!/usr/bin/env python3
"""
Aggregate sales transactions by product category.

Reads an input CSV with columns:
    date, product, category, quantity, unit_price

Outputs a CSV with columns:
    category, total_revenue, average_order_value

Definitions:
- revenue per row = quantity * unit_price
- average_order_value per category = total_revenue / number_of_rows_in_category
"""

from __future__ import annotations

import csv
import sys
from collections import defaultdict
from dataclasses import dataclass
from decimal import Decimal, InvalidOperation, ROUND_HALF_UP
from pathlib import Path
from typing import DefaultDict


REQUIRED_COLUMNS = {"date", "product", "category", "quantity", "unit_price"}
OUTPUT_COLUMNS = ["category", "total_revenue", "average_order_value"]
TWOPLACES = Decimal("0.01")


@dataclass
class CategoryStats:
    """Holds aggregation data for a category."""
    total_revenue: Decimal = Decimal("0")
    order_count: int = 0

    def add_order(self, revenue: Decimal) -> None:
        """Add one transaction's revenue to the category totals."""
        self.total_revenue += revenue
        self.order_count += 1

    @property
    def average_order_value(self) -> Decimal:
        """Compute average order value, rounded to 2 decimal places."""
        if self.order_count == 0:
            return Decimal("0.00")
        return (self.total_revenue / self.order_count).quantize(
            TWOPLACES, rounding=ROUND_HALF_UP
        )


def parse_decimal(value: str, field_name: str, row_number: int) -> Decimal:
    """
    Parse a string into Decimal with validation.

    Raises:
        ValueError: If the value is missing or invalid.
    """
    if value is None or value.strip() == "":
        raise ValueError(f"Row {row_number}: missing value for '{field_name}'")
    try:
        return Decimal(value.strip())
    except (InvalidOperation, AttributeError) as exc:
        raise ValueError(
            f"Row {row_number}: invalid decimal for '{field_name}': {value!r}"
        ) from exc


def aggregate_sales_by_category(input_csv: Path) -> dict[str, CategoryStats]:
    """
    Read the input CSV and aggregate revenue and order counts by category.

    Args:
        input_csv: Path to the source CSV file.

    Returns:
        Mapping of category name to aggregated statistics.

    Raises:
        FileNotFoundError: If the input file does not exist.
        ValueError: If required columns are missing or row data is invalid.
    """
    if not input_csv.exists():
        raise FileNotFoundError(f"Input file not found: {input_csv}")

    stats: DefaultDict[str, CategoryStats] = defaultdict(CategoryStats)

    with input_csv.open("r", newline="", encoding="utf-8") as f:
        reader = csv.DictReader(f)

        if reader.fieldnames is None:
            raise ValueError("Input CSV is empty or missing a header row")

        missing = REQUIRED_COLUMNS - set(reader.fieldnames)
        if missing:
            raise ValueError(
                f"Input CSV is missing required columns: {', '.join(sorted(missing))}"
            )

        for row_number, row in enumerate(reader, start=2):  # header is row 1
            category = (row.get("category") or "").strip()
            if not category:
                raise ValueError(f"Row {row_number}: missing category")

            quantity = parse_decimal(row.get("quantity", ""), "quantity", row_number)
            unit_price = parse_decimal(row.get("unit_price", ""), "unit_price", row_number)

            if quantity < 0:
                raise ValueError(f"Row {row_number}: quantity cannot be negative")
            if unit_price < 0:
                raise ValueError(f"Row {row_number}: unit_price cannot be negative")

            # Revenue for one transaction row.
            revenue = quantity * unit_price
            stats[category].add_order(revenue)

    return dict(stats)


def write_summary_csv(output_csv: Path, stats: dict[str, CategoryStats]) -> None:
    """
    Write aggregated category metrics to an output CSV.

    Args:
        output_csv: Destination CSV path.
        stats: Aggregated category statistics.
    """
    with output_csv.open("w", newline="", encoding="utf-8") as f:
        writer = csv.DictWriter(f, fieldnames=OUTPUT_COLUMNS)
        writer.writeheader()

        # Sort categories for stable, predictable output.
        for category in sorted(stats):
            category_stats = stats[category]
            writer.writerow(
                {
                    "category": category,
                    "total_revenue": str(
                        category_stats.total_revenue.quantize(
                            TWOPLACES, rounding=ROUND_HALF_UP
                        )
                    ),
                    "average_order_value": str(category_stats.average_order_value),
                }
            )


def main(argv: list[str]) -> int:
    """
    CLI entry point.

    Usage:
        python sales_summary.py input.csv output.csv
    """
    if len(argv) != 3:
        print(
            "Usage: python sales_summary.py <input_csv> <output_csv>",
            file=sys.stderr,
        )
        return 1

    input_csv = Path(argv[1])
    output_csv = Path(argv[2])

    try:
        stats = aggregate_sales_by_category(input_csv)
        write_summary_csv(output_csv, stats)
    except (FileNotFoundError, ValueError, OSError) as exc:
        print(f"Error: {exc}", file=sys.stderr)
        return 1

    print(f"Summary written to: {output_csv}")
    return 0


if __name__ == "__main__":
    raise SystemExit(main(sys.argv))
```

### Usage example
```bash
python sales_summary.py sales_transactions.csv category_summary.csv
```

Example input:
```csv
date,product,category,quantity,unit_price
2024-01-01,Keyboard,Electronics,2,49.99
2024-01-02,Mouse,Electronics,1,19.99
2024-01-03,Mug,Home,3,8.50
```

This writes `category_summary.csv` with total revenue and average order value per category.

Grok 4.1 Fast Reasoning 8/10

```python
import argparse
import csv
from collections import defaultdict
from pathlib import Path
from typing import Dict, Any

def process_sales_data(input_path: str, output_path: str) -> None:
    """
    Reads a sales CSV file, groups transactions by 'category',
    computes total revenue (sum(quantity * unit_price)) and average order value
    (total_revenue / transaction_count) per category, and writes to output CSV.

    Handles missing files, invalid columns, non-numeric data gracefully.
    """
    required_columns = {'date', 'product', 'category', 'quantity', 'unit_price'}
    # Use defaultdict for efficient grouping by category
    stats: Dict[str, Dict[str, float]] = defaultdict(lambda: {'total_revenue': 0.0, 'count': 0.0})

    # Read and validate input CSV
    try:
        with open(input_path, 'r', newline='', encoding='utf-8') as infile:
            reader = csv.DictReader(infile)
            # Ensure all required columns exist
            if not required_columns.issubset(set(reader.fieldnames or [])):
                missing = required_columns - set(reader.fieldnames or [])
                raise ValueError(f"Missing columns: {missing}")
            
            for row_num, row in enumerate(reader, start=2):  # Line numbers for errors
                try:
                    # Compute revenue per transaction (row)
                    qty = float(row['quantity'])
                    unit_price = float(row['unit_price'])
                    revenue = qty * unit_price
                    category = row['category'].strip()
                    
                    # Accumulate stats
                    stats[category]['total_revenue'] += revenue
                    stats[category]['count'] += 1.0
                except (ValueError, KeyError) as e:
                    print(f"Warning: Skipping row {row_num} (invalid data): {e}")
                    continue
    except FileNotFoundError:
        raise FileNotFoundError(f"Input file not found: {input_path}")
    except csv.Error as e:
        raise ValueError(f"CSV read error in {input_path}: {e}")

    # Write grouped stats to output CSV, sorted by category
    fieldnames = ['category', 'total_revenue', 'average_order_value']
    try:
        with open(output_path, 'w', newline='', encoding='utf-8') as outfile:
            writer = csv.DictWriter(outfile, fieldnames=fieldnames)
            writer.writeheader()
            for category in sorted(stats.keys()):
                total_rev = stats[category]['total_revenue']
                count = stats[category]['count']
                avg_order_value = total_rev / count if count > 0 else 0.0
                writer.writerow({
                    'category': category,
                    'total_revenue': round(total_rev, 2),
                    'average_order_value': round(avg_order_value, 2)
                })
        print(f"Output written to {output_path} ({len(stats)} categories)")
    except IOError as e:
        raise IOError(f"Failed to write {output_path}: {e}")

if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='Analyze sales by category.')
    parser.add_argument('input', help='Path to input CSV (e.g., sales.csv)')
    parser.add_argument('output', help='Path to output CSV (e.g., summary.csv)')
    args = parser.parse_args()
    process_sales_data(args.input, args.output)
```

**Usage example:**
```
$ python sales_analyzer.py sales.csv category_summary.csv
```
Assumes `sales.csv` has header and data like: `date,product,category,quantity,unit_price\n2023-01-01,Widget A,Electronics,2,10.50`. Outputs `category_summary.csv` with `category,total_revenue,average_order_value`. (98 words)

What makes these work

01

Specify inputs, outputs, and types
ChatGPT writes better code when you tell it exactly what goes in and what comes out. Instead of 'write a function to process orders', say 'write a function that accepts a list of dicts with keys order_id and amount and returns a sorted list of order_ids where amount exceeds 100'. Concrete types eliminate ambiguity and reduce the chance you get back a generic example that does not match your actual data.
02

Name the library you want used
Python has multiple ways to do almost everything. If you want pandas instead of csv, requests instead of urllib, or asyncio instead of threading, say so explicitly. Without a library constraint, ChatGPT picks whatever is most common in training data, which may not match your environment or existing dependencies.
03

Ask for error handling in the prompt
By default, ChatGPT often returns happy-path code with no exception handling. If you add 'handle errors gracefully' or 'raise a custom exception on failure' to your prompt, you get production-closer code. This single addition saves you the most common rewrite cycle where you have to manually add try/except blocks after seeing the first draft.
04

Request comments or docstrings explicitly
ChatGPT will add comments if you ask but omits them if you do not. Adding 'include a docstring for each function' or 'add inline comments explaining non-obvious logic' to your prompt costs you nothing and returns code that is easier to review, hand off, or revisit in six months.

More example scenarios

#01 · CSV data cleaning for a marketing analyst

Input

Write a Python script that reads a CSV file called 'leads.csv', removes any rows where the 'email' column is empty or not a valid email format, deduplicates rows by email keeping the most recent 'date_added' entry, and saves the result to 'clean_leads.csv'. Use pandas. Add comments explaining each step.

Expected output

A pandas script that loads the CSV, applies a regex email validator to filter invalid rows, sorts by date_added descending and drops duplicates on email keeping the first occurrence, then exports to clean_leads.csv. Each block has an inline comment. The script runs from the command line with no arguments required.

#02 · REST API client for a backend developer

Input

Write a Python class called GitHubClient that wraps the GitHub REST API. It should accept a personal access token on init, have a method get_repos(username) that returns a list of repo names, and a method create_issue(owner, repo, title, body) that posts a new issue. Use the requests library and raise custom exceptions on HTTP errors.

Expected output

A GitHubClient class with an __init__ that sets the auth header, a get_repos method that calls GET /users/{username}/repos and returns a list of name strings, and a create_issue method that posts to /repos/{owner}/{repo}/issues. A GitHubAPIError exception class is defined and raised when response status is 4xx or 5xx.

#03 · File automation script for an operations team

Input

Write a Python script that watches a folder called 'incoming' for new PDF files. When a PDF is detected, move it to a subfolder named after today's date in YYYY-MM-DD format, creating the folder if it does not exist. Log each moved file with a timestamp to a file called 'move_log.txt'. Use watchdog for folder monitoring.

Expected output

A script using watchdog's FileSystemEventHandler that triggers on_created events, checks the file extension for .pdf, builds a dated subfolder path with datetime.today(), creates it with os.makedirs(exist_ok=True), moves the file with shutil.move, and appends a timestamped line to move_log.txt. The observer starts in a blocking loop.

#04 · Data pipeline function for a data engineer

Input

Write a Python function called fetch_and_store that accepts a list of URLs, fetches each URL concurrently using asyncio and aiohttp, parses the JSON response, extracts the keys 'id' and 'value' from each response, and inserts the results into a SQLite database table called 'records'. Handle connection errors gracefully and skip failed URLs.

Expected output

An async function using aiohttp.ClientSession with asyncio.gather to fetch all URLs concurrently. Each response is parsed with response.json(), the id and value keys are extracted, and rows are inserted into SQLite using aiosqlite. A try/except around each fetch logs the URL and error message then continues to the next URL rather than stopping execution.

#05 · CLI tool for a DevOps engineer

Input

Write a Python CLI tool using argparse that accepts a hostname and optional port (default 80) as arguments and checks whether the port is open using a TCP socket connection with a 3-second timeout. Print 'OPEN' or 'CLOSED' to stdout. Exit with code 0 if open and code 1 if closed.

Expected output

A script with an ArgumentParser defining hostname as a positional arg and --port as an optional int defaulting to 80. A socket.create_connection call with a 3-second timeout is wrapped in a try/except. On success it prints OPEN and calls sys.exit(0). On socket.timeout or ConnectionRefusedError it prints CLOSED and calls sys.exit(1).

Common mistakes to avoid

Prompt is too vague to be useful
Asking 'write me a Python script to handle data' gives ChatGPT almost nothing to work with. The output will be a skeleton that still requires you to write all the real logic. Every word you spend describing the actual input format, processing logic, and expected output saves you multiple rounds of follow-up prompting.
Assuming the code runs without testing
ChatGPT generates plausible code, not guaranteed-correct code. It occasionally produces function calls with wrong argument orders, references variables it never defined, or uses APIs that have changed since its training data. Always paste the output into a test environment before treating it as done.
Not specifying the Python version or environment
Code written for Python 3.10+ uses features like structural pattern matching that will break on Python 3.8. If your environment has constraints, state them. The same applies to whether you are in a virtual environment, a Lambda function, a Jupyter notebook, or a Docker container, since each has different import and execution assumptions.
Prompting for the whole application at once
Asking ChatGPT to write a complete multi-file application in one prompt produces sprawling, hard-to-verify output. Break the task into focused prompts: one for the data model, one for the processing logic, one for the CLI interface. Smaller prompts produce tighter, more reviewable code and make it easier to catch errors before they compound.
Ignoring the context window when iterating
When you paste a long script back into the chat to ask for changes, ChatGPT must hold all of that in context while generating a response. On long files this leads to dropped logic, inconsistent variable names, or sections that revert to earlier versions. For large refactors, isolate the specific function or class you want changed rather than submitting the full file.

Related queries

Frequently asked questions

What is the best way to prompt ChatGPT to write Python code?

Start with the function or script name, describe the inputs with their types, describe the expected output, name any libraries you want used, and specify error handling behavior. The more concrete your description, the less editing you will need. Treating the prompt like a mini technical spec consistently outperforms open-ended requests.

Can ChatGPT write Python code that actually runs without errors?

Often yes, but not always. ChatGPT produces syntactically correct Python the vast majority of the time, but logical errors and outdated API usage do occur. Always run the output in a test environment before using it in production. Asking ChatGPT to also write unit tests for the code it generates adds a useful sanity check.

How do I get ChatGPT to write Python code for a specific library like pandas or FastAPI?

Name the library directly in your prompt. Say 'use pandas' or 'use FastAPI with Pydantic models'. Without this, ChatGPT defaults to standard library or the most common alternative. If you need a specific version because of a known breaking change, include that too: 'use SQLAlchemy 2.0 style session management'.

Can I use ChatGPT to debug Python code instead of writing it from scratch?

Yes, and it works well for this. Paste your code, describe the error message or unexpected behavior, and ask ChatGPT to identify the bug. It is especially useful for type errors, off-by-one errors, and incorrect use of library methods. For debugging, giving ChatGPT the full traceback alongside the code produces much better results than describing the error in plain language.

What Python tasks is ChatGPT not reliable for?

ChatGPT struggles with tasks that require up-to-date knowledge of library APIs, highly optimized numerical code, or code that must integrate tightly with a proprietary codebase it has not seen. It also tends to underperform on complex algorithmic problems where the correct solution is non-obvious, since it will produce a plausible-looking but potentially incorrect implementation.

How do I get ChatGPT to write Pythonic code instead of beginner-style code?

Add 'write idiomatic Python' or 'use list comprehensions, context managers, and dataclasses where appropriate' to your prompt. You can also specify 'follow PEP 8' or 'target senior-level readability'. Without these signals, ChatGPT calibrates to a middle-ground style that avoids advanced features, which produces readable but not always idiomatic output.

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