Can I use external libraries like Pandas or Requests?

No, n8n Code nodes currently only support the Python standard library. For HTTP requests, use the n8n HTTP Request node or switch to JavaScript.

How do I access data from a Webhook node in Python?

A common mistake is accessing _json directly; webhook data is actually nested under the body key, so you should use _json['body'] or _json.get('body', {}).

What is the correct return format for an n8n Python node?

You must always return a list of dictionaries where each dictionary contains a 'json' key, for example: return [{'json': {'myKey': 'myValue'}}].

When should I use Python instead of JavaScript in n8n?

JavaScript is recommended for 95% of use cases due to better helper support, but Python is preferred if you need specific standard library functions (like statistics or re) or if you are more comfortable with Python syntax.

n8n Python Code Assistant

Name: n8n Python Code Assistant
Author: ProfSynapse

byProfSynapse

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Productivity & Workflow

Guides the creation of optimized Python logic within n8n Code nodes using specific data access patterns and return requirements.

About

This skill provides expert guidance for writing Python code specifically tailored for n8n automation workflows. It helps users navigate the unique constraints of the n8n environment, such as the lack of external libraries and the mandatory list-of-dictionaries return format. By offering standardized patterns for data access via _input and _json helpers, the skill ensures that data transformations, statistical analysis, and complex string manipulations are implemented correctly, preventing common execution errors like the 'Webhook body' nesting mistake or incorrect JSON nesting.

Key Features

36 GitHub stars
Comparison between Python (Beta) and Python (Native) modes
Strict enforcement of required return formats
Detailed handling of Webhook data structure nesting
Optimized n8n Python syntax patterns (_input, _json, _node)
Pre-built patterns for data filtering and statistical analysis

Use Cases

Performing mathematical or statistical operations using the Python standard library
Processing complex string extractions using the built-in regex (re) module
Transforming large datasets using Pythonic list comprehensions and filtering

About

Key Features

36 GitHub stars
Comparison between Python (Beta) and Python (Native) modes
Strict enforcement of required return formats
Detailed handling of Webhook data structure nesting
Optimized n8n Python syntax patterns (_input, _json, _node)
Pre-built patterns for data filtering and statistical analysis

Use Cases

Performing mathematical or statistical operations using the Python standard library
Processing complex string extractions using the built-in regex (re) module
Transforming large datasets using Pythonic list comprehensions and filtering