What is the required return format for Python nodes in n8n?

You must always return a list of dictionaries where the data is wrapped in a 'json' key, for example: return [{'json': {'id': 1, 'status': 'active'}}].

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

Use _input.all() to access all items from the previous node, or use _node['Node Name']['json'] to reference data from a specific node elsewhere in the workflow.

When should I choose Python over JavaScript in n8n?

Choose Python when you need specific Python standard library modules like 'statistics' or 're', or if you are significantly more efficient with Python syntax for complex data processing.

Can I use external Python libraries like Pandas or Requests in n8n?

No, n8n Code nodes are limited to the Python standard library (e.g., json, datetime, re, math). For HTTP requests, use the native n8n HTTP Request node or the JavaScript node.

n8n Python Code Helper

Name: n8n Python Code Helper
Author: thanhtoan105

bythanhtoan105

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Productivity & Workflow

Optimizes Python development in n8n workflows by providing expert guidance on syntax, data access patterns, and standard library utilization.

About

This skill provides comprehensive guidance for developers using Python within n8n Code nodes, specifically focusing on the Python (Beta) environment. It helps users navigate n8n-specific data access patterns like _input.all() and _node, enforces correct return formats to prevent execution errors, and clarifies the limitations regarding external libraries. By offering templates for data transformation, filtering, and statistical analysis using only the standard library, it ensures that Python logic is implemented efficiently and correctly within automated workflows.

Key Features

Expert guidance on n8n-specific Python syntax and helper variables
Standardized return format templates to prevent workflow execution failures
Pattern-matching solutions for complex nested data like webhook payloads
1 GitHub stars
Best practices for data transformation using Python standard library modules
Clear decision frameworks for choosing between Python and JavaScript modes

Use Cases

Implementing regex-based pattern extraction from unstructured API or webhook responses
Calculating descriptive statistics from workflow items using the built-in statistics module
Performing complex data filtering and aggregation using Python list comprehensions

About

Key Features

Expert guidance on n8n-specific Python syntax and helper variables
Standardized return format templates to prevent workflow execution failures
Pattern-matching solutions for complex nested data like webhook payloads
1 GitHub stars
Best practices for data transformation using Python standard library modules
Clear decision frameworks for choosing between Python and JavaScript modes

Use Cases

Implementing regex-based pattern extraction from unstructured API or webhook responses
Calculating descriptive statistics from workflow items using the built-in statistics module
Performing complex data filtering and aggregation using Python list comprehensions