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

You can use the _node helper, for example: _node['Node Name']['json'] to access data from a specific preceding node.

Why should I use JavaScript instead of Python in n8n?

n8n recommends JavaScript for 95% of use cases because it offers full helper functions like $helpers.httpRequest and the Luxon library for dates.

Can I use Pandas or NumPy in n8n Python nodes?

No, n8n Python nodes are limited to the Python standard library. External packages like Pandas, NumPy, or Requests are not available.

How do I handle Webhook data in Python nodes?

Webhook data is nested under the 'body' property. Use _json.get('body', {}) to safely access the incoming payload.

What is the correct return format for n8n Python code?

You must always return a list of dictionaries where each dictionary contains a 'json' key, such as: return [{'json': {'key': 'value'}}].

n8n Python Code Specialist

Name: n8n Python Code Specialist
Author: czlonkowski

byczlonkowski

•

1,244

生产力与工作流

Writes and optimizes Python logic for n8n Code nodes using specialized data access patterns and return formats.

关于

The n8n Python Code Specialist skill empowers Claude to build production-grade n8n workflows using Python. It provides expert guidance on the unique syntax of n8n's Python environment, including the usage of the _input, _json, and _node helpers. This skill ensures your code adheres to critical constraints—such as returning the mandatory list-of-dictionaries format and working within the standard library limitations—while offering optimized patterns for data transformation, regex processing, and statistical analysis.

主要功能

Optimized logic for 'Run Once for All Items' execution mode
Comprehensive guidance on available Python standard libraries
Automatic generation of n8n-compliant return structures
Comparison between Python (Beta) and Python (Native) modes
Expert handling of nested webhook data patterns
1,244 GitHub stars

使用场景

Performing statistical analysis on workflow data using the statistics module
Transforming large JSON datasets using Python list comprehensions
Complex string parsing and data validation via regular expressions

关于

主要功能

Optimized logic for 'Run Once for All Items' execution mode
Comprehensive guidance on available Python standard libraries
Automatic generation of n8n-compliant return structures
Comparison between Python (Beta) and Python (Native) modes
Expert handling of nested webhook data patterns
1,244 GitHub stars

使用场景

Performing statistical analysis on workflow data using the statistics module
Transforming large JSON datasets using Python list comprehensions
Complex string parsing and data validation via regular expressions