viriansemail-sys/claude-code-helper/n8n-code-python
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Write Python code in n8n Code nodes. Use when writing Python in n8n, using _input/_json/_node syntax, working with standard library, or need to understand Python limitations in n8n Code nodes.
Install
npx vskill@latest install viriansemail-sys/claude-code-helper/n8n-code-pythonSKILL.md
Python Code Node (Beta)
Expert guidance for writing Python code in n8n Code nodes.
⚠️ Important: JavaScript First
Recommendation: Use JavaScript for 95% of use cases. Only use Python when:
- You need specific Python standard library functions
- You're significantly more comfortable with Python syntax
- You're doing data transformations better suited to Python
Why JavaScript is preferred:
- Full n8n helper functions ($helpers.httpRequest, etc.)
- Luxon DateTime library for advanced date/time operations
- No external library limitations
- Better n8n documentation and community support
Quick Start
Essential Rules
- Consider JavaScript first - Use Python only when necessary
- Access data:
_input.all(),_input.first(), or_input.item - CRITICAL: Must return
[{"json": {...}}]format - CRITICAL: Webhook data is under
_json["body"](not_jsondirectly) - CRITICAL LIMITATION: No external libraries (no requests, pandas, numpy)
- Standard library only: json, datetime, re, base64, hashlib, urllib.parse, math, random, statistics
Mode Selection Guide
Same as JavaScript - choose based on your use case:
Run Once for All Items (Recommended - Default)
Use this mode for: 95% of use cases
- How it works: Code executes once regardless of input count
- Data access:
_input.all()or_itemsarray (Native mode) - Best for: Aggregation, filtering, batch processing, transformations
- Performance: Faster for multiple items (single execution)
Run Once for Each Item
Use this mode for: Specialized cases only
- How it works: Code executes separately for each input item
- Data access:
_input.itemor_item(Native mode) - Best for: Item-specific logic, independent operations, per-item validation
- Performance: Slower for large datasets (multiple executions)
Python Modes: Beta vs Native
n8n offers two Python execution modes:
Python (Beta) - Recommended
- Use:
_input,_json,_nodehelper syntax - Best for: Most Python use cases
- Helpers available:
_now,_today,_jmespath() - Import:
from datetime import datetime
Python (Native) (Beta)
- Use:
_items,_itemvariables only - No helpers: No
_input,_now, etc. - More limited: Standard Python only
- Use when: Need pure Python without n8n helpers
Recommendation: Use Python (Beta) for better n8n integration.
Data Access Patterns
Pattern 1: _input.all() - Most Common
Use when: Processing arrays, batch operations, aggregations
Pattern 2: _input.first() - Very Common
Use when: Working with single objects, API responses
Pattern 3: _input.item - Each Item Mode Only
Use when: In "Run Once for Each Item" mode
Pattern 4: _node - Reference Other Nodes
Use when: Need data from specific nodes in workflow
See: DATA_ACCESS.md for comprehensive guide
Critical: Webhook Data Structure
MOST COMMON MISTAKE: Webhook data is nested under ["body"]
Why: Webhook node wraps all request data under body property. This includes POST data, query parameters, and JSON payloads.
See: DATA_ACCESS.md for full webhook structure details
Return Format Requirements
CRITICAL RULE: Always return list of dictionaries with "json" key
Correct Return Formats
Incorrect Return Formats
Why it matters: Next nodes expect list format. Incorrect format causes workflow execution to fail.
See: ERROR_PATTERNS.md #2 for detailed error solutions
Critical Limitation: No External Libraries
MOST IMPORTANT PYTHON LIMITATION: Cannot import external packages
What's NOT Available
What IS Available (Standard Library)
Workarounds
Need HTTP requests?
- ✅ Use HTTP Request node before Code node
- ✅ Or switch to JavaScript and use
$helpers.httpRequest()
Need data analysis (pandas/numpy)?
- ✅ Use Python statistics module for basic stats
- ✅ Or switch to JavaScript for most operations
- ✅ Manual calculations with lists and dictionaries
Need web scraping (BeautifulSoup)?
- ✅ Use HTTP Request node + HTML Extract node
- ✅ Or switch to JavaScript with regex/string methods
See: STANDARD_LIBRARY.md for complete reference
Common Patterns Overview
Based on production workflows, here are the most useful Python patterns:
1. Data Transformation
Transform all items with list comprehensions
2. Filtering & Aggregation
Sum, filter, count with built-in functions
3. String Processing with Regex
Extract patterns from text
4. Data Validation
Validate and clean data
5. Statistical Analysis
Calculate statistics with statistics module
See: COMMON_PATTERNS.md for 10 detailed Python patterns
Error Prevention - Top 5 Mistakes
#1: Importing External Libraries (Python-Specific!)
#2: Empty Code or Missing Return
#3: Incorrect Return Format
#4: KeyError on Dictionary Access
This is the start of the file. Read the full SKILL.md on GitHub.
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