synaptic-labs-ai/pact-plugin/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 synaptic-labs-ai/pact-plugin/n8n-code-python

SKILL.md

The same SKILL.md was published earlier as viriansemail-sys/claude-code-helper/n8n-code-python.

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

python
# Basic template for Python Code nodes
items = _input.all()
# Process data
processed = []
for item in items:
processed.append({
"json": {
**item["json"],
"processed": True,
"timestamp": datetime.now().isoformat()
}
})
return processed
Essential Rules
  1. Consider JavaScript first - Use Python only when necessary
  2. Access data: _input.all(), _input.first(), or _input.item
  3. CRITICAL: Must return [{"json": {...}}] format
  4. CRITICAL: Webhook data is under _json["body"] (not _json directly)
  5. CRITICAL LIMITATION: No external libraries (no requests, pandas, numpy)
  6. 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 _items array (Native mode)
  • Best for: Aggregation, filtering, batch processing, transformations
  • Performance: Faster for multiple items (single execution)
python
# Example: Calculate total from all items
all_items = _input.all()
total = sum(item["json"].get("amount", 0) for item in all_items)
return [{
"json": {
"total": total,
"count": len(all_items),
"average": total / len(all_items) if all_items else 0
}
}]
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.item or _item (Native mode)
  • Best for: Item-specific logic, independent operations, per-item validation
  • Performance: Slower for large datasets (multiple executions)
python
# Example: Add processing timestamp to each item
item = _input.item
return [{
"json": {
**item["json"],
"processed": True,
"processed_at": datetime.now().isoformat()
}
}]

Python Modes: Beta vs Native

n8n offers two Python execution modes:

Python (Beta) - Recommended
  • Use: _input, _json, _node helper syntax
  • Best for: Most Python use cases
  • Helpers available: _now, _today, _jmespath()
  • Import: from datetime import datetime
python
# Python (Beta) example
items = _input.all()
now = _now # Built-in datetime object
return [{
"json": {
"count": len(items),
"timestamp": now.isoformat()
}
}]
Python (Native) (Beta)
  • Use: _items, _item variables only
  • No helpers: No _input, _now, etc.
  • More limited: Standard Python only
  • Use when: Need pure Python without n8n helpers
python
# Python (Native) example
processed = []
for item in _items:
processed.append({
"json": {
"id": item["json"].get("id"),
"processed": True
}
})
return processed

Recommendation: Use Python (Beta) for better n8n integration.


Data Access Patterns

Pattern 1: _input.all() - Most Common

Use when: Processing arrays, batch operations, aggregations

python
# Get all items from previous node
all_items = _input.all()
# Filter, transform as needed
valid = [item for item in all_items if item["json"].get("status") == "active"]
processed = []
for item in valid:
processed.append({
"json": {
"id": item["json"]["id"],
"name": item["json"]["name"]
}
})
return processed
Pattern 2: _input.first() - Very Common

Use when: Working with single objects, API responses

python
# Get first item only
first_item = _input.first()
data = first_item["json"]
return [{
"json": {
"result": process_data(data),
"processed_at": datetime.now().isoformat()
}
}]
Pattern 3: _input.item - Each Item Mode Only

Use when: In "Run Once for Each Item" mode

python
# Current item in loop (Each Item mode only)
current_item = _input.item
return [{
"json": {
**current_item["json"],
"item_processed": True
}
}]
Pattern 4: _node - Reference Other Nodes

Use when: Need data from specific nodes in workflow

python
# Get output from specific node
webhook_data = _node["Webhook"]["json"]
http_data = _node["HTTP Request"]["json"]
return [{
"json": {
"combined": {
"webhook": webhook_data,
"api": http_data
}
}
}]

See: DATA_ACCESS.md for comprehensive guide


Critical: Webhook Data Structure

MOST COMMON MISTAKE: Webhook data is nested under ["body"]

python
# ❌ WRONG - Will raise KeyError
name = _json["name"]
email = _json["email"]
# ✅ CORRECT - Webhook data is under ["body"]
name = _json["body"]["name"]
email = _json["body"]["email"]
# ✅ SAFER - Use .get() for safe access
webhook_data = _json.get("body", {})
name = webhook_data.get("name")

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
python
# ✅ Single result
return [{
"json": {
"field1": value1,
"field2": value2
}
}]
# ✅ Multiple results
return [
{"json": {"id": 1, "data": "first"}},
{"json": {"id": 2, "data": "second"}}
]
# ✅ List comprehension
transformed = [
{"json": {"id": item["json"]["id"], "processed": True}}
for item in _input.all()
if item["json"].get("valid")
]
return transformed
# ✅ Empty result (when no data to return)
return []
# ✅ Conditional return
if should_process:
return [{"json": processed_data}]
else:
return []
Incorrect Return Formats
python
# ❌ WRONG: Dictionary without list wrapper
return {
"json": {"field": value}
}
# ❌ WRONG: List without json wrapper
return [{"field": value}]
# ❌ WRONG: Plain string
return "processed"
# ❌ WRONG: Incomplete structure
return [{"data": value}] # Should be {"json": value}

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
python
# ❌ NOT AVAILABLE - Will raise ModuleNotFoundError
import requests # ❌ No
import pandas # ❌ No
import numpy # ❌ No
import scipy # ❌ No
from bs4 import BeautifulSoup # ❌ No
import lxml # ❌ No
What IS Available (Standard Library)
python
# ✅ AVAILABLE - Standard library only
import json # ✅ JSON parsing
import datetime # ✅ Date/time operations
import re # ✅ Regular expressions
import base64 # ✅ Base64 encoding/decoding
import hashlib # ✅ Hashing functions
import urllib.parse # ✅ URL parsing
import math # ✅ Math functions
import random # ✅ Random numbers
import statistics # ✅ Statistical functions
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

python
items = _input.all()
return [
{
"json": {
"id": item["json"].get("id"),
"name": item["json"].get("name", "Unknown").upper(),
"processed": True
}
}
for item in items
]
2. Filtering & Aggregation

Sum, filter, count with built-in functions

python
items = _input.all()
total = sum(item["json"].get("amount", 0) for item in items)
valid_items = [item for item in items if item["json"].get("amount", 0) > 0]
return [{
"json": {
"total": total,
"count": len(valid_items)
}
}]
3. String Processing with Regex

Extract patterns from text

python
import re
items = _input.all()
email_pattern = r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b'
all_emails = []
for item in items:
text = item["json"].get("text", "")
emails = re.findall(email_pattern, text)
all_emails.extend(emails)
# Remove duplicates
unique_emails = list(set(all_emails))
return [{
"json": {
"emails": unique_emails,
"count": len(unique_emails)
}
}]
4. Data Validation

Validate and clean data

python
items = _input.all()
validated = []
for item in items:
data = item["json"]
errors = []
# Validate fields
if not data.get("email"):
errors.append("Email required")
if not data.get("name"):
errors.append("Name required")
validated.append({
"json": {
**data,
"valid": len(errors) == 0,
"errors": errors if errors else None
}
})
return validated
5. Statistical Analysis

Calculate statistics with statistics module

python
from statistics import mean, median, stdev
items = _input.all()
values = [item["json"].get("value", 0) for item in items if "value" in item["json"]]
if values:
return [{
"json": {
"mean": mean(values),
"median": median(values),
"stdev": stdev(values) if len(values) > 1 else 0,
"min": min(values),
"max": max(values),
"count": len(values)
}
}]
else:
return [{"json": {"error": "No values found"}}]

See: COMMON_PATTERNS.md for 10 detailed Python patterns


Error Prevention - Top 5 Mistakes

#1: Importing External Libraries (Python-Specific!)
python
# ❌ WRONG: Trying to import external library
import requests # ModuleNotFoundError!
# ✅ CORRECT: Use HTTP Request node or JavaScript
# Add HTTP Request node before Code node
# OR switch to JavaScript and use $helpers.httpRequest()
#2: Empty Code or Missing Return
python
# ❌ WRONG: No return statement
items = _input.all()
# Processing...
# Forgot to return!
# ✅ CORRECT: Always return data
items = _input.all()
# Processing...
return [{"json": item["json"]} for item in items]
#3: Incorrect Return Format
python
# ❌ WRONG: Returning dict instead of list
return {"json": {"result": "success"}}
# ✅ CORRECT: List wrapper required
return [{"json": {"result": "success"}}]
#4: KeyError on Dictionary Access
python
# ❌ WRONG: Direct access crashes if missing
name = _json["user"]["name"] # KeyError!
# ✅ CORRECT: Use .get() for safe access
name = _json.get("user", {}).get("name", "Unknown")

This is the start of the file. Read the full SKILL.md on GitHub.

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