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:

Why JavaScript is preferred:

Quick Start

# 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

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

# 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

# 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

# Python (Beta) example
items = _input.all()
now = _now  # Built-in datetime object
return [{
    "json": {
        "count": len(items),
        "timestamp": now.isoformat()
    }
}]

Python (Native) (Beta)

# 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

# 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

# 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

# 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

# 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"]

# ❌ 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

# ✅ 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

# ❌ 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

# ❌ 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)

# ✅ 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?

Need data analysis (pandas/numpy)?

Need web scraping (BeautifulSoup)?

See: STANDARD_LIBRARY.md for complete reference

Common Patterns Overview

Based on production workflows, here are the most useful Python patterns:

1. Data Transformation

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

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)
    }
}]

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)
    }
}]

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

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!)

# ❌ 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

# ❌ 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

# ❌ WRONG: Returning dict instead of list
return {"json": {"result": "success"}}
✅ CORRECT: List wrapper required

return [{"json": {"result": "success"}}]

#4: KeyError on Dictionary Access

# ❌ WRONG: Direct access crashes if missing
name = _json["user"]["name"]  # KeyError!
✅ CORRECT: Use .get() for safe access

name = _json.get("user", {}).get("name", "Unknown")

#5: Webhook Body Nesting

# ❌ WRONG: Direct access to webhook data
email = _json["email"]  # KeyError!
✅ CORRECT: Webhook data under ["body"]

email = _json["body"]["email"]
✅ BETTER: Safe access with .get()

email = _json.get("body", {}).get("email", "no-email")

See: ERROR_PATTERNS.md for comprehensive error guide

Standard Library Reference

Most Useful Modules

# JSON operations
import json
data = json.loads(json_string)
json_output = json.dumps({"key": "value"})
Date/time

from datetime import datetime, timedelta
now = datetime.now()
tomorrow = now + timedelta(days=1)
formatted = now.strftime("%Y-%m-%d")
Regular expressions

import re
matches = re.findall(r'\d+', text)
cleaned = re.sub(r'[^\w\s]', '', text)
Base64 encoding

import base64
encoded = base64.b64encode(data).decode()
decoded = base64.b64decode(encoded)
Hashing

import hashlib
hash_value = hashlib.sha256(text.encode()).hexdigest()
URL parsing

import urllib.parse
params = urllib.parse.urlencode({"key": "value"})
parsed = urllib.parse.urlparse(url)
Statistics

from statistics import mean, median, stdev
average = mean([1, 2, 3, 4, 5])

See: STANDARD_LIBRARY.md for complete reference

Best Practices

1. Always Use .get() for Dictionary Access

# ✅ SAFE: Won't crash if field missing
value = item["json"].get("field", "default")
❌ RISKY: Crashes if field doesn't exist

value = item["json"]["field"]

2. Handle None/Null Values Explicitly

# ✅ GOOD: Default to 0 if None
amount = item["json"].get("amount") or 0
✅ GOOD: Check for None explicitly

text = item["json"].get("text")
if text is None:
    text = ""

3. Use List Comprehensions for Filtering

# ✅ PYTHONIC: List comprehension
valid = [item for item in items if item["json"].get("active")]
❌ VERBOSE: Manual loop

valid = []
for item in items:
    if item["json"].get("active"):
        valid.append(item)

4. Return Consistent Structure

# ✅ CONSISTENT: Always list with "json" key
return [{"json": result}]  # Single result
return results  # Multiple results (already formatted)
return []  # No results

5. Debug with print() Statements

# Debug statements appear in browser console (F12)
items = _input.all()
print(f"Processing {len(items)} items")
print(f"First item: {items[0] if items else 'None'}")

When to Use Python vs JavaScript

Use Python When:

Use JavaScript When:

Consider Other Nodes When:

Integration with Other Skills

Works With:

n8n Expression Syntax:

n8n MCP Tools Expert:

n8n Node Configuration:

n8n Workflow Patterns:

n8n Validation Expert:

n8n Code JavaScript:

Quick Reference Checklist

Before deploying Python Code nodes, verify:

Additional Resources

Related Files

n8n Documentation

Ready to write Python in n8n Code nodes - but consider JavaScript first! Use Python for specific needs, reference the error patterns guide to avoid common mistakes, and leverage the standard library effectively.