n8n-code-python

Python Code Node (Beta)

Expert guidance for writing Python code in n8n Code nodes.

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⚠️ 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

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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

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

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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)

# 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)

# Example: Add processing timestamp to each item
item = _input.item

return [{
    "json": {
        **item["json"],
        "processed": True,
        "processed_at": datetime.now().isoformat()
    }
}]

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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 (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 (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.

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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

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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

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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

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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?

• ✅ 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

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Common Patterns Overview

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

1. Data Transformation

Transform all items with list comprehensions

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

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

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

items = _input.all()
validated = []

for item in items:
    data = item["json"]
    errors = []

    # Validate fields
    if not data.get("email"):
        errors.

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