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. Use this skill when the user specifically requests Python for an n8n Code node. Note — JavaScript is recommended for 95% of use cases — only use Python when the user explicitly prefers it or the task requires Python-specific standard library capabilities (regex, hashlib, statistics). EXCEPTION — for Python in the AI-agent-callable Custom Code Tool (@n8n/n8n-nodes-langchain.toolCode), use the n8n-code-tool skill instead (input is _query, return must be a string).
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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. Use this skill when the user specifically requests Python for an n8n Code node. Note — JavaScript is recommended for 95% of use cases — only use Python when the user explicitly prefers it or the task requires Python-specific standard library capabilities (regex, hashlib, statistics). EXCEPTION — for Python in the AI-agent-callable Custom Code Tool (@n8n/n8n-nodes-langchain.toolCode), use the n8n-code-tool skill instead (input is _query, return must be a string).
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 (this.helpers.httpRequest, etc.)
Luxon DateTime library for advanced date/time operations
No external library limitations
Better n8n documentation and community support
Quick Start
# Basic template for Python Code nodes
items = _input.all()
# Process data
processed = []
for item in items:
processed.append({
"json": {
**item["json"],
: ,
: datetime.now().isoformat()
}
})
processed
"processed"
True
"timestamp"
return
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 _json directly)
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 _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 else0
}
}]
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 (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
Access input data through underscore-prefixed variables. Each item is a dict shaped {"json": {...}}, so the actual fields live under ["json"].
# Pattern 1: _input.all() - Most common. Arrays, batch ops, aggregations
all_items = _input.all() # list of {"json": {...}} dicts# Pattern 2: _input.first() - Very common. Single objects, API responses
data = _input.first()["json"] # built-in safety vs all_items[0]# Pattern 3: _input.item - "Run Once for Each Item" mode ONLY
current = _input.item["json"] # None/error in All Items mode# Pattern 4: _node - Reference a specific named node
webhook_data = _node["Webhook"]["json"]
http_data = _node["HTTP Request"]["json"]
See: DATA_ACCESS.md for the comprehensive guide — six _input.all() recipes (filter, transform, aggregate, sort, group, deduplicate), _input.first() and _input.item examples, multi-node combining, the JS-vs-Python variable table, and the decision tree.
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.
MOST IMPORTANT PYTHON LIMITATION: Cannot import external packages on default installs.
Self-hosted exception: external package availability depends entirely on the instance's Python runner configuration. If the user states their self-hosted instance has specific packages available in the Python runner environment, use them — don't refuse. When unsure, ask or write standard-library-only code.
❌ NOT available (raise ModuleNotFoundError): requests, pandas, numpy, scipy, bs4/BeautifulSoup, lxml.
✅ Available (standard library only): json, datetime, re, base64, hashlib, urllib.parse, math, random, statistics.
Workarounds
Need HTTP requests?
✅ Use HTTP Request node before Code node
✅ Or switch to JavaScript and use this.helpers.httpRequest() (the bare $helpers global is undefined in the task-runner sandbox)
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
Based on production workflows, the most useful Python patterns are:
Data Transformation - Transform all items with list comprehensions
Filtering & Aggregation - Sum, filter, count with built-in functions
String Processing with Regex - Extract patterns from text with re
Data Validation - Validate and clean data, attach error lists
Statistical Analysis - Calculate mean/median/stdev with the statistics module
Copy-ready snippets for all five live in COMMON_PATTERNS.md, alongside 10 fully detailed production patterns (multi-source aggregation, markdown parsing, JSON comparison, CRM normalization, dictionary lookup, top-N filtering, and more).
Error Prevention - Top 5 Mistakes
Importing external libraries (Python-specific) → import requests raises ModuleNotFoundError. Use the HTTP Request node or JavaScript instead.
Empty code or missing return → every path must end with return [{"json": ...}].
Incorrect return format → wrap in a list: {"json": {...}} becomes [{"json": {...}}].
KeyError on dictionary access → use .get(): _json.get("user", {}).get("name", "Unknown").
Webhook body nesting → read via ["body"]: _json.get("body", {}).get("email", "no-email").
See: ERROR_PATTERNS.md for the comprehensive guide — each error with wrong-vs-right code, error messages, nested-access fixes, an AttributeError bonus case, a prevention checklist, and a quick-fix table.
Standard Library Reference
Most useful modules: json (parse/generate), datetime (dates + timedelta), re (regex), base64 (encode/decode), hashlib (hashing), urllib.parse (URL ops), and statistics (mean/median/stdev). Also available: math, random, collections, itertools, functools.
For a condensed cheat sheet plus full per-module examples, see STANDARD_LIBRARY.md.
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") or0# ✅ GOOD: Check for None explicitly
text = item["json"].get("text")
if text isNone:
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" keyreturn [{"json": result}] # Single resultreturn 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'}")
Production Gotchas
SplitInBatches Loop Semantics
The SplitInBatches node has two outputs:
main[0] = done — fires ONCE after all batches complete
main[1] = each batch — fires for every batch (the loop body)
Always add a Limit 1 node after the done output.
Correct Node Reference Syntax
# ❌ WRONG
data = _node['HTTP Request']['json']
# ✅ CORRECT - call .first() then access json
data = _node['HTTP Request'].first()['json']
Cross-Iteration Data Not Available in Python
$getWorkflowStaticData('global') may not be available in Python Beta mode. If you need to accumulate data across SplitInBatches iterations, use a JavaScript Code node for the accumulation logic instead.
When to Use Python vs JavaScript
Use Python When:
✅ You need statistics module for statistical operations
✅ You're significantly more comfortable with Python syntax
✅ Your logic maps well to list comprehensions
✅ You need specific standard library functions
Use JavaScript When:
✅ You need HTTP requests (this.helpers.httpRequest())
✅ You need advanced date/time (DateTime/Luxon)
✅ You want better n8n integration
✅ For 95% of use cases (recommended)
Consider Other Nodes When:
❌ Simple field mapping → Use Set node
❌ Basic filtering → Use Filter node
❌ Simple conditionals → Use IF or Switch node
❌ HTTP requests only → Use HTTP Request node
Integration with Other Skills
Works With:
n8n Expression Syntax:
Expressions use {{ }} syntax in other nodes
Code nodes use Python directly (no {{ }})
When to use expressions vs code
n8n MCP Tools Expert:
How to find Code node: search_nodes({query: "code"})
Get configuration help: get_node({nodeType: "nodes-base.code"})
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.
