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 recomm

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 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) 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 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 (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 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"] . See : [DATA ACCESS.md](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"] Why : Webhook node wraps all request data under body property. This includes POST data, query parameters, and JSON payloads. See : [DATA ACCESS.md](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](ERROR PATTERNS.md) 2 for detailed error solutions Critical Limitation: No External Libraries 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 See : [STANDARD LIBRARY.md](STANDARD LIBRARY.md) for complete reference Common Patterns Overview Based on production workflows, the most useful Python patterns are: 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 with re 4. Data Validation Validate and clean data, attach error lists 5. Statistical Analysis Calculate mean/median/stdev with the statistics module Copy ready snippets for all five live in [COMMON PATTERNS.md](COMMON PATTERNS.md quick pattern snippets), 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 1. Importing external libraries (Python specific) → import requests raises ModuleNotFoundError . Use the HTTP Request node or JavaScript instead. 2. Empty code or missing return → every path must end with return [{"json": ...}] . 3. Incorrect return format → wrap in a list: {"json": {...}} becomes [{"json": {...}}] . 4. KeyError on dictionary access → use .get() : json.get("user", {}).get("name", "Unknown") . 5. Webhook body nesting → read via ["body"] : json.get("body", {}).get("email", "no email") . See : [ERROR PATTERNS.md](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](STANDARD LIBRARY.md quick reference most useful modules). Best Practices 1. Always Use .get() for Dictionary Access 2. Handle None/Null Values Explicitly 3. Use List Comprehensions for Filtering 4. Return Consistent Structure 5. Debug with print() Statements 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 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"}) Validate code: validate node({nodeType: "nodes base.code", config: {...}}) n8n Node Configuration : Mode selection (All Items vs Each Item) Language selection (Python vs JavaScript) Understanding property dependencies n8n Workflow Patterns : Code nodes in transformation step When to use Python vs JavaScript in patterns n8n Validation Expert : Validate Code node configuration Handle validation errors Auto fix common issues n8n Code JavaScript : When to use JavaScript instead Comparison of JavaScript vs Python features Migration from Python to JavaScript Quick Reference Checklist Before deploying Python Code nodes, verify: [ ] Considered JavaScript first Using Python only when necessary [ ] Code is not empty Must have meaningful logic [ ] Return statement exists Must return list of dictionaries [ ] Proper return format Each item: {"json": {...}} [ ] Data access correct Using input.all() , input.first() , or input.item [ ] No external imports Only standard library (json, datetime, re, etc.) [ ] Safe dictionary access Using .get() to avoid KeyError [ ] Webhook data Access via ["body"] if from webhook [ ] Mode selection "All Items" for most cases [ ] Output consistent All code paths return same structure Additional Resources Related Files [DATA ACCESS.md](DATA ACCESS.md) Comprehensive Python data access patterns [COMMON PATTERNS.md](COMMON PATTERNS.md) 10 Python patterns for n8n [ERROR PATTERNS.md](ERROR PATTERNS.md) Top 5 errors and solutions [STANDARD LIBRARY.md](STANDARD LIBRARY.md) Complete standard library reference n8n Documentation Code Node Guide: https://docs.n8n.io/code/code node/ Python in n8n: https://docs.n8n.io/code/builtin/python modules/ 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.

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