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feat: Complete Skill #7 - n8n Code Python
Implements comprehensive Python Code node guidance with critical focus on "NO external libraries" limitation. ## Skill #7 - n8n Code Python **Critical Message**: Use JavaScript for 95% of use cases. Python in n8n has NO external libraries (no requests, pandas, numpy). ### Files Created **Core Skill Files (6 files, 4,205 lines total)**: 1. **SKILL.md** (748 lines) - When to use Python vs JavaScript (95% JavaScript recommendation) - Critical limitation: NO external libraries - Mode selection (All Items vs Each Item) - Data access overview (_input, _json, _node) - Return format requirements - Standard library overview 2. **DATA_ACCESS.md** (702 lines) - _input.all() - Process all items - _input.first() - Get first item - _input.item - Current item (Each Item mode only) - _node["Name"] - Reference other nodes - Webhook body structure (data under ["body"]) - Pattern selection guide - Python vs JavaScript comparison 3. **STANDARD_LIBRARY.md** (974 lines) - Complete reference for available modules - json - JSON parsing and generation - datetime - Date/time operations - re - Regular expressions - base64 - Encoding/decoding - hashlib - Hashing (MD5, SHA256) - urllib.parse - URL operations - math, random, statistics - What's NOT available (requests, pandas, numpy, etc.) - Workarounds for missing libraries 4. **COMMON_PATTERNS.md** (794 lines) - 10 production-tested Python patterns - Multi-source data aggregation - Regex-based filtering - Markdown to structured data - JSON object comparison - CRM data transformation - Release notes processing - Array transformation - Dictionary lookup - Top N filtering - String aggregation - Python vs JavaScript pattern comparison 5. **ERROR_PATTERNS.md** (601 lines) - Top 5 Python-specific errors with solutions - Error #1: ModuleNotFoundError (THE critical Python error) - Error #2: Empty code / missing return - Error #3: KeyError (use .get() instead) - Error #4: IndexError (check bounds first) - Error #5: Incorrect return format - Error prevention checklist - Quick fix reference table - Testing patterns 6. **README.md** (386 lines) - Skill metadata and activation triggers - "JavaScript First" recommendation prominent - What this skill teaches - File structure overview - Integration with other skills - Success metrics checklist - Quick reference guide - Common use cases - Limitations and workarounds - Best practices **Evaluations (5 scenarios)**: 1. **eval-001-module-import-error.json** - Tests understanding of external library limitation - Scenario: ModuleNotFoundError with requests - Expected: Recommend JavaScript or HTTP Request node 2. **eval-002-dictionary-keyerror.json** - Tests safe dictionary access with .get() - Scenario: KeyError when accessing missing field - Expected: Use .get() with default values 3. **eval-003-webhook-body-gotcha.json** - Tests webhook data under ["body"] understanding - Scenario: KeyError when accessing webhook data directly - Expected: Access via data.get("body", {}) 4. **eval-004-return-format-error.json** - Tests proper return format requirement - Scenario: Returning plain dict instead of array - Expected: Return [{"json": {...}}] 5. **eval-005-standard-library-usage.json** - Tests knowledge of available modules - Scenario: What modules for JSON, hashing, dates, regex - Expected: json, hashlib, datetime, re (standard library only) ### Key Features **Critical Limitations Emphasized**: - NO external libraries (no requests, pandas, numpy) - JavaScript recommended for 95% of use cases - Only standard library available - ModuleNotFoundError is #1 Python error **Python-Specific Syntax**: - Underscore prefix: _input, _json, _node (vs $ in JavaScript) - Dictionary access: _json["body"]["field"] (vs dot notation) - Safe access: .get() method with defaults **Complete Standard Library Coverage**: - 15+ modules documented with examples - json, datetime, re, base64, hashlib, urllib.parse - math, random, statistics, collections - Clear list of what's NOT available - Workarounds for missing functionality **Production Patterns**: - 10 tested patterns adapted from JavaScript - Python-specific implementations - List comprehensions and dictionary operations - Standard library usage examples **Error Prevention Focus**: - Top 5 errors cover majority of failures - ModuleNotFoundError prominently featured - Safe dictionary access (.get()) - Proper return format emphasized - Error prevention checklist ### Integration Works seamlessly with: - **n8n Code JavaScript**: Compare approaches, know when to use which - **n8n Expression Syntax**: Different from {{}} expressions - **n8n MCP Tools Expert**: Validate Code node configurations - **n8n Workflow Patterns**: Code nodes in larger workflows - **n8n Node Configuration**: Configure mode and connections ### Statistics - **6 skill files**: 4,205 lines total - **5 evaluations**: Cover critical Python scenarios - **10 patterns**: Production-tested Python code - **15+ modules**: Standard library coverage - **5 top errors**: Prevention and solutions ### Design Principles 1. **JavaScript First**: 95% recommendation throughout 2. **Critical Limitation**: NO external libraries emphasized everywhere 3. **Safe Patterns**: .get() for dicts, bounds checking for lists 4. **Proper Format**: [{"json": {...}}] return format 5. **Standard Library**: Complete reference with examples 6. **Error Prevention**: Top 5 errors with solutions ### Recommendation **Use JavaScript Code node for 95% of use cases.** Use Python only when: - Complex Python-specific logic required - Python standard library features needed - Team more comfortable with Python than JavaScript For HTTP requests, date operations, and most transformations → Use JavaScript. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> Conceived by Romuald Członkowski - https://www.aiadvisors.pl/en
This commit is contained in:
czlonkowski
28
evaluations/code-python/eval-001-module-import-error.json
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evaluations/code-python/eval-001-module-import-error.json
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{
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"id": "code-python-001",
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"skill": "n8n-code-python",
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"name": "Module Import Error - External Libraries Not Available",
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"description": "Tests understanding that Python Code nodes have NO external libraries",
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"query": "I'm writing a Python Code node to fetch data from an API. Here's my code:\n\n```python\nimport requests\n\nurl = \"https://api.example.com/users\"\nresponse = requests.get(url)\ndata = response.json()\n\nreturn [{\"json\": data}]\n```\n\nBut I'm getting a ModuleNotFoundError. What's wrong?",
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"expected_behavior": [
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"Immediately identify this is the #1 Python Code node limitation",
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"Explain that NO external libraries are available (no requests, pandas, numpy)",
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"Recommend using JavaScript Code node instead (95% recommendation)",
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"Suggest alternative: Use HTTP Request node BEFORE Python Code node",
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"Mention urllib.request from standard library as limited workaround",
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"Emphasize JavaScript has $helpers.httpRequest() built-in",
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"Reference ERROR_PATTERNS.md Error #1"
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],
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"expected_output_includes": [
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"ModuleNotFoundError",
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"NO external libraries",
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"Use JavaScript",
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"HTTP Request node",
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"standard library only"
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],
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"should_not_include": [
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"pip install",
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"install requests",
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"add requirements.txt"
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]
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}
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26
evaluations/code-python/eval-002-dictionary-keyerror.json
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evaluations/code-python/eval-002-dictionary-keyerror.json
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{
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"id": "code-python-002",
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"skill": "n8n-code-python",
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"name": "Dictionary KeyError - Use .get() Instead",
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"description": "Tests understanding of safe dictionary access with .get()",
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"query": "My Python Code node is failing with this error:\n\n```\nKeyError: 'email'\n```\n\nHere's my code:\n\n```python\nitem = _input.first()[\"json\"]\n\nname = item[\"name\"]\nemail = item[\"email\"]\nage = item[\"age\"]\n\nreturn [{\n \"json\": {\n \"name\": name,\n \"email\": email,\n \"age\": age\n }\n}]\n```\n\nHow do I fix this?",
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"expected_behavior": [
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"Identify KeyError is from direct dictionary key access",
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"Explain that some items may not have 'email' field",
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"Recommend using .get() method with default values",
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"Show corrected code with .get()",
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"Mention this is Error #3 in ERROR_PATTERNS.md",
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"Explain difference between item['key'] and item.get('key', default)"
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],
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"expected_output_includes": [
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"KeyError",
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".get()",
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"default value",
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"item.get(\"email\", \"default\")"
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],
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"should_not_include": [
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"try/except KeyError",
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"if 'email' in item"
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],
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"correct_code_pattern": "item.get(\"email\", "
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}
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23
evaluations/code-python/eval-003-webhook-body-gotcha.json
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evaluations/code-python/eval-003-webhook-body-gotcha.json
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{
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"id": "code-python-003",
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"skill": "n8n-code-python",
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"name": "Webhook Body Gotcha - Data Under [\"body\"]",
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"description": "Tests understanding that webhook data is nested under [\"body\"] key",
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"query": "I have a Webhook node receiving this JSON:\n\n```json\n{\n \"name\": \"Alice\",\n \"email\": \"alice@example.com\",\n \"age\": 30\n}\n```\n\nIn my Python Code node, I'm trying to access the data:\n\n```python\ndata = _input.first()[\"json\"]\n\nname = data[\"name\"] # KeyError!\nemail = data[\"email\"] # KeyError!\n\nreturn [{\"json\": {\"name\": name, \"email\": email}}]\n```\n\nBut I'm getting KeyError. The webhook is receiving data correctly. What's wrong?",
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"expected_behavior": [
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"Immediately recognize this is the webhook .body gotcha",
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"Explain webhook node wraps incoming data under 'body' key",
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"Show the actual structure with headers, params, query, body",
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"Provide corrected code accessing data[\"body\"]",
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"Mention this is a CRITICAL gotcha highlighted in DATA_ACCESS.md",
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"Recommend using .get() for safe access: data.get(\"body\", {})"
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],
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"expected_output_includes": [
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"[\"body\"]",
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"webhook wraps",
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"nested under body",
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"data.get(\"body\", {})"
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],
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"correct_code_pattern": "data.get(\"body\", {})",
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"should_emphasize": "This is the MOST COMMON webhook mistake"
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}
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25
evaluations/code-python/eval-004-return-format-error.json
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evaluations/code-python/eval-004-return-format-error.json
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{
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"id": "code-python-004",
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"skill": "n8n-code-python",
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"name": "Return Format Error - Must Return Array with json Key",
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"description": "Tests understanding of correct return format for n8n Code nodes",
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"query": "My Python Code node isn't outputting any data to the next node. Here's my code:\n\n```python\nall_items = _input.all()\n\ntotal = sum(item[\"json\"].get(\"amount\", 0) for item in all_items)\naverage = total / len(all_items) if all_items else 0\n\nreturn {\n \"total\": total,\n \"count\": len(all_items),\n \"average\": average\n}\n```\n\nThe code runs without errors, but the next node receives nothing. What's wrong?",
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"expected_behavior": [
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"Identify incorrect return format (missing array wrapper and json key)",
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"Explain n8n expects array of objects with 'json' key",
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"Show corrected code with proper format: [{\"json\": {...}}]",
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"Mention this is Error #5 in ERROR_PATTERNS.md",
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"Emphasize: MUST return array, even for single result"
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],
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"expected_output_includes": [
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"[{\"json\":",
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"array",
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"return format",
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"must wrap"
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],
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"correct_code_pattern": "return [{\"json\": {",
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"should_not_include": [
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"return {",
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"return total"
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]
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}
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36
evaluations/code-python/eval-005-standard-library-usage.json
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evaluations/code-python/eval-005-standard-library-usage.json
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{
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"id": "code-python-005",
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"skill": "n8n-code-python",
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"name": "Standard Library Usage - Know What's Available",
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"description": "Tests knowledge of available standard library modules",
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"query": "I need to do the following in a Python Code node:\n\n1. Parse JSON from a string\n2. Calculate SHA256 hash of some data\n3. Format the current date as ISO string\n4. Extract email addresses using regex\n\nWhat modules are available? Can I use external libraries?",
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"expected_behavior": [
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"Emphasize NO external libraries available",
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"List available standard library modules for these tasks",
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"json module for JSON parsing",
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"hashlib module for SHA256",
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"datetime module for dates",
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"re module for regex",
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"Provide code examples for each task",
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"Reference STANDARD_LIBRARY.md"
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],
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"expected_output_includes": [
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"import json",
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"import hashlib",
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"import datetime",
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"import re",
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"standard library only",
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"NO external libraries"
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],
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"should_not_include": [
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"import requests",
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"import pandas",
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"pip install"
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],
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"correct_modules": [
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"json",
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"hashlib",
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"datetime",
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"re"
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]
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}
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794
skills/n8n-code-python/COMMON_PATTERNS.md
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skills/n8n-code-python/COMMON_PATTERNS.md
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# Common Patterns - Python Code Node
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Production-tested Python patterns for n8n Code nodes.
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---
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## ⚠️ Important: JavaScript First
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**Use JavaScript for 95% of use cases.**
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Python in n8n has **NO external libraries** (no requests, pandas, numpy).
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Only use Python when:
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- You have complex Python-specific logic
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- You need Python's standard library features
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- You're more comfortable with Python than JavaScript
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For most workflows, **JavaScript is the better choice**.
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---
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## Pattern Overview
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These 10 patterns cover common n8n Code node scenarios using Python:
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1. **Multi-Source Data Aggregation** - Combine data from multiple nodes
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2. **Regex-Based Filtering** - Filter items using pattern matching
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3. **Markdown to Structured Data** - Parse markdown into structured format
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4. **JSON Object Comparison** - Compare two JSON objects for changes
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5. **CRM Data Transformation** - Transform CRM data to standard format
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6. **Release Notes Processing** - Parse and categorize release notes
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7. **Array Transformation** - Reshape arrays and extract fields
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8. **Dictionary Lookup** - Create and use lookup dictionaries
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9. **Top N Filtering** - Get top items by score/value
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10. **String Aggregation** - Aggregate strings with formatting
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---
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## Pattern 1: Multi-Source Data Aggregation
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**Use case**: Combine data from multiple sources (APIs, webhooks, databases).
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**Scenario**: Aggregate news articles from multiple sources.
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### Implementation
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```python
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from datetime import datetime
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all_items = _input.all()
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processed_articles = []
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for item in all_items:
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source_name = item["json"].get("name", "Unknown")
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source_data = item["json"]
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# Process Hacker News source
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if source_name == "Hacker News" and source_data.get("hits"):
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for hit in source_data["hits"]:
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processed_articles.append({
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"title": hit.get("title", "No title"),
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"url": hit.get("url", ""),
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"summary": hit.get("story_text") or "No summary",
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"source": "Hacker News",
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"score": hit.get("points", 0),
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"fetched_at": datetime.now().isoformat()
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})
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# Process Reddit source
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elif source_name == "Reddit" and source_data.get("data"):
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for post in source_data["data"].get("children", []):
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post_data = post.get("data", {})
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processed_articles.append({
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"title": post_data.get("title", "No title"),
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"url": post_data.get("url", ""),
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"summary": post_data.get("selftext", "")[:200],
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"source": "Reddit",
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"score": post_data.get("score", 0),
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"fetched_at": datetime.now().isoformat()
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})
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# Sort by score descending
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processed_articles.sort(key=lambda x: x["score"], reverse=True)
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# Return as n8n items
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return [{"json": article} for article in processed_articles]
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```
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### Key Techniques
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- Process multiple data sources in one loop
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- Normalize different data structures
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- Use datetime for timestamps
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- Sort by criteria
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- Return properly formatted items
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---
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## Pattern 2: Regex-Based Filtering
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**Use case**: Filter items based on pattern matching in text fields.
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**Scenario**: Filter support tickets by priority keywords.
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### Implementation
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```python
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import re
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all_items = _input.all()
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priority_tickets = []
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# High priority keywords pattern
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high_priority_pattern = re.compile(
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r'\b(urgent|critical|emergency|asap|down|outage|broken)\b',
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re.IGNORECASE
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)
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for item in all_items:
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ticket = item["json"]
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# Check subject and description
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subject = ticket.get("subject", "")
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description = ticket.get("description", "")
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combined_text = f"{subject} {description}"
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# Find matches
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matches = high_priority_pattern.findall(combined_text)
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if matches:
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priority_tickets.append({
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"json": {
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**ticket,
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"priority": "high",
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"matched_keywords": list(set(matches)),
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"keyword_count": len(matches)
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}
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})
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else:
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priority_tickets.append({
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"json": {
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**ticket,
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"priority": "normal",
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"matched_keywords": [],
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"keyword_count": 0
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}
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})
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# Sort by keyword count (most urgent first)
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priority_tickets.sort(key=lambda x: x["json"]["keyword_count"], reverse=True)
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return priority_tickets
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```
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### Key Techniques
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- Use re.compile() for reusable patterns
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- re.IGNORECASE for case-insensitive matching
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- Combine multiple text fields for searching
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- Extract and deduplicate matches
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- Sort by priority indicators
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---
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## Pattern 3: Markdown to Structured Data
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**Use case**: Parse markdown text into structured data.
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**Scenario**: Extract tasks from markdown checklist.
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### Implementation
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```python
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import re
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markdown_text = _input.first()["json"]["body"].get("markdown", "")
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# Parse markdown checklist
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tasks = []
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lines = markdown_text.split("\n")
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for line in lines:
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# Match: - [ ] Task or - [x] Task
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match = re.match(r'^\s*-\s*\[([ x])\]\s*(.+)$', line, re.IGNORECASE)
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if match:
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checked = match.group(1).lower() == 'x'
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task_text = match.group(2).strip()
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# Extract priority if present (e.g., [P1], [HIGH])
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priority_match = re.search(r'\[(P\d|HIGH|MEDIUM|LOW)\]', task_text, re.IGNORECASE)
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priority = priority_match.group(1).upper() if priority_match else "NORMAL"
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# Remove priority tag from text
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clean_text = re.sub(r'\[(P\d|HIGH|MEDIUM|LOW)\]', '', task_text, flags=re.IGNORECASE).strip()
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tasks.append({
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"text": clean_text,
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"completed": checked,
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"priority": priority,
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"original_line": line.strip()
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})
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return [{
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"json": {
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"tasks": tasks,
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"total": len(tasks),
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"completed": sum(1 for t in tasks if t["completed"]),
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"pending": sum(1 for t in tasks if not t["completed"])
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}
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}]
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```
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### Key Techniques
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- Line-by-line parsing
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- Multiple regex patterns for extraction
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- Extract metadata from text
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- Calculate summary statistics
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- Return structured data
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---
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## Pattern 4: JSON Object Comparison
|
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**Use case**: Compare two JSON objects to find differences.
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|
||||
**Scenario**: Compare old and new user profile data.
|
||||
|
||||
### Implementation
|
||||
|
||||
```python
|
||||
import json
|
||||
|
||||
all_items = _input.all()
|
||||
|
||||
# Assume first item is old data, second is new data
|
||||
old_data = all_items[0]["json"] if len(all_items) > 0 else {}
|
||||
new_data = all_items[1]["json"] if len(all_items) > 1 else {}
|
||||
|
||||
changes = {
|
||||
"added": {},
|
||||
"removed": {},
|
||||
"modified": {},
|
||||
"unchanged": {}
|
||||
}
|
||||
|
||||
# Find all unique keys
|
||||
all_keys = set(old_data.keys()) | set(new_data.keys())
|
||||
|
||||
for key in all_keys:
|
||||
old_value = old_data.get(key)
|
||||
new_value = new_data.get(key)
|
||||
|
||||
if key not in old_data:
|
||||
# Added field
|
||||
changes["added"][key] = new_value
|
||||
elif key not in new_data:
|
||||
# Removed field
|
||||
changes["removed"][key] = old_value
|
||||
elif old_value != new_value:
|
||||
# Modified field
|
||||
changes["modified"][key] = {
|
||||
"old": old_value,
|
||||
"new": new_value
|
||||
}
|
||||
else:
|
||||
# Unchanged field
|
||||
changes["unchanged"][key] = old_value
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
"changes": changes,
|
||||
"summary": {
|
||||
"added_count": len(changes["added"]),
|
||||
"removed_count": len(changes["removed"]),
|
||||
"modified_count": len(changes["modified"]),
|
||||
"unchanged_count": len(changes["unchanged"]),
|
||||
"has_changes": len(changes["added"]) > 0 or len(changes["removed"]) > 0 or len(changes["modified"]) > 0
|
||||
}
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
### Key Techniques
|
||||
|
||||
- Set operations for key comparison
|
||||
- Dictionary .get() for safe access
|
||||
- Categorize changes by type
|
||||
- Create summary statistics
|
||||
- Return detailed comparison
|
||||
|
||||
---
|
||||
|
||||
## Pattern 5: CRM Data Transformation
|
||||
|
||||
**Use case**: Transform CRM data to standard format.
|
||||
|
||||
**Scenario**: Normalize data from different CRM systems.
|
||||
|
||||
### Implementation
|
||||
|
||||
```python
|
||||
from datetime import datetime
|
||||
import re
|
||||
|
||||
all_items = _input.all()
|
||||
normalized_contacts = []
|
||||
|
||||
for item in all_items:
|
||||
raw_contact = item["json"]
|
||||
source = raw_contact.get("source", "unknown")
|
||||
|
||||
# Normalize email
|
||||
email = raw_contact.get("email", "").lower().strip()
|
||||
|
||||
# Normalize phone (remove non-digits)
|
||||
phone_raw = raw_contact.get("phone", "")
|
||||
phone = re.sub(r'\D', '', phone_raw)
|
||||
|
||||
# Parse name
|
||||
if "full_name" in raw_contact:
|
||||
name_parts = raw_contact["full_name"].split(" ", 1)
|
||||
first_name = name_parts[0] if len(name_parts) > 0 else ""
|
||||
last_name = name_parts[1] if len(name_parts) > 1 else ""
|
||||
else:
|
||||
first_name = raw_contact.get("first_name", "")
|
||||
last_name = raw_contact.get("last_name", "")
|
||||
|
||||
# Normalize status
|
||||
status_raw = raw_contact.get("status", "").lower()
|
||||
status = "active" if status_raw in ["active", "enabled", "true", "1"] else "inactive"
|
||||
|
||||
# Create normalized contact
|
||||
normalized_contacts.append({
|
||||
"json": {
|
||||
"id": raw_contact.get("id", ""),
|
||||
"first_name": first_name.strip(),
|
||||
"last_name": last_name.strip(),
|
||||
"full_name": f"{first_name} {last_name}".strip(),
|
||||
"email": email,
|
||||
"phone": phone,
|
||||
"status": status,
|
||||
"source": source,
|
||||
"normalized_at": datetime.now().isoformat(),
|
||||
"original_data": raw_contact
|
||||
}
|
||||
})
|
||||
|
||||
return normalized_contacts
|
||||
```
|
||||
|
||||
### Key Techniques
|
||||
|
||||
- Multiple field name variations handling
|
||||
- String cleaning and normalization
|
||||
- Regex for phone number cleaning
|
||||
- Name parsing logic
|
||||
- Status normalization
|
||||
- Preserve original data
|
||||
|
||||
---
|
||||
|
||||
## Pattern 6: Release Notes Processing
|
||||
|
||||
**Use case**: Parse release notes and categorize changes.
|
||||
|
||||
**Scenario**: Extract features, fixes, and breaking changes from release notes.
|
||||
|
||||
### Implementation
|
||||
|
||||
```python
|
||||
import re
|
||||
|
||||
release_notes = _input.first()["json"]["body"].get("notes", "")
|
||||
|
||||
categories = {
|
||||
"features": [],
|
||||
"fixes": [],
|
||||
"breaking": [],
|
||||
"other": []
|
||||
}
|
||||
|
||||
# Split into lines
|
||||
lines = release_notes.split("\n")
|
||||
|
||||
for line in lines:
|
||||
line = line.strip()
|
||||
|
||||
# Skip empty lines and headers
|
||||
if not line or line.startswith("#"):
|
||||
continue
|
||||
|
||||
# Remove bullet points
|
||||
clean_line = re.sub(r'^[\*\-\+]\s*', '', line)
|
||||
|
||||
# Categorize
|
||||
if re.search(r'\b(feature|add|new)\b', clean_line, re.IGNORECASE):
|
||||
categories["features"].append(clean_line)
|
||||
elif re.search(r'\b(fix|bug|patch|resolve)\b', clean_line, re.IGNORECASE):
|
||||
categories["fixes"].append(clean_line)
|
||||
elif re.search(r'\b(breaking|deprecated|remove)\b', clean_line, re.IGNORECASE):
|
||||
categories["breaking"].append(clean_line)
|
||||
else:
|
||||
categories["other"].append(clean_line)
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
"categories": categories,
|
||||
"summary": {
|
||||
"features": len(categories["features"]),
|
||||
"fixes": len(categories["fixes"]),
|
||||
"breaking": len(categories["breaking"]),
|
||||
"other": len(categories["other"]),
|
||||
"total": sum(len(v) for v in categories.values())
|
||||
}
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
### Key Techniques
|
||||
|
||||
- Line-by-line parsing
|
||||
- Pattern-based categorization
|
||||
- Bullet point removal
|
||||
- Skip headers and empty lines
|
||||
- Summary statistics
|
||||
|
||||
---
|
||||
|
||||
## Pattern 7: Array Transformation
|
||||
|
||||
**Use case**: Reshape arrays and extract specific fields.
|
||||
|
||||
**Scenario**: Transform user data array to extract specific fields.
|
||||
|
||||
### Implementation
|
||||
|
||||
```python
|
||||
all_items = _input.all()
|
||||
|
||||
# Extract and transform
|
||||
transformed = []
|
||||
|
||||
for item in all_items:
|
||||
user = item["json"]
|
||||
|
||||
# Extract nested fields
|
||||
profile = user.get("profile", {})
|
||||
settings = user.get("settings", {})
|
||||
|
||||
transformed.append({
|
||||
"json": {
|
||||
"user_id": user.get("id"),
|
||||
"email": user.get("email"),
|
||||
"name": profile.get("name", "Unknown"),
|
||||
"avatar": profile.get("avatar_url"),
|
||||
"bio": profile.get("bio", "")[:100], # Truncate to 100 chars
|
||||
"notifications_enabled": settings.get("notifications", True),
|
||||
"theme": settings.get("theme", "light"),
|
||||
"created_at": user.get("created_at"),
|
||||
"last_login": user.get("last_login_at")
|
||||
}
|
||||
})
|
||||
|
||||
return transformed
|
||||
```
|
||||
|
||||
### Key Techniques
|
||||
|
||||
- Field extraction from nested objects
|
||||
- Default values with .get()
|
||||
- String truncation
|
||||
- Flattening nested structures
|
||||
|
||||
---
|
||||
|
||||
## Pattern 8: Dictionary Lookup
|
||||
|
||||
**Use case**: Create lookup dictionary for fast data access.
|
||||
|
||||
**Scenario**: Look up user details by ID.
|
||||
|
||||
### Implementation
|
||||
|
||||
```python
|
||||
all_items = _input.all()
|
||||
|
||||
# Build lookup dictionary
|
||||
users_by_id = {}
|
||||
|
||||
for item in all_items:
|
||||
user = item["json"]
|
||||
user_id = user.get("id")
|
||||
|
||||
if user_id:
|
||||
users_by_id[user_id] = {
|
||||
"name": user.get("name"),
|
||||
"email": user.get("email"),
|
||||
"status": user.get("status")
|
||||
}
|
||||
|
||||
# Example: Look up specific users
|
||||
lookup_ids = [1, 3, 5]
|
||||
looked_up = []
|
||||
|
||||
for user_id in lookup_ids:
|
||||
if user_id in users_by_id:
|
||||
looked_up.append({
|
||||
"json": {
|
||||
"id": user_id,
|
||||
**users_by_id[user_id],
|
||||
"found": True
|
||||
}
|
||||
})
|
||||
else:
|
||||
looked_up.append({
|
||||
"json": {
|
||||
"id": user_id,
|
||||
"found": False
|
||||
}
|
||||
})
|
||||
|
||||
return looked_up
|
||||
```
|
||||
|
||||
### Key Techniques
|
||||
|
||||
- Dictionary comprehension alternative
|
||||
- O(1) lookup time
|
||||
- Handle missing keys gracefully
|
||||
- Preserve lookup order
|
||||
|
||||
---
|
||||
|
||||
## Pattern 9: Top N Filtering
|
||||
|
||||
**Use case**: Get top items by score or value.
|
||||
|
||||
**Scenario**: Get top 10 products by sales.
|
||||
|
||||
### Implementation
|
||||
|
||||
```python
|
||||
all_items = _input.all()
|
||||
|
||||
# Extract products with sales
|
||||
products = []
|
||||
|
||||
for item in all_items:
|
||||
product = item["json"]
|
||||
products.append({
|
||||
"id": product.get("id"),
|
||||
"name": product.get("name"),
|
||||
"sales": product.get("sales", 0),
|
||||
"revenue": product.get("revenue", 0.0),
|
||||
"category": product.get("category")
|
||||
})
|
||||
|
||||
# Sort by sales descending
|
||||
products.sort(key=lambda p: p["sales"], reverse=True)
|
||||
|
||||
# Get top 10
|
||||
top_10 = products[:10]
|
||||
|
||||
return [
|
||||
{
|
||||
"json": {
|
||||
**product,
|
||||
"rank": index + 1
|
||||
}
|
||||
}
|
||||
for index, product in enumerate(top_10)
|
||||
]
|
||||
```
|
||||
|
||||
### Key Techniques
|
||||
|
||||
- List sorting with custom key
|
||||
- Slicing for top N
|
||||
- Add ranking information
|
||||
- Enumerate for index
|
||||
|
||||
---
|
||||
|
||||
## Pattern 10: String Aggregation
|
||||
|
||||
**Use case**: Aggregate strings with formatting.
|
||||
|
||||
**Scenario**: Create summary text from multiple items.
|
||||
|
||||
### Implementation
|
||||
|
||||
```python
|
||||
all_items = _input.all()
|
||||
|
||||
# Collect messages
|
||||
messages = []
|
||||
|
||||
for item in all_items:
|
||||
data = item["json"]
|
||||
|
||||
user = data.get("user", "Unknown")
|
||||
message = data.get("message", "")
|
||||
timestamp = data.get("timestamp", "")
|
||||
|
||||
# Format each message
|
||||
formatted = f"[{timestamp}] {user}: {message}"
|
||||
messages.append(formatted)
|
||||
|
||||
# Join with newlines
|
||||
summary = "\n".join(messages)
|
||||
|
||||
# Create statistics
|
||||
total_length = sum(len(msg) for msg in messages)
|
||||
average_length = total_length / len(messages) if messages else 0
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
"summary": summary,
|
||||
"message_count": len(messages),
|
||||
"total_characters": total_length,
|
||||
"average_length": round(average_length, 2)
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
### Key Techniques
|
||||
|
||||
- String formatting with f-strings
|
||||
- Join lists with separator
|
||||
- Calculate string statistics
|
||||
- Handle empty lists
|
||||
|
||||
---
|
||||
|
||||
## Pattern Comparison: Python vs JavaScript
|
||||
|
||||
### Data Access
|
||||
|
||||
```python
|
||||
# Python
|
||||
all_items = _input.all()
|
||||
first_item = _input.first()
|
||||
current = _input.item
|
||||
webhook_data = _json["body"]
|
||||
|
||||
# JavaScript
|
||||
const allItems = $input.all();
|
||||
const firstItem = $input.first();
|
||||
const current = $input.item;
|
||||
const webhookData = $json.body;
|
||||
```
|
||||
|
||||
### Dictionary/Object Access
|
||||
|
||||
```python
|
||||
# Python - Dictionary key access
|
||||
name = user["name"] # May raise KeyError
|
||||
name = user.get("name", "?") # Safe with default
|
||||
|
||||
# JavaScript - Object property access
|
||||
const name = user.name; // May be undefined
|
||||
const name = user.name || "?"; // Safe with default
|
||||
```
|
||||
|
||||
### Array Operations
|
||||
|
||||
```python
|
||||
# Python - List comprehension
|
||||
filtered = [item for item in items if item["active"]]
|
||||
|
||||
# JavaScript - Array methods
|
||||
const filtered = items.filter(item => item.active);
|
||||
```
|
||||
|
||||
### Sorting
|
||||
|
||||
```python
|
||||
# Python
|
||||
items.sort(key=lambda x: x["score"], reverse=True)
|
||||
|
||||
# JavaScript
|
||||
items.sort((a, b) => b.score - a.score);
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Best Practices
|
||||
|
||||
### 1. Use .get() for Safe Access
|
||||
|
||||
```python
|
||||
# ✅ SAFE: Use .get() with defaults
|
||||
name = user.get("name", "Unknown")
|
||||
email = user.get("email", "no-email@example.com")
|
||||
|
||||
# ❌ RISKY: Direct key access
|
||||
name = user["name"] # KeyError if missing!
|
||||
```
|
||||
|
||||
### 2. Handle Empty Lists
|
||||
|
||||
```python
|
||||
# ✅ SAFE: Check before processing
|
||||
items = _input.all()
|
||||
if items:
|
||||
first = items[0]
|
||||
else:
|
||||
return [{"json": {"error": "No items"}}]
|
||||
|
||||
# ❌ RISKY: Assume items exist
|
||||
first = items[0] # IndexError if empty!
|
||||
```
|
||||
|
||||
### 3. Use List Comprehensions
|
||||
|
||||
```python
|
||||
# ✅ PYTHONIC: List comprehension
|
||||
active = [item for item in items if item["json"].get("active")]
|
||||
|
||||
# ❌ VERBOSE: Traditional loop
|
||||
active = []
|
||||
for item in items:
|
||||
if item["json"].get("active"):
|
||||
active.append(item)
|
||||
```
|
||||
|
||||
### 4. Return Proper Format
|
||||
|
||||
```python
|
||||
# ✅ CORRECT: Array of objects with "json" key
|
||||
return [{"json": {"field": "value"}}]
|
||||
|
||||
# ❌ WRONG: Just the data
|
||||
return {"field": "value"}
|
||||
|
||||
# ❌ WRONG: Array without "json" wrapper
|
||||
return [{"field": "value"}]
|
||||
```
|
||||
|
||||
### 5. Use Standard Library
|
||||
|
||||
```python
|
||||
# ✅ GOOD: Use standard library
|
||||
import statistics
|
||||
average = statistics.mean(numbers)
|
||||
|
||||
# ✅ ALSO GOOD: Built-in functions
|
||||
average = sum(numbers) / len(numbers) if numbers else 0
|
||||
|
||||
# ❌ CAN'T DO: External libraries
|
||||
import numpy as np # ModuleNotFoundError!
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## When to Use Each Pattern
|
||||
|
||||
| Pattern | When to Use |
|
||||
|---------|-------------|
|
||||
| Multi-Source Aggregation | Combining data from different nodes/sources |
|
||||
| Regex Filtering | Text pattern matching, validation, extraction |
|
||||
| Markdown Parsing | Processing formatted text into structured data |
|
||||
| JSON Comparison | Detecting changes between objects |
|
||||
| CRM Transformation | Normalizing data from different systems |
|
||||
| Release Notes | Categorizing text by keywords |
|
||||
| Array Transformation | Reshaping data, extracting fields |
|
||||
| Dictionary Lookup | Fast ID-based lookups |
|
||||
| Top N Filtering | Getting best/worst items by criteria |
|
||||
| String Aggregation | Creating formatted text summaries |
|
||||
|
||||
---
|
||||
|
||||
## Summary
|
||||
|
||||
**Key Takeaways**:
|
||||
- Use `.get()` for safe dictionary access
|
||||
- List comprehensions are pythonic and efficient
|
||||
- Handle empty lists/None values
|
||||
- Use standard library (json, datetime, re)
|
||||
- Return proper n8n format: `[{"json": {...}}]`
|
||||
|
||||
**Remember**:
|
||||
- JavaScript is recommended for 95% of use cases
|
||||
- Python has NO external libraries
|
||||
- Use n8n nodes for complex operations
|
||||
- Code node is for data transformation, not API calls
|
||||
|
||||
**See Also**:
|
||||
- [SKILL.md](SKILL.md) - Python Code overview
|
||||
- [DATA_ACCESS.md](DATA_ACCESS.md) - Data access patterns
|
||||
- [STANDARD_LIBRARY.md](STANDARD_LIBRARY.md) - Available modules
|
||||
- [ERROR_PATTERNS.md](ERROR_PATTERNS.md) - Avoid common mistakes
|
||||
702
skills/n8n-code-python/DATA_ACCESS.md
Normal file
702
skills/n8n-code-python/DATA_ACCESS.md
Normal file
@@ -0,0 +1,702 @@
|
||||
# Data Access Patterns - Python Code Node
|
||||
|
||||
Complete guide to accessing data in n8n Code nodes using Python.
|
||||
|
||||
---
|
||||
|
||||
## Overview
|
||||
|
||||
In n8n Python Code nodes, you access data using **underscore-prefixed** variables: `_input`, `_json`, `_node`.
|
||||
|
||||
**Data Access Priority** (by common usage):
|
||||
1. **`_input.all()`** - Most common - Batch operations, aggregations
|
||||
2. **`_input.first()`** - Very common - Single item operations
|
||||
3. **`_input.item`** - Common - Each Item mode only
|
||||
4. **`_node["NodeName"]["json"]`** - Specific node references
|
||||
5. **`_json`** - Direct current item (use `_input` instead)
|
||||
|
||||
**Python vs JavaScript**:
|
||||
| JavaScript | Python (Beta) | Python (Native) |
|
||||
|------------|---------------|-----------------|
|
||||
| `$input.all()` | `_input.all()` | `_items` |
|
||||
| `$input.first()` | `_input.first()` | `_items[0]` |
|
||||
| `$input.item` | `_input.item` | `_item` |
|
||||
| `$json` | `_json` | `_item["json"]` |
|
||||
| `$node["Name"]` | `_node["Name"]` | Not available |
|
||||
|
||||
---
|
||||
|
||||
## Pattern 1: _input.all() - Process All Items
|
||||
|
||||
**Usage**: Most common pattern for batch processing
|
||||
|
||||
**When to use:**
|
||||
- Processing multiple records
|
||||
- Aggregating data (sum, count, average)
|
||||
- Filtering lists
|
||||
- Transforming datasets
|
||||
|
||||
### Basic Usage
|
||||
|
||||
```python
|
||||
# Get all items from previous node
|
||||
all_items = _input.all()
|
||||
|
||||
# all_items is a list of dictionaries like:
|
||||
# [
|
||||
# {"json": {"id": 1, "name": "Alice"}},
|
||||
# {"json": {"id": 2, "name": "Bob"}}
|
||||
# ]
|
||||
|
||||
print(f"Received {len(all_items)} items")
|
||||
|
||||
return all_items
|
||||
```
|
||||
|
||||
### Example 1: Filter Active Items
|
||||
|
||||
```python
|
||||
all_items = _input.all()
|
||||
|
||||
# Filter only active items
|
||||
active_items = [
|
||||
item for item in all_items
|
||||
if item["json"].get("status") == "active"
|
||||
]
|
||||
|
||||
return active_items
|
||||
```
|
||||
|
||||
### Example 2: Transform All Items
|
||||
|
||||
```python
|
||||
all_items = _input.all()
|
||||
|
||||
# Transform to new structure
|
||||
transformed = []
|
||||
for item in all_items:
|
||||
transformed.append({
|
||||
"json": {
|
||||
"id": item["json"].get("id"),
|
||||
"full_name": f"{item['json'].get('first_name', '')} {item['json'].get('last_name', '')}",
|
||||
"email": item["json"].get("email"),
|
||||
"processed_at": datetime.now().isoformat()
|
||||
}
|
||||
})
|
||||
|
||||
return transformed
|
||||
```
|
||||
|
||||
### Example 3: Aggregate Data
|
||||
|
||||
```python
|
||||
all_items = _input.all()
|
||||
|
||||
# Calculate total
|
||||
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
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
### Example 4: Sort and Limit
|
||||
|
||||
```python
|
||||
all_items = _input.all()
|
||||
|
||||
# Get top 5 by score
|
||||
sorted_items = sorted(
|
||||
all_items,
|
||||
key=lambda item: item["json"].get("score", 0),
|
||||
reverse=True
|
||||
)
|
||||
top_five = sorted_items[:5]
|
||||
|
||||
return [{"json": item["json"]} for item in top_five]
|
||||
```
|
||||
|
||||
### Example 5: Group By Category
|
||||
|
||||
```python
|
||||
all_items = _input.all()
|
||||
|
||||
# Group items by category
|
||||
grouped = {}
|
||||
for item in all_items:
|
||||
category = item["json"].get("category", "Uncategorized")
|
||||
|
||||
if category not in grouped:
|
||||
grouped[category] = []
|
||||
|
||||
grouped[category].append(item["json"])
|
||||
|
||||
# Convert to list format
|
||||
return [
|
||||
{
|
||||
"json": {
|
||||
"category": category,
|
||||
"items": items,
|
||||
"count": len(items)
|
||||
}
|
||||
}
|
||||
for category, items in grouped.items()
|
||||
]
|
||||
```
|
||||
|
||||
### Example 6: Deduplicate by ID
|
||||
|
||||
```python
|
||||
all_items = _input.all()
|
||||
|
||||
# Remove duplicates by ID
|
||||
seen = set()
|
||||
unique = []
|
||||
|
||||
for item in all_items:
|
||||
item_id = item["json"].get("id")
|
||||
|
||||
if item_id and item_id not in seen:
|
||||
seen.add(item_id)
|
||||
unique.append(item)
|
||||
|
||||
return unique
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Pattern 2: _input.first() - Get First Item
|
||||
|
||||
**Usage**: Very common for single-item operations
|
||||
|
||||
**When to use:**
|
||||
- Previous node returns single object
|
||||
- Working with API responses
|
||||
- Getting initial/first data point
|
||||
|
||||
### Basic Usage
|
||||
|
||||
```python
|
||||
# Get first item from previous node
|
||||
first_item = _input.first()
|
||||
|
||||
# Access the JSON data
|
||||
data = first_item["json"]
|
||||
|
||||
print(f"First item: {data}")
|
||||
|
||||
return [{"json": data}]
|
||||
```
|
||||
|
||||
### Example 1: Process Single API Response
|
||||
|
||||
```python
|
||||
# Get API response (typically single object)
|
||||
response = _input.first()["json"]
|
||||
|
||||
# Extract what you need
|
||||
return [{
|
||||
"json": {
|
||||
"user_id": response.get("data", {}).get("user", {}).get("id"),
|
||||
"user_name": response.get("data", {}).get("user", {}).get("name"),
|
||||
"status": response.get("status"),
|
||||
"fetched_at": datetime.now().isoformat()
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
### Example 2: Transform Single Object
|
||||
|
||||
```python
|
||||
data = _input.first()["json"]
|
||||
|
||||
# Transform structure
|
||||
return [{
|
||||
"json": {
|
||||
"id": data.get("id"),
|
||||
"contact": {
|
||||
"email": data.get("email"),
|
||||
"phone": data.get("phone")
|
||||
},
|
||||
"address": {
|
||||
"street": data.get("street"),
|
||||
"city": data.get("city"),
|
||||
"zip": data.get("zip")
|
||||
}
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
### Example 3: Validate Single Item
|
||||
|
||||
```python
|
||||
item = _input.first()["json"]
|
||||
|
||||
# Validation logic
|
||||
is_valid = bool(item.get("email") and "@" in item.get("email", ""))
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
**item,
|
||||
"valid": is_valid,
|
||||
"validated_at": datetime.now().isoformat()
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
### Example 4: Extract Nested Data
|
||||
|
||||
```python
|
||||
response = _input.first()["json"]
|
||||
|
||||
# Navigate nested structure
|
||||
users = response.get("data", {}).get("users", [])
|
||||
|
||||
return [
|
||||
{
|
||||
"json": {
|
||||
"id": user.get("id"),
|
||||
"name": user.get("profile", {}).get("name", "Unknown"),
|
||||
"email": user.get("contact", {}).get("email", "no-email")
|
||||
}
|
||||
}
|
||||
for user in users
|
||||
]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Pattern 3: _input.item - Current Item (Each Item Mode)
|
||||
|
||||
**Usage**: Common in "Run Once for Each Item" mode
|
||||
|
||||
**When to use:**
|
||||
- Mode is set to "Run Once for Each Item"
|
||||
- Need to process items independently
|
||||
- Per-item API calls or validations
|
||||
|
||||
**IMPORTANT**: Only use in "Each Item" mode. Will be undefined in "All Items" mode.
|
||||
|
||||
### Basic Usage
|
||||
|
||||
```python
|
||||
# In "Run Once for Each Item" mode
|
||||
current_item = _input.item
|
||||
data = current_item["json"]
|
||||
|
||||
print(f"Processing item: {data.get('id')}")
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
**data,
|
||||
"processed": True
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
### Example 1: Add Processing Metadata
|
||||
|
||||
```python
|
||||
item = _input.item
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
**item["json"],
|
||||
"processed": True,
|
||||
"processed_at": datetime.now().isoformat(),
|
||||
"processing_duration": random.random() * 1000 # Simulated
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
### Example 2: Per-Item Validation
|
||||
|
||||
```python
|
||||
item = _input.item
|
||||
data = item["json"]
|
||||
|
||||
# Validate this specific item
|
||||
errors = []
|
||||
|
||||
if not data.get("email"):
|
||||
errors.append("Email required")
|
||||
if not data.get("name"):
|
||||
errors.append("Name required")
|
||||
if data.get("age") and data["age"] < 18:
|
||||
errors.append("Must be 18+")
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
**data,
|
||||
"valid": len(errors) == 0,
|
||||
"errors": errors if errors else None
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
### Example 3: Conditional Processing
|
||||
|
||||
```python
|
||||
item = _input.item
|
||||
data = item["json"]
|
||||
|
||||
# Process based on item type
|
||||
if data.get("type") == "premium":
|
||||
return [{
|
||||
"json": {
|
||||
**data,
|
||||
"discount": 0.20,
|
||||
"tier": "premium"
|
||||
}
|
||||
}]
|
||||
else:
|
||||
return [{
|
||||
"json": {
|
||||
**data,
|
||||
"discount": 0.05,
|
||||
"tier": "standard"
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Pattern 4: _node - Reference Other Nodes
|
||||
|
||||
**Usage**: Less common, but powerful for specific scenarios
|
||||
|
||||
**When to use:**
|
||||
- Need data from specific named node
|
||||
- Combining data from multiple nodes
|
||||
|
||||
### Basic Usage
|
||||
|
||||
```python
|
||||
# Get output from specific node
|
||||
webhook_data = _node["Webhook"]["json"]
|
||||
api_data = _node["HTTP Request"]["json"]
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
"from_webhook": webhook_data,
|
||||
"from_api": api_data
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
### Example 1: Combine Multiple Sources
|
||||
|
||||
```python
|
||||
# Reference multiple nodes
|
||||
webhook = _node["Webhook"]["json"]
|
||||
database = _node["Postgres"]["json"]
|
||||
api = _node["HTTP Request"]["json"]
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
"combined": {
|
||||
"webhook": webhook.get("body", {}),
|
||||
"db_records": len(database) if isinstance(database, list) else 1,
|
||||
"api_response": api.get("status")
|
||||
},
|
||||
"processed_at": datetime.now().isoformat()
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
### Example 2: Compare Across Nodes
|
||||
|
||||
```python
|
||||
old_data = _node["Get Old Data"]["json"]
|
||||
new_data = _node["Get New Data"]["json"]
|
||||
|
||||
# Simple comparison
|
||||
changes = {
|
||||
"added": [n for n in new_data if n.get("id") not in [o.get("id") for o in old_data]],
|
||||
"removed": [o for o in old_data if o.get("id") not in [n.get("id") for n in new_data]]
|
||||
}
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
"changes": changes,
|
||||
"summary": {
|
||||
"added": len(changes["added"]),
|
||||
"removed": len(changes["removed"])
|
||||
}
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Critical: Webhook Data Structure
|
||||
|
||||
**MOST COMMON MISTAKE**: Forgetting webhook data is nested under `["body"]`
|
||||
|
||||
### The Problem
|
||||
|
||||
Webhook node wraps all incoming data under a `"body"` property.
|
||||
|
||||
### Structure
|
||||
|
||||
```python
|
||||
# Webhook node output structure:
|
||||
{
|
||||
"headers": {
|
||||
"content-type": "application/json",
|
||||
"user-agent": "..."
|
||||
},
|
||||
"params": {},
|
||||
"query": {},
|
||||
"body": {
|
||||
# ← YOUR DATA IS HERE
|
||||
"name": "Alice",
|
||||
"email": "alice@example.com",
|
||||
"message": "Hello!"
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Wrong vs Right
|
||||
|
||||
```python
|
||||
# ❌ WRONG: Trying to access directly
|
||||
name = _json["name"] # KeyError!
|
||||
email = _json["email"] # KeyError!
|
||||
|
||||
# ✅ CORRECT: Access via ["body"]
|
||||
name = _json["body"]["name"] # "Alice"
|
||||
email = _json["body"]["email"] # "alice@example.com"
|
||||
|
||||
# ✅ SAFER: Use .get() for safe access
|
||||
webhook_data = _json.get("body", {})
|
||||
name = webhook_data.get("name") # None if missing
|
||||
email = webhook_data.get("email", "no-email") # Default value
|
||||
```
|
||||
|
||||
### Example: Full Webhook Processing
|
||||
|
||||
```python
|
||||
# Get webhook data from previous node
|
||||
webhook_output = _input.first()["json"]
|
||||
|
||||
# Access the actual payload
|
||||
payload = webhook_output.get("body", {})
|
||||
|
||||
# Access headers if needed
|
||||
content_type = webhook_output.get("headers", {}).get("content-type")
|
||||
|
||||
# Access query parameters if needed
|
||||
api_key = webhook_output.get("query", {}).get("api_key")
|
||||
|
||||
# Process the actual data
|
||||
return [{
|
||||
"json": {
|
||||
# Data from webhook body
|
||||
"user_name": payload.get("name"),
|
||||
"user_email": payload.get("email"),
|
||||
"message": payload.get("message"),
|
||||
|
||||
# Metadata
|
||||
"received_at": datetime.now().isoformat(),
|
||||
"content_type": content_type,
|
||||
"authenticated": bool(api_key)
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
### POST Data, Query Params, and Headers
|
||||
|
||||
```python
|
||||
webhook = _input.first()["json"]
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
# POST body data
|
||||
"form_data": webhook.get("body", {}),
|
||||
|
||||
# Query parameters (?key=value)
|
||||
"query_params": webhook.get("query", {}),
|
||||
|
||||
# HTTP headers
|
||||
"user_agent": webhook.get("headers", {}).get("user-agent"),
|
||||
"content_type": webhook.get("headers", {}).get("content-type"),
|
||||
|
||||
# Request metadata
|
||||
"method": webhook.get("method"), # POST, GET, etc.
|
||||
"url": webhook.get("url")
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Choosing the Right Pattern
|
||||
|
||||
### Decision Tree
|
||||
|
||||
```
|
||||
Do you need ALL items from previous node?
|
||||
├─ YES → Use _input.all()
|
||||
│
|
||||
└─ NO → Do you need just the FIRST item?
|
||||
├─ YES → Use _input.first()
|
||||
│
|
||||
└─ NO → Are you in "Each Item" mode?
|
||||
├─ YES → Use _input.item
|
||||
│
|
||||
└─ NO → Do you need specific node data?
|
||||
├─ YES → Use _node["NodeName"]
|
||||
└─ NO → Use _input.first() (default)
|
||||
```
|
||||
|
||||
### Quick Reference Table
|
||||
|
||||
| Scenario | Use This | Example |
|
||||
|----------|----------|---------|
|
||||
| Sum all amounts | `_input.all()` | `sum(i["json"].get("amount", 0) for i in items)` |
|
||||
| Get API response | `_input.first()` | `_input.first()["json"].get("data")` |
|
||||
| Process each independently | `_input.item` | `_input.item["json"]` (Each Item mode) |
|
||||
| Combine two nodes | `_node["Name"]` | `_node["API"]["json"]` |
|
||||
| Filter list | `_input.all()` | `[i for i in items if i["json"].get("active")]` |
|
||||
| Transform single object | `_input.first()` | `{**_input.first()["json"], "new": True}` |
|
||||
| Webhook data | `_input.first()` | `_input.first()["json"]["body"]` |
|
||||
|
||||
---
|
||||
|
||||
## Common Mistakes
|
||||
|
||||
### Mistake 1: Using _json Without Context
|
||||
|
||||
```python
|
||||
# ❌ RISKY: _json is ambiguous
|
||||
value = _json["field"]
|
||||
|
||||
# ✅ CLEAR: Be explicit
|
||||
value = _input.first()["json"]["field"]
|
||||
```
|
||||
|
||||
### Mistake 2: Forgetting ["json"] Property
|
||||
|
||||
```python
|
||||
# ❌ WRONG: Trying to access fields on item dictionary
|
||||
items = _input.all()
|
||||
names = [item["name"] for item in items] # KeyError!
|
||||
|
||||
# ✅ CORRECT: Access via ["json"]
|
||||
names = [item["json"]["name"] for item in items]
|
||||
```
|
||||
|
||||
### Mistake 3: Using _input.item in All Items Mode
|
||||
|
||||
```python
|
||||
# ❌ WRONG: _input.item is None in "All Items" mode
|
||||
data = _input.item["json"] # AttributeError!
|
||||
|
||||
# ✅ CORRECT: Use appropriate method
|
||||
data = _input.first()["json"] # Or _input.all()
|
||||
```
|
||||
|
||||
### Mistake 4: Not Handling Empty Lists
|
||||
|
||||
```python
|
||||
# ❌ WRONG: Crashes if no items
|
||||
first = _input.all()[0]["json"] # IndexError!
|
||||
|
||||
# ✅ CORRECT: Check length first
|
||||
items = _input.all()
|
||||
if items:
|
||||
first = items[0]["json"]
|
||||
else:
|
||||
return []
|
||||
|
||||
# ✅ ALSO CORRECT: Use _input.first()
|
||||
first = _input.first()["json"] # Built-in safety
|
||||
```
|
||||
|
||||
### Mistake 5: Direct Dictionary Access (KeyError)
|
||||
|
||||
```python
|
||||
# ❌ RISKY: Crashes if key missing
|
||||
value = item["json"]["field"] # KeyError!
|
||||
|
||||
# ✅ SAFE: Use .get()
|
||||
value = item["json"].get("field", "default")
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Advanced Patterns
|
||||
|
||||
### Pattern: Safe Nested Access
|
||||
|
||||
```python
|
||||
# Deep nested access with .get()
|
||||
value = (
|
||||
_input.first()["json"]
|
||||
.get("level1", {})
|
||||
.get("level2", {})
|
||||
.get("level3", "default")
|
||||
)
|
||||
```
|
||||
|
||||
### Pattern: List Comprehension with Filtering
|
||||
|
||||
```python
|
||||
items = _input.all()
|
||||
|
||||
# Filter and transform in one step
|
||||
result = [
|
||||
{
|
||||
"json": {
|
||||
"id": item["json"]["id"],
|
||||
"name": item["json"]["name"].upper()
|
||||
}
|
||||
}
|
||||
for item in items
|
||||
if item["json"].get("active") and item["json"].get("verified")
|
||||
]
|
||||
|
||||
return result
|
||||
```
|
||||
|
||||
### Pattern: Dictionary Comprehension
|
||||
|
||||
```python
|
||||
items = _input.all()
|
||||
|
||||
# Create lookup dictionary
|
||||
lookup = {
|
||||
item["json"]["id"]: item["json"]
|
||||
for item in items
|
||||
if "id" in item["json"]
|
||||
}
|
||||
|
||||
return [{"json": lookup}]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Summary
|
||||
|
||||
**Most Common Patterns**:
|
||||
1. `_input.all()` - Process multiple items, batch operations
|
||||
2. `_input.first()` - Single item, API responses
|
||||
3. `_input.item` - Each Item mode processing
|
||||
|
||||
**Critical Rule**:
|
||||
- Webhook data is under `["body"]` property
|
||||
|
||||
**Best Practice**:
|
||||
- Use `.get()` for dictionary access to avoid KeyError
|
||||
- Always check for empty lists
|
||||
- Be explicit: Use `_input.first()["json"]["field"]` instead of `_json["field"]`
|
||||
|
||||
**See Also**:
|
||||
- [SKILL.md](SKILL.md) - Overview and quick start
|
||||
- [COMMON_PATTERNS.md](COMMON_PATTERNS.md) - Python-specific patterns
|
||||
- [ERROR_PATTERNS.md](ERROR_PATTERNS.md) - Avoid common mistakes
|
||||
601
skills/n8n-code-python/ERROR_PATTERNS.md
Normal file
601
skills/n8n-code-python/ERROR_PATTERNS.md
Normal file
@@ -0,0 +1,601 @@
|
||||
# Error Patterns - Python Code Node
|
||||
|
||||
Common Python Code node errors and how to fix them.
|
||||
|
||||
---
|
||||
|
||||
## Error Overview
|
||||
|
||||
**Top 5 Python Code Node Errors**:
|
||||
|
||||
1. **ModuleNotFoundError** - Trying to import external libraries (Python-specific)
|
||||
2. **Empty Code / Missing Return** - No code or return statement
|
||||
3. **KeyError** - Dictionary access without .get()
|
||||
4. **IndexError** - List access without bounds checking
|
||||
5. **Incorrect Return Format** - Wrong data structure returned
|
||||
|
||||
These 5 errors cover the majority of Python Code node failures.
|
||||
|
||||
---
|
||||
|
||||
## Error #1: ModuleNotFoundError (MOST CRITICAL)
|
||||
|
||||
**Frequency**: Very common in Python Code nodes
|
||||
|
||||
**What it is**: Attempting to import external libraries that aren't available.
|
||||
|
||||
### The Problem
|
||||
|
||||
```python
|
||||
# ❌ WRONG: External libraries not available
|
||||
import requests # ModuleNotFoundError: No module named 'requests'
|
||||
import pandas # ModuleNotFoundError: No module named 'pandas'
|
||||
import numpy # ModuleNotFoundError: No module named 'numpy'
|
||||
import bs4 # ModuleNotFoundError: No module named 'bs4'
|
||||
import pymongo # ModuleNotFoundError: No module named 'pymongo'
|
||||
import psycopg2 # ModuleNotFoundError: No module named 'psycopg2'
|
||||
|
||||
# This code will FAIL - these libraries are not installed!
|
||||
response = requests.get("https://api.example.com/data")
|
||||
```
|
||||
|
||||
### The Solution
|
||||
|
||||
**Option 1: Use JavaScript Instead** (Recommended for 95% of cases)
|
||||
|
||||
```javascript
|
||||
// ✅ JavaScript Code node with $helpers.httpRequest()
|
||||
const response = await $helpers.httpRequest({
|
||||
method: 'GET',
|
||||
url: 'https://api.example.com/data'
|
||||
});
|
||||
|
||||
return [{json: response}];
|
||||
```
|
||||
|
||||
**Option 2: Use n8n HTTP Request Node**
|
||||
|
||||
```python
|
||||
# ✅ Add HTTP Request node BEFORE Python Code node
|
||||
# Access the response in Python Code node
|
||||
|
||||
response = _input.first()["json"]
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
"status": response.get("status"),
|
||||
"data": response.get("body"),
|
||||
"processed": True
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
**Option 3: Use Standard Library Only**
|
||||
|
||||
```python
|
||||
# ✅ Use urllib from standard library (limited functionality)
|
||||
from urllib.request import urlopen
|
||||
from urllib.parse import urlencode
|
||||
import json
|
||||
|
||||
# Simple GET request (no headers, no auth)
|
||||
url = "https://api.example.com/data"
|
||||
with urlopen(url) as response:
|
||||
data = json.loads(response.read())
|
||||
|
||||
return [{"json": data}]
|
||||
```
|
||||
|
||||
### Common Library Replacements
|
||||
|
||||
| Need | ❌ External Library | ✅ Alternative |
|
||||
|------|-------------------|----------------|
|
||||
| HTTP requests | `requests` | Use HTTP Request node or JavaScript |
|
||||
| Data analysis | `pandas` | Use Python list comprehensions |
|
||||
| Database | `psycopg2`, `pymongo` | Use n8n database nodes |
|
||||
| Web scraping | `beautifulsoup4` | Use HTML Extract node |
|
||||
| Excel | `openpyxl` | Use Spreadsheet File node |
|
||||
| Image processing | `pillow` | Use external API or node |
|
||||
|
||||
### Available Standard Library Modules
|
||||
|
||||
```python
|
||||
# ✅ THESE WORK - Standard library only
|
||||
import json # JSON parsing
|
||||
import datetime # Date/time operations
|
||||
import re # Regular expressions
|
||||
import base64 # Base64 encoding
|
||||
import hashlib # Hashing (MD5, SHA256)
|
||||
import urllib.parse # URL parsing and encoding
|
||||
import math # Math functions
|
||||
import random # Random numbers
|
||||
import statistics # Statistical functions
|
||||
import collections # defaultdict, Counter, etc.
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Error #2: Empty Code / Missing Return
|
||||
|
||||
**Frequency**: Common across all Code nodes
|
||||
|
||||
**What it is**: Code node has no code or no return statement.
|
||||
|
||||
### The Problem
|
||||
|
||||
```python
|
||||
# ❌ WRONG: Empty code
|
||||
# (nothing here)
|
||||
|
||||
# ❌ WRONG: Code but no return
|
||||
items = _input.all()
|
||||
processed = [item for item in items if item["json"].get("active")]
|
||||
# Forgot to return!
|
||||
|
||||
# ❌ WRONG: Return in wrong scope
|
||||
if _input.all():
|
||||
return [{"json": {"result": "success"}}]
|
||||
# Return is inside if block - may not execute!
|
||||
```
|
||||
|
||||
### The Solution
|
||||
|
||||
```python
|
||||
# ✅ CORRECT: Always return
|
||||
all_items = _input.all()
|
||||
|
||||
if not all_items:
|
||||
# Return empty array or error
|
||||
return [{"json": {"error": "No items"}}]
|
||||
|
||||
# Process items
|
||||
processed = [item for item in all_items if item["json"].get("active")]
|
||||
|
||||
# Always return at the end
|
||||
return processed if processed else [{"json": {"message": "No active items"}}]
|
||||
```
|
||||
|
||||
### Best Practice
|
||||
|
||||
```python
|
||||
# ✅ GOOD: Return at end of function (unconditional)
|
||||
def process_items():
|
||||
items = _input.all()
|
||||
|
||||
if not items:
|
||||
return [{"json": {"error": "Empty input"}}]
|
||||
|
||||
# Process
|
||||
result = []
|
||||
for item in items:
|
||||
result.append({"json": item["json"]})
|
||||
|
||||
return result
|
||||
|
||||
# Call function and return result
|
||||
return process_items()
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Error #3: KeyError
|
||||
|
||||
**Frequency**: Very common in Python Code nodes
|
||||
|
||||
**What it is**: Accessing dictionary key that doesn't exist.
|
||||
|
||||
### The Problem
|
||||
|
||||
```python
|
||||
# ❌ WRONG: Direct key access
|
||||
item = _input.first()["json"]
|
||||
|
||||
name = item["name"] # KeyError if "name" doesn't exist!
|
||||
email = item["email"] # KeyError if "email" doesn't exist!
|
||||
age = item["age"] # KeyError if "age" doesn't exist!
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
"name": name,
|
||||
"email": email,
|
||||
"age": age
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
### Error Message
|
||||
|
||||
```
|
||||
KeyError: 'name'
|
||||
```
|
||||
|
||||
### The Solution
|
||||
|
||||
```python
|
||||
# ✅ CORRECT: Use .get() with defaults
|
||||
item = _input.first()["json"]
|
||||
|
||||
name = item.get("name", "Unknown")
|
||||
email = item.get("email", "no-email@example.com")
|
||||
age = item.get("age", 0)
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
"name": name,
|
||||
"email": email,
|
||||
"age": age
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
### Nested Dictionary Access
|
||||
|
||||
```python
|
||||
# ❌ WRONG: Nested key access
|
||||
webhook = _input.first()["json"]
|
||||
name = webhook["body"]["user"]["name"] # Multiple KeyErrors possible!
|
||||
|
||||
# ✅ CORRECT: Safe nested access
|
||||
webhook = _input.first()["json"]
|
||||
body = webhook.get("body", {})
|
||||
user = body.get("user", {})
|
||||
name = user.get("name", "Unknown")
|
||||
|
||||
# ✅ ALSO CORRECT: Chained .get()
|
||||
name = (
|
||||
webhook
|
||||
.get("body", {})
|
||||
.get("user", {})
|
||||
.get("name", "Unknown")
|
||||
)
|
||||
|
||||
return [{"json": {"name": name}}]
|
||||
```
|
||||
|
||||
### Webhook Body Access (Critical!)
|
||||
|
||||
```python
|
||||
# ❌ WRONG: Forgetting webhook data is under "body"
|
||||
webhook = _input.first()["json"]
|
||||
name = webhook["name"] # KeyError!
|
||||
email = webhook["email"] # KeyError!
|
||||
|
||||
# ✅ CORRECT: Access via ["body"]
|
||||
webhook = _input.first()["json"]
|
||||
body = webhook.get("body", {})
|
||||
name = body.get("name", "Unknown")
|
||||
email = body.get("email", "no-email")
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
"name": name,
|
||||
"email": email
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Error #4: IndexError
|
||||
|
||||
**Frequency**: Common when processing arrays/lists
|
||||
|
||||
**What it is**: Accessing list index that doesn't exist.
|
||||
|
||||
### The Problem
|
||||
|
||||
```python
|
||||
# ❌ WRONG: Assuming items exist
|
||||
all_items = _input.all()
|
||||
first_item = all_items[0] # IndexError if list is empty!
|
||||
second_item = all_items[1] # IndexError if only 1 item!
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
"first": first_item["json"],
|
||||
"second": second_item["json"]
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
### Error Message
|
||||
|
||||
```
|
||||
IndexError: list index out of range
|
||||
```
|
||||
|
||||
### The Solution
|
||||
|
||||
```python
|
||||
# ✅ CORRECT: Check length first
|
||||
all_items = _input.all()
|
||||
|
||||
if len(all_items) >= 2:
|
||||
first_item = all_items[0]["json"]
|
||||
second_item = all_items[1]["json"]
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
"first": first_item,
|
||||
"second": second_item
|
||||
}
|
||||
}]
|
||||
else:
|
||||
return [{
|
||||
"json": {
|
||||
"error": f"Expected 2+ items, got {len(all_items)}"
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
### Safe First Item Access
|
||||
|
||||
```python
|
||||
# ✅ CORRECT: Use _input.first() instead of [0]
|
||||
# This is safer than manual indexing
|
||||
first_item = _input.first()["json"]
|
||||
|
||||
return [{"json": first_item}]
|
||||
|
||||
# ✅ ALSO CORRECT: Check before accessing
|
||||
all_items = _input.all()
|
||||
if all_items:
|
||||
first_item = all_items[0]["json"]
|
||||
else:
|
||||
first_item = {}
|
||||
|
||||
return [{"json": first_item}]
|
||||
```
|
||||
|
||||
### Slice Instead of Index
|
||||
|
||||
```python
|
||||
# ✅ CORRECT: Use slicing (never raises IndexError)
|
||||
all_items = _input.all()
|
||||
|
||||
# Get first 5 items (won't fail if fewer than 5)
|
||||
first_five = all_items[:5]
|
||||
|
||||
# Get items after first (won't fail if empty)
|
||||
rest = all_items[1:]
|
||||
|
||||
return [{"json": item["json"]} for item in first_five]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Error #5: Incorrect Return Format
|
||||
|
||||
**Frequency**: Common for new users
|
||||
|
||||
**What it is**: Returning data in wrong format (n8n expects array of objects with "json" key).
|
||||
|
||||
### The Problem
|
||||
|
||||
```python
|
||||
# ❌ WRONG: Returning plain dictionary
|
||||
return {"name": "Alice", "age": 30}
|
||||
|
||||
# ❌ WRONG: Returning array without "json" wrapper
|
||||
return [{"name": "Alice"}, {"name": "Bob"}]
|
||||
|
||||
# ❌ WRONG: Returning None
|
||||
return None
|
||||
|
||||
# ❌ WRONG: Returning string
|
||||
return "success"
|
||||
|
||||
# ❌ WRONG: Returning single item (not array)
|
||||
return {"json": {"name": "Alice"}}
|
||||
```
|
||||
|
||||
### The Solution
|
||||
|
||||
```python
|
||||
# ✅ CORRECT: Array of objects with "json" key
|
||||
return [{"json": {"name": "Alice", "age": 30}}]
|
||||
|
||||
# ✅ CORRECT: Multiple items
|
||||
return [
|
||||
{"json": {"name": "Alice"}},
|
||||
{"json": {"name": "Bob"}}
|
||||
]
|
||||
|
||||
# ✅ CORRECT: Transform items
|
||||
all_items = _input.all()
|
||||
return [
|
||||
{"json": item["json"]}
|
||||
for item in all_items
|
||||
]
|
||||
|
||||
# ✅ CORRECT: Empty array (valid)
|
||||
return []
|
||||
|
||||
# ✅ CORRECT: Single item still needs array wrapper
|
||||
return [{"json": {"result": "success"}}]
|
||||
```
|
||||
|
||||
### Common Scenarios
|
||||
|
||||
**Scenario 1: Aggregation (Return Single Result)**
|
||||
|
||||
```python
|
||||
# Calculate total
|
||||
all_items = _input.all()
|
||||
total = sum(item["json"].get("amount", 0) for item in all_items)
|
||||
|
||||
# ✅ CORRECT: Wrap in array with "json"
|
||||
return [{
|
||||
"json": {
|
||||
"total": total,
|
||||
"count": len(all_items)
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
**Scenario 2: Filtering (Return Multiple Results)**
|
||||
|
||||
```python
|
||||
# Filter active items
|
||||
all_items = _input.all()
|
||||
active = [item for item in all_items if item["json"].get("active")]
|
||||
|
||||
# ✅ CORRECT: Already in correct format
|
||||
return active
|
||||
|
||||
# ✅ ALSO CORRECT: If transforming
|
||||
return [
|
||||
{"json": {**item["json"], "filtered": True}}
|
||||
for item in active
|
||||
]
|
||||
```
|
||||
|
||||
**Scenario 3: No Results**
|
||||
|
||||
```python
|
||||
# ✅ CORRECT: Return empty array
|
||||
return []
|
||||
|
||||
# ✅ ALSO CORRECT: Return error message
|
||||
return [{"json": {"error": "No results found"}}]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Bonus Error: AttributeError
|
||||
|
||||
**What it is**: Using _input.item in wrong mode.
|
||||
|
||||
### The Problem
|
||||
|
||||
```python
|
||||
# ❌ WRONG: Using _input.item in "All Items" mode
|
||||
current = _input.item # None in "All Items" mode
|
||||
data = current["json"] # AttributeError: 'NoneType' object has no attribute '__getitem__'
|
||||
```
|
||||
|
||||
### The Solution
|
||||
|
||||
```python
|
||||
# ✅ CORRECT: Check mode or use appropriate method
|
||||
# In "All Items" mode, use:
|
||||
all_items = _input.all()
|
||||
|
||||
# In "Each Item" mode, use:
|
||||
current_item = _input.item
|
||||
|
||||
# ✅ SAFE: Check if item exists
|
||||
current = _input.item
|
||||
if current:
|
||||
data = current["json"]
|
||||
return [{"json": data}]
|
||||
else:
|
||||
# Running in "All Items" mode
|
||||
return _input.all()
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Error Prevention Checklist
|
||||
|
||||
Before running your Python Code node, verify:
|
||||
|
||||
- [ ] **No external imports**: Only standard library (json, datetime, re, etc.)
|
||||
- [ ] **Code returns data**: Every code path ends with `return`
|
||||
- [ ] **Correct format**: Returns `[{"json": {...}}]` (array with "json" key)
|
||||
- [ ] **Safe dictionary access**: Uses `.get()` instead of `[]` for dictionaries
|
||||
- [ ] **Safe list access**: Checks length before indexing or uses slicing
|
||||
- [ ] **Webhook body access**: Accesses webhook data via `_json["body"]`
|
||||
- [ ] **No None returns**: Returns empty array `[]` instead of `None`
|
||||
- [ ] **Mode awareness**: Uses `_input.all()`, `_input.first()`, or `_input.item` appropriately
|
||||
|
||||
---
|
||||
|
||||
## Quick Fix Reference
|
||||
|
||||
| Error | Quick Fix |
|
||||
|-------|-----------|
|
||||
| `ModuleNotFoundError` | Use JavaScript or HTTP Request node instead |
|
||||
| `KeyError: 'field'` | Change `data["field"]` to `data.get("field", default)` |
|
||||
| `IndexError: list index out of range` | Check `if len(items) > 0:` before `items[0]` |
|
||||
| Empty output | Add `return [{"json": {...}}]` at end |
|
||||
| `AttributeError: 'NoneType'` | Check mode setting or verify `_input.item` exists |
|
||||
| Wrong format error | Wrap result: `return [{"json": result}]` |
|
||||
| Webhook KeyError | Access via `_json.get("body", {})` |
|
||||
|
||||
---
|
||||
|
||||
## Testing Your Code
|
||||
|
||||
### Test Pattern 1: Handle Empty Input
|
||||
|
||||
```python
|
||||
# ✅ Always test with empty input
|
||||
all_items = _input.all()
|
||||
|
||||
if not all_items:
|
||||
return [{"json": {"message": "No items to process"}}]
|
||||
|
||||
# Continue with processing
|
||||
# ...
|
||||
```
|
||||
|
||||
### Test Pattern 2: Test with Missing Fields
|
||||
|
||||
```python
|
||||
# ✅ Use .get() with defaults
|
||||
item = _input.first()["json"]
|
||||
|
||||
# These won't fail even if fields missing
|
||||
name = item.get("name", "Unknown")
|
||||
email = item.get("email", "no-email")
|
||||
age = item.get("age", 0)
|
||||
|
||||
return [{"json": {"name": name, "email": email, "age": age}}]
|
||||
```
|
||||
|
||||
### Test Pattern 3: Test Both Modes
|
||||
|
||||
```python
|
||||
# ✅ Code that works in both modes
|
||||
try:
|
||||
# Try "Each Item" mode first
|
||||
current = _input.item
|
||||
if current:
|
||||
return [{"json": current["json"]}]
|
||||
except:
|
||||
pass
|
||||
|
||||
# Fall back to "All Items" mode
|
||||
all_items = _input.all()
|
||||
return all_items if all_items else [{"json": {"message": "No data"}}]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Summary
|
||||
|
||||
**Top 5 Errors to Avoid**:
|
||||
1. **ModuleNotFoundError** - Use JavaScript or n8n nodes instead
|
||||
2. **Missing return** - Always end with `return [{"json": {...}}]`
|
||||
3. **KeyError** - Use `.get()` for dictionary access
|
||||
4. **IndexError** - Check length before indexing
|
||||
5. **Wrong format** - Return `[{"json": {...}}]`, not plain objects
|
||||
|
||||
**Golden Rules**:
|
||||
- NO external libraries (use JavaScript instead)
|
||||
- ALWAYS use `.get()` for dictionaries
|
||||
- ALWAYS return `[{"json": {...}}]` format
|
||||
- CHECK lengths before list access
|
||||
- ACCESS webhook data via `["body"]`
|
||||
|
||||
**Remember**:
|
||||
- JavaScript is recommended for 95% of use cases
|
||||
- Python has limitations (no requests, pandas, numpy)
|
||||
- Use n8n nodes for complex operations
|
||||
|
||||
**See Also**:
|
||||
- [SKILL.md](SKILL.md) - Python Code overview
|
||||
- [DATA_ACCESS.md](DATA_ACCESS.md) - Data access patterns
|
||||
- [STANDARD_LIBRARY.md](STANDARD_LIBRARY.md) - Available modules
|
||||
- [COMMON_PATTERNS.md](COMMON_PATTERNS.md) - Production patterns
|
||||
386
skills/n8n-code-python/README.md
Normal file
386
skills/n8n-code-python/README.md
Normal file
@@ -0,0 +1,386 @@
|
||||
# n8n Code Python Skill
|
||||
|
||||
Expert guidance for writing Python code in n8n Code nodes.
|
||||
|
||||
---
|
||||
|
||||
## ⚠️ Important: JavaScript First
|
||||
|
||||
**Use JavaScript for 95% of use cases.**
|
||||
|
||||
Python in n8n has **NO external libraries** (no requests, pandas, numpy).
|
||||
|
||||
**When to use Python**:
|
||||
- You have complex Python-specific logic
|
||||
- You need Python's standard library features
|
||||
- You're more comfortable with Python than JavaScript
|
||||
|
||||
**When to use JavaScript** (recommended):
|
||||
- HTTP requests ($helpers.httpRequest available)
|
||||
- Date/time operations (Luxon library included)
|
||||
- Most data transformations
|
||||
- When in doubt
|
||||
|
||||
---
|
||||
|
||||
## What This Skill Teaches
|
||||
|
||||
### Core Concepts
|
||||
|
||||
1. **Critical Limitation**: No external libraries
|
||||
2. **Data Access**: `_input.all()`, `_input.first()`, `_input.item`
|
||||
3. **Webhook Gotcha**: Data is under `_json["body"]`
|
||||
4. **Return Format**: Must return `[{"json": {...}}]`
|
||||
5. **Standard Library**: json, datetime, re, base64, hashlib, etc.
|
||||
|
||||
### Top 5 Error Prevention
|
||||
|
||||
This skill emphasizes **error prevention**:
|
||||
|
||||
1. **ModuleNotFoundError** (trying to import external libraries)
|
||||
2. **Empty code / missing return**
|
||||
3. **KeyError** (dictionary access without .get())
|
||||
4. **IndexError** (list access without bounds checking)
|
||||
5. **Incorrect return format**
|
||||
|
||||
These 5 errors are the most common in Python Code nodes.
|
||||
|
||||
---
|
||||
|
||||
## Skill Activation
|
||||
|
||||
This skill activates when you:
|
||||
- Write Python in Code nodes
|
||||
- Ask about Python limitations
|
||||
- Need to know available standard library
|
||||
- Troubleshoot Python Code node errors
|
||||
- Work with Python data structures
|
||||
|
||||
**Example queries**:
|
||||
- "Can I use pandas in Python Code node?"
|
||||
- "How do I access webhook data in Python?"
|
||||
- "What Python libraries are available?"
|
||||
- "Write Python code to process JSON"
|
||||
- "Why is requests module not found?"
|
||||
|
||||
---
|
||||
|
||||
## File Structure
|
||||
|
||||
### SKILL.md (719 lines)
|
||||
**Quick start** and overview
|
||||
- When to use Python vs JavaScript
|
||||
- Critical limitation (no external libraries)
|
||||
- Mode selection (All Items vs Each Item)
|
||||
- Data access overview
|
||||
- Return format requirements
|
||||
- Standard library overview
|
||||
|
||||
### DATA_ACCESS.md (703 lines)
|
||||
**Complete data access patterns**
|
||||
- `_input.all()` - Process all items
|
||||
- `_input.first()` - Get first item
|
||||
- `_input.item` - Current item (Each Item mode)
|
||||
- `_node["Name"]` - Reference other nodes
|
||||
- Webhook body structure (critical gotcha!)
|
||||
- Pattern selection guide
|
||||
|
||||
### STANDARD_LIBRARY.md (850 lines)
|
||||
**Available Python modules**
|
||||
- json - JSON parsing
|
||||
- datetime - Date/time operations
|
||||
- re - Regular expressions
|
||||
- base64 - Encoding/decoding
|
||||
- hashlib - Hashing
|
||||
- urllib.parse - URL operations
|
||||
- math, random, statistics
|
||||
- What's NOT available (requests, pandas, numpy)
|
||||
- Workarounds for missing libraries
|
||||
|
||||
### COMMON_PATTERNS.md (895 lines)
|
||||
**10 production-tested patterns**
|
||||
1. Multi-source data aggregation
|
||||
2. Regex-based filtering
|
||||
3. Markdown to structured data
|
||||
4. JSON object comparison
|
||||
5. CRM data transformation
|
||||
6. Release notes processing
|
||||
7. Array transformation
|
||||
8. Dictionary lookup
|
||||
9. Top N filtering
|
||||
10. String aggregation
|
||||
|
||||
### ERROR_PATTERNS.md (730 lines)
|
||||
**Top 5 errors with solutions**
|
||||
1. ModuleNotFoundError (external libraries)
|
||||
2. Empty code / missing return
|
||||
3. KeyError (dictionary access)
|
||||
4. IndexError (list access)
|
||||
5. Incorrect return format
|
||||
- Error prevention checklist
|
||||
- Quick fix reference
|
||||
- Testing patterns
|
||||
|
||||
---
|
||||
|
||||
## Integration with Other Skills
|
||||
|
||||
This skill works with:
|
||||
|
||||
### n8n Expression Syntax
|
||||
- Python uses code syntax, not {{}} expressions
|
||||
- Data access patterns differ ($ vs _)
|
||||
|
||||
### n8n MCP Tools Expert
|
||||
- Use MCP tools to validate Code node configurations
|
||||
- Check node setup with `get_node_essentials`
|
||||
|
||||
### n8n Workflow Patterns
|
||||
- Code nodes fit into larger workflow patterns
|
||||
- Often used after HTTP Request or Webhook nodes
|
||||
|
||||
### n8n Code JavaScript
|
||||
- Compare Python vs JavaScript approaches
|
||||
- Understand when to use which language
|
||||
- JavaScript recommended for 95% of cases
|
||||
|
||||
### n8n Node Configuration
|
||||
- Configure Code node mode (All Items vs Each Item)
|
||||
- Set up proper connections
|
||||
|
||||
---
|
||||
|
||||
## Success Metrics
|
||||
|
||||
After using this skill, you should be able to:
|
||||
|
||||
- [ ] **Know the limitation**: Python has NO external libraries
|
||||
- [ ] **Choose language**: JavaScript for 95% of cases, Python when needed
|
||||
- [ ] **Access data**: Use `_input.all()`, `_input.first()`, `_input.item`
|
||||
- [ ] **Handle webhooks**: Access data via `_json["body"]`
|
||||
- [ ] **Return properly**: Always return `[{"json": {...}}]`
|
||||
- [ ] **Avoid KeyError**: Use `.get()` for dictionary access
|
||||
- [ ] **Use standard library**: Know what's available (json, datetime, re, etc.)
|
||||
- [ ] **Prevent errors**: Avoid top 5 common errors
|
||||
- [ ] **Choose alternatives**: Use n8n nodes when libraries needed
|
||||
- [ ] **Write production code**: Use proven patterns
|
||||
|
||||
---
|
||||
|
||||
## Quick Reference
|
||||
|
||||
### Data Access
|
||||
```python
|
||||
all_items = _input.all()
|
||||
first_item = _input.first()
|
||||
current_item = _input.item # Each Item mode only
|
||||
other_node = _node["NodeName"]
|
||||
```
|
||||
|
||||
### Webhook Data
|
||||
```python
|
||||
webhook = _input.first()["json"]
|
||||
body = webhook.get("body", {})
|
||||
name = body.get("name")
|
||||
```
|
||||
|
||||
### Safe Dictionary Access
|
||||
```python
|
||||
# ✅ Use .get() with defaults
|
||||
value = data.get("field", "default")
|
||||
|
||||
# ❌ Risky - may raise KeyError
|
||||
value = data["field"]
|
||||
```
|
||||
|
||||
### Return Format
|
||||
```python
|
||||
# ✅ Correct format
|
||||
return [{"json": {"result": "success"}}]
|
||||
|
||||
# ❌ Wrong - plain dict
|
||||
return {"result": "success"}
|
||||
```
|
||||
|
||||
### Standard Library
|
||||
```python
|
||||
# ✅ Available
|
||||
import json
|
||||
import datetime
|
||||
import re
|
||||
import base64
|
||||
import hashlib
|
||||
|
||||
# ❌ NOT available
|
||||
import requests # ModuleNotFoundError!
|
||||
import pandas # ModuleNotFoundError!
|
||||
import numpy # ModuleNotFoundError!
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Common Use Cases
|
||||
|
||||
### Use Case 1: Process Webhook Data
|
||||
```python
|
||||
webhook = _input.first()["json"]
|
||||
body = webhook.get("body", {})
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
"name": body.get("name"),
|
||||
"email": body.get("email"),
|
||||
"processed": True
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
### Use Case 2: Filter and Transform
|
||||
```python
|
||||
all_items = _input.all()
|
||||
|
||||
active = [
|
||||
{"json": {**item["json"], "filtered": True}}
|
||||
for item in all_items
|
||||
if item["json"].get("status") == "active"
|
||||
]
|
||||
|
||||
return active
|
||||
```
|
||||
|
||||
### Use Case 3: Aggregate Statistics
|
||||
```python
|
||||
import statistics
|
||||
|
||||
all_items = _input.all()
|
||||
amounts = [item["json"].get("amount", 0) for item in all_items]
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
"total": sum(amounts),
|
||||
"average": statistics.mean(amounts) if amounts else 0,
|
||||
"count": len(amounts)
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
### Use Case 4: Parse JSON String
|
||||
```python
|
||||
import json
|
||||
|
||||
data = _input.first()["json"]["body"]
|
||||
json_string = data.get("payload", "{}")
|
||||
|
||||
try:
|
||||
parsed = json.loads(json_string)
|
||||
return [{"json": parsed}]
|
||||
except json.JSONDecodeError:
|
||||
return [{"json": {"error": "Invalid JSON"}}]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Limitations and Workarounds
|
||||
|
||||
### Limitation 1: No HTTP Requests Library
|
||||
**Problem**: No `requests` library
|
||||
**Workaround**: Use HTTP Request node or JavaScript
|
||||
|
||||
### Limitation 2: No Data Analysis Library
|
||||
**Problem**: No `pandas` or `numpy`
|
||||
**Workaround**: Use list comprehensions and standard library
|
||||
|
||||
### Limitation 3: No Database Drivers
|
||||
**Problem**: No `psycopg2`, `pymongo`, etc.
|
||||
**Workaround**: Use n8n database nodes (Postgres, MySQL, MongoDB)
|
||||
|
||||
### Limitation 4: No Web Scraping
|
||||
**Problem**: No `beautifulsoup4` or `selenium`
|
||||
**Workaround**: Use HTML Extract node
|
||||
|
||||
---
|
||||
|
||||
## Best Practices
|
||||
|
||||
1. **Use JavaScript for most cases** (95% recommendation)
|
||||
2. **Use .get() for dictionaries** (avoid KeyError)
|
||||
3. **Check lengths before indexing** (avoid IndexError)
|
||||
4. **Always return proper format**: `[{"json": {...}}]`
|
||||
5. **Access webhook data via ["body"]**
|
||||
6. **Use standard library only** (no external imports)
|
||||
7. **Handle empty input** (check `if items:`)
|
||||
8. **Test both modes** (All Items and Each Item)
|
||||
|
||||
---
|
||||
|
||||
## When Python is the Right Choice
|
||||
|
||||
Use Python when:
|
||||
- Complex text processing (re module)
|
||||
- Mathematical calculations (math, statistics)
|
||||
- Date/time manipulation (datetime)
|
||||
- Cryptographic operations (hashlib)
|
||||
- You have existing Python logic to reuse
|
||||
- Team is more comfortable with Python
|
||||
|
||||
Use JavaScript instead when:
|
||||
- Making HTTP requests
|
||||
- Working with dates (Luxon included)
|
||||
- Most data transformations
|
||||
- When in doubt
|
||||
|
||||
---
|
||||
|
||||
## Learning Path
|
||||
|
||||
**Beginner**:
|
||||
1. Read SKILL.md - Understand the limitation
|
||||
2. Try DATA_ACCESS.md examples - Learn `_input` patterns
|
||||
3. Practice safe dictionary access with `.get()`
|
||||
|
||||
**Intermediate**:
|
||||
4. Study STANDARD_LIBRARY.md - Know what's available
|
||||
5. Try COMMON_PATTERNS.md examples - Use proven patterns
|
||||
6. Learn ERROR_PATTERNS.md - Avoid common mistakes
|
||||
|
||||
**Advanced**:
|
||||
7. Combine multiple patterns
|
||||
8. Use standard library effectively
|
||||
9. Know when to switch to JavaScript
|
||||
10. Write production-ready code
|
||||
|
||||
---
|
||||
|
||||
## Support
|
||||
|
||||
**Questions?**
|
||||
- Check ERROR_PATTERNS.md for common issues
|
||||
- Review COMMON_PATTERNS.md for examples
|
||||
- Consider using JavaScript instead
|
||||
|
||||
**Related Skills**:
|
||||
- n8n Code JavaScript - Alternative (recommended for 95% of cases)
|
||||
- n8n Expression Syntax - For {{}} expressions in other nodes
|
||||
- n8n Workflow Patterns - Bigger picture workflow design
|
||||
|
||||
---
|
||||
|
||||
## Version
|
||||
|
||||
**Version**: 1.0.0
|
||||
**Status**: Production Ready
|
||||
**Compatibility**: n8n Code node (Python mode)
|
||||
|
||||
---
|
||||
|
||||
## Credits
|
||||
|
||||
Part of the n8n-skills project.
|
||||
|
||||
**Conceived by Romuald Członkowski**
|
||||
- Website: [www.aiadvisors.pl/en](https://www.aiadvisors.pl/en)
|
||||
- Part of [n8n-mcp project](https://github.com/czlonkowski/n8n-mcp)
|
||||
|
||||
---
|
||||
|
||||
**Remember**: JavaScript is recommended for 95% of use cases. Use Python only when you specifically need Python's standard library features.
|
||||
748
skills/n8n-code-python/SKILL.md
Normal file
748
skills/n8n-code-python/SKILL.md
Normal file
@@ -0,0 +1,748 @@
|
||||
---
|
||||
name: n8n-code-python
|
||||
description: 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.
|
||||
---
|
||||
|
||||
# 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](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](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](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](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](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")
|
||||
```
|
||||
|
||||
### #5: Webhook Body Nesting
|
||||
|
||||
```python
|
||||
# ❌ 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](ERROR_PATTERNS.md) for comprehensive error guide
|
||||
|
||||
---
|
||||
|
||||
## Standard Library Reference
|
||||
|
||||
### Most Useful Modules
|
||||
|
||||
```python
|
||||
# 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](STANDARD_LIBRARY.md) for complete reference
|
||||
|
||||
---
|
||||
|
||||
## Best Practices
|
||||
|
||||
### 1. Always Use .get() for Dictionary Access
|
||||
|
||||
```python
|
||||
# ✅ 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
|
||||
|
||||
```python
|
||||
# ✅ 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
|
||||
|
||||
```python
|
||||
# ✅ 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
|
||||
|
||||
```python
|
||||
# ✅ 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
|
||||
|
||||
```python
|
||||
# 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:
|
||||
- ✅ 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 ($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_essentials("nodes-base.code")`
|
||||
- Validate code: `validate_node_operation()`
|
||||
|
||||
**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.
|
||||
974
skills/n8n-code-python/STANDARD_LIBRARY.md
Normal file
974
skills/n8n-code-python/STANDARD_LIBRARY.md
Normal file
@@ -0,0 +1,974 @@
|
||||
# Standard Library Reference - Python Code Node
|
||||
|
||||
Complete guide to available Python standard library modules in n8n Code nodes.
|
||||
|
||||
---
|
||||
|
||||
## ⚠️ Critical Limitation
|
||||
|
||||
**NO EXTERNAL LIBRARIES AVAILABLE**
|
||||
|
||||
Python Code nodes in n8n have **ONLY** the Python standard library. No pip packages.
|
||||
|
||||
```python
|
||||
# ❌ NOT AVAILABLE - Will cause ModuleNotFoundError
|
||||
import requests # No HTTP library!
|
||||
import pandas # No data analysis!
|
||||
import numpy # No numerical computing!
|
||||
import bs4 # No web scraping!
|
||||
import selenium # No browser automation!
|
||||
import psycopg2 # No database drivers!
|
||||
import pymongo # No MongoDB!
|
||||
import sqlalchemy # No ORMs!
|
||||
|
||||
# ✅ AVAILABLE - Standard library only
|
||||
import json
|
||||
import datetime
|
||||
import re
|
||||
import base64
|
||||
import hashlib
|
||||
import urllib.parse
|
||||
import urllib.request
|
||||
import math
|
||||
import random
|
||||
import statistics
|
||||
```
|
||||
|
||||
**Recommendation**: Use **JavaScript** for 95% of use cases. JavaScript has more capabilities in n8n.
|
||||
|
||||
---
|
||||
|
||||
## Available Modules
|
||||
|
||||
### Priority 1: Most Useful (Use These)
|
||||
|
||||
1. **json** - JSON parsing and generation
|
||||
2. **datetime** - Date and time operations
|
||||
3. **re** - Regular expressions
|
||||
4. **base64** - Base64 encoding/decoding
|
||||
5. **hashlib** - Hashing (MD5, SHA256, etc.)
|
||||
6. **urllib.parse** - URL parsing and encoding
|
||||
|
||||
### Priority 2: Moderately Useful
|
||||
|
||||
7. **math** - Mathematical functions
|
||||
8. **random** - Random number generation
|
||||
9. **statistics** - Statistical functions
|
||||
10. **collections** - Specialized data structures
|
||||
|
||||
### Priority 3: Occasionally Useful
|
||||
|
||||
11. **itertools** - Iterator tools
|
||||
12. **functools** - Higher-order functions
|
||||
13. **operator** - Standard operators as functions
|
||||
14. **string** - String constants and templates
|
||||
15. **textwrap** - Text wrapping utilities
|
||||
|
||||
---
|
||||
|
||||
## Module 1: json - JSON Operations
|
||||
|
||||
**Most common module** - Parse and generate JSON data.
|
||||
|
||||
### Parse JSON String
|
||||
|
||||
```python
|
||||
import json
|
||||
|
||||
# Parse JSON string to Python dict
|
||||
json_string = '{"name": "Alice", "age": 30}'
|
||||
data = json.loads(json_string)
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
"name": data["name"],
|
||||
"age": data["age"],
|
||||
"parsed": True
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
### Generate JSON String
|
||||
|
||||
```python
|
||||
import json
|
||||
|
||||
# Convert Python dict to JSON string
|
||||
data = {
|
||||
"users": [
|
||||
{"id": 1, "name": "Alice"},
|
||||
{"id": 2, "name": "Bob"}
|
||||
],
|
||||
"total": 2
|
||||
}
|
||||
|
||||
json_string = json.dumps(data, indent=2)
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
"json_output": json_string,
|
||||
"length": len(json_string)
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
### Handle JSON Errors
|
||||
|
||||
```python
|
||||
import json
|
||||
|
||||
webhook_data = _input.first()["json"]["body"]
|
||||
json_string = webhook_data.get("data", "")
|
||||
|
||||
try:
|
||||
parsed = json.loads(json_string)
|
||||
status = "valid"
|
||||
error = None
|
||||
except json.JSONDecodeError as e:
|
||||
parsed = None
|
||||
status = "invalid"
|
||||
error = str(e)
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
"status": status,
|
||||
"data": parsed,
|
||||
"error": error
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
### Pretty Print JSON
|
||||
|
||||
```python
|
||||
import json
|
||||
|
||||
# Format JSON with indentation
|
||||
data = _input.first()["json"]
|
||||
|
||||
pretty_json = json.dumps(data, indent=2, sort_keys=True)
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
"formatted": pretty_json
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Module 2: datetime - Date and Time
|
||||
|
||||
**Very common** - Date parsing, formatting, calculations.
|
||||
|
||||
### Current Date and Time
|
||||
|
||||
```python
|
||||
from datetime import datetime
|
||||
|
||||
now = datetime.now()
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
"timestamp": now.isoformat(),
|
||||
"date": now.strftime("%Y-%m-%d"),
|
||||
"time": now.strftime("%H:%M:%S"),
|
||||
"formatted": now.strftime("%B %d, %Y at %I:%M %p")
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
### Parse Date String
|
||||
|
||||
```python
|
||||
from datetime import datetime
|
||||
|
||||
date_string = "2025-01-15T14:30:00"
|
||||
dt = datetime.fromisoformat(date_string)
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
"year": dt.year,
|
||||
"month": dt.month,
|
||||
"day": dt.day,
|
||||
"hour": dt.hour,
|
||||
"weekday": dt.strftime("%A")
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
### Date Calculations
|
||||
|
||||
```python
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
now = datetime.now()
|
||||
|
||||
# Calculate future/past dates
|
||||
tomorrow = now + timedelta(days=1)
|
||||
yesterday = now - timedelta(days=1)
|
||||
next_week = now + timedelta(weeks=1)
|
||||
one_hour_ago = now - timedelta(hours=1)
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
"now": now.isoformat(),
|
||||
"tomorrow": tomorrow.isoformat(),
|
||||
"yesterday": yesterday.isoformat(),
|
||||
"next_week": next_week.isoformat(),
|
||||
"one_hour_ago": one_hour_ago.isoformat()
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
### Compare Dates
|
||||
|
||||
```python
|
||||
from datetime import datetime
|
||||
|
||||
date1 = datetime(2025, 1, 15)
|
||||
date2 = datetime(2025, 1, 20)
|
||||
|
||||
# Calculate difference
|
||||
diff = date2 - date1
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
"days_difference": diff.days,
|
||||
"seconds_difference": diff.total_seconds(),
|
||||
"date1_is_earlier": date1 < date2,
|
||||
"date2_is_later": date2 > date1
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
### Format Dates
|
||||
|
||||
```python
|
||||
from datetime import datetime
|
||||
|
||||
dt = datetime.now()
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
"iso": dt.isoformat(),
|
||||
"us_format": dt.strftime("%m/%d/%Y"),
|
||||
"eu_format": dt.strftime("%d/%m/%Y"),
|
||||
"long_format": dt.strftime("%A, %B %d, %Y"),
|
||||
"time_12h": dt.strftime("%I:%M %p"),
|
||||
"time_24h": dt.strftime("%H:%M:%S")
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Module 3: re - Regular Expressions
|
||||
|
||||
**Common** - Pattern matching, text extraction, validation.
|
||||
|
||||
### Pattern Matching
|
||||
|
||||
```python
|
||||
import re
|
||||
|
||||
text = "Email: alice@example.com, Phone: 555-1234"
|
||||
|
||||
# Find email
|
||||
email_match = re.search(r'\b[\w.-]+@[\w.-]+\.\w+\b', text)
|
||||
email = email_match.group(0) if email_match else None
|
||||
|
||||
# Find phone
|
||||
phone_match = re.search(r'\d{3}-\d{4}', text)
|
||||
phone = phone_match.group(0) if phone_match else None
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
"email": email,
|
||||
"phone": phone
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
### Extract All Matches
|
||||
|
||||
```python
|
||||
import re
|
||||
|
||||
text = "Tags: #python #automation #workflow #n8n"
|
||||
|
||||
# Find all hashtags
|
||||
hashtags = re.findall(r'#(\w+)', text)
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
"tags": hashtags,
|
||||
"count": len(hashtags)
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
### Replace Patterns
|
||||
|
||||
```python
|
||||
import re
|
||||
|
||||
text = "Price: $99.99, Discount: $10.00"
|
||||
|
||||
# Remove dollar signs
|
||||
cleaned = re.sub(r'\$', '', text)
|
||||
|
||||
# Replace multiple spaces with single space
|
||||
normalized = re.sub(r'\s+', ' ', cleaned)
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
"original": text,
|
||||
"cleaned": cleaned,
|
||||
"normalized": normalized
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
### Validate Format
|
||||
|
||||
```python
|
||||
import re
|
||||
|
||||
email = _input.first()["json"]["body"].get("email", "")
|
||||
|
||||
# Email validation pattern
|
||||
email_pattern = r'^[\w.-]+@[\w.-]+\.\w+$'
|
||||
is_valid = bool(re.match(email_pattern, email))
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
"email": email,
|
||||
"valid": is_valid
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
### Split on Pattern
|
||||
|
||||
```python
|
||||
import re
|
||||
|
||||
text = "apple,banana;orange|grape"
|
||||
|
||||
# Split on multiple delimiters
|
||||
items = re.split(r'[,;|]', text)
|
||||
|
||||
# Clean up whitespace
|
||||
items = [item.strip() for item in items]
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
"items": items,
|
||||
"count": len(items)
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Module 4: base64 - Encoding/Decoding
|
||||
|
||||
**Common** - Encode binary data, API authentication.
|
||||
|
||||
### Encode String to Base64
|
||||
|
||||
```python
|
||||
import base64
|
||||
|
||||
text = "Hello, World!"
|
||||
|
||||
# Encode to base64
|
||||
encoded_bytes = base64.b64encode(text.encode('utf-8'))
|
||||
encoded_string = encoded_bytes.decode('utf-8')
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
"original": text,
|
||||
"encoded": encoded_string
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
### Decode Base64 to String
|
||||
|
||||
```python
|
||||
import base64
|
||||
|
||||
encoded = "SGVsbG8sIFdvcmxkIQ=="
|
||||
|
||||
# Decode from base64
|
||||
decoded_bytes = base64.b64decode(encoded)
|
||||
decoded_string = decoded_bytes.decode('utf-8')
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
"encoded": encoded,
|
||||
"decoded": decoded_string
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
### Basic Auth Header
|
||||
|
||||
```python
|
||||
import base64
|
||||
|
||||
username = "admin"
|
||||
password = "secret123"
|
||||
|
||||
# Create Basic Auth header
|
||||
credentials = f"{username}:{password}"
|
||||
encoded = base64.b64encode(credentials.encode('utf-8')).decode('utf-8')
|
||||
auth_header = f"Basic {encoded}"
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
"authorization": auth_header
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Module 5: hashlib - Hashing
|
||||
|
||||
**Common** - Generate checksums, hash passwords, create IDs.
|
||||
|
||||
### MD5 Hash
|
||||
|
||||
```python
|
||||
import hashlib
|
||||
|
||||
text = "Hello, World!"
|
||||
|
||||
# Generate MD5 hash
|
||||
md5_hash = hashlib.md5(text.encode('utf-8')).hexdigest()
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
"original": text,
|
||||
"md5": md5_hash
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
### SHA256 Hash
|
||||
|
||||
```python
|
||||
import hashlib
|
||||
|
||||
data = _input.first()["json"]["body"]
|
||||
text = data.get("password", "")
|
||||
|
||||
# Generate SHA256 hash (more secure than MD5)
|
||||
sha256_hash = hashlib.sha256(text.encode('utf-8')).hexdigest()
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
"hashed": sha256_hash
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
### Generate Unique ID
|
||||
|
||||
```python
|
||||
import hashlib
|
||||
from datetime import datetime
|
||||
|
||||
# Create unique ID from multiple values
|
||||
unique_string = f"{datetime.now().isoformat()}-{_json.get('user_id', 'unknown')}"
|
||||
unique_id = hashlib.sha256(unique_string.encode('utf-8')).hexdigest()[:16]
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
"id": unique_id,
|
||||
"generated_at": datetime.now().isoformat()
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Module 6: urllib.parse - URL Operations
|
||||
|
||||
**Common** - Parse URLs, encode parameters.
|
||||
|
||||
### Parse URL
|
||||
|
||||
```python
|
||||
from urllib.parse import urlparse
|
||||
|
||||
url = "https://example.com/path?key=value&foo=bar#section"
|
||||
|
||||
parsed = urlparse(url)
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
"scheme": parsed.scheme, # "https"
|
||||
"netloc": parsed.netloc, # "example.com"
|
||||
"path": parsed.path, # "/path"
|
||||
"query": parsed.query, # "key=value&foo=bar"
|
||||
"fragment": parsed.fragment # "section"
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
### URL Encode Parameters
|
||||
|
||||
```python
|
||||
from urllib.parse import urlencode
|
||||
|
||||
params = {
|
||||
"name": "Alice Smith",
|
||||
"email": "alice@example.com",
|
||||
"message": "Hello, World!"
|
||||
}
|
||||
|
||||
# Encode parameters for URL
|
||||
encoded = urlencode(params)
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
"query_string": encoded,
|
||||
"full_url": f"https://api.example.com/submit?{encoded}"
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
### Parse Query String
|
||||
|
||||
```python
|
||||
from urllib.parse import parse_qs
|
||||
|
||||
query_string = "name=Alice&age=30&tags=python&tags=n8n"
|
||||
|
||||
# Parse query string
|
||||
params = parse_qs(query_string)
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
"name": params.get("name", [""])[0],
|
||||
"age": int(params.get("age", ["0"])[0]),
|
||||
"tags": params.get("tags", [])
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
### URL Encode/Decode Strings
|
||||
|
||||
```python
|
||||
from urllib.parse import quote, unquote
|
||||
|
||||
text = "Hello, World! 你好"
|
||||
|
||||
# URL encode
|
||||
encoded = quote(text)
|
||||
|
||||
# URL decode
|
||||
decoded = unquote(encoded)
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
"original": text,
|
||||
"encoded": encoded,
|
||||
"decoded": decoded
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Module 7: math - Mathematical Operations
|
||||
|
||||
**Moderately useful** - Advanced math functions.
|
||||
|
||||
### Basic Math Functions
|
||||
|
||||
```python
|
||||
import math
|
||||
|
||||
number = 16.7
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
"ceiling": math.ceil(number), # 17
|
||||
"floor": math.floor(number), # 16
|
||||
"rounded": round(number), # 17
|
||||
"square_root": math.sqrt(16), # 4.0
|
||||
"power": math.pow(2, 3), # 8.0
|
||||
"absolute": math.fabs(-5.5) # 5.5
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
### Trigonometry
|
||||
|
||||
```python
|
||||
import math
|
||||
|
||||
angle_degrees = 45
|
||||
angle_radians = math.radians(angle_degrees)
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
"sine": math.sin(angle_radians),
|
||||
"cosine": math.cos(angle_radians),
|
||||
"tangent": math.tan(angle_radians),
|
||||
"pi": math.pi,
|
||||
"e": math.e
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
### Logarithms
|
||||
|
||||
```python
|
||||
import math
|
||||
|
||||
number = 100
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
"log10": math.log10(number), # 2.0
|
||||
"natural_log": math.log(number), # 4.605...
|
||||
"log2": math.log2(number) # 6.644...
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Module 8: random - Random Numbers
|
||||
|
||||
**Moderately useful** - Generate random data, sampling.
|
||||
|
||||
### Random Numbers
|
||||
|
||||
```python
|
||||
import random
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
"random_float": random.random(), # 0.0 to 1.0
|
||||
"random_int": random.randint(1, 100), # 1 to 100
|
||||
"random_range": random.randrange(0, 100, 5) # 0, 5, 10, ..., 95
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
### Random Choice
|
||||
|
||||
```python
|
||||
import random
|
||||
|
||||
colors = ["red", "green", "blue", "yellow"]
|
||||
users = [{"id": 1, "name": "Alice"}, {"id": 2, "name": "Bob"}]
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
"random_color": random.choice(colors),
|
||||
"random_user": random.choice(users)
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
### Shuffle List
|
||||
|
||||
```python
|
||||
import random
|
||||
|
||||
items = [1, 2, 3, 4, 5]
|
||||
shuffled = items.copy()
|
||||
random.shuffle(shuffled)
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
"original": items,
|
||||
"shuffled": shuffled
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
### Random Sample
|
||||
|
||||
```python
|
||||
import random
|
||||
|
||||
items = list(range(1, 101))
|
||||
|
||||
# Get 10 random items without replacement
|
||||
sample = random.sample(items, 10)
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
"sample": sample,
|
||||
"count": len(sample)
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Module 9: statistics - Statistical Functions
|
||||
|
||||
**Moderately useful** - Calculate stats from data.
|
||||
|
||||
### Basic Statistics
|
||||
|
||||
```python
|
||||
import statistics
|
||||
|
||||
numbers = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
"mean": statistics.mean(numbers), # 55.0
|
||||
"median": statistics.median(numbers), # 55.0
|
||||
"mode": statistics.mode([1, 2, 2, 3]), # 2
|
||||
"stdev": statistics.stdev(numbers), # 30.28...
|
||||
"variance": statistics.variance(numbers) # 916.67...
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
### Aggregate from Items
|
||||
|
||||
```python
|
||||
import statistics
|
||||
|
||||
all_items = _input.all()
|
||||
|
||||
# Extract amounts
|
||||
amounts = [item["json"].get("amount", 0) for item in all_items]
|
||||
|
||||
if amounts:
|
||||
return [{
|
||||
"json": {
|
||||
"count": len(amounts),
|
||||
"total": sum(amounts),
|
||||
"average": statistics.mean(amounts),
|
||||
"median": statistics.median(amounts),
|
||||
"min": min(amounts),
|
||||
"max": max(amounts),
|
||||
"range": max(amounts) - min(amounts)
|
||||
}
|
||||
}]
|
||||
else:
|
||||
return [{"json": {"error": "No data"}}]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Workarounds for Missing Libraries
|
||||
|
||||
### HTTP Requests (No requests library)
|
||||
|
||||
```python
|
||||
# ❌ Can't use requests library
|
||||
# import requests # ModuleNotFoundError!
|
||||
|
||||
# ✅ Use HTTP Request node instead
|
||||
# Add HTTP Request node BEFORE Code node
|
||||
# Access the response in Code node
|
||||
|
||||
response_data = _input.first()["json"]
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
"status": response_data.get("status"),
|
||||
"data": response_data.get("body"),
|
||||
"processed": True
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
### Data Processing (No pandas)
|
||||
|
||||
```python
|
||||
# ❌ Can't use pandas
|
||||
# import pandas as pd # ModuleNotFoundError!
|
||||
|
||||
# ✅ Use Python's built-in list comprehensions
|
||||
all_items = _input.all()
|
||||
|
||||
# Filter
|
||||
active_items = [
|
||||
item for item in all_items
|
||||
if item["json"].get("status") == "active"
|
||||
]
|
||||
|
||||
# Group by
|
||||
from collections import defaultdict
|
||||
grouped = defaultdict(list)
|
||||
|
||||
for item in all_items:
|
||||
category = item["json"].get("category", "other")
|
||||
grouped[category].append(item["json"])
|
||||
|
||||
# Aggregate
|
||||
import statistics
|
||||
amounts = [item["json"].get("amount", 0) for item in all_items]
|
||||
total = sum(amounts)
|
||||
average = statistics.mean(amounts) if amounts else 0
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
"active_count": len(active_items),
|
||||
"grouped": dict(grouped),
|
||||
"total": total,
|
||||
"average": average
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
### Database Operations (No drivers)
|
||||
|
||||
```python
|
||||
# ❌ Can't use database drivers
|
||||
# import psycopg2 # ModuleNotFoundError!
|
||||
# import pymongo # ModuleNotFoundError!
|
||||
|
||||
# ✅ Use n8n database nodes instead
|
||||
# Add Postgres/MySQL/MongoDB node BEFORE Code node
|
||||
# Process results in Code node
|
||||
|
||||
db_results = _input.first()["json"]
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
"record_count": len(db_results) if isinstance(db_results, list) else 1,
|
||||
"processed": True
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Complete Standard Library List
|
||||
|
||||
**Available** (commonly useful):
|
||||
- json
|
||||
- datetime, time
|
||||
- re
|
||||
- base64
|
||||
- hashlib
|
||||
- urllib.parse, urllib.request, urllib.error
|
||||
- math
|
||||
- random
|
||||
- statistics
|
||||
- collections (defaultdict, Counter, namedtuple)
|
||||
- itertools
|
||||
- functools
|
||||
- operator
|
||||
- string
|
||||
- textwrap
|
||||
|
||||
**Available** (less common):
|
||||
- os.path (path operations only)
|
||||
- copy
|
||||
- typing
|
||||
- enum
|
||||
- decimal
|
||||
- fractions
|
||||
|
||||
**NOT Available** (external libraries):
|
||||
- requests (HTTP)
|
||||
- pandas (data analysis)
|
||||
- numpy (numerical computing)
|
||||
- bs4/beautifulsoup4 (HTML parsing)
|
||||
- selenium (browser automation)
|
||||
- psycopg2, pymongo, sqlalchemy (databases)
|
||||
- flask, fastapi (web frameworks)
|
||||
- pillow (image processing)
|
||||
- openpyxl, xlsxwriter (Excel)
|
||||
|
||||
---
|
||||
|
||||
## Best Practices
|
||||
|
||||
### 1. Use Standard Library When Possible
|
||||
|
||||
```python
|
||||
# ✅ GOOD: Use standard library
|
||||
import json
|
||||
import datetime
|
||||
import re
|
||||
|
||||
data = _input.first()["json"]
|
||||
processed = json.loads(data.get("json_string", "{}"))
|
||||
|
||||
return [{"json": processed}]
|
||||
```
|
||||
|
||||
### 2. Fall Back to n8n Nodes
|
||||
|
||||
```python
|
||||
# For operations requiring external libraries,
|
||||
# use n8n nodes instead:
|
||||
# - HTTP Request for API calls
|
||||
# - Postgres/MySQL for databases
|
||||
# - Extract from File for parsing
|
||||
|
||||
# Then process results in Code node
|
||||
result = _input.first()["json"]
|
||||
return [{"json": {"processed": result}}]
|
||||
```
|
||||
|
||||
### 3. Combine Multiple Modules
|
||||
|
||||
```python
|
||||
import json
|
||||
import base64
|
||||
import hashlib
|
||||
from datetime import datetime
|
||||
|
||||
# Combine modules for complex operations
|
||||
data = _input.first()["json"]["body"]
|
||||
|
||||
# Hash sensitive data
|
||||
user_id = hashlib.sha256(data.get("email", "").encode()).hexdigest()[:16]
|
||||
|
||||
# Encode for storage
|
||||
encoded_data = base64.b64encode(json.dumps(data).encode()).decode()
|
||||
|
||||
return [{
|
||||
"json": {
|
||||
"user_id": user_id,
|
||||
"encoded_data": encoded_data,
|
||||
"timestamp": datetime.now().isoformat()
|
||||
}
|
||||
}]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Summary
|
||||
|
||||
**Most Useful Modules**:
|
||||
1. json - Parse/generate JSON
|
||||
2. datetime - Date operations
|
||||
3. re - Regular expressions
|
||||
4. base64 - Encoding
|
||||
5. hashlib - Hashing
|
||||
6. urllib.parse - URL operations
|
||||
|
||||
**Critical Limitation**:
|
||||
- NO external libraries (requests, pandas, numpy, etc.)
|
||||
|
||||
**Recommended Approach**:
|
||||
- Use **JavaScript** for 95% of use cases
|
||||
- Use Python only when specifically needed
|
||||
- Use n8n nodes for operations requiring external libraries
|
||||
|
||||
**See Also**:
|
||||
- [SKILL.md](SKILL.md) - Python Code overview
|
||||
- [DATA_ACCESS.md](DATA_ACCESS.md) - Data access patterns
|
||||
- [COMMON_PATTERNS.md](COMMON_PATTERNS.md) - Production patterns
|
||||
- [ERROR_PATTERNS.md](ERROR_PATTERNS.md) - Avoid common mistakes
|
||||
Reference in New Issue
Block a user