Python Code Execution Tool

Execute Python code for calculations, data processing, and automation tasks.

How It Works

This skill provides instructions for the Python Executor tool node. Connect the Python Executor node to Zeenie's input-tools handle to enable Python code execution.

python_code Tool

Execute Python code and return results.

Schema Fields

Field	Type	Required	Description
code	string	Yes	Python code to execute

Available Libraries

Library	Description
`math`	Mathematical functions (sqrt, pow, sin, cos, etc.)
`json`	JSON encoding/decoding
`datetime`	Date and time manipulation
`timedelta`	Time duration calculations
`re`	Regular expressions
`random`	Random number generation
`Counter`	Count hashable objects
`defaultdict`	Dictionary with default values

Built-in Variables

Variable	Description
`input_data`	Data from connected workflow nodes (dict)
`output`	Set this to return a result

Output Methods

•Set output variable: Returns structured data to the workflow
•Use print(): Captured as console output

Examples

Basic calculation:

json

{
  "code": "result = 25 * 4 + 10\nprint(f'Result: {result}')\noutput = result"
}

Calculate tip:

json

{
  "code": "bill = 85.50\ntip_percent = 15\ntip = bill * (tip_percent / 100)\ntotal = bill + tip\nprint(f'Tip: ${tip:.2f}')\nprint(f'Total: ${total:.2f}')\noutput = {'tip': tip, 'total': total}"
}

Generate random numbers:

json

{
  "code": "import random\nnumbers = [random.randint(1, 100) for _ in range(5)]\nprint(f'Random numbers: {numbers}')\noutput = numbers"
}

Date calculations:

json

{
  "code": "from datetime import datetime, timedelta\ntoday = datetime.now()\nfuture = today + timedelta(days=30)\nresult = future.strftime('%Y-%m-%d')\nprint(f'30 days from now: {result}')\noutput = result"
}

Process data:

json

{
  "code": "data = input_data.get('numbers', [1, 2, 3, 4, 5])\ntotal = sum(data)\naverage = total / len(data)\nprint(f'Total: {total}, Average: {average}')\noutput = {'total': total, 'average': average}"
}

Parse JSON:

json

{
  "code": "import json\njson_str = '{\"name\": \"John\", \"age\": 30}'\ndata = json.loads(json_str)\nprint(f'Name: {data[\"name\"]}')\noutput = data"
}

Count items:

json

{
  "code": "from collections import Counter\nitems = ['apple', 'banana', 'apple', 'cherry', 'banana', 'apple']\ncounts = dict(Counter(items))\nprint(f'Counts: {counts}')\noutput = counts"
}

Text processing:

json

{
  "code": "import re\ntext = 'Contact: john@example.com or jane@test.org'\nemails = re.findall(r'\\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\\.[A-Z|a-z]{2,}\\b', text)\nprint(f'Found emails: {emails}')\noutput = emails"
}

Response Format

Success:

json

{
  "success": true,
  "result": {"tip": 12.825, "total": 98.325},
  "output": "Tip: $12.83\nTotal: $98.33"
}

Error:

json

{
  "error": "name 'undefined_var' is not defined"
}

Use Cases

Use Case	Approach
Math calculations	Use `math` library functions
Date/time operations	Use `datetime` and `timedelta`
Data analysis	Use list comprehensions, sum, len
Random generation	Use `random` library
Text parsing	Use `re` regular expressions
JSON manipulation	Use `json.loads()` and `json.dumps()`
Counting	Use `Counter` from collections

Guidelines

•Always set output: This returns data to the workflow
•Use print() for debugging: Output is captured and returned
•Keep code focused: One task per execution
•Handle errors: Use try/except for robust code
•No network access: Use http-skill for web requests
•No file system access: Restricted to safe operations
•Timeout: Default 30 seconds max execution time

Security Restrictions

•No network/socket operations
•No file system access outside designated areas
•No subprocess/shell commands
•Limited execution time (30 seconds)
•Sandboxed environment

Setup Requirements

•Connect the Python Executor node to Zeenie's input-tools handle
•Python must be installed on the server