Python Code

​

When to Use It

• Perform complex calculations not available in existing math/utility nodes
• Transform data in highly specific ways not supported by existing data nodes
• Apply advanced statistical analysis, machine learning, or scientific computing
• Parse or manipulate complex data structures requiring custom logic
• Create sophisticated business logic that combines multiple operations
• Process data with specialized libraries (computer vision, audio analysis, NLP)

​

Inputs

​

Outputs

​

Credit Cost

​

How It Works

• Jupyter notebook-style execution (last line becomes the output)
• Access to 30+ pre-installed data science libraries
• Automatic variable injection from workflow data
• JSON-safe output formatting
• Secure execution environment

​

Available Libraries

​

Installing Additional Packages

• Maximum 5 packages per execution
• Common data science packages (pandas, numpy, requests, etc.) are already available - don’t reinstall them
• Use specific versions when needed: "package==1.2.3"
• Packages are installed at runtime before code execution, which may slightly increase execution time
• Packages are temporary and only available for the current execution

​

Code Requirements

• Works like a Jupyter notebook - only the last line’s result is returned
• No need for explicit return statements
• The final expression becomes the output automatically

• Place your result as the final line of code
• Must be JSON-safe types: dict, list, str, int, float, bool, or None
• No print() statements for output - use the last line instead

# Simple calculation (last line is the result)
total_sales = sum(row['amount'] for row in sales_data)
average_sales = total_sales / len(sales_data)
average_sales  # This becomes the output

# Multiple values as dict
data_summary = {
    "total": sum(amounts), 
    "count": len(amounts),
    "average": sum(amounts) / len(amounts)
}
data_summary  # This becomes the output

# Processed data list
processed = [{"name": item["name"].upper(), "score": item["score"] * 1.1} for item in raw_data]
processed  # This becomes the output

• pandas DataFrames (convert to dict/list first: df.to_dict('records'))
• numpy arrays (use .tolist())
• Custom objects or functions
• Complex nested objects

​

Variables Usage

{
  "sales_data": "{{sheetsNode.data}}",
  "target_amount": "{{inputNode.data}}",
  "account_ids": "{{adsNode.data}}"
}

# Variables are already available - no need to define them
total_sales = sum(row['amount'] for row in sales_data)
performance = total_sales / target_amount

{"total": total_sales, "performance_ratio": performance}

​

Examples

​

Example 1: Calculate Campaign Performance

{
  "campaigns": "{{adsNode.data}}",
  "target_roas": "{{inputNode.data}}"
}

results = []
for campaign in campaigns:
    roas = campaign['revenue'] / campaign['cost'] if campaign['cost'] > 0 else 0
    performance = "Above Target" if roas >= target_roas else "Below Target"
    
    results.append({
        "campaign_name": campaign['name'],
        "roas": round(roas, 2),
        "status": performance
    })

results

​

Example 2: Data Cleaning and Transformation

{
  "raw_data": "{{dataNode.data}}"
}

import pandas as pd

# Convert to DataFrame for easier processing
df = pd.DataFrame(raw_data)

# Clean and transform
df['email'] = df['email'].str.lower().str.strip()
df['phone'] = df['phone'].str.replace(r'[^\d]', '', regex=True)
df = df.dropna(subset=['email'])

# Convert back to list of dicts
df.to_dict('records')

​

FAQ