Python + n8n: Combining No-Code with Custom Scripts

n8n's visual workflow builder is powerful, but some tasks need custom code. Here is how to combine the best of both worlds—no-code simplicity with Python's unlimited flexibility.

Why Combine Python and n8n?

n8n Strengths

• Visual workflow design
• Easy app integrations
• Trigger and scheduling built-in
• Non-developers can understand flows

Python Strengths

• Complex data transformation
• Advanced algorithms
• Machine learning integration
• Unlimited customization

Together

n8n handles orchestration and integrations; Python handles heavy processing.

Methods for Integration

Method 1: Code Node

n8n has a built-in Code node that runs JavaScript. For Python, you can:

• Use a Python-to-JS transpiler
• Call Python via shell commands
• Use Pyodide (Python in browser)

Method 2: HTTP Request to Python API

Best approach for complex Python logic:

1. Build a simple Python API (Flask/FastAPI)
2. Host it (server, serverless, Docker)
3. n8n calls the API via HTTP Request node
4. Python processes and returns results

Method 3: Execute Command Node

If n8n and Python are on the same server:

1. Use Execute Command node
2. Call Python script directly
3. Pass data via arguments or files
4. Read output back into n8n

Example: Data Processing Pipeline

The Workflow

1. n8n: Trigger on new file upload
2. n8n: Download file from cloud storage
3. Python API: Process file (complex transformations)
4. n8n: Take processed data
5. n8n: Update CRM, send notifications

Python API Example (FastAPI)

from fastapi import FastAPI
import pandas as pd

app = FastAPI()

@app.post("/process-data")
async def process_data(data: dict):
    # Convert to DataFrame
    df = pd.DataFrame(data['records'])

    # Complex processing
    df['total'] = df['quantity'] * df['price']
    summary = df.groupby('category')['total'].sum()

    return {"summary": summary.to_dict()}

n8n HTTP Request

• Method: POST
• URL: http://your-api/process-data
• Body: JSON with your data

Use Cases for This Combo

• ML predictions: n8n triggers, Python model predicts
• Document processing: n8n orchestrates, Python parses
• Data enrichment: n8n collects, Python transforms
• Report generation: n8n schedules, Python generates

Deployment Options

Same Server

• Simplest setup
• Use Execute Command or local API
• Good for low volume

Separate Services

• Python API on separate server or serverless
• Better scaling and maintenance
• n8n connects via HTTP

Docker Compose

• n8n and Python API in containers
• Easy deployment and scaling
• Network communication between containers