# Python Code Execution

Run custom Python scripts directly inside your workflow. Use it for data transformation, complex logic, reshaping payloads, or anything that can't be handled by a standard module.

### 1. Add the Module

Add the **Python Code** module to your workflow as an action node.

<figure><img src="/files/DQzH7pbaPefV5zj8afg6" alt=""><figcaption></figcaption></figure>

### 2. Pass Data In Context

In the module configuration panel, add context variables under the **Context** section. Each entry takes a **Name** and a **Value,** use the variable picker to map values from upstream nodes.

Each context entry becomes a key on `WORKFLOW_CONTEXT` inside your script:

```python
def main(WORKFLOW_CONTEXT):
    amount = WORKFLOW_CONTEXT["amount"]
```

<figure><img src="/files/mTv3fGDxjl9bw43NLWWU" alt=""><figcaption></figcaption></figure>

### 3. Write Your Script

Your script must define a `main` function. Stacksync automatically injects `WORKFLOW_CONTEXT` as the argument containing all your context values.

```python
def main(WORKFLOW_CONTEXT):
    amount = WORKFLOW_CONTEXT["amount"]
    return {
        "amount": amount,
    }
```

### 4. Return Data

Whatever `main` returns becomes the node's output, available to downstream nodes via standard node referencing.

<figure><img src="/files/PymN5aBMUVsKS6vLV5CD" alt=""><figcaption></figcaption></figure>

Reference in downstream nodes:

```jinja
{{ module_id.amount }}
```

> **Debugging:** `print()` output is captured and surfaced under `metadata.stdout` in the execution logs. Use it for debugging — it does not pass data downstream.

### Available Libraries

| Library                      | Use case                             |
| ---------------------------- | ------------------------------------ |
| `pandas`                     | Data manipulation and transformation |
| `numpy`                      | Numerical operations                 |
| `phonenumbers`               | Phone number parsing and formatting  |
| `json`                       | JSON parsing                         |
| `os`                         | OS-level utilities                   |
| Python 3.10 standard library | All built-in modules                 |

### Limits

| Limit            | Value                |
| ---------------- | -------------------- |
| Timeout          | 300s (5 min)         |
| Memory           | 700 MB               |
| Filesystem       | Read-only, no writes |
| Runtime packages | Pre-installed only   |

> **Watch out:** Large pandas DataFrames can hit the 700 MB memory limit. Process data in chunks where possible.


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```
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