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What This Node Does

The SQL/Python node (also called the CODE node) lets you write custom code when built-in nodes don’t provide the exact transformation you need. Toggle between SQL and Python modes for advanced calculations, complex business logic, or custom data manipulation. Your code runs in a secure, sandboxed environment and operates on data from the parent node. The result becomes the output that flows to the next node.
The SQL/Python node configuration with a SQL/Python language toggle and a code editor referencing the upstream step.

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When to Use This Node

Use it when:
  • A built-in node can’t do what you need (custom calculations, complex conditional logic, advanced string or date work)
  • You’re preserving existing SQL queries from another tool
  • You need pandas or numpy operations
Skip it when:
  • A built-in node (Transform, Filter, PivotTable) can do the same job faster and more clearly
  • Teammates without coding experience need to read the flow

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Language Toggle

A toggle at the top switches between SQL and Python. Both operate on the same parent data:
  • SQL Mode: Standard SQL SELECT statements.
  • Python Mode: Python with pandas and numpy.

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SQL Mode

The parent node’s output is available as a table named data:
If one of your source columns is also named data, bare unquoted data always refers to the parent table, not the column. Use double quotes ("data") or a table alias (FROM data AS t then t.data) to reference it as a column instead.
You have access to standard SQL: SELECT, WHERE, CASE WHEN, GROUP BY, ORDER BY, joins, and common string, date, and math functions.

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Joining Multiple Parents

When the node has more than one parent, each parent is available as a table named after its step label (shown on the node in the canvas: A1, B1, C1). The position-0 parent is also aliased as data.
Step labels update automatically if you reorder or add parents. Go Fig translates stale labels on your behalf so your queries keep working.

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Example

Creates a custom “tier” column based on revenue thresholds, filters out zero-revenue customers, and sorts by revenue.

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Python Mode

The parent node’s output is available as a pandas DataFrame named data. Access columns with dot or bracket notation (data.revenue or data['Customer Name']). Three libraries are pre-imported: pandas (pd), numpy (np), and fig, Go Fig’s helper library for fuzzy matching, fiscal-year conversion, z-scores, safe percentage change, and deduplication (see Fig Helpers).

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Joining Multiple Parents

Each parent is exposed as a DataFrame named after its step label (A1, B1, C1). The position-0 parent is also bound to data.

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Must Return a DataFrame

Your code must return a DataFrame, not a Series or scalar. The last expression should be a DataFrame:

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Example

Filters customers with revenue over $1,000, calculates a 10% commission, and returns the result.

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Sandbox Restrictions

For security, code runs in an isolated sandbox: no import statements (only the pre-imported pd, np, and fig), no exec/eval/compile, no filesystem or system access, and no network requests. If you need more, preprocess your data outside Go Fig and import it as a data source.

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Tips

Test with small data first: In Python, use data.head() to develop against the first few rows before running on the full dataset.
SQL vs Python: Use SQL for familiar queries and filtering. Use Python for row-wise iteration, custom functions, or pandas-specific operations.
Always use .copy() when creating filtered DataFrames in Python to avoid SettingWithCopyWarning:

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Debugging

If your code isn’t behaving as expected:
  1. Check column names: data.columns (Python) or SELECT * FROM data LIMIT 5 (SQL)
  2. Check data types: data.dtypes (Python)
  3. Handle nulls: .fillna() (Python) or COALESCE() (SQL)
  4. Build incrementally: test each part before adding complexity