Drop columns
Drop columns — dropColumns
Remove one or more columns.
Use cases
- Strip internal IDs, scratch columns, or PII before sharing or saving.
- Slim a wide frame down before a join or export.
What it does
Removes the listed columns; all other columns pass through unchanged.
Before
| id | name | internal_id | temp_notes | amount |
|---|---|---|---|---|
| 1 | Alice | sys-001 | check later | 120.5 |
| 2 | Bob | sys-002 | null | 89 |
2 rows · 5 cols
Drop columns (columns=[internal_id, temp_notes])
After
| id | name | amount |
|---|---|---|
| 1 | Alice | 120.5 |
| 2 | Bob | 89 |
2 rows · 3 cols
Configuration
| Config key | Type | Required | Description |
|---|---|---|---|
columns | string[] | Yes | Columns to remove |
Generated Python code
df_2 = df_1.drop(columns=['internal_id', 'temp_notes'])
Tips & common mistakes
- To keep a few columns, invert the operation with Select columns instead of listing everything you don't want.
- Dropping a column other nodes downstream reference will surface as a validation error when you run the flow.