replace_in_column
Replace text patterns in a CSV column using regex or literal matching. Clean data by substituting, removing, or standardizing values within specified columns.
Instructions
Replace patterns in a column with replacement text.
Returns: ColumnOperationResult with replacement details
Examples: # Replace with regex replace_in_column(ctx, "name", r"Mr.", "Mister")
# Remove non-digits from phone numbers
replace_in_column(ctx, "phone", r"\D", "", regex=True)
# Simple string replacement
replace_in_column(ctx, "status", "N/A", "Unknown", regex=False)
# Replace multiple spaces with single space
replace_in_column(ctx, "description", r"\s+", " ")Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| column | Yes | Column name to apply pattern replacement in | |
| pattern | Yes | Pattern to search for (regex or literal string) | |
| replacement | Yes | Replacement text to use for matches | |
| regex | Yes | Whether to treat pattern as regex (True) or literal string (False) |
Output Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| success | No | Whether operation completed successfully | |
| operation | Yes | Type of operation performed | |
| transform | No | Transform description | |
| part_index | No | Part index for split operations | |
| nulls_filled | No | Number of null values filled | |
| rows_removed | No | Number of rows removed (for remove_duplicates) | |
| rows_affected | Yes | Number of rows affected by operation | |
| values_filled | No | Number of values filled (for fill_missing_values) | |
| updated_sample | No | Sample values after operation | |
| original_sample | No | Sample values before operation | |
| columns_affected | Yes | Names of columns affected |