Drop / Rename Columns
Remove columns you no longer need or rename them to match your output schema. Drop and rename in the same step with validation to prevent invalid names.
Drop / Rename Columns lets you remove columns from the output and rename columns to match your target schema. Every column in your dataset is listed at once. Drop what you do not need, rename what needs a different label, and save. Both actions can happen in the same operation.
Dropped columns are excluded from the output entirely. They are not moved or hidden. Renamed columns keep their data and data type. No new column is created.
When to Use Drop / Rename Columns
- Cleaning up before export. Source datasets often contain internal fields (audit timestamps, system IDs, staging flags) that should not appear in the output. Drop them here.
- Enforcing a naming convention. Snake case, camel case, or a specific abbreviation scheme can be applied across all columns in one pass.
- Matching a downstream schema. A downstream database table or API expects specific column names. Rename to match exactly.
- Reducing dataset width. Removing columns you do not need speeds up all subsequent operations in the pipeline.
- Removing PII before sharing. Drop columns containing personal information before handing a dataset to a lower-privilege team or exporting to a third-party tool.
Sample Dataset
The examples in this doc use the Edilitics sample orders dataset. Download it to follow along in your own workspace.
edilitics_sample_orders.csv
Sample orders dataset for hands-on practice · 500 rows
Relevant columns for Drop / Rename examples:
Prop
Type
How to Drop or Rename Columns
Open the Drop / Rename Columns operation
In your Transform pipeline, click Add Operation and select Drop/Rename Columns from the operation list.
Review the column list
Every column in your dataset is shown. Each row has three fields:
- Source Column: the current column name. Read-only. Cannot be edited.
- Source Data Type: the column's data type. Read-only.
- Rename To: an editable input pre-filled with the current column name. Change it to rename. Leave it as-is to keep the name.
Use the search bar at the top to filter columns by name or data type if your dataset has many columns.
Rename columns (optional)
Click into the Rename To input for any column and type the new name. The input shows a red outline if the name is invalid or already used by another column.
Valid column names:
- Can contain letters, numbers, and underscores
- Cannot start with double underscore (
__) or a number - Must be unique across all columns in the operation
Drop columns (optional)
Click the remove icon to the right of any column row. The row grays out to indicate it is marked for removal. Click the undo icon to restore it.
You can drop multiple columns in one operation. You cannot drop all columns. The Save & Preview button is disabled if all columns are marked for removal.
Save the operation
Click Save & Preview. Edilitics applies the changes: dropped columns are excluded from the output, renamed columns appear under their new names. A success toast confirms: "Column update saved. Preview reflects renamed and dropped columns."
Verify in the preview
Check the data preview to confirm the column list and names match your expectation. If a column is missing that you need, remove the operation and re-add it with corrected settings.
Before and After
Starting columns: order_id, customer_name, created_at, revenue, status
Operations applied:
- Rename
order_idtoid - Rename
revenuetorevenue_usd - Drop
created_at - Drop
customer_name
| Before | After |
|---|---|
order_id | id |
customer_name | (dropped) |
created_at | (dropped) |
revenue | revenue_usd |
status | status |
The output dataset has 3 columns instead of 5.
Code Equivalent
-- Rename and select (drop by omission)
SELECT
order_id AS id,
revenue AS revenue_usd,
status
FROM orders;
-- customer_name and created_at omitted = droppedimport polars as pl
df = df.select([
pl.col("order_id").alias("id"),
pl.col("revenue").alias("revenue_usd"),
pl.col("status"),
# customer_name and created_at omitted = dropped
])After Save & Preview, the pipeline shows a DQ delta badge on this step - green if the table score improved, red if it dropped. See Data Quality Scoring for how scores are calculated.
After Save & Preview, the pipeline shows a DQ delta badge on this step - green if the table score improved, red if it dropped. See Data Quality Scoring for how scores are calculated.
Operation Reference
Prop
Type
Frequently Asked Questions
Next Steps
Cast Data Types
Change a column's data type after renaming it to match the target schema.
Filter
Filter rows after narrowing the dataset to only the columns you need.
Merge Columns
Combine two renamed columns into a single formatted output column.
Code Editor
Use Polars expressions to duplicate, reorder, or derive columns beyond what Drop / Rename supports.
Need help? Email support@edilitics.com with your workspace, job ID, and context. We reply within one business day.
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Sort / Order By
Sort your dataset by one or more columns in ascending or descending order. Add multiple sort keys and control precedence across numeric and text columns.
Cast Data Types
Convert column data types without code. Change string to integer, datetime to date, or boolean. Available target types adapt to your destination database.