Round Off Values
Control decimal precision on float columns: round to 0–5 decimal places across all columns at once or per-column with different precision. Values are updated in place, no new column created.
Round Off Values adjusts decimal precision on float columns, replacing raw values like 6420.497351 with clean, report-ready numbers like 6420.50. The original column is updated in place: no new column is created, and the column name stays the same.
Only float, decimal, numeric, and double columns are eligible. Integer columns do not appear in this operation.
When to Use Round Off Values
- Financial reporting : standardize
revenueanddiscount_pctto 2 decimal places so currency values display correctly in dashboards and exports - Metric simplification : round
conversion_rateormargin_pctto 1 decimal place to reduce noise in KPI summaries - Downstream join accuracy : align float precision across datasets before joining on calculated fields that may differ by trailing decimals
- Export formatting : clean up precision before writing to a destination that has strict column format requirements (e.g. a BI tool expecting 2dp currency)
- Scientific or measurement data : reduce
sensor_readingfrom 8 decimal places to 3 to match the required reporting standard
If your column is an integer type, this operation does not apply; integers have no decimal places. Use Cast Data Type to convert first if needed.
Sample Dataset
edilitics_sample_orders.csv
500 B2B SaaS orders, H1 2024. Key columns for this operation: revenue (Float), discount_pct (Float, 17% null). · 500 rows
Relevant columns:
Prop
Type
The examples below use revenue and discount_pct from this dataset.
Two Modes
Round Off Values has two modes. Choose based on whether you want the same precision across all columns or different precision per column.
Applies one decimal setting to every eligible float column in your dataset at once. You can optionally exclude individual columns using the remove button next to each row.
Best for: Standardizing an entire dataset to consistent precision, for example, rounding all float columns to 2 decimal places before a financial export.
How it works:
- All float/decimal/numeric/double columns load automatically in a list showing: Column Name · Decimal Places · Preview
- Pick one decimal value from the Round Off To dropdown (top right): this sets the same precision for all columns
- Use the search box to find a specific column in large datasets
- Click the remove icon on any row to exclude that column from the operation
- The Preview column shows a live sample of the rounded value from your actual data
Assigns a different decimal precision to each column individually. You build a list of column + decimal pairs.
Best for: Datasets where different columns need different precision, e.g. revenue rounded to 2 places, discount_pct rounded to 4 places.
How it works:
- Each row has: Column Name dropdown · Decimal Places dropdown · Preview
- Use Add Column to add more rows (only enabled after the current row is fully configured)
- Each column can only be selected once. Already-selected columns are hidden from subsequent dropdowns
- The Preview column shows the rounded value from your first data row
How to Round Values in Edilitics
Open Transform and load your dataset
Open the Transform module. Create or open a transformation using edilitics_sample_orders.csv as the source. The column list and data preview appear automatically.
Add the Round Off Values operation
In the left panel under All Transformations, click Round Off Values. The configuration panel opens on the right.
If your dataset has no float, decimal, numeric, or double columns, Edilitics shows an info message and redirects you back to the transformation home screen. Integer-only datasets cannot use this operation.
Choose a mode
At the top of the panel you'll see two controls side by side:
- Select All Columns (checkbox, checked by default): applies one decimal setting to all eligible columns
- Uncheck it to switch to Custom mode. Configure each column individually
For this example, keep Select All Columns checked to round revenue and discount_pct together.
Set decimal precision
In Select All Columns mode: Open the Round Off To dropdown (top right of the panel) and select 2. This sets 2 decimal places for all columns in the list. The Preview column updates immediately with rounded sample values from your data.
In Custom mode: For each row, select a column from Column Name and a decimal count from Decimal Places. The preview updates per row. Click Add Column to configure additional columns. Each column appears only once across all rows.
Choosing your decimal count:
0: whole numbers only, e.g.6421(use for counts, units, or integer-like metrics)2: standard for currency and financial values3–5: scientific measurements, rates, or ratios where precision matters
Exclude columns (Select All mode only)
In Select All mode, every eligible column is included by default. If you want to exclude a column, for example, discount_pct because it needs a different precision later, click the remove icon on that row. The row becomes greyed out. Click the undo icon to restore it.
Preview and apply
Verify the Preview column shows the expected rounded values. When ready, click Save & Preview. All included columns are updated in place. Their values change, their names and positions stay the same.
Before & After
Input (4 rows from edilitics_sample_orders.csv):
| order_id | revenue | discount_pct |
|---|---|---|
| ORD-2024-0001 | 6420.497351 | 0.149873 |
| ORD-2024-0042 | 38915.820014 | 0.249601 |
| ORD-2024-0087 | 72300.750000 | null |
| ORD-2024-0113 | 9850.200000 | 0.050000 |
After Round Off Values (Select All Columns · 2 decimal places):
| order_id | revenue | discount_pct |
|---|---|---|
| ORD-2024-0001 | 6420.50 | 0.15 |
| ORD-2024-0042 | 38915.82 | 0.25 |
| ORD-2024-0087 | 72300.75 | null |
| ORD-2024-0113 | 9850.20 | 0.05 |
Null discount_pct → remains null. Column names and positions unchanged. Original column values replaced.
What Round Off Replaces in Code
Already writing this in SQL or Python? Here is what Round Off Values replaces.
-- Works in PostgreSQL, BigQuery, Snowflake, Redshift, DuckDB
SELECT
order_id,
ROUND(revenue, 2) AS revenue,
ROUND(discount_pct, 2) AS discount_pct
FROM edilitics_sample_orders;import polars as pl
df = pl.read_csv("edilitics_sample_orders.csv")
df = df.with_columns([
pl.col("revenue").round(2),
pl.col("discount_pct").round(2),
])
In Edilitics, select your decimal count from a dropdown and click Save & Preview. No syntax required.
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
Column Aggregation
Sum, average, or count the rounded column values across your dataset.
Group By
Summarize rounded revenue or discount values by region, product tier, or sales rep.
Cast Data Type
Convert an integer column to float before applying Round Off, or cast a rounded float to integer.
Edit Columns
Rename, reorder, or duplicate columns. Use before Round Off to preserve the original values.
Need help? Email support@edilitics.com with your workspace, job ID, and context. We reply within one business day.
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