Manage Timezones
Convert datetime columns to a target IANA timezone. All datetime columns are pre-loaded. Apply one timezone to all selected columns without writing code.
Manage Timezones converts datetime column values to a target timezone you select. All datetime columns in your dataset appear pre-loaded. You choose one target timezone and it applies to every included column. Columns you do not want converted can be excluded with their remove icon. The conversion happens in place - the existing column values are updated, no new columns are created.
Timezone conversion assumes your datetime values have an implicit reference timezone (typically UTC or the source system's local time). The operation converts values to the target timezone. If your datetimes are already timezone-aware and correctly set, conversion is accurate. If not, align your source timezone expectation before applying this operation.
When to Use Manage Timezones
- Normalise to UTC. Convert all timestamp columns to UTC before joining datasets from multiple regional sources.
- Localise for reporting. Convert UTC timestamps to
America/New_YorkorAsia/Kolkatafor region-specific dashboards. - Fix imported data. Correct timestamps loaded from a system that stored times in a non-UTC timezone.
- Align before delta calculation. Ensure
order_dateanddelivery_dateare in the same timezone before computing Datetime Delta. - Standardise for group by. Normalise timestamps before extracting hour or weekday with Datetime Aggregation so groupings are consistent.
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 Manage Timezones examples:
Prop
Type
How to Apply Manage Timezones
Open the Manage Timezones operation
In your Transform pipeline, click Add Operation and select Manage Timezones from the operation list.
If no datetime columns exist in the dataset, an info toast appears and the operation closes automatically. Cast string date columns to datetime first using Cast Data Types.
Select the target timezone
In the Time Zone dropdown at the top, search and select the target timezone. The list uses IANA standard names such as UTC, America/New_York, Asia/Kolkata, Europe/London, Australia/Sydney.
The selected timezone is immediately displayed in the Assigned Timezone column next to every included datetime column.
Review the column list
All datetime columns appear pre-loaded with two columns: Datetime Column (the source column name and type) and Assigned Timezone (the selected target timezone).
All columns are included by default.
Exclude columns (optional)
Click the remove icon next to any column you do not want to convert. The row becomes greyed out and is marked as excluded. Click the undo icon to re-include it.
At least one column must remain included - Save & Preview is disabled if all columns are excluded.
Click Save & Preview
Click Save & Preview. Edilitics converts all included datetime column values to the target timezone in place. The success toast confirms: "Timezone configuration saved. Output preview now reflects adjusted timezones."
Verify in the preview
Check that the datetime values have shifted correctly. The column type and name remain the same - only the values change to reflect the target timezone offset.
Before and After
Target timezone: Asia/Kolkata (UTC+5:30). Applied to order_date.
Input:
| order_id | order_date | revenue |
|---|---|---|
| ORD-2024-0001 | 2024-01-15 08:32:00 | 12450.00 |
| ORD-2024-0047 | 2024-03-22 14:15:00 | 8320.50 |
| ORD-2024-0099 | 2024-06-07 09:45:00 | 83841.00 |
Output (order_date converted to Asia/Kolkata, UTC+5:30):
| order_id | order_date | revenue |
|---|---|---|
| ORD-2024-0001 | 2024-01-15 14:02:00+05:30 | 12450.00 |
| ORD-2024-0047 | 2024-03-22 19:45:00+05:30 | 8320.50 |
| ORD-2024-0099 | 2024-06-07 15:15:00+05:30 | 83841.00 |
Column name unchanged. Values updated in place.
Code Equivalent
-- PostgreSQL: convert from UTC to Asia/Kolkata
SELECT
order_id,
order_date AT TIME ZONE 'UTC' AT TIME ZONE 'Asia/Kolkata' AS order_date,
revenue
FROM orders;
-- BigQuery
SELECT
order_id,
DATETIME(order_date, 'Asia/Kolkata') AS order_date,
revenue
FROM orders;import polars as pl
from pytz import timezone
target_tz = timezone("Asia/Kolkata")
df = df.with_columns(
pl.col("order_date")
.cast(pl.String)
.str.to_datetime(strict=False)
.map_elements(
lambda x: x.astimezone(tz=target_tz) if x else None
)
.alias("order_date")
)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
Datetime Aggregation
Extract hour, weekday, or month after normalising to a consistent timezone.
Datetime Delta
Calculate time differences between columns after ensuring they share the same timezone.
Cast Data Types
Cast string date columns to datetime so they appear in the Manage Timezones column list.
Group By
Group by hour or weekday after timezone normalisation for accurate time-based summaries.
Need help? Email support@edilitics.com with your workspace, job ID, and context. We reply within one business day.
Last updated on
Datetime Aggregation
Extract year, quarter, month, week, day, or time components from a datetime column into a new column. Configure multiple extractions in one operation.
Group By
Group rows by a column and apply aggregations like sum, count, mean, median, min, max, variance, and standard deviation to output grouped summary tables.