Date & Time Transformations in Power Query: Types, Extraction, and Locale Errors

Date & Time — Power Query | analytics.bi
Dates and times are one of the most common problems in Power Query. They often look like dates but are actually text, or are interpreted incorrectly because of regional settings.

If you’re just starting to work withPower Query, it’s very important to understand how dates and times are handled.

Power Query Date transformations pavyzdžiai

1. Change to the Date or DateTime type

If Power Query doesn’t recognize a date, the first step is to set the correct data type.

How to get there:
Select the column → Data Type → Date or Date/Time

2. Extract the year, month, day

Useful when you need to group or analyze data by period.

  • Year
  • Month
  • Day number

This method is often used together withGroup By in Power Query.

How to get there:
Transform → Date → Year / Month / Day

3. Create a date from text

If the date is text (e.g. 20240115 or 01/02/2024), it needs to be converted.

How to get there:
Transform → Date → From Text
or
Custom Column → Date.FromText()

If you need more flexibility, useCustom Column.

4. Calculate the difference between dates

Examples:

  • Days between the order and delivery
  • The customer’s age
How to get there:
Custom Column → Duration.Days or Date.Difference

5. The locale problem (regional settings)

For example, “01/02/2024” can mean different dates depending on the region – in the US format it’s January 2, while in Europe it’s February 1.

Power Query locale problema datos interpretacija

Such errors occur because different regions use different date formats (MM/DD/YYYY vs DD/MM/YYYY).

How to fix it:
Change Type → Using Locale → select the correct region

Such problems often arise together with:incorrect data types.

In short:
First set the correct type.
Use Date transformations.
If the dates look “strange” – check the Locale.

What to read next

Conclusion

Dates in Power Query often cause problems, but once you understand data types and locale settings, they can be solved quickly.

Tidy dates are essential for correct analysis in Power BI.

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