Question

What’s the best way to handle missing values during the extract phase in Power BI?
Some of my data includes nulls or gaps, and I want to know the best Power Query techniques for identifying and handling missing values during extraction.

Answer 1

The most effective method for dealing with missing values in Power BI's extract phase when using Power Query is to:

1. Determine Any Missing Values

To locate null or blank values in columns, use filters.

For quick insights, use the Column Quality and Column Distribution features in Power Query Editor or Table. Profile (in Advanced Editor).

2. Change or Add Missing Values

To replace nulls with defaults (such as zero, "Unknown," or a meaningful placeholder), use Replace Values.

To propagate the last known value in time series or ordered data, use Fill Down or Fill Up.

When data is lacking, use Conditional Columns to set values according to rules.

3. If applicable, eliminate rows that contain missing values.

Remove rows with null critical columns; if there is no data, the row is invalid.

4. Apply Custom Logic to Imputation

For numeric columns, think about using custom M code to fill in the missing values with averages, medians, or values taken from related columns.