Removing missing data
Next, we have the very common issue of missing data or, as most people would recognize, null values. In Chapter 2, Understanding Data Quality and Why Data Cleaning is Important, we understood the reasons why this might happen – for example, due to the type of join between two tables, which might cause many null values to show.
These null values can often either ruin the look of your reporting or potentially skew the numbers being used or analyzed, so it’s often best we look to remove these.
In the example of our products table, we can see that we have a row with blank or 0 values shown in Figure 4.5. If you were viewing this from within the Power Query Editor, then the blank values would be showing as null. While this would otherwise be acceptable as we won’t necessarily see the null product within a visualization, there is a price and cost value against the null product with the 0 value. This could affect the analysis, particularly...
