How to impute missing data values

Real-world data will often have missing values. Sometime you can just remove the rows with the missing values. For example, where you are working with large amounts of data with a few values ‘missing at random’, then removing a small number of rows is unlikely to affect the overall results. In other situations it might be preferable to ‘impute’ the missing values from other data in the same column. You can easily do this in Easy Data Transform using the Impute transform.

For example to impute the missing ages of Titanic passengers based on the average (mean) age of all passengers:

impute example

To impute the missing ages of Titanic passengers based on the median age of all passengers with the same passenger class and sex:

impute missing data

See the video above for more details.

Easy Data Transform can also help with converting, cleaning, filtering and enriching your data. All without coding.

Try it free now!

Windows Logo Windows Download

v1.46.5 for Windows 11 / 10 / 8 / 7 (47 MB)
Zip file version

Apple Logo Mac Download

v1.46.5 for Mac 14.x to 10.13 (79 MB)

No commitments.
You can uninstall any time.
You don't even have to give us your email address.

Questions or problems?