Guided Project: Clean And Analyze Employee Exit Surveys

Hi everyone!

My question is about one of the extra-steps-to-do after completing the project:
“Decide how to handle the rest of the missing values. Then, aggregate the data according to the service_cat column again. How many people in each career stage resigned due to some kind of dissatisfaction?”

I just don’t want to drop 15% of missing data from combined_updated dataset.
Is there any way to randomly select rows in ‘institute_service’ column and assign some random numbers to missing values according to our categories (Veteran, Established, Experienced and New)?

The only way how I see it is to write a function and then apply it to the column, but I can’t get sample() function to work properly.

Or maybe someone just could throw me an idea how to deal with missing service years)

Hi @Kaish ,

If you don’t want to drop those rows, then you can find the mean of service years by position and use it to replace missing values, and if you wish you can also, find the mean service years using position along with region.

Hope this helps :slightly_smiling_face:



Thanks for reply!

I’ve already come to the same decision by myself)

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