Imputing missing observations
Collecting data is messy. Research data collection instruments fail, humans do not want to answer some questions in a questionnaire, or files might get corrupted; these are but a sample of reasons why a dataset might have missing observations. If we want to use the dataset, we have a couple of choices: remove the missing observations altogether or replace them with some value.
Getting ready
To execute this recipe, you will need the pandas module.
No other prerequisites are required.
How to do it…
Once again, we assume that the reader followed the earlier recipes and the csv_read DataFrame is already accessible to us. To impute missing observations, all you need to do is add this snippet to your code (the data_imput.py ...
Get Practical Data Analysis Cookbook now with the O’Reilly learning platform.
O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.
