Editing and imputation in Python

Open to
Government analysts
Training category
Analytical, Data science
Type of training
4 to 6 hours
Analysis Function Capability Team
Analysis Function Capability Team


This is a short course and aims to covers the practical application of editing and imputation in Python. This course doesn’t cover the theory of any of the methods specified. The theory of these methods is covered in introduction to editing and imputation which is one of the prerequisites of this course.

Learning outcomes

By the end of the session, participants will be able to:

  • apply methods of reviewing data such as identifying missing values, visualising the missing value, and finding duplicates in data in Python
  • do automatic editing where you can apply restrictions, check which restrictions have been violated and correct those
  • impute missing values using model-based methods such as mean, median, ratio and regression imputation in Python
  • impute missing values using donor-based imputations such as the random hot deck, sequential hot-deck, hierarchical hot-deck and k-nearest neighbours (KNN) imputation in Python

How to access this course

Please use your Learning Hub account to access this course online.

If you do not have a Learning Hub account, please contact Data.Science.Campus.Faculty@ons.gov.uk.

Related courses

Editing and imputation in R