How to Solve Missing Data in Python
Two data scientists plug a Python pain point with their imputation package Autoimpute.
Written by Philip Baker. 5-minute read
- Data Analytics
- Technology and Innovation
- Working with incomplete data sets is one of the hardest challenges data scientists face.
- Unlike other languages, the Python programming language had no robust approach to imputation before Shahid Barkat and Joseph Kearney built Autoimpute.
- Key to their package’s success is users’ ability to choose the imputation method most relevant to the specific context of their data.
Missing data is one of the main challenges data scientists face when preparing data to model.