WenjieDu/PyGrinder
PyGrinder: a Python toolkit for grinding data beans into the incomplete for real-world data simulation by introducing missing values with different missingness patterns, including MCAR (complete at random), MAR (at random), MNAR (not at random), sub sequence missing, and block missing
This tool helps data scientists and researchers simulate real-world data imperfections by deliberately introducing missing values into complete datasets. You provide your full dataset, and it generates a new version with gaps, simulating various scenarios of missing information. This is useful for anyone evaluating how well their data analysis models perform when confronted with incomplete information.
Used by 1 other package. Available on PyPI.
Use this if you need to test the robustness of your machine learning models or analysis methods against different patterns of missing data in time series or other structured datasets.
Not ideal if you're looking to fill in or clean up existing missing values in your data, as this tool is specifically for creating them.
Stars
65
Forks
6
Language
Python
License
BSD-3-Clause
Category
Last pushed
Dec 16, 2025
Commits (30d)
0
Dependencies
5
Reverse dependents
1
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