Missing Values: Identification and Understanding

79 min
Block 5 — Data cleaning
Objective
define the missing value rigorously, separate it from the coded sentinel and from the non-applicable field, master Rubin's typology (MCAR, MAR, MNAR) together with what each mechanism licenses and what it forbids, know the pandas semantics of NaN, None, pd.NA and NaT precisely enough to trust a count, read patterns and co-occurrences of missingness, treat absence as a variable in its own right, and produce a complete audit that ends in a documented decision for every lacunary column.
Estimated duration
60 minutes
Prerequisites
chapters 005 (dataset structure), 013 (exploratory analysis), 014 (descriptive statistics), 015 (visualization) and 016 (correlation and causation)
Associated quizzes
018.1-quiz-nature-of-absence.md to 018.8-quiz-python-audit.md

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This lesson is part of the “Cleaning the Data” module

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