Text, Date & Category Cleaning
Text, Date & Category Cleaning groups the core ideas a learner needs at the 4 · data cleaning & missing data stage. Work through the lessons in order when new to the area, or use them independently as a reference when implementing an analysis.
Learn the mechanism one decision at a time
Work through the lessons in order if the topic is new. If you already know the basics, open the specific leaf lesson that matches the operation, diagnostic or failure mode you need.
1Definition→
2Mechanism→
3Example→
4Diagnostic→
5Decision
Whitespace and casingWhitespace and casing is a practical concept within Text, Date & Category Cleaning. It helps turn the broader workflow stage “4 · Data Cleaning & Missing Data” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
02String parsing and regular expressionsString parsing and regular expressions is a practical concept within Text, Date & Category Cleaning. It helps turn the broader workflow stage “4 · Data Cleaning & Missing Data” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
03Date parsing and time zonesDate parsing and time zones is a practical concept within Text, Date & Category Cleaning. It helps turn the broader workflow stage “4 · Data Cleaning & Missing Data” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
04Category normalizationCategory normalization is a practical concept within Text, Date & Category Cleaning. It helps turn the broader workflow stage “4 · Data Cleaning & Missing Data” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
05Rare categoriesRare categories is a practical concept within Text, Date & Category Cleaning. It helps turn the broader workflow stage “4 · Data Cleaning & Missing Data” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
06Invalid codes and sentinelsInvalid codes and sentinels is a practical concept within Text, Date & Category Cleaning. It helps turn the broader workflow stage “4 · Data Cleaning & Missing Data” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
07Unicode and encoding issuesUnicode and encoding issues changes how raw variables are represented for analysis or modelling. The transformation should preserve the information needed by the task while making assumptions explicit and reproducible.