Nominal data
Nominal data consists of categorical values whose labels have no inherent or meaningful ordering.
Ensemble Methods for Machine Learning
A nominal feature is a categorical feature whose categories have no inherent ordering or quantitative relationship.
- How it is used
- Distinguishes unordered categorical values before selecting an encoding for machine-learning features.
- How deep it goes
- Defines the category relationship, contrasts ordinal data, and connects encoding choice to ordering and interpretability.
- Examples
- Uses T-shirt colors and sizes, plus scikit-learn encoder configurations and binary output arrays.
- What you can do after
- Apply via scikit-learn by distinguishing unordered categories from ordered ones and choosing a corresponding encoding.
Read how Ensemble Methods for Machine Learning explains nominal data →
D3.js in Action, Third Edition
A nominal value is a categorical value whose labels have no inherent ordering.
- How it is used
- Guides visual-channel and scale choices when designing data visualizations.
- How deep it goes
- Defines unordered qualitative labels and gives encoding guidance, while deferring detailed scale treatment.
- Examples
- Uses gender identity labels, city names, countries, and a dataset-attribute classification exercise.
- What you can do after
- Apply it via D3 scales and categorical visual channels when planning a visualization.
Read how D3.js in Action, Third Edition explains nominal data →
Data Storytelling with Altair and AI
Nominal data contains categorical labels whose values have no intrinsic ordering.
- How it is used
- Maps unordered categories to Altair channels so charts can separate, label, or group observations.
- How deep it goes
- Defines nominal data and demonstrates shorthand encoding, but stops short of deeper categorical-data theory.
- Examples
- Uses names, cities, countries, grouped tourist-arrival lines, and the `Name:N` and `Country:N` shorthand forms.
- What you can do after
- Build charts that correctly encode unordered categorical columns with Altair’s `:N` shorthand or explicit type property.
Read how Data Storytelling with Altair and AI explains nominal data →
Data Without Labels
Nominal data consists of categories with no inherent ordering or sequence.
- How it is used
- Classifies observed labels such as cities, colors, languages, or customer services without imposing an order.
- How deep it goes
- Defines the category type, gives examples, and contrasts it with ordinal data without discussing computation or modeling.
- Examples
- Uses languages, rainbow colors, customer services, and cities as concrete nominal categories.
- What you can do after
- Evaluate whether a categorized variable has labels only or also supports meaningful ordering.
Related concepts
- data typekind of · A data type is a classification that specifies the values data can represent, how they are represented or interpreted, and which operations apply to them.
- categorical datakind of · Categorical data consists of values representing membership in a discrete set of named or ordered categories rather than measured numerical magnitudes.
- qualitative datakind of · Qualitative data is descriptive, nonnumeric information representing characteristics, categories, meanings, experiences, opinions, or context rather than measured quantities.
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