Recap of the levels of data

Understanding the various levels of data is necessary to perform feature engineering. When it comes time to build new features, or fix old ones, we must have ways of identifying how to work with every column.

Here is a quick table to summarize what is and isn't possible at every level:

Level of Measurement	Properties	Examples	Descriptive statistics	Graphs
Nominal	Discrete Orderless	Binary Responses (True or False) Names of People Colors of paint	Frequencies/Percentages Mode	Bar Pie
Ordinal	Ordered categories Comparisons	Likert Scales Grades on an exam	Frequencies Mode Median Percentiles	Bar Pie Stem and leaf
Interval	Differences between ordered values have meaning	Deg. C or F Some Likert Scales (must be specific)	Frequencies Mode Median Mean Standard Deviation	Bar Pie Stem and leaf Box plot Histogram...