Types of data and data types

Types of data and data types#

Please note that this page appears in both the chapters “Statistics — Basics & location” and “Pandas Basics”. This is because the topic is relevant to both the general understanding of data and the specific implementation in Pandas.

Learning objectives#

After working through this topic, you should be able to:

  • determine the correct description of a given data column

    • nominal

    • cardinal

    • ordinal

  • explain how these categories map into Pandas data types:

    • unordered categorical

    • floating point

    • integer

    • ordered categorical

Materials#

Video with English subtitles:

Download the slides.

Video with German subtitles:

(turn subtitles on in the bottom right corner of the video)

Decision tree#

This is the decision tree for the “correct” form of data. Just because a variable arrives as a number, it does not mean that you should think about it as numerical data. Very often this happens when categories are encoded as numbers (e.g., 0, 1, 2 meaning \([0, 30,000)\), \([30,000, 60,000)\), \([60,000, \infty)\), which would be described in some metadata).

        flowchart LR
    A -->|"`Yes`"| C("`Numerical?`")
    A(Ordered?) -->|No| B("`Unordered
    Categorical`")

    C -->|"`Yes`"| E("`Continuous?`")
    C -->|"`No`"| D("`Ordered
    Categorical`")

    E -->|"`Yes`"| G("`Floating
    point`")
    E -->|"`No`"| F("`Integer`")

    style A fill:#FFE5B4
    style C fill:#FFE5B4
    style E fill:#FFE5B4

Note that for numerical data, you still have to decide whether a variable is measured on a cardinal or ordinal scale. Both are possible for continuous or discrete data, it is not embedded in the Pandas data type.

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