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    Choosing the right color palette for data visualization is crucial for effectively conveying information and ensuring that the audience can easily interpret the data. There are three main types of color palettes used in data visualization: categorical, sequential, and diverging.

    Categorical Color Palettes

    Categorical color palettes are used for data with distinct categories that have no inherent order. These palettes are ideal for distinguishing between discrete data groups without implying any order or magnitude. Effective categorical palettes limit colors to around ten unique shades to prevent visual confusion and ensure each category is distinct.

    Examples:

    1. Retro Metro: A vibrant blend of reds, pinks, oranges, yellows, greens, blues, and purples. ["#ea5545", "#f46a9b", "#ef9b20", "#edbf33", "#ede15b", "#bdcf32", "#87bc45", "#27aeef", "#b33dc6"]

    2. Dutch Field: Features bold colors like hot pink, sky blue, lime green, and orange. ["#e60049", "#0bb4ff", "#50e991", "#e6d800", "#9b19f5", "#ffa300", "#dc0ab4", "#b3d4ff", "#00bfa0"]

    Sequential Color Palettes

    Sequential color palettes are used for visualizing data with a natural progression or order, such as time series data or temperature gradients. The colors in a sequential palette transition smoothly from light to dark or from one hue to another to represent increasing or decreasing values.

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