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On the other hand, hue variations are not well suited to representing numeric data. Consider this example, where we need colors to represent the counts in a bivariate histogram. On the left, we use a circular colormap, where gradual changes in the number of observation within each bin correspond to gradual changes in hue. On the right, we use a palette that uses brighter colors to represent bins with larger counts: Because of the way our eyes work, a particular color can be defined using three components. We usually program colors in a computer by specifying their RGB values, which set the intensity of the red, green, and blue channels in a display. But for analyzing the perceptual attributes of a color, it’s better to think in terms of hue, saturation, and luminance channels.In the plot on the right, the orange triangles “pop out”, making it easy to distinguish them from the circles. This pop-out effect happens because our visual system prioritizes color differences. And aesthetics do matter: the more that people want to look at your figures, the greater the chance that they will learn something from them. This is true even when you are making plots for yourself. During exploratory data analysis, you may generate many similar figures. Varying the color palettes will add a sense of novelty, which keeps you engaged and prepared to notice interesting features of your data. Saturation (or chroma) is the colorfulness. Two colors with different hues will look more distinct when they have more saturation: Calling color_palette() with no arguments will return the current default color palette that matplotlib (and most seaborn functions) will use if colors are not otherwise specified. This default palette can be set with the corresponding set_palette() function, which calls color_palette() internally and accepts the same arguments.

Seaborn in fact has six variations of matplotlib’s palette, called deep, muted, pastel, bright, dark, and colorblind. These span a range of average luminance and saturation values:With the hue-based palette, it’s quite difficult to ascertain the shape of the bivariate distribution. In contrast, the luminance palette makes it much more clear that there are two prominent peaks. To motivate the different options that color_palette() provides, it will be useful to introduce a classification scheme for color palettes. Broadly, palettes fall into one of three categories: The second major class of color palettes is called “sequential”. This kind of mapping is appropriate when data range from relatively low or uninteresting values to relatively high or interesting values (or vice versa). As we saw above, the primary dimension of variation in a sequential palette is luminance. Some seaborn functions will default to a sequential palette when you are mapping numeric data. (For historical reasons, both categorical and numeric mappings are specified with the hue parameter in functions like relplot() or displot(), even though numeric mappings use color palettes with relatively little hue variation). Perceptually uniform palettes # Should the worst happen, one of our fully compliant and comprehensive burns kits will help provide the injured party with the effective and immediate treatment required. These kits, which should be stored close to the areas containing temperature hazards, will be sufficient to treat burns or scalding and ease the pain for the sufferer. Dependent on the severity of the injury, a trip to hospital may also be necessary.