![]() Many other data visualization options for Python Matplotlib in particular were designed before Pandas DataFrames became popular data structures in Python. Second, Seaborn has been designed to work well with DataFrames. The relationship between x and y can be shown for different subsets of the data using. In particular, Seaborn has easy-to-use functions for creating plots like scatterplots, line charts, bar charts, box plots, etc. But it's reasonably simple the other answer uses FacetGrid but it's a bit over-engineered because it forgets the hue_kws parameter: palette = Draw a scatter plot with possibility of several semantic groupings. This is less flexible because the plot will have to be in its own figure. ![]() When looking at the actual graph, there’s a pretty apparent trend that majors with. (plt.scatter(,, ec=color, **kws), key) for key, color in ems()Ī third option is to use FacetGrid. Seaborn is built on top of Matplotlib, so a lot of graph aesthetics can be modified using Matplotlib commands. That is somewhat limited because you lose control over the thickness of the circle.Ī hybrid seaborn-matplotlib approach is more flexible, but also more cumbersome (you need to create the legend yourself): palette = The simplest pure seaborn solution is to take advantage of the fact that you can use arbitrary latex symbols as the markers: sns.scatterplot(data=df, x='x', y='y', hue="cat", marker="$\circ$", ec="face", s=100) In principle you should be able to create a circular marker with fillstyle="none", but there are some deep complications there and it doesn't currently work as you'd hope.
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