Blog - complex scatter scatter plot#24
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I'm not sure if this and the other Todo blog post should be merged or held off until those features are merged.
| # Making a Complex Scatter Plot | ||
| At the time of writing, mpl-altair does not support scatter plots with nominal or ordinal color encodings, so this post will show how to create a complex scatter plot in Altair, Matplotlib, and how mpl-altair _should_ implement the chart conversion in the future. | ||
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| In the first part, we made a simple scatter plot. This post will look at a more complex plot. |
| In addition to looking at Horsepower vs Weight, let's color each point by its origin country. | ||
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| ## Altair | ||
| Since Altair is based on linking columns to encodings, we just have to specify |
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No 'just', use "we have to specify"
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| ## Altair | ||
| Since Altair is based on linking columns to encodings, we just have to specify | ||
| that the color encoding comes from the Origin column. |
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Use italics to highlight the origin column
| ```python | ||
| ax.scatter('Weight_in_lbs', 'Horsepower', c='quantitative_column', data=cars) | ||
| ``` | ||
| However, the scatter function currently doesn't allow scalar mappables for categorical data. |
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Categorical color definitions (my guess is that scalar mappables is too weedy for the audience for this post)
| ax.set_xlim([0, None]) | ||
| ax.set_ylim([0, None]) | ||
| ax.legend(title='Origin') | ||
| plt.grid() |
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ax.grid() and fig.show() if you want to stay more Oo
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This is the slightly more complex scatter plot section from the blog post Nabarun and I were working on.