Thread by @Josh_Ebner

How to Make Small Multiple Charts in Python, with Plotly

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sharpsightlabs.com/blog/plotly-sm…

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Remember: small multiple charts break out a visualization by a categorical variable.

We take a simple chart and break it out into panels.

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There are a variety of tools to make small multiple charts in Python, including the Seaborn FacetGrid technique.

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https://twitter.com/Josh_Ebner/status/1470097782279131148?s=20&t=3ljSlVs1kMCvGhKrOpK3nA

BTW: If you're an R user, you can also create small multiple charts with facet_wrap and facet_grid.

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https://twitter.com/Josh_Ebner/status/1324102431916068864?s=20&t=_3l083trefGFu3NbtL_5Bw

But if you're using Python, you can create small multiple charts in Plotly using specific parameters inside of most Plotly Express functions.

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For example, you can create a small multiple from a:

– plotly histogram
– plotly scatterplot
– plotly bar chart
– plotly heatmap (px.imshow)

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The syntax for a Plotly small multiple is pretty simple.

As mentioned previously, you create a small multiple by using specific *parameters* inside existing plotly functions.

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You can use "facet_row=" to specify the variable on which to facet row-wise.

You can use "facet_col=" to specify the variable on which to facet column-wise.

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You can also use "facet_col_wrap=" to specify the number of panels in the horizontal direction (i.e., column wise)

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Here's a quick example faceting a Plotly histogram by column:

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And here's a quick example faceting a Plotly histogram by row:

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And here, we'll facet by row *and* column:

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Small multiples are a very powerful tool for data visualization and analysis.

They're also extremely under-used.

You need to learn them.

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For more tutorials about data visualization and data science, follow me here: @Josh_Ebner

Every day, I post tutorials and threads about data science, data visualization, and ML in Python and R.

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