# Vessel Count Category vs. Trade Share
Now, let’s investigate whether ports with higher vessel counts also tend to play a larger role in their country’s maritime imports and exports.
You computed the variable `VESSEL_COUNT_CATEGORY`, which classifies each port as "low", "medium", or "high" based on its total vessel count. Each port also has two columns: `SHARE_COUNTRY_MARITIME_IMPORT` and `SHARE_COUNTRY_MARITIME_EXPORT`, representing the share of that port in its country’s total maritime imports and exports, respectively.
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### **🔧 Your tasks:**
Your task is to visualize and interpret the relationship between vessel count category and trade share:
* [ ] **Create boxplots** for both import and export shares, grouped by `VESSEL_COUNT_CATEGORY`. They should look something like this (here, outliers aren't show for the purpose of visualization:
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**Interpretation Questions:**
* [ ] 🤔 What do the boxplots reveal about the relationship between vessel traffic and a port’s importance in national trade?
* [ ] 🤔 Are the distributions similar for imports and exports, or do you notice differences between them?
* [ ] 🤔 What is your takeway from the distribution of the "high" category?
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> You really just need to use matplotlib's `boxplot()`. There is no need for actual grouping on the dataset, you simply need to set the `x` and `y` options of the boxplot. To remove the outliers from the boxplots, set `showfliers` option to false.
>
> You really just need to use matplotlib's `boxplot()`. There is no need for actual grouping on the dataset, you simply need to set the `x` and `y` options of the boxplot. To remove the outliers from the boxplots, set `showfliers` option to false.