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?

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.