# Domestically Significant Ports So far, we've focused on **globally significant ports** — the major ones that handle huge volumes of vessels. These are clearly important: if any of them were disrupted, global trade could feel the impact. But what about **smaller ports** — especially in smaller countries?\ Let’s consider this idea: > If a country has only a few ports — or even just one — it likely **depends heavily** on those ports for its imports. This brings us to the next step: identifying **domestically significant ports**, i.e., ports that are vital within their own country. Researchers behind the PortWatch dataset defined: > Domestically significant ports are ports that, together, handle **more than 80%** of a country’s total imports. *** ### **🔧 Your tasks:** * [ ] Define a **dummy variable** where `dummy = 1` if a port is domestically significant * [ ] Calculate: * How many ports are **domestically significant** (`dummy = 1`)? * How many ports **are not** (`dummy = 0`)? * [ ] 💬 Comment your code clearly so that someone else can easily follow your logic > > > * You can use `numpy.where()` **or** `.loc[]` to create a **dummy variable** based on a given condition. > * To **count** how many ports fall into each group (`1` or `0`), try using `.value_counts()` on your dummy column > > 📌 *Tip:* You may already have a column showing each port’s **percent share of imports within its country** — use this to calculate **cumulative shares** for each country.