Document entity-table datasets

docs/book/usage/simulations.md

@@ -209,6 +209,75 @@ print(f"Total income tax from all people: £{total_tax:.2f}")
 The column naming follows the pattern `variable_name__year` for time-varying variables. Note the double underscore `__` between the variable name and year.
 ```
 
+### From entity tables with `UKSingleYearDataset` and `UKMultiYearDataset`
+
+If your data is already split into person-, benefit-unit-, and household-level
+tables, use the UK dataset classes directly:
+
+```python
+import pandas as pd
+
+from policyengine_uk import Simulation
+from policyengine_uk.data import UKMultiYearDataset, UKSingleYearDataset
+
+person_2025 = pd.DataFrame({
+    "person_id": [1, 2],
+    "person_benunit_id": [1, 1],
+    "person_household_id": [1, 1],
+    "age": [35, 7],
+    "employment_income": [32_000, 0],
+})
+
+benunit_2025 = pd.DataFrame({
+    "benunit_id": [1],
+})
+
+household_2025 = pd.DataFrame({
+    "household_id": [1],
+    "region": ["LONDON"],
+    "council_tax": [1_800],
+})
+
+dataset_2025 = UKSingleYearDataset(
+    person=person_2025,
+    benunit=benunit_2025,
+    household=household_2025,
+    fiscal_year=2025,
+)
+
+sim = Simulation(dataset=dataset_2025)
+print(sim.calculate("income_tax", 2025))
+```
+
+Each table should contain the ID columns needed to link entities together:
+
+- `person`: `person_id`, `person_benunit_id`, `person_household_id`, plus any person-level variables
+- `benunit`: `benunit_id`, plus any benefit-unit variables
+- `household`: `household_id`, plus any household-level variables
+
+Use `UKSingleYearDataset` when you have one cross-section and want PolicyEngine
+UK to extend it forward using its standard uprating assumptions.
+
+Use `UKMultiYearDataset` when you already have explicit tables for multiple
+years and want those year-by-year values loaded as-is:
+
+```python
+dataset = UKMultiYearDataset(
+    datasets=[
+        dataset_2025,
+        UKSingleYearDataset(
+            person=person_2026,
+            benunit=benunit_2026,
+            household=household_2026,
+            fiscal_year=2026,
+        ),
+    ]
+)
+
+sim = Simulation(dataset=dataset)
+print(sim.calculate("household_net_income", 2026))
+```
+
 ### From survey datasets
 
 For population-level analysis, use survey data:
@@ -453,4 +522,4 @@ This makes it easy to trace through exactly how a reform affects different compo
 - Cache simulation results when running the same calculation multiple times
 ```
 
-The simulation system is designed to be flexible and powerful. Start with simple examples and gradually build up to more complex analyses as you become familiar with the structure and capabilities.
+The simulation system is designed to be flexible and powerful. Start with simple examples and gradually build up to more complex analyses as you become familiar with the structure and capabilities.