Patch: Pandas 3.x Compatibility (#239)

* replace .values with .to_numpy() for consistent behavior

* force cast to defaul time resolution

* force cost columns to float type

* bump pandas version

* update changelog

* bump version

* fix bug in metrics failure

Author: RHammond2

CHANGELOG.md

@@ -1,5 +1,16 @@
 # CHANGELOG
 
+## v0.13.3. - 8 April 2026
+
+- Enable Pandas v3.
+- Replaces some instances of `pd.DataFrame.values` with `pd.DataFrame.to_numpy()` to avoid
+  compatibility issues in Pandas 2.3.3 and Pandas 3+.
+- Cast the weather datetime index to "us" instead of "ns" to match updated Pandas 3 and Polars
+  defaults.
+- Cost columns are forced to be float columns to ensure for Pandas dtypes compatibility.
+- Fixes a bug in `Metrics.vessel_crew_hours_at_sea` where equipment-wise aggregations fail at
+  frequencies more granular than the project level.
+
 ## v0.13.2 - 10 February 2026
 
 - Temporarily prohibit Pandas 3.0 to resolve failing tests and integrations.

pyproject.toml

@@ -14,7 +14,7 @@ dependencies = [
     "attrs>=24.1.0",
     "numpy>=1.21",
     "scipy>=1.8",
-    "pandas>=2,<3",
+    "pandas>=2",
     "polars>=1.33.1",
     "pyarrow>=10",
     "jupyterlab>=3",

wombat/__init__.py

@@ -4,4 +4,4 @@
 from wombat.core.library import create_library_structure, load_yaml
 
 
-__version__ = "0.13.2"
+__version__ = "0.13.3"

wombat/core/library.py

@@ -330,7 +330,7 @@ def format_weather(weather: pd.DataFrame) -> pl.DataFrame:
         .with_row_index()
         .with_columns(
             [
-                pl.col("datetime").cast(pl.Datetime).dt.cast_time_unit("ns"),
+                pl.col("datetime").cast(pl.Datetime).dt.cast_time_unit("us"),
                 (pl.col("datetime").dt.hour()).alias("hour"),
             ]
         )

wombat/core/post_processor.py

@@ -382,13 +382,16 @@ def _apply_inflation_rate(self, events: pd.DataFrame) -> pd.DataFrame:
         pd.DataFrame
             The events dataframe with costs adjusted for inflation.
         """
+        events = events.astype(dict.fromkeys(self._cost_columns, "float"))
+
         adjusted_inflation = deepcopy(self.inflation_rate)
         years = events.year.unique()
         years.sort()
         for year in years:
-            row_filter = events.year == year
             if year > years[0]:
-                events.loc[row_filter, self._cost_columns] *= adjusted_inflation
+                events.loc[events.year.eq(year), self._cost_columns] *= (
+                    adjusted_inflation
+                )
                 adjusted_inflation *= self.inflation_rate
 
         return events
@@ -1022,15 +1025,15 @@ def vessel_crew_hours_at_sea(
                 return pd.DataFrame(at_sea.sum()[["duration"]]).T.rename(
                     columns={"duration": "Total Crew Hours at Sea"}
                 )
-        additional_cols = frequency.group_cols
+        time_cols = frequency.group_cols
         total_hours = (
             total_hours.drop(columns=frequency.drop_cols)
-            .groupby(group_cols)[["N"]]
+            .groupby(time_cols)[["N"]]
             .sum()
         )
 
-        columns = additional_cols + columns
-        group_cols.extend(additional_cols)
+        columns = time_cols + columns
+        group_cols.extend(time_cols)
         at_sea = at_sea[group_cols + ["duration"]].groupby(group_cols).sum()
         if by_equipment:
             total = []
@@ -1043,7 +1046,7 @@ def vessel_crew_hours_at_sea(
             total_hours = (
                 total_hours.reset_index()
                 .rename(columns={"N": "Total Crew Hours at Sea"})[columns]
-                .set_index(additional_cols)
+                .set_index(time_cols)
             )
             return total_hours
 

wombat/windfarm/windfarm.py

@@ -165,9 +165,9 @@ def _create_graph_layout(self, windfarm_layout: str | pd.DataFrame) -> None:
             windfarm, dict(layout[["id", "type"]].values), name="type"
         )
 
-        self.turbine_id: np.ndarray = layout.loc[turbine_filter, "id"].values
-        self.substation_id = layout.loc[substation_filter, "id"].values
-        self.electrolyzer_id = layout.loc[electrolyzer_filter, "id"].values
+        self.turbine_id: np.ndarray = layout.loc[turbine_filter, "id"].to_numpy()
+        self.substation_id = layout.loc[substation_filter, "id"].to_numpy()
+        self.electrolyzer_id = layout.loc[electrolyzer_filter, "id"].to_numpy()
 
         for substation in self.substation_id:
             windfarm.nodes[substation]["connection"] = layout.loc[