chore(dependencies): support pandas 3

flopy/export/metadata.py

@@ -188,7 +188,7 @@ def time_coverage(self):
             mlen = self.model_time.perlen.sum()
             tunits = self.model_time.time_units
             tc["duration"] = f"{mlen} {tunits}"
-            end = strt + pd.Timedelta(mlen, unit="d")
+            end = strt + pd.Timedelta(mlen, unit="D")
             tc["end"] = str(end)
         return tc
 

flopy/mf6/data/mfdataplist.py

@@ -676,9 +676,11 @@ def set_data(self, data, autofill=False, check_data=True, append=False):
                 if len(data[0]) == len(self._data_item_names):
                     # data most likely being stored with cellids as tuples,
                     # create a dataframe and untuple the cellids
-                    data = pandas.DataFrame(
-                        data, columns=self._data_item_names
-                    )
+                    # In pandas 3+, DataFrame() with recarray requires columns to match
+                    # field names, so create without columns param then rename if needed
+                    data = pandas.DataFrame(data)
+                    if list(data.columns) != self._data_item_names:
+                        data.columns = self._data_item_names
                     data = self._untuple_cellids(data)[0]
                     # make sure columns are still in correct order
                     data = pandas.DataFrame(data, columns=self._header_names)
@@ -691,19 +693,21 @@ def set_data(self, data, autofill=False, check_data=True, append=False):
             else:
                 # data size matches the expected header names, create a pandas
                 # dataframe from the data
-                data_new = pandas.DataFrame(data, columns=self._header_names)
+                # In pandas 3+, DataFrame() with recarray requires columns to match
+                # field names, so create without columns param then rename if needed
+                data_new = pandas.DataFrame(data)
+                if list(data_new.columns) != self._header_names:
+                    data_new.columns = self._header_names
                 if not self._dataframe_check(data_new):
                     data_list = self._untuple_recarray(data)
-                    data = pandas.DataFrame(
-                        data_list, columns=self._header_names
-                    )
+                    data = pandas.DataFrame(data_list)
+                    if list(data.columns) != self._header_names:
+                        data.columns = self._header_names
                 else:
                     data, count = self._untuple_cellids(data_new)
                     if count > 0:
                         # make sure columns are still in correct order
-                        data = pandas.DataFrame(
-                            data, columns=self._header_names
-                        )
+                        data = pandas.DataFrame(data, columns=self._header_names)
         elif isinstance(data, list) or isinstance(data, tuple):
             if not (isinstance(data[0], list) or isinstance(data[0], tuple)):
                 # get data in the format of a tuple of lists (or tuples)

flopy/utils/mtlistfile.py

@@ -51,7 +51,7 @@ def __init__(self, file_name):
 
         return
 
-    def parse(self, forgive=True, diff=True, start_datetime=None, time_unit="d"):
+    def parse(self, forgive=True, diff=True, start_datetime=None, time_unit="D"):
         """
         Main entry point for parsing the list file.
 

pyproject.toml

@@ -28,7 +28,7 @@ requires-python = ">=3.10"
 dependencies = [
     "numpy>=1.20.3,<3.0",
     "matplotlib >=1.4.0",
-    "pandas >=2.0.0,<3.0",
+    "pandas >=2.0.0",
 ]
 dynamic = ["version", "readme"]