Handle set_index properly in to_adjlist

libpysal/weights/tests/test_adjlist.py

@@ -145,3 +145,45 @@ def test_map(self):
             np.testing.assert_allclose(
                 data, mapped["_".join(("subtract", name))].values
             )
+
+    def test_sort(self):
+        from libpysal import examples
+        from libpysal.weights import Rook
+
+        us = geopandas.read_file(examples.get_path("us48.shp"))
+        w = Rook.from_dataframe(us.set_index("STATE_FIPS"), use_index=True)
+        unsorted_al = w.to_adjlist(sort_joins=False)
+        sorted_al = w.to_adjlist(sort_joins=True)
+        sv = ["01"] * 4
+        sv.append("04")
+        sv = np.array(sv)
+        usv = np.array(["53", "53", "30", "30", "30"])
+        np.testing.assert_array_equal(unsorted_al.focal.values[:5], usv)
+        np.testing.assert_array_equal(sorted_al.focal.values[:5], sv)
+
+    def test_ids(self):
+        df = geopandas.read_file(examples.get_path("columbus.dbf")).head()
+        df["my_id"] = range(3, len(df) + 3)
+        W = weights.Queen.from_dataframe(df, ids="my_id")
+        W_adj = W.to_adjlist(drop_islands=True)
+        for i in range(3, 8):
+            assert i in W_adj.focal
+            assert i in W_adj.neighbor
+        for i in W_adj.focal:
+            assert i in list(range(3, len(df) + 3))
+        for i in W_adj.neighbor:
+            assert i in list(range(3, len(df) + 3))
+
+    def test_str_ids(self):
+        df = geopandas.read_file(examples.get_path("columbus.dbf")).head()
+        snakes = ["mamba", "boa", "python", "rattlesnake", "cobra"]
+        df["my_str_id"] = snakes
+        W = weights.Queen.from_dataframe(df, ids="my_str_id")
+        W_adj = W.to_adjlist(drop_islands=True)
+        for i in snakes:
+            (W_adj.focal == i).any()
+            (W_adj.neighbor == i).any()
+        for i in W_adj.focal:
+            assert i in snakes
+        for i in W_adj.neighbor:
+            assert i in snakes

libpysal/weights/weights.py

@@ -391,6 +391,7 @@ def to_adjlist(
         focal_col="focal",
         neighbor_col="neighbor",
         weight_col="weight",
+        sort_joins=False,
     ):
         """
         Compute an adjacency list representation of a weights object.
@@ -416,6 +417,10 @@ def to_adjlist(
             Name of the column in which to store "destination" node ids.
         weight_col : str
             Name of the column in which to store weight information.
+        sort_joins : bool
+            Whether or not to lexicographically sort the adjacency
+            list by (focal_col, neighbor_col). Default is False.
+
         """
         try:
             import pandas
@@ -432,6 +437,9 @@ def to_adjlist(
 
         links = []
         focal_ix, neighbor_ix = self.sparse.nonzero()
+        idxs = np.array(list(self.neighbors.keys()))
+        focal_ix = idxs[focal_ix]
+        neighbor_ix = idxs[neighbor_ix]
         weights = self.sparse.data
         adjlist = pandas.DataFrame(
             {focal_col: focal_ix, neighbor_col: neighbor_ix, weight_col: weights}
@@ -443,7 +451,9 @@ def to_adjlist(
                 {focal_col: self.islands, neighbor_col: self.islands, weight_col: 0}
             )
             adjlist = pandas.concat((adjlist, island_adjlist)).reset_index(drop=True)
-        return adjlist.sort_values([focal_col, neighbor_col])
+        if sort_joins:
+            return adjlist.sort_values([focal_col, neighbor_col])
+        return adjlist
 
     def to_networkx(self):
         """Convert a weights object to a ``networkx`` graph.