diff --git a/autotest/test_prtfile.py b/autotest/test_prtfile.py
new file mode 100644
index 0000000000..39b7850fc4
--- /dev/null
+++ b/autotest/test_prtfile.py
@@ -0,0 +1,45 @@
+from pathlib import Path
+
+import pytest
+
+from autotest.conftest import get_project_root_path
+from flopy.utils.prtfile import PathlineFile
+
+pytestmark = pytest.mark.mf6
+prt_data_path = (
+    get_project_root_path() / "examples" / "data" / "mf6" / "prt_data" / "001"
+)
+
+
+@pytest.mark.parametrize(
+    "path, header_path",
+    [
+        (Path(prt_data_path) / "prt001.trk", None),
+        (
+            Path(prt_data_path) / "prt001.trk",
+            Path(prt_data_path) / "prt001.trk.hdr",
+        ),
+        (Path(prt_data_path) / "prt001.trk.csv", None),
+    ],
+)
+def test_init(path, header_path):
+    file = PathlineFile(path, header_filename=header_path)
+    assert file.fname == path
+    assert file.dtype == PathlineFile.dtypes["full"]
+    if path.suffix == ".csv":
+        assert len(file._data) == len(open(path).readlines()) - 1
+
+
+@pytest.mark.parametrize(
+    "path",
+    [
+        Path(prt_data_path) / "prt001.trk",
+        Path(prt_data_path) / "prt001.trk.csv",
+    ],
+)
+def test_intersect(path):
+    file = PathlineFile(path)
+    nodes = [1, 11, 21]
+    intersection = file.intersect(nodes)
+    assert any(intersection)
+    assert all(d.icell in nodes for d in intersection.itertuples())
diff --git a/examples/data/mf6/prt_data/001/prt001.trk b/examples/data/mf6/prt_data/001/prt001.trk
new file mode 100644
index 0000000000..593ca09302
Binary files /dev/null and b/examples/data/mf6/prt_data/001/prt001.trk differ
diff --git a/examples/data/mf6/prt_data/001/prt001.trk.csv b/examples/data/mf6/prt_data/001/prt001.trk.csv
new file mode 100644
index 0000000000..0b846c480f
--- /dev/null
+++ b/examples/data/mf6/prt_data/001/prt001.trk.csv
@@ -0,0 +1,181 @@
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+1,1,1,1,7,1,27,0,1,1,.000000000000000,12.75938925953459,6.713013462189426,7.000000000000000,.5000000000000000,PRP000000007
+1,1,1,1,7,1,37,0,1,1,.000000000000000,14.07202735612326,7.000000000000000,6.713074786853214,.5000000000000000,PRP000000007
+1,1,1,1,7,1,38,0,1,1,.000000000000000,16.92425797975478,7.554881918015834,6.000000000000000,.5000000000000000,PRP000000007
+1,1,1,1,7,1,48,0,1,1,.000000000000000,19.39266639551562,8.000000000000000,5.279606464532343,.5000000000000000,PRP000000007
+1,1,1,1,7,1,49,0,1,1,.000000000000000,20.18090342207203,8.128644331083812,5.000000000000000,.5000000000000000,PRP000000007
+1,1,1,1,7,1,59,0,1,1,.000000000000000,22.63520742498477,8.558521381003381,4.000000000000000,.5000000000000000,PRP000000007
+1,1,1,1,7,1,69,0,1,1,.000000000000000,24.62882950801737,8.960474586955897,3.000000000000000,.5000000000000000,PRP000000007
+1,1,1,1,7,1,79,0,1,1,.000000000000000,24.75426166980224,9.000000000000000,2.930034354506921,.5000000000000000,PRP000000007
+1,1,1,1,7,1,80,0,1,1,.000000000000000,25.85030962625325,9.290460600648139,2.000000000000000,.5000000000000000,PRP000000007
+1,1,1,1,7,1,90,0,1,1,.000000000000000,26.61730611011759,9.571583833577536,1.000000000000000,.5000000000000000,PRP000000007
+1,1,1,1,7,1,100,0,5,3,.000000000000000,26.61730611011759,9.571583833577536,1.000000000000000,.5000000000000000,PRP000000007
+1,1,1,1,8,1,1,0,1,0,.000000000000000,.000000000000000,.8000000100000000,9.800000010000000,.5000000000000000,PRP000000008
+1,1,1,1,8,1,1,0,1,1,.000000000000000,.1344019512308165,1.000000000000000,9.750000015625000,.5000000000000000,PRP000000008
+1,1,1,1,8,1,2,0,1,1,.000000000000000,.9013729199773104,2.000000000000000,9.585954780392077,.5000000000000000,PRP000000008
+1,1,1,1,8,1,3,0,1,1,.000000000000000,2.097332034353719,3.000000000000000,9.397909864619525,.5000000000000000,PRP000000008
+1,1,1,1,8,1,4,0,1,1,.000000000000000,3.775207521732195,4.000000000000000,9.188640883275196,.5000000000000000,PRP000000008
+1,1,1,1,8,1,5,0,1,1,.000000000000000,5.535959564152013,4.812686202004050,9.000000000000000,.5000000000000000,PRP000000008
+1,1,1,1,8,1,15,0,1,1,.000000000000000,6.037268254292059,5.000000000000000,8.939167000453050,.5000000000000000,PRP000000008
+1,1,1,1,8,1,16,0,1,1,.000000000000000,9.106420632686824,6.000000000000000,8.606288836540338,.5000000000000000,PRP000000008
+1,1,1,1,8,1,17,0,1,1,.000000000000000,13.08389052376908,7.000000000000000,8.110323902719321,.5000000000000000,PRP000000008
+1,1,1,1,8,1,18,0,1,1,.000000000000000,13.86103684171424,7.164201773142255,8.000000000000000,.5000000000000000,PRP000000008
+1,1,1,1,8,1,28,0,1,1,.000000000000000,18.71985641123550,8.000000000000000,7.164512745583430,.5000000000000000,PRP000000008
+1,1,1,1,8,1,29,0,1,1,.000000000000000,19.49877003654247,8.110597839292595,7.000000000000000,.5000000000000000,PRP000000008
+1,1,1,1,8,1,39,0,1,1,.000000000000000,23.47729152323840,8.606711335111470,6.000000000000000,.5000000000000000,PRP000000008
+1,1,1,1,8,1,49,0,1,1,.000000000000000,26.54694642588228,8.939710292575988,5.000000000000000,.5000000000000000,PRP000000008
+1,1,1,1,8,1,59,0,1,1,.000000000000000,27.04342131175283,9.000000000000000,4.814550660372381,.5000000000000000,PRP000000008
+1,1,1,1,8,1,60,0,1,1,.000000000000000,28.80857656933874,9.189227014975872,4.000000000000000,.5000000000000000,PRP000000008
+1,1,1,1,8,1,70,0,1,1,.000000000000000,30.48637535867236,9.398458476147445,3.000000000000000,.5000000000000000,PRP000000008
+1,1,1,1,8,1,80,0,1,1,.000000000000000,31.68233989116573,9.586327136997982,2.000000000000000,.5000000000000000,PRP000000008
+1,1,1,1,8,1,90,0,1,1,.000000000000000,32.44933637503007,9.750226495833470,1.000000000000000,.5000000000000000,PRP000000008
+1,1,1,1,8,1,100,0,5,3,.000000000000000,32.44933637503007,9.750226495833470,1.000000000000000,.5000000000000000,PRP000000008
+1,1,1,1,9,1,1,0,1,0,.000000000000000,.000000000000000,.8999999800000000,9.899999980000000,.5000000000000000,PRP000000009
+1,1,1,1,9,1,1,0,1,1,.000000000000000,.6345987855645296E-01,1.000000000000000,9.888888864197531,.5000000000000000,PRP000000009
+1,1,1,1,9,1,2,0,1,1,.000000000000000,.8304308473029468,2.000000000000000,9.815979850001865,.5000000000000000,PRP000000009
+1,1,1,1,9,1,3,0,1,1,.000000000000000,2.026389961679355,3.000000000000000,9.732404308084922,.5000000000000000,PRP000000009
+1,1,1,1,9,1,4,0,1,1,.000000000000000,3.704265449057832,4.000000000000000,9.639395845450316,.5000000000000000,PRP000000009
+1,1,1,1,9,1,5,0,1,1,.000000000000000,5.928937245555336,5.000000000000000,9.530389317077885,.5000000000000000,PRP000000009
+1,1,1,1,9,1,6,0,1,1,.000000000000000,8.827842291371752,6.000000000000000,9.387644956386470,.5000000000000000,PRP000000009
+1,1,1,1,9,1,7,0,1,1,.000000000000000,12.68120184750453,7.000000000000000,9.168457893117671,.5000000000000000,PRP000000009
+1,1,1,1,9,1,8,0,1,1,.000000000000000,15.14938897572776,7.500051133825171,9.000000000000000,.5000000000000000,PRP000000009
+1,1,1,1,9,1,18,0,1,1,.000000000000000,18.21418594018942,8.000000000000000,8.744028834033367,.5000000000000000,PRP000000009
+1,1,1,1,9,1,19,0,1,1,.000000000000000,24.57630095531389,8.744123620991481,8.000000000000000,.5000000000000000,PRP000000009
+1,1,1,1,9,1,29,0,1,1,.000000000000000,27.64079917480293,9.000000000000000,7.500296728865826,.5000000000000000,PRP000000009
+1,1,1,1,9,1,30,0,1,1,.000000000000000,30.11083202407352,9.168514955661681,7.000000000000000,.5000000000000000,PRP000000009
+1,1,1,1,9,1,40,0,1,1,.000000000000000,33.96452852569760,9.387714534177162,6.000000000000000,.5000000000000000,PRP000000009
+1,1,1,1,9,1,50,0,1,1,.000000000000000,36.86342265981291,9.530525778487238,5.000000000000000,.5000000000000000,PRP000000009
+1,1,1,1,9,1,60,0,1,1,.000000000000000,39.08799210268831,9.639587059267356,4.000000000000000,.5000000000000000,PRP000000009
+1,1,1,1,9,1,70,0,1,1,.000000000000000,40.76579089202193,9.732596727334171,3.000000000000000,.5000000000000000,PRP000000009
+1,1,1,1,9,1,80,0,1,1,.000000000000000,41.96175542451530,9.816109989097118,2.000000000000000,.5000000000000000,PRP000000009
+1,1,1,1,9,1,90,0,1,1,.000000000000000,42.72875190837964,9.888968176275537,1.000000000000000,.5000000000000000,PRP000000009
+1,1,1,1,9,1,100,0,5,3,.000000000000000,42.72875190837964,9.888968176275537,1.000000000000000,.5000000000000000,PRP000000009
diff --git a/examples/data/mf6/prt_data/001/prt001.trk.hdr b/examples/data/mf6/prt_data/001/prt001.trk.hdr
new file mode 100644
index 0000000000..c5fb8481e9
--- /dev/null
+++ b/examples/data/mf6/prt_data/001/prt001.trk.hdr
@@ -0,0 +1,2 @@
+kper,kstp,imdl,iprp,irpt,ilay,icell,izone,istatus,ireason,trelease,t,x,y,z,name
+<i4,<i4,<i4,<i4,<i4,<i4,<i4,<i4,<i4,<i4,<f8,<f8,<f8,<f8,<f8,|S40
diff --git a/flopy/utils/prtfile.py b/flopy/utils/prtfile.py
index 2fdabcdc19..6df2419741 100644
--- a/flopy/utils/prtfile.py
+++ b/flopy/utils/prtfile.py
@@ -54,19 +54,25 @@ class PathlineFile(ParticleTrackFile):
                 ("dest", np.str_),  # destination name, for convenience
                 ("istatus", np.int32),
                 ("ireason", np.int32),
-                ("trelease", np.int32),
-                ("t", np.int32),
-                ("t0", np.int32),  # release time, for convenience
-                ("tt", np.int32),  # termination time, convenience
-                ("time", np.int32),  # conform to canonical dtype
-                ("x", np.int32),
-                ("y", np.int32),
-                ("z", np.int32),
+                ("trelease", np.float32),
+                ("t", np.float32),
+                ("t0", np.float32),  # release time, for convenience
+                ("tt", np.float32),  # termination time, convenience
+                ("time", np.float32),  # conform to canonical dtype
+                ("x", np.float32),
+                ("y", np.float32),
+                ("z", np.float32),
                 ("particleid", np.int32),  # conform to canonical dtype
                 ("name", np.str_),
             ]
         ),
     }
+    """
+    Base (directly from MODFLOW 6 PRT) and full (extended by this class) dtypes for PRT.
+    The extended dtype complies with the canonical `flopy.utils.particletrackfile.dtype`
+    and provides a few other columns for convenience, including release and termination 
+    time and destination zone number/name.
+    """
 
     def __init__(
         self,
@@ -105,7 +111,7 @@ def _load(
             data = pd.read_csv(self.fname, dtype=dtype)
         except UnicodeDecodeError:
             try:
-                data = np.fromfile(self.fname, dtype=dtype)
+                data = pd.DataFrame(np.fromfile(self.fname, dtype=dtype))
             except:
                 raise ValueError("Unreadable file, expected binary or CSV")
 
@@ -113,7 +119,8 @@ def _load(
         data = data.sort_values(self.key_cols)
         data["particleid"] = data.groupby(self.key_cols).ngroup()
 
-        # add release time and termination time columns
+        # add time, release time and termination time columns
+        data["time"] = data["t"]
         data["t0"] = (
             data.groupby("particleid")
             .apply(lambda x: x.t.min())
@@ -135,32 +142,21 @@ def _load(
                 axis=1,
             )
 
-        return dtype, data
+        return self.dtypes["full"], data
 
     def intersect(
-        self, cells, to_recarray
+        self, cells, to_recarray=True
     ) -> Union[List[np.recarray], np.recarray]:
-        if isinstance(cells, (list, tuple)):
-            allint = all(isinstance(el, int) for el in cells)
-            if allint:
-                t = []
-                for el in cells:
-                    t.append((el,))
-                    cells = t
-
-        icell = self._data[["icell"]]
-        cells = np.array(cells, dtype=icell.dtype)
-        inds = np.in1d(icell, cells)
-        epdest = self._data[inds].copy().view(np.recarray)
+        """Find intersection of pathlines with cells."""
+
+        if not all(isinstance(nn, int) for nn in cells):
+            raise ValueError("Expected integer cell numbers")
+
+        idxs = np.in1d(self._data[["icell"]], np.array(cells, dtype=np.int32))
+        sect = self._data[idxs].copy()
+        pids = np.unique(sect["particleid"])
         if to_recarray:
-            # use particle ids to get the rest of the paths
-            inds = np.in1d(self._data["particleid"], epdest.particleid)
-            series = self._data[inds].copy()
-            series.sort(order=["particleid", "time"])
-            series = series.view(np.recarray)
+            idxs = np.in1d(sect["particleid"], pids)
+            return sect[idxs].sort_values(by=["particleid", "time"])
         else:
-            # collect unique particleids in selection
-            partids = np.unique(epdest["particleid"])
-            series = [self.get_data(partid) for partid in partids]
-
-        return series
+            return [self.get_data(pid) for pid in pids]