diff --git a/nptdms/export/pandas_export.py b/nptdms/export/pandas_export.py
index 1d15d4f..796ea38 100644
--- a/nptdms/export/pandas_export.py
+++ b/nptdms/export/pandas_export.py
@@ -114,7 +114,7 @@ def _channels_to_dataframe(
 def _array_for_pd(array):
     """ Convert data array to a format suitable for a Pandas dataframe
     """
-    if np.issubdtype(array.dtype, np.dtype('void')):
+    if np.issubdtype(array.dtype, np.dtype('void')) and len(array.dtype) == 0:
         # If dtype is void then the array must also be empty.
         # Pandas doesn't like void data types, so these are converted to empty float64 arrays
         # and Pandas will fill values with NaN
diff --git a/nptdms/test/test_pandas.py b/nptdms/test/test_pandas.py
index 22c3f21..564aee4 100644
--- a/nptdms/test/test_pandas.py
+++ b/nptdms/test/test_pandas.py
@@ -352,6 +352,86 @@ def check_series(series):
     check_series(channel_df["/'Group'/'Channel1'"])
 
 
+@pytest.mark.parametrize('arrow_dtypes', [False, True])
+def test_bool_data_to_pandas(arrow_dtypes):
+    test_file, expected_data = scenarios.bool_data().values
+    df = test_file.load()['group'].as_dataframe(arrow_dtypes=arrow_dtypes)
+    np.testing.assert_allclose(df['bool_channel'], expected_data[('group', 'bool_channel')])
+
+
+@pytest.mark.parametrize('arrow_dtypes', [False, True])
+def test_string_data_to_pandas(arrow_dtypes):
+    strings = ["abcdefg", "qwertyuiop"]
+
+    test_file = GeneratedFile()
+    toc = ("kTocMetaData", "kTocRawData", "kTocNewObjList")
+    metadata = (
+        # Number of objects
+        "01 00 00 00"
+        # Length of the object path
+        "18 00 00 00")
+    metadata += string_hexlify("/'Group'/'StringChannel'")
+    metadata += (
+        # Length of index information
+        "1C 00 00 00"
+        # Raw data data type
+        "20 00 00 00"
+        # Dimension
+        "01 00 00 00"
+        # Number of raw data values
+        "02 00 00 00"
+        "00 00 00 00"
+        # Number of bytes in data
+        "19 00 00 00"
+        "00 00 00 00"
+        # Number of properties (0)
+        "00 00 00 00")
+    data = (
+        "07 00 00 00"  # index to after first string
+        "11 00 00 00"  # index to after second string
+    )
+    for string in strings:
+        data += string_hexlify(string)
+    test_file.add_segment(toc, metadata, data)
+    tdms_data = test_file.load()
+
+    series = tdms_data["Group"].as_dataframe(arrow_dtypes=arrow_dtypes)["StringChannel"]
+
+    assert len(series) == len(strings)
+    for expected, read in zip(strings, series):
+        assert expected == read
+
+
+def test_dataframe_with_complex_data():
+    test_file, expected_data = scenarios.complex_data().values
+    df = test_file.load()['group'].as_dataframe()
+    np.testing.assert_allclose(df['complex_single_channel'], expected_data[('group', 'complex_single_channel')])
+    np.testing.assert_allclose(df['complex_double_channel'], expected_data[('group', 'complex_double_channel')])
+
+
+def test_dataframe_with_raw_timestamp_data():
+    test_file = GeneratedFile()
+    seconds = 3672033330
+    second_fractions = 1234567890 * 10 ** 10
+    test_file.add_segment(
+        ("kTocMetaData", "kTocRawData", "kTocNewObjList"),
+        segment_objects_metadata(
+            channel_metadata("/'group'/'channel1'", 0x44, 4),
+        ),
+        hexlify_value("<Q", 0) + hexlify_value("<q", seconds) +
+        hexlify_value("<Q", second_fractions) + hexlify_value("<q", seconds) +
+        hexlify_value("<Q", 0) + hexlify_value("<q", seconds + 1) +
+        hexlify_value("<Q", second_fractions) + hexlify_value("<q", seconds + 1)
+    )
+
+    with test_file.get_tempfile() as temp_file:
+        tdms_data = TdmsFile.read(temp_file.file, raw_timestamps=True)
+        with pytest.raises(ValueError) as exc_info:
+            tdms_data['group'].as_dataframe()
+        message = str(exc_info.value)
+        assert "compound dtype" in message
+
+
 def test_export_with_empty_channels():
     """Convert a group to dataframe when a channel has empty data and void data type"""