REF: dispatch TDBlock.to_native_types to TDA._format_native_types

pandas/core/arrays/timedeltas.py

@@ -389,7 +389,7 @@ def _format_native_types(self, na_rep="NaT", date_format=None, **kwargs):
         from pandas.io.formats.format import _get_format_timedelta64
 
         formatter = _get_format_timedelta64(self._data, na_rep)
-        return np.array([formatter(x) for x in self._data])
+        return np.array([formatter(x) for x in self._data.ravel()]).reshape(self.shape)
 
     # ----------------------------------------------------------------
     # Arithmetic Methods

pandas/core/internals/blocks.py

@@ -2366,26 +2366,10 @@ def fillna(self, value, **kwargs):
             )
         return super().fillna(value, **kwargs)
 
-    def to_native_types(self, na_rep=None, quoting=None, **kwargs):
+    def to_native_types(self, na_rep="NaT", **kwargs):
         """ convert to our native types format """
-        values = self.values
-        mask = isna(values)
-
-        rvalues = np.empty(values.shape, dtype=object)
-        if na_rep is None:
-            na_rep = "NaT"
-        rvalues[mask] = na_rep
-        imask = (~mask).ravel()
-
-        # FIXME:
-        # should use the formats.format.Timedelta64Formatter here
-        # to figure what format to pass to the Timedelta
-        # e.g. to not show the decimals say
-        rvalues.flat[imask] = np.array(
-            [Timedelta(val)._repr_base(format="all") for val in values.ravel()[imask]],
-            dtype=object,
-        )
-        return rvalues
+        tda = self.array_values()
+        return tda._format_native_types(na_rep, **kwargs)
 
 
 class BoolBlock(NumericBlock):

pandas/tests/frame/test_dtypes.py

@@ -248,12 +248,7 @@ def test_astype_str(self):
             {
                 "a": list(map(str, map(lambda x: Timestamp(x)._date_repr, a._values))),
                 "b": list(map(str, map(Timestamp, b._values))),
-                "c": list(
-                    map(
-                        str,
-                        map(lambda x: Timedelta(x)._repr_base(format="all"), c._values),
-                    )
-                ),
+                "c": list(map(lambda x: Timedelta(x)._repr_base(), c._values)),
                 "d": list(map(str, d._values)),
                 "e": list(map(str, e._values)),
             }

pandas/tests/io/formats/test_to_csv.py

@@ -578,8 +578,8 @@ def test_to_csv_timedelta_precision(self):
         result = buf.getvalue()
         expected_rows = [
             ",0",
-            "0,0 days 00:00:00.000000001",
-            "1,0 days 00:00:00.000000001",
+            "0,00:00:00.000000001",
+            "1,00:00:00.000000001",
         ]
         expected = tm.convert_rows_list_to_csv_str(expected_rows)
         assert result == expected

pandas/tests/series/test_dtypes.py

@@ -132,7 +132,7 @@ def test_astype_str_map(self, dtype, series):
         expected = series.map(str)
         tm.assert_series_equal(result, expected)
 
-    def test_astype_str_cast(self):
+    def test_astype_str_cast_dt64(self):
         # see gh-9757
         ts = Series([Timestamp("2010-01-04 00:00:00")])
         s = ts.astype(str)
@@ -146,11 +146,14 @@ def test_astype_str_cast(self):
         expected = Series([str("2010-01-04 00:00:00-05:00")])
         tm.assert_series_equal(s, expected)
 
+    def test_astype_str_cast_td64(self):
+        # see gh-9757
+
         td = Series([Timedelta(1, unit="d")])
-        s = td.astype(str)
+        ser = td.astype(str)
 
-        expected = Series([str("1 days 00:00:00.000000000")])
-        tm.assert_series_equal(s, expected)
+        expected = Series([str("1 days")])
+        tm.assert_series_equal(ser, expected)
 
     def test_astype_unicode(self):
         # see gh-7758: A bit of magic is required to set