tocsv_interval _categories

pandas/core/array_algos/take.py

@@ -22,6 +22,7 @@
 from pandas.core.dtypes.common import (
     ensure_platform_int,
     is_1d_only_ea_obj,
+    is_interval_dtype,
 )
 from pandas.core.dtypes.missing import na_value_for_dtype
 
@@ -110,6 +111,9 @@ def take_nd(
             return arr.take(
                 indexer, fill_value=fill_value, allow_fill=allow_fill, axis=axis
             )
+        if arr.dtype.kind in "O" and is_interval_dtype(arr):
+            # # GH46297 Interval
+            return arr.take(indexer, allow_fill=allow_fill)
 
         return arr.take(indexer, fill_value=fill_value, allow_fill=allow_fill)
 

pandas/core/internals/blocks.py

@@ -2281,6 +2281,13 @@ def to_native_types(
             results_converted.append(result.astype(object, copy=False))
         return np.vstack(results_converted)
 
+    elif isinstance(values, (PeriodArray, IntervalArray)):
+        # GH46297
+        values = np.array(values, dtype="object")
+        mask = isna(values)
+        values[mask] = na_rep
+        return values
+
     elif values.dtype.kind == "f" and not is_sparse(values):
         # see GH#13418: no special formatting is desired at the
         # output (important for appropriate 'quoting' behaviour),

pandas/tests/io/formats/test_to_csv.py

@@ -315,6 +315,17 @@ def test_to_csv_date_format_in_categorical(self):
         ser = ser.astype("category")
         assert ser.to_csv(index=False, date_format="%Y-%m-%d") == expected
 
+    def test_to_cvs_interval_format_in_categorical(self):
+        # GH#46297
+        df = DataFrame(index=[0], columns=["a"])
+        df.at[0, "a"] = pd.Interval(
+            pd.Timestamp("2020-01-01"), pd.Timestamp("2020-01-02")
+        )
+        df["a"] = df["a"].astype("category")
+        result = df.to_csv(index=False, date_format="%Y-%m-%d")
+        expected = tm.convert_rows_list_to_csv_str(["a", '"(2020-01-01, 2020-01-02]"'])
+        assert result == expected
+
     def test_to_csv_float_ea_float_format(self):
         # GH#45991
         df = DataFrame({"a": [1.1, 2.02, pd.NA, 6.000006], "b": "c"})