Fix for issue pandas-dev#57268 - ENH: Preserve input start/end type i…

…n interval… (pandas-dev#57399)

* Fix for issue pandas-dev#57268 - ENH: Preserve input start/end type in interval_range

* issue pandas-dev#57268 - github actions resolution

* Use generated datatype from breaks

* Ruff - Pre-commit issue fix

* Fix for issue pandas-dev#57268 - floating point support

* int - float dtype compatability

* whatsnew documentation update

* OS based varaible access

* Fixing failed unit test cases

* pytest - interval passsed

* Python backwards compatability

* Pytest

* Fixing PyLint and mypy issues

* dtype specification

* Conditional statement simplification

* remove redundant code blocks

* Changing whatsnew to interval section

* Passing expected in parameterize

* Update doc/source/whatsnew/v3.0.0.rst

Co-authored-by: Matthew Roeschke <10647082+mroeschke@users.noreply.github.com>

* Update pandas/core/indexes/interval.py

Co-authored-by: Matthew Roeschke <10647082+mroeschke@users.noreply.github.com>

---------

Co-authored-by: Matthew Roeschke <10647082+mroeschke@users.noreply.github.com>

doc/source/whatsnew/v3.0.0.rst

@@ -211,7 +211,7 @@ Strings
 
 Interval
 ^^^^^^^^
--
+- Bug in :func:`interval_range` where start and end numeric types were always cast to 64 bit (:issue:`57268`)
 -
 
 Indexing

pandas/core/indexes/interval.py

@@ -1101,9 +1101,23 @@ def interval_range(
     breaks: np.ndarray | TimedeltaIndex | DatetimeIndex
 
     if is_number(endpoint):
+        dtype: np.dtype = np.dtype("int64")
         if com.all_not_none(start, end, freq):
+            if (
+                isinstance(start, (float, np.float16))
+                or isinstance(end, (float, np.float16))
+                or isinstance(freq, (float, np.float16))
+            ):
+                dtype = np.dtype("float64")
+            elif (
+                isinstance(start, (np.integer, np.floating))
+                and isinstance(end, (np.integer, np.floating))
+                and start.dtype == end.dtype
+            ):
+                dtype = start.dtype
             # 0.1 ensures we capture end
             breaks = np.arange(start, end + (freq * 0.1), freq)
+            breaks = maybe_downcast_numeric(breaks, dtype)
         else:
             # compute the period/start/end if unspecified (at most one)
             if periods is None:
@@ -1122,7 +1136,7 @@ def interval_range(
             # expected "ndarray[Any, Any]"  [
             breaks = maybe_downcast_numeric(
                 breaks,  # type: ignore[arg-type]
-                np.dtype("int64"),
+                dtype,
             )
     else:
         # delegate to the appropriate range function
@@ -1131,4 +1145,9 @@ def interval_range(
         else:
             breaks = timedelta_range(start=start, end=end, periods=periods, freq=freq)
 
-    return IntervalIndex.from_breaks(breaks, name=name, closed=closed)
+    return IntervalIndex.from_breaks(
+        breaks,
+        name=name,
+        closed=closed,
+        dtype=IntervalDtype(subtype=breaks.dtype, closed=closed),
+    )

pandas/tests/indexes/interval/test_interval_range.py

@@ -220,6 +220,20 @@ def test_float_subtype(self, start, end, freq):
         expected = "int64" if is_integer(start + end) else "float64"
         assert result == expected
 
+    @pytest.mark.parametrize(
+        "start, end, expected",
+        [
+            (np.int8(1), np.int8(10), np.dtype("int8")),
+            (np.int8(1), np.float16(10), np.dtype("float64")),
+            (np.float32(1), np.float32(10), np.dtype("float32")),
+            (1, 10, np.dtype("int64")),
+            (1, 10.0, np.dtype("float64")),
+        ],
+    )
+    def test_interval_dtype(self, start, end, expected):
+        result = interval_range(start=start, end=end).dtype.subtype
+        assert result == expected
+
     def test_interval_range_fractional_period(self):
         # float value for periods
         expected = interval_range(start=0, periods=10)