diff --git a/numpy/lib/_function_base_impl.py b/numpy/lib/_function_base_impl.py
index f5690a829fc6..6ebb0c2d6edf 100644
--- a/numpy/lib/_function_base_impl.py
+++ b/numpy/lib/_function_base_impl.py
@@ -4812,11 +4812,38 @@ def _quantile(
"""
# --- Setup
arr = np.asanyarray(arr)
- values_count = arr.shape[axis]
# The dimensions of `q` are prepended to the output shape, so we need the
# axis being sampled from `arr` to be last.
if axis != 0: # But moveaxis is slow, so only call it if necessary.
arr = np.moveaxis(arr, axis, destination=0)
+ values_count = arr.shape[0] # Get length along axis
+ # Handle empty array case immediately
+ if values_count == 0:
+ # Determine output shape: quantiles shape + other dimensions
+ shape = quantiles.shape + arr.shape[1:]
+ # Determine dtype for missing values
+ if out is not None:
+ dtype = out.dtype
+ else:
+ if arr.dtype.kind in 'mM': # datetime/timedelta
+ dtype = arr.dtype
+ elif arr.dtype.kind in 'iub': # integer, unsigned, boolean
+ dtype = np.float64
+ else: # float, etc.
+ dtype = arr.dtype
+ # Choose appropriate missing value
+ if dtype.kind in 'mM':
+ missing_value = dtype.type('NaT')
+ else:
+ missing_value = np.nan
+
+ # Create or fill output array
+ if out is None:
+ return np.full(shape, missing_value, dtype=dtype)
+ else:
+ out.fill(missing_value)
+ return out
+
supports_nans = (
np.issubdtype(arr.dtype, np.inexact) or arr.dtype.kind in 'Mm'
)
@@ -4874,6 +4901,10 @@ def _quantile(
# --- Get values from indexes
previous = arr[previous_indexes]
next = arr[next_indexes]
+ # Fix integer overflow by converting to float for interpolation
+ if arr.dtype.kind in 'iub': # integer, unsigned, boolean
+ previous = previous.astype(np.float64)
+ next = next.astype(np.float64)
# --- Linear interpolation
gamma = _get_gamma(virtual_indexes, previous_indexes, method_props)
result_shape = virtual_indexes.shape + (1,) * (arr.ndim - 1)
diff --git a/numpy/lib/tests/test_function_base.py b/numpy/lib/tests/test_function_base.py
index eccf4bcfb019..999849f78246 100644
--- a/numpy/lib/tests/test_function_base.py
+++ b/numpy/lib/tests/test_function_base.py
@@ -4209,6 +4209,45 @@ def test_closest_observation(self):
assert_equal(4, np.quantile(arr[0:9], q, method=m))
assert_equal(5, np.quantile(arr, q, method=m))
+ def test_quantile_empty(self):
+ # Empty array
+ assert np.isnan(np.quantile([], 0.5))
+
+ # Multiple quantiles
+ result = np.quantile([], [0, 0.5, 1])
+ assert result.shape == (3,)
+ assert np.all(np.isnan(result))
+
+ # Keepdims=True
+ a = np.array([[], [], []]).T # Shape (0, 3)
+ result = np.quantile(a, 0.5, axis=0, keepdims=True)
+ assert result.shape == (1, 3)
+ assert np.all(np.isnan(result))
+
+ # Axis=0
+ a = np.array([])
+ result = np.quantile(a, 0.5, axis=0)
+ assert np.isnan(result)
+
+ # Multiple axes reduction
+ a = np.zeros((3, 0, 2)) # Shape (3, 0, 2)
+ result = np.quantile(a, 0.5, axis=(0, 1))
+ assert result.shape == (2,)
+ assert np.all(np.isnan(result))
+
+ def test_quantile_int_overflow(self):
+ # Signed integer overflow
+ a = np.array([32767, -1], dtype=np.int16)
+ assert np.quantile(a, 0.5) == 16383.0
+
+ # Unsigned integer
+ b = np.array([0, 65535], dtype=np.uint16)
+ assert np.quantile(b, 0.5) == 32767.5
+
+ # Large integers
+ c = np.array([np.iinfo(np.int32).max, np.iinfo(np.int32).min], dtype=np.int32)
+ expected = (np.iinfo(np.int32).max + np.iinfo(np.int32).min) / 2.0
+ assert np.quantile(c, 0.5) == expected
class TestLerp:
@hypothesis.given(t0=st.floats(allow_nan=False, allow_infinity=False,
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