Rename numpy._array_api to numpy.array_api

Instead of the leading underscore, the experimentalness of the module will be
indicated by omitting a warning on import. That we, we do not have to change
the API from underscore to no underscore when the module is no longer
experimental.

1 files changed, 30 insertions, 0 deletions

diff --git a/numpy/array_api/_statistical_functions.py b/numpy/array_api/_statistical_functions.py
new file mode 100644
index 000000000..61fc60c46
--- /dev/null
+++ b/numpy/array_api/_statistical_functions.py
@@ -0,0 +1,30 @@
+from __future__ import annotations
+
+from ._array_object import Array
+
+from typing import Optional, Tuple, Union
+
+import numpy as np
+
+def max(x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> Array:
+    return Array._new(np.max(x._array, axis=axis, keepdims=keepdims))
+
+def mean(x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> Array:
+    return Array._new(np.asarray(np.mean(x._array, axis=axis, keepdims=keepdims)))
+
+def min(x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> Array:
+    return Array._new(np.min(x._array, axis=axis, keepdims=keepdims))
+
+def prod(x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> Array:
+    return Array._new(np.asarray(np.prod(x._array, axis=axis, keepdims=keepdims)))
+
+def std(x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, correction: Union[int, float] = 0.0, keepdims: bool = False) -> Array:
+    # Note: the keyword argument correction is different here
+    return Array._new(np.asarray(np.std(x._array, axis=axis, ddof=correction, keepdims=keepdims)))
+
+def sum(x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> Array:
+    return Array._new(np.asarray(np.sum(x._array, axis=axis, keepdims=keepdims)))
+
+def var(x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, correction: Union[int, float] = 0.0, keepdims: bool = False) -> Array:
+    # Note: the keyword argument correction is different here
+    return Array._new(np.asarray(np.var(x._array, axis=axis, ddof=correction, keepdims=keepdims)))