fix: corrected implementation of where param numpy mean

ivy/functional/frontends/numpy/ndarray/ndarray.py

@@ -165,7 +165,7 @@ def any(self, axis=None, dtype=None, out=None, keepdims=False, *, where=True):
     def argsort(self, *, axis=-1, kind=None, order=None):
         return np_frontend.argsort(self, axis=axis, kind=kind, order=order)
 
-    def mean(self, *, axis=None, dtype=None, out=None, keepdims=False, where=True):
+    def mean(self, axis=None, dtype=None, out=None, keepdims=False, *, where=True):
         return np_frontend.mean(
             self,
             axis=axis,

ivy/functional/frontends/numpy/statistics/averages_and_variances.py

@@ -72,62 +72,30 @@ def cov(
 @handle_numpy_dtype
 @to_ivy_arrays_and_back
 @from_zero_dim_arrays_to_scalar
-def mean(
-    a,
-    /,
-    *,
-    axis=None,
-    keepdims=False,
-    out=None,
-    dtype=None,
-    where=True,
-):
+def mean(a, axis=None, dtype=None, out=None, keepdims=False, *, where=True):
     axis = tuple(axis) if isinstance(axis, list) else axis
-    if dtype:
-        a = ivy.astype(ivy.array(a), ivy.as_ivy_dtype(dtype))
-
-    ret = ivy.mean(a, axis=axis, keepdims=keepdims, out=out)
-    if ivy.is_array(where):
-        ret = ivy.where(where, ret, ivy.default(out, ivy.zeros_like(ret)), out=out)
+    dtype = dtype or a.dtype if not ivy.is_int_dtype(a.dtype) else ivy.float64
+    where = ivy.where(where, ivy.ones_like(a), 0)
+    if where is not True:
+        a = ivy.where(where, a, 0.0)
+        sum = ivy.sum(a, axis=axis, keepdims=keepdims, dtype=dtype)
+        cnt = ivy.sum(where, axis=axis, keepdims=keepdims, dtype=int)
+        ret = ivy.divide(sum, cnt, out=out)
+    else:
+        ret = ivy.mean(a.astype(dtype), axis=axis, keepdims=keepdims, out=out)
 
-    return ret
+    return ret.astype(dtype)
 
 
 @handle_numpy_out
 @handle_numpy_dtype
 @to_ivy_arrays_and_back
 @from_zero_dim_arrays_to_scalar
-def nanmean(
-    a,
-    /,
-    *,
-    axis=None,
-    keepdims=False,
-    out=None,
-    dtype=None,
-    where=True,
-):
-    is_nan = ivy.isnan(a)
-    axis = tuple(axis) if isinstance(axis, list) else axis
-
-    if not ivy.any(is_nan):
-        if dtype:
-            a = ivy.astype(ivy.array(a), ivy.as_ivy_dtype(dtype))
-        ret = ivy.mean(a, axis=axis, keepdims=keepdims, out=out)
-
-        if ivy.is_array(where):
-            ret = ivy.where(where, ret, ivy.default(out, ivy.zeros_like(ret)), out=out)
-
-    else:
-        a = [i for i in a if ivy.isnan(i) is False]
-
-        if dtype:
-            a = ivy.astype(ivy.array(a), ivy.as_ivy_dtype(dtype))
-        ret = ivy.mean(a, axis=axis, keepdims=keepdims, out=out)
-
-        if ivy.is_array(where):
-            ret = ivy.where(where, ret, ivy.default(out, ivy.zeros_like(ret)), out=out)
-
+def nanmean(a, axis=None, dtype=None, out=None, keepdims=False, *, where=True):
+    where = ~ivy.isnan(a) & where
+    ret = mean(a, axis, dtype, keepdims=keepdims, where=where).ivy_array
+    if out is not None:
+        out.data = ret.data
     return ret
 
 

ivy_tests/test_ivy/test_frontends/test_numpy/test_ndarray/test_ndarray.py

@@ -28,6 +28,9 @@
 from ivy_tests.test_ivy.test_frontends.test_numpy.test_manipulation_routines.test_changing_number_of_dimensions import (  # noqa
     _squeeze_helper,
 )
+from ivy_tests.test_ivy.test_functional.test_core.test_statistical import (
+    _statistical_dtype_values,
+)
 
 CLASS_TREE = "ivy.functional.frontends.numpy.ndarray"
 
@@ -2766,35 +2769,42 @@ def test_numpy_ndarray_max(
     class_tree=CLASS_TREE,
     init_tree="numpy.array",
     method_name="mean",
-    dtype_x_axis=helpers.dtype_values_axis(
-        available_dtypes=helpers.get_dtypes("float"),
-        min_axis=-1,
-        max_axis=0,
-        min_num_dims=1,
-        force_int_axis=True,
-    ),
+    dtype_and_x=_statistical_dtype_values(function="mean"),
+    dtype=helpers.get_dtypes("float", full=False, none=True),
+    where=np_frontend_helpers.where(),
+    keep_dims=st.booleans(),
 )
 def test_numpy_ndarray_mean(
-    dtype_x_axis,
+    dtype_and_x,
+    dtype,
+    where,
+    keep_dims,
     frontend_method_data,
     init_flags,
     method_flags,
     backend_fw,
     frontend,
     on_device,
 ):
-    input_dtypes, x, axis = dtype_x_axis
+    input_dtypes, x, axis = dtype_and_x
+    where, input_dtypes, test_flags = np_frontend_helpers.handle_where_and_array_bools(
+        where=where,
+        input_dtype=input_dtypes,
+        test_flags=method_flags,
+    )
     helpers.test_frontend_method(
         init_input_dtypes=input_dtypes,
         backend_to_test=backend_fw,
-        method_input_dtypes=input_dtypes,
+        method_input_dtypes=input_dtypes[1:],
         init_all_as_kwargs_np={
             "object": x[0],
         },
         method_all_as_kwargs_np={
             "axis": axis,
-            "dtype": "float64",
+            "dtype": dtype[0],
             "out": None,
+            "keepdims": keep_dims,
+            "where": where,
         },
         frontend=frontend,
         frontend_method_data=frontend_method_data,

ivy_tests/test_ivy/test_frontends/test_numpy/test_statistics/test_averages_and_variances.py

@@ -223,31 +223,24 @@ def test_numpy_mean(
     keep_dims,
 ):
     input_dtypes, x, axis = dtype_and_x
-    if isinstance(axis, tuple):
-        axis = axis[0]
-
     where, input_dtypes, test_flags = np_frontend_helpers.handle_where_and_array_bools(
         where=where,
         input_dtype=input_dtypes,
         test_flags=test_flags,
     )
-
-    np_frontend_helpers.test_frontend_function(
+    helpers.test_frontend_function(
         input_dtypes=input_dtypes,
         frontend=frontend,
         backend_to_test=backend_fw,
         test_flags=test_flags,
         fn_tree=fn_tree,
         on_device=on_device,
-        atol=1e-2,
-        rtol=1e-2,
-        x=x[0],
+        a=x[0],
         axis=axis,
         dtype=dtype[0],
         out=None,
         keepdims=keep_dims,
         where=where,
-        test_values=False,
     )
 
 
@@ -280,7 +273,7 @@ def test_numpy_nanmean(
         test_flags=test_flags,
     )
 
-    np_frontend_helpers.test_frontend_function(
+    helpers.test_frontend_function(
         input_dtypes=input_dtypes,
         frontend=frontend,
         backend_to_test=backend_fw,
@@ -295,7 +288,6 @@ def test_numpy_nanmean(
         out=None,
         keepdims=keep_dims,
         where=where,
-        test_values=False,
     )