fix numpy doc

mindspore/numpy/array_creations.py

@@ -402,9 +402,10 @@ def arange(start, stop=None, step=None, dtype=None):
         Tensor with evenly spaced values.
 
     Raises:
-        TypeError(PyNative Mode) or RuntimeError(Graph Mode): If input arguments
-            have types not specified above, or arguments are not given in the correct
-            orders specified above.
+        TypeError(PyNative Mode): If input arguments have types not specified above,
+            or arguments are not given in the correct orders specified above.
+        RuntimeError(Graph Mode): The inputs that lead to TypeError in Pynative Mode
+            will lead to RuntimeError in Graph Mode.
 
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``

mindspore/numpy/array_ops.py

@@ -149,8 +149,8 @@ def rollaxis(x, axis, start=0):
             If :math:`start <= axis`, the axis is rolled back until it lies in this position (`start`).
             If :math:`start > axis`: the axis is rolled until it lies before this position (`start`).
 
-            If :math:`start < 0`, the start will be normalized as shown in the table.
-            (Please refer to the source code.)
+            If :math:`start < 0`, the start will be normalized as a non-negative number (more details
+                can be seen in the source code.)
 
             .. table
                 +===========+=================+
@@ -665,13 +665,14 @@ def where(condition, x=None, y=None):
     Returns elements chosen from `x` or `y` depending on `condition`.
 
     Note:
-        As nonzero is not supported, neither `x` or `y` can be None.
+        As nonzero is not supported, both `x` and `y` must be provided Tensor
+    input.
 
     Args:
         condition (Tensor): where True, yield `x`, otherwise yield `y`.
-        x (Tensor): Values from which to choose.
+        x (Tensor): Values from which to choose. Defaults to None.
         y (Tensor): Values from which to choose. `x`, `y` and `condition` need
-            to be broadcastable to some shape.
+            to be broadcastable to some shape. Defaults to None.
 
     Returns:
         Tensor or scalar, with elements from `x` where `condition` is True, and
@@ -2015,7 +2016,7 @@ def select(condlist, choicelist, default=0):
             from which the output elements are taken. It has to be of the same length as
             `condlist`.
         default (scalar, optional): The element inserted in output when all conditions
-            evaluate to `False`.
+            evaluate to `False`. Defaults to 0.
 
     Returns:
         Tensor, the output at position `m` is the `m-th` element of the array in

mindspore/numpy/math_ops.py

@@ -1719,8 +1719,8 @@ def fmod(x1, x2, dtype=None):
         not supported.
 
     Args:
-        x1 (Tensor)
-        x2 (Tensor): input arrays.
+        x1 (Tensor): the first input arrays.
+        x2 (Tensor): the second input arrays.
         dtype (:class:`mindspore.dtype`, optional): defaults to None. Overrides the dtype of the
             output Tensor.
 
@@ -3513,7 +3513,7 @@ def arcsin(x, dtype=None):
             output Tensor.
 
     Returns:
-        Tensor.
+        Output Tensor.
 
     Raises:
         TypeError: If the input is not a tensor.
@@ -4359,8 +4359,10 @@ def interp(x, xp, fp, left=None, right=None):
             x-coordinates of the data points, must be increasing.
         fp (Union[int, float, bool, list, tuple, Tensor]): 1-D sequence of floats, the
             y-coordinates of the data points, same length as `xp`.
-        left (float, optional): Value to return for ``x < xp[0]``, default is ``fp[0]``.
-        right (float, optional): Value to return for ``x > xp[-1]``, default is ``fp[-1]``.
+        left (float, optional): Value to return for ``x < xp[0]``, default is ``fp[0]``
+            once obtained.
+        right (float, optional): Value to return for ``x > xp[-1]``, default is ``fp[-1]``
+            once obtained.
 
     Returns:
         Tensor, the interpolated values, same shape as `x`.
@@ -4670,10 +4672,9 @@ def histogram(a, bins=10, range=None, weights=None, density=False): # pylint: di
             the range are ignored. The first element of the range must be less than
             or equal to the second.
         weights (Union[int, float, bool, list, tuple, Tensor], optional): An array
-            of weights, of the same shape as `a`. Each value in `a` only contributes
-            its associated weight towards the bin count (instead of 1). If density
-            is True, the weights are normalized, so that the integral of the density
-            over the range remains 1.
+            of weights, of the same shape as `a`. If density is True, the weights
+            are normalized, so that the integral of the density over the range
+            remains 1.
         density (boolean, optional): If False, the result will contain the number of
             samples in each bin. If True, the result is the value of the probability
             density function at the bin, normalized such that the integral over the
@@ -5319,7 +5320,7 @@ def unwrap(p, discont=3.141592653589793, axis=-1):
     Args:
         p (Union[int, float, bool, list, tuple, Tensor): Input array.
         discont (float, optional): Maximum discontinuity between values, default is pi.
-        axis (int, optional): Axis along which unwrap will operate, default is the last axis.
+        axis (int, optional): Axis along which unwrap will operate, default is -1.
 
     Returns:
         Tensor.