Jul 7, 2023 · Jul 5, 2023 · Jul 5, 2023 · Jul 5, 2023 · Jul 5, 2023 · Jul 5, 2023
diff --git a/numpy/lib/nanfunctions.py b/numpy/lib/nanfunctions.py


 def _nanvar_dispatcher(a, axis=None, dtype=None, out=None, ddof=None,
                       keepdims=None, *, where=None):
                       keepdims=None, *, where=None, mean=None):
    return (a, out)


 @array_function_dispatch(_nanvar_dispatcher)
 def nanvar(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue,
           *, where=np._NoValue):
           *, where=np._NoValue, mean=np._NoValue):
    """
    Compute the variance along the specified axis, while ignoring NaNs.

    where : array_like of bool, optional
        Elements to include in the variance. See `~numpy.ufunc.reduce` for
        details.
    mean : optionally provide the mean to prevent recalculation, the mean
          should have a shape as if it was calculated with keepdims = True
          and the axis the same as used in the call to this var function.
          For masked arrays this argument is ignored.

        .. versionadded:: 1.22.0

    arr, mask = _replace_nan(a, 0)
    if mask is None:
        return np.var(arr, axis=axis, dtype=dtype, out=out, ddof=ddof,
                      keepdims=keepdims, where=where)
                      keepdims=keepdims, where=where, mean=mean)

    if dtype is not None:
        dtype = np.dtype(dtype)
        _keepdims = np._NoValue
    else:
        _keepdims = True
    # we need to special case matrix for reverse compatibility
    # in order for this to work, these sums need to be called with
    # keepdims=True, however matrix now raises an error in this case, but
    # the reason that it drops the keepdims kwarg is to force keepdims=True
    # so this used to work by serendipity.
    cnt = np.sum(~mask, axis=axis, dtype=np.intp, keepdims=_keepdims,
                 where=where)
    avg = np.sum(arr, axis=axis, dtype=dtype, keepdims=_keepdims, where=where)
    avg = _divide_by_count(avg, cnt)

    if mean is not np._NoValue:
        avg = mean
    else:
        # we need to special case matrix for reverse compatibility
        # in order for this to work, these sums need to be called with
        # keepdims=True, however matrix now raises an error in this case, but
        # the reason that it drops the keepdims kwarg is to force keepdims=True
        # so this used to work by serendipity.
        cnt = np.sum(~mask, axis=axis, dtype=np.intp, keepdims=_keepdims,
                     where=where)
        avg = np.sum(arr, axis=axis, dtype=dtype, keepdims=_keepdims, where=where)
        avg = _divide_by_count(avg, cnt)

    # Compute squared deviation from mean.
    np.subtract(arr, avg, out=arr, casting='unsafe', where=where)


 def _nanstd_dispatcher(a, axis=None, dtype=None, out=None, ddof=None,
                       keepdims=None, *, where=None):
                       keepdims=None, *, where=None, mean=None):
    return (a, out)


 @array_function_dispatch(_nanstd_dispatcher)
 def nanstd(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue,
           *, where=np._NoValue):
           *, where=np._NoValue, mean=np._NoValue):
    """
    Compute the standard deviation along the specified axis, while
    ignoring NaNs.
    where : array_like of bool, optional
        Elements to include in the standard deviation.
        See `~numpy.ufunc.reduce` for details.
    mean : optionally provide the mean to prevent recalculation, the mean
          should have a shape as if it was calculated with keepdims = True
          and the axis the same as used in the call to this var function.
          For masked arrays this argument is ignored.

        .. versionadded:: 1.22.0


    """
    var = nanvar(a, axis=axis, dtype=dtype, out=out, ddof=ddof,
                 keepdims=keepdims, where=where)
                 keepdims=keepdims, where=where, mean=mean)
    if isinstance(var, np.ndarray):
        std = np.sqrt(var, out=var)
    elif hasattr(var, 'dtype'):
Original file line number	Diff line number	Diff line change
Expand Up		@@ -1610,13 +1610,13 @@ def _nanquantile_1d(arr1d, q, overwrite_input=False, method="linear"):


		def _nanvar_dispatcher(a, axis=None, dtype=None, out=None, ddof=None,
		keepdims=None, *, where=None):
		keepdims=None, *, where=None, mean=None):
		return (a, out)


		@array_function_dispatch(_nanvar_dispatcher)
		def nanvar(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue,
		*, where=np._NoValue):
		*, where=np._NoValue, mean=np._NoValue):
		"""
		Compute the variance along the specified axis, while ignoring NaNs.

Expand DownExpand Up		@@ -1656,6 +1656,10 @@ def nanvar(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue,
		where : array_like of bool, optional
		Elements to include in the variance. See `~numpy.ufunc.reduce` for
		details.
		mean : optionally provide the mean to prevent recalculation, the mean
		should have a shape as if it was calculated with keepdims = True
		and the axis the same as used in the call to this var function.
		For masked arrays this argument is ignored.

		.. versionadded:: 1.22.0

Expand DownExpand Up		@@ -1713,7 +1717,7 @@ def nanvar(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue,
		arr, mask = _replace_nan(a, 0)
		if mask is None:
		return np.var(arr, axis=axis, dtype=dtype, out=out, ddof=ddof,
		keepdims=keepdims, where=where)
		keepdims=keepdims, where=where, mean=mean)

		if dtype is not None:
		dtype = np.dtype(dtype)
Expand All		@@ -1727,15 +1731,19 @@ def nanvar(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue,
		_keepdims = np._NoValue
		else:
		_keepdims = True
		# we need to special case matrix for reverse compatibility
		# in order for this to work, these sums need to be called with
		# keepdims=True, however matrix now raises an error in this case, but
		# the reason that it drops the keepdims kwarg is to force keepdims=True
		# so this used to work by serendipity.
		cnt = np.sum(~mask, axis=axis, dtype=np.intp, keepdims=_keepdims,
		where=where)
		avg = np.sum(arr, axis=axis, dtype=dtype, keepdims=_keepdims, where=where)
		avg = _divide_by_count(avg, cnt)

		if mean is not np._NoValue:
		avg = mean
		else:
		# we need to special case matrix for reverse compatibility
		# in order for this to work, these sums need to be called with
		# keepdims=True, however matrix now raises an error in this case, but
		# the reason that it drops the keepdims kwarg is to force keepdims=True
		# so this used to work by serendipity.
		cnt = np.sum(~mask, axis=axis, dtype=np.intp, keepdims=_keepdims,
		where=where)
		avg = np.sum(arr, axis=axis, dtype=dtype, keepdims=_keepdims, where=where)
		avg = _divide_by_count(avg, cnt)

		# Compute squared deviation from mean.
		np.subtract(arr, avg, out=arr, casting='unsafe', where=where)
Expand DownExpand Up		@@ -1771,13 +1779,13 @@ def nanvar(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue,


		def _nanstd_dispatcher(a, axis=None, dtype=None, out=None, ddof=None,
		keepdims=None, *, where=None):
		keepdims=None, *, where=None, mean=None):
		return (a, out)


		@array_function_dispatch(_nanstd_dispatcher)
		def nanstd(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue,
		*, where=np._NoValue):
		*, where=np._NoValue, mean=np._NoValue):
		"""
		Compute the standard deviation along the specified axis, while
		ignoring NaNs.
Expand DownExpand Up		@@ -1824,6 +1832,10 @@ def nanstd(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue,
		where : array_like of bool, optional
		Elements to include in the standard deviation.
		See `~numpy.ufunc.reduce` for details.
		mean : optionally provide the mean to prevent recalculation, the mean
		should have a shape as if it was calculated with keepdims = True
		and the axis the same as used in the call to this var function.
		For masked arrays this argument is ignored.

		.. versionadded:: 1.22.0
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Expand DownExpand Up		@@ -1877,7 +1889,7 @@ def nanstd(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue,

		"""
		var = nanvar(a, axis=axis, dtype=dtype, out=out, ddof=ddof,
		keepdims=keepdims, where=where)
		keepdims=keepdims, where=where, mean=mean)
		if isinstance(var, np.ndarray):
		std = np.sqrt(var, out=var)
		elif hasattr(var, 'dtype'):
Expand Down