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from numpy.lib.function_base import _ureduce as _ureduce
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'nansum', 'nanmax', 'nanmin', 'nanargmax', 'nanargmin', 'nanmean',
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'nanmedian', 'nanpercentile', 'nanvar', 'nanstd', 'nanprod',
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return np.divide(a, b, out=out, casting='unsafe')
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def nanmin(a, axis=None, out=None, keepdims=False):
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def nanmin(a, axis=None, out=None, keepdims=np._NoValue):
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Return minimum of an array or minimum along an axis, ignoring any NaNs.
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When all-NaN slices are encountered a ``RuntimeWarning`` is raised and
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.. versionadded:: 1.8.0
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keepdims : bool, optional
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If this is set to True, the axes which are reduced are left in the
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result as dimensions with size one. With this option, the result
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will broadcast correctly against the original `a`.
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If this is set to True, the axes which are reduced are left
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in the result as dimensions with size one. With this option,
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the result will broadcast correctly against the original `a`.
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If the value is anything but the default, then
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`keepdims` will be passed through to the `min` method
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of sub-classes of `ndarray`. If the sub-classes methods
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does not implement `keepdims` any exceptions will be raised.
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.. versionadded:: 1.8.0
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if keepdims is not np._NoValue:
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kwargs['keepdims'] = keepdims
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if not isinstance(a, np.ndarray) or type(a) is np.ndarray:
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# Fast, but not safe for subclasses of ndarray
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res = np.fmin.reduce(a, axis=axis, out=out, keepdims=keepdims)
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res = np.fmin.reduce(a, axis=axis, out=out, **kwargs)
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if np.isnan(res).any():
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warnings.warn("All-NaN axis encountered", RuntimeWarning)
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# Slow, but safe for subclasses of ndarray
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a, mask = _replace_nan(a, +np.inf)
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res = np.amin(a, axis=axis, out=out, keepdims=keepdims)
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res = np.amin(a, axis=axis, out=out, **kwargs)
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# Check for all-NaN axis
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mask = np.all(mask, axis=axis, keepdims=keepdims)
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mask = np.all(mask, axis=axis, **kwargs)
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res = _copyto(res, np.nan, mask)
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warnings.warn("All-NaN axis encountered", RuntimeWarning)
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def nanmax(a, axis=None, out=None, keepdims=False):
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def nanmax(a, axis=None, out=None, keepdims=np._NoValue):
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Return the maximum of an array or maximum along an axis, ignoring any
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NaNs. When all-NaN slices are encountered a ``RuntimeWarning`` is
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.. versionadded:: 1.8.0
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keepdims : bool, optional
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If this is set to True, the axes which are reduced are left in the
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result as dimensions with size one. With this option, the result
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will broadcast correctly against the original `a`.
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If this is set to True, the axes which are reduced are left
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in the result as dimensions with size one. With this option,
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the result will broadcast correctly against the original `a`.
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If the value is anything but the default, then
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`keepdims` will be passed through to the `max` method
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of sub-classes of `ndarray`. If the sub-classes methods
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does not implement `keepdims` any exceptions will be raised.
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.. versionadded:: 1.8.0
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if keepdims is not np._NoValue:
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kwargs['keepdims'] = keepdims
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if not isinstance(a, np.ndarray) or type(a) is np.ndarray:
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# Fast, but not safe for subclasses of ndarray
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res = np.fmax.reduce(a, axis=axis, out=out, keepdims=keepdims)
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res = np.fmax.reduce(a, axis=axis, out=out, **kwargs)
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if np.isnan(res).any():
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warnings.warn("All-NaN slice encountered", RuntimeWarning)
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# Slow, but safe for subclasses of ndarray
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a, mask = _replace_nan(a, -np.inf)
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res = np.amax(a, axis=axis, out=out, keepdims=keepdims)
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res = np.amax(a, axis=axis, out=out, **kwargs)
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# Check for all-NaN axis
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mask = np.all(mask, axis=axis, keepdims=keepdims)
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mask = np.all(mask, axis=axis, **kwargs)
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res = _copyto(res, np.nan, mask)
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warnings.warn("All-NaN axis encountered", RuntimeWarning)
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def nansum(a, axis=None, dtype=None, out=None, keepdims=0):
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def nansum(a, axis=None, dtype=None, out=None, keepdims=np._NoValue):
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Return the sum of array elements over a given axis treating Not a
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Numbers (NaNs) as zero.
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.. versionadded:: 1.8.0
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keepdims : bool, optional
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If True, the axes which are reduced are left in the result as
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dimensions with size one. With this option, the result will
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broadcast correctly against the original `arr`.
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If this is set to True, the axes which are reduced are left
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in the result as dimensions with size one. With this option,
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the result will broadcast correctly against the original `a`.
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If the value is anything but the default, then
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`keepdims` will be passed through to the `mean` or `sum` methods
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of sub-classes of `ndarray`. If the sub-classes methods
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does not implement `keepdims` any exceptions will be raised.
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.. versionadded:: 1.8.0
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return np.sum(a, axis=axis, dtype=dtype, out=out, keepdims=keepdims)
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def nanprod(a, axis=None, dtype=None, out=None, keepdims=0):
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def nanprod(a, axis=None, dtype=None, out=None, keepdims=np._NoValue):
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Return the product of array elements over a given axis treating Not a
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Numbers (NaNs) as zero.
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return np.prod(a, axis=axis, dtype=dtype, out=out, keepdims=keepdims)
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def nanmean(a, axis=None, dtype=None, out=None, keepdims=False):
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def nanmean(a, axis=None, dtype=None, out=None, keepdims=np._NoValue):
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Compute the arithmetic mean along the specified axis, ignoring NaNs.
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expected output, but the type will be cast if necessary. See
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`doc.ufuncs` for details.
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keepdims : bool, optional
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If this is set to True, the axes which are reduced are left in the
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result as dimensions with size one. With this option, the result
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will broadcast correctly against the original `arr`.
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If this is set to True, the axes which are reduced are left
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in the result as dimensions with size one. With this option,
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the result will broadcast correctly against the original `a`.
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If the value is anything but the default, then
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`keepdims` will be passed through to the `mean` or `sum` methods
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of sub-classes of `ndarray`. If the sub-classes methods
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does not implement `keepdims` any exceptions will be raised.
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return m.filled(np.nan)
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def nanmedian(a, axis=None, out=None, overwrite_input=False, keepdims=False):
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def nanmedian(a, axis=None, out=None, overwrite_input=False, keepdims=np._NoValue):
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Compute the median along the specified axis, while ignoring NaNs.
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False. If `overwrite_input` is ``True`` and `a` is not already an
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`ndarray`, an error will be raised.
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keepdims : bool, optional
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If this is set to True, the axes which are reduced are left in
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the result as dimensions with size one. With this option, the
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result will broadcast correctly against the original `arr`.
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If this is set to True, the axes which are reduced are left
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in the result as dimensions with size one. With this option,
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the result will broadcast correctly against the original `a`.
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If this is anything but the default value it will be passed
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through (in the special case of an empty array) to the
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`mean` function of the underlying array. If the array is
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a sub-class and `mean` does not have the kwarg `keepdims` this
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will raise a RuntimeError.
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r, k = _ureduce(a, func=_nanmedian, axis=axis, out=out,
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overwrite_input=overwrite_input)
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if keepdims and keepdims is not np._NoValue:
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return r.reshape(k)
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def nanpercentile(a, q, axis=None, out=None, overwrite_input=False,
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interpolation='linear', keepdims=False):
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interpolation='linear', keepdims=np._NoValue):
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Compute the qth percentile of the data along the specified axis,
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while ignoring nan values.
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* nearest: ``i`` or ``j``, whichever is nearest.
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* midpoint: ``(i + j) / 2``.
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keepdims : bool, optional
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If this is set to True, the axes which are reduced are left in
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the result as dimensions with size one. With this option, the
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result will broadcast correctly against the original array `a`.
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If this is set to True, the axes which are reduced are left
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in the result as dimensions with size one. With this option,
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the result will broadcast correctly against the original `a`.
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If this is anything but the default value it will be passed
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through (in the special case of an empty array) to the
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`mean` function of the underlying array. If the array is
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a sub-class and `mean` does not have the kwarg `keepdims` this
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will raise a RuntimeError.
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If `q` is a single percentile and `axis=None`, then the result
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is a scalar. If multiple percentiles are given, first axis of
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the result corresponds to the percentiles. The other axes are
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the axes that remain after the reduction of `a`. If the input
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the axes that remain after the reduction of `a`. If the input
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contains integers or floats smaller than ``float64``, the output
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data-type is ``float64``. Otherwise, the output data-type is the
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same as that of the input. If `out` is specified, that array is
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r, k = _ureduce(a, func=_nanpercentile, q=q, axis=axis, out=out,
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overwrite_input=overwrite_input,
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interpolation=interpolation)
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if keepdims and keepdims is not np._NoValue:
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return r.reshape(k)
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def _nanpercentile(a, q, axis=None, out=None, overwrite_input=False,
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interpolation='linear', keepdims=False):
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interpolation='linear'):
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Private function that doesn't support extended axis or keepdims.
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These methods are extended to this function using _ureduce
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interpolation=interpolation)
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def nanvar(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
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def nanvar(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue):
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Compute the variance along the specified axis, while ignoring NaNs.
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keepdims : bool, optional
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If this is set to True, the axes which are reduced are left
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in the result as dimensions with size one. With this option,
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the result will broadcast correctly against the original `arr`.
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the result will broadcast correctly against the original `a`.
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below). Specifying a higher-accuracy accumulator using the ``dtype``
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keyword can alleviate this issue.
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For this function to work on sub-classes of ndarray, they must define
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`sum` with the kwarg `keepdims`
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>>> a = np.array([[1, np.nan], [3, 4]])
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warnings.simplefilter('ignore')
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cnt = np.sum(~mask, axis=axis, dtype=np.intp, keepdims=True)
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avg = np.sum(arr, axis=axis, dtype=dtype, keepdims=True)
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if type(arr) is np.matrix:
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_keepdims = np._NoValue
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# we need to special case matrix for reverse compatibility
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# in order for this to work, these sums need to be called with
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# keepdims=True, however matrix now raises an error in this case, but
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# the reason that it drops the keepdims kwarg is to force keepdims=True
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# so this used to work by serendipity.
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cnt = np.sum(~mask, axis=axis, dtype=np.intp, keepdims=_keepdims)
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avg = np.sum(arr, axis=axis, dtype=dtype, keepdims=_keepdims)
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avg = _divide_by_count(avg, cnt)
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# Compute squared deviation from mean.
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def nanstd(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
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def nanstd(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue):
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Compute the standard deviation along the specified axis, while
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Means Delta Degrees of Freedom. The divisor used in calculations
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is ``N - ddof``, where ``N`` represents the number of non-NaN
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elements. By default `ddof` is zero.
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keepdims : bool, optional
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If this is set to True, the axes which are reduced are left
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in the result as dimensions with size one. With this option,
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the result will broadcast correctly against the original `arr`.
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the result will broadcast correctly against the original `a`.
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If this value is anything but the default it is passed through
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as-is to the relevant functions of the sub-classes. If these
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functions do not have a `keepdims` kwarg, a RuntimeError will
added
removed
python/Lib/site-packages/numpy/lib/nanfunctions.py
