def _nanquantile_unchecked(a, q, axis=None, out=None, overwrite_input=False, interpolation='linear', keepdims=np._NoValue): """Assumes that q is in [0, 1], and is an ndarray""" # apply_along_axis in _nanpercentile doesn't handle empty arrays well, # so deal them upfront if a.size == 0: return np.nanmean(a, axis, out=out, keepdims=keepdims) r, k = function_base._ureduce( a, func=_nanquantile_ureduce_func, q=q, axis=axis, out=out, overwrite_input=overwrite_input, interpolation=interpolation ) if keepdims and keepdims is not np._NoValue: return r.reshape(q.shape + k) else: return r
def nanmedian(a, axis=None, out=None, overwrite_input=False, keepdims=np._NoValue): """ Compute the median along the specified axis, while ignoring NaNs. Returns the median of the array elements. .. versionadded:: 1.9.0 Parameters ---------- a : array_like Input array or object that can be converted to an array. axis : {int, sequence of int, None}, optional Axis or axes along which the medians are computed. The default is to compute the median along a flattened version of the array. A sequence of axes is supported since version 1.9.0. out : ndarray, optional Alternative output array in which to place the result. It must have the same shape and buffer length as the expected output, but the type (of the output) will be cast if necessary. overwrite_input : bool, optional If True, then allow use of memory of input array `a` for calculations. The input array will be modified by the call to `median`. This will save memory when you do not need to preserve the contents of the input array. Treat the input as undefined, but it will probably be fully or partially sorted. Default is False. If `overwrite_input` is ``True`` and `a` is not already an `ndarray`, an error will be raised. keepdims : bool, optional If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original `a`. If this is anything but the default value it will be passed through (in the special case of an empty array) to the `mean` function of the underlying array. If the array is a sub-class and `mean` does not have the kwarg `keepdims` this will raise a RuntimeError. Returns ------- median : ndarray A new array holding the result. If the input contains integers or floats smaller than ``float64``, then the output data-type is ``np.float64``. Otherwise, the data-type of the output is the same as that of the input. If `out` is specified, that array is returned instead. See Also -------- mean, median, percentile Notes ----- Given a vector ``V`` of length ``N``, the median of ``V`` is the middle value of a sorted copy of ``V``, ``V_sorted`` - i.e., ``V_sorted[(N-1)/2]``, when ``N`` is odd and the average of the two middle values of ``V_sorted`` when ``N`` is even. Examples -------- >>> a = np.array([[10.0, 7, 4], [3, 2, 1]]) >>> a[0, 1] = np.nan >>> a array([[ 10., nan, 4.], [ 3., 2., 1.]]) >>> np.median(a) nan >>> np.nanmedian(a) 3.0 >>> np.nanmedian(a, axis=0) array([ 6.5, 2., 2.5]) >>> np.median(a, axis=1) array([ 7., 2.]) >>> b = a.copy() >>> np.nanmedian(b, axis=1, overwrite_input=True) array([ 7., 2.]) >>> assert not np.all(a==b) >>> b = a.copy() >>> np.nanmedian(b, axis=None, overwrite_input=True) 3.0 >>> assert not np.all(a==b) """ a = np.asanyarray(a) # apply_along_axis in _nanmedian doesn't handle empty arrays well, # so deal them upfront if a.size == 0: return np.nanmean(a, axis, out=out, keepdims=keepdims) r, k = _ureduce(a, func=_nanmedian, axis=axis, out=out, overwrite_input=overwrite_input) if keepdims and keepdims is not np._NoValue: return r.reshape(k) else: return r
def nanpercentile(a, q, axis=None, out=None, overwrite_input=False, interpolation='linear', keepdims=np._NoValue): """ Compute the qth percentile of the data along the specified axis, while ignoring nan values. Returns the qth percentile(s) of the array elements. .. versionadded:: 1.9.0 Parameters ---------- a : array_like Input array or object that can be converted to an array. q : float in range of [0,100] (or sequence of floats) Percentile to compute, which must be between 0 and 100 inclusive. axis : {int, sequence of int, None}, optional Axis or axes along which the percentiles are computed. The default is to compute the percentile(s) along a flattened version of the array. A sequence of axes is supported since version 1.9.0. out : ndarray, optional Alternative output array in which to place the result. It must have the same shape and buffer length as the expected output, but the type (of the output) will be cast if necessary. overwrite_input : bool, optional If True, then allow use of memory of input array `a` for calculations. The input array will be modified by the call to `percentile`. This will save memory when you do not need to preserve the contents of the input array. In this case you should not make any assumptions about the contents of the input `a` after this function completes -- treat it as undefined. Default is False. If `a` is not already an array, this parameter will have no effect as `a` will be converted to an array internally regardless of the value of this parameter. interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'} This optional parameter specifies the interpolation method to use when the desired quantile lies between two data points ``i < j``: * linear: ``i + (j - i) * fraction``, where ``fraction`` is the fractional part of the index surrounded by ``i`` and ``j``. * lower: ``i``. * higher: ``j``. * nearest: ``i`` or ``j``, whichever is nearest. * midpoint: ``(i + j) / 2``. keepdims : bool, optional If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original array `a`. If this is anything but the default value it will be passed through (in the special case of an empty array) to the `mean` function of the underlying array. If the array is a sub-class and `mean` does not have the kwarg `keepdims` this will raise a RuntimeError. Returns ------- percentile : scalar or ndarray If `q` is a single percentile and `axis=None`, then the result is a scalar. If multiple percentiles are given, first axis of the result corresponds to the percentiles. The other axes are the axes that remain after the reduction of `a`. If the input contains integers or floats smaller than ``float64``, the output data-type is ``float64``. Otherwise, the output data-type is the same as that of the input. If `out` is specified, that array is returned instead. See Also -------- nanmean, nanmedian, percentile, median, mean Notes ----- Given a vector ``V`` of length ``N``, the ``q``-th percentile of ``V`` is the value ``q/100`` of the way from the minimum to the maximum in a sorted copy of ``V``. The values and distances of the two nearest neighbors as well as the `interpolation` parameter will determine the percentile if the normalized ranking does not match the location of ``q`` exactly. This function is the same as the median if ``q=50``, the same as the minimum if ``q=0`` and the same as the maximum if ``q=100``. Examples -------- >>> a = np.array([[10., 7., 4.], [3., 2., 1.]]) >>> a[0][1] = np.nan >>> a array([[ 10., nan, 4.], [ 3., 2., 1.]]) >>> np.percentile(a, 50) nan >>> np.nanpercentile(a, 50) 3.5 >>> np.nanpercentile(a, 50, axis=0) array([ 6.5, 2., 2.5]) >>> np.nanpercentile(a, 50, axis=1, keepdims=True) array([[ 7.], [ 2.]]) >>> m = np.nanpercentile(a, 50, axis=0) >>> out = np.zeros_like(m) >>> np.nanpercentile(a, 50, axis=0, out=out) array([ 6.5, 2., 2.5]) >>> m array([ 6.5, 2. , 2.5]) >>> b = a.copy() >>> np.nanpercentile(b, 50, axis=1, overwrite_input=True) array([ 7., 2.]) >>> assert not np.all(a==b) """ a = np.asanyarray(a) q = np.asanyarray(q) # apply_along_axis in _nanpercentile doesn't handle empty arrays well, # so deal them upfront if a.size == 0: return np.nanmean(a, axis, out=out, keepdims=keepdims) r, k = _ureduce(a, func=_nanpercentile, q=q, axis=axis, out=out, overwrite_input=overwrite_input, interpolation=interpolation) if keepdims and keepdims is not np._NoValue: return r.reshape(q.shape + k) else: return r
def nanpercentile(a, q, axis=None, out=None, overwrite_input=False, interpolation='linear', keepdims=False): """ Compute the qth percentile of the data along the specified axis, while ignoring nan values. Returns the qth percentile of the array elements. Parameters ---------- a : array_like Input array or object that can be converted to an array. q : float in range of [0,100] (or sequence of floats) Percentile to compute which must be between 0 and 100 inclusive. axis : int or sequence of int, optional Axis along which the percentiles are computed. The default (None) is to compute the percentiles along a flattened version of the array. A sequence of axes is supported since version 1.9.0. out : ndarray, optional Alternative output array in which to place the result. It must have the same shape and buffer length as the expected output, but the type (of the output) will be cast if necessary. overwrite_input : bool, optional If True, then allow use of memory of input array `a` for calculations. The input array will be modified by the call to percentile. This will save memory when you do not need to preserve the contents of the input array. In this case you should not make any assumptions about the content of the passed in array `a` after this function completes -- treat it as undefined. Default is False. Note that, if the `a` input is not already an array this parameter will have no effect, `a` will be converted to an array internally regardless of the value of this parameter. interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'} This optional parameter specifies the interpolation method to use, when the desired quantile lies between two data points `i` and `j`: * linear: `i + (j - i) * fraction`, where `fraction` is the fractional part of the index surrounded by `i` and `j`. * lower: `i`. * higher: `j`. * nearest: `i` or `j` whichever is nearest. * midpoint: (`i` + `j`) / 2. keepdims : bool, optional If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original `arr`. Returns ------- nanpercentile : scalar or ndarray If a single percentile `q` is given and axis=None a scalar is returned. If multiple percentiles `q` are given an array holding the result is returned. The results are listed in the first axis. (If `out` is specified, in which case that array is returned instead). If the input contains integers, or floats of smaller precision than 64, then the output data-type is float64. Otherwise, the output data-type is the same as that of the input. See Also -------- nanmean, nanmedian, percentile, median, mean Notes ----- Given a vector V of length N, the q-th percentile of V is the q-th ranked value in a sorted copy of V. The values and distances of the two nearest neighbors as well as the `interpolation` parameter will determine the percentile if the normalized ranking does not match q exactly. This function is the same as the median if ``q=50``, the same as the minimum if ``q=0``and the same as the maximum if ``q=100``. Examples -------- >>> a = np.array([[10., 7., 4.], [3., 2., 1.]]) >>> a[0][1] = np.nan >>> a array([[ 10., nan, 4.], [ 3., 2., 1.]]) >>> np.percentile(a, 50) nan >>> np.nanpercentile(a, 50) 3.5 >>> np.nanpercentile(a, 50, axis=0) array([[ 6.5, 4.5, 2.5]]) >>> np.nanpercentile(a, 50, axis=1) array([[ 7.], [ 2.]]) >>> m = np.nanpercentile(a, 50, axis=0) >>> out = np.zeros_like(m) >>> np.nanpercentile(a, 50, axis=0, out=m) array([[ 6.5, 4.5, 2.5]]) >>> m array([[ 6.5, 4.5, 2.5]]) >>> b = a.copy() >>> np.nanpercentile(b, 50, axis=1, overwrite_input=True) array([[ 7.], [ 2.]]) >>> assert not np.all(a==b) >>> b = a.copy() >>> np.nanpercentile(b, 50, axis=None, overwrite_input=True) array([ 3.5]) """ a = np.asanyarray(a) q = np.asanyarray(q) # apply_along_axis in _nanpercentile doesn't handle empty arrays well, # so deal them upfront if a.size == 0: return np.nanmean(a, axis, out=out, keepdims=keepdims) r, k = _ureduce(a, func=_nanpercentile, q=q, axis=axis, out=out, overwrite_input=overwrite_input, interpolation=interpolation) if keepdims: if q.ndim == 0: return r.reshape(k) else: return r.reshape([len(q)] + k) else: return r