Merge branch 'master' into doc-nditer

15 files changed, 363 insertions, 26 deletions

diff --git a/doc/release/1.15.0-notes.rst b/doc/release/1.15.0-notes.rst
index 68e8566ee..8ef1d5c42 100644
--- a/doc/release/1.15.0-notes.rst
+++ b/doc/release/1.15.0-notes.rst
@@ -16,6 +16,9 @@ New functions
   common multiple.
 * `np.ma.stack`, the `np.stack` array-joining function generalized to masked
   arrays.
+* ``quantile`` function, an interface to ``percentile`` without factors of 100
+* ``nanquantile`` function, an interface to ``nanpercentile`` without factors
+  of 100
 
 * `np.printoptions`, the context manager which sets print options temporarily
   for the scope of the ``with`` block::
@@ -141,6 +144,13 @@ Creating a full iOS-compatible NumPy package requires building for the 5
 architectures supported by iOS (i386, x86_64, armv7, armv7s and arm64), and
 combining these 5 compiled builds products into a single "fat" binary.
 
+``np.quantile`` and ``np.nanquantile``
+--------------------------------------
+Like ``np.percentile`` and ``np.nanpercentile``, but takes quantiles in [0, 1]
+rather than percentiles in [0, 100]. ``np.percentile`` is now a thin wrapper
+around ``np.quantile`` with the extra step of dividing by 100.
+
+
 Build system
 ------------
 Added experimental support for the 64-bit RISC-V architecture.
diff --git a/doc/source/reference/c-api.array.rst b/doc/source/reference/c-api.array.rst
index ad7c725a8..5ea7bfcfc 100644
--- a/doc/source/reference/c-api.array.rst
+++ b/doc/source/reference/c-api.array.rst
@@ -1360,7 +1360,7 @@ Special functions for NPY_OBJECT
 .. c:function:: int PyArray_SetWritebackIfCopyBase(PyArrayObject* arr, PyArrayObject* base)
 
     Precondition: ``arr`` is a copy of ``base`` (though possibly with different
-    strides, ordering, etc.) Sets the :c:data:`NPY_ARRAY_WRITEBACKIFCOPY` flag 
+    strides, ordering, etc.) Sets the :c:data:`NPY_ARRAY_WRITEBACKIFCOPY` flag
     and ``arr->base``, and set ``base`` to READONLY. Call
     :c:func:`PyArray_ResolveWritebackIfCopy` before calling
     `Py_DECREF`` in order copy any changes back to ``base`` and
@@ -3260,12 +3260,14 @@ Memory management
 .. c:function:: int PyArray_ResolveWritebackIfCopy(PyArrayObject* obj)
 
     If ``obj.flags`` has :c:data:`NPY_ARRAY_WRITEBACKIFCOPY` or (deprecated)
-    :c:data:`NPY_ARRAY_UPDATEIFCOPY`, this function copies ``obj->data`` to
-    `obj->base->data`, clears the flags, `DECREF` s `obj->base` and makes it
-    writeable, and sets ``obj->base`` to NULL. This is the opposite of 
+    :c:data:`NPY_ARRAY_UPDATEIFCOPY`, this function clears the flags, `DECREF` s
+    `obj->base` and makes it writeable, and sets ``obj->base`` to NULL. It then
+    copies ``obj->data`` to `obj->base->data`, and returns the error state of
+    the copy operation. This is the opposite of
     :c:func:`PyArray_SetWritebackIfCopyBase`. Usually this is called once
     you are finished with ``obj``, just before ``Py_DECREF(obj)``. It may be called
-    multiple times, or with ``NULL`` input.
+    multiple times, or with ``NULL`` input. See also
+    :c:func:`PyArray_DiscardWritebackIfCopy`.
 
     Returns 0 if nothing was done, -1 on error, and 1 if action was taken.
 
@@ -3487,12 +3489,14 @@ Miscellaneous Macros
 
 .. c:function:: PyArray_DiscardWritebackIfCopy(PyObject* obj)
 
-    Reset the :c:data:`NPY_ARRAY_WRITEBACKIFCOPY` and deprecated
-    :c:data:`NPY_ARRAY_UPDATEIFCOPY` flag. Resets the
-    :c:data:`NPY_ARRAY_WRITEABLE` flag on the base object. It also
-    discards pending changes to the base object. This is
-    useful for recovering from an error condition when
-    writeback semantics are used.
+    If ``obj.flags`` has :c:data:`NPY_ARRAY_WRITEBACKIFCOPY` or (deprecated)
+    :c:data:`NPY_ARRAY_UPDATEIFCOPY`, this function clears the flags, `DECREF` s
+    `obj->base` and makes it writeable, and sets ``obj->base`` to NULL. In
+    contrast to :c:func:`PyArray_DiscardWritebackIfCopy` it makes no attempt
+    to copy the data from `obj->base` This undoes
+    :c:func:`PyArray_SetWritebackIfCopyBase`. Usually this is called after an
+    error when you are finished with ``obj``, just before ``Py_DECREF(obj)``.
+    It may be called multiple times, or with ``NULL`` input.
 
 .. c:function:: PyArray_XDECREF_ERR(PyObject* obj)
 
diff --git a/doc/source/reference/c-api.iterator.rst b/doc/source/reference/c-api.iterator.rst
index 6a9ec6005..392dcb730 100644
--- a/doc/source/reference/c-api.iterator.rst
+++ b/doc/source/reference/c-api.iterator.rst
@@ -779,7 +779,7 @@ Construction and Destruction
     :c:func:`NpyIter_Close` should be called before this. If not, and if
     writeback is needed, it will be performed at this point in order to maintain
     backward-compatibility with older code, and a deprecation warning will be
-    emmitted. Old code should be updated to call `NpyIter_Close` beforehand.
+    emitted. Old code should be updated to call `NpyIter_Close` beforehand.
 
     Returns ``NPY_SUCCEED`` or ``NPY_FAIL``.
 
diff --git a/doc/source/reference/routines.statistics.rst b/doc/source/reference/routines.statistics.rst
index d359541aa..e287fe9c8 100644
--- a/doc/source/reference/routines.statistics.rst
+++ b/doc/source/reference/routines.statistics.rst
@@ -17,6 +17,8 @@ Order statistics
    ptp
    percentile
    nanpercentile
+   quantile
+   nanquantile
 
 Averages and variances
 ----------------------
diff --git a/numpy/core/arrayprint.py b/numpy/core/arrayprint.py
index adbbab6ed..f39248bd0 100644
--- a/numpy/core/arrayprint.py
+++ b/numpy/core/arrayprint.py
@@ -647,6 +647,9 @@ def array2string(a, max_line_width=None, precision=None,
     options.update(overrides)
 
     if options['legacy'] == '1.13':
+        if style is np._NoValue:
+            style = repr
+
         if a.shape == () and not a.dtype.names:
             return style(a.item())
     elif style is not np._NoValue:
diff --git a/numpy/core/include/numpy/ndarrayobject.h b/numpy/core/include/numpy/ndarrayobject.h
index ec0fd1ee9..12fc7098c 100644
--- a/numpy/core/include/numpy/ndarrayobject.h
+++ b/numpy/core/include/numpy/ndarrayobject.h
@@ -170,14 +170,17 @@ extern "C" CONFUSE_EMACS
                                             (k)*PyArray_STRIDES(obj)[2] + \
                                             (l)*PyArray_STRIDES(obj)[3]))
 
+/* Move to arrayobject.c once PyArray_XDECREF_ERR is removed */
 static NPY_INLINE void
 PyArray_DiscardWritebackIfCopy(PyArrayObject *arr)
 {
-    if (arr != NULL) {
-        if ((PyArray_FLAGS(arr) & NPY_ARRAY_WRITEBACKIFCOPY) ||
-            (PyArray_FLAGS(arr) & NPY_ARRAY_UPDATEIFCOPY)) {
-            PyArrayObject *base = (PyArrayObject *)PyArray_BASE(arr);
-            PyArray_ENABLEFLAGS(base, NPY_ARRAY_WRITEABLE);
+    PyArrayObject_fields *fa = (PyArrayObject_fields *)arr;
+    if (fa && fa->base) {
+        if ((fa->flags & NPY_ARRAY_UPDATEIFCOPY) ||
+                (fa->flags & NPY_ARRAY_WRITEBACKIFCOPY)) {
+            PyArray_ENABLEFLAGS((PyArrayObject*)fa->base, NPY_ARRAY_WRITEABLE);
+            Py_DECREF(fa->base);
+            fa->base = NULL;
             PyArray_CLEARFLAGS(arr, NPY_ARRAY_WRITEBACKIFCOPY);
             PyArray_CLEARFLAGS(arr, NPY_ARRAY_UPDATEIFCOPY);
         }
diff --git a/numpy/core/src/multiarray/_multiarray_tests.c.src b/numpy/core/src/multiarray/_multiarray_tests.c.src
index 38698887a..0299f1a1b 100644
--- a/numpy/core/src/multiarray/_multiarray_tests.c.src
+++ b/numpy/core/src/multiarray/_multiarray_tests.c.src
@@ -687,6 +687,18 @@ npy_resolve(PyObject* NPY_UNUSED(self), PyObject* args)
     Py_RETURN_NONE;
 }
 
+/* resolve WRITEBACKIFCOPY */
+static PyObject*
+npy_discard(PyObject* NPY_UNUSED(self), PyObject* args)
+{
+    if (!PyArray_Check(args)) {
+        PyErr_SetString(PyExc_TypeError, "test needs ndarray input");
+        return NULL;
+    }
+    PyArray_DiscardWritebackIfCopy((PyArrayObject*)args);
+    Py_RETURN_NONE;
+}
+
 #if !defined(NPY_PY3K)
 static PyObject *
 int_subclass(PyObject *dummy, PyObject *args)
@@ -1857,6 +1869,9 @@ static PyMethodDef Multiarray_TestsMethods[] = {
     {"npy_resolve",
         npy_resolve,
         METH_O, NULL},
+    {"npy_discard",
+        npy_discard,
+        METH_O, NULL},
 #if !defined(NPY_PY3K)
     {"test_int_subclass",
         int_subclass,
diff --git a/numpy/core/tests/test_arrayprint.py b/numpy/core/tests/test_arrayprint.py
index 2c142f82b..6214e325c 100644
--- a/numpy/core/tests/test_arrayprint.py
+++ b/numpy/core/tests/test_arrayprint.py
@@ -491,6 +491,8 @@ class TestPrintOptions(object):
                                          np.array(1.), style=repr)
         # but not in legacy mode
         np.array2string(np.array(1.), style=repr, legacy='1.13')
+        # gh-10934 style was broken in legacy mode, check it works
+        np.array2string(np.array(1.), legacy='1.13')
 
     def test_float_spacing(self):
         x = np.array([1., 2., 3.])
diff --git a/numpy/core/tests/test_multiarray.py b/numpy/core/tests/test_multiarray.py
index 806a3b083..ed3102f66 100644
--- a/numpy/core/tests/test_multiarray.py
+++ b/numpy/core/tests/test_multiarray.py
@@ -7246,16 +7246,20 @@ class TestWritebackIfCopy(object):
 
     def test_view_assign(self):
         from numpy.core._multiarray_tests import npy_create_writebackifcopy, npy_resolve
+
         arr = np.arange(9).reshape(3, 3).T
         arr_wb = npy_create_writebackifcopy(arr)
         assert_(arr_wb.flags.writebackifcopy)
         assert_(arr_wb.base is arr)
-        arr_wb[:] = -100
+        arr_wb[...] = -100
         npy_resolve(arr_wb)
+        # arr changes after resolve, even though we assigned to arr_wb
         assert_equal(arr, -100)
         # after resolve, the two arrays no longer reference each other
-        assert_(not arr_wb.ctypes.data == 0)
-        arr_wb[:] = 100
+        assert_(arr_wb.ctypes.data != 0)
+        assert_equal(arr_wb.base, None)
+        # assigning to arr_wb does not get transfered to arr
+        arr_wb[...] = 100
         assert_equal(arr, -100)
 
     def test_dealloc_warning(self):
@@ -7266,6 +7270,30 @@ class TestWritebackIfCopy(object):
             _multiarray_tests.npy_abuse_writebackifcopy(v)
             assert len(sup.log) == 1
 
+    def test_view_discard_refcount(self):
+        from numpy.core._multiarray_tests import npy_create_writebackifcopy, npy_discard
+
+        arr = np.arange(9).reshape(3, 3).T
+        orig = arr.copy()
+        if HAS_REFCOUNT:
+            arr_cnt = sys.getrefcount(arr)
+        arr_wb = npy_create_writebackifcopy(arr)
+        assert_(arr_wb.flags.writebackifcopy)
+        assert_(arr_wb.base is arr)
+        arr_wb[...] = -100
+        npy_discard(arr_wb)
+        # arr remains unchanged after discard
+        assert_equal(arr, orig)
+        # after discard, the two arrays no longer reference each other
+        assert_(arr_wb.ctypes.data != 0)
+        assert_equal(arr_wb.base, None)
+        if HAS_REFCOUNT:
+            assert_equal(arr_cnt, sys.getrefcount(arr))
+        # assigning to arr_wb does not get transfered to arr
+        arr_wb[...] = 100
+        assert_equal(arr, orig)
+
+
 class TestArange(object):
     def test_infinite(self):
         assert_raises_regex(
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py
index 099b63c40..72beef471 100644
--- a/numpy/lib/function_base.py
+++ b/numpy/lib/function_base.py
@@ -54,7 +54,8 @@ __all__ = [
     'bincount', 'digitize', 'cov', 'corrcoef',
     'msort', 'median', 'sinc', 'hamming', 'hanning', 'bartlett',
     'blackman', 'kaiser', 'trapz', 'i0', 'add_newdoc', 'add_docstring',
-    'meshgrid', 'delete', 'insert', 'append', 'interp', 'add_newdoc_ufunc'
+    'meshgrid', 'delete', 'insert', 'append', 'interp', 'add_newdoc_ufunc',
+    'quantile'
     ]
 
 
@@ -3427,7 +3428,7 @@ def percentile(a, q, axis=None, out=None,
 
     interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}
         This optional parameter specifies the interpolation method to
-        use when the desired quantile lies between two data points
+        use when the desired percentile lies between two data points
         ``i < j``:
 
         * 'linear': ``i + (j - i) * fraction``, where ``fraction``
@@ -3463,6 +3464,7 @@ def percentile(a, q, axis=None, out=None,
     mean
     median : equivalent to ``percentile(..., 50)``
     nanpercentile
+    quantile : equivalent to percentile, except with q in the range [0, 1].
 
     Notes
     -----
@@ -3539,6 +3541,110 @@ def percentile(a, q, axis=None, out=None,
         a, q, axis, out, overwrite_input, interpolation, keepdims)
 
 
+def quantile(a, q, axis=None, out=None,
+             overwrite_input=False, interpolation='linear', keepdims=False):
+    """
+    Compute the `q`th quantile of the data along the specified axis.
+    ..versionadded:: 1.15.0
+
+    Parameters
+    ----------
+    a : array_like
+        Input array or object that can be converted to an array.
+    q : array_like of float
+        Quantile or sequence of quantiles to compute, which must be between
+        0 and 1 inclusive.
+    axis : {int, tuple of int, None}, optional
+        Axis or axes along which the quantiles are computed. The
+        default is to compute the quantile(s) along a flattened
+        version of the array.
+    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 the input array `a` to be modified by intermediate
+        calculations, to save memory. In this case, the contents of the input
+        `a` after this function completes is undefined.
+    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`.
+
+    Returns
+    -------
+    quantile : scalar or ndarray
+        If `q` is a single quantile and `axis=None`, then the result
+        is a scalar. If multiple quantiles are given, first axis of
+        the result corresponds to the quantiles. 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
+    --------
+    mean
+    percentile : equivalent to quantile, but with q in the range [0, 100].
+    median : equivalent to ``quantile(..., 0.5)``
+    nanquantile
+
+    Notes
+    -----
+    Given a vector ``V`` of length ``N``, the ``q``-th quantile of
+    ``V`` is the value ``q`` 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 quantile if the normalized ranking does not
+    match the location of ``q`` exactly. This function is the same as
+    the median if ``q=0.5``, the same as the minimum if ``q=0.0`` and the
+    same as the maximum if ``q=1.0``.
+
+    Examples
+    --------
+    >>> a = np.array([[10, 7, 4], [3, 2, 1]])
+    >>> a
+    array([[10,  7,  4],
+           [ 3,  2,  1]])
+    >>> np.quantile(a, 0.5)
+    3.5
+    >>> np.quantile(a, 0.5, axis=0)
+    array([[ 6.5,  4.5,  2.5]])
+    >>> np.quantile(a, 0.5, axis=1)
+    array([ 7.,  2.])
+    >>> np.quantile(a, 0.5, axis=1, keepdims=True)
+    array([[ 7.],
+           [ 2.]])
+    >>> m = np.quantile(a, 0.5, axis=0)
+    >>> out = np.zeros_like(m)
+    >>> np.quantile(a, 0.5, axis=0, out=out)
+    array([[ 6.5,  4.5,  2.5]])
+    >>> m
+    array([[ 6.5,  4.5,  2.5]])
+    >>> b = a.copy()
+    >>> np.quantile(b, 0.5, axis=1, overwrite_input=True)
+    array([ 7.,  2.])
+    >>> assert not np.all(a == b)
+    """
+    q = np.asanyarray(q)
+    if not _quantile_is_valid(q):
+        raise ValueError("Quantiles must be in the range [0, 1]")
+    return _quantile_unchecked(
+        a, q, axis, out, overwrite_input, interpolation, keepdims)
+
+
 def _quantile_unchecked(a, q, axis=None, out=None, overwrite_input=False,
                         interpolation='linear', keepdims=False):
     """Assumes that q is in [0, 1], and is an ndarray"""
diff --git a/numpy/lib/nanfunctions.py b/numpy/lib/nanfunctions.py
index dddc0e5b8..abd2da1a2 100644
--- a/numpy/lib/nanfunctions.py
+++ b/numpy/lib/nanfunctions.py
@@ -16,6 +16,7 @@ Functions
 - `nanvar` -- variance of non-NaN values
 - `nanstd` -- standard deviation of non-NaN values
 - `nanmedian` -- median of non-NaN values
+- `nanquantile` -- qth quantile of non-NaN values
 - `nanpercentile` -- qth percentile of non-NaN values
 
 """
@@ -29,7 +30,7 @@ from numpy.lib import function_base
 __all__ = [
     'nansum', 'nanmax', 'nanmin', 'nanargmax', 'nanargmin', 'nanmean',
     'nanmedian', 'nanpercentile', 'nanvar', 'nanstd', 'nanprod',
-    'nancumsum', 'nancumprod'
+    'nancumsum', 'nancumprod', 'nanquantile'
     ]
 
 
@@ -1057,7 +1058,7 @@ def nanpercentile(a, q, axis=None, out=None, overwrite_input=False,
         `a` after this function completes is undefined.
     interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}
         This optional parameter specifies the interpolation method to
-        use when the desired quantile lies between two data points
+        use when the desired percentile lies between two data points
         ``i < j``:
 
         * 'linear': ``i + (j - i) * fraction``, where ``fraction``
@@ -1095,6 +1096,7 @@ def nanpercentile(a, q, axis=None, out=None, overwrite_input=False,
     nanmean
     nanmedian : equivalent to ``nanpercentile(..., 50)``
     percentile, median, mean
+    nanquantile : equivalent to nanpercentile, but with q in the range [0, 1].
 
     Notes
     -----
@@ -1144,6 +1146,110 @@ def nanpercentile(a, q, axis=None, out=None, overwrite_input=False,
         a, q, axis, out, overwrite_input, interpolation, keepdims)
 
 
+def nanquantile(a, q, axis=None, out=None, overwrite_input=False,
+                interpolation='linear', keepdims=np._NoValue):
+    """
+    Compute the qth quantile of the data along the specified axis,
+    while ignoring nan values.
+    Returns the qth quantile(s) of the array elements.
+    .. versionadded:: 1.15.0
+
+    Parameters
+    ----------
+    a : array_like
+        Input array or object that can be converted to an array, containing
+        nan values to be ignored
+    q : array_like of float
+        Quantile or sequence of quantiles to compute, which must be between
+        0 and 1 inclusive.
+    axis : {int, tuple of int, None}, optional
+        Axis or axes along which the quantiles are computed. The
+        default is to compute the quantile(s) along a flattened
+        version of the array.
+    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 the input array `a` to be modified by intermediate
+        calculations, to save memory. In this case, the contents of the input
+        `a` after this function completes is undefined.
+    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
+    -------
+    quantile : scalar or ndarray
+        If `q` is a single percentile and `axis=None`, then the result
+        is a scalar. If multiple quantiles are given, first axis of
+        the result corresponds to the quantiles. 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
+    --------
+    quantile
+    nanmean, nanmedian
+    nanmedian : equivalent to ``nanquantile(..., 0.5)``
+    nanpercentile : same as nanquantile, but with q in the range [0, 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.quantile(a, 0.5)
+    nan
+    >>> np.nanquantile(a, 0.5)
+    3.5
+    >>> np.nanquantile(a, 0.5, axis=0)
+    array([ 6.5,  2.,   2.5])
+    >>> np.nanquantile(a, 0.5, axis=1, keepdims=True)
+    array([[ 7.],
+           [ 2.]])
+    >>> m = np.nanquantile(a, 0.5, axis=0)
+    >>> out = np.zeros_like(m)
+    >>> np.nanquantile(a, 0.5, axis=0, out=out)
+    array([ 6.5,  2.,   2.5])
+    >>> m
+    array([ 6.5,  2. ,  2.5])
+    >>> b = a.copy()
+    >>> np.nanquantile(b, 0.5, axis=1, overwrite_input=True)
+    array([  7.,  2.])
+    >>> assert not np.all(a==b)
+    """
+    a = np.asanyarray(a)
+    q = np.asanyarray(q)
+    if not function_base._quantile_is_valid(q):
+        raise ValueError("Quantiles must be in the range [0, 1]")
+    return _nanquantile_unchecked(
+        a, q, axis, out, overwrite_input, interpolation, keepdims)
+
+
 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"""
diff --git a/numpy/lib/npyio.py b/numpy/lib/npyio.py
index 59379bdda..67585443b 100644
--- a/numpy/lib/npyio.py
+++ b/numpy/lib/npyio.py
@@ -936,7 +936,7 @@ def loadtxt(fname, dtype=float, comments='#', delimiter=None,
     if encoding is not None:
         fencoding = encoding
     # we must assume local encoding
-    # TOOD emit portability warning?
+    # TODO emit portability warning?
     elif fencoding is None:
         import locale
         fencoding = locale.getpreferredencoding()
diff --git a/numpy/lib/tests/test_function_base.py b/numpy/lib/tests/test_function_base.py
index 6653b5ba1..43d62a7ff 100644
--- a/numpy/lib/tests/test_function_base.py
+++ b/numpy/lib/tests/test_function_base.py
@@ -2749,6 +2749,28 @@ class TestPercentile(object):
                 a, [0.3, 0.6], (0, 2), interpolation='nearest'), b)
 
 
+class TestQuantile(object):
+    # most of this is already tested by TestPercentile
+
+    def test_basic(self):
+        x = np.arange(8) * 0.5
+        assert_equal(np.quantile(x, 0), 0.)
+        assert_equal(np.quantile(x, 1), 3.5)
+        assert_equal(np.quantile(x, 0.5), 1.75)
+
+    def test_no_p_overwrite(self):
+        # this is worth retesting, beause quantile does not make a copy
+        p0 = np.array([0, 0.75, 0.25, 0.5, 1.0])
+        p = p0.copy()
+        np.quantile(np.arange(100.), p, interpolation="midpoint")
+        assert_array_equal(p, p0)
+
+        p0 = p0.tolist()
+        p = p.tolist()
+        np.quantile(np.arange(100.), p, interpolation="midpoint")
+        assert_array_equal(p, p0)
+
+
 class TestMedian(object):
 
     def test_basic(self):
diff --git a/numpy/lib/tests/test_io.py b/numpy/lib/tests/test_io.py
index 0ce44f28b..f58c9e33d 100644
--- a/numpy/lib/tests/test_io.py
+++ b/numpy/lib/tests/test_io.py
@@ -937,7 +937,7 @@ class TestLoadTxt(LoadTxtBase):
         assert_equal(res, tgt)
 
     def test_complex_misformatted(self):
-        # test for backward compatability
+        # test for backward compatibility
         # some complex formats used to generate x+-yj
         a = np.zeros((2, 2), dtype=np.complex128)
         re = np.pi
diff --git a/numpy/lib/tests/test_nanfunctions.py b/numpy/lib/tests/test_nanfunctions.py
index 1f403f7b8..e69d9dd7d 100644
--- a/numpy/lib/tests/test_nanfunctions.py
+++ b/numpy/lib/tests/test_nanfunctions.py
@@ -886,3 +886,39 @@ class TestNanFunctions_Percentile(object):
 
         megamat = np.ones((3, 4, 5, 6))
         assert_equal(np.nanpercentile(megamat, perc, axis=(1, 2)).shape, (2, 3, 6))
+
+
+class TestNanFunctions_Quantile(object):
+    # most of this is already tested by TestPercentile
+
+    def test_regression(self):
+        ar = np.arange(24).reshape(2, 3, 4).astype(float)
+        ar[0][1] = np.nan
+
+        assert_equal(np.nanquantile(ar, q=0.5), np.nanpercentile(ar, q=50))
+        assert_equal(np.nanquantile(ar, q=0.5, axis=0),
+                     np.nanpercentile(ar, q=50, axis=0))
+        assert_equal(np.nanquantile(ar, q=0.5, axis=1),
+                     np.nanpercentile(ar, q=50, axis=1))
+        assert_equal(np.nanquantile(ar, q=[0.5], axis=1),
+                     np.nanpercentile(ar, q=[50], axis=1))
+        assert_equal(np.nanquantile(ar, q=[0.25, 0.5, 0.75], axis=1),
+                     np.nanpercentile(ar, q=[25, 50, 75], axis=1))
+
+    def test_basic(self):
+        x = np.arange(8) * 0.5
+        assert_equal(np.nanquantile(x, 0), 0.)
+        assert_equal(np.nanquantile(x, 1), 3.5)
+        assert_equal(np.nanquantile(x, 0.5), 1.75)
+
+    def test_no_p_overwrite(self):
+        # this is worth retesting, beause quantile does not make a copy
+        p0 = np.array([0, 0.75, 0.25, 0.5, 1.0])
+        p = p0.copy()
+        np.nanquantile(np.arange(100.), p, interpolation="midpoint")
+        assert_array_equal(p, p0)
+
+        p0 = p0.tolist()
+        p = p.tolist()
+        np.nanquantile(np.arange(100.), p, interpolation="midpoint")
+        assert_array_equal(p, p0)