MAINT: Factor out code duplicated by nanmedian and nanpercentile

1 files changed, 53 insertions, 43 deletions

diff --git a/numpy/lib/nanfunctions.py b/numpy/lib/nanfunctions.py
index 97bd33492..791218886 100644
--- a/numpy/lib/nanfunctions.py
+++ b/numpy/lib/nanfunctions.py
@@ -106,6 +106,46 @@ def _copyto(a, val, mask):
     return a
 
 
+def _remove_nan_1d(arr1d, overwrite_input=False):
+    """
+    Equivalent to arr1d[~arr1d.isnan()], but in a different order
+
+    Presumably faster as it incurs fewer copies
+
+    Parameters
+    ----------
+    arr1d : ndarray
+        Array to remove nans from
+    overwrite_input : bool
+        True if `arr1d` can be modified in place
+
+    Returns
+    -------
+    res : ndarray
+        Array with nan elements removed
+    overwrite_input : bool
+        True if `res` can be modified in place, given the constraint on the
+        input
+    """
+
+    c = np.isnan(arr1d)
+    s = np.nonzero(c)[0]
+    if s.size == arr1d.size:
+        warnings.warn("All-NaN slice encountered", RuntimeWarning, stacklevel=4)
+        return arr1d[:0], True
+    elif s.size == 0:
+        return arr1d, overwrite_input
+    else:
+        if not overwrite_input:
+            arr1d = arr1d.copy()
+        # select non-nans at end of array
+        enonan = arr1d[-s.size:][~c[-s.size:]]
+        # fill nans in beginning of array with non-nans of end
+        arr1d[s[:enonan.size]] = enonan
+
+        return arr1d[:-s.size], True
+
+
 def _divide_by_count(a, b, out=None):
     """
     Compute a/b ignoring invalid results. If `a` is an array the division
@@ -836,24 +876,12 @@ def _nanmedian1d(arr1d, overwrite_input=False):
     Private function for rank 1 arrays. Compute the median ignoring NaNs.
     See nanmedian for parameter usage
     """
-    c = np.isnan(arr1d)
-    s = np.nonzero(c)[0]
-    if s.size == arr1d.size:
-        warnings.warn("All-NaN slice encountered", RuntimeWarning, stacklevel=3)
+    arr1d, overwrite_input = _remove_nan_1d(arr1d,
+        overwrite_input=overwrite_input)
+    if arr1d.size == 0:
         return np.nan
-    elif s.size == 0:
-        return np.median(arr1d, overwrite_input=overwrite_input)
-    else:
-        if overwrite_input:
-            x = arr1d
-        else:
-            x = arr1d.copy()
-        # select non-nans at end of array
-        enonan = arr1d[-s.size:][~c[-s.size:]]
-        # fill nans in beginning of array with non-nans of end
-        x[s[:enonan.size]] = enonan
-        # slice nans away
-        return np.median(x[:-s.size], overwrite_input=True)
+
+    return np.median(arr1d, overwrite_input=overwrite_input)
 
 
 def _nanmedian(a, axis=None, out=None, overwrite_input=False):
@@ -1155,34 +1183,16 @@ def _nanpercentile(a, q, axis=None, out=None, overwrite_input=False,
 
 def _nanpercentile1d(arr1d, q, overwrite_input=False, interpolation='linear'):
     """
-    Private function for rank 1 arrays. Compute percentile ignoring
-    NaNs.
-
+    Private function for rank 1 arrays. Compute percentile ignoring NaNs.
     See nanpercentile for parameter usage
     """
-    c = np.isnan(arr1d)
-    s = np.nonzero(c)[0]
-    if s.size == arr1d.size:
-        warnings.warn("All-NaN slice encountered", RuntimeWarning, stacklevel=3)
-        if q.ndim == 0:
-            return np.nan
-        else:
-            return np.nan * np.ones((len(q),))
-    elif s.size == 0:
-        return np.percentile(arr1d, q, overwrite_input=overwrite_input,
-                             interpolation=interpolation)
-    else:
-        if overwrite_input:
-            x = arr1d
-        else:
-            x = arr1d.copy()
-        # select non-nans at end of array
-        enonan = arr1d[-s.size:][~c[-s.size:]]
-        # fill nans in beginning of array with non-nans of end
-        x[s[:enonan.size]] = enonan
-        # slice nans away
-        return np.percentile(x[:-s.size], q, overwrite_input=True,
-                             interpolation=interpolation)
+    arr1d, overwrite_input = _remove_nan_1d(arr1d,
+        overwrite_input=overwrite_input)
+    if arr1d.size == 0:
+        return np.full(q.shape, np.nan)[()]  # convert to scalar
+
+    return np.percentile(arr1d, q, overwrite_input=overwrite_input,
+                         interpolation=interpolation)
 
 
 def nanvar(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue):