summaryrefslogtreecommitdiff
path: root/numpy/lib/nanfunctions.py
diff options
context:
space:
mode:
Diffstat (limited to 'numpy/lib/nanfunctions.py')
-rw-r--r--numpy/lib/nanfunctions.py128
1 files changed, 79 insertions, 49 deletions
diff --git a/numpy/lib/nanfunctions.py b/numpy/lib/nanfunctions.py
index d73d84467..77c851fcf 100644
--- a/numpy/lib/nanfunctions.py
+++ b/numpy/lib/nanfunctions.py
@@ -40,6 +40,33 @@ __all__ = [
]
+def _nan_mask(a, out=None):
+ """
+ Parameters
+ ----------
+ a : array-like
+ Input array with at least 1 dimension.
+ out : ndarray, optional
+ Alternate output array in which to place the result. The default
+ is ``None``; if provided, it must have the same shape as the
+ expected output and will prevent the allocation of a new array.
+
+ Returns
+ -------
+ y : bool ndarray or True
+ A bool array where ``np.nan`` positions are marked with ``False``
+ and other positions are marked with ``True``. If the type of ``a``
+ is such that it can't possibly contain ``np.nan``, returns ``True``.
+ """
+ # we assume that a is an array for this private function
+
+ if a.dtype.kind not in 'fc':
+ return True
+
+ y = np.isnan(a, out=out)
+ y = np.invert(y, out=y)
+ return y
+
def _replace_nan(a, val):
"""
If `a` is of inexact type, make a copy of `a`, replace NaNs with
@@ -271,9 +298,9 @@ def nanmin(a, axis=None, out=None, keepdims=np._NoValue):
>>> np.nanmin(a)
1.0
>>> np.nanmin(a, axis=0)
- array([ 1., 2.])
+ array([1., 2.])
>>> np.nanmin(a, axis=1)
- array([ 1., 3.])
+ array([1., 3.])
When positive infinity and negative infinity are present:
@@ -384,9 +411,9 @@ def nanmax(a, axis=None, out=None, keepdims=np._NoValue):
>>> np.nanmax(a)
3.0
>>> np.nanmax(a, axis=0)
- array([ 3., 2.])
+ array([3., 2.])
>>> np.nanmax(a, axis=1)
- array([ 2., 3.])
+ array([2., 3.])
When positive infinity and negative infinity are present:
@@ -601,12 +628,15 @@ def nansum(a, axis=None, dtype=None, out=None, keepdims=np._NoValue):
>>> np.nansum(a)
3.0
>>> np.nansum(a, axis=0)
- array([ 2., 1.])
+ array([2., 1.])
>>> np.nansum([1, np.nan, np.inf])
inf
>>> np.nansum([1, np.nan, np.NINF])
-inf
- >>> np.nansum([1, np.nan, np.inf, -np.inf]) # both +/- infinity present
+ >>> from numpy.testing import suppress_warnings
+ >>> with suppress_warnings() as sup:
+ ... sup.filter(RuntimeWarning)
+ ... np.nansum([1, np.nan, np.inf, -np.inf]) # both +/- infinity present
nan
"""
@@ -677,7 +707,7 @@ def nanprod(a, axis=None, dtype=None, out=None, keepdims=np._NoValue):
>>> np.nanprod(a)
6.0
>>> np.nanprod(a, axis=0)
- array([ 3., 2.])
+ array([3., 2.])
"""
a, mask = _replace_nan(a, 1)
@@ -738,16 +768,16 @@ def nancumsum(a, axis=None, dtype=None, out=None):
>>> np.nancumsum([1])
array([1])
>>> np.nancumsum([1, np.nan])
- array([ 1., 1.])
+ array([1., 1.])
>>> a = np.array([[1, 2], [3, np.nan]])
>>> np.nancumsum(a)
- array([ 1., 3., 6., 6.])
+ array([1., 3., 6., 6.])
>>> np.nancumsum(a, axis=0)
- array([[ 1., 2.],
- [ 4., 2.]])
+ array([[1., 2.],
+ [4., 2.]])
>>> np.nancumsum(a, axis=1)
- array([[ 1., 3.],
- [ 3., 3.]])
+ array([[1., 3.],
+ [3., 3.]])
"""
a, mask = _replace_nan(a, 0)
@@ -805,16 +835,16 @@ def nancumprod(a, axis=None, dtype=None, out=None):
>>> np.nancumprod([1])
array([1])
>>> np.nancumprod([1, np.nan])
- array([ 1., 1.])
+ array([1., 1.])
>>> a = np.array([[1, 2], [3, np.nan]])
>>> np.nancumprod(a)
- array([ 1., 2., 6., 6.])
+ array([1., 2., 6., 6.])
>>> np.nancumprod(a, axis=0)
- array([[ 1., 2.],
- [ 3., 2.]])
+ array([[1., 2.],
+ [3., 2.]])
>>> np.nancumprod(a, axis=1)
- array([[ 1., 2.],
- [ 3., 3.]])
+ array([[1., 2.],
+ [3., 3.]])
"""
a, mask = _replace_nan(a, 1)
@@ -895,9 +925,9 @@ def nanmean(a, axis=None, dtype=None, out=None, keepdims=np._NoValue):
>>> np.nanmean(a)
2.6666666666666665
>>> np.nanmean(a, axis=0)
- array([ 2., 4.])
+ array([2., 4.])
>>> np.nanmean(a, axis=1)
- array([ 1., 3.5])
+ array([1., 3.5]) # may vary
"""
arr, mask = _replace_nan(a, 0)
@@ -1049,19 +1079,19 @@ def nanmedian(a, axis=None, out=None, overwrite_input=False, keepdims=np._NoValu
>>> a = np.array([[10.0, 7, 4], [3, 2, 1]])
>>> a[0, 1] = np.nan
>>> a
- array([[ 10., nan, 4.],
- [ 3., 2., 1.]])
+ 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])
+ array([6.5, 2. , 2.5])
>>> np.median(a, axis=1)
- array([ 7., 2.])
+ array([nan, 2.])
>>> b = a.copy()
>>> np.nanmedian(b, axis=1, overwrite_input=True)
- array([ 7., 2.])
+ array([7., 2.])
>>> assert not np.all(a==b)
>>> b = a.copy()
>>> np.nanmedian(b, axis=None, overwrite_input=True)
@@ -1177,27 +1207,27 @@ def nanpercentile(a, q, axis=None, out=None, overwrite_input=False,
>>> a = np.array([[10., 7., 4.], [3., 2., 1.]])
>>> a[0][1] = np.nan
>>> a
- array([[ 10., nan, 4.],
- [ 3., 2., 1.]])
+ array([[10., nan, 4.],
+ [ 3., 2., 1.]])
>>> np.percentile(a, 50)
nan
>>> np.nanpercentile(a, 50)
- 3.5
+ 3.0
>>> np.nanpercentile(a, 50, axis=0)
- array([ 6.5, 2., 2.5])
+ array([6.5, 2. , 2.5])
>>> np.nanpercentile(a, 50, axis=1, keepdims=True)
- array([[ 7.],
- [ 2.]])
+ 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])
+ array([6.5, 2. , 2.5])
>>> m
- array([ 6.5, 2. , 2.5])
+ array([6.5, 2. , 2.5])
>>> b = a.copy()
>>> np.nanpercentile(b, 50, axis=1, overwrite_input=True)
- array([ 7., 2.])
+ array([7., 2.])
>>> assert not np.all(a==b)
"""
@@ -1291,26 +1321,26 @@ def nanquantile(a, q, axis=None, out=None, overwrite_input=False,
>>> a = np.array([[10., 7., 4.], [3., 2., 1.]])
>>> a[0][1] = np.nan
>>> a
- array([[ 10., nan, 4.],
- [ 3., 2., 1.]])
+ array([[10., nan, 4.],
+ [ 3., 2., 1.]])
>>> np.quantile(a, 0.5)
nan
>>> np.nanquantile(a, 0.5)
- 3.5
+ 3.0
>>> np.nanquantile(a, 0.5, axis=0)
- array([ 6.5, 2., 2.5])
+ array([6.5, 2. , 2.5])
>>> np.nanquantile(a, 0.5, axis=1, keepdims=True)
- array([[ 7.],
- [ 2.]])
+ 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])
+ array([6.5, 2. , 2.5])
>>> m
- array([ 6.5, 2. , 2.5])
+ array([6.5, 2. , 2.5])
>>> b = a.copy()
>>> np.nanquantile(b, 0.5, axis=1, overwrite_input=True)
- array([ 7., 2.])
+ array([7., 2.])
>>> assert not np.all(a==b)
"""
a = np.asanyarray(a)
@@ -1465,12 +1495,12 @@ def nanvar(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue):
Examples
--------
>>> a = np.array([[1, np.nan], [3, 4]])
- >>> np.var(a)
+ >>> np.nanvar(a)
1.5555555555555554
>>> np.nanvar(a, axis=0)
- array([ 1., 0.])
+ array([1., 0.])
>>> np.nanvar(a, axis=1)
- array([ 0., 0.25])
+ array([0., 0.25]) # may vary
"""
arr, mask = _replace_nan(a, 0)
@@ -1619,9 +1649,9 @@ def nanstd(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue):
>>> np.nanstd(a)
1.247219128924647
>>> np.nanstd(a, axis=0)
- array([ 1., 0.])
+ array([1., 0.])
>>> np.nanstd(a, axis=1)
- array([ 0., 0.5])
+ array([0., 0.5]) # may vary
"""
var = nanvar(a, axis=axis, dtype=dtype, out=out, ddof=ddof,
View Lorry Controller Status