ENH: Add new function nancov

doc/release/upcoming_changes/14784.new_function.rst

@@ -0,0 +1,7 @@
+new function numpy.nancov added to numpy
+---------------------------------------------------------------------
+``numpy.nancov`` will estimate a covariance matrix while ignoring nan-values.
+
+Using ``np.nancov(x, pairwise=True)`` calculates pairwise covariance where the covariance of 2 variables will include all observations where neither variable is nan.
+
+The default behavior is ``pairwise=False`` to ignore all observations where any variable is nan.

doc/source/reference/routines.statistics.rst

@@ -45,6 +45,7 @@ Correlating
    corrcoef
    correlate
    cov
+   nancov
 
 Histograms
 ----------

numpy/lib/nanfunctions.py

@@ -14,6 +14,7 @@
 - `nancumprod` -- cumulative product of non-NaN values
 - `nanmean` -- mean of non-NaN values
 - `nanvar` -- variance of non-NaN values
+- `nancov` -- covariance of non-NaN values
 - `nanstd` -- standard deviation of non-NaN values
 - `nanmedian` -- median of non-NaN values
 - `nanquantile` -- qth quantile of non-NaN values
@@ -35,7 +36,7 @@
 
 __all__ = [
     'nansum', 'nanmax', 'nanmin', 'nanargmax', 'nanargmin', 'nanmean',
-    'nanmedian', 'nanpercentile', 'nanvar', 'nanstd', 'nanprod',
+    'nanmedian', 'nanpercentile', 'nanvar', 'nancov', 'nanstd', 'nanprod',
     'nancumsum', 'nancumprod', 'nanquantile'
     ]
 
@@ -1562,6 +1563,137 @@ def nanvar(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue):
     return var
 
 
+def _nancov_dispatcher(m, y=None, pairwise=None, ddof=None):
+    return (m, y, pairwise)
+
+
+@array_function_dispatch(_nancov_dispatcher)
+def nancov(m, y=None, pairwise=False, ddof=1):
+    """
+    Estimate a covariance matrix, while ignoring NaNs.
+
+    Returns a covariance matrix using only slices with all non-nan values.
+    All slices with nan values will be ignored by default.
+
+    If `pairwise=True` is set, the covariance of variables x and y will be
+    computed using slices where x and y are non-nan values. Only slices
+    where x and y are nan will be ignored. The returned covariance matrix
+    my not be a positive definite matrix.
+
+    For all-NaN slices or slices with zero degrees of freedom, NaN is
+    returned.
+
+    Parameters
+    ----------
+    m : array_like
+        A 1-D or 2-D array containing multiple variables and observations.
+        Each row of `m` represents a variable, and each column a single
+        observation of all those variables.
+    y : array_like, optional
+        An additional set of variables and observations. `y` has the same form
+        as that of `m`.
+    pairwise : bool, option
+        By default (False), any observation(column) with nan values are
+        ignored. If `pairwise` is True, then the covariance of x and y will
+        be computed pairwise, using rows where both x and y are non-nan values
+        and ignoring only rows where either x or y is a nan value.
+    ddof : int, optional
+        "Delta Degrees of Freedom": the divisor used in the calculation is
+        ``N - ddof``, where ``N`` represents the number of
+        observations(columns) with no nan-values. If `pairwise` is set to True,
+        ``N`` is computed separately for each pair of variables as the number
+        of observations where both x and y are non-nan values.
+        By default ddof is 1.
+
+
+    Returns
+    -------
+    out : ndarray
+        The covariance matrix of the variables
+
+    See Also
+    --------
+    cov : Estimated covariance matrix
+
+    Notes
+    -----
+    In the regular covariance matrix function, `cov`, if the i'th variable
+    has a nan value in any observation, all entries :math:`C_{i,j}` and
+    :math:`C_{j,i}` for all variables `j` will have a nan value in the
+    returned matrix.
+
+
+    With `nancov`, if `pairwise` is False, then the covariance matrix will
+    be calculated after removing all columns(observations) with nan values.
+
+    If `pairwise` is True, then pairwise covariances will be calculated where
+    the covariance of `x` and `y` will include all columns where `x` and `y`
+    are not nan values.
+
+    In either case, the normalization will be equal to the number of columns
+    used in calculating the covariance of two variables minus `ddof`.
+
+
+    Examples
+    --------
+
+    >>> x = np.array([
+            [1,      2, 3],
+            [2, np.nan, 6],
+            [3,    1.5, 1]])
+
+    Without pairwise covariances::
+    Notice how all covariances are calculated without the center column.
+
+    >>> np.nancov(x)
+    >>> array([
+            [ 2,  4, -2],
+            [ 4,  8, -4],
+            [-2, -4,  2]])
+
+    With pairwise covariances::
+    Covariance between variables :math:`x_0` and :math:`x_2` are calculated
+    with all 3 columns since they do not have any nan values, but all
+    covariances involving :math:`x_1` exclude the 2nd column with a nan value.
+
+    >>> np.nancov(x, pairwise=True)
+    >>> array([
+            [ 1,  4,         -1],
+            [ 4,  8,         -4],
+            [-1, -4, 1.08333333]])
+
+    >>> np.nanvar(a)
+    1.5555555555555554
+    >>> np.nanvar(a, axis=0)
+    array([1.,  0.])
+    >>> np.nanvar(a, axis=1)
+    array([0.,  0.25])  # may vary
+
+    """
+
+    X = np.array(m, ndmin=2)
+    if y is not None:
+        y = np.array(y, ndmin=2)
+        X = np.concatenate((X, y), axis=0)
+
+    if not pairwise:
+        print(X)
+        nanmask = np.any(np.isnan(X.T), axis=1)
+        X = np.delete(X, np.argwhere(nanmask), axis=1)
+        print(nanmask)
+        print(X)
+        return (np.cov(X, ddof=ddof))
+    else:
+        rows = X.shape[0]
+        out = np.empty((rows, rows))
+        for i in range(rows):
+            out[i][i] = np.nanvar(X[i], ddof=ddof)
+            for j in range(i+1, rows):
+                out[i][j] = np.nancov(X[i], X[j], ddof=ddof)[0][1]
+                out[j][i] = out[i][j]
+        return out
+
+
 def _nanstd_dispatcher(
         a, axis=None, dtype=None, out=None, ddof=None, keepdims=None):
     return (a, out)

numpy/lib/tests/test_nanfunctions.py

@@ -7,7 +7,7 @@
 from numpy.lib.nanfunctions import _nan_mask
 from numpy.testing import (
     assert_, assert_equal, assert_almost_equal, assert_no_warnings,
-    assert_raises, assert_array_equal, suppress_warnings
+    assert_raises, assert_array_equal, assert_allclose,  suppress_warnings
     )
 
 
@@ -298,6 +298,11 @@ def test_nanvar(self):
         for mat in self.integer_arrays():
             assert_equal(np.nanvar(mat, ddof=1), tgt)
 
+    def test_nancov(self):
+        tgt = np.cov(self.mat)
+        for mat in self.integer_arrays():
+            assert_equal(np.nancov(mat), tgt)
+
     def test_nanstd(self):
         tgt = np.std(self.mat)
         for mat in self.integer_arrays():
@@ -447,6 +452,63 @@ def test_empty(self):
             assert_equal(res, tgt)
 
 
+class TestNanFunctions_NanCov(object):
+    x1 = np.array([
+            [0, 1, 4],
+            [1, 1, 3],
+            [2, 1, 2],
+            [3, 1, 1],
+            [4, 1, 0]])
+    res1 = np.array([
+            [2.5,  0., -2.5],
+            [0.,   0.,   0.],
+            [-2.5, 0.,  2.5]])
+
+    x2 = np.array([
+            [1, np.nan, 2],
+            [np.nan, 3, 1],
+            [5, 2, np.nan],
+            [9, 4, 2],
+            [3, 8, 9]])
+    res2_default = np.array([
+            [18,  -12,   -21],
+            [-12,   8,    14],
+            [-21,  14,  24.5]])
+    res2_pairwise = np.array([
+            [11.6666666, -4.6666666, -4.6666666],
+            [-4.6666666,  6.9166666,       11.5],
+            [-4.6666666,       11.5, 13.6666666]])
+
+    x3 = np.array([0, 1, 2, 3, 4])
+    y3 = np.array([4, 3, np.nan, 1, 0])
+    res3_default = np.array([
+            [3.3333333,  -3.3333333],
+            [-3.3333333,  3.3333333]])
+    res3_pairwise = np.array([
+            [2.5,       -3.3333333],
+            [-3.3333333, 3.3333333]])
+
+    def test_default_no_nans(self):
+        assert_allclose(np.nancov(self.x1.T), self.res1)
+
+    def test_pairwise_no_nans(self):
+        assert_allclose(np.nancov(self.x1.T, pairwise=True), self.res1)
+
+    def test_default_with_nans(self):
+        assert_allclose(np.nancov(self.x2.T), self.res2_default)
+
+    def test_pairwise_with_nans(self):
+        assert_allclose(np.nancov(self.x2.T, pairwise=True),
+                        self.res2_pairwise)
+
+    def test_default_xy(self):
+        assert_allclose(np.nancov(self.x3.T, self.y3), self.res3_default)
+
+    def test_pairwise_xy(self):
+        assert_allclose(np.nancov(self.x3.T, self.y3, pairwise=True),
+                        self.res3_pairwise)
+
+
 class TestNanFunctions_CumSumProd(SharedNanFunctionsTestsMixin):
 
     nanfuncs = [np.nancumsum, np.nancumprod]