GCXS.var
- GCXS.var(axis=None, dtype=None, out=None, ddof=0, keepdims=False)
Compute the variance along the given axes. Uses all axes by default.
- Parameters:
axis (Union[int, Iterable[int]], optional) – The axes along which to compute the variance. Uses all axes by default.
dtype (numpy.dtype, optional) – The output datatype.
out (SparseArray, optional) – The array to write the output to.
ddof (int) – The degrees of freedom.
keepdims (bool, optional) – Whether or not to keep the dimensions of the original array.
- Returns:
The reduced output sparse array.
- Return type:
See also
numpy.ndarray.var
Equivalent numpy method.
Notes
This function internally calls
COO.sum_duplicates
to bring the array into canonical form.
Examples
You can use
COO.var
to compute the variance of an array across any dimension.>>> from sparse import COO >>> x = np.array([[1, 2, 0, 0], ... [0, 1, 0, 0]], dtype='i8') >>> s = COO.from_numpy(x) >>> s2 = s.var(axis=1) >>> s2.todense() array([0.6875, 0.1875])
You can also use the
keepdims
argument to keep the dimensions after the variance.>>> s3 = s.var(axis=0, keepdims=True) >>> s3.shape (1, 4)
You can pass in an output datatype, if needed.
>>> s4 = s.var(axis=0, dtype=np.float16) >>> s4.dtype dtype('float16')
By default, this reduces the array down to one number, computing the variance along all axes.
>>> s.var() 0.5