COO.mean¶
-
COO.
mean
(axis=None, keepdims=False, dtype=None, out=None)[source]¶ Compute the mean along the given axes. Uses all axes by default.
Parameters: - axis (Union[int, Iterable[int]], optional) – The axes along which to compute the mean. Uses all axes by default.
- keepdims (bool, optional) – Whether or not to keep the dimensions of the original array.
- dtype (numpy.dtype) – The data type of the output array.
Returns: The reduced output sparse array.
Return type: See also
numpy.ndarray.mean()
- Equivalent numpy method.
scipy.sparse.coo_matrix.mean()
- Equivalent Scipy method.
Notes
- This function internally calls
COO.sum_duplicates
to bring the array into canonical form. - The
out
parameter is provided just for compatibility with Numpy and isn’t actually supported.
Examples
You can use
COO.mean
to compute the mean of an array across any dimension.>>> x = np.array([[1, 2, 0, 0], ... [0, 1, 0, 0]], dtype='i8') >>> s = COO.from_numpy(x) >>> s2 = s.mean(axis=1) >>> s2.todense() # doctest: +SKIP array([0.5, 1.5, 0., 0.])
You can also use the
keepdims
argument to keep the dimensions after the mean.>>> s3 = s.mean(axis=0, keepdims=True) >>> s3.shape (1, 4)
You can pass in an output datatype, if needed.
>>> s4 = s.mean(axis=0, dtype=np.float16) >>> s4.dtype dtype('float16')
By default, this reduces the array down to one number, computing the mean along all axes.
>>> s.mean() 0.5