7 examples of 'sum of all elements in array python' in Python

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def test_sum(self):
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    self.create_node('Sum', {'input[0]': 5.0, 'input[1]': -3.0, 'input[2]': 2.0}, 4.0)

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def _prod(array):
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    prod = 1
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    for value in array:
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        prod *= value
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    return prod

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def prod(array):
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    """Element-wise product of a numpy array across all the slave processes.
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    The result is only available in the main_slave (rank 1)."""
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    _reduce(array, "PROD")

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def countArrayElements(array):
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    '''
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        Simple method to count the repetitions of elements in an array
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		@param array: An array of elements
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		@return: A tuple (elements,counters), where elements is a list with the distinct elements and counters is the list with the number of times they appear in the array
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	'''
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    elements = []
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    counters = []
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    for element in array:
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        if element in elements:
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            indx = elements.index(element)
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            counters[indx] += 1
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        else:
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            elements.append(element)
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            counters.append(1)
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    return elements,counters

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def sum(self, axis=None, dtype=None, out=None):
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    """
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    Returns the sum of the matrix elements, along the given axis.
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    Refer to `numpy.sum` for full documentation.
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    See Also
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    --------
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    numpy.sum
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    Notes
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    -----
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    This is the same as `ndarray.sum`, except that where an `ndarray` would
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    be returned, a `matrix` object is returned instead.
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    Examples
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    --------
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    >>> x = np.matrix([[1, 2], [4, 3]])
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    >>> x.sum()
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    10
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    >>> x.sum(axis=1)
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    matrix([[3],
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            [7]])
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    >>> x.sum(axis=1, dtype='float')
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    matrix([[ 3.],
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            [ 7.]])
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    >>> out = np.zeros((1, 2), dtype='float')
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    >>> x.sum(axis=1, dtype='float', out=out)
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    matrix([[ 3.],
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            [ 7.]])
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    """
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    return N.ndarray.sum(self, axis, dtype, out)._align(axis)

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def sum_sum(values: TypeValList) -> float:
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    """Calculate the sum of ``values``."""
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    if len(values) == 0:
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        return np.nan
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    return sum(values)

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@jit(native=True, xsimd=True)
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def row_sum(arr, columns):
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    return arr.T[columns].sum(0)