NumPy | sum method
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sum(~) computes the sum of the values in the input array.
The input array for which you would like to compute the sum.
The axis along which to compute the sum. For 2D arrays, the allowed values are as follows:
Compute the sum column-wise
Compute the sum row-wise
Compute the sum of all values
The desired data type of the returned array.
dtype will also be the type used during the computation of the sum. By default, the
a is used.
Numpy array |
Instead of creating a new array, you can place the computed sum into the array specified by
The initial value used for the computation of the sum. By default,
A boolean mask, where values that are flagged as False will be ignored, while those flagged as True will be used in the computation.
Computing the sum of a 2D array
Consider the following 2D array:
a = np.array([[1,2],[3,4]])aarray([[1, 2],[3, 4]])
np.sum(a, axis=0)array([4, 6])
np.sum(a, axis=1)array([3, 7])
Specifying an output array
a = np.zeros(2)np.sum([[1,2],[3,4]], axis=1, out=a) # row-wise summationaarray([3., 7.])
Here, we've outputted the results to the array
Specifying an initial value
Here, since we set an initial value of 10, we have
Specifying a boolean mask
np.sum([4,5,6,7], where=[False, True, True, False])11
Here, only the second and third values were included in the computation of the sum.