In NumPy the sum function is utilized to compute the sum of elements in an array. It add all the elements, in the array and Provides the outcome. In this tutorial, we'll explore how to perform summation operations using NumPy.
We can use np.sum() function to calculate the sum of elements in the Numpy array.
Example:
import numpy as np
arr = np.array([1, 2, 3, 4, 5])
total_sum = np.sum(arr)
print("Sum of the array:", total_sum)
Result:
Sum of the array: 15
NumPy allows us to perform sum operation along specific axes of multi-dimensional arrays. When dealing with multi-dimensional arrays, we can specify the axis along which the sum should be calculated. Axis 0 corresponds to rows, and axis 1 corresponds to columns.
Example:
import numpy as np
matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
column_sum = np.sum(matrix, axis=0)
row_sum = np.sum(matrix, axis=1)
print("Sum along columns (axis 0):", column_sum)
print("Sum along rows (axis 1):", row_sum)
Result:
Sum along columns (axis 0): [12 15 18]
Sum along rows (axis 1): [ 6 15 24]
We can also perform sum operations along multiple axes by providing a tuple of axis values. It is very useful when working with higher-dimensional arrays.
Example:
import numpy as np
arr = np.array([[[1, 2], [3, 4]],[[5, 6], [7, 8]]])
sum_axes = np.sum(arr, axis=(0, 1))
print("Sum along axis 0 and axis 1:", sum_axes)
Result:
Sum along axis 0 and axis 1: [16 20]
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