How to use the NumPy sum function?

Introduction to NumPy's sum() Function

NumPy's sum() function is a powerful tool for performing summation operations on arrays. It allows for quick and efficient computation of the sum of array elements, supporting multi-dimensional arrays and offering various options to customize the summation process.

Basic Usage

To compute the sum of all elements in a one-dimensional array, simply pass the array to the sum() function:

import numpy as np
arr = np.array([1, 2, 3, 4])
total = np.sum(arr)
print(total)  # Output: 10

Summing Along Specific Axes

For multi-dimensional arrays, you can specify the axis along which to perform the summation. For example, to sum along rows (axis=0) or columns (axis=1) in a 2D array:

arr = np.array([[1, 2], [3, 4]])
sum_rows = np.sum(arr, axis=0)  # Output: [4 6]
sum_columns = np.sum(arr, axis=1)  # Output: [3 7]

Using the initial Parameter

The initial parameter allows you to specify a starting value for the summation:

arr = np.array([1, 2, 3])
total = np.sum(arr, initial=10)
print(total)  # Output: 16

Handling NaN Values with np.nansum()

When working with arrays that contain NaN (Not a Number) values, np.sum() will return NaN if any element is NaN. To ignore NaN values during summation, use np.nansum():

arr = np.array([1, 2, np.nan, 4])
total = np.nansum(arr)
print(total)  # Output: 7.0

Performance Considerations

Using np.sum() is generally more efficient than using Python's built-in sum() function, especially for large arrays. NumPy's implementation is optimized for performance, making it suitable for numerical computations on large datasets.

Combining np.sum() with numpy.moveaxis()

When working with multi-dimensional arrays, you might need to rearrange the axes before performing summation. The numpy.moveaxis() function allows you to move axes of an array to new positions. For instance, to move the first axis to the last position:

arr = np.zeros((2, 3, 4))
arr_moved = np.moveaxis(arr, 0, -1)
print(arr_moved.shape)  # Output: (3, 4, 2)

After rearranging the axes, you can perform summation along the desired axis:

total = np.sum(arr_moved, axis=0)
print(total.shape)  # Output: (4, 2)

Conclusion

The numpy.sum() function is a versatile tool for performing summation operations on arrays. By understanding its parameters and combining it with other functions like numpy.moveaxis(), you can efficiently compute sums across different dimensions of your data.