NumPy sum: A step-by-step Guide with Examples

 

NumPy is a powerful Python library for numerical computations, and it provides a wide range of functions for various operations, including summing arrays. Let's go through some step-by-step examples of how to use the numpy.sum() function: 

 

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Summing a 1D Array:

import numpy as np

arr = np.array([1, 2, 3, 4, 5])
result = np.sum(arr)
print(result)

In this example, we import NumPy, create a 1D array, and use np.sum() to calculate the sum of all elements in the array. The result will be 15.

Summing a 2D Array (Matrix):

import numpy as np

matrix = np.array([[101, 102, 103], [104, 105, 106], [107, 108, 109]])
result = np.sum(matrix)
print(result)

945

Here, we create a 2D array (matrix) and calculate the sum of all elements. The result will be 45.

 

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Summing Along a Specific Axis:

import numpy as np

matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9])
# Sum along rows (axis 0)
row_sum = np.sum(matrix, axis=0)
# Sum along columns (axis 1)
col_sum = np.sum(matrix, axis=1)

print("Row Sum:")
print(row_sum)
print("Column Sum:")
print(col_sum)

Row Sum:
[12 15 18]
Column Sum:
[ 6 15 24]

 

In this example, we create a matrix and calculate the sum along different axes. axis=0 sums along rows, and axis=1 sums along columns.

Summing a Subset of Elements:

import numpy as np

arr = np.array([1, 2, 3, 4, 5])
subset_sum = np.sum(arr[1:4])  # Sum elements from index 1 to 3
print(subset_sum)

9

Here, we calculate the sum of a subset of elements in a 1D array.

Ignoring NaN Values:

import numpy as np
np.seterr(divide='ignore', invalid='ignore')

arr = np.array([1.0, 2.0, np.nan, 4.0, 5.0])
result = np.nansum(arr)
print(result)

You can use np.nansum() to calculate the sum of elements while ignoring NaN values.

 

 

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Cumulative Sum:

import numpy as np

arr = np.array([1, 2, 3, 4, 5])
cumulative_sum = np.cumsum(arr)
print(cumulative_sum)

[ 1  3  6 10 15]

To calculate the cumulative sum of an array, you can use np.cumsum().

These are some step-by-step examples of using the numpy.sum() function in various scenarios. NumPy is a versatile library, and you can adapt these examples to suit your specific needs.