Sum of natural numbers

Calculate the Σ symbol expression that represents the sum of natural numbers using NumPy.

What is NumPy?

NumPy is a Python library for numerical computation. It’s particularly strong in array operations (vectors and matrices) and allows for fast calculations. We can efficiently perform calculations like summing natural numbers.

import numpy as np

# Create an array of integers from 1 to 10
numbers = np.arange(1, 11)  # arange creates an array with integers spaced evenly within a specified range

# Calculate the sum of the elements in the array
total_sum = np.sum(numbers)

# Display the result
print("Sum of natural numbers from 1 to 10:", total_sum)

import numpy as np

# Create an array of integers from 1 to 10
numbers = np.arange(1, 11)  # arange creates an array with integers spaced evenly within a specified range

# Calculate the sum of the elements in the array
total_sum = np.sum(numbers)

# Display the result
print("Sum of natural numbers from 1 to 10:", total_sum)

Code Explanation

1. import numpy as np: Imports the NumPy library. We use the alias np to easily call NumPy functions.
2. numbers = np.arange(1, 11): Uses the np.arange() function to create an array containing integers from 1 to 10.
   • It’s specified in the format arange(start_value, end_value).
   • The end value is not included, so we specify 11 to create an array from 1 to 10.
3. total_sum = np.sum(numbers): Uses the np.sum() function to calculate the sum of all elements in the numbers array.
4. print("Sum of natural numbers from 1 to 10:", total_sum): Displays the calculated result on the screen.

Why isn’t the end value included?

There are several reasons for this design:

1. Half-Open Interval: In mathematical convention, intervals are often represented as “(start_value, end_value),” including the start value but excluding the end value. np.arange() follows this half-open interval concept.
2. Intuitive Usability: For example, if you want to generate numbers from 0 to 9, you can easily create the desired array by writing np.arange(0, 10). Not including the end value allows for this intuitive description.