NumPy Sum in Python

What is numpy sum()?

numpy.sum() is a built-in function in the NumPy library used to calculate the sum of array elements. It allows summing all elements or performing summation along specific axes in multi-dimensional arrays.

The function efficiently computes sums with optional parameters like axis selection, data type conversion, and initial value addition. This makes it highly flexible for various use cases.

Summation is a fundamental operation in numerical computations, data analysis, and machine learning. It helps in aggregating data, performing mathematical operations, and simplifying complex calculations.

Syntax of Python numpy.sum() Function

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numpy.sum(arr, axis=None, dtype=None, out=None, initial=0)

Explanation of Syntax Components:

• arr: The input NumPy array.
• axis(optional): Specifies the axis along which to sum. Default is None (sums all elements).
• dtype(optional): Defines the data type of the output sum.
• out(optional): Stores the result in a provided array.
• initial(optional): An initial value added to the sum.

Parameters of numpy.sum()

1. arr (Required): Input array containing numerical values.
2. axis (Optional): If None, sums all elements. If an integer, sums along that axis.
3. dtype (Optional): Data type of the result (e.g., int, float).
4. out (Optional): Specifies an output array to store the sum result.
5. initial (Optional): A starting value added before summing array elements.

Python numpy.sum() Examples

1. All Elements sum in an Array

Problem Statement:

Given a NumPy array, find the total sum of all its elements.

How to Solve:

• Import NumPy.
• Create an array.
• Use numpy.sum() without specifying an axis.
• Print the result.

Code:

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

Explanation:

• The array [1, 2, 3, 4, 5] is passed to np.sum().
• Since no axis is specified, it sums all elements: 1+2+3+4+5 = 15.
• The output is 15.

2. Array Elements Sum Along an Axis

Problem Statement:

Given a 2D NumPy array, compute row-wise and column-wise sums.

How to Solve:

• Import NumPy.
• Define a 2D array.
• Use np.sum() with axis=0 for column-wise sum.
• Use np.sum() with axis=1 for row-wise sum.
• Print the results.

Code:

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import numpy as np
arr = np.array([[1, 2, 3], [4, 5, 6]])
col_sum = np.sum(arr, axis=0)
row_sum = np.sum(arr, axis=1)
print("Column-wise Sum:", col_sum)
print("Row-wise Sum:", row_sum)

Explanation:

• The 2D array:

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[[1 2 3]
 [4 5 6]]

• axis=0 sums down each column: [1+4, 2+5, 3+6] = [5, 7, 9].
• axis=1 sums across each row: [1+2+3, 4+5+6] = [6, 15].
• Outputs: Column-wise Sum: [5 7 9], Row-wise Sum: [6 15].

3. Output Data Type Specifying with Sum

Problem Statement:

Given an integer array, compute the sum but return the result as a floating-point number.

How to Solve:

• Import NumPy.
• Define an integer array.
• Use np.sum() with dtype=float.
• Print the result.

Code:

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import numpy as np
arr = np.array([1, 2, 3, 4])
result = np.sum(arr, dtype=float)
print(result)

Explanation:

• The integer array [1, 2, 3, 4] normally returns an integer sum.
• Using dtype=float ensures the result is 10.0 instead of 10.
• Output: 10.0.

4. Initializing Value for the Sum

Problem Statement:

Given a NumPy array, compute its sum but start with an initial value.

How to Solve:

• Import NumPy.
• Define an array.
• Use np.sum() with initial=10.
• Print the result.

Code:

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import numpy as np
arr = np.array([1, 2, 3])
result = np.sum(arr, initial=10)
print(result)

Explanation:

• The array [1, 2, 3] sums to 6.
• The initial=10 adds 10 before summing: 10 + (1+2+3) = 16.
• Output: 16.

Key Summary of numpy sum()

• numpy.sum() is a function for calculating the sum of elements in arrays.
• It works with multi-dimensional arrays, allowing summation along specific axes.
• The dtype parameter ensures the output type matches the desired format.
• The initial parameter allows starting the sum from a predefined value.
• Summing along axis=0 provides column-wise totals, while axis=1 gives row-wise totals.
• Used in numerical analysis, machine learning, and statistical computations for efficient data aggregation.