Python NumPy minimum Function

The minimum function in Python's NumPy library is used to compute the element-wise minimum of two arrays. This function is essential in various fields such as data analysis, scientific computing, engineering, and machine learning where comparisons between arrays are required.

Table of Contents

1. Introduction
2. Importing the numpy Module
3. minimum Function Syntax
4. Understanding minimum
5. Examples
   • Basic Usage
   • Applying minimum to Arrays
   • Broadcasting in Minimum Calculation
6. Real-World Use Case
7. Conclusion
8. Reference

Introduction

The minimum function in Python's NumPy library allows you to compute the element-wise minimum of two arrays. This function is particularly useful in numerical computations where finding the minimum values between corresponding elements of arrays is necessary.

Importing the numpy Module

Before using the minimum function, you need to import the numpy module, which provides the array object.

import numpy as np

minimum Function Syntax

The syntax for the minimum function is as follows:

np.minimum(x1, x2, out=None, where=True, casting='same_kind', order='K', dtype=None, subok=True)

Parameters:

• x1: The first input array.
• x2: The second input array. Must be broadcastable to the shape of x1.
• out: Optional. A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to.
• where: Optional. This condition is broadcast over the input. At locations where the condition is True, the out array will be set to the ufunc result. Otherwise, it will retain its original value.
• casting: Optional. Controls what kind of data casting may occur. Defaults to 'same_kind'.
• order: Optional. Controls the memory layout order of the result. Defaults to 'K'.
• dtype: Optional. Overrides the data type of the output array.
• subok: Optional. If True, then sub-classes will be passed through, otherwise the returned array will be forced to be a base-class array.

Returns:

• An array with the element-wise minimum of x1 and x2.

Understanding minimum

The minimum function computes the element-wise minimum of each element in the input array x1 with the corresponding element in the input array x2. If the shapes of the input arrays are not the same, they must be broadcastable to a common shape (according to the broadcasting rules).

Examples

Basic Usage

To demonstrate the basic usage of minimum, we will compute the element-wise minimum of two single values.

Example

import numpy as np

# Values
x1 = 5
x2 = 3

# Computing the element-wise minimum
result = np.minimum(x1, x2)
print(result)

Output:

3

Applying minimum to Arrays

This example demonstrates how to apply the minimum function to arrays of values.

Example

import numpy as np

# Arrays of values
x1 = np.array([1, 4, 3])
x2 = np.array([2, 2, 5])

# Computing the element-wise minimum
result = np.minimum(x1, x2)
print(result)

Output:

[1 2 3]

Broadcasting in Minimum Calculation

This example demonstrates how broadcasting works in the minimum function when comparing arrays of different shapes.

Example

import numpy as np

# Arrays of values
x1 = np.array([[1, 4, 3], [2, 5, 1]])
x2 = np.array([3, 2, 1])

# Computing the element-wise minimum with broadcasting
result = np.minimum(x1, x2)
print(result)

Output:

[[1 2 1]
 [2 2 1]]

Real-World Use Case

Data Analysis: Finding Minimum Values

In data analysis, the minimum function can be used to find the minimum values between corresponding elements of two datasets.

Example

import numpy as np

# Example datasets
data1 = np.array([10, 20, 30, 40, 50])
data2 = np.array([15, 18, 35, 25, 55])

# Finding the element-wise minimum values
min_values = np.minimum(data1, data2)
print(f"Minimum Values: {min_values}")

Output:

Minimum Values: [10 18 30 25 50]

Conclusion

The minimum function in Python's NumPy library is used for computing the element-wise minimum of arrays. This function is useful in various numerical and data processing applications, particularly those involving comparisons between arrays. Proper usage of this function can enhance the accuracy and efficiency of your computations.

Reference

Python NumPy minimum Function