How to Calculate minimum() of Array in NumPy?

Python NumPy minimum() or min() function is used to get the minimum value (lowest value) of a given array, or compare the two arrays element-wise and return the minimum values. While comparing, one of the elements of two arrays is a NaN, then that element is returned as NaN. If both elements of two arrays are NaNs then the first element is returned. Use the maximum() function to get the maximum values of the NumPy array.

In this article, I will explain how to get the minimum values of 1-D arrays in element-wise and multi-D arrays along with specified axes using syntax and parameters of numpy.minimum() function with examples.

1. Quick Examples of Calculate Minimum of Array

If you are in a hurry, below are some quick examples of how to use the NumPy minimum() function.

# Quick examples of calculate minimum of array

# Example :1 Get the minimum value 
# Of two scalars
arr = 34
arr1 = 65
min_value = np.minimum(arr, arr1) 

# Example :2 Get the minimum value
arr = np.array([14, 8, 12, 20, 17, 15])
min_value = np.minimum(arr)

# Example :3 Get the element-wise minimum 
arr = np.array([26,38,68,79])
arr1 = np.array([43,28,55,84])
min_value = np.minimum(arr,arr1) 

# Example :4 Use numpy.minimum() function & NaN
arr = np.array([np.nan, 8, 17, np.nan, 48])
arr1 = np.array([np.nan, 24, np.nan, np.nan, 35])
min_value = np.minimum(arr,arr1)

# Example :5 Get the minimum value of 2-D arrays 
arr = np.array([[26,38,68,79],[34,47,np.nan,20]])
arr1 = np.array([[43,28,55,84],[np.nan,32,43,np.nan]])   
max_value = np.minimum(arr,arr1) 

2. Syntax of minimum()

Following is the syntax of the numpy.minimum() function.

# Syntax of numpy.minimum()
numpy.minimum(arr1, arr2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, ufunc 'minimum')

2.1 Parameters of minimum()

Following are the parameters of minimum().

• arr,arr1– Input arrays to be compared element-wise. They must be broadcastable to the same shape, or one of them should be a scalar.
• out – ndarray, optional] A location in which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly allocated array is returned.
• where – This parameter is optional. It is a boolean array that indicates where the result should be calculated. If True, the calculation is performed; if False, the value is propagated. By default, it is set to True.
• order – This parameter is optional and specifies the memory layout of the output array. It can be C (row-major), F (column-major), or K (match input layout).
• dtype – Data type of the output array. If not specified, the data type is determined from the inputs.
• **kwargs – allows you to pass keyword variable length of argument to a function. It is used when we want to handle a named argument in a function.

2.2 Return Value

It returns the minimum of arr1 and arr2 arrays, element-wise. This returns a scalar if both arr1 and arr2 are scalars.

3. Usage of Numpy minimum()

NumPy minimum() function is used to get a new array that contains element-wise minimum values of two arrays. It compares two arrays and returns a new array containing the minimum values. If one of the elements being compared is NaN (Not a Number), the result for that pair is also NaN. Similarly, if both elements being compared are NaN, the result is NaN.

3.1 Get Minimum Value of Two Scalars

You can get the minimum value of two scalar variables arr and arr1. In this case, arr has a value of 34, and arr1 has a value of 65. For instance, minimum(arr, arr1) returns the minimum value between arr and arr1, and the result is stored in the variable min_value. The print() statement then displays the minimum value.

# Import numpy
import numpy as np

# Initialize variables
arr = 34
arr1 = 65
  
# Get minimum value of two scalars
min_value = np.minimum(arr, arr1) 
print ("Minimum value:", min_value)

Yields below output.

3.2 Get Minimum Values of 1-D Array

To get the minimum value of a 1-D array in Python, you can use the numpy.minimum() function in NumPy. For instance, np.minimum(arr) returns the minimum value of the 1-D array arr, and the result is stored in the variable min_value. The print() statement then displays the minimum value.

# Create a 1-D NumPy array
arr = np.array([14, 8, 12, 20, 17, 15])
print("Original array:", arr)

# Get the minimum value
min_value = np.minimum(arr)
print("Minimum value of 1-D array:", min_value)

# Output:
# Original array: [14  8 12 20 17 15]
# Minimum value of 1-D array: 8

4. Get a Minimum of Two NumPy Arrays

Alternatively, to get the element-wise minimum of two NumPy arrays, you can use the numpy.minimum() function.

In the below example, np.minimum(arr, arr1) returns a new array where each element is the minimum of the corresponding elements in arr and arr1. The resulting array is stored in the variable arr2, and the print() statement displays the original arrays and the element-wise minimum.

# Create 1-D arrays 
arr = np.array([26,38,68,79])
print("First array:\n",arr)
arr1 = np.array([43,28,55,84])
print("Second array:\n",arr1)

# Get the element-wise minimum 
min_value = np.minimum(arr,arr1) 
print("Element-wise Minimum:\n",min_value)

Yields below output.

5. Using NaN in NumPy Arrays

If there is a NaN in the given NumPy array then minimum() will return NaN as the minimum value. If both elements of two arrays are NaNs then it returns the first element.

You can use the numpy.minimum() function to perform an element-wise minimum comparison between two NumPy arrays containing NaN values. The numpy.minimum() function handles NaN values according to the specified rules.

# Create two NumPy arrays with NaN values
arr = np.array([np.nan, 8, 17, np.nan, 48])
arr1 = np.array([np.nan, 24, np.nan, np.nan, 35])

# Use numpy.minimum() function & NaN
min_value = np.minimum(arr,arr1)
print("After the Element-wise minimum of the arrays:\n",min_value)

# Output:
# After the Element-wise minimum of the arrays:
#  [nan  8. nan nan 35.]

In the result:

• The minimum of (NaN, NaN) is NaN.
• The minimum of (NaN, 8) is 8.
• The minimum of (17, NaN) is NaN.
• The minimum of (NaN, NaN) is NaN.
• The minimum of (48, 35) is 35.

So, the resulting array (min_value) contains the element-wise minimum values of the corresponding elements in arr and arr1, and NaN values are handled appropriately.

6. Get the Minimum Value of 2-D Arrays

You can also use the numpy.minimum() function to perform an element-wise minimum comparison between two 2-D NumPy arrays. The numpy.minimum() function handles NaN values according to the specified rules.

So, the resulting array (min_value) contains the element-wise minimum values of the corresponding elements in arr and arr1, and NaN values are handled appropriately.

# Create 2-D arrays 
arr = np.array([[26,38,68,79],[34,47,np.nan,20]])
arr1 = np.array([[43,28,55,84],[np.nan,32,43,np.nan]])

# Get minimum value of 2-D arrays    
max_value = np.minimum(arr,arr1) 
print("Minimum value of the entire array:\n", max_value)

# Output:
# Minimum value of the entire array:
#  [[26. 28. 55. 79.]
#  [nan 32. nan nan]]

Frequently Asked Questions

Conclusion

In this article, I have explained how to get the minimum values of Numpy arrays using minimum() or min() function with examples. While comparing, one of the elements of two arrays is a NaN, then that element is returned. If both elements of two arrays are NaNs then the first element is returned.

Happy Learning!!

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References

• https://numpy.org/doc/stable/reference/generated/numpy.amin.html