Set Operations In Numpy Ufunc
In this article, we will explore Numpy Ufunc Set Operations and their applications. You can utilize NumPy set operations to perform different tasks on arrays and sets of arrays.
Set Operations – What are they?
Set operations are mathematical operations that involve sets, which are collections of distinct objects.
There are several set operations that are commonly used, including union, intersection, difference, and symmetric difference.
These operations are useful in various fields, such as computer science, mathematics, and data analysis.
NumPy Ufunc Set Operations
NumPy provides several ufuncs to perform set operations on arrays.
These ufuncs are implemented in C for faster performance and provide an easy-to-use interface for performing set operations on arrays. Let’s explore each of these ufuncs in detail.
According to set operations, NumPy’s unique() method can be applied to identify unique items.
As an example, construct a set array, but keep in mind that the array must only be 1-dimensional.
Modify the duplicate item array to the distinct array:
Example:
Apply the unique() method to the following palindrome array:
Example:
np.union1d()
The np.union1d() ufunc computes the union of two input arrays, which is the set of elements that are present in either of the input arrays.
The output of this ufunc is a sorted, one-dimensional array containing all the unique elements from both input arrays.Implement the union1d() method in set operations to identify the distinctive data in two arrays.
Combine the distinct values of two set arrays by utilizing the union1d() method:
Example:
By considering the following two set arrays, identify the union:
Example:
Find Intersection
The np.intersect1d() ufunc computes the intersection of two input arrays, which is the set of elements that are present in both input arrays.
The output of this ufunc is a sorted, one-dimensional array containing all the common elements from both input arrays.
You can invoke intersect1d() according to set operations to retrieve only the values included in both arrays.
Check the intersection of the below two set arrays and apply the optional argument assume_unique:
Example:
Implement the intersect1d() method in the following example:
Example:
Find Difference – setdiff1d()
The np.setdiff1d() ufunc computes the difference between two input arrays, which is the set of elements that are present in the first input array but not in the second input array.
The output of this ufunc is a sorted, one-dimensional array containing all the unique elements from the first input array that are not present in the second input array.
Apply the setdiff1d() method to retrieve only the values in the initial set that are not found in the second set.
Initialize the two sets array, then implement the setdiff1d() method to display the difference:
Example:
In the following example execute the setdiff1d() method with optional argument assume_unique:
Example:
Find Symmetric Difference – setxor1d()
The np.setxor1d() ufunc computes the symmetric difference between two input arrays, which is the set of elements that are present in either of the input arrays but not in both input arrays.
The output of this ufunc is a sorted, one-dimensional array containing all the unique elements that are present in either of the input arrays but not in both input arrays.
You can utilize the setxor1d() method to retrieve values that don’t appear in BOTH sets.
Calculate the symmetric difference between even_set1 and even_set2:
Example:
Invoke the setxor1d() method in the below example with assume_unique = True:
Example:
Example Explanation
In this example, we have two NumPy arrays named odd_set1 and odd_set2 that represent two sets of odd numbers. The odd_set1 contains the elements [1, 3, 5, 7, 9, 11] and the odd_set2 contains the elements [13, 9, 15, 3, 17, 19].
We then call the setxor1d() function on the two sets and store the result in the mrx_arr variable.
The setxor1d() function returns the exclusive values between the two sets, which means it returns the values that are present only in one of the sets and not in both. In this case, the function returns [1, 5, 7, 11, 13, 15, 17, 19], which are the values that are not present in both sets.
The assume_unique parameter is set to True, which indicates that the input arrays are already unique. If this parameter is not set to True, the function will first perform a unique sort on the input arrays, which can affect the performance of the function for large arrays.
