NumPy Union Explained – Python np.union1d() Function with Examples

NumPy – Union

In data analysis, we often need to combine two datasets and remove duplicates.

In NumPy, this is done using the function np.union1d().

It is widely used in:

• Data science
• Machine learning
• Database merging
• Data cleaning
• Set theory operations

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What is Union?

Union means:

Combining all unique elements from two arrays.

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Import NumPy

import numpy as np

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1. Basic Union of Two Arrays

import numpy as np

A = np.array([1, 2, 3, 4])
B = np.array([3, 4, 5, 6])

result = np.union1d(A, B)

print(result)

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Output:

[1 2 3 4 5 6]

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2. Union with Duplicate Values

import numpy as np

A = np.array([1, 1, 2, 3])
B = np.array([3, 4, 4, 5])

result = np.union1d(A, B)

print(result)

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Output:

[1 2 3 4 5]

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3. Union of 2D Arrays

NumPy flattens arrays before union operation.

import numpy as np

A = np.array([[1, 2],
              [3, 4]])

B = np.array([[4, 5],
              [6, 7]])

result = np.union1d(A, B)

print(result)

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Output:

[1 2 3 4 5 6 7]

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4. Real Dataset Example

import numpy as np

users_A = np.array([101, 102, 103])
users_B = np.array([103, 104, 105])

all_users = np.union1d(users_A, users_B)

print(all_users)

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Output:

[101 102 103 104 105]

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5. Union vs Concatenation

import numpy as np

A = np.array([1, 2, 3])
B = np.array([3, 4, 5])

print("Union:", np.union1d(A, B))
print("Concatenate:", np.concatenate((A, B)))

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Difference:

Operation	Behavior
Union	Removes duplicates
Concatenate	Keeps all values

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Set Operations in NumPy

NumPy provides full set operations:

• Union → np.union1d()
• Intersection → np.intersect1d()
• Difference → np.setdiff1d()
• Symmetric Difference → np.setxor1d()

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Real-World Applications

1. Data Science

• Merging datasets
• Combining feature sets

2. Machine Learning

• Feature union
• Data preprocessing

3. Databases

• Record merging
• Unique ID collection

4. Web Applications

• User data aggregation
• Search indexing

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Why Use NumPy Union?

Using NumPy provides:

• Fast set operations
• Automatic duplicate removal
• Efficient large-scale processing
• Clean and readable code

Combined with Python, it becomes powerful for data engineering and analysis.

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Summary

NumPy allows efficient union operations using:

np.union1d(A, B)

It automatically removes duplicates and returns sorted unique values.

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Conclusion

Union is a fundamental operation in data science for merging datasets. NumPy makes it fast, simple, and highly efficient for real-world applications.