In this tutorial, we will look at how to get the union of two numpy arrays with the help of some examples.
You can use the numpy
union1d() function to find the union of two arrays. Pass the two arrays as arguments to the function. The following is the syntax:
import numpy as np union_ar = np.union1d(ar1, ar2)
It returns the unique, sorted array of values that are in either of the two input arrays. Note that if the input arrays are not 1d then they are flattened before applying the union operation.
Let’s look at some examples of using the numpy union1d() function.
Union of two 1d numpy arrays
Pass the two arrays as arguments to the numpy union1d() function.
import numpy as np # create two 1d arrays ar1 = np.array([1, 3, 3, -2, 0, 0, 1]) ar2 = np.array([4, 1, 2, 7]) # union of ar1 and ar2 ar_union = np.union1d(ar1, ar2) # display the union array print(ar_union) # display the type of ar_union print(type(ar_union))
[-2 0 1 2 3 4 7] <class 'numpy.ndarray'>
Here, we created two 1d numpy arrays and then applied the union1d() function. Note that the returned array contains the unique values present in both the arrays in a sorted manner.
Union of two 2d numpy arrays
If the arrays are not 1d, they are flattened first, and then the union operation is applied. For example, if you apply the union1d() function on two 2d arrays:
- Harvard University Data Science: Learn R Basics for Data Science
- Standford University Data Science: Introduction to Machine Learning
- UC Davis Data Science: Learn SQL Basics for Data Science
- IBM Data Science: Professional Certificate in Data Science
- IBM Data Analysis: Professional Certificate in Data Analytics
- Google Data Analysis: Professional Certificate in Data Analytics
- IBM Data Science: Professional Certificate in Python Data Science
- IBM Data Engineering Fundamentals: Python Basics for Data Science
- Harvard University Learning Python for Data Science: Introduction to Data Science with Python
- Harvard University Computer Science Courses: Using Python for Research
- IBM Python Data Science: Visualizing Data with Python
- DeepLearning.AI Data Science and Machine Learning: Deep Learning Specialization
- UC San Diego Data Science: Python for Data Science
- UC San Diego Data Science: Probability and Statistics in Data Science using Python
- Google Data Analysis: Professional Certificate in Advanced Data Analytics
- MIT Statistics and Data Science: Machine Learning with Python - from Linear Models to Deep Learning
- MIT Statistics and Data Science: MicroMasters® Program in Statistics and Data Science
🔎 Find Data Science Programs 👨💻 111,889 already enrolled
Disclaimer: Data Science Parichay is reader supported. When you purchase a course through a link on this site, we may earn a small commission at no additional cost to you. Earned commissions help support this website and its team of writers.
# create two 2d arrays ar1 = np.array([[1, 3], [3, 2]]) ar2 = np.array([[2, 3], [0, 5]]) # union of ar1 and ar2 ar_union = np.union1d(ar1, ar2) # display the union array print(ar_union)
[0 1 2 3 5]
You can see that we get a 1d numpy array containing the unique values in both the arrays as output.
Union of more than two arrays in python
You can only get the union of two numpy arrays at a time with the np.union1d() function.
To get the union of more than two arrays, you can use the
np.union1d() function with the
reduce() function. See the example below:
from functools import reduce # create three 1d numpy arrays ar1 = np.array([1, 2, 2]) ar2 = np.array([2, 3]) ar3 = np.array([5, 1, 7]) # union of three arrays ar_union = reduce(np.union1d, (ar1, ar2, ar3)) # display the union array print(ar_union)
[1 2 3 5 7]
We get the unique values from the three arrays as a numpy array.
For more on the numpy union1d() function, refer to its documentation.
With this, we come to the end of this tutorial. The code examples and results presented in this tutorial have been implemented in a Jupyter Notebook with a python (version 3.8.3) kernel having numpy version 1.18.5
Subscribe to our newsletter for more informative guides and tutorials.
We do not spam and you can opt out any time.