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When working with Numpy arrays, we will sometimes feel the need to compare them and check if they are equal, i.e., if they are equal when comparisons are done element-wise. The numpy.equal() function takes two numpy arrays as input and does precisely this - after doing an element-wise comparison returning an output boolean array of results.
Here is a simple program to explore this:
In this program, we create two arrays with elements [1,2] and [1,3] respectively and then use numpy.equal() on these arrays. The output is:
revealing that the (respective) first elements are equal but the second elements are not.
If we try this:
we will get this error:
meaning that the two arrays are not broadcastable to a common shape (which becomes the shape of the output).
Here we will use numpy.arange() to first generate values in a certain interval and then numpy.reshape() to organize them into a two dimensional array:
The first array will have values from 0 to 8 and the second will have values from 1 to 9. As a result even though they are of the same size (4 rows and 2 columns), they will not have element-wise the same values, so np.equal() will return all False’s. The output is thus:
In this example, we will create two arrays with the same values but reshape them into different sizes. As a result numpy.equall() will fail.
The output is:
We can use the == operator to do the same operations we have been doing thus far.
Thus, the following program:
returns:
To summarize, checking for equality between two numpy arrays is really easy, either with numpy.equal() or the == operator. The array sizes have to match up exactly, in order to allow an element-wise comparison. An aggregate True or False is not returned; instead an element-wise True or False is returned (which we can then aggregate if needed).
For more understanding of numpy arrays, checkout our blogpost on creating numpy arrays from lists.
For more Python content, checkout the math.ceil() and math.floor() functions! Also
learn about the math domain error in Python and how to fix it!
Interested in more things Python? Checkout our post on Python queues. Also see our blogpost on Python's enumerate() capability. Also if you like Python+math content, see our blogpost on Magic Squares. Finally, master the Python print function!
Want to learn Python with us? Sign up for 1:1 or small group classes.
import numpy as np
array1 = np.array([1,2])
array2 = np.array([1,3])
print(np.equal(array1,array2))[ True False]import numpy as np
array1 = np.array([1,2,3])
array2 = np.array([1,3])
print(np.equal(array1,array2))Traceback (most recent call last):
File "main.py", line 5, in <module>
print(np.equal(array1,array2))
ValueError: operands could not be broadcast together with shapes (3,) (2,)import numpy as np
array1 = np.arange(8).reshape(4,2)
array2 = np.arange(1,9).reshape(4,2)
print(array1)
print(array2)
print(np.equal(array1,array2))[[0 1]
[2 3]
[4 5]
[6 7]]
[[1 2]
[3 4]
[5 6]
[7 8]]
[[False False]
[False False]
[False False]
[False False]]import numpy as np
array1 = np.arange(8).reshape(4,2)
array2 = np.arange(8).reshape(2,4)
print(array1)
print(array2)
print(np.equal(array1,array2))[[0 1]
[2 3]
[4 5]
[6 7]]
[[0 1 2 3]
[4 5 6 7]]
Traceback (most recent call last):
File "main.py", line 7, in <module>
print(np.equal(array1,array2))
ValueError: operands could not be broadcast together with shapes (4,2) (2,4)import numpy as np
array1 = np.arange(8).reshape(4,2)
array2 = np.arange(8).reshape(4,2)
print(array1 == array2)[[ True True]
[ True True]
[ True True]
[ True True]]