# Numpy – Set difference between two arrays

The set difference operation is used in sets to find elements in one set that are not present in the other set. For example, for two sets a and be, `a-b` results in a set with elements of set a that are not present in set b. In this tutorial, we will look at how to apply the set difference operation between two numpy arrays with the help of some examples.

The numpy `setdiff1d()` function is used to find the set difference of two arrays. The following is the syntax:

```import numpy as np
diff = np.setdiff1d(ar1, ar2, assume_unique=False)```

It returns a numpy array with the unique values in the first array that are not present in the second array. For numpy arrays of higher dimensions (2 or more), the arrays are first flattened, and then the set difference operation is applied.

Also, if the input arrays contain unique values you can pass `True` to the `assume_unique` parameter. This can speed up the calculation. Note that it is `False` by default.

Let’s look at some examples of using this function to find the set difference.

Pass the two arrays as arguments to the numpy sefdiff1d() function.

```import numpy as np

# create two 1d arrays
ar1 = np.array([3, 1, 1, 2, 0, 2])
ar2 = np.array([2, 3, 3, 4])
# set difference ar1 - ar2
ar1_2_diff = np.setdiff1d(ar1, ar2)
# display the array
print(ar1_2_diff)
# display the type
print(type(ar1_2_diff))```

Output:

```[0 1]
<class 'numpy.ndarray'>```

The returned array contains elements of ar1 that are not present in ar2.

Note that the set difference operation `a-b` is not the same as `b-a`. Thus, the order in which you pass the two arrays matters. For example, to get the elements of ar2 that are not in ar1, pass ar2 as the first array and ar1 as the second array.

```# create two 1d arrays
ar1 = np.array([3, 1, 1, 2, 0, 2])
ar2 = np.array([2, 3, 3, 4])
# set difference ar2 - ar1
ar2_1_diff = np.setdiff1d(ar2, ar1)
# display the array
print(ar2_1_diff)```

Output:

``

We get the elements of ar2 that are not in ar1.

If the input arrays are unique, you can pass `assume_unique=True` to speed up the calculation.

```# create two 1d arrays
ar1 = np.array([3, 1, 2, 0])
ar2 = np.array([2, 3, 4])
# set difference ar1 - ar2
ar1_2_diff = np.setdiff1d(ar1, ar2, assume_unique=True)
# display the array
print(ar1_2_diff)```

Output:

`[1 0]`

We get the same output as above. Note that with `assume_unique=True` the resulting array is not sorted.

If the input arrays are not 1d, they will be flattened before calculating the set difference. For example, if we apply the np.setdiff1d() function on two 2d arrays –

```# create two 2d arrays
ar1 = np.array([[1, 2, 3],
[4, 4, 1]])
ar2 = np.array([[3, 4, 3],
[5, 6, 7]])
# set difference ar1 - ar2
ar1_2_diff = np.setdiff1d(ar1, ar2)
# display the array
print(ar1_2_diff)```

Output:

`[1 2]`

We get a 1d numpy array with elements of ar1 that are not present in ar2.

For more on the numpy setdiff1d() 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

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