np.unique() for unique elements in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to find unique elements in the array.

NumPy

import numpy as np
arr = np.array([1, 2, 2, 3, 4, 4, 5])
unique_elements = np.[1](arr)
print(unique_elements)

Drag options to blanks, or click blank then click option'

Aunique

Barray

Csort

Dmean

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.sort() instead of np.unique()

Trying to use list methods instead of numpy functions

The np.unique() function returns the sorted unique elements of an array.

2fill in blank

medium

Complete the code to find unique elements and their counts.

NumPy

import numpy as np
arr = np.array([1, 2, 2, 3, 3, 3, 4])
unique_elements, counts = np.unique(arr, [1]=True)
print(unique_elements)
print(counts)

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Areturn_inverse

Breturn_index

Creturn_counts

Dreturn_sorted

Attempts:

3 left

💡 Hint

Common Mistakes

Using return_index instead of return_counts

Forgetting to set the argument to True

The return_counts=True argument makes np.unique() return the counts of each unique element.

3fill in blank

hard

Fix the error in the code to get unique elements from a 2D array.

NumPy

import numpy as np
arr = np.array([[1, 2], [2, 3]])
unique_elements = np.unique(arr, [1]=0)
print(unique_elements)

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Areturn_counts

Baxis

Csort

Dreturn_index

Attempts:

3 left

💡 Hint

Common Mistakes

Using return_counts instead of axis

Not specifying axis when working with 2D arrays

The axis parameter specifies the axis along which to find unique rows or columns in a 2D array.

4fill in blank

hard

Fill both blanks to create a dictionary of unique elements and their counts.

NumPy

import numpy as np
arr = np.array([5, 6, 5, 7, 6, 8])
unique, counts = np.unique(arr, [1]=True)
result = [2](zip(unique, counts))
print(result)

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Areturn_counts

Bdict

Clist

Dset

Attempts:

3 left

💡 Hint

Common Mistakes

Using list() instead of dict() for the second blank

Forgetting to set return_counts to True

Use return_counts=True to get counts and dict() to create a dictionary from zipped pairs.

5fill in blank

hard

Fill both blanks to filter unique elements greater than 3 and create a list.

NumPy

import numpy as np
arr = np.array([1, 4, 4, 5, 2, 6, 3])
unique = np.unique(arr)
filtered = [x for x in unique if x [1] 3]
result = [2](filtered)
print(result)

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A>

Blist

C<

Dset

Attempts:

3 left

💡 Hint

Common Mistakes

Using < instead of > for filtering

Using set() instead of list() for the result

Use > to filter values greater than 3 and list() to create a list from the filtered values.