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

Practice - 5 Tasks

1fill in blank

easy

Complete the code to find unique values in the array.

NumPy

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

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Aunique

Bsort

Carray

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 values and return their counts.

NumPy

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

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Areturn_index

Breturn_sorted

Creturn_inverse

Dreturn_counts

Attempts:

3 left

💡 Hint

Common Mistakes

Using return_index instead of return_counts

Not setting any argument and expecting counts

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

3fill in blank

hard

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

NumPy

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

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Aaxis

Breturn_index

Caxis=0

Dreturn_counts

Attempts:

3 left

💡 Hint

Common Mistakes

Using return_counts instead of axis

Passing axis as a keyword argument value instead of parameter name

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 values and their counts from a list.

NumPy

import numpy as np
words = ['apple', 'banana', 'apple', 'orange', 'banana', 'banana']
unique_words, counts = np.unique(words, [1]=True)
word_count = { [2]: counts[i] for i, [2] in enumerate(unique_words) }
print(word_count)

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Areturn_counts

Breturn_index

Cword

Dcount

Attempts:

3 left

💡 Hint

Common Mistakes

Using return_index instead of return_counts

Using count as dictionary key instead of word

Use return_counts=True to get counts, then create a dictionary with words as keys and counts as values.

5fill in blank

hard

Fill all three blanks to filter unique numbers greater than 3 and create a list of them.

NumPy

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

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A>

B3

Clist

Dset

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>'

Forgetting to convert filtered result to list

Filter unique numbers greater than 3 using > and 3, then convert the filtered result to a list.