np.unique() for unique values in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the function np.unique() do in NumPy?

np.unique() finds all the unique values in a NumPy array and returns them sorted.

intermediate

How can you get the indices of the unique values in the original array using np.unique()?

Use the argument return_index=True. It returns the indices of the first occurrences of the unique values.

intermediate

What does the return_counts=True option do in np.unique()?

It returns how many times each unique value appears in the original array.

intermediate

If you want to find unique rows in a 2D NumPy array, can np.unique() help?

Yes, by using axis=0, np.unique() returns unique rows.

beginner

What is the order of the unique values returned by np.unique()?

The unique values are always returned sorted in ascending order.

What does np.unique() return by default?

A Original array with duplicates removed but order kept

B Indices of duplicates

C Sorted unique values from the array

D Sum of unique values

By default, np.unique() returns the sorted unique values from the input array.

How do you get the count of each unique value using np.unique()?

A Set <code>return_counts=True</code>

B Set <code>return_index=True</code>

C Use <code>np.count()</code>

D Use <code>np.sum()</code>

The return_counts=True option returns the count of each unique value.

Which argument helps find unique rows in a 2D array?

A <code>sorted=False</code>

B <code>axis=1</code>

C <code>return_index=True</code>

D <code>axis=0</code>

Using axis=0 tells np.unique() to find unique rows.

What type of object does np.unique() return?

A A Python list of unique values

B A sorted NumPy array of unique values

C A dictionary with counts

D A scalar value

np.unique() returns a sorted NumPy array of unique values.

If you want the indices of unique values in the original array, which parameter do you use?

A <code>return_index=True</code>

B <code>return_inverse=True</code>

C <code>return_counts=True</code>

D <code>axis=1</code>

return_index=True returns the indices of the first occurrences of unique values.

Explain how to use np.unique() to find unique values and their counts in a NumPy array.

Think about the arguments that give extra information besides unique values.

Describe how to find unique rows in a 2D NumPy array using np.unique().

Consider the axis argument for row-wise uniqueness.

Practice

1. What does the np.unique() function do in NumPy?

easy

A. Calculates the sum of all elements

B. Sorts the array in descending order

C. Reverses the order of elements

D. Finds all unique values in an array

Solution

1. Step 1: Understand the purpose of np.unique()

   This function is designed to find all different (unique) values in a NumPy array or list.

2. Step 2: Compare with other options

   Sorting, summing, or reversing are different operations and not what np.unique() does.

3. Final Answer:

   Finds all unique values in an array -> Option D

4. Quick Check:

   np.unique() = unique values [OK]

Hint: Remember: unique means different values only [OK]

Common Mistakes:

• Confusing unique with sorting
• Thinking it sums values
• Assuming it reverses array

2. Which of the following is the correct syntax to get unique values from a NumPy array arr?

easy

A. np.arr.unique()

B. np.unique(arr)

C. unique(arr)

D. arr.unique()

Solution

1. Step 1: Recall the correct function call

   The function unique is part of the NumPy module and is called as np.unique().

2. Step 2: Check other options for errors

   arr.unique() is not a NumPy array method, unique(arr) misses the module prefix, and np.arr.unique() is invalid syntax.

3. Final Answer:

   np.unique(arr) -> Option B

4. Quick Check:

   Correct syntax = np.unique(arr) [OK]

Hint: Use np.unique(array) always [OK]

Common Mistakes:

• Calling unique as method on array
• Missing np prefix
• Wrong module usage

3. What is the output of this code?

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

medium

A. [1 2 3 4]

B. [3 1 2 4]

C. [4 3 2 1]

D. [1 1 2 2 3 3 4]

Solution

1. Step 1: Identify unique values in the array

   The array contains values 3, 1, 2, 3, 2, 1, 4. Unique values are 1, 2, 3, and 4.

2. Step 2: Understand np.unique() output order

   np.unique() returns sorted unique values, so output is [1 2 3 4].

3. Final Answer:

   [1 2 3 4] -> Option A

4. Quick Check:

   Unique sorted values = [1 2 3 4] [OK]

Hint: Unique values are sorted by default [OK]

Common Mistakes:

• Ignoring sorting order
• Listing duplicates
• Wrong output format

4. The following code throws an error. What is the problem?

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

medium

A. Incorrect argument name, should be count_return=True

B. List input causes error, must convert to np.array first

C. No error, code runs fine

D. np.unique() does not support return_counts argument

Solution

1. Step 1: Check input type compatibility

   np.unique() accepts lists or arrays as input without error.

2. Step 2: Verify return_counts argument

   The argument return_counts=True is valid and returns counts of unique values.

3. Final Answer:

   No error, code runs fine -> Option C

4. Quick Check:

   List input + return_counts works [OK]

Hint: np.unique accepts lists and arrays [OK]

Common Mistakes:

• Thinking list input causes error
• Wrong argument name
• Assuming return_counts unsupported

5. You have a 2D NumPy array:

arr = np.array([[1, 2, 2], [3, 1, 4]])

How do you get all unique values from this 2D array as a sorted 1D array?

hard

A. np.unique(arr)

B. np.unique(arr, axis=1)

C. np.unique(arr, axis=0)

D. arr.unique()

Solution

1. Step 1: Understand np.unique() on 2D arrays

   Calling np.unique() without axis flattens the array and returns unique sorted values.

2. Step 2: Check axis arguments

   Using axis=0 or axis=1 returns unique rows or columns, not unique elements overall.

3. Final Answer:

   np.unique(arr) -> Option A

4. Quick Check:

   Flatten and unique = np.unique(arr) [OK]

Hint: No axis means flatten and find unique [OK]

Common Mistakes:

• Using axis to get unique elements
• Calling unique as method
• Expecting 2D unique output