np.union1d() for union in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does np.union1d() do in NumPy?

np.union1d() returns the sorted unique values that are in either of two input arrays. It combines both arrays without duplicates.

beginner

How does np.union1d() handle duplicate values within the input arrays?

It removes duplicates and returns only unique values from both arrays combined.

beginner

What is the output type of np.union1d()?

The output is a NumPy array containing sorted unique elements from both input arrays.

beginner

Write a simple example of np.union1d() with arrays [1, 2, 3] and [3, 4, 5].

<pre>import numpy as np
arr1 = np.array([1, 2, 3])
arr2 = np.array([3, 4, 5])
result = np.union1d(arr1, arr2)
print(result)  # Output: [1 2 3 4 5]</pre>

beginner

Why is np.union1d() useful in data science?

It helps combine datasets or lists by merging unique values, which is useful for data cleaning and analysis.

What does np.union1d() return when given two arrays?

A Only values common to both arrays

B The first array unchanged

C All values including duplicates from both arrays

D Sorted unique values from both arrays combined

np.union1d() returns sorted unique values from both arrays combined, like a set union.

If arr1 = [1, 2, 2] and arr2 = [2, 3], what is np.union1d(arr1, arr2)?

A [1, 2, 2, 3]

B [2, 3]

C [1, 2, 3]

D [1, 3]

Duplicates are removed, so the union is [1, 2, 3].

Which of these is NOT true about np.union1d()?

A It modifies the original arrays

B It removes duplicates

C It combines values from both arrays

D It sorts the output array

np.union1d() does not change the original arrays; it returns a new array.

What data type does np.union1d() return?

A Tuple

B NumPy array

C List

D Set

The function returns a NumPy array with unique sorted elements.

Which NumPy function is best to combine two arrays and keep only unique sorted values?

A np.union1d()

B np.intersect1d()

C np.concatenate()

D np.append()

np.union1d() combines arrays and returns unique sorted values.

Explain how np.union1d() works and give a simple example.

Think about how sets work in math.

Why might you use np.union1d() in data science projects?

Consider cleaning and combining data.

Practice

1. What does the function np.union1d() do when applied to two arrays?

easy

A. Finds the common elements between two arrays

B. Combines two arrays and returns unique sorted elements

C. Concatenates two arrays without removing duplicates

D. Sorts a single array in descending order

Solution

1. Step 1: Understand the purpose of np.union1d()

   This function merges two arrays and removes any duplicate values.

2. Step 2: Check the output characteristics

   The result is sorted and contains only unique elements from both arrays.

3. Final Answer:

   Combines two arrays and returns unique sorted elements -> Option B

4. Quick Check:

   Union = unique sorted merge [OK]

Hint: Think of union as merging without duplicates [OK]

Common Mistakes:

• Confusing union with intersection
• Assuming duplicates remain
• Thinking it sorts in descending order

2. Which of the following is the correct syntax to find the union of arrays a and b using numpy?

easy

A. np.union1d[a, b]

B. np.union(a, b)

C. np.union_1d(a, b)

D. np.union1d(a, b)

Solution

1. Step 1: Recall the correct function name

   The correct numpy function is np.union1d() with parentheses.

2. Step 2: Check syntax details

   Arguments are passed inside parentheses, not square brackets, and spelling must be exact.

3. Final Answer:

   np.union1d(a, b) -> Option D

4. Quick Check:

   Correct function call syntax [OK]

Hint: Use parentheses and exact function name np.union1d() [OK]

Common Mistakes:

• Using square brackets instead of parentheses
• Misspelling the function name
• Using a non-existent function np.union

3. What is the output of the following code?

import numpy as np
x = np.array([1, 3, 5])
y = np.array([3, 4, 5, 6])
result = np.union1d(x, y)
print(result)

medium

A. [1 3 4 5 6]

B. [1 3 5]

C. [3 4 5 6]

D. [1 3 5 3 4 5 6]

Solution

1. Step 1: Identify unique elements from both arrays

   Array x has [1, 3, 5], array y has [3, 4, 5, 6]. The union combines all unique values.

2. Step 2: Sort and remove duplicates

   Unique elements combined are [1, 3, 4, 5, 6], sorted in ascending order.

3. Final Answer:

   [1 3 4 5 6] -> Option A

4. Quick Check:

   Union = unique sorted merge [OK]

Hint: Union merges unique sorted elements from both arrays [OK]

Common Mistakes:

• Forgetting to remove duplicates
• Not sorting the result
• Printing only one array

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

import numpy as np
arr1 = [1, 2, 3]
arr2 = [3, 4, 5]
result = np.union1d(arr1 arr2)
print(result)

medium

A. Missing comma between arr1 and arr2 in function call

B. np.union1d() does not accept lists as input

C. np.union1d() requires arrays to be sorted first

D. print() function is used incorrectly

Solution

1. Step 1: Check function call syntax

   The function call np.union1d(arr1 arr2) is missing a comma between arguments.

2. Step 2: Confirm input types and print usage

   np.union1d accepts lists or arrays, and print() is correctly used.

3. Final Answer:

   Missing comma between arr1 and arr2 in function call -> Option A

4. Quick Check:

   Comma separates arguments [OK]

Hint: Check commas between function arguments [OK]

Common Mistakes:

• Omitting commas between arguments
• Thinking input must be numpy arrays only
• Assuming print() causes error

5. You have two datasets of customer IDs:

dataset1 = np.array([101, 102, 103, 104])
dataset2 = np.array([103, 104, 105, 106])

You want to create a combined list of all unique customer IDs sorted in ascending order. Which code snippet correctly achieves this?

hard

A. combined = np.intersect1d(dataset1, dataset2)

B. combined = np.concatenate((dataset1, dataset2))

C. combined = np.union1d(dataset1, dataset2)

D. combined = np.sort(np.append(dataset1, dataset2))

Solution

1. Step 1: Understand the goal

   We want all unique customer IDs from both datasets, sorted ascending.

2. Step 2: Evaluate each option

   combined = np.union1d(dataset1, dataset2) uses np.union1d which merges and sorts unique elements. combined = np.concatenate((dataset1, dataset2)) concatenates but keeps duplicates. combined = np.intersect1d(dataset1, dataset2) finds only common IDs. combined = np.sort(np.append(dataset1, dataset2)) appends and sorts but does not remove duplicates.

3. Final Answer:

   combined = np.union1d(dataset1, dataset2) -> Option C

4. Quick Check:

   Union merges unique sorted elements [OK]

Hint: Use np.union1d to merge unique sorted values [OK]

Common Mistakes:

• Using concatenate without removing duplicates
• Using intersect1d which finds only common elements
• Appending and sorting without removing duplicates