np.intersect1d() for intersection in NumPy - Step-by-Step Execution

Concept Flow - np.intersect1d() for intersection

Input: Array1, Array2

↓

Find common elements

↓

Sort and remove duplicates

↓

Return intersection array

The function takes two arrays, finds elements present in both, removes duplicates, sorts them, and returns the result.

Execution Sample

NumPy

import numpy as np
arr1 = np.array([1, 2, 3, 4])
arr2 = np.array([3, 4, 5, 6])
result = np.intersect1d(arr1, arr2)
print(result)

This code finds the common elements between arr1 and arr2 and prints the sorted intersection without duplicates.

Execution Table

Step	Action	Input Arrays	Intermediate Result	Output
1	Receive input arrays	[1, 2, 3, 4], [3, 4, 5, 6]	N/A	N/A
2	Find common elements	Same	[3 4]	N/A
3	Sort and remove duplicates	[3 4]	[3 4]	N/A
4	Return intersection array	N/A	N/A	[3 4]
5	Print result	N/A	N/A	[3 4]

💡 All common elements found, duplicates removed, sorted, and returned.

Variable Tracker

Variable	Start	After Step 2	After Step 3	Final
arr1	[1 2 3 4]	[1 2 3 4]	[1 2 3 4]	[1 2 3 4]
arr2	[3 4 5 6]	[3 4 5 6]	[3 4 5 6]	[3 4 5 6]
result	N/A	[3 4]	[3 4]	[3 4]

Key Moments - 2 Insights

Why does the output not include duplicates even if input arrays have duplicates?

np.intersect1d automatically removes duplicates from the intersection result, as shown in step 3 of the execution_table where duplicates are removed before returning.

Are the elements in the output always sorted?

Yes, np.intersect1d returns a sorted array of common elements, confirmed in step 3 where sorting happens before output.

Visual Quiz - 3 Questions

Look at the execution_table at step 2, what is the intermediate result?

A [3 4]

B [1, 2]

C [5, 6]

D [2, 3]

💡 Hint

Check the 'Intermediate Result' column at step 2 in execution_table.

At which step does np.intersect1d remove duplicates and sort the result?

A Step 1

B Step 2

C Step 3

D Step 4

💡 Hint

Look at the 'Action' column describing sorting and duplicate removal.

If arr1 was [1, 2, 2, 3] and arr2 was [2, 2, 4], what would the final result be?

A [2, 2]

B [2]

C [1, 2]

D [1, 2, 4]

💡 Hint

np.intersect1d removes duplicates in the output, see variable_tracker for 'result'.

Concept Snapshot

np.intersect1d(arr1, arr2)
- Finds common elements in two arrays
- Removes duplicates automatically
- Returns sorted intersection array
- Useful for comparing datasets
- Output is always 1D sorted array

Full Transcript

This visual execution traces np.intersect1d which finds common elements between two numpy arrays. First, it takes two input arrays. Then it finds elements present in both arrays. Next, it removes duplicates and sorts the common elements. Finally, it returns the sorted array of unique common elements. The example code shows arrays [1, 2, 3, 4] and [3, 4, 5, 6] intersecting to [3, 4]. Key points are that duplicates are removed and output is sorted. The variable tracker shows how the result variable changes from empty to the final intersection. The quizzes test understanding of intermediate results, sorting step, and duplicate removal behavior.

Practice

1. What does the function np.intersect1d() do in NumPy?

easy

A. Finds common elements between two arrays

B. Combines two arrays into one

C. Sorts an array in descending order

D. Removes duplicates from an array

Solution

1. Step 1: Understand the function purpose

   np.intersect1d() is designed to find elements that appear in both input arrays.

2. Step 2: Compare with other options

   Options A, B, and D describe different functions: sorting descending, combining arrays, and removing duplicates, which are not the purpose of np.intersect1d().

3. Final Answer:

   Finds common elements between two arrays -> Option A

4. Quick Check:

   Intersection = common elements [OK]

Hint: Think 'intersection' means shared items [OK]

Common Mistakes:

• Confusing intersection with concatenation
• Thinking it sorts descending
• Mixing with duplicate removal

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

easy

A. np.intersect(a, b)

B. np.intersection(a, b)

C. np.intersect1d(a, b)

D. np.intersect1d([a, b])

Solution

1. Step 1: Recall the correct function name and parameters

   The correct function is np.intersect1d() and it takes two arrays as separate arguments.

2. Step 2: Check each option

   np.intersect1d(a, b) uses the correct function and syntax. Options B and C use incorrect function names. np.intersect1d([a, b]) incorrectly passes a list of arrays instead of two separate arguments.

3. Final Answer:

   np.intersect1d(a, b) -> Option C

4. Quick Check:

   Correct function and parameters [OK]

Hint: Use exact function name and separate arrays as arguments [OK]

Common Mistakes:

• Using wrong function names
• Passing arrays inside a list
• Missing one argument

3. What is the output of the following code?

import numpy as np
x = np.array([3, 1, 4, 1, 5])
y = np.array([5, 9, 2, 6, 5])
print(np.intersect1d(x, y))

medium

A. [1 5]

B. [3 5]

C. [1 5 9]

D. [5]

Solution

1. Step 1: Identify unique elements in both arrays

   Array x has elements {1, 3, 4, 5} (1 appears twice but counted once). Array y has elements {2, 5, 6, 9} (5 appears twice but counted once).

2. Step 2: Find common elements

   The only common element between x and y is 5.

3. Final Answer:

   [5] -> Option D

4. Quick Check:

   Common elements = [5] [OK]

Hint: Look for numbers appearing in both arrays [OK]

Common Mistakes:

• Including duplicates
• Adding elements not in both arrays
• Confusing order of output

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

import numpy as np
arr1 = [1, 2, 3]
arr2 = [2, 3, 4]
result = np.intersect1d(arr1 arr2)
print(result)

medium

A. Missing comma between arguments in np.intersect1d()

B. Arrays must be NumPy arrays, not lists

C. np.intersect1d() does not accept two arguments

D. print() function is used incorrectly

Solution

1. Step 1: Check the function call syntax

   The call np.intersect1d(arr1 arr2) is missing a comma between the two arguments.

2. Step 2: Verify other parts

   Passing lists is allowed because NumPy converts them internally. The function accepts two arguments. The print statement is correct.

3. Final Answer:

   Missing comma between arguments in np.intersect1d() -> Option A

4. Quick Check:

   Arguments must be separated by commas [OK]

Hint: Check commas between function arguments [OK]

Common Mistakes:

• Forgetting commas
• Thinking lists are invalid inputs
• Misreading error source

5. You have two arrays representing product IDs sold in two stores:

store1 = np.array([101, 102, 103, 104, 105])
store2 = np.array([104, 105, 106, 107])

How can you find the sorted list of product IDs sold in both stores using np.intersect1d()?

hard

A. np.union1d(store1, store2)

B. np.intersect1d(store1, store2)

C. np.setdiff1d(store1, store2)

D. np.concatenate((store1, store2))

Solution

1. Step 1: Understand the problem

   We want product IDs common to both stores, which means intersection.

2. Step 2: Choose the correct function

   np.intersect1d(store1, store2) returns sorted common elements. Other options return union, difference, or concatenation, which are not correct here.

3. Final Answer:

   np.intersect1d(store1, store2) -> Option B

4. Quick Check:

   Intersection = common products [OK]

Hint: Use intersect1d for common elements between arrays [OK]

Common Mistakes:

• Using union instead of intersection
• Using difference or concatenation
• Not sorting output