Comparison operations in NumPy

Introduction

Comparison operations help us check if values are equal, bigger, or smaller. This is useful to find patterns or filter data.

Checking which students scored above a passing grade in a test.

Finding products that cost less than a certain price in a store.

Selecting days when the temperature was higher than average.

Filtering emails that arrived before a specific date.

Syntax

NumPy

array1 == array2
array1 != array2
array1 > array2
array1 >= array2
array1 < array2
array1 <= array2

These operations compare elements of arrays element-wise.

They return a new array of True/False values showing the comparison result.

Examples

Checks which elements are greater than 2.

NumPy

import numpy as np

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

Checks element-wise equality between two arrays.

NumPy

arr1 = np.array([1, 2, 3])
arr2 = np.array([3, 2, 1])
print(arr1 == arr2)

Checks which elements are less than or equal to 10.

NumPy

arr = np.array([5, 10, 15])
print(arr <= 10)

Sample Program

This program checks which scores are greater than or equal to 60. It prints the original scores, a True/False array showing who passed, and then only the passing scores.

NumPy

import numpy as np

scores = np.array([55, 70, 85, 40, 90])
passing_score = 60
passed = scores >= passing_score
print("Scores:", scores)
print("Passed:", passed)

# Filter scores that passed
passed_scores = scores[passed]
print("Scores that passed:", passed_scores)

Important Notes

Comparison results can be used to filter or select data easily.

All arrays compared must have the same shape or be broadcastable.

Summary

Comparison operations compare arrays element-wise and return True/False arrays.

They help find or filter data based on conditions.

Common operators: ==, !=, >, >=, <, <=.

Practice

1. What does the numpy expression arr == 5 return when arr is a numpy array?

easy

A. The sum of all elements equal to 5

B. An array of True/False values showing where elements equal 5

C. The count of elements equal to 5

D. A single True or False value

Solution

1. Step 1: Understand element-wise comparison

   The expression arr == 5 compares each element of the array to 5 individually.

2. Step 2: Result type of comparison

   This returns a new array of the same shape with True where the element equals 5, and False otherwise.

3. Final Answer:

   An array of True/False values showing where elements equal 5 -> Option B

4. Quick Check:

   Element-wise comparison = Boolean array [OK]

Hint: Remember == returns True/False array element-wise [OK]

Common Mistakes:

• Thinking it returns a count or sum
• Expecting a single True/False value
• Confusing == with assignment =

2. Which of the following is the correct syntax to check if elements in numpy array arr are greater than 10?

easy

A. arr >> 10

B. arr => 10

C. arr > 10

D. arr >= 10

Solution

1. Step 1: Identify correct comparison operator

   The operator for 'greater than' is >, so arr > 10 checks elements greater than 10.

2. Step 2: Check other options

   arr => 10 is invalid syntax, arr >> 10 is a bitwise shift, and arr >= 10 checks greater or equal, not strictly greater.

3. Final Answer:

   arr > 10 -> Option C

4. Quick Check:

   Greater than operator is > [OK]

Hint: Use > for greater than, >= for greater or equal [OK]

Common Mistakes:

• Using => instead of >
• Confusing bitwise >> with comparison
• Using >= when > is asked

3. What is the output of the following code?

import numpy as np
arr = np.array([3, 7, 2, 9])
result = arr >= 5
print(result)

medium

A. [False False False False]

B. [True False True False]

C. [True True True True]

D. [False True False True]

Solution

1. Step 1: Compare each element with 5

   Elements: 3, 7, 2, 9. Check if each is >= 5: 3>=5 is False, 7>=5 is True, 2>=5 is False, 9>=5 is True.

2. Step 2: Form boolean array

   The result is [False, True, False, True].

3. Final Answer:

   [False True False True] -> Option D

4. Quick Check:

   Element-wise >= 5 = [False True False True] [OK]

Hint: Check each element against 5 carefully [OK]

Common Mistakes:

• Mixing up >= with >
• Misreading element values
• Expecting integer output instead of boolean

4. Identify the error in this numpy comparison code:

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

medium

A. Using single = instead of == for comparison

B. Array must be converted to list before comparison

C. print() cannot display numpy arrays

D. Missing parentheses in print statement

Solution

1. Step 1: Check comparison syntax

   The code uses arr = 2 which assigns 2 to arr, not compares. Comparison needs ==.

2. Step 2: Understand effect of assignment

   Assignment overwrites arr with 2, so print(result) prints 2, not a boolean array.

3. Final Answer:

   Using single = instead of == for comparison -> Option A

4. Quick Check:

   Comparison needs ==, not = [OK]

Hint: Use == for comparison, = is assignment [OK]

Common Mistakes:

• Confusing = and ==
• Thinking print can't show arrays
• Believing arrays must convert to lists

5. Given two numpy arrays:

import numpy as np
arr1 = np.array([1, 4, 6, 8])
arr2 = np.array([2, 4, 5, 10])

Which expression returns a boolean array showing where elements in arr1 are less than or equal to elements in arr2?

hard

A. arr1 <= arr2

B. arr1 < arr2

C. arr1 == arr2

D. arr1 >= arr2

Solution

1. Step 1: Understand element-wise comparison between arrays

   Comparing two arrays with operators like <= compares each element pair at the same index.

2. Step 2: Identify operator for 'less than or equal'

   The operator <= checks if elements in arr1 are less than or equal to corresponding elements in arr2.

3. Final Answer:

   arr1 <= arr2 -> Option A

4. Quick Check:

   Use <= for less or equal element-wise [OK]

Hint: Use <= to compare arrays element-wise for less or equal [OK]

Common Mistakes:

• Using < instead of <= when equal counts
• Using == which only checks equality
• Using >= which is opposite