Counting with boolean arrays in NumPy - Mini Project: Build & Apply

Counting with boolean arrays

📖 Scenario: You work in a store and have a list of daily sales numbers. You want to find out how many days had sales above a certain target.

🎯 Goal: Build a small program that counts how many days had sales greater than a target using boolean arrays in NumPy.

📋 What You'll Learn

Use NumPy to create and work with arrays

Create a boolean array by comparing sales to a target

Count the number of True values in the boolean array

💡 Why This Matters

🌍 Real World

Stores and businesses often want to quickly find how many days or items meet a sales goal to track performance.

💼 Career

Data analysts and scientists use boolean arrays and counting techniques to summarize and analyze data efficiently.

1

Create the sales data array

Import NumPy as np and create a NumPy array called sales with these exact daily sales values: 100, 150, 90, 200, 130.

NumPy

# Import numpy as np and create the sales array
# Your code here

Hint

Use np.array([...]) to create the array with the exact sales numbers.

2

Set the sales target

Create a variable called target and set it to the value 120.

NumPy

import numpy as np
sales = np.array([100, 150, 90, 200, 130])
# Create the target variable with value 120
# Your code here

Hint

Just assign the number 120 to the variable target.

3

Create a boolean array for sales above target

Create a boolean NumPy array called above_target by comparing sales to target using the greater than operator >.

NumPy

import numpy as np
sales = np.array([100, 150, 90, 200, 130])
target = 120
# Create above_target array where sales are greater than target
# Your code here

Hint

Use sales > target to get a boolean array.

4

Count and print the number of days above target

Use np.sum() on the above_target array to count how many days had sales above the target. Print the result.

NumPy

import numpy as np
sales = np.array([100, 150, 90, 200, 130])
target = 120
above_target = sales > target
# Print the count of days with sales above target
# Your code here

Hint

Use np.sum(above_target) to count True values, then print it.

Practice

1. What does summing a boolean array in NumPy do?
arr = np.array([True, False, True])
What is arr.sum()?

easy

A. Returns the length of the array

B. Counts how many False values are in the array

C. Counts how many True values are in the array

D. Returns an error because booleans cannot be summed

Solution

1. Step 1: Understand boolean values in NumPy

   In NumPy, True is treated as 1 and False as 0 when summed.

2. Step 2: Sum the boolean array

   Summing [True, False, True] is 1 + 0 + 1 = 2.

3. Final Answer:

   Counts how many True values are in the array -> Option C

4. Quick Check:

   True count = 2 [OK]

Hint: Sum boolean arrays to count True values quickly [OK]

Common Mistakes:

• Thinking sum counts False values
• Believing sum returns array length
• Expecting an error when summing booleans

2. Which of the following is the correct syntax to count how many elements in arr = np.array([1, 2, 3, 4]) are greater than 2 using boolean arrays?

easy

A. (arr > 2).sum()

B. arr > 2.sum()

C. arr.sum() > 2

D. arr > 2).sum()

Solution

1. Step 1: Create boolean array for condition

   arr > 2 creates a boolean array: [False, False, True, True].

2. Step 2: Sum the boolean array correctly

   We must sum the boolean array, so use parentheses: (arr > 2).sum().

3. Final Answer:

   (arr > 2).sum() -> Option A

4. Quick Check:

   Correct syntax uses parentheses [OK]

Hint: Use parentheses around condition before sum() [OK]

Common Mistakes:

• Missing parentheses causing wrong order
• Using sum() on condition without parentheses
• Confusing comparison and sum order

3. What is the output of this code?

import numpy as np
arr = np.array([5, 3, 8, 1, 6])
count = (arr % 2 == 0).sum()
print(count)

medium

A. 3

B. 0

C. 5

D. 2

Solution

1. Step 1: Create boolean array for even numbers

   arr % 2 == 0 checks which elements are even: [False, False, True, False, True].

2. Step 2: Sum True values to count evens

   Sum is 0 + 0 + 1 + 0 + 1 = 2.

3. Final Answer:

   2 -> Option D

4. Quick Check:

   Count of even numbers = 2 [OK]

Hint: Sum boolean condition to count matching elements [OK]

Common Mistakes:

• Counting odd numbers instead
• Forgetting to use parentheses
• Misunderstanding modulo operator

4. The code below is intended to count how many values in arr are less than 10, but it raises an error. What is the error and how to fix it?

import numpy as np
arr = np.array([7, 12, 5, 20])
count = arr < 10.sum()
print(count)

medium

A. Error: Missing import statement

B. Error: 10.sum() is invalid; fix by using (arr < 10).sum()

C. No error; output is boolean array

D. Error: arr < 10 is invalid; fix by using arr.sum() < 10

Solution

1. Step 1: Identify the error in expression

   10.sum() is invalid because 10 is an integer, not an array.

2. Step 2: Correct the syntax to sum boolean array

   Use parentheses to sum the boolean array: (arr < 10).sum().

3. Final Answer:

   Error: 10.sum() is invalid; fix by using (arr < 10).sum() -> Option B

4. Quick Check:

   Parentheses needed before sum() [OK]

Hint: Always put parentheses around condition before sum() [OK]

Common Mistakes:

• Calling sum() on number instead of boolean array
• Confusing comparison and sum order
• Ignoring error message details

5. Given a 2D NumPy array data = np.array([[3, 7, 2], [5, 1, 8], [6, 4, 9]]), how can you count how many elements are greater than 5 across the entire array?

hard

A. (data > 5).sum()

B. data[data > 5].count()

C. data.sum() > 5

D. np.count(data > 5)

Solution

1. Step 1: Create boolean array for elements > 5

   data > 5 creates a boolean array marking elements greater than 5.

2. Step 2: Sum True values to count elements

   Use (data > 5).sum() to count all True values in the 2D array.

3. Final Answer:

   (data > 5).sum() -> Option A

4. Quick Check:

   Sum boolean mask counts elements > 5 [OK]

Hint: Sum boolean mask over entire array to count matches [OK]

Common Mistakes:

• Using sum() on data directly
• Trying to use count() method on NumPy array
• Using non-existent np.count() function