np.count_nonzero() for counting in NumPy - Mini Project: Build & Apply

Counting Non-Zero Elements with np.count_nonzero()

📖 Scenario: Imagine you work in a store that tracks daily sales of different products. Some days, some products sell zero items. You want to find out how many products sold at least one item each day.

🎯 Goal: You will create a numpy array representing daily sales, then use np.count_nonzero() to count how many products sold more than zero items each day.

📋 What You'll Learn

Create a numpy array called daily_sales with the exact values given.

Create a variable called day_index to select a specific day.

Use np.count_nonzero() to count non-zero sales for the selected day.

Print the count of products sold on that day.

💡 Why This Matters

🌍 Real World

Stores and businesses often track sales data daily. Counting how many products sold helps understand customer demand and stock management.

💼 Career

Data analysts and scientists use numpy functions like <code>np.count_nonzero()</code> to quickly analyze datasets and extract useful insights.

1

Create the daily sales data

Create a numpy array called daily_sales with these exact values: [[3, 0, 5, 0], [0, 2, 0, 1], [4, 0, 0, 0]]. This array represents sales of 4 products over 3 days.

NumPy

import numpy as np
# Create the numpy array daily_sales with the given values
# Your code here

Hint

Use np.array() and type the exact nested list of sales numbers.

2

Select the day to analyze

Create a variable called day_index and set it to 1 to select the second day (indexing starts at 0).

NumPy

import numpy as np

daily_sales = np.array([[3, 0, 5, 0], [0, 2, 0, 1], [4, 0, 0, 0]])
# Create day_index variable and set it to 1
# Your code here

Hint

Remember, Python counts from zero, so the second day is index 1.

3

Count products sold on the selected day

Use np.count_nonzero() on daily_sales[day_index] to count how many products sold more than zero items on that day. Store the result in a variable called products_sold.

NumPy

import numpy as np

daily_sales = np.array([[3, 0, 5, 0], [0, 2, 0, 1], [4, 0, 0, 0]])
day_index = 1
# Use np.count_nonzero() on daily_sales[day_index] and save to products_sold
# Your code here

Hint

Pass the selected day's sales array to np.count_nonzero() to count non-zero values.

4

Print the count of products sold

Print the value of products_sold to show how many products sold on the selected day.

NumPy

import numpy as np

daily_sales = np.array([[3, 0, 5, 0], [0, 2, 0, 1], [4, 0, 0, 0]])
day_index = 1
products_sold = np.count_nonzero(daily_sales[day_index])
# Print products_sold
# Your code here

Hint

Use print(products_sold) to display the count.

Practice

1.

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

easy

A. Calculates the sum of all elements in an array

B. Returns the shape of the array

C. Finds the maximum value in an array

D. Counts how many elements in an array are not zero

Solution

1. Step 1: Understand the function purpose

   np.count_nonzero() counts elements that are not zero in the array.

2. Step 2: Compare with other options

   Other options describe different functions like sum, max, or shape, which are not what np.count_nonzero() does.

3. Final Answer:

   Counts how many elements in an array are not zero -> Option D

4. Quick Check:

   Counting non-zero elements = Counts how many elements are not zero [OK]

Hint: Remember: count_nonzero counts non-zero values only [OK]

Common Mistakes:

• Confusing count_nonzero with sum or max functions
• Thinking it returns the array shape
• Assuming it counts zero elements

2.

Which of the following is the correct syntax to count non-zero elements in a NumPy array arr?

arr = np.array([1, 0, 3, 0, 5])

easy

A. np.count_nonzero = arr

B. np.count_nonzero(arr)

C. arr.count_nonzero()

D. np.count(arr != 0)

Solution

1. Step 1: Identify correct function usage

   The function np.count_nonzero() is called with the array as argument: np.count_nonzero(arr).

2. Step 2: Check other options for errors

   np.count_nonzero = arr tries to assign instead of call; arr.count_nonzero() uses method not available on array; np.count(arr != 0) uses a non-existent function np.count().

3. Final Answer:

   np.count_nonzero(arr) -> Option B

4. Quick Check:

   Correct syntax is np.count_nonzero(array) [OK]

Hint: Use np.count_nonzero(array) to count non-zero values [OK]

Common Mistakes:

• Using assignment instead of function call
• Calling count_nonzero as a method on array
• Using non-existent np.count function

3.

What is the output of the following code?

import numpy as np
arr = np.array([[0, 1, 2], [3, 0, 0], [4, 5, 6]])
count = np.count_nonzero(arr, axis=0)
print(count)

medium

A. [3 2 3]

B. [3 3 3]

C. [2 3 2]

D. [2 2 2]

Solution

1. Step 1: Understand axis=0 counting

   Counting non-zero elements along columns (axis=0) means counting down each column.

2. Step 2: Count non-zero per column

   Column 1: values [0,3,4] -> non-zero count = 2 (3 and 4)
   Column 2: values [1,0,5] -> non-zero count = 2 (1 and 5)
   Column 3: values [2,0,6] -> non-zero count = 2 (2 and 6)

3. Final Answer:

   [3 2 3] -> Option A

4. Quick Check:

   Count non-zero per column = [3 2 3] [OK]

Hint: axis=0 counts down columns, axis=1 counts across rows [OK]

Common Mistakes:

• Counting zeros instead of non-zero
• Confusing axis=0 with axis=1
• Miscounting elements per column

4.

Find the error in this code snippet and choose the correct fix:

import numpy as np
arr = np.array([1, 0, 2, 0, 3])
count = np.count_nonzero(arr, axis=1)
print(count)

medium

A. Remove axis=1 because arr is 1D, use np.count_nonzero(arr) instead

B. Change axis=1 to axis=0 to fix the error

C. Use arr.count_nonzero() method instead

D. No error, code runs fine

Solution

1. Step 1: Identify array dimension

   Array arr is 1D, so axis=1 is invalid (no second axis).

2. Step 2: Correct function call

   Remove axis argument to count all non-zero elements: np.count_nonzero(arr).

3. Final Answer:

   Remove axis=1 because arr is 1D, use np.count_nonzero(arr) instead -> Option A

4. Quick Check:

   1D arrays have no axis=1, so omit axis [OK]

Hint: Check array shape before using axis in count_nonzero [OK]

Common Mistakes:

• Using axis=1 on 1D arrays causes errors
• Trying to call count_nonzero as array method
• Assuming axis=0 fixes all axis errors

5.

You have a 2D NumPy array representing attendance (1 for present, 0 for absent) of 4 students over 5 days:

attendance = np.array([
  [1, 0, 1, 1, 0],
  [0, 0, 1, 0, 0],
  [1, 1, 1, 1, 1],
  [0, 0, 0, 0, 0]
])

Which code correctly counts how many days each student was present?

hard

A. np.sum(attendance, axis=0)

B. np.count_nonzero(attendance, axis=0)

C. np.count_nonzero(attendance, axis=1)

D. np.count_nonzero(attendance)

Solution

1. Step 1: Understand data layout

   Rows represent students, columns represent days. Counting days present per student means counting non-zero per row (axis=1).

2. Step 2: Choose correct axis

   Use np.count_nonzero(attendance, axis=1) to count non-zero values per student (row).

3. Final Answer:

   np.count_nonzero(attendance, axis=1) -> Option C

4. Quick Check:

   Count per row (student) = axis=1 [OK]

Hint: Count per student = count_nonzero with axis=1 [OK]

Common Mistakes:

• Using axis=0 counts per day, not per student
• Using np.sum instead of count_nonzero (works but different function)
• Counting total non-zero without axis