Array arithmetic (element-wise) in Data Analysis Python - Mini Project: Build & Apply

Array arithmetic (element-wise)

📖 Scenario: You work in a small shop that tracks daily sales of two products. You want to quickly find the total sales each day by adding the sales of both products.

🎯 Goal: Build a simple program that creates two lists of daily sales numbers, then adds them element-wise to find total daily sales.

📋 What You'll Learn

Create two lists named sales_product_a and sales_product_b with exact daily sales numbers

Create a variable named days to store the number of days

Use a for loop with index variable i to add sales element-wise

Store the element-wise sums in a list named total_sales

Print the total_sales list

💡 Why This Matters

🌍 Real World

Shops and businesses often track daily sales of multiple products. Adding sales data element-wise helps find total sales per day quickly.

💼 Career

Data analysts and scientists frequently perform element-wise operations on arrays or lists to combine or compare data from different sources.

1

Create daily sales lists

Create a list called sales_product_a with values [10, 15, 20, 25, 30] and a list called sales_product_b with values [5, 10, 15, 20, 25].

Data Analysis Python

# Create the sales lists for product A and product B
# Your code here

Hint

Use square brackets [] to create lists with the exact numbers given.

2

Set the number of days

Create a variable called days and set it to the length of the sales_product_a list using the len() function.

Data Analysis Python

sales_product_a = [10, 15, 20, 25, 30]
sales_product_b = [5, 10, 15, 20, 25]
# Create a variable days with the length of sales_product_a
# Your code here

Hint

Use len(sales_product_a) to find how many days of sales data there are.

3

Add sales element-wise

Create an empty list called total_sales. Use a for loop with index variable i from 0 to days - 1. Inside the loop, add sales_product_a[i] and sales_product_b[i] and append the result to total_sales.

Data Analysis Python

sales_product_a = [10, 15, 20, 25, 30]
sales_product_b = [5, 10, 15, 20, 25]
days = len(sales_product_a)
# Create total_sales list and add sales element-wise using a for loop
# Your code here

Hint

Use range(days) to loop over all days and append() to add sums to the list.

4

Print the total sales

Write a print() statement to display the total_sales list.

Data Analysis Python

sales_product_a = [10, 15, 20, 25, 30]
sales_product_b = [5, 10, 15, 20, 25]
days = len(sales_product_a)
total_sales = []
for i in range(days):
    total_sales.append(sales_product_a[i] + sales_product_b[i])
# Print the total_sales list
# Your code here

Hint

Use print(total_sales) to show the final list of sums.

Practice

1. What does element-wise addition of two arrays mean in simple terms?

easy

A. Combining the arrays end to end without any math

B. Adding each number in one array to the corresponding number in the other array

C. Multiplying each number in one array by the total sum of the other array

D. Adding all numbers in both arrays together as a single sum

Solution

1. Step 1: Understand element-wise operation

   Element-wise means doing the operation on each pair of matching positions in two arrays.

2. Step 2: Apply to addition

   For addition, each number in the first array is added to the number in the same position in the second array.

3. Final Answer:

   Adding each number in one array to the corresponding number in the other array -> Option B

4. Quick Check:

   Element-wise addition = add matching elements [OK]

Hint: Think pair by pair addition, not total sums [OK]

Common Mistakes:

• Confusing element-wise with total sum
• Thinking arrays are joined instead of added
• Mixing addition with multiplication

2. Which of the following is the correct syntax to add two NumPy arrays a and b element-wise?

easy

A. c = a + b

B. c = a.add(b)

C. c = add(a, b)

D. c = a & b

Solution

1. Step 1: Recall NumPy addition syntax

   NumPy supports element-wise addition using the + operator directly between arrays.

2. Step 2: Check options

   c = a + b uses a + b, which is correct. c = a.add(b) is invalid syntax. c = add(a, b) requires importing add function explicitly. c = a & b uses bitwise AND, not addition.

3. Final Answer:

   c = a + b -> Option A

4. Quick Check:

   Use + for element-wise addition in NumPy [OK]

Hint: Use + operator for element-wise addition in NumPy [OK]

Common Mistakes:

• Using bitwise operators instead of +
• Trying to call add as a method on array
• Forgetting to import add function if used

3. What is the output of this code?

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

medium

A. [4 5 6]

B. [5 7 9]

C. [4 10 18]

D. [1 2 3 4 5 6]

Solution

1. Step 1: Understand element-wise multiplication

   Multiplying two arrays with * multiplies each element in the first array by the corresponding element in the second array.

2. Step 2: Calculate each element

   1*4=4, 2*5=10, 3*6=18, so the result is [4, 10, 18].

3. Final Answer:

   [4 10 18] -> Option C

4. Quick Check:

   Element-wise multiply = multiply matching elements [OK]

Hint: Multiply arrays element by element with * operator [OK]

Common Mistakes:

• Adding instead of multiplying
• Concatenating arrays instead of multiplying
• Confusing output format with list instead of array

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

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

medium

A. You forgot to convert arrays to lists first

B. You must use np.add() instead of + operator

C. The print statement syntax is incorrect

D. Arrays have different sizes, so element-wise addition is not possible

Solution

1. Step 1: Check array sizes

   Array x has 3 elements, y has 2 elements. Element-wise operations require arrays to be the same size or broadcastable.

2. Step 2: Understand error cause

   Since sizes differ and no broadcasting applies here, addition fails with a ValueError.

3. Final Answer:

   Arrays have different sizes, so element-wise addition is not possible -> Option D

4. Quick Check:

   Array sizes must match for element-wise ops [OK]

Hint: Check array lengths before element-wise operations [OK]

Common Mistakes:

• Assuming + works regardless of size
• Thinking np.add fixes size mismatch
• Blaming print syntax for error

5. You have two arrays representing daily sales for two stores:

store1 = np.array([10, 20, 30, 40])
store2 = np.array([5, 15, 25, 35])

You want to find the total sales per day by adding these arrays element-wise. Then, you want to apply a 10% tax on the total sales. Which code correctly does this?

hard

A. total = store1 + store2
final = total * 1.10
print(final)

B. total = store1 + store2
final = total + 0.10
print(final)

C. total = store1 * store2
final = total * 1.10
print(final)

D. final = (store1 + store2) / 1.10
print(final)

Solution

1. Step 1: Calculate total sales element-wise

   Add store1 and store2 arrays element-wise using + operator to get daily totals.

2. Step 2: Apply 10% tax

   Multiply the total sales array by 1.10 to add 10% tax to each day's total.

3. Final Answer:

   total = store1 + store2 final = total * 1.10 print(final) -> Option A

4. Quick Check:

   Add then multiply by 1.10 for tax [OK]

Hint: Add arrays first, then multiply by 1.10 for tax [OK]

Common Mistakes:

• Adding 0.10 instead of multiplying by 1.10
• Multiplying arrays instead of adding
• Dividing instead of multiplying for tax