Array arithmetic (element-wise) in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Element-wise addition of NumPy arrays

What is the output of this code snippet that adds two NumPy arrays element-wise?

Data Analysis Python

import numpy as np

arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
result = arr1 + arr2
print(result)

A [1 2 3 4 5 6]

B TypeError

C [4 7 9]

D [5 7 9]

💡 Hint

Think about how NumPy adds arrays of the same shape.

NumPy adds arrays element-wise when they have the same shape. So each element in arr1 is added to the corresponding element in arr2.

❓ data_output

intermediate

Element-wise multiplication with broadcasting

Given the arrays below, what is the output of the element-wise multiplication?

Data Analysis Python

import numpy as np

arr1 = np.array([[1, 2, 3], [4, 5, 6]])
arr2 = np.array([10, 20, 30])
result = arr1 * arr2
print(result)

A

[[10 20 30]
 [40 50 60]]

B

[[10 40 90]
 [40 100 180]]

C

[[10 20 30 10 20 30]
 [40 50 60 40 50 60]]

D ValueError

💡 Hint

Remember how broadcasting works when multiplying arrays of different shapes.

The 1D array arr2 is broadcast across the rows of arr1, so each element in arr2 multiplies the corresponding column in arr1.

🔧 Debug

advanced

Identify the error in element-wise division

What error does this code raise when performing element-wise division?

Data Analysis Python

import numpy as np

arr1 = np.array([1, 2, 0])
arr2 = np.array([0, 2, 3])
result = arr1 / arr2
print(result)

A RuntimeWarning: divide by zero encountered in true_divide

B ValueError

C TypeError

D ZeroDivisionError

💡 Hint

Look carefully at division by zero in NumPy arrays.

NumPy raises a RuntimeWarning when dividing by zero but does not stop execution. It returns 'inf' or 'nan' in those positions.

❓ visualization

advanced

Plotting element-wise addition result

Which option correctly plots the element-wise addition of two arrays as a line chart?

Data Analysis Python

import numpy as np
import matplotlib.pyplot as plt

arr1 = np.array([1, 3, 5, 7])
arr2 = np.array([2, 4, 6, 8])
result = arr1 + arr2
plt.plot(result)
plt.show()

A A scatter plot with points [1, 3, 5, 7]

B A bar chart with bars at heights [3, 7, 11, 15]

C A line chart with points [3, 7, 11, 15] connected by lines

D An empty plot with no data

💡 Hint

plt.plot draws a line chart by default connecting the points.

plt.plot draws a line chart connecting the values in the array. The result array is [3, 7, 11, 15].

🧠 Conceptual

expert

Effect of element-wise operations on array shape

If you multiply a (3,1) shaped NumPy array by a (1,4) shaped array element-wise, what is the shape of the result?

A (3, 4)

B (3, 1)

C (1, 4)

D ValueError

💡 Hint

Think about how broadcasting expands dimensions to match.

Broadcasting expands the (3,1) array across columns and the (1,4) array across rows, resulting in a (3,4) array.

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