np.sum() and axis parameter in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of np.sum() with axis=0 on 2D array

What is the output of the following code?

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

NumPy

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

A [6 8 10]

B [5 7 9]

C [3 7 11]

D [1 2 3 4 5 6]

💡 Hint

axis=0 sums down the rows for each column.

np.sum with axis=0 adds elements vertically down each column. So, 1+4=5, 2+5=7, 3+6=9.

❓ Predict Output

intermediate

Result of np.sum() with axis=1 on 2D array

What does this code print?

import numpy as np
arr = np.array([[7, 8], [9, 10], [11, 12]])
print(np.sum(arr, axis=1))

NumPy

import numpy as np
arr = np.array([[7, 8], [9, 10], [11, 12]])
print(np.sum(arr, axis=1))

A [26 29]

B [7 9 11]

C [15 19 23]

D [7 8 9 10 11 12]

💡 Hint

axis=1 sums across the columns for each row.

Summing along axis=1 adds elements horizontally in each row: 7+8=15, 9+10=19, 11+12=23.

❓ data_output

advanced

Shape of result after np.sum with axis=1

Given this array:

import numpy as np
arr = np.array([[[1,2],[3,4]], [[5,6],[7,8]]])
result = np.sum(arr, axis=1)
print(result)
print(result.shape)

What is the shape of result?

NumPy

import numpy as np
arr = np.array([[[1,2],[3,4]], [[5,6],[7,8]]])
result = np.sum(arr, axis=1)
print(result)
print(result.shape)

A (2, 2)

B (2, 1, 2)

C (1, 2, 2)

D (2, 4)

💡 Hint

Summing over axis=1 removes that dimension.

The original array shape is (2, 2, 2). Summing over axis=1 collapses the second dimension, resulting in shape (2, 2).

❓ visualization

advanced

Visualize np.sum() with axis=0 and axis=1

You have this 2D array:

import numpy as np
import matplotlib.pyplot as plt
arr = np.array([[1, 3, 5], [2, 4, 6]])

Which plot correctly shows the sums along axis=0 and axis=1?

NumPy

import numpy as np
import matplotlib.pyplot as plt
arr = np.array([[1, 3, 5], [2, 4, 6]])
sum_axis0 = np.sum(arr, axis=0)
sum_axis1 = np.sum(arr, axis=1)

plt.figure(figsize=(8,4))
plt.subplot(1,2,1)
plt.bar(['col0','col1','col2'], sum_axis0, color='blue')
plt.title('Sum along axis=0')

plt.subplot(1,2,2)
plt.bar(['row0','row1'], sum_axis1, color='green')
plt.title('Sum along axis=1')

plt.tight_layout()
plt.show()

A Left plot bars: [3,7,11], Right plot bars: [9,12]

B Left plot bars: [3,7,11], Right plot bars: [6,15]

C Left plot bars: [1,3,5], Right plot bars: [2,4,6]

D Left plot bars: [9,12], Right plot bars: [3,7,11]

💡 Hint

Sum axis=0 adds columns, sum axis=1 adds rows.

Sum along axis=0: 1+2=3, 3+4=7, 5+6=11. Sum along axis=1: 1+3+5=9, 2+4+6=12.

🧠 Conceptual

expert

Effect of axis=None in np.sum() on 3D array

Consider a 3D numpy array with shape (3, 4, 5). What does np.sum(arr, axis=None) return?

A An array with the same shape as the original, unchanged

B A 2D array with shape (3, 4) summing over the last axis

C A 1D array with length 3 summing over the last two axes

D A single number representing the sum of all elements in the array

💡 Hint

axis=None means sum everything into one value.

When axis=None, np.sum adds all elements in the entire array, returning a single scalar number.

Practice

1. What does the axis parameter in np.sum() control?

easy

A. The data type of the output array.

B. The direction along which the sum is calculated (rows or columns).

C. The size of the array to sum.

D. The number of times the sum is repeated.

Solution

1. Step 1: Understand the role of axis in np.sum()

   The axis parameter tells numpy whether to sum across rows (axis=1) or columns (axis=0).

2. Step 2: Clarify what axis does not affect

   It does not change data type, size, or repetition of sums.

3. Final Answer:

   The direction along which the sum is calculated (rows or columns). -> Option B

4. Quick Check:

   axis controls sum direction [OK]

Hint: Axis decides row or column sum direction [OK]

Common Mistakes:

• Thinking axis changes data type
• Confusing axis with array size
• Assuming axis repeats sums

2. Which of the following is the correct syntax to sum all elements of a 2D numpy array arr?

easy

A. np.sum(arr)

B. np.sum(arr, axis=1, 0)

C. np.sum(arr, axis=2)

D. np.sum(arr, axis='all')

Solution

1. Step 1: Recall default behavior of np.sum()

   Without axis, np.sum(arr) sums all elements in the array.

2. Step 2: Check invalid axis values

   axis=2 is invalid for 2D arrays, axis=1,0 is incorrect syntax, axis='all' is invalid.

3. Final Answer:

   np.sum(arr) -> Option A

4. Quick Check:

   Default np.sum sums all elements [OK]

Hint: No axis sums all elements [OK]

Common Mistakes:

• Using invalid axis values
• Passing multiple axis values incorrectly
• Using string instead of integer for axis

3. Given the array arr = np.array([[1, 2, 3], [4, 5, 6]]), what is the output of np.sum(arr, axis=0)?

medium

A. [5 7 9]

B. [3 7]

C. [6 15]

D. [1 2 3 4 5 6]

Solution

1. Step 1: Understand axis=0 sums columns

   Summing along axis=0 adds elements vertically: (1+4), (2+5), (3+6).

2. Step 2: Calculate sums for each column

   1+4=5, 2+5=7, 3+6=9, so result is [5 7 9].

3. Final Answer:

   [5 7 9] -> Option A

4. Quick Check:

   Sum columns = [5 7 9] [OK]

Hint: axis=0 sums columns vertically [OK]

Common Mistakes:

• Confusing axis=0 with axis=1
• Adding rows instead of columns
• Expecting a scalar output

4. What is wrong with this code snippet?

import numpy as np
arr = np.array([[1, 2], [3, 4]])
result = np.sum(arr, axis=2)

medium

A. The array is not defined correctly.

B. np.sum() cannot be used on 2D arrays.

C. The axis value 2 is invalid for a 2D array.

D. The import statement is missing.

Solution

1. Step 1: Check array dimensions

   The array has shape (2, 2), so valid axis values are 0 or 1.

2. Step 2: Validate axis parameter

   axis=2 is out of bounds for this 2D array, causing an error.

3. Final Answer:

   The axis value 2 is invalid for a 2D array. -> Option C

4. Quick Check:

   Axis must be less than array dimensions [OK]

Hint: Axis must be less than array dimensions [OK]

Common Mistakes:

• Using axis larger than array dimensions
• Thinking np.sum can't handle 2D arrays
• Ignoring error messages

5. You have a 3D numpy array arr = np.array([[[1,2],[3,4]], [[5,6],[7,8]]]). What is the shape of the result when you run np.sum(arr, axis=1)?

hard

A. (1, 2)

B. (3, 2)

C. (2, 4)

D. (2, 2)

Solution

1. Step 1: Understand the shape of arr

   arr has shape (2, 2, 2): 2 blocks, each with 2 rows and 2 columns.

2. Step 2: Summing along axis=1

   axis=1 sums over the second dimension (rows), reducing it from 2 to 1, so shape changes from (2, 2, 2) to (2, 2).

3. Final Answer:

   (2, 2) -> Option D

4. Quick Check:

   Sum axis=1 reduces dimension 2 to 1 [OK]

Hint: Sum along axis removes that dimension [OK]

Common Mistakes:

• Confusing which axis is summed
• Expecting shape to stay same
• Mixing up dimensions in 3D arrays