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.