Aggregation along specific axes in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Sum aggregation along axis in a 2D array

What is the output of the following code that sums elements along axis 0 of a 2D numpy array?

NumPy

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

A [6 15]

B [3 7 11]

C [5 7 9]

D [1 2 3 4 5 6]

💡 Hint

Sum along axis 0 means summing down each column.

Summing along axis 0 adds elements vertically: [1+4, 2+5, 3+6] = [5, 7, 9].

❓ data_output

intermediate

Mean aggregation along axis 1 in a 3x3 array

What is the output array after computing the mean along axis 1 of this numpy array?

NumPy

import numpy as np
arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
result = arr.mean(axis=1)
print(result)

A [5. 5. 5.]

B [1. 4. 7.]

C [3. 6. 9.]

D [2. 5. 8.]

💡 Hint

Mean along axis 1 means averaging each row.

Each row's mean: (1+2+3)/3=2, (4+5+6)/3=5, (7+8+9)/3=8.

❓ visualization

advanced

Visualizing sum aggregation along axis 0 and axis 1

Which option correctly describes the bar plots generated by summing a 2D array along axis 0 and axis 1?

NumPy

import numpy as np
import matplotlib.pyplot as plt
arr = np.array([[1, 2, 3], [4, 5, 6]])
sum_axis0 = arr.sum(axis=0)
sum_axis1 = arr.sum(axis=1)
plt.subplot(1,2,1)
plt.bar(range(len(sum_axis0)), sum_axis0)
plt.title('Sum along axis 0')
plt.subplot(1,2,2)
plt.bar(range(len(sum_axis1)), sum_axis1)
plt.title('Sum along axis 1')
plt.show()

A Left plot bars heights: [5,7,9]; Right plot bars heights: [6,15]

B Left plot bars heights: [6,15]; Right plot bars heights: [5,7,9]

C Both plots have bars heights: [5,7,9]

D Both plots have bars heights: [6,15]

💡 Hint

Sum along axis 0 sums columns; axis 1 sums rows.

Sum axis 0: [1+4, 2+5, 3+6] = [5,7,9]. Sum axis 1: [1+2+3, 4+5+6] = [6,15].

🔧 Debug

advanced

Identify the error in axis aggregation

What error will this code raise when trying to sum along axis 2 of a 2D numpy array?

NumPy

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

A AxisError: axis 2 is out of bounds for array of dimension 2

B TypeError: unsupported operand type(s) for +: 'int' and 'str'

C ValueError: operands could not be broadcast together with shapes (2,3) (3,2)

D No error, output: [6 15]

💡 Hint

Check the number of dimensions of the array and the axis argument.

The array has 2 dimensions (axes 0 and 1). Axis 2 does not exist, causing AxisError.

🚀 Application

expert

Calculate total sales per product and per region

Given a 3D numpy array representing sales data with shape (regions, products, months), which code correctly computes total sales per product across all regions and months?

NumPy

import numpy as np
sales = np.array([
  [[10, 20], [30, 40]],  # Region 1
  [[5, 15], [25, 35]]    # Region 2
])
# Compute total sales per product across regions and months
result = None  # Fill in the correct aggregation

A sales.sum(axis=2)

B sales.sum(axis=(0,2))

C sales.sum(axis=1)

D sales.sum(axis=0)

💡 Hint

Sum over regions and months to get totals per product.

Summing over axis 0 (regions) and axis 2 (months) leaves axis 1 (products) summed per product.