Why aggregation matters in NumPy - Challenge Your Understanding

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of numpy aggregation with axis

What is the output of this code that uses numpy to sum values along axis 0?

NumPy

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

A [1 2 3 4 5 6]

B [5 7 9]

C [6 15]

D [3 7 11]

💡 Hint

Think about summing down the rows for each column.

Summing along axis 0 adds elements vertically, so each column's values are added: [1+4, 2+5, 3+6] = [5,7,9].

❓ data_output

intermediate

Number of elements after aggregation

After applying np.mean(arr, axis=1) on the array below, how many elements does the resulting array have?

NumPy

import numpy as np
arr = np.array([[10, 20, 30], [40, 50, 60], [70, 80, 90]])
result = np.mean(arr, axis=1)
print(result)

A 3

B 1

C 9

D 6

💡 Hint

Mean along axis 1 reduces columns, keeping rows count.

Mean along axis 1 calculates average of each row, so output length equals number of rows, which is 3.

❓ visualization

advanced

Visualizing aggregation effect on data

Which plot correctly shows the effect of np.cumsum on the array below?

NumPy

import numpy as np
import matplotlib.pyplot as plt
arr = np.array([1, 2, 3, 4, 5])
cumsum = np.cumsum(arr)
plt.plot(arr, label='Original')
plt.plot(cumsum, label='Cumulative Sum')
plt.legend()
plt.show()

A Line plot with original values increasing by 1 and cumulative sum increasing faster

B Pie chart showing distribution of original values

C Scatter plot with original values decreasing and cumulative sum decreasing

D Bar plot with original values and cumulative sum as constant line

💡 Hint

Cumulative sum adds all previous values, so it grows faster than original.

The cumulative sum plot rises faster than the original because each point adds all previous values, showing aggregation effect.

🧠 Conceptual

advanced

Why aggregation reduces data size

Why does aggregation like np.sum or np.mean reduce the size of data when applied along an axis?

A Because aggregation removes all rows and columns except the first

B Because aggregation duplicates data to fill missing values

C Because aggregation combines multiple values into a single summary value along the specified axis

D Because aggregation sorts data and removes duplicates

💡 Hint

Think about what sum or mean does to multiple numbers.

Aggregation functions combine many values into one summary value per group or axis, reducing the number of elements.

🔧 Debug

expert

Identify the error in aggregation code

What error will this code raise and why?

NumPy

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

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

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

C No error, prints [3 7]

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

💡 Hint

Check the dimensions of the array and the axis argument.

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