np.mean() for average in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

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

What is the output of this code snippet using np.mean() on a 2D array with axis=0?

NumPy

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

A [2.5 3.5 4.5]

B [2. 3. 4.]

C [3. 4. 5.]

D [1.5 2.5 3.5]

💡 Hint

Remember, axis=0 means averaging down the rows for each column.

The mean along axis=0 calculates the average of each column: (1+4)/2=2.5, (2+5)/2=3.5, (3+6)/2=4.5.

❓ data_output

intermediate

Number of elements in np.mean() output with axis=1

Given a 3x4 numpy array, how many elements does the output have after applying np.mean() with axis=1?

NumPy

import numpy as np
arr = np.arange(12).reshape(3,4)
result = np.mean(arr, axis=1)
print(result)
print(len(result))

A 12

B 3

C 4

D 1

💡 Hint

Axis=1 means averaging across columns for each row.

The array has 3 rows, so averaging across columns (axis=1) produces 3 mean values, one per row.

🔧 Debug

advanced

Identify the error in np.mean() usage

What error does this code raise when trying to compute the mean?

NumPy

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

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

B ValueError: could not broadcast input array from shape (3,) into shape (1,)

C AxisError: axis 1 is out of bounds for array of dimension 1

D No error, prints 2.0

💡 Hint

Check the shape of the array and the axis parameter.

The array is 1D, so axis=1 is invalid and causes an AxisError.

🧠 Conceptual

advanced

Effect of np.mean() with keepdims=True

What is the shape of the output when applying np.mean() on a 2D array with axis=1 and keepdims=True?

NumPy

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

A (2, 1)

B (2,)

C (1, 3)

D (3, 2)

💡 Hint

Keepdims keeps the reduced dimension as size 1.

With keepdims=True, the output keeps the axis dimension with size 1, so shape is (2,1).

🚀 Application

expert

Calculate weighted average using np.mean()

You have values [10, 20, 30] and weights [1, 2, 3]. Which code correctly calculates the weighted average using numpy?

A np.mean([10, 20, 30], weights=[1, 2, 3])

B np.average([10, 20, 30], weights=[1, 2, 3])

C np.mean([10, 20, 30]) * np.mean([1, 2, 3])

D np.sum(np.array([10, 20, 30]) * np.array([1, 2, 3])) / np.sum([1, 2, 3])

💡 Hint

np.mean() does not support weights parameter; use np.average or manual calculation.

np.mean() does not accept weights. np.average supports weights. Option D manually computes weighted average correctly.