np.clip() for bounding values in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of np.clip() with 1D array

What is the output of this code snippet using np.clip()?

NumPy

import numpy as np
arr = np.array([1, 5, 10, 15, 20])
result = np.clip(arr, 5, 15)
print(result)

A [ 1 5 10 15 15]

B [ 1 5 10 15 20]

C [ 5 5 10 10 15]

D [ 5 5 10 15 15]

💡 Hint

np.clip() limits values below the minimum to the minimum, and above the maximum to the maximum.

Values less than 5 become 5, values greater than 15 become 15, others stay the same.

❓ data_output

intermediate

Resulting array shape after clipping 2D array

Given this 2D array and clipping operation, what is the shape of the resulting array?

NumPy

import numpy as np
arr = np.array([[2, 8, 12], [20, 5, 0]])
clipped = np.clip(arr, 3, 10)
print(clipped.shape)

A (2, 3)

B (3, 2)

C (6,)

D (3,)

💡 Hint

Clipping does not change the shape of the array.

np.clip returns an array of the same shape as input.

🔧 Debug

advanced

Identify the error in np.clip usage

What error does this code raise?

NumPy

import numpy as np
arr = np.array([1, 2, 3])
result = np.clip(arr, 5)
print(result)

A TypeError: clip() missing 1 required positional argument: 'a_max'

B SyntaxError: invalid syntax

C ValueError: min cannot be greater than max

D No error, outputs [5 5 5]

💡 Hint

np.clip requires both minimum and maximum values unless using keyword arguments.

np.clip requires both a_min and a_max arguments unless specified by keywords.

🚀 Application

advanced

Using np.clip to limit sensor readings

You have sensor readings in an array. You want to limit all values below 0 to 0 and above 100 to 100. Which code correctly does this?

NumPy

import numpy as np
sensor_data = np.array([-10, 20, 150, 50, 0])

A np.clip(sensor_data, 100, 0)

B np.clip(sensor_data, 0, 100)

C np.clip(sensor_data, min=0, max=100)

D np.clip(sensor_data, a_min=0)

💡 Hint

np.clip needs minimum and maximum values in correct order.

Option B correctly clips values below 0 to 0 and above 100 to 100.

🧠 Conceptual

expert

Effect of np.clip on data distribution

If you apply np.clip() to a large dataset to limit values between the 5th and 95th percentile, what is the main effect on the data distribution?

A It randomly samples 5% of data from both ends.

B It normalizes the data to have mean 0 and standard deviation 1.

C It removes outliers by capping extreme values, reducing skewness.

D It increases the variance by stretching values beyond original range.

💡 Hint

Clipping limits extreme values but does not change mean or variance directly.

Clipping caps extreme values, which reduces the effect of outliers and can reduce skewness.