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.

Practice

1. What does the np.clip() function do in NumPy?

easy

A. Removes all negative values from the array

B. Sorts the array in ascending order

C. Limits values in an array to a specified minimum and maximum range

D. Calculates the cumulative sum of the array elements

Solution

1. Step 1: Understand the purpose of np.clip()

   The function np.clip() is designed to keep all values within a given range by replacing values below the minimum with the minimum, and values above the maximum with the maximum.

2. Step 2: Compare with other options

   Sorting, removing negatives, or cumulative sums are different operations and not what np.clip() does.

3. Final Answer:

   Limits values in an array to a specified minimum and maximum range -> Option C

4. Quick Check:

   np.clip() bounds values [OK]

Hint: Remember: clip means cut off outside limits [OK]

Common Mistakes:

• Confusing clip with sorting functions
• Thinking clip removes values instead of bounding
• Assuming clip changes array shape

2. Which of the following is the correct syntax to clip values of array arr between 0 and 10 using NumPy?

easy

A. np.clip(arr, min=0, max=10)

B. np.clip(0, 10, arr)

C. arr.clip(min=0, max=10)

D. np.clip(arr, 0, 10)

Solution

1. Step 1: Recall np.clip() parameter order

   The correct order is np.clip(array, min_value, max_value). So the array comes first, then min, then max.

2. Step 2: Check each option

   np.clip(arr, 0, 10) matches the correct order. np.clip(0, 10, arr) swaps parameters incorrectly. arr.clip(min=0, max=10) uses keyword arguments that arr.clip() does not support. np.clip(arr, min=0, max=10) uses keyword arguments that np.clip() does not support.

3. Final Answer:

   np.clip(arr, 0, 10) -> Option D

4. Quick Check:

   np.clip(array, min, max) syntax [OK]

Hint: Remember: array first, then min, then max in np.clip() [OK]

Common Mistakes:

• Swapping min and max arguments
• Using invalid keyword arguments with array.clip()
• Using keyword arguments min= or max= which are invalid

3. What is the output of the following code?

import numpy as np
arr = np.array([5, 15, -3, 7])
result = np.clip(arr, 0, 10)
print(result)

medium

A. [ 5 10 0 7]

B. [ 5 15 -3 7]

C. [10 10 0 10]

D. [ 0 10 0 0]

Solution

1. Step 1: Apply np.clip() to each element

   Values below 0 become 0, above 10 become 10, others stay the same. So 5 stays 5, 15 becomes 10, -3 becomes 0, 7 stays 7.

2. Step 2: Write the resulting array

   The clipped array is [5, 10, 0, 7].

3. Final Answer:

   [ 5 10 0 7] -> Option A

4. Quick Check:

   Clip caps values outside [0,10] [OK]

Hint: Clip caps values below min and above max [OK]

Common Mistakes:

• Forgetting to clip negative values to 0
• Not clipping values above max to max
• Expecting original array unchanged

4. The code below throws an error. What is the problem?

import numpy as np
arr = np.array([1, 2, 3])
result = np.clip(arr, max=5, min=0)
print(result)

medium

A. np.clip() does not accept keyword arguments named 'min' and 'max'

B. The array must be a list, not a NumPy array

C. The min value cannot be zero

D. The print statement is missing parentheses

Solution

1. Step 1: Check np.clip() parameter usage

   np.clip() expects positional arguments: array, min, max. It does not accept keyword arguments named 'min' or 'max'.

2. Step 2: Identify the error cause

   Using 'max=5' and 'min=0' causes a TypeError because these keywords are not defined in np.clip().

3. Final Answer:

   np.clip() does not accept keyword arguments named 'min' and 'max' -> Option A

4. Quick Check:

   np.clip() uses positional args only [OK]

Hint: Use positional args in np.clip(), no min= or max= [OK]

Common Mistakes:

• Trying to use keyword arguments with np.clip()
• Assuming np.clip() works on lists only
• Misreading error as print syntax issue

5. You have a NumPy array of temperatures in Celsius: temps = np.array([-5, 0, 15, 40, 50]). You want to limit the temperatures to a safe range between 0 and 35 degrees before analysis. Which code correctly applies np.clip() and what is the resulting array?

hard

A. np.clip(temps, min=0, max=35) -> [ 0 0 15 35 35]

B. np.clip(temps, 0, 35) -> [ 0 0 15 35 35]

C. temps.clip(min=0, max=35) -> [ 0 0 15 35 35]

D. np.clip(temps, 35, 0) -> [35 35 35 35 35]

Solution

1. Step 1: Apply np.clip() with correct parameter order

   The correct call is np.clip(temps, 0, 35) to limit values below 0 to 0 and above 35 to 35.

2. Step 2: Calculate the clipped array

   Values: -5 -> 0, 0 -> 0, 15 -> 15, 40 -> 35, 50 -> 35. Result: [0, 0, 15, 35, 35].

3. Final Answer:

   np.clip(temps, 0, 35) -> [ 0 0 15 35 35] -> Option B

4. Quick Check:

   Clip bounds temps to safe range [OK]

Hint: Use np.clip(array, min, max) to limit values [OK]

Common Mistakes:

• Swapping min and max values
• Using invalid keyword arguments with array.clip()
• Trying to use keyword arguments min= or max=