np.clip() for bounding values in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to clip the values in the array to a minimum of 0.

NumPy

import numpy as np
arr = np.array([-5, 0, 5, 10])
clipped = np.clip(arr, [1], None)
print(clipped)

Drag options to blanks, or click blank then click option'

A-1

B10

C5

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Using None as minimum value causes error.

Using a positive number greater than 0 clips too many values.

np.clip() limits values below the minimum to the minimum value. Here, minimum is 0.

2fill in blank

medium

Complete the code to clip the values in the array between 2 and 8.

NumPy

import numpy as np
arr = np.array([1, 3, 5, 9])
clipped = np.clip(arr, [1], 8)
print(clipped)

Drag options to blanks, or click blank then click option'

A2

B0

C5

D9

Attempts:

3 left

💡 Hint

Common Mistakes

Setting minimum to 0 clips too many values.

Setting minimum higher than 2 clips values incorrectly.

Setting minimum to 2 and maximum to 8 clips values below 2 to 2 and above 8 to 8.

3fill in blank

hard

Fix the error in the code to clip values between 1 and 4.

NumPy

import numpy as np
arr = np.array([0, 2, 5, 7])
clipped = np.clip(arr, 1, [1])
print(clipped)

Drag options to blanks, or click blank then click option'

A7

B4

C5

D3

Attempts:

3 left

💡 Hint

Common Mistakes

Using a maximum bound larger than 4.

Using a minimum bound larger than maximum.

The maximum bound should be 4 to clip values above 4 down to 4.

4fill in blank

hard

Fill both blanks to clip values below 10 to 10 and above 20 to 20.

NumPy

import numpy as np
arr = np.array([5, 10, 15, 25])
clipped = np.clip(arr, [1], [2])
print(clipped)

Drag options to blanks, or click blank then click option'

A10

B15

C20

D25

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping minimum and maximum values.

Using values outside the specified range.

Minimum is 10 and maximum is 20 to clip values outside this range.

5fill in blank

hard

Fill all three blanks to create a dictionary with words as keys and their lengths clipped to max 4.

NumPy

words = ['apple', 'bat', 'cat', 'dolphin']
lengths = {word: len(word) if len(word) < [1] else [2] for word in words if len(word) > [3]
print(lengths)

Drag options to blanks, or click blank then click option'

A3

B4

C5

D6

Attempts:

3 left

💡 Hint

Common Mistakes

Using inconsistent max length values.

Filtering words incorrectly.

Clip lengths to max 4: if length less than 4, keep it; else set to 4. Include words longer than 3.

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=