np.sign() for sign detection in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to find the sign of each number in the array using np.sign().

NumPy

import numpy as np
arr = np.array([-3, 0, 4])
signs = np.[1](arr)
print(signs)

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

Aabs

Bsign

Csqrt

Dlog

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.abs() which returns absolute values, not signs.

Using np.sqrt() which calculates square roots.

Using np.log() which calculates logarithms.

The np.sign() function returns -1 for negative numbers, 0 for zero, and 1 for positive numbers.

2fill in blank

medium

Complete the code to create a boolean mask for positive numbers using np.sign().

NumPy

import numpy as np
arr = np.array([-2, 5, 0, 3])
mask = np.sign(arr) [1] 1
print(mask)

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

A<

B!=

C>

D==

Attempts:

3 left

💡 Hint

Common Mistakes

Using '!=' which selects non-positive numbers.

Using '<' or '>' which do not correctly select only positive signs.

Using np.sign(arr) == 1 creates a mask where only positive numbers are True.

3fill in blank

hard

Fix the error in the code to correctly compute signs of values in the list.

NumPy

import numpy as np
values = [-1, 0, 2]
signs = np.sign([1])
print(signs)

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

Anp.array(values)

B[values]

Clist(values)

Dvalues

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the list directly without conversion.

Wrapping the list in another list which creates a nested list.

Using list() which does not convert to numpy array.

np.sign() works best with numpy arrays, so convert the list to an array first.

4fill in blank

hard

Fill both blanks to create a dictionary with words as keys and their sign of length comparison to 3 as values.

NumPy

import numpy as np
words = ['cat', 'dog', 'elephant']
sign_dict = {word: np.sign(len(word) [1] 3) for word in words if len(word) [2] 2}
print(sign_dict)

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

A>

B==

C>=

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using '==' in the first blank which returns 0 or 1 only.

Using '<' in the second blank which filters out longer words.

The dictionary comprehension uses len(word) > 3 inside np.sign() to get the sign, and filters words with length >= 2.

5fill in blank

hard

Fill both blanks to create a dictionary with uppercase words as keys, their lengths as values, and only include words with positive sign difference from 4.

NumPy

import numpy as np
words = ['apple', 'bat', 'carrot', 'dog']
result = {word[1]: len(word) for word in words if np.sign(len(word) [2] 4) == 1}
print(result)

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

A.upper()

C>

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

Not converting keys to uppercase.

Using '<=' instead of '>' in the sign comparison.

Adding an operator in the value part which is not needed.

The keys are uppercase words using .upper(), values are lengths, and only words longer than 4 are included.

Practice

1. What does the np.sign() function return when applied to a negative number?

easy

A. -1

B. 0

C. 1

D. The original number

Solution

1. Step 1: Understand np.sign() behavior

   The function returns -1 for negative numbers, 0 for zero, and 1 for positive numbers.

2. Step 2: Apply to a negative number

   Since the input is negative, np.sign() returns -1.

3. Final Answer:

   -1 -> Option A

4. Quick Check:

   Negative number sign = -1 [OK]

Hint: Negative input always gives -1 from np.sign() [OK]

Common Mistakes:

• Confusing negative with zero
• Expecting original number as output
• Thinking it returns boolean

2. Which of the following is the correct syntax to get the sign of each element in a numpy array arr?

easy

A. np.sign(arr)

B. arr.sign()

C. sign(arr)

D. np.sign_of(arr)

Solution

1. Step 1: Recall numpy function usage

   Functions in numpy are called with the syntax np.function_name(arguments).

2. Step 2: Identify correct function call

   The correct function to get sign is np.sign(), so np.sign(arr) is correct.

3. Final Answer:

   np.sign(arr) -> Option A

4. Quick Check:

   Correct numpy function call = np.sign(arr) [OK]

Hint: Use np.sign(array) to get signs of all elements [OK]

Common Mistakes:

• Using method on array like arr.sign()
• Calling sign() without np prefix
• Using non-existent np.sign_of()

3. What is the output of the following code?

import numpy as np
arr = np.array([-3, 0, 4])
sign_arr = np.sign(arr)
print(sign_arr)

medium

A. [-3 0 4]

B. [3 0 4]

C. [0 0 0]

D. [-1 0 1]

Solution

1. Step 1: Understand input array values

   The array has values -3 (negative), 0 (zero), and 4 (positive).

2. Step 2: Apply np.sign() to each element

   np.sign(-3) = -1, np.sign(0) = 0, np.sign(4) = 1, so the output array is [-1, 0, 1].

3. Final Answer:

   [-1 0 1] -> Option D

4. Quick Check:

   Signs of [-3,0,4] = [-1,0,1] [OK]

Hint: np.sign() maps negative to -1, zero to 0, positive to 1 [OK]

Common Mistakes:

• Expecting original values
• Confusing zero with positive
• Outputting boolean instead of sign

4. The following code throws an error. What is the mistake?

import numpy as np
arr = [-1, 2, 0]
signs = np.sign arr
print(signs)

medium

A. print() syntax error

B. Using list instead of numpy array

C. Missing parentheses in function call

D. np.sign does not exist

Solution

1. Step 1: Check function call syntax

   The code uses np.sign arr without parentheses, which is invalid syntax in Python.

2. Step 2: Correct the syntax

   It should be np.sign(arr) with parentheses to call the function properly.

3. Final Answer:

   Missing parentheses in function call -> Option C

4. Quick Check:

   Function calls need parentheses [OK]

Hint: Always use parentheses when calling functions [OK]

Common Mistakes:

• Omitting parentheses
• Thinking lists cause error here
• Assuming np.sign is undefined

5. You have a numpy array data = np.array([-5, 0, 3, -2, 7]). How can you create a new array that replaces all negative values with 0, using np.sign()?

hard

A. data * np.sign(data)

B. data * (np.sign(data) + 1) / 2

C. np.sign(data) * 2

D. data + np.sign(data)

Solution

1. Step 1: Understand np.sign() output

   np.sign(data) gives -1 for negatives, 0 for zero, 1 for positives.

2. Step 2: Transform sign to mask for positives and zero

   Adding 1 to sign gives 0 for -1, 1 for 0, 2 for 1. Dividing by 2 maps negatives to 0, zero to 0.5, positives to 1.

3. Step 3: Multiply original data by this mask

   Multiplying data by this mask sets negative values to 0, keeps zero and positive values unchanged (zero times 0.5 is 0).

4. Final Answer:

   data * (np.sign(data) + 1) / 2 -> Option B

5. Quick Check:

   Mask negatives to zero using (sign+1)/2 [OK]

Hint: Use (sign+1)/2 as mask to zero negatives [OK]

Common Mistakes:

• Using sign directly multiplies negatives
• Adding sign to data changes values wrongly
• Confusing mask calculation