np.sign() for sign detection in NumPy - Mini Project: Build & Apply

Detecting Number Signs Using np.sign()

📖 Scenario: Imagine you have a list of daily temperature changes in degrees Celsius. Some days are warmer (positive change), some are colder (negative change), and some days have no change (zero). You want to quickly find out which days were warmer, colder, or stayed the same.

🎯 Goal: You will create a program that uses np.sign() to detect the sign of each temperature change and output the results as an array of -1, 0, or 1.

📋 What You'll Learn

Create a numpy array with exact temperature changes

Create a variable to hold the numpy array

Use np.sign() to find the sign of each number

Print the resulting sign array

💡 Why This Matters

🌍 Real World

Detecting signs of numbers is useful in finance to know if stock prices went up or down, in weather to track temperature changes, or in any data where direction matters.

💼 Career

Data scientists often need to quickly classify data points by their sign to make decisions or prepare data for further analysis.

1

Create the temperature changes array

Create a numpy array called temp_changes with these exact values: [-3, 0, 4, -1, 2, 0].

NumPy

import numpy as np
# Create the numpy array temp_changes with values [-3, 0, 4, -1, 2, 0]
# Your code here

Hint

Use np.array() to create the array with the exact values.

2

Prepare to detect signs

Create a variable called signs and set it to None as a placeholder before detecting signs.

NumPy

import numpy as np

temp_changes = np.array([-3, 0, 4, -1, 2, 0])

# Create a variable signs and set it to None
# Your code here

Hint

Just create the variable signs and assign None to it.

3

Detect the sign of each temperature change

Use np.sign() on the temp_changes array and assign the result to the variable signs.

NumPy

import numpy as np

temp_changes = np.array([-3, 0, 4, -1, 2, 0])

signs = None

# Use np.sign() on temp_changes and assign to signs
# Your code here

Hint

Call np.sign(temp_changes) and save it in signs.

4

Print the sign detection result

Print the variable signs to display the sign of each temperature change.

NumPy

import numpy as np

temp_changes = np.array([-3, 0, 4, -1, 2, 0])

signs = np.sign(temp_changes)

# Print the signs array
# Your code here

Hint

Use print(signs) to show the output.

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