np.sign() for sign detection in NumPy - Step-by-Step Execution

Concept Flow - np.sign() for sign detection

Input array

↓

Apply np.sign()

↓

Check each element's sign

↓

Output array with -1, 0, or 1

↓

Use sign info for analysis

np.sign() takes each number and returns -1 if negative, 0 if zero, and 1 if positive, helping detect the sign of each element.

Execution Sample

NumPy

import numpy as np
arr = np.array([-3, 0, 4, -1, 5])
signs = np.sign(arr)
print(signs)

This code finds the sign of each number in the array, outputting -1, 0, or 1 for each element.

Execution Table

Step	Input Element	np.sign() Result	Explanation
1	-3	-1	Negative number returns -1
2	0	0	Zero returns 0
3	4	1	Positive number returns 1
4	-1	-1	Negative number returns -1
5	5	1	Positive number returns 1
6	All elements processed	[-1 0 1 -1 1]	Final output array with signs

💡 All elements processed, np.sign() returned sign for each element

Variable Tracker

Variable	Start	After 1	After 2	After 3	After 4	After 5	Final
arr	[-3, 0, 4, -1, 5]	[-3, 0, 4, -1, 5]	[-3, 0, 4, -1, 5]	[-3, 0, 4, -1, 5]	[-3, 0, 4, -1, 5]	[-3, 0, 4, -1, 5]	[-3, 0, 4, -1, 5]
signs	[]	[-1]	[-1, 0]	[-1, 0, 1]	[-1, 0, 1, -1]	[-1, 0, 1, -1, 1]	[-1, 0, 1, -1, 1]

Key Moments - 2 Insights

Why does np.sign() return 0 for zero but not for other numbers?

np.sign() returns 0 only when the input element is exactly zero, as shown in execution_table step 2. This distinguishes zero from positive or negative numbers.

Can np.sign() handle arrays with mixed positive, negative, and zero values?

Yes, as shown in the execution_table, np.sign() processes each element independently and returns the correct sign for each, producing an array of -1, 0, or 1.

Visual Quiz - 3 Questions

Look at the execution_table, what is the np.sign() result for the input element -1 at step 4?

A -1

B 0

C 1

D None

💡 Hint

Check execution_table row with Step 4 for input -1 and its np.sign() result.

At which step does np.sign() return 0 according to the execution_table?

A Step 1

B Step 3

C Step 2

D Step 5

💡 Hint

Look for the step where the input element is zero and np.sign() returns 0.

If the input array had an extra element 7 at the end, how would the final signs array change?

A [-1, 0, 1, -1, 1, -1]

B [-1, 0, 1, -1, 1, 1]

C [-1, 0, 1, -1, 1, 0]

D [-1, 0, 1, -1, 1]

💡 Hint

Positive number 7 would add a 1 at the end of the signs array.

Concept Snapshot

np.sign(array) returns an array with -1 for negatives, 0 for zeros, and 1 for positives.
Useful to quickly detect the sign of each element.
Works element-wise on numpy arrays.
Output array has same shape as input.
Simple way to classify numbers by sign.

Full Transcript

This lesson shows how np.sign() works in numpy. It takes each number in an array and returns -1 if the number is negative, 0 if it is zero, and 1 if it is positive. We traced an example array with values -3, 0, 4, -1, and 5. Step by step, np.sign() returned -1, 0, 1, -1, and 1 respectively. The final output is an array of these sign values. This helps us quickly understand the sign of each number in data. We also answered common questions about zero handling and mixed values. Finally, quiz questions tested understanding of the sign results at different steps and how adding a positive number changes the output.