np.sign() for sign detection in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

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

The np.sign() function returns the sign of each element in an array. It outputs 1 for positive numbers, -1 for negative numbers, and 0 for zeros.

beginner

What output does np.sign() give for the input array [-3, 0, 4]?

It returns [-1, 0, 1] because -3 is negative, 0 is zero, and 4 is positive.

intermediate

Why is np.sign() useful in data science?

It helps quickly identify if values are positive, negative, or zero. This is useful for tasks like filtering data, feature engineering, or understanding trends.

intermediate

How does np.sign() handle floating point numbers close to zero?

It treats any positive number (even very small) as 1, any negative number as -1, and exactly zero as 0. It does not round or threshold values.

beginner

Can np.sign() be applied to multi-dimensional arrays?

Yes, np.sign() works element-wise on arrays of any shape, returning an array of the same shape with the sign of each element.

What does np.sign(-7) return?

A 0

B -1

C 1

D 7

Since -7 is negative, np.sign() returns -1.

What is the output of np.sign([3, -2, 0])?

A [1, 1, 0]

B [-1, 0, 1]

C [3, -2, 0]

D [1, -1, 0]

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

Which of these is NOT a valid output of np.sign()?

A 2

B -1

C 0

D 1

np.sign() only returns -1, 0, or 1.

If an array contains only zeros, what will np.sign() return?

A An array of zeros

B An array of negative ones

C An array of ones

D An error

Zeros remain zeros after applying np.sign().

How does np.sign() treat very small positive numbers like 0.00001?

A Returns -1

B Returns 0

C Returns 1

D Returns the original number

Any positive number, no matter how small, returns 1.

Explain how np.sign() works and give an example with a small array.

Think about how it tells if numbers are positive, negative, or zero.

Describe a real-life situation where detecting the sign of numbers using np.sign() could be helpful.

Consider when knowing if a value is above or below zero matters.