np.clip() for bounding values in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the function np.clip() do in numpy?

np.clip() limits the values in an array to a specified minimum and maximum range. Values below the minimum become the minimum, and values above the maximum become the maximum.

beginner

How would you use np.clip() to ensure all values in an array are between 0 and 10?

Use np.clip(array, 0, 10). This sets any value less than 0 to 0, and any value greater than 10 to 10.

intermediate

If you call np.clip(arr, a_min=None, a_max=5), what happens to values in arr?

Values greater than 5 become 5. Values less than 5 remain unchanged because a_min=None means no lower bound.

beginner

Why is np.clip() useful in real-life data science tasks?

It helps keep data within expected limits, like bounding sensor readings or normalizing scores, preventing extreme values from affecting analysis.

beginner

What is the output of np.clip(np.array([1, 5, 10, 15]), 3, 12)?

The output array is [3, 5, 10, 12]. Values below 3 become 3, values above 12 become 12, others stay the same.

What does np.clip(array, 0, 1) do to the array values?

A Sets all values below 0 to 0 and above 1 to 1

B Removes values outside 0 and 1

C Multiplies all values by 0 or 1

D Leaves the array unchanged

np.clip() limits values to the given range by replacing values outside the range with the boundary values.

If you want to only limit the upper bound of an array to 10, what should you pass as a_min?

A None

B 0

C 10

D -10

Passing a_min=None means no lower bound is applied, so only the upper bound is enforced.

Which numpy function is best to keep values within a fixed range?

A np.unique()

B np.sort()

C np.mean()

D np.clip()

np.clip() is designed to bound values within a specified minimum and maximum.

What happens if a_min is greater than a_max in np.clip()?

A All values become <code>a_min</code>

B Values are clipped normally

C An error is raised

D All values become <code>a_max</code>

Setting a_min greater than a_max causes a ValueError because the bounds are invalid.

Which of these is a valid use of np.clip()?

A np.clip(array, 5, 3)

B np.clip(array, 0, 100)

C np.clip(array, 'low', 'high')

D np.clip(array, None, None)

np.clip(array, 0, 100) correctly clips values between 0 and 100 using valid numeric bounds in proper order. A raises ValueError (a_min > a_max), B applies no clipping, C uses invalid types.

Explain how np.clip() can help when working with noisy sensor data.

Think about keeping data within a safe range.

Describe the parameters of np.clip() and what happens if you set one of the bounds to None.

Consider how clipping works with only one bound.