Percentiles with np.percentile() in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the 50th percentile (median) of the data array.

NumPy

import numpy as np

data = np.array([10, 20, 30, 40, 50])
median = np.percentile(data, [1])
print(median)

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

A25

B75

C50th

D50

Attempts:

3 left

💡 Hint

Common Mistakes

Using the value 50th as a string instead of the number 50.

Passing the wrong percentile number like 25 or 75.

The 50th percentile corresponds to the median, so we pass 50 to np.percentile().

2fill in blank

medium

Complete the code to calculate the 90th percentile of the data array.

NumPy

import numpy as np

data = np.array([5, 15, 25, 35, 45, 55])
ninetieth = np.percentile(data, [1])
print(ninetieth)

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A90

B75

C85

D95

Attempts:

3 left

💡 Hint

Common Mistakes

Using 75 or 85 instead of 90.

Passing the percentile as a string like '90' instead of number 90.

The 90th percentile means the value below which 90% of the data falls, so we pass 90 to np.percentile().

3fill in blank

hard

Complete the code to calculate the 25th percentile.

NumPy

import numpy as np

data = [1, 2, 3, 4, 5]
percentile_25 = np.percentile([1], 25)
print(percentile_25)

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Adata

Bnp.array(data)

Clist(data)

Ddata.tolist()

Attempts:

3 left

💡 Hint

Common Mistakes

Selecting D: lists do not have a tolist() method, causing an AttributeError.

Unnecessarily converting when not needed (though B and C also work).

np.percentile() accepts Python lists directly because they are array_like.

4fill in blank

hard

Fill both blanks to create a dictionary of percentiles for 25th and 75th percentiles.

NumPy

import numpy as np

data = np.array([2, 4, 6, 8, 10])
percentiles = {25: np.percentile(data, [1]), 75: np.percentile(data, [2])}
print(percentiles)

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A25

B50

C75

D100

Attempts:

3 left

💡 Hint

Common Mistakes

Using 50 or 100 instead of 25 or 75.

Mixing up the percentile values for the keys.

To get the 25th and 75th percentiles, pass 25 and 75 respectively to np.percentile().

5fill in blank

hard

Fill all three blanks to create a dictionary of percentiles for 10th, 50th, and 90th percentiles.

NumPy

import numpy as np

data = np.array([3, 6, 9, 12, 15])
percentiles = {10: np.percentile(data, [1]), 50: np.percentile(data, [2]), 90: np.percentile(data, [3])}
print(percentiles)

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

A10

B50

C90

D100

Attempts:

3 left

💡 Hint

Common Mistakes

Using 100 instead of 90 for the last percentile.

Mixing up the percentile numbers.

Each percentile value matches the key: 10th, 50th, and 90th percentiles.