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)

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

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)

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

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)

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

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.

Practice

1. What does the function np.percentile() calculate in a dataset?

easy

A. The value below which a given percentage of data falls

B. The average value of the dataset

C. The total sum of all data points

D. The number of data points in the dataset

Solution

1. Step 1: Understand percentile meaning

   A percentile indicates the value below which a certain percentage of data lies.

2. Step 2: Connect to np.percentile()

   The function np.percentile() returns this value for the given percentage.

3. Final Answer:

   The value below which a given percentage of data falls -> Option A

4. Quick Check:

   Percentile = value below % data [OK]

Hint: Percentile = cutoff value for given percent [OK]

Common Mistakes:

• Confusing percentile with mean or sum
• Thinking percentile counts data points
• Mixing percentile with total dataset size

2. Which of the following is the correct syntax to find the 25th percentile of a numpy array data?

easy

A. np.percentile(25, data)

B. np.percentile(data, 25)

C. np.percentile(data, percentile=25)

D. np.percentile(25%)

Solution

1. Step 1: Recall np.percentile() parameters

   The first argument is the data array, the second is the percentile number.

2. Step 2: Check each option

   np.percentile(data, 25) correctly uses np.percentile(data, 25). Others have wrong argument order or syntax.

3. Final Answer:

   np.percentile(data, 25) -> Option B

4. Quick Check:

   np.percentile(array, percentile) [OK]

Hint: First argument data, second percentile number [OK]

Common Mistakes:

• Swapping arguments order
• Using keyword 'percentile' incorrectly
• Adding % sign inside function

3. Given the array arr = np.array([10, 20, 30, 40, 50]), what is the output of np.percentile(arr, 40)?

medium

A. 20.0

B. 30.0

C. 26.0

D. 40.0

Solution

1. Step 1: Understand percentile calculation

   40th percentile means value below which 40% of data lies. Data sorted: [10,20,30,40,50]

2. Step 2: Calculate 40th percentile

   Position = (40/100)*(5-1) = 1.6. Interpolate between arr[1]=20 and arr[2]=30: 20 + 0.6*(30-20) = 26.0

3. Final Answer:

   26.0 -> Option C

4. Quick Check:

   40th percentile = 26.0 [OK]

Hint: Percentile index = p/100*(n-1), interpolate if needed [OK]

Common Mistakes:

• Picking nearest data point without interpolation
• Using wrong index calculation
• Confusing percentile with quartile

4. What is wrong with this code snippet to find the 90th percentile of data?

np.percentile(data, percentile=90)

medium

A. The data array must be sorted before calling np.percentile()

B. The percentile value must be between 0 and 1, not 90

C. The function should be called as data.percentile(90)

D. The keyword argument 'percentile' is invalid; percentile should be positional

Solution

1. Step 1: Check np.percentile() argument rules

   The function expects the percentile as the second positional argument, not as a keyword 'percentile'.

2. Step 2: Validate other options

   Percentile can be 90 (not 0.9), function is not a method of array, and sorting is handled internally.

3. Final Answer:

   The keyword argument 'percentile' is invalid; percentile should be positional -> Option D

4. Quick Check:

   Percentile argument is positional [OK]

Hint: Percentile must be second positional argument [OK]

Common Mistakes:

• Using invalid keyword argument
• Assuming percentile is fraction (0-1)
• Trying to sort data manually

5. You have a dataset data = np.array([5, 7, 8, 12, 15, 18, 22, 27, 30]). You want to find the value below which 75% of the data falls and then filter the dataset to only include values below this percentile. Which code correctly does this?

hard

A. threshold = np.percentile(data, 75)
filtered = data[data <= threshold]

B. threshold = np.percentile(data, 0.75)
filtered = data[data <= threshold]

C. threshold = np.percentile(data, 75)
filtered = data[data < threshold]

D. threshold = np.percentile(data, 75)
filtered = data[data > threshold]

Solution

1. Step 1: Calculate 75th percentile threshold

   Use np.percentile(data, 75) to get the value below which 75% data lies.

2. Step 2: Filter data including values equal to threshold

   Use <= to include values equal to threshold, so filtered = data[data <= threshold].

3. Final Answer:

   threshold = np.percentile(data, 75) filtered = data[data <= threshold] -> Option A

4. Quick Check:

   Percentile 75 + filter <= threshold [OK]

Hint: Use percentile value and filter with <= threshold [OK]

Common Mistakes:

• Using 0.75 instead of 75 for percentile
• Filtering with < instead of <=
• Filtering with > instead of <= or <