Percentiles with np.percentile() in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of np.percentile() on a simple array

What is the output of this code snippet?

NumPy

import numpy as np
arr = np.array([10, 20, 30, 40, 50])
result = np.percentile(arr, 40)
print(result)

A 26.0

B 20.0

C 30.0

D 40.0

💡 Hint

Percentile 40 means the value below which 40% of data falls.

The 40th percentile is calculated by linear interpolation between 20 and 30, resulting in 26.0.

❓ data_output

intermediate

Percentiles of a 2D array along axis 0

What is the output of np.percentile(arr, 50, axis=0) for the given 2D array?

NumPy

import numpy as np
arr = np.array([[1, 3, 5], [2, 4, 6], [3, 5, 7]])
result = np.percentile(arr, 50, axis=0)
print(result)

A [3. 5. 7.]

B [1. 3. 5.]

C [2. 4. 6.]

D [2.5 4.5 6.5]

💡 Hint

50th percentile is the median along the specified axis.

The median along axis 0 is the middle value of each column: [2, 4, 6].

❓ visualization

advanced

Visualizing percentiles on a dataset

Which option correctly describes the plot generated by this code?

NumPy

import numpy as np
import matplotlib.pyplot as plt
np.random.seed(0)
data = np.random.normal(0, 1, 1000)
percentiles = np.percentile(data, [25, 50, 75])
plt.hist(data, bins=30, alpha=0.5)
for p in percentiles:
    plt.axvline(p, color='r', linestyle='--')
plt.show()

A Histogram with red dashed lines at the minimum, median, and maximum values

B Scatter plot of data points with red dashed lines at quartiles

C Line plot of data with red dashed lines at mean and standard deviation

D Histogram with red dashed lines at the 25th, 50th, and 75th percentiles

💡 Hint

Percentiles 25, 50, and 75 correspond to quartiles.

The code plots a histogram and draws vertical red dashed lines at the 25th, 50th (median), and 75th percentiles.

🧠 Conceptual

advanced

Understanding interpolation in np.percentile()

If np.percentile() is called with interpolation='nearest' on the array [1, 2, 3, 4], what is the 30th percentile value?

NumPy

import numpy as np
arr = np.array([1, 2, 3, 4])
result = np.percentile(arr, 30, interpolation='nearest')
print(result)

A 1

B 2

C 4

D 3

💡 Hint

Nearest interpolation picks the closest data point to the percentile rank.

The 30th percentile rank corresponds closest to the second element (value 2) when using nearest interpolation.

🔧 Debug

expert

Error raised by incorrect np.percentile() usage

What error does this code raise?

NumPy

import numpy as np
arr = np.array([1, 2, 3])
result = np.percentile(arr, 110)
print(result)

A ValueError: percentile must be in the range [0, 100]

B IndexError: index out of bounds

C No error, returns 3

D TypeError: unsupported operand type(s)

💡 Hint

Percentiles must be between 0 and 100 inclusive.

Percentile values outside 0-100 range cause a ValueError in np.percentile().

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 <