Percentiles with np.percentile() in NumPy - Mini Project: Build & Apply

Calculate Percentiles with np.percentile()

📖 Scenario: You work in a small bakery that tracks daily sales of different bread types. You want to understand how sales vary by calculating percentiles, which help you see the sales distribution.

🎯 Goal: Build a simple program that calculates the 25th, 50th, and 75th percentiles of daily bread sales using np.percentile().

📋 What You'll Learn

Create a numpy array with daily sales data

Set a list of percentiles to calculate

Use np.percentile() to find the percentile values

Print the percentile results clearly

💡 Why This Matters

🌍 Real World

Percentiles help businesses understand data spread, like how many sales fall below a certain value.

💼 Career

Data analysts and scientists use percentiles to summarize and explain data distributions clearly.

1

Create the sales data array

Create a numpy array called sales with these exact daily sales numbers: 20, 35, 30, 35, 27, 25, 40, 50, 29, 30.

NumPy

import numpy as np

# Create the sales array with the exact values
# Your code here

Hint

Use np.array() and put the numbers inside square brackets.

2

Set the percentiles to calculate

Create a list called percentiles with these exact values: 25, 50, 75.

NumPy

import numpy as np

sales = np.array([20, 35, 30, 35, 27, 25, 40, 50, 29, 30])

# Create the percentiles list with 25, 50, and 75
# Your code here

Hint

Use square brackets to create a list with the three numbers.

3

Calculate the percentile values

Use np.percentile() with the sales array and the percentiles list to create a variable called percentile_values that stores the results.

NumPy

import numpy as np

sales = np.array([20, 35, 30, 35, 27, 25, 40, 50, 29, 30])
percentiles = [25, 50, 75]

# Calculate percentile_values using np.percentile
# Your code here

Hint

Call np.percentile() with the sales array and the percentiles list as arguments.

4

Print the percentile results

Print the percentile_values variable to display the 25th, 50th, and 75th percentiles of sales.

NumPy

import numpy as np

sales = np.array([20, 35, 30, 35, 27, 25, 40, 50, 29, 30])
percentiles = [25, 50, 75]
percentile_values = np.percentile(sales, percentiles)

# Print the percentile_values
# Your code here

Hint

Use print() to show the percentile_values array.

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 <