Percentiles with np.percentile() in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a percentile in data analysis?

A percentile is a value below which a certain percentage of data falls. For example, the 25th percentile is the value below which 25% of the data lies.

beginner

What does the function np.percentile() do?

np.percentile() calculates the value below which a given percentage of data in an array falls. It helps find specific percentiles like median (50th percentile) or quartiles.

beginner

How do you find the median of an array arr using np.percentile()?

Use np.percentile(arr, 50). The 50th percentile is the median, the middle value of the data.

intermediate

What is the difference between the 25th and 75th percentiles?

The 25th percentile (Q1) is the value below which 25% of data falls, and the 75th percentile (Q3) is the value below which 75% of data falls. The difference between them is called the interquartile range (IQR), showing data spread.

intermediate

How does np.percentile() handle multi-dimensional arrays?

np.percentile() can calculate percentiles along a specified axis using the axis parameter. This lets you find percentiles for rows or columns separately.

What does np.percentile(arr, 90) return?

A The average of the data in <code>arr</code>

B The value below which 90% of the data in <code>arr</code> falls

C The maximum value in <code>arr</code>

D The value above which 90% of the data falls

np.percentile(arr, 90) returns the 90th percentile, meaning 90% of data is below this value.

Which percentile corresponds to the median?

A 50th percentile

B 25th percentile

C 75th percentile

D 100th percentile

The median is the 50th percentile, the middle value of the data.

How do you calculate the interquartile range (IQR) using percentiles?

A 50th percentile minus 25th percentile

B Maximum minus minimum

C 75th percentile minus 25th percentile

D 25th percentile plus 75th percentile

IQR = Q3 (75th percentile) - Q1 (25th percentile), showing the middle 50% spread.

What parameter do you use in np.percentile() to calculate percentiles along columns of a 2D array?

A axis=2

B axis=1

C axis=None

D axis=0

Use axis=0 to calculate percentiles down columns (each column separately).

If you want the 0th percentile of data, what value does np.percentile() return?

A Minimum value

B Maximum value

C Mean value

D Median value

The 0th percentile is the smallest value in the data.

Explain what a percentile is and how you can use np.percentile() to find the median and quartiles in a dataset.

Think about how percentiles split data into parts and how np.percentile helps find those split points.

Describe how to calculate the interquartile range (IQR) using np.percentile() and why it is useful.

IQR is the difference between two key percentiles that capture the middle half of data.

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 <