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The **interquartile** **range**, often denoted “IQR”, is a way to measure the spread of the middle 50% of a dataset. It is calculated as the difference between the first quartile* (the 25th percentile) and the third quartile (the 75th percentile) of a dataset.

Fortunately it’s easy to calculate the interquartile range of a dataset in Python using the numpy.percentile() function.

This tutorial shows several examples of how to use this function in practice.

**Example 1: Interquartile Range of One Array**

The following code shows how to calculate the interquartile range of values in a single array:

import numpy as np #define array of data data = np.array([14, 19, 20, 22, 24, 26, 27, 30, 30, 31, 36, 38, 44, 47]) #calculate interquartile range q3, q1 = np.percentile(data, [75 ,25]) iqr = q3 - q1 #display interquartile range iqr 12.25

The interquartile range of this dataset turns out to be **12.25**. This is the spread of the middle 50% of values in this dataset.

**Example 2: Interquartile Range of a Data Frame Column**

The following code shows how to calculate the interquartile range of a single column in a data frame:

import numpy as np import pandas as pd #create data frame df = pd.DataFrame({'rating': [90, 85, 82, 88, 94, 90, 76, 75, 87, 86], 'points': [25, 20, 14, 16, 27, 20, 12, 15, 14, 19], 'assists': [5, 7, 7, 8, 5, 7, 6, 9, 9, 5], 'rebounds': [11, 8, 10, 6, 6, 9, 6, 10, 10, 7]}) #calculate interquartile range of values in the 'points' column q75, q25 = np.percentile(df['points'], [75 ,25]) iqr = q75 - q25 #display interquartile range iqr 5.75

The interquartile range of values in the points column turns out to be **5.75**.

**Example 3: Interquartile Range of Multiple Data Frame Columns**

The following code shows how to calculate the interquartile range of multiple columns in a data frame at once:

import numpy as np import pandas as pd #create data frame df = pd.DataFrame({'rating': [90, 85, 82, 88, 94, 90, 76, 75, 87, 86], 'points': [25, 20, 14, 16, 27, 20, 12, 15, 14, 19], 'assists': [5, 7, 7, 8, 5, 7, 6, 9, 9, 5], 'rebounds': [11, 8, 10, 6, 6, 9, 6, 10, 10, 7]}) #define function to calculate interquartile range def find_iqr(x): return np.subtract(*np.percentile(x, [75, 25])) #calculate IQR for 'rating' and 'points' columns df[['rating', 'points']].apply(find_iqr) rating 6.75 points 5.75 dtype: float64 #calculate IQR for all columns df.apply(find_iqr) rating 6.75 points 5.75 assists 2.50 rebounds 3.75 dtype: float64

**Note: **We use the pandas.DataFrame.apply() function to calculate the IQR for multiple columns in the data frame above.

**Additional Resources**

Is the Interquartile Range (IQR) Affected By Outliers?

How to Calculate the Interquartile Range (IQR) in Excel

Interquartile Range Calculator