# How to Calculate The Interquartile Range in Python

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.