How one can Calculate The Interquartile Space in Python

The interquartile dimension, steadily denoted “IQR”, is a strategy to measure the unfold of the center 50% of a dataset. It’s calculated because the excess between the primary quartile* (the twenty fifth percentile) and the 3rd quartile (the seventy fifth percentile) of a dataset. 

Thankfully it’s simple to calculate the interquartile dimension of a dataset in Python the use of the numpy.percentile() serve as.

This instructional displays a number of examples of find out how to worth this serve as in follow.

Instance 1: Interquartile Space of One Array

Please see code displays find out how to calculate the interquartile dimension of values in one array:

import numpy as np

#outline array of information
knowledge = np.array([14, 19, 20, 22, 24, 26, 27, 30, 30, 31, 36, 38, 44, 47])

#calculate interquartile dimension 
q3, q1 = np.percentile(knowledge, [75 ,25])
iqr = q3 - q1

#show interquartile dimension 
iqr

12.25

The interquartile dimension of this dataset seems to be 12.25. That is the unfold of the center 50% of values on this dataset.

Instance 2: Interquartile Space of a Information Body Column

Please see code displays find out how to calculate the interquartile dimension of a unmarried column in an information body:

import numpy as np
import pandas as pd

#develop knowledge body
df = pd.DataFrame({'ranking': [90, 85, 82, 88, 94, 90, 76, 75, 87, 86],
                   'issues': [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 dimension of values within the 'issues' column
q75, q25 = np.percentile(df['points'], [75 ,25])
iqr = q75 - q25

#show interquartile dimension 
iqr

5.75

The interquartile dimension of values within the issues column seems to be 5.75.

Instance 3: Interquartile Space of More than one Information Body Columns

Please see code displays find out how to calculate the interquartile dimension of more than one columns in an information body immediately:

import numpy as np
import pandas as pd

#develop knowledge body
df = pd.DataFrame({'ranking': [90, 85, 82, 88, 94, 90, 76, 75, 87, 86],
                   'issues': [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]})

#outline serve as to calculate interquartile dimension
def find_iqr(x):
  go back np.subtract(*np.percentile(x, [75, 25]))

#calculate IQR for 'ranking' and 'issues' columns
df[['rating', 'points']].observe(find_iqr)

ranking    6.75
issues    5.75
dtype: float64

#calculate IQR for all columns
df.observe(find_iqr)

ranking      6.75
issues      5.75
assists     2.50
rebounds    3.75
dtype: float64

Observe: We worth the pandas.DataFrame.observe() serve as to calculate the IQR for more than one columns within the knowledge body above.

Backup Sources

Is the Interquartile Space (IQR) Affected Through Outliers?
How one can Calculate the Interquartile Space (IQR) in Excel
Interquartile Space Calculator