Lower cutoff of age:  -6.6875
Upper cutoff of age:  64.8125
     survived  pclass   sex   age  ...  deck  embark_town  alive  alone
33          0       2  male  66.0  ...   NaN  Southampton     no   True
54          0       1  male  65.0  ...     B    Cherbourg     no  False
96          0       1  male  71.0  ...     A    Cherbourg     no   True
116         0       3  male  70.5  ...   NaN   Queenstown     no   True
280         0       3  male  65.0  ...   NaN   Queenstown     no   True
456         0       1  male  65.0  ...     E  Southampton     no   True
493         0       1  male  71.0  ...   NaN    Cherbourg     no   True
630         1       1  male  80.0  ...     A  Southampton    yes   True
672         0       2  male  70.0  ...   NaN  Southampton     no   True
745         0       1  male  70.0  ...     B  Southampton     no  False
851         0       3  male  74.0  ...   NaN  Southampton     no   True

[11 rows x 15 columns]

How to perform outlier trimming using Inter Quartile Range (IQR)?

So, till now, we have detected outliers in a dataset. Now, we will perform outlier trimming based on Inter Quartile Range (IQR). We can use the following Python code for that purpose:

import seaborn
from matplotlib import pyplot

df = seaborn.load_dataset("titanic")
seaborn.boxplot(data=df, x="age")
pyplot.savefig("titanic-age-outliers.png")
pyplot.close()

q3 = df["age"].quantile(q=0.75)
q1 = df["age"].quantile(q=0.25)
iqr = q3 - q1
lower_cutoff = q1 - 1.5 * iqr
upper_cutoff = q3 + 1.5 * iqr

print("Lower cutoff of age: ", lower_cutoff)
print("Upper cutoff of age: ", upper_cutoff)

print(df[(df["age"] > upper_cutoff) | (df["age"] < lower_cutoff)]) df = df[(df["age"] >= lower_cutoff) & (df["age"] <= upper_cutoff)]
print(df.head())

seaborn.boxplot(data=df, x="age")
pyplot.savefig("titanic-age-without-outliers.png")
pyplot.close()

Here, we are using the following Python statement to select only those rows from the dataset where the value in the age column …