is the most frequent value in the column.

import seaborn
from sklearn.preprocessing import LabelEncoder

df = seaborn.load_dataset("titanic")

df["embark_town"].fillna(value="Southampton", inplace=True)
print("Missing values after filling missing values: \n", df.isnull().sum())

After data imputation, the number of missing values in the embark town column becomes zero.

Missing values after filling in missing values: 
survived         0
pclass           0
sex              0
age            177
sibsp            0
parch            0
fare             0
embarked         2
class            0
who              0
adult_male       0
deck           688
embark_town      0
alive            0
alone            0
dtype: int64

Now, let’s start with label encoding. We can use the following Python code for label encoding.

import seaborn
from sklearn.preprocessing import LabelEncoder

df = seaborn.load_dataset("titanic")

df["embark_town"].fillna(value="Southampton", inplace=True)

print("Value counts before label encoding: \n", df.embark_town.value_counts())

label_encoder = LabelEncoder()
df["embark_town"] = label_encoder.fit_transform(df["embark_town"])

print("Value counts after label encoding: \n", df.embark_town.value_counts())

Here, we are using the LabelEncoder class from the sklearn.preprocessing module to perform the label encoding. The …