the embark town column. We can use the following Python code for that purpose.

import seaborn

df = seaborn.load_dataset("titanic")

df.dropna(inplace=True)

frequency_counts = (df["embark_town"].value_counts()/len(df)).to_dict()
print(frequency_counts)


df["embark_town"] = df["embark_town"].map(frequency_counts)
print(df.head())

The output of the above program will be:

{'Southampton': 0.6318681318681318, 'Cherbourg': 0.35714285714285715, 'Queenstown': 0.01098901098901099}
    survived  pclass     sex   age  ...  deck  embark_town  alive  alone
1          1       1  female  38.0  ...     C     0.357143    yes  False
3          1       1  female  35.0  ...     C     0.631868    yes  False
6          0       1    male  54.0  ...     E     0.631868     no   True
10         1       3  female   4.0  ...     G     0.631868    yes  False
11         1       1  female  58.0  ...     C     0.631868    yes   True

[5 rows x 15 columns]