Replacing similar strings in the column by using the same for both

I'm encountering the following issue during a small project of mine. I'm having a large dataset where some string values are accidentally not written properly. My goal is to write a function that ensures that all names that look fairly similar (.75) will be looked for in a loop, and will get the same name. In the example below I described a subset of the data where "Bob Fisherman", "Bob Felony" & "Bob Haris" are the correct names. I would like to have the misspelled names changes to the above if they match

Here is a subset of the dataframe:

columns = ["Name", "Type","Amount", "Year"]
data = [("Bob fisherman", "Income", 150, 2022), ("Bob fisherman","Income", 100, 2021), ("Bob Felony", "Income", 100, 2021), ("Bob Felany", "Expense", 50, 2022), ("Bob Haris", "Expense", 100, 2022), ("Bob Disherman", "Expense", 100, 2021)]
data = spark.createDataFrame(data).toDF(*columns)

So eventually I would like to have something like this:

In the example it only goes about Bob. But in the total sample, I have much more names so the use of pre-specified list is not going to cut it unfortunately.

I tried to get some inspiration from the following question but I didn't seem to make it work: Replace similar strings in a column with the same string

With the following toy dataframe:

import pandas as pd

df = pd.DataFrame(
    [
        ("Bob Fisherman", "Income", 150, 2022),
        ("Bob Heris", "Income", 100, 2021),
        ("Bob Felony", "Income", 100, 2021),
        ("Bob Felany", "Expense", 50, 2022),
        ("Bob Haris", "Expense", 100, 2022),
        ("Bob Disherman", "Expense", 100, 2021),
    ],
    columns=["Name", "Type", "Amount", "Year"],
)
print(df)
# Output
            Name     Type  Amount  Year
0  Bob Fisherman   Income     150  2022
1      Bob Heris   Income     100  2021
2     Bob Felony   Income     100  2021
3     Bob Felany  Expense      50  2022
4      Bob Haris  Expense     100  2022
5  Bob Disherman  Expense     100  2021

Here is one way to do it with the help of SequenceMatcher class from Python standard library difflib module:

from difflib import SequenceMatcher

def similar(a, b):
    """Get similarity ratio between a and b.

    Args:
        a: value.
        b: other value.

    Returns:
        Similatity ratio.

    """
    return SequenceMatcher(None, a, b).ratio()


def replace_similar_values(df, col, value):
    """Main helper function.

    Args:
        df: target dataframe.
        col: target column.
        value: value to use for replacement.

    Returns:
        Modified dataframe.

    """
    df = df.assign(
        Match=df[col].map(
            lambda x: max(
                [similar(x, value)],
                key=lambda x: x if x != 1 else 0,
            )
        )
    )
    df["Name"] = df.apply(lambda x: value if x["Match"] >= 0.8 else x["Name"], axis=1)
    df = df.drop(columns="Match")
    return df

And then:

correct_names = ["Bob Fisherman", "Bob Felony", "Bob Haris"]

for correct_name in correct_names:
    df = replace_similar_values(df, "Name", correct_name)

print(df)
# Output
            Name     Type  Amount  Year
0  Bob Fisherman   Income     150  2022
1      Bob Haris   Income     100  2021
2     Bob Felony   Income     100  2021
3     Bob Felony  Expense      50  2022
4      Bob Haris  Expense     100  2022
5  Bob Fisherman  Expense     100  2021