More efficient way to replace `.loc` method with chaining in Pandas DataFrame operations

I'm currently working on a project where I need to perform conditional replacements in a Pandas DataFrame. I've implemented a solution, but I'm wondering if there's a more efficient way to achieve the same result.

import pandas as pd

data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Emma'],
    'Age': [25, 30, 35, 40, 45],
    'City': ['New York', 'Los Angeles', 'Chicago', 'Houston', 'Phoenix']
}
df_init = pd.DataFrame(data)

# Using `.loc`
df_init['Group'] = ['A', 'B', 'C', 'D', 'E']
df_init.loc[df_init.loc[
            (df_init.City=='New York')
            &(df_init.Name=='Alice')].index, 'City'] = 'Hamburg' 

# Using method chaining
def _replace(dataframe): 
    dataframe.loc[dataframe.loc[
                 (dataframe.City=='New York')
                 &(dataframe.Name=='Alice')].index, 'City'] = 'Hamburg' 
    return dataframe

(df_init
 .assign(Group=['A', 'B', 'C', 'D', 'E'])
 .pipe(_replace)
)

In the first method, I'm using the .loc method to locate the row where the conditions are met and then perform the replacement. In the second approach, I'm using method chaining with .assign and .pipe to achieve the same result.

My question is: Is there a more efficient way to replace the .loc method with method chaining in Pandas DataFrame operations? I should mention that I am very new to the methods chaining world of pandas.

Code

make condition and use mask func.

cond = df_init['City'].eq('New York') & df_init['Name'].eq('Alice')
out = df_init.assign(
    Group = ['A', 'B', 'C', 'D', 'E'], 
    City=df_init['City'].mask(cond, 'Hamburg')
)

out

      Name  Age         City Group
0    Alice   25      Hamburg     A
1      Bob   30  Los Angeles     B
2  Charlie   35      Chicago     C
3    David   40      Houston     D
4     Emma   45      Phoenix     E