Python : my code is slow, using for loop with Pandas dataframe

I got this code below which works fine but takes too much time to execute (>30s):

df['Date_engagement'] = np.nan
for i in range(df.shape[0]):
if not(pd.isna(df.loc[i,'Engagement'])):
    df.loc[i, 'Date_engagement'] = np.busday_offset(
        df.loc[i, 'Date_réception'].date(),
        df.loc[i,'Engagement'], roll='backward', busdaycal=france_bdc
    )

I guess this is because I loop through the Pandas dataframe, and I wonder if there is a way to achieve the same result, not using any loop.

Basically what this code does is creating the 'Date_engagement' column which will be the result of adding 'Engagement' days to 'Date_réception' if *'Engagement' *is not empty. I use *'np.busday_offset()' *to avoid holidays.

Thanks in advance for your help...

I tried to use Pandas features for dataframes (like DataFrame.apply()), but can't find how to do it correctly to get the same result as explained.

Suppose the input dataframe:

import pandas as pd
import numpy as np

df = pd.DataFrame({'Date_réception': ['2023-05-12', '2023-05-13'],
                   'Engagement': [10, np.nan]})
print(df)

# Output
  Date_réception  Engagement
0     2023-05-12        10.0
1     2023-05-13         NaN

You can vectorize your code:

france_bdc = np.busdaycalendar(weekmask='1111100', holidays=['2023-05-18'])
m = df['Engagement'].notna()

df.loc[m, 'Date_engagement'] = (
    np.busday_offset(df.loc[m, 'Date_réception'].values.astype('datetime64[D]'),
                     df.loc[m, 'Engagement'], roll='backward', busdaycal=france_bdc)
)

Output:

>>> df
  Date_réception  Engagement Date_engagement
0     2023-05-12        10.0      2023-05-29
1     2023-05-13         NaN             NaT