Change date to next trading date - python-3.x

Question

I have two tables:
-event dates
-return dates
Some event dates are not at a trading day.
How can I change the event date to the next trading day?
So if event date is not in return dates, take the next day in return dates.
The approach to change weekend days to working days does not work because of days like Christmas.
The best would be to look up the next day in the return table.
for i in event['date']:
if i is not in return ['date'].values:
event ['date']=i+datetime.timedelta(days=1)
but this doenst work
I am working with dataframes and dates have the format datetime64[ns]. If the event date does not exist in return date than event date plus one day

Answer 1

Edit
After the clarifications concerning the desired logic, here is the new solution
from datetime import datetime, timedelta
import numpy as np
import pandas as pd
# Create two df
event_date = datetime.now()
event_dates = pd.DataFrame([datetime(2020, 2, _) for _ in range(1, 29)], columns=['date'])
print(event_dates.date[0])
# 2020-02-01 00:00:00
return_dates = pd.DataFrame([datetime(2020, 1, _) for _ in range(1, 32)], columns=['date'])
# Apply logic
event_dates.date = [_ if _ in return_dates.date else _ + timedelta(days=1) for _ in event_dates.date]
print(event_dates.date[0])
# 2020-02-02 00:00:00
Base Python
Here is a solution using the standard datetime library
from datetime import datetime
from typing import List
def get_next_trade_date(date: datetime, date_list: List[datetime]) -> datetime: # The annotations here are just to specify the types of the objects
if date in date_list: # Check if the date is contained in the list
return date
delta, res = None, None # Initialize both to None
for _ in date_list:
tmp = abs((date - _).days) # Time difference in current iteration
if not delta or tmp < delta: # See bullet point 1.
delta, res = tmp, _
return res
if __name__ == '__main__':
event_date = datetime.now()
return_dates = [datetime(2020, 1, _) for _ in range(1, 32)]
print(get_next_trade_date(event_date, return_dates))
# 2020-01-01 00:00:00
Notice that
The condition not delta or tmp < delta is twofold: in the first iteration delta, res are both None so we will overwrite them with tmp, _. We catch this by using not delta. The other part (tmp < delta) is more obvious: if we have a new minimal delta then we overwrite delta, res.
I only considered days intervals ((date - _).days), you could go further into details (see datetime.timedelta for more info)
coming from R I believe there must be a simpler solution using numpy - see below
Numpy
This solution uses numpy. (date_list - date) is an array of timedeltas, (date_list - date).argmin() returns the index of the minimal value.
from datetime import datetime
import numpy as np
def get_next_trade_date(date: datetime, date_list: np.ndarray) -> datetime:
return date_list[(date_list - date).argmin()]
if __name__ == '__main__':
event_date = datetime.now()
return_dates = np.array([datetime(2020, 1, _) for _ in range(1, 32)])
print(get_next_trade_date(event_date, return_dates))
# 2020-01-01 00:00:00