Pandas Timestamp and .isin functionality

I am trying to create a function that I will .apply() to a dataframe to:

Remove one business day from the provided argument into the function
Check to see if this new day is in a specific collection of dates (formatted as a datetime index)

I have simplified the logic of my function to iron out this issue - I will add more logic later.

My function:

def test(trade_date):
    if (trade_date - BDay(1)).isin(pricing_date):
        return True
    else:
        return False

Error:

AttributeError: 'Timestamp' object has no attribute 'isin'

Looks like there is an issue with using .isin with a Timestamp. However, when I run code to test within the dataframe itself:

df['Check'] = df['test_date'].isin(pricing_date)

The expected output is returned - isin() does work properly with this data.

TradeDate
2023-01-03    False
2023-01-03    False
2023-01-03    False
2023-01-03    False
2023-01-03    False
              ...  
2023-03-22     True
2023-03-22     True
2023-03-22     True
2023-03-22     True
2023-03-22     True
Name: Check, Length: 14324, dtype: bool

The column that .isin() is being called on is of datatype: datetime64[ns], but unsure how to convert the timestamp in my function to this data type - I have read in many places that they are virtually equivalent, just types from python vs pandas.

Name: test_date, Length: 14324, dtype: datetime64[ns]

Any help is appreciated!

Tried passing in a timestamp into .isin - expected output from running it directly on the dataframe.

Solution

Pandas dataframe apply runs the function on all the values inside the pd.Series rather than the function in the pd.Series. Thus, trade_date would be a timestamp, that doesn't have the isin method. What you should do is something like this:

def test(trade_date):
    return (trade_date - BDay(1)) in pricing_date

Or, much simpler:

df['Check'] = (df['test_date']-BDay(1)).isin(pricing_date)