performance – I have code that loops through a column of a dataframe. How can I make this code faster? (Python/Pandas)

I have datasetA with 90,000 rows and datasetB with 5,000 rows. Each dataset has a column called “ID” with employee IDs. My goal is to to create another column in datasetA that identifies whether the employee ID in datasetA is also in datasetB with a True/False. Additionally, there are most likely some multiples for certain employees/employee ids in both datasets. I am fairly certain that the code I wrote works, but it is way too slow, and I was wondering what I could change to make it faster? Thanks!

#Creating the new column to identify whether the ID in datasetA is also in datasetB.

datasetA("inB") = "Empty"

# Looping through

for id_num in datasetA("ID"):
    filt = (datasetA("ID") == id_num)
    if (datasetB("ID") == id_num).any():
        datasetA.loc(filt, "inB") = True
    else:
        datasetA.loc(filt, "inB") = False
```