Comparing Dictionary Key to df column names to find missing columns

I want to compare the keys in the dictionary to the df column names to find the missing column names.

import pandas as pd

df= pd.DataFrame(columns=['industry','System_Type__c','AccountType','email','firstname','lastName','country','company'])
req_cols={"firstname":[],"lastName":[],"country":[],"company":[],"email":[], "Existing_Customer__c":[]}

errors= {}

if req_cols.items() in df.columns.values:
    pass
else:
    errors= {"There is a missing required column"}

I have the above code to write an error message but I would like it to be more detailed. Here’s an example of the error message I would like

{'Existing_Customer_c is missing in the column names'}

obviously the output would change depending on what column value is missing and if not value is missing than no error message needs to be wrote.

How would I compare the dictionary key to the df column names to find which variable is missing and write that error message?

>Solution :

You can use set operations:

missing = set(req_cols)-set(df)

if missing:
    errors = {f'missing columns: {", ".join(missing)}'}
else:
    errors = {}
    
print(errors)

Output: {'missing columns: Existing_Customer__c'}