pandas python flag transactions across rows

I have a data as below. I would like to flag transactions -

when a same employee has one of the ('Car Rental', 'Car Rental - Gas' in the column expense type) and 'Car Mileage' on the same day - so in this case employee a and c's transactions would be highlighted. Employee b's transactions won't be highlighted as they don't meet the condition - he doesn't have a 'Car Mileage'

i want the column zflag. Different numbers in that column indicate group of instances when the above condition was met

d = {'emp': ['a',   'a',    'a',    'a',    'b',    'b',    'b',    'c',    'c',    'c',    'c' ], 
 'date': ['1',  '1',    '1',    '1',    '2',    '2',    '2',    '3',    '3',    '3',    '3' ], 
 'usd':[1,  2,  3,  4,  5,  6,  7,  8,  9,  10,     11 ], 
 'expense type':['Car Mileage',     'Car Rental',   'Car Rental - Gas',     'food',     'Car Rental',   'Car Rental - Gas',     'food',     'Car Mileage',  'Car Rental',   'food',     'wine' ],
 'zflag':['1',  '1', '1',   ' ',' ',' ',' ','2','2',' ',' ' ]
 }

df = pd.DataFrame(data=d)



    df
Out[253]: 
   date emp      expense type  usd zflag
0     1   a       Car Mileage    1     1
1     1   a        Car Rental    2     1
2     1   a  Car Rental - Gas    3     1
3     1   a              food    4      
4     2   b        Car Rental    5      
5     2   b  Car Rental - Gas    6      
6     2   b              food    7      
7     3   c       Car Mileage    8     2
8     3   c        Car Rental    9     2
9     3   c              food   10      
10    3   c              wine   11

I would appreciate if i could get pointers regarding functions to use. I am thinking of using groupby...but not sure

I understand that date+emp will be my primary key

Here is an approach. It's not the cleanest but what you're describing is quite specific. Some of this might be able to be simplified with a function.

temp_df = df.groupby(["emp", "date"], axis=0)["expense type"].apply(lambda x: 1 if "Car Mileage" in x.values and any([k in x.values for k in ["Car Rental", "Car Rental - Gas"]]) else 0).rename("zzflag")
temp_df = temp_df.loc[temp_df!=0,:].cumsum()
final_df = pd.merge(df, temp_df.reset_index(), how="left").fillna(0)

Steps:

Groupby emp/date and search for criteria, 1 if met, 0 if not
Remove rows with 0's and cumsum to produce unique values
Join back to the original frame

Edit:

To answer your question below. The join works because after you run .reset_index() that takes "emp" and "date" from the index and moves them to columns.