How to convert a whole column from string type to date type in mongodb with pymongo

My data consist of 1million rows. A sample look like this:

_id:object("603678958a6eade21c0790b8")
    id1:3758
    date2:2010-01-01
    time3:00:05:00
    date4 :2009-12-31
    time5:19:05:00
    id6 :2
    id7:-79.09
    id8:35.97
    id9:5.5
    id10:0
    id11:-99999
    id12 :0
    id13 :-9999
    c14:"U"
    id15:0
    id16:99
    id17:0
    id18:-99
    id19:-9999
    id20:33
    id21:0
    id22:-99
    id23:0

The thing is that date2 and date4 are in the form that i want but they are string and i want to convert them to date. The code i have used look like this:

    df['date4'] = df['date4'].astype('datetime64[ns]') 
    df['date2'] = df['date2'].astype('datetime64[ns]') 

    
    df['time3'] = df['time3'].apply(lambda x:datetime.datetime.strptime(x[0]+x[1]+":"+x[2]+x[3], '%H:%M'))
    df['time5'] = df['time5'].apply( lambda x: datetime.datetime.strptime(x[0] + x[1] + ":" + x[2] + x[3], '%H:%M'))

    df['date2'] = df['date2'].apply(lambda x: arrow.get(x).format("YYYY-MM-DD"))
    df['date4'] = df['date4'].apply(lambda x: arrow.get(x).format("YYYY-MM-DD"))
    df['time3'] = df['time3'].apply(lambda x: arrow.get(x).format("HH:mm:ss"))
    df['time5'] = df['time5'].apply(lambda x: arrow.get(x).format("HH:mm:ss"))

Do i need to convert them before inserting or after? Does anyone know how i can do that?

Answer

If it were me, I’d want to combine date2/time3 into one column, and date4/time5, as in:

df['date2'] = (df['date2']+'T'+df['time3']).astype('datetime64')
df['date4'] = (df['date4']+'T'+df['time5']).astype('datetime64')