Pandas: Parsing / converting a column as date after importing from read_sql

Question

I have the following code:

from pandas.io import sql  
import sqlite3  
conn = sqlite3.connect('breakDownRecs.sqlite')  
query = "SELECT * FROM indRecs"  
df = sql.read_sql(query, con=conn)  #parse_dates=['DateDayId'])  
print(df) 

I get (correctly) something like:

               DateID           Usr EventTypeM  DateDayID  DateMonthID
0      20151007221243        Noelle        LVL   20151007       201510
1      20151007225607         Morak        QST   20151007       201510
2      20151007225621   Austinpower        QST   20151007       201510
...

The question is how can I parse it so to become DateDayID column as a date, so to be indexed later? If I provide:

df = sql.read_sql(query, con=conn, parse_dates=['DateDayID'])

It gives me

               DateID           Usr EventTypeM DateDayID  DateMonthID
0      20151007221243        Noelle        LVL       NaT       201510
1      20151007225607         Morak        QST       NaT       201510
...

Thank you!

Answer 1

df = sql.read_sql(query, con=conn, parse_dates=['DateID'])

or

df = sql.read_sql(query, con=conn, parse_dates={'DateID': {'format': '%Y-%m-%d'}})