I'm querying an underlying PostgreSQL database using Pandas 0.8. Pandas is returning the DataFrame properly but the underlying timestamp column in my database is being returned as a generic "object" type in Pandas. As I would eventually like to seasonal normalization of my data I am curious as to how to convert this generic "object" column to something that is appropriate for analysis. Here is my current code to retrieve the data: # get records from db example import pandas.io.sql as psql import psycopg2 # define query to get all subs created this year QRY = """ select i i, i * random() f, case when random() > 0.5 then true else false end t, (current_date - (i*random())::int)::timestamp with time zone tsz from generate_series(1,1000) as s(i) order by 4 ; """ CONN_STRING = "host='localhost' port=5432 dbname='postgres' user='postgres'" # connect to db conn = psycopg2.connect(CONN_STRING) # get some data set index on relid column df = psql.frame_query(QRY, con=conn) print "Row count retrieved: %i" % (len(df),) Thanks for any help you can render. M