Using Postgres 9.6.1, sqlachemy 1.1.4, and psycopg2 2.6.2:
Convert your data structure to a dictionary. From Pandas it is
import pandas
from sqlalchemy import MetaData
from sqlalchemy.dialects.postgresql import insert
import psycopg2
# The dictionary should include all the values including index values
insrt_vals = df.to_dict(orient='records')
Connect to database through sqlalchemy . Instead try psycog2 driver underneath and the native COPY function, which bypasses all the postgres indexing.
csv_data = os.path.realpath('test.csv')
con = psycopg2.connect(database = 'db01', user = 'postgres')
cur = con.cursor()
cur.execute("\copy stamm_data from '%s' DELIMITER ';' csv header" % csv_data)
con.commit()
Execute
results = engine.execute(do_nothing_stmt)
# Get number of rows inserted
rowcount = results.rowcount
Warning:
This method does not work with NaT
s out of the box.
Everything together
tst_df = pd.DataFrame({'colA':['a','b','c','a','z', 'q'],
'colB': pd.date_range(end=datetime.datetime.now() , periods=6),
'colC' : ['a1','b2','c3','a4','z5', 'q6']})
insrt_vals = tst_df.to_dict(orient='records')
engine = sqlalchemy.create_engine("postgresql://user:password@localhost/postgres")
connect = engine.connect()
meta = MetaData(bind=engine)
meta.reflect(bind=engine)
table = meta.tables['tstbl']
insrt_stmnt = insert(table).values(insrt_vals)
do_nothing_stmt = insrt_stmnt.on_conflict_do_nothing(index_elements=['colA','colB'])
results = engine.execute(do_nothing_stmt)
Instead of step 2 and 3 , using psycog2
driver with the copy command in postgres is faster for larger files (approaching a gig) because it sets all the table indexing off.
csv_data = os.path.realpath('test.csv')