There is DataFrame.to_sql method, but it works only for mysql, sqlite and oracle databases. I cant pass to this method postgres connection or sqlalchemy engine.

Starting from pandas 0.14 (released end of May 2014), postgresql is supported. The sql module now uses sqlalchemy to support different database flavors. You can pass a sqlalchemy engine for a postgresql database (see docs). E.g.:

from sqlalchemy import create_engine
engine = create_engine('postgresql://scott:tiger@localhost:5432/mydatabase')
df.to_sql('table_name', engine)

You are correct that in pandas up to version 0.13.1 postgresql was not supported. If you need to use an older version of pandas, here is a patched version of pandas.io.sql: https://gist.github.com/jorisvandenbossche/10841234.
I wrote this a time ago, so cannot fully guarantee that it always works, buth the basis should be there). If you put that file in your working directory and import it, then you should be able to do (where con is a postgresql connection):

import sql  # the patched version (file is named sql.py)
sql.write_frame(df, 'table_name', con, flavor='postgresql')

Faster option:

The following code will copy your Pandas DF to postgres DB much faster than df.to_sql method and you won’t need any intermediate csv file to store the df.

Create an engine based on your DB specifications.

Create a table in your postgres DB that has equal number of columns as the Dataframe (df).

Data in DF will get inserted in your postgres table.

from sqlalchemy import create_engine
import psycopg2 
import io

if you want to replace the table, we can replace it with normal to_sql method using headers from our df and then load the entire big time consuming df into DB.

engine = create_engine('postgresql+psycopg2://username:password@host:port/database')

df.head(0).to_sql('table_name', engine, if_exists='replace',index=False) #truncates the table

conn = engine.raw_connection()
cur = conn.cursor()
output = io.StringIO()
df.to_csv(output, sep='\t', header=False, index=False)
output.seek(0)
contents = output.getvalue()
cur.copy_from(output, 'table_name', null="") # null values become ''
conn.commit()

Pandas 0.24.0+ solution

In Pandas 0.24.0 a new feature was introduced specifically designed for fast writes to Postgres. You can learn more about it here: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#io-sql-method

import csv
from io import StringIO

from sqlalchemy import create_engine

def psql_insert_copy(table, conn, keys, data_iter):
    # gets a DBAPI connection that can provide a cursor
    dbapi_conn = conn.connection
    with dbapi_conn.cursor() as cur:
        s_buf = StringIO()
        writer = csv.writer(s_buf)
        writer.writerows(data_iter)
        s_buf.seek(0)

        columns = ', '.join('"{}"'.format(k) for k in keys)
        if table.schema:
            table_name = '{}.{}'.format(table.schema, table.name)
        else:
            table_name = table.name

        sql = 'COPY {} ({}) FROM STDIN WITH CSV'.format(
            table_name, columns)
        cur.copy_expert(sql=sql, file=s_buf)

engine = create_engine('postgresql://myusername:mypassword@myhost:5432/mydatabase')
df.to_sql('table_name', engine, method=psql_insert_copy)

This is how I did it.

It may be faster because it is using execute_batch:

# df is the dataframe
if len(df) > 0:
    df_columns = list(df)
    # create (col1,col2,...)
    columns = ",".join(df_columns)

    # create VALUES('%s', '%s",...) one '%s' per column
    values = "VALUES({})".format(",".join(["%s" for _ in df_columns])) 

    #create INSERT INTO table (columns) VALUES('%s',...)
    insert_stmt = "INSERT INTO {} ({}) {}".format(table,columns,values)

    cur = conn.cursor()
    psycopg2.extras.execute_batch(cur, insert_stmt, df.values)
    conn.commit()
    cur.close()

For Python 2.7 and Pandas 0.24.2 and using Psycopg2

Psycopg2 Connection Module

def dbConnect (db_parm, username_parm, host_parm, pw_parm):
    # Parse in connection information
    credentials = {'host': host_parm, 'database': db_parm, 'user': username_parm, 'password': pw_parm}
    conn = psycopg2.connect(**credentials)
    conn.autocommit = True  # auto-commit each entry to the database
    conn.cursor_factory = RealDictCursor
    cur = conn.cursor()
    print ("Connected Successfully to DB: " + str(db_parm) + "@" + str(host_parm))
    return conn, cur

Connect to the database

conn, cur = dbConnect(databaseName, dbUser, dbHost, dbPwd)

Assuming dataframe to be present already as df

output = io.BytesIO() # For Python3 use StringIO
df.to_csv(output, sep='\t', header=True, index=False)
output.seek(0) # Required for rewinding the String object
copy_query = "COPY mem_info FROM STDOUT csv DELIMITER '\t' NULL ''  ESCAPE '\\' HEADER "  # Replace your table name in place of mem_info
cur.copy_expert(copy_query, output)
conn.commit()