COPY command in psycopg2

I have a process which is reading from 4 databases with 4 tables each. I am consolidating that data into 1 postgres database with 4 tables total. (Each of the original 4 databases have the same 4 tables which need to be consolidated).

The way I am doing it now works using pandas. I read one table from all 4 databases at a time, concatenate the data into one dataframe, then I use to_sql to save it on my postgres database. I then loop through to the remaining databases doing the same thing for the other tables.

My issue is speed. One of my tables has about 1 - 2mil rows per date so it can take about 5,000 - 6,000 seconds to finish writing the data to postgres. It is much quicker to write it to a .csv file and then use COPY FROM in pgadmin.

Here is my current code. Note that there are some function calls but it is just referring to the table names basically. I also have some basic logging being done but that is not too necessary. I am adding a column for the source database which is required though. I am stripping .0 from fields which are actually strings but pandas sees them as a float too, and I fill blank integers with 0 and make sure the columns are really type int.

def query_database(table, table_name, query_date):
    df_list = []
    log_list = []
    for db in ['NJ', 'NJ2', 'LA', 'NA']:
        start_time = time.clock()
        query_timestamp = dt.datetime.now(pytz.timezone('UTC')).strftime('%Y-%m-%d %H:%M:%S')
        engine_name = '{}{}{}{}'.format(connection_type, server_name, '/', db)
        print('Accessing {} from {}'.format((select_database(db)[0][table]), engine_name))
        engine = create_engine(engine_name)
        df = pd.read_sql_query(query.format(select_database(db)[0][table]), engine, params={query_date})
        query_end = time.clock() - start_time
        df['source_database'] = db
        df['insert_date_utc'] = query_timestamp
        df['row_count'] = df.shape[0]
        df['column_count'] = df.shape[1]
        df['query_time'] = round(query_end, 0)
        df['maximum_id'] = df['Id'].max()
        df['minimum_id'] = df['Id'].min()
        df['source_table'] = table_dict.get(table)
        log = df[['insert_date_utc', 'row_date', 'source_database', 'source_table', 'row_count', 'column_count', 'query_time', 'maximum_id', 'minimum_id']].copy()
        df.drop(['row_count', 'column_count', 'query_time', 'maximum_id', 'minimum_id', 'source_table'], inplace=True, axis=1)
        df_list.append(df)
        log_list.append(log)
    log = pd.concat(log_list)
    log.drop_duplicates(subset=['row_date', 'source_database', 'source_table'], inplace=True, keep='last')
    result = pd.concat(df_list)
    result.drop_duplicates('Id', inplace=True)
    cols = [i.strip() for i in (create_columns(select_database(db)[0][table]))]
    result = result[cols]
    print('Creating string columns for {}'.format(table_name))
    for col in modify_str_cols(select_database(db)[0][table]):
        create_string(result, col)
    print('Creating integer columns for {}'.format(table_name))
    for col in modify_int_cols(select_database(db)[0][table]):
        create_int(result, col)
    log.to_sql('raw_query_log', cms_dtypes.pg_engine, index=False, if_exists='append', dtype=cms_dtypes.log_dtypes)
    print('Inserting {} data into PostgreSQL'.format(table_name))
    result.to_sql(create_table(select_database(db)[0][table]), cms_dtypes.pg_engine, index=False, if_exists='append', chunksize=50000, dtype=create_dtypes(select_database(db)[0][table]))

How can I insert a COPY TO and COPY FROM into this to speed it up? Should I just write the .csv files and then loop over those or can I COPY from memory to my postgres?