python postgresql create database and populate table from pandas dataframe [duplicate]

I am quite new to Python and also very new to using postgresql, so please forgive me if this is something basic (which I - so far - have failed to achieve). I'm trying to write a python code that:

• creates a new database (testdb)
• reads a csv file into a pandas dataframe
• creates and populates a new table within the database out of the pandas dataframe.

I have 3 different files so far: a) an .ini-File where I store the database info I need to create a new database, b) a .csv-File (from here, named 100_recs.csv) and c) my python code.

database.ini:

[postgresql]
host=localhost
user=postgres
password=creator
port=5432

db_creator.py:

from config import config
import sqlalchemy
from sqlalchemy import create_engine
from sqlalchemy_utils import database_exists, create_database
import pandas as pd

# useful info for psycopg2:
# https://stackoverflow.com/questions/34484066/create-a-postgres-database-using-python


class MyDB(object):
    def __init__(self):
        self.params = config()

    def create_new_db(self, newdb):
        user, host, port = self.params['user'], self.params['host'], testdb.params['port']
        pw = self.params['password']
        url = 'postgresql://{}:{}@{}:{}/{}'
        url = url.format(user, pw, host, port, newdb)

        engine = create_engine(url)
        if not database_exists(engine.url):
            create_database(engine.url)

        print(database_exists(engine.url))


if __name__ == '__main__':

    testdb = MyDB()
    testdb.create_new_db('testdb')

When I try to do it like that, I'm getting the following Error:

sqlalchemy.exc.OperationalError: (psycopg2.OperationalError)

However, it works when I do it as suggested in this SO post. Unfortunately, the answer in this post uses psycopg2 to create a new database, but I would like to do it with sqlalchemy (also because I think that working with Pandas dataframe further on will be easier with sqlalchemy (as e.g. shown here. Or am I wrong?). I thought that when doing it with sqlqlchemy then something like the following should be possible to read the data from the csv-file into a pandas dataframe and then populate a table in the new database:

def connect_alchemy(user, host, port, db, password):
    url = 'postgresql://{}:{}@{}:{}/{}'
    url = url.format(user, password, host, port, db)
    con = sqlalchemy.create_engine(url, client_encoding='utf8')
    mydata = pd.read_csv('100_recs.csv', delimiter=';', quotechar='"')
    data_db = mydata.to_sql(name='100_records', con=con, if_exists='replace', index=True, chunksize=10)

    print(con.execute('SELECT * from 100_records'))

But honestly, I'm stuck here and need some help... It would be great if someone could point me into the right direction.

EDIT: Ah stupid me! So I had an old typo in the follwing lines in db_creator.py

user, host, port = testdb.params['user'], testdb.params['host'], testdb.params['port']
        pw = testdb.params['password']

should be:

user, host, port = self.params['user'], self.params['host'], self.params['port']
        pw = self.params['password']

I already changed this.

Then I also forgot to add the config.py file here. Apologies for that.

Here you go:

config.py

# source: http://www.postgresqltutorial.com/postgresql-python/connect/
from configparser import ConfigParser


def config(filename='database.ini', section='postgresql'):
    # create a parser
    parser = ConfigParser()
    # read config file
    parser.read(filename)

    # get section, default to postgresql
    db = {}
    if parser.has_section(section):
        params = parser.items(section)
        for param in params:
            db[param[0]] = param[1]
    else:
        raise Exception('Section {0} not found in the {1} file'.format(section, filename))

    return db

EDIT 2:

It works now with the following setup:

database.ini:

[postgresql]
host=localhost
user=postgres
password=postgres
port=5432

config.py:

# source: http://www.postgresqltutorial.com/postgresql-python/connect/
from configparser import ConfigParser


def config(filename='database.ini', section='postgresql'):
    # create a parser
    parser = ConfigParser()
    # read config file
    parser.read(filename)

    # get section, default to postgresql
    db = {}
    if parser.has_section(section):
        params = parser.items(section)
        for param in params:
            db[param[0]] = param[1]
    else:
        raise Exception('Section {0} not found in the {1} file'.format(section, filename))

    return db

the csv file: from here

db_creator.py

from config import config
import sqlalchemy
from sqlalchemy import create_engine
from sqlalchemy_utils import database_exists, create_database
import pandas as pd

# useful info for psycopg2:
# https://stackoverflow.com/questions/34484066/create-a-postgres-database-using-python


class MyDB(object):
    def __init__(self):
        self.params = config()

    def create_new_db(self, newdb):
        user, host, port = self.params['user'], self.params['host'], self.params['port']
        pw = self.params['password']
        url = 'postgresql://{}:{}@{}:{}/{}'
        url = url.format(user, pw, host, port, newdb)

        self.engine = create_engine(url, client_encoding='utf8')
        if not database_exists(self.engine.url):
            create_database(self.engine.url)
        # print(database_exists(engine.url))

def df2postgres(engine, df):
    con = engine.connect()
    df.to_sql(name='records', con=con, if_exists='replace', index=True, chunksize=10)

    return con



if __name__ == '__main__':

    testdb = MyDB()
    testdb.create_new_db('testdb')
    engn = testdb.engine
    df = pd.read_csv('100_recs.csv', delimiter=';', quotechar='"', encoding='utf-8')
    con = df2postgres(engine=engn, df=df)
    dta = con.execute('SELECT * FROM records LIMIT 5;')
    print(dta.fetchall())

Apologies for the stupid mistakes...