Connect to RedShift and S3 from Python with credentials in .env

aws-connections.py

import os
from dotenv import load_dotenv, find_dotenv
import psycopg2
import pandas as pd
import boto3

class Redshift:
    def __init__(self):
        ''' Constructor for this class. '''
        # Create some members
		# find .env automagically by walking up directories until it's found
        dotenv_path = find_dotenv()
        # load up the entries as environment variables
        load_dotenv(dotenv_path)
		#credentials
        self.database_endpoint = os.environ.get("DATABASE_ENDPOINT")
        self.database_name = os.environ.get("DATABASE_NAME")
        self.database_user = os.environ.get("DATABASE_USER")
        self.database_password = os.environ.get("DATABASE_PASSWORD")
        self.port = os.environ.get("PORT")

    def connect(self):	        
        #connect using psycopg2
        connection = psycopg2.connect(dbname=self.database_name, host=self.database_endpoint,
                                           port=self.port, user=self.database_user, password=self.database_password)

        return connection
	


class S3:
    def __init__(self):
        ''' Constructor for this class. '''
        # Create some members
		# find .env automagically by walking up directories until it's found
        dotenv_path = find_dotenv()
        # load up t`he entries as environment variables
        load_dotenv(dotenv_path)
		#credentials
        self.AWS_KEY = os.environ.get("AWS_ACCESS_KEY")
        self.AWS_SECRET = os.environ.get("AWS_SECRET_ACCESS_KEY")		
        self.AWS_BUCKET = os.environ.get("AWS_BUCKET")
        
     

    def connect(self):         		
      client = boto3.client('s3', aws_access_key_id=self.AWS_ACCESS_KEY,aws_secret_access_key=self.AWS_SECRET_ACCESS_KEY)
      return client
	
    def read_file(self,file_name):        
        client = self.connect()		
        file_nm=client.get_object(Bucket=self.AWS_BUCKET, Key=file_name)
        df=pd.read_csv(file_nm['Body'],header=0)
        return df