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Object Relational Mapping with SQL Alchemy
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‎Advanced SQL for Application Development/03.Object Relational Mapping with SQL-Alchemy.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import psycopg2\n",
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"import os\n",
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"\n",
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"from sqlalchemy import create_engine\n",
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"from sqlalchemy import Column, String, Integer\n",
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"from sqlalchemy.ext.declarative import declarative_base\n",
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"from sqlalchemy.orm import sessionmaker"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Connect to DB with SQL Alchemy"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"USER = os.environ['POSTGRE_USERNAME']\n",
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"PASSWORD = os.environ['POSTGRE_PASSWORD']"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"('postgres', 'admin')"
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]
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},
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"execution_count": 17,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"USER, PASSWORD"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### create engine"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 44,
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"metadata": {},
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"outputs": [],
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"source": [
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"db_conn_string = 'postgresql+psycopg2://'+USER+':'+PASSWORD+'@localhost/db_Advanced_SQL_Application_Development'\n",
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"engine = create_engine(db_conn_string)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### create session"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 45,
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"metadata": {},
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"outputs": [],
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"source": [
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"Session = sessionmaker(engine)\n",
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"session = Session()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Creating Classes"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 46,
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"metadata": {},
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"outputs": [],
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"source": [
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"# create base class\n",
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"base = declarative_base()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 47,
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"metadata": {},
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"outputs": [],
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"source": [
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"# product class\n",
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"class Product(base):\n",
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" __tablename__ = 'products'\n",
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" product_id = Column(Integer, primary_key=True)\n",
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" product_name = Column(String)\n",
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" product_type = Column(String)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"----------"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Query products using Product class"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 48,
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"metadata": {},
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"outputs": [],
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"source": [
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"products = session.query(Product)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 49,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<sqlalchemy.orm.query.Query at 0xfdc8b5fdc0>"
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]
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},
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"execution_count": 49,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"products"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 50,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Reflector oven\n",
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"Convection microwave\n",
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"Pressure fryer\n",
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"Multicooker\n",
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"Food steamer\n",
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"Chapati maker\n",
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"Mess kit\n",
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"Rotisserie\n",
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"Sous-vide cooker\n",
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"Rocket mass heater\n",
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"Cheesemelter\n",
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"Hot plate\n",
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"Flattop grill\n",
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"Wet grinder\n",
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"Masonry oven\n",
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"Chocolatera\n",
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"Turkey fryer\n",
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"Bread machine\n",
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"Roasting jack\n",
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"Brasero (heater)\n",
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"Susceptor\n",
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"Slow cooker\n",
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"Butane torch\n",
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"Microwave oven\n",
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"Solar cooker\n",
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"Deep fryer\n",
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"Popcorn maker\n",
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"Russian oven\n",
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"Clome oven\n",
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"Convection oven\n",
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"Beehive oven\n",
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"Toaster and toaster ovens\n",
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"Field kitchen\n",
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"Corn roaster\n",
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"Self-cleaning oven\n",
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"Wood-fired oven\n",
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"Kitchener range\n",
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"Rice polisher\n",
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"Soy milk maker\n",
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"Crepe maker\n",
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"Oven\n",
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"Hot box (appliance)\n",
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"Combi steamer\n",
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"Rice cooker\n",
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"Fire pot\n",
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"Salamander broiler\n",
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"Vacuum fryer\n",
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"Fufu Machine\n",
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"Tabun oven\n",
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"Pancake machine\n",
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"Barbecue grill\n",
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"Panini sandwich grill\n",
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"Air fryer\n",
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"Chorkor oven\n",
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"Communal oven\n",
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"Pressure cooker\n",
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"Halogen oven\n",
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"Instant Pot\n",
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"Waffle iron\n",
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"Stove\n",
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"Earth oven\n",
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"Electric cooker\n",
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"Espresso machine\n",
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"Coffee pot\n"
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]
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}
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],
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"source": [
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"for product in products:\n",
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" print(product.product_name)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"------"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Search for specific product\n",
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"- filter"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 52,
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"metadata": {},
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"outputs": [],
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"source": [
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"products = session.query(Product).filter(Product.product_type=='fryer')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 53,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Pressure fryer\n",
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"Turkey fryer\n",
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"Deep fryer\n",
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"Vacuum fryer\n",
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"Air fryer\n"
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]
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}
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],
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"source": [
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"for product in products:\n",
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" print(product.product_name)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"----------\n",
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"----------"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Challenge\n",
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"define a class using SQLalchemy that corresponds to a supplier table which has four column:\n",
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"\n",
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"- supplier_id (integer)\n",
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"- supplier_name(variable character string)\n",
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"- supplier_region(varaible character string)\n",
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"- supplier_level (integer)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"class Supplier(base):\n",
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" __tablename__ = 'suppliers'\n",
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" supplier_id = Column(Integer, primary_key=True)\n",
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" supplier_name = Column(String)\n",
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" supplier_region = Column(String)\n",
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" supplier_level = Column(Integer)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

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