airBnb_dataModeling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a747ff44",
   "metadata": {},
   "source": [
    "### AirBnB Datamodeling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d694f507",
   "metadata": {},
   "source": [
    "The notebook requires the Airbnb dataset loaded into a database server, and the credentials for that are available to connect with. The table names used will be different from your database. So review the notebook before starting to execute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "247bf59d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import configparser\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d6cdeb7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "config = configparser.ConfigParser()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5b50a309",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['clusterair.config']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "config.read('clusterair.config')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c4bb5934",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'airbnb'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "config['POSTGRES']['PG_DB']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4d719646",
   "metadata": {},
   "outputs": [],
   "source": [
    "db = config['POSTGRES']['PG_DB']\n",
    "user = config['POSTGRES']['PG_UNAME']\n",
    "passwd = config['POSTGRES']['PG_PASS']\n",
    "port = config['POSTGRES']['PG_PORT']\n",
    "host = config['POSTGRES']['PG_HOST']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc18ed89",
   "metadata": {},
   "source": [
    "Using the pandas internal read_sql method to get the data from the DB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "86635a7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "credentials = \"postgresql://{}:{}@{}:{}/{}\".format(user,passwd,host,port,db)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "53435a3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'postgresql://postgres:1234@172.17.0.2:5432/airbnb'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credentials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fd428ffd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#using psycopg2 to test connection since there are no tables\n",
    "\n",
    "import psycopg2\n",
    "try:\n",
    "    conn = psycopg2.connect(host=host,dbname=db,user=user,password=passwd,port=port)\n",
    "except Exception as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e4856ca9",
   "metadata": {},
   "outputs": [],
   "source": [
    "conn.set_session(autocommit=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f464add2",
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    cur = conn.cursor()\n",
    "    \n",
    "except:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a43078b",
   "metadata": {},
   "source": [
    "Following steps to start the data modeling\n",
    "\n",
    "1) Get the schema from the airbnb database that is already in pg\n",
    "\n",
    "2) Design the star schema and decide the facttable and dimension table\n",
    "\n",
    "3) Decide the columns in the dimtables and facttable\n",
    "\n",
    "4) Create tables and insert data into the tables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da620015",
   "metadata": {},
   "source": [
    "#### Questions to model this Dataset\n",
    "\n",
    "When modeling data, you typically ask questions like:\n",
    "\n",
    "- What are the main entities/concepts in my application?\n",
    "   \n",
    "    - Listing (location, host, property details)\n",
    "    \n",
    "    - Review (comment and stars)\n",
    "    \n",
    "    - Date / Day details (date, day, month, year)\n",
    "\n",
    "- How do they relate to each other?\n",
    "     * Review, Review stars, Price, Sales are the drivers\n",
    "     \n",
    "     * Reviews are given by customers\n",
    "     \n",
    "     * Customers are related to the listings they stay\n",
    "     \n",
    "     * Property have hosts, location\n",
    "     \n",
    "     * Hosts can have multiple properties\n",
    "     \n",
    "     * Listing will have one property linked to it\n",
    "\n",
    "- What are their main characteristics/properties?\n",
    "    * Reviews have \n",
    "        \n",
    "        - Review Id\n",
    "        \n",
    "        - Customer Id\n",
    "        \n",
    "        - Review Comment\n",
    "        \n",
    "        - Date \n",
    "        \n",
    "    * Listings\n",
    "    \n",
    "        The number columns are huge for the listings_Details table, so to unDerstanD we will be using a panDas columns. Once we can view the columns, then the requireD columns can be selecteD from the list. \n",
    "\n",
    "    Listings table can be split into \n",
    "\n",
    "    - listingsDimension\n",
    "\n",
    "    - hostDimension\n",
    "\n",
    "    - reviewDimension\n",
    "\n",
    "    - locationDimension\n",
    "\n",
    "    - miscelDimension\n",
    "\n",
    "\n",
    "- How can they be represented with my technology stack?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "419d82d1",
   "metadata": {},
   "source": [
    "#### Writing additional helpers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "70edda81",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using pandas read_sql for getting schema\n",
    "def getSchema(tableName, credentials):\n",
    "    schema = pd.read_sql(\"\"\"SELECT * FROM information_schema.columns where table_name='{}'\"\"\".format(tableName),con=credentials)\n",
    "    return schema"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0d3944a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Issue is in using pd.read_sql to write data to the database. so using psycopg2\n",
    "def queryTable(query):\n",
    "    try:\n",
    "        schema = cur.execute(query)\n",
    "        return \n",
    "    except Exception as e:\n",
    "        print(e)\n",
    "        \n",
    "#This doesn't return anything"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5d1587d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using the pd.read_sql for getting data from db\n",
    "def queryBase(query):\n",
    "    requiredTable = pd.read_sql(query,con=credentials)\n",
    "    return requiredTable\n",
    "\n",
    "#This returns the dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3beb7697",
   "metadata": {},
   "source": [
    "What is the Goal of the Data model?\n",
    "\n",
    "- To find which hotel is having the highest revenue\n",
    "\n",
    "- To find how any reviews each of the listings have and how their pricing is correlated\n",
    "\n",
    "- On which date there were more bookings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "88f166b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>table_catalog</th>\n",
       "      <th>table_schema</th>\n",
       "      <th>table_name</th>\n",
       "      <th>column_name</th>\n",
       "      <th>ordinal_position</th>\n",
       "      <th>column_default</th>\n",
       "      <th>is_nullable</th>\n",
       "      <th>data_type</th>\n",
       "      <th>character_maximum_length</th>\n",
       "      <th>character_octet_length</th>\n",
       "      <th>...</th>\n",
       "      <th>is_identity</th>\n",
       "      <th>identity_generation</th>\n",
       "      <th>identity_start</th>\n",
       "      <th>identity_increment</th>\n",
       "      <th>identity_maximum</th>\n",
       "      <th>identity_minimum</th>\n",
       "      <th>identity_cycle</th>\n",
       "      <th>is_generated</th>\n",
       "      <th>generation_expression</th>\n",
       "      <th>is_updatable</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>airbnb</td>\n",
       "      <td>public</td>\n",
       "      <td>listings_details</td>\n",
       "      <td>bedrooms</td>\n",
       "      <td>51</td>\n",
       "      <td>None</td>\n",
       "      <td>YES</td>\n",
       "      <td>real</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NO</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NO</td>\n",
       "      <td>NEVER</td>\n",
       "      <td>None</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>airbnb</td>\n",
       "      <td>public</td>\n",
       "      <td>listings_details</td>\n",
       "      <td>beds</td>\n",
       "      <td>52</td>\n",
       "      <td>None</td>\n",
       "      <td>YES</td>\n",
       "      <td>real</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NO</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NO</td>\n",
       "      <td>NEVER</td>\n",
       "      <td>None</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>airbnb</td>\n",
       "      <td>public</td>\n",
       "      <td>listings_details</td>\n",
       "      <td>availability_30</td>\n",
       "      <td>67</td>\n",
       "      <td>None</td>\n",
       "      <td>YES</td>\n",
       "      <td>numeric</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NO</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NO</td>\n",
       "      <td>NEVER</td>\n",
       "      <td>None</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>airbnb</td>\n",
       "      <td>public</td>\n",
       "      <td>listings_details</td>\n",
       "      <td>availability_60</td>\n",
       "      <td>68</td>\n",
       "      <td>None</td>\n",
       "      <td>YES</td>\n",
       "      <td>numeric</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NO</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NO</td>\n",
       "      <td>NEVER</td>\n",
       "      <td>None</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>airbnb</td>\n",
       "      <td>public</td>\n",
       "      <td>listings_details</td>\n",
       "      <td>availability_90</td>\n",
       "      <td>69</td>\n",
       "      <td>None</td>\n",
       "      <td>YES</td>\n",
       "      <td>numeric</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NO</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NO</td>\n",
       "      <td>NEVER</td>\n",
       "      <td>None</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>airbnb</td>\n",
       "      <td>public</td>\n",
       "      <td>listings_details</td>\n",
       "      <td>host_is_superhost</td>\n",
       "      <td>25</td>\n",
       "      <td>None</td>\n",
       "      <td>YES</td>\n",
       "      <td>character varying</td>\n",
       "      <td>None</td>\n",
       "      <td>1.073742e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>NO</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NO</td>\n",
       "      <td>NEVER</td>\n",
       "      <td>None</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>airbnb</td>\n",
       "      <td>public</td>\n",
       "      <td>listings_details</td>\n",
       "      <td>host_thumbnail_url</td>\n",
       "      <td>26</td>\n",
       "      <td>None</td>\n",
       "      <td>YES</td>\n",
       "      <td>character varying</td>\n",
       "      <td>None</td>\n",
       "      <td>1.073742e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>NO</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NO</td>\n",
       "      <td>NEVER</td>\n",
       "      <td>None</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>airbnb</td>\n",
       "      <td>public</td>\n",
       "      <td>listings_details</td>\n",
       "      <td>host_picture_url</td>\n",
       "      <td>27</td>\n",
       "      <td>None</td>\n",
       "      <td>YES</td>\n",
       "      <td>character varying</td>\n",
       "      <td>None</td>\n",
       "      <td>1.073742e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>NO</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NO</td>\n",
       "      <td>NEVER</td>\n",
       "      <td>None</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>airbnb</td>\n",
       "      <td>public</td>\n",
       "      <td>listings_details</td>\n",
       "      <td>host_neighbourhood</td>\n",
       "      <td>28</td>\n",
       "      <td>None</td>\n",
       "      <td>YES</td>\n",
       "      <td>character varying</td>\n",
       "      <td>None</td>\n",
       "      <td>1.073742e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>NO</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NO</td>\n",
       "      <td>NEVER</td>\n",
       "      <td>None</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>airbnb</td>\n",
       "      <td>public</td>\n",
       "      <td>listings_details</td>\n",
       "      <td>host_listings_count</td>\n",
       "      <td>29</td>\n",
       "      <td>None</td>\n",
       "      <td>YES</td>\n",
       "      <td>character varying</td>\n",
       "      <td>None</td>\n",
       "      <td>1.073742e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>NO</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NO</td>\n",
       "      <td>NEVER</td>\n",
       "      <td>None</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>91 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   table_catalog table_schema        table_name          column_name  \\\n",
       "0         airbnb       public  listings_details             bedrooms   \n",
       "1         airbnb       public  listings_details                 beds   \n",
       "2         airbnb       public  listings_details      availability_30   \n",
       "3         airbnb       public  listings_details      availability_60   \n",
       "4         airbnb       public  listings_details      availability_90   \n",
       "..           ...          ...               ...                  ...   \n",
       "86        airbnb       public  listings_details    host_is_superhost   \n",
       "87        airbnb       public  listings_details   host_thumbnail_url   \n",
       "88        airbnb       public  listings_details     host_picture_url   \n",
       "89        airbnb       public  listings_details   host_neighbourhood   \n",
       "90        airbnb       public  listings_details  host_listings_count   \n",
       "\n",
       "    ordinal_position column_default is_nullable          data_type  \\\n",
       "0                 51           None         YES               real   \n",
       "1                 52           None         YES               real   \n",
       "2                 67           None         YES            numeric   \n",
       "3                 68           None         YES            numeric   \n",
       "4                 69           None         YES            numeric   \n",
       "..               ...            ...         ...                ...   \n",
       "86                25           None         YES  character varying   \n",
       "87                26           None         YES  character varying   \n",
       "88                27           None         YES  character varying   \n",
       "89                28           None         YES  character varying   \n",
       "90                29           None         YES  character varying   \n",
       "\n",
       "   character_maximum_length  character_octet_length  ...  is_identity  \\\n",
       "0                      None                     NaN  ...           NO   \n",
       "1                      None                     NaN  ...           NO   \n",
       "2                      None                     NaN  ...           NO   \n",
       "3                      None                     NaN  ...           NO   \n",
       "4                      None                     NaN  ...           NO   \n",
       "..                      ...                     ...  ...          ...   \n",
       "86                     None            1.073742e+09  ...           NO   \n",
       "87                     None            1.073742e+09  ...           NO   \n",
       "88                     None            1.073742e+09  ...           NO   \n",
       "89                     None            1.073742e+09  ...           NO   \n",
       "90                     None            1.073742e+09  ...           NO   \n",
       "\n",
       "    identity_generation identity_start identity_increment identity_maximum  \\\n",
       "0                  None           None               None             None   \n",
       "1                  None           None               None             None   \n",
       "2                  None           None               None             None   \n",
       "3                  None           None               None             None   \n",
       "4                  None           None               None             None   \n",
       "..                  ...            ...                ...              ...   \n",
       "86                 None           None               None             None   \n",
       "87                 None           None               None             None   \n",
       "88                 None           None               None             None   \n",
       "89                 None           None               None             None   \n",
       "90                 None           None               None             None   \n",
       "\n",
       "   identity_minimum identity_cycle is_generated generation_expression  \\\n",
       "0              None             NO        NEVER                  None   \n",
       "1              None             NO        NEVER                  None   \n",
       "2              None             NO        NEVER                  None   \n",
       "3              None             NO        NEVER                  None   \n",
       "4              None             NO        NEVER                  None   \n",
       "..              ...            ...          ...                   ...   \n",
       "86             None             NO        NEVER                  None   \n",
       "87             None             NO        NEVER                  None   \n",
       "88             None             NO        NEVER                  None   \n",
       "89             None             NO        NEVER                  None   \n",
       "90             None             NO        NEVER                  None   \n",
       "\n",
       "   is_updatable  \n",
       "0           YES  \n",
       "1           YES  \n",
       "2           YES  \n",
       "3           YES  \n",
       "4           YES  \n",
       "..          ...  \n",
       "86          YES  \n",
       "87          YES  \n",
       "88          YES  \n",
       "89          YES  \n",
       "90          YES  \n",
       "\n",
       "[91 rows x 44 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "getSchema('listings_details',credentials)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9c4781a1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'listing_url', 'scrape_id', 'last_scraped', 'name', 'summary',\n",
       "       'space', 'description', 'experiences_offered', 'neighborhood_overview',\n",
       "       'notes', 'transit', 'access', 'interaction', 'house_rules',\n",
       "       'picture_url', 'host_id', 'host_url', 'host_name', 'host_since',\n",
       "       'host_location', 'host_about', 'host_response_time',\n",
       "       'host_response_rate', 'host_is_superhost', 'host_thumbnail_url',\n",
       "       'host_picture_url', 'host_neighbourhood', 'host_listings_count',\n",
       "       'host_total_listings_count', 'host_verifications',\n",
       "       'host_has_profile_pic', 'host_identity_verified', 'street',\n",
       "       'neighbourhood', 'neighbourhood_cleansed', 'city', 'state', 'zipcode',\n",
       "       'market', 'smart_location', 'country_code', 'country', 'latitude',\n",
       "       'longitude', 'is_location_exact', 'property_type', 'room_type',\n",
       "       'accommodates', 'bathrooms', 'bedrooms', 'beds', 'bed_type',\n",
       "       'amenities', 'square_feet', 'price', 'weekly_price', 'monthly_price',\n",
       "       'security_deposit', 'cleaning_fee', 'guests_included', 'extra_people',\n",
       "       'minimum_nights', 'maximum_nights', 'calendar_updated',\n",
       "       'has_availability', 'availability_30', 'availability_60',\n",
       "       'availability_90', 'availability_365', 'calendar_last_scraped',\n",
       "       'number_of_reviews', 'first_review', 'last_review',\n",
       "       'review_scores_rating', 'review_scores_accuracy',\n",
       "       'review_scores_cleanliness', 'review_scores_checkin',\n",
       "       'review_scores_communication', 'review_scores_location',\n",
       "       'review_scores_value', 'requires_license', 'license',\n",
       "       'jurisdiction_names', 'instant_bookable', 'is_business_travel_ready',\n",
       "       'cancellation_policy', 'require_guest_profile_picture',\n",
       "       'require_guest_phone_verification', 'calculated_host_listings_count',\n",
       "       'reviews_per_month'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "queryListings0 = \"\"\"SELECT * FROM listings_details LIMIT 1\"\"\"\n",
    "queryBase(queryListings0).columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90a6b4f4",
   "metadata": {},
   "source": [
    "### Listing summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2af01e37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>listing_url</th>\n",
       "      <th>scrape_id</th>\n",
       "      <th>last_scraped</th>\n",
       "      <th>name</th>\n",
       "      <th>summary</th>\n",
       "      <th>space</th>\n",
       "      <th>description</th>\n",
       "      <th>experiences_offered</th>\n",
       "      <th>interaction</th>\n",
       "      <th>house_rules</th>\n",
       "      <th>picture_url</th>\n",
       "      <th>instant_bookable</th>\n",
       "      <th>is_business_travel_ready</th>\n",
       "      <th>cancellation_policy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2818.0</td>\n",
       "      <td>https://www.airbnb.com/rooms/2818</td>\n",
       "      <td>2.018121e+13</td>\n",
       "      <td>2018-12-06</td>\n",
       "      <td>Quiet Garden View Room &amp; Super Fast WiFi</td>\n",
       "      <td>Quiet Garden View Room &amp; Super Fast WiFi</td>\n",
       "      <td>I'm renting a bedroom (room overlooking the ga...</td>\n",
       "      <td>Quiet Garden View Room &amp; Super Fast WiFi I'm r...</td>\n",
       "      <td>none</td>\n",
       "      <td>noData</td>\n",
       "      <td>Please: - Leave your shoes in the entrance  - ...</td>\n",
       "      <td>https://a0.muscache.com/im/pictures/10272854/8...</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id                        listing_url     scrape_id last_scraped  \\\n",
       "0  2818.0  https://www.airbnb.com/rooms/2818  2.018121e+13   2018-12-06   \n",
       "\n",
       "                                       name  \\\n",
       "0  Quiet Garden View Room & Super Fast WiFi   \n",
       "\n",
       "                                    summary  \\\n",
       "0  Quiet Garden View Room & Super Fast WiFi   \n",
       "\n",
       "                                               space  \\\n",
       "0  I'm renting a bedroom (room overlooking the ga...   \n",
       "\n",
       "                                         description experiences_offered  \\\n",
       "0  Quiet Garden View Room & Super Fast WiFi I'm r...                none   \n",
       "\n",
       "  interaction                                        house_rules  \\\n",
       "0      noData  Please: - Leave your shoes in the entrance  - ...   \n",
       "\n",
       "                                         picture_url instant_bookable  \\\n",
       "0  https://a0.muscache.com/im/pictures/10272854/8...                t   \n",
       "\n",
       "  is_business_travel_ready          cancellation_policy  \n",
       "0                        f  strict_14_with_grace_period  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "queryListings5 = \"\"\"SELECT id, listing_url, scrape_id, last_scraped, name, summary,\n",
    "       space, description, experiences_offered, interaction, house_rules,\n",
    "       picture_url,instant_bookable, is_business_travel_ready,\n",
    "       cancellation_policy FROM listings_details LIMIT 1\"\"\"\n",
    "queryBase(queryListings5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ac6cdb1",
   "metadata": {},
   "source": [
    "### Host summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "dc3d4032",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>host_id</th>\n",
       "      <th>host_url</th>\n",
       "      <th>host_name</th>\n",
       "      <th>host_since</th>\n",
       "      <th>host_location</th>\n",
       "      <th>host_about</th>\n",
       "      <th>host_response_time</th>\n",
       "      <th>host_response_rate</th>\n",
       "      <th>host_is_superhost</th>\n",
       "      <th>host_thumbnail_url</th>\n",
       "      <th>host_picture_url</th>\n",
       "      <th>host_neighbourhood</th>\n",
       "      <th>host_listings_count</th>\n",
       "      <th>host_total_listings_count</th>\n",
       "      <th>host_verifications</th>\n",
       "      <th>host_has_profile_pic</th>\n",
       "      <th>host_identity_verified</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3159.0</td>\n",
       "      <td>https://www.airbnb.com/users/show/3159</td>\n",
       "      <td>Daniel</td>\n",
       "      <td>2008-09-24</td>\n",
       "      <td>Amsterdam, Noord-Holland, The Netherlands</td>\n",
       "      <td>Upon arriving in Amsterdam, one can imagine as...</td>\n",
       "      <td>within an hour</td>\n",
       "      <td>100%</td>\n",
       "      <td>t</td>\n",
       "      <td>https://a0.muscache.com/im/users/3159/profile_...</td>\n",
       "      <td>https://a0.muscache.com/im/users/3159/profile_...</td>\n",
       "      <td>Indische Buurt</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>['email', 'phone', 'reviews', 'jumio', 'offlin...</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3806.0</td>\n",
       "      <td>https://www.airbnb.com/users/show/3806</td>\n",
       "      <td>Maartje</td>\n",
       "      <td>2008-10-24</td>\n",
       "      <td>Amsterdam, Noord-Holland, The Netherlands</td>\n",
       "      <td>I am a freelance radio producer and journalist...</td>\n",
       "      <td>within an hour</td>\n",
       "      <td>100%</td>\n",
       "      <td>f</td>\n",
       "      <td>https://a0.muscache.com/im/users/3806/profile_...</td>\n",
       "      <td>https://a0.muscache.com/im/users/3806/profile_...</td>\n",
       "      <td>Spaarndammer en Zeeheldenbuurt</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>['email', 'phone', 'reviews', 'jumio']</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   host_id                                host_url host_name  host_since  \\\n",
       "0   3159.0  https://www.airbnb.com/users/show/3159    Daniel  2008-09-24   \n",
       "1   3806.0  https://www.airbnb.com/users/show/3806   Maartje  2008-10-24   \n",
       "\n",
       "                               host_location  \\\n",
       "0  Amsterdam, Noord-Holland, The Netherlands   \n",
       "1  Amsterdam, Noord-Holland, The Netherlands   \n",
       "\n",
       "                                          host_about host_response_time  \\\n",
       "0  Upon arriving in Amsterdam, one can imagine as...     within an hour   \n",
       "1  I am a freelance radio producer and journalist...     within an hour   \n",
       "\n",
       "  host_response_rate host_is_superhost  \\\n",
       "0               100%                 t   \n",
       "1               100%                 f   \n",
       "\n",
       "                                  host_thumbnail_url  \\\n",
       "0  https://a0.muscache.com/im/users/3159/profile_...   \n",
       "1  https://a0.muscache.com/im/users/3806/profile_...   \n",
       "\n",
       "                                    host_picture_url  \\\n",
       "0  https://a0.muscache.com/im/users/3159/profile_...   \n",
       "1  https://a0.muscache.com/im/users/3806/profile_...   \n",
       "\n",
       "               host_neighbourhood host_listings_count  \\\n",
       "0                  Indische Buurt                 1.0   \n",
       "1  Spaarndammer en Zeeheldenbuurt                 1.0   \n",
       "\n",
       "   host_total_listings_count  \\\n",
       "0                        1.0   \n",
       "1                        1.0   \n",
       "\n",
       "                                  host_verifications host_has_profile_pic  \\\n",
       "0  ['email', 'phone', 'reviews', 'jumio', 'offlin...                    t   \n",
       "1             ['email', 'phone', 'reviews', 'jumio']                    t   \n",
       "\n",
       "  host_identity_verified  \n",
       "0                      f  \n",
       "1                      t  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "queryListings1 = \"\"\"SELECT host_id, host_url, host_name, host_since,\n",
    "       host_location, host_about, host_response_time,\n",
    "       host_response_rate, host_is_superhost, host_thumbnail_url,\n",
    "       host_picture_url, host_neighbourhood, host_listings_count,\n",
    "       host_total_listings_count, host_verifications,\n",
    "       host_has_profile_pic, host_identity_verified FROM listings_details LIMIT 2\"\"\"\n",
    "queryBase(queryListings1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8eb1cbf6",
   "metadata": {},
   "source": [
    "### location summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "77829aec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>street</th>\n",
       "      <th>neighborhood_overview</th>\n",
       "      <th>notes</th>\n",
       "      <th>transit</th>\n",
       "      <th>access</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>neighbourhood_cleansed</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>market</th>\n",
       "      <th>smart_location</th>\n",
       "      <th>country_code</th>\n",
       "      <th>country</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>is_location_exact</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Amsterdam, North Holland, Netherlands</td>\n",
       "      <td>Indische Buurt (\"Indies Neighborhood\") is a ne...</td>\n",
       "      <td>From week 38 to week 47 maintenance work to th...</td>\n",
       "      <td>The neighbourhood is well served by 24 hours p...</td>\n",
       "      <td>noData</td>\n",
       "      <td>Indische Buurt</td>\n",
       "      <td>Oostelijk Havengebied - Indische Buurt</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>North Holland</td>\n",
       "      <td>0</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>Amsterdam, Netherlands</td>\n",
       "      <td>NL</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>52.365753</td>\n",
       "      <td>4.941419</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Amsterdam, Noord-Holland, Netherlands</td>\n",
       "      <td>Welcome to the Spaarndammerbuurt! From the beg...</td>\n",
       "      <td>noData</td>\n",
       "      <td>From Central Station, walk towards the busstop...</td>\n",
       "      <td>You will have the entire house to yourself.</td>\n",
       "      <td>Spaarndammer en Zeeheldenbuurt</td>\n",
       "      <td>Westerpark</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>Noord-Holland</td>\n",
       "      <td>1013 XE</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>Amsterdam, Netherlands</td>\n",
       "      <td>NL</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>52.390224</td>\n",
       "      <td>4.873924</td>\n",
       "      <td>t</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Amsterdam, North Holland, Netherlands</td>\n",
       "      <td>Located just in between famous central canals....</td>\n",
       "      <td>Check-in time from 2pm till 10pm Checkout anyt...</td>\n",
       "      <td>No need to use any transport! All is within a ...</td>\n",
       "      <td>noData</td>\n",
       "      <td>Grachtengordel</td>\n",
       "      <td>Centrum-Oost</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>North Holland</td>\n",
       "      <td>1017</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>Amsterdam, Netherlands</td>\n",
       "      <td>NL</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>52.365086</td>\n",
       "      <td>4.893541</td>\n",
       "      <td>t</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Amsterdam, North Holland, Netherlands</td>\n",
       "      <td>noData</td>\n",
       "      <td>noData</td>\n",
       "      <td>noData</td>\n",
       "      <td>The apartment is about 75 meters or 800 square...</td>\n",
       "      <td>Grachtengordel</td>\n",
       "      <td>Centrum-West</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>North Holland</td>\n",
       "      <td>1016</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>Amsterdam, Netherlands</td>\n",
       "      <td>NL</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>52.373116</td>\n",
       "      <td>4.883668</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Amsterdam, North Holland, Netherlands</td>\n",
       "      <td>Central, quiet, safe, clean and beautiful.</td>\n",
       "      <td>we have a canadian canoe for you as well, free...</td>\n",
       "      <td>cental station aprox. 10 minutes on foot, buss...</td>\n",
       "      <td>Your own apartment, nothing shared</td>\n",
       "      <td>Westelijke Eilanden</td>\n",
       "      <td>Centrum-West</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>North Holland</td>\n",
       "      <td>1013</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>Amsterdam, Netherlands</td>\n",
       "      <td>NL</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>52.386726</td>\n",
       "      <td>4.892078</td>\n",
       "      <td>t</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  street  \\\n",
       "0  Amsterdam, North Holland, Netherlands   \n",
       "1  Amsterdam, Noord-Holland, Netherlands   \n",
       "2  Amsterdam, North Holland, Netherlands   \n",
       "3  Amsterdam, North Holland, Netherlands   \n",
       "4  Amsterdam, North Holland, Netherlands   \n",
       "\n",
       "                               neighborhood_overview  \\\n",
       "0  Indische Buurt (\"Indies Neighborhood\") is a ne...   \n",
       "1  Welcome to the Spaarndammerbuurt! From the beg...   \n",
       "2  Located just in between famous central canals....   \n",
       "3                                             noData   \n",
       "4         Central, quiet, safe, clean and beautiful.   \n",
       "\n",
       "                                               notes  \\\n",
       "0  From week 38 to week 47 maintenance work to th...   \n",
       "1                                             noData   \n",
       "2  Check-in time from 2pm till 10pm Checkout anyt...   \n",
       "3                                             noData   \n",
       "4  we have a canadian canoe for you as well, free...   \n",
       "\n",
       "                                             transit  \\\n",
       "0  The neighbourhood is well served by 24 hours p...   \n",
       "1  From Central Station, walk towards the busstop...   \n",
       "2  No need to use any transport! All is within a ...   \n",
       "3                                             noData   \n",
       "4  cental station aprox. 10 minutes on foot, buss...   \n",
       "\n",
       "                                              access  \\\n",
       "0                                             noData   \n",
       "1        You will have the entire house to yourself.   \n",
       "2                                             noData   \n",
       "3  The apartment is about 75 meters or 800 square...   \n",
       "4                 Your own apartment, nothing shared   \n",
       "\n",
       "                    neighbourhood                  neighbourhood_cleansed  \\\n",
       "0                  Indische Buurt  Oostelijk Havengebied - Indische Buurt   \n",
       "1  Spaarndammer en Zeeheldenbuurt                              Westerpark   \n",
       "2                  Grachtengordel                            Centrum-Oost   \n",
       "3                  Grachtengordel                            Centrum-West   \n",
       "4             Westelijke Eilanden                            Centrum-West   \n",
       "\n",
       "        city          state  zipcode     market          smart_location  \\\n",
       "0  Amsterdam  North Holland        0  Amsterdam  Amsterdam, Netherlands   \n",
       "1  Amsterdam  Noord-Holland  1013 XE  Amsterdam  Amsterdam, Netherlands   \n",
       "2  Amsterdam  North Holland     1017  Amsterdam  Amsterdam, Netherlands   \n",
       "3  Amsterdam  North Holland     1016  Amsterdam  Amsterdam, Netherlands   \n",
       "4  Amsterdam  North Holland     1013  Amsterdam  Amsterdam, Netherlands   \n",
       "\n",
       "  country_code      country   latitude  longitude is_location_exact  \n",
       "0           NL  Netherlands  52.365753   4.941419                 f  \n",
       "1           NL  Netherlands  52.390224   4.873924                 t  \n",
       "2           NL  Netherlands  52.365086   4.893541                 t  \n",
       "3           NL  Netherlands  52.373116   4.883668                 f  \n",
       "4           NL  Netherlands  52.386726   4.892078                 t  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Needs location id for each listing_id\n",
    "queryListings2 = \"\"\"SELECT street, neighborhood_overview,\n",
    "       notes, transit, access, neighbourhood, neighbourhood_cleansed, city, state, zipcode,\n",
    "       market, smart_location, country_code, country, latitude,\n",
    "       longitude, is_location_exact FROM listings_details LIMIT 5\"\"\"\n",
    "queryBase(queryListings2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc369a06",
   "metadata": {},
   "source": [
    "### reviews summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "12cb9404",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>number_of_reviews</th>\n",
       "      <th>first_review</th>\n",
       "      <th>last_review</th>\n",
       "      <th>review_scores_rating</th>\n",
       "      <th>review_scores_accuracy</th>\n",
       "      <th>review_scores_cleanliness</th>\n",
       "      <th>review_scores_checkin</th>\n",
       "      <th>review_scores_communication</th>\n",
       "      <th>review_scores_location</th>\n",
       "      <th>review_scores_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>248.0</td>\n",
       "      <td>2009-03-30</td>\n",
       "      <td>2018-11-28</td>\n",
       "      <td>97.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42.0</td>\n",
       "      <td>2015-07-31</td>\n",
       "      <td>2018-08-29</td>\n",
       "      <td>96.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>233.0</td>\n",
       "      <td>2010-03-02</td>\n",
       "      <td>2018-11-30</td>\n",
       "      <td>87.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2018-01-21</td>\n",
       "      <td>2018-01-21</td>\n",
       "      <td>100.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>171.0</td>\n",
       "      <td>2012-01-09</td>\n",
       "      <td>2018-11-25</td>\n",
       "      <td>99.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   number_of_reviews first_review last_review  review_scores_rating  \\\n",
       "0              248.0   2009-03-30  2018-11-28                  97.0   \n",
       "1               42.0   2015-07-31  2018-08-29                  96.0   \n",
       "2              233.0   2010-03-02  2018-11-30                  87.0   \n",
       "3                1.0   2018-01-21  2018-01-21                 100.0   \n",
       "4              171.0   2012-01-09  2018-11-25                  99.0   \n",
       "\n",
       "   review_scores_accuracy  review_scores_cleanliness  review_scores_checkin  \\\n",
       "0                    10.0                       10.0                   10.0   \n",
       "1                    10.0                        9.0                   10.0   \n",
       "2                     9.0                       10.0                    9.0   \n",
       "3                    10.0                       10.0                   10.0   \n",
       "4                    10.0                       10.0                   10.0   \n",
       "\n",
       "   review_scores_communication  review_scores_location  review_scores_value  \n",
       "0                         10.0                     9.0                 10.0  \n",
       "1                         10.0                     9.0                  9.0  \n",
       "2                          9.0                    10.0                  9.0  \n",
       "3                         10.0                    10.0                 10.0  \n",
       "4                         10.0                    10.0                 10.0  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this is kind of fact table and better to reference it to the listings_id\n",
    "queryListings3 = \"\"\"SELECT number_of_reviews, reviews_per_month ,first_review, last_review,\n",
    "       review_scores_rating, review_scores_accuracy,\n",
    "       review_scores_cleanliness, review_scores_checkin,\n",
    "       review_scores_communication, review_scores_location,\n",
    "       review_scores_value FROM listings_details LIMIT 5\"\"\"\n",
    "queryBase(queryListings3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ad5a3525",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>requires_license</th>\n",
       "      <th>license</th>\n",
       "      <th>jurisdiction_names</th>\n",
       "      <th>require_guest_profile_picture</th>\n",
       "      <th>require_guest_phone_verification</th>\n",
       "      <th>calculated_host_listings_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>f</td>\n",
       "      <td>noData</td>\n",
       "      <td>{Amsterdam}</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>f</td>\n",
       "      <td>noData</td>\n",
       "      <td>{Amsterdam}</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  requires_license license jurisdiction_names require_guest_profile_picture  \\\n",
       "0                f  noData        {Amsterdam}                             f   \n",
       "1                f  noData        {Amsterdam}                             f   \n",
       "\n",
       "  require_guest_phone_verification  calculated_host_listings_count  \n",
       "0                                f                             1.0  \n",
       "1                                f                             1.0  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "queryListings6 = \"\"\"SELECT requires_license, license,\n",
    "       jurisdiction_names, require_guest_profile_picture,\n",
    "       require_guest_phone_verification, calculated_host_listings_count\n",
    "       FROM listings_details LIMIT 2\"\"\"\n",
    "queryBase(queryListings6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "61497f3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>listing_id</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2818.0</td>\n",
       "      <td>2009-03-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2818.0</td>\n",
       "      <td>2009-04-24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   listing_id        date\n",
       "0      2818.0  2009-03-30\n",
       "1      2818.0  2009-04-24"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "queryReviews1 = \"\"\"SELECT * FROM reviews LIMIT 2;\"\"\"\n",
    "queryBase(queryReviews1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f84003a9",
   "metadata": {},
   "source": [
    "As you can observe in the below table, the \"review\" table is redundant, so can be completely ignored"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "285574a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>listing_id</th>\n",
       "      <th>id</th>\n",
       "      <th>date</th>\n",
       "      <th>reviewer_id</th>\n",
       "      <th>reviewer_name</th>\n",
       "      <th>comments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2818.0</td>\n",
       "      <td>1191.0</td>\n",
       "      <td>2009-03-30</td>\n",
       "      <td>10952.0</td>\n",
       "      <td>Lam</td>\n",
       "      <td>Daniel is really cool. The place was nice and ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2818.0</td>\n",
       "      <td>1771.0</td>\n",
       "      <td>2009-04-24</td>\n",
       "      <td>12798.0</td>\n",
       "      <td>Alice</td>\n",
       "      <td>Daniel is the most amazing host! His place is ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   listing_id      id        date  reviewer_id reviewer_name  \\\n",
       "0      2818.0  1191.0  2009-03-30      10952.0           Lam   \n",
       "1      2818.0  1771.0  2009-04-24      12798.0         Alice   \n",
       "\n",
       "                                            comments  \n",
       "0  Daniel is really cool. The place was nice and ...  \n",
       "1  Daniel is the most amazing host! His place is ...  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "queryReviews2 = \"\"\"SELECT * FROM reviews_details LIMIT 2;\"\"\"\n",
    "queryBase(queryReviews2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "edb6a679",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>listing_id</th>\n",
       "      <th>id</th>\n",
       "      <th>date</th>\n",
       "      <th>reviewer_id</th>\n",
       "      <th>reviewer_name</th>\n",
       "      <th>comments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2818.0</td>\n",
       "      <td>1191.0</td>\n",
       "      <td>2009-03-30</td>\n",
       "      <td>10952.0</td>\n",
       "      <td>Lam</td>\n",
       "      <td>Daniel is really cool. The place was nice and ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2818.0</td>\n",
       "      <td>1771.0</td>\n",
       "      <td>2009-04-24</td>\n",
       "      <td>12798.0</td>\n",
       "      <td>Alice</td>\n",
       "      <td>Daniel is the most amazing host! His place is ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   listing_id      id        date  reviewer_id reviewer_name  \\\n",
       "0      2818.0  1191.0  2009-03-30      10952.0           Lam   \n",
       "1      2818.0  1771.0  2009-04-24      12798.0         Alice   \n",
       "\n",
       "                                            comments  \n",
       "0  Daniel is really cool. The place was nice and ...  \n",
       "1  Daniel is the most amazing host! His place is ...  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "queryReviewsA = \"\"\"SELECT * FROM reviews_details ORDER BY date ASC LIMIT 2;\"\"\"\n",
    "queryBase(queryReviewsA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "7505a657",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>listing_id</th>\n",
       "      <th>id</th>\n",
       "      <th>date</th>\n",
       "      <th>reviewer_id</th>\n",
       "      <th>reviewer_name</th>\n",
       "      <th>comments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>600983.0</td>\n",
       "      <td>355876792.0</td>\n",
       "      <td>2018-12-06</td>\n",
       "      <td>184394649.0</td>\n",
       "      <td>Margaret</td>\n",
       "      <td>Ben will be there to welcome you and is very f...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2946162.0</td>\n",
       "      <td>355892010.0</td>\n",
       "      <td>2018-12-06</td>\n",
       "      <td>217385749.0</td>\n",
       "      <td>Patrick</td>\n",
       "      <td>Sehr coole, problemlose und Stadtnahe Unterkun...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   listing_id           id        date  reviewer_id reviewer_name  \\\n",
       "0    600983.0  355876792.0  2018-12-06  184394649.0      Margaret   \n",
       "1   2946162.0  355892010.0  2018-12-06  217385749.0       Patrick   \n",
       "\n",
       "                                            comments  \n",
       "0  Ben will be there to welcome you and is very f...  \n",
       "1  Sehr coole, problemlose und Stadtnahe Unterkun...  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "queryReviewsD = \"\"\"SELECT * FROM reviews_details ORDER BY date DESC LIMIT 2;\"\"\"\n",
    "queryBase(queryReviewsD)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24b5acfd",
   "metadata": {},
   "source": [
    "There is total 14,621,901 rows in the calendar table. Below query to sort the query result will take more time than usual, if you are running the database server on docker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "8e13b8dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>listing_id</th>\n",
       "      <th>date</th>\n",
       "      <th>available</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>73208.0</td>\n",
       "      <td>2018-12-06</td>\n",
       "      <td>f</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>255809.0</td>\n",
       "      <td>2018-12-06</td>\n",
       "      <td>f</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   listing_id        date available price\n",
       "0     73208.0  2018-12-06         f  None\n",
       "1    255809.0  2018-12-06         f  None"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "querycalendar = \"\"\"SELECT * FROM calendar ORDER BY date ASC LIMIT 2;\"\"\"\n",
    "queryBase(querycalendar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "18051092",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>listing_id</th>\n",
       "      <th>date</th>\n",
       "      <th>available</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>264628.0</td>\n",
       "      <td>2019-12-06</td>\n",
       "      <td>t</td>\n",
       "      <td>$100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>147270.0</td>\n",
       "      <td>2019-12-06</td>\n",
       "      <td>f</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   listing_id        date available    price\n",
       "0    264628.0  2019-12-06         t  $100.00\n",
       "1    147270.0  2019-12-06         f     None"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "querycalendar = \"\"\"SELECT * FROM calendar ORDER BY date DESC LIMIT 2;\"\"\"\n",
    "queryBase(querycalendar)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45dbcd74",
   "metadata": {},
   "source": [
    "Seems the Calendar table has exactly one year of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "b89eb940",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>neighbourhood_group</th>\n",
       "      <th>neighbourhood</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None</td>\n",
       "      <td>Bijlmer-Centrum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>None</td>\n",
       "      <td>Bijlmer-Oost</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>None</td>\n",
       "      <td>Bos en Lommer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>None</td>\n",
       "      <td>Buitenveldert - Zuidas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>None</td>\n",
       "      <td>Centrum-Oost</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>None</td>\n",
       "      <td>Oud-Oost</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>None</td>\n",
       "      <td>Slotervaart</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>None</td>\n",
       "      <td>Watergraafsmeer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>None</td>\n",
       "      <td>Westerpark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>None</td>\n",
       "      <td>Zuid</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>66 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   neighbourhood_group           neighbourhood\n",
       "0                 None         Bijlmer-Centrum\n",
       "1                 None            Bijlmer-Oost\n",
       "2                 None           Bos en Lommer\n",
       "3                 None  Buitenveldert - Zuidas\n",
       "4                 None            Centrum-Oost\n",
       "..                 ...                     ...\n",
       "61                None                Oud-Oost\n",
       "62                None             Slotervaart\n",
       "63                None         Watergraafsmeer\n",
       "64                None              Westerpark\n",
       "65                None                    Zuid\n",
       "\n",
       "[66 rows x 2 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "queryNeigh = \"\"\"SELECT * FROM neighbourhoods;\"\"\"\n",
    "queryBase(queryNeigh)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9daed908",
   "metadata": {},
   "source": [
    "#### The Crux\n",
    "\n",
    "The crux of the data modeling problem is defining useful entities and identifying whether and how they relate to one another in a web or graph of connections. These entities represent classes of physical objects or intangible concepts -- and more than a few things in between, such as locations, shipments, or assignments. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a89940aa",
   "metadata": {},
   "source": [
    "#### Star Schema\n",
    "\n",
    "The star schema is a different way of organizing your data. It is an excellent design approach for storing and analyzing massive amounts of data, and it relies on the usage of \"facts\" and \"dimensions.\"\n",
    "\n",
    "A **\"fact\"** is a numerical data point that drives business processes, and a **\"dimension\"** is a description of that fact. Using car sales numbers, for example, the \"fact\" table would contain information about the number of units sold, and a corresponding \"dimensional\" table would have the colors of those cars.  \n",
    "\n",
    "The same can be extended to **Snow Flake** schema when the dimensions tables have their own child tables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3de0869d",
   "metadata": {},
   "source": [
    "#### Remember Types of Schemas\n",
    "\n",
    "The six database schema designs covered in this article are:\n",
    "\n",
    "1. The flat model is for small, simple applications.\n",
    "\n",
    "2. The hierarchical model is for nested data, like XML or JSON.\n",
    "\n",
    "3. The network model is useful in mapping and spatial data, also for depicting workflows.\n",
    "\n",
    "4. The relational model best reflects Object-Oriented Programming applications.\n",
    "\n",
    "5. The star schema and snowflake schema are for analyzing large datasets.\n",
    "\n",
    "We have to use the Star schema for the AirBnB dataset. After the above analysis, below is the table configuration we can have for the dimensions and the facts."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09a5b778",
   "metadata": {},
   "source": [
    "#### Find the Unique IDs in tables given\n",
    "\n",
    "The tables needs to be connected with each other, and that is going to happen with a column that is having unique values for each record. "
   ]
  },
  {
   "attachments": {
    "image-2.png": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "0d81653a",
   "metadata": {},
   "source": [
    "![image-2.png](attachment:image-2.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6b712706",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>counted</th>\n",
       "      <th>listing_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>248</td>\n",
       "      <td>2818.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42</td>\n",
       "      <td>3209.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>233</td>\n",
       "      <td>20168.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>25428.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>171</td>\n",
       "      <td>27886.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   counted  listing_id\n",
       "0      248      2818.0\n",
       "1       42      3209.0\n",
       "2      233     20168.0\n",
       "3        1     25428.0\n",
       "4      171     27886.0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "queryBase(\"\"\"SELECT COUNT(listing_id) AS counted, listing_id FROM reviews_details GROUP BY listing_id ORDER BY listing_id ASC LIMIT 5;\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3576d9ca",
   "metadata": {},
   "source": [
    "For the \"reviews_details\" the id column is unique, even though changing randomly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c41e1dad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>counted</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1191.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1771.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1989.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2797.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>3151.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431825</th>\n",
       "      <td>1</td>\n",
       "      <td>355932552.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431826</th>\n",
       "      <td>1</td>\n",
       "      <td>355935460.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431827</th>\n",
       "      <td>1</td>\n",
       "      <td>355978587.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431828</th>\n",
       "      <td>1</td>\n",
       "      <td>355992396.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431829</th>\n",
       "      <td>1</td>\n",
       "      <td>355996918.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>431830 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        counted           id\n",
       "0             1       1191.0\n",
       "1             1       1771.0\n",
       "2             1       1989.0\n",
       "3             1       2797.0\n",
       "4             1       3151.0\n",
       "...         ...          ...\n",
       "431825        1  355932552.0\n",
       "431826        1  355935460.0\n",
       "431827        1  355978587.0\n",
       "431828        1  355992396.0\n",
       "431829        1  355996918.0\n",
       "\n",
       "[431830 rows x 2 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "queryBase(\"\"\"SELECT COUNT(id) AS counted, id FROM reviews_details GROUP BY id ORDER BY id ASC;\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac50e4d5",
   "metadata": {},
   "source": [
    "For the \"listings_details\" the id column is unique, even though changing randomly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0b7864e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>counted</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2818.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3209.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>20168.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>25428.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>27886.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20025</th>\n",
       "      <td>1</td>\n",
       "      <td>30576148.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20026</th>\n",
       "      <td>1</td>\n",
       "      <td>30577727.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20027</th>\n",
       "      <td>1</td>\n",
       "      <td>30578037.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20028</th>\n",
       "      <td>1</td>\n",
       "      <td>30579673.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20029</th>\n",
       "      <td>1</td>\n",
       "      <td>30580413.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20030 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       counted          id\n",
       "0            1      2818.0\n",
       "1            1      3209.0\n",
       "2            1     20168.0\n",
       "3            1     25428.0\n",
       "4            1     27886.0\n",
       "...        ...         ...\n",
       "20025        1  30576148.0\n",
       "20026        1  30577727.0\n",
       "20027        1  30578037.0\n",
       "20028        1  30579673.0\n",
       "20029        1  30580413.0\n",
       "\n",
       "[20030 rows x 2 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "queryBase(\"\"\"SELECT COUNT(id) AS counted, id FROM listings_details GROUP BY id ORDER BY id ASC;\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16596f28",
   "metadata": {},
   "source": [
    "**Row Counts:**\n",
    "Reviews Listings table: 431,000 records \n",
    "\n",
    "Calendar table : 14 Million+ records\n",
    "\n",
    "listings details : 20,000+ records\n",
    "\n",
    "**Column Counts:**\n",
    "Listings details : 90+ columnsn\n",
    "\n",
    "Review details : 6 column\n",
    "\n",
    "calendar : 4 columns\n",
    "\n",
    "**Data modeling objective:** \n",
    "\n",
    "1) Connect these data tables with diverse dimensions in a way to efficiently query them. \n",
    "\n",
    "2) Reduce the amount of data that is touched during the query \n",
    "\n",
    "3) Subsequently increase the speed of query response.\n",
    "\n",
    "### I had dropped the tables atleast 15 times \n",
    "\n",
    "Getting the correct set of unique_ids \n",
    "\n",
    "- needs practice, \n",
    "\n",
    "- understanding the data, and \n",
    "\n",
    "- knowing the final objective of using the data.  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "897d92a1",
   "metadata": {},
   "source": [
    "#### Next steps are to create the tables as per the schema above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c7c2607f",
   "metadata": {},
   "outputs": [],
   "source": [
    "createDimInfo = \"\"\"CREATE TABLE dim_information(\n",
    "                    info_id NUMERIC PRIMARY KEY NOT NULL,  \n",
    "                    listing_url VARCHAR, \n",
    "                    scrape_id BIGINT,  \n",
    "                    name VARCHAR, \n",
    "                    summary VARCHAR,\n",
    "                    space VARCHAR, \n",
    "                    description VARCHAR, \n",
    "                    experiences_offered VARCHAR, \n",
    "                    interaction VARCHAR, \n",
    "                    house_rules VARCHAR, \n",
    "                    picture_url VARCHAR,\n",
    "                    instant_bookable VARCHAR(5),  \n",
    "                    is_business_travel_ready VARCHAR(5),\n",
    "                    cancellation_policy VARCHAR) \"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "578d7fec",
   "metadata": {},
   "outputs": [],
   "source": [
    "queryTable(createDimInfo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b1c91ba6",
   "metadata": {},
   "outputs": [],
   "source": [
    "loadDimInfo = \"\"\"INSERT INTO dim_information(info_id,listing_url, scrape_id, \n",
    "                    name, summary,space,description, experiences_offered, interaction, \n",
    "                    house_rules, picture_url,instant_bookable, is_business_travel_ready,\n",
    "                    cancellation_policy)\n",
    "                SELECT id as info_id,\n",
    "                listing_url,scrape_id, name, summary,space, \n",
    "                description, experiences_offered, interaction, house_rules, picture_url,\n",
    "                instant_bookable, is_business_travel_ready,cancellation_policy \n",
    "                FROM listings_details;\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5188833f",
   "metadata": {},
   "outputs": [],
   "source": [
    "queryTable(loadDimInfo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "5bd0e8f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>info_id</th>\n",
       "      <th>listing_url</th>\n",
       "      <th>scrape_id</th>\n",
       "      <th>name</th>\n",
       "      <th>summary</th>\n",
       "      <th>space</th>\n",
       "      <th>description</th>\n",
       "      <th>experiences_offered</th>\n",
       "      <th>interaction</th>\n",
       "      <th>house_rules</th>\n",
       "      <th>picture_url</th>\n",
       "      <th>instant_bookable</th>\n",
       "      <th>is_business_travel_ready</th>\n",
       "      <th>cancellation_policy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2818.0</td>\n",
       "      <td>https://www.airbnb.com/rooms/2818</td>\n",
       "      <td>20181206172549</td>\n",
       "      <td>Quiet Garden View Room &amp; Super Fast WiFi</td>\n",
       "      <td>Quiet Garden View Room &amp; Super Fast WiFi</td>\n",
       "      <td>I'm renting a bedroom (room overlooking the ga...</td>\n",
       "      <td>Quiet Garden View Room &amp; Super Fast WiFi I'm r...</td>\n",
       "      <td>none</td>\n",
       "      <td>noData</td>\n",
       "      <td>Please: - Leave your shoes in the entrance  - ...</td>\n",
       "      <td>https://a0.muscache.com/im/pictures/10272854/8...</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   info_id                        listing_url       scrape_id  \\\n",
       "0   2818.0  https://www.airbnb.com/rooms/2818  20181206172549   \n",
       "\n",
       "                                       name  \\\n",
       "0  Quiet Garden View Room & Super Fast WiFi   \n",
       "\n",
       "                                    summary  \\\n",
       "0  Quiet Garden View Room & Super Fast WiFi   \n",
       "\n",
       "                                               space  \\\n",
       "0  I'm renting a bedroom (room overlooking the ga...   \n",
       "\n",
       "                                         description experiences_offered  \\\n",
       "0  Quiet Garden View Room & Super Fast WiFi I'm r...                none   \n",
       "\n",
       "  interaction                                        house_rules  \\\n",
       "0      noData  Please: - Leave your shoes in the entrance  - ...   \n",
       "\n",
       "                                         picture_url instant_bookable  \\\n",
       "0  https://a0.muscache.com/im/pictures/10272854/8...                t   \n",
       "\n",
       "  is_business_travel_ready          cancellation_policy  \n",
       "0                        f  strict_14_with_grace_period  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "queryBase('SELECT * FROM dim_information LIMIT 1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b7cc99e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "createDimHost = \"\"\"CREATE TABLE dim_host(\n",
    "                    dimhost_id NUMERIC,\n",
    "                    host_id NUMERIC NOT NULL, \n",
    "                    host_url VARCHAR,\n",
    "                    host_name VARCHAR, \n",
    "                    host_since VARCHAR,     \n",
    "                    host_location VARCHAR, \n",
    "                    host_about VARCHAR, \n",
    "                    host_response_time VARCHAR, \n",
    "                    host_response_rate VARCHAR, \n",
    "                    host_is_superhost VARCHAR, \n",
    "                    host_thumbnail_url VARCHAR, \n",
    "                    host_picture_url VARCHAR, \n",
    "                    host_neighbourhood VARCHAR, \n",
    "                    host_listings_count VARCHAR,   \n",
    "                    host_total_listings_count VARCHAR, \n",
    "                    host_verifications VARCHAR, \n",
    "                    host_has_profile_pic VARCHAR, \n",
    "                    host_identity_verified VARCHAR) \"\"\"\n",
    "queryTable(createDimHost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "1b0d0458",
   "metadata": {},
   "outputs": [
    {
     "ename": "ResourceClosedError",
     "evalue": "This result object does not return rows. It has been closed automatically.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mResourceClosedError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [42], line 13\u001b[0m\n\u001b[1;32m      1\u001b[0m loadDimHost \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\"\"\u001b[39m\u001b[38;5;124mINSERT INTO dim_host(dimhost_id, host_id, host_url,host_name, \u001b[39m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;124m                    host_since,host_location, host_about, \u001b[39m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;124m                    host_response_time,host_response_rate, host_is_superhost, host_thumbnail_url, \u001b[39m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[38;5;124m                host_has_profile_pic, host_identity_verified\u001b[39m\n\u001b[1;32m     12\u001b[0m \u001b[38;5;124m                FROM listings_details;\u001b[39m\u001b[38;5;124m\"\"\"\u001b[39m\n\u001b[0;32m---> 13\u001b[0m queryBase(loadDimHost)\n",
      "Cell \u001b[0;32mIn [13], line 3\u001b[0m, in \u001b[0;36mqueryBase\u001b[0;34m(query)\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mqueryBase\u001b[39m(query):\n\u001b[0;32m----> 3\u001b[0m     requiredTable \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_sql\u001b[49m\u001b[43m(\u001b[49m\u001b[43mquery\u001b[49m\u001b[43m,\u001b[49m\u001b[43mcon\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcredentials\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      4\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m requiredTable\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/io/sql.py:590\u001b[0m, in \u001b[0;36mread_sql\u001b[0;34m(sql, con, index_col, coerce_float, params, parse_dates, columns, chunksize)\u001b[0m\n\u001b[1;32m    581\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m pandas_sql\u001b[38;5;241m.\u001b[39mread_table(\n\u001b[1;32m    582\u001b[0m         sql,\n\u001b[1;32m    583\u001b[0m         index_col\u001b[38;5;241m=\u001b[39mindex_col,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    587\u001b[0m         chunksize\u001b[38;5;241m=\u001b[39mchunksize,\n\u001b[1;32m    588\u001b[0m     )\n\u001b[1;32m    589\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 590\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mpandas_sql\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_query\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    591\u001b[0m \u001b[43m        \u001b[49m\u001b[43msql\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    592\u001b[0m \u001b[43m        \u001b[49m\u001b[43mindex_col\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindex_col\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    593\u001b[0m \u001b[43m        \u001b[49m\u001b[43mparams\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    594\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcoerce_float\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcoerce_float\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    595\u001b[0m \u001b[43m        \u001b[49m\u001b[43mparse_dates\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparse_dates\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    596\u001b[0m \u001b[43m        \u001b[49m\u001b[43mchunksize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunksize\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    597\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/io/sql.py:1574\u001b[0m, in \u001b[0;36mSQLDatabase.read_query\u001b[0;34m(self, sql, index_col, coerce_float, parse_dates, params, chunksize, dtype)\u001b[0m\n\u001b[1;32m   1564\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_query_iterator(\n\u001b[1;32m   1565\u001b[0m         result,\n\u001b[1;32m   1566\u001b[0m         chunksize,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1571\u001b[0m         dtype\u001b[38;5;241m=\u001b[39mdtype,\n\u001b[1;32m   1572\u001b[0m     )\n\u001b[1;32m   1573\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1574\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[43mresult\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfetchall\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1575\u001b[0m     frame \u001b[38;5;241m=\u001b[39m _wrap_result(\n\u001b[1;32m   1576\u001b[0m         data,\n\u001b[1;32m   1577\u001b[0m         columns,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1581\u001b[0m         dtype\u001b[38;5;241m=\u001b[39mdtype,\n\u001b[1;32m   1582\u001b[0m     )\n\u001b[1;32m   1583\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m frame\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/sqlalchemy/engine/result.py:1058\u001b[0m, in \u001b[0;36mResult.fetchall\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1055\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfetchall\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m   1056\u001b[0m     \u001b[38;5;124;03m\"\"\"A synonym for the :meth:`_engine.Result.all` method.\"\"\"\u001b[39;00m\n\u001b[0;32m-> 1058\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_allrows\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/sqlalchemy/engine/result.py:399\u001b[0m, in \u001b[0;36mResultInternal._allrows\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    395\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_allrows\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    397\u001b[0m     post_creational_filter \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_post_creational_filter\n\u001b[0;32m--> 399\u001b[0m     make_row \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_row_getter\u001b[49m\n\u001b[1;32m    401\u001b[0m     rows \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fetchall_impl()\n\u001b[1;32m    402\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m make_row:\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/sqlalchemy/util/langhelpers.py:1184\u001b[0m, in \u001b[0;36mHasMemoized.memoized_attribute.__get__\u001b[0;34m(self, obj, cls)\u001b[0m\n\u001b[1;32m   1182\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m obj \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   1183\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n\u001b[0;32m-> 1184\u001b[0m obj\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__dict__\u001b[39m[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m] \u001b[38;5;241m=\u001b[39m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfget\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1185\u001b[0m obj\u001b[38;5;241m.\u001b[39m_memoized_keys \u001b[38;5;241m|\u001b[39m\u001b[38;5;241m=\u001b[39m {\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m}\n\u001b[1;32m   1186\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/sqlalchemy/engine/result.py:320\u001b[0m, in \u001b[0;36mResultInternal._row_getter\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    317\u001b[0m key_style \u001b[38;5;241m=\u001b[39m real_result\u001b[38;5;241m.\u001b[39m_process_row\u001b[38;5;241m.\u001b[39m_default_key_style\n\u001b[1;32m    318\u001b[0m metadata \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_metadata\n\u001b[0;32m--> 320\u001b[0m keymap \u001b[38;5;241m=\u001b[39m \u001b[43mmetadata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_keymap\u001b[49m\n\u001b[1;32m    321\u001b[0m processors \u001b[38;5;241m=\u001b[39m metadata\u001b[38;5;241m.\u001b[39m_processors\n\u001b[1;32m    322\u001b[0m tf \u001b[38;5;241m=\u001b[39m metadata\u001b[38;5;241m.\u001b[39m_tuplefilter\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/sqlalchemy/engine/cursor.py:1221\u001b[0m, in \u001b[0;36m_NoResultMetaData._keymap\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1219\u001b[0m \u001b[38;5;129m@property\u001b[39m\n\u001b[1;32m   1220\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_keymap\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m-> 1221\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_we_dont_return_rows\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/sqlalchemy/engine/cursor.py:1202\u001b[0m, in \u001b[0;36m_NoResultMetaData._we_dont_return_rows\u001b[0;34m(self, err)\u001b[0m\n\u001b[1;32m   1201\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_we_dont_return_rows\u001b[39m(\u001b[38;5;28mself\u001b[39m, err\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m-> 1202\u001b[0m     \u001b[43mutil\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mraise_\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1203\u001b[0m \u001b[43m        \u001b[49m\u001b[43mexc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mResourceClosedError\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1204\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mThis result object does not return rows. \u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n\u001b[1;32m   1205\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mIt has been closed automatically.\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n\u001b[1;32m   1206\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1207\u001b[0m \u001b[43m        \u001b[49m\u001b[43mreplace_context\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merr\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1208\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/sqlalchemy/util/compat.py:208\u001b[0m, in \u001b[0;36mraise_\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m    205\u001b[0m     exception\u001b[38;5;241m.\u001b[39m__cause__ \u001b[38;5;241m=\u001b[39m replace_context\n\u001b[1;32m    207\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 208\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m exception\n\u001b[1;32m    209\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m    210\u001b[0m     \u001b[38;5;66;03m# credit to\u001b[39;00m\n\u001b[1;32m    211\u001b[0m     \u001b[38;5;66;03m# https://cosmicpercolator.com/2016/01/13/exception-leaks-in-python-2-and-3/\u001b[39;00m\n\u001b[1;32m    212\u001b[0m     \u001b[38;5;66;03m# as the __traceback__ object creates a cycle\u001b[39;00m\n\u001b[1;32m    213\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m exception, replace_context, from_, with_traceback\n",
      "\u001b[0;31mResourceClosedError\u001b[0m: This result object does not return rows. It has been closed automatically."
     ]
    }
   ],
   "source": [
    "loadDimHost = \"\"\"INSERT INTO dim_host(dimhost_id, host_id, host_url,host_name, \n",
    "                    host_since,host_location, host_about, \n",
    "                    host_response_time,host_response_rate, host_is_superhost, host_thumbnail_url, \n",
    "                    host_picture_url, host_neighbourhood, host_listings_count,\n",
    "                    host_total_listings_count, host_verifications, host_has_profile_pic, \n",
    "                    host_identity_verified)\n",
    "                SELECT id as dimhost_id, host_id, host_url,host_name, host_since,host_location, \n",
    "                host_about, host_response_time, host_response_rate, host_is_superhost, \n",
    "                host_thumbnail_url, host_picture_url, host_neighbourhood, \n",
    "                host_listings_count,host_total_listings_count, host_verifications, \n",
    "                host_has_profile_pic, host_identity_verified\n",
    "                FROM listings_details;\"\"\"\n",
    "queryBase(loadDimHost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e91965cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dimhost_id</th>\n",
       "      <th>host_id</th>\n",
       "      <th>host_url</th>\n",
       "      <th>host_name</th>\n",
       "      <th>host_since</th>\n",
       "      <th>host_location</th>\n",
       "      <th>host_about</th>\n",
       "      <th>host_response_time</th>\n",
       "      <th>host_response_rate</th>\n",
       "      <th>host_is_superhost</th>\n",
       "      <th>host_thumbnail_url</th>\n",
       "      <th>host_picture_url</th>\n",
       "      <th>host_neighbourhood</th>\n",
       "      <th>host_listings_count</th>\n",
       "      <th>host_total_listings_count</th>\n",
       "      <th>host_verifications</th>\n",
       "      <th>host_has_profile_pic</th>\n",
       "      <th>host_identity_verified</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2818.0</td>\n",
       "      <td>3159.0</td>\n",
       "      <td>https://www.airbnb.com/users/show/3159</td>\n",
       "      <td>Daniel</td>\n",
       "      <td>2008-09-24</td>\n",
       "      <td>Amsterdam, Noord-Holland, The Netherlands</td>\n",
       "      <td>Upon arriving in Amsterdam, one can imagine as...</td>\n",
       "      <td>within an hour</td>\n",
       "      <td>100%</td>\n",
       "      <td>t</td>\n",
       "      <td>https://a0.muscache.com/im/users/3159/profile_...</td>\n",
       "      <td>https://a0.muscache.com/im/users/3159/profile_...</td>\n",
       "      <td>Indische Buurt</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>['email', 'phone', 'reviews', 'jumio', 'offlin...</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3209.0</td>\n",
       "      <td>3806.0</td>\n",
       "      <td>https://www.airbnb.com/users/show/3806</td>\n",
       "      <td>Maartje</td>\n",
       "      <td>2008-10-24</td>\n",
       "      <td>Amsterdam, Noord-Holland, The Netherlands</td>\n",
       "      <td>I am a freelance radio producer and journalist...</td>\n",
       "      <td>within an hour</td>\n",
       "      <td>100%</td>\n",
       "      <td>f</td>\n",
       "      <td>https://a0.muscache.com/im/users/3806/profile_...</td>\n",
       "      <td>https://a0.muscache.com/im/users/3806/profile_...</td>\n",
       "      <td>Spaarndammer en Zeeheldenbuurt</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>['email', 'phone', 'reviews', 'jumio']</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   dimhost_id  host_id                                host_url host_name  \\\n",
       "0      2818.0   3159.0  https://www.airbnb.com/users/show/3159    Daniel   \n",
       "1      3209.0   3806.0  https://www.airbnb.com/users/show/3806   Maartje   \n",
       "\n",
       "   host_since                              host_location  \\\n",
       "0  2008-09-24  Amsterdam, Noord-Holland, The Netherlands   \n",
       "1  2008-10-24  Amsterdam, Noord-Holland, The Netherlands   \n",
       "\n",
       "                                          host_about host_response_time  \\\n",
       "0  Upon arriving in Amsterdam, one can imagine as...     within an hour   \n",
       "1  I am a freelance radio producer and journalist...     within an hour   \n",
       "\n",
       "  host_response_rate host_is_superhost  \\\n",
       "0               100%                 t   \n",
       "1               100%                 f   \n",
       "\n",
       "                                  host_thumbnail_url  \\\n",
       "0  https://a0.muscache.com/im/users/3159/profile_...   \n",
       "1  https://a0.muscache.com/im/users/3806/profile_...   \n",
       "\n",
       "                                    host_picture_url  \\\n",
       "0  https://a0.muscache.com/im/users/3159/profile_...   \n",
       "1  https://a0.muscache.com/im/users/3806/profile_...   \n",
       "\n",
       "               host_neighbourhood host_listings_count  \\\n",
       "0                  Indische Buurt                 1.0   \n",
       "1  Spaarndammer en Zeeheldenbuurt                 1.0   \n",
       "\n",
       "  host_total_listings_count  \\\n",
       "0                         1   \n",
       "1                         1   \n",
       "\n",
       "                                  host_verifications host_has_profile_pic  \\\n",
       "0  ['email', 'phone', 'reviews', 'jumio', 'offlin...                    t   \n",
       "1             ['email', 'phone', 'reviews', 'jumio']                    t   \n",
       "\n",
       "  host_identity_verified  \n",
       "0                      f  \n",
       "1                      t  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "queryBase(\"\"\"SELECT * FROM dim_host LIMIT 2\"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "066ce177",
   "metadata": {},
   "outputs": [
    {
     "ename": "ResourceClosedError",
     "evalue": "This result object does not return rows. It has been closed automatically.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mResourceClosedError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [44], line 12\u001b[0m\n\u001b[1;32m      1\u001b[0m createdimRew \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\"\"\u001b[39m\u001b[38;5;124mCREATE TABLE dim_review(\u001b[39m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;124m                    prop_review_id NUMERIC,\u001b[39m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;124m                    first_review VARCHAR, \u001b[39m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[38;5;124m                    review_scores_location REAL, \u001b[39m\n\u001b[1;32m     11\u001b[0m \u001b[38;5;124m                    review_scores_value REAL)\u001b[39m\u001b[38;5;124m\"\"\"\u001b[39m\n\u001b[0;32m---> 12\u001b[0m queryBase(createdimRew)\n",
      "Cell \u001b[0;32mIn [13], line 3\u001b[0m, in \u001b[0;36mqueryBase\u001b[0;34m(query)\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mqueryBase\u001b[39m(query):\n\u001b[0;32m----> 3\u001b[0m     requiredTable \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_sql\u001b[49m\u001b[43m(\u001b[49m\u001b[43mquery\u001b[49m\u001b[43m,\u001b[49m\u001b[43mcon\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcredentials\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      4\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m requiredTable\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/io/sql.py:590\u001b[0m, in \u001b[0;36mread_sql\u001b[0;34m(sql, con, index_col, coerce_float, params, parse_dates, columns, chunksize)\u001b[0m\n\u001b[1;32m    581\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m pandas_sql\u001b[38;5;241m.\u001b[39mread_table(\n\u001b[1;32m    582\u001b[0m         sql,\n\u001b[1;32m    583\u001b[0m         index_col\u001b[38;5;241m=\u001b[39mindex_col,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    587\u001b[0m         chunksize\u001b[38;5;241m=\u001b[39mchunksize,\n\u001b[1;32m    588\u001b[0m     )\n\u001b[1;32m    589\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 590\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mpandas_sql\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_query\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    591\u001b[0m \u001b[43m        \u001b[49m\u001b[43msql\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    592\u001b[0m \u001b[43m        \u001b[49m\u001b[43mindex_col\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindex_col\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    593\u001b[0m \u001b[43m        \u001b[49m\u001b[43mparams\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    594\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcoerce_float\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcoerce_float\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    595\u001b[0m \u001b[43m        \u001b[49m\u001b[43mparse_dates\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparse_dates\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    596\u001b[0m \u001b[43m        \u001b[49m\u001b[43mchunksize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunksize\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    597\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/io/sql.py:1574\u001b[0m, in \u001b[0;36mSQLDatabase.read_query\u001b[0;34m(self, sql, index_col, coerce_float, parse_dates, params, chunksize, dtype)\u001b[0m\n\u001b[1;32m   1564\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_query_iterator(\n\u001b[1;32m   1565\u001b[0m         result,\n\u001b[1;32m   1566\u001b[0m         chunksize,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1571\u001b[0m         dtype\u001b[38;5;241m=\u001b[39mdtype,\n\u001b[1;32m   1572\u001b[0m     )\n\u001b[1;32m   1573\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1574\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[43mresult\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfetchall\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1575\u001b[0m     frame \u001b[38;5;241m=\u001b[39m _wrap_result(\n\u001b[1;32m   1576\u001b[0m         data,\n\u001b[1;32m   1577\u001b[0m         columns,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1581\u001b[0m         dtype\u001b[38;5;241m=\u001b[39mdtype,\n\u001b[1;32m   1582\u001b[0m     )\n\u001b[1;32m   1583\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m frame\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/sqlalchemy/engine/result.py:1058\u001b[0m, in \u001b[0;36mResult.fetchall\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1055\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfetchall\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m   1056\u001b[0m     \u001b[38;5;124;03m\"\"\"A synonym for the :meth:`_engine.Result.all` method.\"\"\"\u001b[39;00m\n\u001b[0;32m-> 1058\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_allrows\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/sqlalchemy/engine/result.py:399\u001b[0m, in \u001b[0;36mResultInternal._allrows\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    395\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_allrows\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    397\u001b[0m     post_creational_filter \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_post_creational_filter\n\u001b[0;32m--> 399\u001b[0m     make_row \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_row_getter\u001b[49m\n\u001b[1;32m    401\u001b[0m     rows \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fetchall_impl()\n\u001b[1;32m    402\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m make_row:\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/sqlalchemy/util/langhelpers.py:1184\u001b[0m, in \u001b[0;36mHasMemoized.memoized_attribute.__get__\u001b[0;34m(self, obj, cls)\u001b[0m\n\u001b[1;32m   1182\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m obj \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   1183\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n\u001b[0;32m-> 1184\u001b[0m obj\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__dict__\u001b[39m[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m] \u001b[38;5;241m=\u001b[39m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfget\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1185\u001b[0m obj\u001b[38;5;241m.\u001b[39m_memoized_keys \u001b[38;5;241m|\u001b[39m\u001b[38;5;241m=\u001b[39m {\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m}\n\u001b[1;32m   1186\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/sqlalchemy/engine/result.py:320\u001b[0m, in \u001b[0;36mResultInternal._row_getter\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    317\u001b[0m key_style \u001b[38;5;241m=\u001b[39m real_result\u001b[38;5;241m.\u001b[39m_process_row\u001b[38;5;241m.\u001b[39m_default_key_style\n\u001b[1;32m    318\u001b[0m metadata \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_metadata\n\u001b[0;32m--> 320\u001b[0m keymap \u001b[38;5;241m=\u001b[39m \u001b[43mmetadata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_keymap\u001b[49m\n\u001b[1;32m    321\u001b[0m processors \u001b[38;5;241m=\u001b[39m metadata\u001b[38;5;241m.\u001b[39m_processors\n\u001b[1;32m    322\u001b[0m tf \u001b[38;5;241m=\u001b[39m metadata\u001b[38;5;241m.\u001b[39m_tuplefilter\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/sqlalchemy/engine/cursor.py:1221\u001b[0m, in \u001b[0;36m_NoResultMetaData._keymap\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1219\u001b[0m \u001b[38;5;129m@property\u001b[39m\n\u001b[1;32m   1220\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_keymap\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m-> 1221\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_we_dont_return_rows\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/sqlalchemy/engine/cursor.py:1202\u001b[0m, in \u001b[0;36m_NoResultMetaData._we_dont_return_rows\u001b[0;34m(self, err)\u001b[0m\n\u001b[1;32m   1201\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_we_dont_return_rows\u001b[39m(\u001b[38;5;28mself\u001b[39m, err\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m-> 1202\u001b[0m     \u001b[43mutil\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mraise_\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1203\u001b[0m \u001b[43m        \u001b[49m\u001b[43mexc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mResourceClosedError\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1204\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mThis result object does not return rows. \u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n\u001b[1;32m   1205\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mIt has been closed automatically.\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n\u001b[1;32m   1206\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1207\u001b[0m \u001b[43m        \u001b[49m\u001b[43mreplace_context\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merr\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1208\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/sqlalchemy/util/compat.py:208\u001b[0m, in \u001b[0;36mraise_\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m    205\u001b[0m     exception\u001b[38;5;241m.\u001b[39m__cause__ \u001b[38;5;241m=\u001b[39m replace_context\n\u001b[1;32m    207\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 208\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m exception\n\u001b[1;32m    209\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m    210\u001b[0m     \u001b[38;5;66;03m# credit to\u001b[39;00m\n\u001b[1;32m    211\u001b[0m     \u001b[38;5;66;03m# https://cosmicpercolator.com/2016/01/13/exception-leaks-in-python-2-and-3/\u001b[39;00m\n\u001b[1;32m    212\u001b[0m     \u001b[38;5;66;03m# as the __traceback__ object creates a cycle\u001b[39;00m\n\u001b[1;32m    213\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m exception, replace_context, from_, with_traceback\n",
      "\u001b[0;31mResourceClosedError\u001b[0m: This result object does not return rows. It has been closed automatically."
     ]
    }
   ],
   "source": [
    "createdimRew = \"\"\"CREATE TABLE dim_review(\n",
    "                    prop_review_id NUMERIC,\n",
    "                    first_review VARCHAR, \n",
    "                    last_review VARCHAR,\n",
    "                    review_scores_rating REAL, \n",
    "                    review_scores_accuracy REAL, \n",
    "                    review_scores_cleanliness REAL, \n",
    "                    review_scores_checkin REAL, \n",
    "                    review_scores_communication REAL, \n",
    "                    review_scores_location REAL, \n",
    "                    review_scores_value REAL)\"\"\"\n",
    "queryBase(createdimRew)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b7c5681d",
   "metadata": {},
   "outputs": [
    {
     "ename": "ResourceClosedError",
     "evalue": "This result object does not return rows. It has been closed automatically.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mResourceClosedError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [46], line 12\u001b[0m\n\u001b[1;32m      1\u001b[0m loaddimreview \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\"\"\u001b[39m\u001b[38;5;124mINSERT INTO dim_review(prop_review_id,\u001b[39m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;124m                    first_review, last_review,\u001b[39m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;124m                    review_scores_rating,review_scores_accuracy, \u001b[39m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[38;5;124m                   review_scores_value\u001b[39m\n\u001b[1;32m     11\u001b[0m \u001b[38;5;124m                   FROM listings_details;\u001b[39m\u001b[38;5;124m\"\"\"\u001b[39m\n\u001b[0;32m---> 12\u001b[0m queryBase(loaddimreview)\n",
      "Cell \u001b[0;32mIn [13], line 3\u001b[0m, in \u001b[0;36mqueryBase\u001b[0;34m(query)\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mqueryBase\u001b[39m(query):\n\u001b[0;32m----> 3\u001b[0m     requiredTable \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_sql\u001b[49m\u001b[43m(\u001b[49m\u001b[43mquery\u001b[49m\u001b[43m,\u001b[49m\u001b[43mcon\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcredentials\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      4\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m requiredTable\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/io/sql.py:590\u001b[0m, in \u001b[0;36mread_sql\u001b[0;34m(sql, con, index_col, coerce_float, params, parse_dates, columns, chunksize)\u001b[0m\n\u001b[1;32m    581\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m pandas_sql\u001b[38;5;241m.\u001b[39mread_table(\n\u001b[1;32m    582\u001b[0m         sql,\n\u001b[1;32m    583\u001b[0m         index_col\u001b[38;5;241m=\u001b[39mindex_col,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    587\u001b[0m         chunksize\u001b[38;5;241m=\u001b[39mchunksize,\n\u001b[1;32m    588\u001b[0m     )\n\u001b[1;32m    589\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 590\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mpandas_sql\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_query\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    591\u001b[0m \u001b[43m        \u001b[49m\u001b[43msql\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    592\u001b[0m \u001b[43m        \u001b[49m\u001b[43mindex_col\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindex_col\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    593\u001b[0m \u001b[43m        \u001b[49m\u001b[43mparams\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    594\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcoerce_float\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcoerce_float\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    595\u001b[0m \u001b[43m        \u001b[49m\u001b[43mparse_dates\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparse_dates\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    596\u001b[0m \u001b[43m        \u001b[49m\u001b[43mchunksize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunksize\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    597\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/io/sql.py:1574\u001b[0m, in \u001b[0;36mSQLDatabase.read_query\u001b[0;34m(self, sql, index_col, coerce_float, parse_dates, params, chunksize, dtype)\u001b[0m\n\u001b[1;32m   1564\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_query_iterator(\n\u001b[1;32m   1565\u001b[0m         result,\n\u001b[1;32m   1566\u001b[0m         chunksize,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1571\u001b[0m         dtype\u001b[38;5;241m=\u001b[39mdtype,\n\u001b[1;32m   1572\u001b[0m     )\n\u001b[1;32m   1573\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1574\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[43mresult\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfetchall\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1575\u001b[0m     frame \u001b[38;5;241m=\u001b[39m _wrap_result(\n\u001b[1;32m   1576\u001b[0m         data,\n\u001b[1;32m   1577\u001b[0m         columns,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1581\u001b[0m         dtype\u001b[38;5;241m=\u001b[39mdtype,\n\u001b[1;32m   1582\u001b[0m     )\n\u001b[1;32m   1583\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m frame\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/sqlalchemy/engine/result.py:1058\u001b[0m, in \u001b[0;36mResult.fetchall\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1055\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfetchall\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m   1056\u001b[0m     \u001b[38;5;124;03m\"\"\"A synonym for the :meth:`_engine.Result.all` method.\"\"\"\u001b[39;00m\n\u001b[0;32m-> 1058\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_allrows\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/sqlalchemy/engine/result.py:399\u001b[0m, in \u001b[0;36mResultInternal._allrows\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    395\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_allrows\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    397\u001b[0m     post_creational_filter \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_post_creational_filter\n\u001b[0;32m--> 399\u001b[0m     make_row \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_row_getter\u001b[49m\n\u001b[1;32m    401\u001b[0m     rows \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fetchall_impl()\n\u001b[1;32m    402\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m make_row:\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/sqlalchemy/util/langhelpers.py:1184\u001b[0m, in \u001b[0;36mHasMemoized.memoized_attribute.__get__\u001b[0;34m(self, obj, cls)\u001b[0m\n\u001b[1;32m   1182\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m obj \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   1183\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n\u001b[0;32m-> 1184\u001b[0m obj\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__dict__\u001b[39m[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m] \u001b[38;5;241m=\u001b[39m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfget\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1185\u001b[0m obj\u001b[38;5;241m.\u001b[39m_memoized_keys \u001b[38;5;241m|\u001b[39m\u001b[38;5;241m=\u001b[39m {\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m}\n\u001b[1;32m   1186\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/sqlalchemy/engine/result.py:320\u001b[0m, in \u001b[0;36mResultInternal._row_getter\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    317\u001b[0m key_style \u001b[38;5;241m=\u001b[39m real_result\u001b[38;5;241m.\u001b[39m_process_row\u001b[38;5;241m.\u001b[39m_default_key_style\n\u001b[1;32m    318\u001b[0m metadata \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_metadata\n\u001b[0;32m--> 320\u001b[0m keymap \u001b[38;5;241m=\u001b[39m \u001b[43mmetadata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_keymap\u001b[49m\n\u001b[1;32m    321\u001b[0m processors \u001b[38;5;241m=\u001b[39m metadata\u001b[38;5;241m.\u001b[39m_processors\n\u001b[1;32m    322\u001b[0m tf \u001b[38;5;241m=\u001b[39m metadata\u001b[38;5;241m.\u001b[39m_tuplefilter\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/sqlalchemy/engine/cursor.py:1221\u001b[0m, in \u001b[0;36m_NoResultMetaData._keymap\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1219\u001b[0m \u001b[38;5;129m@property\u001b[39m\n\u001b[1;32m   1220\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_keymap\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m-> 1221\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_we_dont_return_rows\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/sqlalchemy/engine/cursor.py:1202\u001b[0m, in \u001b[0;36m_NoResultMetaData._we_dont_return_rows\u001b[0;34m(self, err)\u001b[0m\n\u001b[1;32m   1201\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_we_dont_return_rows\u001b[39m(\u001b[38;5;28mself\u001b[39m, err\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m-> 1202\u001b[0m     \u001b[43mutil\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mraise_\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1203\u001b[0m \u001b[43m        \u001b[49m\u001b[43mexc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mResourceClosedError\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1204\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mThis result object does not return rows. \u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n\u001b[1;32m   1205\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mIt has been closed automatically.\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n\u001b[1;32m   1206\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1207\u001b[0m \u001b[43m        \u001b[49m\u001b[43mreplace_context\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merr\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1208\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/sqlalchemy/util/compat.py:208\u001b[0m, in \u001b[0;36mraise_\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m    205\u001b[0m     exception\u001b[38;5;241m.\u001b[39m__cause__ \u001b[38;5;241m=\u001b[39m replace_context\n\u001b[1;32m    207\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 208\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m exception\n\u001b[1;32m    209\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m    210\u001b[0m     \u001b[38;5;66;03m# credit to\u001b[39;00m\n\u001b[1;32m    211\u001b[0m     \u001b[38;5;66;03m# https://cosmicpercolator.com/2016/01/13/exception-leaks-in-python-2-and-3/\u001b[39;00m\n\u001b[1;32m    212\u001b[0m     \u001b[38;5;66;03m# as the __traceback__ object creates a cycle\u001b[39;00m\n\u001b[1;32m    213\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m exception, replace_context, from_, with_traceback\n",
      "\u001b[0;31mResourceClosedError\u001b[0m: This result object does not return rows. It has been closed automatically."
     ]
    }
   ],
   "source": [
    "loaddimreview = \"\"\"INSERT INTO dim_review(prop_review_id,\n",
    "                    first_review, last_review,\n",
    "                    review_scores_rating,review_scores_accuracy, \n",
    "                    review_scores_cleanliness, review_scores_checkin, \n",
    "                    review_scores_communication, review_scores_location, \n",
    "                    review_scores_value)\n",
    "                   SELECT id as prop_review_id, first_review, last_review,\n",
    "                   review_scores_rating, review_scores_accuracy, review_scores_cleanliness, \n",
    "                   review_scores_checkin, review_scores_communication, review_scores_location, \n",
    "                   review_scores_value\n",
    "                   FROM listings_details;\"\"\"\n",
    "queryBase(loaddimreview)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "50ca2869",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prop_review_id</th>\n",
       "      <th>first_review</th>\n",
       "      <th>last_review</th>\n",
       "      <th>review_scores_rating</th>\n",
       "      <th>review_scores_accuracy</th>\n",
       "      <th>review_scores_cleanliness</th>\n",
       "      <th>review_scores_checkin</th>\n",
       "      <th>review_scores_communication</th>\n",
       "      <th>review_scores_location</th>\n",
       "      <th>review_scores_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2818.0</td>\n",
       "      <td>2009-03-30</td>\n",
       "      <td>2018-11-28</td>\n",
       "      <td>97.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
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       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3209.0</td>\n",
       "      <td>2015-07-31</td>\n",
       "      <td>2018-08-29</td>\n",
       "      <td>96.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20168.0</td>\n",
       "      <td>2010-03-02</td>\n",
       "      <td>2018-11-30</td>\n",
       "      <td>87.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25428.0</td>\n",
       "      <td>2018-01-21</td>\n",
       "      <td>2018-01-21</td>\n",
       "      <td>100.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27886.0</td>\n",
       "      <td>2012-01-09</td>\n",
       "      <td>2018-11-25</td>\n",
       "      <td>99.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
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       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   prop_review_id first_review last_review  review_scores_rating  \\\n",
       "0          2818.0   2009-03-30  2018-11-28                  97.0   \n",
       "1          3209.0   2015-07-31  2018-08-29                  96.0   \n",
       "2         20168.0   2010-03-02  2018-11-30                  87.0   \n",
       "3         25428.0   2018-01-21  2018-01-21                 100.0   \n",
       "4         27886.0   2012-01-09  2018-11-25                  99.0   \n",
       "\n",
       "   review_scores_accuracy  review_scores_cleanliness  review_scores_checkin  \\\n",
       "0                    10.0                       10.0                   10.0   \n",
       "1                    10.0                        9.0                   10.0   \n",
       "2                     9.0                       10.0                    9.0   \n",
       "3                    10.0                       10.0                   10.0   \n",
       "4                    10.0                       10.0                   10.0   \n",
       "\n",
       "   review_scores_communication  review_scores_location  review_scores_value  \n",
       "0                         10.0                     9.0                 10.0  \n",
       "1                         10.0                     9.0                  9.0  \n",
       "2                          9.0                    10.0                  9.0  \n",
       "3                         10.0                    10.0                 10.0  \n",
       "4                         10.0                    10.0                 10.0  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "queryBase(\"\"\"SELECT * FROM dim_review LIMIT 5\"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "77268ec6",
   "metadata": {},
   "outputs": [],
   "source": [
    "createDimLoc = \"\"\"CREATE TABLE dim_location(\n",
    "                        location_id NUMERIC PRIMARY KEY,\n",
    "                        street VARCHAR, \n",
    "                        neighborhood_overview VARCHAR,\n",
    "                        notes VARCHAR, \n",
    "                        transit VARCHAR, \n",
    "                        access VARCHAR, \n",
    "                        neighbourhood VARCHAR, \n",
    "                        neighbourhood_cleansed VARCHAR, \n",
    "                        city VARCHAR, \n",
    "                        state VARCHAR, \n",
    "                        zipcode VARCHAR,\n",
    "                        market VARCHAR, \n",
    "                        smart_location VARCHAR, \n",
    "                        country_code VARCHAR, \n",
    "                        country VARCHAR, \n",
    "                        latitude REAL,\n",
    "                        longitude REAL, \n",
    "                        is_location_exact VARCHAR)\"\"\"\n",
    "queryTable(createDimLoc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "d2e0b671",
   "metadata": {},
   "outputs": [],
   "source": [
    "insertDimLoc = \"\"\"INSERT INTO dim_location(location_id,transit, access, neighbourhood, neighbourhood_cleansed, city, \n",
    "                    state, zipcode,market, smart_location, country_code, \n",
    "                    country, latitude,longitude, is_location_exact)\n",
    "                  SELECT id as location_id, transit, access, neighbourhood, neighbourhood_cleansed, city, \n",
    "                    state, zipcode,market, smart_location, country_code, \n",
    "                    country, latitude,longitude, is_location_exact\n",
    "                  FROM listings_details\"\"\"\n",
    "queryTable(insertDimLoc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "d57d6738",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location_id</th>\n",
       "      <th>street</th>\n",
       "      <th>neighborhood_overview</th>\n",
       "      <th>notes</th>\n",
       "      <th>transit</th>\n",
       "      <th>access</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>neighbourhood_cleansed</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>market</th>\n",
       "      <th>smart_location</th>\n",
       "      <th>country_code</th>\n",
       "      <th>country</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>is_location_exact</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2818.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>The neighbourhood is well served by 24 hours p...</td>\n",
       "      <td>noData</td>\n",
       "      <td>Indische Buurt</td>\n",
       "      <td>Oostelijk Havengebied - Indische Buurt</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>North Holland</td>\n",
       "      <td>0</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>Amsterdam, Netherlands</td>\n",
       "      <td>NL</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>52.365753</td>\n",
       "      <td>4.941419</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3209.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>From Central Station, walk towards the busstop...</td>\n",
       "      <td>You will have the entire house to yourself.</td>\n",
       "      <td>Spaarndammer en Zeeheldenbuurt</td>\n",
       "      <td>Westerpark</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>Noord-Holland</td>\n",
       "      <td>1013 XE</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>Amsterdam, Netherlands</td>\n",
       "      <td>NL</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>52.390224</td>\n",
       "      <td>4.873924</td>\n",
       "      <td>t</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   location_id street neighborhood_overview notes  \\\n",
       "0       2818.0   None                  None  None   \n",
       "1       3209.0   None                  None  None   \n",
       "\n",
       "                                             transit  \\\n",
       "0  The neighbourhood is well served by 24 hours p...   \n",
       "1  From Central Station, walk towards the busstop...   \n",
       "\n",
       "                                        access  \\\n",
       "0                                       noData   \n",
       "1  You will have the entire house to yourself.   \n",
       "\n",
       "                    neighbourhood                  neighbourhood_cleansed  \\\n",
       "0                  Indische Buurt  Oostelijk Havengebied - Indische Buurt   \n",
       "1  Spaarndammer en Zeeheldenbuurt                              Westerpark   \n",
       "\n",
       "        city          state  zipcode     market          smart_location  \\\n",
       "0  Amsterdam  North Holland        0  Amsterdam  Amsterdam, Netherlands   \n",
       "1  Amsterdam  Noord-Holland  1013 XE  Amsterdam  Amsterdam, Netherlands   \n",
       "\n",
       "  country_code      country   latitude  longitude is_location_exact  \n",
       "0           NL  Netherlands  52.365753   4.941419                 f  \n",
       "1           NL  Netherlands  52.390224   4.873924                 t  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "queryBase(\"\"\"SELECT * FROM dim_location LIMIT 2\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "531001c4",
   "metadata": {},
   "source": [
    "In order to reference the keys have to primary keys so the below command is used on all the tables.\n",
    "\n",
    "ALTER TABLE dim_tabName ADD PRIMARY KEY (idName_id);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "3ac32e61",
   "metadata": {},
   "outputs": [],
   "source": [
    "createFact = \"\"\"CREATE TABLE fact_listing(\n",
    "                    listing_id NUMERIC,\n",
    "                    location_key integer REFERENCES dim_location(location_id),\n",
    "                    review_key integer REFERENCES dim_review(prop_review_id),\n",
    "                    host_key integer REFERENCES dim_host(dimhost_id),\n",
    "                    info_key integer REFERENCES dim_information(info_id)\n",
    "                    )\"\"\"\n",
    "\n",
    "queryTable(createFact)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "08c007fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "insertdimcalendar = \"\"\"CREATE TABLE dim_calendar(\n",
    "                            calender_id BIGSERIAL NOT NULL PRIMARY KEY,\n",
    "                            listing_id NUMERIC,\n",
    "                            date VARCHAR,\n",
    "                            available VARCHAR,\n",
    "                            price VARCHAR)\"\"\"\n",
    "queryTable(insertdimcalendar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "38f7c865",
   "metadata": {},
   "outputs": [],
   "source": [
    "createdimcalendar = \"\"\"INSERT INTO dim_calendar(listing_id, date,\n",
    "                            available, price)\n",
    "                        SELECT listing_id, date, available, price\n",
    "                        FROM calendar;\"\"\"\n",
    "queryTable(createdimcalendar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "2fdd8703",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>calender_id</th>\n",
       "      <th>listing_id</th>\n",
       "      <th>date</th>\n",
       "      <th>available</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2818.0</td>\n",
       "      <td>2019-12-05</td>\n",
       "      <td>f</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2818.0</td>\n",
       "      <td>2019-12-05</td>\n",
       "      <td>f</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>73208.0</td>\n",
       "      <td>2019-08-30</td>\n",
       "      <td>f</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>73208.0</td>\n",
       "      <td>2019-08-29</td>\n",
       "      <td>f</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>73208.0</td>\n",
       "      <td>2019-08-28</td>\n",
       "      <td>f</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   calender_id  listing_id        date available price\n",
       "0            1      2818.0  2019-12-05         f      \n",
       "1            2      2818.0  2019-12-05         f  None\n",
       "2            3     73208.0  2019-08-30         f  None\n",
       "3            4     73208.0  2019-08-29         f  None\n",
       "4            5     73208.0  2019-08-28         f  None"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "queryBase(\"\"\"SELECT * FROM dim_calendar LIMIT 5\"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "15b478d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "insertFact=\"\"\"INSERT INTO fact_listing(listing_id,location_key,review_key,\n",
    "                    host_key,info_key)\n",
    "                SELECT\n",
    "                    ll.id as listing_id,\n",
    "                    dl.location_id as location_key,\n",
    "                    dr.prop_review_id as review_key,\n",
    "                    dh.dimhost_id as host_key,\n",
    "                    di.info_id as info_key\n",
    "                FROM listings_details AS ll\n",
    "                JOIN dim_location AS dl ON ll.id = dl.location_id\n",
    "                JOIN dim_review AS dr ON dl.location_id = dr.prop_review_id\n",
    "                JOIN dim_information AS di ON dr.prop_review_id = di.info_id\n",
    "                JOIN dim_host AS dh ON di.info_id = dh.dimhost_id;\"\"\"\n",
    "queryTable(insertFact)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "f84e5f05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>listing_id</th>\n",
       "      <th>location_key</th>\n",
       "      <th>review_key</th>\n",
       "      <th>host_key</th>\n",
       "      <th>info_key</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30580413.0</td>\n",
       "      <td>30580413</td>\n",
       "      <td>30580413</td>\n",
       "      <td>30580413</td>\n",
       "      <td>30580413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30579673.0</td>\n",
       "      <td>30579673</td>\n",
       "      <td>30579673</td>\n",
       "      <td>30579673</td>\n",
       "      <td>30579673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30578037.0</td>\n",
       "      <td>30578037</td>\n",
       "      <td>30578037</td>\n",
       "      <td>30578037</td>\n",
       "      <td>30578037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30577727.0</td>\n",
       "      <td>30577727</td>\n",
       "      <td>30577727</td>\n",
       "      <td>30577727</td>\n",
       "      <td>30577727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30576148.0</td>\n",
       "      <td>30576148</td>\n",
       "      <td>30576148</td>\n",
       "      <td>30576148</td>\n",
       "      <td>30576148</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   listing_id  location_key  review_key  host_key  info_key\n",
       "0  30580413.0      30580413    30580413  30580413  30580413\n",
       "1  30579673.0      30579673    30579673  30579673  30579673\n",
       "2  30578037.0      30578037    30578037  30578037  30578037\n",
       "3  30577727.0      30577727    30577727  30577727  30577727\n",
       "4  30576148.0      30576148    30576148  30576148  30576148"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "queryBase(\"\"\"SELECT * FROM fact_listing ORDER BY listing_id DESC LIMIT 5;\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c70b5ae1",
   "metadata": {},
   "source": [
    "We have divided the massive dataset, which was spanning more than 100 columns into something very manageable, and efficiently workable.If we want to query the latitude, longitude, review counts it is possible"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "699d9553",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>review_scores_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52.365753</td>\n",
       "      <td>4.941419</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>52.390224</td>\n",
       "      <td>4.873924</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>52.365086</td>\n",
       "      <td>4.893541</td>\n",
       "      <td>87.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>52.373116</td>\n",
       "      <td>4.883668</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>52.386726</td>\n",
       "      <td>4.892078</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    latitude  longitude  review_scores_rating\n",
       "0  52.365753   4.941419                  97.0\n",
       "1  52.390224   4.873924                  96.0\n",
       "2  52.365086   4.893541                  87.0\n",
       "3  52.373116   4.883668                 100.0\n",
       "4  52.386726   4.892078                  99.0"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testExample1 = \"\"\"SELECT dl.latitude, dl.longitude, dr.review_scores_rating \n",
    "                FROM dim_location AS dl\n",
    "                JOIN dim_review AS dr ON dl.location_id = dr.prop_review_id\n",
    "                LIMIT 5\"\"\"\n",
    "queryBase(testExample1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "466d0f23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>summary</th>\n",
       "      <th>host_name</th>\n",
       "      <th>host_about</th>\n",
       "      <th>review_scores_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Quiet Garden View Room &amp; Super Fast WiFi</td>\n",
       "      <td>Quiet Garden View Room &amp; Super Fast WiFi</td>\n",
       "      <td>Daniel</td>\n",
       "      <td>Upon arriving in Amsterdam, one can imagine as...</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Quiet apt near center, great view</td>\n",
       "      <td>You will love our spacious (90 m2) bright apar...</td>\n",
       "      <td>Maartje</td>\n",
       "      <td>I am a freelance radio producer and journalist...</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100%Centre-Studio 1 Private Floor/Bathroom</td>\n",
       "      <td>Cozy studio on your own private floor, 100% in...</td>\n",
       "      <td>Alex</td>\n",
       "      <td>Secondary phone nr. + (Phone number hidden by ...</td>\n",
       "      <td>87.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lovely apt in City Centre (Jordaan)</td>\n",
       "      <td>noData</td>\n",
       "      <td>Joan</td>\n",
       "      <td>We are a retired couple who live in NYC, and h...</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Romantic, stylish B&amp;B houseboat in canal district</td>\n",
       "      <td>Stylish and romantic houseboat on fantastic hi...</td>\n",
       "      <td>Flip</td>\n",
       "      <td>Marjan works in \"eye\" the dutch filmmuseum, an...</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                name  \\\n",
       "0           Quiet Garden View Room & Super Fast WiFi   \n",
       "1                  Quiet apt near center, great view   \n",
       "2         100%Centre-Studio 1 Private Floor/Bathroom   \n",
       "3                Lovely apt in City Centre (Jordaan)   \n",
       "4  Romantic, stylish B&B houseboat in canal district   \n",
       "\n",
       "                                             summary host_name  \\\n",
       "0           Quiet Garden View Room & Super Fast WiFi    Daniel   \n",
       "1  You will love our spacious (90 m2) bright apar...   Maartje   \n",
       "2  Cozy studio on your own private floor, 100% in...      Alex   \n",
       "3                                             noData      Joan   \n",
       "4  Stylish and romantic houseboat on fantastic hi...      Flip   \n",
       "\n",
       "                                          host_about  review_scores_rating  \n",
       "0  Upon arriving in Amsterdam, one can imagine as...                  97.0  \n",
       "1  I am a freelance radio producer and journalist...                  96.0  \n",
       "2  Secondary phone nr. + (Phone number hidden by ...                  87.0  \n",
       "3  We are a retired couple who live in NYC, and h...                 100.0  \n",
       "4  Marjan works in \"eye\" the dutch filmmuseum, an...                  99.0  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testExample2 = \"\"\"SELECT di.name, di.summary,dh.host_name, dh.host_about, dr.review_scores_rating \n",
    "                FROM dim_location AS dl\n",
    "                JOIN dim_review AS dr ON dl.location_id = dr.prop_review_id\n",
    "                JOIN dim_information AS di on dr.prop_review_id = di.info_id\n",
    "                JOIN dim_host AS dh on di.info_id = dh.dimhost_id\n",
    "                LIMIT 5\"\"\"\n",
    "queryBase(testExample2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e337f246",
   "metadata": {},
   "source": [
    "Our target is to analyse the relation between the dimensions of the listing with \n",
    "\n",
    "- review_comments\n",
    "\n",
    "- price\n",
    "\n",
    "- availability status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "d54236ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>comments</th>\n",
       "      <th>reviewer_name</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Daniel is really cool. The place was nice and ...</td>\n",
       "      <td>Lam</td>\n",
       "      <td>Quiet Garden View Room &amp; Super Fast WiFi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Daniel is the most amazing host! His place is ...</td>\n",
       "      <td>Alice</td>\n",
       "      <td>Quiet Garden View Room &amp; Super Fast WiFi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>We had such a great time in Amsterdam. Daniel ...</td>\n",
       "      <td>Natalja</td>\n",
       "      <td>Quiet Garden View Room &amp; Super Fast WiFi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Very professional operation. Room is very clea...</td>\n",
       "      <td>Enrique</td>\n",
       "      <td>Quiet Garden View Room &amp; Super Fast WiFi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Daniel is highly recommended.  He provided all...</td>\n",
       "      <td>Sherwin</td>\n",
       "      <td>Quiet Garden View Room &amp; Super Fast WiFi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Daniel was a great host! He made everything so...</td>\n",
       "      <td>Jie</td>\n",
       "      <td>Quiet Garden View Room &amp; Super Fast WiFi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Daniele is an amazing host! He provided everyt...</td>\n",
       "      <td>Vanessa</td>\n",
       "      <td>Quiet Garden View Room &amp; Super Fast WiFi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>You can´t have a nicer start in Amsterdam. Dan...</td>\n",
       "      <td>Katja</td>\n",
       "      <td>Quiet Garden View Room &amp; Super Fast WiFi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Daniel was a fantastic host. His place is calm...</td>\n",
       "      <td>Marie-Eve</td>\n",
       "      <td>Quiet Garden View Room &amp; Super Fast WiFi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Daniel was great. He couldn.t do enough for us...</td>\n",
       "      <td>Graham</td>\n",
       "      <td>Quiet Garden View Room &amp; Super Fast WiFi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            comments reviewer_name  \\\n",
       "0  Daniel is really cool. The place was nice and ...           Lam   \n",
       "1  Daniel is the most amazing host! His place is ...         Alice   \n",
       "2  We had such a great time in Amsterdam. Daniel ...       Natalja   \n",
       "3  Very professional operation. Room is very clea...       Enrique   \n",
       "4  Daniel is highly recommended.  He provided all...       Sherwin   \n",
       "5  Daniel was a great host! He made everything so...           Jie   \n",
       "6  Daniele is an amazing host! He provided everyt...       Vanessa   \n",
       "7  You can´t have a nicer start in Amsterdam. Dan...         Katja   \n",
       "8  Daniel was a fantastic host. His place is calm...     Marie-Eve   \n",
       "9  Daniel was great. He couldn.t do enough for us...        Graham   \n",
       "\n",
       "                                       name  \n",
       "0  Quiet Garden View Room & Super Fast WiFi  \n",
       "1  Quiet Garden View Room & Super Fast WiFi  \n",
       "2  Quiet Garden View Room & Super Fast WiFi  \n",
       "3  Quiet Garden View Room & Super Fast WiFi  \n",
       "4  Quiet Garden View Room & Super Fast WiFi  \n",
       "5  Quiet Garden View Room & Super Fast WiFi  \n",
       "6  Quiet Garden View Room & Super Fast WiFi  \n",
       "7  Quiet Garden View Room & Super Fast WiFi  \n",
       "8  Quiet Garden View Room & Super Fast WiFi  \n",
       "9  Quiet Garden View Room & Super Fast WiFi  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testComplicated1 = \"\"\"SELECT r.comments, r.reviewer_name, di.name\n",
    "                    FROM dim_information AS di\n",
    "                    JOIN reviews_details AS r ON di.info_id = r.listing_id\n",
    "                    LIMIT 10;\"\"\"\n",
    "queryBase(testComplicated1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "1a05fa75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>available</th>\n",
       "      <th>price</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>f</td>\n",
       "      <td></td>\n",
       "      <td>Quiet Garden View Room &amp; Super Fast WiFi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>t</td>\n",
       "      <td>$100.00</td>\n",
       "      <td>TOP LOCATED Canalhouse B&amp;B Jordaan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>t</td>\n",
       "      <td>$100.00</td>\n",
       "      <td>TOP LOCATED Canalhouse B&amp;B Jordaan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>t</td>\n",
       "      <td>$100.00</td>\n",
       "      <td>TOP LOCATED Canalhouse B&amp;B Jordaan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>t</td>\n",
       "      <td>$100.00</td>\n",
       "      <td>TOP LOCATED Canalhouse B&amp;B Jordaan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>t</td>\n",
       "      <td>$100.00</td>\n",
       "      <td>TOP LOCATED Canalhouse B&amp;B Jordaan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>t</td>\n",
       "      <td>$100.00</td>\n",
       "      <td>TOP LOCATED Canalhouse B&amp;B Jordaan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>t</td>\n",
       "      <td>$100.00</td>\n",
       "      <td>Garden Suite Studiohttps://www.airbnb.com/mana...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>t</td>\n",
       "      <td>$100.00</td>\n",
       "      <td>TOP LOCATED Canalhouse B&amp;B Jordaan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>t</td>\n",
       "      <td>$100.00</td>\n",
       "      <td>TOP LOCATED Canalhouse B&amp;B Jordaan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  available    price                                               name\n",
       "0         f                    Quiet Garden View Room & Super Fast WiFi\n",
       "1         t  $100.00                 TOP LOCATED Canalhouse B&B Jordaan\n",
       "2         t  $100.00                 TOP LOCATED Canalhouse B&B Jordaan\n",
       "3         t  $100.00                 TOP LOCATED Canalhouse B&B Jordaan\n",
       "4         t  $100.00                 TOP LOCATED Canalhouse B&B Jordaan\n",
       "5         t  $100.00                 TOP LOCATED Canalhouse B&B Jordaan\n",
       "6         t  $100.00                 TOP LOCATED Canalhouse B&B Jordaan\n",
       "7         t  $100.00  Garden Suite Studiohttps://www.airbnb.com/mana...\n",
       "8         t  $100.00                 TOP LOCATED Canalhouse B&B Jordaan\n",
       "9         t  $100.00                 TOP LOCATED Canalhouse B&B Jordaan"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testComplicated2 = \"\"\"SELECT c.available, c.price, di.name\n",
    "                    FROM dim_information AS di\n",
    "                    JOIN dim_calendar AS c ON di.info_id = c.listing_id\n",
    "                    ORDER BY price ASC\n",
    "                    LIMIT 10;\"\"\"\n",
    "queryBase(testComplicated2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e226e71f",
   "metadata": {},
   "source": [
    "### That brings us to end of the Data modeling exercise. \n",
    "\n",
    "There were many lessons learnt and the important point is creating unique reference points for each record. When multiple table with different record counts are involved, the Data Modeling can be very interesting.\n",
    "\n",
    "Bringing these diverse dimensions together requires a reliable way, and has been achieved. "
   ]
  }
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