Case Study #1 - Danny's Diner.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Import Libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyodbc  # For SQL Use\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**SQL Connection**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Set up the database connection\n",
    "conn = pyodbc.connect(\n",
    "    \"Driver={SQL Server};\"\n",
    "    \"Server=SHORYA;\"\n",
    "    \"Database=dannys_diner;\"\n",
    "    \"Trusted_Connection=yes;\"\n",
    ")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create Cursor and Fetch List Of Tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['sales', 'menu', 'members']\n"
     ]
    }
   ],
   "source": [
    "cursor = conn.cursor()\n",
    "\n",
    "# Get a list of all tables in the database\n",
    "tables_query = \"SELECT name FROM sys.tables\"\n",
    "cursor.execute(tables_query)\n",
    "\n",
    "# Fetch the results\n",
    "tables = cursor.fetchall()\n",
    "\n",
    "# Process the results\n",
    "table_list = [table[0] for table in tables]\n",
    "print(table_list)\n",
    "\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>customer_id</th>\n",
       "      <th>order_date</th>\n",
       "      <th>product_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A</td>\n",
       "      <td>2021-01-07</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>2021-01-10</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A</td>\n",
       "      <td>2021-01-11</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A</td>\n",
       "      <td>2021-01-11</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>B</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>B</td>\n",
       "      <td>2021-01-02</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>B</td>\n",
       "      <td>2021-01-04</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>B</td>\n",
       "      <td>2021-01-11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>B</td>\n",
       "      <td>2021-01-16</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>B</td>\n",
       "      <td>2021-02-01</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>C</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>C</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>C</td>\n",
       "      <td>2021-01-07</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_id  order_date  product_id\n",
       "0            A  2021-01-01           1\n",
       "1            A  2021-01-01           2\n",
       "2            A  2021-01-07           2\n",
       "3            A  2021-01-10           3\n",
       "4            A  2021-01-11           3\n",
       "5            A  2021-01-11           3\n",
       "6            B  2021-01-01           2\n",
       "7            B  2021-01-02           2\n",
       "8            B  2021-01-04           1\n",
       "9            B  2021-01-11           1\n",
       "10           B  2021-01-16           3\n",
       "11           B  2021-02-01           3\n",
       "12           C  2021-01-01           3\n",
       "13           C  2021-01-01           3\n",
       "14           C  2021-01-07           3"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales= pd.read_sql_query('select * from sales',conn)\n",
    "sales\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_id</th>\n",
       "      <th>product_name</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>sushi</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>curry</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>ramen</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   product_id product_name  price\n",
       "0           1        sushi     10\n",
       "1           2        curry     15\n",
       "2           3        ramen     12"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "menu= pd.read_sql_query('select * from menu',conn)\n",
    "menu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\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>customer_id</th>\n",
       "      <th>join_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>2021-01-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>2021-01-09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  customer_id   join_date\n",
       "0           A  2021-01-07\n",
       "1           B  2021-01-09"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "members= pd.read_sql_query('select * from members',conn)\n",
    "members"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Type Of Each Column From Each Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column Name:customer_id, Data Type:varchar, Table Name:sales\n",
      "Column Name:order_date, Data Type:date, Table Name:sales\n",
      "Column Name:product_id, Data Type:int, Table Name:sales\n",
      "Column Name:product_id, Data Type:int, Table Name:menu\n",
      "Column Name:product_name, Data Type:varchar, Table Name:menu\n",
      "Column Name:price, Data Type:int, Table Name:menu\n",
      "Column Name:customer_id, Data Type:varchar, Table Name:members\n",
      "Column Name:join_date, Data Type:date, Table Name:members\n"
     ]
    }
   ],
   "source": [
    "cursor.execute(\"SELECT c.name AS ColumnName, t.name AS DataType, ta.name AS TableName \\\n",
    "                FROM sys.columns c \\\n",
    "                INNER JOIN sys.types t ON c.user_type_id = t.user_type_id \\\n",
    "                INNER JOIN sys.tables ta ON c.object_id = ta.object_id\")\n",
    "\n",
    "# Fetch the results\n",
    "columns = cursor.fetchall()\n",
    "\n",
    "for column in columns:\n",
    "    column_name= column.ColumnName\n",
    "    data_type= column.DataType\n",
    "    table_name= column.TableName\n",
    "\n",
    "    print(f\"Column Name:{column_name}, Data Type:{data_type}, Table Name:{table_name}\")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**EDA And Visualization**"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " \n",
    " 1. What is the total amount each customer spent at the restaurant?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>total_amount_each_customer_spent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  customer_id  total_amount_each_customer_spent\n",
       "0           A                                76\n",
       "1           B                                74\n",
       "2           C                                36"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "Total_Amt_Each_Spent=pd.read_sql_query('SELECT s.customer_id, SUM(m.price) as total_amount_each_customer_spent \\\n",
    "                                   FROM sales as s\\\n",
    "                                   JOIN menu as m\\\n",
    "                                   ON s.product_id = m.product_id \\\n",
    "                                   GROUP BY s.customer_id',conn)\n",
    "Total_Amt_Each_Spent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize= (7,10))\n",
    "sns.barplot(data= Total_Amt_Each_Spent,x= Total_Amt_Each_Spent['customer_id'],\n",
    "            y= 'total_amount_each_customer_spent')\n",
    "\n",
    "plt.yticks(np.arange(2,78,2))\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. How many days has each customer visited the restaurant?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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>customer_id</th>\n",
       "      <th>Visited_Days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  customer_id  Visited_Days\n",
       "0           A             6\n",
       "1           B             6\n",
       "2           C             3"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Visited_Days=pd.read_sql_query('''SELECT customer_id, count(*) as Visited_Days\n",
    "                  FROM sales\n",
    "                  GROUP BY customer_id\n",
    "                  ''',conn)\n",
    "Visited_Days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize= (4,3))\n",
    "sns.lineplot(data=Visited_Days,x= Visited_Days['customer_id'],y= Visited_Days['Visited_Days'],\n",
    "             marker='o', markersize=10, color= 'red')\n",
    "plt.yticks(np.arange(1,8,1))\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. What was the first item from the menu purchased by each customer?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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>customer_id</th>\n",
       "      <th>order_date</th>\n",
       "      <th>first_Purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>sushi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>curry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>ramen</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  customer_id  order_date first_Purchase\n",
       "0           A  2021-01-01          sushi\n",
       "1           B  2021-01-01          curry\n",
       "2           C  2021-01-01          ramen"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "First_Purchase= pd.read_sql_query('''SELECT customer_id,order_date,product_name as first_Purchase\n",
    "FROM(SELECT s.customer_id,m.product_name,s.order_date, ROW_NUMBER() OVER(PARTITION BY s.customer_id ORDER BY s.order_date ASC) as rn\n",
    "FROM sales s \n",
    "JOIN menu m\n",
    "ON m.product_id=s.product_id) subquery\n",
    "WHERE rn=1''',conn)\n",
    "\n",
    "First_Purchase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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>customer_id</th>\n",
       "      <th>order_date</th>\n",
       "      <th>first_Purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>sushi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>curry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>ramen</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  customer_id  order_date first_Purchase\n",
       "0           A  2021-01-01          sushi\n",
       "1           B  2021-01-01          curry\n",
       "2           C  2021-01-01          ramen"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df= pd.DataFrame(First_Purchase)\n",
    "df"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. What is the most purchased item on the menu and how many times was it purchased by all customers?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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>product_name</th>\n",
       "      <th>product_id</th>\n",
       "      <th>Total_Purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ramen</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  product_name  product_id  Total_Purchase\n",
       "0        ramen           3               8"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Most_Purchased_item= pd.read_sql_query('''SELECT TOP 1 m.product_name,m.product_id,count(*) as Total_Purchase\n",
    "FROM sales as s\n",
    "JOIN menu as m\n",
    "ON m.product_id=s.product_id\n",
    "GROUP BY m.product_name,m.product_id\n",
    "ORDER BY Total_Purchase DESC''',conn)\n",
    "\n",
    "Most_Purchased_item"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5. Which item was the most popular for each customer?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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>customer_id</th>\n",
       "      <th>product_name</th>\n",
       "      <th>product_id</th>\n",
       "      <th>Total_Orders</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>ramen</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>curry</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>ramen</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  customer_id product_name  product_id  Total_Orders\n",
       "0           A        ramen           3             3\n",
       "1           B        curry           2             2\n",
       "2           C        ramen           3             3"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Most_Popular_By_Each_Customer= pd.read_sql_query('''SELECT  customer_id,product_name,product_id, Total_Orders\n",
    "FROM ( SELECT s.customer_id,m.product_name,m.product_id, count (*) as Total_Orders,\n",
    "ROW_NUMBER() OVER(PARTITION BY s.customer_id ORDER BY count(*) DESC) as rn\n",
    "FROM sales as s\n",
    "JOIN menu as m\n",
    "ON m.product_id=s.product_id\n",
    "GROUP BY s.customer_id,m.product_name,m.product_id) as subquery\n",
    "WHERE rn= 1''',conn)\n",
    "Most_Popular_By_Each_Customer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize= (4,3))\n",
    "# Create a bar plot\n",
    "sns.barplot(x='customer_id', y='Total_Orders', hue='product_name', \n",
    "            data=Most_Popular_By_Each_Customer,dodge=False, width= 0.5)\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel('Customer ID')\n",
    "plt.ylabel('Total Orders')\n",
    "plt.yticks(range(0,4))\n",
    "plt.xticks()\n",
    "plt.title('Most_Popular_By_Each_Customer')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6. Which item was purchased first by the customer after they became a member?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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>customer_id</th>\n",
       "      <th>first_purchase_date</th>\n",
       "      <th>product_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>2021-01-10</td>\n",
       "      <td>ramen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>2021-01-11</td>\n",
       "      <td>sushi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  customer_id first_purchase_date product_name\n",
       "0           A          2021-01-10        ramen\n",
       "1           B          2021-01-11        sushi"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "First_Item_After_Membership= pd.read_sql_query('''SELECT t.customer_id, first_purchase_date,m.product_name\n",
    "FROM(SELECT s.customer_id,MIN(order_date) as first_purchase_date\n",
    "FROM sales as s\n",
    "JOIN menu as m\n",
    "ON s.product_id = m.product_id\n",
    "JOIN members as mem\n",
    "ON s.customer_id = mem.customer_id\n",
    "Where s.order_date > mem.join_date\n",
    "GROUP BY s.customer_id) as t\n",
    "JOIN sales AS s ON t.customer_id = s.customer_id AND t.first_purchase_date = s.order_date\n",
    "JOIN menu AS m ON s.product_id = m.product_id;''',conn)\n",
    "\n",
    "First_Item_After_Membership"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7. Which item was purchased just before the customer became a member?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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>customer_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>last_order</th>\n",
       "      <th>product_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>sushi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-01-04</td>\n",
       "      <td>sushi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  customer_id  product_id  last_order product_name\n",
       "0           A           1  2021-01-01        sushi\n",
       "1           B           1  2021-01-04        sushi"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "last_purchase_before_membership= pd.read_sql_query('''SELECT customer_id, product_id, last_order, product_name\n",
    "FROM (\n",
    "    SELECT s.customer_id, s.product_id, MAX(s.order_date) AS last_order, product_name,\n",
    "    ROW_NUMBER() OVER (PARTITION BY s.customer_id ORDER BY s.order_date DESC) AS rn\n",
    "    FROM sales AS s\n",
    "    JOIN menu AS m ON s.product_id = m.product_id\n",
    "    JOIN members AS mm ON s.customer_id = mm.customer_id\n",
    "    WHERE s.order_date < mm.join_date\n",
    "    GROUP BY s.customer_id, s.product_id, product_name,order_date\n",
    ") subquery\n",
    "WHERE rn = 1;''',conn)\n",
    "\n",
    "last_purchase_before_membership"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "8. What is the total items and amount spent for each member before they became a member?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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>customer_id</th>\n",
       "      <th>total_items</th>\n",
       "      <th>total_spent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>3</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  customer_id  total_items  total_spent\n",
       "0           A            2           25\n",
       "1           B            3           40"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_item_amtspent_before_membership=pd.read_sql_query('''SELECT s.customer_id,COUNT(s.product_id) as total_items,SUM(m.price) as total_spent\n",
    "FROM sales s\n",
    "JOIN menu m\n",
    "ON s.product_id =m.product_id\n",
    "JOIN members mm\n",
    "ON s.customer_id=mm.customer_id\n",
    "WHERE order_date<join_date\n",
    "GROUP BY s.customer_id''',conn)\n",
    "\n",
    "total_item_amtspent_before_membership"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize= (8,3))\n",
    "plt.subplot(1, 2, 1)\n",
    "\n",
    "plt.pie(data=total_item_amtspent_before_membership, x= 'total_spent',autopct='%1.1f%%',\n",
    "        labels= total_item_amtspent_before_membership['total_spent'])\n",
    "plt.title('Total Amount Spent Before Membership')\n",
    "plt.legend(total_item_amtspent_before_membership['customer_id'],loc=1)\n",
    "\n",
    "plt.subplot(1, 2, 2) \n",
    "plt.pie(data=total_item_amtspent_before_membership, x= 'total_items',autopct='%1.1f%%',\n",
    "        labels= total_item_amtspent_before_membership['total_items'])\n",
    "plt.title('Total Item Purchased Before Membership')\n",
    "plt.legend(total_item_amtspent_before_membership['customer_id'],loc=1)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "9.  If each $1 spent equates to 10 points and sushi has a 2x points multiplier - how many points would each customer have?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "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>customer_id</th>\n",
       "      <th>total_points</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>360</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  customer_id  total_points\n",
       "0           A           860\n",
       "1           B           940\n",
       "2           C           360"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Total_points= pd.read_sql('''SELECT s.customer_id,SUM(CASE WHEN m.product_name= 'sushi' Then 2*m.price ELSE m.price END)*10 AS total_points\n",
    "FROM sales s\n",
    "JOIN menu m\n",
    "ON s.product_id= m.product_id\n",
    "GROUP BY customer_id''',conn)\n",
    "\n",
    "Total_points\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FDBERETGEQoaIiIgYQiFDREREDKGQIXKLMxcV2rsEuYn085bbSYV94qdIeWFycCQnbSQXcvbYuxQxmHO1BlSLes/eZYiUGYUMkXLgQs4eLvz5i73LEBGxiaZLRERExBAKGSIiImIIhQwRERExhEKGiIiIGEIhQ0RERAyhkCEiIiKGUMgQERERQyhkiIiIiCEUMkRERMQQChkiIiJiCIUMERERMYRChoiIiBhCIUNEREQMoZAhIiIihlDIEBEREUMoZJRSYVGRvUuQm0g/bxER2znZu4DyytHBgTc++pa9R07auxQxWP0aXrz7bAt7lyEiUu4oZNyAvUdOsvPAMXuXISIickvSdImIiIgYQiFDREREDKGQISIiIoZQyBARERFDKGSIiIiIIRQyRERExBAKGSIiImIIu4aMQ4cO0b9/fxo3bkybNm2YM2eOpW3Hjh107dqVkJAQOnfuzPbt24ttu3z5ciIiIggJCSE6Oppjx/S8ChERkVuJXUPG4MGDqVSpEmlpabz++uu8//77rF69mtzcXPr160fTpk1JS0sjLCyM/v37k5ubC8BPP/1EXFwcgwYNIjU1lVOnThEbG2vPUxEREZHL2C1knDx5koyMDAYMGEC9evWIiIigRYsWbNy4kZUrV+Lq6srw4cO55557iIuLo3Llynz11VcALFiwgA4dOtCpUycaNmzIhAkTWLduHVlZWfY6HREREbmM3R4r7ubmhru7O2lpaQwZMoSsrCy2bNnC4MGDyczMpEmTJphMJgBMJhONGzcmIyODqKgoMjMz6du3r2VfNWvWpFatWmRmZlK7dm2b6igsLCxV/Y6OjqXaTsqv0vaVG6W+VvHYq6+JWMvaPmq3kOHq6sqoUaN45513mDdvHoWFhURFRdG1a1fWrFmDn59fsfWrVq3Krl27ADhy5Ag1atQo0f7nn3/aXMe2bdts3sbd3Z3AwECbt5Py7ddffyUvL++mHlN9rWKyR18TMYJdPyBt9+7dtG7dmueff55du3bxzjvv0KxZM/Ly8nBxcSm2rouLC/n5+QCcO3fumu22CAoK0jtFsYq/v7+9S5AKQn1NbnWFhYVWvUm3W8jYuHEjS5YsYd26dbi5uREUFMThw4eZPn06tWvXLhEY8vPzcXNzAy6Oglyp3d3d3eY6HB0dFTLEKuoncrOor8ntwm4Xfm7fvp26detaggNAYGAgBw8exNvbm5ycnGLr5+TkWKZIrtZevXp14wsXERERq9gtZNSoUYN9+/YVG5HYs2cPvr6+hISEsHXrVsxmMwBms5ktW7YQEhICQEhICJs3b7Zsd+jQIQ4dOmRpFxEREfuzW8ho06YNzs7OvPHGG+zdu5dvvvmGGTNm0LNnT9q3b8+pU6cYO3Ysv//+O2PHjiUvL48OHToA0L17dz777DMWL17Mzp07GT58OK1atbL5zhIRERExjt1ChqenJ3PmzCE7O5suXbowbtw4BgwYwDPPPIOHhwcffPABmzdvttyympycTKVKlQAICwvj7bffJjExke7du+Pl5cW4cePsdSoiIiJyBXa9u8TPz48PP/zwim3BwcF8+umnV902KiqKqKgoo0oTERGRG6QPSBMRERFDKGSIiIiIIRQyRERExBAKGSIiImIIhQwRERExhEKGiIiIGEIhQ0RERAyhkCEiIiKGUMgQERERQyhkiIiIiCEUMkRERMQQChkiIiJiCIUMERERMYRChoiIiBhCIUNEREQMoZAhIiIihlDIEBEREUMoZIiIiIghFDJERETEEAoZIiIiYgiFDBERETGEQoaIiIgYQiFDREREDKGQISIiIoZQyBARERFDKGSIiIiIIRQyRERExBAKGSIiImIIhQwRERExhEKGiIiIGKJUIWP37t2cPn0agG+//ZYxY8awePHiMi1MREREyjebQ0ZqaipPPvkkv/zyCzt27GDAgAFkZWUxbdo0pk2bZkSNIiIiUg7ZHDJmzZrF+PHjCQ8PZ+nSpQQEBDBr1iymTp2q0QwRERGxsDlkHD58mCZNmgCwdu1aIiIiALj77rs5e/Zs2VYnIiIi5ZbNIaNBgwZ88cUXLFmyhIMHDxIREcGFCxeYPXs2DRs2tGlf+fn5jBkzhgceeIDmzZszZcoUzGYzADt27KBr166EhITQuXNntm/fXmzb5cuXExERQUhICNHR0Rw7dszWUxERERED2RwyRowYQUpKCm+88QbPPvss99xzD+PGjWP16tXExcXZtK93332X77//npSUFCZPnswnn3xCamoqubm59OvXj6ZNm5KWlkZYWBj9+/cnNzcXgJ9++om4uDgGDRpEamoqp06dIjY21tZTEREREQM52bpBs2bN2LhxI6dPn8bLywuAgQMHEhsba7njxBonTpxg6dKlfPjhhwQHBwPwwgsvkJmZiZOTE66urgwfPhyTyURcXBzr16/nq6++IioqigULFtChQwc6deoEwIQJE2jdujVZWVnUrl3b1lMSERERA9g8khEQEMCJEycsAQOgWrVqHDlyhLZt21q9n82bN+Ph4UF4eLhlWb9+/Rg3bhyZmZk0adIEk8kEgMlkonHjxmRkZACQmZlJ06ZNLdvVrFmTWrVqkZmZaevpiIiIiEGsGslYtmwZaWlpAJjNZqKjo3F2di62zpEjR6hevbrVB87KysLHx4dly5YxY8YMLly4QFRUFAMGDCA7Oxs/P79i61etWpVdu3ZZjlWjRo0S7X/++afVx7+ksLDQ5m0AHB0dS7WdlF+l7Ss3Sn2t4rFXXxOxlrV91KqQ0a5dO/bv3w9Aeno6oaGhVK5cudg6lSpVol27dlYXmJuby759+1i0aBHjxo0jOzubUaNG4e7uTl5eHi4uLsXWd3FxIT8/H4Bz585ds90W27Zts3kbd3d3AgMDbd5Oyrdff/2VvLy8m3pM9bWKyR59TcQIVoWMypUrM2jQIAB8fHzo2LEjrq6uN3ZgJyfOnDnD5MmT8fHxAeDgwYN8/PHH1K1bt0RgyM/Px83NDQBXV9crtru7u9tcR1BQkN4pilX8/f3tXYJUEOprcqsrLCy06k26zRd+Pv300+zbt4/t27dz4cKFEu2XLsa8nurVq+Pq6moJGAD169fn0KFDhIeHk5OTU2z9nJwcyxSJt7f3Fdttma65xNHRUSFDrKJ+IjeL+prcLmwOGbNmzWLSpEl4eXmVmDIxmUxWh4yQkBDOnz/P3r17qV+/PgB79uzBx8eHkJAQZs6cidlsxmQyYTab2bJlCy+99JJl282bNxMVFQXAoUOHOHToECEhIbaejoiIiBjE5pAxe/Zshg0bRp8+fW7owA0aNKBVq1bExsby1ltvkZ2dTXJyMgMGDKB9+/ZMnjyZsWPH0q1bNxYtWkReXh4dOnQAoHv37vTs2ZPQ0FCCgoIYO3YsrVq10u2rIiIitxCbb2E9f/48f/vb38rk4JMmTaJOnTp0796dESNG0KNHD3r27ImHhwcffPCBZbQiMzOT5ORkKlWqBEBYWBhvv/02iYmJdO/eHS8vL8aNG1cmNYmIiEjZsHkk44knnuCjjz6yPCjrRnh6ejJhwoQrtgUHB/Ppp59edduoqCjLdImIiIjcemwOGWfOnGHJkiUsX74cX1/fEs/LmDdvXpkVJyIiIuWXzSGjXr16lgswRURERK7G5pBx6XkZIiIiItdiVciIjY0lLi4ODw+P637aqS7AFBERESjF3SUiIiIi1rBqJOOvoxMaqRARERFr2HxNBsC//vUvZs2axZ49eygsLKR+/fo899xzVj/tU0RERG5/NoeMRYsWMX78eJ577jn69etHUVERW7ZsYcyYMVy4cIGuXbsaUaeIiIiUM6X67JLRo0cXG7WIiIjg3nvvZcaMGQoZIiIiApTiws+jR48SGhpaYnlYWBiHDh0qi5pERETkNmBzyAgICGDZsmUlln/66af4+fmVRU0iIiJyG7B5umTYsGH07t2bTZs2WT5aPSMjg507dzJjxowyL1BERETKJ5tHMsLCwkhLSyMkJITdu3ezf/9+HnjgAb788kseeughI2oUERGRcsimkYzTp0/j7OzMPffcw8iRI42qSURERG4DVo1kZGdn07t3b8LDw2ncuDF9+vTRRZ4iIiJyTVaFjClTpnD06FEmT57MlClTOHHiBCNGjDC6NhERESnHrJouWbt2LSkpKTRq1AiAhg0b0rFjR/Ly8nB3dze0QBERESmfrBrJOHXqFN7e3pbX9erVw9HRkWPHjhlWmIiIiJRvVoWMoqIiTCZTsWVOTk4UFRUZUpSIiIiUf1aFDJPJVCJkXP5aRERE5K+suibDbDYTHR2Ns7OzZdm5c+cYOnQorq6uxdadN29e2VYoIiIi5ZJVIWPQoEElloWHh5d5MSIiInL7KHXIEBEREbkWmx8rLiIiImINhQwRERExhEKGiIiIGEIhQ0RERAxh1YWfCQkJVu9QF4mKiIgIWBkyNm3aZNXO9IAuERERucSqkDF//nyj6xAREZHbjFUh43K//PILu3btsnx2idlsJj8/nx07djBmzJgyLVBERETKJ5tDRkJCAgkJCVSrVo2jR4/i7e1NTk4OhYWFtGvXzogaRUREpByy+e6S1NRUxowZw3fffUfNmjWZP38+33//Pc2bN6dOnTpG1CgiIiLlkM0h4/jx47Ro0QKAgIAAtm7dyh133MGrr77KypUry7xAERERKZ9sDhne3t5kZWUBcM8997Bjxw4APDw8OHbsWNlWJyIiIuWWzSGja9euvPbaa6xbt46IiAg++eQTZs+ezbvvvkvDhg1LXUi/fv0YOXKk5fWOHTvo2rUrISEhdO7cme3btxdbf/ny5URERBASEkJ0dLQCjoiIyC3G5pDx0ksvMWzYMNzd3QkODiY2NpYVK1ZgNpsZN25cqYpYsWIF69ats7zOzc2lX79+NG3alLS0NMLCwujfvz+5ubkA/PTTT8TFxTFo0CBSU1M5deoUsbGxpTq2iIiIGMPmkLFs2TI6duxIeHg4cHFkY+nSpcTHx/Ptt9/aXMCJEyeYMGECQUFBlmUrV67E1dWV4cOHc8899xAXF0flypX56quvAFiwYAEdOnSgU6dONGzYkAkTJrBu3TrLNI6IiIjYn1Uh49ixYxw8eJCDBw8SGxvLrl27LK8v/fnhhx+YNGmSzQWMHz+ep556Cj8/P8uyzMxMmjRpYnmCqMlkonHjxmRkZFjamzZtalm/Zs2a1KpVi8zMTJuPLyIiIsaw6jkZ6enpDB482PJLv0uXLsDFh3D91ZNPPmnTwTdu3MiPP/7IF198wVtvvWVZnp2dXSx0AFStWpVdu3YBcOTIEWrUqFGi/c8//7Tp+ACFhYU2bwPg6OhYqu2k/CptX7lR6msVj736moi1rO2jVoWM9u3b880331BUVERERASLFy+mSpUqlnaTyYS7uzt33XWX1QWeP3+e0aNHM2rUKNzc3Iq15eXl4eLiUmyZi4sL+fn5AJw7d+6a7bbYtm2bzdu4u7sTGBho83ZSvv3666/k5eXd1GOqr1VM9uhrIkaw+omftWrVAmDnzp3AxSCwb98+ioqKqFOnDh4eHjYdOCEhgfvvv9/yzI2/cnV1LREY8vPzLWHkau3u7u421QAQFBSkd4piFX9/f3uXIBWE+prc6goLC616k27zY8UvXLjAxIkT+eijjygoKLi4EycnnnjiCcaMGVNihOFqVqxYQU5ODmFhYQCW0LBq1SoiIyPJyckptn5OTo5liuTSo8wvb69evbqtp4Ojo6NChlhF/URuFvU1uV3YfHfJ+PHjWbt2LdOnT+fHH38kPT2dxMREfvzxR6ZOnWr1fubPn88XX3zBsmXLWLZsGW3atKFNmzYsW7aMkJAQtm7darnmw2w2s2XLFkJCQgAICQlh8+bNln0dOnSIQ4cOWdpFRETE/mweyVi+fDnTpk3jwQcftCxr2bIlrq6uDB06lBEjRli1Hx8fn2KvK1euDEDdunWpWrUqkydPZuzYsXTr1o1FixaRl5dHhw4dAOjevTs9e/YkNDSUoKAgxo4dS6tWrahdu7atpyMiIiIGsXkkw2w2U7Vq1RLLq1SpwtmzZ8ukKA8PDz744AM2b95MVFQUmZmZJCcnU6lSJQDCwsJ4++23SUxMpHv37nh5eZX6QWAiIiJiDJtHMh566CEmTZrEpEmTLBd7njp1iilTphQb3bDVe++9V+x1cHAwn3766VXXj4qKIioqqtTHExEREWNZFTL+85//EBYWhpOTE6+//jq9evWiRYsW1K9fH4C9e/dSu3Ztpk+fbmixIiIiUn5YFTJ69erFd999R9WqVfH29mb58uWsX7+ePXv24OrqSv369Xn44YdxcLB59kVERERuU1aFjMuf7Ons7Ezbtm1p27atIUWJiMjNV1hUiKODbp+tKG7Gz9vqazIuPVJcRERuT44OjoxZNYY/jv9h71LEYPXuqsfox0YbfhyrQ0bnzp2tmg5Zs2bNDRUkIiL288fxP/gt+zd7lyG3CatDxvPPP4+np6eRtYiIiMhtxKqQYTKZePzxx6/4fAwRERGRK7HqdpDLL/wUERERuR6rQsbTTz+Nq6ur0bWIiIjIbcSq6RI9sltERERspadniYiIiCEUMkRERMQQChkiIiJiCIUMERERMYRChoiIiBhCIUNEREQMoZAhIiIihlDIEBEREUMoZIiIiIghFDJERETEEAoZIiIiYgiFDBERETGEQoaIiIgYQiFDREREDKGQISIiIoZQyBARERFDKGSIiIiIIRQyRERExBAKGSIiImIIhQwRERExhEKGiIiIGEIhQ0RERAyhkCEiIiKGUMgQERERQyhkiIiIiCHsGjIOHz5MTEwM4eHhtGjRgnHjxnH+/HkAsrKy6N27N6GhoXTs2JHvvvuu2Lbff/89kZGRhISE0KtXL7KysuxxCiIiInIVdgsZZrOZmJgY8vLyWLhwIVOnTmXt2rW8//77mM1moqOjqVatGkuXLuWpp55i0KBBHDx4EICDBw8SHR1NVFQUS5YsoUqVKgwcOBCz2Wyv0xEREZHLONnrwHv27CEjI4MNGzZQrVo1AGJiYhg/fjyPPvooWVlZLFq0iEqVKnHPPfewceNGli5dyssvv8zixYu5//77eeGFFwAYN24cDz/8MOnp6Tz44IP2OiURERH5C7uNZFSvXp1Zs2ZZAsYlZ86cITMzk8DAQCpVqmRZ3qRJEzIyMgDIzMykadOmljZ3d3caNWpkaRcRERH7s9tIxh133EGLFi0sr4uKiliwYAEPPfQQ2dnZ1KhRo9j6VatW5c8//wS4brstCgsLS1E9ODo6lmo7Kb9K21dulPpaxaO+JjdLafuatdvZLWRcbuLEiezYsYMlS5YwZ84cXFxcirW7uLiQn58PQF5e3jXbbbFt2zabt3F3dycwMNDm7aR8+/XXX8nLy7upx1Rfq5jU1+RmMbqv3RIhY+LEicydO5epU6dy33334erqyokTJ4qtk5+fj5ubGwCurq4lAkV+fj533HGHzccOCgpSeher+Pv727sEqSDU1+RmKW1fKywstOpNut1DxjvvvMPHH3/MxIkTeeyxxwDw9vbm999/L7ZeTk6OZYrE29ubnJycEu0BAQE2H9/R0VEhQ6yifiI3i/qa3CxG9zW7PicjISGBRYsWMWXKFB5//HHL8pCQEH7++WfOnTtnWbZ582ZCQkIs7Zs3b7a05eXlsWPHDku7iIiI2J/dQsbu3btJSkqib9++NGnShOzsbMuf8PBwatasSWxsLLt27SI5OZmffvqJLl26ANC5c2e2bNlCcnIyu3btIjY2Fl9fX92+KiIicguxW8hYs2YNhYWFTJ8+nUceeaTYH0dHR5KSksjOziYqKorPP/+cxMREatWqBYCvry/x8fEsXbqULl26cOLECRITEzGZTPY6HREREbmM3a7J6NevH/369btqe926dVmwYMFV21u2bEnLli2NKE1ERETKgD4gTURERAyhkCEiIiKGUMgQERERQyhkiIiIiCEUMkRERMQQChkiIiJiCIUMERERMYRChoiIiBhCIUNEREQMoZAhIiIihlDIEBEREUMoZIiIiIghFDJERETEEAoZIiIiYgiFDBERETGEQoaIiIgYQiFDREREDKGQISIiIoZQyBARERFDKGSIiIiIIRQyRERExBAKGSIiImIIhQwRERExhEKGiIiIGEIhQ0RERAyhkCEiIiKGUMgQERERQyhkiIiIiCEUMkRERMQQChkiIiJiCIUMERERMYRChoiIiBhCIUNEREQMoZAhIiIihlDIEBEREUOU65Bx/vx5Xn/9dZo2bcojjzzC7Nmz7V2SiIiI/H9O9i7gRkyYMIHt27czd+5cDh48yIgRI6hVqxbt27e3d2kiIiIVXrkNGbm5uSxevJiZM2fSqFEjGjVqxK5du1i4cKFChoiIyC2g3E6X7Ny5k4KCAsLCwizLmjRpQmZmJkVFRXasTERERKAcj2RkZ2dz11134eLiYllWrVo1zp8/z4kTJ6hSpco1tzebzQDk5+fj6Oho8/EdHR25924vXBxNNm8r5Uvd6ndQWFhIYWGhXY7v6OiIY/X7KHJwuf7KUq45Vq1n977mV8UPZwdnuxxfbp66d9a9ob52abtLv0uvptyGjLy8vGIBA7C8zs/Pv+72l0Y7duzYUeoanri3EtxbqdTbS/mRkZFh3wLqPA117FuC3BxZdu5r7au2h6p2LUFukrL4f+16MwflNmS4urqWCBOXXru5uV13eycnJ4KCgnBwcMBk0miEiIiItcxmM0VFRTg5XTtGlNuQ4e3tzfHjxykoKLCcZHZ2Nm5ubtxxxx3X3d7BwaHESIiIiIiUnXJ74WdAQABOTk7Fhns2b95sGZ0QERER+yq3v43d3d3p1KkTb731Fj/99BP/+te/mD17Nr169bJ3aSIiIgKYzNe7NPQWlpeXx1tvvcXXX3+Nh4cHffr0oXfv3vYuS0RERCjnIUNERERuXeV2ukRERERubQoZIiIiYgiFDBERETGEQoaIiFRIuiTReAoZck1/faqq/kGKyO1ET3s2Xrl94qcYLzk5mSNHjtC6dWsefvhhyz/IwsLCUn2onMi1bNu2DWdnZ0wmE/7+/sDFYKtfBFLW1q9fz3//+1+OHTtG3bp1eeqpp+xd0m1Lt7DKFR0/fpx//OMf5OTkUFBQwKOPPspjjz1Gu3bt7F2a3IYmTZrE6tWrAbhw4QI9evSgT58+dq5KbkeTJk1i1apVeHt7c+HCBXJzc0lJSaFGjRr2Lu22pJAhVzVt2jS+/fZbhgwZwtSpU/nzzz+pWbMmAwcOxN/fn7vvvtveJcpt4J///CdpaWnMmjULR0dHdu7cyaRJk5gwYQItW7a0d3lyG1mwYAHJycnEx8cTEBCAi4sLJ0+exNPTUx9HYRB9V6WES7nzxRdf5MCBA2zfvp3U1FSGDh2Kq6srgwcPpm/fvixfvpwDBw7YuVopzyZMmMBnn33GwoULCQ0NJSgoiA4dOtC0aVPLByCK3Ciz2UxhYSH/+c9/6NOnDyEhIZYPyPTy8ioWMPLy8uxV5m1JIUNKMJlMFBYWUrlyZfr27cvnn3/Ozz//zJNPPsm8efOoXr06Bw8eZOjQoQwePJhRo0Zx/vx5e5ct5czBgweZPXs2/fv3x9/fn8LCQgA8PDw4e/YsKSkpPP744wwePJj169fbuVopz0wmE6dPn2bLli3Uq1cPuPKF7CtWrCA5OfkmV3d7U8iQK7p0YWfz5s05fvw4GzZsACA2NpazZ8/y4YcfkpycTI0aNVi/fj3Z2dn2LFfKoVq1apGcnMz06dP56quvLO8mk5OTycjIoF27dvTu3ZtffvmFxMREjh8/bueKpTzz8vLCy8uLvXv3Ale+s6RKlSpMnz6drVu33uzyblu6u0SuqWHDhvTq1Yvk5GQ2bdrEb7/9RnJyMo0aNQKgSZMmAFSuXNmeZUo59eijjzJ58mRiYmKoVq0aW7ZsISUlhfj4eFq0aAHAI488Qrt27fjPf/7D3/72NztXLOWR2WwmPz8fX19fNm7cSOfOnfH09CyxXqNGjfD29ubo0aN2qPL2pJEMua5WrVpx5513smfPHpKSkiwBAy6GCwUMuRGPPvoo06ZN47nnniMxMZFJkybRokULzGYzBQUFuLi4EBAQcMVfCiLWMJlMuLq6MnDgQDZs2MDMmTOLtRcVFQEX76rz8vKiatWq9ijztqSQIdd133330apVK/Ly8vD19QX+949SpCy0bNmSuXPncv78eQoLCykoKMBkMuHk5MQnn3zC6dOnadCggb3LlHIuODiYUaNGMWvWLN577z0OHz4MYJmqW7x4MWazmTp16tizzNuKbmGVa7r0MKRjx47RtWtX2rRpQ1xcnL3LktvUunXreOWVV5g4cSLt2rUjPj6emTNn8vHHHxcbQRMpraKiIr744gtGjx5NYGAgAQEBeHl5kZWVxbfffsuHH35IQECAvcu8bShkiFUuXLjAsGHDOHXqFImJibi7u9u7JLlNrVu3jmHDhtG4cWM2bdrE/Pnzuf/+++1dltxmdu/ezeLFi9m2bRsODg74+/vz7LPPasSsjClkiNX27dsHQN26de1cidzu1q1bR//+/UlLSyMwMNDe5UgFoEfYG0MhQ0RuSXl5eRoxE0P9NVgoZBhDIUNEREQMobtLRERExBAKGSIiImIIhQwRERExhEKGiIiIGEIhQ0RERAyhkCEiIiKGUMgQERERQ+ij3kWEkydPMn36dL7++muOHj1KrVq1eOaZZ+jVq5flw6NuxJdffkl4ePgt+emWI0eOBOC9994DwN/f39JmMpmoXLkywcHBvPrqqwQHB9ulRpHySiFDpII7fvw4zzzzDDVq1GDs2LH4+vqybds23nnnHbKysnjzzTdvaP8HDhxg8ODBrFmzpowqNl58fDxhYWEUFRVx9OhR5s6dyz/+8Q9SU1O577777F2eSLmhkCFSwU2ePBkXFxdSUlJwdXUFoHbt2ri5uTFw4ECee+456tevX+r9l8eHCnt5eVG9enUAvL29ee+999i/fz9TpkxhxowZdq5OpPzQNRkiFVh+fj4rVqygR48eloBxSevWrZkzZw4+Pj7AxWmETZs2WdrT0tJo06aN5fWUKVN45JFHCA4OpmfPnuzatQuAtm3bWv5OS0sDYO3atTz99NMEBwfTsWNHvv76a8t+evbsSUpKCs8//zzBwcF06dKFffv28eabbxIWFsbf/vY30tPTLev/9ttv9OzZk+DgYB577DEWLlxoaYuPj2fgwIH06NGD8PDwYtvZwmQy0bVrV7777jvOnTtXqn2IVEQKGSIV2H//+19yc3MJCgoq0WYymXjooYdwcXG57n5Wr15Namoq77//PsuXL6datWrExsYCsHjxYsvfHTt2ZOPGjbz88ss89dRTfPbZZ3Tt2pVXX32V7du3W/aXmJjI3//+d9LS0jh9+jRdunShWrVqLFmyhHvvvZd3330XgHPnztG3b1+aNGnC559/zogRI0hKSmLZsmWWfa1Zs4bIyEjmzp17Q9dU+Pn5ceHCBf74449S70OkotF0iUgFdurUKQA8PT1vaD8HDhzA2dmZWrVqUatWLd5880327NkDQJUqVSx/u7m5sXDhQh577DF69+4NQP369fnpp5+YPXs2U6ZMAS6OonTo0AGAiIgIVq5cSUxMDCaTib///e9ER0cD8MUXX1C1alUGDx4MQL169Thw4ADz5s2jU6dOAFSrVo3u3bvf0PnB/75HZ8+eveF9iVQUChkiFdidd94JXLy75EY8/vjjLFiwgLZt2xIaGkpERARdunS54rq7d++mW7duxZaFhYWxdOlSy2tfX1/L125ubtSqVcvyMdxubm5cuHABgD179rBz507CwsIs6xcWFuLo6Gh5fWm650adOXMGAA8PjzLZn0hFoJAhUoHVqVMHT09Pfv755ytOJQwYMICePXvSvHnzEm2FhYWWr6tXr86XX37Jhg0bWLt2LSkpKXzyySfFpi0uufzaD4CioiKKioosr52civ/XdLXbaAsKCmjWrBmjRo266jle6Xil8euvv+Ls7Ey9evXKZH8iFYGuyRCpwJycnOjYsSMLFy4kPz+/WNs333zDN998Q40aNQBwdnYuNlWQlZVl+frf//43ixcvplWrVowZM4bPPvuMP/74g99++80yAnFJ/fr1yczMLLZs69atpbqDpX79+uzduxdfX1/q1q1L3bp1ycjIYP78+Tbv63ouXehaVqFFpCJQyBCp4F5++WXOnDlDnz59SE9P57///S+LFy9m5MiR9OrVCz8/PwCCgoJYsGABf/zxB2vWrLHcKQIXRyImTJjA6tWr2b9/P2lpabi7u1OvXj3c3d0B2LlzJ2fPnqV3796sWrWKuXPn8scffzBnzhxWr15dqusmnnzySc6dO8eoUaPYvXs369atY+zYsTf80K+TJ0+SnZ3N4cOH+fnnnxkyZAg///yz5doPEbGOpktEKrjq1avz8ccfEx8fz9ChQzlx4gR16tQhJiam2C/+N998kzfeeIPIyEiCgoKIiYmxPDOiTZs2xMTEMG7cOLKzs2nQoAFJSUl4eXkBF8PA4MGDGTp0KL1792bChAnEx8czceJE6tevz/vvv0+zZs1srt3Dw4OZM2fyz3/+k06dOnHnnXfSo0cP+vfvf0Pfk5dffhm4OE1TtWpVGjduzKJFi2jQoMEN7VekojGZy+OTckREROSWp+kSERERMYRChoiIiBhCIUNEREQMoZAhIiIihlDIEBEREUMoZIiIiIghFDJERETEEAoZIiIiYgiFDBERETGEQoaIiIgYQiFDREREDPH/AGAhsyHwG0/rAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize= (6,3))\n",
    "\n",
    "# Assuming you have a DataFrame named 'customer_points' with columns 'customer_id' and 'total_points'\n",
    "\n",
    "# Create a bar plot\n",
    "sns.barplot(x='customer_id', y='total_points', data=Total_points)\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel('Customer ID')\n",
    "plt.ylabel('Total Points')\n",
    "plt.title('Total Points of Customers')\n",
    "\n",
    "# Add some styling\n",
    "sns.set_style('whitegrid')  # Set the style of the plot\n",
    "plt.xticks(rotation=45)  # Rotate the x-axis labels if needed\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "10. In the first week after a customer joins the program (including their join date) they earn 2x points on all items, \n",
    "not just sushi - how many points do customer A and B have at the end of January?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "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>customer_id</th>\n",
       "      <th>Total_Points</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  customer_id  Total_Points\n",
       "0           A           152\n",
       "1           B           124"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Total_points=pd.read_sql_query('''SELECT s.customer_id,SUM(CASE WHEN join_date<= DATEADD(DAY,6,join_date) THEN 2*m.price ELSE m.price END)as Total_Points\n",
    "FROM sales s\n",
    "JOIN menu m\n",
    "ON s.product_id =m.product_id\n",
    "JOIN members mm\n",
    "ON s.customer_id=mm.customer_id\n",
    "WHERE YEAR(order_date)= 2021 AND MONTH(order_date)=1\n",
    "GROUP BY s.customer_id''',conn)\n",
    "\n",
    "Total_points\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4,3))\n",
    "sns.barplot(data=Total_points, x='customer_id', y='Total_Points',width=0.5)\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel('Customer ID')\n",
    "plt.ylabel('Total Points')\n",
    "plt.title('Total Points of Customers')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.1"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}