assets/scripts/Scenario_9.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pymysql #Allows the connection to mysql\n",
    "import pandas as pd #We need pandas to obtain a Data Frame\n",
    "import getpass # function to prompt a cell to write down our password\n",
    "from sqlalchemy import create_engine #Method to produce an engine based on a URL, that includes, host, user....\n",
    "import matplotlib.pyplot as plt #Importing this library for plot generation\n",
    "import pylab #It allows to manipulate matplotlib labels\n",
    "import warnings #It helps to get rid of annoying warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please enter your MySQL password········\n"
     ]
    }
   ],
   "source": [
    "user = getpass.getuser()\n",
    "password = getpass.getpass(prompt=\"Please enter your MySQL password\",stream=None)\n",
    "engine = create_engine('mysql+pymysql://{}:{}@localhost:3306/classicmodels'.format(user,password))\n",
    "connection = pymysql.connect(host='localhost',user=user,password=password,db='classicmodels')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=\"\"\"SELECT \n",
    "        CONCAT(firstName, ' ', lastName) AS employee,\n",
    "        SUM(quantityOrdered*priceEach) AS totalSales\n",
    "FROM    orderdetails JOIN orders USING (orderNumber)\n",
    "        JOIN customers USING (customerNumber)\n",
    "        JOIN employees ON\n",
    "        customers.salesRepEmployeeNumber = employees.employeeNumber\n",
    "GROUP BY    salesRepEmployeeNumber\n",
    "ORDER BY    totalSales DESC;\"\"\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "salesRep=pd.read_sql_query(a,engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>employee</th>\n",
       "      <th>totalSales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gerard Hernandez</td>\n",
       "      <td>1258577.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Leslie Jennings</td>\n",
       "      <td>1081530.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Pamela Castillo</td>\n",
       "      <td>868220.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Larry Bott</td>\n",
       "      <td>732096.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Barry Jones</td>\n",
       "      <td>704853.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>George Vanauf</td>\n",
       "      <td>669377.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Peter Marsh</td>\n",
       "      <td>584593.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Loui Bondur</td>\n",
       "      <td>569485.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Andy Fixter</td>\n",
       "      <td>562582.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Steve Patterson</td>\n",
       "      <td>505875.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Foon Yue Tseng</td>\n",
       "      <td>488212.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Mami Nishi</td>\n",
       "      <td>457110.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Martin Gerard</td>\n",
       "      <td>387477.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Julie Firrelli</td>\n",
       "      <td>386663.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Leslie Thompson</td>\n",
       "      <td>347533.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            employee  totalSales\n",
       "0   Gerard Hernandez  1258577.81\n",
       "1    Leslie Jennings  1081530.54\n",
       "2    Pamela Castillo   868220.55\n",
       "3         Larry Bott   732096.79\n",
       "4        Barry Jones   704853.91\n",
       "5      George Vanauf   669377.05\n",
       "6        Peter Marsh   584593.76\n",
       "7        Loui Bondur   569485.75\n",
       "8        Andy Fixter   562582.59\n",
       "9    Steve Patterson   505875.42\n",
       "10    Foon Yue Tseng   488212.67\n",
       "11        Mami Nishi   457110.07\n",
       "12     Martin Gerard   387477.47\n",
       "13    Julie Firrelli   386663.20\n",
       "14   Leslie Thompson   347533.03"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(salesRep, columns=['employee', 'totalSales' ])#Creating the data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the scatter chart for above dataframe\n",
    "salesRep.plot.scatter(x='employee',y='totalSales',s=100)\n",
    "ax = plt.gca()\n",
    "ax.tick_params(axis='x', labelrotation = 45)\n",
    "current_values = plt.gca().get_yticks()\n",
    "warnings.filterwarnings('ignore')\n",
    "plt.gca().set_yticklabels(['{:.0f}'.format(x) for x in current_values]);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}