Advanced Python Practical Database Examples_2.ipynb

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   "source": [
    "# LinkedIn Learning\n",
    "# Advanced Python Practical Database Examples_2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e37962b8-fbf2-4a35-92b5-2d1e21e21933",
   "metadata": {},
   "source": [
    "## 2. Developing Analysis Applications with Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf359c62-1c20-47f7-bbe1-143bab59fab3",
   "metadata": {},
   "source": [
    "### A. Setting Up Pandas and Jupyter Notebook"
   ]
  },
  {
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    }
   },
   "cell_type": "markdown",
   "id": "77e980de-e680-4a47-ba16-391636f0143d",
   "metadata": {},
   "source": [
    "![image.png](attachment:0a755046-43fb-4a51-92a2-9ddf665c6620.png)"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "![image.png](attachment:befbbce2-9647-44ac-84ad-f9b6ad4d8c80.png)"
   ]
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59PeU8rE0daGp04WOUQrKfGnO2XLHDU9uvhyVj2mrzV5jnC/veGiYtl10aS9zTjNuzylpVvbBtO3E+4X2mLJm46onvz0r8q5sq67OD22aZzgfK3K8yGtjhcZeB+NGVt5Dmu9f+W0ju96dfpdRtjFNe3ZC01Zz0qMuH2W+cscozXY046HNe/65aK687VRbch9817oK0+il76QzkJ3zp/GxQJEvF9/j1DTlozzueIWQe66Vm2ZNvXtyxx+br9navKXOe46X36kX9f+YL6nBLD/t2Ks5/xkx6U87z1Tmy019OTrmWos7n88/jzJy0qMp55z0FBl7nYx1noXVe9S0tqrYl+o8XFM+rvq70f9lYwSHaes2e2lRXc/05PV3zbihPabkUW0np76KnK+6OMbllU+R9ORyOrb4qvuHhespJqsdpsq/dqrjajvZZi4fIGEU9PUHX39gNoPIdeyOhKF0r5IPyU7ehRB+5kfk7+bPDGLhrHu/g7zIbzv+atLsa5y27ImpiNeLD73PV7/Itv2/od+OeS7y42gJAP+ZGIN5rK/qDb7xi26z7WvmMsxNj6ZtBH9z9+B9OtQX80iIcf0o/Xn1arImmeOx4zLMlJee0cHU//K2dXQz+v/bfZG8a+pLUxdj4UlDvKyKcdUvsvupz+5rVGb+l5Zx3gu2MVMv5gTQnETFTla9MjxIv/PSvAqetxXWP2umn+hETrabvZq0fxTsNwW4qNPRsiyxNl6VVmvcekOadhguh1L6BCl33FCOh4Hp46pHM0YFMvPlaOydzMNQHu68ftb4Ns7rIsfMwPS8a/KlrK8CdWG/OIdf3kftX8u/K7VxHf2yVZfLa68QE8uoZY9jYzO3eSV1f59aPpq60NSpnqvjTjZlGyvQdzLHDY+rfGVvJ63/+0tavUQToxRvzylpVvbB5HaS5VzsmLJe42pevefnfUl9cOo4FuTBxTm/J7uNFRx7XfWvqXkfy+zvirZRpM3PSrWvhZ0nKNuqq/QU6aeRdljqWOD4XHQ65XZM4NfrK4POiddX+unP0FOWz+zf4zSU+XJ1Hu7JTXOR9pNxXHbW5ouc907x+auXx5A59xvNYhzK75fworh+7DXyzn+srPPwUucS5erLzTF3bKHn81ljVCAzPQ7aj46rsc63mHqPyGqrufvSnIe7H+vyDE3n9kbtL9Gkh7w8hdcvNdczp/d3/bihOaZo5G5HM64q5bbDXMXG1UXRji1WdV9Mtst8pzQy22GKqddOC3K1nVwzlg+QNAr6mo5q78y4OZL399vxHZ2Wf1eDuTMlGtzYvXgOfu8ZPog5Nm1re58DW1tH8m082nkdxPtiMgpWK9Ls2draiR0EJ9Ov2461wGcXJJf7KL78ms9FGRqZ6VG2jfr3ttQGHRlNjLB38TTi6dMIlqUxy/NmLbniqgxzKdMzu9Xspy76RX4/DST2Vf1iTtnGXxoLtzHbH679u9VGy6F4J/f3/p2Jaa/keb1Lps2adpr6pT9ysv32eCh3e/4SMnPhoE7tneDRLzMz80/ASz+LPref6sfD7HHVU2hMyM6Xi7HX/7IV+XJv/y6x2odnYWPmyJS8q/KlrC9lXdgb43YvSgZ8PcFdqRNfuj9/9d5NPH87Zxwbm7HNayn6e3b5LOHY5GCMyufoHCmQO24YrvKVuZ2UixD9n3Lx/F6ufvLSrB0PE9tJ9otix5Q1G1dzyjA/78vvgzOfj2XJKp8Zxt7ScvJuaPp7Xttwfx41nXZfCzlPKNBWXaWnTD9Nq/fc7Tg8F81WYDve94vrhknTse2jac/Qy8uXq+9x+ZT5cnge7qTejazxx2WbV+V9OHGDc/SzNo+DV6+0TdLu5Pn9eXI54AJjr2Y8zD0Pd3QuYeSVoZNjbsxyzuen96+M9BTqO7PQlmF2Wx1ZSL37NN8Zs/elOQ+fx1inUPsqyS5dVGZ/V44b6mNKDu12VGO4hroPTlHknHaRSuSrTNB8RNkOTb2Zupr1hlZX2ylipvIBIiaXd/ZO7M3dOWlLepg7cqLBDRtcKLs8xIK4SvMq5d0MOrHlPrzXxPI+DuXl3Vl6grtawhPM/i/vkBa7e00rDMo9TT3JWWwZ5qfHpXXspwtTtI3Zu5eb0qu15XHJZWiWcjEnG7El1abxTp5PvS9r8Vk0q8S/E9w1exG59F2Oun7qpn8VGxOm5cvd2Ou3l7Bf2CVIa4cSvZFx0ced0Gx1qqkvXV3YG+PMTXG3x/O7maKEWcvHFU35bOqxaWHnSAtmbgA9Di9wHN/afJa53yHfLOdI4Z3exY8pmzOu6vO+Un3Q2Tn/ZspvG/M5j0qn29eqjXWu0jPbdsazUXTbWeS5qHY72fW/avVu5OfLzXm4xiLLx10bCz8z3k5seVobYPAv6P95FTm/ORex3zn9QFTZAEyW/PPMWc4lxvLLcD7H3OWdz6fPmJueHjflrKXvy9HfJ26YVHBZ73ltVTsmaM7DXY91uYKbPT6SRY7hmyV7Nu5MyyQr2mGha6cZXG2nqHkvI42PI/WZvvVvjcRs389iVnEJv5inCpZBiE9D95fqyjavL62KNKu42o4rQZku5M5uTd4V6VG3De+kxL9C5v3W/D7y3LNS4ic542erLbIMo6alx5V59tNFcjcmTC7/UaCNmeWSw7s0Y8vSTN5RGn3NZXlnLy3mzk2zVM4iTzbmpyr2BsBpy76UZS8iTz4ncFJWG5vWT+dxLFCOCan5UoxjBfq7Oe779eEvKRVfYnNZY6YnLe+qfBWtL0Vd1C/tc4fMXdWF+/m0O3DtslvZs9sylzFSt/kFmFo+m3JsSnJ0jrRy/DEgeSFpPgHfqGLnSLZfjO70LnFM2ZhxVZP35ffBmc7HZjHD2OtK0aXodG1jTudRqTT7ctWeFVRt1VV6ym8nPkYV3c68z0X12xl2T6QzMBe8b7we05Nm7JE386334n2naPnMch6eLp5mR+UzlzZf7JgbE6TH7vPlUPbrXvnInfjFE8xCm8fYqzoPL3EuUai+HB1zk5ZwPh8foxJy0zND+1FxNdalm0+9R0xtq5p9ac7D5zTWZSg8K7WMFThniys6rhZT+Bi3cuWTLnNsmXFlIVU7dHXtdBnXYF2vvIQPLzXoK3X/mQHjWWL+EhPmbqXpJ1j+MiDjvzEPum/K7XvWUhUmeLJnl6pwT5NmDVfbCQQnyeVPYPwDvHfWP/rCZe4+MnefuKfJuyY9mrYRqH+zXyiPPx1Lz9nB1b/rdHy3TIEynLm+0iTT44qrfjpm7mwygcz5zWyb45gw7MpJZzA5c0TRxmy+gzsqJ5flCU+a4yfi4avMhXHT/kw5V9IqzgSfvbSYkw31srLe35j2HL/AvFrsHcTm4lEsz179dyfLQN8O/fYUX6mibBtL9lNN/yorb0xIy9d8xt5fXXOimfzS4Pi4U2hcTcu7/nyjeH1l14V5vrS5u/f2OOUGj6x8hbP+muEy8YaX7mb+agOp49hIWvksT3r5aOqi2LFpNWjytchzNrfcnvcUoThHCvpF9BhX5JjiS+s76zmu5ud9yX1whvOxmc/Dy469ruSO4Wl0bUPd5h18l8nf1yLHOk1bLZCezPIpma+JMaps+czrXFS5neCCp//8QP+5gWbp0pPExIC51HupvlO2fMqch6eYSLOr8nHc5mMUx9wJZl8DuTu5kpfDfa90TPmIdMz/vd5kr1XPaezNPA+P0Z9LFK0vN8fcJGUbc2VijErSpqdM+wlkjr1lylBpbvUel95W9f00+5hdtnxmqC97zjbnGxOWfc42ocC4WvRcq8wxrkj5ODj3KyVnbLErH3ktqXSAOq8dFrh2aurSxTXYzO0UNHP5AAnpQV+ve5q7r81JfXgTkDlombuVzEHLNOjwFW3Y9ctHaUtHdu3vjkWu3yaWPqi2fki7Zr7gm8/syt3ho91uVHiB3/zeXJg3BzO7r4JLUWjSrOFqOz7vC9Ojd8LQa463M3pWqIZXNz+8vx+E5fxJrr4uswx16UlrG8nP+PwHv5vnKySfe6Y1znv4OpaX9mPk7hxdmn3Z9aUp5/z06Gj2Vbafpud9MTRjglZYJva1eyHbXt4myzmnjdkTFZHzp9lmNoUH//w+mDXr1PsicWVuL0zkLXiF1Trel/+qNMUuQVM0/a7GDRXvpNU+LznWt05E9mc7w6m2Tu1F5Oj5bbHjTvia7KeujgVlxoTJfM1n7L29uBCvY3j/i3J93Cl2HEyrU02+NPVVpi7Mvs2NcWa78arPzlf98k2uG2E7nL4v3Tg2llY+mrrQ11cxaeXj6tg0rzSn0ezL1TmSK27Kx0uz+Q4Q/G30VXSs0xinOXwl+8X4wtboM0G/iB3jShxT1mFcVVHkfdHnh2n1NTmOac75Z/3eZPKlG3td0eU9i7JtqNv87N9l8velS7OrMTy/rRbpX1nlo9mOZowqWj7ha7KtujoXzd1O5ILnKB/BY8DGs9eKlHO+vL6jaT+a8tGUc1LauZaRnWZ35eOqzZfJ+yR/1uNgIHIYPLPAPsfee2MQmd01r7HXlEXyPFObLyf15eiYmzS/83nleVRCdnrC12Q5a9Lsyz42uRrrjEXVe9JkW9Xsy/uM4jx8XmPddMHNJ3O+MWFe40Y5yrZh5Z+vZrdDXd/Rl8/s5375nyk6tviz2GebOZ3VDnXXTn2zX4P1Za9Sqb8mbLgoHyBu67///su7/W8mppE3ZfpD7IEkc3DZuzuUx9iSukAW78B8sCedbd1YQxvbbPa489LeuPrd1HxpfIy8FxvHoj5y24Bjw64c7HVEkhcQgvfzLhKuG/qOax/5fKz8GA58bOvRd+LHC/r7elnHNrZ863+OtIb9dKXPw/1VBnoNxr1iOF4Y9py/sy3XU55trreZ7dBd+WAT/PPPP/Lvv/8G/ytvykxfYEn6Z95A553j/ODiG+aENrbx7PJLg/DZUptjU/Ol8ZHzrkH5YN785aaCZ/VtEPrOEnE+BmCNcLzAvK1aG6PNr47VOA8fzx6d3+PfsJHCc/5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    }
   },
   "cell_type": "markdown",
   "id": "9f5a0c97-c4c9-4548-8b4a-0d93a8b77ce2",
   "metadata": {},
   "source": [
    "![image.png](attachment:4dd73f61-b85b-4449-ac83-70da601416dd.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "503d11cf-e835-4c26-8be1-aeca75dd1f37",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello World\n"
     ]
    }
   ],
   "source": [
    "print(\"Hello World\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2455a237-d8e4-4fc9-a755-a2538af28e07",
   "metadata": {},
   "source": [
    "### B. Analyze Data with Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9ab12d6d-5649-474f-b683-0a2d67298563",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4e57b478-e458-4777-8e84-daee02554c9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel('red_30.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "84b58472-d352-4222-b816-364e358af641",
   "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>OrderNum</th>\n",
       "      <th>OrderDate</th>\n",
       "      <th>OrderType</th>\n",
       "      <th>CustomerType</th>\n",
       "      <th>CustName</th>\n",
       "      <th>CustState</th>\n",
       "      <th>ProdCategory</th>\n",
       "      <th>ProdNumber</th>\n",
       "      <th>ProdName</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Price</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Order Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1100934</td>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>Wholesale</td>\n",
       "      <td>Business</td>\n",
       "      <td>Gusikowski Group</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>Blueprints</td>\n",
       "      <td>BP102</td>\n",
       "      <td>Bsquare Robot Blueprint</td>\n",
       "      <td>10</td>\n",
       "      <td>8.99</td>\n",
       "      <td>1.798</td>\n",
       "      <td>88.102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1100935</td>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Spencer Educators</td>\n",
       "      <td>Delaware</td>\n",
       "      <td>Drone Kits</td>\n",
       "      <td>DK204</td>\n",
       "      <td>BYOD-300</td>\n",
       "      <td>2</td>\n",
       "      <td>89.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>178.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1100936</td>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>Wholesale</td>\n",
       "      <td>Business</td>\n",
       "      <td>Schinner Inc.</td>\n",
       "      <td>Florida</td>\n",
       "      <td>Training Videos</td>\n",
       "      <td>TV801</td>\n",
       "      <td>Aerial Security</td>\n",
       "      <td>10</td>\n",
       "      <td>36.99</td>\n",
       "      <td>7.398</td>\n",
       "      <td>362.502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1100937</td>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Saxon Laviss</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>Robot Kits</td>\n",
       "      <td>RK602</td>\n",
       "      <td>BYOR-1000</td>\n",
       "      <td>1</td>\n",
       "      <td>189.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>189.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1100938</td>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Business</td>\n",
       "      <td>Wilderman Technologies</td>\n",
       "      <td>Texas</td>\n",
       "      <td>eBooks</td>\n",
       "      <td>EB502</td>\n",
       "      <td>Building Your First Robot</td>\n",
       "      <td>4</td>\n",
       "      <td>24.95</td>\n",
       "      <td>0.000</td>\n",
       "      <td>99.800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   OrderNum  OrderDate  OrderType CustomerType                CustName  \\\n",
       "0   1100934 2017-09-01  Wholesale     Business        Gusikowski Group   \n",
       "1   1100935 2017-09-01     Retail   Individual       Spencer Educators   \n",
       "2   1100936 2017-09-01  Wholesale     Business           Schinner Inc.   \n",
       "3   1100937 2017-09-01     Retail   Individual            Saxon Laviss   \n",
       "4   1100938 2017-09-01     Retail     Business  Wilderman Technologies   \n",
       "\n",
       "        CustState     ProdCategory ProdNumber                   ProdName  \\\n",
       "0  North Carolina       Blueprints      BP102    Bsquare Robot Blueprint   \n",
       "1        Delaware       Drone Kits      DK204                   BYOD-300   \n",
       "2         Florida  Training Videos      TV801            Aerial Security   \n",
       "3        Virginia       Robot Kits      RK602                  BYOR-1000   \n",
       "4           Texas           eBooks      EB502  Building Your First Robot   \n",
       "\n",
       "   Quantity   Price  Discount  Order Total  \n",
       "0        10    8.99     1.798       88.102  \n",
       "1         2   89.00     0.000      178.000  \n",
       "2        10   36.99     7.398      362.502  \n",
       "3         1  189.00     0.000      189.000  \n",
       "4         4   24.95     0.000       99.800  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "82f45210-1f30-4dda-82f0-de0c66a93c11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OrderNum</th>\n",
       "      <th>OrderDate</th>\n",
       "      <th>OrderType</th>\n",
       "      <th>CustomerType</th>\n",
       "      <th>CustName</th>\n",
       "      <th>CustState</th>\n",
       "      <th>ProdCategory</th>\n",
       "      <th>ProdNumber</th>\n",
       "      <th>ProdName</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Price</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Order Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4971</th>\n",
       "      <td>1105905</td>\n",
       "      <td>2019-08-29</td>\n",
       "      <td>Wholesale</td>\n",
       "      <td>Business</td>\n",
       "      <td>Rau-Dickinson</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>eBooks</td>\n",
       "      <td>EB503</td>\n",
       "      <td>Building Your Own Drone</td>\n",
       "      <td>5</td>\n",
       "      <td>24.99</td>\n",
       "      <td>4.998</td>\n",
       "      <td>119.952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4972</th>\n",
       "      <td>1105906</td>\n",
       "      <td>2019-08-29</td>\n",
       "      <td>Wholesale</td>\n",
       "      <td>Business</td>\n",
       "      <td>Reichel and Sons</td>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>eBooks</td>\n",
       "      <td>EB502</td>\n",
       "      <td>Building Your First Robot</td>\n",
       "      <td>5</td>\n",
       "      <td>24.95</td>\n",
       "      <td>4.990</td>\n",
       "      <td>119.760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4973</th>\n",
       "      <td>1105907</td>\n",
       "      <td>2019-08-29</td>\n",
       "      <td>Wholesale</td>\n",
       "      <td>Business</td>\n",
       "      <td>Wolff LLC</td>\n",
       "      <td>South Carolina</td>\n",
       "      <td>Blueprints</td>\n",
       "      <td>BP104</td>\n",
       "      <td>Cat Robot Blueprint</td>\n",
       "      <td>10</td>\n",
       "      <td>4.99</td>\n",
       "      <td>0.998</td>\n",
       "      <td>48.902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4974</th>\n",
       "      <td>1105908</td>\n",
       "      <td>2019-08-29</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Business</td>\n",
       "      <td>McClure Inc.</td>\n",
       "      <td>Utah</td>\n",
       "      <td>Drone Kits</td>\n",
       "      <td>DK209</td>\n",
       "      <td>BYOD-550</td>\n",
       "      <td>64</td>\n",
       "      <td>179.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>11456.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4975</th>\n",
       "      <td>1105909</td>\n",
       "      <td>2019-08-30</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Gussi Strodder</td>\n",
       "      <td>New York</td>\n",
       "      <td>Drone Kits</td>\n",
       "      <td>DK204</td>\n",
       "      <td>BYOD-300</td>\n",
       "      <td>1</td>\n",
       "      <td>89.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>89.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      OrderNum  OrderDate  OrderType CustomerType          CustName  \\\n",
       "4971   1105905 2019-08-29  Wholesale     Business     Rau-Dickinson   \n",
       "4972   1105906 2019-08-29  Wholesale     Business  Reichel and Sons   \n",
       "4973   1105907 2019-08-29  Wholesale     Business         Wolff LLC   \n",
       "4974   1105908 2019-08-29     Retail     Business      McClure Inc.   \n",
       "4975   1105909 2019-08-30     Retail   Individual    Gussi Strodder   \n",
       "\n",
       "           CustState ProdCategory ProdNumber                   ProdName  \\\n",
       "4971        Kentucky       eBooks      EB503    Building Your Own Drone   \n",
       "4972        Oklahoma       eBooks      EB502  Building Your First Robot   \n",
       "4973  South Carolina   Blueprints      BP104        Cat Robot Blueprint   \n",
       "4974            Utah   Drone Kits      DK209                   BYOD-550   \n",
       "4975        New York   Drone Kits      DK204                   BYOD-300   \n",
       "\n",
       "      Quantity   Price  Discount  Order Total  \n",
       "4971         5   24.99     4.998      119.952  \n",
       "4972         5   24.95     4.990      119.760  \n",
       "4973        10    4.99     0.998       48.902  \n",
       "4974        64  179.00     0.000    11456.000  \n",
       "4975         1   89.00     0.000       89.000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f94d18f7-f51c-45ba-a447-dac854cba8dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4976 entries, 0 to 4975\n",
      "Data columns (total 13 columns):\n",
      " #   Column        Non-Null Count  Dtype         \n",
      "---  ------        --------------  -----         \n",
      " 0   OrderNum      4976 non-null   int64         \n",
      " 1   OrderDate     4976 non-null   datetime64[ns]\n",
      " 2   OrderType     4976 non-null   object        \n",
      " 3   CustomerType  4976 non-null   object        \n",
      " 4   CustName      4976 non-null   object        \n",
      " 5   CustState     4976 non-null   object        \n",
      " 6   ProdCategory  4976 non-null   object        \n",
      " 7   ProdNumber    4976 non-null   object        \n",
      " 8   ProdName      4976 non-null   object        \n",
      " 9   Quantity      4976 non-null   int64         \n",
      " 10  Price         4976 non-null   float64       \n",
      " 11  Discount      4976 non-null   float64       \n",
      " 12  Order Total   4976 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(3), int64(2), object(7)\n",
      "memory usage: 505.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4e07a606-fee1-44f6-8b92-f4127c69af17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>OrderNum</th>\n",
       "      <th>OrderDate</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Price</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Order Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.976000e+03</td>\n",
       "      <td>4976</td>\n",
       "      <td>4976.000000</td>\n",
       "      <td>4976.000000</td>\n",
       "      <td>4976.000000</td>\n",
       "      <td>4976.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.103422e+06</td>\n",
       "      <td>2018-08-28 14:31:03.665594880</td>\n",
       "      <td>11.107516</td>\n",
       "      <td>126.849068</td>\n",
       "      <td>14.369103</td>\n",
       "      <td>1386.502483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.100934e+06</td>\n",
       "      <td>2017-09-01 00:00:00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.990000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.990000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.102178e+06</td>\n",
       "      <td>2018-03-03 00:00:00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>14.990000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>42.990000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.103422e+06</td>\n",
       "      <td>2018-08-23 00:00:00</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>31.470000</td>\n",
       "      <td>2.198000</td>\n",
       "      <td>179.036000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.104665e+06</td>\n",
       "      <td>2019-02-26 00:00:00</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>179.000000</td>\n",
       "      <td>8.598000</td>\n",
       "      <td>691.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.105909e+06</td>\n",
       "      <td>2019-08-30 00:00:00</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>899.000000</td>\n",
       "      <td>179.800000</td>\n",
       "      <td>83708.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.436592e+03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.367634</td>\n",
       "      <td>200.362183</td>\n",
       "      <td>32.763964</td>\n",
       "      <td>5107.252411</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           OrderNum                      OrderDate     Quantity        Price  \\\n",
       "count  4.976000e+03                           4976  4976.000000  4976.000000   \n",
       "mean   1.103422e+06  2018-08-28 14:31:03.665594880    11.107516   126.849068   \n",
       "min    1.100934e+06            2017-09-01 00:00:00     1.000000     4.990000   \n",
       "25%    1.102178e+06            2018-03-03 00:00:00     1.000000    14.990000   \n",
       "50%    1.103422e+06            2018-08-23 00:00:00     5.000000    31.470000   \n",
       "75%    1.104665e+06            2019-02-26 00:00:00    10.000000   179.000000   \n",
       "max    1.105909e+06            2019-08-30 00:00:00   109.000000   899.000000   \n",
       "std    1.436592e+03                            NaN    20.367634   200.362183   \n",
       "\n",
       "          Discount   Order Total  \n",
       "count  4976.000000   4976.000000  \n",
       "mean     14.369103   1386.502483  \n",
       "min       0.000000      4.990000  \n",
       "25%       0.000000     42.990000  \n",
       "50%       2.198000    179.036000  \n",
       "75%       8.598000    691.200000  \n",
       "max     179.800000  83708.400000  \n",
       "std      32.763964   5107.252411  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7702d55b-9073-4196-bc08-9f6559aec246",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ProdCategory\n",
       "eBooks             1296\n",
       "Training Videos     992\n",
       "Blueprints          912\n",
       "Drone Kits          552\n",
       "Robot Kits          480\n",
       "Drones              408\n",
       "Robots              336\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ProdCategory'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "578ecbdb-40d1-4bae-82a2-687cebfc200b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderType\n",
       "Wholesale    2833\n",
       "Retail       2143\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['OrderType'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d9d92a8d-d69c-4346-b884-f4da8aae757f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     4976.000000\n",
       "mean      1386.502483\n",
       "std       5107.252411\n",
       "min          4.990000\n",
       "25%         42.990000\n",
       "50%        179.036000\n",
       "75%        691.200000\n",
       "max      83708.400000\n",
       "Name: Order Total, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Order Total'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dd63cc45-620e-442b-acd6-bac1b125bf48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Order Total\n",
       "12.000       90\n",
       "8.990        90\n",
       "49.000       87\n",
       "4.990        86\n",
       "23.990       84\n",
       "             ..\n",
       "607.200       1\n",
       "375.782       1\n",
       "3714.200      1\n",
       "1035.400      1\n",
       "11456.000     1\n",
       "Name: count, Length: 1217, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Order Total'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47b9dc32-a6fd-4909-a399-4aa0e2f5a239",
   "metadata": {},
   "source": [
    "### C. Analyze Data with Matplotlib "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4f78e3b1-df4d-42b4-9716-a3fcac33753c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b021b663-3535-4278-8426-e5f4a22044df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Quantity'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2ff5da56-f3be-49d2-8d44-853bf93b00b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='ProdCategory'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['ProdCategory'].value_counts().plot(kind = 'barh')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cbe6609-e2c3-445e-b355-1b29993b3087",
   "metadata": {},
   "source": [
    "### D. Connect to a DB using SQLALCHEMY within Jupyter Notebook "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "614d87b6-7c9b-41d0-85a2-d9a156dac3c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sqlalchemy import create_engine\n",
    "from os import environ"
   ]
  },
  {
   "attachments": {
    "d1cd854d-f829-42f6-9f87-0f5625577d8b.png": {
     "image/png": 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    }
   },
   "cell_type": "markdown",
   "id": "a2ff8d4e-0fdd-4c60-9fcc-1bf132da930e",
   "metadata": {},
   "source": [
    "![image.png](attachment:d1cd854d-f829-42f6-9f87-0f5625577d8b.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "40b0e864-ab39-4889-9136-2e31cf3ccf57",
   "metadata": {},
   "outputs": [],
   "source": [
    "db_url = environ.get('SALESPEOPLE_DB_URL')\n",
    "engine = create_engine(\"sqlite:///salespeople.sqlite\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95409ffd-43b0-4fef-813b-e9c6a1f1278c",
   "metadata": {},
   "source": [
    "### E. Visualize data using SQL Alchemy, pandas, and Matplotlib "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1e0d3010-b9b4-4548-b143-3d291fb96101",
   "metadata": {},
   "outputs": [],
   "source": [
    "sales_df = pd.read_sql_table(\"salespeople\", con = engine.connect())\n",
    "#index_col = 'idColumn', coerce_float = True, columns = [\"city\", \"state\"]\n",
    "#parse_dates = [\"dateColumn\"], chunksize = 250"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "345dd365-a189-4648-94c8-d9c21144a1f0",
   "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>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>email_address</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jack</td>\n",
       "      <td>Powell</td>\n",
       "      <td>jpowell0@hplussport.com</td>\n",
       "      <td>Lynchburg</td>\n",
       "      <td>Virginia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Emily</td>\n",
       "      <td>Garcia</td>\n",
       "      <td>egarcia1@hplussport.com</td>\n",
       "      <td>Manchester</td>\n",
       "      <td>New Hampshire</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Richard</td>\n",
       "      <td>Dean</td>\n",
       "      <td>rdean2@hplussport.com</td>\n",
       "      <td>Houston</td>\n",
       "      <td>Texas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jane</td>\n",
       "      <td>Porter</td>\n",
       "      <td>jporter3@hplussport.com</td>\n",
       "      <td>Alexandria</td>\n",
       "      <td>Virginia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Robin</td>\n",
       "      <td>Vasquez</td>\n",
       "      <td>rvasquez4@hplussport.com</td>\n",
       "      <td>El Paso</td>\n",
       "      <td>Texas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Douglas</td>\n",
       "      <td>Flores</td>\n",
       "      <td>dflores5@hplussport.com</td>\n",
       "      <td>Houston</td>\n",
       "      <td>Texas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Craig</td>\n",
       "      <td>Johnston</td>\n",
       "      <td>cjohnston6@hplussport.com</td>\n",
       "      <td>Las Cruces</td>\n",
       "      <td>New Mexico</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Norma</td>\n",
       "      <td>Johnson</td>\n",
       "      <td>njohnson7@hplussport.com</td>\n",
       "      <td>Amarillo</td>\n",
       "      <td>Texas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Kathryn</td>\n",
       "      <td>Bishop</td>\n",
       "      <td>kbishop8@hplussport.com</td>\n",
       "      <td>Roanoke</td>\n",
       "      <td>Virginia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Joe</td>\n",
       "      <td>Stewart</td>\n",
       "      <td>jstewart9@hplussport.com</td>\n",
       "      <td>Mesquite</td>\n",
       "      <td>Texas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Dennis</td>\n",
       "      <td>Price</td>\n",
       "      <td>dpricea@hplussport.com</td>\n",
       "      <td>Lynn</td>\n",
       "      <td>Massachusetts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Jason</td>\n",
       "      <td>Reid</td>\n",
       "      <td>jreidb@hplussport.com</td>\n",
       "      <td>Garland</td>\n",
       "      <td>Texas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Randy</td>\n",
       "      <td>Gomez</td>\n",
       "      <td>rgomezc@hplussport.com</td>\n",
       "      <td>Wichita</td>\n",
       "      <td>Kansas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Jerry</td>\n",
       "      <td>George</td>\n",
       "      <td>jgeorged@hplussport.com</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>Arizona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Jane</td>\n",
       "      <td>Larson</td>\n",
       "      <td>jlarsone@hplussport.com</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>Washington</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Joyce</td>\n",
       "      <td>Carr</td>\n",
       "      <td>jcarrf@hplussport.com</td>\n",
       "      <td>Bloomington</td>\n",
       "      <td>Indiana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>James</td>\n",
       "      <td>Ortiz</td>\n",
       "      <td>jortizg@hplussport.com</td>\n",
       "      <td>Oklahoma City</td>\n",
       "      <td>Oklahoma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Jimmy</td>\n",
       "      <td>Fowler</td>\n",
       "      <td>jfowlerh@hplussport.com</td>\n",
       "      <td>Jacksonville</td>\n",
       "      <td>Florida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Benjamin</td>\n",
       "      <td>Sims</td>\n",
       "      <td>bsimsi@hplussport.com</td>\n",
       "      <td>Louisville</td>\n",
       "      <td>Kentucky</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Jack</td>\n",
       "      <td>Wagner</td>\n",
       "      <td>jwagnerj@hplussport.com</td>\n",
       "      <td>Monticello</td>\n",
       "      <td>Minnesota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Sharon</td>\n",
       "      <td>Evans</td>\n",
       "      <td>sevansk@hplussport.com</td>\n",
       "      <td>Springfield</td>\n",
       "      <td>Illinois</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Tina</td>\n",
       "      <td>Mccoy</td>\n",
       "      <td>tmccoyl@hplussport.com</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Joan</td>\n",
       "      <td>Ruiz</td>\n",
       "      <td>jruizm@hplussport.com</td>\n",
       "      <td>Fort Myers</td>\n",
       "      <td>Florida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Sara</td>\n",
       "      <td>Cox</td>\n",
       "      <td>scoxn@hplussport.com</td>\n",
       "      <td>Oklahoma City</td>\n",
       "      <td>Oklahoma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Jessica</td>\n",
       "      <td>Alvarez</td>\n",
       "      <td>jalvarezo@hplussport.com</td>\n",
       "      <td>Akron</td>\n",
       "      <td>Ohio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Amanda</td>\n",
       "      <td>Butler</td>\n",
       "      <td>abutlerp@hplussport.com</td>\n",
       "      <td>Minneapolis</td>\n",
       "      <td>Minnesota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Louis</td>\n",
       "      <td>Gonzales</td>\n",
       "      <td>lgonzalesq@hplussport.com</td>\n",
       "      <td>El Paso</td>\n",
       "      <td>Texas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Victor</td>\n",
       "      <td>Moore</td>\n",
       "      <td>vmoorer@hplussport.com</td>\n",
       "      <td>New York City</td>\n",
       "      <td>New York</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Harold</td>\n",
       "      <td>Lawson</td>\n",
       "      <td>hlawsons@hplussport.com</td>\n",
       "      <td>Raleigh</td>\n",
       "      <td>North Carolina</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Carlos</td>\n",
       "      <td>James</td>\n",
       "      <td>cjamest@hplussport.com</td>\n",
       "      <td>Visalia</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Edward</td>\n",
       "      <td>Kelley</td>\n",
       "      <td>ekelleyu@hplussport.com</td>\n",
       "      <td>Flint</td>\n",
       "      <td>Michigan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Gregory</td>\n",
       "      <td>Wells</td>\n",
       "      <td>gwellsv@hplussport.com</td>\n",
       "      <td>Kingsport</td>\n",
       "      <td>Tennessee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Shirley</td>\n",
       "      <td>Carpenter</td>\n",
       "      <td>scarpenterw@hplussport.com</td>\n",
       "      <td>Van Nuys</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>Fowler</td>\n",
       "      <td>vfowlerx@hplussport.com</td>\n",
       "      <td>Salt Lake City</td>\n",
       "      <td>Utah</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Janet</td>\n",
       "      <td>Harvey</td>\n",
       "      <td>jharveyy@hplussport.com</td>\n",
       "      <td>Anderson</td>\n",
       "      <td>Indiana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Diana</td>\n",
       "      <td>Parker</td>\n",
       "      <td>dparkerz@hplussport.com</td>\n",
       "      <td>Montgomery</td>\n",
       "      <td>Alabama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Sara</td>\n",
       "      <td>Simpson</td>\n",
       "      <td>ssimpson10@hplussport.com</td>\n",
       "      <td>Tampa</td>\n",
       "      <td>Florida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Stephen</td>\n",
       "      <td>Duncan</td>\n",
       "      <td>sduncan11@hplussport.com</td>\n",
       "      <td>Denver</td>\n",
       "      <td>Colorado</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Daniel</td>\n",
       "      <td>Myers</td>\n",
       "      <td>dmyers12@hplussport.com</td>\n",
       "      <td>Tallahassee</td>\n",
       "      <td>Florida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Beverly</td>\n",
       "      <td>Hamilton</td>\n",
       "      <td>bhamilton13@hplussport.com</td>\n",
       "      <td>Memphis</td>\n",
       "      <td>Tennessee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Tina</td>\n",
       "      <td>Holmes</td>\n",
       "      <td>tholmes14@hplussport.com</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>Washington</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Bobby</td>\n",
       "      <td>Garcia</td>\n",
       "      <td>bgarcia15@hplussport.com</td>\n",
       "      <td>Norfolk</td>\n",
       "      <td>Virginia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Wanda</td>\n",
       "      <td>Bennett</td>\n",
       "      <td>wbennett16@hplussport.com</td>\n",
       "      <td>Warren</td>\n",
       "      <td>Ohio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Steven</td>\n",
       "      <td>Johnson</td>\n",
       "      <td>sjohnson17@hplussport.com</td>\n",
       "      <td>Monticello</td>\n",
       "      <td>Minnesota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Marie</td>\n",
       "      <td>Hall</td>\n",
       "      <td>mhall18@hplussport.com</td>\n",
       "      <td>Canton</td>\n",
       "      <td>Ohio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Wanda</td>\n",
       "      <td>Evans</td>\n",
       "      <td>wevans19@hplussport.com</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>Texas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Nancy</td>\n",
       "      <td>Wagner</td>\n",
       "      <td>nwagner1a@hplussport.com</td>\n",
       "      <td>Waterbury</td>\n",
       "      <td>Connecticut</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Roger</td>\n",
       "      <td>Sullivan</td>\n",
       "      <td>rsullivan1b@hplussport.com</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>Alabama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Dorothy</td>\n",
       "      <td>Boyd</td>\n",
       "      <td>dboyd1c@hplussport.com</td>\n",
       "      <td>Kansas City</td>\n",
       "      <td>Kansas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Walter</td>\n",
       "      <td>Rivera</td>\n",
       "      <td>wrivera1d@hplussport.com</td>\n",
       "      <td>San Antonio</td>\n",
       "      <td>Texas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Teresa</td>\n",
       "      <td>Riley</td>\n",
       "      <td>triley1e@hplussport.com</td>\n",
       "      <td>Saint Paul</td>\n",
       "      <td>Minnesota</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   first_name  last_name               email_address            city  \\\n",
       "0        Jack     Powell     jpowell0@hplussport.com       Lynchburg   \n",
       "1       Emily     Garcia     egarcia1@hplussport.com      Manchester   \n",
       "2     Richard       Dean       rdean2@hplussport.com         Houston   \n",
       "3        Jane     Porter     jporter3@hplussport.com      Alexandria   \n",
       "4       Robin    Vasquez    rvasquez4@hplussport.com         El Paso   \n",
       "5     Douglas     Flores     dflores5@hplussport.com         Houston   \n",
       "6       Craig   Johnston   cjohnston6@hplussport.com      Las Cruces   \n",
       "7       Norma    Johnson    njohnson7@hplussport.com        Amarillo   \n",
       "8     Kathryn     Bishop     kbishop8@hplussport.com         Roanoke   \n",
       "9         Joe    Stewart    jstewart9@hplussport.com        Mesquite   \n",
       "10     Dennis      Price      dpricea@hplussport.com            Lynn   \n",
       "11      Jason       Reid       jreidb@hplussport.com         Garland   \n",
       "12      Randy      Gomez      rgomezc@hplussport.com         Wichita   \n",
       "13      Jerry     George     jgeorged@hplussport.com         Phoenix   \n",
       "14       Jane     Larson     jlarsone@hplussport.com         Seattle   \n",
       "15      Joyce       Carr       jcarrf@hplussport.com     Bloomington   \n",
       "16      James      Ortiz      jortizg@hplussport.com   Oklahoma City   \n",
       "17      Jimmy     Fowler     jfowlerh@hplussport.com    Jacksonville   \n",
       "18   Benjamin       Sims       bsimsi@hplussport.com      Louisville   \n",
       "19       Jack     Wagner     jwagnerj@hplussport.com      Monticello   \n",
       "20     Sharon      Evans      sevansk@hplussport.com     Springfield   \n",
       "21       Tina      Mccoy      tmccoyl@hplussport.com        San Jose   \n",
       "22       Joan       Ruiz       jruizm@hplussport.com      Fort Myers   \n",
       "23       Sara        Cox        scoxn@hplussport.com   Oklahoma City   \n",
       "24    Jessica    Alvarez    jalvarezo@hplussport.com           Akron   \n",
       "25     Amanda     Butler     abutlerp@hplussport.com     Minneapolis   \n",
       "26      Louis   Gonzales   lgonzalesq@hplussport.com         El Paso   \n",
       "27     Victor      Moore      vmoorer@hplussport.com   New York City   \n",
       "28     Harold     Lawson     hlawsons@hplussport.com         Raleigh   \n",
       "29     Carlos      James      cjamest@hplussport.com         Visalia   \n",
       "30     Edward     Kelley     ekelleyu@hplussport.com           Flint   \n",
       "31    Gregory      Wells      gwellsv@hplussport.com       Kingsport   \n",
       "32    Shirley  Carpenter  scarpenterw@hplussport.com        Van Nuys   \n",
       "33   Virginia     Fowler     vfowlerx@hplussport.com  Salt Lake City   \n",
       "34      Janet     Harvey     jharveyy@hplussport.com        Anderson   \n",
       "35      Diana     Parker     dparkerz@hplussport.com      Montgomery   \n",
       "36       Sara    Simpson   ssimpson10@hplussport.com           Tampa   \n",
       "37    Stephen     Duncan    sduncan11@hplussport.com          Denver   \n",
       "38     Daniel      Myers     dmyers12@hplussport.com     Tallahassee   \n",
       "39    Beverly   Hamilton  bhamilton13@hplussport.com         Memphis   \n",
       "40       Tina     Holmes    tholmes14@hplussport.com         Seattle   \n",
       "41      Bobby     Garcia    bgarcia15@hplussport.com         Norfolk   \n",
       "42      Wanda    Bennett   wbennett16@hplussport.com          Warren   \n",
       "43     Steven    Johnson   sjohnson17@hplussport.com      Monticello   \n",
       "44      Marie       Hall      mhall18@hplussport.com          Canton   \n",
       "45      Wanda      Evans     wevans19@hplussport.com          Dallas   \n",
       "46      Nancy     Wagner    nwagner1a@hplussport.com       Waterbury   \n",
       "47      Roger   Sullivan  rsullivan1b@hplussport.com      Birmingham   \n",
       "48    Dorothy       Boyd      dboyd1c@hplussport.com     Kansas City   \n",
       "49     Walter     Rivera    wrivera1d@hplussport.com     San Antonio   \n",
       "50     Teresa      Riley     triley1e@hplussport.com      Saint Paul   \n",
       "\n",
       "             state  \n",
       "0         Virginia  \n",
       "1    New Hampshire  \n",
       "2            Texas  \n",
       "3         Virginia  \n",
       "4            Texas  \n",
       "5            Texas  \n",
       "6       New Mexico  \n",
       "7            Texas  \n",
       "8         Virginia  \n",
       "9            Texas  \n",
       "10   Massachusetts  \n",
       "11           Texas  \n",
       "12          Kansas  \n",
       "13         Arizona  \n",
       "14      Washington  \n",
       "15         Indiana  \n",
       "16        Oklahoma  \n",
       "17         Florida  \n",
       "18        Kentucky  \n",
       "19       Minnesota  \n",
       "20        Illinois  \n",
       "21      California  \n",
       "22         Florida  \n",
       "23        Oklahoma  \n",
       "24            Ohio  \n",
       "25       Minnesota  \n",
       "26           Texas  \n",
       "27        New York  \n",
       "28  North Carolina  \n",
       "29      California  \n",
       "30        Michigan  \n",
       "31       Tennessee  \n",
       "32      California  \n",
       "33            Utah  \n",
       "34         Indiana  \n",
       "35         Alabama  \n",
       "36         Florida  \n",
       "37        Colorado  \n",
       "38         Florida  \n",
       "39       Tennessee  \n",
       "40      Washington  \n",
       "41        Virginia  \n",
       "42            Ohio  \n",
       "43       Minnesota  \n",
       "44            Ohio  \n",
       "45           Texas  \n",
       "46     Connecticut  \n",
       "47         Alabama  \n",
       "48          Kansas  \n",
       "49           Texas  \n",
       "50       Minnesota  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3a4c6d6f-7e81-4130-b5b9-06075843eac3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        jpowell0@hplussport.com\n",
       "1        egarcia1@hplussport.com\n",
       "2          rdean2@hplussport.com\n",
       "3        jporter3@hplussport.com\n",
       "4       rvasquez4@hplussport.com\n",
       "5        dflores5@hplussport.com\n",
       "6      cjohnston6@hplussport.com\n",
       "7       njohnson7@hplussport.com\n",
       "8        kbishop8@hplussport.com\n",
       "9       jstewart9@hplussport.com\n",
       "10        dpricea@hplussport.com\n",
       "11         jreidb@hplussport.com\n",
       "12        rgomezc@hplussport.com\n",
       "13       jgeorged@hplussport.com\n",
       "14       jlarsone@hplussport.com\n",
       "15         jcarrf@hplussport.com\n",
       "16        jortizg@hplussport.com\n",
       "17       jfowlerh@hplussport.com\n",
       "18         bsimsi@hplussport.com\n",
       "19       jwagnerj@hplussport.com\n",
       "20        sevansk@hplussport.com\n",
       "21        tmccoyl@hplussport.com\n",
       "22         jruizm@hplussport.com\n",
       "23          scoxn@hplussport.com\n",
       "24      jalvarezo@hplussport.com\n",
       "25       abutlerp@hplussport.com\n",
       "26     lgonzalesq@hplussport.com\n",
       "27        vmoorer@hplussport.com\n",
       "28       hlawsons@hplussport.com\n",
       "29        cjamest@hplussport.com\n",
       "30       ekelleyu@hplussport.com\n",
       "31        gwellsv@hplussport.com\n",
       "32    scarpenterw@hplussport.com\n",
       "33       vfowlerx@hplussport.com\n",
       "34       jharveyy@hplussport.com\n",
       "35       dparkerz@hplussport.com\n",
       "36     ssimpson10@hplussport.com\n",
       "37      sduncan11@hplussport.com\n",
       "38       dmyers12@hplussport.com\n",
       "39    bhamilton13@hplussport.com\n",
       "40      tholmes14@hplussport.com\n",
       "41      bgarcia15@hplussport.com\n",
       "42     wbennett16@hplussport.com\n",
       "43     sjohnson17@hplussport.com\n",
       "44        mhall18@hplussport.com\n",
       "45       wevans19@hplussport.com\n",
       "46      nwagner1a@hplussport.com\n",
       "47    rsullivan1b@hplussport.com\n",
       "48        dboyd1c@hplussport.com\n",
       "49      wrivera1d@hplussport.com\n",
       "50       triley1e@hplussport.com\n",
       "Name: email_address, dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_df['email_address']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "bc934804-1b2d-43aa-bd59-f6e7e804ab87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           Virginia\n",
       "1      New Hampshire\n",
       "2              Texas\n",
       "3           Virginia\n",
       "4              Texas\n",
       "5              Texas\n",
       "6         New Mexico\n",
       "7              Texas\n",
       "8           Virginia\n",
       "9              Texas\n",
       "10     Massachusetts\n",
       "11             Texas\n",
       "12            Kansas\n",
       "13           Arizona\n",
       "14        Washington\n",
       "15           Indiana\n",
       "16          Oklahoma\n",
       "17           Florida\n",
       "18          Kentucky\n",
       "19         Minnesota\n",
       "20          Illinois\n",
       "21        California\n",
       "22           Florida\n",
       "23          Oklahoma\n",
       "24              Ohio\n",
       "25         Minnesota\n",
       "26             Texas\n",
       "27          New York\n",
       "28    North Carolina\n",
       "29        California\n",
       "30          Michigan\n",
       "31         Tennessee\n",
       "32        California\n",
       "33              Utah\n",
       "34           Indiana\n",
       "35           Alabama\n",
       "36           Florida\n",
       "37          Colorado\n",
       "38           Florida\n",
       "39         Tennessee\n",
       "40        Washington\n",
       "41          Virginia\n",
       "42              Ohio\n",
       "43         Minnesota\n",
       "44              Ohio\n",
       "45             Texas\n",
       "46       Connecticut\n",
       "47           Alabama\n",
       "48            Kansas\n",
       "49             Texas\n",
       "50         Minnesota\n",
       "Name: state, dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_df['state']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9efa0913-67ce-47dc-91fd-76cc685731ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state\n",
       "Texas             9\n",
       "Virginia          4\n",
       "Florida           4\n",
       "Minnesota         4\n",
       "California        3\n",
       "Ohio              3\n",
       "Washington        2\n",
       "Indiana           2\n",
       "Oklahoma          2\n",
       "Alabama           2\n",
       "Tennessee         2\n",
       "Kansas            2\n",
       "North Carolina    1\n",
       "Colorado          1\n",
       "Utah              1\n",
       "Michigan          1\n",
       "Massachusetts     1\n",
       "New York          1\n",
       "Illinois          1\n",
       "New Hampshire     1\n",
       "Kentucky          1\n",
       "New Mexico        1\n",
       "Arizona           1\n",
       "Connecticut       1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_df['state'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "acb3dc19-e417-4f28-9fa1-fcebc872cdb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='state'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sales_df['state'].value_counts().plot(kind = 'barh')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e39e713-05b3-47e3-ad61-ff1303826451",
   "metadata": {},
   "source": [
    "### Challenge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b6db2996-6743-4b88-96eb-d8e9fbafae77",
   "metadata": {},
   "outputs": [],
   "source": [
    "engine_2 = create_engine(\"sqlite:///landon.sqlite\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d742a786-04f2-42d8-8d20-407c2b73291b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2 = pd.read_sql_table(\"sales\", con = engine_2.connect(), index_col = \"order_id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "897ec69e-75d1-4cb1-a3ed-df423d41be16",
   "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>property_id</th>\n",
       "      <th>property_city</th>\n",
       "      <th>property_state</th>\n",
       "      <th>product_id</th>\n",
       "      <th>product_category</th>\n",
       "      <th>product_name</th>\n",
       "      <th>quantity</th>\n",
       "      <th>product_price</th>\n",
       "      <th>order_total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>order_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3768</th>\n",
       "      <td>18</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>81</td>\n",
       "      <td>Public Areas</td>\n",
       "      <td>Flyer Holder</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2027</th>\n",
       "      <td>2</td>\n",
       "      <td>Cincinnati</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>81</td>\n",
       "      <td>Public Areas</td>\n",
       "      <td>Flyer Holder</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>4</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>Washington</td>\n",
       "      <td>22</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Paper Clips</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4482</th>\n",
       "      <td>19</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>California</td>\n",
       "      <td>81</td>\n",
       "      <td>Public Areas</td>\n",
       "      <td>Flyer Holder</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4581</th>\n",
       "      <td>13</td>\n",
       "      <td>New Orleans</td>\n",
       "      <td>Louisiana</td>\n",
       "      <td>81</td>\n",
       "      <td>Public Areas</td>\n",
       "      <td>Flyer Holder</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2743</th>\n",
       "      <td>16</td>\n",
       "      <td>Denver</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>37</td>\n",
       "      <td>Furnishings</td>\n",
       "      <td>Bed (King)</td>\n",
       "      <td>3</td>\n",
       "      <td>300.0</td>\n",
       "      <td>900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>774</th>\n",
       "      <td>11</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>37</td>\n",
       "      <td>Furnishings</td>\n",
       "      <td>Bed (King)</td>\n",
       "      <td>3</td>\n",
       "      <td>300.0</td>\n",
       "      <td>900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2755</th>\n",
       "      <td>14</td>\n",
       "      <td>Nashville</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>37</td>\n",
       "      <td>Furnishings</td>\n",
       "      <td>Bed (King)</td>\n",
       "      <td>3</td>\n",
       "      <td>300.0</td>\n",
       "      <td>900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>12</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>Texas</td>\n",
       "      <td>37</td>\n",
       "      <td>Furnishings</td>\n",
       "      <td>Bed (King)</td>\n",
       "      <td>3</td>\n",
       "      <td>300.0</td>\n",
       "      <td>900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1723</th>\n",
       "      <td>1</td>\n",
       "      <td>New York</td>\n",
       "      <td>New York</td>\n",
       "      <td>37</td>\n",
       "      <td>Furnishings</td>\n",
       "      <td>Bed (King)</td>\n",
       "      <td>3</td>\n",
       "      <td>300.0</td>\n",
       "      <td>900.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5000 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          property_id property_city property_state  product_id  \\\n",
       "order_id                                                         \n",
       "3768               18       Phoenix        Arizona          81   \n",
       "2027                2    Cincinnati           Ohio          81   \n",
       "423                 4       Seattle     Washington          22   \n",
       "4482               19   Los Angeles     California          81   \n",
       "4581               13   New Orleans      Louisiana          81   \n",
       "...               ...           ...            ...         ...   \n",
       "2743               16        Denver       Colorado          37   \n",
       "774                11       Atlanta        Georgia          37   \n",
       "2755               14     Nashville      Tennessee          37   \n",
       "1980               12        Dallas          Texas          37   \n",
       "1723                1      New York       New York          37   \n",
       "\n",
       "         product_category  product_name  quantity  product_price  order_total  \n",
       "order_id                                                                       \n",
       "3768         Public Areas  Flyer Holder         1            3.0          3.0  \n",
       "2027         Public Areas  Flyer Holder         1            3.0          3.0  \n",
       "423       Office Supplies   Paper Clips         1            3.0          3.0  \n",
       "4482         Public Areas  Flyer Holder         1            3.0          3.0  \n",
       "4581         Public Areas  Flyer Holder         1            3.0          3.0  \n",
       "...                   ...           ...       ...            ...          ...  \n",
       "2743          Furnishings    Bed (King)         3          300.0        900.0  \n",
       "774           Furnishings    Bed (King)         3          300.0        900.0  \n",
       "2755          Furnishings    Bed (King)         3          300.0        900.0  \n",
       "1980          Furnishings    Bed (King)         3          300.0        900.0  \n",
       "1723          Furnishings    Bed (King)         3          300.0        900.0  \n",
       "\n",
       "[5000 rows x 9 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2.sort_values(\"order_total\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3b6c8f2d-1410-4e25-a05f-00873b2a04c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "property_state\n",
       "California       505\n",
       "Virginia         504\n",
       "Pennsylvania     272\n",
       "Missouri         267\n",
       "Nevada           261\n",
       "Louisiana        260\n",
       "Washington       257\n",
       "Illinois         257\n",
       "Tennessee        257\n",
       "Ohio             254\n",
       "Georgia          253\n",
       "Texas            252\n",
       "Florida          238\n",
       "Massachusetts    237\n",
       "Colorado         235\n",
       "Arizona          233\n",
       "Oregon           231\n",
       "New York         227\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2[\"property_state\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6d8913ce-d39b-4732-99e2-3efaa964a8ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x238c3dfdcd0>,\n",
       "  <matplotlib.patches.Wedge at 0x238c3dd36e0>,\n",
       "  <matplotlib.patches.Wedge at 0x238c3dfd910>,\n",
       "  <matplotlib.patches.Wedge at 0x238c3dfcbf0>,\n",
       "  <matplotlib.patches.Wedge at 0x238c3d9e9c0>],\n",
       " [Text(0.7034012670934939, 0.8457107410050244, 'California'),\n",
       "  Text(-0.958775597663662, 0.5392117889333353, 'Virginia'),\n",
       "  Text(-0.7380290570056299, -0.8156672796032589, 'Pennsylvania'),\n",
       "  Text(0.219164255061859, -1.0779457450647414, 'Missouri'),\n",
       "  Text(0.9889245084675504, -0.4816931767756464, 'Nevada')])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(df_2[\"property_state\"].value_counts().head(), labels = df_2[\"property_state\"].value_counts().head().index.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b50734f3-5343-416b-be9d-a921a981c67b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x238c43ccf50>,\n",
       "  <matplotlib.patches.Wedge at 0x238c43d2360>,\n",
       "  <matplotlib.patches.Wedge at 0x238c44d4cb0>,\n",
       "  <matplotlib.patches.Wedge at 0x238c44d4ef0>,\n",
       "  <matplotlib.patches.Wedge at 0x238c44d56d0>,\n",
       "  <matplotlib.patches.Wedge at 0x238c44d5cd0>,\n",
       "  <matplotlib.patches.Wedge at 0x238c44d6120>,\n",
       "  <matplotlib.patches.Wedge at 0x238c44d5ee0>,\n",
       "  <matplotlib.patches.Wedge at 0x238c44d6ea0>,\n",
       "  <matplotlib.patches.Wedge at 0x238c44d72c0>],\n",
       " [Text(0.9585288546080237, 0.5396502894318046, 'California'),\n",
       "  Text(0.03685142488712066, 1.0993825414676135, 'Virginia'),\n",
       "  Text(-0.7533600471128238, 0.8015289386005748, 'Pennsylvania'),\n",
       "  Text(-1.0604185785239426, 0.2924250986513661, 'Missouri'),\n",
       "  Text(-1.0610101836456403, -0.2902712355715684, 'Nevada'),\n",
       "  Text(-0.7694806075353592, -0.7860658971275976, 'Louisiana'),\n",
       "  Text(-0.27191402797249314, -1.0658624495645648, 'Washington'),\n",
       "  Text(0.2956541753339997, -1.059522821182995, 'Illinois'),\n",
       "  Text(0.7845016796352393, -0.7710752976522386, 'Tennessee'),\n",
       "  Text(1.0636185489465253, -0.2805629739236593, 'Ohio')])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(df_2[\"property_state\"].value_counts().head(10), labels = df_2[\"property_state\"].value_counts().head(10).index.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f231cc96-0cbb-4a44-a4a4-2cbefa572bf4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}