DMA project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4db994f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting mysql-connector\n",
      "  Downloading mysql-connector-2.2.9.tar.gz (11.9 MB)\n",
      "     ---------------------------------------- 11.9/11.9 MB 6.3 MB/s eta 0:00:00\n",
      "  Preparing metadata (setup.py): started\n",
      "  Preparing metadata (setup.py): finished with status 'done'"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: You are using pip version 22.0.4; however, version 22.3.1 is available.\n",
      "You should consider upgrading via the 'c:\\users\\aditb\\appdata\\local\\programs\\python\\python39\\python.exe -m pip install --upgrade pip' command.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Building wheels for collected packages: mysql-connector\n",
      "  Building wheel for mysql-connector (setup.py): started\n",
      "  Building wheel for mysql-connector (setup.py): finished with status 'done'\n",
      "  Created wheel for mysql-connector: filename=mysql_connector-2.2.9-cp39-cp39-win_amd64.whl size=247945 sha256=98f6eb3c805fa87863385a37a27d58b1878a64814b45c1d759938dc3e4d5ab56\n",
      "  Stored in directory: c:\\users\\aditb\\appdata\\local\\pip\\cache\\wheels\\7b\\14\\39\\5aad423666e827dfe9a1fbcd111ac17171e7c9865d570780ce\n",
      "Successfully built mysql-connector\n",
      "Installing collected packages: mysql-connector\n",
      "Successfully installed mysql-connector-2.2.9\n"
     ]
    }
   ],
   "source": [
    "!pip install mysql-connector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3d9b6f61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pymysql\n",
      "  Downloading PyMySQL-1.0.2-py3-none-any.whl (43 kB)\n",
      "     ---------------------------------------- 43.8/43.8 KB 1.1 MB/s eta 0:00:00\n",
      "Installing collected packages: pymysql\n",
      "Successfully installed pymysql-1.0.2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: You are using pip version 22.0.4; however, version 22.3.1 is available.\n",
      "You should consider upgrading via the 'c:\\users\\aditb\\appdata\\local\\programs\\python\\python39\\python.exe -m pip install --upgrade pip' command.\n"
     ]
    }
   ],
   "source": [
    "!pip install pymysql"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8928447d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import mysql.connector\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as mp\n",
    "import os\n",
    "import pymysql\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "0112bb58",
   "metadata": {},
   "outputs": [],
   "source": [
    "host= os.getenv( 'Local' )\n",
    "conn= pymysql.connect(\n",
    "    host=host,\n",
    "    port=int (3306),\n",
    "    user= \"root\",\n",
    "    passwd='<password>', # Put your password in this place\n",
    "    db=\"restaurant\",\n",
    "    charset='utf8mb4')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8ac3d67b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\aditb\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning:\n",
      "\n",
      "pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "\n",
      "c:\\users\\aditb\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning:\n",
      "\n",
      "pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "\n",
      "c:\\users\\aditb\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning:\n",
      "\n",
      "pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "\n"
     ]
    }
   ],
   "source": [
    "orders = pd.read_sql('SELECT * FROM orders',conn)\n",
    "waiters= pd.read_sql('SELECT * FROM waiter',conn)\n",
    "dishes= pd.read_sql('SELECT * FROM dishes',conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "03c74c29",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ORDERID</th>\n",
       "      <th>CUSTOMERID</th>\n",
       "      <th>WAITERID</th>\n",
       "      <th>TIP</th>\n",
       "      <th>MONTH_AND_DATE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>74</td>\n",
       "      <td>2</td>\n",
       "      <td>6.92</td>\n",
       "      <td>1975-10-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>97</td>\n",
       "      <td>5</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1986-06-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>91</td>\n",
       "      <td>3</td>\n",
       "      <td>9.05</td>\n",
       "      <td>1985-01-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "      <td>6</td>\n",
       "      <td>8.78</td>\n",
       "      <td>2008-10-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>46</td>\n",
       "      <td>4</td>\n",
       "      <td>6.12</td>\n",
       "      <td>2003-09-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>196</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "      <td>6.28</td>\n",
       "      <td>1988-09-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>197</td>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "      <td>1.68</td>\n",
       "      <td>2017-10-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>198</td>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "      <td>2.60</td>\n",
       "      <td>1987-12-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>199</td>\n",
       "      <td>61</td>\n",
       "      <td>5</td>\n",
       "      <td>2.05</td>\n",
       "      <td>2015-02-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>200</td>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "      <td>8.04</td>\n",
       "      <td>1997-08-21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     ORDERID  CUSTOMERID  WAITERID   TIP MONTH_AND_DATE\n",
       "0          1          74         2  6.92     1975-10-25\n",
       "1          2          97         5  3.92     1986-06-22\n",
       "2          3          91         3  9.05     1985-01-23\n",
       "3          4          26         6  8.78     2008-10-27\n",
       "4          5          46         4  6.12     2003-09-09\n",
       "..       ...         ...       ...   ...            ...\n",
       "195      196          28         5  6.28     1988-09-27\n",
       "196      197          62         1  1.68     2017-10-24\n",
       "197      198          38         2  2.60     1987-12-08\n",
       "198      199          61         5  2.05     2015-02-13\n",
       "199      200          38         2  8.04     1997-08-21\n",
       "\n",
       "[200 rows x 5 columns]"
      ]
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     "execution_count": 40,
     "metadata": {},
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   "source": [
    "orders"
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  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d8633b27",
   "metadata": {},
   "outputs": [
    {
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       "      <th>WAITERID</th>\n",
       "      <th>NAME</th>\n",
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       "      <th>DOB</th>\n",
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       "      <td>1990-04-09</td>\n",
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       "      <td>Erika</td>\n",
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       "      <td>Auberon</td>\n",
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       "      <td>100</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Kingsley</td>\n",
       "      <td>kandreuzzi3@studiopress.com</td>\n",
       "      <td>1970-05-19</td>\n",
       "      <td>700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Collin</td>\n",
       "      <td>cgraffham4@japanpost.jp</td>\n",
       "      <td>1972-11-26</td>\n",
       "      <td>599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Dari</td>\n",
       "      <td>dslinn5@purevolume.com</td>\n",
       "      <td>1993-04-08</td>\n",
       "      <td>425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Donovan</td>\n",
       "      <td>dcheasman6@irs.gov</td>\n",
       "      <td>1976-06-10</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   WAITERID      NAME                        EMAIL        DOB  \\\n",
       "0         1      Sean           sgrima0@sphinn.com 1990-04-09   \n",
       "1         2     Erika        egleder1@newsvine.com 1982-09-13   \n",
       "2         3   Auberon            agreep2@wiley.com 1961-08-31   \n",
       "3         4  Kingsley  kandreuzzi3@studiopress.com 1970-05-19   \n",
       "4         5    Collin      cgraffham4@japanpost.jp 1972-11-26   \n",
       "5         6      Dari       dslinn5@purevolume.com 1993-04-08   \n",
       "6         7   Donovan           dcheasman6@irs.gov 1976-06-10   \n",
       "\n",
       "   PERIOD_OF_SERVICE  \n",
       "0                521  \n",
       "1                311  \n",
       "2                100  \n",
       "3                700  \n",
       "4                599  \n",
       "5                425  \n",
       "6                135  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
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   "source": [
    "waiters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "9551d269",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>ORDERID</th>\n",
       "      <th>CUSTOMERID</th>\n",
       "      <th>WAITERID</th>\n",
       "      <th>TIP</th>\n",
       "      <th>MONTH_AND_DATE</th>\n",
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       "      <td>6.92</td>\n",
       "      <td>1975-10-25</td>\n",
       "      <td>Erika</td>\n",
       "      <td>egleder1@newsvine.com</td>\n",
       "      <td>1982-09-13</td>\n",
       "      <td>311</td>\n",
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       "    <tr>\n",
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       "      <td>2</td>\n",
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       "      <td>5</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1986-06-22</td>\n",
       "      <td>Collin</td>\n",
       "      <td>cgraffham4@japanpost.jp</td>\n",
       "      <td>1972-11-26</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>91</td>\n",
       "      <td>3</td>\n",
       "      <td>9.05</td>\n",
       "      <td>1985-01-23</td>\n",
       "      <td>Auberon</td>\n",
       "      <td>agreep2@wiley.com</td>\n",
       "      <td>1961-08-31</td>\n",
       "      <td>100</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
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       "      <td>6</td>\n",
       "      <td>8.78</td>\n",
       "      <td>2008-10-27</td>\n",
       "      <td>Dari</td>\n",
       "      <td>dslinn5@purevolume.com</td>\n",
       "      <td>1993-04-08</td>\n",
       "      <td>425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>46</td>\n",
       "      <td>4</td>\n",
       "      <td>6.12</td>\n",
       "      <td>2003-09-09</td>\n",
       "      <td>Kingsley</td>\n",
       "      <td>kandreuzzi3@studiopress.com</td>\n",
       "      <td>1970-05-19</td>\n",
       "      <td>700</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>196</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "      <td>6.28</td>\n",
       "      <td>1988-09-27</td>\n",
       "      <td>Collin</td>\n",
       "      <td>cgraffham4@japanpost.jp</td>\n",
       "      <td>1972-11-26</td>\n",
       "      <td>599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>197</td>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "      <td>1.68</td>\n",
       "      <td>2017-10-24</td>\n",
       "      <td>Sean</td>\n",
       "      <td>sgrima0@sphinn.com</td>\n",
       "      <td>1990-04-09</td>\n",
       "      <td>521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>198</td>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "      <td>2.60</td>\n",
       "      <td>1987-12-08</td>\n",
       "      <td>Erika</td>\n",
       "      <td>egleder1@newsvine.com</td>\n",
       "      <td>1982-09-13</td>\n",
       "      <td>311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>199</td>\n",
       "      <td>61</td>\n",
       "      <td>5</td>\n",
       "      <td>2.05</td>\n",
       "      <td>2015-02-13</td>\n",
       "      <td>Collin</td>\n",
       "      <td>cgraffham4@japanpost.jp</td>\n",
       "      <td>1972-11-26</td>\n",
       "      <td>599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>200</td>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "      <td>8.04</td>\n",
       "      <td>1997-08-21</td>\n",
       "      <td>Erika</td>\n",
       "      <td>egleder1@newsvine.com</td>\n",
       "      <td>1982-09-13</td>\n",
       "      <td>311</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 9 columns</p>\n",
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      ],
      "text/plain": [
       "     ORDERID  CUSTOMERID  WAITERID   TIP MONTH_AND_DATE      NAME  \\\n",
       "0          1          74         2  6.92     1975-10-25     Erika   \n",
       "1          2          97         5  3.92     1986-06-22    Collin   \n",
       "2          3          91         3  9.05     1985-01-23   Auberon   \n",
       "3          4          26         6  8.78     2008-10-27      Dari   \n",
       "4          5          46         4  6.12     2003-09-09  Kingsley   \n",
       "..       ...         ...       ...   ...            ...       ...   \n",
       "195      196          28         5  6.28     1988-09-27    Collin   \n",
       "196      197          62         1  1.68     2017-10-24      Sean   \n",
       "197      198          38         2  2.60     1987-12-08     Erika   \n",
       "198      199          61         5  2.05     2015-02-13    Collin   \n",
       "199      200          38         2  8.04     1997-08-21     Erika   \n",
       "\n",
       "                           EMAIL        DOB  PERIOD_OF_SERVICE  \n",
       "0          egleder1@newsvine.com 1982-09-13                311  \n",
       "1        cgraffham4@japanpost.jp 1972-11-26                599  \n",
       "2              agreep2@wiley.com 1961-08-31                100  \n",
       "3         dslinn5@purevolume.com 1993-04-08                425  \n",
       "4    kandreuzzi3@studiopress.com 1970-05-19                700  \n",
       "..                           ...        ...                ...  \n",
       "195      cgraffham4@japanpost.jp 1972-11-26                599  \n",
       "196           sgrima0@sphinn.com 1990-04-09                521  \n",
       "197        egleder1@newsvine.com 1982-09-13                311  \n",
       "198      cgraffham4@japanpost.jp 1972-11-26                599  \n",
       "199        egleder1@newsvine.com 1982-09-13                311  \n",
       "\n",
       "[200 rows x 9 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged=orders.merge(waiters, on='WAITERID', how='left')\n",
    "merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "2f05f45f",
   "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>WAITERID</th>\n",
       "      <th>NAME</th>\n",
       "      <th>ORDERID</th>\n",
       "      <th>CUSTOMERID</th>\n",
       "      <th>TIP</th>\n",
       "      <th>PERIOD_OF_SERVICE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Sean</td>\n",
       "      <td>2914</td>\n",
       "      <td>1348</td>\n",
       "      <td>146.06</td>\n",
       "      <td>14588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Erika</td>\n",
       "      <td>3751</td>\n",
       "      <td>1546</td>\n",
       "      <td>165.90</td>\n",
       "      <td>10263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Auberon</td>\n",
       "      <td>1924</td>\n",
       "      <td>1235</td>\n",
       "      <td>136.95</td>\n",
       "      <td>2300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Kingsley</td>\n",
       "      <td>3017</td>\n",
       "      <td>1824</td>\n",
       "      <td>172.53</td>\n",
       "      <td>21700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Collin</td>\n",
       "      <td>4381</td>\n",
       "      <td>2438</td>\n",
       "      <td>216.51</td>\n",
       "      <td>26356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Dari</td>\n",
       "      <td>1760</td>\n",
       "      <td>1065</td>\n",
       "      <td>110.75</td>\n",
       "      <td>8500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Donovan</td>\n",
       "      <td>2353</td>\n",
       "      <td>982</td>\n",
       "      <td>107.11</td>\n",
       "      <td>2835</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   WAITERID      NAME  ORDERID  CUSTOMERID     TIP  PERIOD_OF_SERVICE\n",
       "0         1      Sean     2914        1348  146.06              14588\n",
       "1         2     Erika     3751        1546  165.90              10263\n",
       "2         3   Auberon     1924        1235  136.95               2300\n",
       "3         4  Kingsley     3017        1824  172.53              21700\n",
       "4         5    Collin     4381        2438  216.51              26356\n",
       "5         6      Dari     1760        1065  110.75               8500\n",
       "6         7   Donovan     2353         982  107.11               2835"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grouping on wiater id, adding new column, plotting tip\n",
    "grouped=merged.groupby(['WAITERID','NAME']).sum().reset_index()\n",
    "grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "fce5d2ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Scatter plot for waiter id and the tip \n",
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(x=grouped['NAME'], y=grouped['TIP'], color='purple')\n",
    "#ax.set_facecolor('white')\n",
    "plt.xlabel('Waiter ID')\n",
    "plt.ylabel('Total tip (in $)')\n",
    "plt.title('Tip and the Waiter Name')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "16e90d45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Count of orders taken')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#how many times a waiter has taken order\n",
    "import seaborn as sns\n",
    "a=sns.countplot(data=orders, x=\"WAITERID\")\n",
    "for p in a.patches:\n",
    "   a.annotate('{:.1f}'.format(p.get_height()), (p.get_x()+0.25, p.get_height()+0.01))\n",
    "a.set_title('Count of orders taken')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "9654e4fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "marker": {
          "size": [
           40,
           60,
           80,
           100,
           120,
           140,
           160
          ]
         },
         "mode": "markers",
         "type": "scatter",
         "x": [
          "Erika",
          "Collin",
          "Auberon",
          "Dari",
          "Kingsley",
          "Dari",
          "Collin",
          "Auberon",
          "Dari",
          "Collin",
          "Dari",
          "Auberon",
          "Auberon",
          "Kingsley",
          "Auberon",
          "Donovan",
          "Collin",
          "Dari",
          "Collin",
          "Collin",
          "Kingsley",
          "Sean",
          "Auberon",
          "Kingsley",
          "Collin",
          "Kingsley",
          "Collin",
          "Auberon",
          "Collin",
          "Dari",
          "Sean",
          "Auberon",
          "Kingsley",
          "Erika",
          "Sean",
          "Collin",
          "Auberon",
          "Collin",
          "Auberon",
          "Kingsley",
          "Dari",
          "Kingsley",
          "Kingsley",
          "Sean",
          "Donovan",
          "Collin",
          "Collin",
          "Sean",
          "Sean",
          "Collin",
          "Dari",
          "Donovan",
          "Erika",
          "Collin",
          "Sean",
          "Sean",
          "Collin",
          "Erika",
          "Donovan",
          "Erika",
          "Erika",
          "Sean",
          "Kingsley",
          "Erika",
          "Erika",
          "Auberon",
          "Collin",
          "Collin",
          "Kingsley",
          "Erika",
          "Donovan",
          "Sean",
          "Auberon",
          "Kingsley",
          "Collin",
          "Kingsley",
          "Sean",
          "Sean",
          "Donovan",
          "Sean",
          "Auberon",
          "Erika",
          "Dari",
          "Donovan",
          "Kingsley",
          "Donovan",
          "Dari",
          "Kingsley",
          "Kingsley",
          "Kingsley",
          "Erika",
          "Erika",
          "Erika",
          "Sean",
          "Collin",
          "Dari",
          "Kingsley",
          "Kingsley",
          "Donovan",
          "Erika",
          "Kingsley",
          "Erika",
          "Collin",
          "Sean",
          "Erika",
          "Erika",
          "Dari",
          "Dari",
          "Donovan",
          "Donovan",
          "Collin",
          "Sean",
          "Collin",
          "Collin",
          "Donovan",
          "Sean",
          "Dari",
          "Collin",
          "Auberon",
          "Auberon",
          "Donovan",
          "Collin",
          "Auberon",
          "Collin",
          "Erika",
          "Sean",
          "Collin",
          "Kingsley",
          "Sean",
          "Sean",
          "Donovan",
          "Kingsley",
          "Erika",
          "Dari",
          "Donovan",
          "Collin",
          "Erika",
          "Collin",
          "Erika",
          "Erika",
          "Collin",
          "Erika",
          "Collin",
          "Erika",
          "Collin",
          "Sean",
          "Auberon",
          "Kingsley",
          "Erika",
          "Collin",
          "Donovan",
          "Dari",
          "Auberon",
          "Collin",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.graph_objects as go\n",
    "\n",
    "fig = go.Figure(data=[go.Scatter(\n",
    "    x=merged['NAME'], y=merged['TIP'],\n",
    "    mode='markers',\n",
    "    marker_size=[40, 60, 80,100,120,140,160])\n",
    "])\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "908d5bd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "dishes['TYPEOFCUISINE']=dishes['TYPEOFCUISINE'].str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "138183fa",
   "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>TYPEOFCUISINE</th>\n",
       "      <th>DISHID</th>\n",
       "      <th>PRICE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Continental</td>\n",
       "      <td>10</td>\n",
       "      <td>659.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>English</td>\n",
       "      <td>37</td>\n",
       "      <td>714.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Italian</td>\n",
       "      <td>31</td>\n",
       "      <td>866.19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  TYPEOFCUISINE  DISHID   PRICE\n",
       "0   Continental      10  659.12\n",
       "1       English      37  714.12\n",
       "2       Italian      31  866.19"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_dishes=dishes.groupby('TYPEOFCUISINE').sum().reset_index()\n",
    "grouped_dishes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "c23bf477",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Sum of prices and the cuisines')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a=sns.barplot(data=grouped_dishes, x=\"TYPEOFCUISINE\", y=\"PRICE\")\n",
    "for p in a.patches:\n",
    "   a.annotate('{:.1f}'.format(p.get_height()), (p.get_x()+0.25, p.get_height()+0.01))\n",
    "sns.set(rc={'figure.figsize':(5,5)})\n",
    "a.set_title('Sum of prices and the cuisines')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8bff98e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
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