10-hafta.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e123ae63",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ba4427a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>Sector</th>\n",
       "      <th>Industry</th>\n",
       "      <th>Location</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Profits</th>\n",
       "      <th>Employees</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rank</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>1</th>\n",
       "      <td>Walmart</td>\n",
       "      <td>Retailing</td>\n",
       "      <td>General Merchandisers</td>\n",
       "      <td>Bentonville, AR</td>\n",
       "      <td>482130</td>\n",
       "      <td>14694</td>\n",
       "      <td>2300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Exxon Mobil</td>\n",
       "      <td>Energy</td>\n",
       "      <td>Petroleum Refining</td>\n",
       "      <td>Irving, TX</td>\n",
       "      <td>246204</td>\n",
       "      <td>16150</td>\n",
       "      <td>75600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Computers, Office Equipment</td>\n",
       "      <td>Cupertino, CA</td>\n",
       "      <td>233715</td>\n",
       "      <td>53394</td>\n",
       "      <td>110000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Berkshire Hathaway</td>\n",
       "      <td>Financials</td>\n",
       "      <td>Insurance: Property and Casualty (Stock)</td>\n",
       "      <td>Omaha, NE</td>\n",
       "      <td>210821</td>\n",
       "      <td>24083</td>\n",
       "      <td>331000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>McKesson</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>Wholesalers: Health Care</td>\n",
       "      <td>San Francisco, CA</td>\n",
       "      <td>181241</td>\n",
       "      <td>1476</td>\n",
       "      <td>70400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Company       Sector  \\\n",
       "Rank                                    \n",
       "1                Walmart    Retailing   \n",
       "2            Exxon Mobil       Energy   \n",
       "3                  Apple   Technology   \n",
       "4     Berkshire Hathaway   Financials   \n",
       "5               McKesson  Health Care   \n",
       "\n",
       "                                      Industry           Location  Revenue  \\\n",
       "Rank                                                                         \n",
       "1                        General Merchandisers    Bentonville, AR   482130   \n",
       "2                           Petroleum Refining         Irving, TX   246204   \n",
       "3                  Computers, Office Equipment      Cupertino, CA   233715   \n",
       "4     Insurance: Property and Casualty (Stock)          Omaha, NE   210821   \n",
       "5                     Wholesalers: Health Care  San Francisco, CA   181241   \n",
       "\n",
       "      Profits  Employees  \n",
       "Rank                      \n",
       "1       14694    2300000  \n",
       "2       16150      75600  \n",
       "3       53394     110000  \n",
       "4       24083     331000  \n",
       "5        1476      70400  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fortune=pd.read_csv(\"resources/resources/fortune1000.csv\",index_col=\"Rank\")\n",
    "df_fortune.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "009b7a86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x0000028D494D1D60>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fortune.groupby(\"Sector\") #dataframe groupby objesi olarak döner.\n",
    "#groupby ile sectorleri gruplamış"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fc09339f",
   "metadata": {},
   "outputs": [],
   "source": [
    "sector=df_fortune.groupby(\"Sector\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "355f2c98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(sector) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "81ed99ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fortune[\"Sector\"].nunique() \n",
    "#uzunluk olarak bakınca da unique sayısı olarak bakınca da aynı şey çıkar.Sebebi groupby dememiz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2e8ef1cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sector\n",
       "Aerospace & Defense              20\n",
       "Apparel                          15\n",
       "Business Services                51\n",
       "Chemicals                        30\n",
       "Energy                          122\n",
       "Engineering & Construction       26\n",
       "Financials                      139\n",
       "Food and Drug Stores             15\n",
       "Food, Beverages & Tobacco        43\n",
       "Health Care                      75\n",
       "Hotels, Resturants & Leisure     25\n",
       "Household Products               28\n",
       "Industrials                      46\n",
       "Materials                        43\n",
       "Media                            25\n",
       "Motor Vehicles & Parts           24\n",
       "Retailing                        80\n",
       "Technology                      102\n",
       "Telecommunications               15\n",
       "Transportation                   36\n",
       "Wholesalers                      40\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sector.size()\n",
    "#sektörlerin içindeki şirket sayısı"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "42217f5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Financials                      139\n",
       "Energy                          122\n",
       "Technology                      102\n",
       "Retailing                        80\n",
       "Health Care                      75\n",
       "Business Services                51\n",
       "Industrials                      46\n",
       "Materials                        43\n",
       "Food, Beverages & Tobacco        43\n",
       "Wholesalers                      40\n",
       "Transportation                   36\n",
       "Chemicals                        30\n",
       "Household Products               28\n",
       "Engineering & Construction       26\n",
       "Media                            25\n",
       "Hotels, Resturants & Leisure     25\n",
       "Motor Vehicles & Parts           24\n",
       "Aerospace & Defense              20\n",
       "Telecommunications               15\n",
       "Apparel                          15\n",
       "Food and Drug Stores             15\n",
       "Name: Sector, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fortune[\"Sector\"].value_counts() #sıralama yaparak sectorleri ve sayılarını gösterir "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "86e98f6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>Industry</th>\n",
       "      <th>Location</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Profits</th>\n",
       "      <th>Employees</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sector</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>Aerospace &amp; Defense</th>\n",
       "      <td>Boeing</td>\n",
       "      <td>Aerospace and Defense</td>\n",
       "      <td>Chicago, IL</td>\n",
       "      <td>96114</td>\n",
       "      <td>5176</td>\n",
       "      <td>161400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apparel</th>\n",
       "      <td>Nike</td>\n",
       "      <td>Apparel</td>\n",
       "      <td>Beaverton, OR</td>\n",
       "      <td>30601</td>\n",
       "      <td>3273</td>\n",
       "      <td>62600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Business Services</th>\n",
       "      <td>ManpowerGroup</td>\n",
       "      <td>Temporary Help</td>\n",
       "      <td>Milwaukee, WI</td>\n",
       "      <td>19330</td>\n",
       "      <td>419</td>\n",
       "      <td>27000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chemicals</th>\n",
       "      <td>Dow Chemical</td>\n",
       "      <td>Chemicals</td>\n",
       "      <td>Midland, MI</td>\n",
       "      <td>48778</td>\n",
       "      <td>7685</td>\n",
       "      <td>49495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Energy</th>\n",
       "      <td>Exxon Mobil</td>\n",
       "      <td>Petroleum Refining</td>\n",
       "      <td>Irving, TX</td>\n",
       "      <td>246204</td>\n",
       "      <td>16150</td>\n",
       "      <td>75600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Engineering &amp; Construction</th>\n",
       "      <td>Fluor</td>\n",
       "      <td>Engineering, Construction</td>\n",
       "      <td>Irving, TX</td>\n",
       "      <td>18114</td>\n",
       "      <td>413</td>\n",
       "      <td>38758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Financials</th>\n",
       "      <td>Berkshire Hathaway</td>\n",
       "      <td>Insurance: Property and Casualty (Stock)</td>\n",
       "      <td>Omaha, NE</td>\n",
       "      <td>210821</td>\n",
       "      <td>24083</td>\n",
       "      <td>331000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Food and Drug Stores</th>\n",
       "      <td>CVS Health</td>\n",
       "      <td>Food and Drug Stores</td>\n",
       "      <td>Woonsocket, RI</td>\n",
       "      <td>153290</td>\n",
       "      <td>5237</td>\n",
       "      <td>199000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Food, Beverages &amp; Tobacco</th>\n",
       "      <td>Archer Daniels Midland</td>\n",
       "      <td>Food Production</td>\n",
       "      <td>Chicago, IL</td>\n",
       "      <td>67702</td>\n",
       "      <td>1849</td>\n",
       "      <td>32300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Health Care</th>\n",
       "      <td>McKesson</td>\n",
       "      <td>Wholesalers: Health Care</td>\n",
       "      <td>San Francisco, CA</td>\n",
       "      <td>181241</td>\n",
       "      <td>1476</td>\n",
       "      <td>70400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hotels, Resturants &amp; Leisure</th>\n",
       "      <td>McDonald’s</td>\n",
       "      <td>Food Services</td>\n",
       "      <td>Oak Brook, IL</td>\n",
       "      <td>25413</td>\n",
       "      <td>4529</td>\n",
       "      <td>420000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Household Products</th>\n",
       "      <td>Procter &amp; Gamble</td>\n",
       "      <td>Household and Personal Products</td>\n",
       "      <td>Cincinnati, OH</td>\n",
       "      <td>78756</td>\n",
       "      <td>7036</td>\n",
       "      <td>110000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Industrials</th>\n",
       "      <td>General Electric</td>\n",
       "      <td>Industrial Machinery</td>\n",
       "      <td>Fairfield, CT</td>\n",
       "      <td>140389</td>\n",
       "      <td>-6126</td>\n",
       "      <td>333000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Materials</th>\n",
       "      <td>Alcoa</td>\n",
       "      <td>Metals</td>\n",
       "      <td>New York, NY</td>\n",
       "      <td>22534</td>\n",
       "      <td>-322</td>\n",
       "      <td>60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Media</th>\n",
       "      <td>Disney</td>\n",
       "      <td>Entertainment</td>\n",
       "      <td>Burbank, CA</td>\n",
       "      <td>52465</td>\n",
       "      <td>8382</td>\n",
       "      <td>185000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Motor Vehicles &amp; Parts</th>\n",
       "      <td>General Motors</td>\n",
       "      <td>Motor Vehicles and Parts</td>\n",
       "      <td>Detroit, MI</td>\n",
       "      <td>152356</td>\n",
       "      <td>9687</td>\n",
       "      <td>215000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Retailing</th>\n",
       "      <td>Walmart</td>\n",
       "      <td>General Merchandisers</td>\n",
       "      <td>Bentonville, AR</td>\n",
       "      <td>482130</td>\n",
       "      <td>14694</td>\n",
       "      <td>2300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Technology</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Computers, Office Equipment</td>\n",
       "      <td>Cupertino, CA</td>\n",
       "      <td>233715</td>\n",
       "      <td>53394</td>\n",
       "      <td>110000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Telecommunications</th>\n",
       "      <td>AT&amp;T</td>\n",
       "      <td>Telecommunications</td>\n",
       "      <td>Dallas, TX</td>\n",
       "      <td>146801</td>\n",
       "      <td>13345</td>\n",
       "      <td>281450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transportation</th>\n",
       "      <td>UPS</td>\n",
       "      <td>Mail, Package, and Freight Delivery</td>\n",
       "      <td>Atlanta, GA</td>\n",
       "      <td>58363</td>\n",
       "      <td>4844</td>\n",
       "      <td>341240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wholesalers</th>\n",
       "      <td>Sysco</td>\n",
       "      <td>Wholesalers: Food and Grocery</td>\n",
       "      <td>Houston, TX</td>\n",
       "      <td>48681</td>\n",
       "      <td>687</td>\n",
       "      <td>51700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             Company  \\\n",
       "Sector                                                 \n",
       "Aerospace & Defense                           Boeing   \n",
       "Apparel                                         Nike   \n",
       "Business Services                      ManpowerGroup   \n",
       "Chemicals                               Dow Chemical   \n",
       "Energy                                   Exxon Mobil   \n",
       "Engineering & Construction                     Fluor   \n",
       "Financials                        Berkshire Hathaway   \n",
       "Food and Drug Stores                      CVS Health   \n",
       "Food, Beverages & Tobacco     Archer Daniels Midland   \n",
       "Health Care                                 McKesson   \n",
       "Hotels, Resturants & Leisure              McDonald’s   \n",
       "Household Products                  Procter & Gamble   \n",
       "Industrials                         General Electric   \n",
       "Materials                                      Alcoa   \n",
       "Media                                         Disney   \n",
       "Motor Vehicles & Parts                General Motors   \n",
       "Retailing                                    Walmart   \n",
       "Technology                                     Apple   \n",
       "Telecommunications                              AT&T   \n",
       "Transportation                                   UPS   \n",
       "Wholesalers                                    Sysco   \n",
       "\n",
       "                                                              Industry  \\\n",
       "Sector                                                                   \n",
       "Aerospace & Defense                              Aerospace and Defense   \n",
       "Apparel                                                        Apparel   \n",
       "Business Services                                       Temporary Help   \n",
       "Chemicals                                                    Chemicals   \n",
       "Energy                                              Petroleum Refining   \n",
       "Engineering & Construction                   Engineering, Construction   \n",
       "Financials                    Insurance: Property and Casualty (Stock)   \n",
       "Food and Drug Stores                              Food and Drug Stores   \n",
       "Food, Beverages & Tobacco                              Food Production   \n",
       "Health Care                                   Wholesalers: Health Care   \n",
       "Hotels, Resturants & Leisure                             Food Services   \n",
       "Household Products                     Household and Personal Products   \n",
       "Industrials                                       Industrial Machinery   \n",
       "Materials                                                       Metals   \n",
       "Media                                                    Entertainment   \n",
       "Motor Vehicles & Parts                        Motor Vehicles and Parts   \n",
       "Retailing                                        General Merchandisers   \n",
       "Technology                                 Computers, Office Equipment   \n",
       "Telecommunications                                  Telecommunications   \n",
       "Transportation                     Mail, Package, and Freight Delivery   \n",
       "Wholesalers                              Wholesalers: Food and Grocery   \n",
       "\n",
       "                                       Location  Revenue  Profits  Employees  \n",
       "Sector                                                                        \n",
       "Aerospace & Defense                 Chicago, IL    96114     5176     161400  \n",
       "Apparel                           Beaverton, OR    30601     3273      62600  \n",
       "Business Services                 Milwaukee, WI    19330      419      27000  \n",
       "Chemicals                           Midland, MI    48778     7685      49495  \n",
       "Energy                               Irving, TX   246204    16150      75600  \n",
       "Engineering & Construction           Irving, TX    18114      413      38758  \n",
       "Financials                            Omaha, NE   210821    24083     331000  \n",
       "Food and Drug Stores             Woonsocket, RI   153290     5237     199000  \n",
       "Food, Beverages & Tobacco           Chicago, IL    67702     1849      32300  \n",
       "Health Care                   San Francisco, CA   181241     1476      70400  \n",
       "Hotels, Resturants & Leisure      Oak Brook, IL    25413     4529     420000  \n",
       "Household Products               Cincinnati, OH    78756     7036     110000  \n",
       "Industrials                       Fairfield, CT   140389    -6126     333000  \n",
       "Materials                          New York, NY    22534     -322      60000  \n",
       "Media                               Burbank, CA    52465     8382     185000  \n",
       "Motor Vehicles & Parts              Detroit, MI   152356     9687     215000  \n",
       "Retailing                       Bentonville, AR   482130    14694    2300000  \n",
       "Technology                        Cupertino, CA   233715    53394     110000  \n",
       "Telecommunications                   Dallas, TX   146801    13345     281450  \n",
       "Transportation                      Atlanta, GA    58363     4844     341240  \n",
       "Wholesalers                         Houston, TX    48681      687      51700  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sector.first() #grupların ilk değerini alır"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9bbfad0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>Industry</th>\n",
       "      <th>Location</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Profits</th>\n",
       "      <th>Employees</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sector</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>Aerospace &amp; Defense</th>\n",
       "      <td>Delta Tucker Holdings</td>\n",
       "      <td>Aerospace and Defense</td>\n",
       "      <td>McLean, VA</td>\n",
       "      <td>1923</td>\n",
       "      <td>-133</td>\n",
       "      <td>12000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apparel</th>\n",
       "      <td>Guess</td>\n",
       "      <td>Apparel</td>\n",
       "      <td>Los Angeles, CA</td>\n",
       "      <td>2204</td>\n",
       "      <td>82</td>\n",
       "      <td>13500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Business Services</th>\n",
       "      <td>DeVry Education Group</td>\n",
       "      <td>Education</td>\n",
       "      <td>Downers Grove, IL</td>\n",
       "      <td>1910</td>\n",
       "      <td>140</td>\n",
       "      <td>11770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chemicals</th>\n",
       "      <td>H.B. Fuller</td>\n",
       "      <td>Chemicals</td>\n",
       "      <td>St. Paul, MN</td>\n",
       "      <td>2084</td>\n",
       "      <td>87</td>\n",
       "      <td>4425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Energy</th>\n",
       "      <td>Portland General Electric</td>\n",
       "      <td>Utilities: Gas and Electric</td>\n",
       "      <td>Portland, OR</td>\n",
       "      <td>1898</td>\n",
       "      <td>172</td>\n",
       "      <td>2646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Engineering &amp; Construction</th>\n",
       "      <td>MDC Holdings</td>\n",
       "      <td>Homebuilders</td>\n",
       "      <td>Denver, CO</td>\n",
       "      <td>1909</td>\n",
       "      <td>66</td>\n",
       "      <td>1225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Financials</th>\n",
       "      <td>New York Community Bancorp</td>\n",
       "      <td>Commercial Banks</td>\n",
       "      <td>Westbury, NY</td>\n",
       "      <td>1902</td>\n",
       "      <td>-47</td>\n",
       "      <td>3448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Food and Drug Stores</th>\n",
       "      <td>Fred’s</td>\n",
       "      <td>Food and Drug Stores</td>\n",
       "      <td>Memphis, TN</td>\n",
       "      <td>2151</td>\n",
       "      <td>-7</td>\n",
       "      <td>7103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Food, Beverages &amp; Tobacco</th>\n",
       "      <td>Alliance One International</td>\n",
       "      <td>Tobacco</td>\n",
       "      <td>Morrisville, NC</td>\n",
       "      <td>2066</td>\n",
       "      <td>-15</td>\n",
       "      <td>6835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Health Care</th>\n",
       "      <td>Providence Service</td>\n",
       "      <td>Health Care: Pharmacy and Other Services</td>\n",
       "      <td>Tucson, AZ</td>\n",
       "      <td>1987</td>\n",
       "      <td>84</td>\n",
       "      <td>9072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hotels, Resturants &amp; Leisure</th>\n",
       "      <td>Wendy’s</td>\n",
       "      <td>Food Services</td>\n",
       "      <td>Dublin, OH</td>\n",
       "      <td>1896</td>\n",
       "      <td>161</td>\n",
       "      <td>21200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Household Products</th>\n",
       "      <td>Revlon</td>\n",
       "      <td>Household and Personal Products</td>\n",
       "      <td>New York, NY</td>\n",
       "      <td>1914</td>\n",
       "      <td>56</td>\n",
       "      <td>5700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Industrials</th>\n",
       "      <td>Briggs &amp; Stratton</td>\n",
       "      <td>Industrial Machinery</td>\n",
       "      <td>Wauwatosa, WI</td>\n",
       "      <td>1895</td>\n",
       "      <td>46</td>\n",
       "      <td>5480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Materials</th>\n",
       "      <td>Schnitzer Steel Industries</td>\n",
       "      <td>Metals</td>\n",
       "      <td>Portland, OR</td>\n",
       "      <td>1924</td>\n",
       "      <td>-197</td>\n",
       "      <td>2955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Media</th>\n",
       "      <td>Cenveo</td>\n",
       "      <td>Publishing, Printing</td>\n",
       "      <td>Stamford, CT</td>\n",
       "      <td>1921</td>\n",
       "      <td>-31</td>\n",
       "      <td>7300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Motor Vehicles &amp; Parts</th>\n",
       "      <td>Allison Transmission Holdings</td>\n",
       "      <td>Motor Vehicles and Parts</td>\n",
       "      <td>Indianaoplis, IN</td>\n",
       "      <td>1986</td>\n",
       "      <td>182</td>\n",
       "      <td>2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Retailing</th>\n",
       "      <td>99 Cents Only Stores</td>\n",
       "      <td>Specialty Retailers: Other</td>\n",
       "      <td>Commerce, CA</td>\n",
       "      <td>1999</td>\n",
       "      <td>-232</td>\n",
       "      <td>18200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Technology</th>\n",
       "      <td>F5 Networks</td>\n",
       "      <td>Network and Other Communications Equipment</td>\n",
       "      <td>Seattle, WA</td>\n",
       "      <td>1920</td>\n",
       "      <td>365</td>\n",
       "      <td>4178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Telecommunications</th>\n",
       "      <td>Equinix</td>\n",
       "      <td>Telecommunications</td>\n",
       "      <td>Redwood City, CA</td>\n",
       "      <td>2726</td>\n",
       "      <td>188</td>\n",
       "      <td>5042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transportation</th>\n",
       "      <td>Roadrunner Transportation Systems</td>\n",
       "      <td>Transportation and Logistics</td>\n",
       "      <td>Cudahy, WI</td>\n",
       "      <td>1995</td>\n",
       "      <td>48</td>\n",
       "      <td>4502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wholesalers</th>\n",
       "      <td>BlueLinx Holdings</td>\n",
       "      <td>Wholesalers: Diversified</td>\n",
       "      <td>Atlanta, GA</td>\n",
       "      <td>1917</td>\n",
       "      <td>-12</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "                                                        Company  \\\n",
       "Sector                                                            \n",
       "Aerospace & Defense                       Delta Tucker Holdings   \n",
       "Apparel                                                   Guess   \n",
       "Business Services                         DeVry Education Group   \n",
       "Chemicals                                           H.B. Fuller   \n",
       "Energy                                Portland General Electric   \n",
       "Engineering & Construction                         MDC Holdings   \n",
       "Financials                           New York Community Bancorp   \n",
       "Food and Drug Stores                                     Fred’s   \n",
       "Food, Beverages & Tobacco            Alliance One International   \n",
       "Health Care                                  Providence Service   \n",
       "Hotels, Resturants & Leisure                            Wendy’s   \n",
       "Household Products                                       Revlon   \n",
       "Industrials                                   Briggs & Stratton   \n",
       "Materials                            Schnitzer Steel Industries   \n",
       "Media                                                    Cenveo   \n",
       "Motor Vehicles & Parts            Allison Transmission Holdings   \n",
       "Retailing                                  99 Cents Only Stores   \n",
       "Technology                                          F5 Networks   \n",
       "Telecommunications                                      Equinix   \n",
       "Transportation                Roadrunner Transportation Systems   \n",
       "Wholesalers                                   BlueLinx Holdings   \n",
       "\n",
       "                                                                Industry  \\\n",
       "Sector                                                                     \n",
       "Aerospace & Defense                                Aerospace and Defense   \n",
       "Apparel                                                          Apparel   \n",
       "Business Services                                              Education   \n",
       "Chemicals                                                      Chemicals   \n",
       "Energy                                       Utilities: Gas and Electric   \n",
       "Engineering & Construction                                  Homebuilders   \n",
       "Financials                                              Commercial Banks   \n",
       "Food and Drug Stores                                Food and Drug Stores   \n",
       "Food, Beverages & Tobacco                                        Tobacco   \n",
       "Health Care                     Health Care: Pharmacy and Other Services   \n",
       "Hotels, Resturants & Leisure                               Food Services   \n",
       "Household Products                       Household and Personal Products   \n",
       "Industrials                                         Industrial Machinery   \n",
       "Materials                                                         Metals   \n",
       "Media                                               Publishing, Printing   \n",
       "Motor Vehicles & Parts                          Motor Vehicles and Parts   \n",
       "Retailing                                     Specialty Retailers: Other   \n",
       "Technology                    Network and Other Communications Equipment   \n",
       "Telecommunications                                    Telecommunications   \n",
       "Transportation                              Transportation and Logistics   \n",
       "Wholesalers                                     Wholesalers: Diversified   \n",
       "\n",
       "                                       Location  Revenue  Profits  Employees  \n",
       "Sector                                                                        \n",
       "Aerospace & Defense                  McLean, VA     1923     -133      12000  \n",
       "Apparel                         Los Angeles, CA     2204       82      13500  \n",
       "Business Services             Downers Grove, IL     1910      140      11770  \n",
       "Chemicals                          St. Paul, MN     2084       87       4425  \n",
       "Energy                             Portland, OR     1898      172       2646  \n",
       "Engineering & Construction           Denver, CO     1909       66       1225  \n",
       "Financials                         Westbury, NY     1902      -47       3448  \n",
       "Food and Drug Stores                Memphis, TN     2151       -7       7103  \n",
       "Food, Beverages & Tobacco       Morrisville, NC     2066      -15       6835  \n",
       "Health Care                          Tucson, AZ     1987       84       9072  \n",
       "Hotels, Resturants & Leisure         Dublin, OH     1896      161      21200  \n",
       "Household Products                 New York, NY     1914       56       5700  \n",
       "Industrials                       Wauwatosa, WI     1895       46       5480  \n",
       "Materials                          Portland, OR     1924     -197       2955  \n",
       "Media                              Stamford, CT     1921      -31       7300  \n",
       "Motor Vehicles & Parts         Indianaoplis, IN     1986      182       2700  \n",
       "Retailing                          Commerce, CA     1999     -232      18200  \n",
       "Technology                          Seattle, WA     1920      365       4178  \n",
       "Telecommunications             Redwood City, CA     2726      188       5042  \n",
       "Transportation                       Cudahy, WI     1995       48       4502  \n",
       "Wholesalers                         Atlanta, GA     1917      -12       1600  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sector.last() #grupların son değerini alır  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4a0bb76d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Aerospace & Defense': [24, 45, 60, 88, 118, 120, 209, 245, 282, 378, 389, 490, 560, 605, 785, 788, 836, 903, 958, 987], 'Apparel': [91, 231, 340, 354, 448, 547, 575, 597, 683, 695, 726, 794, 877, 882, 917], 'Business Services': [144, 186, 199, 204, 221, 248, 249, 294, 307, 312, 355, 392, 404, 440, 467, 468, 481, 485, 492, 503, 545, 626, 635, 652, 677, 694, 714, 729, 734, 735, 737, 744, 767, 776, 777, 783, 791, 792, 796, 801, 803, 816, 819, 820, 869, 870, 886, 939, 951, 952, 993], 'Chemicals': [56, 101, 182, 189, 206, 253, 262, 277, 288, 296, 316, 538, 549, 555, 566, 580, 613, 624, 654, 668, 717, 720, 724, 758, 761, 829, 865, 898, 934, 949], 'Energy': [2, 14, 30, 32, 42, 65, 90, 95, 98, 104, 115, 117, 121, 162, 163, 165, 166, 175, 178, 188, 190, 192, 193, 198, 214, 216, 217, 223, 225, 229, 243, 246, 247, 257, 272, 274, 279, 289, 319, 322, 324, 343, 348, 349, 350, 363, 364, 384, 387, 388, 394, 402, 403, 410, 425, 437, 438, 445, 458, 475, 483, 493, 507, 522, 541, 548, 556, 558, 569, 571, 579, 598, 600, 606, 633, 650, 656, 662, 679, 692, 696, 703, 703, 706, 711, 715, 732, 742, 743, 745, 754, 772, 784, 798, 802, 810, 817, 826, 843, 850, ...], 'Engineering & Construction': [155, 156, 235, 260, 301, 314, 352, 381, 433, 478, 498, 501, 516, 572, 576, 640, 719, 738, 822, 863, 874, 892, 929, 963, 985, 994], 'Financials': [4, 16, 23, 26, 27, 29, 35, 40, 43, 49, 50, 61, 69, 73, 74, 76, 78, 81, 82, 83, 85, 100, 105, 112, 114, 131, 135, 137, 152, 171, 179, 184, 205, 210, 222, 226, 227, 232, 236, 250, 252, 259, 264, 265, 271, 273, 275, 283, 298, 306, 311, 318, 326, 329, 332, 344, 347, 367, 368, 376, 382, 398, 401, 411, 421, 436, 442, 449, 453, 453, 471, 472, 472, 476, 479, 482, 486, 488, 494, 497, 499, 506, 509, 510, 520, 529, 531, 536, 540, 551, 561, 568, 573, 590, 603, 609, 611, 646, 651, 663, ...], 'Food and Drug Stores': [7, 17, 19, 87, 107, 160, 181, 595, 614, 631, 669, 756, 809, 849, 928], 'Food, Beverages & Tobacco': [41, 44, 62, 66, 84, 94, 106, 149, 153, 161, 176, 207, 215, 242, 266, 304, 336, 337, 362, 397, 413, 429, 452, 456, 460, 535, 542, 567, 574, 587, 608, 615, 636, 664, 686, 702, 770, 787, 795, 804, 889, 902, 954], 'Health Care': [5, 6, 12, 21, 22, 33, 39, 46, 52, 55, 63, 72, 79, 86, 123, 124, 125, 130, 138, 140, 141, 168, 172, 200, 201, 202, 263, 268, 278, 286, 287, 290, 291, 305, 325, 358, 359, 372, 420, 430, 431, 447, 457, 465, 469, 515, 527, 537, 553, 559, 583, 618, 629, 661, 684, 693, 697, 708, 746, 749, 766, 775, 790, 799, 815, 818, 845, 868, 881, 883, 935, 960, 965, 977, 978], 'Hotels, Resturants & Leisure': [109, 146, 195, 218, 241, 254, 309, 371, 444, 466, 534, 546, 557, 562, 585, 731, 763, 764, 779, 797, 895, 915, 918, 941, 999], 'Household Products': [34, 151, 174, 256, 261, 328, 338, 345, 370, 434, 441, 450, 455, 533, 550, 554, 601, 665, 699, 716, 856, 890, 900, 907, 932, 950, 967, 992], 'Industrials': [11, 59, 75, 93, 97, 128, 134, 148, 211, 224, 360, 377, 396, 412, 419, 539, 544, 596, 622, 623, 630, 645, 655, 658, 666, 674, 690, 757, 782, 789, 806, 813, 823, 831, 832, 835, 841, 847, 866, 867, 873, 887, 942, 947, 964, 1000], 'Materials': [126, 127, 170, 244, 251, 303, 321, 341, 356, 373, 375, 383, 417, 418, 426, 435, 446, 480, 489, 514, 518, 577, 586, 616, 617, 620, 625, 628, 637, 639, 641, 660, 667, 687, 691, 751, 774, 793, 857, 885, 911, 982, 986], 'Media': [53, 96, 99, 203, 213, 255, 327, 366, 406, 414, 525, 530, 681, 705, 707, 722, 741, 752, 759, 762, 821, 913, 968, 969, 989], 'Motor Vehicles & Parts': [8, 9, 70, 147, 154, 169, 281, 310, 334, 339, 424, 428, 470, 588, 591, 604, 647, 670, 718, 739, 811, 957, 961, 979], 'Retailing': [1, 15, 28, 38, 47, 71, 89, 103, 111, 132, 136, 139, 143, 145, 177, 180, 191, 196, 197, 228, 234, 237, 238, 240, 258, 267, 269, 280, 293, 297, 299, 302, 330, 342, 346, 361, 365, 374, 379, 380, 393, 415, 427, 439, 495, 500, 502, 511, 512, 517, 523, 582, 592, 602, 607, 610, 621, 643, 644, 648, 657, 675, 689, 721, 748, 771, 773, 807, 812, 824, 825, 827, 840, 876, 894, 899, 922, 937, 940, 973], 'Technology': [3, 18, 20, 25, 31, 36, 51, 54, 77, 110, 113, 133, 150, 157, 158, 164, 173, 194, 219, 220, 230, 233, 284, 295, 300, 308, 313, 331, 385, 386, 400, 408, 409, 422, 451, 462, 464, 487, 491, 504, 505, 508, 513, 519, 524, 532, 543, 552, 563, 565, 584, 589, 593, 594, 612, 632, 634, 638, 649, 659, 673, 676, 678, 682, 688, 700, 725, 733, 736, 753, 760, 769, 778, 805, 830, 834, 842, 854, 864, 871, 888, 891, 893, 896, 904, 905, 906, 909, 912, 914, 919, 927, 931, 943, 944, 946, 956, 970, 971, 975, ...], 'Telecommunications': [10, 13, 37, 116, 159, 187, 292, 333, 399, 443, 461, 496, 526, 619, 786], 'Transportation': [48, 58, 67, 68, 80, 129, 142, 208, 239, 270, 353, 390, 395, 405, 407, 416, 432, 459, 521, 528, 570, 578, 642, 672, 709, 713, 740, 800, 814, 858, 884, 930, 933, 945, 972, 974], 'Wholesalers': [57, 64, 92, 102, 108, 119, 122, 167, 183, 185, 212, 276, 285, 315, 317, 320, 323, 335, 351, 357, 369, 391, 423, 463, 474, 477, 484, 564, 581, 599, 627, 653, 685, 727, 747, 780, 808, 837, 875, 991]}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sector.groups #grupların bulunduğu satır idleri ile verir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "29fe8aa6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>Sector</th>\n",
       "      <th>Industry</th>\n",
       "      <th>Location</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Profits</th>\n",
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       "      <th>Rank</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>2</th>\n",
       "      <td>Exxon Mobil</td>\n",
       "      <td>Energy</td>\n",
       "      <td>Petroleum Refining</td>\n",
       "      <td>Irving, TX</td>\n",
       "      <td>246204</td>\n",
       "      <td>16150</td>\n",
       "      <td>75600</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Chevron</td>\n",
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       "      <td>San Ramon, CA</td>\n",
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       "      <td>4587</td>\n",
       "      <td>61500</td>\n",
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       "      <th>30</th>\n",
       "      <td>Phillips 66</td>\n",
       "      <td>Energy</td>\n",
       "      <td>Petroleum Refining</td>\n",
       "      <td>Houston, TX</td>\n",
       "      <td>87169</td>\n",
       "      <td>4227</td>\n",
       "      <td>14000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Valero Energy</td>\n",
       "      <td>Energy</td>\n",
       "      <td>Petroleum Refining</td>\n",
       "      <td>San Antonio, TX</td>\n",
       "      <td>81824</td>\n",
       "      <td>3990</td>\n",
       "      <td>10103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Marathon Petroleum</td>\n",
       "      <td>Energy</td>\n",
       "      <td>Petroleum Refining</td>\n",
       "      <td>Findlay, OH</td>\n",
       "      <td>64566</td>\n",
       "      <td>2852</td>\n",
       "      <td>45440</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981</th>\n",
       "      <td>WPX Energy</td>\n",
       "      <td>Energy</td>\n",
       "      <td>Mining, Crude-Oil Production</td>\n",
       "      <td>Tulsa, OK</td>\n",
       "      <td>1958</td>\n",
       "      <td>-1727</td>\n",
       "      <td>1040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>983</th>\n",
       "      <td>Adams Resources &amp; Energy</td>\n",
       "      <td>Energy</td>\n",
       "      <td>Petroleum Refining</td>\n",
       "      <td>Houston, TX</td>\n",
       "      <td>1944</td>\n",
       "      <td>-1</td>\n",
       "      <td>809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>EP Energy</td>\n",
       "      <td>Energy</td>\n",
       "      <td>Mining, Crude-Oil Production</td>\n",
       "      <td>Houston, TX</td>\n",
       "      <td>1908</td>\n",
       "      <td>-3748</td>\n",
       "      <td>665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>Portland General Electric</td>\n",
       "      <td>Energy</td>\n",
       "      <td>Utilities: Gas and Electric</td>\n",
       "      <td>Portland, OR</td>\n",
       "      <td>1898</td>\n",
       "      <td>172</td>\n",
       "      <td>2646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>Portland General Electric</td>\n",
       "      <td>Energy</td>\n",
       "      <td>Utilities: Gas and Electric</td>\n",
       "      <td>Portland, OR</td>\n",
       "      <td>1898</td>\n",
       "      <td>172</td>\n",
       "      <td>2646</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>122 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Company  Sector                      Industry  \\\n",
       "Rank                                                                    \n",
       "2                   Exxon Mobil  Energy            Petroleum Refining   \n",
       "14                      Chevron  Energy            Petroleum Refining   \n",
       "30                  Phillips 66  Energy            Petroleum Refining   \n",
       "32                Valero Energy  Energy            Petroleum Refining   \n",
       "42           Marathon Petroleum  Energy            Petroleum Refining   \n",
       "...                         ...     ...                           ...   \n",
       "981                  WPX Energy  Energy  Mining, Crude-Oil Production   \n",
       "983    Adams Resources & Energy  Energy            Petroleum Refining   \n",
       "995                   EP Energy  Energy  Mining, Crude-Oil Production   \n",
       "997   Portland General Electric  Energy   Utilities: Gas and Electric   \n",
       "997   Portland General Electric  Energy   Utilities: Gas and Electric   \n",
       "\n",
       "             Location  Revenue  Profits  Employees  \n",
       "Rank                                                \n",
       "2          Irving, TX   246204    16150      75600  \n",
       "14      San Ramon, CA   131118     4587      61500  \n",
       "30        Houston, TX    87169     4227      14000  \n",
       "32    San Antonio, TX    81824     3990      10103  \n",
       "42        Findlay, OH    64566     2852      45440  \n",
       "...               ...      ...      ...        ...  \n",
       "981         Tulsa, OK     1958    -1727       1040  \n",
       "983       Houston, TX     1944       -1        809  \n",
       "995       Houston, TX     1908    -3748        665  \n",
       "997      Portland, OR     1898      172       2646  \n",
       "997      Portland, OR     1898      172       2646  \n",
       "\n",
       "[122 rows x 7 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sector.get_group(\"Energy\") #Energy grubundaki değerleri döner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "dfd93871",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>Sector</th>\n",
       "      <th>Industry</th>\n",
       "      <th>Location</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Profits</th>\n",
       "      <th>Employees</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rank</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>2</th>\n",
       "      <td>Exxon Mobil</td>\n",
       "      <td>Energy</td>\n",
       "      <td>Petroleum Refining</td>\n",
       "      <td>Irving, TX</td>\n",
       "      <td>246204</td>\n",
       "      <td>16150</td>\n",
       "      <td>75600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Chevron</td>\n",
       "      <td>Energy</td>\n",
       "      <td>Petroleum Refining</td>\n",
       "      <td>San Ramon, CA</td>\n",
       "      <td>131118</td>\n",
       "      <td>4587</td>\n",
       "      <td>61500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Phillips 66</td>\n",
       "      <td>Energy</td>\n",
       "      <td>Petroleum Refining</td>\n",
       "      <td>Houston, TX</td>\n",
       "      <td>87169</td>\n",
       "      <td>4227</td>\n",
       "      <td>14000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Valero Energy</td>\n",
       "      <td>Energy</td>\n",
       "      <td>Petroleum Refining</td>\n",
       "      <td>San Antonio, TX</td>\n",
       "      <td>81824</td>\n",
       "      <td>3990</td>\n",
       "      <td>10103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Marathon Petroleum</td>\n",
       "      <td>Energy</td>\n",
       "      <td>Petroleum Refining</td>\n",
       "      <td>Findlay, OH</td>\n",
       "      <td>64566</td>\n",
       "      <td>2852</td>\n",
       "      <td>45440</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981</th>\n",
       "      <td>WPX Energy</td>\n",
       "      <td>Energy</td>\n",
       "      <td>Mining, Crude-Oil Production</td>\n",
       "      <td>Tulsa, OK</td>\n",
       "      <td>1958</td>\n",
       "      <td>-1727</td>\n",
       "      <td>1040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>983</th>\n",
       "      <td>Adams Resources &amp; Energy</td>\n",
       "      <td>Energy</td>\n",
       "      <td>Petroleum Refining</td>\n",
       "      <td>Houston, TX</td>\n",
       "      <td>1944</td>\n",
       "      <td>-1</td>\n",
       "      <td>809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>EP Energy</td>\n",
       "      <td>Energy</td>\n",
       "      <td>Mining, Crude-Oil Production</td>\n",
       "      <td>Houston, TX</td>\n",
       "      <td>1908</td>\n",
       "      <td>-3748</td>\n",
       "      <td>665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>Portland General Electric</td>\n",
       "      <td>Energy</td>\n",
       "      <td>Utilities: Gas and Electric</td>\n",
       "      <td>Portland, OR</td>\n",
       "      <td>1898</td>\n",
       "      <td>172</td>\n",
       "      <td>2646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>Portland General Electric</td>\n",
       "      <td>Energy</td>\n",
       "      <td>Utilities: Gas and Electric</td>\n",
       "      <td>Portland, OR</td>\n",
       "      <td>1898</td>\n",
       "      <td>172</td>\n",
       "      <td>2646</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>122 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Company  Sector                      Industry  \\\n",
       "Rank                                                                    \n",
       "2                   Exxon Mobil  Energy            Petroleum Refining   \n",
       "14                      Chevron  Energy            Petroleum Refining   \n",
       "30                  Phillips 66  Energy            Petroleum Refining   \n",
       "32                Valero Energy  Energy            Petroleum Refining   \n",
       "42           Marathon Petroleum  Energy            Petroleum Refining   \n",
       "...                         ...     ...                           ...   \n",
       "981                  WPX Energy  Energy  Mining, Crude-Oil Production   \n",
       "983    Adams Resources & Energy  Energy            Petroleum Refining   \n",
       "995                   EP Energy  Energy  Mining, Crude-Oil Production   \n",
       "997   Portland General Electric  Energy   Utilities: Gas and Electric   \n",
       "997   Portland General Electric  Energy   Utilities: Gas and Electric   \n",
       "\n",
       "             Location  Revenue  Profits  Employees  \n",
       "Rank                                                \n",
       "2          Irving, TX   246204    16150      75600  \n",
       "14      San Ramon, CA   131118     4587      61500  \n",
       "30        Houston, TX    87169     4227      14000  \n",
       "32    San Antonio, TX    81824     3990      10103  \n",
       "42        Findlay, OH    64566     2852      45440  \n",
       "...               ...      ...      ...        ...  \n",
       "981         Tulsa, OK     1958    -1727       1040  \n",
       "983       Houston, TX     1944       -1        809  \n",
       "995       Houston, TX     1908    -3748        665  \n",
       "997      Portland, OR     1898      172       2646  \n",
       "997      Portland, OR     1898      172       2646  \n",
       "\n",
       "[122 rows x 7 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fortune[df_fortune[\"Sector\"] == \"Energy\"] \n",
    "#Bu sorgu yukardaki sorgu ile ayn işlemi yapar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3dd7fbe4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>Industry</th>\n",
       "      <th>Location</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Profits</th>\n",
       "      <th>Employees</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sector</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>Aerospace &amp; Defense</th>\n",
       "      <td>Woodward</td>\n",
       "      <td>Aerospace and Defense</td>\n",
       "      <td>Wichita, KS</td>\n",
       "      <td>96114</td>\n",
       "      <td>7608</td>\n",
       "      <td>197200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apparel</th>\n",
       "      <td>Wolverine World Wide</td>\n",
       "      <td>Apparel</td>\n",
       "      <td>Winston-Salem, NC</td>\n",
       "      <td>30601</td>\n",
       "      <td>3273</td>\n",
       "      <td>65300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Business Services</th>\n",
       "      <td>Western Union</td>\n",
       "      <td>Waste Management</td>\n",
       "      <td>Troy, MI</td>\n",
       "      <td>19330</td>\n",
       "      <td>6328</td>\n",
       "      <td>216500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chemicals</th>\n",
       "      <td>Westlake Chemical</td>\n",
       "      <td>Chemicals</td>\n",
       "      <td>Wilmington, DE</td>\n",
       "      <td>48778</td>\n",
       "      <td>7685</td>\n",
       "      <td>52000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Energy</th>\n",
       "      <td>Xcel Energy</td>\n",
       "      <td>Utilities: Gas and Electric</td>\n",
       "      <td>Washington, DC</td>\n",
       "      <td>246204</td>\n",
       "      <td>16150</td>\n",
       "      <td>75600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Engineering &amp; Construction</th>\n",
       "      <td>Tutor Perini</td>\n",
       "      <td>Homebuilders</td>\n",
       "      <td>Watsonville, CA</td>\n",
       "      <td>18114</td>\n",
       "      <td>803</td>\n",
       "      <td>92000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Financials</th>\n",
       "      <td>Zions Bancorp.</td>\n",
       "      <td>Securities</td>\n",
       "      <td>Worcester, MA</td>\n",
       "      <td>210821</td>\n",
       "      <td>24442</td>\n",
       "      <td>331000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Food and Drug Stores</th>\n",
       "      <td>Whole Foods Market</td>\n",
       "      <td>Food and Drug Stores</td>\n",
       "      <td>Woonsocket, RI</td>\n",
       "      <td>153290</td>\n",
       "      <td>5237</td>\n",
       "      <td>431000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Food, Beverages &amp; Tobacco</th>\n",
       "      <td>WhiteWave Foods</td>\n",
       "      <td>Tobacco</td>\n",
       "      <td>Winston-Salem, NC</td>\n",
       "      <td>67702</td>\n",
       "      <td>7351</td>\n",
       "      <td>263000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Health Care</th>\n",
       "      <td>inVentiv Health</td>\n",
       "      <td>Wholesalers: Health Care</td>\n",
       "      <td>York, PA</td>\n",
       "      <td>181241</td>\n",
       "      <td>18108</td>\n",
       "      <td>203500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hotels, Resturants &amp; Leisure</th>\n",
       "      <td>Yum Brands</td>\n",
       "      <td>Hotels, Casinos, Resorts</td>\n",
       "      <td>Wyomissing, PA</td>\n",
       "      <td>25413</td>\n",
       "      <td>5920</td>\n",
       "      <td>505000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Household Products</th>\n",
       "      <td>Vista Outdoor</td>\n",
       "      <td>Miscellaneous</td>\n",
       "      <td>Zeeland, MI</td>\n",
       "      <td>78756</td>\n",
       "      <td>7036</td>\n",
       "      <td>110000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Industrials</th>\n",
       "      <td>Zebra Technologies</td>\n",
       "      <td>Miscellaneous</td>\n",
       "      <td>Wilmerding, PA</td>\n",
       "      <td>140389</td>\n",
       "      <td>4833</td>\n",
       "      <td>333000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Materials</th>\n",
       "      <td>Worthington Industries</td>\n",
       "      <td>Packaging, Containers</td>\n",
       "      <td>Wyomissing, PA</td>\n",
       "      <td>22534</td>\n",
       "      <td>991</td>\n",
       "      <td>60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Media</th>\n",
       "      <td>iHeartMedia</td>\n",
       "      <td>Publishing, Printing</td>\n",
       "      <td>Sussex, WI</td>\n",
       "      <td>52465</td>\n",
       "      <td>8382</td>\n",
       "      <td>185000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Motor Vehicles &amp; Parts</th>\n",
       "      <td>Wabash National</td>\n",
       "      <td>Motor Vehicles and Parts</td>\n",
       "      <td>Van Buren Township, MI</td>\n",
       "      <td>152356</td>\n",
       "      <td>9687</td>\n",
       "      <td>215000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Retailing</th>\n",
       "      <td>hhgregg</td>\n",
       "      <td>Specialty Retailers: Other</td>\n",
       "      <td>York, PA</td>\n",
       "      <td>482130</td>\n",
       "      <td>14694</td>\n",
       "      <td>2300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Technology</th>\n",
       "      <td>salesforce.com</td>\n",
       "      <td>Semiconductors and Other Electronic Components</td>\n",
       "      <td>Woburn, MA</td>\n",
       "      <td>233715</td>\n",
       "      <td>53394</td>\n",
       "      <td>411798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Telecommunications</th>\n",
       "      <td>Windstream Holdings</td>\n",
       "      <td>Telecommunications</td>\n",
       "      <td>Stamford, CT</td>\n",
       "      <td>146801</td>\n",
       "      <td>17879</td>\n",
       "      <td>281450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transportation</th>\n",
       "      <td>YRC Worldwide</td>\n",
       "      <td>Trucking, Truck Leasing</td>\n",
       "      <td>Thomasville, NC</td>\n",
       "      <td>58363</td>\n",
       "      <td>7610</td>\n",
       "      <td>341240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wholesalers</th>\n",
       "      <td>World Fuel Services</td>\n",
       "      <td>Wholesalers: Food and Grocery</td>\n",
       "      <td>Waltham, MA</td>\n",
       "      <td>48681</td>\n",
       "      <td>1472</td>\n",
       "      <td>78500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             Company  \\\n",
       "Sector                                                 \n",
       "Aerospace & Defense                         Woodward   \n",
       "Apparel                         Wolverine World Wide   \n",
       "Business Services                      Western Union   \n",
       "Chemicals                          Westlake Chemical   \n",
       "Energy                                   Xcel Energy   \n",
       "Engineering & Construction              Tutor Perini   \n",
       "Financials                            Zions Bancorp.   \n",
       "Food and Drug Stores              Whole Foods Market   \n",
       "Food, Beverages & Tobacco            WhiteWave Foods   \n",
       "Health Care                          inVentiv Health   \n",
       "Hotels, Resturants & Leisure              Yum Brands   \n",
       "Household Products                     Vista Outdoor   \n",
       "Industrials                       Zebra Technologies   \n",
       "Materials                     Worthington Industries   \n",
       "Media                                    iHeartMedia   \n",
       "Motor Vehicles & Parts               Wabash National   \n",
       "Retailing                                    hhgregg   \n",
       "Technology                            salesforce.com   \n",
       "Telecommunications               Windstream Holdings   \n",
       "Transportation                         YRC Worldwide   \n",
       "Wholesalers                      World Fuel Services   \n",
       "\n",
       "                                                                    Industry  \\\n",
       "Sector                                                                         \n",
       "Aerospace & Defense                                    Aerospace and Defense   \n",
       "Apparel                                                              Apparel   \n",
       "Business Services                                           Waste Management   \n",
       "Chemicals                                                          Chemicals   \n",
       "Energy                                           Utilities: Gas and Electric   \n",
       "Engineering & Construction                                      Homebuilders   \n",
       "Financials                                                        Securities   \n",
       "Food and Drug Stores                                    Food and Drug Stores   \n",
       "Food, Beverages & Tobacco                                            Tobacco   \n",
       "Health Care                                         Wholesalers: Health Care   \n",
       "Hotels, Resturants & Leisure                        Hotels, Casinos, Resorts   \n",
       "Household Products                                             Miscellaneous   \n",
       "Industrials                                                    Miscellaneous   \n",
       "Materials                                              Packaging, Containers   \n",
       "Media                                                   Publishing, Printing   \n",
       "Motor Vehicles & Parts                              Motor Vehicles and Parts   \n",
       "Retailing                                         Specialty Retailers: Other   \n",
       "Technology                    Semiconductors and Other Electronic Components   \n",
       "Telecommunications                                        Telecommunications   \n",
       "Transportation                                       Trucking, Truck Leasing   \n",
       "Wholesalers                                    Wholesalers: Food and Grocery   \n",
       "\n",
       "                                            Location  Revenue  Profits  \\\n",
       "Sector                                                                   \n",
       "Aerospace & Defense                      Wichita, KS    96114     7608   \n",
       "Apparel                            Winston-Salem, NC    30601     3273   \n",
       "Business Services                           Troy, MI    19330     6328   \n",
       "Chemicals                             Wilmington, DE    48778     7685   \n",
       "Energy                                Washington, DC   246204    16150   \n",
       "Engineering & Construction           Watsonville, CA    18114      803   \n",
       "Financials                             Worcester, MA   210821    24442   \n",
       "Food and Drug Stores                  Woonsocket, RI   153290     5237   \n",
       "Food, Beverages & Tobacco          Winston-Salem, NC    67702     7351   \n",
       "Health Care                                 York, PA   181241    18108   \n",
       "Hotels, Resturants & Leisure          Wyomissing, PA    25413     5920   \n",
       "Household Products                       Zeeland, MI    78756     7036   \n",
       "Industrials                           Wilmerding, PA   140389     4833   \n",
       "Materials                             Wyomissing, PA    22534      991   \n",
       "Media                                     Sussex, WI    52465     8382   \n",
       "Motor Vehicles & Parts        Van Buren Township, MI   152356     9687   \n",
       "Retailing                                   York, PA   482130    14694   \n",
       "Technology                                Woburn, MA   233715    53394   \n",
       "Telecommunications                      Stamford, CT   146801    17879   \n",
       "Transportation                       Thomasville, NC    58363     7610   \n",
       "Wholesalers                              Waltham, MA    48681     1472   \n",
       "\n",
       "                              Employees  \n",
       "Sector                                   \n",
       "Aerospace & Defense              197200  \n",
       "Apparel                           65300  \n",
       "Business Services                216500  \n",
       "Chemicals                         52000  \n",
       "Energy                            75600  \n",
       "Engineering & Construction        92000  \n",
       "Financials                       331000  \n",
       "Food and Drug Stores             431000  \n",
       "Food, Beverages & Tobacco        263000  \n",
       "Health Care                      203500  \n",
       "Hotels, Resturants & Leisure     505000  \n",
       "Household Products               110000  \n",
       "Industrials                      333000  \n",
       "Materials                         60000  \n",
       "Media                            185000  \n",
       "Motor Vehicles & Parts           215000  \n",
       "Retailing                       2300000  \n",
       "Technology                       411798  \n",
       "Telecommunications               281450  \n",
       "Transportation                   341240  \n",
       "Wholesalers                       78500  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sector.max(numeric_only=False) #sadece sayısal değerleri getirir "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d9e5a0d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>Industry</th>\n",
       "      <th>Location</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Profits</th>\n",
       "      <th>Employees</th>\n",
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       "    <tr>\n",
       "      <th>Sector</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>Aerospace &amp; Defense</th>\n",
       "      <td>B/E Aerospace</td>\n",
       "      <td>Aerospace and Defense</td>\n",
       "      <td>Berwyn, PA</td>\n",
       "      <td>1923</td>\n",
       "      <td>-240</td>\n",
       "      <td>6955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apparel</th>\n",
       "      <td>Carter’s</td>\n",
       "      <td>Apparel</td>\n",
       "      <td>Atlanta, GA</td>\n",
       "      <td>2204</td>\n",
       "      <td>82</td>\n",
       "      <td>5978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Business Services</th>\n",
       "      <td>ABM Industries</td>\n",
       "      <td>Advertising, marketing</td>\n",
       "      <td>Arlington, VA</td>\n",
       "      <td>1910</td>\n",
       "      <td>-1481</td>\n",
       "      <td>2400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chemicals</th>\n",
       "      <td>A. Schulman</td>\n",
       "      <td>Chemicals</td>\n",
       "      <td>Allentown, PA</td>\n",
       "      <td>2084</td>\n",
       "      <td>-816</td>\n",
       "      <td>1979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Energy</th>\n",
       "      <td>AES</td>\n",
       "      <td>Energy</td>\n",
       "      <td>Akron, OH</td>\n",
       "      <td>1898</td>\n",
       "      <td>-23119</td>\n",
       "      <td>480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Engineering &amp; Construction</th>\n",
       "      <td>AECOM</td>\n",
       "      <td>Engineering, Construction</td>\n",
       "      <td>Atlanta, GA</td>\n",
       "      <td>1909</td>\n",
       "      <td>-155</td>\n",
       "      <td>1036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Financials</th>\n",
       "      <td>AIG</td>\n",
       "      <td>Commercial Banks</td>\n",
       "      <td>Atlanta, GA</td>\n",
       "      <td>1902</td>\n",
       "      <td>-1194</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Food and Drug Stores</th>\n",
       "      <td>CVS Health</td>\n",
       "      <td>Food and Drug Stores</td>\n",
       "      <td>Austin, TX</td>\n",
       "      <td>2151</td>\n",
       "      <td>-62</td>\n",
       "      <td>1616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Food, Beverages &amp; Tobacco</th>\n",
       "      <td>Alliance One International</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Arden Hills, MN</td>\n",
       "      <td>2066</td>\n",
       "      <td>-253</td>\n",
       "      <td>1857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Health Care</th>\n",
       "      <td>AbbVie</td>\n",
       "      <td>Health Care: Insurance and Managed Care</td>\n",
       "      <td>Abbott Park, IL</td>\n",
       "      <td>1987</td>\n",
       "      <td>-458</td>\n",
       "      <td>2924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hotels, Resturants &amp; Leisure</th>\n",
       "      <td>Bloomin’ Brands</td>\n",
       "      <td>Food Services</td>\n",
       "      <td>Ann Arbor, MI</td>\n",
       "      <td>1896</td>\n",
       "      <td>-1394</td>\n",
       "      <td>8500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Household Products</th>\n",
       "      <td>Avon Products</td>\n",
       "      <td>Home Equipment, Furnishings</td>\n",
       "      <td>Atlanta, GA</td>\n",
       "      <td>1914</td>\n",
       "      <td>-1149</td>\n",
       "      <td>4406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Industrials</th>\n",
       "      <td>3M</td>\n",
       "      <td>Construction and Farm Machinery</td>\n",
       "      <td>Annapolis Junction, MD</td>\n",
       "      <td>1895</td>\n",
       "      <td>-6126</td>\n",
       "      <td>4104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Materials</th>\n",
       "      <td>A-Mark Precious Metals</td>\n",
       "      <td>Building Materials, Glass</td>\n",
       "      <td>Atlanta, GA</td>\n",
       "      <td>1924</td>\n",
       "      <td>-1642</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Media</th>\n",
       "      <td>AMC Networks</td>\n",
       "      <td>Entertainment</td>\n",
       "      <td>Beverly Hills, CA</td>\n",
       "      <td>1921</td>\n",
       "      <td>-881</td>\n",
       "      <td>2341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Motor Vehicles &amp; Parts</th>\n",
       "      <td>Allison Transmission Holdings</td>\n",
       "      <td>Motor Vehicles and Parts</td>\n",
       "      <td>Akron, OH</td>\n",
       "      <td>1986</td>\n",
       "      <td>-889</td>\n",
       "      <td>2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Retailing</th>\n",
       "      <td>99 Cents Only Stores</td>\n",
       "      <td>Automotive Retailing, Services</td>\n",
       "      <td>Ankeny, IA</td>\n",
       "      <td>1999</td>\n",
       "      <td>-1243</td>\n",
       "      <td>2155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Technology</th>\n",
       "      <td>Activision Blizzard</td>\n",
       "      <td>Computer Peripherals</td>\n",
       "      <td>Angleton, TX</td>\n",
       "      <td>1920</td>\n",
       "      <td>-4359</td>\n",
       "      <td>2285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Telecommunications</th>\n",
       "      <td>AT&amp;T</td>\n",
       "      <td>Telecommunications</td>\n",
       "      <td>Bethpage, NY</td>\n",
       "      <td>2726</td>\n",
       "      <td>-271</td>\n",
       "      <td>2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transportation</th>\n",
       "      <td>Alaska Air Group</td>\n",
       "      <td>Airlines</td>\n",
       "      <td>Atlanta, GA</td>\n",
       "      <td>1995</td>\n",
       "      <td>-191</td>\n",
       "      <td>1223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wholesalers</th>\n",
       "      <td>Airgas</td>\n",
       "      <td>Miscellaneous</td>\n",
       "      <td>Atlanta, GA</td>\n",
       "      <td>1917</td>\n",
       "      <td>-502</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Company  \\\n",
       "Sector                                                        \n",
       "Aerospace & Defense                           B/E Aerospace   \n",
       "Apparel                                            Carter’s   \n",
       "Business Services                            ABM Industries   \n",
       "Chemicals                                       A. Schulman   \n",
       "Energy                                                  AES   \n",
       "Engineering & Construction                            AECOM   \n",
       "Financials                                              AIG   \n",
       "Food and Drug Stores                             CVS Health   \n",
       "Food, Beverages & Tobacco        Alliance One International   \n",
       "Health Care                                          AbbVie   \n",
       "Hotels, Resturants & Leisure                Bloomin’ Brands   \n",
       "Household Products                            Avon Products   \n",
       "Industrials                                              3M   \n",
       "Materials                            A-Mark Precious Metals   \n",
       "Media                                          AMC Networks   \n",
       "Motor Vehicles & Parts        Allison Transmission Holdings   \n",
       "Retailing                              99 Cents Only Stores   \n",
       "Technology                              Activision Blizzard   \n",
       "Telecommunications                                     AT&T   \n",
       "Transportation                             Alaska Air Group   \n",
       "Wholesalers                                          Airgas   \n",
       "\n",
       "                                                             Industry  \\\n",
       "Sector                                                                  \n",
       "Aerospace & Defense                             Aerospace and Defense   \n",
       "Apparel                                                       Apparel   \n",
       "Business Services                              Advertising, marketing   \n",
       "Chemicals                                                   Chemicals   \n",
       "Energy                                                         Energy   \n",
       "Engineering & Construction                  Engineering, Construction   \n",
       "Financials                                           Commercial Banks   \n",
       "Food and Drug Stores                             Food and Drug Stores   \n",
       "Food, Beverages & Tobacco                                   Beverages   \n",
       "Health Care                   Health Care: Insurance and Managed Care   \n",
       "Hotels, Resturants & Leisure                            Food Services   \n",
       "Household Products                        Home Equipment, Furnishings   \n",
       "Industrials                           Construction and Farm Machinery   \n",
       "Materials                                   Building Materials, Glass   \n",
       "Media                                                   Entertainment   \n",
       "Motor Vehicles & Parts                       Motor Vehicles and Parts   \n",
       "Retailing                              Automotive Retailing, Services   \n",
       "Technology                                       Computer Peripherals   \n",
       "Telecommunications                                 Telecommunications   \n",
       "Transportation                                               Airlines   \n",
       "Wholesalers                                             Miscellaneous   \n",
       "\n",
       "                                            Location  Revenue  Profits  \\\n",
       "Sector                                                                   \n",
       "Aerospace & Defense                       Berwyn, PA     1923     -240   \n",
       "Apparel                                  Atlanta, GA     2204       82   \n",
       "Business Services                      Arlington, VA     1910    -1481   \n",
       "Chemicals                              Allentown, PA     2084     -816   \n",
       "Energy                                     Akron, OH     1898   -23119   \n",
       "Engineering & Construction               Atlanta, GA     1909     -155   \n",
       "Financials                               Atlanta, GA     1902    -1194   \n",
       "Food and Drug Stores                      Austin, TX     2151      -62   \n",
       "Food, Beverages & Tobacco            Arden Hills, MN     2066     -253   \n",
       "Health Care                          Abbott Park, IL     1987     -458   \n",
       "Hotels, Resturants & Leisure           Ann Arbor, MI     1896    -1394   \n",
       "Household Products                       Atlanta, GA     1914    -1149   \n",
       "Industrials                   Annapolis Junction, MD     1895    -6126   \n",
       "Materials                                Atlanta, GA     1924    -1642   \n",
       "Media                              Beverly Hills, CA     1921     -881   \n",
       "Motor Vehicles & Parts                     Akron, OH     1986     -889   \n",
       "Retailing                                 Ankeny, IA     1999    -1243   \n",
       "Technology                              Angleton, TX     1920    -4359   \n",
       "Telecommunications                      Bethpage, NY     2726     -271   \n",
       "Transportation                           Atlanta, GA     1995     -191   \n",
       "Wholesalers                              Atlanta, GA     1917     -502   \n",
       "\n",
       "                              Employees  \n",
       "Sector                                   \n",
       "Aerospace & Defense                6955  \n",
       "Apparel                            5978  \n",
       "Business Services                  2400  \n",
       "Chemicals                          1979  \n",
       "Energy                              480  \n",
       "Engineering & Construction         1036  \n",
       "Financials                          187  \n",
       "Food and Drug Stores               1616  \n",
       "Food, Beverages & Tobacco          1857  \n",
       "Health Care                        2924  \n",
       "Hotels, Resturants & Leisure       8500  \n",
       "Household Products                 4406  \n",
       "Industrials                        4104  \n",
       "Materials                            52  \n",
       "Media                              2341  \n",
       "Motor Vehicles & Parts             2700  \n",
       "Retailing                          2155  \n",
       "Technology                         2285  \n",
       "Telecommunications                 2700  \n",
       "Transportation                     1223  \n",
       "Wholesalers                         500  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sector.min() #sectorlerdeki şirketlerin alfabetik olarak minimumunu alır"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "da32f6fc",
   "metadata": {},
   "outputs": [
    {
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       "      <td>Troy, MI</td>\n",
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       "      <td>Chemicals</td>\n",
       "      <td>Wilmington, DE</td>\n",
       "      <td>48778</td>\n",
       "      <td>7685</td>\n",
       "      <td>52000</td>\n",
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       "    <tr>\n",
       "      <th>Energy</th>\n",
       "      <td>Xcel Energy</td>\n",
       "      <td>Utilities: Gas and Electric</td>\n",
       "      <td>Washington, DC</td>\n",
       "      <td>246204</td>\n",
       "      <td>16150</td>\n",
       "      <td>75600</td>\n",
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       "    <tr>\n",
       "      <th>Engineering &amp; Construction</th>\n",
       "      <td>Tutor Perini</td>\n",
       "      <td>Homebuilders</td>\n",
       "      <td>Watsonville, CA</td>\n",
       "      <td>18114</td>\n",
       "      <td>803</td>\n",
       "      <td>92000</td>\n",
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       "    <tr>\n",
       "      <th>Financials</th>\n",
       "      <td>Zions Bancorp.</td>\n",
       "      <td>Securities</td>\n",
       "      <td>Worcester, MA</td>\n",
       "      <td>210821</td>\n",
       "      <td>24442</td>\n",
       "      <td>331000</td>\n",
       "    </tr>\n",
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       "      <th>Food and Drug Stores</th>\n",
       "      <td>Whole Foods Market</td>\n",
       "      <td>Food and Drug Stores</td>\n",
       "      <td>Woonsocket, RI</td>\n",
       "      <td>153290</td>\n",
       "      <td>5237</td>\n",
       "      <td>431000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Food, Beverages &amp; Tobacco</th>\n",
       "      <td>WhiteWave Foods</td>\n",
       "      <td>Tobacco</td>\n",
       "      <td>Winston-Salem, NC</td>\n",
       "      <td>67702</td>\n",
       "      <td>7351</td>\n",
       "      <td>263000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Health Care</th>\n",
       "      <td>inVentiv Health</td>\n",
       "      <td>Wholesalers: Health Care</td>\n",
       "      <td>York, PA</td>\n",
       "      <td>181241</td>\n",
       "      <td>18108</td>\n",
       "      <td>203500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hotels, Resturants &amp; Leisure</th>\n",
       "      <td>Yum Brands</td>\n",
       "      <td>Hotels, Casinos, Resorts</td>\n",
       "      <td>Wyomissing, PA</td>\n",
       "      <td>25413</td>\n",
       "      <td>5920</td>\n",
       "      <td>505000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Household Products</th>\n",
       "      <td>Vista Outdoor</td>\n",
       "      <td>Miscellaneous</td>\n",
       "      <td>Zeeland, MI</td>\n",
       "      <td>78756</td>\n",
       "      <td>7036</td>\n",
       "      <td>110000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Industrials</th>\n",
       "      <td>Zebra Technologies</td>\n",
       "      <td>Miscellaneous</td>\n",
       "      <td>Wilmerding, PA</td>\n",
       "      <td>140389</td>\n",
       "      <td>4833</td>\n",
       "      <td>333000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Materials</th>\n",
       "      <td>Worthington Industries</td>\n",
       "      <td>Packaging, Containers</td>\n",
       "      <td>Wyomissing, PA</td>\n",
       "      <td>22534</td>\n",
       "      <td>991</td>\n",
       "      <td>60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Media</th>\n",
       "      <td>iHeartMedia</td>\n",
       "      <td>Publishing, Printing</td>\n",
       "      <td>Sussex, WI</td>\n",
       "      <td>52465</td>\n",
       "      <td>8382</td>\n",
       "      <td>185000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Motor Vehicles &amp; Parts</th>\n",
       "      <td>Wabash National</td>\n",
       "      <td>Motor Vehicles and Parts</td>\n",
       "      <td>Van Buren Township, MI</td>\n",
       "      <td>152356</td>\n",
       "      <td>9687</td>\n",
       "      <td>215000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Retailing</th>\n",
       "      <td>hhgregg</td>\n",
       "      <td>Specialty Retailers: Other</td>\n",
       "      <td>York, PA</td>\n",
       "      <td>482130</td>\n",
       "      <td>14694</td>\n",
       "      <td>2300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Technology</th>\n",
       "      <td>salesforce.com</td>\n",
       "      <td>Semiconductors and Other Electronic Components</td>\n",
       "      <td>Woburn, MA</td>\n",
       "      <td>233715</td>\n",
       "      <td>53394</td>\n",
       "      <td>411798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Telecommunications</th>\n",
       "      <td>Windstream Holdings</td>\n",
       "      <td>Telecommunications</td>\n",
       "      <td>Stamford, CT</td>\n",
       "      <td>146801</td>\n",
       "      <td>17879</td>\n",
       "      <td>281450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transportation</th>\n",
       "      <td>YRC Worldwide</td>\n",
       "      <td>Trucking, Truck Leasing</td>\n",
       "      <td>Thomasville, NC</td>\n",
       "      <td>58363</td>\n",
       "      <td>7610</td>\n",
       "      <td>341240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wholesalers</th>\n",
       "      <td>World Fuel Services</td>\n",
       "      <td>Wholesalers: Food and Grocery</td>\n",
       "      <td>Waltham, MA</td>\n",
       "      <td>48681</td>\n",
       "      <td>1472</td>\n",
       "      <td>78500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             Company  \\\n",
       "Sector                                                 \n",
       "Aerospace & Defense                         Woodward   \n",
       "Apparel                         Wolverine World Wide   \n",
       "Business Services                      Western Union   \n",
       "Chemicals                          Westlake Chemical   \n",
       "Energy                                   Xcel Energy   \n",
       "Engineering & Construction              Tutor Perini   \n",
       "Financials                            Zions Bancorp.   \n",
       "Food and Drug Stores              Whole Foods Market   \n",
       "Food, Beverages & Tobacco            WhiteWave Foods   \n",
       "Health Care                          inVentiv Health   \n",
       "Hotels, Resturants & Leisure              Yum Brands   \n",
       "Household Products                     Vista Outdoor   \n",
       "Industrials                       Zebra Technologies   \n",
       "Materials                     Worthington Industries   \n",
       "Media                                    iHeartMedia   \n",
       "Motor Vehicles & Parts               Wabash National   \n",
       "Retailing                                    hhgregg   \n",
       "Technology                            salesforce.com   \n",
       "Telecommunications               Windstream Holdings   \n",
       "Transportation                         YRC Worldwide   \n",
       "Wholesalers                      World Fuel Services   \n",
       "\n",
       "                                                                    Industry  \\\n",
       "Sector                                                                         \n",
       "Aerospace & Defense                                    Aerospace and Defense   \n",
       "Apparel                                                              Apparel   \n",
       "Business Services                                           Waste Management   \n",
       "Chemicals                                                          Chemicals   \n",
       "Energy                                           Utilities: Gas and Electric   \n",
       "Engineering & Construction                                      Homebuilders   \n",
       "Financials                                                        Securities   \n",
       "Food and Drug Stores                                    Food and Drug Stores   \n",
       "Food, Beverages & Tobacco                                            Tobacco   \n",
       "Health Care                                         Wholesalers: Health Care   \n",
       "Hotels, Resturants & Leisure                        Hotels, Casinos, Resorts   \n",
       "Household Products                                             Miscellaneous   \n",
       "Industrials                                                    Miscellaneous   \n",
       "Materials                                              Packaging, Containers   \n",
       "Media                                                   Publishing, Printing   \n",
       "Motor Vehicles & Parts                              Motor Vehicles and Parts   \n",
       "Retailing                                         Specialty Retailers: Other   \n",
       "Technology                    Semiconductors and Other Electronic Components   \n",
       "Telecommunications                                        Telecommunications   \n",
       "Transportation                                       Trucking, Truck Leasing   \n",
       "Wholesalers                                    Wholesalers: Food and Grocery   \n",
       "\n",
       "                                            Location  Revenue  Profits  \\\n",
       "Sector                                                                   \n",
       "Aerospace & Defense                      Wichita, KS    96114     7608   \n",
       "Apparel                            Winston-Salem, NC    30601     3273   \n",
       "Business Services                           Troy, MI    19330     6328   \n",
       "Chemicals                             Wilmington, DE    48778     7685   \n",
       "Energy                                Washington, DC   246204    16150   \n",
       "Engineering & Construction           Watsonville, CA    18114      803   \n",
       "Financials                             Worcester, MA   210821    24442   \n",
       "Food and Drug Stores                  Woonsocket, RI   153290     5237   \n",
       "Food, Beverages & Tobacco          Winston-Salem, NC    67702     7351   \n",
       "Health Care                                 York, PA   181241    18108   \n",
       "Hotels, Resturants & Leisure          Wyomissing, PA    25413     5920   \n",
       "Household Products                       Zeeland, MI    78756     7036   \n",
       "Industrials                           Wilmerding, PA   140389     4833   \n",
       "Materials                             Wyomissing, PA    22534      991   \n",
       "Media                                     Sussex, WI    52465     8382   \n",
       "Motor Vehicles & Parts        Van Buren Township, MI   152356     9687   \n",
       "Retailing                                   York, PA   482130    14694   \n",
       "Technology                                Woburn, MA   233715    53394   \n",
       "Telecommunications                      Stamford, CT   146801    17879   \n",
       "Transportation                       Thomasville, NC    58363     7610   \n",
       "Wholesalers                              Waltham, MA    48681     1472   \n",
       "\n",
       "                              Employees  \n",
       "Sector                                   \n",
       "Aerospace & Defense              197200  \n",
       "Apparel                           65300  \n",
       "Business Services                216500  \n",
       "Chemicals                         52000  \n",
       "Energy                            75600  \n",
       "Engineering & Construction        92000  \n",
       "Financials                       331000  \n",
       "Food and Drug Stores             431000  \n",
       "Food, Beverages & Tobacco        263000  \n",
       "Health Care                      203500  \n",
       "Hotels, Resturants & Leisure     505000  \n",
       "Household Products               110000  \n",
       "Industrials                      333000  \n",
       "Materials                         60000  \n",
       "Media                            185000  \n",
       "Motor Vehicles & Parts           215000  \n",
       "Retailing                       2300000  \n",
       "Technology                       411798  \n",
       "Telecommunications               281450  \n",
       "Transportation                   341240  \n",
       "Wholesalers                       78500  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sector.max() #sectorlerdeki şirketlerin alfabetik olarak maximumunu alır"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "093b61dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1517809"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sector.get_group(\"Energy\")[\"Revenue\"].sum()  #Energy sektöründekilerin revenuelarının toplamını getirir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d430a612",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1517809"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#energy nin toplam revenue sunu çekmek için\n",
    "sector.sum().loc[\"Energy\",\"Revenue\"] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "69053aa6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sector\n",
       "Aerospace & Defense              357940\n",
       "Apparel                           95968\n",
       "Business Services                272195\n",
       "Chemicals                        243897\n",
       "Energy                          1517809\n",
       "Engineering & Construction       153983\n",
       "Financials                      2217159\n",
       "Food and Drug Stores             483769\n",
       "Food, Beverages & Tobacco        555967\n",
       "Health Care                     1614707\n",
       "Hotels, Resturants & Leisure     169546\n",
       "Household Products               234737\n",
       "Industrials                      497581\n",
       "Materials                        259145\n",
       "Media                            220764\n",
       "Motor Vehicles & Parts           482540\n",
       "Retailing                       1465076\n",
       "Technology                      1377600\n",
       "Telecommunications               461834\n",
       "Transportation                   408508\n",
       "Wholesalers                      444800\n",
       "Name: Revenue, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sector[\"Revenue\"].sum() #her sektördeki revenuların toplamını verir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c963753a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sector\n",
       "Aerospace & Defense             1923\n",
       "Apparel                         2204\n",
       "Business Services               1910\n",
       "Chemicals                       2084\n",
       "Energy                          1898\n",
       "Engineering & Construction      1909\n",
       "Financials                      1902\n",
       "Food and Drug Stores            2151\n",
       "Food, Beverages & Tobacco       2066\n",
       "Health Care                     1987\n",
       "Hotels, Resturants & Leisure    1896\n",
       "Household Products              1914\n",
       "Industrials                     1895\n",
       "Materials                       1924\n",
       "Media                           1921\n",
       "Motor Vehicles & Parts          1986\n",
       "Retailing                       1999\n",
       "Technology                      1920\n",
       "Telecommunications              2726\n",
       "Transportation                  1995\n",
       "Wholesalers                     1917\n",
       "Name: Revenue, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sector[\"Revenue\"].min()  #her sektördeki revenuların minimumunu verir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9246a38f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sector\n",
       "Aerospace & Defense              96114\n",
       "Apparel                          30601\n",
       "Business Services                19330\n",
       "Chemicals                        48778\n",
       "Energy                          246204\n",
       "Engineering & Construction       18114\n",
       "Financials                      210821\n",
       "Food and Drug Stores            153290\n",
       "Food, Beverages & Tobacco        67702\n",
       "Health Care                     181241\n",
       "Hotels, Resturants & Leisure     25413\n",
       "Household Products               78756\n",
       "Industrials                     140389\n",
       "Materials                        22534\n",
       "Media                            52465\n",
       "Motor Vehicles & Parts          152356\n",
       "Retailing                       482130\n",
       "Technology                      233715\n",
       "Telecommunications              146801\n",
       "Transportation                   58363\n",
       "Wholesalers                      48681\n",
       "Name: Revenue, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sector[\"Revenue\"].max()  #her sektördeki revenuların maximumunu verir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5909c01b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sector\n",
       "Aerospace & Defense             48402.850000\n",
       "Apparel                         23093.133333\n",
       "Business Services               26687.254902\n",
       "Chemicals                       15455.033333\n",
       "Energy                           9745.303279\n",
       "Engineering & Construction      15642.615385\n",
       "Financials                      24172.287770\n",
       "Food and Drug Stores            93026.533333\n",
       "Food, Beverages & Tobacco       28177.488372\n",
       "Health Care                     35710.520000\n",
       "Hotels, Resturants & Leisure    99369.800000\n",
       "Household Products              23072.785714\n",
       "Industrials                     33591.934783\n",
       "Materials                       14840.069767\n",
       "Media                           22012.560000\n",
       "Motor Vehicles & Parts          45106.666667\n",
       "Retailing                       77845.362500\n",
       "Technology                      35087.735294\n",
       "Telecommunications              55497.866667\n",
       "Transportation                  42688.694444\n",
       "Wholesalers                     13139.925000\n",
       "Name: Employees, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sector[\"Employees\"].mean()  #her sektördeki revenuların ortalamasını verir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4db4638a",
   "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>Revenue</th>\n",
       "      <th>Profits</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sector</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Aerospace &amp; Defense</th>\n",
       "      <td>357940</td>\n",
       "      <td>28742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apparel</th>\n",
       "      <td>95968</td>\n",
       "      <td>8236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Business Services</th>\n",
       "      <td>272195</td>\n",
       "      <td>28227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chemicals</th>\n",
       "      <td>243897</td>\n",
       "      <td>22628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Energy</th>\n",
       "      <td>1517809</td>\n",
       "      <td>-73447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Engineering &amp; Construction</th>\n",
       "      <td>153983</td>\n",
       "      <td>5304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Financials</th>\n",
       "      <td>2217159</td>\n",
       "      <td>260209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Food and Drug Stores</th>\n",
       "      <td>483769</td>\n",
       "      <td>16759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Food, Beverages &amp; Tobacco</th>\n",
       "      <td>555967</td>\n",
       "      <td>51417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Health Care</th>\n",
       "      <td>1614707</td>\n",
       "      <td>106114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hotels, Resturants &amp; Leisure</th>\n",
       "      <td>169546</td>\n",
       "      <td>20697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Household Products</th>\n",
       "      <td>234737</td>\n",
       "      <td>14428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Industrials</th>\n",
       "      <td>497581</td>\n",
       "      <td>20764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Materials</th>\n",
       "      <td>259145</td>\n",
       "      <td>4428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Media</th>\n",
       "      <td>220764</td>\n",
       "      <td>24347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Motor Vehicles &amp; Parts</th>\n",
       "      <td>482540</td>\n",
       "      <td>25898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Retailing</th>\n",
       "      <td>1465076</td>\n",
       "      <td>47830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Technology</th>\n",
       "      <td>1377600</td>\n",
       "      <td>180473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Telecommunications</th>\n",
       "      <td>461834</td>\n",
       "      <td>48637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transportation</th>\n",
       "      <td>408508</td>\n",
       "      <td>44169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wholesalers</th>\n",
       "      <td>444800</td>\n",
       "      <td>8233</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Revenue  Profits\n",
       "Sector                                        \n",
       "Aerospace & Defense            357940    28742\n",
       "Apparel                         95968     8236\n",
       "Business Services              272195    28227\n",
       "Chemicals                      243897    22628\n",
       "Energy                        1517809   -73447\n",
       "Engineering & Construction     153983     5304\n",
       "Financials                    2217159   260209\n",
       "Food and Drug Stores           483769    16759\n",
       "Food, Beverages & Tobacco      555967    51417\n",
       "Health Care                   1614707   106114\n",
       "Hotels, Resturants & Leisure   169546    20697\n",
       "Household Products             234737    14428\n",
       "Industrials                    497581    20764\n",
       "Materials                      259145     4428\n",
       "Media                          220764    24347\n",
       "Motor Vehicles & Parts         482540    25898\n",
       "Retailing                     1465076    47830\n",
       "Technology                    1377600   180473\n",
       "Telecommunications             461834    48637\n",
       "Transportation                 408508    44169\n",
       "Wholesalers                    444800     8233"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sector[[\"Revenue\",\"Profits\"]].sum() # Revenue ve Profits sütünlarının kendi sektörleri içinde toplamı"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3e36263f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_twogroup=df_fortune.groupby([\"Sector\",\"Industry\"]) #ikili de gruplayabiliriz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c7b23bcb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sector               Industry                                     \n",
       "Aerospace & Defense  Aerospace and Defense                            20\n",
       "Apparel              Apparel                                          15\n",
       "Business Services    Advertising, marketing                            2\n",
       "                     Diversified Outsourcing Services                 14\n",
       "                     Education                                         3\n",
       "                                                                      ..\n",
       "Transportation       Trucking, Truck Leasing                           9\n",
       "Wholesalers          Miscellaneous                                     1\n",
       "                     Wholesalers: Diversified                         25\n",
       "                     Wholesalers: Electronics and Office Equipment     8\n",
       "                     Wholesalers: Food and Grocery                     6\n",
       "Length: 79, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twogroup.size()#NE NE DÖNDÜRÜR ÖNEMLİ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "492c200a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df_twogroup.size()) #seri döndürür"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f42fccc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.groupby.generic.DataFrameGroupBy"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df_twogroup) #döndürdüğü önemli"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "44bb1f4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Profits</th>\n",
       "      <th>Employees</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sector</th>\n",
       "      <th>Industry</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Aerospace &amp; Defense</th>\n",
       "      <th>Aerospace and Defense</th>\n",
       "      <td>357940</td>\n",
       "      <td>28742</td>\n",
       "      <td>968057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apparel</th>\n",
       "      <th>Apparel</th>\n",
       "      <td>95968</td>\n",
       "      <td>8236</td>\n",
       "      <td>346397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Business Services</th>\n",
       "      <th>Advertising, marketing</th>\n",
       "      <td>22748</td>\n",
       "      <td>1549</td>\n",
       "      <td>124100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Diversified Outsourcing Services</th>\n",
       "      <td>64829</td>\n",
       "      <td>4305</td>\n",
       "      <td>708330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Education</th>\n",
       "      <td>7485</td>\n",
       "      <td>69</td>\n",
       "      <td>46755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>Transportation</th>\n",
       "      <th>Trucking, Truck Leasing</th>\n",
       "      <td>35950</td>\n",
       "      <td>1910</td>\n",
       "      <td>170456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Wholesalers</th>\n",
       "      <th>Miscellaneous</th>\n",
       "      <td>8982</td>\n",
       "      <td>17</td>\n",
       "      <td>9200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wholesalers: Diversified</th>\n",
       "      <td>176138</td>\n",
       "      <td>5193</td>\n",
       "      <td>233831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wholesalers: Electronics and Office Equipment</th>\n",
       "      <td>147906</td>\n",
       "      <td>1857</td>\n",
       "      <td>166661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wholesalers: Food and Grocery</th>\n",
       "      <td>111774</td>\n",
       "      <td>1166</td>\n",
       "      <td>115905</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>79 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                   Revenue  \\\n",
       "Sector              Industry                                                 \n",
       "Aerospace & Defense Aerospace and Defense                           357940   \n",
       "Apparel             Apparel                                          95968   \n",
       "Business Services   Advertising, marketing                           22748   \n",
       "                    Diversified Outsourcing Services                 64829   \n",
       "                    Education                                         7485   \n",
       "...                                                                    ...   \n",
       "Transportation      Trucking, Truck Leasing                          35950   \n",
       "Wholesalers         Miscellaneous                                     8982   \n",
       "                    Wholesalers: Diversified                        176138   \n",
       "                    Wholesalers: Electronics and Office Equipment   147906   \n",
       "                    Wholesalers: Food and Grocery                   111774   \n",
       "\n",
       "                                                                   Profits  \\\n",
       "Sector              Industry                                                 \n",
       "Aerospace & Defense Aerospace and Defense                            28742   \n",
       "Apparel             Apparel                                           8236   \n",
       "Business Services   Advertising, marketing                            1549   \n",
       "                    Diversified Outsourcing Services                  4305   \n",
       "                    Education                                           69   \n",
       "...                                                                    ...   \n",
       "Transportation      Trucking, Truck Leasing                           1910   \n",
       "Wholesalers         Miscellaneous                                       17   \n",
       "                    Wholesalers: Diversified                          5193   \n",
       "                    Wholesalers: Electronics and Office Equipment     1857   \n",
       "                    Wholesalers: Food and Grocery                     1166   \n",
       "\n",
       "                                                                   Employees  \n",
       "Sector              Industry                                                  \n",
       "Aerospace & Defense Aerospace and Defense                             968057  \n",
       "Apparel             Apparel                                           346397  \n",
       "Business Services   Advertising, marketing                            124100  \n",
       "                    Diversified Outsourcing Services                  708330  \n",
       "                    Education                                          46755  \n",
       "...                                                                      ...  \n",
       "Transportation      Trucking, Truck Leasing                           170456  \n",
       "Wholesalers         Miscellaneous                                       9200  \n",
       "                    Wholesalers: Diversified                          233831  \n",
       "                    Wholesalers: Electronics and Office Equipment     166661  \n",
       "                    Wholesalers: Food and Grocery                     115905  \n",
       "\n",
       "[79 rows x 3 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twogroup.sum() #her iki yer için sayısal toplam "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1e50e1de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sector               Industry                                     \n",
       "Aerospace & Defense  Aerospace and Defense                            48402.850000\n",
       "Apparel              Apparel                                          23093.133333\n",
       "Business Services    Advertising, marketing                           62050.000000\n",
       "                     Diversified Outsourcing Services                 50595.000000\n",
       "                     Education                                        15585.000000\n",
       "                                                                          ...     \n",
       "Transportation       Trucking, Truck Leasing                          18939.555556\n",
       "Wholesalers          Miscellaneous                                     9200.000000\n",
       "                     Wholesalers: Diversified                          9353.240000\n",
       "                     Wholesalers: Electronics and Office Equipment    20832.625000\n",
       "                     Wholesalers: Food and Grocery                    19317.500000\n",
       "Name: Employees, Length: 79, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twogroup[\"Employees\"].mean() #Employees columnunun aritmetik ortalamaları "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "655d4871",
   "metadata": {},
   "outputs": [],
   "source": [
    "#agrigate func ? sum,mean,count...\n",
    "#bir grup üzerinde birden fazla agrigate func çalıştırmak istersek dictionary göndermemiz gerekiyor (key-value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f5c93970",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>6955</td>\n",
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       "    <tr>\n",
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       "      <td>3273</td>\n",
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       "      <th>Business Services</th>\n",
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       "      <td>16150</td>\n",
       "      <td>480</td>\n",
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       "    <tr>\n",
       "      <th>Engineering &amp; Construction</th>\n",
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       "      <td>803</td>\n",
       "      <td>1036</td>\n",
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       "    <tr>\n",
       "      <th>Financials</th>\n",
       "      <td>2217159</td>\n",
       "      <td>24442</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Food and Drug Stores</th>\n",
       "      <td>483769</td>\n",
       "      <td>5237</td>\n",
       "      <td>1616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Food, Beverages &amp; Tobacco</th>\n",
       "      <td>555967</td>\n",
       "      <td>7351</td>\n",
       "      <td>1857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Health Care</th>\n",
       "      <td>1614707</td>\n",
       "      <td>18108</td>\n",
       "      <td>2924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hotels, Resturants &amp; Leisure</th>\n",
       "      <td>169546</td>\n",
       "      <td>5920</td>\n",
       "      <td>8500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Household Products</th>\n",
       "      <td>234737</td>\n",
       "      <td>7036</td>\n",
       "      <td>4406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Industrials</th>\n",
       "      <td>497581</td>\n",
       "      <td>4833</td>\n",
       "      <td>4104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Materials</th>\n",
       "      <td>259145</td>\n",
       "      <td>991</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Media</th>\n",
       "      <td>220764</td>\n",
       "      <td>8382</td>\n",
       "      <td>2341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Motor Vehicles &amp; Parts</th>\n",
       "      <td>482540</td>\n",
       "      <td>9687</td>\n",
       "      <td>2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Retailing</th>\n",
       "      <td>1465076</td>\n",
       "      <td>14694</td>\n",
       "      <td>2155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Technology</th>\n",
       "      <td>1377600</td>\n",
       "      <td>53394</td>\n",
       "      <td>2285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Telecommunications</th>\n",
       "      <td>461834</td>\n",
       "      <td>17879</td>\n",
       "      <td>2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transportation</th>\n",
       "      <td>408508</td>\n",
       "      <td>7610</td>\n",
       "      <td>1223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wholesalers</th>\n",
       "      <td>444800</td>\n",
       "      <td>1472</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Revenue  Profits  Employees\n",
       "Sector                                                   \n",
       "Aerospace & Defense            357940     7608       6955\n",
       "Apparel                         95968     3273       5978\n",
       "Business Services              272195     6328       2400\n",
       "Chemicals                      243897     7685       1979\n",
       "Energy                        1517809    16150        480\n",
       "Engineering & Construction     153983      803       1036\n",
       "Financials                    2217159    24442        187\n",
       "Food and Drug Stores           483769     5237       1616\n",
       "Food, Beverages & Tobacco      555967     7351       1857\n",
       "Health Care                   1614707    18108       2924\n",
       "Hotels, Resturants & Leisure   169546     5920       8500\n",
       "Household Products             234737     7036       4406\n",
       "Industrials                    497581     4833       4104\n",
       "Materials                      259145      991         52\n",
       "Media                          220764     8382       2341\n",
       "Motor Vehicles & Parts         482540     9687       2700\n",
       "Retailing                     1465076    14694       2155\n",
       "Technology                    1377600    53394       2285\n",
       "Telecommunications             461834    17879       2700\n",
       "Transportation                 408508     7610       1223\n",
       "Wholesalers                    444800     1472        500"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sector.agg({\n",
    "    \"Revenue\":\"sum\",\n",
    "    \"Profits\":\"max\",\n",
    "    \"Employees\":\"min\"\n",
    "}) #sectorleri groupby ile gruplayarak 3 ayrı fonku 3 ayrı columnda çalıştırdık "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e1450a37",
   "metadata": {},
   "outputs": [
    {
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       "      <td>357940</td>\n",
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       "      <td>7608</td>\n",
       "      <td>6955</td>\n",
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       "      <th>Apparel</th>\n",
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       "      <td>2400</td>\n",
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       "    <tr>\n",
       "      <th>Chemicals</th>\n",
       "      <td>243897</td>\n",
       "      <td>8129.900000</td>\n",
       "      <td>7685</td>\n",
       "      <td>1979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Energy</th>\n",
       "      <td>1517809</td>\n",
       "      <td>12441.057377</td>\n",
       "      <td>16150</td>\n",
       "      <td>480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Engineering &amp; Construction</th>\n",
       "      <td>153983</td>\n",
       "      <td>5922.423077</td>\n",
       "      <td>803</td>\n",
       "      <td>1036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Financials</th>\n",
       "      <td>2217159</td>\n",
       "      <td>15950.784173</td>\n",
       "      <td>24442</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Food and Drug Stores</th>\n",
       "      <td>483769</td>\n",
       "      <td>32251.266667</td>\n",
       "      <td>5237</td>\n",
       "      <td>1616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Food, Beverages &amp; Tobacco</th>\n",
       "      <td>555967</td>\n",
       "      <td>12929.465116</td>\n",
       "      <td>7351</td>\n",
       "      <td>1857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Health Care</th>\n",
       "      <td>1614707</td>\n",
       "      <td>21529.426667</td>\n",
       "      <td>18108</td>\n",
       "      <td>2924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hotels, Resturants &amp; Leisure</th>\n",
       "      <td>169546</td>\n",
       "      <td>6781.840000</td>\n",
       "      <td>5920</td>\n",
       "      <td>8500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Household Products</th>\n",
       "      <td>234737</td>\n",
       "      <td>8383.464286</td>\n",
       "      <td>7036</td>\n",
       "      <td>4406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Industrials</th>\n",
       "      <td>497581</td>\n",
       "      <td>10816.978261</td>\n",
       "      <td>4833</td>\n",
       "      <td>4104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Materials</th>\n",
       "      <td>259145</td>\n",
       "      <td>6026.627907</td>\n",
       "      <td>991</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Media</th>\n",
       "      <td>220764</td>\n",
       "      <td>8830.560000</td>\n",
       "      <td>8382</td>\n",
       "      <td>2341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Motor Vehicles &amp; Parts</th>\n",
       "      <td>482540</td>\n",
       "      <td>20105.833333</td>\n",
       "      <td>9687</td>\n",
       "      <td>2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Retailing</th>\n",
       "      <td>1465076</td>\n",
       "      <td>18313.450000</td>\n",
       "      <td>14694</td>\n",
       "      <td>2155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Technology</th>\n",
       "      <td>1377600</td>\n",
       "      <td>13505.882353</td>\n",
       "      <td>53394</td>\n",
       "      <td>2285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Telecommunications</th>\n",
       "      <td>461834</td>\n",
       "      <td>30788.933333</td>\n",
       "      <td>17879</td>\n",
       "      <td>2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transportation</th>\n",
       "      <td>408508</td>\n",
       "      <td>11347.444444</td>\n",
       "      <td>7610</td>\n",
       "      <td>1223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wholesalers</th>\n",
       "      <td>444800</td>\n",
       "      <td>11120.000000</td>\n",
       "      <td>1472</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Revenue               Profits Employees\n",
       "                                  sum          mean     max       min\n",
       "Sector                                                               \n",
       "Aerospace & Defense            357940  17897.000000    7608      6955\n",
       "Apparel                         95968   6397.866667    3273      5978\n",
       "Business Services              272195   5337.156863    6328      2400\n",
       "Chemicals                      243897   8129.900000    7685      1979\n",
       "Energy                        1517809  12441.057377   16150       480\n",
       "Engineering & Construction     153983   5922.423077     803      1036\n",
       "Financials                    2217159  15950.784173   24442       187\n",
       "Food and Drug Stores           483769  32251.266667    5237      1616\n",
       "Food, Beverages & Tobacco      555967  12929.465116    7351      1857\n",
       "Health Care                   1614707  21529.426667   18108      2924\n",
       "Hotels, Resturants & Leisure   169546   6781.840000    5920      8500\n",
       "Household Products             234737   8383.464286    7036      4406\n",
       "Industrials                    497581  10816.978261    4833      4104\n",
       "Materials                      259145   6026.627907     991        52\n",
       "Media                          220764   8830.560000    8382      2341\n",
       "Motor Vehicles & Parts         482540  20105.833333    9687      2700\n",
       "Retailing                     1465076  18313.450000   14694      2155\n",
       "Technology                    1377600  13505.882353   53394      2285\n",
       "Telecommunications             461834  30788.933333   17879      2700\n",
       "Transportation                 408508  11347.444444    7610      1223\n",
       "Wholesalers                    444800  11120.000000    1472       500"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sector.agg({\n",
    "    \"Revenue\":[\"sum\",\"mean\"],\n",
    "    \"Profits\":\"max\",\n",
    "    \"Employees\":\"min\" \n",
    "}) #Aynı columnda birden fazla işlem yaptırabiliriz. sum-mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c8beece5",
   "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>Company</th>\n",
       "      <th>Sector</th>\n",
       "      <th>Industry</th>\n",
       "      <th>Location</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Profits</th>\n",
       "      <th>Employees</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Company, Sector, Industry, Location, Revenue, Profits, Employees]\n",
       "Index: []"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#her sektördeki en yüksek kara sahip şirketi listelemek bir dataframe e aktarıp göstersin\n",
    "df=pd.DataFrame(columns=df_fortune.columns) \n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "13fc88c9",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\msika\\AppData\\Local\\Temp\\ipykernel_15780\\1372547261.py:3: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df=df.append(highest_revenue_company_in_group)\n",
      "C:\\Users\\msika\\AppData\\Local\\Temp\\ipykernel_15780\\1372547261.py:3: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df=df.append(highest_revenue_company_in_group)\n",
      "C:\\Users\\msika\\AppData\\Local\\Temp\\ipykernel_15780\\1372547261.py:3: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df=df.append(highest_revenue_company_in_group)\n",
      "C:\\Users\\msika\\AppData\\Local\\Temp\\ipykernel_15780\\1372547261.py:3: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df=df.append(highest_revenue_company_in_group)\n",
      "C:\\Users\\msika\\AppData\\Local\\Temp\\ipykernel_15780\\1372547261.py:3: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df=df.append(highest_revenue_company_in_group)\n",
      "C:\\Users\\msika\\AppData\\Local\\Temp\\ipykernel_15780\\1372547261.py:3: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df=df.append(highest_revenue_company_in_group)\n",
      "C:\\Users\\msika\\AppData\\Local\\Temp\\ipykernel_15780\\1372547261.py:3: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df=df.append(highest_revenue_company_in_group)\n",
      "C:\\Users\\msika\\AppData\\Local\\Temp\\ipykernel_15780\\1372547261.py:3: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df=df.append(highest_revenue_company_in_group)\n",
      "C:\\Users\\msika\\AppData\\Local\\Temp\\ipykernel_15780\\1372547261.py:3: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df=df.append(highest_revenue_company_in_group)\n",
      "C:\\Users\\msika\\AppData\\Local\\Temp\\ipykernel_15780\\1372547261.py:3: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df=df.append(highest_revenue_company_in_group)\n",
      "C:\\Users\\msika\\AppData\\Local\\Temp\\ipykernel_15780\\1372547261.py:3: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df=df.append(highest_revenue_company_in_group)\n",
      "C:\\Users\\msika\\AppData\\Local\\Temp\\ipykernel_15780\\1372547261.py:3: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df=df.append(highest_revenue_company_in_group)\n",
      "C:\\Users\\msika\\AppData\\Local\\Temp\\ipykernel_15780\\1372547261.py:3: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df=df.append(highest_revenue_company_in_group)\n",
      "C:\\Users\\msika\\AppData\\Local\\Temp\\ipykernel_15780\\1372547261.py:3: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df=df.append(highest_revenue_company_in_group)\n",
      "C:\\Users\\msika\\AppData\\Local\\Temp\\ipykernel_15780\\1372547261.py:3: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df=df.append(highest_revenue_company_in_group)\n",
      "C:\\Users\\msika\\AppData\\Local\\Temp\\ipykernel_15780\\1372547261.py:3: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df=df.append(highest_revenue_company_in_group)\n",
      "C:\\Users\\msika\\AppData\\Local\\Temp\\ipykernel_15780\\1372547261.py:3: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df=df.append(highest_revenue_company_in_group)\n",
      "C:\\Users\\msika\\AppData\\Local\\Temp\\ipykernel_15780\\1372547261.py:3: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df=df.append(highest_revenue_company_in_group)\n",
      "C:\\Users\\msika\\AppData\\Local\\Temp\\ipykernel_15780\\1372547261.py:3: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df=df.append(highest_revenue_company_in_group)\n",
      "C:\\Users\\msika\\AppData\\Local\\Temp\\ipykernel_15780\\1372547261.py:3: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df=df.append(highest_revenue_company_in_group)\n",
      "C:\\Users\\msika\\AppData\\Local\\Temp\\ipykernel_15780\\1372547261.py:3: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df=df.append(highest_revenue_company_in_group)\n"
     ]
    },
    {
     "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>Company</th>\n",
       "      <th>Sector</th>\n",
       "      <th>Industry</th>\n",
       "      <th>Location</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Profits</th>\n",
       "      <th>Employees</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Boeing</td>\n",
       "      <td>Aerospace &amp; Defense</td>\n",
       "      <td>Aerospace and Defense</td>\n",
       "      <td>Chicago, IL</td>\n",
       "      <td>96114</td>\n",
       "      <td>5176</td>\n",
       "      <td>161400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Nike</td>\n",
       "      <td>Apparel</td>\n",
       "      <td>Apparel</td>\n",
       "      <td>Beaverton, OR</td>\n",
       "      <td>30601</td>\n",
       "      <td>3273</td>\n",
       "      <td>62600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>ManpowerGroup</td>\n",
       "      <td>Business Services</td>\n",
       "      <td>Temporary Help</td>\n",
       "      <td>Milwaukee, WI</td>\n",
       "      <td>19330</td>\n",
       "      <td>419</td>\n",
       "      <td>27000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Dow Chemical</td>\n",
       "      <td>Chemicals</td>\n",
       "      <td>Chemicals</td>\n",
       "      <td>Midland, MI</td>\n",
       "      <td>48778</td>\n",
       "      <td>7685</td>\n",
       "      <td>49495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Exxon Mobil</td>\n",
       "      <td>Energy</td>\n",
       "      <td>Petroleum Refining</td>\n",
       "      <td>Irving, TX</td>\n",
       "      <td>246204</td>\n",
       "      <td>16150</td>\n",
       "      <td>75600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>Fluor</td>\n",
       "      <td>Engineering &amp; Construction</td>\n",
       "      <td>Engineering, Construction</td>\n",
       "      <td>Irving, TX</td>\n",
       "      <td>18114</td>\n",
       "      <td>413</td>\n",
       "      <td>38758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Berkshire Hathaway</td>\n",
       "      <td>Financials</td>\n",
       "      <td>Insurance: Property and Casualty (Stock)</td>\n",
       "      <td>Omaha, NE</td>\n",
       "      <td>210821</td>\n",
       "      <td>24083</td>\n",
       "      <td>331000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CVS Health</td>\n",
       "      <td>Food and Drug Stores</td>\n",
       "      <td>Food and Drug Stores</td>\n",
       "      <td>Woonsocket, RI</td>\n",
       "      <td>153290</td>\n",
       "      <td>5237</td>\n",
       "      <td>199000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Archer Daniels Midland</td>\n",
       "      <td>Food, Beverages &amp; Tobacco</td>\n",
       "      <td>Food Production</td>\n",
       "      <td>Chicago, IL</td>\n",
       "      <td>67702</td>\n",
       "      <td>1849</td>\n",
       "      <td>32300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>McKesson</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>Wholesalers: Health Care</td>\n",
       "      <td>San Francisco, CA</td>\n",
       "      <td>181241</td>\n",
       "      <td>1476</td>\n",
       "      <td>70400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>McDonald’s</td>\n",
       "      <td>Hotels, Resturants &amp; Leisure</td>\n",
       "      <td>Food Services</td>\n",
       "      <td>Oak Brook, IL</td>\n",
       "      <td>25413</td>\n",
       "      <td>4529</td>\n",
       "      <td>420000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Procter &amp; Gamble</td>\n",
       "      <td>Household Products</td>\n",
       "      <td>Household and Personal Products</td>\n",
       "      <td>Cincinnati, OH</td>\n",
       "      <td>78756</td>\n",
       "      <td>7036</td>\n",
       "      <td>110000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>General Electric</td>\n",
       "      <td>Industrials</td>\n",
       "      <td>Industrial Machinery</td>\n",
       "      <td>Fairfield, CT</td>\n",
       "      <td>140389</td>\n",
       "      <td>-6126</td>\n",
       "      <td>333000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>Alcoa</td>\n",
       "      <td>Materials</td>\n",
       "      <td>Metals</td>\n",
       "      <td>New York, NY</td>\n",
       "      <td>22534</td>\n",
       "      <td>-322</td>\n",
       "      <td>60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Disney</td>\n",
       "      <td>Media</td>\n",
       "      <td>Entertainment</td>\n",
       "      <td>Burbank, CA</td>\n",
       "      <td>52465</td>\n",
       "      <td>8382</td>\n",
       "      <td>185000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>General Motors</td>\n",
       "      <td>Motor Vehicles &amp; Parts</td>\n",
       "      <td>Motor Vehicles and Parts</td>\n",
       "      <td>Detroit, MI</td>\n",
       "      <td>152356</td>\n",
       "      <td>9687</td>\n",
       "      <td>215000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Walmart</td>\n",
       "      <td>Retailing</td>\n",
       "      <td>General Merchandisers</td>\n",
       "      <td>Bentonville, AR</td>\n",
       "      <td>482130</td>\n",
       "      <td>14694</td>\n",
       "      <td>2300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Computers, Office Equipment</td>\n",
       "      <td>Cupertino, CA</td>\n",
       "      <td>233715</td>\n",
       "      <td>53394</td>\n",
       "      <td>110000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>AT&amp;T</td>\n",
       "      <td>Telecommunications</td>\n",
       "      <td>Telecommunications</td>\n",
       "      <td>Dallas, TX</td>\n",
       "      <td>146801</td>\n",
       "      <td>13345</td>\n",
       "      <td>281450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>UPS</td>\n",
       "      <td>Transportation</td>\n",
       "      <td>Mail, Package, and Freight Delivery</td>\n",
       "      <td>Atlanta, GA</td>\n",
       "      <td>58363</td>\n",
       "      <td>4844</td>\n",
       "      <td>341240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Sysco</td>\n",
       "      <td>Wholesalers</td>\n",
       "      <td>Wholesalers: Food and Grocery</td>\n",
       "      <td>Houston, TX</td>\n",
       "      <td>48681</td>\n",
       "      <td>687</td>\n",
       "      <td>51700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Company                        Sector  \\\n",
       "24                   Boeing           Aerospace & Defense   \n",
       "91                     Nike                       Apparel   \n",
       "144           ManpowerGroup             Business Services   \n",
       "56             Dow Chemical                     Chemicals   \n",
       "2               Exxon Mobil                        Energy   \n",
       "155                   Fluor    Engineering & Construction   \n",
       "4        Berkshire Hathaway                    Financials   \n",
       "7                CVS Health          Food and Drug Stores   \n",
       "41   Archer Daniels Midland     Food, Beverages & Tobacco   \n",
       "5                  McKesson                   Health Care   \n",
       "109              McDonald’s  Hotels, Resturants & Leisure   \n",
       "34         Procter & Gamble            Household Products   \n",
       "11         General Electric                   Industrials   \n",
       "126                   Alcoa                     Materials   \n",
       "53                   Disney                         Media   \n",
       "8            General Motors        Motor Vehicles & Parts   \n",
       "1                   Walmart                     Retailing   \n",
       "3                     Apple                    Technology   \n",
       "10                     AT&T            Telecommunications   \n",
       "48                      UPS                Transportation   \n",
       "57                    Sysco                   Wholesalers   \n",
       "\n",
       "                                     Industry           Location Revenue  \\\n",
       "24                      Aerospace and Defense        Chicago, IL   96114   \n",
       "91                                    Apparel      Beaverton, OR   30601   \n",
       "144                            Temporary Help      Milwaukee, WI   19330   \n",
       "56                                  Chemicals        Midland, MI   48778   \n",
       "2                          Petroleum Refining         Irving, TX  246204   \n",
       "155                 Engineering, Construction         Irving, TX   18114   \n",
       "4    Insurance: Property and Casualty (Stock)          Omaha, NE  210821   \n",
       "7                        Food and Drug Stores     Woonsocket, RI  153290   \n",
       "41                            Food Production        Chicago, IL   67702   \n",
       "5                    Wholesalers: Health Care  San Francisco, CA  181241   \n",
       "109                             Food Services      Oak Brook, IL   25413   \n",
       "34            Household and Personal Products     Cincinnati, OH   78756   \n",
       "11                       Industrial Machinery      Fairfield, CT  140389   \n",
       "126                                    Metals       New York, NY   22534   \n",
       "53                              Entertainment        Burbank, CA   52465   \n",
       "8                    Motor Vehicles and Parts        Detroit, MI  152356   \n",
       "1                       General Merchandisers    Bentonville, AR  482130   \n",
       "3                 Computers, Office Equipment      Cupertino, CA  233715   \n",
       "10                         Telecommunications         Dallas, TX  146801   \n",
       "48        Mail, Package, and Freight Delivery        Atlanta, GA   58363   \n",
       "57              Wholesalers: Food and Grocery        Houston, TX   48681   \n",
       "\n",
       "    Profits Employees  \n",
       "24     5176    161400  \n",
       "91     3273     62600  \n",
       "144     419     27000  \n",
       "56     7685     49495  \n",
       "2     16150     75600  \n",
       "155     413     38758  \n",
       "4     24083    331000  \n",
       "7      5237    199000  \n",
       "41     1849     32300  \n",
       "5      1476     70400  \n",
       "109    4529    420000  \n",
       "34     7036    110000  \n",
       "11    -6126    333000  \n",
       "126    -322     60000  \n",
       "53     8382    185000  \n",
       "8      9687    215000  \n",
       "1     14694   2300000  \n",
       "3     53394    110000  \n",
       "10    13345    281450  \n",
       "48     4844    341240  \n",
       "57      687     51700  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for sector_name,data in sector: #grup ismi ve grup bilgilerini döndürür\n",
    "    highest_revenue_company_in_group=data.nlargest(1,\"Revenue\")\n",
    "    df=df.append(highest_revenue_company_in_group)\n",
    "df\n",
    "# append kullanımdan kaldırıldı bunun yerine concat kullan\n",
    "#revenue hasılat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "95538047",
   "metadata": {
    "scrolled": false
   },
   "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>Company</th>\n",
       "      <th>Sector</th>\n",
       "      <th>Industry</th>\n",
       "      <th>Location</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Profits</th>\n",
       "      <th>Employees</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Boeing</td>\n",
       "      <td>Aerospace &amp; Defense</td>\n",
       "      <td>Aerospace and Defense</td>\n",
       "      <td>Chicago, IL</td>\n",
       "      <td>96114</td>\n",
       "      <td>5176</td>\n",
       "      <td>161400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Nike</td>\n",
       "      <td>Apparel</td>\n",
       "      <td>Apparel</td>\n",
       "      <td>Beaverton, OR</td>\n",
       "      <td>30601</td>\n",
       "      <td>3273</td>\n",
       "      <td>62600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>ManpowerGroup</td>\n",
       "      <td>Business Services</td>\n",
       "      <td>Temporary Help</td>\n",
       "      <td>Milwaukee, WI</td>\n",
       "      <td>19330</td>\n",
       "      <td>419</td>\n",
       "      <td>27000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Dow Chemical</td>\n",
       "      <td>Chemicals</td>\n",
       "      <td>Chemicals</td>\n",
       "      <td>Midland, MI</td>\n",
       "      <td>48778</td>\n",
       "      <td>7685</td>\n",
       "      <td>49495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Exxon Mobil</td>\n",
       "      <td>Energy</td>\n",
       "      <td>Petroleum Refining</td>\n",
       "      <td>Irving, TX</td>\n",
       "      <td>246204</td>\n",
       "      <td>16150</td>\n",
       "      <td>75600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>Fluor</td>\n",
       "      <td>Engineering &amp; Construction</td>\n",
       "      <td>Engineering, Construction</td>\n",
       "      <td>Irving, TX</td>\n",
       "      <td>18114</td>\n",
       "      <td>413</td>\n",
       "      <td>38758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Berkshire Hathaway</td>\n",
       "      <td>Financials</td>\n",
       "      <td>Insurance: Property and Casualty (Stock)</td>\n",
       "      <td>Omaha, NE</td>\n",
       "      <td>210821</td>\n",
       "      <td>24083</td>\n",
       "      <td>331000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CVS Health</td>\n",
       "      <td>Food and Drug Stores</td>\n",
       "      <td>Food and Drug Stores</td>\n",
       "      <td>Woonsocket, RI</td>\n",
       "      <td>153290</td>\n",
       "      <td>5237</td>\n",
       "      <td>199000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Archer Daniels Midland</td>\n",
       "      <td>Food, Beverages &amp; Tobacco</td>\n",
       "      <td>Food Production</td>\n",
       "      <td>Chicago, IL</td>\n",
       "      <td>67702</td>\n",
       "      <td>1849</td>\n",
       "      <td>32300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>McKesson</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>Wholesalers: Health Care</td>\n",
       "      <td>San Francisco, CA</td>\n",
       "      <td>181241</td>\n",
       "      <td>1476</td>\n",
       "      <td>70400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>McDonald’s</td>\n",
       "      <td>Hotels, Resturants &amp; Leisure</td>\n",
       "      <td>Food Services</td>\n",
       "      <td>Oak Brook, IL</td>\n",
       "      <td>25413</td>\n",
       "      <td>4529</td>\n",
       "      <td>420000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Procter &amp; Gamble</td>\n",
       "      <td>Household Products</td>\n",
       "      <td>Household and Personal Products</td>\n",
       "      <td>Cincinnati, OH</td>\n",
       "      <td>78756</td>\n",
       "      <td>7036</td>\n",
       "      <td>110000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>General Electric</td>\n",
       "      <td>Industrials</td>\n",
       "      <td>Industrial Machinery</td>\n",
       "      <td>Fairfield, CT</td>\n",
       "      <td>140389</td>\n",
       "      <td>-6126</td>\n",
       "      <td>333000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>Alcoa</td>\n",
       "      <td>Materials</td>\n",
       "      <td>Metals</td>\n",
       "      <td>New York, NY</td>\n",
       "      <td>22534</td>\n",
       "      <td>-322</td>\n",
       "      <td>60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Disney</td>\n",
       "      <td>Media</td>\n",
       "      <td>Entertainment</td>\n",
       "      <td>Burbank, CA</td>\n",
       "      <td>52465</td>\n",
       "      <td>8382</td>\n",
       "      <td>185000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>General Motors</td>\n",
       "      <td>Motor Vehicles &amp; Parts</td>\n",
       "      <td>Motor Vehicles and Parts</td>\n",
       "      <td>Detroit, MI</td>\n",
       "      <td>152356</td>\n",
       "      <td>9687</td>\n",
       "      <td>215000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Walmart</td>\n",
       "      <td>Retailing</td>\n",
       "      <td>General Merchandisers</td>\n",
       "      <td>Bentonville, AR</td>\n",
       "      <td>482130</td>\n",
       "      <td>14694</td>\n",
       "      <td>2300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Computers, Office Equipment</td>\n",
       "      <td>Cupertino, CA</td>\n",
       "      <td>233715</td>\n",
       "      <td>53394</td>\n",
       "      <td>110000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>AT&amp;T</td>\n",
       "      <td>Telecommunications</td>\n",
       "      <td>Telecommunications</td>\n",
       "      <td>Dallas, TX</td>\n",
       "      <td>146801</td>\n",
       "      <td>13345</td>\n",
       "      <td>281450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>UPS</td>\n",
       "      <td>Transportation</td>\n",
       "      <td>Mail, Package, and Freight Delivery</td>\n",
       "      <td>Atlanta, GA</td>\n",
       "      <td>58363</td>\n",
       "      <td>4844</td>\n",
       "      <td>341240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Sysco</td>\n",
       "      <td>Wholesalers</td>\n",
       "      <td>Wholesalers: Food and Grocery</td>\n",
       "      <td>Houston, TX</td>\n",
       "      <td>48681</td>\n",
       "      <td>687</td>\n",
       "      <td>51700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Boeing</td>\n",
       "      <td>Aerospace &amp; Defense</td>\n",
       "      <td>Aerospace and Defense</td>\n",
       "      <td>Chicago, IL</td>\n",
       "      <td>96114</td>\n",
       "      <td>5176</td>\n",
       "      <td>161400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Nike</td>\n",
       "      <td>Apparel</td>\n",
       "      <td>Apparel</td>\n",
       "      <td>Beaverton, OR</td>\n",
       "      <td>30601</td>\n",
       "      <td>3273</td>\n",
       "      <td>62600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>ManpowerGroup</td>\n",
       "      <td>Business Services</td>\n",
       "      <td>Temporary Help</td>\n",
       "      <td>Milwaukee, WI</td>\n",
       "      <td>19330</td>\n",
       "      <td>419</td>\n",
       "      <td>27000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Dow Chemical</td>\n",
       "      <td>Chemicals</td>\n",
       "      <td>Chemicals</td>\n",
       "      <td>Midland, MI</td>\n",
       "      <td>48778</td>\n",
       "      <td>7685</td>\n",
       "      <td>49495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Exxon Mobil</td>\n",
       "      <td>Energy</td>\n",
       "      <td>Petroleum Refining</td>\n",
       "      <td>Irving, TX</td>\n",
       "      <td>246204</td>\n",
       "      <td>16150</td>\n",
       "      <td>75600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>Fluor</td>\n",
       "      <td>Engineering &amp; Construction</td>\n",
       "      <td>Engineering, Construction</td>\n",
       "      <td>Irving, TX</td>\n",
       "      <td>18114</td>\n",
       "      <td>413</td>\n",
       "      <td>38758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Berkshire Hathaway</td>\n",
       "      <td>Financials</td>\n",
       "      <td>Insurance: Property and Casualty (Stock)</td>\n",
       "      <td>Omaha, NE</td>\n",
       "      <td>210821</td>\n",
       "      <td>24083</td>\n",
       "      <td>331000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CVS Health</td>\n",
       "      <td>Food and Drug Stores</td>\n",
       "      <td>Food and Drug Stores</td>\n",
       "      <td>Woonsocket, RI</td>\n",
       "      <td>153290</td>\n",
       "      <td>5237</td>\n",
       "      <td>199000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Archer Daniels Midland</td>\n",
       "      <td>Food, Beverages &amp; Tobacco</td>\n",
       "      <td>Food Production</td>\n",
       "      <td>Chicago, IL</td>\n",
       "      <td>67702</td>\n",
       "      <td>1849</td>\n",
       "      <td>32300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>McKesson</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>Wholesalers: Health Care</td>\n",
       "      <td>San Francisco, CA</td>\n",
       "      <td>181241</td>\n",
       "      <td>1476</td>\n",
       "      <td>70400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>McDonald’s</td>\n",
       "      <td>Hotels, Resturants &amp; Leisure</td>\n",
       "      <td>Food Services</td>\n",
       "      <td>Oak Brook, IL</td>\n",
       "      <td>25413</td>\n",
       "      <td>4529</td>\n",
       "      <td>420000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Procter &amp; Gamble</td>\n",
       "      <td>Household Products</td>\n",
       "      <td>Household and Personal Products</td>\n",
       "      <td>Cincinnati, OH</td>\n",
       "      <td>78756</td>\n",
       "      <td>7036</td>\n",
       "      <td>110000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>General Electric</td>\n",
       "      <td>Industrials</td>\n",
       "      <td>Industrial Machinery</td>\n",
       "      <td>Fairfield, CT</td>\n",
       "      <td>140389</td>\n",
       "      <td>-6126</td>\n",
       "      <td>333000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>Alcoa</td>\n",
       "      <td>Materials</td>\n",
       "      <td>Metals</td>\n",
       "      <td>New York, NY</td>\n",
       "      <td>22534</td>\n",
       "      <td>-322</td>\n",
       "      <td>60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Disney</td>\n",
       "      <td>Media</td>\n",
       "      <td>Entertainment</td>\n",
       "      <td>Burbank, CA</td>\n",
       "      <td>52465</td>\n",
       "      <td>8382</td>\n",
       "      <td>185000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>General Motors</td>\n",
       "      <td>Motor Vehicles &amp; Parts</td>\n",
       "      <td>Motor Vehicles and Parts</td>\n",
       "      <td>Detroit, MI</td>\n",
       "      <td>152356</td>\n",
       "      <td>9687</td>\n",
       "      <td>215000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Walmart</td>\n",
       "      <td>Retailing</td>\n",
       "      <td>General Merchandisers</td>\n",
       "      <td>Bentonville, AR</td>\n",
       "      <td>482130</td>\n",
       "      <td>14694</td>\n",
       "      <td>2300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Computers, Office Equipment</td>\n",
       "      <td>Cupertino, CA</td>\n",
       "      <td>233715</td>\n",
       "      <td>53394</td>\n",
       "      <td>110000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>AT&amp;T</td>\n",
       "      <td>Telecommunications</td>\n",
       "      <td>Telecommunications</td>\n",
       "      <td>Dallas, TX</td>\n",
       "      <td>146801</td>\n",
       "      <td>13345</td>\n",
       "      <td>281450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>UPS</td>\n",
       "      <td>Transportation</td>\n",
       "      <td>Mail, Package, and Freight Delivery</td>\n",
       "      <td>Atlanta, GA</td>\n",
       "      <td>58363</td>\n",
       "      <td>4844</td>\n",
       "      <td>341240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Sysco</td>\n",
       "      <td>Wholesalers</td>\n",
       "      <td>Wholesalers: Food and Grocery</td>\n",
       "      <td>Houston, TX</td>\n",
       "      <td>48681</td>\n",
       "      <td>687</td>\n",
       "      <td>51700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Company                        Sector  \\\n",
       "24                   Boeing           Aerospace & Defense   \n",
       "91                     Nike                       Apparel   \n",
       "144           ManpowerGroup             Business Services   \n",
       "56             Dow Chemical                     Chemicals   \n",
       "2               Exxon Mobil                        Energy   \n",
       "155                   Fluor    Engineering & Construction   \n",
       "4        Berkshire Hathaway                    Financials   \n",
       "7                CVS Health          Food and Drug Stores   \n",
       "41   Archer Daniels Midland     Food, Beverages & Tobacco   \n",
       "5                  McKesson                   Health Care   \n",
       "109              McDonald’s  Hotels, Resturants & Leisure   \n",
       "34         Procter & Gamble            Household Products   \n",
       "11         General Electric                   Industrials   \n",
       "126                   Alcoa                     Materials   \n",
       "53                   Disney                         Media   \n",
       "8            General Motors        Motor Vehicles & Parts   \n",
       "1                   Walmart                     Retailing   \n",
       "3                     Apple                    Technology   \n",
       "10                     AT&T            Telecommunications   \n",
       "48                      UPS                Transportation   \n",
       "57                    Sysco                   Wholesalers   \n",
       "24                   Boeing           Aerospace & Defense   \n",
       "91                     Nike                       Apparel   \n",
       "144           ManpowerGroup             Business Services   \n",
       "56             Dow Chemical                     Chemicals   \n",
       "2               Exxon Mobil                        Energy   \n",
       "155                   Fluor    Engineering & Construction   \n",
       "4        Berkshire Hathaway                    Financials   \n",
       "7                CVS Health          Food and Drug Stores   \n",
       "41   Archer Daniels Midland     Food, Beverages & Tobacco   \n",
       "5                  McKesson                   Health Care   \n",
       "109              McDonald’s  Hotels, Resturants & Leisure   \n",
       "34         Procter & Gamble            Household Products   \n",
       "11         General Electric                   Industrials   \n",
       "126                   Alcoa                     Materials   \n",
       "53                   Disney                         Media   \n",
       "8            General Motors        Motor Vehicles & Parts   \n",
       "1                   Walmart                     Retailing   \n",
       "3                     Apple                    Technology   \n",
       "10                     AT&T            Telecommunications   \n",
       "48                      UPS                Transportation   \n",
       "57                    Sysco                   Wholesalers   \n",
       "\n",
       "                                     Industry           Location Revenue  \\\n",
       "24                      Aerospace and Defense        Chicago, IL   96114   \n",
       "91                                    Apparel      Beaverton, OR   30601   \n",
       "144                            Temporary Help      Milwaukee, WI   19330   \n",
       "56                                  Chemicals        Midland, MI   48778   \n",
       "2                          Petroleum Refining         Irving, TX  246204   \n",
       "155                 Engineering, Construction         Irving, TX   18114   \n",
       "4    Insurance: Property and Casualty (Stock)          Omaha, NE  210821   \n",
       "7                        Food and Drug Stores     Woonsocket, RI  153290   \n",
       "41                            Food Production        Chicago, IL   67702   \n",
       "5                    Wholesalers: Health Care  San Francisco, CA  181241   \n",
       "109                             Food Services      Oak Brook, IL   25413   \n",
       "34            Household and Personal Products     Cincinnati, OH   78756   \n",
       "11                       Industrial Machinery      Fairfield, CT  140389   \n",
       "126                                    Metals       New York, NY   22534   \n",
       "53                              Entertainment        Burbank, CA   52465   \n",
       "8                    Motor Vehicles and Parts        Detroit, MI  152356   \n",
       "1                       General Merchandisers    Bentonville, AR  482130   \n",
       "3                 Computers, Office Equipment      Cupertino, CA  233715   \n",
       "10                         Telecommunications         Dallas, TX  146801   \n",
       "48        Mail, Package, and Freight Delivery        Atlanta, GA   58363   \n",
       "57              Wholesalers: Food and Grocery        Houston, TX   48681   \n",
       "24                      Aerospace and Defense        Chicago, IL   96114   \n",
       "91                                    Apparel      Beaverton, OR   30601   \n",
       "144                            Temporary Help      Milwaukee, WI   19330   \n",
       "56                                  Chemicals        Midland, MI   48778   \n",
       "2                          Petroleum Refining         Irving, TX  246204   \n",
       "155                 Engineering, Construction         Irving, TX   18114   \n",
       "4    Insurance: Property and Casualty (Stock)          Omaha, NE  210821   \n",
       "7                        Food and Drug Stores     Woonsocket, RI  153290   \n",
       "41                            Food Production        Chicago, IL   67702   \n",
       "5                    Wholesalers: Health Care  San Francisco, CA  181241   \n",
       "109                             Food Services      Oak Brook, IL   25413   \n",
       "34            Household and Personal Products     Cincinnati, OH   78756   \n",
       "11                       Industrial Machinery      Fairfield, CT  140389   \n",
       "126                                    Metals       New York, NY   22534   \n",
       "53                              Entertainment        Burbank, CA   52465   \n",
       "8                    Motor Vehicles and Parts        Detroit, MI  152356   \n",
       "1                       General Merchandisers    Bentonville, AR  482130   \n",
       "3                 Computers, Office Equipment      Cupertino, CA  233715   \n",
       "10                         Telecommunications         Dallas, TX  146801   \n",
       "48        Mail, Package, and Freight Delivery        Atlanta, GA   58363   \n",
       "57              Wholesalers: Food and Grocery        Houston, TX   48681   \n",
       "\n",
       "    Profits Employees  \n",
       "24     5176    161400  \n",
       "91     3273     62600  \n",
       "144     419     27000  \n",
       "56     7685     49495  \n",
       "2     16150     75600  \n",
       "155     413     38758  \n",
       "4     24083    331000  \n",
       "7      5237    199000  \n",
       "41     1849     32300  \n",
       "5      1476     70400  \n",
       "109    4529    420000  \n",
       "34     7036    110000  \n",
       "11    -6126    333000  \n",
       "126    -322     60000  \n",
       "53     8382    185000  \n",
       "8      9687    215000  \n",
       "1     14694   2300000  \n",
       "3     53394    110000  \n",
       "10    13345    281450  \n",
       "48     4844    341240  \n",
       "57      687     51700  \n",
       "24     5176    161400  \n",
       "91     3273     62600  \n",
       "144     419     27000  \n",
       "56     7685     49495  \n",
       "2     16150     75600  \n",
       "155     413     38758  \n",
       "4     24083    331000  \n",
       "7      5237    199000  \n",
       "41     1849     32300  \n",
       "5      1476     70400  \n",
       "109    4529    420000  \n",
       "34     7036    110000  \n",
       "11    -6126    333000  \n",
       "126    -322     60000  \n",
       "53     8382    185000  \n",
       "8      9687    215000  \n",
       "1     14694   2300000  \n",
       "3     53394    110000  \n",
       "10    13345    281450  \n",
       "48     4844    341240  \n",
       "57      687     51700  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for sector_name,data in sector: #grup ismi ve grup bilgilerini döndürür\n",
    "    highest_revenue_company_in_group=data.nlargest(1,\"Revenue\") #nlargest en yüksek alanı getirir\n",
    "    df=pd.concat([df,highest_revenue_company_in_group]) #concat dataframe döndürür\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c133f0ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "#eyaletlere göre grupla,her gruptaki en yüksek kara sahip şirketi göster\n",
    "cities=df_fortune.groupby(\"Location\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "174e1142",
   "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>Company</th>\n",
       "      <th>Sector</th>\n",
       "      <th>Industry</th>\n",
       "      <th>Location</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Profits</th>\n",
       "      <th>Employees</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Boeing</td>\n",
       "      <td>Aerospace &amp; Defense</td>\n",
       "      <td>Aerospace and Defense</td>\n",
       "      <td>Chicago, IL</td>\n",
       "      <td>96114</td>\n",
       "      <td>5176</td>\n",
       "      <td>161400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Nike</td>\n",
       "      <td>Apparel</td>\n",
       "      <td>Apparel</td>\n",
       "      <td>Beaverton, OR</td>\n",
       "      <td>30601</td>\n",
       "      <td>3273</td>\n",
       "      <td>62600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>ManpowerGroup</td>\n",
       "      <td>Business Services</td>\n",
       "      <td>Temporary Help</td>\n",
       "      <td>Milwaukee, WI</td>\n",
       "      <td>19330</td>\n",
       "      <td>419</td>\n",
       "      <td>27000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Dow Chemical</td>\n",
       "      <td>Chemicals</td>\n",
       "      <td>Chemicals</td>\n",
       "      <td>Midland, MI</td>\n",
       "      <td>48778</td>\n",
       "      <td>7685</td>\n",
       "      <td>49495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Exxon Mobil</td>\n",
       "      <td>Energy</td>\n",
       "      <td>Petroleum Refining</td>\n",
       "      <td>Irving, TX</td>\n",
       "      <td>246204</td>\n",
       "      <td>16150</td>\n",
       "      <td>75600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>Fluor</td>\n",
       "      <td>Engineering &amp; Construction</td>\n",
       "      <td>Engineering, Construction</td>\n",
       "      <td>Irving, TX</td>\n",
       "      <td>18114</td>\n",
       "      <td>413</td>\n",
       "      <td>38758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Berkshire Hathaway</td>\n",
       "      <td>Financials</td>\n",
       "      <td>Insurance: Property and Casualty (Stock)</td>\n",
       "      <td>Omaha, NE</td>\n",
       "      <td>210821</td>\n",
       "      <td>24083</td>\n",
       "      <td>331000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CVS Health</td>\n",
       "      <td>Food and Drug Stores</td>\n",
       "      <td>Food and Drug Stores</td>\n",
       "      <td>Woonsocket, RI</td>\n",
       "      <td>153290</td>\n",
       "      <td>5237</td>\n",
       "      <td>199000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Archer Daniels Midland</td>\n",
       "      <td>Food, Beverages &amp; Tobacco</td>\n",
       "      <td>Food Production</td>\n",
       "      <td>Chicago, IL</td>\n",
       "      <td>67702</td>\n",
       "      <td>1849</td>\n",
       "      <td>32300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>McKesson</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>Wholesalers: Health Care</td>\n",
       "      <td>San Francisco, CA</td>\n",
       "      <td>181241</td>\n",
       "      <td>1476</td>\n",
       "      <td>70400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>McDonald’s</td>\n",
       "      <td>Hotels, Resturants &amp; Leisure</td>\n",
       "      <td>Food Services</td>\n",
       "      <td>Oak Brook, IL</td>\n",
       "      <td>25413</td>\n",
       "      <td>4529</td>\n",
       "      <td>420000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Procter &amp; Gamble</td>\n",
       "      <td>Household Products</td>\n",
       "      <td>Household and Personal Products</td>\n",
       "      <td>Cincinnati, OH</td>\n",
       "      <td>78756</td>\n",
       "      <td>7036</td>\n",
       "      <td>110000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>General Electric</td>\n",
       "      <td>Industrials</td>\n",
       "      <td>Industrial Machinery</td>\n",
       "      <td>Fairfield, CT</td>\n",
       "      <td>140389</td>\n",
       "      <td>-6126</td>\n",
       "      <td>333000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>Alcoa</td>\n",
       "      <td>Materials</td>\n",
       "      <td>Metals</td>\n",
       "      <td>New York, NY</td>\n",
       "      <td>22534</td>\n",
       "      <td>-322</td>\n",
       "      <td>60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Disney</td>\n",
       "      <td>Media</td>\n",
       "      <td>Entertainment</td>\n",
       "      <td>Burbank, CA</td>\n",
       "      <td>52465</td>\n",
       "      <td>8382</td>\n",
       "      <td>185000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>General Motors</td>\n",
       "      <td>Motor Vehicles &amp; Parts</td>\n",
       "      <td>Motor Vehicles and Parts</td>\n",
       "      <td>Detroit, MI</td>\n",
       "      <td>152356</td>\n",
       "      <td>9687</td>\n",
       "      <td>215000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Walmart</td>\n",
       "      <td>Retailing</td>\n",
       "      <td>General Merchandisers</td>\n",
       "      <td>Bentonville, AR</td>\n",
       "      <td>482130</td>\n",
       "      <td>14694</td>\n",
       "      <td>2300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Computers, Office Equipment</td>\n",
       "      <td>Cupertino, CA</td>\n",
       "      <td>233715</td>\n",
       "      <td>53394</td>\n",
       "      <td>110000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>AT&amp;T</td>\n",
       "      <td>Telecommunications</td>\n",
       "      <td>Telecommunications</td>\n",
       "      <td>Dallas, TX</td>\n",
       "      <td>146801</td>\n",
       "      <td>13345</td>\n",
       "      <td>281450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>UPS</td>\n",
       "      <td>Transportation</td>\n",
       "      <td>Mail, Package, and Freight Delivery</td>\n",
       "      <td>Atlanta, GA</td>\n",
       "      <td>58363</td>\n",
       "      <td>4844</td>\n",
       "      <td>341240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Sysco</td>\n",
       "      <td>Wholesalers</td>\n",
       "      <td>Wholesalers: Food and Grocery</td>\n",
       "      <td>Houston, TX</td>\n",
       "      <td>48681</td>\n",
       "      <td>687</td>\n",
       "      <td>51700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Boeing</td>\n",
       "      <td>Aerospace &amp; Defense</td>\n",
       "      <td>Aerospace and Defense</td>\n",
       "      <td>Chicago, IL</td>\n",
       "      <td>96114</td>\n",
       "      <td>5176</td>\n",
       "      <td>161400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Nike</td>\n",
       "      <td>Apparel</td>\n",
       "      <td>Apparel</td>\n",
       "      <td>Beaverton, OR</td>\n",
       "      <td>30601</td>\n",
       "      <td>3273</td>\n",
       "      <td>62600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>ManpowerGroup</td>\n",
       "      <td>Business Services</td>\n",
       "      <td>Temporary Help</td>\n",
       "      <td>Milwaukee, WI</td>\n",
       "      <td>19330</td>\n",
       "      <td>419</td>\n",
       "      <td>27000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Dow Chemical</td>\n",
       "      <td>Chemicals</td>\n",
       "      <td>Chemicals</td>\n",
       "      <td>Midland, MI</td>\n",
       "      <td>48778</td>\n",
       "      <td>7685</td>\n",
       "      <td>49495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Exxon Mobil</td>\n",
       "      <td>Energy</td>\n",
       "      <td>Petroleum Refining</td>\n",
       "      <td>Irving, TX</td>\n",
       "      <td>246204</td>\n",
       "      <td>16150</td>\n",
       "      <td>75600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>Fluor</td>\n",
       "      <td>Engineering &amp; Construction</td>\n",
       "      <td>Engineering, Construction</td>\n",
       "      <td>Irving, TX</td>\n",
       "      <td>18114</td>\n",
       "      <td>413</td>\n",
       "      <td>38758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Berkshire Hathaway</td>\n",
       "      <td>Financials</td>\n",
       "      <td>Insurance: Property and Casualty (Stock)</td>\n",
       "      <td>Omaha, NE</td>\n",
       "      <td>210821</td>\n",
       "      <td>24083</td>\n",
       "      <td>331000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CVS Health</td>\n",
       "      <td>Food and Drug Stores</td>\n",
       "      <td>Food and Drug Stores</td>\n",
       "      <td>Woonsocket, RI</td>\n",
       "      <td>153290</td>\n",
       "      <td>5237</td>\n",
       "      <td>199000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Archer Daniels Midland</td>\n",
       "      <td>Food, Beverages &amp; Tobacco</td>\n",
       "      <td>Food Production</td>\n",
       "      <td>Chicago, IL</td>\n",
       "      <td>67702</td>\n",
       "      <td>1849</td>\n",
       "      <td>32300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>McKesson</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>Wholesalers: Health Care</td>\n",
       "      <td>San Francisco, CA</td>\n",
       "      <td>181241</td>\n",
       "      <td>1476</td>\n",
       "      <td>70400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>McDonald’s</td>\n",
       "      <td>Hotels, Resturants &amp; Leisure</td>\n",
       "      <td>Food Services</td>\n",
       "      <td>Oak Brook, IL</td>\n",
       "      <td>25413</td>\n",
       "      <td>4529</td>\n",
       "      <td>420000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Procter &amp; Gamble</td>\n",
       "      <td>Household Products</td>\n",
       "      <td>Household and Personal Products</td>\n",
       "      <td>Cincinnati, OH</td>\n",
       "      <td>78756</td>\n",
       "      <td>7036</td>\n",
       "      <td>110000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>General Electric</td>\n",
       "      <td>Industrials</td>\n",
       "      <td>Industrial Machinery</td>\n",
       "      <td>Fairfield, CT</td>\n",
       "      <td>140389</td>\n",
       "      <td>-6126</td>\n",
       "      <td>333000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>Alcoa</td>\n",
       "      <td>Materials</td>\n",
       "      <td>Metals</td>\n",
       "      <td>New York, NY</td>\n",
       "      <td>22534</td>\n",
       "      <td>-322</td>\n",
       "      <td>60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Disney</td>\n",
       "      <td>Media</td>\n",
       "      <td>Entertainment</td>\n",
       "      <td>Burbank, CA</td>\n",
       "      <td>52465</td>\n",
       "      <td>8382</td>\n",
       "      <td>185000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>General Motors</td>\n",
       "      <td>Motor Vehicles &amp; Parts</td>\n",
       "      <td>Motor Vehicles and Parts</td>\n",
       "      <td>Detroit, MI</td>\n",
       "      <td>152356</td>\n",
       "      <td>9687</td>\n",
       "      <td>215000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Walmart</td>\n",
       "      <td>Retailing</td>\n",
       "      <td>General Merchandisers</td>\n",
       "      <td>Bentonville, AR</td>\n",
       "      <td>482130</td>\n",
       "      <td>14694</td>\n",
       "      <td>2300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Computers, Office Equipment</td>\n",
       "      <td>Cupertino, CA</td>\n",
       "      <td>233715</td>\n",
       "      <td>53394</td>\n",
       "      <td>110000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>AT&amp;T</td>\n",
       "      <td>Telecommunications</td>\n",
       "      <td>Telecommunications</td>\n",
       "      <td>Dallas, TX</td>\n",
       "      <td>146801</td>\n",
       "      <td>13345</td>\n",
       "      <td>281450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>UPS</td>\n",
       "      <td>Transportation</td>\n",
       "      <td>Mail, Package, and Freight Delivery</td>\n",
       "      <td>Atlanta, GA</td>\n",
       "      <td>58363</td>\n",
       "      <td>4844</td>\n",
       "      <td>341240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Sysco</td>\n",
       "      <td>Wholesalers</td>\n",
       "      <td>Wholesalers: Food and Grocery</td>\n",
       "      <td>Houston, TX</td>\n",
       "      <td>48681</td>\n",
       "      <td>687</td>\n",
       "      <td>51700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>932</th>\n",
       "      <td>Herman Miller</td>\n",
       "      <td>Household Products</td>\n",
       "      <td>Home Equipment, Furnishings</td>\n",
       "      <td>Zeeland, MI</td>\n",
       "      <td>2142</td>\n",
       "      <td>98</td>\n",
       "      <td>7510</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Company                        Sector  \\\n",
       "24                   Boeing           Aerospace & Defense   \n",
       "91                     Nike                       Apparel   \n",
       "144           ManpowerGroup             Business Services   \n",
       "56             Dow Chemical                     Chemicals   \n",
       "2               Exxon Mobil                        Energy   \n",
       "155                   Fluor    Engineering & Construction   \n",
       "4        Berkshire Hathaway                    Financials   \n",
       "7                CVS Health          Food and Drug Stores   \n",
       "41   Archer Daniels Midland     Food, Beverages & Tobacco   \n",
       "5                  McKesson                   Health Care   \n",
       "109              McDonald’s  Hotels, Resturants & Leisure   \n",
       "34         Procter & Gamble            Household Products   \n",
       "11         General Electric                   Industrials   \n",
       "126                   Alcoa                     Materials   \n",
       "53                   Disney                         Media   \n",
       "8            General Motors        Motor Vehicles & Parts   \n",
       "1                   Walmart                     Retailing   \n",
       "3                     Apple                    Technology   \n",
       "10                     AT&T            Telecommunications   \n",
       "48                      UPS                Transportation   \n",
       "57                    Sysco                   Wholesalers   \n",
       "24                   Boeing           Aerospace & Defense   \n",
       "91                     Nike                       Apparel   \n",
       "144           ManpowerGroup             Business Services   \n",
       "56             Dow Chemical                     Chemicals   \n",
       "2               Exxon Mobil                        Energy   \n",
       "155                   Fluor    Engineering & Construction   \n",
       "4        Berkshire Hathaway                    Financials   \n",
       "7                CVS Health          Food and Drug Stores   \n",
       "41   Archer Daniels Midland     Food, Beverages & Tobacco   \n",
       "5                  McKesson                   Health Care   \n",
       "109              McDonald’s  Hotels, Resturants & Leisure   \n",
       "34         Procter & Gamble            Household Products   \n",
       "11         General Electric                   Industrials   \n",
       "126                   Alcoa                     Materials   \n",
       "53                   Disney                         Media   \n",
       "8            General Motors        Motor Vehicles & Parts   \n",
       "1                   Walmart                     Retailing   \n",
       "3                     Apple                    Technology   \n",
       "10                     AT&T            Telecommunications   \n",
       "48                      UPS                Transportation   \n",
       "57                    Sysco                   Wholesalers   \n",
       "932           Herman Miller            Household Products   \n",
       "\n",
       "                                     Industry           Location Revenue  \\\n",
       "24                      Aerospace and Defense        Chicago, IL   96114   \n",
       "91                                    Apparel      Beaverton, OR   30601   \n",
       "144                            Temporary Help      Milwaukee, WI   19330   \n",
       "56                                  Chemicals        Midland, MI   48778   \n",
       "2                          Petroleum Refining         Irving, TX  246204   \n",
       "155                 Engineering, Construction         Irving, TX   18114   \n",
       "4    Insurance: Property and Casualty (Stock)          Omaha, NE  210821   \n",
       "7                        Food and Drug Stores     Woonsocket, RI  153290   \n",
       "41                            Food Production        Chicago, IL   67702   \n",
       "5                    Wholesalers: Health Care  San Francisco, CA  181241   \n",
       "109                             Food Services      Oak Brook, IL   25413   \n",
       "34            Household and Personal Products     Cincinnati, OH   78756   \n",
       "11                       Industrial Machinery      Fairfield, CT  140389   \n",
       "126                                    Metals       New York, NY   22534   \n",
       "53                              Entertainment        Burbank, CA   52465   \n",
       "8                    Motor Vehicles and Parts        Detroit, MI  152356   \n",
       "1                       General Merchandisers    Bentonville, AR  482130   \n",
       "3                 Computers, Office Equipment      Cupertino, CA  233715   \n",
       "10                         Telecommunications         Dallas, TX  146801   \n",
       "48        Mail, Package, and Freight Delivery        Atlanta, GA   58363   \n",
       "57              Wholesalers: Food and Grocery        Houston, TX   48681   \n",
       "24                      Aerospace and Defense        Chicago, IL   96114   \n",
       "91                                    Apparel      Beaverton, OR   30601   \n",
       "144                            Temporary Help      Milwaukee, WI   19330   \n",
       "56                                  Chemicals        Midland, MI   48778   \n",
       "2                          Petroleum Refining         Irving, TX  246204   \n",
       "155                 Engineering, Construction         Irving, TX   18114   \n",
       "4    Insurance: Property and Casualty (Stock)          Omaha, NE  210821   \n",
       "7                        Food and Drug Stores     Woonsocket, RI  153290   \n",
       "41                            Food Production        Chicago, IL   67702   \n",
       "5                    Wholesalers: Health Care  San Francisco, CA  181241   \n",
       "109                             Food Services      Oak Brook, IL   25413   \n",
       "34            Household and Personal Products     Cincinnati, OH   78756   \n",
       "11                       Industrial Machinery      Fairfield, CT  140389   \n",
       "126                                    Metals       New York, NY   22534   \n",
       "53                              Entertainment        Burbank, CA   52465   \n",
       "8                    Motor Vehicles and Parts        Detroit, MI  152356   \n",
       "1                       General Merchandisers    Bentonville, AR  482130   \n",
       "3                 Computers, Office Equipment      Cupertino, CA  233715   \n",
       "10                         Telecommunications         Dallas, TX  146801   \n",
       "48        Mail, Package, and Freight Delivery        Atlanta, GA   58363   \n",
       "57              Wholesalers: Food and Grocery        Houston, TX   48681   \n",
       "932               Home Equipment, Furnishings        Zeeland, MI    2142   \n",
       "\n",
       "    Profits Employees  \n",
       "24     5176    161400  \n",
       "91     3273     62600  \n",
       "144     419     27000  \n",
       "56     7685     49495  \n",
       "2     16150     75600  \n",
       "155     413     38758  \n",
       "4     24083    331000  \n",
       "7      5237    199000  \n",
       "41     1849     32300  \n",
       "5      1476     70400  \n",
       "109    4529    420000  \n",
       "34     7036    110000  \n",
       "11    -6126    333000  \n",
       "126    -322     60000  \n",
       "53     8382    185000  \n",
       "8      9687    215000  \n",
       "1     14694   2300000  \n",
       "3     53394    110000  \n",
       "10    13345    281450  \n",
       "48     4844    341240  \n",
       "57      687     51700  \n",
       "24     5176    161400  \n",
       "91     3273     62600  \n",
       "144     419     27000  \n",
       "56     7685     49495  \n",
       "2     16150     75600  \n",
       "155     413     38758  \n",
       "4     24083    331000  \n",
       "7      5237    199000  \n",
       "41     1849     32300  \n",
       "5      1476     70400  \n",
       "109    4529    420000  \n",
       "34     7036    110000  \n",
       "11    -6126    333000  \n",
       "126    -322     60000  \n",
       "53     8382    185000  \n",
       "8      9687    215000  \n",
       "1     14694   2300000  \n",
       "3     53394    110000  \n",
       "10    13345    281450  \n",
       "48     4844    341240  \n",
       "57      687     51700  \n",
       "932      98      7510  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for city,data in cities:\n",
    "    highest_revenue_company_in_group=data.nlargest(1,\"Profits\")\n",
    "    df_city=pd.concat([df,highest_revenue_company_in_group])\n",
    "df_city"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "456e91fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "week1=pd.read_csv(\"resources/resources/Restaurant - Week 1 Sales.csv\")\n",
    "week2=pd.read_csv(\"resources/resources/Restaurant - Week 2 Sales.csv\")\n",
    "customer=pd.read_csv(\"resources/resources/Restaurant - Customers.csv\")\n",
    "foods=pd.read_csv(\"resources/resources/Restaurant - Foods.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "77b0be37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Food ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <th>1</th>\n",
       "      <td>97</td>\n",
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       "      <th>2</th>\n",
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       "      <th>3</th>\n",
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       "      <td>155</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>783</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>556</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>547</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>252</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>249</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Customer ID  Food ID\n",
       "0            537        9\n",
       "1             97        4\n",
       "2            658        1\n",
       "3            202        2\n",
       "4            155        9\n",
       "..           ...      ...\n",
       "245          783       10\n",
       "246          556       10\n",
       "247          547        9\n",
       "248          252        9\n",
       "249          249        6\n",
       "\n",
       "[500 rows x 2 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat(objs=[week1,week2]) #indexler yeniden düzenlenmez varolanlar korunur\n",
    "# week1 ve week2 yi birleştirdi\n",
    "#iterable ->liste,map,dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d168833a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Food ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>658</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <td>556</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>547</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>252</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>249</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Customer ID  Food ID\n",
       "0            537        9\n",
       "1             97        4\n",
       "2            658        1\n",
       "3            202        2\n",
       "4            155        9\n",
       "..           ...      ...\n",
       "495          783       10\n",
       "496          556       10\n",
       "497          547        9\n",
       "498          252        9\n",
       "499          249        6\n",
       "\n",
       "[500 rows x 2 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([week1,week2],ignore_index=True) #ignore_index default olarak false dur.\n",
    "#true yaparsak indexleri yeniden düzenleriz\n",
    "# bu yöntemle varolan indexleri kaybederiz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2ec2e4e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th></th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Food ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Week1</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Week2</th>\n",
       "      <th>245</th>\n",
       "      <td>783</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>556</td>\n",
       "      <td>10</td>\n",
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       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>252</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>249</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Customer ID  Food ID\n",
       "Week1 0            537        9\n",
       "      1             97        4\n",
       "      2            658        1\n",
       "      3            202        2\n",
       "      4            155        9\n",
       "...                ...      ...\n",
       "Week2 245          783       10\n",
       "      246          556       10\n",
       "      247          547        9\n",
       "      248          252        9\n",
       "      249          249        6\n",
       "\n",
       "[500 rows x 2 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_multi=pd.concat([week1,week2],keys=[\"Week1\",\"Week2\"]) #indexlerin nerden başladığını görebilmek içimn\n",
    "df_multi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "1e29169f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Food ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>537</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>97</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>658</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>202</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>155</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>413</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>926</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>134</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>396</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>535</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>250 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Customer ID  Food ID\n",
       "0            537        9\n",
       "1             97        4\n",
       "2            658        1\n",
       "3            202        2\n",
       "4            155        9\n",
       "..           ...      ...\n",
       "245          413        9\n",
       "246          926        6\n",
       "247          134        3\n",
       "248          396        6\n",
       "249          535       10\n",
       "\n",
       "[250 rows x 2 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_multi.loc[(\"Week1\")] #virgülsüzde aynı şeyi verir.loc içindeki () olmadan da çalışır"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "19fc5491",
   "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></th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Food ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Week1</th>\n",
       "      <th>0</th>\n",
       "      <td>537</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>97</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>658</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>202</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>155</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Week2</th>\n",
       "      <th>245</th>\n",
       "      <td>783</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>556</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>547</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>252</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>249</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Customer ID  Food ID\n",
       "Week1 0            537        9\n",
       "      1             97        4\n",
       "      2            658        1\n",
       "      3            202        2\n",
       "      4            155        9\n",
       "...                ...      ...\n",
       "Week2 245          783       10\n",
       "      246          556       10\n",
       "      247          547        9\n",
       "      248          252        9\n",
       "      249          249        6\n",
       "\n",
       "[500 rows x 2 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_multi.loc[(\"Week1\"):] #week1 den başlar hepsini getirir.loc içinde() yazmazsak getirmez"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "060f8788",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Customer ID    493\n",
       "Food ID          9\n",
       "Name: (Week1, 200), dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_multi.loc[(\"Week1\",200)] #week1 in 200.indexini getirir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "315153c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "493"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_multi.loc[(\"Week1\",200),\"Customer ID\"] #week1 in 200.indexindeki customerıd değerine ulaştık"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ecfe09b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Customer ID    493\n",
       "Food ID          9\n",
       "Name: (Week1, 200), dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_multi.loc[(\"Week1\",200),[\"Customer ID\",\"Food ID\"]] #week1 in 200.indexindeki customerıd ve foodid değerlerine ulaştık"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "707bc080",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Customer ID    97\n",
       "Food ID         4\n",
       "Name: (Week1, 1), dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_multi.iloc[1] #iloc üzerinden herzaman indexlere ulaşabiliriz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "91900b04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Food ID_x</th>\n",
       "      <th>Food ID_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>503</td>\n",
       "      <td>5</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>945</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>343</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>343</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>343</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>621</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Customer ID  Food ID_x  Food ID_y\n",
       "0           537          9          5\n",
       "1           155          9          3\n",
       "2           155          1          3\n",
       "3           503          5          8\n",
       "4           503          5          9\n",
       "..          ...        ...        ...\n",
       "57          945          5          4\n",
       "58          343          3          5\n",
       "59          343          3          2\n",
       "60          343          3          7\n",
       "61          621          9          6\n",
       "\n",
       "[62 rows x 3 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "week1.merge(right=week2,how=\"inner\",on=\"Customer ID\") #how default inner\n",
    "#customer id üzerinden inner yöntemiyle week1 ve week2 yi merge ettik\n",
    "#week1 foodid->Food ID_x \n",
    "#week2 foodid->Food ID_y\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "0959dc39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Food ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Customer ID  Food ID\n",
       "4           155        9\n",
       "17          155        1"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "week1[week1[\"Customer ID\"]==155] #customerid si 155 olanlar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "9fb77923",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>Food ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>155</td>\n",
       "      <td>3</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Customer ID  Food ID\n",
       "208          155        3"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "week2[week2[\"Customer ID\"]==155] #ikisini eşleştiriyo 9 u alan 3ü  aldı 1i alan da 3ü aldı\n",
    "#week1 week2 eşleyecek 9-3 1-3 olacak"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "8e733a5a",
   "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",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>Food ID week2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>2</th>\n",
       "      <td>155</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>503</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>503</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>945</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>343</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>343</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>343</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>621</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Customer ID  Food ID week1  Food ID week2\n",
       "0           537              9              5\n",
       "1           155              9              3\n",
       "2           155              1              3\n",
       "3           503              5              8\n",
       "4           503              5              9\n",
       "..          ...            ...            ...\n",
       "57          945              5              4\n",
       "58          343              3              5\n",
       "59          343              3              2\n",
       "60          343              3              7\n",
       "61          621              9              6\n",
       "\n",
       "[62 rows x 3 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "week1.merge(right=week2,how=\"inner\",on=\"Customer ID\",suffixes=(\" week1\",\" week2\"))\n",
    "#suffixes diyerek columndaki isimleri değiştirdi "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "9aba9cc2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Food ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>304</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>540</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>937</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>233</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>922</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>578</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>578</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer ID  Food ID\n",
       "0          304        3\n",
       "1          540        3\n",
       "2          937       10\n",
       "3          233        3\n",
       "4           21        4\n",
       "5           21        4\n",
       "6          922        1\n",
       "7          578        5\n",
       "8          578        5"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "week1.merge(right=week2,how=\"inner\",on=[\"Customer ID\",\"Food ID\"]) #customerid ve foodid si ortak olanları aldı"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "69d6d337",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Food ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
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       "      <th>212</th>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Customer ID  Food ID\n",
       "101           21        4\n",
       "212           21        4"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "week1[week1[\"Customer ID\"]==21] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "eb5000ef",
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Food ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Customer ID  Food ID\n",
       "30           21        4"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "week2[week2[\"Customer ID\"]==21] #üstteki muhabbet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "3c69f318",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Food ID_Week1</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>97</td>\n",
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       "      <th>2</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>202</td>\n",
       "      <td>2.0</td>\n",
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       "      <th>4</th>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>855</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>559</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>451</th>\n",
       "      <td>276</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452</th>\n",
       "      <td>556</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>252</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>454 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Customer ID  Food ID_Week1  Food ID_Week2\n",
       "0            537            9.0            5.0\n",
       "1             97            4.0            NaN\n",
       "2            658            1.0            NaN\n",
       "3            202            2.0            NaN\n",
       "4            155            9.0            3.0\n",
       "..           ...            ...            ...\n",
       "449          855            NaN            4.0\n",
       "450          559            NaN           10.0\n",
       "451          276            NaN            4.0\n",
       "452          556            NaN           10.0\n",
       "453          252            NaN            9.0\n",
       "\n",
       "[454 rows x 3 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "week1.merge(right=week2,how=\"outer\",on=\"Customer ID\",suffixes=[\"_Week1\",\"_Week2\"]) # ortak olmayanları nan gösterir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "129ddc3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Food ID week1</th>\n",
       "      <th>Food ID week2</th>\n",
       "      <th>_merge</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>451</th>\n",
       "      <td>276</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452</th>\n",
       "      <td>556</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>252</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>454 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Customer ID  Food ID week1  Food ID week2      _merge\n",
       "0            537            9.0            5.0        both\n",
       "1             97            4.0            NaN   left_only\n",
       "2            658            1.0            NaN   left_only\n",
       "3            202            2.0            NaN   left_only\n",
       "4            155            9.0            3.0        both\n",
       "..           ...            ...            ...         ...\n",
       "449          855            NaN            4.0  right_only\n",
       "450          559            NaN           10.0  right_only\n",
       "451          276            NaN            4.0  right_only\n",
       "452          556            NaN           10.0  right_only\n",
       "453          252            NaN            9.0  right_only\n",
       "\n",
       "[454 rows x 4 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deneme=week1.merge(right=week2,how=\"outer\",on=\"Customer ID\",suffixes=(\" week1\",\" week2\"),indicator=True)\n",
    "deneme\n",
    "#outer ->full outer \n",
    "#indicator -> true dersek merge in nası yapıldığını söyler\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "28ab580e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <td>right_only</td>\n",
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       "      <th>451</th>\n",
       "      <td>276</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452</th>\n",
       "      <td>556</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>252</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>392 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Customer ID  Food ID week1  Food ID week2      _merge\n",
       "1             97            4.0            NaN   left_only\n",
       "2            658            1.0            NaN   left_only\n",
       "3            202            2.0            NaN   left_only\n",
       "6            213            8.0            NaN   left_only\n",
       "7            600            1.0            NaN   left_only\n",
       "..           ...            ...            ...         ...\n",
       "449          855            NaN            4.0  right_only\n",
       "450          559            NaN           10.0  right_only\n",
       "451          276            NaN            4.0  right_only\n",
       "452          556            NaN           10.0  right_only\n",
       "453          252            NaN            9.0  right_only\n",
       "\n",
       "[392 rows x 4 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask=deneme[\"_merge\"].isin([\"left_only\",\"right_only\"]) #sadece left ve right ları getirdi ikisinde de olanları getirmedi\n",
    "deneme[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "661c8d3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <th>2</th>\n",
       "      <td>658</td>\n",
       "      <td>Sushi</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>202</td>\n",
       "      <td>Burrito</td>\n",
       "      <td>9.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>155</td>\n",
       "      <td>Donut</td>\n",
       "      <td>0.99</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>413</td>\n",
       "      <td>Donut</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>926</td>\n",
       "      <td>Pasta</td>\n",
       "      <td>13.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>134</td>\n",
       "      <td>Taco</td>\n",
       "      <td>2.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>396</td>\n",
       "      <td>Pasta</td>\n",
       "      <td>13.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>535</td>\n",
       "      <td>Drink</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>250 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Customer ID   Food Item  Price\n",
       "0            537       Donut   0.99\n",
       "1             97  Quesadilla   4.25\n",
       "2            658       Sushi   3.99\n",
       "3            202     Burrito   9.99\n",
       "4            155       Donut   0.99\n",
       "..           ...         ...    ...\n",
       "245          413       Donut   0.99\n",
       "246          926       Pasta  13.99\n",
       "247          134        Taco   2.99\n",
       "248          396       Pasta  13.99\n",
       "249          535       Drink   1.75\n",
       "\n",
       "[250 rows x 3 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#left join\n",
    "week1.merge(foods,how=\"left\",on=\"Food ID\").drop(\"Food ID\",axis=\"columns\") \n",
    "# önce left join yaptık daha sonra foodid column ını sildik"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "809e9034",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Food ID</th>\n",
       "      <th>ID</th>\n",
       "      <th>First Name</th>\n",
       "      <th>Last Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Company</th>\n",
       "      <th>Occupation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>688</td>\n",
       "      <td>10</td>\n",
       "      <td>688</td>\n",
       "      <td>Carl</td>\n",
       "      <td>Williamson</td>\n",
       "      <td>Male</td>\n",
       "      <td>Thoughtmix</td>\n",
       "      <td>Graphic Designer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>813</td>\n",
       "      <td>7</td>\n",
       "      <td>813</td>\n",
       "      <td>Johnny</td>\n",
       "      <td>Walker</td>\n",
       "      <td>Male</td>\n",
       "      <td>Kayveo</td>\n",
       "      <td>Developer II</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>495</td>\n",
       "      <td>10</td>\n",
       "      <td>495</td>\n",
       "      <td>Deborah</td>\n",
       "      <td>Little</td>\n",
       "      <td>Female</td>\n",
       "      <td>Babbleblab</td>\n",
       "      <td>VP Accounting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>189</td>\n",
       "      <td>5</td>\n",
       "      <td>189</td>\n",
       "      <td>Roger</td>\n",
       "      <td>Gordon</td>\n",
       "      <td>Male</td>\n",
       "      <td>Skilith</td>\n",
       "      <td>Operator</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>267</td>\n",
       "      <td>3</td>\n",
       "      <td>267</td>\n",
       "      <td>Matthew</td>\n",
       "      <td>Wood</td>\n",
       "      <td>Male</td>\n",
       "      <td>Agimba</td>\n",
       "      <td>Product Engineer</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>783</td>\n",
       "      <td>10</td>\n",
       "      <td>783</td>\n",
       "      <td>Phyllis</td>\n",
       "      <td>Meyer</td>\n",
       "      <td>Female</td>\n",
       "      <td>Voolia</td>\n",
       "      <td>Information Systems Manager</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>556</td>\n",
       "      <td>10</td>\n",
       "      <td>556</td>\n",
       "      <td>Samuel</td>\n",
       "      <td>Bailey</td>\n",
       "      <td>Male</td>\n",
       "      <td>Oyoloo</td>\n",
       "      <td>Nurse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>547</td>\n",
       "      <td>9</td>\n",
       "      <td>547</td>\n",
       "      <td>Tina</td>\n",
       "      <td>Watkins</td>\n",
       "      <td>Female</td>\n",
       "      <td>Thoughtstorm</td>\n",
       "      <td>Accountant II</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>252</td>\n",
       "      <td>9</td>\n",
       "      <td>252</td>\n",
       "      <td>Douglas</td>\n",
       "      <td>Powell</td>\n",
       "      <td>Male</td>\n",
       "      <td>Jetwire</td>\n",
       "      <td>Geologist IV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>249</td>\n",
       "      <td>6</td>\n",
       "      <td>249</td>\n",
       "      <td>Anthony</td>\n",
       "      <td>Carpenter</td>\n",
       "      <td>Male</td>\n",
       "      <td>Shuffletag</td>\n",
       "      <td>Health Coach I</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>250 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Customer ID  Food ID   ID First Name   Last Name  Gender       Company  \\\n",
       "0            688       10  688       Carl  Williamson    Male    Thoughtmix   \n",
       "1            813        7  813     Johnny      Walker    Male        Kayveo   \n",
       "2            495       10  495    Deborah      Little  Female    Babbleblab   \n",
       "3            189        5  189      Roger      Gordon    Male       Skilith   \n",
       "4            267        3  267    Matthew        Wood    Male        Agimba   \n",
       "..           ...      ...  ...        ...         ...     ...           ...   \n",
       "245          783       10  783    Phyllis       Meyer  Female        Voolia   \n",
       "246          556       10  556     Samuel      Bailey    Male        Oyoloo   \n",
       "247          547        9  547       Tina     Watkins  Female  Thoughtstorm   \n",
       "248          252        9  252    Douglas      Powell    Male       Jetwire   \n",
       "249          249        6  249    Anthony   Carpenter    Male    Shuffletag   \n",
       "\n",
       "                      Occupation  \n",
       "0               Graphic Designer  \n",
       "1                   Developer II  \n",
       "2                  VP Accounting  \n",
       "3                       Operator  \n",
       "4               Product Engineer  \n",
       "..                           ...  \n",
       "245  Information Systems Manager  \n",
       "246                        Nurse  \n",
       "247                Accountant II  \n",
       "248                 Geologist IV  \n",
       "249               Health Coach I  \n",
       "\n",
       "[250 rows x 8 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# alan isimleri faklıysa merge etmek->left_on-right_on ile \n",
    "week2.merge(customer,how=\"left\",left_on=\"Customer ID\",right_on=\"ID\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "e8886ad2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<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>Gender</th>\n",
       "      <th>Company</th>\n",
       "      <th>Occupation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</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>1</th>\n",
       "      <td>Joseph</td>\n",
       "      <td>Perkins</td>\n",
       "      <td>Male</td>\n",
       "      <td>Dynazzy</td>\n",
       "      <td>Community Outreach Specialist</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jennifer</td>\n",
       "      <td>Alvarez</td>\n",
       "      <td>Female</td>\n",
       "      <td>DabZ</td>\n",
       "      <td>Senior Quality Engineer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Roger</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>Tagfeed</td>\n",
       "      <td>Account Executive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Steven</td>\n",
       "      <td>Evans</td>\n",
       "      <td>Male</td>\n",
       "      <td>Fatz</td>\n",
       "      <td>Registered Nurse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Judy</td>\n",
       "      <td>Morrison</td>\n",
       "      <td>Female</td>\n",
       "      <td>Demivee</td>\n",
       "      <td>Legal Assistant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>Debra</td>\n",
       "      <td>Garcia</td>\n",
       "      <td>Female</td>\n",
       "      <td>Dazzlesphere</td>\n",
       "      <td>Structural Engineer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>Douglas</td>\n",
       "      <td>Bishop</td>\n",
       "      <td>Male</td>\n",
       "      <td>Livepath</td>\n",
       "      <td>Developer I</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>Frank</td>\n",
       "      <td>Franklin</td>\n",
       "      <td>Male</td>\n",
       "      <td>Brainverse</td>\n",
       "      <td>Nurse Practicioner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>Jessica</td>\n",
       "      <td>Burns</td>\n",
       "      <td>Female</td>\n",
       "      <td>Babbleblab</td>\n",
       "      <td>Financial Advisor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>Brian</td>\n",
       "      <td>Daniels</td>\n",
       "      <td>Male</td>\n",
       "      <td>Tazzy</td>\n",
       "      <td>Physical Therapy Assistant</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     First Name Last Name  Gender       Company                     Occupation\n",
       "ID                                                                            \n",
       "1        Joseph   Perkins    Male       Dynazzy  Community Outreach Specialist\n",
       "2      Jennifer   Alvarez  Female          DabZ        Senior Quality Engineer\n",
       "3         Roger     Black    Male       Tagfeed              Account Executive\n",
       "4        Steven     Evans    Male          Fatz               Registered Nurse\n",
       "5          Judy  Morrison  Female       Demivee                Legal Assistant\n",
       "...         ...       ...     ...           ...                            ...\n",
       "996       Debra    Garcia  Female  Dazzlesphere            Structural Engineer\n",
       "997     Douglas    Bishop    Male      Livepath                    Developer I\n",
       "998       Frank  Franklin    Male    Brainverse             Nurse Practicioner\n",
       "999     Jessica     Burns  Female    Babbleblab              Financial Advisor\n",
       "1000      Brian   Daniels    Male         Tazzy     Physical Therapy Assistant\n",
       "\n",
       "[1000 rows x 5 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_customer=pd.read_csv(\"resources/resources/Restaurant - Customers.csv\",index_col=\"ID\")\n",
    "df_customer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "aad0772b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Food Item</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Food ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sushi</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Burrito</td>\n",
       "      <td>9.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Taco</td>\n",
       "      <td>2.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Quesadilla</td>\n",
       "      <td>4.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Pizza</td>\n",
       "      <td>2.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Pasta</td>\n",
       "      <td>13.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Steak</td>\n",
       "      <td>24.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Salad</td>\n",
       "      <td>11.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Donut</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Drink</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Food Item  Price\n",
       "Food ID                   \n",
       "1             Sushi   3.99\n",
       "2           Burrito   9.99\n",
       "3              Taco   2.99\n",
       "4        Quesadilla   4.25\n",
       "5             Pizza   2.49\n",
       "6             Pasta  13.99\n",
       "7             Steak  24.99\n",
       "8             Salad  11.25\n",
       "9             Donut   0.99\n",
       "10            Drink   1.75"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_foods=pd.read_csv(\"resources/resources/Restaurant - Foods.csv\",index_col=\"Food ID\")\n",
    "df_foods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "afa18d15",
   "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>Customer ID</th>\n",
       "      <th>Food ID</th>\n",
       "      <th>First Name</th>\n",
       "      <th>Last Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Company</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>537</td>\n",
       "      <td>9</td>\n",
       "      <td>Cheryl</td>\n",
       "      <td>Carroll</td>\n",
       "      <td>Female</td>\n",
       "      <td>Zoombeat</td>\n",
       "      <td>Registered Nurse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>97</td>\n",
       "      <td>4</td>\n",
       "      <td>Amanda</td>\n",
       "      <td>Watkins</td>\n",
       "      <td>Female</td>\n",
       "      <td>Ozu</td>\n",
       "      <td>Account Coordinator</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>658</td>\n",
       "      <td>1</td>\n",
       "      <td>Patrick</td>\n",
       "      <td>Webb</td>\n",
       "      <td>Male</td>\n",
       "      <td>Browsebug</td>\n",
       "      <td>Community Outreach Specialist</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>202</td>\n",
       "      <td>2</td>\n",
       "      <td>Louis</td>\n",
       "      <td>Campbell</td>\n",
       "      <td>Male</td>\n",
       "      <td>Rhynoodle</td>\n",
       "      <td>Account Representative III</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>155</td>\n",
       "      <td>9</td>\n",
       "      <td>Carolyn</td>\n",
       "      <td>Diaz</td>\n",
       "      <td>Female</td>\n",
       "      <td>Gigazoom</td>\n",
       "      <td>Database Administrator III</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>413</td>\n",
       "      <td>9</td>\n",
       "      <td>Diane</td>\n",
       "      <td>Bailey</td>\n",
       "      <td>Female</td>\n",
       "      <td>Wikibox</td>\n",
       "      <td>Technical Writer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>926</td>\n",
       "      <td>6</td>\n",
       "      <td>Anne</td>\n",
       "      <td>Wagner</td>\n",
       "      <td>Female</td>\n",
       "      <td>Skyba</td>\n",
       "      <td>Legal Assistant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>134</td>\n",
       "      <td>3</td>\n",
       "      <td>Diana</td>\n",
       "      <td>Hall</td>\n",
       "      <td>Female</td>\n",
       "      <td>Quinu</td>\n",
       "      <td>Financial Advisor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>396</td>\n",
       "      <td>6</td>\n",
       "      <td>Juan</td>\n",
       "      <td>Romero</td>\n",
       "      <td>Male</td>\n",
       "      <td>Zoonder</td>\n",
       "      <td>Analyst Programmer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>535</td>\n",
       "      <td>10</td>\n",
       "      <td>Nancy</td>\n",
       "      <td>Cox</td>\n",
       "      <td>Female</td>\n",
       "      <td>Trudeo</td>\n",
       "      <td>Research Associate</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>250 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Customer ID  Food ID First Name Last Name  Gender    Company  \\\n",
       "0            537        9     Cheryl   Carroll  Female   Zoombeat   \n",
       "1             97        4     Amanda   Watkins  Female        Ozu   \n",
       "2            658        1    Patrick      Webb    Male  Browsebug   \n",
       "3            202        2      Louis  Campbell    Male  Rhynoodle   \n",
       "4            155        9    Carolyn      Diaz  Female   Gigazoom   \n",
       "..           ...      ...        ...       ...     ...        ...   \n",
       "245          413        9      Diane    Bailey  Female    Wikibox   \n",
       "246          926        6       Anne    Wagner  Female      Skyba   \n",
       "247          134        3      Diana      Hall  Female      Quinu   \n",
       "248          396        6       Juan    Romero    Male    Zoonder   \n",
       "249          535       10      Nancy       Cox  Female     Trudeo   \n",
       "\n",
       "                        Occupation  \n",
       "0                 Registered Nurse  \n",
       "1              Account Coordinator  \n",
       "2    Community Outreach Specialist  \n",
       "3       Account Representative III  \n",
       "4       Database Administrator III  \n",
       "..                             ...  \n",
       "245               Technical Writer  \n",
       "246                Legal Assistant  \n",
       "247              Financial Advisor  \n",
       "248             Analyst Programmer  \n",
       "249             Research Associate  \n",
       "\n",
       "[250 rows x 7 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "week1.merge(df_customer,how=\"left\",left_on=\"Customer ID\",right_index=True) \n",
    "#week1deki customer id ile df_customerın indexini (right_index=True diyerek) kullanrak birleştirdi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "5d36c2f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Food ID</th>\n",
       "      <th>First Name</th>\n",
       "      <th>Last Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Company</th>\n",
       "      <th>Occupation</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>537</td>\n",
       "      <td>9</td>\n",
       "      <td>Cheryl</td>\n",
       "      <td>Carroll</td>\n",
       "      <td>Female</td>\n",
       "      <td>Zoombeat</td>\n",
       "      <td>Registered Nurse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>97</td>\n",
       "      <td>4</td>\n",
       "      <td>Amanda</td>\n",
       "      <td>Watkins</td>\n",
       "      <td>Female</td>\n",
       "      <td>Ozu</td>\n",
       "      <td>Account Coordinator</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>658</td>\n",
       "      <td>1</td>\n",
       "      <td>Patrick</td>\n",
       "      <td>Webb</td>\n",
       "      <td>Male</td>\n",
       "      <td>Browsebug</td>\n",
       "      <td>Community Outreach Specialist</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>202</td>\n",
       "      <td>2</td>\n",
       "      <td>Louis</td>\n",
       "      <td>Campbell</td>\n",
       "      <td>Male</td>\n",
       "      <td>Rhynoodle</td>\n",
       "      <td>Account Representative III</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>155</td>\n",
       "      <td>9</td>\n",
       "      <td>Carolyn</td>\n",
       "      <td>Diaz</td>\n",
       "      <td>Female</td>\n",
       "      <td>Gigazoom</td>\n",
       "      <td>Database Administrator III</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",
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       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>413</td>\n",
       "      <td>9</td>\n",
       "      <td>Diane</td>\n",
       "      <td>Bailey</td>\n",
       "      <td>Female</td>\n",
       "      <td>Wikibox</td>\n",
       "      <td>Technical Writer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>926</td>\n",
       "      <td>6</td>\n",
       "      <td>Anne</td>\n",
       "      <td>Wagner</td>\n",
       "      <td>Female</td>\n",
       "      <td>Skyba</td>\n",
       "      <td>Legal Assistant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>134</td>\n",
       "      <td>3</td>\n",
       "      <td>Diana</td>\n",
       "      <td>Hall</td>\n",
       "      <td>Female</td>\n",
       "      <td>Quinu</td>\n",
       "      <td>Financial Advisor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>396</td>\n",
       "      <td>6</td>\n",
       "      <td>Juan</td>\n",
       "      <td>Romero</td>\n",
       "      <td>Male</td>\n",
       "      <td>Zoonder</td>\n",
       "      <td>Analyst Programmer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>535</td>\n",
       "      <td>10</td>\n",
       "      <td>Nancy</td>\n",
       "      <td>Cox</td>\n",
       "      <td>Female</td>\n",
       "      <td>Trudeo</td>\n",
       "      <td>Research Associate</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>250 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Customer ID  Food ID First Name Last Name  Gender    Company  \\\n",
       "0            537        9     Cheryl   Carroll  Female   Zoombeat   \n",
       "1             97        4     Amanda   Watkins  Female        Ozu   \n",
       "2            658        1    Patrick      Webb    Male  Browsebug   \n",
       "3            202        2      Louis  Campbell    Male  Rhynoodle   \n",
       "4            155        9    Carolyn      Diaz  Female   Gigazoom   \n",
       "..           ...      ...        ...       ...     ...        ...   \n",
       "245          413        9      Diane    Bailey  Female    Wikibox   \n",
       "246          926        6       Anne    Wagner  Female      Skyba   \n",
       "247          134        3      Diana      Hall  Female      Quinu   \n",
       "248          396        6       Juan    Romero    Male    Zoonder   \n",
       "249          535       10      Nancy       Cox  Female     Trudeo   \n",
       "\n",
       "                        Occupation  \n",
       "0                 Registered Nurse  \n",
       "1              Account Coordinator  \n",
       "2    Community Outreach Specialist  \n",
       "3       Account Representative III  \n",
       "4       Database Administrator III  \n",
       "..                             ...  \n",
       "245               Technical Writer  \n",
       "246                Legal Assistant  \n",
       "247              Financial Advisor  \n",
       "248             Analyst Programmer  \n",
       "249             Research Associate  \n",
       "\n",
       "[250 rows x 7 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#aynı işlemi pd.merge ile yapabiliriz\n",
    "pd.merge(week1,df_customer,how=\"left\",left_on=\"Customer ID\",right_on=\"ID\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "ed7845c3",
   "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>Customer ID</th>\n",
       "      <th>Food ID</th>\n",
       "      <th>First Name</th>\n",
       "      <th>Last Name</th>\n",
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       "      <td>Cheryl</td>\n",
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       "      <td>Female</td>\n",
       "      <td>Zoombeat</td>\n",
       "      <td>Registered Nurse</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>97</td>\n",
       "      <td>4</td>\n",
       "      <td>Amanda</td>\n",
       "      <td>Watkins</td>\n",
       "      <td>Female</td>\n",
       "      <td>Ozu</td>\n",
       "      <td>Account Coordinator</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>658</td>\n",
       "      <td>1</td>\n",
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       "      <td>Webb</td>\n",
       "      <td>Male</td>\n",
       "      <td>Browsebug</td>\n",
       "      <td>Community Outreach Specialist</td>\n",
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       "      <th>3</th>\n",
       "      <td>202</td>\n",
       "      <td>2</td>\n",
       "      <td>Louis</td>\n",
       "      <td>Campbell</td>\n",
       "      <td>Male</td>\n",
       "      <td>Rhynoodle</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>155</td>\n",
       "      <td>9</td>\n",
       "      <td>Carolyn</td>\n",
       "      <td>Diaz</td>\n",
       "      <td>Female</td>\n",
       "      <td>Gigazoom</td>\n",
       "      <td>Database Administrator III</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",
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       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>413</td>\n",
       "      <td>9</td>\n",
       "      <td>Diane</td>\n",
       "      <td>Bailey</td>\n",
       "      <td>Female</td>\n",
       "      <td>Wikibox</td>\n",
       "      <td>Technical Writer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>926</td>\n",
       "      <td>6</td>\n",
       "      <td>Anne</td>\n",
       "      <td>Wagner</td>\n",
       "      <td>Female</td>\n",
       "      <td>Skyba</td>\n",
       "      <td>Legal Assistant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>134</td>\n",
       "      <td>3</td>\n",
       "      <td>Diana</td>\n",
       "      <td>Hall</td>\n",
       "      <td>Female</td>\n",
       "      <td>Quinu</td>\n",
       "      <td>Financial Advisor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>396</td>\n",
       "      <td>6</td>\n",
       "      <td>Juan</td>\n",
       "      <td>Romero</td>\n",
       "      <td>Male</td>\n",
       "      <td>Zoonder</td>\n",
       "      <td>Analyst Programmer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>535</td>\n",
       "      <td>10</td>\n",
       "      <td>Nancy</td>\n",
       "      <td>Cox</td>\n",
       "      <td>Female</td>\n",
       "      <td>Trudeo</td>\n",
       "      <td>Research Associate</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>250 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Customer ID  Food ID First Name Last Name  Gender    Company  \\\n",
       "0            537        9     Cheryl   Carroll  Female   Zoombeat   \n",
       "1             97        4     Amanda   Watkins  Female        Ozu   \n",
       "2            658        1    Patrick      Webb    Male  Browsebug   \n",
       "3            202        2      Louis  Campbell    Male  Rhynoodle   \n",
       "4            155        9    Carolyn      Diaz  Female   Gigazoom   \n",
       "..           ...      ...        ...       ...     ...        ...   \n",
       "245          413        9      Diane    Bailey  Female    Wikibox   \n",
       "246          926        6       Anne    Wagner  Female      Skyba   \n",
       "247          134        3      Diana      Hall  Female      Quinu   \n",
       "248          396        6       Juan    Romero    Male    Zoonder   \n",
       "249          535       10      Nancy       Cox  Female     Trudeo   \n",
       "\n",
       "                        Occupation  \n",
       "0                 Registered Nurse  \n",
       "1              Account Coordinator  \n",
       "2    Community Outreach Specialist  \n",
       "3       Account Representative III  \n",
       "4       Database Administrator III  \n",
       "..                             ...  \n",
       "245               Technical Writer  \n",
       "246                Legal Assistant  \n",
       "247              Financial Advisor  \n",
       "248             Analyst Programmer  \n",
       "249             Research Associate  \n",
       "\n",
       "[250 rows x 7 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "week1.merge(df_customer,how=\"left\",left_on=\"Customer ID\",right_index=True)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "8bbad576",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\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>Satisfaction Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>2</td>\n",
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       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>250 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Satisfaction Rating\n",
       "0                      2\n",
       "1                      7\n",
       "2                      3\n",
       "3                      7\n",
       "4                     10\n",
       "..                   ...\n",
       "245                    1\n",
       "246                    2\n",
       "247                    8\n",
       "248                   10\n",
       "249                    3\n",
       "\n",
       "[250 rows x 1 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "week1_satification=pd.read_csv(\"resources/resources/Restaurant - Week 1 Satisfaction.csv\")\n",
    "week1_satification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "26c44ee3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Food ID</th>\n",
       "      <th>Satisfaction Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>537</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>97</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>658</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>202</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>155</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>413</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>926</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>134</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>396</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>535</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>250 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Customer ID  Food ID  Satisfaction Rating\n",
       "0            537        9                    2\n",
       "1             97        4                    7\n",
       "2            658        1                    3\n",
       "3            202        2                    7\n",
       "4            155        9                   10\n",
       "..           ...      ...                  ...\n",
       "245          413        9                    1\n",
       "246          926        6                    2\n",
       "247          134        3                    8\n",
       "248          396        6                   10\n",
       "249          535       10                    3\n",
       "\n",
       "[250 rows x 3 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "week1.merge(week1_satification,how=\"left\",left_index=True,right_index=True)\n",
    "#soldaki ve sağdaki dataframelerin kendi indexlerine göre left join işlemi yaptırıyo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "8ca74bd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Food ID</th>\n",
       "      <th>Satisfaction Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>537</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>97</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>658</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>202</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>413</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>926</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>134</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>396</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>535</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>250 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Customer ID  Food ID  Satisfaction Rating\n",
       "0            537        9                    2\n",
       "1             97        4                    7\n",
       "2            658        1                    3\n",
       "3            202        2                    7\n",
       "4            155        9                   10\n",
       "..           ...      ...                  ...\n",
       "245          413        9                    1\n",
       "246          926        6                    2\n",
       "247          134        3                    8\n",
       "248          396        6                   10\n",
       "249          535       10                    3\n",
       "\n",
       "[250 rows x 3 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "week1.join(week1_satification) #join ile week1 dataframeine week1_satification columnunu ekler"
   ]
  }
 ],
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