09-hafta.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3b8ea293",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df_city=pd.read_csv(\"resources/resources/chicago.csv\").dropna(how=\"all\")\n",
    "df_city[\"Department\"]=df_city[\"Department\"].astype(\"category\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cfcac68c",
   "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>Name</th>\n",
       "      <th>Position Title</th>\n",
       "      <th>Department</th>\n",
       "      <th>Employee Annual Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AARON,  ELVIA J</td>\n",
       "      <td>WATER RATE TAKER</td>\n",
       "      <td>WATER MGMNT</td>\n",
       "      <td>$90744.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AARON,  JEFFERY M</td>\n",
       "      <td>POLICE OFFICER</td>\n",
       "      <td>POLICE</td>\n",
       "      <td>$84450.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AARON,  KARINA</td>\n",
       "      <td>POLICE OFFICER</td>\n",
       "      <td>POLICE</td>\n",
       "      <td>$84450.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AARON,  KIMBERLEI R</td>\n",
       "      <td>CHIEF CONTRACT EXPEDITER</td>\n",
       "      <td>GENERAL SERVICES</td>\n",
       "      <td>$89880.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ABAD JR,  VICENTE M</td>\n",
       "      <td>CIVIL ENGINEER IV</td>\n",
       "      <td>WATER MGMNT</td>\n",
       "      <td>$106836.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32057</th>\n",
       "      <td>ZYGADLO,  MICHAEL J</td>\n",
       "      <td>FRM OF MACHINISTS - AUTOMOTIVE</td>\n",
       "      <td>GENERAL SERVICES</td>\n",
       "      <td>$99528.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32058</th>\n",
       "      <td>ZYGOWICZ,  PETER J</td>\n",
       "      <td>POLICE OFFICER</td>\n",
       "      <td>POLICE</td>\n",
       "      <td>$87384.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32059</th>\n",
       "      <td>ZYMANTAS,  MARK E</td>\n",
       "      <td>POLICE OFFICER</td>\n",
       "      <td>POLICE</td>\n",
       "      <td>$84450.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32060</th>\n",
       "      <td>ZYRKOWSKI,  CARLO E</td>\n",
       "      <td>POLICE OFFICER</td>\n",
       "      <td>POLICE</td>\n",
       "      <td>$87384.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32061</th>\n",
       "      <td>ZYSKOWSKI,  DARIUSZ</td>\n",
       "      <td>CHIEF DATA BASE ANALYST</td>\n",
       "      <td>DoIT</td>\n",
       "      <td>$113664.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32062 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Name                  Position Title        Department  \\\n",
       "0          AARON,  ELVIA J                WATER RATE TAKER       WATER MGMNT   \n",
       "1        AARON,  JEFFERY M                  POLICE OFFICER            POLICE   \n",
       "2           AARON,  KARINA                  POLICE OFFICER            POLICE   \n",
       "3      AARON,  KIMBERLEI R        CHIEF CONTRACT EXPEDITER  GENERAL SERVICES   \n",
       "4      ABAD JR,  VICENTE M               CIVIL ENGINEER IV       WATER MGMNT   \n",
       "...                    ...                             ...               ...   \n",
       "32057  ZYGADLO,  MICHAEL J  FRM OF MACHINISTS - AUTOMOTIVE  GENERAL SERVICES   \n",
       "32058   ZYGOWICZ,  PETER J                  POLICE OFFICER            POLICE   \n",
       "32059    ZYMANTAS,  MARK E                  POLICE OFFICER            POLICE   \n",
       "32060  ZYRKOWSKI,  CARLO E                  POLICE OFFICER            POLICE   \n",
       "32061  ZYSKOWSKI,  DARIUSZ         CHIEF DATA BASE ANALYST              DoIT   \n",
       "\n",
       "      Employee Annual Salary  \n",
       "0                  $90744.00  \n",
       "1                  $84450.00  \n",
       "2                  $84450.00  \n",
       "3                  $89880.00  \n",
       "4                 $106836.00  \n",
       "...                      ...  \n",
       "32057              $99528.00  \n",
       "32058              $87384.00  \n",
       "32059              $84450.00  \n",
       "32060              $87384.00  \n",
       "32061             $113664.00  \n",
       "\n",
       "[32062 rows x 4 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_city"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "db6c1014",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MICHAEL     1153\n",
       "JOHN         899\n",
       "JAMES        676\n",
       "ROBERT       622\n",
       "JOSEPH       537\n",
       "            ... \n",
       "DEENA          1\n",
       "CHERRISE       1\n",
       "EARTHA         1\n",
       "ERNIKA         1\n",
       "MAC            1\n",
       "Name: Name, Length: 5091, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_city[\"Name\"].str.split(\",\").str.get(1).str.strip().str.split(\" \").str.get(0).value_counts()\n",
    "#tek bir satırda bir alandaki tüm bilgileri parçalayıp istedğimizi aldık onlarında kaç defa geçtiğini bulduk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4f6801ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AARON</td>\n",
       "      <td>ELVIA J</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AARON</td>\n",
       "      <td>JEFFERY M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AARON</td>\n",
       "      <td>KARINA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AARON</td>\n",
       "      <td>KIMBERLEI R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ABAD JR</td>\n",
       "      <td>VICENTE M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32057</th>\n",
       "      <td>ZYGADLO</td>\n",
       "      <td>MICHAEL J</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32058</th>\n",
       "      <td>ZYGOWICZ</td>\n",
       "      <td>PETER J</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32059</th>\n",
       "      <td>ZYMANTAS</td>\n",
       "      <td>MARK E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32060</th>\n",
       "      <td>ZYRKOWSKI</td>\n",
       "      <td>CARLO E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32061</th>\n",
       "      <td>ZYSKOWSKI</td>\n",
       "      <td>DARIUSZ</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32062 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               0              1\n",
       "0          AARON        ELVIA J\n",
       "1          AARON      JEFFERY M\n",
       "2          AARON         KARINA\n",
       "3          AARON    KIMBERLEI R\n",
       "4        ABAD JR      VICENTE M\n",
       "...          ...            ...\n",
       "32057    ZYGADLO      MICHAEL J\n",
       "32058   ZYGOWICZ        PETER J\n",
       "32059   ZYMANTAS         MARK E\n",
       "32060  ZYRKOWSKI        CARLO E\n",
       "32061  ZYSKOWSKI        DARIUSZ\n",
       "\n",
       "[32062 rows x 2 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# bi columndaki bilgileri ayırıp yeni bir column da yapabiliriz\n",
    "df_city[\"Name\"].str.split(\",\",expand=True) #expand ı True verirsek bize direkt dataframe olarak  geri döndürür\n",
    "#isim ve soyisim olarak alanları geri döner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e9ea2a97",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Position Title</th>\n",
       "      <th>Department</th>\n",
       "      <th>Employee Annual Salary</th>\n",
       "      <th>Last_Name</th>\n",
       "      <th>First_Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AARON,  ELVIA J</td>\n",
       "      <td>WATER RATE TAKER</td>\n",
       "      <td>WATER MGMNT</td>\n",
       "      <td>$90744.00</td>\n",
       "      <td>AARON</td>\n",
       "      <td>ELVIA J</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AARON,  JEFFERY M</td>\n",
       "      <td>POLICE OFFICER</td>\n",
       "      <td>POLICE</td>\n",
       "      <td>$84450.00</td>\n",
       "      <td>AARON</td>\n",
       "      <td>JEFFERY M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AARON,  KARINA</td>\n",
       "      <td>POLICE OFFICER</td>\n",
       "      <td>POLICE</td>\n",
       "      <td>$84450.00</td>\n",
       "      <td>AARON</td>\n",
       "      <td>KARINA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AARON,  KIMBERLEI R</td>\n",
       "      <td>CHIEF CONTRACT EXPEDITER</td>\n",
       "      <td>GENERAL SERVICES</td>\n",
       "      <td>$89880.00</td>\n",
       "      <td>AARON</td>\n",
       "      <td>KIMBERLEI R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ABAD JR,  VICENTE M</td>\n",
       "      <td>CIVIL ENGINEER IV</td>\n",
       "      <td>WATER MGMNT</td>\n",
       "      <td>$106836.00</td>\n",
       "      <td>ABAD JR</td>\n",
       "      <td>VICENTE M</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32057</th>\n",
       "      <td>ZYGADLO,  MICHAEL J</td>\n",
       "      <td>FRM OF MACHINISTS - AUTOMOTIVE</td>\n",
       "      <td>GENERAL SERVICES</td>\n",
       "      <td>$99528.00</td>\n",
       "      <td>ZYGADLO</td>\n",
       "      <td>MICHAEL J</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32058</th>\n",
       "      <td>ZYGOWICZ,  PETER J</td>\n",
       "      <td>POLICE OFFICER</td>\n",
       "      <td>POLICE</td>\n",
       "      <td>$87384.00</td>\n",
       "      <td>ZYGOWICZ</td>\n",
       "      <td>PETER J</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32059</th>\n",
       "      <td>ZYMANTAS,  MARK E</td>\n",
       "      <td>POLICE OFFICER</td>\n",
       "      <td>POLICE</td>\n",
       "      <td>$84450.00</td>\n",
       "      <td>ZYMANTAS</td>\n",
       "      <td>MARK E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32060</th>\n",
       "      <td>ZYRKOWSKI,  CARLO E</td>\n",
       "      <td>POLICE OFFICER</td>\n",
       "      <td>POLICE</td>\n",
       "      <td>$87384.00</td>\n",
       "      <td>ZYRKOWSKI</td>\n",
       "      <td>CARLO E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32061</th>\n",
       "      <td>ZYSKOWSKI,  DARIUSZ</td>\n",
       "      <td>CHIEF DATA BASE ANALYST</td>\n",
       "      <td>DoIT</td>\n",
       "      <td>$113664.00</td>\n",
       "      <td>ZYSKOWSKI</td>\n",
       "      <td>DARIUSZ</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32062 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Name                  Position Title        Department  \\\n",
       "0          AARON,  ELVIA J                WATER RATE TAKER       WATER MGMNT   \n",
       "1        AARON,  JEFFERY M                  POLICE OFFICER            POLICE   \n",
       "2           AARON,  KARINA                  POLICE OFFICER            POLICE   \n",
       "3      AARON,  KIMBERLEI R        CHIEF CONTRACT EXPEDITER  GENERAL SERVICES   \n",
       "4      ABAD JR,  VICENTE M               CIVIL ENGINEER IV       WATER MGMNT   \n",
       "...                    ...                             ...               ...   \n",
       "32057  ZYGADLO,  MICHAEL J  FRM OF MACHINISTS - AUTOMOTIVE  GENERAL SERVICES   \n",
       "32058   ZYGOWICZ,  PETER J                  POLICE OFFICER            POLICE   \n",
       "32059    ZYMANTAS,  MARK E                  POLICE OFFICER            POLICE   \n",
       "32060  ZYRKOWSKI,  CARLO E                  POLICE OFFICER            POLICE   \n",
       "32061  ZYSKOWSKI,  DARIUSZ         CHIEF DATA BASE ANALYST              DoIT   \n",
       "\n",
       "      Employee Annual Salary  Last_Name     First_Name  \n",
       "0                  $90744.00      AARON        ELVIA J  \n",
       "1                  $84450.00      AARON      JEFFERY M  \n",
       "2                  $84450.00      AARON         KARINA  \n",
       "3                  $89880.00      AARON    KIMBERLEI R  \n",
       "4                 $106836.00    ABAD JR      VICENTE M  \n",
       "...                      ...        ...            ...  \n",
       "32057              $99528.00    ZYGADLO      MICHAEL J  \n",
       "32058              $87384.00   ZYGOWICZ        PETER J  \n",
       "32059              $84450.00   ZYMANTAS         MARK E  \n",
       "32060              $87384.00  ZYRKOWSKI        CARLO E  \n",
       "32061             $113664.00  ZYSKOWSKI        DARIUSZ  \n",
       "\n",
       "[32062 rows x 6 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_city[[\"Last_Name\",\"First_Name\"]]=df_city[\"Name\"].str.split(\",\",expand=True) #kaydettik istersek bu alanı drop yapabiliriz\n",
    "df_city "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c8f51b9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WATER</td>\n",
       "      <td>RATE</td>\n",
       "      <td>TAKER</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>POLICE</td>\n",
       "      <td>OFFICER</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>POLICE</td>\n",
       "      <td>OFFICER</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHIEF</td>\n",
       "      <td>CONTRACT</td>\n",
       "      <td>EXPEDITER</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CIVIL</td>\n",
       "      <td>ENGINEER</td>\n",
       "      <td>IV</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32057</th>\n",
       "      <td>FRM</td>\n",
       "      <td>OF</td>\n",
       "      <td>MACHINISTS</td>\n",
       "      <td>-</td>\n",
       "      <td>AUTOMOTIVE</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32058</th>\n",
       "      <td>POLICE</td>\n",
       "      <td>OFFICER</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32059</th>\n",
       "      <td>POLICE</td>\n",
       "      <td>OFFICER</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32060</th>\n",
       "      <td>POLICE</td>\n",
       "      <td>OFFICER</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32061</th>\n",
       "      <td>CHIEF</td>\n",
       "      <td>DATA</td>\n",
       "      <td>BASE</td>\n",
       "      <td>ANALYST</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32062 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1           2        3           4     5     6     7  \\\n",
       "0       WATER      RATE       TAKER     None        None  None  None  None   \n",
       "1      POLICE   OFFICER        None     None        None  None  None  None   \n",
       "2      POLICE   OFFICER        None     None        None  None  None  None   \n",
       "3       CHIEF  CONTRACT   EXPEDITER     None        None  None  None  None   \n",
       "4       CIVIL  ENGINEER          IV     None        None  None  None  None   \n",
       "...       ...       ...         ...      ...         ...   ...   ...   ...   \n",
       "32057     FRM        OF  MACHINISTS        -  AUTOMOTIVE  None  None  None   \n",
       "32058  POLICE   OFFICER        None     None        None  None  None  None   \n",
       "32059  POLICE   OFFICER        None     None        None  None  None  None   \n",
       "32060  POLICE   OFFICER        None     None        None  None  None  None   \n",
       "32061   CHIEF      DATA        BASE  ANALYST        None  None  None  None   \n",
       "\n",
       "          8  \n",
       "0      None  \n",
       "1      None  \n",
       "2      None  \n",
       "3      None  \n",
       "4      None  \n",
       "...     ...  \n",
       "32057  None  \n",
       "32058  None  \n",
       "32059  None  \n",
       "32060  None  \n",
       "32061  None  \n",
       "\n",
       "[32062 rows x 9 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_city[\"Position Title\"].str.split(\" \",expand=True)\n",
    "#split ile bölerken en yüksek kelime sayısına bakarak column oluşturur. \n",
    "#bu sayıdan az olan kelimelerin boş kalanlarına dataframe da nan yazar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b9c871f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    </tr>\n",
       "  </thead>\n",
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       "      <td>WATER</td>\n",
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       "      <th>1</th>\n",
       "      <td>POLICE</td>\n",
       "      <td>OFFICER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>POLICE</td>\n",
       "      <td>OFFICER</td>\n",
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       "      <th>3</th>\n",
       "      <td>CHIEF</td>\n",
       "      <td>CONTRACT EXPEDITER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CIVIL</td>\n",
       "      <td>ENGINEER IV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32057</th>\n",
       "      <td>FRM</td>\n",
       "      <td>OF MACHINISTS - AUTOMOTIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32058</th>\n",
       "      <td>POLICE</td>\n",
       "      <td>OFFICER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32059</th>\n",
       "      <td>POLICE</td>\n",
       "      <td>OFFICER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32060</th>\n",
       "      <td>POLICE</td>\n",
       "      <td>OFFICER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32061</th>\n",
       "      <td>CHIEF</td>\n",
       "      <td>DATA BASE ANALYST</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32062 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0                           1\n",
       "0       WATER                  RATE TAKER\n",
       "1      POLICE                     OFFICER\n",
       "2      POLICE                     OFFICER\n",
       "3       CHIEF          CONTRACT EXPEDITER\n",
       "4       CIVIL                 ENGINEER IV\n",
       "...       ...                         ...\n",
       "32057     FRM  OF MACHINISTS - AUTOMOTIVE\n",
       "32058  POLICE                     OFFICER\n",
       "32059  POLICE                     OFFICER\n",
       "32060  POLICE                     OFFICER\n",
       "32061   CHIEF           DATA BASE ANALYST\n",
       "\n",
       "[32062 rows x 2 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_city[\"Position Title\"].str.split(\" \",expand=True,n=1) \n",
    "#n ile böldükten sonra kaç tane sütun olarak göstermesini ayarlayabilir bu parametre ile\n",
    "#1.column dahil gösterir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5fc8f891",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>Name</th>\n",
       "      <th>Position Title</th>\n",
       "      <th>Department</th>\n",
       "      <th>Employee Annual Salary</th>\n",
       "      <th>Last_Name</th>\n",
       "      <th>First_Name</th>\n",
       "      <th>First_Title</th>\n",
       "      <th>Second_Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AARON,  ELVIA J</td>\n",
       "      <td>WATER RATE TAKER</td>\n",
       "      <td>WATER MGMNT</td>\n",
       "      <td>$90744.00</td>\n",
       "      <td>AARON</td>\n",
       "      <td>ELVIA J</td>\n",
       "      <td>WATER</td>\n",
       "      <td>RATE TAKER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AARON,  JEFFERY M</td>\n",
       "      <td>POLICE OFFICER</td>\n",
       "      <td>POLICE</td>\n",
       "      <td>$84450.00</td>\n",
       "      <td>AARON</td>\n",
       "      <td>JEFFERY M</td>\n",
       "      <td>POLICE</td>\n",
       "      <td>OFFICER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AARON,  KARINA</td>\n",
       "      <td>POLICE OFFICER</td>\n",
       "      <td>POLICE</td>\n",
       "      <td>$84450.00</td>\n",
       "      <td>AARON</td>\n",
       "      <td>KARINA</td>\n",
       "      <td>POLICE</td>\n",
       "      <td>OFFICER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AARON,  KIMBERLEI R</td>\n",
       "      <td>CHIEF CONTRACT EXPEDITER</td>\n",
       "      <td>GENERAL SERVICES</td>\n",
       "      <td>$89880.00</td>\n",
       "      <td>AARON</td>\n",
       "      <td>KIMBERLEI R</td>\n",
       "      <td>CHIEF</td>\n",
       "      <td>CONTRACT EXPEDITER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ABAD JR,  VICENTE M</td>\n",
       "      <td>CIVIL ENGINEER IV</td>\n",
       "      <td>WATER MGMNT</td>\n",
       "      <td>$106836.00</td>\n",
       "      <td>ABAD JR</td>\n",
       "      <td>VICENTE M</td>\n",
       "      <td>CIVIL</td>\n",
       "      <td>ENGINEER IV</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>32057</th>\n",
       "      <td>ZYGADLO,  MICHAEL J</td>\n",
       "      <td>FRM OF MACHINISTS - AUTOMOTIVE</td>\n",
       "      <td>GENERAL SERVICES</td>\n",
       "      <td>$99528.00</td>\n",
       "      <td>ZYGADLO</td>\n",
       "      <td>MICHAEL J</td>\n",
       "      <td>FRM</td>\n",
       "      <td>OF MACHINISTS - AUTOMOTIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32058</th>\n",
       "      <td>ZYGOWICZ,  PETER J</td>\n",
       "      <td>POLICE OFFICER</td>\n",
       "      <td>POLICE</td>\n",
       "      <td>$87384.00</td>\n",
       "      <td>ZYGOWICZ</td>\n",
       "      <td>PETER J</td>\n",
       "      <td>POLICE</td>\n",
       "      <td>OFFICER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32059</th>\n",
       "      <td>ZYMANTAS,  MARK E</td>\n",
       "      <td>POLICE OFFICER</td>\n",
       "      <td>POLICE</td>\n",
       "      <td>$84450.00</td>\n",
       "      <td>ZYMANTAS</td>\n",
       "      <td>MARK E</td>\n",
       "      <td>POLICE</td>\n",
       "      <td>OFFICER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32060</th>\n",
       "      <td>ZYRKOWSKI,  CARLO E</td>\n",
       "      <td>POLICE OFFICER</td>\n",
       "      <td>POLICE</td>\n",
       "      <td>$87384.00</td>\n",
       "      <td>ZYRKOWSKI</td>\n",
       "      <td>CARLO E</td>\n",
       "      <td>POLICE</td>\n",
       "      <td>OFFICER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32061</th>\n",
       "      <td>ZYSKOWSKI,  DARIUSZ</td>\n",
       "      <td>CHIEF DATA BASE ANALYST</td>\n",
       "      <td>DoIT</td>\n",
       "      <td>$113664.00</td>\n",
       "      <td>ZYSKOWSKI</td>\n",
       "      <td>DARIUSZ</td>\n",
       "      <td>CHIEF</td>\n",
       "      <td>DATA BASE ANALYST</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32062 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Name                  Position Title        Department  \\\n",
       "0          AARON,  ELVIA J                WATER RATE TAKER       WATER MGMNT   \n",
       "1        AARON,  JEFFERY M                  POLICE OFFICER            POLICE   \n",
       "2           AARON,  KARINA                  POLICE OFFICER            POLICE   \n",
       "3      AARON,  KIMBERLEI R        CHIEF CONTRACT EXPEDITER  GENERAL SERVICES   \n",
       "4      ABAD JR,  VICENTE M               CIVIL ENGINEER IV       WATER MGMNT   \n",
       "...                    ...                             ...               ...   \n",
       "32057  ZYGADLO,  MICHAEL J  FRM OF MACHINISTS - AUTOMOTIVE  GENERAL SERVICES   \n",
       "32058   ZYGOWICZ,  PETER J                  POLICE OFFICER            POLICE   \n",
       "32059    ZYMANTAS,  MARK E                  POLICE OFFICER            POLICE   \n",
       "32060  ZYRKOWSKI,  CARLO E                  POLICE OFFICER            POLICE   \n",
       "32061  ZYSKOWSKI,  DARIUSZ         CHIEF DATA BASE ANALYST              DoIT   \n",
       "\n",
       "      Employee Annual Salary  Last_Name     First_Name First_Title  \\\n",
       "0                  $90744.00      AARON        ELVIA J       WATER   \n",
       "1                  $84450.00      AARON      JEFFERY M      POLICE   \n",
       "2                  $84450.00      AARON         KARINA      POLICE   \n",
       "3                  $89880.00      AARON    KIMBERLEI R       CHIEF   \n",
       "4                 $106836.00    ABAD JR      VICENTE M       CIVIL   \n",
       "...                      ...        ...            ...         ...   \n",
       "32057              $99528.00    ZYGADLO      MICHAEL J         FRM   \n",
       "32058              $87384.00   ZYGOWICZ        PETER J      POLICE   \n",
       "32059              $84450.00   ZYMANTAS         MARK E      POLICE   \n",
       "32060              $87384.00  ZYRKOWSKI        CARLO E      POLICE   \n",
       "32061             $113664.00  ZYSKOWSKI        DARIUSZ       CHIEF   \n",
       "\n",
       "                     Second_Title  \n",
       "0                      RATE TAKER  \n",
       "1                         OFFICER  \n",
       "2                         OFFICER  \n",
       "3              CONTRACT EXPEDITER  \n",
       "4                     ENGINEER IV  \n",
       "...                           ...  \n",
       "32057  OF MACHINISTS - AUTOMOTIVE  \n",
       "32058                     OFFICER  \n",
       "32059                     OFFICER  \n",
       "32060                     OFFICER  \n",
       "32061           DATA BASE ANALYST  \n",
       "\n",
       "[32062 rows x 8 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_city[[\"First_Title\",\"Second_Title\"]]=df_city[\"Position Title\"].str.split(\" \",expand=True,n=1)\n",
    "df_city #first title ve second title olmak üzere; Position title sütununu bölüp kaydetti"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7df43264",
   "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>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th>Price in US Dollars</th>\n",
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       "      <th>0</th>\n",
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       "      <td>Argentina</td>\n",
       "      <td>2.39</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2016</td>\n",
       "      <td>Australia</td>\n",
       "      <td>3.74</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/2016</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>3.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/2016</td>\n",
       "      <td>Britain</td>\n",
       "      <td>4.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/2016</td>\n",
       "      <td>Canada</td>\n",
       "      <td>4.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>647</th>\n",
       "      <td>1/2010</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>3.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>648</th>\n",
       "      <td>1/2010</td>\n",
       "      <td>UAE</td>\n",
       "      <td>2.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>649</th>\n",
       "      <td>1/2010</td>\n",
       "      <td>Ukraine</td>\n",
       "      <td>1.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650</th>\n",
       "      <td>1/2010</td>\n",
       "      <td>United States</td>\n",
       "      <td>3.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>651</th>\n",
       "      <td>1/2010</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>3.32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>652 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Date        Country  Price in US Dollars\n",
       "0    1/2016      Argentina                 2.39\n",
       "1    1/2016      Australia                 3.74\n",
       "2    1/2016         Brazil                 3.35\n",
       "3    1/2016        Britain                 4.22\n",
       "4    1/2016         Canada                 4.14\n",
       "..      ...            ...                  ...\n",
       "647  1/2010         Turkey                 3.83\n",
       "648  1/2010            UAE                 2.99\n",
       "649  1/2010        Ukraine                 1.83\n",
       "650  1/2010  United States                 3.58\n",
       "651  1/2010        Uruguay                 3.32\n",
       "\n",
       "[652 rows x 3 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(\"resources/resources/bigmac.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c909eaf3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th>Price in US Dollars</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>Australia</td>\n",
       "      <td>3.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>3.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>Britain</td>\n",
       "      <td>4.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>Canada</td>\n",
       "      <td>4.14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date    Country  Price in US Dollars\n",
       "0 2016-01-01  Argentina                 2.39\n",
       "1 2016-01-01  Australia                 3.74\n",
       "2 2016-01-01     Brazil                 3.35\n",
       "3 2016-01-01    Britain                 4.22\n",
       "4 2016-01-01     Canada                 4.14"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# date->otomatik parse etmek\n",
    "df_bigmac=pd.read_csv(\"resources/resources/bigmac.csv\",parse_dates=[\"Date\"])\n",
    "df_bigmac.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bf3afe04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=652, step=1)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bigmac.index #index i verir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dfca430f",
   "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>Country</th>\n",
       "      <th>Price in US Dollars</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>Australia</td>\n",
       "      <td>3.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>3.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>Britain</td>\n",
       "      <td>4.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>Canada</td>\n",
       "      <td>4.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>3.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>UAE</td>\n",
       "      <td>2.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>Ukraine</td>\n",
       "      <td>1.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>United States</td>\n",
       "      <td>3.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>3.32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>652 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Country  Price in US Dollars\n",
       "Date                                          \n",
       "2016-01-01      Argentina                 2.39\n",
       "2016-01-01      Australia                 3.74\n",
       "2016-01-01         Brazil                 3.35\n",
       "2016-01-01        Britain                 4.22\n",
       "2016-01-01         Canada                 4.14\n",
       "...                   ...                  ...\n",
       "2010-01-01         Turkey                 3.83\n",
       "2010-01-01            UAE                 2.99\n",
       "2010-01-01        Ukraine                 1.83\n",
       "2010-01-01  United States                 3.58\n",
       "2010-01-01        Uruguay                 3.32\n",
       "\n",
       "[652 rows x 2 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bigmac.set_index(keys=\"Date\") #yeni index belirliyoruz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d923fa2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Price in US Dollars</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2016-01-01</th>\n",
       "      <th>Argentina</th>\n",
       "      <td>2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>3.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>3.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Britain</th>\n",
       "      <td>4.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>4.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2010-01-01</th>\n",
       "      <th>Turkey</th>\n",
       "      <td>3.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UAE</th>\n",
       "      <td>2.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ukraine</th>\n",
       "      <td>1.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>3.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uruguay</th>\n",
       "      <td>3.32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>652 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Price in US Dollars\n",
       "Date       Country                           \n",
       "2016-01-01 Argentina                     2.39\n",
       "           Australia                     3.74\n",
       "           Brazil                        3.35\n",
       "           Britain                       4.22\n",
       "           Canada                        4.14\n",
       "...                                       ...\n",
       "2010-01-01 Turkey                        3.83\n",
       "           UAE                           2.99\n",
       "           Ukraine                       1.83\n",
       "           United States                 3.58\n",
       "           Uruguay                       3.32\n",
       "\n",
       "[652 rows x 1 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bigmac.set_index([\"Date\",\"Country\"]) #iki alanı index olarak belirledik author index inner index\n",
    "#inplace=False default olarak\n",
    "#peki bu author-inner nası belirlenir?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f5196669",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date                    12\n",
       "Country                 58\n",
       "Price in US Dollars    330\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bigmac.nunique() #dicez \n",
    "# unique olarak geçen alanlardan en az geçeni author diğerini de inner olarak belirleriz. MULTIINDEX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0e58697b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Price in US Dollars</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2016-01-01</th>\n",
       "      <th>Argentina</th>\n",
       "      <td>2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>3.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>3.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Britain</th>\n",
       "      <td>4.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>4.14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Price in US Dollars\n",
       "Date       Country                       \n",
       "2016-01-01 Argentina                 2.39\n",
       "           Australia                 3.74\n",
       "           Brazil                    3.35\n",
       "           Britain                   4.22\n",
       "           Canada                    4.14"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bigmac.set_index([\"Date\",\"Country\"],inplace=True) #indexleri belirledik ve kaydettik\n",
    "df_bigmac.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6797cedc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Price in US Dollars</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2010-01-01</th>\n",
       "      <th>Argentina</th>\n",
       "      <td>1.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>3.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>4.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Britain</th>\n",
       "      <td>3.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>3.97</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Price in US Dollars\n",
       "Date       Country                       \n",
       "2010-01-01 Argentina                 1.84\n",
       "           Australia                 3.98\n",
       "           Brazil                    4.76\n",
       "           Britain                   3.67\n",
       "           Canada                    3.97"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bigmac.sort_index(inplace=True) #index e göre sıralama yaptı->küçükten büyüğe\n",
    "df_bigmac.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "94a85acd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex([('2010-01-01',      'Argentina'),\n",
       "            ('2010-01-01',      'Australia'),\n",
       "            ('2010-01-01',         'Brazil'),\n",
       "            ('2010-01-01',        'Britain'),\n",
       "            ('2010-01-01',         'Canada'),\n",
       "            ('2010-01-01',          'Chile'),\n",
       "            ('2010-01-01',          'China'),\n",
       "            ('2010-01-01',       'Colombia'),\n",
       "            ('2010-01-01',     'Costa Rica'),\n",
       "            ('2010-01-01', 'Czech Republic'),\n",
       "            ...\n",
       "            ('2016-01-01',    'Switzerland'),\n",
       "            ('2016-01-01',         'Taiwan'),\n",
       "            ('2016-01-01',       'Thailand'),\n",
       "            ('2016-01-01',         'Turkey'),\n",
       "            ('2016-01-01',            'UAE'),\n",
       "            ('2016-01-01',        'Ukraine'),\n",
       "            ('2016-01-01',  'United States'),\n",
       "            ('2016-01-01',        'Uruguay'),\n",
       "            ('2016-01-01',      'Venezuela'),\n",
       "            ('2016-01-01',        'Vietnam')],\n",
       "           names=['Date', 'Country'], length=652)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bigmac.index #index ler tupple olarak tutulur tuple lar değiştirilemez"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c42e9073",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FrozenList(['Date', 'Country'])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bigmac.index.names #index isimleri\n",
    "#biz değiştirmezsek index isimleri alan isimleriyle aynı"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3117e1b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.indexes.multi.MultiIndex"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df_bigmac.index) #type ı MultiIndex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "24896bf4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Timestamp('2010-01-01 00:00:00'), 'Argentina')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#istediğimiz index e ulaşabiliriz\n",
    "df_bigmac.index[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ba4ccde2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Date</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2016-01-01</th>\n",
       "      <th>Argentina</th>\n",
       "      <td>2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>3.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>3.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Britain</th>\n",
       "      <td>4.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>4.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2010-01-01</th>\n",
       "      <th>Turkey</th>\n",
       "      <td>3.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UAE</th>\n",
       "      <td>2.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ukraine</th>\n",
       "      <td>1.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>3.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uruguay</th>\n",
       "      <td>3.32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>652 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Price in US Dollars\n",
       "Date       Country                           \n",
       "2016-01-01 Argentina                     2.39\n",
       "           Australia                     3.74\n",
       "           Brazil                        3.35\n",
       "           Britain                       4.22\n",
       "           Canada                        4.14\n",
       "...                                       ...\n",
       "2010-01-01 Turkey                        3.83\n",
       "           UAE                           2.99\n",
       "           Ukraine                       1.83\n",
       "           United States                 3.58\n",
       "           Uruguay                       3.32\n",
       "\n",
       "[652 rows x 1 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bigmac1=pd.read_csv(\"resources/resources/bigmac.csv\",parse_dates=[\"Date\"],index_col=[\"Date\",\"Country\"])\n",
    "df_bigmac1\n",
    " #index_col ile index belirlemeyi böyle de yapabiliriz \n",
    "#ya set_index kullanacaz ya da index_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2afbe1b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Argentina', 'Australia', 'Brazil', 'Britain', 'Canada', 'Chile',\n",
       "       'China', 'Colombia', 'Costa Rica', 'Czech Republic',\n",
       "       ...\n",
       "       'Switzerland', 'Taiwan', 'Thailand', 'Turkey', 'UAE', 'Ukraine',\n",
       "       'United States', 'Uruguay', 'Venezuela', 'Vietnam'],\n",
       "      dtype='object', name='Country', length=652)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#indexlerin değerlerini get_level_values ile öğrenebiliriz\n",
    "df_bigmac1.index.get_level_values(\"Date\") #level ya index ya da index ismi olabailir\n",
    "df_bigmac.index.get_level_values(1) #\"Country\" de yazsak aynı olurdu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9e0f8309",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Price in US Dollars</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day</th>\n",
       "      <th>Location</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2016-01-01</th>\n",
       "      <th>Argentina</th>\n",
       "      <td>2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>3.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>3.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Britain</th>\n",
       "      <td>4.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>4.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2010-01-01</th>\n",
       "      <th>Turkey</th>\n",
       "      <td>3.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UAE</th>\n",
       "      <td>2.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ukraine</th>\n",
       "      <td>1.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>3.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uruguay</th>\n",
       "      <td>3.32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>652 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Price in US Dollars\n",
       "Day        Location                          \n",
       "2016-01-01 Argentina                     2.39\n",
       "           Australia                     3.74\n",
       "           Brazil                        3.35\n",
       "           Britain                       4.22\n",
       "           Canada                        4.14\n",
       "...                                       ...\n",
       "2010-01-01 Turkey                        3.83\n",
       "           UAE                           2.99\n",
       "           Ukraine                       1.83\n",
       "           United States                 3.58\n",
       "           Uruguay                       3.32\n",
       "\n",
       "[652 rows x 1 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#index adı değşitirme->index.set_names\n",
    "df_bigmac1.index.set_names(names=[\"Day\",\"Location\"],inplace=True)\n",
    "df_bigmac1 # date ve country yi day ve location olarak değiştirdik \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "515179d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Price in US Dollars</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yusuf</th>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2010-01-01</th>\n",
       "      <th>Argentina</th>\n",
       "      <td>1.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>3.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>4.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Britain</th>\n",
       "      <td>3.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>3.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2016-01-01</th>\n",
       "      <th>Ukraine</th>\n",
       "      <td>1.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>4.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uruguay</th>\n",
       "      <td>3.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Venezuela</th>\n",
       "      <td>0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vietnam</th>\n",
       "      <td>2.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>652 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Price in US Dollars\n",
       "Yusuf      Country                           \n",
       "2010-01-01 Argentina                     1.84\n",
       "           Australia                     3.98\n",
       "           Brazil                        4.76\n",
       "           Britain                       3.67\n",
       "           Canada                        3.97\n",
       "...                                       ...\n",
       "2016-01-01 Ukraine                       1.54\n",
       "           United States                 4.93\n",
       "           Uruguay                       3.74\n",
       "           Venezuela                     0.66\n",
       "           Vietnam                       2.67\n",
       "\n",
       "[652 rows x 1 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bigmac.index.set_names(names=\"Yusuf\",level=0,inplace=True) #değiştireceğimiz index i level ile verdik \n",
    "df_bigmac \n",
    "#İndexlerin isimlerini ayrı ayrı değiştirdik #"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "569890de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Price in US Dollars</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yusuf</th>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <th>Argentina</th>\n",
       "      <td>1.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-01</th>\n",
       "      <th>Argentina</th>\n",
       "      <td>3.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01</th>\n",
       "      <th>Argentina</th>\n",
       "      <td>4.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <th>Argentina</th>\n",
       "      <td>4.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-07-01</th>\n",
       "      <th>Argentina</th>\n",
       "      <td>4.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01</th>\n",
       "      <th>Vietnam</th>\n",
       "      <td>2.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-01</th>\n",
       "      <th>Vietnam</th>\n",
       "      <td>2.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <th>Vietnam</th>\n",
       "      <td>2.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-01</th>\n",
       "      <th>Vietnam</th>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <th>Vietnam</th>\n",
       "      <td>2.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>652 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Price in US Dollars\n",
       "Yusuf      Country                       \n",
       "2010-01-01 Argentina                 1.84\n",
       "2010-07-01 Argentina                 3.56\n",
       "2011-07-01 Argentina                 4.84\n",
       "2012-01-01 Argentina                 4.64\n",
       "2012-07-01 Argentina                 4.16\n",
       "...                                   ...\n",
       "2014-01-01 Vietnam                   2.84\n",
       "2014-07-01 Vietnam                   2.83\n",
       "2015-01-01 Vietnam                   2.81\n",
       "2015-07-01 Vietnam                   2.75\n",
       "2016-01-01 Vietnam                   2.67\n",
       "\n",
       "[652 rows x 1 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bigmac.sort_index(level=\"Country\") #Önce country indexini sıralar daha sonrada country ye göre date i sıralar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b4ae7107",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Price in US Dollars</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yusuf</th>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2010-01-01</th>\n",
       "      <th>Uruguay</th>\n",
       "      <td>3.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>3.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ukraine</th>\n",
       "      <td>1.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UAE</th>\n",
       "      <td>2.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkey</th>\n",
       "      <td>3.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2016-01-01</th>\n",
       "      <th>Brazil</th>\n",
       "      <td>3.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>4.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>3.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>3.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>2.39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>652 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Price in US Dollars\n",
       "Yusuf      Country                           \n",
       "2010-01-01 Uruguay                       3.32\n",
       "           United States                 3.58\n",
       "           Ukraine                       1.83\n",
       "           UAE                           2.99\n",
       "           Turkey                        3.83\n",
       "...                                       ...\n",
       "2016-01-01 Brazil                        3.35\n",
       "           Belgium                       4.25\n",
       "           Austria                       3.76\n",
       "           Australia                     3.74\n",
       "           Argentina                     2.39\n",
       "\n",
       "[652 rows x 1 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bigmac.sort_index(ascending=[True,False])\n",
    "#ilk index için true ikincisi için false "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "00cc54cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.39"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bigmac.loc[('2016-01-01','Argentina'),\"Price in US Dollars\"] #bu alandaki kaydın price ını normal değer olarak döndürür"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ac0e86ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>2.39</td>\n",
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       "      <th>Australia</th>\n",
       "      <td>3.74</td>\n",
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       "      <th>Austria</th>\n",
       "      <td>3.76</td>\n",
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       "      <th>Belgium</th>\n",
       "      <td>4.25</td>\n",
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       "      <th>Brazil</th>\n",
       "      <td>3.35</td>\n",
       "    </tr>\n",
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       "      <th>Britain</th>\n",
       "      <td>4.22</td>\n",
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       "      <th>Canada</th>\n",
       "      <td>4.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chile</th>\n",
       "      <td>2.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>2.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colombia</th>\n",
       "      <td>2.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Costa Rica</th>\n",
       "      <td>4.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Czech Republic</th>\n",
       "      <td>2.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denmark</th>\n",
       "      <td>4.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Egypt</th>\n",
       "      <td>2.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Estonia</th>\n",
       "      <td>3.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Euro area</th>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finland</th>\n",
       "      <td>4.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>4.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>3.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greece</th>\n",
       "      <td>3.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hong Kong</th>\n",
       "      <td>2.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hungary</th>\n",
       "      <td>3.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>1.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indonesia</th>\n",
       "      <td>2.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ireland</th>\n",
       "      <td>4.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Israel</th>\n",
       "      <td>4.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>4.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>3.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malaysia</th>\n",
       "      <td>1.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mexico</th>\n",
       "      <td>2.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>3.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Zealand</th>\n",
       "      <td>3.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</th>\n",
       "      <td>5.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pakistan</th>\n",
       "      <td>2.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Peru</th>\n",
       "      <td>2.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Philippines</th>\n",
       "      <td>2.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poland</th>\n",
       "      <td>2.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portugal</th>\n",
       "      <td>3.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Russia</th>\n",
       "      <td>1.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saudi Arabia</th>\n",
       "      <td>3.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singapore</th>\n",
       "      <td>3.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Africa</th>\n",
       "      <td>1.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Korea</th>\n",
       "      <td>3.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>3.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sri Lanka</th>\n",
       "      <td>2.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>5.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>6.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Taiwan</th>\n",
       "      <td>2.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thailand</th>\n",
       "      <td>3.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkey</th>\n",
       "      <td>3.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UAE</th>\n",
       "      <td>3.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ukraine</th>\n",
       "      <td>1.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>4.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uruguay</th>\n",
       "      <td>3.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Venezuela</th>\n",
       "      <td>0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vietnam</th>\n",
       "      <td>2.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Price in US Dollars\n",
       "Country                            \n",
       "Argentina                      2.39\n",
       "Australia                      3.74\n",
       "Austria                        3.76\n",
       "Belgium                        4.25\n",
       "Brazil                         3.35\n",
       "Britain                        4.22\n",
       "Canada                         4.14\n",
       "Chile                          2.94\n",
       "China                          2.68\n",
       "Colombia                       2.43\n",
       "Costa Rica                     4.02\n",
       "Czech Republic                 2.98\n",
       "Denmark                        4.32\n",
       "Egypt                          2.16\n",
       "Estonia                        3.23\n",
       "Euro area                      4.00\n",
       "Finland                        4.41\n",
       "France                         4.41\n",
       "Germany                        3.86\n",
       "Greece                         3.60\n",
       "Hong Kong                      2.48\n",
       "Hungary                        3.08\n",
       "India                          1.90\n",
       "Indonesia                      2.19\n",
       "Ireland                        4.25\n",
       "Israel                         4.29\n",
       "Italy                          4.30\n",
       "Japan                          3.12\n",
       "Malaysia                       1.82\n",
       "Mexico                         2.81\n",
       "Netherlands                    3.71\n",
       "New Zealand                    3.91\n",
       "Norway                         5.21\n",
       "Pakistan                       2.86\n",
       "Peru                           2.93\n",
       "Philippines                    2.79\n",
       "Poland                         2.37\n",
       "Portugal                       3.23\n",
       "Russia                         1.53\n",
       "Saudi Arabia                   3.20\n",
       "Singapore                      3.27\n",
       "South Africa                   1.77\n",
       "South Korea                    3.59\n",
       "Spain                          3.76\n",
       "Sri Lanka                      2.43\n",
       "Sweden                         5.23\n",
       "Switzerland                    6.44\n",
       "Taiwan                         2.08\n",
       "Thailand                       3.09\n",
       "Turkey                         3.41\n",
       "UAE                            3.54\n",
       "Ukraine                        1.54\n",
       "United States                  4.93\n",
       "Uruguay                        3.74\n",
       "Venezuela                      0.66\n",
       "Vietnam                        2.67"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bigmac.loc[('2016-01-01')] # bu tarihteki fiyatları verdi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7543ade4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price in US Dollars</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
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       "      <th>Argentina</th>\n",
       "      <td>2.39</td>\n",
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       "      <th>Australia</th>\n",
       "      <td>3.74</td>\n",
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       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>3.76</td>\n",
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       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>4.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>3.35</td>\n",
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       "    <tr>\n",
       "      <th>Britain</th>\n",
       "      <td>4.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>4.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chile</th>\n",
       "      <td>2.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>2.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colombia</th>\n",
       "      <td>2.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Costa Rica</th>\n",
       "      <td>4.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Czech Republic</th>\n",
       "      <td>2.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denmark</th>\n",
       "      <td>4.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Egypt</th>\n",
       "      <td>2.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Estonia</th>\n",
       "      <td>3.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Euro area</th>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finland</th>\n",
       "      <td>4.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>4.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>3.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greece</th>\n",
       "      <td>3.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hong Kong</th>\n",
       "      <td>2.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hungary</th>\n",
       "      <td>3.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>1.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indonesia</th>\n",
       "      <td>2.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ireland</th>\n",
       "      <td>4.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Israel</th>\n",
       "      <td>4.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>4.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>3.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malaysia</th>\n",
       "      <td>1.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mexico</th>\n",
       "      <td>2.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>3.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Zealand</th>\n",
       "      <td>3.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</th>\n",
       "      <td>5.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pakistan</th>\n",
       "      <td>2.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Peru</th>\n",
       "      <td>2.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Philippines</th>\n",
       "      <td>2.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poland</th>\n",
       "      <td>2.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portugal</th>\n",
       "      <td>3.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Russia</th>\n",
       "      <td>1.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saudi Arabia</th>\n",
       "      <td>3.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singapore</th>\n",
       "      <td>3.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Africa</th>\n",
       "      <td>1.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Korea</th>\n",
       "      <td>3.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>3.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sri Lanka</th>\n",
       "      <td>2.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>5.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>6.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Taiwan</th>\n",
       "      <td>2.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thailand</th>\n",
       "      <td>3.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkey</th>\n",
       "      <td>3.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UAE</th>\n",
       "      <td>3.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ukraine</th>\n",
       "      <td>1.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>4.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uruguay</th>\n",
       "      <td>3.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Venezuela</th>\n",
       "      <td>0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vietnam</th>\n",
       "      <td>2.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Price in US Dollars\n",
       "Country                            \n",
       "Argentina                      2.39\n",
       "Australia                      3.74\n",
       "Austria                        3.76\n",
       "Belgium                        4.25\n",
       "Brazil                         3.35\n",
       "Britain                        4.22\n",
       "Canada                         4.14\n",
       "Chile                          2.94\n",
       "China                          2.68\n",
       "Colombia                       2.43\n",
       "Costa Rica                     4.02\n",
       "Czech Republic                 2.98\n",
       "Denmark                        4.32\n",
       "Egypt                          2.16\n",
       "Estonia                        3.23\n",
       "Euro area                      4.00\n",
       "Finland                        4.41\n",
       "France                         4.41\n",
       "Germany                        3.86\n",
       "Greece                         3.60\n",
       "Hong Kong                      2.48\n",
       "Hungary                        3.08\n",
       "India                          1.90\n",
       "Indonesia                      2.19\n",
       "Ireland                        4.25\n",
       "Israel                         4.29\n",
       "Italy                          4.30\n",
       "Japan                          3.12\n",
       "Malaysia                       1.82\n",
       "Mexico                         2.81\n",
       "Netherlands                    3.71\n",
       "New Zealand                    3.91\n",
       "Norway                         5.21\n",
       "Pakistan                       2.86\n",
       "Peru                           2.93\n",
       "Philippines                    2.79\n",
       "Poland                         2.37\n",
       "Portugal                       3.23\n",
       "Russia                         1.53\n",
       "Saudi Arabia                   3.20\n",
       "Singapore                      3.27\n",
       "South Africa                   1.77\n",
       "South Korea                    3.59\n",
       "Spain                          3.76\n",
       "Sri Lanka                      2.43\n",
       "Sweden                         5.23\n",
       "Switzerland                    6.44\n",
       "Taiwan                         2.08\n",
       "Thailand                       3.09\n",
       "Turkey                         3.41\n",
       "UAE                            3.54\n",
       "Ukraine                        1.54\n",
       "United States                  4.93\n",
       "Uruguay                        3.74\n",
       "Venezuela                      0.66\n",
       "Vietnam                        2.67"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bigmac.loc[('2016-01-01',)] #böyle de kullabiliriz "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "04d51b74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Price in US Dollars</th>\n",
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       "      <th rowspan=\"4\" valign=\"top\">2010-01-01</th>\n",
       "      <th>Argentina</th>\n",
       "      <td>1.84</td>\n",
       "    </tr>\n",
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       "      <th>Brazil</th>\n",
       "      <td>4.76</td>\n",
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       "      <th>Canada</th>\n",
       "      <td>3.97</td>\n",
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       "      <th>China</th>\n",
       "      <td>1.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-07-01</th>\n",
       "      <th>Turkey</th>\n",
       "      <td>4.52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Price in US Dollars\n",
       "Yusuf      Country                       \n",
       "2010-01-01 Argentina                 1.84\n",
       "           Brazil                    4.76\n",
       "           Canada                    3.97\n",
       "           China                     1.83\n",
       "2012-07-01 Turkey                    4.52"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bigmac.iloc[[0,2,4,6,250]] #sayısal değerlere göre,girilen indexlerdeki değerleri dataframe olarak döndürür\n",
    "#0-2-4-6 2010 da 250 2012 de"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a4f4539f",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Yusuf</th>\n",
       "      <th colspan=\"10\" halign=\"left\">2010-01-01</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"10\" halign=\"left\">2016-01-01</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th>Argentina</th>\n",
       "      <th>Australia</th>\n",
       "      <th>Brazil</th>\n",
       "      <th>Britain</th>\n",
       "      <th>Canada</th>\n",
       "      <th>Chile</th>\n",
       "      <th>China</th>\n",
       "      <th>Colombia</th>\n",
       "      <th>Costa Rica</th>\n",
       "      <th>Czech Republic</th>\n",
       "      <th>...</th>\n",
       "      <th>Switzerland</th>\n",
       "      <th>Taiwan</th>\n",
       "      <th>Thailand</th>\n",
       "      <th>Turkey</th>\n",
       "      <th>UAE</th>\n",
       "      <th>Ukraine</th>\n",
       "      <th>United States</th>\n",
       "      <th>Uruguay</th>\n",
       "      <th>Venezuela</th>\n",
       "      <th>Vietnam</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Price in US Dollars</th>\n",
       "      <td>1.84</td>\n",
       "      <td>3.98</td>\n",
       "      <td>4.76</td>\n",
       "      <td>3.67</td>\n",
       "      <td>3.97</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1.83</td>\n",
       "      <td>3.91</td>\n",
       "      <td>3.52</td>\n",
       "      <td>3.71</td>\n",
       "      <td>...</td>\n",
       "      <td>6.44</td>\n",
       "      <td>2.08</td>\n",
       "      <td>3.09</td>\n",
       "      <td>3.41</td>\n",
       "      <td>3.54</td>\n",
       "      <td>1.54</td>\n",
       "      <td>4.93</td>\n",
       "      <td>3.74</td>\n",
       "      <td>0.66</td>\n",
       "      <td>2.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 652 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Yusuf               2010-01-01                                              \\\n",
       "Country              Argentina Australia Brazil Britain Canada Chile China   \n",
       "Price in US Dollars       1.84      3.98   4.76    3.67   3.97  3.18  1.83   \n",
       "\n",
       "Yusuf                                                   ...  2016-01-01  \\\n",
       "Country             Colombia Costa Rica Czech Republic  ... Switzerland   \n",
       "Price in US Dollars     3.91       3.52           3.71  ...        6.44   \n",
       "\n",
       "Yusuf                                                                   \\\n",
       "Country             Taiwan Thailand Turkey   UAE Ukraine United States   \n",
       "Price in US Dollars   2.08     3.09   3.41  3.54    1.54          4.93   \n",
       "\n",
       "Yusuf                                          \n",
       "Country             Uruguay Venezuela Vietnam  \n",
       "Price in US Dollars    3.74      0.66    2.67  \n",
       "\n",
       "[1 rows x 652 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_bigmac=df_bigmac.transpose() #satırları sütun sütunları satır yaptık matrisin transpozunu aldık\n",
    "new_bigmac"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9ae8f057",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Price in US Dollars'], dtype='object')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_bigmac.index #indexi tek satır olarak aldı"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ee676a87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.98"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_bigmac.loc[(\"Price in US Dollars\"),(\"2010-01-01\",\"Australia\")] #değer döndürür"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "24c582fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Yusuf       Country       \n",
       "2010-01-01  Australia         3.98\n",
       "            Brazil            4.76\n",
       "            Britain           3.67\n",
       "            Canada            3.97\n",
       "            Chile             3.18\n",
       "            China             1.83\n",
       "            Colombia          3.91\n",
       "            Costa Rica        3.52\n",
       "            Czech Republic    3.71\n",
       "            Denmark           5.99\n",
       "            Egypt             2.38\n",
       "            Euro area         4.84\n",
       "            Hong Kong         1.91\n",
       "            Hungary           3.86\n",
       "            Indonesia         2.24\n",
       "            Israel            3.99\n",
       "            Japan             3.50\n",
       "            Latvia            3.09\n",
       "            Lithuania         2.87\n",
       "            Malaysia          2.08\n",
       "            Mexico            2.50\n",
       "            New Zealand       3.61\n",
       "            Norway            7.02\n",
       "            Pakistan          2.42\n",
       "            Peru              2.81\n",
       "            Philippines       2.21\n",
       "            Poland            2.86\n",
       "            Russia            2.34\n",
       "            Saudi Arabia      2.67\n",
       "            Singapore         3.19\n",
       "            South Africa      2.46\n",
       "            South Korea       2.98\n",
       "            Sri Lanka         1.83\n",
       "            Sweden            5.51\n",
       "            Switzerland       6.30\n",
       "            Taiwan            2.36\n",
       "            Thailand          2.11\n",
       "            Turkey            3.83\n",
       "            UAE               2.99\n",
       "            Ukraine           1.83\n",
       "            United States     3.58\n",
       "            Uruguay           3.32\n",
       "Name: Price in US Dollars, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_bigmac.loc[(\"Price in US Dollars\"),(\"2010-01-01\",\"Australia\"):(\"2010-01-01\")] #seri döndürür "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7d7cbbc2",
   "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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       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Yusuf</th>\n",
       "      <th colspan=\"8\" halign=\"left\">2010-01-01</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th>Australia</th>\n",
       "      <th>Brazil</th>\n",
       "      <th>Britain</th>\n",
       "      <th>Canada</th>\n",
       "      <th>Chile</th>\n",
       "      <th>China</th>\n",
       "      <th>Colombia</th>\n",
       "      <th>Costa Rica</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Price in US Dollars</th>\n",
       "      <td>3.98</td>\n",
       "      <td>4.76</td>\n",
       "      <td>3.67</td>\n",
       "      <td>3.97</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1.83</td>\n",
       "      <td>3.91</td>\n",
       "      <td>3.52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Yusuf               2010-01-01                                             \\\n",
       "Country              Australia Brazil Britain Canada Chile China Colombia   \n",
       "Price in US Dollars       3.98   4.76    3.67   3.97  3.18  1.83     3.91   \n",
       "\n",
       "Yusuf                           \n",
       "Country             Costa Rica  \n",
       "Price in US Dollars       3.52  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_bigmac.loc[(\"Price in US Dollars\",),(\"2010-01-01\",\"Australia\"):(\"2010-01-01\",\"Costa Rica\")] #dataframe döndürür"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "63b70328",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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></th>\n",
       "      <th>Price in US Dollars</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2016-01-01</th>\n",
       "      <th>Argentina</th>\n",
       "      <td>2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>3.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>3.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Britain</th>\n",
       "      <td>4.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>4.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2010-01-01</th>\n",
       "      <th>Turkey</th>\n",
       "      <td>3.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UAE</th>\n",
       "      <td>2.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ukraine</th>\n",
       "      <td>1.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>3.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uruguay</th>\n",
       "      <td>3.32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>652 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Price in US Dollars\n",
       "Date       Country                           \n",
       "2016-01-01 Argentina                     2.39\n",
       "           Australia                     3.74\n",
       "           Brazil                        3.35\n",
       "           Britain                       4.22\n",
       "           Canada                        4.14\n",
       "...                                       ...\n",
       "2010-01-01 Turkey                        3.83\n",
       "           UAE                           2.99\n",
       "           Ukraine                       1.83\n",
       "           United States                 3.58\n",
       "           Uruguay                       3.32\n",
       "\n",
       "[652 rows x 1 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bigmac1=pd.read_csv(\"resources/resources/bigmac.csv\",parse_dates=[\"Date\"],index_col=[\"Date\",\"Country\"])\n",
    "df_bigmac1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "0c45bf2c",
   "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",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Yusuf</th>\n",
       "      <th colspan=\"8\" halign=\"left\">2010-01-01</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th>Australia</th>\n",
       "      <th>Brazil</th>\n",
       "      <th>Britain</th>\n",
       "      <th>Canada</th>\n",
       "      <th>Chile</th>\n",
       "      <th>China</th>\n",
       "      <th>Colombia</th>\n",
       "      <th>Costa Rica</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Price in US Dollars</th>\n",
       "      <td>3.98</td>\n",
       "      <td>4.76</td>\n",
       "      <td>3.67</td>\n",
       "      <td>3.97</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1.83</td>\n",
       "      <td>3.91</td>\n",
       "      <td>3.52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Yusuf               2010-01-01                                             \\\n",
       "Country              Australia Brazil Britain Canada Chile China Colombia   \n",
       "Price in US Dollars       3.98   4.76    3.67   3.97  3.18  1.83     3.91   \n",
       "\n",
       "Yusuf                           \n",
       "Country             Costa Rica  \n",
       "Price in US Dollars       3.52  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_bigmac.loc[(\"Price in US Dollars\",),(\"2010-01-01\",\"Australia\"):(\"2010-01-01\",\"Costa Rica\")] \n",
    "#(\"Price in US Dollars\",) virgüllü dataframe,virgülsüz seri döndürür."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9e1e3b6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\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></th>\n",
       "      <th>Price in US Dollars</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th>Yusuf</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>1.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>3.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>4.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Britain</th>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>3.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>3.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ukraine</th>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>1.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>4.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uruguay</th>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>3.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Venezuela</th>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vietnam</th>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>2.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>652 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Price in US Dollars\n",
       "Country       Yusuf                          \n",
       "Argentina     2010-01-01                 1.84\n",
       "Australia     2010-01-01                 3.98\n",
       "Brazil        2010-01-01                 4.76\n",
       "Britain       2010-01-01                 3.67\n",
       "Canada        2010-01-01                 3.97\n",
       "...                                       ...\n",
       "Ukraine       2016-01-01                 1.54\n",
       "United States 2016-01-01                 4.93\n",
       "Uruguay       2016-01-01                 3.74\n",
       "Venezuela     2016-01-01                 0.66\n",
       "Vietnam       2016-01-01                 2.67\n",
       "\n",
       "[652 rows x 1 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#index lerin yerini değiştirmek için swaplevel i kullanırız\n",
    "df_bigmac.swaplevel()\n",
    "#axis i -2,-1 oluyor default olarak"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "58874b2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    .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>Price in US Dollars</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th>Yusuf</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>1.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>3.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>4.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Britain</th>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>3.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>3.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ukraine</th>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>1.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>4.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uruguay</th>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>3.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Venezuela</th>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vietnam</th>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>2.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>652 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Price in US Dollars\n",
       "Country       Yusuf                          \n",
       "Argentina     2010-01-01                 1.84\n",
       "Australia     2010-01-01                 3.98\n",
       "Brazil        2010-01-01                 4.76\n",
       "Britain       2010-01-01                 3.67\n",
       "Canada        2010-01-01                 3.97\n",
       "...                                       ...\n",
       "Ukraine       2016-01-01                 1.54\n",
       "United States 2016-01-01                 4.93\n",
       "Uruguay       2016-01-01                 3.74\n",
       "Venezuela     2016-01-01                 0.66\n",
       "Vietnam       2016-01-01                 2.67\n",
       "\n",
       "[652 rows x 1 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bigmac.swaplevel(\"Country\",\"Yusuf\") #inplace diye bi şeyi yok\n",
    "#böyle de swap işlemi yapabiliriz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "3fd7facc",
   "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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       "    }\n",
       "\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></th>\n",
       "      <th>Price in US Dollars</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yusuf</th>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2010-01-01</th>\n",
       "      <th>Argentina</th>\n",
       "      <td>1.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>3.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>4.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Britain</th>\n",
       "      <td>3.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>3.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2016-01-01</th>\n",
       "      <th>Ukraine</th>\n",
       "      <td>1.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>4.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uruguay</th>\n",
       "      <td>3.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Venezuela</th>\n",
       "      <td>0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vietnam</th>\n",
       "      <td>2.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>652 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Price in US Dollars\n",
       "Yusuf      Country                           \n",
       "2010-01-01 Argentina                     1.84\n",
       "           Australia                     3.98\n",
       "           Brazil                        4.76\n",
       "           Britain                       3.67\n",
       "           Canada                        3.97\n",
       "...                                       ...\n",
       "2016-01-01 Ukraine                       1.54\n",
       "           United States                 4.93\n",
       "           Uruguay                       3.74\n",
       "           Venezuela                     0.66\n",
       "           Vietnam                       2.67\n",
       "\n",
       "[652 rows x 1 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bigmac"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "3dc67e46",
   "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",
       "    .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>Population</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Arab World</th>\n",
       "      <th>2015</th>\n",
       "      <td>392022276.0</td>\n",
       "      <td>2.530102e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>384222592.0</td>\n",
       "      <td>2.873600e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>376504253.0</td>\n",
       "      <td>2.846994e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>368802611.0</td>\n",
       "      <td>2.773270e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>361031820.0</td>\n",
       "      <td>2.497945e+12</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\">Zimbabwe</th>\n",
       "      <th>1964</th>\n",
       "      <td>4279561.0</td>\n",
       "      <td>1.217138e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963</th>\n",
       "      <td>4140804.0</td>\n",
       "      <td>1.159512e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962</th>\n",
       "      <td>4006262.0</td>\n",
       "      <td>1.117602e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961</th>\n",
       "      <td>3876638.0</td>\n",
       "      <td>1.096647e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960</th>\n",
       "      <td>3752390.0</td>\n",
       "      <td>1.052990e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11211 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Population           GDP\n",
       "country    year                           \n",
       "Arab World 2015  392022276.0  2.530102e+12\n",
       "           2014  384222592.0  2.873600e+12\n",
       "           2013  376504253.0  2.846994e+12\n",
       "           2012  368802611.0  2.773270e+12\n",
       "           2011  361031820.0  2.497945e+12\n",
       "...                      ...           ...\n",
       "Zimbabwe   1964    4279561.0  1.217138e+09\n",
       "           1963    4140804.0  1.159512e+09\n",
       "           1962    4006262.0  1.117602e+09\n",
       "           1961    3876638.0  1.096647e+09\n",
       "           1960    3752390.0  1.052990e+09\n",
       "\n",
       "[11211 rows x 2 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_world=pd.read_csv(\"resources/resources/worldstats.csv\",index_col=[\"country\",\"year\"])\n",
    "df_world"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ed5e8bef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country     year            \n",
       "Arab World  2015  Population    3.920223e+08\n",
       "                  GDP           2.530102e+12\n",
       "            2014  Population    3.842226e+08\n",
       "                  GDP           2.873600e+12\n",
       "            2013  Population    3.765043e+08\n",
       "                                    ...     \n",
       "Zimbabwe    1962  GDP           1.117602e+09\n",
       "            1961  Population    3.876638e+06\n",
       "                  GDP           1.096647e+09\n",
       "            1960  Population    3.752390e+06\n",
       "                  GDP           1.052990e+09\n",
       "Length: 22422, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_world.stack() #stack() index dışındaki sütunları alıp satır haline getirir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e76551fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df_world.stack()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "f3aeb3fb",
   "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>Population</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Afghanistan</th>\n",
       "      <th>1960</th>\n",
       "      <td>8994793.0</td>\n",
       "      <td>5.377778e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961</th>\n",
       "      <td>9164945.0</td>\n",
       "      <td>5.488889e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962</th>\n",
       "      <td>9343772.0</td>\n",
       "      <td>5.466667e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963</th>\n",
       "      <td>9531555.0</td>\n",
       "      <td>7.511112e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964</th>\n",
       "      <td>9728645.0</td>\n",
       "      <td>8.000000e+08</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\">Zimbabwe</th>\n",
       "      <th>2011</th>\n",
       "      <td>14255592.0</td>\n",
       "      <td>1.095623e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>14565482.0</td>\n",
       "      <td>1.239272e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>14898092.0</td>\n",
       "      <td>1.349023e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>15245855.0</td>\n",
       "      <td>1.419691e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>15602751.0</td>\n",
       "      <td>1.389294e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11211 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Population           GDP\n",
       "country     year                          \n",
       "Afghanistan 1960   8994793.0  5.377778e+08\n",
       "            1961   9164945.0  5.488889e+08\n",
       "            1962   9343772.0  5.466667e+08\n",
       "            1963   9531555.0  7.511112e+08\n",
       "            1964   9728645.0  8.000000e+08\n",
       "...                      ...           ...\n",
       "Zimbabwe    2011  14255592.0  1.095623e+10\n",
       "            2012  14565482.0  1.239272e+10\n",
       "            2013  14898092.0  1.349023e+10\n",
       "            2014  15245855.0  1.419691e+10\n",
       "            2015  15602751.0  1.389294e+10\n",
       "\n",
       "[11211 rows x 2 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#stackle yaptığımız şeyi geri eski haline getirmek için\n",
    "#inplace yok stack te\n",
    "s=df_world.stack() #stack dediğimiz kadar unstack dicez.\n",
    "s.unstack() #birden fazla unstack yapabiliriz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e04267bc",
   "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>Date</th>\n",
       "      <th>Salesman</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>Bob</td>\n",
       "      <td>7172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>Bob</td>\n",
       "      <td>6362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-03</td>\n",
       "      <td>Bob</td>\n",
       "      <td>5982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>Bob</td>\n",
       "      <td>7917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>Bob</td>\n",
       "      <td>7837</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date Salesman  Revenue\n",
       "0 2016-01-01      Bob     7172\n",
       "1 2016-01-02      Bob     6362\n",
       "2 2016-01-03      Bob     5982\n",
       "3 2016-01-04      Bob     7917\n",
       "4 2016-01-05      Bob     7837"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_salesmen=pd.read_csv(\"resources/resources/salesmen.csv\",parse_dates=[\"Date\"])\n",
    "df_salesmen.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "aabc56cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1830"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_salesmen) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "d8305001",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bob       366\n",
       "Ronald    366\n",
       "Dave      366\n",
       "Jeb       366\n",
       "Oscar     366\n",
       "Name: Salesman, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_salesmen[\"Salesman\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "9d35af7d",
   "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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       "    }\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>Date</th>\n",
       "      <th>Salesman</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>Bob</td>\n",
       "      <td>7172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>Bob</td>\n",
       "      <td>6362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-03</td>\n",
       "      <td>Bob</td>\n",
       "      <td>5982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>Bob</td>\n",
       "      <td>7917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>Bob</td>\n",
       "      <td>7837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1825</th>\n",
       "      <td>2016-12-27</td>\n",
       "      <td>Oscar</td>\n",
       "      <td>835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1826</th>\n",
       "      <td>2016-12-28</td>\n",
       "      <td>Oscar</td>\n",
       "      <td>3073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1827</th>\n",
       "      <td>2016-12-29</td>\n",
       "      <td>Oscar</td>\n",
       "      <td>6424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1828</th>\n",
       "      <td>2016-12-30</td>\n",
       "      <td>Oscar</td>\n",
       "      <td>7088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1829</th>\n",
       "      <td>2016-12-31</td>\n",
       "      <td>Oscar</td>\n",
       "      <td>8408</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1830 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date Salesman  Revenue\n",
       "0    2016-01-01      Bob     7172\n",
       "1    2016-01-02      Bob     6362\n",
       "2    2016-01-03      Bob     5982\n",
       "3    2016-01-04      Bob     7917\n",
       "4    2016-01-05      Bob     7837\n",
       "...         ...      ...      ...\n",
       "1825 2016-12-27    Oscar      835\n",
       "1826 2016-12-28    Oscar     3073\n",
       "1827 2016-12-29    Oscar     6424\n",
       "1828 2016-12-30    Oscar     7088\n",
       "1829 2016-12-31    Oscar     8408\n",
       "\n",
       "[1830 rows x 3 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_salesmen[\"Salesman\"]=df_salesmen[\"Salesman\"].astype(\"category\")\n",
    "df_salesmen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "e055b7d9",
   "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",
       "    .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>Salesman</th>\n",
       "      <th>Bob</th>\n",
       "      <th>Dave</th>\n",
       "      <th>Jeb</th>\n",
       "      <th>Oscar</th>\n",
       "      <th>Ronald</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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>2016-01-01</th>\n",
       "      <td>7172</td>\n",
       "      <td>1864</td>\n",
       "      <td>4430</td>\n",
       "      <td>5250</td>\n",
       "      <td>2639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-02</th>\n",
       "      <td>6362</td>\n",
       "      <td>8278</td>\n",
       "      <td>8026</td>\n",
       "      <td>8661</td>\n",
       "      <td>4951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-03</th>\n",
       "      <td>5982</td>\n",
       "      <td>4226</td>\n",
       "      <td>5188</td>\n",
       "      <td>7075</td>\n",
       "      <td>2703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>7917</td>\n",
       "      <td>3868</td>\n",
       "      <td>3144</td>\n",
       "      <td>2524</td>\n",
       "      <td>4258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>7837</td>\n",
       "      <td>2287</td>\n",
       "      <td>938</td>\n",
       "      <td>2793</td>\n",
       "      <td>7771</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>2016-12-27</th>\n",
       "      <td>2045</td>\n",
       "      <td>2843</td>\n",
       "      <td>6666</td>\n",
       "      <td>835</td>\n",
       "      <td>2981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-28</th>\n",
       "      <td>100</td>\n",
       "      <td>8888</td>\n",
       "      <td>1243</td>\n",
       "      <td>3073</td>\n",
       "      <td>6129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29</th>\n",
       "      <td>4115</td>\n",
       "      <td>9490</td>\n",
       "      <td>3498</td>\n",
       "      <td>6424</td>\n",
       "      <td>7662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30</th>\n",
       "      <td>2577</td>\n",
       "      <td>3594</td>\n",
       "      <td>8858</td>\n",
       "      <td>7088</td>\n",
       "      <td>2570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31</th>\n",
       "      <td>3845</td>\n",
       "      <td>6830</td>\n",
       "      <td>9717</td>\n",
       "      <td>8408</td>\n",
       "      <td>2619</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>366 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Salesman     Bob  Dave   Jeb  Oscar  Ronald\n",
       "Date                                       \n",
       "2016-01-01  7172  1864  4430   5250    2639\n",
       "2016-01-02  6362  8278  8026   8661    4951\n",
       "2016-01-03  5982  4226  5188   7075    2703\n",
       "2016-01-04  7917  3868  3144   2524    4258\n",
       "2016-01-05  7837  2287   938   2793    7771\n",
       "...          ...   ...   ...    ...     ...\n",
       "2016-12-27  2045  2843  6666    835    2981\n",
       "2016-12-28   100  8888  1243   3073    6129\n",
       "2016-12-29  4115  9490  3498   6424    7662\n",
       "2016-12-30  2577  3594  8858   7088    2570\n",
       "2016-12-31  3845  6830  9717   8408    2619\n",
       "\n",
       "[366 rows x 5 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pivot elimizdeki tabloyu özet halinde bize sunar\n",
    "df_salesmen.pivot(index=\"Date\",columns=\"Salesman\",values=\"Revenue\") #values columnlarda hangi değerleri göstereceğimizi belirler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "bcfd48c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>City</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Item</th>\n",
       "      <th>Spend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Wanda</td>\n",
       "      <td>Female</td>\n",
       "      <td>Stamford</td>\n",
       "      <td>Weekly</td>\n",
       "      <td>Burger</td>\n",
       "      <td>15.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Eric</td>\n",
       "      <td>Male</td>\n",
       "      <td>Stamford</td>\n",
       "      <td>Daily</td>\n",
       "      <td>Chalupa</td>\n",
       "      <td>10.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Charles</td>\n",
       "      <td>Male</td>\n",
       "      <td>New York</td>\n",
       "      <td>Never</td>\n",
       "      <td>Sushi</td>\n",
       "      <td>42.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Anna</td>\n",
       "      <td>Female</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>Once</td>\n",
       "      <td>Ice Cream</td>\n",
       "      <td>11.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Deborah</td>\n",
       "      <td>Female</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>Daily</td>\n",
       "      <td>Chalupa</td>\n",
       "      <td>23.49</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>Donna</td>\n",
       "      <td>Female</td>\n",
       "      <td>New York</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>Sushi</td>\n",
       "      <td>83.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>Albert</td>\n",
       "      <td>Male</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>Daily</td>\n",
       "      <td>Sushi</td>\n",
       "      <td>72.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>Jean</td>\n",
       "      <td>Female</td>\n",
       "      <td>Stamford</td>\n",
       "      <td>Weekly</td>\n",
       "      <td>Donut</td>\n",
       "      <td>5.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>Jessica</td>\n",
       "      <td>Female</td>\n",
       "      <td>New York</td>\n",
       "      <td>Daily</td>\n",
       "      <td>Chalupa</td>\n",
       "      <td>43.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>Brian</td>\n",
       "      <td>Male</td>\n",
       "      <td>New York</td>\n",
       "      <td>Daily</td>\n",
       "      <td>Chalupa</td>\n",
       "      <td>28.36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    First Name  Gender          City Frequency       Item  Spend\n",
       "0        Wanda  Female      Stamford    Weekly     Burger  15.66\n",
       "1         Eric    Male      Stamford     Daily    Chalupa  10.56\n",
       "2      Charles    Male      New York     Never      Sushi  42.14\n",
       "3         Anna  Female  Philadelphia      Once  Ice Cream  11.01\n",
       "4      Deborah  Female  Philadelphia     Daily    Chalupa  23.49\n",
       "..         ...     ...           ...       ...        ...    ...\n",
       "995      Donna  Female      New York   Monthly      Sushi  83.53\n",
       "996     Albert    Male  Philadelphia     Daily      Sushi  72.88\n",
       "997       Jean  Female      Stamford    Weekly      Donut   5.85\n",
       "998    Jessica  Female      New York     Daily    Chalupa  43.19\n",
       "999      Brian    Male      New York     Daily    Chalupa  28.36\n",
       "\n",
       "[1000 rows x 6 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_foods=pd.read_csv(\"resources/resources/foods.csv\")\n",
    "df_foods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "ea30bf2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>Spend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>50.709629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>49.397623</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Spend\n",
       "Gender           \n",
       "Female  50.709629\n",
       "Male    49.397623"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_foods.pivot_table(values=\"Spend\",index=\"Gender\",aggfunc=\"mean\") #mean yerine sum vs de kullanabiliriz\n",
    "#hangi cinsiyettekiler ort ne kadar harcama yapmış bunu bulduk\n",
    "#aritmetik işlemlerde pivot_table ı kullanırız."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "47757816",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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></th>\n",
       "      <th>Frequency</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th>Item</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">Female</th>\n",
       "      <th>Burger</th>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burrito</th>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chalupa</th>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Donut</th>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ice Cream</th>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sushi</th>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">Male</th>\n",
       "      <th>Burger</th>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burrito</th>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chalupa</th>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Donut</th>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ice Cream</th>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sushi</th>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Frequency\n",
       "Gender Item                \n",
       "Female Burger            82\n",
       "       Burrito           85\n",
       "       Chalupa           76\n",
       "       Donut             95\n",
       "       Ice Cream         81\n",
       "       Sushi             93\n",
       "Male   Burger            74\n",
       "       Burrito           83\n",
       "       Chalupa           71\n",
       "       Donut             92\n",
       "       Ice Cream         95\n",
       "       Sushi             73"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_foods.pivot_table(values=\"Frequency\",index=[\"Gender\",\"Item\"],aggfunc=\"count\") #mean yerine sum vs de kullanabiliriz\n",
    "#hangi cinsiyettekiler hangi üründen kaç tane almış bunu bulduk\n",
    "#count kullandığımızdan values a hangi columnu yazacağımız çok fark etmiyor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "fa62d668",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>City</th>\n",
       "      <th>New York</th>\n",
       "      <th>Philadelphia</th>\n",
       "      <th>Stamford</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th>Item</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">Female</th>\n",
       "      <th>Burger</th>\n",
       "      <td>24</td>\n",
       "      <td>31</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burrito</th>\n",
       "      <td>23</td>\n",
       "      <td>28</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chalupa</th>\n",
       "      <td>19</td>\n",
       "      <td>32</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Donut</th>\n",
       "      <td>31</td>\n",
       "      <td>30</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ice Cream</th>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sushi</th>\n",
       "      <td>31</td>\n",
       "      <td>30</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">Male</th>\n",
       "      <th>Burger</th>\n",
       "      <td>22</td>\n",
       "      <td>21</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burrito</th>\n",
       "      <td>25</td>\n",
       "      <td>30</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chalupa</th>\n",
       "      <td>25</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Donut</th>\n",
       "      <td>30</td>\n",
       "      <td>33</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ice Cream</th>\n",
       "      <td>29</td>\n",
       "      <td>41</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sushi</th>\n",
       "      <td>27</td>\n",
       "      <td>28</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "City              New York  Philadelphia  Stamford\n",
       "Gender Item                                       \n",
       "Female Burger           24            31        27\n",
       "       Burrito          23            28        34\n",
       "       Chalupa          19            32        25\n",
       "       Donut            31            30        34\n",
       "       Ice Cream        27            32        22\n",
       "       Sushi            31            30        32\n",
       "Male   Burger           22            21        31\n",
       "       Burrito          25            30        28\n",
       "       Chalupa          25            23        23\n",
       "       Donut            30            33        29\n",
       "       Ice Cream        29            41        25\n",
       "       Sushi            27            28        18"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_foods.pivot_table(values=\"Spend\",index=[\"Gender\",\"Item\"],aggfunc=\"count\",columns=\"City\")\n",
    "#count yerine sum vs de kullanabiliriz\n",
    "#columns ile ekranda görmek istediğimiz column u yazabiliriz bu kullanımda spend in bi anlamı kalmadı\n",
    "#hangi cinsiyettekiler hangi üründen hangi şehirde kaç tane alınmış"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "14a10b16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Frequency</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Daily</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Monthly</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Never</th>\n",
       "      <th>Often</th>\n",
       "      <th>...</th>\n",
       "      <th>Once</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Seldom</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Weekly</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Yearly</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>New York</th>\n",
       "      <th>Philadelphia</th>\n",
       "      <th>Stamford</th>\n",
       "      <th>New York</th>\n",
       "      <th>Philadelphia</th>\n",
       "      <th>Stamford</th>\n",
       "      <th>New York</th>\n",
       "      <th>Philadelphia</th>\n",
       "      <th>Stamford</th>\n",
       "      <th>New York</th>\n",
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       "      <th>New York</th>\n",
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       "      <th>Stamford</th>\n",
       "      <th>New York</th>\n",
       "      <th>Philadelphia</th>\n",
       "      <th>Stamford</th>\n",
       "      <th>New York</th>\n",
       "      <th>Philadelphia</th>\n",
       "      <th>Stamford</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th>Item</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">Female</th>\n",
       "      <th>Burger</th>\n",
       "      <td>43.778333</td>\n",
       "      <td>77.226667</td>\n",
       "      <td>48.220000</td>\n",
       "      <td>57.286667</td>\n",
       "      <td>53.762500</td>\n",
       "      <td>59.622500</td>\n",
       "      <td>97.890000</td>\n",
       "      <td>54.742500</td>\n",
       "      <td>45.485000</td>\n",
       "      <td>23.740000</td>\n",
       "      <td>...</td>\n",
       "      <td>31.683333</td>\n",
       "      <td>31.580000</td>\n",
       "      <td>58.435714</td>\n",
       "      <td>48.765000</td>\n",
       "      <td>92.17500</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>31.0040</td>\n",
       "      <td>64.825000</td>\n",
       "      <td>61.585000</td>\n",
       "      <td>51.171667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burrito</th>\n",
       "      <td>44.890000</td>\n",
       "      <td>53.595000</td>\n",
       "      <td>39.126000</td>\n",
       "      <td>40.913333</td>\n",
       "      <td>17.140000</td>\n",
       "      <td>67.940000</td>\n",
       "      <td>47.432500</td>\n",
       "      <td>63.716667</td>\n",
       "      <td>52.334286</td>\n",
       "      <td>34.533333</td>\n",
       "      <td>...</td>\n",
       "      <td>56.003333</td>\n",
       "      <td>83.770000</td>\n",
       "      <td>49.527500</td>\n",
       "      <td>78.163333</td>\n",
       "      <td>13.23000</td>\n",
       "      <td>31.410000</td>\n",
       "      <td>46.1820</td>\n",
       "      <td>35.630000</td>\n",
       "      <td>38.916667</td>\n",
       "      <td>43.245000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chalupa</th>\n",
       "      <td>43.190000</td>\n",
       "      <td>23.490000</td>\n",
       "      <td>95.700000</td>\n",
       "      <td>79.185000</td>\n",
       "      <td>72.490000</td>\n",
       "      <td>80.990000</td>\n",
       "      <td>35.150000</td>\n",
       "      <td>30.492500</td>\n",
       "      <td>52.120000</td>\n",
       "      <td>39.730000</td>\n",
       "      <td>...</td>\n",
       "      <td>40.590000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>54.902000</td>\n",
       "      <td>58.416667</td>\n",
       "      <td>42.88000</td>\n",
       "      <td>28.136667</td>\n",
       "      <td>68.2300</td>\n",
       "      <td>52.606667</td>\n",
       "      <td>56.048889</td>\n",
       "      <td>69.632000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Donut</th>\n",
       "      <td>39.841667</td>\n",
       "      <td>61.850000</td>\n",
       "      <td>41.450000</td>\n",
       "      <td>71.132500</td>\n",
       "      <td>50.250000</td>\n",
       "      <td>45.860000</td>\n",
       "      <td>56.070000</td>\n",
       "      <td>72.263333</td>\n",
       "      <td>52.443333</td>\n",
       "      <td>32.657500</td>\n",
       "      <td>...</td>\n",
       "      <td>79.120000</td>\n",
       "      <td>30.270000</td>\n",
       "      <td>45.812500</td>\n",
       "      <td>34.886667</td>\n",
       "      <td>71.39000</td>\n",
       "      <td>69.600000</td>\n",
       "      <td>55.0075</td>\n",
       "      <td>62.950000</td>\n",
       "      <td>58.410000</td>\n",
       "      <td>56.120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ice Cream</th>\n",
       "      <td>65.547500</td>\n",
       "      <td>59.230000</td>\n",
       "      <td>46.440000</td>\n",
       "      <td>46.265000</td>\n",
       "      <td>37.255000</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>68.716667</td>\n",
       "      <td>39.007500</td>\n",
       "      <td>77.660000</td>\n",
       "      <td>58.065000</td>\n",
       "      <td>...</td>\n",
       "      <td>55.866000</td>\n",
       "      <td>80.783333</td>\n",
       "      <td>50.775000</td>\n",
       "      <td>58.865000</td>\n",
       "      <td>56.90500</td>\n",
       "      <td>47.546667</td>\n",
       "      <td>25.0060</td>\n",
       "      <td>37.917500</td>\n",
       "      <td>39.965000</td>\n",
       "      <td>15.240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sushi</th>\n",
       "      <td>40.535000</td>\n",
       "      <td>58.088333</td>\n",
       "      <td>56.181429</td>\n",
       "      <td>46.580000</td>\n",
       "      <td>78.710000</td>\n",
       "      <td>54.195000</td>\n",
       "      <td>69.330000</td>\n",
       "      <td>47.645000</td>\n",
       "      <td>19.560000</td>\n",
       "      <td>49.134286</td>\n",
       "      <td>...</td>\n",
       "      <td>38.950000</td>\n",
       "      <td>87.700000</td>\n",
       "      <td>62.848000</td>\n",
       "      <td>27.820000</td>\n",
       "      <td>51.36125</td>\n",
       "      <td>55.666667</td>\n",
       "      <td>52.5600</td>\n",
       "      <td>46.482000</td>\n",
       "      <td>48.616667</td>\n",
       "      <td>45.342500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">Male</th>\n",
       "      <th>Burger</th>\n",
       "      <td>63.892000</td>\n",
       "      <td>37.566667</td>\n",
       "      <td>49.430000</td>\n",
       "      <td>62.430000</td>\n",
       "      <td>71.046667</td>\n",
       "      <td>13.580000</td>\n",
       "      <td>90.320000</td>\n",
       "      <td>8.655000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.735000</td>\n",
       "      <td>...</td>\n",
       "      <td>36.293333</td>\n",
       "      <td>75.226667</td>\n",
       "      <td>47.015000</td>\n",
       "      <td>53.250000</td>\n",
       "      <td>69.69000</td>\n",
       "      <td>33.296667</td>\n",
       "      <td>77.5525</td>\n",
       "      <td>24.805000</td>\n",
       "      <td>49.340000</td>\n",
       "      <td>45.014000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burrito</th>\n",
       "      <td>78.736667</td>\n",
       "      <td>41.440000</td>\n",
       "      <td>69.057500</td>\n",
       "      <td>49.180000</td>\n",
       "      <td>29.860000</td>\n",
       "      <td>39.866667</td>\n",
       "      <td>28.926667</td>\n",
       "      <td>47.290000</td>\n",
       "      <td>70.368000</td>\n",
       "      <td>47.480000</td>\n",
       "      <td>...</td>\n",
       "      <td>15.075000</td>\n",
       "      <td>67.466667</td>\n",
       "      <td>27.710000</td>\n",
       "      <td>9.840000</td>\n",
       "      <td>64.18500</td>\n",
       "      <td>48.208333</td>\n",
       "      <td>40.4625</td>\n",
       "      <td>55.175000</td>\n",
       "      <td>59.255000</td>\n",
       "      <td>32.830000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chalupa</th>\n",
       "      <td>27.045000</td>\n",
       "      <td>68.702500</td>\n",
       "      <td>48.160000</td>\n",
       "      <td>66.752000</td>\n",
       "      <td>45.350000</td>\n",
       "      <td>57.293333</td>\n",
       "      <td>39.818000</td>\n",
       "      <td>48.596000</td>\n",
       "      <td>46.233333</td>\n",
       "      <td>62.880000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.690000</td>\n",
       "      <td>65.375000</td>\n",
       "      <td>34.804000</td>\n",
       "      <td>54.40000</td>\n",
       "      <td>33.920000</td>\n",
       "      <td>44.3700</td>\n",
       "      <td>55.913333</td>\n",
       "      <td>34.405000</td>\n",
       "      <td>58.095000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Donut</th>\n",
       "      <td>46.000000</td>\n",
       "      <td>47.677500</td>\n",
       "      <td>64.710000</td>\n",
       "      <td>45.932500</td>\n",
       "      <td>51.858000</td>\n",
       "      <td>29.882500</td>\n",
       "      <td>43.926000</td>\n",
       "      <td>26.825000</td>\n",
       "      <td>54.910000</td>\n",
       "      <td>46.600000</td>\n",
       "      <td>...</td>\n",
       "      <td>27.978000</td>\n",
       "      <td>16.250000</td>\n",
       "      <td>33.003333</td>\n",
       "      <td>40.827500</td>\n",
       "      <td>37.22000</td>\n",
       "      <td>38.600000</td>\n",
       "      <td>62.2540</td>\n",
       "      <td>35.775000</td>\n",
       "      <td>22.305000</td>\n",
       "      <td>16.520000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ice Cream</th>\n",
       "      <td>59.406667</td>\n",
       "      <td>29.738000</td>\n",
       "      <td>15.170000</td>\n",
       "      <td>50.733333</td>\n",
       "      <td>64.425000</td>\n",
       "      <td>74.360000</td>\n",
       "      <td>28.770000</td>\n",
       "      <td>80.485000</td>\n",
       "      <td>39.923333</td>\n",
       "      <td>69.510000</td>\n",
       "      <td>...</td>\n",
       "      <td>36.542000</td>\n",
       "      <td>67.605000</td>\n",
       "      <td>69.220000</td>\n",
       "      <td>20.415000</td>\n",
       "      <td>44.77200</td>\n",
       "      <td>55.310000</td>\n",
       "      <td>47.8840</td>\n",
       "      <td>53.233333</td>\n",
       "      <td>47.988000</td>\n",
       "      <td>32.106667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sushi</th>\n",
       "      <td>56.392500</td>\n",
       "      <td>46.821667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53.857500</td>\n",
       "      <td>77.795000</td>\n",
       "      <td>93.030000</td>\n",
       "      <td>54.670000</td>\n",
       "      <td>40.961667</td>\n",
       "      <td>68.300000</td>\n",
       "      <td>9.310000</td>\n",
       "      <td>...</td>\n",
       "      <td>62.930000</td>\n",
       "      <td>53.496667</td>\n",
       "      <td>91.260000</td>\n",
       "      <td>74.716667</td>\n",
       "      <td>50.05000</td>\n",
       "      <td>52.066000</td>\n",
       "      <td>84.5400</td>\n",
       "      <td>39.813333</td>\n",
       "      <td>33.800000</td>\n",
       "      <td>48.710000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Frequency             Daily                            Monthly               \\\n",
       "City               New York Philadelphia   Stamford   New York Philadelphia   \n",
       "Gender Item                                                                   \n",
       "Female Burger     43.778333    77.226667  48.220000  57.286667    53.762500   \n",
       "       Burrito    44.890000    53.595000  39.126000  40.913333    17.140000   \n",
       "       Chalupa    43.190000    23.490000  95.700000  79.185000    72.490000   \n",
       "       Donut      39.841667    61.850000  41.450000  71.132500    50.250000   \n",
       "       Ice Cream  65.547500    59.230000  46.440000  46.265000    37.255000   \n",
       "       Sushi      40.535000    58.088333  56.181429  46.580000    78.710000   \n",
       "Male   Burger     63.892000    37.566667  49.430000  62.430000    71.046667   \n",
       "       Burrito    78.736667    41.440000  69.057500  49.180000    29.860000   \n",
       "       Chalupa    27.045000    68.702500  48.160000  66.752000    45.350000   \n",
       "       Donut      46.000000    47.677500  64.710000  45.932500    51.858000   \n",
       "       Ice Cream  59.406667    29.738000  15.170000  50.733333    64.425000   \n",
       "       Sushi      56.392500    46.821667        NaN  53.857500    77.795000   \n",
       "\n",
       "Frequency                        Never                              Often  \\\n",
       "City               Stamford   New York Philadelphia   Stamford   New York   \n",
       "Gender Item                                                                 \n",
       "Female Burger     59.622500  97.890000    54.742500  45.485000  23.740000   \n",
       "       Burrito    67.940000  47.432500    63.716667  52.334286  34.533333   \n",
       "       Chalupa    80.990000  35.150000    30.492500  52.120000  39.730000   \n",
       "       Donut      45.860000  56.070000    72.263333  52.443333  32.657500   \n",
       "       Ice Cream  41.950000  68.716667    39.007500  77.660000  58.065000   \n",
       "       Sushi      54.195000  69.330000    47.645000  19.560000  49.134286   \n",
       "Male   Burger     13.580000  90.320000     8.655000        NaN  27.735000   \n",
       "       Burrito    39.866667  28.926667    47.290000  70.368000  47.480000   \n",
       "       Chalupa    57.293333  39.818000    48.596000  46.233333  62.880000   \n",
       "       Donut      29.882500  43.926000    26.825000  54.910000  46.600000   \n",
       "       Ice Cream  74.360000  28.770000    80.485000  39.923333  69.510000   \n",
       "       Sushi      93.030000  54.670000    40.961667  68.300000   9.310000   \n",
       "\n",
       "Frequency         ...       Once     Seldom                            Weekly  \\\n",
       "City              ...   Stamford   New York Philadelphia   Stamford  New York   \n",
       "Gender Item       ...                                                           \n",
       "Female Burger     ...  31.683333  31.580000    58.435714  48.765000  92.17500   \n",
       "       Burrito    ...  56.003333  83.770000    49.527500  78.163333  13.23000   \n",
       "       Chalupa    ...  40.590000  40.000000    54.902000  58.416667  42.88000   \n",
       "       Donut      ...  79.120000  30.270000    45.812500  34.886667  71.39000   \n",
       "       Ice Cream  ...  55.866000  80.783333    50.775000  58.865000  56.90500   \n",
       "       Sushi      ...  38.950000  87.700000    62.848000  27.820000  51.36125   \n",
       "Male   Burger     ...  36.293333  75.226667    47.015000  53.250000  69.69000   \n",
       "       Burrito    ...  15.075000  67.466667    27.710000   9.840000  64.18500   \n",
       "       Chalupa    ...        NaN  11.690000    65.375000  34.804000  54.40000   \n",
       "       Donut      ...  27.978000  16.250000    33.003333  40.827500  37.22000   \n",
       "       Ice Cream  ...  36.542000  67.605000    69.220000  20.415000  44.77200   \n",
       "       Sushi      ...  62.930000  53.496667    91.260000  74.716667  50.05000   \n",
       "\n",
       "Frequency                                  Yearly                          \n",
       "City             Philadelphia Stamford   New York Philadelphia   Stamford  \n",
       "Gender Item                                                                \n",
       "Female Burger       16.000000  31.0040  64.825000    61.585000  51.171667  \n",
       "       Burrito      31.410000  46.1820  35.630000    38.916667  43.245000  \n",
       "       Chalupa      28.136667  68.2300  52.606667    56.048889  69.632000  \n",
       "       Donut        69.600000  55.0075  62.950000    58.410000  56.120000  \n",
       "       Ice Cream    47.546667  25.0060  37.917500    39.965000  15.240000  \n",
       "       Sushi        55.666667  52.5600  46.482000    48.616667  45.342500  \n",
       "Male   Burger       33.296667  77.5525  24.805000    49.340000  45.014000  \n",
       "       Burrito      48.208333  40.4625  55.175000    59.255000  32.830000  \n",
       "       Chalupa      33.920000  44.3700  55.913333    34.405000  58.095000  \n",
       "       Donut        38.600000  62.2540  35.775000    22.305000  16.520000  \n",
       "       Ice Cream    55.310000  47.8840  53.233333    47.988000  32.106667  \n",
       "       Sushi        52.066000  84.5400  39.813333    33.800000  48.710000  \n",
       "\n",
       "[12 rows x 24 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_foods.pivot_table(values=\"Spend\",index=[\"Gender\",\"Item\"],aggfunc=\"mean\",columns=[\"Frequency\",\"City\"]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "72fb3a48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th colspan=\"8\" halign=\"left\">New York</th>\n",
       "      <th colspan=\"5\" halign=\"left\">Philadelphia</th>\n",
       "      <th colspan=\"8\" halign=\"left\">Stamford</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Daily</th>\n",
       "      <th>Monthly</th>\n",
       "      <th>Never</th>\n",
       "      <th>Often</th>\n",
       "      <th>Once</th>\n",
       "      <th>Seldom</th>\n",
       "      <th>Weekly</th>\n",
       "      <th>Yearly</th>\n",
       "      <th>Daily</th>\n",
       "      <th>Monthly</th>\n",
       "      <th>...</th>\n",
       "      <th>Weekly</th>\n",
       "      <th>Yearly</th>\n",
       "      <th>Daily</th>\n",
       "      <th>Monthly</th>\n",
       "      <th>Never</th>\n",
       "      <th>Often</th>\n",
       "      <th>Once</th>\n",
       "      <th>Seldom</th>\n",
       "      <th>Weekly</th>\n",
       "      <th>Yearly</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th>Item</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">Female</th>\n",
       "      <th>Burger</th>\n",
       "      <td>43.778333</td>\n",
       "      <td>57.286667</td>\n",
       "      <td>97.890000</td>\n",
       "      <td>23.740000</td>\n",
       "      <td>52.425000</td>\n",
       "      <td>31.580000</td>\n",
       "      <td>92.17500</td>\n",
       "      <td>64.825000</td>\n",
       "      <td>77.226667</td>\n",
       "      <td>53.762500</td>\n",
       "      <td>...</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>61.585000</td>\n",
       "      <td>48.220000</td>\n",
       "      <td>59.622500</td>\n",
       "      <td>45.485000</td>\n",
       "      <td>43.635000</td>\n",
       "      <td>31.683333</td>\n",
       "      <td>48.765000</td>\n",
       "      <td>31.0040</td>\n",
       "      <td>51.171667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burrito</th>\n",
       "      <td>44.890000</td>\n",
       "      <td>40.913333</td>\n",
       "      <td>47.432500</td>\n",
       "      <td>34.533333</td>\n",
       "      <td>59.940000</td>\n",
       "      <td>83.770000</td>\n",
       "      <td>13.23000</td>\n",
       "      <td>35.630000</td>\n",
       "      <td>53.595000</td>\n",
       "      <td>17.140000</td>\n",
       "      <td>...</td>\n",
       "      <td>31.410000</td>\n",
       "      <td>38.916667</td>\n",
       "      <td>39.126000</td>\n",
       "      <td>67.940000</td>\n",
       "      <td>52.334286</td>\n",
       "      <td>63.968333</td>\n",
       "      <td>56.003333</td>\n",
       "      <td>78.163333</td>\n",
       "      <td>46.1820</td>\n",
       "      <td>43.245000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chalupa</th>\n",
       "      <td>43.190000</td>\n",
       "      <td>79.185000</td>\n",
       "      <td>35.150000</td>\n",
       "      <td>39.730000</td>\n",
       "      <td>36.933333</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>42.88000</td>\n",
       "      <td>52.606667</td>\n",
       "      <td>23.490000</td>\n",
       "      <td>72.490000</td>\n",
       "      <td>...</td>\n",
       "      <td>28.136667</td>\n",
       "      <td>56.048889</td>\n",
       "      <td>95.700000</td>\n",
       "      <td>80.990000</td>\n",
       "      <td>52.120000</td>\n",
       "      <td>59.945714</td>\n",
       "      <td>40.590000</td>\n",
       "      <td>58.416667</td>\n",
       "      <td>68.2300</td>\n",
       "      <td>69.632000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Donut</th>\n",
       "      <td>39.841667</td>\n",
       "      <td>71.132500</td>\n",
       "      <td>56.070000</td>\n",
       "      <td>32.657500</td>\n",
       "      <td>29.065000</td>\n",
       "      <td>30.270000</td>\n",
       "      <td>71.39000</td>\n",
       "      <td>62.950000</td>\n",
       "      <td>61.850000</td>\n",
       "      <td>50.250000</td>\n",
       "      <td>...</td>\n",
       "      <td>69.600000</td>\n",
       "      <td>58.410000</td>\n",
       "      <td>41.450000</td>\n",
       "      <td>45.860000</td>\n",
       "      <td>52.443333</td>\n",
       "      <td>36.423333</td>\n",
       "      <td>79.120000</td>\n",
       "      <td>34.886667</td>\n",
       "      <td>55.0075</td>\n",
       "      <td>56.120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ice Cream</th>\n",
       "      <td>65.547500</td>\n",
       "      <td>46.265000</td>\n",
       "      <td>68.716667</td>\n",
       "      <td>58.065000</td>\n",
       "      <td>35.616667</td>\n",
       "      <td>80.783333</td>\n",
       "      <td>56.90500</td>\n",
       "      <td>37.917500</td>\n",
       "      <td>59.230000</td>\n",
       "      <td>37.255000</td>\n",
       "      <td>...</td>\n",
       "      <td>47.546667</td>\n",
       "      <td>39.965000</td>\n",
       "      <td>46.440000</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>77.660000</td>\n",
       "      <td>66.103333</td>\n",
       "      <td>55.866000</td>\n",
       "      <td>58.865000</td>\n",
       "      <td>25.0060</td>\n",
       "      <td>15.240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sushi</th>\n",
       "      <td>40.535000</td>\n",
       "      <td>46.580000</td>\n",
       "      <td>69.330000</td>\n",
       "      <td>49.134286</td>\n",
       "      <td>32.358000</td>\n",
       "      <td>87.700000</td>\n",
       "      <td>51.36125</td>\n",
       "      <td>46.482000</td>\n",
       "      <td>58.088333</td>\n",
       "      <td>78.710000</td>\n",
       "      <td>...</td>\n",
       "      <td>55.666667</td>\n",
       "      <td>48.616667</td>\n",
       "      <td>56.181429</td>\n",
       "      <td>54.195000</td>\n",
       "      <td>19.560000</td>\n",
       "      <td>19.010000</td>\n",
       "      <td>38.950000</td>\n",
       "      <td>27.820000</td>\n",
       "      <td>52.5600</td>\n",
       "      <td>45.342500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">Male</th>\n",
       "      <th>Burger</th>\n",
       "      <td>63.892000</td>\n",
       "      <td>62.430000</td>\n",
       "      <td>90.320000</td>\n",
       "      <td>27.735000</td>\n",
       "      <td>59.314000</td>\n",
       "      <td>75.226667</td>\n",
       "      <td>69.69000</td>\n",
       "      <td>24.805000</td>\n",
       "      <td>37.566667</td>\n",
       "      <td>71.046667</td>\n",
       "      <td>...</td>\n",
       "      <td>33.296667</td>\n",
       "      <td>49.340000</td>\n",
       "      <td>49.430000</td>\n",
       "      <td>13.580000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.152500</td>\n",
       "      <td>36.293333</td>\n",
       "      <td>53.250000</td>\n",
       "      <td>77.5525</td>\n",
       "      <td>45.014000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burrito</th>\n",
       "      <td>78.736667</td>\n",
       "      <td>49.180000</td>\n",
       "      <td>28.926667</td>\n",
       "      <td>47.480000</td>\n",
       "      <td>59.110000</td>\n",
       "      <td>67.466667</td>\n",
       "      <td>64.18500</td>\n",
       "      <td>55.175000</td>\n",
       "      <td>41.440000</td>\n",
       "      <td>29.860000</td>\n",
       "      <td>...</td>\n",
       "      <td>48.208333</td>\n",
       "      <td>59.255000</td>\n",
       "      <td>69.057500</td>\n",
       "      <td>39.866667</td>\n",
       "      <td>70.368000</td>\n",
       "      <td>88.140000</td>\n",
       "      <td>15.075000</td>\n",
       "      <td>9.840000</td>\n",
       "      <td>40.4625</td>\n",
       "      <td>32.830000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chalupa</th>\n",
       "      <td>27.045000</td>\n",
       "      <td>66.752000</td>\n",
       "      <td>39.818000</td>\n",
       "      <td>62.880000</td>\n",
       "      <td>23.340000</td>\n",
       "      <td>11.690000</td>\n",
       "      <td>54.40000</td>\n",
       "      <td>55.913333</td>\n",
       "      <td>68.702500</td>\n",
       "      <td>45.350000</td>\n",
       "      <td>...</td>\n",
       "      <td>33.920000</td>\n",
       "      <td>34.405000</td>\n",
       "      <td>48.160000</td>\n",
       "      <td>57.293333</td>\n",
       "      <td>46.233333</td>\n",
       "      <td>70.290000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.804000</td>\n",
       "      <td>44.3700</td>\n",
       "      <td>58.095000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Donut</th>\n",
       "      <td>46.000000</td>\n",
       "      <td>45.932500</td>\n",
       "      <td>43.926000</td>\n",
       "      <td>46.600000</td>\n",
       "      <td>56.386667</td>\n",
       "      <td>16.250000</td>\n",
       "      <td>37.22000</td>\n",
       "      <td>35.775000</td>\n",
       "      <td>47.677500</td>\n",
       "      <td>51.858000</td>\n",
       "      <td>...</td>\n",
       "      <td>38.600000</td>\n",
       "      <td>22.305000</td>\n",
       "      <td>64.710000</td>\n",
       "      <td>29.882500</td>\n",
       "      <td>54.910000</td>\n",
       "      <td>80.387500</td>\n",
       "      <td>27.978000</td>\n",
       "      <td>40.827500</td>\n",
       "      <td>62.2540</td>\n",
       "      <td>16.520000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ice Cream</th>\n",
       "      <td>59.406667</td>\n",
       "      <td>50.733333</td>\n",
       "      <td>28.770000</td>\n",
       "      <td>69.510000</td>\n",
       "      <td>53.535000</td>\n",
       "      <td>67.605000</td>\n",
       "      <td>44.77200</td>\n",
       "      <td>53.233333</td>\n",
       "      <td>29.738000</td>\n",
       "      <td>64.425000</td>\n",
       "      <td>...</td>\n",
       "      <td>55.310000</td>\n",
       "      <td>47.988000</td>\n",
       "      <td>15.170000</td>\n",
       "      <td>74.360000</td>\n",
       "      <td>39.923333</td>\n",
       "      <td>58.128000</td>\n",
       "      <td>36.542000</td>\n",
       "      <td>20.415000</td>\n",
       "      <td>47.8840</td>\n",
       "      <td>32.106667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sushi</th>\n",
       "      <td>56.392500</td>\n",
       "      <td>53.857500</td>\n",
       "      <td>54.670000</td>\n",
       "      <td>9.310000</td>\n",
       "      <td>68.160000</td>\n",
       "      <td>53.496667</td>\n",
       "      <td>50.05000</td>\n",
       "      <td>39.813333</td>\n",
       "      <td>46.821667</td>\n",
       "      <td>77.795000</td>\n",
       "      <td>...</td>\n",
       "      <td>52.066000</td>\n",
       "      <td>33.800000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>93.030000</td>\n",
       "      <td>68.300000</td>\n",
       "      <td>44.220000</td>\n",
       "      <td>62.930000</td>\n",
       "      <td>74.716667</td>\n",
       "      <td>84.5400</td>\n",
       "      <td>48.710000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "City               New York                                              \\\n",
       "Frequency             Daily    Monthly      Never      Often       Once   \n",
       "Gender Item                                                               \n",
       "Female Burger     43.778333  57.286667  97.890000  23.740000  52.425000   \n",
       "       Burrito    44.890000  40.913333  47.432500  34.533333  59.940000   \n",
       "       Chalupa    43.190000  79.185000  35.150000  39.730000  36.933333   \n",
       "       Donut      39.841667  71.132500  56.070000  32.657500  29.065000   \n",
       "       Ice Cream  65.547500  46.265000  68.716667  58.065000  35.616667   \n",
       "       Sushi      40.535000  46.580000  69.330000  49.134286  32.358000   \n",
       "Male   Burger     63.892000  62.430000  90.320000  27.735000  59.314000   \n",
       "       Burrito    78.736667  49.180000  28.926667  47.480000  59.110000   \n",
       "       Chalupa    27.045000  66.752000  39.818000  62.880000  23.340000   \n",
       "       Donut      46.000000  45.932500  43.926000  46.600000  56.386667   \n",
       "       Ice Cream  59.406667  50.733333  28.770000  69.510000  53.535000   \n",
       "       Sushi      56.392500  53.857500  54.670000   9.310000  68.160000   \n",
       "\n",
       "City                                             Philadelphia             ...  \\\n",
       "Frequency            Seldom    Weekly     Yearly        Daily    Monthly  ...   \n",
       "Gender Item                                                               ...   \n",
       "Female Burger     31.580000  92.17500  64.825000    77.226667  53.762500  ...   \n",
       "       Burrito    83.770000  13.23000  35.630000    53.595000  17.140000  ...   \n",
       "       Chalupa    40.000000  42.88000  52.606667    23.490000  72.490000  ...   \n",
       "       Donut      30.270000  71.39000  62.950000    61.850000  50.250000  ...   \n",
       "       Ice Cream  80.783333  56.90500  37.917500    59.230000  37.255000  ...   \n",
       "       Sushi      87.700000  51.36125  46.482000    58.088333  78.710000  ...   \n",
       "Male   Burger     75.226667  69.69000  24.805000    37.566667  71.046667  ...   \n",
       "       Burrito    67.466667  64.18500  55.175000    41.440000  29.860000  ...   \n",
       "       Chalupa    11.690000  54.40000  55.913333    68.702500  45.350000  ...   \n",
       "       Donut      16.250000  37.22000  35.775000    47.677500  51.858000  ...   \n",
       "       Ice Cream  67.605000  44.77200  53.233333    29.738000  64.425000  ...   \n",
       "       Sushi      53.496667  50.05000  39.813333    46.821667  77.795000  ...   \n",
       "\n",
       "City                                     Stamford                        \\\n",
       "Frequency            Weekly     Yearly      Daily    Monthly      Never   \n",
       "Gender Item                                                               \n",
       "Female Burger     16.000000  61.585000  48.220000  59.622500  45.485000   \n",
       "       Burrito    31.410000  38.916667  39.126000  67.940000  52.334286   \n",
       "       Chalupa    28.136667  56.048889  95.700000  80.990000  52.120000   \n",
       "       Donut      69.600000  58.410000  41.450000  45.860000  52.443333   \n",
       "       Ice Cream  47.546667  39.965000  46.440000  41.950000  77.660000   \n",
       "       Sushi      55.666667  48.616667  56.181429  54.195000  19.560000   \n",
       "Male   Burger     33.296667  49.340000  49.430000  13.580000        NaN   \n",
       "       Burrito    48.208333  59.255000  69.057500  39.866667  70.368000   \n",
       "       Chalupa    33.920000  34.405000  48.160000  57.293333  46.233333   \n",
       "       Donut      38.600000  22.305000  64.710000  29.882500  54.910000   \n",
       "       Ice Cream  55.310000  47.988000  15.170000  74.360000  39.923333   \n",
       "       Sushi      52.066000  33.800000        NaN  93.030000  68.300000   \n",
       "\n",
       "City                                                                   \n",
       "Frequency             Often       Once     Seldom   Weekly     Yearly  \n",
       "Gender Item                                                            \n",
       "Female Burger     43.635000  31.683333  48.765000  31.0040  51.171667  \n",
       "       Burrito    63.968333  56.003333  78.163333  46.1820  43.245000  \n",
       "       Chalupa    59.945714  40.590000  58.416667  68.2300  69.632000  \n",
       "       Donut      36.423333  79.120000  34.886667  55.0075  56.120000  \n",
       "       Ice Cream  66.103333  55.866000  58.865000  25.0060  15.240000  \n",
       "       Sushi      19.010000  38.950000  27.820000  52.5600  45.342500  \n",
       "Male   Burger     48.152500  36.293333  53.250000  77.5525  45.014000  \n",
       "       Burrito    88.140000  15.075000   9.840000  40.4625  32.830000  \n",
       "       Chalupa    70.290000        NaN  34.804000  44.3700  58.095000  \n",
       "       Donut      80.387500  27.978000  40.827500  62.2540  16.520000  \n",
       "       Ice Cream  58.128000  36.542000  20.415000  47.8840  32.106667  \n",
       "       Sushi      44.220000  62.930000  74.716667  84.5400  48.710000  \n",
       "\n",
       "[12 rows x 24 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(df_foods,values=\"Spend\",index=[\"Gender\",\"Item\"],aggfunc=\"mean\",columns=[\"City\",\"Frequency\"])\n",
    "#df ile aynı pandasınki ama\n",
    "#ilk parametreye hangi df üzerinde işlem yapacağımızı belirtmeliyiz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "a2874649",
   "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>Salesman</th>\n",
       "      <th>Q1</th>\n",
       "      <th>Q2</th>\n",
       "      <th>Q3</th>\n",
       "      <th>Q4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Boris</td>\n",
       "      <td>602908</td>\n",
       "      <td>233879</td>\n",
       "      <td>354479</td>\n",
       "      <td>32704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>43790</td>\n",
       "      <td>514863</td>\n",
       "      <td>297151</td>\n",
       "      <td>544493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tommy</td>\n",
       "      <td>392668</td>\n",
       "      <td>113579</td>\n",
       "      <td>430882</td>\n",
       "      <td>247231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Travis</td>\n",
       "      <td>834663</td>\n",
       "      <td>266785</td>\n",
       "      <td>749238</td>\n",
       "      <td>570524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Donald</td>\n",
       "      <td>580935</td>\n",
       "      <td>411379</td>\n",
       "      <td>110390</td>\n",
       "      <td>651572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Ted</td>\n",
       "      <td>656644</td>\n",
       "      <td>70803</td>\n",
       "      <td>375948</td>\n",
       "      <td>321388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jeb</td>\n",
       "      <td>486141</td>\n",
       "      <td>600753</td>\n",
       "      <td>742716</td>\n",
       "      <td>404995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Stacy</td>\n",
       "      <td>479662</td>\n",
       "      <td>742806</td>\n",
       "      <td>770712</td>\n",
       "      <td>2501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Morgan</td>\n",
       "      <td>992673</td>\n",
       "      <td>879183</td>\n",
       "      <td>37945</td>\n",
       "      <td>293710</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Salesman      Q1      Q2      Q3      Q4\n",
       "0    Boris  602908  233879  354479   32704\n",
       "1      Bob   43790  514863  297151  544493\n",
       "2    Tommy  392668  113579  430882  247231\n",
       "3   Travis  834663  266785  749238  570524\n",
       "4   Donald  580935  411379  110390  651572\n",
       "5      Ted  656644   70803  375948  321388\n",
       "6      Jeb  486141  600753  742716  404995\n",
       "7    Stacy  479662  742806  770712    2501\n",
       "8   Morgan  992673  879183   37945  293710"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd_quarters=pd.read_csv(\"resources/resources/quarters.csv\")\n",
    "pd_quarters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "9e67be37",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Salesman</th>\n",
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       "      <th>4</th>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Morgan</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Boris</td>\n",
       "      <td>Q2</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Bob</td>\n",
       "      <td>Q2</td>\n",
       "      <td>514863</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Tommy</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Donald</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Jeb</td>\n",
       "      <td>Q2</td>\n",
       "      <td>600753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Stacy</td>\n",
       "      <td>Q2</td>\n",
       "      <td>742806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Morgan</td>\n",
       "      <td>Q2</td>\n",
       "      <td>879183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Boris</td>\n",
       "      <td>Q3</td>\n",
       "      <td>354479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Bob</td>\n",
       "      <td>Q3</td>\n",
       "      <td>297151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Tommy</td>\n",
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       "    <tr>\n",
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       "      <td>749238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Donald</td>\n",
       "      <td>Q3</td>\n",
       "      <td>110390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Ted</td>\n",
       "      <td>Q3</td>\n",
       "      <td>375948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Jeb</td>\n",
       "      <td>Q3</td>\n",
       "      <td>742716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Stacy</td>\n",
       "      <td>Q3</td>\n",
       "      <td>770712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Morgan</td>\n",
       "      <td>Q3</td>\n",
       "      <td>37945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Boris</td>\n",
       "      <td>Q4</td>\n",
       "      <td>32704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Bob</td>\n",
       "      <td>Q4</td>\n",
       "      <td>544493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Tommy</td>\n",
       "      <td>Q4</td>\n",
       "      <td>247231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Travis</td>\n",
       "      <td>Q4</td>\n",
       "      <td>570524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Donald</td>\n",
       "      <td>Q4</td>\n",
       "      <td>651572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Ted</td>\n",
       "      <td>Q4</td>\n",
       "      <td>321388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Jeb</td>\n",
       "      <td>Q4</td>\n",
       "      <td>404995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Stacy</td>\n",
       "      <td>Q4</td>\n",
       "      <td>2501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Morgan</td>\n",
       "      <td>Q4</td>\n",
       "      <td>293710</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Salesman variable   value\n",
       "0     Boris       Q1  602908\n",
       "1       Bob       Q1   43790\n",
       "2     Tommy       Q1  392668\n",
       "3    Travis       Q1  834663\n",
       "4    Donald       Q1  580935\n",
       "5       Ted       Q1  656644\n",
       "6       Jeb       Q1  486141\n",
       "7     Stacy       Q1  479662\n",
       "8    Morgan       Q1  992673\n",
       "9     Boris       Q2  233879\n",
       "10      Bob       Q2  514863\n",
       "11    Tommy       Q2  113579\n",
       "12   Travis       Q2  266785\n",
       "13   Donald       Q2  411379\n",
       "14      Ted       Q2   70803\n",
       "15      Jeb       Q2  600753\n",
       "16    Stacy       Q2  742806\n",
       "17   Morgan       Q2  879183\n",
       "18    Boris       Q3  354479\n",
       "19      Bob       Q3  297151\n",
       "20    Tommy       Q3  430882\n",
       "21   Travis       Q3  749238\n",
       "22   Donald       Q3  110390\n",
       "23      Ted       Q3  375948\n",
       "24      Jeb       Q3  742716\n",
       "25    Stacy       Q3  770712\n",
       "26   Morgan       Q3   37945\n",
       "27    Boris       Q4   32704\n",
       "28      Bob       Q4  544493\n",
       "29    Tommy       Q4  247231\n",
       "30   Travis       Q4  570524\n",
       "31   Donald       Q4  651572\n",
       "32      Ted       Q4  321388\n",
       "33      Jeb       Q4  404995\n",
       "34    Stacy       Q4    2501\n",
       "35   Morgan       Q4  293710"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.melt(pd_quarters,id_vars=\"Salesman\")  \n",
    "#melt bir pivotu normal tablo haline çevirmemizi sağlar "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "46a3142a",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Salesman</th>\n",
       "      <th>Quarter</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Boris</td>\n",
       "      <td>Q1</td>\n",
       "      <td>602908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>Q1</td>\n",
       "      <td>43790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tommy</td>\n",
       "      <td>Q1</td>\n",
       "      <td>392668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Travis</td>\n",
       "      <td>Q1</td>\n",
       "      <td>834663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Donald</td>\n",
       "      <td>Q1</td>\n",
       "      <td>580935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Ted</td>\n",
       "      <td>Q1</td>\n",
       "      <td>656644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jeb</td>\n",
       "      <td>Q1</td>\n",
       "      <td>486141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Stacy</td>\n",
       "      <td>Q1</td>\n",
       "      <td>479662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Morgan</td>\n",
       "      <td>Q1</td>\n",
       "      <td>992673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Boris</td>\n",
       "      <td>Q2</td>\n",
       "      <td>233879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Bob</td>\n",
       "      <td>Q2</td>\n",
       "      <td>514863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Tommy</td>\n",
       "      <td>Q2</td>\n",
       "      <td>113579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Travis</td>\n",
       "      <td>Q2</td>\n",
       "      <td>266785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Donald</td>\n",
       "      <td>Q2</td>\n",
       "      <td>411379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Ted</td>\n",
       "      <td>Q2</td>\n",
       "      <td>70803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Jeb</td>\n",
       "      <td>Q2</td>\n",
       "      <td>600753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Stacy</td>\n",
       "      <td>Q2</td>\n",
       "      <td>742806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Morgan</td>\n",
       "      <td>Q2</td>\n",
       "      <td>879183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Boris</td>\n",
       "      <td>Q3</td>\n",
       "      <td>354479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Bob</td>\n",
       "      <td>Q3</td>\n",
       "      <td>297151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Tommy</td>\n",
       "      <td>Q3</td>\n",
       "      <td>430882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Travis</td>\n",
       "      <td>Q3</td>\n",
       "      <td>749238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Donald</td>\n",
       "      <td>Q3</td>\n",
       "      <td>110390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Ted</td>\n",
       "      <td>Q3</td>\n",
       "      <td>375948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Jeb</td>\n",
       "      <td>Q3</td>\n",
       "      <td>742716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Stacy</td>\n",
       "      <td>Q3</td>\n",
       "      <td>770712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Morgan</td>\n",
       "      <td>Q3</td>\n",
       "      <td>37945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Boris</td>\n",
       "      <td>Q4</td>\n",
       "      <td>32704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Bob</td>\n",
       "      <td>Q4</td>\n",
       "      <td>544493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Tommy</td>\n",
       "      <td>Q4</td>\n",
       "      <td>247231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Travis</td>\n",
       "      <td>Q4</td>\n",
       "      <td>570524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Donald</td>\n",
       "      <td>Q4</td>\n",
       "      <td>651572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Ted</td>\n",
       "      <td>Q4</td>\n",
       "      <td>321388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Jeb</td>\n",
       "      <td>Q4</td>\n",
       "      <td>404995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Stacy</td>\n",
       "      <td>Q4</td>\n",
       "      <td>2501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Morgan</td>\n",
       "      <td>Q4</td>\n",
       "      <td>293710</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Salesman Quarter  Revenue\n",
       "0     Boris      Q1   602908\n",
       "1       Bob      Q1    43790\n",
       "2     Tommy      Q1   392668\n",
       "3    Travis      Q1   834663\n",
       "4    Donald      Q1   580935\n",
       "5       Ted      Q1   656644\n",
       "6       Jeb      Q1   486141\n",
       "7     Stacy      Q1   479662\n",
       "8    Morgan      Q1   992673\n",
       "9     Boris      Q2   233879\n",
       "10      Bob      Q2   514863\n",
       "11    Tommy      Q2   113579\n",
       "12   Travis      Q2   266785\n",
       "13   Donald      Q2   411379\n",
       "14      Ted      Q2    70803\n",
       "15      Jeb      Q2   600753\n",
       "16    Stacy      Q2   742806\n",
       "17   Morgan      Q2   879183\n",
       "18    Boris      Q3   354479\n",
       "19      Bob      Q3   297151\n",
       "20    Tommy      Q3   430882\n",
       "21   Travis      Q3   749238\n",
       "22   Donald      Q3   110390\n",
       "23      Ted      Q3   375948\n",
       "24      Jeb      Q3   742716\n",
       "25    Stacy      Q3   770712\n",
       "26   Morgan      Q3    37945\n",
       "27    Boris      Q4    32704\n",
       "28      Bob      Q4   544493\n",
       "29    Tommy      Q4   247231\n",
       "30   Travis      Q4   570524\n",
       "31   Donald      Q4   651572\n",
       "32      Ted      Q4   321388\n",
       "33      Jeb      Q4   404995\n",
       "34    Stacy      Q4     2501\n",
       "35   Morgan      Q4   293710"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.melt(pd_quarters,id_vars=\"Salesman\",var_name=\"Quarter\",value_name=\"Revenue\")\n",
    "#var name e ve value name e isim verdik, üstte böyle bir şey yapmamıştık\n",
    "#melt bir pivotu normal tablo haline çevirmemizi sağlar "
   ]
  }
 ],
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