01_Veri_Cercevesi_Olustur.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4c02393b",
   "metadata": {},
   "source": [
    "# Veri Çerçevesi (Data Frame) Oluştur\n",
    "\n",
    "Bu bölümde sıfırdan **Veri Çerçevesi** (**Data Frame**) oluşturmayı ya da harici kaynaktan **(MS Excel, LibreOfis Calc, CSV, JSON, URL ve Pano'dan)** verileri okuyarak veri çerçevesine dönüştürmeyi öğreneceğiz.\n",
    "\n",
    "## import Fonksiyonu\n",
    "\n",
    "Öncelikle **Pandas** Kütüphanesini projemize dahil edip (içe aktarıp), kodlama esnasında hızlı olması adına bu kütüphaneye **pd** adını atayalım;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dbd7eb3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "368c5e8b",
   "metadata": {},
   "source": [
    "## DataFrame() Fonksiyonu\n",
    "\n",
    "Veri Çerçevesi (Data Frame) Oluşturmak ya da Dönüştürmek için **DataFrame()** fonksiyonunu kullanıyoruz. Veri Çerçevesine dönüştürmek istediğimiz veriyi, parantez içine, parametre olarak yazmalıyız.\n",
    "\n",
    "***pd.DataFrame(dosya, iterable veri ya da sözlük yapısı)***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f9a3f80",
   "metadata": {},
   "source": [
    "örneğin bir **sözlük (dict)** yapısı oluşturup bu yapıyı **Veri Çerçevesi**ne (**Data Frame**'e) dönüştürelim;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f18449e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "sozluk = {\"isim\" : [\"Mustafa\", \"Halil\", \"Burak\", \"Emre\", \"Ersin\", \"Sertaç\", \"Furkan\",\"Murat\",\"Ahmet\",\"Abdülkadir\"],\n",
    "                    \"yaş\" : [25, 38, 41, 23, 37, 52, 30, 23, 40, 38],\n",
    "                   \"iş-meslek\" : [\"mühendis\", \"programcı\", \"akademisyen\", \"yönetici\",\"amir\",\"mühendis\", \"yönetici\",\"müdür\",\"veteriner\",\"yönetici\"]}\n",
    "veri = pd.DataFrame(sozluk)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81909669",
   "metadata": {},
   "source": [
    "Oluşturduğumuz Veri Çerçevesinin içeriğini görelim;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fe072612",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>isim</th>\n",
       "      <th>yaş</th>\n",
       "      <th>iş-meslek</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mustafa</td>\n",
       "      <td>25</td>\n",
       "      <td>mühendis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Halil</td>\n",
       "      <td>38</td>\n",
       "      <td>programcı</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Burak</td>\n",
       "      <td>41</td>\n",
       "      <td>akademisyen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Emre</td>\n",
       "      <td>23</td>\n",
       "      <td>yönetici</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ersin</td>\n",
       "      <td>37</td>\n",
       "      <td>amir</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Sertaç</td>\n",
       "      <td>52</td>\n",
       "      <td>mühendis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Furkan</td>\n",
       "      <td>30</td>\n",
       "      <td>yönetici</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Murat</td>\n",
       "      <td>23</td>\n",
       "      <td>müdür</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Ahmet</td>\n",
       "      <td>40</td>\n",
       "      <td>veteriner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Abdülkadir</td>\n",
       "      <td>38</td>\n",
       "      <td>yönetici</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         isim  yaş    iş-meslek\n",
       "0     Mustafa   25     mühendis\n",
       "1       Halil   38    programcı\n",
       "2       Burak   41  akademisyen\n",
       "3        Emre   23     yönetici\n",
       "4       Ersin   37         amir\n",
       "5      Sertaç   52     mühendis\n",
       "6      Furkan   30     yönetici\n",
       "7       Murat   23        müdür\n",
       "8       Ahmet   40    veteriner\n",
       "9  Abdülkadir   38     yönetici"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veri"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe63b0b7",
   "metadata": {},
   "source": [
    "Gördüğünüz gibi verimiz, **DataFrame()** fonksiyonu ile, SQL ya da Excel tablosuna benzer şekilde satır ve sütunlardan oluşan yapıya dönüştürüldü. Artık bu yapıyı yönetmek ve analiz etmek oldukça kolaylaşmış oldu."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4728aa4",
   "metadata": {},
   "source": [
    "Örnek olması açısından, bir de liste (list) veri yapısındaki değerlerin Veri Çerçevesine (Data Frame'e) nasıl dönüştürüldüğünü görelim;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2c61b906",
   "metadata": {},
   "outputs": [],
   "source": [
    "veri1 = [\"Kerem\", 14, \"öğrenci\"]\n",
    "df = pd.DataFrame([veri1], columns=[\"isim\", \"yaş\", \"meslek\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c369e6d7",
   "metadata": {},
   "source": [
    "DataFrame() metodunun içine yazdığımız liste veri yapısının, ayrı bir liste içinde belirtildiğine dikkat edin.\n",
    "\n",
    "Aynı kodu aşağıdaki şekilde yazarak ta aynı sonuca ulaşabiliriz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b9f526df",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame([[\"Kerem\", 14, \"öğrenci\"]], columns=[\"isim\", \"yaş\", \"meslek\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a74efa5d",
   "metadata": {},
   "source": [
    "Oluşturduğumuz Veri Çerçevesinin içeriğini görelim;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b9ea87bd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>isim</th>\n",
       "      <th>yaş</th>\n",
       "      <th>meslek</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kerem</td>\n",
       "      <td>14</td>\n",
       "      <td>öğrenci</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    isim  yaş   meslek\n",
       "0  Kerem   14  öğrenci"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba254d2f",
   "metadata": {},
   "source": [
    "Birden fazla satır kaydı oluşturmak için, aşağıdaki şekilde liste yapısını kullanabilirsiniz;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1e452e39",
   "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>isim</th>\n",
       "      <th>yaş</th>\n",
       "      <th>meslek</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kerem</td>\n",
       "      <td>23</td>\n",
       "      <td>öğrenci</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Safa</td>\n",
       "      <td>18</td>\n",
       "      <td>öğrenci</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    isim  yaş   meslek\n",
       "0  Kerem   23  öğrenci\n",
       "1   Safa   18  öğrenci"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame([[\"Kerem\", 23, \"öğrenci\"], [\"Safa\",18,\"öğrenci\"]], columns=[\"isim\", \"yaş\", \"meslek\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58b0d168",
   "metadata": {},
   "source": [
    "# Harici Dosyalardan Veri Çerçevesi Oluştur\n",
    "\n",
    "## CSV Dosyasını İçe Aktarmak\n",
    "\n",
    "## read_csv() Fonksiyonu\n",
    "\n",
    "csv uzantılı dosyaların içeriğini çalışmalarımıza eklemek/dahil etmek istersek kullanabileceğimiz fonksiyon, **read_csv()** fonksiyonudur.\n",
    "\n",
    "örneğin **nba.csv** isimli dosyasınının içeriğini çalışmamıza aktaralıp, veri çerçevesine dönüştürelim;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8a4f6de8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Team</th>\n",
       "      <th>Number</th>\n",
       "      <th>Position</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>College</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avery Bradley</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-2</td>\n",
       "      <td>180.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>7730337.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jae Crowder</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>99.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-6</td>\n",
       "      <td>235.0</td>\n",
       "      <td>Marquette</td>\n",
       "      <td>6796117.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John Holland</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>30.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>205.0</td>\n",
       "      <td>Boston University</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R.J. Hunter</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>28.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>185.0</td>\n",
       "      <td>Georgia State</td>\n",
       "      <td>1148640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jonas Jerebko</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>29.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>231.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>Shelvin Mack</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6-3</td>\n",
       "      <td>203.0</td>\n",
       "      <td>Butler</td>\n",
       "      <td>2433333.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>Raul Neto</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>25.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6-1</td>\n",
       "      <td>179.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>900000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>Tibor Pleiss</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>21.0</td>\n",
       "      <td>C</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7-3</td>\n",
       "      <td>256.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2900000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>Jeff Withey</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>24.0</td>\n",
       "      <td>C</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7-0</td>\n",
       "      <td>231.0</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>947276.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>458 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Name            Team  Number Position   Age Height  Weight  \\\n",
       "0    Avery Bradley  Boston Celtics     0.0       PG  25.0    6-2   180.0   \n",
       "1      Jae Crowder  Boston Celtics    99.0       SF  25.0    6-6   235.0   \n",
       "2     John Holland  Boston Celtics    30.0       SG  27.0    6-5   205.0   \n",
       "3      R.J. Hunter  Boston Celtics    28.0       SG  22.0    6-5   185.0   \n",
       "4    Jonas Jerebko  Boston Celtics     8.0       PF  29.0   6-10   231.0   \n",
       "..             ...             ...     ...      ...   ...    ...     ...   \n",
       "453   Shelvin Mack       Utah Jazz     8.0       PG  26.0    6-3   203.0   \n",
       "454      Raul Neto       Utah Jazz    25.0       PG  24.0    6-1   179.0   \n",
       "455   Tibor Pleiss       Utah Jazz    21.0        C  26.0    7-3   256.0   \n",
       "456    Jeff Withey       Utah Jazz    24.0        C  26.0    7-0   231.0   \n",
       "457            NaN             NaN     NaN      NaN   NaN    NaN     NaN   \n",
       "\n",
       "               College     Salary  \n",
       "0                Texas  7730337.0  \n",
       "1            Marquette  6796117.0  \n",
       "2    Boston University        NaN  \n",
       "3        Georgia State  1148640.0  \n",
       "4                  NaN  5000000.0  \n",
       "..                 ...        ...  \n",
       "453             Butler  2433333.0  \n",
       "454                NaN   900000.0  \n",
       "455                NaN  2900000.0  \n",
       "456             Kansas   947276.0  \n",
       "457                NaN        NaN  \n",
       "\n",
       "[458 rows x 9 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "nba_csv = pd.read_csv(\"Veri_Setleri/nba.csv\")\n",
    "nba_csv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b766b91c",
   "metadata": {},
   "source": [
    "## index_col Parametresi\n",
    "\n",
    "Harici kaynaktan veri alarak oluşturulan veri çerçevesinin indeks değerini, istediğimiz sütuna eşitleyebiliriz. Bunu yapmak için **index_col** parametresini kullanmalıyız. Örneğin NBA oyuncularının verileri barındıran CSV uzantılı dosyanın **Name** Sütununu, veri çerçevemizin **indeks sütunu** haline getirip veri çerçevemize göz atalım."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f2d89e64",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>Team</th>\n",
       "      <th>Number</th>\n",
       "      <th>Position</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>College</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</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>Avery Bradley</th>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-2</td>\n",
       "      <td>180.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>7730337.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jae Crowder</th>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>99.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-6</td>\n",
       "      <td>235.0</td>\n",
       "      <td>Marquette</td>\n",
       "      <td>6796117.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>John Holland</th>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>30.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>205.0</td>\n",
       "      <td>Boston University</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R.J. Hunter</th>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>28.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>185.0</td>\n",
       "      <td>Georgia State</td>\n",
       "      <td>1148640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jonas Jerebko</th>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>29.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>231.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5000000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Team  Number Position   Age Height  Weight  \\\n",
       "Name                                                                  \n",
       "Avery Bradley  Boston Celtics     0.0       PG  25.0    6-2   180.0   \n",
       "Jae Crowder    Boston Celtics    99.0       SF  25.0    6-6   235.0   \n",
       "John Holland   Boston Celtics    30.0       SG  27.0    6-5   205.0   \n",
       "R.J. Hunter    Boston Celtics    28.0       SG  22.0    6-5   185.0   \n",
       "Jonas Jerebko  Boston Celtics     8.0       PF  29.0   6-10   231.0   \n",
       "\n",
       "                         College     Salary  \n",
       "Name                                         \n",
       "Avery Bradley              Texas  7730337.0  \n",
       "Jae Crowder            Marquette  6796117.0  \n",
       "John Holland   Boston University        NaN  \n",
       "R.J. Hunter        Georgia State  1148640.0  \n",
       "Jonas Jerebko                NaN  5000000.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba_csv = pd.read_csv(\"Veri_Setleri/nba.csv\", index_col=\"Name\")\n",
    "nba_csv.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f43f575",
   "metadata": {},
   "source": [
    "Gördüğünüz gibi artık **indeks bilgileri**, **Oyuncu isimleri**ne dönüşmüş oldu.\n",
    "\n",
    "## read_table() Fonksiyonu\n",
    "\n",
    "**read_table()** fonksiyonu ile **csv** uzantılı dosyalar içe aktarıldığında, dosyanın her satırı, tablonun bir sütununa yazılır. Yani tabloda 2 sütun oluşturulur birinde index bilgisi, ikinci sütunda ise **csv** dosyasının satırlarındaki bilgiler bulunur."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "465d4c90",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Name,Team,Number,Position,Age,Height,Weight,College,Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avery Bradley,Boston Celtics,0.0,PG,25.0,6-2,1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jae Crowder,Boston Celtics,99.0,SF,25.0,6-6,23...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John Holland,Boston Celtics,30.0,SG,27.0,6-5,2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R.J. Hunter,Boston Celtics,28.0,SG,22.0,6-5,18...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jonas Jerebko,Boston Celtics,8.0,PF,29.0,6-10,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>Shelvin Mack,Utah Jazz,8.0,PG,26.0,6-3,203.0,B...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>Raul Neto,Utah Jazz,25.0,PG,24.0,6-1,179.0,,90...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>Tibor Pleiss,Utah Jazz,21.0,C,26.0,7-3,256.0,,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>Jeff Withey,Utah Jazz,24.0,C,26.0,7-0,231.0,Ka...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>,,,,,,,,</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>458 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name,Team,Number,Position,Age,Height,Weight,College,Salary\n",
       "0    Avery Bradley,Boston Celtics,0.0,PG,25.0,6-2,1...        \n",
       "1    Jae Crowder,Boston Celtics,99.0,SF,25.0,6-6,23...        \n",
       "2    John Holland,Boston Celtics,30.0,SG,27.0,6-5,2...        \n",
       "3    R.J. Hunter,Boston Celtics,28.0,SG,22.0,6-5,18...        \n",
       "4    Jonas Jerebko,Boston Celtics,8.0,PF,29.0,6-10,...        \n",
       "..                                                 ...        \n",
       "453  Shelvin Mack,Utah Jazz,8.0,PG,26.0,6-3,203.0,B...        \n",
       "454  Raul Neto,Utah Jazz,25.0,PG,24.0,6-1,179.0,,90...        \n",
       "455  Tibor Pleiss,Utah Jazz,21.0,C,26.0,7-3,256.0,,...        \n",
       "456  Jeff Withey,Utah Jazz,24.0,C,26.0,7-0,231.0,Ka...        \n",
       "457                                           ,,,,,,,,        \n",
       "\n",
       "[458 rows x 1 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba = pd.read_table(\"Veri_Setleri/nba.csv\")\n",
    "nba"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2168dd3",
   "metadata": {},
   "source": [
    "## delimiter Parametresi\n",
    "\n",
    "CSV dosyaları **read_table()** fonksiyonu ile içe aktarılırken **delimiter = \",\"** parametresi eklenirse, dosya içeriği, **read_csv** fonksiyonu ile aynı çıktıyı verir. Satırlardaki veriler, **\",\"** virgül kısımlarından ayrılarak, ayrı sütunlara yazılır."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cd2f0d50",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>Team</th>\n",
       "      <th>Number</th>\n",
       "      <th>Position</th>\n",
       "      <th>Age</th>\n",
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       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avery Bradley</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-2</td>\n",
       "      <td>180.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>7730337.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jae Crowder</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>99.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-6</td>\n",
       "      <td>235.0</td>\n",
       "      <td>Marquette</td>\n",
       "      <td>6796117.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John Holland</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>30.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>205.0</td>\n",
       "      <td>Boston University</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R.J. Hunter</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>28.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>185.0</td>\n",
       "      <td>Georgia State</td>\n",
       "      <td>1148640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jonas Jerebko</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>29.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>231.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>Shelvin Mack</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6-3</td>\n",
       "      <td>203.0</td>\n",
       "      <td>Butler</td>\n",
       "      <td>2433333.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>Raul Neto</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>25.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6-1</td>\n",
       "      <td>179.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>900000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>Tibor Pleiss</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>21.0</td>\n",
       "      <td>C</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7-3</td>\n",
       "      <td>256.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2900000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>Jeff Withey</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>24.0</td>\n",
       "      <td>C</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7-0</td>\n",
       "      <td>231.0</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>947276.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>458 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Name            Team  Number Position   Age Height  Weight  \\\n",
       "0    Avery Bradley  Boston Celtics     0.0       PG  25.0    6-2   180.0   \n",
       "1      Jae Crowder  Boston Celtics    99.0       SF  25.0    6-6   235.0   \n",
       "2     John Holland  Boston Celtics    30.0       SG  27.0    6-5   205.0   \n",
       "3      R.J. Hunter  Boston Celtics    28.0       SG  22.0    6-5   185.0   \n",
       "4    Jonas Jerebko  Boston Celtics     8.0       PF  29.0   6-10   231.0   \n",
       "..             ...             ...     ...      ...   ...    ...     ...   \n",
       "453   Shelvin Mack       Utah Jazz     8.0       PG  26.0    6-3   203.0   \n",
       "454      Raul Neto       Utah Jazz    25.0       PG  24.0    6-1   179.0   \n",
       "455   Tibor Pleiss       Utah Jazz    21.0        C  26.0    7-3   256.0   \n",
       "456    Jeff Withey       Utah Jazz    24.0        C  26.0    7-0   231.0   \n",
       "457            NaN             NaN     NaN      NaN   NaN    NaN     NaN   \n",
       "\n",
       "               College     Salary  \n",
       "0                Texas  7730337.0  \n",
       "1            Marquette  6796117.0  \n",
       "2    Boston University        NaN  \n",
       "3        Georgia State  1148640.0  \n",
       "4                  NaN  5000000.0  \n",
       "..                 ...        ...  \n",
       "453             Butler  2433333.0  \n",
       "454                NaN   900000.0  \n",
       "455                NaN  2900000.0  \n",
       "456             Kansas   947276.0  \n",
       "457                NaN        NaN  \n",
       "\n",
       "[458 rows x 9 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba = pd.read_table(\"Veri_Setleri/nba.csv\", delimiter=\",\")\n",
    "nba"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b7edb0c",
   "metadata": {},
   "source": [
    "## Excel dosyasını içe aktarmak\n",
    "\n",
    "## read_excel() Fonksiyonu\n",
    "\n",
    "**MS Excel** ve **Libre Ofis Calc** programlarıyla oluşturulmuş tabloları çalışmamıza/projemize dahil etmek için **read_excel()** fonksiyonunu kullanmalıyız.\n",
    "\n",
    "<u>Bu fonksiyon;</u>\n",
    "\n",
    "- **xls, xlsx, xlsm, xlsb, odf, ods** ve odt **uzantılı** dosyaları destekler\n",
    "  \n",
    "- Yerel bir dosya sisteminde veya bir URL'den depolanan excel, calc dosyalarını yükleyebilir.\n",
    "  \n",
    "- Bir tek çalışma sayfasından veya bir sayfa listesinden içerik okumayı da destekler.\n",
    "  \n",
    "- İki sayfa okunurken, **DataFrame** Sözlük (Dict) yapısına  dönüşür.\n",
    "\n",
    "<code>pd.read_excel(dosya_adi)</code>\n",
    "\n",
    "Bu fonksiyon kullanıldığında, varsayılan olarak, excel dosyasının **ilk çalışma sayfası** yüklenir ve bu sayfanın **ilk satır**, bir Veri Çerçevesi **başlığı (sütun adı)** olarak ayarlanır."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "69b12f97",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Yıl</th>\n",
       "      <th>Toplam</th>\n",
       "      <th>Ocak</th>\n",
       "      <th>Şubat</th>\n",
       "      <th>Mart</th>\n",
       "      <th>Nisan</th>\n",
       "      <th>Mayıs</th>\n",
       "      <th>Haziran</th>\n",
       "      <th>Temmuz</th>\n",
       "      <th>Ağustos</th>\n",
       "      <th>Eylül</th>\n",
       "      <th>Ekim</th>\n",
       "      <th>Kasım</th>\n",
       "      <th>Aralık</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2001</td>\n",
       "      <td>1323341</td>\n",
       "      <td>170397</td>\n",
       "      <td>103476</td>\n",
       "      <td>107912</td>\n",
       "      <td>102585</td>\n",
       "      <td>110391</td>\n",
       "      <td>111722</td>\n",
       "      <td>119752</td>\n",
       "      <td>120963</td>\n",
       "      <td>109590</td>\n",
       "      <td>103662</td>\n",
       "      <td>92554</td>\n",
       "      <td>70337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2002</td>\n",
       "      <td>1229555</td>\n",
       "      <td>155065</td>\n",
       "      <td>103446</td>\n",
       "      <td>102175</td>\n",
       "      <td>95976</td>\n",
       "      <td>99501</td>\n",
       "      <td>102627</td>\n",
       "      <td>109747</td>\n",
       "      <td>108061</td>\n",
       "      <td>99701</td>\n",
       "      <td>96216</td>\n",
       "      <td>89285</td>\n",
       "      <td>67755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2003</td>\n",
       "      <td>1198927</td>\n",
       "      <td>138670</td>\n",
       "      <td>89548</td>\n",
       "      <td>101046</td>\n",
       "      <td>92574</td>\n",
       "      <td>99531</td>\n",
       "      <td>104644</td>\n",
       "      <td>109225</td>\n",
       "      <td>109159</td>\n",
       "      <td>98766</td>\n",
       "      <td>94838</td>\n",
       "      <td>89542</td>\n",
       "      <td>71384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2004</td>\n",
       "      <td>1222484</td>\n",
       "      <td>141538</td>\n",
       "      <td>94596</td>\n",
       "      <td>100696</td>\n",
       "      <td>100801</td>\n",
       "      <td>102214</td>\n",
       "      <td>105728</td>\n",
       "      <td>111102</td>\n",
       "      <td>110425</td>\n",
       "      <td>98492</td>\n",
       "      <td>94840</td>\n",
       "      <td>90833</td>\n",
       "      <td>71219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2005</td>\n",
       "      <td>1244041</td>\n",
       "      <td>142311</td>\n",
       "      <td>94234</td>\n",
       "      <td>100529</td>\n",
       "      <td>97441</td>\n",
       "      <td>106833</td>\n",
       "      <td>108536</td>\n",
       "      <td>111066</td>\n",
       "      <td>111430</td>\n",
       "      <td>103273</td>\n",
       "      <td>103310</td>\n",
       "      <td>92364</td>\n",
       "      <td>72714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Yıl   Toplam    Ocak   Şubat    Mart   Nisan   Mayıs  Haziran  Temmuz  \\\n",
       "0  2001  1323341  170397  103476  107912  102585  110391   111722  119752   \n",
       "1  2002  1229555  155065  103446  102175   95976   99501   102627  109747   \n",
       "2  2003  1198927  138670   89548  101046   92574   99531   104644  109225   \n",
       "3  2004  1222484  141538   94596  100696  100801  102214   105728  111102   \n",
       "4  2005  1244041  142311   94234  100529   97441  106833   108536  111066   \n",
       "\n",
       "   Ağustos   Eylül    Ekim  Kasım  Aralık  \n",
       "0   120963  109590  103662  92554   70337  \n",
       "1   108061   99701   96216  89285   67755  \n",
       "2   109159   98766   94838  89542   71384  \n",
       "3   110425   98492   94840  90833   71219  \n",
       "4   111430  103273  103310  92364   72714  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogumlar = pd.read_excel(\"Veri_Setleri/AyaGöreDoğumlar.xlsx\")\n",
    "dogumlar.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a6f5445",
   "metadata": {},
   "source": [
    "**MS Excel** ve **Lire Ofis Calc** tabloları ile çalışma konusunu ayrı sayfada detaylı olarak açıkladık. İlgili bağlantıya [buradan](Excel_Dosyasi_ile_Calis.ipynb) erişebilirsiniz.\n",
    "\n",
    "## JSON dosyasını içe aktarmak\n",
    "\n",
    "## read_json() Fonksiyonu\n",
    "\n",
    "**read_json()** fonksiyonu ile **json** uzantılı dosyaları çalışmalarınıza dahil edebilirsiniz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d24bd61c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Duration</th>\n",
       "      <th>Pulse</th>\n",
       "      <th>Maxpulse</th>\n",
       "      <th>Calories</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60</td>\n",
       "      <td>110</td>\n",
       "      <td>130</td>\n",
       "      <td>409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>60</td>\n",
       "      <td>117</td>\n",
       "      <td>145</td>\n",
       "      <td>479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>103</td>\n",
       "      <td>135</td>\n",
       "      <td>340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45</td>\n",
       "      <td>109</td>\n",
       "      <td>175</td>\n",
       "      <td>282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>45</td>\n",
       "      <td>117</td>\n",
       "      <td>148</td>\n",
       "      <td>406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>60</td>\n",
       "      <td>102</td>\n",
       "      <td>127</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Duration  Pulse  Maxpulse  Calories\n",
       "0        60    110       130       409\n",
       "1        60    117       145       479\n",
       "2        60    103       135       340\n",
       "3        45    109       175       282\n",
       "4        45    117       148       406\n",
       "5        60    102       127       300"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veri_json = pd.read_json(\"Veri_Setleri/json_verisi.json\")\n",
    "veri_json"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cccc483",
   "metadata": {},
   "source": [
    "Yerel diskinizde bulunan **json** dosyası dışında, <u>bir websitesindeki **json** dosyasını da</u> çalışmanıza dahil etmek isterseniz yine **read_json()** fonksiyonunu kullanabilirsiniz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "404b15c6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>provider</th>\n",
       "      <th>WARNING_UPGRADE_TO_V6</th>\n",
       "      <th>terms</th>\n",
       "      <th>base</th>\n",
       "      <th>date</th>\n",
       "      <th>time_last_updated</th>\n",
       "      <th>rates</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AED</th>\n",
       "      <td>https://www.exchangerate-api.com</td>\n",
       "      <td>https://www.exchangerate-api.com/docs/free</td>\n",
       "      <td>https://www.exchangerate-api.com/terms</td>\n",
       "      <td>USD</td>\n",
       "      <td>2022-07-11</td>\n",
       "      <td>1657497602</td>\n",
       "      <td>3.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AFN</th>\n",
       "      <td>https://www.exchangerate-api.com</td>\n",
       "      <td>https://www.exchangerate-api.com/docs/free</td>\n",
       "      <td>https://www.exchangerate-api.com/terms</td>\n",
       "      <td>USD</td>\n",
       "      <td>2022-07-11</td>\n",
       "      <td>1657497602</td>\n",
       "      <td>87.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALL</th>\n",
       "      <td>https://www.exchangerate-api.com</td>\n",
       "      <td>https://www.exchangerate-api.com/docs/free</td>\n",
       "      <td>https://www.exchangerate-api.com/terms</td>\n",
       "      <td>USD</td>\n",
       "      <td>2022-07-11</td>\n",
       "      <td>1657497602</td>\n",
       "      <td>114.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMD</th>\n",
       "      <td>https://www.exchangerate-api.com</td>\n",
       "      <td>https://www.exchangerate-api.com/docs/free</td>\n",
       "      <td>https://www.exchangerate-api.com/terms</td>\n",
       "      <td>USD</td>\n",
       "      <td>2022-07-11</td>\n",
       "      <td>1657497602</td>\n",
       "      <td>409.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANG</th>\n",
       "      <td>https://www.exchangerate-api.com</td>\n",
       "      <td>https://www.exchangerate-api.com/docs/free</td>\n",
       "      <td>https://www.exchangerate-api.com/terms</td>\n",
       "      <td>USD</td>\n",
       "      <td>2022-07-11</td>\n",
       "      <td>1657497602</td>\n",
       "      <td>1.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XPF</th>\n",
       "      <td>https://www.exchangerate-api.com</td>\n",
       "      <td>https://www.exchangerate-api.com/docs/free</td>\n",
       "      <td>https://www.exchangerate-api.com/terms</td>\n",
       "      <td>USD</td>\n",
       "      <td>2022-07-11</td>\n",
       "      <td>1657497602</td>\n",
       "      <td>117.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YER</th>\n",
       "      <td>https://www.exchangerate-api.com</td>\n",
       "      <td>https://www.exchangerate-api.com/docs/free</td>\n",
       "      <td>https://www.exchangerate-api.com/terms</td>\n",
       "      <td>USD</td>\n",
       "      <td>2022-07-11</td>\n",
       "      <td>1657497602</td>\n",
       "      <td>249.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZAR</th>\n",
       "      <td>https://www.exchangerate-api.com</td>\n",
       "      <td>https://www.exchangerate-api.com/docs/free</td>\n",
       "      <td>https://www.exchangerate-api.com/terms</td>\n",
       "      <td>USD</td>\n",
       "      <td>2022-07-11</td>\n",
       "      <td>1657497602</td>\n",
       "      <td>16.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZMW</th>\n",
       "      <td>https://www.exchangerate-api.com</td>\n",
       "      <td>https://www.exchangerate-api.com/docs/free</td>\n",
       "      <td>https://www.exchangerate-api.com/terms</td>\n",
       "      <td>USD</td>\n",
       "      <td>2022-07-11</td>\n",
       "      <td>1657497602</td>\n",
       "      <td>16.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZWL</th>\n",
       "      <td>https://www.exchangerate-api.com</td>\n",
       "      <td>https://www.exchangerate-api.com/docs/free</td>\n",
       "      <td>https://www.exchangerate-api.com/terms</td>\n",
       "      <td>USD</td>\n",
       "      <td>2022-07-11</td>\n",
       "      <td>1657497602</td>\n",
       "      <td>374.49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>161 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             provider  \\\n",
       "AED  https://www.exchangerate-api.com   \n",
       "AFN  https://www.exchangerate-api.com   \n",
       "ALL  https://www.exchangerate-api.com   \n",
       "AMD  https://www.exchangerate-api.com   \n",
       "ANG  https://www.exchangerate-api.com   \n",
       "..                                ...   \n",
       "XPF  https://www.exchangerate-api.com   \n",
       "YER  https://www.exchangerate-api.com   \n",
       "ZAR  https://www.exchangerate-api.com   \n",
       "ZMW  https://www.exchangerate-api.com   \n",
       "ZWL  https://www.exchangerate-api.com   \n",
       "\n",
       "                          WARNING_UPGRADE_TO_V6  \\\n",
       "AED  https://www.exchangerate-api.com/docs/free   \n",
       "AFN  https://www.exchangerate-api.com/docs/free   \n",
       "ALL  https://www.exchangerate-api.com/docs/free   \n",
       "AMD  https://www.exchangerate-api.com/docs/free   \n",
       "ANG  https://www.exchangerate-api.com/docs/free   \n",
       "..                                          ...   \n",
       "XPF  https://www.exchangerate-api.com/docs/free   \n",
       "YER  https://www.exchangerate-api.com/docs/free   \n",
       "ZAR  https://www.exchangerate-api.com/docs/free   \n",
       "ZMW  https://www.exchangerate-api.com/docs/free   \n",
       "ZWL  https://www.exchangerate-api.com/docs/free   \n",
       "\n",
       "                                      terms base       date  \\\n",
       "AED  https://www.exchangerate-api.com/terms  USD 2022-07-11   \n",
       "AFN  https://www.exchangerate-api.com/terms  USD 2022-07-11   \n",
       "ALL  https://www.exchangerate-api.com/terms  USD 2022-07-11   \n",
       "AMD  https://www.exchangerate-api.com/terms  USD 2022-07-11   \n",
       "ANG  https://www.exchangerate-api.com/terms  USD 2022-07-11   \n",
       "..                                      ...  ...        ...   \n",
       "XPF  https://www.exchangerate-api.com/terms  USD 2022-07-11   \n",
       "YER  https://www.exchangerate-api.com/terms  USD 2022-07-11   \n",
       "ZAR  https://www.exchangerate-api.com/terms  USD 2022-07-11   \n",
       "ZMW  https://www.exchangerate-api.com/terms  USD 2022-07-11   \n",
       "ZWL  https://www.exchangerate-api.com/terms  USD 2022-07-11   \n",
       "\n",
       "     time_last_updated   rates  \n",
       "AED         1657497602    3.67  \n",
       "AFN         1657497602   87.53  \n",
       "ALL         1657497602  114.07  \n",
       "AMD         1657497602  409.31  \n",
       "ANG         1657497602    1.79  \n",
       "..                 ...     ...  \n",
       "XPF         1657497602  117.34  \n",
       "YER         1657497602  249.15  \n",
       "ZAR         1657497602   16.83  \n",
       "ZMW         1657497602   16.38  \n",
       "ZWL         1657497602  374.49  \n",
       "\n",
       "[161 rows x 7 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = \"https://api.exchangerate-api.com/v4/latest/USD\"\n",
    "df = pd.read_json(url)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7c699c2",
   "metadata": {},
   "source": [
    "## URL ya da HTML dosyası içe aktarmak\n",
    "\n",
    "## read_html() Fonksiyonu\n",
    "\n",
    "**read_html()** fonksiyonu ile web sitelerinde ya da htm/html uzantılı dosyalardaki tabloları, çalışmalarınıza dahil edebilirsiniz. Öncelikle veri almak istediğimiz sayfanın içerisinde kaç adet tablo olduğunu bulalım."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5a0692a0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sayfada bulunan toplam tablo sayısı: 11\n"
     ]
    }
   ],
   "source": [
    "url_tablo = pd.read_html(\"https://tr.wikipedia.org/wiki/Van\")\n",
    "print(f\"Sayfada bulunan toplam tablo sayısı: {len(url_tablo)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d83ec5aa",
   "metadata": {},
   "source": [
    "Hangi tabloyu kullanmak (içe aktarmak) istiyorsak öncelikle o tabloyu, tam olarak belirtmemiz/tanımlamamız gerekiyor. Bunu da **read_html()** fonksiyonunun **match (eşle)** parametresi ile yapmamız gerekir. Benim kullanmak istediğim tablonun içerisinde **Van il nüfus bilgileri** ibaresi bulunduğu için ben bu \"*kelime grubunu*\" **match** parametresine dahil ediyorum."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "90f11c21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url_tablo = pd.read_html(\"https://tr.wikipedia.org/wiki/Van\", match=\"Van il nüfus bilgileri\")\n",
    "len(url_tablo)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "978900e8",
   "metadata": {},
   "source": [
    "Bu demek oluyor ki, sayfa içeriğinde, **Van il nüfus bilgileri** ibaresi bulunan <u>1 adet eşleşmiş tablo</u> var, artık **url** değişkeni sayfadaki tek tabloyu temsil ediyor ve bu tabloya erişmek için ilk indis olan **0 sıfır**'ı kullanmamız gerekir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "140c3097",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
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       "\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></th>\n",
       "      <th colspan=\"5\" halign=\"left\">Van il nüfus bilgileri</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Yıl</th>\n",
       "      <th>Toplam</th>\n",
       "      <th>Sıra</th>\n",
       "      <th>Fark</th>\n",
       "      <th>Şehir - Kır</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1965[11]</td>\n",
       "      <td>266.840</td>\n",
       "      <td>48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>%23 60.686206.154 %77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1970[12]</td>\n",
       "      <td>325.763</td>\n",
       "      <td>43</td>\n",
       "      <td>%22</td>\n",
       "      <td>%27 88.227237.536 %73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1975[13]</td>\n",
       "      <td>386.314</td>\n",
       "      <td>42</td>\n",
       "      <td>%19</td>\n",
       "      <td>%30 115.830270.484 %70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1980[14]</td>\n",
       "      <td>468.646</td>\n",
       "      <td>39</td>\n",
       "      <td>%21</td>\n",
       "      <td>%33 156.852311.794 %67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1985[15]</td>\n",
       "      <td>547.216</td>\n",
       "      <td>35</td>\n",
       "      <td>%17</td>\n",
       "      <td>%35 189.269357.947 %65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1990[16]</td>\n",
       "      <td>637.433</td>\n",
       "      <td>32</td>\n",
       "      <td>%16</td>\n",
       "      <td>%41 258.967378.466 %59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2000[17]</td>\n",
       "      <td>877.524</td>\n",
       "      <td>23</td>\n",
       "      <td>%38</td>\n",
       "      <td>%51 446.976430.548 %49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2007[18]</td>\n",
       "      <td>979.671</td>\n",
       "      <td>19</td>\n",
       "      <td>%12</td>\n",
       "      <td>%52 511.678467.993 %48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2008[19]</td>\n",
       "      <td>1.004.369</td>\n",
       "      <td>19</td>\n",
       "      <td>%3</td>\n",
       "      <td>%51 514.481489.888 %49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2009[20]</td>\n",
       "      <td>1.022.310</td>\n",
       "      <td>19</td>\n",
       "      <td>%2</td>\n",
       "      <td>%52 527.525494.785 %48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2010[21]</td>\n",
       "      <td>1.035.418</td>\n",
       "      <td>19</td>\n",
       "      <td>%1</td>\n",
       "      <td>%52 539.619495.799 %48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2011[22]</td>\n",
       "      <td>1.022.532</td>\n",
       "      <td>19</td>\n",
       "      <td>-%1</td>\n",
       "      <td>%52 526.725495.807 %48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2012[23]</td>\n",
       "      <td>1.051.975</td>\n",
       "      <td>19</td>\n",
       "      <td>%3</td>\n",
       "      <td>%52 548.717503.258 %48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2013[24]</td>\n",
       "      <td>1.070.113</td>\n",
       "      <td>19</td>\n",
       "      <td>%2</td>\n",
       "      <td>Şehir ve kır ayrımı kaldırılmıştır.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2014[25]</td>\n",
       "      <td>1.085.542</td>\n",
       "      <td>19</td>\n",
       "      <td>%1</td>\n",
       "      <td>Şehir ve kır ayrımı kaldırılmıştır.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2015[26]</td>\n",
       "      <td>1.096.397</td>\n",
       "      <td>19</td>\n",
       "      <td>%1</td>\n",
       "      <td>Şehir ve kır ayrımı kaldırılmıştır.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2016[26]</td>\n",
       "      <td>1.100.190</td>\n",
       "      <td>19</td>\n",
       "      <td>%0</td>\n",
       "      <td>Şehir ve kır ayrımı kaldırılmıştır.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2017[26]</td>\n",
       "      <td>1.106.891</td>\n",
       "      <td>19</td>\n",
       "      <td>%1</td>\n",
       "      <td>Şehir ve kır ayrımı kaldırılmıştır.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2018[26]</td>\n",
       "      <td>1.123.784</td>\n",
       "      <td>19</td>\n",
       "      <td>%2</td>\n",
       "      <td>Şehir ve kır ayrımı kaldırılmıştır.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2019[26]</td>\n",
       "      <td>1.136.757</td>\n",
       "      <td>19</td>\n",
       "      <td>%1</td>\n",
       "      <td>Şehir ve kır ayrımı kaldırılmıştır.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2020[26]</td>\n",
       "      <td>1.149.342</td>\n",
       "      <td>19</td>\n",
       "      <td>%1</td>\n",
       "      <td>Şehir ve kır ayrımı kaldırılmıştır.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2021[26]</td>\n",
       "      <td>1.141.015</td>\n",
       "      <td>19</td>\n",
       "      <td>-%1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Van il nüfus bilgileri                       \\\n",
       "                      Yıl     Toplam Sıra Fark   \n",
       "0                1965[11]    266.840   48  NaN   \n",
       "1                1970[12]    325.763   43  %22   \n",
       "2                1975[13]    386.314   42  %19   \n",
       "3                1980[14]    468.646   39  %21   \n",
       "4                1985[15]    547.216   35  %17   \n",
       "5                1990[16]    637.433   32  %16   \n",
       "6                2000[17]    877.524   23  %38   \n",
       "7                2007[18]    979.671   19  %12   \n",
       "8                2008[19]  1.004.369   19   %3   \n",
       "9                2009[20]  1.022.310   19   %2   \n",
       "10               2010[21]  1.035.418   19   %1   \n",
       "11               2011[22]  1.022.532   19  -%1   \n",
       "12               2012[23]  1.051.975   19   %3   \n",
       "13               2013[24]  1.070.113   19   %2   \n",
       "14               2014[25]  1.085.542   19   %1   \n",
       "15               2015[26]  1.096.397   19   %1   \n",
       "16               2016[26]  1.100.190   19   %0   \n",
       "17               2017[26]  1.106.891   19   %1   \n",
       "18               2018[26]  1.123.784   19   %2   \n",
       "19               2019[26]  1.136.757   19   %1   \n",
       "20               2020[26]  1.149.342   19   %1   \n",
       "21               2021[26]  1.141.015   19  -%1   \n",
       "\n",
       "                                         \n",
       "                            Şehir - Kır  \n",
       "0                 %23 60.686206.154 %77  \n",
       "1                 %27 88.227237.536 %73  \n",
       "2                %30 115.830270.484 %70  \n",
       "3                %33 156.852311.794 %67  \n",
       "4                %35 189.269357.947 %65  \n",
       "5                %41 258.967378.466 %59  \n",
       "6                %51 446.976430.548 %49  \n",
       "7                %52 511.678467.993 %48  \n",
       "8                %51 514.481489.888 %49  \n",
       "9                %52 527.525494.785 %48  \n",
       "10               %52 539.619495.799 %48  \n",
       "11               %52 526.725495.807 %48  \n",
       "12               %52 548.717503.258 %48  \n",
       "13  Şehir ve kır ayrımı kaldırılmıştır.  \n",
       "14  Şehir ve kır ayrımı kaldırılmıştır.  \n",
       "15  Şehir ve kır ayrımı kaldırılmıştır.  \n",
       "16  Şehir ve kır ayrımı kaldırılmıştır.  \n",
       "17  Şehir ve kır ayrımı kaldırılmıştır.  \n",
       "18  Şehir ve kır ayrımı kaldırılmıştır.  \n",
       "19  Şehir ve kır ayrımı kaldırılmıştır.  \n",
       "20  Şehir ve kır ayrımı kaldırılmıştır.  \n",
       "21                                  NaN  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url_tablo[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57be1260",
   "metadata": {},
   "source": [
    "**len(url_tablo)** çıktısı **1**'den büyük, yani sayfada aranan kelime ile eşleşen 1'den fazla tablo olsaydı, o durumda **url_tablo[0]** kodundaki indis değerini değiştirerek istediğimiz tabloya erişebilecektik. Örneğin; **url_tablo[3]**\n",
    "\n",
    "Aşağıdaki kodu çalıştırırsanız, ilgili sayfada **ilçe** ibaresi geçen 5 adet tablo bulunduğunu göreceksiniz.\n",
    "<code>\n",
    "url_tablo = pd.read_html(\"https://tr.wikipedia.org/wiki/Ankara\", match=\"İlçe\")\n",
    "len(url_tablo)\n",
    "</code>\n",
    "Bu tablolardan 3. tabloya ulaşmak için **print(url_tablo[2])** komutunu çalıştırmanız gerekecektir (indis değeri 0'dan başladığı için 3 yerine 2 yazmalıyız).\n",
    "\n",
    "Pandas kütüphanesi, tabloda okumayı kolaylaştırır ve ayrıca birden çok satıra yayılan (birleştirilmiş hücrelerdeki verileri) sütunu işleyerek, her hücreye aynı değeri kopyalar. Orjinal tablomuz aşağıdaki gibidir.\n",
    "\n",
    "![nba](Veri_Setleri/nba.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b612430c",
   "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>Team</th>\n",
       "      <th>Number</th>\n",
       "      <th>Position</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>College</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avery Bradley</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-2</td>\n",
       "      <td>180.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>7730337.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jae Crowder</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>99.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-6</td>\n",
       "      <td>235.0</td>\n",
       "      <td>Marquette</td>\n",
       "      <td>6796117.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John Holland</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>30.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>205.0</td>\n",
       "      <td>Boston University</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R.J. Hunter</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>28.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>185.0</td>\n",
       "      <td>Georgia State</td>\n",
       "      <td>1148640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Thomas Robinson</td>\n",
       "      <td>Brooklyn Nets</td>\n",
       "      <td>41.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>237.0</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>981348.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Henry Sims</td>\n",
       "      <td>Brooklyn Nets</td>\n",
       "      <td>14.0</td>\n",
       "      <td>C</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>248.0</td>\n",
       "      <td>Georgetown</td>\n",
       "      <td>947276.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Donald Sloan</td>\n",
       "      <td>Brooklyn Nets</td>\n",
       "      <td>15.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>28.0</td>\n",
       "      <td>6-3</td>\n",
       "      <td>205.0</td>\n",
       "      <td>Texas A&amp;M</td>\n",
       "      <td>947276.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Thaddeus Young</td>\n",
       "      <td>Brooklyn Nets</td>\n",
       "      <td>30.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6-8</td>\n",
       "      <td>221.0</td>\n",
       "      <td>Georgia Tech</td>\n",
       "      <td>11235955.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Arron Afflalo</td>\n",
       "      <td>New York Knicks</td>\n",
       "      <td>4.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>30.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>210.0</td>\n",
       "      <td>UCLA</td>\n",
       "      <td>8000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Lou Amundson</td>\n",
       "      <td>New York Knicks</td>\n",
       "      <td>17.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6-9</td>\n",
       "      <td>220.0</td>\n",
       "      <td>UNLV</td>\n",
       "      <td>1635476.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Thanasis Antetokounmpo</td>\n",
       "      <td>New York Knicks</td>\n",
       "      <td>43.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>23.0</td>\n",
       "      <td>6-7</td>\n",
       "      <td>205.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30888.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Carmelo Anthony</td>\n",
       "      <td>New York Knicks</td>\n",
       "      <td>7.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>32.0</td>\n",
       "      <td>6-8</td>\n",
       "      <td>240.0</td>\n",
       "      <td>Syracuse</td>\n",
       "      <td>22875000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Jose Calderon</td>\n",
       "      <td>New York Knicks</td>\n",
       "      <td>3.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>34.0</td>\n",
       "      <td>6-3</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7402812.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Cleanthony Early</td>\n",
       "      <td>New York Knicks</td>\n",
       "      <td>11.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-8</td>\n",
       "      <td>210.0</td>\n",
       "      <td>Wichita State</td>\n",
       "      <td>845059.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Name             Team  Number Position   Age Height  \\\n",
       "0            Avery Bradley   Boston Celtics     0.0       PG  25.0    6-2   \n",
       "1              Jae Crowder   Boston Celtics    99.0       SF  25.0    6-6   \n",
       "2             John Holland   Boston Celtics    30.0       SG  27.0    6-5   \n",
       "3              R.J. Hunter   Boston Celtics    28.0       SG  22.0    6-5   \n",
       "4          Thomas Robinson    Brooklyn Nets    41.0       PF  25.0   6-10   \n",
       "5               Henry Sims    Brooklyn Nets    14.0        C  26.0   6-10   \n",
       "6             Donald Sloan    Brooklyn Nets    15.0       PG  28.0    6-3   \n",
       "7           Thaddeus Young    Brooklyn Nets    30.0       PF  27.0    6-8   \n",
       "8            Arron Afflalo  New York Knicks     4.0       SG  30.0    6-5   \n",
       "9             Lou Amundson  New York Knicks    17.0       PF  33.0    6-9   \n",
       "10  Thanasis Antetokounmpo  New York Knicks    43.0       SF  23.0    6-7   \n",
       "11         Carmelo Anthony  New York Knicks     7.0       SF  32.0    6-8   \n",
       "12           Jose Calderon  New York Knicks     3.0       PG  34.0    6-3   \n",
       "13        Cleanthony Early  New York Knicks    11.0       SF  25.0    6-8   \n",
       "\n",
       "    Weight            College      Salary  \n",
       "0    180.0              Texas   7730337.0  \n",
       "1    235.0          Marquette   6796117.0  \n",
       "2    205.0  Boston University         NaN  \n",
       "3    185.0      Georgia State   1148640.0  \n",
       "4    237.0             Kansas    981348.0  \n",
       "5    248.0         Georgetown    947276.0  \n",
       "6    205.0          Texas A&M    947276.0  \n",
       "7    221.0       Georgia Tech  11235955.0  \n",
       "8    210.0               UCLA   8000000.0  \n",
       "9    220.0               UNLV   1635476.0  \n",
       "10   205.0                NaN     30888.0  \n",
       "11   240.0           Syracuse  22875000.0  \n",
       "12   200.0                NaN   7402812.0  \n",
       "13   210.0      Wichita State    845059.0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba2 = pd.read_excel(\"Veri_Setleri/nba.xlsx\")\n",
    "nba2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62249d21",
   "metadata": {},
   "source": [
    "Pandas kütüphanesi sayesinde, **Team** Sütunundaki birleştirilmiş hücrelerde bulunan veriler, hücrelere ayrı ayrı kopyalanarak düzgün, bir veri çevçevesi oluşturmamızı sağlamış oldu. Benzer işlemi, [Eksik / Kayıp Veri Tespiti ve Düzenleme Yöntemleri](Eksik_Kayip_Veri_Yontemleri.ipynb) bölümündeki `fillna` fonksiyonunun `method` parametresini kullanarak ta gerçekleştirebiliriz.\n",
    "\n",
    "\n",
    "## Panodan İçe Aktarmak\n",
    "\n",
    "## read_clipboard() Fonksiyonu\n",
    "\n",
    "**read_clipboard()** fonksiyonu ile panoya kopyaladığımız veriyi (örneğin bir Excel tablosu ya da websitesindeki tabloyu) Veri Çerçevesine dönüştürebiliriz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a30f425f",
   "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>İlçe</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "      <th>Fark</th>\n",
       "      <th>Nüfus art. %</th>\n",
       "      <th>Mah. say.</th>\n",
       "      <th>Alanı km2</th>\n",
       "      <th>Yoğunluk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adalar</td>\n",
       "      <td>16.033</td>\n",
       "      <td>16.372</td>\n",
       "      <td>339.000</td>\n",
       "      <td>2.11</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Arnavutköy</td>\n",
       "      <td>296.709</td>\n",
       "      <td>312.023</td>\n",
       "      <td>15.314</td>\n",
       "      <td>5.16</td>\n",
       "      <td>38</td>\n",
       "      <td>453</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ataşehir</td>\n",
       "      <td>422.594</td>\n",
       "      <td>427.217</td>\n",
       "      <td>4.623</td>\n",
       "      <td>1.09</td>\n",
       "      <td>17</td>\n",
       "      <td>25</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Avcılar</td>\n",
       "      <td>436.897</td>\n",
       "      <td>457.981</td>\n",
       "      <td>21.084</td>\n",
       "      <td>4.82</td>\n",
       "      <td>10</td>\n",
       "      <td>50</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bağcılar</td>\n",
       "      <td>737.206</td>\n",
       "      <td>744.351</td>\n",
       "      <td>7.145</td>\n",
       "      <td>0.96</td>\n",
       "      <td>22</td>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Bahçelievler</td>\n",
       "      <td>592.371</td>\n",
       "      <td>605.300</td>\n",
       "      <td>12.929</td>\n",
       "      <td>2.18</td>\n",
       "      <td>11</td>\n",
       "      <td>17</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Bakırköy</td>\n",
       "      <td>226.229</td>\n",
       "      <td>228.759</td>\n",
       "      <td>2.530</td>\n",
       "      <td>1.11</td>\n",
       "      <td>15</td>\n",
       "      <td>29</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Başakşehir</td>\n",
       "      <td>469.924</td>\n",
       "      <td>503.243</td>\n",
       "      <td>33.319</td>\n",
       "      <td>7.09</td>\n",
       "      <td>11</td>\n",
       "      <td>107</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           İlçe     2020     2021     Fark   Nüfus art. %   Mah. say.   \\\n",
       "0        Adalar    16.033   16.372  339.000           2.11           5   \n",
       "1    Arnavutköy   296.709  312.023   15.314           5.16          38   \n",
       "2      Ataşehir   422.594  427.217    4.623           1.09          17   \n",
       "3       Avcılar   436.897  457.981   21.084           4.82          10   \n",
       "4      Bağcılar   737.206  744.351    7.145           0.96          22   \n",
       "5  Bahçelievler   592.371  605.300   12.929           2.18          11   \n",
       "6      Bakırköy   226.229  228.759    2.530           1.11          15   \n",
       "7    Başakşehir   469.924  503.243   33.319           7.09          11   \n",
       "\n",
       "   Alanı km2   Yoğunluk  \n",
       "0          11       NaN  \n",
       "1         453       NaN  \n",
       "2          25       NaN  \n",
       "3          50       NaN  \n",
       "4          23       NaN  \n",
       "5          17       NaN  \n",
       "6          29       NaN  \n",
       "7         107       NaN  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pano = pd.read_clipboard()\n",
    "pano"
   ]
  }
 ],
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