Pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   #                        PANDAS\n",
    " \n",
    "* Panel Data\n",
    "* Veri Manipülasyonu ve veri analizi için yazılmış açık kaynak kodlu bir Python kütüphanesidir\n",
    "* Ekonometrik ve finansal çalışmalar için doğmuştur\n",
    "* Temeli 2008 yılında atılmıştır\n",
    "* R DataFrame yapısını Python dünyasına taşımış ve DataFrame'ler üzerinde hızlı ve etkili çalışabilme imkanı sağlamıştır.\n",
    "* Bir çok farklı veri tipini okuma ve yazma imkanı sağlar."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas Serisi Oluşturma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "3    4\n",
       "4    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series([1,2,3,4,5]) \n",
    "#Numpy Arrayleri gibi yani bir  veri tipidir\n",
    "# indeks bilgili tutar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    15\n",
       "1    35\n",
       "2    98\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series([15,35,98]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seri=pd.Series([15,35,98]) \n",
    "type(seri)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[RangeIndex(start=0, stop=3, step=1)]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seri.axes \n",
    "#serinin indeks bilgilerine ulaşırız\n",
    "#0dan 3e kadar 1er 1er artmış"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seri.size #içerisindeki eleman sayyısı"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seri.ndim #boyut bilgisi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([15, 35, 98], dtype=int64)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seri.values\n",
    "#indekssiz değer bilgilerine ulaşmak istediğimizde(array )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    15\n",
       "1    35\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seri.head(2) #ilk 2 gözlemini getirir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    98\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seri.tail(1) #son bir elemanını"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#indeks isimlendirilmesi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     99\n",
       "1     22\n",
       "2    332\n",
       "3     94\n",
       "4      5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series([99,22,332,94,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a     99\n",
       "b     22\n",
       "c    332\n",
       "d     94\n",
       "e      5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series([99,22,332,94,5],index=[\"a\",\"b\",\"c\",\"d\",\"e\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seri=pd.Series([99,22,332,94,5],\n",
    "               index=[\"a\",\"b\",\"c\",\"d\",\"e\"])\n",
    "seri[\"a\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sozluk üzerinden pandas serisi oluşturmak"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "reg     10\n",
       "log     11\n",
       "cart    12\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sozluk=pd.Series({\"reg\":10,\"log\":11,\"cart\":12})\n",
    "sozluk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(sozluk)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#iki seri birleştirerek seri oluşturma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "reg     10\n",
       "log     11\n",
       "cart    12\n",
       "reg     10\n",
       "log     11\n",
       "cart    12\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([sozluk,sozluk]) #alt alta birleştirdi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ##### ****Eleman İşlemleri****"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1\n",
       "1      2\n",
       "2     33\n",
       "3    444\n",
       "4     75\n",
       "dtype: int32"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "a=np.array([1,2,33,444,75])\n",
    "seri=pd.Series(a)\n",
    "seri"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seri[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     1\n",
       "1     2\n",
       "2    33\n",
       "dtype: int32"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seri[0:3] #3.indekse kadar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "seri = pd.Series([121,200,150,99],\n",
    "                 index = [\"reg\",\"loj\",\"cart\",\"rf\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "reg     121\n",
       "loj     200\n",
       "cart    150\n",
       "rf       99\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seri"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Series.keys of reg     121\n",
       "loj     200\n",
       "cart    150\n",
       "rf       99\n",
       "dtype: int64>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seri.keys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('reg', 121), ('loj', 200), ('cart', 150), ('rf', 99)]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(seri.items()) #key-value şeklinde gelmesini istersek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['reg', 'loj', 'cart', 'rf'], dtype='object')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seri.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Eleman Sorgulama"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " \"reg\" in seri #reg serinin içinde mi - true(evet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"a\" in seri"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#fancy işlem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rf      99\n",
       "reg    121\n",
       "dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seri[[\"rf\",\"reg\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "reg    121\n",
       "loj    200\n",
       "dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seri[\"reg\":\"loj\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas DataFrame Oluşturma\n",
    "* Pandas DataFrame:yapısal bir veri tipidir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 39, 67, 90]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "l = [1,2,39,67,90]\n",
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>degisken_ismi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   degisken_ismi\n",
       "0              1\n",
       "1              2\n",
       "2             39\n",
       "3             67\n",
       "4             90"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(l,columns=[\"degisken_ismi\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6],\n",
       "       [7, 8, 9]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "m=np.arange(1,10).reshape((3,3))\n",
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>var1</th>\n",
       "      <th>var2</th>\n",
       "      <th>var3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   var1  var2  var3\n",
       "0     1     2     3\n",
       "1     4     5     6\n",
       "2     7     8     9"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(m,columns=[\"var1\",\"var2\",\"var3\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df isimlendirme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame(m,columns=[\"var1\",\"var2\",\"var3\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>var1</th>\n",
       "      <th>var2</th>\n",
       "      <th>var3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   var1  var2  var3\n",
       "0     1     2     3\n",
       "1     4     5     6\n",
       "2     7     8     9"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns=(\"deg1\",\"deg2\",\"deg3\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>deg1</th>\n",
       "      <th>deg2</th>\n",
       "      <th>deg3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   deg1  deg2  deg3\n",
       "0     1     2     3\n",
       "1     4     5     6\n",
       "2     7     8     9"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[RangeIndex(start=0, stop=3, step=1),\n",
       " Index(['deg1', 'deg2', 'deg3'], dtype='object')]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.axes #satır ve sütun bilgileri"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 3)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape #boyut bilgisini"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ndim #boyutsorgusu(2boyut-satır,sütun)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6],\n",
       "       [7, 8, 9]])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.values #arraye çevirdi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([1,2,3,4,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>deg1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   deg1\n",
       "0     1\n",
       "1     2\n",
       "2     3\n",
       "3     4\n",
       "4     5"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(a,columns=[\"deg1\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ### ****Eleman İşlemleri****"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "s1=np.random.randint(10,size=5)\n",
    "s2=np.random.randint(10,size=5)\n",
    "s3=np.random.randint(10,size=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "sozluk={\"var1\":s1,\n",
    "       \"var2\":s2,\n",
    "       \"var3\":s3}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'var1': array([9, 3, 9, 1, 0]),\n",
       " 'var2': array([8, 4, 1, 6, 9]),\n",
       " 'var3': array([2, 8, 1, 4, 2])}"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sozluk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   var1  var2  var3\n",
       "0     9     8     2\n",
       "1     3     4     8\n",
       "2     9     1     1\n",
       "3     1     6     4\n",
       "4     0     9     2"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame(sozluk)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>var1</th>\n",
       "      <th>var2</th>\n",
       "      <th>var3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>9</td>\n",
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       "      <td>2</td>\n",
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      "text/plain": [
       "   var1  var2  var3\n",
       "a     9     8     2"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[0:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>var1</th>\n",
       "      <th>var2</th>\n",
       "      <th>var3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   var1  var2  var3\n",
       "a     9     8     2\n",
       "b     3     4     8\n",
       "c     9     1     1\n",
       "d     1     6     4\n",
       "e     0     9     2"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index=[\"a\",\"b\",\"c\",\"d\",\"e\"]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>var3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>0</td>\n",
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       "</table>\n",
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      "text/plain": [
       "   var1  var2  var3\n",
       "c     9     1     1\n",
       "d     1     6     4\n",
       "e     0     9     2"
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     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"c\":\"e\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "#SİlME"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "      <th>var3</th>\n",
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       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "   var1  var2  var3\n",
       "b     3     4     8\n",
       "c     9     1     1\n",
       "d     1     6     4\n",
       "e     0     9     2"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(\"a\",axis=0) \n",
    "#veri setinde kalıcı bir değişiklik olmadı"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <td>2</td>\n",
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       "      <th>b</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   var1  var2  var3\n",
       "a     9     8     2\n",
       "b     3     4     8\n",
       "c     9     1     1\n",
       "d     1     6     4\n",
       "e     0     9     2"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df #a duruyor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(\"a\",axis=0,inplace=True)  \n",
    "#inplace argümanıyla kalıcı hale getirdik\n",
    "#axis=0 satırı,axis=1 sütunu gösterir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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>var1</th>\n",
       "      <th>var2</th>\n",
       "      <th>var3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   var1  var2  var3\n",
       "b     3     4     8\n",
       "c     9     1     1\n",
       "d     1     6     4\n",
       "e     0     9     2"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df # a kalıcı olarak silindi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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>var1</th>\n",
       "      <th>var2</th>\n",
       "      <th>var3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   var1  var2  var3\n",
       "b     3     4     8\n",
       "c     9     1     1\n",
       "d     1     6     4\n",
       "e     0     9     2"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop([\"c\",\"d\"],axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "False\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "l=[\"var1\",\"var4\",\"var3\"]\n",
    "\n",
    "for i in l:\n",
    "    print(i in df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"var4\"] =df[\"var1\"] * df[\"var2\"] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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>var1</th>\n",
       "      <th>var2</th>\n",
       "      <th>var3</th>\n",
       "      <th>var4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   var1  var2  var3  var4\n",
       "b     3     4     8    12\n",
       "c     9     1     1     9\n",
       "d     1     6     4     6\n",
       "e     0     9     2     0"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "#degisken silmek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(\"var4\",axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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>var1</th>\n",
       "      <th>var2</th>\n",
       "      <th>var3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   var1  var2  var3\n",
       "b     3     4     8\n",
       "c     9     1     1\n",
       "d     1     6     4\n",
       "e     0     9     2"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  ****Gözlem ve Değişken Seçimi****\n",
    "\n",
    "* LOC\n",
    "* ILOC\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "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>var1</th>\n",
       "      <th>var2</th>\n",
       "      <th>var3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>26</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19</td>\n",
       "      <td>27</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>23</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>19</td>\n",
       "      <td>8</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8</td>\n",
       "      <td>26</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   var1  var2  var3\n",
       "0    13    12    13\n",
       "1    18    26     7\n",
       "2     9     6     7\n",
       "3    19    27    21\n",
       "4    12    23    13\n",
       "5     2     6    20\n",
       "6     2    21    19\n",
       "7    10    17     6\n",
       "8    19     8    27\n",
       "9     8    26     4"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "m=np.random.randint(1,30,size=(10,3))\n",
    "df=pd.DataFrame(m,columns=[\"var1\",\"var2\",\"var3\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "#LOC : Tanımlandığı şekli ile seçim yapmak için kullanılır"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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>var1</th>\n",
       "      <th>var2</th>\n",
       "      <th>var3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>26</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19</td>\n",
       "      <td>27</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   var1  var2  var3\n",
       "0    13    12    13\n",
       "1    18    26     7\n",
       "2     9     6     7\n",
       "3    19    27    21"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[0:3] #3de alır"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "#iloc : alışık olduğumuz indeksleme mantığıyla seçim yapar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "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>var1</th>\n",
       "      <th>var2</th>\n",
       "      <th>var3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>26</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   var1  var2  var3\n",
       "0    13    12    13\n",
       "1    18    26     7\n",
       "2     9     6     7"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0:3] #3 e kadar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "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>var1</th>\n",
       "      <th>var2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   var1  var2\n",
       "0    13    12\n",
       "1    18    26\n",
       "2     9     6"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:3,:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    13\n",
       "1     7\n",
       "2     7\n",
       "3    21\n",
       "Name: var3, dtype: int32"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[0:3,\"var3\"]  #indekse sadık kalıyor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    13\n",
       "1     7\n",
       "2     7\n",
       "Name: var3, dtype: int32"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0:3][\"var3\"] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  ****Koşullu  Eleman  İşlemleri****"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "   var1  var2  var3\n",
       "0    13    12    13\n",
       "1    18    26     7\n",
       "2     9     6     7\n",
       "3    19    27    21\n",
       "4    12    23    13\n",
       "5     2     6    20\n",
       "6     2    21    19\n",
       "7    10    17     6\n",
       "8    19     8    27\n",
       "9     8    26     4"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    13\n",
       "1    18\n",
       "2     9\n",
       "3    19\n",
       "4    12\n",
       "5     2\n",
       "6     2\n",
       "7    10\n",
       "8    19\n",
       "9     8\n",
       "Name: var1, dtype: int32"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"var1\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "#birinci değişkende ki değerlerin 15den büyük olanlarını istersek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    18\n",
       "3    19\n",
       "8    19\n",
       "Name: var1, dtype: int32"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.var1 > 15][\"var1\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "   var1  var2  var3\n",
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    "df[(df.var1 > 15) & (df.var3 < 21)]"
   ]
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   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
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       "   var1  var2\n",
       "1    18    26\n",
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       "8    19     8"
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     "execution_count": 121,
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   "source": [
    "df[df.var1 > 15][[\"var1\",\"var2\"]]"
   ]
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  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "   var1  var2\n",
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   "source": [
    "df.loc[(df.var1 > 15),[\"var1\",\"var2\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  ****Birleştirme(Join) İşlemleri****\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
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       "   var1  var2  var3\n",
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       "3    19    27    21\n",
       "4    12    23    13\n",
       "5     2     6    20\n",
       "6     2    21    19\n",
       "7    10    17     6\n",
       "8    19     8    27\n",
       "9     8    26     4"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2= df + 99"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
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       "   var1  var2  var3\n",
       "0   112   111   112\n",
       "1   117   125   106\n",
       "2   108   105   106\n",
       "3   118   126   120\n",
       "4   111   122   112\n",
       "5   101   105   119\n",
       "6   101   120   118\n",
       "7   109   116   105\n",
       "8   118   107   126\n",
       "9   107   125   103"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
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       "      <td>112</td>\n",
       "      <td>111</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>117</td>\n",
       "      <td>125</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>108</td>\n",
       "      <td>105</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>118</td>\n",
       "      <td>126</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>111</td>\n",
       "      <td>122</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>101</td>\n",
       "      <td>105</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>101</td>\n",
       "      <td>120</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>109</td>\n",
       "      <td>116</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>118</td>\n",
       "      <td>107</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>107</td>\n",
       "      <td>125</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   var1  var2  var3\n",
       "0    13    12    13\n",
       "1    18    26     7\n",
       "2     9     6     7\n",
       "3    19    27    21\n",
       "4    12    23    13\n",
       "5     2     6    20\n",
       "6     2    21    19\n",
       "7    10    17     6\n",
       "8    19     8    27\n",
       "9     8    26     4\n",
       "0   112   111   112\n",
       "1   117   125   106\n",
       "2   108   105   106\n",
       "3   118   126   120\n",
       "4   111   122   112\n",
       "5   101   105   119\n",
       "6   101   120   118\n",
       "7   109   116   105\n",
       "8   118   107   126\n",
       "9   107   125   103"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([df,df2]) \n",
    "#indeksler iki kere 0dan başlıyor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>11</th>\n",
       "      <td>117</td>\n",
       "      <td>125</td>\n",
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       "      <th>14</th>\n",
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       "      <th>16</th>\n",
       "      <td>101</td>\n",
       "      <td>120</td>\n",
       "      <td>118</td>\n",
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       "      <th>17</th>\n",
       "      <td>109</td>\n",
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       "      <td>105</td>\n",
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       "      <td>103</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "    var1  var2  var3\n",
       "0     13    12    13\n",
       "1     18    26     7\n",
       "2      9     6     7\n",
       "3     19    27    21\n",
       "4     12    23    13\n",
       "5      2     6    20\n",
       "6      2    21    19\n",
       "7     10    17     6\n",
       "8     19     8    27\n",
       "9      8    26     4\n",
       "10   112   111   112\n",
       "11   117   125   106\n",
       "12   108   105   106\n",
       "13   118   126   120\n",
       "14   111   122   112\n",
       "15   101   105   119\n",
       "16   101   120   118\n",
       "17   109   116   105\n",
       "18   118   107   126\n",
       "19   107   125   103"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([df,df2],ignore_index=True) \n",
    "#indeks sorunu çözüldü"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   var1  var2  deg3\n",
       "0    13    12    13\n",
       "1    18    26     7\n",
       "2     9     6     7\n",
       "3    19    27    21\n",
       "4    12    23    13\n",
       "5     2     6    20\n",
       "6     2    21    19\n",
       "7    10    17     6\n",
       "8    19     8    27\n",
       "9     8    26     4"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns=[\"var1\",\"var2\",\"deg3\"]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
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       "      <td>120.0</td>\n",
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       "      <th>4</th>\n",
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       "      <td>112.0</td>\n",
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       "      <th>5</th>\n",
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       "      <td>118.0</td>\n",
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       "      <th>9</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>103.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   var1  var2  deg3   var3\n",
       "0    13    12  13.0    NaN\n",
       "1    18    26   7.0    NaN\n",
       "2     9     6   7.0    NaN\n",
       "3    19    27  21.0    NaN\n",
       "4    12    23  13.0    NaN\n",
       "5     2     6  20.0    NaN\n",
       "6     2    21  19.0    NaN\n",
       "7    10    17   6.0    NaN\n",
       "8    19     8  27.0    NaN\n",
       "9     8    26   4.0    NaN\n",
       "0   112   111   NaN  112.0\n",
       "1   117   125   NaN  106.0\n",
       "2   108   105   NaN  106.0\n",
       "3   118   126   NaN  120.0\n",
       "4   111   122   NaN  112.0\n",
       "5   101   105   NaN  119.0\n",
       "6   101   120   NaN  118.0\n",
       "7   109   116   NaN  105.0\n",
       "8   118   107   NaN  126.0\n",
       "9   107   125   NaN  103.0"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([df,df2]) #birinde deg3 var birinde deg3 var. aynı sütun değil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
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       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>19</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>112</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>117</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>108</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>118</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>111</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>101</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>101</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>109</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>118</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>107</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   var1  var2\n",
       "0    13    12\n",
       "1    18    26\n",
       "2     9     6\n",
       "3    19    27\n",
       "4    12    23\n",
       "5     2     6\n",
       "6     2    21\n",
       "7    10    17\n",
       "8    19     8\n",
       "9     8    26\n",
       "0   112   111\n",
       "1   117   125\n",
       "2   108   105\n",
       "3   118   126\n",
       "4   111   122\n",
       "5   101   105\n",
       "6   101   120\n",
       "7   109   116\n",
       "8   118   107\n",
       "9   107   125"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([df,df2],join=\"inner\") \n",
    "#değişkenler bazında kesişimlerine göre birleştiri)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[1;31mSignature:\u001b[0m\n",
       "\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mobjs\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mIterable\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m~\u001b[0m\u001b[0mFrameOrSeries\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mMapping\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mUnion\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mHashable\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mNoneType\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m~\u001b[0m\u001b[0mFrameOrSeries\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mjoin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'outer'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mignore_index\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mkeys\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mlevels\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mnames\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mverify_integrity\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0msort\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mcopy\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mUnion\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mForwardRef\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'DataFrame'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mForwardRef\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Series'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
       "\u001b[1;31mDocstring:\u001b[0m\n",
       "Concatenate pandas objects along a particular axis with optional set logic\n",
       "along the other axes.\n",
       "\n",
       "Can also add a layer of hierarchical indexing on the concatenation axis,\n",
       "which may be useful if the labels are the same (or overlapping) on\n",
       "the passed axis number.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "objs : a sequence or mapping of Series or DataFrame objects\n",
       "    If a mapping is passed, the sorted keys will be used as the `keys`\n",
       "    argument, unless it is passed, in which case the values will be\n",
       "    selected (see below). Any None objects will be dropped silently unless\n",
       "    they are all None in which case a ValueError will be raised.\n",
       "axis : {0/'index', 1/'columns'}, default 0\n",
       "    The axis to concatenate along.\n",
       "join : {'inner', 'outer'}, default 'outer'\n",
       "    How to handle indexes on other axis (or axes).\n",
       "ignore_index : bool, default False\n",
       "    If True, do not use the index values along the concatenation axis. The\n",
       "    resulting axis will be labeled 0, ..., n - 1. This is useful if you are\n",
       "    concatenating objects where the concatenation axis does not have\n",
       "    meaningful indexing information. Note the index values on the other\n",
       "    axes are still respected in the join.\n",
       "keys : sequence, default None\n",
       "    If multiple levels passed, should contain tuples. Construct\n",
       "    hierarchical index using the passed keys as the outermost level.\n",
       "levels : list of sequences, default None\n",
       "    Specific levels (unique values) to use for constructing a\n",
       "    MultiIndex. Otherwise they will be inferred from the keys.\n",
       "names : list, default None\n",
       "    Names for the levels in the resulting hierarchical index.\n",
       "verify_integrity : bool, default False\n",
       "    Check whether the new concatenated axis contains duplicates. This can\n",
       "    be very expensive relative to the actual data concatenation.\n",
       "sort : bool, default False\n",
       "    Sort non-concatenation axis if it is not already aligned when `join`\n",
       "    is 'outer'.\n",
       "    This has no effect when ``join='inner'``, which already preserves\n",
       "    the order of the non-concatenation axis.\n",
       "\n",
       "    .. versionadded:: 0.23.0\n",
       "    .. versionchanged:: 1.0.0\n",
       "\n",
       "       Changed to not sort by default.\n",
       "\n",
       "copy : bool, default True\n",
       "    If False, do not copy data unnecessarily.\n",
       "\n",
       "Returns\n",
       "-------\n",
       "object, type of objs\n",
       "    When concatenating all ``Series`` along the index (axis=0), a\n",
       "    ``Series`` is returned. When ``objs`` contains at least one\n",
       "    ``DataFrame``, a ``DataFrame`` is returned. When concatenating along\n",
       "    the columns (axis=1), a ``DataFrame`` is returned.\n",
       "\n",
       "See Also\n",
       "--------\n",
       "Series.append : Concatenate Series.\n",
       "DataFrame.append : Concatenate DataFrames.\n",
       "DataFrame.join : Join DataFrames using indexes.\n",
       "DataFrame.merge : Merge DataFrames by indexes or columns.\n",
       "\n",
       "Notes\n",
       "-----\n",
       "The keys, levels, and names arguments are all optional.\n",
       "\n",
       "A walkthrough of how this method fits in with other tools for combining\n",
       "pandas objects can be found `here\n",
       "<https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html>`__.\n",
       "\n",
       "Examples\n",
       "--------\n",
       "Combine two ``Series``.\n",
       "\n",
       ">>> s1 = pd.Series(['a', 'b'])\n",
       ">>> s2 = pd.Series(['c', 'd'])\n",
       ">>> pd.concat([s1, s2])\n",
       "0    a\n",
       "1    b\n",
       "0    c\n",
       "1    d\n",
       "dtype: object\n",
       "\n",
       "Clear the existing index and reset it in the result\n",
       "by setting the ``ignore_index`` option to ``True``.\n",
       "\n",
       ">>> pd.concat([s1, s2], ignore_index=True)\n",
       "0    a\n",
       "1    b\n",
       "2    c\n",
       "3    d\n",
       "dtype: object\n",
       "\n",
       "Add a hierarchical index at the outermost level of\n",
       "the data with the ``keys`` option.\n",
       "\n",
       ">>> pd.concat([s1, s2], keys=['s1', 's2'])\n",
       "s1  0    a\n",
       "    1    b\n",
       "s2  0    c\n",
       "    1    d\n",
       "dtype: object\n",
       "\n",
       "Label the index keys you create with the ``names`` option.\n",
       "\n",
       ">>> pd.concat([s1, s2], keys=['s1', 's2'],\n",
       "...           names=['Series name', 'Row ID'])\n",
       "Series name  Row ID\n",
       "s1           0         a\n",
       "             1         b\n",
       "s2           0         c\n",
       "             1         d\n",
       "dtype: object\n",
       "\n",
       "Combine two ``DataFrame`` objects with identical columns.\n",
       "\n",
       ">>> df1 = pd.DataFrame([['a', 1], ['b', 2]],\n",
       "...                    columns=['letter', 'number'])\n",
       ">>> df1\n",
       "  letter  number\n",
       "0      a       1\n",
       "1      b       2\n",
       ">>> df2 = pd.DataFrame([['c', 3], ['d', 4]],\n",
       "...                    columns=['letter', 'number'])\n",
       ">>> df2\n",
       "  letter  number\n",
       "0      c       3\n",
       "1      d       4\n",
       ">>> pd.concat([df1, df2])\n",
       "  letter  number\n",
       "0      a       1\n",
       "1      b       2\n",
       "0      c       3\n",
       "1      d       4\n",
       "\n",
       "Combine ``DataFrame`` objects with overlapping columns\n",
       "and return everything. Columns outside the intersection will\n",
       "be filled with ``NaN`` values.\n",
       "\n",
       ">>> df3 = pd.DataFrame([['c', 3, 'cat'], ['d', 4, 'dog']],\n",
       "...                    columns=['letter', 'number', 'animal'])\n",
       ">>> df3\n",
       "  letter  number animal\n",
       "0      c       3    cat\n",
       "1      d       4    dog\n",
       ">>> pd.concat([df1, df3], sort=False)\n",
       "  letter  number animal\n",
       "0      a       1    NaN\n",
       "1      b       2    NaN\n",
       "0      c       3    cat\n",
       "1      d       4    dog\n",
       "\n",
       "Combine ``DataFrame`` objects with overlapping columns\n",
       "and return only those that are shared by passing ``inner`` to\n",
       "the ``join`` keyword argument.\n",
       "\n",
       ">>> pd.concat([df1, df3], join=\"inner\")\n",
       "  letter  number\n",
       "0      a       1\n",
       "1      b       2\n",
       "0      c       3\n",
       "1      d       4\n",
       "\n",
       "Combine ``DataFrame`` objects horizontally along the x axis by\n",
       "passing in ``axis=1``.\n",
       "\n",
       ">>> df4 = pd.DataFrame([['bird', 'polly'], ['monkey', 'george']],\n",
       "...                    columns=['animal', 'name'])\n",
       ">>> pd.concat([df1, df4], axis=1)\n",
       "  letter  number  animal    name\n",
       "0      a       1    bird   polly\n",
       "1      b       2  monkey  george\n",
       "\n",
       "Prevent the result from including duplicate index values with the\n",
       "``verify_integrity`` option.\n",
       "\n",
       ">>> df5 = pd.DataFrame([1], index=['a'])\n",
       ">>> df5\n",
       "   0\n",
       "a  1\n",
       ">>> df6 = pd.DataFrame([2], index=['a'])\n",
       ">>> df6\n",
       "   0\n",
       "a  2\n",
       ">>> pd.concat([df5, df6], verify_integrity=True)\n",
       "Traceback (most recent call last):\n",
       "    ...\n",
       "ValueError: Indexes have overlapping values: ['a']\n",
       "\u001b[1;31mFile:\u001b[0m      c:\\users\\basak\\anaconda3\\lib\\site-packages\\pandas\\core\\reshape\\concat.py\n",
       "\u001b[1;31mType:\u001b[0m      function\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "?pd.concat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "#İLERİ BİRLEŞTİRME İŞLEMLERİ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "#birebir birleştirme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>calisanlar</th>\n",
       "      <th>grup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ali</td>\n",
       "      <td>Muhasebe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Veli</td>\n",
       "      <td>Muhendislik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ayse</td>\n",
       "      <td>Muhendislik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fatma</td>\n",
       "      <td>IK</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  calisanlar         grup\n",
       "0        Ali     Muhasebe\n",
       "1       Veli  Muhendislik\n",
       "2       Ayse  Muhendislik\n",
       "3      Fatma           IK"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1=pd.DataFrame({\"calisanlar\":[\"Ali\",\"Veli\",\"Ayse\",\"Fatma\"],\n",
    "                 \"grup\":[\"Muhasebe\",\"Muhendislik\",\"Muhendislik\",\"IK\"]})\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>calisanlar</th>\n",
       "      <th>ilk_giris</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ali</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Veli</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ayse</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fatma</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  calisanlar  ilk_giris\n",
       "0        Ali       2010\n",
       "1       Veli       2009\n",
       "2       Ayse       2014\n",
       "3      Fatma       2019"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2=pd.DataFrame({\"calisanlar\":[\"Ali\",\"Veli\",\"Ayse\",\"Fatma\"],\n",
    "                 \"ilk_giris\":[2010,2009,2014,2019]})\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>calisanlar</th>\n",
       "      <th>grup</th>\n",
       "      <th>ilk_giris</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ali</td>\n",
       "      <td>Muhasebe</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Veli</td>\n",
       "      <td>Muhendislik</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ayse</td>\n",
       "      <td>Muhendislik</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fatma</td>\n",
       "      <td>IK</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  calisanlar         grup  ilk_giris\n",
       "0        Ali     Muhasebe       2010\n",
       "1       Veli  Muhendislik       2009\n",
       "2       Ayse  Muhendislik       2014\n",
       "3      Fatma           IK       2019"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df1,df2)\n",
    "#birebir birleştirme(hangi degişkene göre yapacagini kendi anlar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>calisanlar</th>\n",
       "      <th>grup</th>\n",
       "      <th>ilk_giris</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ali</td>\n",
       "      <td>Muhasebe</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Veli</td>\n",
       "      <td>Muhendislik</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ayse</td>\n",
       "      <td>Muhendislik</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fatma</td>\n",
       "      <td>IK</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  calisanlar         grup  ilk_giris\n",
       "0        Ali     Muhasebe       2010\n",
       "1       Veli  Muhendislik       2009\n",
       "2       Ayse  Muhendislik       2014\n",
       "3      Fatma           IK       2019"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df1,df2,on=\"calisanlar\")\n",
    "#hangi degskenin uzerine birlestirmek istedigimizi yazadabiliriz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#coktan teke birlestirme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3=pd.merge(df1,df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>calisanlar</th>\n",
       "      <th>grup</th>\n",
       "      <th>ilk_giris</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ali</td>\n",
       "      <td>Muhasebe</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Veli</td>\n",
       "      <td>Muhendislik</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ayse</td>\n",
       "      <td>Muhendislik</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fatma</td>\n",
       "      <td>IK</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  calisanlar         grup  ilk_giris\n",
       "0        Ali     Muhasebe       2010\n",
       "1       Veli  Muhendislik       2009\n",
       "2       Ayse  Muhendislik       2014\n",
       "3      Fatma           IK       2019"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>grup</th>\n",
       "      <th>mudur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Muhasebe</td>\n",
       "      <td>Caner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Muhendislik</td>\n",
       "      <td>Mustafa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IK</td>\n",
       "      <td>Berkcan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          grup    mudur\n",
       "0     Muhasebe    Caner\n",
       "1  Muhendislik  Mustafa\n",
       "2           IK  Berkcan"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4=pd.DataFrame({\"grup\":[\"Muhasebe\",\"Muhendislik\",\"IK\"],\n",
    "                 \"mudur\":[\"Caner\",\"Mustafa\",\"Berkcan\"]})\n",
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>calisanlar</th>\n",
       "      <th>grup</th>\n",
       "      <th>ilk_giris</th>\n",
       "      <th>mudur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ali</td>\n",
       "      <td>Muhasebe</td>\n",
       "      <td>2010</td>\n",
       "      <td>Caner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Veli</td>\n",
       "      <td>Muhendislik</td>\n",
       "      <td>2009</td>\n",
       "      <td>Mustafa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ayse</td>\n",
       "      <td>Muhendislik</td>\n",
       "      <td>2014</td>\n",
       "      <td>Mustafa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fatma</td>\n",
       "      <td>IK</td>\n",
       "      <td>2019</td>\n",
       "      <td>Berkcan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  calisanlar         grup  ilk_giris    mudur\n",
       "0        Ali     Muhasebe       2010    Caner\n",
       "1       Veli  Muhendislik       2009  Mustafa\n",
       "2       Ayse  Muhendislik       2014  Mustafa\n",
       "3      Fatma           IK       2019  Berkcan"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5=pd.merge(df3,df4)\n",
    "df5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#coktan coka birlestirme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>grup</th>\n",
       "      <th>yetenekler</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Muhasebe</td>\n",
       "      <td>matematik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Muhasebe</td>\n",
       "      <td>excel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Muhendislik</td>\n",
       "      <td>kodlama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Muhendislik</td>\n",
       "      <td>linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IK</td>\n",
       "      <td>excel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>IK</td>\n",
       "      <td>yonetim</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          grup yetenekler\n",
       "0     Muhasebe  matematik\n",
       "1     Muhasebe      excel\n",
       "2  Muhendislik    kodlama\n",
       "3  Muhendislik      linux\n",
       "4           IK      excel\n",
       "5           IK    yonetim"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6=pd.DataFrame({\"grup\":[\"Muhasebe\",\"Muhasebe\",\"Muhendislik\",\"Muhendislik\",\"IK\",\"IK\"],\n",
    "                 \"yetenekler\":[\"matematik\",\"excel\",\"kodlama\",\"linux\",\"excel\",\"yonetim\"]})\n",
    "df6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>calisanlar</th>\n",
       "      <th>grup</th>\n",
       "      <th>ilk_giris</th>\n",
       "      <th>mudur</th>\n",
       "      <th>yetenekler</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ali</td>\n",
       "      <td>Muhasebe</td>\n",
       "      <td>2010</td>\n",
       "      <td>Caner</td>\n",
       "      <td>matematik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ali</td>\n",
       "      <td>Muhasebe</td>\n",
       "      <td>2010</td>\n",
       "      <td>Caner</td>\n",
       "      <td>excel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Veli</td>\n",
       "      <td>Muhendislik</td>\n",
       "      <td>2009</td>\n",
       "      <td>Mustafa</td>\n",
       "      <td>kodlama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Veli</td>\n",
       "      <td>Muhendislik</td>\n",
       "      <td>2009</td>\n",
       "      <td>Mustafa</td>\n",
       "      <td>linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ayse</td>\n",
       "      <td>Muhendislik</td>\n",
       "      <td>2014</td>\n",
       "      <td>Mustafa</td>\n",
       "      <td>kodlama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Ayse</td>\n",
       "      <td>Muhendislik</td>\n",
       "      <td>2014</td>\n",
       "      <td>Mustafa</td>\n",
       "      <td>linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Fatma</td>\n",
       "      <td>IK</td>\n",
       "      <td>2019</td>\n",
       "      <td>Berkcan</td>\n",
       "      <td>excel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Fatma</td>\n",
       "      <td>IK</td>\n",
       "      <td>2019</td>\n",
       "      <td>Berkcan</td>\n",
       "      <td>yonetim</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  calisanlar         grup  ilk_giris    mudur yetenekler\n",
       "0        Ali     Muhasebe       2010    Caner  matematik\n",
       "1        Ali     Muhasebe       2010    Caner      excel\n",
       "2       Veli  Muhendislik       2009  Mustafa    kodlama\n",
       "3       Veli  Muhendislik       2009  Mustafa      linux\n",
       "4       Ayse  Muhendislik       2014  Mustafa    kodlama\n",
       "5       Ayse  Muhendislik       2014  Mustafa      linux\n",
       "6      Fatma           IK       2019  Berkcan      excel\n",
       "7      Fatma           IK       2019  Berkcan    yonetim"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df5,df6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  ****Toplulaştırma(Aggregation) ve Gruplama(Grouping)****\n",
    "\n",
    "**Basit Toplulaştırma fonksiyonları:**\n",
    "\n",
    "* count()\n",
    "* first()\n",
    "* last()\n",
    "* mean()\n",
    "* median()\n",
    "* min()\n",
    "* max()\n",
    "* std()\n",
    "* var()\n",
    "* sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[1;31mSignature:\u001b[0m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_dataset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcache\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata_home\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkws\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
       "\u001b[1;31mDocstring:\u001b[0m\n",
       "Load an example dataset from the online repository (requires internet).\n",
       "\n",
       "This function provides quick access to a small number of example datasets\n",
       "that are useful for documenting seaborn or generating reproducible examples\n",
       "for bug reports. It is not necessary for normal usage.\n",
       "\n",
       "Note that some of the datasets have a small amount of preprocessing applied\n",
       "to define a proper ordering for categorical variables.\n",
       "\n",
       "Use :func:`get_dataset_names` to see a list of available datasets.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "name : str\n",
       "    Name of the dataset (``{name}.csv`` on\n",
       "    https://github.com/mwaskom/seaborn-data).\n",
       "cache : boolean, optional\n",
       "    If True, try to load from the local cache first, and save to the cache\n",
       "    if a download is required.\n",
       "data_home : string, optional\n",
       "    The directory in which to cache data; see :func:`get_data_home`.\n",
       "kws : keys and values, optional\n",
       "    Additional keyword arguments are passed to passed through to\n",
       "    :func:`pandas.read_csv`.\n",
       "\n",
       "Returns\n",
       "-------\n",
       "df : :class:`pandas.DataFrame`\n",
       "    Tabular data, possibly with some preprocessing applied.\n",
       "\u001b[1;31mFile:\u001b[0m      c:\\users\\basak\\anaconda3\\lib\\site-packages\\seaborn\\utils.py\n",
       "\u001b[1;31mType:\u001b[0m      function\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "?sns.load_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = sns.load_dataset(\"planets\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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 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>method</th>\n",
       "      <th>number</th>\n",
       "      <th>orbital_period</th>\n",
       "      <th>mass</th>\n",
       "      <th>distance</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>269.300</td>\n",
       "      <td>7.10</td>\n",
       "      <td>77.40</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>874.774</td>\n",
       "      <td>2.21</td>\n",
       "      <td>56.95</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>763.000</td>\n",
       "      <td>2.60</td>\n",
       "      <td>19.84</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>326.030</td>\n",
       "      <td>19.40</td>\n",
       "      <td>110.62</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>516.220</td>\n",
       "      <td>10.50</td>\n",
       "      <td>119.47</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            method  number  orbital_period   mass  distance  year\n",
       "0  Radial Velocity       1         269.300   7.10     77.40  2006\n",
       "1  Radial Velocity       1         874.774   2.21     56.95  2008\n",
       "2  Radial Velocity       1         763.000   2.60     19.84  2011\n",
       "3  Radial Velocity       1         326.030  19.40    110.62  2007\n",
       "4  Radial Velocity       1         516.220  10.50    119.47  2009"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "number               1.785507\n",
       "orbital_period    2002.917596\n",
       "mass                 2.638161\n",
       "distance           264.069282\n",
       "year              2009.070531\n",
       "dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean() #bütün değişkenlerin ortalaması"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.6381605847953233"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"mass\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>number</th>\n",
       "      <td>1035.0</td>\n",
       "      <td>1.785507</td>\n",
       "      <td>1.240976</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>orbital_period</th>\n",
       "      <td>992.0</td>\n",
       "      <td>2002.917596</td>\n",
       "      <td>26014.728304</td>\n",
       "      <td>0.090706</td>\n",
       "      <td>5.44254</td>\n",
       "      <td>39.9795</td>\n",
       "      <td>526.005</td>\n",
       "      <td>730000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mass</th>\n",
       "      <td>513.0</td>\n",
       "      <td>2.638161</td>\n",
       "      <td>3.818617</td>\n",
       "      <td>0.003600</td>\n",
       "      <td>0.22900</td>\n",
       "      <td>1.2600</td>\n",
       "      <td>3.040</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance</th>\n",
       "      <td>808.0</td>\n",
       "      <td>264.069282</td>\n",
       "      <td>733.116493</td>\n",
       "      <td>1.350000</td>\n",
       "      <td>32.56000</td>\n",
       "      <td>55.2500</td>\n",
       "      <td>178.500</td>\n",
       "      <td>8500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>1035.0</td>\n",
       "      <td>2009.070531</td>\n",
       "      <td>3.972567</td>\n",
       "      <td>1989.000000</td>\n",
       "      <td>2007.00000</td>\n",
       "      <td>2010.0000</td>\n",
       "      <td>2012.000</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 count         mean           std          min         25%  \\\n",
       "number          1035.0     1.785507      1.240976     1.000000     1.00000   \n",
       "orbital_period   992.0  2002.917596  26014.728304     0.090706     5.44254   \n",
       "mass             513.0     2.638161      3.818617     0.003600     0.22900   \n",
       "distance         808.0   264.069282    733.116493     1.350000    32.56000   \n",
       "year            1035.0  2009.070531      3.972567  1989.000000  2007.00000   \n",
       "\n",
       "                      50%       75%       max  \n",
       "number             1.0000     2.000       7.0  \n",
       "orbital_period    39.9795   526.005  730000.0  \n",
       "mass               1.2600     3.040      25.0  \n",
       "distance          55.2500   178.500    8500.0  \n",
       "year            2010.0000  2012.000    2014.0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().T #bütün istatistiksel sonuçları verilmiş"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>number</th>\n",
       "      <td>498.0</td>\n",
       "      <td>1.734940</td>\n",
       "      <td>1.175720</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>orbital_period</th>\n",
       "      <td>498.0</td>\n",
       "      <td>835.778671</td>\n",
       "      <td>1469.128259</td>\n",
       "      <td>1.3283</td>\n",
       "      <td>38.27225</td>\n",
       "      <td>357.000</td>\n",
       "      <td>999.6000</td>\n",
       "      <td>17337.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mass</th>\n",
       "      <td>498.0</td>\n",
       "      <td>2.509320</td>\n",
       "      <td>3.636274</td>\n",
       "      <td>0.0036</td>\n",
       "      <td>0.21250</td>\n",
       "      <td>1.245</td>\n",
       "      <td>2.8675</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance</th>\n",
       "      <td>498.0</td>\n",
       "      <td>52.068213</td>\n",
       "      <td>46.596041</td>\n",
       "      <td>1.3500</td>\n",
       "      <td>24.49750</td>\n",
       "      <td>39.940</td>\n",
       "      <td>59.3325</td>\n",
       "      <td>354.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>498.0</td>\n",
       "      <td>2007.377510</td>\n",
       "      <td>4.167284</td>\n",
       "      <td>1989.0000</td>\n",
       "      <td>2005.00000</td>\n",
       "      <td>2009.000</td>\n",
       "      <td>2011.0000</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                count         mean          std        min         25%  \\\n",
       "number          498.0     1.734940     1.175720     1.0000     1.00000   \n",
       "orbital_period  498.0   835.778671  1469.128259     1.3283    38.27225   \n",
       "mass            498.0     2.509320     3.636274     0.0036     0.21250   \n",
       "distance        498.0    52.068213    46.596041     1.3500    24.49750   \n",
       "year            498.0  2007.377510     4.167284  1989.0000  2005.00000   \n",
       "\n",
       "                     50%        75%      max  \n",
       "number             1.000     2.0000      6.0  \n",
       "orbital_period   357.000   999.6000  17337.5  \n",
       "mass               1.245     2.8675     25.0  \n",
       "distance          39.940    59.3325    354.0  \n",
       "year            2009.000  2011.0000   2014.0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna().describe().T #eksik gözlemleri silip ssonuçları ver"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ### **Gruplama İşlemleri**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ****Gruplama İşlemi:**** Veri setinde yer alan Kategorik değişkenli gruplarının yakalanması ve bu grupların özelinde bazı işlemler yapılması demektir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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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       "        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>gruplar</th>\n",
       "      <th>veri</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>C</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gruplar  veri\n",
       "0       A    10\n",
       "1       B    11\n",
       "2       C    52\n",
       "3       A    23\n",
       "4       B    43\n",
       "5       C    55"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame({\"gruplar\":[\"A\",\"B\",\"C\",\"A\",\"B\",\"C\"],\n",
    "                 \"veri\":[10,11,52,23,43,55]},columns=[\"gruplar\",\"veri\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>veri</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gruplar</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>53.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         veri\n",
       "gruplar      \n",
       "A        16.5\n",
       "B        27.0\n",
       "C        53.5"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"gruplar\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>veri</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gruplar</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         veri\n",
       "gruplar      \n",
       "A          33\n",
       "B          54\n",
       "C         107"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"gruplar\").sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = sns.load_dataset(\"planets\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<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>method</th>\n",
       "      <th>number</th>\n",
       "      <th>orbital_period</th>\n",
       "      <th>mass</th>\n",
       "      <th>distance</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>269.300</td>\n",
       "      <td>7.10</td>\n",
       "      <td>77.40</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>874.774</td>\n",
       "      <td>2.21</td>\n",
       "      <td>56.95</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>763.000</td>\n",
       "      <td>2.60</td>\n",
       "      <td>19.84</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>326.030</td>\n",
       "      <td>19.40</td>\n",
       "      <td>110.62</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>516.220</td>\n",
       "      <td>10.50</td>\n",
       "      <td>119.47</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            method  number  orbital_period   mass  distance  year\n",
       "0  Radial Velocity       1         269.300   7.10     77.40  2006\n",
       "1  Radial Velocity       1         874.774   2.21     56.95  2008\n",
       "2  Radial Velocity       1         763.000   2.60     19.84  2011\n",
       "3  Radial Velocity       1         326.030  19.40    110.62  2007\n",
       "4  Radial Velocity       1         516.220  10.50    119.47  2009"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "method\n",
       "Astrometry                          631.180000\n",
       "Eclipse Timing Variations          4751.644444\n",
       "Imaging                          118247.737500\n",
       "Microlensing                       3153.571429\n",
       "Orbital Brightness Modulation         0.709307\n",
       "Pulsar Timing                      7343.021201\n",
       "Pulsation Timing Variations        1170.000000\n",
       "Radial Velocity                     823.354680\n",
       "Transit                              21.102073\n",
       "Transit Timing Variations            79.783500\n",
       "Name: orbital_period, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"method\")[\"orbital_period\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>method</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>Astrometry</th>\n",
       "      <td>2.0</td>\n",
       "      <td>631.180000</td>\n",
       "      <td>544.217663</td>\n",
       "      <td>246.360000</td>\n",
       "      <td>438.770000</td>\n",
       "      <td>631.180000</td>\n",
       "      <td>823.590000</td>\n",
       "      <td>1016.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eclipse Timing Variations</th>\n",
       "      <td>9.0</td>\n",
       "      <td>4751.644444</td>\n",
       "      <td>2499.130945</td>\n",
       "      <td>1916.250000</td>\n",
       "      <td>2900.000000</td>\n",
       "      <td>4343.500000</td>\n",
       "      <td>5767.000000</td>\n",
       "      <td>10220.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Imaging</th>\n",
       "      <td>12.0</td>\n",
       "      <td>118247.737500</td>\n",
       "      <td>213978.177277</td>\n",
       "      <td>4639.150000</td>\n",
       "      <td>8343.900000</td>\n",
       "      <td>27500.000000</td>\n",
       "      <td>94250.000000</td>\n",
       "      <td>730000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Microlensing</th>\n",
       "      <td>7.0</td>\n",
       "      <td>3153.571429</td>\n",
       "      <td>1113.166333</td>\n",
       "      <td>1825.000000</td>\n",
       "      <td>2375.000000</td>\n",
       "      <td>3300.000000</td>\n",
       "      <td>3550.000000</td>\n",
       "      <td>5100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Orbital Brightness Modulation</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.709307</td>\n",
       "      <td>0.725493</td>\n",
       "      <td>0.240104</td>\n",
       "      <td>0.291496</td>\n",
       "      <td>0.342887</td>\n",
       "      <td>0.943908</td>\n",
       "      <td>1.544929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pulsar Timing</th>\n",
       "      <td>5.0</td>\n",
       "      <td>7343.021201</td>\n",
       "      <td>16313.265573</td>\n",
       "      <td>0.090706</td>\n",
       "      <td>25.262000</td>\n",
       "      <td>66.541900</td>\n",
       "      <td>98.211400</td>\n",
       "      <td>36525.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pulsation Timing Variations</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1170.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1170.000000</td>\n",
       "      <td>1170.000000</td>\n",
       "      <td>1170.000000</td>\n",
       "      <td>1170.000000</td>\n",
       "      <td>1170.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Radial Velocity</th>\n",
       "      <td>553.0</td>\n",
       "      <td>823.354680</td>\n",
       "      <td>1454.926210</td>\n",
       "      <td>0.736540</td>\n",
       "      <td>38.021000</td>\n",
       "      <td>360.200000</td>\n",
       "      <td>982.000000</td>\n",
       "      <td>17337.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transit</th>\n",
       "      <td>397.0</td>\n",
       "      <td>21.102073</td>\n",
       "      <td>46.185893</td>\n",
       "      <td>0.355000</td>\n",
       "      <td>3.160630</td>\n",
       "      <td>5.714932</td>\n",
       "      <td>16.145700</td>\n",
       "      <td>331.600590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transit Timing Variations</th>\n",
       "      <td>3.0</td>\n",
       "      <td>79.783500</td>\n",
       "      <td>71.599884</td>\n",
       "      <td>22.339500</td>\n",
       "      <td>39.675250</td>\n",
       "      <td>57.011000</td>\n",
       "      <td>108.505500</td>\n",
       "      <td>160.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               count           mean            std  \\\n",
       "method                                                               \n",
       "Astrometry                       2.0     631.180000     544.217663   \n",
       "Eclipse Timing Variations        9.0    4751.644444    2499.130945   \n",
       "Imaging                         12.0  118247.737500  213978.177277   \n",
       "Microlensing                     7.0    3153.571429    1113.166333   \n",
       "Orbital Brightness Modulation    3.0       0.709307       0.725493   \n",
       "Pulsar Timing                    5.0    7343.021201   16313.265573   \n",
       "Pulsation Timing Variations      1.0    1170.000000            NaN   \n",
       "Radial Velocity                553.0     823.354680    1454.926210   \n",
       "Transit                        397.0      21.102073      46.185893   \n",
       "Transit Timing Variations        3.0      79.783500      71.599884   \n",
       "\n",
       "                                       min          25%           50%  \\\n",
       "method                                                                  \n",
       "Astrometry                      246.360000   438.770000    631.180000   \n",
       "Eclipse Timing Variations      1916.250000  2900.000000   4343.500000   \n",
       "Imaging                        4639.150000  8343.900000  27500.000000   \n",
       "Microlensing                   1825.000000  2375.000000   3300.000000   \n",
       "Orbital Brightness Modulation     0.240104     0.291496      0.342887   \n",
       "Pulsar Timing                     0.090706    25.262000     66.541900   \n",
       "Pulsation Timing Variations    1170.000000  1170.000000   1170.000000   \n",
       "Radial Velocity                   0.736540    38.021000    360.200000   \n",
       "Transit                           0.355000     3.160630      5.714932   \n",
       "Transit Timing Variations        22.339500    39.675250     57.011000   \n",
       "\n",
       "                                        75%            max  \n",
       "method                                                      \n",
       "Astrometry                       823.590000    1016.000000  \n",
       "Eclipse Timing Variations       5767.000000   10220.000000  \n",
       "Imaging                        94250.000000  730000.000000  \n",
       "Microlensing                    3550.000000    5100.000000  \n",
       "Orbital Brightness Modulation      0.943908       1.544929  \n",
       "Pulsar Timing                     98.211400   36525.000000  \n",
       "Pulsation Timing Variations     1170.000000    1170.000000  \n",
       "Radial Velocity                  982.000000   17337.500000  \n",
       "Transit                           16.145700     331.600590  \n",
       "Transit Timing Variations        108.505500     160.000000  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"method\")[\"orbital_period\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **İleri Toplulaştırma İşlemleri**\n",
    "* Aggregate\n",
    "* filter\n",
    "* transform\n",
    "* apply"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>gruplar</th>\n",
       "      <th>degisken1</th>\n",
       "      <th>degisken2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>23</td>\n",
       "      <td>253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>33</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>22</td>\n",
       "      <td>262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>11</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>C</td>\n",
       "      <td>99</td>\n",
       "      <td>969</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gruplar  degisken1  degisken2\n",
       "0       A         10        100\n",
       "1       B         23        253\n",
       "2       C         33        333\n",
       "3       A         22        262\n",
       "4       B         11        111\n",
       "5       C         99        969"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame({\"gruplar\":[\"A\",\"B\",\"C\",\"A\",\"B\",\"C\"],\n",
    "                 \"degisken1\":[10,23,33,22,11,99],\n",
    "                \"degisken2\":[100,253,333,262,111,969]},\n",
    "                columns=[\"gruplar\",\"degisken1\",\"degisken2\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    " \n",
    " ## **aggregate**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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",
       "        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>degisken1</th>\n",
       "      <th>degisken2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gruplar</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>16</td>\n",
       "      <td>181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>17</td>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>66</td>\n",
       "      <td>651</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         degisken1  degisken2\n",
       "gruplar                      \n",
       "A               16        181\n",
       "B               17        182\n",
       "C               66        651"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"gruplar\").mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#KENDİMİZ ÖZELLİKLE BİR İSTATİSTİK İSTİYORSAK YANİ DESCRİBE'NİN İÇERİSİNDEKİLER DEĞİL SADECE MOD MEDYAN DA DEĞİL.\n",
    "\n",
    "#KİŞİSEL İSTEKLERİMİZİ AGGREGATE FONKSİYONUNU KULLANARAK ÇAĞIRABİLİRİZ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">degisken1</th>\n",
       "      <th colspan=\"4\" halign=\"left\">degisken2</th>\n",
       "    </tr>\n",
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       "      <th>max</th>\n",
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       "    <tr>\n",
       "      <th>gruplar</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>A</th>\n",
       "      <td>10</td>\n",
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       "      <td>16</td>\n",
       "      <td>22</td>\n",
       "      <td>100</td>\n",
       "      <td>2</td>\n",
       "      <td>181</td>\n",
       "      <td>262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>23</td>\n",
       "      <td>111</td>\n",
       "      <td>2</td>\n",
       "      <td>182</td>\n",
       "      <td>253</td>\n",
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       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>33</td>\n",
       "      <td>2</td>\n",
       "      <td>66</td>\n",
       "      <td>99</td>\n",
       "      <td>333</td>\n",
       "      <td>2</td>\n",
       "      <td>651</td>\n",
       "      <td>969</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        degisken1                  degisken2                  \n",
       "              min count median max       min count median  max\n",
       "gruplar                                                       \n",
       "A              10     2     16  22       100     2    181  262\n",
       "B              11     2     17  23       111     2    182  253\n",
       "C              33     2     66  99       333     2    651  969"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"gruplar\").aggregate([\"min\",\"count\",np.median,max])\n",
    "\n",
    "#dışardan başka bir fonksiyon çağırma ihtiyacını np.median deyip numpydan ağırdık\n",
    "#pandasın içerisinde olan fonksiyonu tırnaklı ya da tırnaksız çağırabiliriz(\"min\" ya da max dedik)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>degisken1</th>\n",
       "      <th>degisken2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gruplar</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>10</td>\n",
       "      <td>262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>11</td>\n",
       "      <td>253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>33</td>\n",
       "      <td>969</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         degisken1  degisken2\n",
       "gruplar                      \n",
       "A               10        262\n",
       "B               11        253\n",
       "C               33        969"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"gruplar\").aggregate({\"degisken1\":\"min\",\n",
    "                                \"degisken2\":\"max\"})\n",
    "\n",
    "#degisken 1 in minlerini getir y degisken2 nin maxlarını dediğimizde sözlük yapısı kullanırız"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## **filter**\n",
    " \n",
    " **Fonksiyonumu  kendim tanımlamam ve bunu bir koşulcasına dfnin içerisindeki değişkenlerin üzerine göndermek gerekiyor..**\n",
    " \n",
    " **Kendi Kişisel fonksiyonlarımızı bir filtreleme aracımız değişkenlerin üzerinde sorgulamak işlemini filter işlemiyle ele alınacak**\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "def filter_func(x):\n",
    "    return x[\"degisken1\"].std() > 9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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>gruplar</th>\n",
       "      <th>degisken1</th>\n",
       "      <th>degisken2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>23</td>\n",
       "      <td>253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>33</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>22</td>\n",
       "      <td>262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>11</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>C</td>\n",
       "      <td>99</td>\n",
       "      <td>969</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gruplar  degisken1  degisken2\n",
       "0       A         10        100\n",
       "1       B         23        253\n",
       "2       C         33        333\n",
       "3       A         22        262\n",
       "4       B         11        111\n",
       "5       C         99        969"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>degisken1</th>\n",
       "      <th>degisken2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gruplar</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>8.485281</td>\n",
       "      <td>114.551299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>8.485281</td>\n",
       "      <td>100.409163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>46.669048</td>\n",
       "      <td>449.719913</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         degisken1   degisken2\n",
       "gruplar                       \n",
       "A         8.485281  114.551299\n",
       "B         8.485281  100.409163\n",
       "C        46.669048  449.719913"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"gruplar\").std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gruplar</th>\n",
       "      <th>degisken1</th>\n",
       "      <th>degisken2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>33</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>C</td>\n",
       "      <td>99</td>\n",
       "      <td>969</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gruplar  degisken1  degisken2\n",
       "2       C         33        333\n",
       "5       C         99        969"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"gruplar\").filter(filter_func)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## **transform**\n",
    " \n",
    "**Veri setimizin değişkenlerine bir  dönüştürme yapmak istiyoruz ve bu dönüştürme işleminide kendimiz tanımlamak istiyoruz**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>degisken1</th>\n",
       "      <th>degisken2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23</td>\n",
       "      <td>253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22</td>\n",
       "      <td>262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>99</td>\n",
       "      <td>969</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   degisken1  degisken2\n",
       "0         10        100\n",
       "1         23        253\n",
       "2         33        333\n",
       "3         22        262\n",
       "4         11        111\n",
       "5         99        969"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_a=df.iloc[:,1:]\n",
    "df_a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>degisken1</th>\n",
       "      <th>degisken2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.687871</td>\n",
       "      <td>-0.738461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.299074</td>\n",
       "      <td>-0.263736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.015514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.328982</td>\n",
       "      <td>-0.235811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.657963</td>\n",
       "      <td>-0.704331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.973890</td>\n",
       "      <td>1.957853</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   degisken1  degisken2\n",
       "0  -0.687871  -0.738461\n",
       "1  -0.299074  -0.263736\n",
       "2   0.000000  -0.015514\n",
       "3  -0.328982  -0.235811\n",
       "4  -0.657963  -0.704331\n",
       "5   1.973890   1.957853"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_a.transform(lambda x: (x-x.mean())/x.std())  #standart normal dönüşümü"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## **Apply**\n",
    "**Değişkenlerin üzerinde gezinme yeteneği olan ve aggregation yani toplulaştırma amacıyla kullanılan bir fonksiyondur**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>degisken1</th>\n",
       "      <th>degisken2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23</td>\n",
       "      <td>253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22</td>\n",
       "      <td>262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>99</td>\n",
       "      <td>969</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   degisken1  degisken2\n",
       "0         10        100\n",
       "1         23        253\n",
       "2         33        333\n",
       "3         22        262\n",
       "4         11        111\n",
       "5         99        969"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame({\"degisken1\":[10,23,33,22,11,99],\n",
    "                \"degisken2\":[100,253,333,262,111,969]},\n",
    "                columns=[\"degisken1\",\"degisken2\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "degisken1     198\n",
       "degisken2    2028\n",
       "dtype: int64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(np.sum) #sütun bazında"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "degisken1     33.0\n",
       "degisken2    338.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(np.mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **PİVOT TABLOLAR**\n",
    "\n",
    "**Veri setleri üzerinde bazı satır ve sütun işlemleri yaparak veri setine amaca uygun hale getirmek için kullanılan yapılardır**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "0    man        True  NaN  Southampton    no  False  \n",
       "1  woman       False    C    Cherbourg   yes  False  \n",
       "2  woman       False  NaN  Southampton   yes   True  \n",
       "3  woman       False    C  Southampton   yes  False  \n",
       "4    man        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "titanic=sns.load_dataset(\"titanic\")\n",
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cinsiyete göre gruplayıp sağ kalma(survived) değişkenini inceleyelim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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>survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        survived\n",
       "sex             \n",
       "female  0.742038\n",
       "male    0.188908"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.groupby(\"sex\")[[\"survived\"]].mean() \n",
    "#ilkel bir pivotlama işlemi yapmış olduk.sağ kalmaya göre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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",
       "        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>survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th>class</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">female</th>\n",
       "      <th>First</th>\n",
       "      <td>0.968085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Second</th>\n",
       "      <td>0.921053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Third</th>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">male</th>\n",
       "      <th>First</th>\n",
       "      <td>0.368852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Second</th>\n",
       "      <td>0.157407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Third</th>\n",
       "      <td>0.135447</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               survived\n",
       "sex    class           \n",
       "female First   0.968085\n",
       "       Second  0.921053\n",
       "       Third   0.500000\n",
       "male   First   0.368852\n",
       "       Second  0.157407\n",
       "       Third   0.135447"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.groupby([\"sex\",\"class\"])[[\"survived\"]].aggregate(\"mean\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
       "    .dataframe thead tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.968085</td>\n",
       "      <td>0.921053</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.368852</td>\n",
       "      <td>0.157407</td>\n",
       "      <td>0.135447</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        survived                    \n",
       "class      First    Second     Third\n",
       "sex                                 \n",
       "female  0.968085  0.921053  0.500000\n",
       "male    0.368852  0.157407  0.135447"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.groupby([\"sex\",\"class\"])[[\"survived\"]].aggregate(\"mean\") .unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.968085</td>\n",
       "      <td>0.921053</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.368852</td>\n",
       "      <td>0.157407</td>\n",
       "      <td>0.135447</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        survived                    \n",
       "class      First    Second     Third\n",
       "sex                                 \n",
       "female  0.968085  0.921053  0.500000\n",
       "male    0.368852  0.157407  0.135447"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.groupby([\"sex\",\"class\"])[[\"survived\"]].mean().unstack()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**pivot ile pivot table**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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>class</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.968085</td>\n",
       "      <td>0.921053</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.368852</td>\n",
       "      <td>0.157407</td>\n",
       "      <td>0.135447</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "class      First    Second     Third\n",
       "sex                                 \n",
       "female  0.968085  0.921053  0.500000\n",
       "male    0.368852  0.157407  0.135447"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.pivot_table(\"survived\",index=\"sex\",columns=\"class\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    (18, 90]\n",
       "1    (18, 90]\n",
       "2    (18, 90]\n",
       "3    (18, 90]\n",
       "4    (18, 90]\n",
       "Name: age, dtype: category\n",
       "Categories (2, interval[int64]): [(0, 18] < (18, 90]]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age = pd.cut(titanic[\"age\"],[0,18,90])\n",
    "age.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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>class</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">female</th>\n",
       "      <th>(0, 18]</th>\n",
       "      <td>0.909091</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.511628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(18, 90]</th>\n",
       "      <td>0.972973</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.423729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">male</th>\n",
       "      <th>(0, 18]</th>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.215686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(18, 90]</th>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.133663</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "class               First    Second     Third\n",
       "sex    age                                   \n",
       "female (0, 18]   0.909091  1.000000  0.511628\n",
       "       (18, 90]  0.972973  0.900000  0.423729\n",
       "male   (0, 18]   0.800000  0.600000  0.215686\n",
       "       (18, 90]  0.375000  0.071429  0.133663"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.pivot_table(\"survived\",[\"sex\",age],\"class\") \n",
    "#age'i dışardan aldık"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **DIŞ KAYNAKLI VERİ OKUMAK**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#yüklemek istediğimiz dosyaları çalışma dizinimize getirememiz lazım"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.read_csv(\"dizin/ornekcsv.csv\",sep=\";\") \n",
    "pd.read_csv(\"dizin/duz_metin.txt\")  #txt dosyamızı csv yüklemesiyle yaptık\n",
    "pd.read_excel(\"dizin/ornekx.xlsx\")  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kaynakça:https://www.udemy.com/course/python-egitimi/"
   ]
  }
 ],
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