Pandas_01.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "608f6f77",
   "metadata": {},
   "source": [
    "## Pandas Serisi Oluşturmak"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dd6d9519",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "df0a10ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "seri = pandas.Series([42, 34, 25, 38])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "68344a0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    42\n",
       "1    34\n",
       "2    25\n",
       "3    38\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seri"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "131ec0d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(seri)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e79c11d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[RangeIndex(start=0, stop=4, step=1)]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seri.axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6e73ecf0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seri.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3bde2da9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seri.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b70b633f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seri.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e91a583f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([42, 34, 25, 38])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seri.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cc1338aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    42\n",
       "1    34\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seri.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c886089b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    25\n",
       "3    38\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seri.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6b5c495d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#index isimlendirme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "880d4035",
   "metadata": {},
   "outputs": [],
   "source": [
    "seri_2 = pandas.Series([54, 45, 34, 87, 56], index=[1, 3, 5, 7, 9])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4535759e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    54\n",
       "3    45\n",
       "5    34\n",
       "7    87\n",
       "9    56\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seri_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "af520f5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "seri_3 = pandas.Series([54, 45, 34, 87, 56], index=['a', 'b', 'c', 'd', 'e'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3e28b4f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    54\n",
       "b    45\n",
       "c    34\n",
       "d    87\n",
       "e    56\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seri_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "778653f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "54"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seri_3['a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ce0a7ae3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    54\n",
       "b    45\n",
       "c    34\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seri_3['a': 'c']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1bb0b6b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#sözlük üzerinden veri oluşturma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fc28c5ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "sozluk = {'log': 10, 'cart': 11}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "142ba7f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "seri_4 = pandas.Series(sozluk)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "792eb45f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "log     10\n",
       "cart    11\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seri_4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a7e95cd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#iki seriyi birleştirme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b725de66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    42\n",
       "1    34\n",
       "2    25\n",
       "3    38\n",
       "1    54\n",
       "3    45\n",
       "5    34\n",
       "7    87\n",
       "9    56\n",
       "dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pandas.concat([seri, seri_2])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4231eef4",
   "metadata": {},
   "source": [
    "## Eleman İşlemleri"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "618e6ab4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0f8c6929",
   "metadata": {},
   "outputs": [],
   "source": [
    "array = numpy.array([1, 2, 3, 4, 5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "71ad6449",
   "metadata": {},
   "outputs": [],
   "source": [
    "seri_5 = pandas.Series(array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "05972cb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "3    4\n",
       "4    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seri_5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5600b667",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seri_5[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a4ea72be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    2\n",
       "2    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seri_5[1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b2cd22be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=5, step=1)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seri_5.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f6c5c473",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Series.keys of 0    1\n",
       "1    2\n",
       "2    3\n",
       "3    4\n",
       "4    5\n",
       "dtype: int64>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seri_5.keys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "0c57be54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 1), (1, 2), (2, 3), (3, 4), (4, 5)]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(seri_5.items())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ee51c373",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0 in seri_5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "b819e755",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "6 in seri_5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddab400a",
   "metadata": {},
   "source": [
    "## Pandas DataFrame Oluşturma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "91d3b0d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "l = [1, 34, 54, 76]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e1789c2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 34, 54, 76]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "86f0f75c",
   "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>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   degisken_ismi\n",
       "0              1\n",
       "1             34\n",
       "2             54\n",
       "3             76"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pandas.DataFrame(l, columns=['degisken_ismi'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "547a4c3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "mat = numpy.arange(1,10).reshape((3, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7d4276cd",
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pandas.DataFrame(mat, columns=['var1', 'var2', 'var3'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e017bd6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#dataframe isimlendirme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4a940430",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pandas.DataFrame(mat, columns=['var1', 'var2', 'var3'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a3e70e31",
   "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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "952088e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['var1', 'var2', 'var3'], dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9897b9c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = ['deg1', 'deg2', 'deg3']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ec233364",
   "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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ca281725",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "63f1a37e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[RangeIndex(start=0, stop=3, step=1),\n",
       " Index(['deg1', 'deg2', 'deg3'], dtype='object')]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d9b37af7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 3)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fa16af97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1c025f4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8420ea1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6],\n",
       "       [7, 8, 9]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "96f65de6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "91f4248c",
   "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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23595f3e",
   "metadata": {},
   "source": [
    "## Eleman İşlemleri"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e02fff7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "s1 = numpy.random.randint(10, size=5)\n",
    "s2 = numpy.random.randint(10, size=5)\n",
    "s3 = numpy.random.randint(10, size=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "bc449f21",
   "metadata": {},
   "outputs": [],
   "source": [
    "sozluk = {'var1': s1, 'var2': s2, 'var3': s3}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "44cbd652",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'var1': array([0, 1, 9, 3, 0]),\n",
       " 'var2': array([9, 0, 1, 0, 7]),\n",
       " 'var3': array([0, 7, 1, 8, 6])}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sozluk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "aa11b836",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2 = pandas.DataFrame(sozluk)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c5489adc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   var1  var2  var3\n",
       "0     0     9     0\n",
       "1     1     0     7\n",
       "2     9     1     1\n",
       "3     3     0     8\n",
       "4     0     7     6"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1c494126",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   var1  var2  var3\n",
       "0     0     9     0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2[0:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cf0d59dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=5, step=1)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a9f189a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2.index = ['a', 'b', 'c', 'd', 'e']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4fbecb99",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>3</td>\n",
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       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   var1  var2  var3\n",
       "a     0     9     0\n",
       "b     1     0     7\n",
       "c     9     1     1\n",
       "d     3     0     8\n",
       "e     0     7     6"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "665de9c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>d</th>\n",
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       "      <th>e</th>\n",
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      "text/plain": [
       "   var1  var2  var3\n",
       "c     9     1     1\n",
       "d     3     0     8\n",
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     "execution_count": 37,
     "metadata": {},
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   ],
   "source": [
    "df_2['c': 'e']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "088d5a7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#silme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "976e8220",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>c</th>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>3</td>\n",
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       "      <th>e</th>\n",
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      "text/plain": [
       "   var1  var2  var3\n",
       "b     1     0     7\n",
       "c     9     1     1\n",
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       "e     0     7     6"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2.drop('a', axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "29f91686",
   "metadata": {},
   "outputs": [
    {
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       "    </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>3</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   var1  var2  var3\n",
       "a     0     9     0\n",
       "b     1     0     7\n",
       "c     9     1     1\n",
       "d     3     0     8\n",
       "e     0     7     6"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "6376488e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2.drop('a', axis = 0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "4e27d04d",
   "metadata": {},
   "outputs": [
    {
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      "text/html": [
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       "      <td>6</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "   var1  var2  var3\n",
       "b     1     0     7\n",
       "c     9     1     1\n",
       "d     3     0     8\n",
       "e     0     7     6"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e19d4243",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2['var4'] = df_2['var1'] * df_2['var2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "1480322b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>var4</th>\n",
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       "      <td>0</td>\n",
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       "      <th>c</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <th>e</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   var1  var2  var3  var4\n",
       "b     1     0     7     0\n",
       "c     9     1     1     9\n",
       "d     3     0     8     0\n",
       "e     0     7     6     0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "41fe878e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#değişken silmek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "532670e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>3</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
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       "      <th>e</th>\n",
       "      <td>0</td>\n",
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       "      <td>6</td>\n",
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       "</div>"
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      "text/plain": [
       "   var1  var2  var3\n",
       "b     1     0     7\n",
       "c     9     1     1\n",
       "d     3     0     8\n",
       "e     0     7     6"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2.drop('var4', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7fbf1905",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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