{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# SORU 3 (1)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "# Helper functions\n",
    "def pprint(title, val):\n",
    "    space = (40 - len(title)) * \" \"\n",
    "\n",
    "    if type(val) == int or type(val) == float:\n",
    "        vs = \"{:.0f}\".format(val)\n",
    "        indent = (5 - len(vs)) * \" \"\n",
    "    else:\n",
    "        indent = \" \"\n",
    "\n",
    "    if type(val) == list:\n",
    "        tmpVal = val[0]\n",
    "        val = \",\".join([str(elem) for elem in val])\n",
    "        if type(tmpVal) == int or type(tmpVal) == float:\n",
    "            vs = \"{:.0f}\".format(tmpVal)\n",
    "            indent = (5 - len(vs)) * \" \"\n",
    "        else:\n",
    "            indent = (5 - len(val)) * \" \"\n",
    "\n",
    "    print(title, space, \":\", indent, val)\n",
    "\n",
    "\n",
    "def tprint(t):\n",
    "    dash = len(t) * \"-\"\n",
    "    print(t)\n",
    "    print(dash)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-08T21:43:59.797089Z",
     "end_time": "2023-04-08T21:43:59.827273Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "start_time": "2023-04-08T21:43:59.827188Z",
     "end_time": "2023-04-08T21:43:59.854064Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Verilerin Toplamı                         :   1350\n",
      "Ortalama                                  :     45\n",
      "\n",
      "Ortalama Farkları\n",
      "------------------\n",
      "[-35, -31, -31, -28, -24, -24, -21, -18, -17, -15, -12, -10, -7, -4, -4, -3, 0, 6, 8, 9, 9, 10, 17, 26, 29, 31, 32, 32, 32, 43]\n",
      "Ortalamadan Farkların Toplamı             :      0\n",
      "\n",
      "\n",
      "Ortalama Fark Kareleri\n",
      "-----------------------\n",
      "[1225, 961, 961, 784, 576, 576, 441, 324, 289, 225, 144, 100, 49, 16, 16, 9, 0, 36, 64, 81, 81, 100, 289, 676, 841, 961, 1024, 1024, 1024, 1849]\n",
      "Ortalama Farkların Karesi Toplamı         :  14746\n",
      "\n",
      "Varyans                                   :    508.48\n",
      "Std. Sapma                                :     22.55\n",
      "Medyan                                    :     41.5\n",
      "Mod                                       :     77\n",
      "Çeyreklikler                              :     26.25,41.5,64.25\n",
      "Aralık                                    :     78\n",
      "Çeyreklikler Aralığı                      :     38.0\n"
     ]
    }
   ],
   "source": [
    "# Soru 1-A, C, E\n",
    "import statistics\n",
    "\n",
    "veri = [10, 14, 14, 17, 21, 21, 24, 27, 28, 30, 33, 35, 38, 41, 41, 42, 45, 51, 53, 54, 54, 55, 62, 71, 74, 76, 77, 77,\n",
    "        77, 88]\n",
    "pprint(\"Verilerin Toplamı\", sum(veri))\n",
    "\n",
    "mean = statistics.mean(veri)\n",
    "pprint(\"Ortalama\", statistics.mean(veri))\n",
    "\n",
    "ortFark = [(x - mean) for x in veri]\n",
    "tprint(\"\\nOrtalama Farkları\")\n",
    "print(ortFark)\n",
    "pprint(\"Ortalamadan Farkların Toplamı\", sum(ortFark))\n",
    "print()\n",
    "\n",
    "ortFarkKare = [(x - mean) ** 2 for x in veri]\n",
    "tprint(\"\\nOrtalama Fark Kareleri\")\n",
    "print(ortFarkKare)\n",
    "pprint(\"Ortalama Farkların Karesi Toplamı\", sum(ortFarkKare))\n",
    "\n",
    "print()\n",
    "\n",
    "pprint(\"Varyans\", round(statistics.variance(veri), 2))\n",
    "pprint(\"Std. Sapma\", round(statistics.stdev(veri), 2))\n",
    "pprint(\"Medyan\", statistics.median(veri))\n",
    "pprint(\"Mod\", statistics.multimode(veri))\n",
    "\n",
    "qs = statistics.quantiles(veri)\n",
    "pprint(\"Çeyreklikler\", qs)\n",
    "\n",
    "pprint(\"Aralık\", veri[-1] - veri[0])\n",
    "pprint(\"Çeyreklikler Aralığı\", qs[2] - qs[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "(<Figure size 750x275 with 1 Axes>, <Axes: >)"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 750x275 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Soru 1-B\n",
    "# Dal - yaprak grafiği\n",
    "import stemgraphic\n",
    "\n",
    "stemgraphic.stem_graphic(veri, asc=False)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-08T21:43:59.854227Z",
     "end_time": "2023-04-08T21:44:22.375718Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Çarpıklık Katsayısı                       :      0.24165031687153363\n",
      "Çarpıklık Yönü                            :   Sağ\n"
     ]
    }
   ],
   "source": [
    "# Soru 1-D\n",
    "# Çarpıklık\n",
    "from scipy.stats import skew\n",
    "\n",
    "sk = skew(veri)\n",
    "if sk > 0:\n",
    "    yon = \"Sağ\"\n",
    "elif sk < 0:\n",
    "    yon = \"Sol\"\n",
    "else:\n",
    "    yon = \"Simetrik\"\n",
    "pprint(\"Çarpıklık Katsayısı\", sk)\n",
    "pprint(\"Çarpıklık Yönü\", yon)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-08T21:44:22.344717Z",
     "end_time": "2023-04-08T21:44:22.376411Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          Frekans 0\n",
      "0                  \n",
      "[10, 24)          6\n",
      "[24, 38)          6\n",
      "[38, 52)          6\n",
      "[52, 66)          5\n",
      "[66, 80)          6\n",
      "[80, 94)          1\n",
      "[10, 24, 38, 52, 66, 80, 94]\n"
     ]
    }
   ],
   "source": [
    "# Soru 1-F\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "w = 14\n",
    "binEdges = [int(x) for x in range(min(veri), max(veri) + w, w)]\n",
    "\n",
    "df = pd.DataFrame(veri)\n",
    "res = df.apply(lambda x: pd.cut(x, bins=binEdges, right=False).value_counts()).sort_index().add_prefix('Frekans ')\n",
    "print(res)\n",
    "print(binEdges)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-08T22:23:21.965987Z",
     "end_time": "2023-04-08T22:23:21.982373Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Soru 1-G / Histogram\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.hist(veri, bins=binEdges, edgecolor='black')\n",
    "plt.axis([0, 100, 0, 7])\n",
    "\n",
    "plt.xticks([i for i in binEdges])\n",
    "\n",
    "plt.title(\"Soru 3(1) - G / Histogram\")\n",
    "plt.xlabel(\"Gözlem Aralıkları\")\n",
    "plt.ylabel(\"Frekans\")\n",
    "\n",
    "plt.show()\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-08T22:26:29.559883Z",
     "end_time": "2023-04-08T22:26:29.620662Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x136735fd0>]"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Soru 1-G / Ogive\n",
    "values, base = np.histogram(veri, binEdges)\n",
    "kum = np.cumsum(values)\n",
    "plt.plot(base[1:], kum, marker=\"o\", linestyle='-')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-08T21:44:22.503169Z",
     "end_time": "2023-04-08T21:44:22.599266Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Soru 4 (2)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cov                                       :    111.86666666666667\n",
      "Cor                                       :      0.9987897067500333\n"
     ]
    }
   ],
   "source": [
    "# Soru 2-B\n",
    "x = [5, 12, 20, 23, 30, 32]\n",
    "y = [8, 16, 24, 28, 34, 36]\n",
    "\n",
    "# python 3.10'dan itibaren covarianca ve corelation dahili statistics modülüne eklendi\n",
    "# <3.10 için numpy kullanılmalı ie:\n",
    "# numpy.cov(x, y, bias=False)[0][1] // bis=False ise sample, yani n-1\n",
    "import statistics\n",
    "\n",
    "cov = statistics.covariance(x, y)\n",
    "pprint(\"Cov\", cov)\n",
    "\n",
    "# numpy.corrcoef(x,y)[0][1]\n",
    "cor = statistics.correlation(x, y)\n",
    "pprint(\"Cor\", cor)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-08T22:26:49.765762Z",
     "end_time": "2023-04-08T22:26:49.769079Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Soru 2-C\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.scatter(x, y)\n",
    "plt.title(\"Soru 4(2) - C\")\n",
    "plt.xlabel(\"x değerleri\")\n",
    "plt.ylabel(\"y değerleri\")\n",
    "plt.axis(\"equal\") # hem y hem de x ayn\n",
    "plt.show()\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-08T22:16:27.237364Z",
     "end_time": "2023-04-08T22:16:27.311169Z"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}