UVBMOB_ODEV1_HATICE_CANDAN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy.stats as st\n",
    "from scipy.stats import norm\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "import scipy\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Books():\n",
    "    populasyon=[]\n",
    "    def __init__(self,name,job,age,book_number_month): \n",
    "        print(\"New member added ! Welcome to book club\",name)\n",
    "        self.name = name\n",
    "        self.job = job\n",
    "        self.age = age\n",
    "        self.book_number_month = book_number_month\n",
    "        self.day = 0\n",
    "        self.populasyon.append(self.book_number_month)\n",
    "    def distribution():\n",
    "        orneklem=Books.populasyon\n",
    "        print(\"Mean  : \", np.mean(orneklem))\n",
    "        print(\"Std   : \",np.std(orneklem))\n",
    "        [a,b]=st.t.interval(alpha = 0.95, df = len(orneklem) - 1, loc=np.mean(orneklem), scale=st.sem(orneklem))\n",
    "        print(\"Scale : \",st.t.interval(alpha = 0.95, df = len(orneklem) - 1, loc=np.mean(orneklem), scale=st.sem(orneklem)))\n",
    "        orneklem = norm.rvs(size=len(Books.populasyon), loc=0,scale=1)\n",
    "        ax = sns.distplot(orneklem)\n",
    "        ax.set(xlabel='Number of books',ylabel ='Frequency',title = 'Distribution Diagram')\n",
    "        for i in range(len(orneklem)):\n",
    "            pdf_val=stats.norm(0,0.1).pdf(i)\n",
    "            print('Pdf[%d]:'%(i),pdf_val)\n",
    "        for k in range(len(orneklem)):\n",
    "            cdf_val = scipy.stats.norm.cdf(k)\n",
    "            print('Cdf[%d]:'%(k),cdf_val) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New member added ! Welcome to book club Hatice\n",
      "New member added ! Welcome to book club Ayse\n",
      "New member added ! Welcome to book club Arzu\n",
      "New member added ! Welcome to book club Binnaz\n",
      "New member added ! Welcome to book club Gamze\n",
      "New member added ! Welcome to book club Mehmet\n",
      "New member added ! Welcome to book club Ecrin\n",
      "New member added ! Welcome to book club Ahmet\n",
      "New member added ! Welcome to book club Hakan\n",
      "New member added ! Welcome to book club Aysu\n"
     ]
    }
   ],
   "source": [
    "hatice = Books(\"Hatice\",\"Engineer\",24,5)  \n",
    "ayse = Books(\"Ayse\",\"Teacher\",28,7)    \n",
    "arzu = Books(\"Arzu\",\"Doctor\",29,3)    \n",
    "binnaz = Books(\"Binnaz\",\"Dentist\",30,3) \n",
    "gamze = Books(\"Gamze\",\"Engineer\",38,6)     \n",
    "mehmet = Books(\"Mehmet\",\"Cook\",24,1) \n",
    "ecrin = Books(\"Ecrin\",\"Scientist\",43,4)    \n",
    "ahmet = Books(\"Ahmet\",\"Retired\",68,10) \n",
    "hakan = Books(\"Hakan\",\"Director\",38,2)   \n",
    "aysu = Books(\"Aysu\",\"Student\",19,8)          \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean  :  4.9\n",
      "Std   :  2.7\n",
      "Scale :  (2.864058553533108, 6.935941446466893)\n",
      "Pdf[0]: 3.989422804014327\n",
      "Pdf[1]: 7.694598626706419e-22\n",
      "Pdf[2]: 5.5209483621597635e-87\n",
      "Pdf[3]: 1.4736461348785476e-195\n",
      "Pdf[4]: 0.0\n",
      "Pdf[5]: 0.0\n",
      "Pdf[6]: 0.0\n",
      "Pdf[7]: 0.0\n",
      "Pdf[8]: 0.0\n",
      "Pdf[9]: 0.0\n",
      "Cdf[0]: 0.5\n",
      "Cdf[1]: 0.8413447460685429\n",
      "Cdf[2]: 0.9772498680518208\n",
      "Cdf[3]: 0.9986501019683699\n",
      "Cdf[4]: 0.9999683287581669\n",
      "Cdf[5]: 0.9999997133484281\n",
      "Cdf[6]: 0.9999999990134123\n",
      "Cdf[7]: 0.9999999999987201\n",
      "Cdf[8]: 0.9999999999999993\n",
      "Cdf[9]: 1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Books.distribution() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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