{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4.877804138352662, 0.02720420624699369, 1, array([[58.13207547, 20.86792453],\n",
      "       [19.86792453,  7.13207547]]))\n",
      "Ttest_indResult(statistic=2.2558785269477974, pvalue=0.02686599410805479)\n",
      "(9.417431088992272, 0.009016351249902618, 2, array([[58.13207547, 19.86792453],\n",
      "       [ 2.23584906,  0.76415094],\n",
      "       [18.63207547,  6.36792453]]))\n",
      "Ttest_indResult(statistic=-8.26430309338257, pvalue=4.402977956690523e-09)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(inf, 0.013361462728551347)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy\n",
    "import scipy.stats\n",
    "gender=numpy.array([[63,16],[15,12]])\n",
    "print(scipy.stats.chi2_contingency(gender))\n",
    "print(scipy.stats.ttest_ind_from_stats(39.48, 38.26, 79, 25.78, 22.26, 27, equal_var=False))\n",
    "cvar=numpy.array([[61,17],[0,3],[18,7]])\n",
    "cvar1=numpy.array([[61,17],[0,3]])\n",
    "print(scipy.stats.chi2_contingency(cvar))\n",
    "print(scipy.stats.ttest_ind_from_stats(1.72, 1.07, 79, 6.19, 2.74, 27, equal_var=False))\n",
    "scipy.stats.fisher_exact(cvar1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5.742882730015083, 0.016555599001289927, 1, array([[48.13483146, 14.86516854],\n",
      "       [19.86516854,  6.13483146]]))\n",
      "Ttest_indResult(statistic=0.507116003909028, pvalue=0.6140574223641995)\n",
      "(0.5543478260869565, 0.5384774958157479)\n",
      "\n",
      "\tFisher's Exact Test for Count Data\n",
      "\n",
      "data:  \n",
      "p-value = 0.4703\n",
      "alternative hypothesis: two.sided\n",
      "\n",
      "\n",
      "(inf, 0.017701863354037183)\n",
      "Ttest_indResult(statistic=-7.670035520868973, pvalue=1.8453617356830517e-08)\n"
     ]
    }
   ],
   "source": [
    "import numpy\n",
    "import scipy.stats\n",
    "gender=numpy.array([[53,10],[15,11]])\n",
    "print(scipy.stats.chi2_contingency(gender))\n",
    "print(scipy.stats.ttest_ind_from_stats(26.85,25.49, 63, 24.2 , 21.02 , 26, equal_var=False))\n",
    "cvar=numpy.array([[51,12],[23,3]])\n",
    "print(scipy.stats.fisher_exact(cvar))\n",
    "\n",
    "import rpy2.robjects\n",
    "rpy2.robjects.r['pi']\n",
    "dataframe=rpy2.robjects.DataFrame({'hidrokolon':rpy2.robjects.IntVector([2,3,3,4]),'operacija':rpy2.robjects.IntVector([1,0,2,0])})\n",
    "dataframe.rownames=rpy2.robjects.StrVector(['<12hr','12-24h','24-48h','>48h'])\n",
    "#print(dataframe)\n",
    "#import rpy2.robjects.packages\n",
    "#base=rpy2.robjects.packages.importr('base')\n",
    "#utils=rpy2.robjects.packages.importr('utils')\n",
    "#dir(utils)\n",
    "fisherTest=rpy2.robjects.r['fisher.test']\n",
    "res=fisherTest(dataframe)\n",
    "print(res)\n",
    "cvar1=numpy.array([[51,16],[0,3]])\n",
    "print(scipy.stats.fisher_exact(cvar1))\n",
    "print(scipy.stats.ttest_ind_from_stats(1.98,1.19 , 63, 6.23,2.72 , 26, equal_var=False))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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