{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[50, 40, 20],\n",
       "       [30, 25, 15]])"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "table = np.array([[50,40,20],[30,25,15]])\n",
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[80 65 35]\n",
      "[110  70]\n",
      "180\n"
     ]
    }
   ],
   "source": [
    "print(table.sum(axis=0))\n",
    "print(table.sum(axis=1))\n",
    "print(table.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.44444444  0.36111111  0.19444444]\n",
      "[ 0.61111111  0.38888889]\n"
     ]
    }
   ],
   "source": [
    "n = table.sum()\n",
    "col = table.sum(axis=0)/n\n",
    "row = table.sum(axis=1)/n\n",
    "print(col)\n",
    "print(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, 0) 50\n",
      "(0, 1) 40\n",
      "(0, 2) 20\n",
      "(1, 0) 30\n",
      "(1, 1) 25\n",
      "(1, 2) 15\n"
     ]
    }
   ],
   "source": [
    "for index, x in np.ndenumerate(table):\n",
    "    print(index, x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 48.88888889  39.72222222  21.38888889]\n",
      " [ 31.11111111  25.27777778  13.61111111]]\n"
     ]
    }
   ],
   "source": [
    "expected = np.empty(table.shape)\n",
    "for index, x in np.ndenumerate(table):\n",
    "    pA = row[index[0]]\n",
    "    pB = col[index[1]]\n",
    "    expected[index] = pA*pB*n\n",
    "print(expected)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.30184101612673114"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(((table-expected)**2)/expected).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.99146454711\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import chi2\n",
    "chi = chi2.isf(q=0.05, df=2)\n",
    "print(chi)"
   ]
  }
 ],
 "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",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}