{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4be81fd7",
   "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>Gender</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>174</td>\n",
       "      <td>96</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>189</td>\n",
       "      <td>87</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Female</td>\n",
       "      <td>185</td>\n",
       "      <td>110</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Female</td>\n",
       "      <td>195</td>\n",
       "      <td>104</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>149</td>\n",
       "      <td>61</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  Height  Weight  Index\n",
       "0    Male     174      96      4\n",
       "1    Male     189      87      2\n",
       "2  Female     185     110      4\n",
       "3  Female     195     104      3\n",
       "4    Male     149      61      3"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "data = pd.read_csv(\"obese.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c90a3afb",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['obese'] = (data.Index >= 4).astype('int')\n",
    "data.drop('Index', axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d0ad044a",
   "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>Gender</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>obese</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>174</td>\n",
       "      <td>96</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>189</td>\n",
       "      <td>87</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Female</td>\n",
       "      <td>185</td>\n",
       "      <td>110</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Female</td>\n",
       "      <td>195</td>\n",
       "      <td>104</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>149</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  Height  Weight  obese\n",
       "0    Male     174      96      1\n",
       "1    Male     189      87      0\n",
       "2  Female     185     110      1\n",
       "3  Female     195     104      0\n",
       "4    Male     149      61      0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dde37f0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Misclassified when cutting at 100kg: 18 \n",
      " Misclassified when cutting at 80kg: 63\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "  \" Misclassified when cutting at 100kg:\",\n",
    "  data.loc[(data['Weight']>=100) & (data['obese']==0),:].shape[0], \"\\n\",\n",
    "  \"Misclassified when cutting at 80kg:\",\n",
    "  data.loc[(data['Weight']>=80) & (data['obese']==0),:].shape[0]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0d2e9b73",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Female    0.51\n",
      "Male      0.49\n",
      "Name: Gender, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.4998"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def gini_impurity(y):\n",
    "  '''\n",
    "  Given a Pandas Series, it calculates the Gini Impurity. \n",
    "  y: variable with which calculate Gini Impurity.\n",
    "  '''\n",
    "  if isinstance(y, pd.Series):\n",
    "    p = y.value_counts()/y.shape[0]\n",
    "    print(p)\n",
    "    gini = 1-np.sum(p**2)\n",
    "    return(gini)\n",
    "\n",
    "  else:\n",
    "    raise('Object must be a Pandas Series.')\n",
    "\n",
    "gini_impurity(data.Gender) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b60120ac",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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