_posts/[Encoding] OHE/1. get_dummies 문제점.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# get_dummies() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`get_dummies()`를 활용하여 범주형 자료를 더미화하고, 아래의 문제가 있을 때 `get_dummies()`는 어떤 결과를 내놓는지 확인해보자. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 1. numeric value가 string 형태로 들어가있을 때.  \n",
    "> 2. missing value가 있을 때. \n",
    "> 3. 다른 데이터셋에 대입할 때."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 데이터 만들기 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    " \n",
    "data = {'Wealth':[27, 27, 22, 32],\n",
    "        'bld':['A', 'B', 'A', 'O'],\n",
    "        'Qual':['Msc', 'MA', 'MA', 'Msc']}\n",
    " \n",
    "\n",
    "data = pd.DataFrame(data, index = ['Jai', 'Princi', 'Salah', 'Anuj'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Wealth</th>\n",
       "      <th>bld</th>\n",
       "      <th>Qual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Jai</th>\n",
       "      <td>27</td>\n",
       "      <td>A</td>\n",
       "      <td>Msc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Princi</th>\n",
       "      <td>27</td>\n",
       "      <td>B</td>\n",
       "      <td>MA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salah</th>\n",
       "      <td>22</td>\n",
       "      <td>A</td>\n",
       "      <td>MA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anuj</th>\n",
       "      <td>32</td>\n",
       "      <td>O</td>\n",
       "      <td>Msc</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Wealth bld Qual\n",
       "Jai         27   A  Msc\n",
       "Princi      27   B   MA\n",
       "Salah       22   A   MA\n",
       "Anuj        32   O  Msc"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 더미화 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Wealth</th>\n",
       "      <th>bld_A</th>\n",
       "      <th>bld_B</th>\n",
       "      <th>bld_O</th>\n",
       "      <th>Qual_MA</th>\n",
       "      <th>Qual_Msc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Jai</th>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Princi</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salah</th>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anuj</th>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Wealth  bld_A  bld_B  bld_O  Qual_MA  Qual_Msc\n",
       "Jai         27      1      0      0        0         1\n",
       "Princi      27      0      1      0        1         0\n",
       "Salah       22      1      0      0        1         0\n",
       "Anuj        32      0      0      1        0         1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy = pd.get_dummies(data)\n",
    "dummy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## numeric value가 string으로 기입되어 있는 경우  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Wealth_22</th>\n",
       "      <th>Wealth_27</th>\n",
       "      <th>Wealth_32</th>\n",
       "      <th>bld_A</th>\n",
       "      <th>bld_B</th>\n",
       "      <th>bld_O</th>\n",
       "      <th>Qual_MA</th>\n",
       "      <th>Qual_Msc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Jai</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Princi</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salah</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anuj</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Wealth_22  Wealth_27  Wealth_32  bld_A  bld_B  bld_O  Qual_MA  \\\n",
       "Jai             0          1          0      1      0      0        0   \n",
       "Princi          0          1          0      0      1      0        1   \n",
       "Salah           1          0          0      1      0      0        1   \n",
       "Anuj            0          0          1      0      0      1        0   \n",
       "\n",
       "        Qual_Msc  \n",
       "Jai            1  \n",
       "Princi         0  \n",
       "Salah          0  \n",
       "Anuj           1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {'Wealth':['27', '27', '22', '32'],\n",
    "        'bld':['A', 'B', 'A', 'O'],\n",
    "        'Qual':['Msc', 'MA', 'MA', 'Msc']}\n",
    "\n",
    "data = pd.DataFrame(data, index = ['Jai', 'Princi', 'Salah', 'Anuj'])\n",
    "dummy = pd.get_dummies(data)\n",
    "dummy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 완전한 수치형 자료임에도 불구하고  불필요하게 재산(wealth)가 더미변수화 되었다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## missing value가 있는 경우  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Wealth</th>\n",
       "      <th>bld_A</th>\n",
       "      <th>bld_O</th>\n",
       "      <th>Qual_MA</th>\n",
       "      <th>Qual_Msc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Jai</th>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Princi</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salah</th>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anuj</th>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Wealth  bld_A  bld_O  Qual_MA  Qual_Msc\n",
       "Jai         27      1      0        0         1\n",
       "Princi      27      0      0        1         0\n",
       "Salah       22      1      0        1         0\n",
       "Anuj        32      0      1        0         1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {'Wealth':[27, 27, 22, 32],\n",
    "        'bld':['A', None, 'A', 'O'],\n",
    "        'Qual':['Msc', 'MA', 'MA', 'Msc']}\n",
    "\n",
    "data = pd.DataFrame(data, index = ['Jai', 'Princi', 'Salah', 'Anuj'])\n",
    "dummy = pd.get_dummies(data)\n",
    "dummy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "보기에는 문제가 없으나, 더미변수화를 하고 다중공선성 문제를 해결하기 위해 `drop_first=True`를 하는 순간 문제가 생긴다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Wealth</th>\n",
       "      <th>bld_O</th>\n",
       "      <th>Qual_Msc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Jai</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Princi</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salah</th>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anuj</th>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Wealth  bld_O  Qual_Msc\n",
       "Jai         27      0         1\n",
       "Princi      27      0         0\n",
       "Salah       22      0         0\n",
       "Anuj        32      1         1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy = pd.get_dummies(data, drop_first = True)\n",
    "dummy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Princi의 혈액형은 분명 기입되지 않았지만, drop을 하고 난 뒤에는 Jai, Anuj와 같은 혈액형으로 읽혀지고 있다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 다른 데이터셋에 적용할 경우  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {'Wealth':[27, 27, 22, 32],\n",
    "        'bld':['A', 'AB', 'A', 'O'],\n",
    "        'Qual':['Msc', 'MA', 'MA', 'Msc']}\n",
    "\n",
    "train = pd.DataFrame(data, index = ['Jai', 'Princi', 'Salah', 'Anuj'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {'Wealth':[51, 27, 22, 32],\n",
    "        'bld':['A', 'B', 'AB', 'O'],\n",
    "        'Qual':['MB', 'MA', 'MA', 'Msc']}\n",
    "\n",
    "test = pd.DataFrame(data, index = ['Hwan', 'Jason', 'Lee', 'Andrew'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Wealth</th>\n",
       "      <th>bld</th>\n",
       "      <th>Qual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Jai</th>\n",
       "      <td>27</td>\n",
       "      <td>A</td>\n",
       "      <td>Msc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Princi</th>\n",
       "      <td>27</td>\n",
       "      <td>AB</td>\n",
       "      <td>MA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salah</th>\n",
       "      <td>22</td>\n",
       "      <td>A</td>\n",
       "      <td>MA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anuj</th>\n",
       "      <td>32</td>\n",
       "      <td>O</td>\n",
       "      <td>Msc</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Wealth bld Qual\n",
       "Jai         27   A  Msc\n",
       "Princi      27  AB   MA\n",
       "Salah       22   A   MA\n",
       "Anuj        32   O  Msc"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Wealth</th>\n",
       "      <th>bld</th>\n",
       "      <th>Qual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Hwan</th>\n",
       "      <td>51</td>\n",
       "      <td>A</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jason</th>\n",
       "      <td>27</td>\n",
       "      <td>B</td>\n",
       "      <td>MA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lee</th>\n",
       "      <td>22</td>\n",
       "      <td>AB</td>\n",
       "      <td>MA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andrew</th>\n",
       "      <td>32</td>\n",
       "      <td>O</td>\n",
       "      <td>Msc</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Wealth bld Qual\n",
       "Hwan        51   A   MB\n",
       "Jason       27   B   MA\n",
       "Lee         22  AB   MA\n",
       "Andrew      32   O  Msc"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "현재 train과 달리 test 데이터에는 B와 MB 같은 새로운 데이터셋이 추가되었다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "dummy1 = pd.get_dummies(train)\n",
    "dummy2 = pd.get_dummies(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Jai</th>\n",
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       "      <td>0</td>\n",
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       "      <th>Princi</th>\n",
       "      <td>27</td>\n",
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       "    <tr>\n",
       "      <th>Salah</th>\n",
       "      <td>22</td>\n",
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       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "        Wealth  bld_A  bld_AB  bld_O  Qual_MA  Qual_Msc\n",
       "Jai         27      1       0      0        0         1\n",
       "Princi      27      0       1      0        1         0\n",
       "Salah       22      1       0      0        1         0\n",
       "Anuj        32      0       0      1        0         1"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>bld_A</th>\n",
       "      <th>bld_AB</th>\n",
       "      <th>bld_B</th>\n",
       "      <th>bld_O</th>\n",
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       "      <th>Hwan</th>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>Jason</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <th>Lee</th>\n",
       "      <td>22</td>\n",
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       "      <td>0</td>\n",
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       "      <td>32</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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      ],
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       "        Wealth  bld_A  bld_AB  bld_B  bld_O  Qual_MA  Qual_MB  Qual_Msc\n",
       "Hwan        51      1       0      0      0        0        1         0\n",
       "Jason       27      0       0      1      0        1        0         0\n",
       "Lee         22      0       1      0      0        1        0         0\n",
       "Andrew      32      0       0      0      1        0        0         1"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "새로운 혈액형이 추가되면서 아예 컬럼 갯수가 달라졌다. 따라서 train 데이터를 활용해서 모델을 피팅하면 나중에 test에서 사용할 수 없게 된다."
   ]
  }
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