06장_2절_결측치처리_클래스레이블_원핫.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 결측치 처리"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame([\n",
    "    [42, 'male', 12,'reading','class2'],\n",
    "    [35, 'unknown', 3,'cooking', 'class1'],\n",
    "    [1000, 'female', 7,'cycling', 'class3'],\n",
    "    [1000, 'unknown', 21,'unknown', 'unknown']\n",
    "])\n",
    "df.columns=['age',\n",
    "            'gender',\n",
    "            'month_birth',\n",
    "            'hobby','target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>month_birth</th>\n",
       "      <th>hobby</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42</td>\n",
       "      <td>male</td>\n",
       "      <td>12</td>\n",
       "      <td>reading</td>\n",
       "      <td>class2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35</td>\n",
       "      <td>unknown</td>\n",
       "      <td>3</td>\n",
       "      <td>cooking</td>\n",
       "      <td>class1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000</td>\n",
       "      <td>female</td>\n",
       "      <td>7</td>\n",
       "      <td>cycling</td>\n",
       "      <td>class3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000</td>\n",
       "      <td>unknown</td>\n",
       "      <td>21</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "    age   gender  month_birth    hobby   target\n",
       "0    42     male           12  reading   class2\n",
       "1    35  unknown            3  cooking   class1\n",
       "2  1000   female            7  cycling   class3\n",
       "3  1000  unknown           21  unknown  unknown"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  42,   35, 1000])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['age'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['male', 'unknown', 'female'], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['gender'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12,  3,  7, 21])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['month_birth'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['reading', 'cooking', 'cycling', 'unknown'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['hobby'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['class2', 'class1', 'class3', 'unknown'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['target'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df['age']>150, ['age']] = np.nan\n",
    "df.loc[df['gender']=='unknown', ['gender']] = np.nan\n",
    "df.loc[df['month_birth']>12, ['month_birth']] = np.nan\n",
    "df.loc[df['hobby']=='unknown', ['hobby']] = np.nan\n",
    "df.loc[df['target']=='unknown', ['target']] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "      <th>hobby</th>\n",
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       "      <th>0</th>\n",
       "      <td>42.0</td>\n",
       "      <td>male</td>\n",
       "      <td>12.0</td>\n",
       "      <td>reading</td>\n",
       "      <td>class2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>cooking</td>\n",
       "      <td>class1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>female</td>\n",
       "      <td>7.0</td>\n",
       "      <td>cycling</td>\n",
       "      <td>class3</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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      "text/plain": [
       "    age  gender  month_birth    hobby  target\n",
       "0  42.0    male         12.0  reading  class2\n",
       "1  35.0     NaN          3.0  cooking  class1\n",
       "2   NaN  female          7.0  cycling  class3\n",
       "3   NaN     NaN          NaN      NaN     NaN"
      ]
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     "execution_count": 10,
     "metadata": {},
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    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>gender</th>\n",
       "      <th>month_birth</th>\n",
       "      <th>hobby</th>\n",
       "      <th>target</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42.0</td>\n",
       "      <td>male</td>\n",
       "      <td>12.0</td>\n",
       "      <td>reading</td>\n",
       "      <td>class2</td>\n",
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      "text/plain": [
       "    age gender  month_birth    hobby  target\n",
       "0  42.0   male         12.0  reading  class2"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 결측치를 포함한 행(row) 삭제\n",
    "df2 = df.dropna(axis=0)\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: [0, 1, 2, 3]"
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     "execution_count": 12,
     "metadata": {},
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   ],
   "source": [
    "# 결측치를 포함한 열(column) 삭제\n",
    "df3 = df.dropna(axis=1)\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "      <td>42.0</td>\n",
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       "      <td>12.0</td>\n",
       "      <td>reading</td>\n",
       "      <td>class2</td>\n",
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       "      <th>1</th>\n",
       "      <td>35.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>female</td>\n",
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       "    age  gender  month_birth    hobby  target\n",
       "0  42.0    male         12.0  reading  class2\n",
       "1  35.0     NaN          3.0  cooking  class1\n",
       "2   NaN  female          7.0  cycling  class3"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 모든 값이 결측치인 행 삭제\n",
    "df4 = df.dropna(how='all')\n",
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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       "      <td>42.0</td>\n",
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       "      <td>12.0</td>\n",
       "      <td>reading</td>\n",
       "      <td>class2</td>\n",
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       "      <td>35.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>female</td>\n",
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      "text/plain": [
       "    age  gender  month_birth    hobby  target\n",
       "0  42.0    male         12.0  reading  class2\n",
       "1  35.0     NaN          3.0  cooking  class1\n",
       "2   NaN  female          7.0  cycling  class3"
      ]
     },
     "execution_count": 14,
     "metadata": {},
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    }
   ],
   "source": [
    "# 값이 2개 미만인 행 삭제\n",
    "df5 = df.dropna(thresh=2)\n",
    "df5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "      <td>class2</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>female</td>\n",
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      "text/plain": [
       "    age  gender  month_birth    hobby  target\n",
       "0  42.0    male         12.0  reading  class2\n",
       "2   NaN  female          7.0  cycling  class3"
      ]
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     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# 특정 열에 결측치가 있는 경우 행 삭제\n",
    "df6 = df.dropna(subset=['gender'])\n",
    "df6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  gender  month_birth    hobby  target\n",
       "0  42.0    male         12.0  reading  class2\n",
       "1  35.0     NaN          3.0  cooking  class1\n",
       "2   NaN  female          7.0  cycling  class3\n",
       "3   NaN     NaN          NaN      NaN     NaN"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age            2\n",
       "gender         2\n",
       "month_birth    1\n",
       "hobby          1\n",
       "target         1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 결측치 대체하기\n",
    "alter_values = {'age': 0, \n",
    "                'gender': 'U', \n",
    "                'month_birth': 0,\n",
    "                'hobby': 'U',\n",
    "                'target': 'class4'}\n",
    "df7 = df.fillna(value = alter_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>month_birth</th>\n",
       "      <th>hobby</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42.0</td>\n",
       "      <td>male</td>\n",
       "      <td>12.0</td>\n",
       "      <td>reading</td>\n",
       "      <td>class2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35.0</td>\n",
       "      <td>U</td>\n",
       "      <td>3.0</td>\n",
       "      <td>cooking</td>\n",
       "      <td>class1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>female</td>\n",
       "      <td>7.0</td>\n",
       "      <td>cycling</td>\n",
       "      <td>class3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>U</td>\n",
       "      <td>0.0</td>\n",
       "      <td>U</td>\n",
       "      <td>class4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  gender  month_birth    hobby  target\n",
       "0  42.0    male         12.0  reading  class2\n",
       "1  35.0       U          3.0  cooking  class1\n",
       "2   0.0  female          7.0  cycling  class3\n",
       "3   0.0       U          0.0        U  class4"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 클래스 라벨 설정"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 2, 3])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "df8 = df7\n",
    "class_label = LabelEncoder()\n",
    "data_value = df8['target'].values\n",
    "y_new = class_label.fit_transform(data_value)\n",
    "y_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>month_birth</th>\n",
       "      <th>hobby</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42.0</td>\n",
       "      <td>male</td>\n",
       "      <td>12.0</td>\n",
       "      <td>reading</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35.0</td>\n",
       "      <td>U</td>\n",
       "      <td>3.0</td>\n",
       "      <td>cooking</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>female</td>\n",
       "      <td>7.0</td>\n",
       "      <td>cycling</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>U</td>\n",
       "      <td>0.0</td>\n",
       "      <td>U</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  gender  month_birth    hobby  target\n",
       "0  42.0    male         12.0  reading       1\n",
       "1  35.0       U          3.0  cooking       0\n",
       "2   0.0  female          7.0  cycling       2\n",
       "3   0.0       U          0.0        U       3"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df8['target'] = y_new\n",
    "df8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['class2', 'class1', 'class3', 'class4'], dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_ori = class_label.inverse_transform(y_new)\n",
    "y_ori"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>month_birth</th>\n",
       "      <th>hobby</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42.0</td>\n",
       "      <td>male</td>\n",
       "      <td>12.0</td>\n",
       "      <td>reading</td>\n",
       "      <td>class2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35.0</td>\n",
       "      <td>U</td>\n",
       "      <td>3.0</td>\n",
       "      <td>cooking</td>\n",
       "      <td>class1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>female</td>\n",
       "      <td>7.0</td>\n",
       "      <td>cycling</td>\n",
       "      <td>class3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>U</td>\n",
       "      <td>0.0</td>\n",
       "      <td>U</td>\n",
       "      <td>class4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  gender  month_birth    hobby  target\n",
       "0  42.0    male         12.0  reading  class2\n",
       "1  35.0       U          3.0  cooking  class1\n",
       "2   0.0  female          7.0  cycling  class3\n",
       "3   0.0       U          0.0        U  class4"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df8['target'] = y_ori\n",
    "df8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['class1', 'class2', 'class3', 'class4'], dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 클래스 라벨링 without 라이브러리 \n",
    "y_arr = df8['target'].values\n",
    "y_arr.sort()\n",
    "y_arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_y = 0\n",
    "dic_y = {}\n",
    "for ith_y in y_arr:\n",
    "    dic_y[ith_y] = num_y\n",
    "    num_y += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'class1': 0, 'class2': 1, 'class3': 2, 'class4': 3}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dic_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df8['target'] = df8['target'].replace(dic_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>month_birth</th>\n",
       "      <th>hobby</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42.0</td>\n",
       "      <td>male</td>\n",
       "      <td>12.0</td>\n",
       "      <td>reading</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35.0</td>\n",
       "      <td>U</td>\n",
       "      <td>3.0</td>\n",
       "      <td>cooking</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>female</td>\n",
       "      <td>7.0</td>\n",
       "      <td>cycling</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>U</td>\n",
       "      <td>0.0</td>\n",
       "      <td>U</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  gender  month_birth    hobby  target\n",
       "0  42.0    male         12.0  reading       0\n",
       "1  35.0       U          3.0  cooking       1\n",
       "2   0.0  female          7.0  cycling       2\n",
       "3   0.0       U          0.0        U       3"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 원-핫 인코딩"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   0  1  2  3\n",
      "0  1  0  0  0\n",
      "1  0  1  0  0\n",
      "2  0  0  1  0\n",
      "3  0  0  0  1\n"
     ]
    }
   ],
   "source": [
    "df9 = df8\n",
    "df9['target'] = df9['target'].astype(str)\n",
    "df10 = pd.get_dummies(df9['target'])\n",
    "print(df10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   1  2  3\n",
      "0  0  0  0\n",
      "1  1  0  0\n",
      "2  0  1  0\n",
      "3  0  0  1\n"
     ]
    }
   ],
   "source": [
    "df9['target'] = df9['target'].astype(str)\n",
    "df11 = pd.get_dummies(df9['target'], drop_first=True)\n",
    "print(df11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>age</th>\n",
       "      <th>month_birth</th>\n",
       "      <th>gender_U</th>\n",
       "      <th>gender_female</th>\n",
       "      <th>gender_male</th>\n",
       "      <th>hobby_U</th>\n",
       "      <th>hobby_cooking</th>\n",
       "      <th>hobby_cycling</th>\n",
       "      <th>hobby_reading</th>\n",
       "      <th>target_0</th>\n",
       "      <th>target_1</th>\n",
       "      <th>target_2</th>\n",
       "      <th>target_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0</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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  month_birth  gender_U  gender_female  gender_male  hobby_U  \\\n",
       "0  42.0         12.0         0              0            1        0   \n",
       "1  35.0          3.0         1              0            0        0   \n",
       "2   0.0          7.0         0              1            0        0   \n",
       "3   0.0          0.0         1              0            0        1   \n",
       "\n",
       "   hobby_cooking  hobby_cycling  hobby_reading  target_0  target_1  target_2  \\\n",
       "0              0              0              1         1         0         0   \n",
       "1              1              0              0         0         1         0   \n",
       "2              0              1              0         0         0         1   \n",
       "3              0              0              0         0         0         0   \n",
       "\n",
       "   target_3  \n",
       "0         0  \n",
       "1         0  \n",
       "2         0  \n",
       "3         1  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df12 = df8\n",
    "df13 = pd.get_dummies(df12)\n",
    "df13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    }\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>age</th>\n",
       "      <th>month_birth</th>\n",
       "      <th>gender_female</th>\n",
       "      <th>gender_male</th>\n",
       "      <th>hobby_cooking</th>\n",
       "      <th>hobby_cycling</th>\n",
       "      <th>hobby_reading</th>\n",
       "      <th>target_1</th>\n",
       "      <th>target_2</th>\n",
       "      <th>target_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42.0</td>\n",
       "      <td>12.0</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>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35.0</td>\n",
       "      <td>3.0</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\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>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  month_birth  gender_female  gender_male  hobby_cooking  \\\n",
       "0  42.0         12.0              0            1              0   \n",
       "1  35.0          3.0              0            0              1   \n",
       "2   0.0          7.0              1            0              0   \n",
       "3   0.0          0.0              0            0              0   \n",
       "\n",
       "   hobby_cycling  hobby_reading  target_1  target_2  target_3  \n",
       "0              0              1         0         0         0  \n",
       "1              0              0         1         0         0  \n",
       "2              1              0         0         1         0  \n",
       "3              0              0         0         0         1  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df14 = pd.get_dummies(df12, drop_first=True)\n",
    "df14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 두번째 방법"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 0. 0. 0.]\n",
      " [0. 1. 0. 0.]\n",
      " [0. 0. 1. 0.]\n",
      " [0. 0. 0. 1.]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "hot_encoder = OneHotEncoder()\n",
    "y = df7[['target']]\n",
    "y_hot = hot_encoder.fit_transform(y)\n",
    "print(y_hot.toarray())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 텐서플로 라이브러리를 활용한 원핫 인코딩"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 0. 0. 0.]\n",
      " [0. 1. 0. 0.]\n",
      " [0. 0. 1. 0.]\n",
      " [0. 0. 0. 1.]]\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.utils import to_categorical\n",
    "y_hotec = to_categorical(y) \n",
    "print(y_hotec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 스케일링"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>month_birth</th>\n",
       "      <th>hobby</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42.0</td>\n",
       "      <td>male</td>\n",
       "      <td>12.0</td>\n",
       "      <td>reading</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35.0</td>\n",
       "      <td>U</td>\n",
       "      <td>3.0</td>\n",
       "      <td>cooking</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>female</td>\n",
       "      <td>7.0</td>\n",
       "      <td>cycling</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>U</td>\n",
       "      <td>0.0</td>\n",
       "      <td>U</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  gender  month_birth    hobby target\n",
       "0  42.0    male         12.0  reading      0\n",
       "1  35.0       U          3.0  cooking      1\n",
       "2   0.0  female          7.0  cycling      2\n",
       "3   0.0       U          0.0        U      3"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 표준화 스케일링"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 표준화 스케일링\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "std = StandardScaler()\n",
    "std.fit(df8[['month_birth']])\n",
    "x_std = std.transform(df8[['month_birth']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.44444444],\n",
       "       [-0.55555556],\n",
       "       [ 0.33333333],\n",
       "       [-1.22222222]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_std2 = std.fit_transform(df8[['month_birth']])\n",
    "x_std2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-5.551115123125783e-17"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(x_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(x_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.44444444],\n",
       "       [-0.55555556],\n",
       "       [ 0.33333333],\n",
       "       [-1.22222222]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_std"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 로버스트 스케일링"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 로버스트 스케일링\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "\n",
    "robust= RobustScaler()\n",
    "robust.fit(df8[['month_birth']])\n",
    "x_robust = robust.transform(df8[['month_birth']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.16666667],\n",
       "       [-0.33333333],\n",
       "       [ 0.33333333],\n",
       "       [-0.83333333]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_robust"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 최소-최대 스케일링"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 최소-최대 스케일링\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "minmax = MinMaxScaler()\n",
    "minmax.fit(df8[['month_birth']])\n",
    "x_minmax = minmax.transform(df8[['month_birth']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        ],\n",
       "       [0.25      ],\n",
       "       [0.58333333],\n",
       "       [0.        ]])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_minmax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 노멀 스케일링"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 노멀 스케일링\n",
    "from sklearn.preprocessing import Normalizer\n",
    "\n",
    "normal = Normalizer()\n",
    "normal.fit(df8[['age','month_birth']])\n",
    "x_normal = normal.transform(df8[['age','month_birth']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.96152395, 0.27472113],\n",
       "       [0.99634665, 0.08540114],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 0.        ]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_normal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "display_name": "Python 3",
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