01_1_데이터_전처리.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 중복/누락 데이터 처리"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 누락 데이터 제거"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df=pd.read_csv('datacleaning.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Duration</th>\n",
       "      <th>Pulse</th>\n",
       "      <th>Maxpulse</th>\n",
       "      <th>Calories</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60</td>\n",
       "      <td>117.0</td>\n",
       "      <td>145</td>\n",
       "      <td>479.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>60</td>\n",
       "      <td>103.0</td>\n",
       "      <td>135</td>\n",
       "      <td>340.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45</td>\n",
       "      <td>109.0</td>\n",
       "      <td>175</td>\n",
       "      <td>282.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>148</td>\n",
       "      <td>406.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60</td>\n",
       "      <td>100.0</td>\n",
       "      <td>120</td>\n",
       "      <td>250.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>450</td>\n",
       "      <td>104.0</td>\n",
       "      <td>134</td>\n",
       "      <td>253.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>30</td>\n",
       "      <td>109.0</td>\n",
       "      <td>133</td>\n",
       "      <td>195.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>124</td>\n",
       "      <td>269.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>60</td>\n",
       "      <td>103.0</td>\n",
       "      <td>147</td>\n",
       "      <td>329.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>60</td>\n",
       "      <td>100.0</td>\n",
       "      <td>120</td>\n",
       "      <td>250.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>60</td>\n",
       "      <td>100.0</td>\n",
       "      <td>120</td>\n",
       "      <td>250.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>60</td>\n",
       "      <td>98.0</td>\n",
       "      <td>120</td>\n",
       "      <td>215.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>45</td>\n",
       "      <td>90.0</td>\n",
       "      <td>112</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>60</td>\n",
       "      <td>103.0</td>\n",
       "      <td>123</td>\n",
       "      <td>323.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>60</td>\n",
       "      <td>100.0</td>\n",
       "      <td>120</td>\n",
       "      <td>250.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>132</td>\n",
       "      <td>246.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>60</td>\n",
       "      <td>102.0</td>\n",
       "      <td>126</td>\n",
       "      <td>334.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>60</td>\n",
       "      <td>100.0</td>\n",
       "      <td>120</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>60</td>\n",
       "      <td>103.0</td>\n",
       "      <td>132</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "    Duration  Pulse  Maxpulse  Calories\n",
       "0         60  117.0       145     479.0\n",
       "1         60  103.0       135     340.0\n",
       "2         45  109.0       175     282.4\n",
       "3         45    NaN       148     406.0\n",
       "4         60  100.0       120     250.7\n",
       "5        450  104.0       134     253.3\n",
       "6         30  109.0       133     195.1\n",
       "7         60    NaN       124     269.0\n",
       "8         60  103.0       147     329.3\n",
       "9         60  100.0       120     250.7\n",
       "10        60  100.0       120     250.7\n",
       "11        60   98.0       120     215.2\n",
       "12        45   90.0       112       NaN\n",
       "13        60  103.0       123     323.0\n",
       "14        60  100.0       120     250.7\n",
       "15        45    NaN       132     246.0\n",
       "16        60  102.0       126     334.5\n",
       "17        60  100.0       120     250.0\n",
       "18        60  103.0       132       NaN"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1-1. 누락 데이터 확인하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Duration    0\n",
       "Pulse       3\n",
       "Maxpulse    0\n",
       "Calories    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 열단위로 누락 데이터 확인하기\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1-2. 결측치가 있는 행 날리기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Pulse</th>\n",
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       "      <td>117.0</td>\n",
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       "      <td>479.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>60</td>\n",
       "      <td>103.0</td>\n",
       "      <td>135</td>\n",
       "      <td>340.0</td>\n",
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       "      <td>45</td>\n",
       "      <td>109.0</td>\n",
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       "      <td>329.3</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>60</td>\n",
       "      <td>100.0</td>\n",
       "      <td>120</td>\n",
       "      <td>250.7</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>60</td>\n",
       "      <td>100.0</td>\n",
       "      <td>120</td>\n",
       "      <td>250.7</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>60</td>\n",
       "      <td>98.0</td>\n",
       "      <td>120</td>\n",
       "      <td>215.2</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>60</td>\n",
       "      <td>103.0</td>\n",
       "      <td>123</td>\n",
       "      <td>323.0</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>60</td>\n",
       "      <td>100.0</td>\n",
       "      <td>120</td>\n",
       "      <td>250.7</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>60</td>\n",
       "      <td>102.0</td>\n",
       "      <td>126</td>\n",
       "      <td>334.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>60</td>\n",
       "      <td>100.0</td>\n",
       "      <td>120</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Duration  Pulse  Maxpulse  Calories\n",
       "0         60  117.0       145     479.0\n",
       "1         60  103.0       135     340.0\n",
       "2         45  109.0       175     282.4\n",
       "4         60  100.0       120     250.7\n",
       "5        450  104.0       134     253.3\n",
       "6         30  109.0       133     195.1\n",
       "8         60  103.0       147     329.3\n",
       "9         60  100.0       120     250.7\n",
       "10        60  100.0       120     250.7\n",
       "11        60   98.0       120     215.2\n",
       "13        60  103.0       123     323.0\n",
       "14        60  100.0       120     250.7\n",
       "16        60  102.0       126     334.5\n",
       "17        60  100.0       120     250.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2=df.dropna(axis=0) # 행단위로 제거\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1-3. 결측치가 있는 열 날리기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "    Duration  Maxpulse\n",
       "0         60       145\n",
       "1         60       135\n",
       "2         45       175\n",
       "3         45       148\n",
       "4         60       120\n",
       "5        450       134\n",
       "6         30       133\n",
       "7         60       124\n",
       "8         60       147\n",
       "9         60       120\n",
       "10        60       120\n",
       "11        60       120\n",
       "12        45       112\n",
       "13        60       123\n",
       "14        60       120\n",
       "15        45       132\n",
       "16        60       126\n",
       "17        60       120\n",
       "18        60       132"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2=df.dropna(axis=1) #열단위로 제거\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1-4. 결측치 메꾸기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>4</th>\n",
       "      <td>60</td>\n",
       "      <td>100.0</td>\n",
       "      <td>120</td>\n",
       "      <td>250.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>450</td>\n",
       "      <td>104.0</td>\n",
       "      <td>134</td>\n",
       "      <td>253.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>30</td>\n",
       "      <td>109.0</td>\n",
       "      <td>133</td>\n",
       "      <td>195.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>60</td>\n",
       "      <td>130.0</td>\n",
       "      <td>124</td>\n",
       "      <td>269.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>60</td>\n",
       "      <td>103.0</td>\n",
       "      <td>147</td>\n",
       "      <td>329.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>60</td>\n",
       "      <td>100.0</td>\n",
       "      <td>120</td>\n",
       "      <td>250.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>60</td>\n",
       "      <td>100.0</td>\n",
       "      <td>120</td>\n",
       "      <td>250.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>60</td>\n",
       "      <td>98.0</td>\n",
       "      <td>120</td>\n",
       "      <td>215.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>45</td>\n",
       "      <td>90.0</td>\n",
       "      <td>112</td>\n",
       "      <td>130.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>60</td>\n",
       "      <td>103.0</td>\n",
       "      <td>123</td>\n",
       "      <td>323.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>60</td>\n",
       "      <td>100.0</td>\n",
       "      <td>120</td>\n",
       "      <td>250.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>45</td>\n",
       "      <td>130.0</td>\n",
       "      <td>132</td>\n",
       "      <td>246.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>60</td>\n",
       "      <td>102.0</td>\n",
       "      <td>126</td>\n",
       "      <td>334.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>60</td>\n",
       "      <td>100.0</td>\n",
       "      <td>120</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>60</td>\n",
       "      <td>103.0</td>\n",
       "      <td>132</td>\n",
       "      <td>130.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Duration  Pulse  Maxpulse  Calories\n",
       "0         60  117.0       145     479.0\n",
       "1         60  103.0       135     340.0\n",
       "2         45  109.0       175     282.4\n",
       "3         45  130.0       148     406.0\n",
       "4         60  100.0       120     250.7\n",
       "5        450  104.0       134     253.3\n",
       "6         30  109.0       133     195.1\n",
       "7         60  130.0       124     269.0\n",
       "8         60  103.0       147     329.3\n",
       "9         60  100.0       120     250.7\n",
       "10        60  100.0       120     250.7\n",
       "11        60   98.0       120     215.2\n",
       "12        45   90.0       112     130.0\n",
       "13        60  103.0       123     323.0\n",
       "14        60  100.0       120     250.7\n",
       "15        45  130.0       132     246.0\n",
       "16        60  102.0       126     334.5\n",
       "17        60  100.0       120     250.0\n",
       "18        60  103.0       132     130.0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna(130)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     479.0\n",
       "1     340.0\n",
       "2     282.4\n",
       "3     406.0\n",
       "4     250.7\n",
       "5     253.3\n",
       "6     195.1\n",
       "7     269.0\n",
       "8     329.3\n",
       "9     250.7\n",
       "10    250.7\n",
       "11    215.2\n",
       "12    130.0\n",
       "13    323.0\n",
       "14    250.7\n",
       "15    246.0\n",
       "16    334.5\n",
       "17    250.0\n",
       "18    130.0\n",
       "Name: Calories, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Calories'].fillna(130)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1-5. 평균값으로 결측치 채우기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "289.74117647058824"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m =df['Calories'].mean()\n",
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     479.000000\n",
       "1     340.000000\n",
       "2     282.400000\n",
       "3     406.000000\n",
       "4     250.700000\n",
       "5     253.300000\n",
       "6     195.100000\n",
       "7     269.000000\n",
       "8     329.300000\n",
       "9     250.700000\n",
       "10    250.700000\n",
       "11    215.200000\n",
       "12    289.741176\n",
       "13    323.000000\n",
       "14    250.700000\n",
       "15    246.000000\n",
       "16    334.500000\n",
       "17    250.000000\n",
       "18    289.741176\n",
       "Name: Calories, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Calories'].fillna(m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1-6. 중복값 제거"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Duration</th>\n",
       "      <th>Pulse</th>\n",
       "      <th>Maxpulse</th>\n",
       "      <th>Calories</th>\n",
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       "      <td>340.0</td>\n",
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       "    <tr>\n",
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       "      <td>109.0</td>\n",
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       "      <td>250.7</td>\n",
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       "      <td>253.3</td>\n",
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       "    <tr>\n",
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       "      <td>124</td>\n",
       "      <td>269.0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>60</td>\n",
       "      <td>103.0</td>\n",
       "      <td>147</td>\n",
       "      <td>329.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>60</td>\n",
       "      <td>98.0</td>\n",
       "      <td>120</td>\n",
       "      <td>215.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>45</td>\n",
       "      <td>90.0</td>\n",
       "      <td>112</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>60</td>\n",
       "      <td>103.0</td>\n",
       "      <td>123</td>\n",
       "      <td>323.0</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>132</td>\n",
       "      <td>246.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>60</td>\n",
       "      <td>102.0</td>\n",
       "      <td>126</td>\n",
       "      <td>334.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>60</td>\n",
       "      <td>100.0</td>\n",
       "      <td>120</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>60</td>\n",
       "      <td>103.0</td>\n",
       "      <td>132</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Duration  Pulse  Maxpulse  Calories\n",
       "0         60  117.0       145     479.0\n",
       "1         60  103.0       135     340.0\n",
       "2         45  109.0       175     282.4\n",
       "3         45    NaN       148     406.0\n",
       "4         60  100.0       120     250.7\n",
       "5        450  104.0       134     253.3\n",
       "6         30  109.0       133     195.1\n",
       "7         60    NaN       124     269.0\n",
       "8         60  103.0       147     329.3\n",
       "11        60   98.0       120     215.2\n",
       "12        45   90.0       112       NaN\n",
       "13        60  103.0       123     323.0\n",
       "15        45    NaN       132     246.0\n",
       "16        60  102.0       126     334.5\n",
       "17        60  100.0       120     250.0\n",
       "18        60  103.0       132       NaN"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Duration</th>\n",
       "      <th>Pulse</th>\n",
       "      <th>Maxpulse</th>\n",
       "      <th>Calories</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>60</td>\n",
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       "      <td>479.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>60</td>\n",
       "      <td>103.0</td>\n",
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       "      <td>340.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>253.3</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>30</td>\n",
       "      <td>109.0</td>\n",
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       "      <td>195.1</td>\n",
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       "      <td>269.0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>60</td>\n",
       "      <td>103.0</td>\n",
       "      <td>147</td>\n",
       "      <td>329.3</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>60</td>\n",
       "      <td>98.0</td>\n",
       "      <td>120</td>\n",
       "      <td>215.2</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>45</td>\n",
       "      <td>90.0</td>\n",
       "      <td>112</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>13</th>\n",
       "      <td>60</td>\n",
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       "      <td>323.0</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>60</td>\n",
       "      <td>100.0</td>\n",
       "      <td>120</td>\n",
       "      <td>250.7</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>132</td>\n",
       "      <td>246.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>60</td>\n",
       "      <td>102.0</td>\n",
       "      <td>126</td>\n",
       "      <td>334.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>60</td>\n",
       "      <td>100.0</td>\n",
       "      <td>120</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>60</td>\n",
       "      <td>103.0</td>\n",
       "      <td>132</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Duration  Pulse  Maxpulse  Calories\n",
       "0         60  117.0       145     479.0\n",
       "1         60  103.0       135     340.0\n",
       "2         45  109.0       175     282.4\n",
       "3         45    NaN       148     406.0\n",
       "5        450  104.0       134     253.3\n",
       "6         30  109.0       133     195.1\n",
       "7         60    NaN       124     269.0\n",
       "8         60  103.0       147     329.3\n",
       "11        60   98.0       120     215.2\n",
       "12        45   90.0       112       NaN\n",
       "13        60  103.0       123     323.0\n",
       "14        60  100.0       120     250.7\n",
       "15        45    NaN       132     246.0\n",
       "16        60  102.0       126     334.5\n",
       "17        60  100.0       120     250.0\n",
       "18        60  103.0       132       NaN"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# keep 패러미터를 통해 중복값중 처음값을 살릴지 마지막값을 살릴지 선택 가능\n",
    "df.drop_duplicates(keep='last')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1-7. 연쇄적 명령"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45</td>\n",
       "      <td>109.0</td>\n",
       "      <td>175</td>\n",
       "      <td>282.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60</td>\n",
       "      <td>100.0</td>\n",
       "      <td>120</td>\n",
       "      <td>250.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>450</td>\n",
       "      <td>104.0</td>\n",
       "      <td>134</td>\n",
       "      <td>253.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>30</td>\n",
       "      <td>109.0</td>\n",
       "      <td>133</td>\n",
       "      <td>195.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>60</td>\n",
       "      <td>103.0</td>\n",
       "      <td>147</td>\n",
       "      <td>329.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>60</td>\n",
       "      <td>98.0</td>\n",
       "      <td>120</td>\n",
       "      <td>215.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>60</td>\n",
       "      <td>103.0</td>\n",
       "      <td>123</td>\n",
       "      <td>323.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>60</td>\n",
       "      <td>102.0</td>\n",
       "      <td>126</td>\n",
       "      <td>334.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>60</td>\n",
       "      <td>100.0</td>\n",
       "      <td>120</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Duration  Pulse  Maxpulse  Calories\n",
       "0         60  117.0       145     479.0\n",
       "1         60  103.0       135     340.0\n",
       "2         45  109.0       175     282.4\n",
       "4         60  100.0       120     250.7\n",
       "5        450  104.0       134     253.3\n",
       "6         30  109.0       133     195.1\n",
       "8         60  103.0       147     329.3\n",
       "11        60   98.0       120     215.2\n",
       "13        60  103.0       123     323.0\n",
       "16        60  102.0       126     334.5\n",
       "17        60  100.0       120     250.0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 여려 명령어를 다음과 같이 연쇄적으로 사용할 수 있음\n",
    "df.dropna().drop_duplicates()\n",
    "# df.dropna().drop_duplicates().describe()\n",
    "# df.dropna().drop_duplicates().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MinMax Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2=df.dropna(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>Duration</th>\n",
       "      <th>Pulse</th>\n",
       "      <th>Maxpulse</th>\n",
       "      <th>Calories</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.736842</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.489609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.964286</td>\n",
       "      <td>0.421053</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.692497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.804156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.684211</td>\n",
       "      <td>0.745455</td>\n",
       "      <td>0.794998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.421053</td>\n",
       "      <td>0.763636</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.736842</td>\n",
       "      <td>0.509091</td>\n",
       "      <td>0.527298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.804156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.804156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.928571</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.929200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.736842</td>\n",
       "      <td>0.945455</td>\n",
       "      <td>0.549489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.804156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.789474</td>\n",
       "      <td>0.890909</td>\n",
       "      <td>0.508982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.806622</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Duration     Pulse  Maxpulse  Calories\n",
       "0   0.928571  0.000000  0.545455  0.000000\n",
       "1   0.928571  0.736842  0.727273  0.489609\n",
       "2   0.964286  0.421053  0.000000  0.692497\n",
       "4   0.928571  0.894737  1.000000  0.804156\n",
       "5   0.000000  0.684211  0.745455  0.794998\n",
       "6   1.000000  0.421053  0.763636  1.000000\n",
       "8   0.928571  0.736842  0.509091  0.527298\n",
       "9   0.928571  0.894737  1.000000  0.804156\n",
       "10  0.928571  0.894737  1.000000  0.804156\n",
       "11  0.928571  1.000000  1.000000  0.929200\n",
       "13  0.928571  0.736842  0.945455  0.549489\n",
       "14  0.928571  0.894737  1.000000  0.804156\n",
       "16  0.928571  0.789474  0.890909  0.508982\n",
       "17  0.928571  0.894737  1.000000  0.806622"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df2.max()-df2)/(df2.max()-df2.min())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Standard Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "        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>Duration</th>\n",
       "      <th>Pulse</th>\n",
       "      <th>Maxpulse</th>\n",
       "      <th>Calories</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.233557</td>\n",
       "      <td>2.663829</td>\n",
       "      <td>0.869933</td>\n",
       "      <td>2.699922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.233557</td>\n",
       "      <td>-0.084121</td>\n",
       "      <td>0.235607</td>\n",
       "      <td>0.754987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.375723</td>\n",
       "      <td>1.093572</td>\n",
       "      <td>2.772913</td>\n",
       "      <td>-0.050972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.233557</td>\n",
       "      <td>-0.672967</td>\n",
       "      <td>-0.715883</td>\n",
       "      <td>-0.494529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.462742</td>\n",
       "      <td>0.112161</td>\n",
       "      <td>0.172174</td>\n",
       "      <td>-0.458149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.517888</td>\n",
       "      <td>1.093572</td>\n",
       "      <td>0.108742</td>\n",
       "      <td>-1.272503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.233557</td>\n",
       "      <td>-0.084121</td>\n",
       "      <td>0.996799</td>\n",
       "      <td>0.605269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.233557</td>\n",
       "      <td>-0.672967</td>\n",
       "      <td>-0.715883</td>\n",
       "      <td>-0.494529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-0.233557</td>\n",
       "      <td>-0.672967</td>\n",
       "      <td>-0.715883</td>\n",
       "      <td>-0.494529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-0.233557</td>\n",
       "      <td>-1.065532</td>\n",
       "      <td>-0.715883</td>\n",
       "      <td>-0.991257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-0.233557</td>\n",
       "      <td>-0.084121</td>\n",
       "      <td>-0.525585</td>\n",
       "      <td>0.517117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-0.233557</td>\n",
       "      <td>-0.672967</td>\n",
       "      <td>-0.715883</td>\n",
       "      <td>-0.494529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-0.233557</td>\n",
       "      <td>-0.280403</td>\n",
       "      <td>-0.335287</td>\n",
       "      <td>0.678029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-0.233557</td>\n",
       "      <td>-0.672967</td>\n",
       "      <td>-0.715883</td>\n",
       "      <td>-0.504324</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Duration     Pulse  Maxpulse  Calories\n",
       "0  -0.233557  2.663829  0.869933  2.699922\n",
       "1  -0.233557 -0.084121  0.235607  0.754987\n",
       "2  -0.375723  1.093572  2.772913 -0.050972\n",
       "4  -0.233557 -0.672967 -0.715883 -0.494529\n",
       "5   3.462742  0.112161  0.172174 -0.458149\n",
       "6  -0.517888  1.093572  0.108742 -1.272503\n",
       "8  -0.233557 -0.084121  0.996799  0.605269\n",
       "9  -0.233557 -0.672967 -0.715883 -0.494529\n",
       "10 -0.233557 -0.672967 -0.715883 -0.494529\n",
       "11 -0.233557 -1.065532 -0.715883 -0.991257\n",
       "13 -0.233557 -0.084121 -0.525585  0.517117\n",
       "14 -0.233557 -0.672967 -0.715883 -0.494529\n",
       "16 -0.233557 -0.280403 -0.335287  0.678029\n",
       "17 -0.233557 -0.672967 -0.715883 -0.504324"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df2-df2.mean())/df2.std()"
   ]
  }
 ],
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