데이터전처리작업1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터 전처리 작업 1\n",
    "### 데이터 정제하기\n",
    "#### 정제가 필요한 결측값, 이상값 등이 무엇인지 파악할 수 있다. <br><br> 결측값와 이상값에 대한 처리 기준을 정하고 제거 또는 임의의 값으로 대체할 수 있다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ ### 모듈 불러오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "#데이터셋\n",
    "\n",
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ ### 데이터 불러오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "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>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "   target  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris = load_iris()\n",
    "data = pd.DataFrame(iris.data, columns=iris.feature_names)\n",
    "data['target'] = iris.target\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 5)\n"
     ]
    }
   ],
   "source": [
    "print(data.shape) # 150개 데이터, 4개 feature & Target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <td>0.685694</td>\n",
       "      <td>-0.042434</td>\n",
       "      <td>1.274315</td>\n",
       "      <td>0.516271</td>\n",
       "      <td>0.530872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <td>-0.042434</td>\n",
       "      <td>0.189979</td>\n",
       "      <td>-0.329656</td>\n",
       "      <td>-0.121639</td>\n",
       "      <td>-0.152349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal length (cm)</th>\n",
       "      <td>1.274315</td>\n",
       "      <td>-0.329656</td>\n",
       "      <td>3.116278</td>\n",
       "      <td>1.295609</td>\n",
       "      <td>1.372483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal width (cm)</th>\n",
       "      <td>0.516271</td>\n",
       "      <td>-0.121639</td>\n",
       "      <td>1.295609</td>\n",
       "      <td>0.581006</td>\n",
       "      <td>0.597315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>0.530872</td>\n",
       "      <td>-0.152349</td>\n",
       "      <td>1.372483</td>\n",
       "      <td>0.597315</td>\n",
       "      <td>0.671141</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   sepal length (cm)  sepal width (cm)  petal length (cm)  \\\n",
       "sepal length (cm)           0.685694         -0.042434           1.274315   \n",
       "sepal width (cm)           -0.042434          0.189979          -0.329656   \n",
       "petal length (cm)           1.274315         -0.329656           3.116278   \n",
       "petal width (cm)            0.516271         -0.121639           1.295609   \n",
       "target                      0.530872         -0.152349           1.372483   \n",
       "\n",
       "                   petal width (cm)    target  \n",
       "sepal length (cm)          0.516271  0.530872  \n",
       "sepal width (cm)          -0.121639 -0.152349  \n",
       "petal length (cm)          1.295609  1.372483  \n",
       "petal width (cm)           0.581006  0.597315  \n",
       "target                     0.597315  0.671141  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.cov() #변수간의 상관성이 존재하는가? 다중공선성 확인하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.117570</td>\n",
       "      <td>0.871754</td>\n",
       "      <td>0.817941</td>\n",
       "      <td>0.782561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <td>-0.117570</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.428440</td>\n",
       "      <td>-0.366126</td>\n",
       "      <td>-0.426658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal length (cm)</th>\n",
       "      <td>0.871754</td>\n",
       "      <td>-0.428440</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.962865</td>\n",
       "      <td>0.949035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal width (cm)</th>\n",
       "      <td>0.817941</td>\n",
       "      <td>-0.366126</td>\n",
       "      <td>0.962865</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.956547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>0.782561</td>\n",
       "      <td>-0.426658</td>\n",
       "      <td>0.949035</td>\n",
       "      <td>0.956547</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   sepal length (cm)  sepal width (cm)  petal length (cm)  \\\n",
       "sepal length (cm)           1.000000         -0.117570           0.871754   \n",
       "sepal width (cm)           -0.117570          1.000000          -0.428440   \n",
       "petal length (cm)           0.871754         -0.428440           1.000000   \n",
       "petal width (cm)            0.817941         -0.366126           0.962865   \n",
       "target                      0.782561         -0.426658           0.949035   \n",
       "\n",
       "                   petal width (cm)    target  \n",
       "sepal length (cm)          0.817941  0.782561  \n",
       "sepal width (cm)          -0.366126 -0.426658  \n",
       "petal length (cm)          0.962865  0.949035  \n",
       "petal width (cm)           1.000000  0.956547  \n",
       "target                     0.956547  1.000000  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr() #변수간의 상관성이 얼마나 존재하며 무시해도 되는 정도인가?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ ### 이상치(outlier) 확인 및 처리"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "식별방법\n",
    "\n",
    "Boxplot outer fence 벗어난 값 <br>\n",
    "EDS(Extreme Studentized Deviation) 평균에서 3표준편차 이상 떨어진 값 \n",
    "\n",
    "\n",
    "처리 방법\n",
    "\n",
    "레코드 삭제(행 삭제)\n",
    "대체(상한, 하한으로 조정)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Boxplot 식별"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IMgAkQZABIIn/AeWGTHFNLriUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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XmbsHPZbnIyJuA67NzP96no9zNvCHmXl5d0amUeAZs7S499P6IeDztR740y48jkaIZ8ySVIxnzJJUjGGWpGIMsyQVY5glqRjDLEnFGGZJKub/AVXbf4NmAC1IAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWYAAAEKCAYAAAAhEP83AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAADOFJREFUeJzt3X+MZWddx/HPt91Ct1F+1GLBFVjJGgmgVFoJyA8RYzSNoX/YSAkKJTFG0WU1MdGoAUKCif9o6qo0JWKhEm1SjRYoIfJDfimELWlpm4IODQRWpC2VFrO1pPTxj3sWxnF25+525t5v77xeyaZ355577vPsmXn3zLlzn6kxRgDo44xlDwCA/0uYAZoRZoBmhBmgGWEGaEaYAZoRZoBmhBmgGWEGaGbP6TzovPPOG/v379/moQCsrhtvvPHuMcYT5tn2tMK8f//+HDly5HQeCrArVdUX593WpQyAZoQZoBlhBmhGmAGaEWaAZoQZoBlhBmhGmAGaEWaAZoQZoBlhBmhGmAGaEWaAZoQZoBlhBmhGmAGaEWaAZoQZoBlhBmjmtH7nX2eHDx/O2trasofBEhw9ejRJsm/fviWPZDkOHDiQgwcPLnsYbIOVC/Pa2lpuuvX2fOucc5c9FBbszGP3Jkn+84GV+7Te0pnH7ln2ENhGK/kZ/K1zzs39T7942cNgwfZ+9oYk2ZXH/vjcWQ2uMQM0I8wAzQgzQDPCDNCMMAM0I8wAzQgzQDPCDNCMMAM0I8wAzQgzQDPCDNCMMAM0I8wAzQgzQDPCDNCMMAM0I8wAzQgzQDPCDNCMMAM0I8wAzQgzQDPCDNCMMAM0I8wAzQgzQDPCDNCMMAM0I8wAzQgzQDPCDNCMMAM0I8wAzQgzQDPCDNCMMAM0I8wAzQgzQDPCDNCMMAM0s9AwHz58OIcPH17kUwJsi0X2a89CnmWytra2yKcD2DaL7JdLGQDNCDNAM8IM0IwwAzQjzADNCDNAM8IM0IwwAzQjzADNCDNAM8IM0IwwAzQjzADNCDNAM8IM0IwwAzQjzADNCDNAM8IM0IwwAzQjzADNCDNAM8IM0IwwAzQjzADNCDNAM8IM0IwwAzQjzADNCDNAM8IM0IwwAzQjzADNCDNAM8IM0IwwAzQjzADNCDNAM8IM0IwwAzQjzADN7Fnkkx09ejT3339/Dh06tGPPsba2ljO+OXZs/9DRGf9zX9bWvrGjX1u73draWvbu3buQ55r7jLmqfqWqjlTVkbvuumsnxwSwq819xjzGuCrJVUly0UUXndYp6b59+5IkV1xxxek8fC6HDh3KjXd8dcf2Dx09dPZjcuBp5+/o19Zut8jvRlxjBmhGmAGaEWaAZoQZoBlhBmhGmAGaEWaAZoQZoBlhBmhGmAGaEWaAZoQZoBlhBmhGmAGaEWaAZoQZoBlhBmhGmAGaEWaAZoQZoBlhBmhGmAGaEWaAZoQZoBlhBmhGmAGaEWaAZoQZoBlhBmhGmAGaEWaAZoQZoBlhBmhGmAGaEWaAZoQZoBlhBmhGmAGaEWaAZoQZoBlhBmhGmAGa2bPIJztw4MAinw5g2yyyXwsN88GDBxf5dADbZpH9cikDoBlhBmhGmAGaEWaAZoQZoBlhBmhGmAGaEWaAZoQZoBlhBmhGmAGaEWaAZoQZoBlhBmhGmAGaEWaAZoQZoBlhBmhGmAGaEWaAZoQZoBlhBmhGmAGaEWaAZoQZoBlhBmhGmAGaEWaAZoQZoBlhBmhGmAGaEWaAZoQZoBlhBmhGmAGaEWaAZoQZoBlhBmhGmAGaEWaAZvYsewA74cxj92TvZ29Y9jBYsDOPfS1JduWxP/PYPUnOX/Yw2CYrF+YDBw4sewgsydGjDyZJ9u3bjYE63+f+Clm5MB88eHDZQwB4WFxjBmhGmAGaEWaAZoQZoBlhBmhGmAGaEWaAZoQZoBlhBmhGmAGaEWaAZoQZoBlhBmhGmAGaEWaAZoQZoBlhBmhGmAGaEWaAZoQZoJkaY5z6g6ruSvLFJOcluXu7B/UIsZvnnuzu+e/muSe7e/4PZ+5PHWM8YZ4NTyvM335w1ZExxkWnvYNHsN0892R3z383zz3Z3fNf1NxdygBoRpgBmnm4Yb5qW0bxyLSb557s7vnv5rknu3v+C5n7w7rGDMD2cykDoJm5wlxVP1tVn6uqtar63U3uf3RVXTvd/8mq2r/dA12WOeZ+eVXdVVU3TX9+eRnj3AlV9baqurOqbj3B/VVVfzr923ymqp6z6DHulDnm/pKqunfdcX/9ose4k6rqyVX1oaq6vapuq6pDm2yzksd/zrnv7PEfY5z0T5Izk3w+ydOSPCrJzUmesWGb1ya5crp9WZJrt9rvI+HPnHO/PMmfLXusOzT/Fyd5TpJbT3D/xUnem6SSPC/JJ5c95gXO/SVJ3r3sce7g/J+U5DnT7e9O8m+bfO6v5PGfc+47evznOWN+bpK1McYdY4xvJvnbJJds2OaSJG+fbl+X5KeqqubYd3fzzH1ljTE+kuSek2xySZJ3jJlPJHlcVT1pMaPbWXPMfaWNMb4yxvj0dPsbSW5Psm/DZit5/Oec+46aJ8z7knxp3d+/nP8/yG9vM8Z4MMm9Sb5nOwa4ZPPMPUl+fvpW7rqqevJihtbCvP8+q+r5VXVzVb23qp657MHslOnS5I8m+eSGu1b++J9k7skOHv95wrzZme/GH+WYZ5tHonnm9a4k+8cYP5Lk/fnOdw67waoe93l8OrO32D47yeEk/7Dk8eyIqvquJH+X5DfHGPdtvHuTh6zM8d9i7jt6/OcJ85eTrD8L/P4k/3GibapqT5LHZjW+Ddxy7mOMr40xHpj++tYkFy5obB3M87mxksYY940x/nu6fUOSs6rqvCUPa1tV1VmZhemdY4y/32STlT3+W819p4//PGH+VJIfrKofqKpHZfbi3vUbtrk+yaun25cm+eCYrpA/wm059w3X1F6W2fWo3eL6JK+aXp1/XpJ7xxhfWfagFqGqnnj8dZSqem5mX0tfW+6ots80t79McvsY449PsNlKHv955r7Tx3/PVhuMMR6sqt9I8r7MfkrhbWOM26rqTUmOjDGunyZxTVWtZXamfNl2DXCZ5pz766rqZUkezGzuly9twNusqv4ms1efz6uqLyd5Q5KzkmSMcWWSGzJ7ZX4tybEkr1nOSLffHHO/NMmvVdWDSe5PctmKnIwc94Ikv5Tklqq6afrY7yV5SrLyx3+eue/o8ffOP4BmvPMPoBlhBmhGmAGaEWaAZoQZoBlhZkdNq+993xzbXV1Vl57G/n+1ql61ycf3H18ZrqouqKqL1933xqr67Tn2XVX1wap6zKmOa5N9vb+qHv9w98PuIMzstMuTbBnm0zXGuHKM8Y4tNrsgs5+3PVUXJ7l5k7fjno5rMluFEbYkzMxtOgv9bFW9fd2iTedM911YVR+uqhur6n1V9aTpDPiiJO+c1qzdW1Wvr6pPVdWtVXXVyVYhrKrvraobp9vPrqpRVU+Z/v75qjpn/dnvNIabq+pfk/z69LFHJXlTkpdPY3j5tPtnVNU/V9UdVfW6EwzhlUn+cd14XjXN++aqumb62NVV9Zaard97R1X9RM3Wcr69qq5et6/rk7ziFP/J2aWEmVP1Q0mumhZtui/Ja6d1BQ4nuXSMcWGStyV58xjjuiRHkrxyjHHBGOP+zNau/rExxrOS7E3ycyd6ojHGnUnOni4lvGja14uq6qlJ7hxjHNvwkL9K8roxxvPX7eObSV6f2RrhF4wxrp3uenqSn8lsadc3THPY6AVJjv+P4ZlJfj/JS6eFa9Yvnv74JC9N8luZLWr1J0memeSHq+qCaRz/leTRVbUKqy6yw4SZU/WlMcbHp9t/neSFmcX6WUn+aXoL6x9ktqDNZn6yZr/l5pbMYrbVcon/klkgX5zkD6f/vijJR9dvVFWPTfK4McaHpw9ds8V+3zPGeGCMcXeSO5Ocv8k2507r8WYa63XT9hljrF+k613T23FvSfLVMcYtY4yHktyWZP+67e7MDl7WYXVsuVYGbLDxPfwjs+Ufb1t/prqZqjo7yV8kuWiM8aWqemOSs7d4vo9mFuKnZnZZ4Xem53z3xt1vMraTeWDd7W9l86+FB6vqjCmyJ9v/8X09tGG/D23Y79mZrasAJ+WMmVP1lKo6HuBXJPlYks8lecLxj1fVWfWdhcO/kdmv50m+E+G7a7bW7Tw/hfGRJL+Y5N+nQN6T2YtyH1+/0Rjj60nuraoXTh965bq714/hVHwus18rliQfSPILxy9FVNW5p7Kj6Vr6E5N84TTGwS4jzJyq25O8uqo+k+TcJG+ZruNemuSPqurmJDcl+fFp+6uTXDld4nggszWrb8lsYfFPbfVkY4wvTDc/Mv33Y0m+Pl2z3eg1Sf58evFv/ZnphzJ7sW/9i3/zeE9mK8xljHFbkjcn+fA0xxMthXkiFyb5xPQbfuCkrC7H3Gr2a3bePb1wt/Jqttb2O8YYP70N+7oiyfVjjA88/JGx6pwxwwlMi76/dTveYJLZb9sWZebijBmgGWfMAM0IM0AzwgzQjDADNCPMAM0IM0Az/wuqi83EyZrOSQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for count,i in enumerate(data.columns):\n",
    "    sns.boxplot(data[i])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " sepal width에서만 이상치 발견"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23ab5982400>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data['sepal width (cm)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "q25, q75 = np.quantile(data['sepal width (cm)'],0.25), np.quantile(data['sepal width (cm)'],0.75)\n",
    "cut_off = (q75- q25)*1.5 #IQR*1.5\n",
    "\n",
    "lower = q25 - cut_off \n",
    "upper = q75 + cut_off"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "lower_list = data['sepal width (cm)'][data['sepal width (cm)'] <lower].index.tolist()\n",
    "upper_list = data['sepal width (cm)'][data['sepal width (cm)'] >upper].index.tolist()\n",
    "drop_list = lower_list + upper_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[60, 15, 32, 33]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drop_list # lower = 60, upper = 15,32,33"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "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>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>146 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                  5.1               3.5                1.4               0.2   \n",
       "1                  4.9               3.0                1.4               0.2   \n",
       "2                  4.7               3.2                1.3               0.2   \n",
       "3                  4.6               3.1                1.5               0.2   \n",
       "4                  5.0               3.6                1.4               0.2   \n",
       "..                 ...               ...                ...               ...   \n",
       "145                6.7               3.0                5.2               2.3   \n",
       "146                6.3               2.5                5.0               1.9   \n",
       "147                6.5               3.0                5.2               2.0   \n",
       "148                6.2               3.4                5.4               2.3   \n",
       "149                5.9               3.0                5.1               1.8   \n",
       "\n",
       "     target  \n",
       "0         0  \n",
       "1         0  \n",
       "2         0  \n",
       "3         0  \n",
       "4         0  \n",
       "..      ...  \n",
       "145       2  \n",
       "146       2  \n",
       "147       2  \n",
       "148       2  \n",
       "149       2  \n",
       "\n",
       "[146 rows x 5 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop(drop_list,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "data['sepal width (cm)'][60] = lower"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "data['sepal width (cm)'][upper_list] = upper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23ab7110438>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RJ822W4fXn5F0Y0S8u4TpTV26/pGqKS23qJpIfUzSH2rXK9e5+pzNs7XlnSq3N9rsUDW/gdR5CsZOx3q+9vp57fre2qccE5gTtywwJ9uvVHU74BuSviDpBFUTrbzC9kllm0V+YZJ9SZq5z/wmSU9HxNOqZpHbWtaf063fqH5jyo8l/baqCZjWSPqUXny7Qqpub5xV+jxO1UQ3M/633GLpx12SJksdz0h6aGbSdFe/f23fPo/3GlUT7gBzIpDRzeslrXP1myAukPRXJSzPlPR52xtVzXS2srbPk7bXSvqyql8MIEl/o+oK+4eqbnH0Yo2kR8uDsTWq5qPtFMj/IGn/cqviPFUzds24VNKm2kO9Xlyr6jbIjLMlfaIcf62q6VP7cbKqmeGAOTHbGwbK9k2SPhURA/vN1KNg+2pJ50XEfbt5nGWSzo2IswdTGV7OuEIGOjtf1cO93TUm6c8GcBzsAbhCBoAkuEIGgCQIZABIgkAGgCQIZABIgkAGgCQIZABI4v8AkK3FfeehP6UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data['sepal width (cm)'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ESD 식별"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ESD(Extreme Studentized Deviation)\n",
    "\n",
    "평균으로부터 3 표준편차 떨어진 값(각 0.15%)를 이상치로 판단 - "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sepal length (cm)\n",
      "0\n",
      "0\n",
      "sepal width (cm)\n",
      "0\n",
      "0\n",
      "petal length (cm)\n",
      "0\n",
      "0\n",
      "petal width (cm)\n",
      "0\n",
      "0\n",
      "target\n",
      "0\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "for i in data.columns:\n",
    "    print(i)\n",
    "    print(sum(data[i] >data[i].mean() + data[i].std()*3 ))\n",
    "    print(sum(data[i] <data[i].mean() - data[i].std()*3 ))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}