2.1 데이터 전처리.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "709593fe",
   "metadata": {},
   "source": [
    "# 데이터 전처리"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a68dfc37",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "50bed2bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import missingno as msno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "34cf3946",
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic = sns.load_dataset(\"titanic\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4f237fe1",
   "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>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>Second</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>B</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n",
       "0           0       3    male  22.0      1      0   7.2500        S   Third   \n",
       "1           1       1  female  38.0      1      0  71.2833        C   First   \n",
       "2           1       3  female  26.0      0      0   7.9250        S   Third   \n",
       "3           1       1  female  35.0      1      0  53.1000        S   First   \n",
       "4           0       3    male  35.0      0      0   8.0500        S   Third   \n",
       "..        ...     ...     ...   ...    ...    ...      ...      ...     ...   \n",
       "886         0       2    male  27.0      0      0  13.0000        S  Second   \n",
       "887         1       1  female  19.0      0      0  30.0000        S   First   \n",
       "888         0       3  female   NaN      1      2  23.4500        S   Third   \n",
       "889         1       1    male  26.0      0      0  30.0000        C   First   \n",
       "890         0       3    male  32.0      0      0   7.7500        Q   Third   \n",
       "\n",
       "       who  adult_male deck  embark_town alive  alone  \n",
       "0      man        True  NaN  Southampton    no  False  \n",
       "1    woman       False    C    Cherbourg   yes  False  \n",
       "2    woman       False  NaN  Southampton   yes   True  \n",
       "3    woman       False    C  Southampton   yes  False  \n",
       "4      man        True  NaN  Southampton    no   True  \n",
       "..     ...         ...  ...          ...   ...    ...  \n",
       "886    man        True  NaN  Southampton    no   True  \n",
       "887  woman       False    B  Southampton   yes   True  \n",
       "888  woman       False  NaN  Southampton    no  False  \n",
       "889    man        True    C    Cherbourg   yes   True  \n",
       "890    man        True  NaN   Queenstown    no   True  \n",
       "\n",
       "[891 rows x 15 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "431bd4cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.matrix(titanic)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "990356ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.bar(titanic)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "df09a742",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic2 = titanic.dropna(thresh=int(len(titanic)*0.5), axis = 1)\n",
    "msno.matrix(titanic2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6756b914",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4819e832",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      Southampton\n",
       "1        Cherbourg\n",
       "2      Southampton\n",
       "3      Southampton\n",
       "4      Southampton\n",
       "          ...     \n",
       "886    Southampton\n",
       "887    Southampton\n",
       "888    Southampton\n",
       "889      Cherbourg\n",
       "890     Queenstown\n",
       "Name: embark_town, Length: 891, dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[\"embark_town\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a35a872f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEXCAYAAABCjVgAAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAbe0lEQVR4nO3de7hcdX3v8feHgIACAibQQNIGPfES2oo1Ui1esNBCLxqqYmNFU0vL6XNQi09v0Pa0VBtra3tOvdEWLRAolQZvIKdVaSwX8RKDopAAkhKECIZA6wWraOB7/li/vZgke292QmbvZOf9ep79zFq/Wes33z1rZj6z1pr5TaoKSZIA9pjqAiRJOw9DQZLUMxQkST1DQZLUMxQkST1DQZLUMxQkST1DQbu9JFcl+fUd2N8FSf5sR/UnTSZDQdoJGCTaWRgK0g6UZMZU1yA9FoaCdllJDkvywSQbk6xL8sbWfnaSS5P8Y5JvJ7kxyVOTnJXk3iR3JfnZLbp7SpKVSb6Z5LIkBw/czqVJvt6uuybJkQPXXZDkb5P8S5LvAC/eosb9k/x7kncmyRj/x2nAq4HfS/JAko+29me0Q1vfSLI6yUtb+xGtbY82/74k9w70949JzmjTVyV5S5Lr2n3xiSQzt/tO17RnKGiX1F4QPwp8CTgcOA44I8kJbZGXABcBBwFfBD5O93g/HHgz8PdbdPla4NeAw4BNwDsHrvtXYD5wCPAF4OIt1v0VYCmwP/CpgRqfBKwArquqN9YYA41V1bmtz7+sqv2q6iVJ9mr/3yfa7b4BuDjJ06pqHfAt4FmtixcADyR5Rpt/IXD1FvW9rvXzOOB3RqtDAkNBu67nALOq6s1V9f2quh14L7C4XX9tVX28qjYBlwKzgLdV1Q+AS4B5SQ4c6O+iqrqpqr4D/G/glSOHgqrqvKr6dlU9CJwNPDPJEwfWvayqrquqh6vqe63tMLoX5kur6o+24/97LrBfq/n7VfVJ4ArgVe36q4EXJfmhNv+BNn8EcABdWI44v6q+UlXfBZYDR21HPdpN7DnVBUjb6UeAw5J8Y6BtBnAt8FVgw0D7d4H7quqhgXnoXnRH1r9rYPmvAnsBM5PcR7cXcDJdsDzclpkJfHOUdUf8AvAA8Hfb8k8NOAy4q6oeHmj7Kt2eDnSh8FJgPXANcBXwGuB7dIE4uN7XB6b/m+7/lkblnoJ2VXcB66rqwIG//avq57ezv7kD0z8M/AC4j+7QyyLgeOCJwLy2zOD5gdEOC70X+BjwL0meMIHb37KPu4G5I+cNBur6Wpu+mu6w0bFt+lPAMcCL2PzQkbRNDAXtqlYC30ry+0n2TTIjyY8mec529ndKkgVJHk93zuEDbc9if+BB4H7g8cBbt6HP1wO3Alck2fdRlt0APHlg/nPAd+hOPu+V5Fi68ySXAFTVbXR7PKcA11TVt1ofL8dQ0GNgKGiX1F6wX0J3fHwd3bv699G9m98eFwEX0B1q2Qd4Y2u/kO6wzdeANcBnt6HGAk6j26u5LMk+4yz+D8CC9qmij1TV9+kOD/0c3f92DvDaqrplYJ2rgfur6s6B+dCdWJe2S/zlNUnSCPcUJEk9Q0GaJO0LaA+M8vfqqa5NGuHhI0lSb5f+nsLMmTNr3rx5U12GJO1Srr/++vuqatZo1+3SoTBv3jxWrVo11WVI0i4lyVfHus5zCpKknqEgSeoZCpKknqEgSeoZCpKknqEgSeoZCpKknqEgSeoZCpKk3i79jeZt8ezfvXCqS9gtXP/21051CZIeA/cUJEk9Q0GS1DMUJEk9Q0GS1DMUJEk9Q0GS1DMUJEk9Q0GS1DMUJEk9Q0GS1BtqKCQ5MMkHktyS5OYkz0tycJIrk9zWLg8aWP6sJGuT3JrkhGHWJkna2rD3FN4BfKyqng48E7gZOBNYUVXzgRVtniQLgMXAkcCJwDlJZgy5PknSgKGFQpIDgBcC/wBQVd+vqm8Ai4BlbbFlwEltehFwSVU9WFXrgLXA0cOqT5K0tWHuKTwZ2Aicn+SLSd6X5AnAoVV1D0C7PKQtfzhw18D661vbZpKclmRVklUbN24cYvmStPsZZijsCfwE8LdV9SzgO7RDRWPIKG21VUPVuVW1sKoWzpo1a8dUKkkChhsK64H1VfW5Nv8BupDYkGQ2QLu8d2D5uQPrzwHuHmJ9kqQtDC0UqurrwF1JntaajgPWAJcDS1rbEuCyNn05sDjJ3kmOAOYDK4dVnyRpa8P+5bU3ABcneRxwO/A6uiBanuRU4E7gZICqWp1kOV1wbAJOr6qHhlyfJGnAUEOhqm4AFo5y1XFjLL8UWDrMmiRJY/MbzZKknqEgSeoZCpKknqEgSeoZCpKknqEgSeoZCpKknqEgSeoZCpKknqEgSeoZCpKknqEgSeoZCpKknqEgSeoZCpKknqEgSeoZCpKknqEgSeoZCpKknqEgSeoZCpKknqEgSeoZCpKk3lBDIckdSW5MckOSVa3t4CRXJrmtXR40sPxZSdYmuTXJCcOsTZK0tcnYU3hxVR1VVQvb/JnAiqqaD6xo8yRZACwGjgROBM5JMmMS6pMkNVNx+GgRsKxNLwNOGmi/pKoerKp1wFrg6MkvT5J2X8MOhQI+keT6JKe1tkOr6h6AdnlIaz8cuGtg3fWtbTNJTkuyKsmqjRs3DrF0Sdr97Dnk/o+pqruTHAJcmeSWcZbNKG21VUPVucC5AAsXLtzqeknS9hvqnkJV3d0u7wU+THc4aEOS2QDt8t62+Hpg7sDqc4C7h1mfJGlzQwuFJE9Isv/INPCzwE3A5cCSttgS4LI2fTmwOMneSY4A5gMrh1WfJGlrwzx8dCjw4SQjt/NPVfWxJJ8Hlic5FbgTOBmgqlYnWQ6sATYBp1fVQ0OsT5K0haGFQlXdDjxzlPb7gePGWGcpsHRYNUmSxuc3miVJPUNBktQzFCRJPUNBktQzFCRJPUNBktQzFCRJPUNBktQzFCRJPUNBktQzFCRJPUNBktQzFCRJPUNBktQzFCRJPUNBktQzFCRJPUNBktQzFCRJPUNBktQzFCRJPUNBktQzFCRJvaGHQpIZSb6Y5Io2f3CSK5Pc1i4PGlj2rCRrk9ya5IRh1yZJ2txk7Cn8FnDzwPyZwIqqmg+saPMkWQAsBo4ETgTOSTJjEuqTJDVDDYUkc4BfAN430LwIWNamlwEnDbRfUlUPVtU6YC1w9DDrkyRtbth7Cn8D/B7w8EDboVV1D0C7PKS1Hw7cNbDc+ta2mSSnJVmVZNXGjRuHUrQk7a6GFgpJfhG4t6qun+gqo7TVVg1V51bVwqpaOGvWrMdUoyRpc3sOse9jgJcm+XlgH+CAJP8IbEgyu6ruSTIbuLctvx6YO7D+HODuIdYnSdrC0PYUquqsqppTVfPoTiB/sqpOAS4HlrTFlgCXtenLgcVJ9k5yBDAfWDms+iRJWxvmnsJY3gYsT3IqcCdwMkBVrU6yHFgDbAJOr6qHpqA+SdptTUooVNVVwFVt+n7guDGWWwosnYyaJElb8xvNkqSeoSBJ6hkKkqTehEIhyYqJtEmSdm3jnmhOsg/weGBmG7hu5AtmBwCHDbk2SdIke7RPH/1P4Ay6ALieR0LhW8B7hleWJGkqjBsKVfUO4B1J3lBV75qkmiRJU2RC31Ooqncl+Slg3uA6VXXhkOqSJE2BCYVCkouApwA3ACPfMi7AUJCkaWSi32heCCyoqq1GLZUkTR8T/Z7CTcAPDbMQSdLUm+iewkxgTZKVwIMjjVX10qFUJUmaEhMNhbOHWYQkaecw0U8fXT3sQiRJU2+inz76No/8NObjgL2A71TVAcMqTJI0+Sa6p7D/4HySk4Cjh1GQJGnqbNcoqVX1EeCnd2wpkqSpNtHDRy8bmN2D7nsLfmdBkqaZiX766CUD05uAO4BFO7waSdKUmug5hdcNuxBJ0tSb6I/szEny4ST3JtmQ5INJ5gy7OEnS5JroiebzgcvpflfhcOCjrU2SNI1MNBRmVdX5VbWp/V0AzBpiXZKkKTDRULgvySlJZrS/U4D7x1shyT5JVib5UpLVSf60tR+c5Mokt7XLgwbWOSvJ2iS3Jjlh+/8tSdL2mGgo/BrwSuDrwD3AK4BHO/n8IPDTVfVM4CjgxCTPBc4EVlTVfGBFmyfJAmAxcCRwInBOkhnb9N9Ikh6TiYbCW4AlVTWrqg6hC4mzx1uhOg+02b3aX9F9lHVZa18GnNSmFwGXVNWDVbUOWIvfmpakSTXRUPjxqvqvkZmq+k/gWY+2UjvUdANwL3BlVX0OOLSq7mn93AMc0hY/HLhrYPX1rW3LPk9LsirJqo0bN06wfEnSREw0FPbY4tj/wUzgOw5V9VBVHQXMAY5O8qPjLJ7Ruhilz3OramFVLZw1y3PdkrQjTfQbzX8NfDrJB+heqF8JLJ3ojVTVN5JcRXeuYEOS2VV1T5LZdHsR0O0ZzB1YbQ5w90RvQ5L02E1oT6GqLgReDmwANgIvq6qLxlsnyawkB7bpfYHjgVvovu+wpC22BLisTV8OLE6yd5IjgPnAym36byRJj8lE9xSoqjXAmm3oezawrH2CaA9geVVdkeQzwPIkpwJ3Aie3/lcnWd5uYxNwelU9tA23J0l6jCYcCtuqqr7MKCejq+p+4Lgx1lnKNhyWkiTtWNv1ewqSpOnJUJAk9QwFSVLPUJAk9QwFSVLPUJAk9QwFSVLPUJAk9QwFSVLPUJAk9QwFSVLPUJAk9QwFSVLPUJAk9QwFSVLPUJAk9QwFSVLPUJAk9QwFSVLPUJAk9QwFSVLPUJAk9QwFSVJvaKGQZG6Sf09yc5LVSX6rtR+c5Mokt7XLgwbWOSvJ2iS3JjlhWLVJkkY3zD2FTcBvV9UzgOcCpydZAJwJrKiq+cCKNk+7bjFwJHAicE6SGUOsT5K0haGFQlXdU1VfaNPfBm4GDgcWAcvaYsuAk9r0IuCSqnqwqtYBa4Gjh1WfJGlrk3JOIck84FnA54BDq+oe6IIDOKQtdjhw18Bq61ubJGmSDD0UkuwHfBA4o6q+Nd6io7TVKP2dlmRVklUbN27cUWVKkhhyKCTZiy4QLq6qD7XmDUlmt+tnA/e29vXA3IHV5wB3b9lnVZ1bVQurauGsWbOGV7wk7YaG+emjAP8A3FxV/2fgqsuBJW16CXDZQPviJHsnOQKYD6wcVn2SpK3tOcS+jwFeA9yY5IbW9gfA24DlSU4F7gROBqiq1UmWA2voPrl0elU9NMT6JElbGFooVNWnGP08AcBxY6yzFFg6rJokSePzG82SpJ6hIEnqGQqSpJ6hIEnqGQqSpN4wP5Iq7TB3vvnHprqEae+H//jGqS5BOwH3FCRJPUNBktQzFCRJPUNBktQzFCRJPUNBktQzFCRJPUNBktQzFCRJPUNBktQzFCRJPUNBktQzFCRJPUNBktQzFCRJPUNBktQzFCRJvaGFQpLzktyb5KaBtoOTXJnktnZ50MB1ZyVZm+TWJCcMqy5J0tiGuadwAXDiFm1nAiuqaj6wos2TZAGwGDiyrXNOkhlDrE2SNIqhhUJVXQP85xbNi4BlbXoZcNJA+yVV9WBVrQPWAkcPqzZJ0ugm+5zCoVV1D0C7PKS1Hw7cNbDc+tYmSZpEO8uJ5ozSVqMumJyWZFWSVRs3bhxyWZK0e5nsUNiQZDZAu7y3ta8H5g4sNwe4e7QOqurcqlpYVQtnzZo11GIlaXcz2aFwObCkTS8BLhtoX5xk7yRHAPOBlZNcmyTt9vYcVsdJ3g8cC8xMsh74E+BtwPIkpwJ3AicDVNXqJMuBNcAm4PSqemhYtUmSRje0UKiqV41x1XFjLL8UWDqseiRJj25nOdEsSdoJGAqSpN7QDh9J0ohj3nXMVJcw7V33hut2SD/uKUiSeoaCJKlnKEiSeoaCJKlnKEiSeoaCJKlnKEiSeoaCJKlnKEiSeoaCJKlnKEiSeoaCJKlnKEiSeoaCJKlnKEiSeoaCJKlnKEiSeoaCJKlnKEiSeoaCJKlnKEiSejtdKCQ5McmtSdYmOXOq65Gk3clOFQpJZgDvAX4OWAC8KsmCqa1KknYfO1UoAEcDa6vq9qr6PnAJsGiKa5Kk3Uaqaqpr6CV5BXBiVf16m38N8JNV9fqBZU4DTmuzTwNunfRCJ89M4L6pLkLbze2365ru2+5HqmrWaFfsOdmVPIqM0rZZalXVucC5k1PO1EqyqqoWTnUd2j5uv13X7rztdrbDR+uBuQPzc4C7p6gWSdrt7Gyh8HlgfpIjkjwOWAxcPsU1SdJuY6c6fFRVm5K8Hvg4MAM4r6pWT3FZU2m3OEw2jbn9dl277bbbqU40S5Km1s52+EiSNIUMBUlSz1AYR5I/TLI6yZeT3JDkJ7ejj2OT/NTA/AXt+xiTIsm8JL8yWbe3q0jyQ0kuSfIfSdYk+ZckpyW54jH2O6nbd3eQZE6Sy5LcluT2JO9OsvcU1nNgkv81Vbc/bIbCGJI8D/hF4Ceq6seB44G7tqOrY4GferSFhmgeYCgMSBLgw8BVVfWUqloA/AFw6GPsd2gf3GhDwOx22rb6EPCRqpoPzAf2Bf5yCss6EDAUdkOzgfuq6kGAqrqvqu5OclySLya5Mcl5I+9YktyRZGabXpjkqiTzgN8E3tT2NF7Q+n5hkk+3dz2vaOvsl2RFki+0vhe19nlJbknyviQ3Jbk4yfFJrmvvnI5uy52d5KIkn2ztv9Fu623AC9rtvynJPknOb7fxxSQvbuv/apIPJflYW38qn3TD9mLgB1X1dyMNVXUDcC2wX5IPtPv84vaiRJJnJ7k6yfVJPp5kdmu/Kslbk1wN/Fbr7vgk1yb5SpJfbMuNd7+/e6SOJFckObZNP5DkzUk+Bzwvyamtz6uSvHdwvWnsp4HvVdX5AFX1EPAm4LVJXj/OffezST7Tnk+XJtmvtY+3Hf8iycp2H7+gtR/Z2m5Id8RgPt1z6imt7e3pvL09P29M8stt3XOSvLRNfzjJeW361CR/1p7bN7dtuTrJJ5LsOyn36niqyr9R/oD9gBuArwDnAC8C9qHbW3hqW+ZC4Iw2fQcws00vpHsXCnA28DsD/V4AXEoXyAvoxnqC7uPBB7TpmcBaum94zwM2AT/W1rkeOK9dt4juHdTI7XyJ7l3UzFbnYXR7KlcM3P5vA+e36acDd7b/61eB24EntvmvAnOnejsMadu+Efi/o7QfC3yT7kuTewCfAZ4P7AV8GpjVlvtluo9LA1wFnLPF9v1YW38+3Rcy93mU+/3dA+tfARzbpgt4ZZs+rD3GDm71XDu43nT9G2dbfRE4Y7T7rj3+rwGe0Np/H/jjCWzHv27TPw/8W5t+F/DqNv249vyaB9w0cLsvB66k+xj9oW3bzqb7ntXb2zIrgc+26fOBE3jkuX1Ua18OnDLV9/lO9T2FnUlVPZDk2cAL6N5Z/jPw58C6qvpKW2wZcDrwN9vY/Ueq6mFgTZKRQxYB3prkhcDDwOE8cjhjXVXdCJBkNbCiqirJjXQPrBGXVdV3ge8m+Xe6AQa/scVtP5/ugU5V3ZLkq8BT23Urquqb7XbWAD/C9h0y25WtrKr1AEluoLt/vwH8KHBl23GYAdwzsM4/b9HH8rZ9b0tyO10IjHe/j+Uh4INt+mjg6qr6z1bbpRNYfzoIWwx1M9A+lufSveG6rm2vx9EF/NMYfzt+qF1ezyPPq88Af5hkDvChqrqtrTvo+cD7q9uL2dD2Gp9DF9xnpBvpeQ1wUNszeR5d2D2J7rl9wyi3O2UMhXG0jXwVcFV7AV4yzuKbeORw3D6P0vWDA9Mjj7BXA7OAZ1fVD5LcMdDP4PIPD8w/zObbcMsnz7Y+mQZv5yGm7+NjNTDWyeDR7oMAq6vqeWOs850t5kfbDmPd74OPG9j8sfO99hhknPWnu9V078R7SQ6ge8N0P5sH48h9F+DKqnrVFuv9GONvx5Ft3z/2q+qf2uG7XwA+nuTX6faoN+t6tM6q6mtJDgJOpNtzORh4JfBAVX07yZPY+vE25YePPKcwhiRPa8cPRxwFbADmJfkfre01wNVt+g7g2W168EH8bWD/CdzkE4F7WyC8mO5d+rZa1I5dP4luN/rzo9z+NXQBRJKnAj/M9B5pdjSfBPbOI+ddSPIcukOEo7kVmJXuwwck2SvJkeP0f3KSPZI8BXhyW3+s+/0O4Ki2/Fy6PYLRrARelOSgdCe0Xz7GctPNCuDxSV4L/Qn3vwbeDaxj9Pvus8AxI8/TJI9v9/m2bkeSPBm4vareSTfkzo8z+nPql5PMSDILeCHd9oJuT+OMtsy1wO+0y52WoTC2/YBl6T6u+GW63dEzgdcBl7Y9h4eBkZOVfwq8I8m1dIk/4qPAL2XzE82juRhYmGQV3YvHLdtR80rg/9E9Kd5SVXcDXwY2JflSkjfRnR+Z0er/Z+BXq51M311UdwD3l4CfSfeR1NV052RGHXyxut/2eAXwF0m+RHeuabxPlN1K92bhX4HfrKrvMfb9fh3di9uNwF8BXxijhq8BbwU+B/wb3eGIb078v941DWyrVyS5jW7v4OGqWsoY911VbaQ7V/P+9tz9LPD07diO0J13uKkdSnw6cGFV3U93aOqmJG+n+yTbl+nO6X0S+L2q+npb/1pgz6pa2+o7mJ08FBzmYppIcjbdbulfTXUtGo4k+7VzXXvSvRCdV1Ufnuq6JlO67/y8H3hZVV0/1fVMR9P1mLE0HZ2d5Hi6Y+efAD4yteVMvqr6NNt3aFUT5J6CJKnnOQVJUs9QkCT1DAVJUs9QkCT1DAVpC9likLrt7KMfIPFRlpvWwzBr12MoSDtYtm2Y6wOZxsMwa9djKGjaSnLKwLDHf9+GIXgg3RDJ1yf5tyRHpxs2+faRYY6buemGEb81yZ8M9PmRtu7qJKcNtG82zPVA+76tn99gdNN7GGbtcgwFTUtJnkE3RMExVXUU3dAjrwaeQDes+bPpxrD5M+Bn6IZSePNAF0e35Y+iG8toYWv/tbbuQuCNbZwpWr83VdVPVtWnWtt+dMOc/FNVvXeMUs8E/qOqjqqq3wVe1m7zmXQ/7PT2dCNrXkM3Yi90I+guaNPP55FhE+YD76mqI+lGdt1dxkfSDmQoaLo6jm6Aws+3cWuOoxuc7vt0v3cA3Zg5V1fVD9r0vIH1r6yq+9tQ5B+ie/GFLgi+RDeezly6F2LYfJjrEZfR/YbChdtQdz8Mc1VtoBtDaWQY5hfkkWGYN+SRYZg/3dbd6YZh1q7HUNB0FWBZewd+VFU9rarOpvvFtZGv8ffDkLffPxh3GPJ0v+p1PPC8qnom3Q+9jAzXPDjM9YjrgJ9Lth6A/1Hq3kobEG9wGOZrGRiGuS22uwx9riEyFDRdraAbWfMQgCQHJ9mWMXN+pq2zL3AS3Qv8E4H/qqr/TvJ0uh9zGc8f043qec44y0zrYZi16zEUNC1V1Rrgj4BPtOGTr6T7icSJ+hRwEd3wyh+sqlV0h532bP29he4Q0qM5A9gnY/zm9XQfhlm7HgfEkyT13FOQJPU8ESVNgvbR1RWjXHVcO4Qk7RQ8fCRJ6nn4SJLUMxQkST1DQZLUMxQkSb3/D+Yh6Cg/5sX/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(titanic.embark_town)\n",
    "plt.title(\"embark_town\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "5593c3ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic3 = titanic2\n",
    "imputer_embark_town = SimpleImputer(strategy = \"most_frequent\")\n",
    "titanic[\"embark_town\"] = imputer_embark_town.fit_transform(titanic2[[\"embark_town\"]])\n",
    "titanic[\"embarked\"] = imputer_embark_town.fit_transform(titanic[[\"embarked\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "95d178f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.matrix(titanic3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a380e016",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'embark_town' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[1;32mIn [31]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m titanic\u001b[38;5;241m.\u001b[39miloc(\u001b[43membark_town\u001b[49m)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'embark_town' is not defined"
     ]
    }
   ],
   "source": [
    "titanic.iloc(embark_town)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d6a83c85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      Southampton\n",
       "1        Cherbourg\n",
       "2      Southampton\n",
       "3      Southampton\n",
       "4      Southampton\n",
       "          ...     \n",
       "886    Southampton\n",
       "887    Southampton\n",
       "888    Southampton\n",
       "889      Cherbourg\n",
       "890     Queenstown\n",
       "Name: embark_town, Length: 891, dtype: object"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.loc[:,\"embark_town\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "06a4f89a",
   "metadata": {},
   "outputs": [
    {
     "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.kdeplot(titanic.age)\n",
    "plt.title(\"CBH\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "1480446f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\UserK\\AppData\\Local\\Temp\\ipykernel_4088\\396162891.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  titanic3.loc[:,\"age\"] = imputer_age.fit_transform(titanic2[[\"age\"]])\n"
     ]
    }
   ],
   "source": [
    "titanic3 = titanic2\n",
    "imputer_age = SimpleImputer(strategy = \"median\")\n",
    "titanic3.loc[:,\"age\"] = imputer_age.fit_transform(titanic2[[\"age\"]]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "d5958acf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      22.0\n",
       "1      38.0\n",
       "2      26.0\n",
       "3      35.0\n",
       "4      35.0\n",
       "       ... \n",
       "886    27.0\n",
       "887    19.0\n",
       "888    28.0\n",
       "889    26.0\n",
       "890    32.0\n",
       "Name: age, Length: 891, dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic2.loc[:,\"age\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "9c9c3592",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.matrix(titanic3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58f921e2",
   "metadata": {},
   "source": [
    "## patsy 패키지"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "150a5140",
   "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>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>x3</th>\n",
       "      <th>x4</th>\n",
       "      <th>x5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.764052</td>\n",
       "      <td>-0.977278</td>\n",
       "      <td>0.144044</td>\n",
       "      <td>0.333674</td>\n",
       "      <td>-2.552990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.400157</td>\n",
       "      <td>0.950088</td>\n",
       "      <td>1.454274</td>\n",
       "      <td>1.494079</td>\n",
       "      <td>0.653619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.978738</td>\n",
       "      <td>-0.151357</td>\n",
       "      <td>0.761038</td>\n",
       "      <td>-0.205158</td>\n",
       "      <td>0.864436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.240893</td>\n",
       "      <td>-0.103219</td>\n",
       "      <td>0.121675</td>\n",
       "      <td>0.313068</td>\n",
       "      <td>-0.742165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.867558</td>\n",
       "      <td>0.410599</td>\n",
       "      <td>0.443863</td>\n",
       "      <td>-0.854096</td>\n",
       "      <td>2.269755</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         x1        x2        x3        x4        x5\n",
       "0  1.764052 -0.977278  0.144044  0.333674 -2.552990\n",
       "1  0.400157  0.950088  1.454274  1.494079  0.653619\n",
       "2  0.978738 -0.151357  0.761038 -0.205158  0.864436\n",
       "3  2.240893 -0.103219  0.121675  0.313068 -0.742165\n",
       "4  1.867558  0.410599  0.443863 -0.854096  2.269755"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import patsy\n",
    "from patsy import demo_data\n",
    "\n",
    "df = pd.DataFrame(demo_data(\"x1\", \"x2\", \"x3\", \"x4\", \"x5\"))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "5485f522",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DesignMatrix with shape (5, 1)\n",
       "       x1\n",
       "  1.76405\n",
       "  0.40016\n",
       "  0.97874\n",
       "  2.24089\n",
       "  1.86756\n",
       "  Terms:\n",
       "    'x1' (column 0)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from patsy import dmatrix\n",
    "dmatrix(\"x1 + 0\", data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "065ed718",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{EvalFactor('x1'): FactorInfo(factor=EvalFactor('x1'),\n",
       "            type='numerical',\n",
       "            state=<factor state>,\n",
       "            num_columns=1),\n",
       " EvalFactor('center(x1)'): FactorInfo(factor=EvalFactor('center(x1)'),\n",
       "            type='numerical',\n",
       "            state=<factor state>,\n",
       "            num_columns=1)}"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dm = dmatrix(\"x1 + center(x1)+0\",data=df)\n",
    "dm.design_info.factor_infos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "6c20009f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'x1': array([1.76405235, 0.40015721, 0.97873798, 2.2408932 , 1.86755799])}"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demo_data(\"x1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "313e59ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DesignMatrix with shape (5, 3)\n",
       "  Intercept       x1  np.sum(df, axis=1)\n",
       "          1  1.76405            -1.28850\n",
       "          1  0.40016             4.95222\n",
       "          1  0.97874             2.24770\n",
       "          1  2.24089             1.83025\n",
       "          1  1.86756             4.13768\n",
       "  Terms:\n",
       "    'Intercept' (column 0)\n",
       "    'x1' (column 1)\n",
       "    'np.sum(df, axis=1)' (column 2)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dmatrix(\"x1 + np.sum(df,axis=1)\", data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "41c61120",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   -1.288497\n",
       "1    4.952217\n",
       "2    2.247696\n",
       "3    1.830252\n",
       "4    4.137679\n",
       "dtype: float64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(df,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "b9c05af4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DesignMatrix with shape (5, 3)\n",
       "       x1        x2     x1:x2\n",
       "  1.76405  -0.97728  -1.72397\n",
       "  0.40016   0.95009   0.38018\n",
       "  0.97874  -0.15136  -0.14814\n",
       "  2.24089  -0.10322  -0.23130\n",
       "  1.86756   0.41060   0.76682\n",
       "  Terms:\n",
       "    'x1' (column 0)\n",
       "    'x2' (column 1)\n",
       "    'x1:x2' (column 2)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dmatrix(\"x1+x2+ x1:x2 + 0\",data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "e99f2520",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'design_info'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Input \u001b[1;32mIn [86]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdesign_info\u001b[49m\u001b[38;5;241m.\u001b[39mfactor_infos\n",
      "File \u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:5575\u001b[0m, in \u001b[0;36mNDFrame.__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   5568\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[0;32m   5569\u001b[0m     name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_internal_names_set\n\u001b[0;32m   5570\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_metadata\n\u001b[0;32m   5571\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_accessors\n\u001b[0;32m   5572\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_info_axis\u001b[38;5;241m.\u001b[39m_can_hold_identifiers_and_holds_name(name)\n\u001b[0;32m   5573\u001b[0m ):\n\u001b[0;32m   5574\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m[name]\n\u001b[1;32m-> 5575\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mobject\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__getattribute__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'design_info'"
     ]
    }
   ],
   "source": [
    "df.design_info.factor_infos"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae3c0d20",
   "metadata": {},
   "source": [
    "# sklearn.preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "f750d317",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0],\n",
       "       [1],\n",
       "       [2],\n",
       "       [3],\n",
       "       [4],\n",
       "       [5],\n",
       "       [6]])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = np.arange(7).reshape(-1,1)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "4fd76052",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.5],\n",
       "       [-1. ],\n",
       "       [-0.5],\n",
       "       [ 0. ],\n",
       "       [ 0.5],\n",
       "       [ 1. ],\n",
       "       [ 1.5]])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "X = np.arange(7).reshape(-1,1)\n",
    "scaler = StandardScaler()\n",
    "scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "fd1072ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   0],\n",
       "       [   1],\n",
       "       [   2],\n",
       "       [   3],\n",
       "       [   4],\n",
       "       [   5],\n",
       "       [   6],\n",
       "       [1000]])"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X2 = np.vstack([X, [[1000]]])\n",
    "X2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "32d848b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.00000000e+00],\n",
       "       [-7.14285714e-01],\n",
       "       [-4.28571429e-01],\n",
       "       [-1.42857143e-01],\n",
       "       [ 1.42857143e-01],\n",
       "       [ 4.28571429e-01],\n",
       "       [ 7.14285714e-01],\n",
       "       [ 2.84714286e+02]])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import RobustScaler\n",
    "\n",
    "scaler = RobustScaler()\n",
    "scaler.fit_transform(X2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "cae28cff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "930.4199999999997"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.quantile(X2,0.99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "ab2fc51a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a33b2a6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "display_name": "Python 3 (ipykernel)",
   "language": "python",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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