11.7.1.범주형 변수의 가변수 처리.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 필요한 패키지 설치\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "plt.rcParams['figure.dpi'] = 300"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Device Name</th>\n",
       "      <th>Compute Type</th>\n",
       "      <th>OS</th>\n",
       "      <th>Median Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12X Intel Xeon CPU E5-2450 0 @ 2.10GHz</td>\n",
       "      <td>CPU</td>\n",
       "      <td>Linux</td>\n",
       "      <td>78.570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24X Intel Xeon CPU E5-2690 v3 @ 2.60GHz</td>\n",
       "      <td>CPU</td>\n",
       "      <td>Windows</td>\n",
       "      <td>297.940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2X 12th Gen Intel Core i9-12900KF</td>\n",
       "      <td>CPU</td>\n",
       "      <td>Windows</td>\n",
       "      <td>424.470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2X AMD EPYC 7343 16-Core Processor</td>\n",
       "      <td>CPU</td>\n",
       "      <td>Linux</td>\n",
       "      <td>439.210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2X AMD Opteron Processor 6276</td>\n",
       "      <td>CPU</td>\n",
       "      <td>Windows</td>\n",
       "      <td>81.478</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Device Name Compute Type       OS  Median Score\n",
       "0   12X Intel Xeon CPU E5-2450 0 @ 2.10GHz          CPU    Linux        78.570\n",
       "1  24X Intel Xeon CPU E5-2690 v3 @ 2.60GHz          CPU  Windows       297.940\n",
       "2        2X 12th Gen Intel Core i9-12900KF          CPU  Windows       424.470\n",
       "3       2X AMD EPYC 7343 16-Core Processor          CPU    Linux       439.210\n",
       "4            2X AMD Opteron Processor 6276          CPU  Windows        81.478"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 데이터 불러오기\n",
    "# https://www.kaggle.com/datasets/sewonghwang/GPU-CPU-benchmark\n",
    "df = pd.read_csv(\"datasets/GPU_CPU_benchmark.csv\")\n",
    "\n",
    "# 데이터 샘플 확인\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x1200 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 변경할 컬럼 범주 별 분포 확인 시각화\n",
    "fig, ax = plt.subplots(nrows=2)\n",
    "\n",
    "# Compute Type 컬럼 범주 별 분포\n",
    "sns.countplot(x=\"Compute Type\", data=df, ax=ax[0])\n",
    "\n",
    "# OS 컬럼 범주 별 분포\n",
    "sns.countplot(x=\"OS\", data=df, ax=ax[1])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Median Score</th>\n",
       "      <th>Device Name_12X Intel Xeon CPU E5-2450 0 @ 2.10GHz</th>\n",
       "      <th>Device Name_24X Intel Xeon CPU E5-2690 v3 @ 2.60GHz</th>\n",
       "      <th>Device Name_2X 12th Gen Intel Core i9-12900KF</th>\n",
       "      <th>Device Name_2X AMD EPYC 7343 16-Core Processor</th>\n",
       "      <th>Device Name_2X AMD Opteron Processor 6276</th>\n",
       "      <th>Device Name_2X AMD Opteron(tm) Processor 6328</th>\n",
       "      <th>Device Name_2X AMD Ryzen Threadripper PRO 3995WX 64-Cores</th>\n",
       "      <th>Device Name_2X Genuine Intel CPU 0000 @ 2.00GHz</th>\n",
       "      <th>Device Name_2X Genuine Intel CPU 0000 @ 2.10GHz</th>\n",
       "      <th>...</th>\n",
       "      <th>Device Name_T500</th>\n",
       "      <th>Device Name_Virtual CPU @ 2.99GHz</th>\n",
       "      <th>Compute Type_CPU</th>\n",
       "      <th>Compute Type_CUDA</th>\n",
       "      <th>Compute Type_HIP</th>\n",
       "      <th>Compute Type_METAL</th>\n",
       "      <th>Compute Type_OPTIX</th>\n",
       "      <th>OS_Linux</th>\n",
       "      <th>OS_Windows</th>\n",
       "      <th>OS_macOS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>78.570</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>297.940</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>424.470</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>439.210</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>81.478</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 740 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Median Score  Device Name_12X Intel Xeon CPU E5-2450 0 @ 2.10GHz  \\\n",
       "0        78.570                                                  1    \n",
       "1       297.940                                                  0    \n",
       "2       424.470                                                  0    \n",
       "3       439.210                                                  0    \n",
       "4        81.478                                                  0    \n",
       "\n",
       "   Device Name_24X Intel Xeon CPU E5-2690 v3 @ 2.60GHz  \\\n",
       "0                                                  0     \n",
       "1                                                  1     \n",
       "2                                                  0     \n",
       "3                                                  0     \n",
       "4                                                  0     \n",
       "\n",
       "   Device Name_2X 12th Gen Intel Core i9-12900KF  \\\n",
       "0                                              0   \n",
       "1                                              0   \n",
       "2                                              1   \n",
       "3                                              0   \n",
       "4                                              0   \n",
       "\n",
       "   Device Name_2X AMD EPYC 7343 16-Core Processor  \\\n",
       "0                                               0   \n",
       "1                                               0   \n",
       "2                                               0   \n",
       "3                                               1   \n",
       "4                                               0   \n",
       "\n",
       "   Device Name_2X AMD Opteron Processor 6276  \\\n",
       "0                                          0   \n",
       "1                                          0   \n",
       "2                                          0   \n",
       "3                                          0   \n",
       "4                                          1   \n",
       "\n",
       "   Device Name_2X AMD Opteron(tm) Processor 6328  \\\n",
       "0                                              0   \n",
       "1                                              0   \n",
       "2                                              0   \n",
       "3                                              0   \n",
       "4                                              0   \n",
       "\n",
       "   Device Name_2X AMD Ryzen Threadripper PRO 3995WX 64-Cores  \\\n",
       "0                                                  0           \n",
       "1                                                  0           \n",
       "2                                                  0           \n",
       "3                                                  0           \n",
       "4                                                  0           \n",
       "\n",
       "   Device Name_2X Genuine Intel CPU 0000 @ 2.00GHz  \\\n",
       "0                                                0   \n",
       "1                                                0   \n",
       "2                                                0   \n",
       "3                                                0   \n",
       "4                                                0   \n",
       "\n",
       "   Device Name_2X Genuine Intel CPU 0000 @ 2.10GHz  ...  Device Name_T500  \\\n",
       "0                                                0  ...                 0   \n",
       "1                                                0  ...                 0   \n",
       "2                                                0  ...                 0   \n",
       "3                                                0  ...                 0   \n",
       "4                                                0  ...                 0   \n",
       "\n",
       "   Device Name_Virtual CPU @ 2.99GHz  Compute Type_CPU  Compute Type_CUDA  \\\n",
       "0                                  0                 1                  0   \n",
       "1                                  0                 1                  0   \n",
       "2                                  0                 1                  0   \n",
       "3                                  0                 1                  0   \n",
       "4                                  0                 1                  0   \n",
       "\n",
       "   Compute Type_HIP  Compute Type_METAL  Compute Type_OPTIX  OS_Linux  \\\n",
       "0                 0                   0                   0         1   \n",
       "1                 0                   0                   0         0   \n",
       "2                 0                   0                   0         0   \n",
       "3                 0                   0                   0         1   \n",
       "4                 0                   0                   0         0   \n",
       "\n",
       "   OS_Windows  OS_macOS  \n",
       "0           0         0  \n",
       "1           1         0  \n",
       "2           1         0  \n",
       "3           0         0  \n",
       "4           1         0  \n",
       "\n",
       "[5 rows x 740 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 전체 컬럼 일괄 가변수 처리\n",
    "\n",
    "df1 = pd.get_dummies(df)\n",
    "\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>12X Intel Xeon CPU E5-2450 0 @ 2.10GHz</td>\n",
       "      <td>78.570</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>24X Intel Xeon CPU E5-2690 v3 @ 2.60GHz</td>\n",
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       "      <th>2</th>\n",
       "      <td>2X 12th Gen Intel Core i9-12900KF</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2X AMD EPYC 7343 16-Core Processor</td>\n",
       "      <td>439.210</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2X AMD Opteron Processor 6276</td>\n",
       "      <td>81.478</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "                               Device Name  Median Score  Compute Type_CPU  \\\n",
       "0   12X Intel Xeon CPU E5-2450 0 @ 2.10GHz        78.570                 1   \n",
       "1  24X Intel Xeon CPU E5-2690 v3 @ 2.60GHz       297.940                 1   \n",
       "2        2X 12th Gen Intel Core i9-12900KF       424.470                 1   \n",
       "3       2X AMD EPYC 7343 16-Core Processor       439.210                 1   \n",
       "4            2X AMD Opteron Processor 6276        81.478                 1   \n",
       "\n",
       "   Compute Type_CUDA  Compute Type_HIP  Compute Type_METAL  \\\n",
       "0                  0                 0                   0   \n",
       "1                  0                 0                   0   \n",
       "2                  0                 0                   0   \n",
       "3                  0                 0                   0   \n",
       "4                  0                 0                   0   \n",
       "\n",
       "   Compute Type_OPTIX  OS_Linux  OS_Windows  OS_macOS  \n",
       "0                   0         1           0         0  \n",
       "1                   0         0           1         0  \n",
       "2                   0         0           1         0  \n",
       "3                   0         1           0         0  \n",
       "4                   0         0           1         0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 특정 컬럼만 가변수 처리\n",
    "\n",
    "df2 = pd.get_dummies(df, columns = ['Compute Type', 'OS'])\n",
    "\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>12X Intel Xeon CPU E5-2450 0 @ 2.10GHz</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "                               Device Name  Median Score  Compute Type_CUDA  \\\n",
       "0   12X Intel Xeon CPU E5-2450 0 @ 2.10GHz        78.570                  0   \n",
       "1  24X Intel Xeon CPU E5-2690 v3 @ 2.60GHz       297.940                  0   \n",
       "2        2X 12th Gen Intel Core i9-12900KF       424.470                  0   \n",
       "3       2X AMD EPYC 7343 16-Core Processor       439.210                  0   \n",
       "4            2X AMD Opteron Processor 6276        81.478                  0   \n",
       "\n",
       "   Compute Type_HIP  Compute Type_METAL  Compute Type_OPTIX  OS_Windows  \\\n",
       "0                 0                   0                   0           0   \n",
       "1                 0                   0                   0           1   \n",
       "2                 0                   0                   0           1   \n",
       "3                 0                   0                   0           0   \n",
       "4                 0                   0                   0           1   \n",
       "\n",
       "   OS_macOS  \n",
       "0         0  \n",
       "1         0  \n",
       "2         0  \n",
       "3         0  \n",
       "4         0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 하나의 가변수 범주 제거 옵션 적용\n",
    "df3 = pd.get_dummies(df, columns = ['Compute Type','OS'],drop_first=True)\n",
    "\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>1</th>\n",
       "      <td>24X Intel Xeon CPU E5-2690 v3 @ 2.60GHz</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2X 12th Gen Intel Core i9-12900KF</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2X AMD EPYC 7343 16-Core Processor</td>\n",
       "      <td>439.210</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2X AMD Opteron Processor 6276</td>\n",
       "      <td>81.478</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "                               Device Name  Median Score  Compute Type_CUDA  \\\n",
       "0   12X Intel Xeon CPU E5-2450 0 @ 2.10GHz        78.570                  0   \n",
       "1  24X Intel Xeon CPU E5-2690 v3 @ 2.60GHz       297.940                  0   \n",
       "2        2X 12th Gen Intel Core i9-12900KF       424.470                  0   \n",
       "3       2X AMD EPYC 7343 16-Core Processor       439.210                  0   \n",
       "4            2X AMD Opteron Processor 6276        81.478                  0   \n",
       "\n",
       "   Compute Type_HIP  Compute Type_METAL  Compute Type_OPTIX  Compute Type_nan  \\\n",
       "0                 0                   0                   0                 0   \n",
       "1                 0                   0                   0                 0   \n",
       "2                 0                   0                   0                 0   \n",
       "3                 0                   0                   0                 0   \n",
       "4                 0                   0                   0                 0   \n",
       "\n",
       "   OS_Windows  OS_macOS  OS_nan  \n",
       "0           0         0       0  \n",
       "1           1         0       0  \n",
       "2           1         0       0  \n",
       "3           0         0       0  \n",
       "4           1         0       0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 결측값을 별도의 컬럼으로 처리 옵션 적용\n",
    "\n",
    "df4 = pd.get_dummies(df, columns = ['Compute Type','OS']\n",
    "                     , drop_first=True, dummy_na=True)\n",
    "\n",
    "df4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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