GS_Data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from scipy.stats import norm\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from scipy import stats\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_excel('F:\\\\Downloads\\\\GS Project Big leader\\\\GS Data Edited.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>P001</th>\n",
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       "      <th>OTH7</th>\n",
       "      <th>OTH8</th>\n",
       "      <th>OTH9</th>\n",
       "      <th>비중</th>\n",
       "      <th>굴곡탄성률</th>\n",
       "      <th>굴곡강도</th>\n",
       "      <th>HDT</th>\n",
       "      <th>IZOD 충격강도</th>\n",
       "      <th>MI</th>\n",
       "      <th>인장강도</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.032</td>\n",
       "      <td>2792.0</td>\n",
       "      <td>41.4</td>\n",
       "      <td>120.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>18.9</td>\n",
       "      <td>27.1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "<p>811 rows × 97 columns</p>\n",
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       "     P001  P002  P003  P004  P005       P006  P007       P008  P009  P010  \\\n",
       "0     0.0   0.0  85.0   0.0   0.0   0.000000     0   0.000000   0.0   0.0   \n",
       "1     0.0   0.0   0.0   0.0   0.0   9.803922     0  58.823529   0.0   0.0   \n",
       "2     0.0   0.0   0.0   0.0   0.0  19.607843     0  58.823529   0.0   0.0   \n",
       "3     0.0   0.0   0.0   0.0   0.0  14.705882     0  58.823529   0.0   0.0   \n",
       "4     0.0   0.0   0.0   0.0   0.0   0.000000     0   0.000000   0.0   0.0   \n",
       "..    ...   ...   ...   ...   ...        ...   ...        ...   ...   ...   \n",
       "806   0.0   0.0   0.0   0.0   0.0   0.000000     0   0.000000   0.0   0.0   \n",
       "807   0.0   0.0   0.0   0.0   0.0   0.000000     0   0.000000   0.0   0.0   \n",
       "808   0.0   0.0   0.0   0.0   0.0   0.000000     0   0.000000   0.0   0.0   \n",
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       "810   0.0   0.0   0.0   0.0   0.0   0.000000     0   0.000000   0.0   0.0   \n",
       "\n",
       "     ...  OTH7      OTH8  OTH9     비중   굴곡탄성률   굴곡강도    HDT  IZOD 충격강도    MI  \\\n",
       "0    ...   0.0  0.000000   0.0  1.007  2607.0   53.4  120.2        NaN  26.3   \n",
       "1    ...   0.0  0.000000   0.0  1.118  3289.0   43.5  117.5        3.4  20.7   \n",
       "2    ...   0.0  0.000000   0.0  1.032  2792.0   41.4  120.1        2.3  18.9   \n",
       "3    ...   0.0  0.000000   0.0  1.077  2979.0   43.1  127.2        3.0  21.7   \n",
       "4    ...   0.0  0.000000   0.0  1.219  8274.0  150.2  151.8       18.3  25.3   \n",
       "..   ...   ...       ...   ...    ...     ...    ...    ...        ...   ...   \n",
       "806  ...   0.0  0.000000   0.0  1.029  2385.0   36.8  117.1        6.1   NaN   \n",
       "807  ...   0.0  2.857143   0.0  1.032  2454.0   37.5  116.5        9.8   NaN   \n",
       "808  ...   0.0  0.000000   0.0  1.030  2542.0   38.3  113.5        8.3   NaN   \n",
       "809  ...   0.0  0.000000   0.0  1.028  2577.0   38.5  114.0       17.7   NaN   \n",
       "810  ...   0.0  2.857143   0.0  1.027  2567.0   38.8  116.0       15.0   NaN   \n",
       "\n",
       "      인장강도  \n",
       "0     36.3  \n",
       "1     27.5  \n",
       "2     27.1  \n",
       "3     27.5  \n",
       "4    102.0  \n",
       "..     ...  \n",
       "806   24.9  \n",
       "807   25.3  \n",
       "808   25.2  \n",
       "809   25.4  \n",
       "810   25.7  \n",
       "\n",
       "[811 rows x 97 columns]"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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  {
   "cell_type": "code",
   "execution_count": 4,
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      " 80  R022       811 non-null    float64\n",
      " 81  OTH1       811 non-null    float64\n",
      " 82  OTH2       811 non-null    float64\n",
      " 83  OTH3       811 non-null    float64\n",
      " 84  OTH4       811 non-null    float64\n",
      " 85  OTH5       811 non-null    float64\n",
      " 86  OTH6       811 non-null    float64\n",
      " 87  OTH7       811 non-null    float64\n",
      " 88  OTH8       811 non-null    float64\n",
      " 89  OTH9       811 non-null    float64\n",
      " 90  비중         799 non-null    float64\n",
      " 91  굴곡탄성률      807 non-null    float64\n",
      " 92  굴곡강도       804 non-null    float64\n",
      " 93  HDT        807 non-null    float64\n",
      " 94  IZOD 충격강도  655 non-null    float64\n",
      " 95  MI         480 non-null    float64\n",
      " 96  인장강도       802 non-null    float64\n",
      "dtypes: float64(93), int64(4)\n",
      "memory usage: 614.7 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
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       "      <th>810</th>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>811 rows × 97 columns</p>\n",
       "</div>"
      ],
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       "      P001   P002   P003   P004   P005   P006   P007   P008   P009   P010  \\\n",
       "0    False  False  False  False  False  False  False  False  False  False   \n",
       "1    False  False  False  False  False  False  False  False  False  False   \n",
       "2    False  False  False  False  False  False  False  False  False  False   \n",
       "3    False  False  False  False  False  False  False  False  False  False   \n",
       "4    False  False  False  False  False  False  False  False  False  False   \n",
       "..     ...    ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "806  False  False  False  False  False  False  False  False  False  False   \n",
       "807  False  False  False  False  False  False  False  False  False  False   \n",
       "808  False  False  False  False  False  False  False  False  False  False   \n",
       "809  False  False  False  False  False  False  False  False  False  False   \n",
       "810  False  False  False  False  False  False  False  False  False  False   \n",
       "\n",
       "     ...   OTH7   OTH8   OTH9     비중  굴곡탄성률   굴곡강도    HDT  IZOD 충격강도     MI  \\\n",
       "0    ...  False  False  False  False  False  False  False       True  False   \n",
       "1    ...  False  False  False  False  False  False  False      False  False   \n",
       "2    ...  False  False  False  False  False  False  False      False  False   \n",
       "3    ...  False  False  False  False  False  False  False      False  False   \n",
       "4    ...  False  False  False  False  False  False  False      False  False   \n",
       "..   ...    ...    ...    ...    ...    ...    ...    ...        ...    ...   \n",
       "806  ...  False  False  False  False  False  False  False      False   True   \n",
       "807  ...  False  False  False  False  False  False  False      False   True   \n",
       "808  ...  False  False  False  False  False  False  False      False   True   \n",
       "809  ...  False  False  False  False  False  False  False      False   True   \n",
       "810  ...  False  False  False  False  False  False  False      False   True   \n",
       "\n",
       "      인장강도  \n",
       "0    False  \n",
       "1    False  \n",
       "2    False  \n",
       "3    False  \n",
       "4    False  \n",
       "..     ...  \n",
       "806  False  \n",
       "807  False  \n",
       "808  False  \n",
       "809  False  \n",
       "810  False  \n",
       "\n",
       "[811 rows x 97 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
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  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
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       "      <td>3.442622</td>\n",
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       "      <td>2.949639</td>\n",
       "      <td>13.452392</td>\n",
       "      <td>6.149266</td>\n",
       "      <td>3.816834</td>\n",
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       "      <td>0.768806</td>\n",
       "      <td>0.724224</td>\n",
       "      <td>0.095078</td>\n",
       "      <td>2117.114158</td>\n",
       "      <td>33.104157</td>\n",
       "      <td>23.891549</td>\n",
       "      <td>16.565691</td>\n",
       "      <td>12.118263</td>\n",
       "      <td>24.381658</td>\n",
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       "      <th>min</th>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>29.000000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>30.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.974000</td>\n",
       "      <td>1706.500000</td>\n",
       "      <td>29.600000</td>\n",
       "      <td>96.100000</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>14.380000</td>\n",
       "      <td>20.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.022000</td>\n",
       "      <td>2059.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>105.100000</td>\n",
       "      <td>7.300000</td>\n",
       "      <td>23.220000</td>\n",
       "      <td>22.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.047000</td>\n",
       "      <td>2516.500000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>114.500000</td>\n",
       "      <td>21.100000</td>\n",
       "      <td>30.217500</td>\n",
       "      <td>26.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>71.928072</td>\n",
       "      <td>98.039216</td>\n",
       "      <td>85.210578</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>98.039216</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>98.039216</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>98.039216</td>\n",
       "      <td>...</td>\n",
       "      <td>3.381643</td>\n",
       "      <td>5.660377</td>\n",
       "      <td>14.563107</td>\n",
       "      <td>1.516000</td>\n",
       "      <td>16294.000000</td>\n",
       "      <td>198.700000</td>\n",
       "      <td>163.800000</td>\n",
       "      <td>91.700000</td>\n",
       "      <td>72.410000</td>\n",
       "      <td>148.600000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 97 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             P001        P002        P003        P004        P005        P006  \\\n",
       "count  811.000000  811.000000  811.000000  811.000000  811.000000  811.000000   \n",
       "mean     0.273463    1.623350    1.629551    0.516042    0.120887    3.534684   \n",
       "std      3.974863   10.724968    9.555728    5.606937    3.442622   15.629119   \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "50%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "75%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "max     71.928072   98.039216   85.210578   90.000000   98.039216  100.000000   \n",
       "\n",
       "             P007        P008        P009        P010  ...        OTH7  \\\n",
       "count  811.000000  811.000000  811.000000  811.000000  ...  811.000000   \n",
       "mean     0.103576    3.068800    0.590962    0.178913  ...    0.216712   \n",
       "std      2.949639   13.452392    6.149266    3.816834  ...    0.554780   \n",
       "min      0.000000    0.000000    0.000000    0.000000  ...    0.000000   \n",
       "25%      0.000000    0.000000    0.000000    0.000000  ...    0.000000   \n",
       "50%      0.000000    0.000000    0.000000    0.000000  ...    0.000000   \n",
       "75%      0.000000    0.000000    0.000000    0.000000  ...    0.000000   \n",
       "max     84.000000   98.039216  100.000000   98.039216  ...    3.381643   \n",
       "\n",
       "             OTH8        OTH9          비중         굴곡탄성률        굴곡강도  \\\n",
       "count  811.000000  811.000000  799.000000    807.000000  804.000000   \n",
       "mean     0.263926    0.038297    1.023700   2564.840644   42.564055   \n",
       "std      0.768806    0.724224    0.095078   2117.114158   33.104157   \n",
       "min      0.000000    0.000000    0.588000     29.000000    1.200000   \n",
       "25%      0.000000    0.000000    0.974000   1706.500000   29.600000   \n",
       "50%      0.000000    0.000000    1.022000   2059.000000   33.000000   \n",
       "75%      0.000000    0.000000    1.047000   2516.500000   40.000000   \n",
       "max      5.660377   14.563107    1.516000  16294.000000  198.700000   \n",
       "\n",
       "              HDT   IZOD 충격강도          MI        인장강도  \n",
       "count  807.000000  655.000000  480.000000  802.000000  \n",
       "mean   105.707993   15.224122   23.288690   30.006110  \n",
       "std     23.891549   16.565691   12.118263   24.381658  \n",
       "min     30.000000    1.400000    0.025000    1.500000  \n",
       "25%     96.100000    4.300000   14.380000   20.400000  \n",
       "50%    105.100000    7.300000   23.220000   22.600000  \n",
       "75%    114.500000   21.100000   30.217500   26.750000  \n",
       "max    163.800000   91.700000   72.410000  148.600000  \n",
       "\n",
       "[8 rows x 97 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>P001</th>\n",
       "      <th>P002</th>\n",
       "      <th>P003</th>\n",
       "      <th>P004</th>\n",
       "      <th>P005</th>\n",
       "      <th>P006</th>\n",
       "      <th>P007</th>\n",
       "      <th>P008</th>\n",
       "      <th>P009</th>\n",
       "      <th>P010</th>\n",
       "      <th>...</th>\n",
       "      <th>OTH7</th>\n",
       "      <th>OTH8</th>\n",
       "      <th>OTH9</th>\n",
       "      <th>비중</th>\n",
       "      <th>굴곡탄성률</th>\n",
       "      <th>굴곡강도</th>\n",
       "      <th>HDT</th>\n",
       "      <th>IZOD 충격강도</th>\n",
       "      <th>MI</th>\n",
       "      <th>인장강도</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.007</td>\n",
       "      <td>2607.0</td>\n",
       "      <td>53.4</td>\n",
       "      <td>120.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.3</td>\n",
       "      <td>36.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.803922</td>\n",
       "      <td>0</td>\n",
       "      <td>58.823529</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.118</td>\n",
       "      <td>3289.0</td>\n",
       "      <td>43.5</td>\n",
       "      <td>117.5</td>\n",
       "      <td>3.4</td>\n",
       "      <td>20.7</td>\n",
       "      <td>27.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.607843</td>\n",
       "      <td>0</td>\n",
       "      <td>58.823529</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.032</td>\n",
       "      <td>2792.0</td>\n",
       "      <td>41.4</td>\n",
       "      <td>120.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>18.9</td>\n",
       "      <td>27.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.705882</td>\n",
       "      <td>0</td>\n",
       "      <td>58.823529</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.077</td>\n",
       "      <td>2979.0</td>\n",
       "      <td>43.1</td>\n",
       "      <td>127.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>21.7</td>\n",
       "      <td>27.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.219</td>\n",
       "      <td>8274.0</td>\n",
       "      <td>150.2</td>\n",
       "      <td>151.8</td>\n",
       "      <td>18.3</td>\n",
       "      <td>25.3</td>\n",
       "      <td>102.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 97 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   P001  P002  P003  P004  P005       P006  P007       P008  P009  P010  ...  \\\n",
       "0   0.0   0.0  85.0   0.0   0.0   0.000000     0   0.000000   0.0   0.0  ...   \n",
       "1   0.0   0.0   0.0   0.0   0.0   9.803922     0  58.823529   0.0   0.0  ...   \n",
       "2   0.0   0.0   0.0   0.0   0.0  19.607843     0  58.823529   0.0   0.0  ...   \n",
       "3   0.0   0.0   0.0   0.0   0.0  14.705882     0  58.823529   0.0   0.0  ...   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.000000     0   0.000000   0.0   0.0  ...   \n",
       "\n",
       "   OTH7  OTH8  OTH9     비중   굴곡탄성률   굴곡강도    HDT  IZOD 충격강도    MI   인장강도  \n",
       "0   0.0   0.0   0.0  1.007  2607.0   53.4  120.2        NaN  26.3   36.3  \n",
       "1   0.0   0.0   0.0  1.118  3289.0   43.5  117.5        3.4  20.7   27.5  \n",
       "2   0.0   0.0   0.0  1.032  2792.0   41.4  120.1        2.3  18.9   27.1  \n",
       "3   0.0   0.0   0.0  1.077  2979.0   43.1  127.2        3.0  21.7   27.5  \n",
       "4   0.0   0.0   0.0  1.219  8274.0  150.2  151.8       18.3  25.3  102.0  \n",
       "\n",
       "[5 rows x 97 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.describe of 0       0.000000\n",
       "1      58.823529\n",
       "2      58.823529\n",
       "3      58.823529\n",
       "4       0.000000\n",
       "         ...    \n",
       "806     0.000000\n",
       "807     0.000000\n",
       "808     0.000000\n",
       "809     0.000000\n",
       "810     0.000000\n",
       "Name: P008, Length: 811, dtype: float64>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['P008'].describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.06880044143514"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['P008'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['P008'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19ab87b8438>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['P008'].plot(kind='box', vert=False, figsize=(14,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19ab90d0630>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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O0Bjm1h/V4gvFIHDNMJwOAAAkFMEI6ACNYW4DxagWX8iGz0MwAgAAyUQwAjrASjCKcI7R6ucBAABIGoIR0AHOD6WLblW64HkIRgAAIJkIRkAHaHRy+ruj7hgxlA4AACQTwQjoANOlinZ0ZZRJR/NPnqF0AAAg6QhGQAeYKpUjW6pbkrpzaeUzKU7yCgAAEotgBHSAqVIlsoUXGvoLWeYYAQCAxCIYAR1gulSObOGFhoFCjjlGAAAgsQhGQAcIhtLRMQIAANgIwQjoANMLFTpGAAAAF0EwAtpcpVbX3HI10sUXpOAcSXSMAABAUhGMgDbXWCluoBjtULqBQlbTpYrcPdLnAQAAiALBCGhzjS5OM4bSVeuuueVqpM8DAAAQBYIR0OYa836iXnyhL3z86QXmGQEAgOQhGAFtbqrRMeqOvmMkSdOLzDMCAADJQzAC2tz5oXTRzzGSxMp0AAAgkQhGQJtbGUpXjH5VOkmsTAcAABKJYAS0ualSWdm0qZhLR/o8Kx2jBYIRAABIHoIR0OYaJ3c1s0ifp6+boXQAACC5CEZAm5sqlSNfkU6SMumUersyDKUDAACJRDAC2tx0qRL5OYwaBoo5OkYAACCRIg1GZna7mT1pZsfM7APr3J43s8+Ft3/TzA6uuu3FZvZPZvaomT1iZl1R1gq0q+nF5nSMJKm/O6vpRYIRAABInsiCkZmlJX1C0hsk3STp7WZ205rd3iVpyt2vk/QxSR8N75uR9CeSftHdf0DSayRxtAVcgalSZeUcQ1HrL+QYSgcAABIpyo7RrZKOuftxdy9L+qykO9bsc4eke8LLX5D0WgtmiL9e0nfc/duS5O4T7l6LsFagLbm7pkvl5g2lK2RXTigLAACQJFEGo72STq26PhpuW3cfd69KmpE0JOkGSW5m95vZQ2b2f0VYJ9C2Fso1VWrevKF0hZymF2juAgCA5MlE+NjrrQ3sm9wnI+l/kvSDkkqSvmJmD7r7Vy64s9m7Jb1bkg4cOLDlgoF20zinULOG0g0UcppbrqpSqyubZm0XAACQHJs6cjGzvzCzf25ml3OkMypp/6rr+ySd3mifcF5Rn6TJcPs/uvu4u5ck3SfpZWufwN0/6e6H3f3wyMjIZZQGdIbpcIW4/iZ1jAaK2QueFwAAICk2G3T+k6R3SPq+mf22md24ifs8IOl6MztkZjlJd0q6d80+90q6K7z8VklfdXeXdL+kF5tZIQxMPyzpsU3WCiDUmO8zUGze4guSWIABAAAkzqaCkbt/2d1/VkHX5oSkL5nZ183sX5nZul9Fh3OG3qMg5Dwu6fPu/qiZfcTM3hzu9ilJQ2Z2TNL7JH0gvO+UpN9XEK6OSnrI3f/blf4hgU61EoyauFx38Lx0jAAAQLJseo6RmQ1Jeqekn5P0sKQ/VTAP6C4Fy2k/j7vfp2AY3OptH1p1eUnS2za4758oWLIbwBU6P5SueXOMguelYwQAAJJlU8HIzP5S0o2SPi3pTe5+Jrzpc2Z2JKriAGxNo2PU6ORErTGXiTlGAAAgaTbbMfovYfdnhZnl3X3Z3Q9HUBeAbTBdqmhHV0aZJq0Q15jLxLmMAABA0mz2aOm31tn2T9tZCIDtN1UqN22pbkkq5tLKpo05RgAAIHEu2jEys90KTsLabWYv1fnzDvVKKkRcG4AtmipVmrbwgiSZWXCSVzpGAAAgYS41lO7HJf1LBecg+v1V2+ck/VpENQHYJtNN7hhJwQp4DKUDAABJc9Fg5O73SLrHzH7K3f+iSTUB2CZTpbKuGS429Tn7u3MMpQMAAIlzqaF07wyXzT5oZu9be7u7//46dwPQIqYXKk1bqruhv5DVMxOlpj4nAADAVl1qKF3jq+aeqAsBsL0qtbrmlqsxDKXL6eip6aY+JwAAwFZdaijdH4a/f6M55QDYLo1zCQ0Um7f4giT1F7OaLlXk7jKzS98BAACgBWxquW4z+x0z6zWzrJl9xczGzeydURcH4Mo1VoZr9lC6gUJO5VpdpXKtqc8LAACwFZs9j9Hr3X1W0k9IGpV0g6T/M7KqAGxZYwGEZi7Xvfr5WJkOAAAkyWaDUePI6o2SPuPukxHVA2CbNIJJs+cYNTpU06xMBwAAEuRSiy80/Fcze0LSoqRfMrMRSUvRlQVgq84PpWt2xygIRnSMAABAkmyqY+TuH5D0zyQddveKpAVJd0RZGICtOT+UrvnLda9+fgAAgCTYbMdIkl6o4HxGq+/zx9tcD4BtMlUqK5dOqZBLN/V5G8Foho4RAABIkE0FIzP7tKRrJR2V1FhqykUwAlpWcHLXbNOXzO7vbgylo2MEAACSY7Mdo8OSbnJ3j7IYANtnqlRu+jA6ScplUurJZ5hjBAAAEmWzq9J9V9LuKAsBsL2mS5WmL7zQ0F/IsiodAABIlM12jIYlPWZm35K03Njo7m+OpCoAWzZVKuvakZ5YnnugkKNjBAAAEmWzwejDURYBYPtNlSoaKDZ/KJ0UdIyYYwQAAJJkU8HI3f/RzK6WdL27f9nMCpKau9QVgE1zd02XyhqIbShdTicnS7E8NwAAwJXY1BwjM/tfJH1B0h+Gm/ZK+mJURQHYmrnlqqp112BMHaMB5hgBAICE2eziC78s6VWSZiXJ3b8vaWdURQHYmqmFYH5PHKvSSUHHaHapolqdhSwBAEAybDYYLbv7ykzq8CSvHPEALWqyEYyK8QylGyhk5S7NLNI1AgAAybDZYPSPZvZrkrrN7HWS/lzSf42uLABb0RjGFlfHqPG8rEwHAACSYrPB6AOSxiQ9IukXJN0n6d9GVRSArWl0jOKaY9Q4f9I0wQgAACTEZlelq5vZFyV90d3HIq4JwBY1OjX9cXeMFhhKBwAAkuGiHSMLfNjMxiU9IelJMxszsw81pzwAV2KqVFY6Zert2uypyrYXQ+kAAEDSXGoo3XsVrEb3g+4+5O6Dkm6T9Coz+9XIqwNwRSYXKhoo5GRmsTx/38pQOjpGAAAgGS4VjH5e0tvd/enGBnc/Lumd4W0AWtDUQlmDMa1IJ0m9XRmlU6bpRTpGAAAgGS4VjLLuPr52YzjPKL6jLgAXNVUqxza/SJLMTP3dWU3RMQIAAAlxqWB0sa97+SoYaFFTpbIGYwxGUrAyHavSAQCApLjUzOyXmNnsOttNUlcE9QDYBpMLFb386niD0UAhx6p0AAAgMS4ajNw93axCAGwPd9d0qayBQryjXfsLOY1OlWKtAQAAYLM2e4JXAAkxt1xVte6xndy1YaCQZVU6AACQGAQjoM1MLQTzegZaYI4R5zECAABJQTAC2sxkGIzi7hj1F3Jarta1VKnFWgcAAMBmEIyANtMYvtYf8xyjRseKrhEAAEgCghHQZlqlY9RY/IGV6QAAQBIQjIA20+jQDLTAUDpJnMsIAAAkAsEIaDNTpbIyKdOO/KVOUxatgWLYMWJlOgAAkAAEI6DNTC5U1F/IycxirYM5RgAAIEkIRkCbmVooa7AY78ILktTXHdTAUDoAAJAEBCOgzUyVyivze+LUlU2rO5tmKB0AAEgEghHQZqZKZQ22QDCSgpXppglGAAAgAQhGQJuZXKjEviJdQ38hx1A6AACQCAQjoI3U667pUnnlHEJxGyhmWXwBAAAkQqTByMxuN7MnzeyYmX1gndvzZva58PZvmtnBNbcfMLN5M/s/oqwTaBezSxVV667hnnzcpUhqdIwYSgcAAFpfZMHIzNKSPiHpDZJukvR2M7tpzW7vkjTl7tdJ+pikj665/WOS/jaqGoF2Mz4fdGeGelpjKN1AgY4RAABIhig7RrdKOubux929LOmzku5Ys88dku4JL39B0mstPPmKmb1F0nFJj0ZYI9BWJuaXJallOkYDhZxmFiuq1z3uUgAAAC4qymC0V9KpVddHw23r7uPuVUkzkobMrCjp/ZJ+42JPYGbvNrMjZnZkbGxs2woHkmpiobU6Rv2FnOoeDPEDAABoZVEGI1tn29qvjTfa5zckfczd5y/2BO7+SXc/7O6HR0ZGrrBMoH00OkZDxdboGA2Fq+M1AhsAAECrykT42KOS9q+6vk/S6Q32GTWzjKQ+SZOSbpP0VjP7HUn9kupmtuTu/zHCeoHEG58vy0wtsypdY0jf+Nyyrh3pibkaAACAjUUZjB6QdL2ZHZL0rKQ7Jb1jzT73SrpL0j9Jequkr7q7S/qhxg5m9mFJ84Qi4NImFpY1UMgpk26NlfgbQ/roGAEAgFYXWTBy96qZvUfS/ZLSku5290fN7COSjrj7vZI+JenTZnZMQafozqjqATrB5EJ5ZfhaK1jpGIVD/AAAAFpVlB0juft9ku5bs+1Dqy4vSXrbJR7jw5EUB7Sh8flyyyy8IAVD+szOLyMOAADQqlpjvA2AbTExv6yhFlmqW5Iy6ZQGCjk6RgAAoOURjIA2MrFQ1nALDaWTpOGe3MpqeQAAAK2KYAS0iUqtrulSpaU6RlKwdDhD6QAAQKsjGAFtYipc+W2w1TpGO/J0jAAAQMsjGAFtotGVGW6hxRek4CSvdIwAAECrIxgBbWJiIejKtNpQupEdec0vV7VUqcVdCgAAwIYIRkCbmAi7Mq10HiPpfD2sTAcAAFoZwQhoE43g0Wodo8ZJXicYTgcAAFoYwQhoExMLZWXTpt6uSM/bfNkaJ5xtDPUDAABoRQQjoE1MzC9rqJiXmcVdygUaHaPxOTpGAACgdRGMgDYxMV9e6c60kkZN43SMAABACyMYAW1ifKHccvOLJKmQy6iQS9MxAgAALY1gBLSJifllDbfYinQNwz155hgBAICWRjAC2kSrDqWTguF0LNcNAABaGcEIaAOlclWLlVpLDqWTwo4Ry3UDAIAWRjAC2sDYXHgOo5YdSkfHCAAAtDaCEdAGGsFoZ29XzJWsb7gnr8mFsmp1j7sUAACAdRGMgDZwrhGMdrTmULqhYk51l6ZKDKcDAACtiWAEtIFzs0uSWjcYDYd1MZwOAAC0KoIR0AbOzS0rkzINFFp1jlEYjDiXEQAAaFEEI6ANnJtb1nBPXqmUxV3KunaFc5+eCztbAAAArYZgBLSBc3PL2tnbmsPopPND/BpzoQAAAFoNwQhoA2Nzyy07v0iSivmMevIZOkYAAKBlEYyANjA2t6SRHa25VHfDzh35lWXFAQAAWg3BCEi4aq2uiYVyS3eMJGlnb56OEQAAaFkEIyDhxufLcpdGWj0Y7ehijhEAAGhZBCMg4c7NtfY5jBp2hR0jd4+7FAAAgOchGAEJ15i3s7O31ecYdWm5WtfsUjXuUgAAAJ6HYAQkXGN4Wqt3jBrLiY/NMc8IAAC0HoIRkHDnZoNgNNzT4sFoR+Mkr8wzAgAArYdgBCTc2dklDRVzymVa+5/zrt7GSV7pGAEAgNbT2kdSAC7p7Myidve19vwi6fwcKDpGAACgFRGMgIQ7M7OkPQkIRj35jIq59MrQPwAAgFZCMAIS7uzsUiI6RpK0q7eLk7wCAICWRDACEmypUtN0qaI9fd1xl7Ipu/u6dHpmMe4yAAAAnodgBCTY2Zmg+7K7xc9h1LCnr1tnpukYAQCA1kMwAhLsTBiMkjDHSJKu6u/SubklVWv1uEsBAAC4AMEISLCzs8GwtF0JCUZ7+rpV9/MnpQUAAGgVBCMgwc4kbShdf1DnGeYZAQCAFkMwAhLs7MySersyKuYzcZeyKVeFi0ScZp4RAABoMQQjIMGCcxglY0U6iY4RAABoXQQjIMHOziTnHEaStCM8ySsdIwAA0GoIRkCCnZlZSsz8IkkyM+3p715ZZhwAAKBVEIyAhFqq1DQ+v6y9A8kZSicFS4szlA4AALQaghGQUKeng3CxL2HB6Kq+bp2mYwQAAFoMwQhIqNGpRjAqxFzJ5dnT36Xx+WWVq5zkFQAAtI5Ig5GZ3W5mT5rZMTP7wDq3583sc+Ht3zSzg+H215nZg2b2SPj7R6OsE0ii88EoWR2jvf3dcj/f8QIAAGgFkQUjM0tL+oSkN0i6SdLbzeymNbu9S9KUu18n6WOSPhpuH5f0Jne/WdJdkj4dVZ1AUo1OlZRJmXYlaPEFSdo/GHS4Tk2VYq4EAADgvCg7RrdKOubux929LOmzku5Ys88dku4JL39B0mvNzNz9YXc/HW5/VFKXmeUjrGiX7hUAABeYSURBVBVInNGpRe3p71I6ZXGXcllWgtEkHSMAANA6ogxGeyWdWnV9NNy27j7uXpU0I2lozT4/Jelhd1+OqE4gkZ6dXtS+/mTNL5Kk3b1dyqaNjhEAAGgpUQaj9b7G9svZx8x+QMHwul9Y9wnM3m1mR8zsyNjY2BUXCiTR6FQpcfOLJCmdMl3V361TkwQjAADQOqIMRqOS9q+6vk/S6Y32MbOMpD5Jk+H1fZL+StLPu/tT6z2Bu3/S3Q+7++GRkZFtLh9oXcvVmp6bXU7cinQN+wcKOjXFUDoAANA6ogxGD0i63swOmVlO0p2S7l2zz70KFleQpLdK+qq7u5n1S/pvkj7o7l+LsEYgkU5PB+cBSmLHSJL2D3ZrlI4RAABoIZEFo3DO0Hsk3S/pcUmfd/dHzewjZvbmcLdPSRoys2OS3iepsaT3eyRdJ+nfmdnR8GdnVLUCSTMazs9JajDaN1DQxEJZC8vVuEsBAACQJGWifHB3v0/SfWu2fWjV5SVJb1vnfr8l6beirA1IspNht2XfYEKH0oV1j04t6gW7d8RcDQAAQMQneAUQjRPjC8pnUtqTsHMYNewPO10swAAAAFoFwQhIoKfHS7p6qKBUws5h1MBJXgEAQKshGAEJdGJiQQeHinGXccWGijn15DN6ZoJgBAAAWgPBCEiYWt11cqKkQ8PJDUZmpkPDRT01Nh93KQAAAJIIRkDinJ5eVLlW18EEByNJumakqONjC3GXAQAAIIlgBCTOiYkgTCR5KJ0kHRou6vTMopYqtbhLAQAAIBgBSXNiPAhGSR5KJ0nXjPTI/XzQAwAAiBPBCEiYp8dL6s6mtas3H3cpW3JNGOyeZjgdAABoAQQjIGFOTCzo6qGCzJK5VHdDo+N1fJxgBAAA4kcwAhLmqbF5XTvSE3cZW1bMZ7S7t4uV6QAAQEsgGAEJUipXdXKypBt27Yi7lG1xaLiop+kYAQCAFkAwAhLk2Ll5uUs37Ep+x0iSrt1ZDP9MHncpAACgwxGMgAT53nPBsLMbdrdHx+gFu3s1t1TVmZmluEsBAAAdjmAEJMj3nptTLpPS1YOFuEvZFi8MA94TZ2djrgQAAHQ6ghGQIE+endN1Iz3KpNvjn26j8/X4mbmYKwEAAJ2uPY6ugA7xvefm2mZ+kST1dmW1t79bT54lGAEAgHgRjICEmF2q6MzMUtvML2p44Z4dDKUDAACxIxgBCdHoqrygTZbqbnjB7h16amxBy9Va3KUAAIAORjACEuI7ozOSpJv39sVcyfa6cXevanXXU+c4nxEAAIgPwQhIiEdGp7W7t0s7e7viLmVbvXBPryTpu6dnYq4EAAB0MoIRkBDfeXZGN+9rr26RJF0zXNSOfEbfPjUddykAAKCDEYyABJhdquj42IJe0obBKJUyvXh/n44SjAAAQIwIRkACfPfZcH7Rvv6YK4nGLfv79cTZOS2WWYABAADEg2AEJMAj4cILL26zhRcabtk/oFrdmWcEAABiQzACEuDhk9PaP9itgWIu7lIiccv+oBN29CTD6QAAQDwIRkCLq9dd3zoxqdsODcVdSmRGduS1t7+beUYAACA2BCOgxX3/3LwmF8p6xTXtG4wk6fDBAX3z6Um5e9ylAACADkQwAlrcN45PSJJuOzQYcyXRetV1wxqfX9b3npuPuxQAANCBCEZAi/vG8Qnt7e/W/sFC3KVE6lXXDUuSvnZsPOZKAABAJyIYAS3M3fXNpyfbfhidJO3t79bBoYK+/hTBCAAANB/BCGhhjzw7o8mFsl55bfsHI0l65XXD+sbxSVVr9bhLAQAAHYZgBLSwLz/2nFIm/ciNO+MupSl+6LphzS9X9eAzU3GXAgAAOgzBCGhhf//Yczp8cFCDbXr+orVefcOI8pmU/va7Z+MuBQAAdBiCEdCiTk2W9MTZOb3+pl1xl9I0xXxGr75hRH/33bOq11m2GwAANA/BCGhR9z8adE1+7IWdE4wk6Y0379bZ2SUdHeVkrwAAoHkIRkALcnd94cFRvXhfnw4OF+Mup6l+9MZdyqZNf/PtM3GXAgAAOgjBCGhBjzw7oyfOzumnD++Pu5Sm6+vO6nU37dJfPjyqpUot7nIAAECHIBgBLehzD5xSPpPSm15yVdylxOJnb7ta06WK7nuErhEAAGgOghHQYmZKFf310dP65zfvUV93Nu5yYvHKa4d0zXBRf/KNZ+IuBQAAdAiCEdBi7v7a05pfrurdP3xN3KXExsz0zldcrYdOTusbxyfiLgcAAHQAghHQQmaXKrr7a0/r9Tft0o27e+MuJ1bvuO2AdvXm9Xv3Pyl3lu4GAADRIhgBLeTjX/6+5paq+pXXXh93KbHryqb1K6+9XkeemdKXHz8XdzkAAKDNZeIuAEDgkdEZ/dHXntY7bjugF+3ti7uclvDTh/frnq+f0L/94iO69eCg+gqdOecKyVSvu56dXtS5uSWdnVnWzGJFlVpdlVpduUxKvV1Z9XZntKu3SwcGC9rRxfsbAOJEMAJawNxSRb/6+aMa6snr/bffGHc5LSObTuk/vO0WveX/+Zr+3V9/Vx+/8xaZWdxlAeuaXaro6MlpPXRySg8+M6Wjp6Y1t1Td9P0HiznduHuHbt7bpx/Y26cX7+3T1UMF3vMA0CQEIyBm1Vpd7/3sUT09vqBP/+tbO3Yluo3cvK9P73vdDfrd+5/UoeGifvV1N8RdEiB31/HxBT30zJQeOjmlh56Z1vfOzcldMpNesGuH3vSSq3Tz3j5d1d+tXb159XfnlE2bMumUqrW6Zpeqmlms6PT0ok5OlnRifEGPnZnVH33thMq1uiRpoJDVLfv7dcv+Ab30QL9esr+fzwgAiAjBCIjRUqWmf/OZh/WVJ87pN9/yIr3yuuG4S2pJv/Saa/XMxII+/pXvy9313h+7QakU36KjeeaWKnpkdCYIQWFXaLpUkSTt6MroZQcG9Mab9+jlVw/oJfv7NjUsbqgnL0m6ZX//BdvL1bq+f25O3z41o6Ongs7TP3xvTI01SK4dKeqlBwZ0y/5+vfRAv16wa4cyaaYMA8BWWbus9nT48GE/cuRI3GUAm/b4mVm97/Pf1hNnZ/XhN/2A7nrlwbhLammVWl2/9peP6M8fHNWP3rhTv/mWF2lvf3fcZaHN1Ouus7NLenp8QU+endMjz87o26PTOj62sLLPdTt79LID/Xr51QN62YEBXTvSE3lQnw2D2cMnp/TwyWkdPTWtiYWyJKk7m9bN+/r00gP9eun+fr14X7/29HW13BC8Wt01H3bJFis1VWp1Veuuaq2uSs2VTpnymZRymZTymZTy2bR68hn1dmVa7s8CINnM7EF3P/y87VEGIzO7XdLHJaUl/Rd3/+01t+cl/bGkl0uakPQz7n4ivO2Dkt4lqSbpV9z9/os9F8EISeDu+s7ojO75+gl98eizGizm9dGfulmvfeGuuEtLBHfXH33thH73/iflcr3t5fv1tsP79KKr+uggxahed80uVTRdqmh6saLpUjm4XCprdqmquaWK5paqml35HWwrV+uq1lzVenBgXK3VVXcpkzJlM6ngdzqlbNrCg+W0urLB73wmpa5seuUAOvidUlcmfcHv1fcxSUvVmpYqdS1WaiotVzU+v6zx+bLG5pZ1bm5JJydLWqrUV/5su3rzunlvv168ry8IH/v71V/Ixfdih9xdpyYX9fCpICg9fGpaj52eUaUW/J/elU3pwGBBBwaLunqooF29eQ0V8xrekddQMae+7qwKubQKuYy6sqlLBg93V7lW11KlruVKTaVyTTOLwd/3TPgzG/7dz1ywLQhCjb/7K5FOmfq7s+ovZDVQyGmgmNNQMafBYk5DPXkN94SXi3kNhZezdNAAXETTg5GZpSV9T9LrJI1KekDS2939sVX7/JKkF7v7L5rZnZL+hbv/jJndJOkzkm6VdJWkL0u6wd1rGz0fwQitpF53lSo1PTe7pFOTJZ2aLOnhk9P65tOTenZ6UV3ZlH7uFVfrf33NdRosxn+QlTSnJkv6g698X3999LTKtbqGe/L6wYMDun5nj/YNFtTXnVV/d1b5bHAw3DjmaxwQL5ZrWqrUVi4vVmoqlasqlYPrpfBnsfL8beXq+Y+hxsFk4/EzKVM6ZUqbKZ02ZVIppVOmTMqUMlMmbSvX0ys/qQvuV3dX3V21uqtaDy5Xa+e31equ2gbbarXwd93DelLKpE25dPA7kwpCRja8nk2vDh/n98llbOW+2XRKy5XGa3T+dZtfrmmmVF45ML7YfyXd2bR2dGXCn6x6u7Pakc8on0mFr0lQVyaVUsoUdBHqQWgq18Lf1bqWqzUtV+taqgS/L7hcqWmpWle5Wt+4kHXkMymN7MhruCf4uXqooEPDRV0zXNR1O3u0s7frsh4vTkuVmh47M6vvPjujZyZKOjlZ0snw92Jlw/8+ZRb8HTXCROP9bJJcwdC+pUpN9U0cLnRlU+rvzqm3O6O+7qz6urPh6nvBT3A9o0IuE76/zr/X3BX8HVfO/93OL1c1VSprKgzakwtB6J5YCC7XNiiqtyuj4Z7zQWmoJwiEQ8UgWBVyGRVzaXXn0irmMyshsZBLr/x7jLJL5e6qu1b+DbtLtfDffr0e3BZs93C7wu3BZZOUMpOZlEqZUrbquln4E3xGpVZtO3/7+f3pxqETxRGM/pmkD7v7j4fXPyhJ7v7vV+1zf7jPP5lZRtJZSSOSPrB639X7bfR8rRSM7rr7W5pdqqxcX+8lXvdVX2fH9fZb//HWue96+22yls28L7ZUxyafc/3X6dKbNvtY2/1a1uquheWq5svV590+3JPT4asH9ZoXjOgNN+9hAvU2mFwo6x+ePKf//uSYHn12RicmFjZ18LaedMpUyAYHSoVcWt25xsFSWt3Z89vymQu/iW6811xaCSnVenBwU125Xr/gttW/L9xeXzmAyaTD3ylTKgxN621Lp8Pbwm2N31KwsEe15qrUXZVq/Xxnpl5XpeqqhOGjsYR0MKxp1fVaUFcuk1p5Hbobr0c+s/ItfvA7F1wuZNXXndNAITwI7s429dv7ej0IU8HBddAdagSquru6sml1hV2kQj44OG73A0N310K5pvG5ZU0sLGtsrqzZpcpK4F8sV7VQrq0ciEsXfl7m0kF3risbdumyaRWy6eALiPDvufF33ZVNN+3P1ehUjs8HIWliflkTC2VNzJc1sdC4vBzeVtZkqXzRAL9WJmUrXxas/E4FYcI9+L/CXeFnjofbzoce97WBJwxC4fZWsTosXTpINfa7eNh63m2p8/df/WUVOseNu3v173/y5rjLWLFRMIpy8YW9kk6tuj4q6baN9nH3qpnNSBoKt39jzX33rn0CM3u3pHdL0oEDB7at8K3qyWdUX/Opt95/vOt9Lqz3YbH+flf+eOvtuZnnXX+fTT7WJu+7yU1X/OffymOtX9uFG1Mm9XRltCOfUU9XRiM78to3UND+gWAoS7sfgDXbYDGnn3zZPv3ky/ZJCr5tPjcbnC9mulRRuVYLDlbCf47dubS6shce4Dcu5zIMvWkHqZSpK5UOD9D58kEKPuN68hn15DM6OFyMu5xtk0pZGMg313Wv1V3TYfdpsVzTQrm68rtUDoZWLlbqwRcKq7qWjS8TGvOhGisPmoIDfinsvOh8YAgun+/cNDpQ6dTqjk5wPdh+YSBZuR52m9NhyEiHwUzSStDysPPU6ECt7kY1AtqFt2ulK7Xp/Vdt81Uhz3WRGuobPyY6TyHXvC9NtiLKYLTeEeDafw0b7bOZ+8rdPynpk1LQMbrcAqPyiZ99WdwlAB0pn0lr/2BB++MuBEDLSacsGFIXrgYIAGtF+RXpqHTB8ck+Sac32iccStcnaXKT9wUAAACAbRFlMHpA0vVmdsjMcpLulHTvmn3ulXRXePmtkr7qwQDneyXdaWZ5Mzsk6XpJ34qwVgAAAAAdLLKhdOGcofdIul/Bct13u/ujZvYRSUfc/V5Jn5L0aTM7pqBTdGd430fN7POSHpNUlfTLF1uRDgAAAAC2ghO8AgAAAOgYG61KxzJMAAAAADoewQgAAABAxyMYAQAAAOh4BCMAAAAAHY9gBAAAAKDjEYwAAAAAdDyCEQAAAICORzACAAAA0PEIRgAAAAA6nrl73DVsCzMbk/RMjCUMSxqP8fk7Ca91c/A6Nw+vdXPwOjcPr3Xz8Fo3B69z8zTjtb7a3UfWbmybYBQ3Mzvi7ofjrqMT8Fo3B69z8/BaNwevc/PwWjcPr3Vz8Do3T5yvNUPpAAAAAHQ8ghEAAACAjkcw2j6fjLuADsJr3Ry8zs3Da90cvM7Nw2vdPLzWzcHr3DyxvdbMMQIAAADQ8egYAQAAAOh4BKMtMrMPm9mzZnY0/Hnjqts+aGbHzOxJM/vxOOtMOjP7XTN7wsy+Y2Z/ZWb94faDZra46vX/z3HX2g7M7PbwfXvMzD4Qdz3twsz2m9l/N7PHzexRM/vfwu0bfo7gypnZCTN7JHxNj4TbBs3sS2b2/fD3QNx1JpmZvWDV+/aomc2a2Xt5T28PM7vbzM6Z2XdXbVv3PWyBPwg/t79jZi+Lr/Lk2eC15thjm23wOrfMsTRD6bbIzD4sad7df2/N9pskfUbSrZKukvRlSTe4e63pRbYBM3u9pK+6e9XMPipJ7v5+Mzso6W/c/UVx1tdOzCwt6XuSXidpVNIDkt7u7o/FWlgbMLM9kva4+0NmtkPSg5LeIumntc7nCLbGzE5IOuzu46u2/Y6kSXf/7TD0D7j7++OqsZ2Enx3PSrpN0r8S7+ktM7NXS5qX9MeN/+c2eg+HB5P/RtIbFfwdfNzdb4ur9qTZ4LXm2GObbfA6f1gtcixNxyg6d0j6rLsvu/vTko4p+IvFFXD3v3f3anj1G5L2xVlPm7tV0jF3P+7uZUmfVfB+xha5+xl3fyi8PCfpcUl7462q49wh6Z7w8j0Kgim2x2slPeXucZ5sva24+/8naXLN5o3ew3coONh0d/+GpP7wyxhswnqvNcce22+D9/RGmn4sTTDaHu8J26x3rxqWsVfSqVX7jIoDoO3yryX97arrh8zsYTP7RzP7obiKaiO8d5sg/MbxpZK+GW5a73MEW+OS/t7MHjSzd4fbdrn7GSkIqpJ2xlZd+7lTwbe7Dbyno7HRe5jP7mhx7BGtljiWJhhtgpl92cy+u87PHZL+k6RrJd0i6Yyk/9C42zoPxbjFi7jE69zY5/+WVJX0p+GmM5IOuPtLJb1P0p+ZWW/zq28rvHcjZmY9kv5C0nvdfVYbf45ga17l7i+T9AZJvxwO4UAEzCwn6c2S/jzcxHu6+fjsjgjHHpFrmWPpTJQP3i7c/cc2s5+Z/b+S/ia8Oipp/6qb90k6vc2ltZVLvc5mdpekn5D0Wg8nx7n7sqTl8PKDZvaUpBskHYm43HbGezdCZpZVEIr+1N3/UpLc/blVt6/+HMEWuPvp8Pc5M/srBUMwnjOzPe5+JhxmdC7WItvHGyQ91Hgv856O1EbvYT67I8CxR/Qu8nnR9Pc0HaMtWjN+919Iaqyyca+kO80sb2aHJF0v6VvNrq9dmNntkt4v6c3uXlq1fSSc8Cszu0bB63w8nirbxgOSrjezQ+G3wHcqeD9ji8zMJH1K0uPu/vurtm/0OYIrZGbFcIELmVlR0usVvK73Sror3O0uSX8dT4Vt5+1aNYyO93SkNnoP3yvp5y3wCkkzjSF3uDIcezRHKx1L0zHaut8xs1sUtPZOSPoFSXL3R83s85IeU9B+/WVWpNuS/ygpL+lLwbGlvuHuvyjp1ZI+YmZVSTVJv+jum53Uh3WEq++8R9L9ktKS7nb3R2Muq128StLPSXrEzI6G235N0tvX+xzBluyS9Ffh50VG0p+5+9+Z2QOSPm9m75J0UtLbYqyxLZhZQcEqlqvft+v+34jLY2afkfQaScNmNirp1yX9ttZ/D9+nYEW6Y5JKClYGxCZt8Fp/UBx7bKsNXufXtMqxNMt1AwAAAOh4DKUDAAAA0PEIRgAAAAA6HsEIAAAAQMcjGAEAAADoeAQjAAAAAB2PYAQAAACg4xGMAAAAAHQ8ghEAAACAjvf/A7AftZHSfZd2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['P008'].plot(kind='density', figsize=(14,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x19ab8527320>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = data['P008'].plot(kind='density', figsize=(14,6))\n",
    "ax.axvline(data['P008'].mean(), color = 'red')\n",
    "ax.axvline(data['P008'].median(), color = 'green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'materials')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = data['P008'].plot(kind='hist', figsize=(14,6))\n",
    "ax.axvline(data['P008'].mean(), color = 'red')\n",
    "ax.axvline(data['P008'].median(), color = 'green')\n",
    "ax.set_ylabel('sum')\n",
    "ax.set_xlabel('materials')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.000000     767\n",
       "58.823529      5\n",
       "78.431373      4\n",
       "59.405941      4\n",
       "59.113300      4\n",
       "63.663075      3\n",
       "19.960080      2\n",
       "44.878049      2\n",
       "42.156863      1\n",
       "80.000000      1\n",
       "30.000000      1\n",
       "68.627451      1\n",
       "76.470588      1\n",
       "58.422590      1\n",
       "29.411765      1\n",
       "56.862745      1\n",
       "83.333333      1\n",
       "98.039216      1\n",
       "29.853717      1\n",
       "19.607843      1\n",
       "72.549020      1\n",
       "43.349754      1\n",
       "62.376238      1\n",
       "54.187192      1\n",
       "51.960784      1\n",
       "48.039216      1\n",
       "41.747573      1\n",
       "39.215686      1\n",
       "Name: P008, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['P008'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19ab8691630>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ngP8CMMjMdxLR2QC+BOCi01zfyZDgE0JEJPjEIccKvoCZdwAAEVWY+U4AYOYdRLRgxwcz2ZyCDmshhACAvmYXIBaOYwVfnoheBX1qjxki+gsAXwNwHYDS6S7uJHRjcU/cEUI8MYlmFyAWjmOFw0uhuzPXAnhmeNkPAbwIwCtPY10nS9bsCCHmasq5R8XCdNTgY+b9zPwqZr6JmceY+cPMfA4zD0ZdoAuUtPaEEHOdcPARUZyIfkVE24noASJ6b3j5NUT0GyK6n4g+R0SPeQ4iuoCI/ie8371E9OI5172OiHYSEc+dLU9ETyeiWSK6J/x695zrOono60T0EBHtIKLL5zzPneHt7yKiSw97vHvCGv47vGwlEf0kfIwHiOgNc25/y5znHiKiew57TauIqEREb5lz2RAR3Rc9/5zLu4noNiJ6NPzeFV7eRUTfCt+TXxHROXPu8xdhTfcT0VeIKH609+tEHGtWp0lEryKi/wwL3B7+/Coisk7miU8zr9kFCCEWlJNp8dWhh3cCAEMAnk1EH4Tu/boQwNMB7IXuITuEiK4G8BUAHQBc6DPGfJKIovkHvwDwcwA85z4fBvDp8NckgAwzvy+8zoc+k/ylAB4BcD6AHUR0B4CfAeiFHst0Afw9Ef0BEd0P4FboY+LvA/i98LFfDmAVAB/AgwBeR0Sbieh1AC4OH/s6AN8A8M25YQzg3vB9iGoeArAs/NVj5q3h5X8N4CEAWwDsAfArAFkieh6AhwFcBsAB8BEAHw3v8wkA7w8f668BGABuDFcRvDa8fR3AGpyEY7WMvgB99uL3Qs/uHAx/vgDAF0/miU8zCT4hxFwnHHzMzNBDO6sBbAVgQR+AA+iQ+T50eLzgsPv9BMDnobdMS4X3GQawnIh+BeAWAM8HMHcf0e+Fj0sAZgB8CwCI6A/C69vCx3gXMzvMnAfwKIBYWNf/ALgbwAiAa6DPRZgH0AXgFmY+SETLAfwZgMnweZ4BoAxgefj4Xvi6bgFwI3R4A8B+6HD1AMyGdT0DwNLweh/AW8PL0wCeBz0/ZBrAFQB+J3y9twP4PwCfgA7ZvwWQCVudN815Lz4W3n8Eem5JJ3RYlwG8CSfhWP8YLmLmjYddNgzgTiJ65GSe+DTzm12AWBiUXxvvdcfut0jFlGmBfBgxBBSwBYtNMBRgGkTMBGIQMQACkQKBCLpbhUCKASYGwGDo7wEQ/U4I/wvmQ7cAhd9x2HfZMPlJFoBGT/S+RLQCwDbohoKCDqYXQB+A09AtsL8Nf597v83QwfFP0IGQgl5IvwO6NfUd6MbF+wDcRURfA/ASAG8H8M/h415IRP8D4H40wvYcAL8JW4DvBPDZ8PoPAFgJHVovhJ6fcS+A+wD8FoDziehjAP4hrKMEoAYdmGsB3AW9TO2LAF4P4GkAJpj5USIahA5+A8DO8Oc+6PAshe/DJgD/RUQIn/sy6IA0oYPZBNDLzKVwVcB7w+dfFj7u+eHj9YaPRQAuYeYfEVEJwKuhA1ABGDvW3+1ojhV8M0T0ewC+wcwBAIQF/x70p5GFSlp8AgAQGPF+n1PFc9xd92yujhRMilGprZPJrqJuOUp5dSRrdQYMRZxAoAxC4CsGs8eeUr6vAp8AV7HtByrmBWwxyAbIAqBgUMAWAIsCtmCwQRabMGHADEyYMJQBAyCToAyQMomJmEkRKwUmpaB0DjIxKTCDQGHvFxOYQBxdQgATgQEi0rfTPwIKIAJAQHRV+DsfuhUBOt4R3fPQDXUJxIcu05fr+0Lp66LQZw6DnhmMILps7vWNn8E6/pnnfXBofDx47M8gfZ9Dl0UfIOjQ9eF3PPZ30h9hotuDGEwWGyfzz+ifoQ/MB6FPd5aCPmB/CMC79HuFNdABM9fzAPwE+gAfDy+bgg6ZG6BD4S/Dy4vQa6V/N7zOgP4AH4TPb0IHVHS5F36tYOafEdE3w9uWobtdPwfdfdgP3aoyoYPzRugwvBfAeeHjcVhXOnwtXniZCd2SjV5LLHyMqJvx5cz8ViIqh9dFb3IA3VID9L+ddeH3nvA1ALp1yeH7Ugmfcy90izMA8PXwvUgR0UcB/CGAb4fvPQBcTUQZZh4iogz0h4mHw+vuZOa5LcfHOFbw3Qjd3/pPRDQTvimdAH4cXrdQSfCJQybN3nW3m73rbgew0Xjk/kvKd+7KTAV1rvcgafTzwb40nFg5YHPKNx0fdc9H3CWkvE6OeRzEY1Xidg91u52LqXZMxJKogFCFTayY4yiRzUWOo0KEEnvKQT0IqIoAdTAC30GiXuNEvY5E3aGEU0fCdRFzHUq4HtuOB+WbykMs8BCjIIizz3EEiJEPm8AxMoIYDI5Bsc0G26TYBsECNRqqBAABgX0isAJ8ImZiBAQKCAjCOGBiCsDExCoMFQJ8Ajwwh8dU9hnwwewT4DPgE5h1k4dIEQHE+gdFRApERGE7GaQUKVK6qUyKlFJQUKQCRYoVjECRwYqMw35XrMhgBQOKDCb9OxTC76SgYIBIIbyciFT0XT8jou9EREoRdMvdY5eP9m/k8RDRcwBsDn81oI+Bt0CHyXvRGC7KQ4+/zbUKunsP0MfaCoDt0GOBfw4dDlFdBD0GZ0GHgRNep6CD7iUAfgPdkopamkMA1oZjaT3h7fcAOBDWfAV06MztYYhBj0mugG6pdYTX29DHzV8CeMuc+/xL+H1lWEsqfB4C0EZE/xi+rmpY96rw+ueEgVyADrJ4+MVEdBN0+AP6A8S+8HG/BeDd4fO8KHwdPwtf683QY3tAo0fv/QCiyUK7mPkCHKejBh8zD0UPTEQ9AIiZJ4/3wZtIgk8c0cP+hnMejm84R8UD/1rjf+9elvjFyLrhMa5UzoNdW0EEE3Z6gLyE63HbTDDdneQ95srApnEUzAm2Kvs5NeZzT6ULiZrPcbem4kjCQg/F0MNENtVjJgVtDpvJgjITRXYTjEJ3G2ZjKZq2YzxOBkqBydXARN23UPdN8n3ihOdS0nM46dWpza9R2q9St1+gNr/IHmZRh0M+OfDJgW/U2VNO4BkuucojV/kqgEe2GyjlAuRSAIdIucTKIVYukeESGS6U6UIZLlHMhUrUgbgLWJ6C4hgbbLFiGwoxIraZyAZgg8gOAIuYbGJlwzdigf6y2Tdi8JWNwLDhKwuBslRgWMqDaQQq/IIymNgI45cCChSiWOaAQD6YfR/wfLDnA57P7PlgJwC8AOwxh9/BHochzY2Q9kiHtq+AAGD9nTkwAB9EVnA2nnsi/2T+GDokCHqczID+0L8PwHOhu94C6AD57GH3DQB8EsA7oFsja6DDKAUdFO3QoZECcDb0uNcrAPwAOjy+DH38jYLjpvA5lkCHQTQx5d8AZKGP56vDx6oA+CPoeRn/CuBHAK6CnjCTDl/Lg9BB1QU9hDUR/r4JwB3QXZlvD19jHXpsbhl0yC6FDvubw9tsBvDVsG4furfwauhWbRJ6Ms1XoD8cvAJADsBvh7ffCD1O+jB0K+/b0JN3NgC4MnwPb4Ru1XVCB/96ACso7Fd9oo454EtEm6Cbucuh03oEwHeY+aETecIniQSfOKoAyrjNv3wrSpcj3lmv3thz+93L0j+dLTh5Sj0aC1JTZ5sJnGt2JOJsmLNGu5n0Ouz1nmP78NrgY1k52Nvehh1kB75XxdLgoF83DlLRraNzosx9U0VKzSQR43bUvDjqnkNpN88r0I6U1UV2LEGubcE1fbhGCZ5ymJIOzGQVZqIKo61MrlXBtG3zjJ3CrLEEJSvFJTOpKpRAzbeo7lqq6sS56sS55ti+49jKgWkkVJ06zZLqsgpBt1+g7qCATr9AbVxSHUGJOlBWHSjDVFV2lev7yoWrPDhGgWrKp7oRUJ0CVTaAolJcUIqLSnFJKVVRRF5AMF1ShgNluiDDJbIckKXDlBJ1+Mk6/ESd3WQN9YQDTjmguAOKu0wxF4btwrA9mKYPy/RhKVZGQJYNsojJUqwsCgzb95Xt+obt+UYs+vJ9w/Z9Ixb4Khb4RjsHhg3fsNlXNgeGhUDZ5BsWB8rkQJnMZEIFjnuC/1T2o9H6ibry4gAy0ONhSTRabT8HACLaCh1SdQBvhG7FbQgfpwdAgZkHiGgy/B3hdS+EDgoO7/NC6BbaD6DHBP8SjW7GqKa/hZ7MEtUWtchMAH8F4HUACtATWAzoQNkO4DnQY2pOeL9NAN4W3v+b0OHK0Md9ABgH8PfQ44jRZJYS9Djkf4T389HoKv0EgDvD+pdAhx4D+HhY84WYP7nyYughtHdAB33UUl0OPfvVhp5UqaA3IwjC54vevzVEdHf4Wt/JzHfgKI4afET0Nugm9lehp6IC+tPPV4noq8y87Wj3byIJPnHcaoglPuvfcAXyN6ALhek/W517YHn8f72fp3/DlZGYv+Ehov7CWrueujBW7lxvVIoV0ztQcbt5zN1od1AqluZqYl29YhOhfZqKy2b93evTGEfKzzsJr8+bxjJ3ApWgShXeR/HyPVg6U8CSQhVGpQNVLGMn6FOmZ7HlMpTP5JtJtmPL0B1rp+VmDHFlkmd4cA0frknsGgzPrIPiM0TxEiGWZ8QLASWKBLtslM0YFa0kymYKRbMNk0YKQ6qfiiptFIM2rjiJoObEuO7YcFyLXMckzzEUu0zkBExuAKvioCsoUmdQRGdQ5E4uYSAooRMl7qZS0IVi0EVFdKJM7VRWbVbVtG3HqKd9s6YC01FMNcWqqpiKSnmzhvImlfIK+sstKIWSUlRWSlUUqxoFVp0c2yGyPaKY8tiwXMRiDhD3yI/XUU847CTrcBN1eMk6vKQDP1HnIFkHJ+qgeAVB0mEVc8ExF2x7gO2B6ibcRq/YE/Iz6BAj6IO4gu6eS4ZfCK/Tg4ph6DHzK4joYjQ2+oi6Bz8OYDpc0hCDHlvrgQ6G3w5vHx3UkwBGoUPwbugJNYxGyF3GzIVwBmUUhD50C+kN0K0/hUZ3JqA394hmjXpojLltDx93bXh5FErGnPv9CPPDahl0eKbC+5jh8/8pdKtyI3T4h2PG2AngH6EbUk8LHyOAHttbBd1qjGrm8Lrt0F2aeTRCDtCTjW4MbzcKYBUzT4Xv+beJaAszF/A4jtXi+1MAW5h53qclIvoQgAfCJ1+IJPjECZlBe/f7vZdcidJLsKo8Pvx6+1s7z9v667Zb2++t/hI7ec3DCk99gL2O2srUTM9F5cmeTeYYJXv86oGi7+4qG8GU1W13GNfFU44d766VYxQUTNsqxyzm9gPBTNrC6NKN3t3U6eRrSYbDWFadxvL6FKtgAlWaobrKc3exjN58VfEsUKz0wMEAm0E/rKCHTE+pgHyumwE7ygOZpkqaSyhlLA9SRixIGHFlKkO5piLXJK4bgGMEVFeBqqoaIzbsIzbLFCtCxYpK2WXDildgt1fJMxVKZoqLRltQVGkqGW2qQO2YQTcPY5m/Ax0oBUnluLbhuobhOQZ5rqHYJUbd98gJ6uT4ICcguIGiOtvkB1Y6KFvtXLI7UKYOlKkbJaeLik4GRb+bin4XFf1OlNwOqtTSqBgpqllx1GMxuHELftwhtqqEWMVUfsmmarFD1QtKOQWl3FlDeQWl/ANKBQWlKGydckURV0kFNSLEmWtPP7F/EgegWxg+9OSW6PRGO6FbcRGCHoN7J/T0ezDzr4moAt1ascPb/TF0y+ZX0BM52qAP3t+HHteKxhGjA3+0Pm44vM5FI3zOAnDPnPoM6Nbos6BbjpeicSw0oUPo1dDdhwhvPxveZw10C84JbxsF2f7wto8COHfO5QH0BMc/Cuv1oUM0QGNfVDqstmjCy/OhxzM/Ah3+NnR4rQnvj/A7h6+/Oue+kT8Jn++N0EEaENFBAC8DsAv6b3MXHscxN6kOn3jvYZcvnVPgQiTBJ07aPu5f8Rb3phVwgfMrOx95o/mN0fP7Hlj6n+viU/+a3lczJw60P+3e785e8iCXTO7vONh3cXViyXVcsAeWz/izQZDfcyBwdzuBP9HZYaZjA4nVM+cmVtXZTpgFy09MGmWaNvKK26bJbJvCbFtbdSK11pmgTv/BWpLypTR3VSu0pj6KjfUh8jGCKXMHF2MlJq+i2vMuYnmD43nFXiGtStwPl/u4yL0M1cWuAaoZXuAYHnlUVz5qlFDkJQ2bU5ZBtYIAACAASURBVEYaSaNPJY2UkTTbiU2TXcV+3QA6jMCokkdlVeMy1blOVaLYFKnYXkPZJZixMsViFd+OVQLbrpLdVjPZZKOkUnZJtVGB2lRBdfp5dLkz6K7l0VWdRQeV0GtOYbVdYzvmu0bMdynBLoHcwCEnqKPuu+QEPjmBC8d3yeU6vKBKHtsIOIGAUza7djsq7R1UrnWiVO2iYq2bitSNIvqoyBtR4k4qopPKnEYFbahSkupU4Xj1BP8ZRAdgQJ/hIepGjEEHSWzObXcy8+eJaBkR/YCZb4ButZlohNltzPwLIooCNGoJfhp6Dd4V0GNYdehp/YDuSpwIf3bQCNFPQC9TqKDRcmPobsvXQI+XxaFbV354vw7obtpItJh+BrpLdVv4eOnw8vOJ6GboUPTRCF0V1vgr6AXqUU0WgM3M/DEiqkEHX9Rq7IRuiX4UerlELKy3A3pG7Az0B4rz5jzPNPTYX1Rz9H71QnfJ/gP0JJ83Qwdz1C16+ESjeY4VfG8EcDsRPYpG8q+C7n993THu2zRD2wY5k81FnzKEOGnbed2Gl7tv2wCX+Rrn7srN+W8Vzqbdy//7ojh/8up08WE10X3Ro7cmrrnnPysXHWDHs3pi430XBxO9l6pS+/KOGqnlQ/7orl35XROBt5fYn16aMBKpgfiq4YHEhnJX7CqjaiE9ZVSCcWPWnaKC7cWHEom2icDuLRYPJruqI4kL/YOqm/KVNkwXOtmLGWpd5wFsqe/h9d5eJppSs7H7grxdDmYtx0AZHJtRSM4YlJ4l6inF/FjQGxjoB6leo2LFjKmYQt0seC45cJWjfFQUB7NAUEbKsNFmxIM+I6GSZprazJV+0mynmJEk1yCzrgLUlE9V5QRFqtXrVGOX6kRwYimjbqbtkrnGHlZW7JHAjlVqsVjZi8Uqrm1XK5ZVM03Liau4lyhTKlGktmSROtw8uqoz6HJm0O3m0eXPotMvot0rI+XWkHBrsJMlNlIHPKTIY1O3Kn0iJ2A4AVPd98kJGG7A5AQML2DyGWxQad+J/em3QHdtRsdKgp4UcgF06+t+6KAxoVtsgG4sjBDRZdBBMHfm5guIaCd0sLhotPAUdGvl36FbZEk0Wle/BHB5+BgJNA7+FxHRs6AD8/o5z7Gbmf+ZiH4HjS7FqLvxzdBLGaLu1Cgwu6BDV6ERepHfgg6a10OP80XX/wv0EozrMf+0T1cR0YsQzuKc8/xW+F69ELpFFr2OOPT4aB9012nU/Yvwuf4RupX4D9BrGK8P75eFboG/A40Zn+0AXsbM0ziKY83qvJWINkA3mZeHTzYM4P9YT6FayCYhZ10WpxzRj4OLzv+xcxEseM7vej/bc3Pp+9YaGh74TVes9oXnp8vvT8T9jkIe12y/LbjiwR9Z/Xl01e3O6YN9F04f7L2IS22XLwuUeVYApzTq7jcOFHYi8H5ucVBcZZDZviS2fPd5yTUzvfGLvcRMR3vBrHePq9niMpV3p1UpWVdT3Ym2u9329okZXuKWR5N97i/i5wRjtMSYLnXSTKEd5VKCB9QEzknvxtnxISztHybTnAlmYyOYtnerMTtQE4HBsVnlt80oas+TMTANWlIwYAa9gVK9FJi9qh7vRCXRh7ydJEe5yqNZ5WOUOJh12c8zB0VTwTVTZtJIGknqMtNBm9nppKyeWtJoV3EjZStlxWrKS9WURxVynCLV6iWquSWquRWqV2rkxlx4KVb1mGlX48tjVWdNbE8lFru/FotVvJhd8Wy76ptWzTdNxzMMz1dmkK6ZsXgpkaZZdHIeXcEMugL9vZtn0YkC2rmENKpIUhy1Ez2bzNOgD8xzZw9GLZ/oQG4AADNPEtEyAO9j5hvCISFg/sH/y9DjZVEQRMdRk5l3E9EWNFp1USvxYegWHGH+h/nboFtJz5jzHAzgy+H+lpegES4+dNC+BHo8MRpH88Lrbw/rmTuGF4S//xv0ovZPzrmuDD3D9AfQYRNAd/VuhW69vn7ObaMa7oPumv085q/5M8L7fiK8rAe6ezMGvWXa88P35HVzXud4+H79PfRsz/OgA/NqZo5ax4/rWJNboim068KiP8PMZ0o34jAk+MRp5MK0b/GvufQW/xqkUC2+3Lt16h21H7UtweRZQ7Y5/IUL08E7n5pMlkCJc/cW9l1390+dcx/4SW/CwRrXSk8d7L1g58G+i+uF9NVLA2WvA5HioDA25e6pTBR3B8HMnZ3g6lkESnbYvUP9icz4efHV5a5Yv4GK1TM1VfJG1YwXqLwdo3J6GSaWxxO7nXR6ary9c6JUXm45I/EBetBeqW7jS3m61B3MFlJUKcWMeKnKm5yh4Gx3iNYl9xsbUsNAZjaYtqsYsXepvfYe7LEs+BVF6Rnijjxh6RR4+RS8ngKZJrqNwOhVntWLanJVvZLs41qs2yqRbR0IapbvVMDBfoeDmRr7+RoHRZO5mrTJiCfNVCJppt1+s7PaZnU6KbOnkjDS9biRjFsq1uYR4lVy4hXllEtUKxep6pSo5pdRd6pU5zp57MBDgEApw4lbsZpv21VvpV3218UOBrFYhe1YhW27CsuqwTRd8jxrRg99PWF3Qc91GIVu1Sjo3q+zoQ/m0axOAgBmHoFegA7MD8voAH8ldMvwjXMuAwCTiAagJ4qMQbcao9bYHui1g0899E+vsWauDD3x5crw8qhFWg9r7oTuukzOeawPQy+Kn4BuZfnQLasvQ0++iV6PAd3F+R3oULoqfN0WgP8HvaTjk9Bjlhno0GPoGZrfh24pzh0SWwsdutugZ19GG01z+J7+EDqYfeiW7TB0N6iC/iAQ1cVojPltgJ5v0gm91vDD0Ivdj+pYXZ2fg34z74BuXp4N/Qc7EwxD/wGEOO3KSKQ/7v/OUz/u/w76MX3wNf539r7JuaP33VMzm6eUmrylKz37peelkmOGsTZdRf7K+8uPPP2+O4Lzt9+x0WAMuGYyP7HkvEcO9m2tzrZv7PXt87aCyGDmgP2JPSV31+hsaYgfnr27H3DXAzCTRnqsL7Gqek5iTaUndlY9abZX6zUvebBQMEZHZ6igZuNxqnSuhLdqjbUHbW2/Hk63T+RTfdPO1KpOPhBbSsPmSuP/gvMwXe4MSoUE1YsmUcnDQGmCzvaGeFOwT22mfbyu8wAHS4pqwnZoxGLsjFW83dY4TcGwU3mKdeWJlk7DXTHBlVUzrLqKiBvoiPlmb7we73XLyRW1SrKvUIv3mI7VnppRiE1yNc61Qp2DiTL7j7gczJY5KDrM1RTYTceNRDpppK2U1VHqNjurq6xOL2UudRNGG2wjqSyyDSLVWa94yQrVnTLV3SJV3RLVUKIapqjOVThwyCWLzZnnDJ7Qn3YKjTV8hEZYOZh/+rNoVudzAWxl5ndDd9sBeqPms8L7rWDm+8ItuOaeI7DIzGPhuNhS6HDxoFuChbARoqCDqC28jwG9LdhW6O7YyKeY+avhVmsKjTFGQId3LPy9H41u1nj42GrObRmAxcyV8HWtQCOo3wPgg9AzN5+G+a3aB6G7J98S1hoF+P3h9w481ho0jtdRq3MFdDh/FXoSjY9GZn0r/J6BzqYd0GF/IxG9LtzD9HEdK/g2M/O5AEBEn0FjScOZYLjZBYjWNI7uvvd4L+97j/dybKD9e95gfnPvK2buWvOa/OzqClH5e22pR2+5oC34z0usLUzUtXaUH7327urI1kfvTJ8/dudlBMQ8I1ac7Dn34fG+reXZjrXdXvyyp5iJyy0AYHargXdgR93dNb23ss8aKu1YAwSrAMAku7gkvtwYSKzBxvhKSlvdpklWrOTUZsYrs9XRyRlvXBXsIlV7bPir16qaeU7yruH29MREe/tE1VxexmhsgA+Yy2lIrcH3+Vr+UqVdVYox8otgKrq+Xapaa/Mj1oZgn7qC9rln094gY43ZzvKKPZHx7b0x5d9l25Xdlk+j5myCa7viXXmKL51CfeUkV1eMYbYvz1ZHGW0KqbhrL4lVE32lcmqgWkmsdauJnqJjd7iemegow0uWuGyOeXnFzkyF/WGXg7zHQanKXDXArq2AjrjZlkiZHbU2s9NJW500YHZy0hxA3EghphLksXPM7q/H8Qvog/rckJqFDr2oBXJoOQN0CzHaLisad9qIxtZgdSJKYv6kGEC3ygDdfRmdQcEAgPAUPL8Mn2fu+Nt/MPNniOiPoFtjkf8Kv2+HDqVoEglDh3G0Y8zcFmkBugVJh10X1ZlEI/Ty4WOk0Jh0E92+Ej7OBsxffgDoYIvex7lBydBdrZug1x7OrWsgvKwO3brtgf7w8dPw+rY5t0+Ez78eejOAx3Ws4Du0jIGZvRNcJN8sB5pdgBCP8Mo1r3XfsAYALlMPPPgG45sTLyw8tPnFxVKvB3j/nUzc88XOdOEz18fW/ssNtMJ2ufKUh/iua7bXyxtG7lo1cPCurQDgK7sy1b35/vH+rbP5zvWdrrl6k2Flov0fwUFlKnCH9vjurtJYbaRtrLrnHIQtEgJ5nXZ/aSCRqa9PrMYl9qpkTCX7QYjlqbxvzMkfHC3ka2MjBbtM9T4fweokwbzAHpt4avqBkfb2iUI6PRm4nVAHrOXmPrWK99A675e43Lq18gyrVjIJxcClgutQ2bV7yvnY+mC/fTbtd36bhiob1HBtOU0mq531xGgvx3fGrPLPLbu+2zbzI2Y5UUSlvb2wv3NgBrGVEyiunGR36RhXlhTAbVWAOJZy4j2oJPu5nOznSnKNV00s8eqxzqprJe1AWQmf64nZoFDJB/kaV2dUEOwK9CSdEpirAMzxN+OEmnzRri0RQuOk3HPH4o50cPwFgKvRCA9CI5SirciiMIlaQd+BPrg/NbzOADAVjh8e+nOH398f7p18BfRen1EoRicQmNsijWrcAT39//CAW4NGd2i0+0q0kB5ozNoEdLeiBz0E9sLDXrMNHUCPYn64AcA/ha3aaKZsNHuUADwFep3gO9BYyhEt35gOHzd6H0egu3Y/Ax180ebhD0DP7DzqjE7g2MF3frjXWlR8IvydoM/WcfjaioVEWnxiQbkz2LL5zmALFAL/BvW/v36d+e3qNeX9519bqaYBYHvMfvjzHe1jP9sSX3rHueZWAOif4eFrtgd7rtjhWH2T95zdN3lPBwAEZNanuzZuH++/ZGa6a2O7a6U3GbHNW43Y5kPPF/jTewN394HA3e3NuAe7Zpyxi3fM3nkoLFNmx0hffPXBgUSmdmlsRTxpppcpMlb6CJwpKj465uYnxqp5d2KymNgLZymDV4BAPcqrrkrdv7+9/SdT6fbJeqptxip0pWLD5grsobX13VhXfBDnx39VuyLmlFSMil5FFZwSlby6WXbaVhXHOzZif22rGipvpn2VNTTq9VG+vWK6qf2rDePRdVbhHtuu77bN6ohpcl65MJyxdH9+nJdPsr9qgoPlUwhWzrDbVUIt4aAGmB31WI9VTfYZ5WQ/VZIDqCbWci3WRa7VpnwzPn6Cf7Znh98fhD6gr4Fu5QxAH4x3QXeD9hxh+6wvQZ95wYU+kMegl4ZF3YnT0CE39zg8CD2ZsAwdMAEz85xz+M0gDChmrhPRKjROkxR5E3QrKRE+zn7okDKhu25VePsydBBeEf4eBWYNjTV4kWjj6tnw+VLQ6xp/Av3hoAuNiTLbobtOo9cNNDajBnQwpTBnJigzf5uIng49vtkLHXpV6AAexPxJN/1obPr9fei9TzdAL37/K2aewjGQ3kd98clkc1dDb6YtxIIVR736EuPH97zC/IGxDFMXEulP2PtMc/gLHendt6aS7XmlzgWRoQL2ztvDD153D0+fO8R9cQebKDwgBKTcfMf6h8f7L5mc6j67zbE7NoBo3gdTZt9lb3SX7+4+GHhDBvszA4B/Fua0ViwVm10SWzG0NLFmdkl8hUpbXb0GWWuIyHbhlSdUce+ompkep7w/o8ptNTgrmaIFy8zxeHEknZ4aa2+fKLelp8iKl1OT5hJrn8rUd2F9ZQ/OwjgG2spOIhGU4FPRLahZ16WSa1LNb2/3S21rMepsVkP5LTRU26iGaSUdTHai1FNSQWLItvKP2lb+Eduq77ItGjHNxIxSnQ7Q11Wi2rJpnlw5gcLKSXaWTTEtKSCeqsHfun3HFSfy9wlPgPpfaLSOAgD/Dd2FGLXWooNoP/TBN9q55fOYP9GCoJcr9EIfzB00ZlJWocfNboYO1CgMq9CTQt4KPb8iqsOBnszxRehZnYc7BzqAovV2RviYz4BeOL4xfC1RS+5H0ONmn0Jj1mo0G3QLgF9j/k41LvTSiJuhW5bRwnoDet1hEXqROUMHaXQ2iW+gsVC/gMb2Z1dDt+DOmvMaCtDdn1+B7r6MJhd5AL7CzH9MRK+F3jRgIry8ysy/dYT3Y57FHHzr0fgEI8SC14nizKvM79/3EuPHHR0onxedLGhGqemvpdt2fDPdZo6YxrnQY0RIV3j6affzQ1fdF/CqCaxT3JjFzCB/tuOsR8b6Ljk43bM5Xot1bwBR1+HPyVwvBO6+Xb67azbwhpMIiqsBnjcbmqDcrlj/noFE5mBffHXQafd22CqRIaIOAKjCmT6oZvePqpnZg2qW8lTpcOCtAh1aHA3TrM+2tU0Pp9sn8u3pSS+ZyicCK2gfVcuwh9bO7sL62l5kjEn0dVTdWDuXg6oquHlVcGtUck2q+mnyvCXLMYlNtH9yCw0VN6u9/lk0ag7QTEcK1b6iQdYeyxx/xLZnH7Gt2i7LogOWGa8Sjf78Tx58/hP9e4QTSvZDty6ivSgVdKttDRrjZtFZFAzobce2MvO7iSgKNqARWHdBh997oMNrBXRL6EHo7r7pOc9lQneXvhl6csfr5zzWDHTL8N+h1/iZc2rxoE+XFJ3JfK5vQIeuBd1ii2Z1fhT65LQd0EFlQwdjHfoUQdEOLS50S7ELeizyg2hMQlkS3uf70BNO4tBdnh3QrVeC3px6K3TgRd29FeguzveEj1GHbrRshl4usTl83P+APjltDcAoM68PT55QClu/rwbwMWY+9h7Uizj4kjjsxJBCnClW0sEDf25889HfNu5cniBnfXR5jaj6g1Tyvq+0p52Hbets1v/jAwDWjvAj190TjGx9lNvbKziH5ozLMMDF9Oqd431bRyaXnGtX4z3rQKr38OcFAPYLo767e2/g7q4H/lgHuLYOjZmEh7SZnQf6EqsPDCQyle7Y0mTCSC9XpKJNjVFCbWxc5Q+MqnzpoJo1ilTtduFnQIdaDiAKvERydn80uSbVNmPG46XOqhFr30+rizuxPr8b691hrLSn0dNV9+0eKnuzquBOU8GpqqKrqOq3wQ36klxrX0sj45vV3uktNFTbpPZjFR1MWPB+s+S9+496frYjId1i3gc9DhXtkXkvdAsshcbygWhmpAF9QP809DT/7+CxY38fYOa/DLsu74MOjRno9WfbiWgf9ME/Dt1qIujW0Dj0koZLobsKl0NvKn0JdOvzJ8z8p3POjfcZ6FMi2dBBvSV8/r+D7hZMQ0+ISYSv8e3Qrbd1aHSHxqGD6U7oFmTUrRs91gD0B4Bvhu9HB/R6vHvC+94IHe7RWF20DGEofJ+mof9dlZi5n4i+AOAPoI/bTnjb70G3pM+HnlSzHDrYa8ycJqKlzDwa/r1eAH2W+dYNPgDIZHPT0H8oIc5Y59GuR99ofuPA09S9600KDgWLD/g/T8Qf+EJH+8yv47E1nh7vAQDEHC5f9jA/ePX2oLJ+BKss/9Cu/ocUU8t3j/dvHZ7sOc+sJPvOAqmBw28DAHpJxcHdvrtrNHCHwP5kH+CtxRHmCNgqnu+NrxzqT2Rme+MrjDazq98gcw0RmQDA4GCWKsNjKj82qvLVSSpYJar1+QhWgw61jvRj2ZWJtvRUNLmGE4lC0rJq/TPUZe2ls8Z3Yn1hN9YGI1iRmEXHEi8we6niT6qCM0mzblUVXaaqlwLjO3vfd/3fPNH3nYieAd1CiXZfiVo8BuaPORWhw2ZFeNtPQx+cn4P5k0iiUw150N190cL4WQBfD7tH5+7SEk0w2QzdDZmE7kqdCH9+N3QrMdox5tApgaDX8J0D3Xq6FzrQuqFbj/8POhCj11WF7jbdAt2tG02qGYdujRH0TE0f83d7saBboR9EI9xugd5hJQfdSutHY0brndDBtheP3RDgMuhdxN8S3rYc1v1l6AB9KhpdyrdDn+HiBeH5AK9FY2Nt/3jOy7fYg+9e6Fk+QiwCzNeou+/7c/Nb+fNp17mK5n+ou9+2H/18R3rkp8lEf1WpTXOv65/m4Wu3B7sv38F23yw202M3/UU50b93vP/ifRNLLlCV5MBqVsaKx6/ErQTe8K7A2TUdePtsDgorgeCIt1cw6t2xgT39icxkX3xV0GH3dtoqnqE5Y5ABAneKSnvHVH5iTM04U6qUqKA+EOgJNWre4ymvmkrN7E+3T061t084qdSMFYtVelgFyw7SwMxurD24CxsqQzgLYxhoUwg+8tA113zheN/lCBFdD93VNnf25QPQQfQ96NZWN3TojUO3Yi6GXs5wJXTL8CboltczATjMnCKir0F3E1ahD9YPQS8c/zr05A4T+sA/GT7+G6BbkEPhc62GDqTLoSff/D10q60Hejb7DjQm3xSg11/3hz93Q0+4eRsasza/y8zPJ6LvQC8TeBf0vw8X+hRCfwLdCqxDt9C2QIfYVdB7a0atsAR09+k10Gu/o23Xog8K50PvH3opdJDFw9tcFb6vd0EH5svDx7oFehu3XuhzAl4aPs4jAAbDnVv+Dvq8iB50C/LVx3PKvMUefDk0dlEQYtGw4Dm/Y9xxz03G9/w1NHYB0bwZeDhgGiNfak/vzLWl0tN6csyh1pkK2Dt/Nz9w3T08c85e7os7OJuOMB2/Gu85MN538dBE7wVBKbV8JSszc7SaOChP+O7QUODuqgTeaApcXofGJsiPuXna6t7fH1890p/I1LtjA8m40bZCkVo690Yu/MokFfaOqfz0mMp706rUVoOznAlHaJ0yx+OlkXR68tDkmkSimGbGa65/9oP/c7TajyTcSSU6+Smgu9/uhR6j+ifoMbFZ6MC5lpl/HG5Z9hnomZJJ6Kn3FeixtGgWaLRergPAvzLzq4joAuhWXQKN3VYOQIfAZ6C35Jo7zX8HM28morugAzY6W0IXdLC9GzqoEtBhugl6T88kdAj2o9F63Qc9EeYG6NCdCWsrQbfQvgEdkJPQwbYReuF4BsB/hrfdDh2wZehxvQugg70AHaIe9Ez7Puhu1jboEPwygL8B8Cro7tL0nNdYg/5AEU3A2g3dMp0F8HZm/vTR/n5Hs9iD7wPQTXshFq0UqsWXGT/c/lLzh4lezF5ANH9z9lml8l9Ptz34jXRK7TfNc0A0b7yuvcxTV93HDz/t/iBYOYkNig+dVmaemt05frDvot0Hey90S20rlweGtfZYtQX+1N7A3T3su7t99iZ6AGcdHrt4+5CYSk71xlfuG0hkCkviK6w2s6NP6a7Sea+pDnf2oJrdN6ry+XGV5zyVO+rwVoLmrV2LLLn55puPOcX9cKQnA01Ab9yhoE+euhs6RP4LusVVADDAzGrO/X4feucSEzocPxtedRszP4+IfgHgovD6zwF4LXQLMmrlvRw6NF4b/v7nAG6FHp+zAYwx84rwuarQLaCl0GH5I+i9Ld8AfQqk+6BbVGuYuS98HzuhlztcH/78VuhJOdcD+C50a6wdetH82dCzWH8fOniHAOxl5meHra0Xhs+9BXpsbyx87X8FvaziQ9CBNsrMW8KNs78MHaQ16C3KPgPd4g2gPzAsCd+PdPje3Qa9Fdm3oWeXvo2ZP3XUP94xLPbg+0MAT7iLQ4gzVT+mD77G/O6OFxg/622j2ubDr3eA+q1tqXu/3N5We9C2NzHR/AkuzLxuBI9cd08wevFO7mivYAvNX7zceCwrPdnYb3TVQKDs9dALqh8Xs++wN7LTd3dPBt5exf7McsDP4MgLwAEAioxaT2zp7oHEmqne+CrusJZ0WSq2hg4LcAAoo3ZwXM0Oj6qZ4kFVMIpUVX/13ncec3r7kRDRw9DT6F00Tuvzf9DjUS70wbsOYISZz5pz5vXboWcfdkIfzKPz113LzP9NRNHYXbTU4JfQLaI14ZcH3e03AH2gH4UO0HHorkoHOlTeDN2yik4sOw0dgtG56LZCtwAZwLOY+XYi+ksAf43Gbi43hLf5I+hxwN6w7nr4Wn4fOpCfEd6nCN3iegn0SXqjST27oUPzGdChfQEaY3tfgQ7Gl4Xvnxm+f0PQoemHz7cE82d6/jN06P0QjXWEVegW9s0Iz8MH3Qp9WbhP6nFZ7MF3DvQnHiFaznoaHnqD+Y2hZ6pfZ2zyModfHwDBLxPxB77Ynp76VSKecYkec5uYw+XLH+IHrt4eVNeNYLUV4DG3iUT7jY73ba0W2tf0+kZsIw5rqR0JB7XZwNu3y3d3FQNvOIGgtAbgI844nXu3dqtnX39i9Uh/IuN02QOphJFaSaQO35j+xyu2XXntsWo4EiKKpugD85cKjEK3iLqgZxq+h5n/MdqnE3qW48+gWzpRoE9CH+SfBj11fxg6nIYBvAJ6/dy3oUOmCh0Et0OPi2XCn/8Qeqzts9Chshl6Qs2noEPqfdCB+1PoHbmj3Vx+w8xXENFy6NP/vDK8Txv0qX2+BB1az5pTbzRJZk94/5uI6BPQY5UW9BkblkB3ez41rPuj4Wt+cfjYS6DH5XrC1xiF+qrw+W3ojax/Aj2rlKGP12eF78VPoCfPXAIdbtFJbn8J4LnRGdaJ6PXQ22se98zdxR58JvQfP36s2wqxmD2FHnzwjeY3Jy9VOzYZxEfsytxhW7u+0N4+/ONUYkmZaDOOsEfhwDTvv3Z7sOfyHRzvncXZ9Nhztx3ymP1GzcQmEFmPd/u5An92JNBLKpzAH+8Ml1SkjnW/uJGa6I2v3D+QyBSXxFZYprJv3/CB70DJSQAAIABJREFUG959PM85FxE9B7rL8CrolhBDt6i6obvz+tHYNHpJuI4sCj4H+kDvQYeWASDHzM8JJ7ZY0BMydkGPAd4J3XpZFj7m/2/vzuOjrK7Hj39OEvbVBamCOlpAVFRQBK0KrpU6rVSr1bZW/bUudV+rw6uL1mo77l1c0Grr/tWqVZGx1bpWrVRRQEA2wQfZEWQLSyDJ+f1x7pghJCRA4EnynPfrlRc4mXnmzETm5N57zr0LsaSxCJtK7RK+ZmHv95dYQczAcO0WWBJ+AUuEe4V4W1F1SG2+CncUlhhPw0Zu+faB+8LrzJ+MMBdbJ7wBmzUbiiX7r2Ejz4XYVOVB4brLsWrNf2HFLbuE16DY+t8wLJmVhtcyg6rjjKZjCbA8xNse+4XgSOxIpnJsvbMz0Kb6jmEiMgzYTVUv2PAnWbNmnfgAUpncaPyUBucAKKKy4ltF74+9uOT51b3l8wNEak5c84uL5z/escO0ke3btV1UXLR/TQmruELXHTBDPzlmnC7pE2nX1uvoXVORTF7Yb3RK1X6j7XpjTeJ1Uq2s0IoFMyrWzVhQuS5SrVjcNbRU1DWiPOuqp0Y+Up/nKBTWry7FRl4dqKrszFdC5g+JBRvx3VAw1dkW+B72gd6FqqbsocBfwm0tscSRT3qLseOPdg7X2BMb2aWwBPdnrDq0JzYK+x+WLMCSTDGWKPMnnBceK9SdqmKYK7B1N7AT43uGkdyeWPHLNVjCXIKtET6LJchlWOLpgiXDS8J7sV3B+3AjVrCSPyZoEJbIb1DVG0VkJlUtER2wXx5+g1XAHhqec2fsF4cLsOOW7sdGiEuxX3yKVLVb+BndFGJcRj3P4ctLQuL7C/bbl3OuQGvKVp9e/MbYc0peKurGon4iNa/lLS+SZc+1bz/x6Y7tmWnFMTXu0dtppS4aPF6nDppQqd2tSGaj05W232jvKQu69v8yv99oflea+rCWilnTKtdOX1pZPqulVi7bFbR6S0Wfq54aObG+18wLrRbjsObxN4CJYcTWGyve2AGbhixX1dbVHns79pkzHRvB7ION+H4sIquwEdMNWIJ4Cks6i4F7VfUXInIJVsxxGrb2diyWKPIbOf9XVYcUPN9krMWhLZY4JlHVg1eETQ8ej1VvjgqxX4tNuf4aK9Y5F2tLWBqu05eqc/1uxtbUPsASXb/C/TBD07xgU5K3hVgfx9Yh2wHdwn6jd2C/TEzCioMuxZLaaKCDqrYXkXuxkfYeWB/iqPBzuCK89j+q6rXV3u9hQGtVvW6DH2QtkpD4zsOG8c65WnRmxZJzS3ITflj8esfOlH61XVp1a2Htv9u1Hf94xw6rJrZq2atSpObDnlW151ymHjumct5Bn2rnDqvZV2Cj05z12W+0LlpZGloqZqysLJ/XCl15+FVPjays+5HrC+0F92PJ4jtYItlbVVcWjDS6AWNV9cDCc/hE5HRsVJU/i24hcIuq3h22MavEPvznY0UfB2EFJ6diiaIkfP88rLjkx1hiXIVNS36pqieEOHfApiUn5Ru3ReTh8LhybBSlWHXmEGwU+VtsnfJHWOvBa1iiyp8qr8AMVe0hIkuomk79grD9mBYkjpDMFZuaXIn9otA5POY54IdhKvhb2Nl6q7BpztmqupeI/AzrU8yvbc4L7+9+2Ai5AhsdVwDvqep6x2yIyO7YLxZ9NvpDLXxMAhLffljvjXOuHmy7tOc+/U7xezu3kbW9arufgr7futUnj3TquGhUm9a7rhXZs7b7tl6rpYdM0k+O/rhyTY+57F5Sye51xVG43+jiHfZpU9Zq+5417Te6EW9dNPzoIzfh/l8RkbnY6Omz8Of9WK3AgeHPCButRKo6tNpjX8eSwDQs2XyMlffPx0Z4+dHRQCxBHgE8go3apmEjmwuxKdLJ2FTmjPDYc7A1t3uxJLIHlmSOUNX/iUgrbHqyN5Ys5wKHqOrOInIrVlnZBttP8wCsH7EIG/EJts63Loy+bqVqf9B/YkUl14d4DsVGi9tj07ErwnNeEZ6jc3iPRmMj35uxpvZe2Lrd0vBe7oUlt3wF8lvhNR6iqqNF5G1shLojllA/D1u+9VTVaeH9vgQYrKrVj0iqVRISXxH2Jte6CO+cq9l+MmPa5SXPzhlcNG697dJqMrVFi88e7dTh81fbtt2+tEj61FQck7fzYv38mLGV0aGTtfWOy9lHatgLtLqw3+i0+V37z1+8w34tNrbfaJC9aPjRw+q6bk1EZBb2ob4c+9BdgsX4AJbMOmIFGK1VtU9oXH9AVU8II5hbsRHPFGz3l45Yr1oO+yxaFq7dC5uKXItNP56Lrcktwqb6vo1tSdYDS7TTsbW0k7Dq0auw0dzPVfVeEbkQaynog01ZKjaN2ElEHqfqZASwEeFjWOXlgBDDGqAs7DDzzXD//bG1xdXYLwJXYTuplGPFLguBK1X1HyJySHgdQ7ER2lIs+d4X4l6JjWiXYiO7N7CWinyTe2+sheMOVX1GRG7Ddo7pTDiCSFWXiMizVJ0wMRM7EaPeZ7A2+8QHkMrkXsMqjZxzm0X1yKJx4y8t+cfSvvLpBtulVbewuHjh/3VsP2VE+3ZtFhYX74eNRGpUXKHr+s7QiceM1aV9ZmrXVnUUyRRa0a7b9AVd+89dtMP+xTXsN/qdi4YfPbKeL3A9IhJhH8xnq+oUEbkeq0zso6rTRGRXrLH93eojjdAkPh77fN1bRP4GjA5TnVlsDesMLJHeqqoHi0gPLKmlsFHSGqrOufsQK2T5J1Zc0gfr1/salky6AN1V9XgReRk7oWBk2B/1S+ATVT1ERL4brrMSSzCLsBHW8ap6Toi9DPiVqt4S/nsI1rP3PDY1OhDbT3MoVpzyoqr+odrr3xkbuXbA1irXYM3/+f03b8SmjtdgxS0nh682WNGOYNPLy7EG/4+BU1R113r86OolKYnvRmwh1zm3hUooX3dS8TtjLih+sXwPmdev+nZp1ZWKrHihQ7sJf+/QQWe0KNmXcJxRbTqV6hdHjtdpgyZUarfFdRfJFFrZtuvnC3Y6aOYXO/aVle27nXjR8KOX1PexhUTkM+yDeTdstDeaqmbrjthocCdgd1Wdk6/oLEggZ2Cjw2lY4jpPVdeGUxmmh2t+ER4zTkTuwqb4WmD7X/5UVd8JSfQzLAkuwwpGXgRGhTW4GdiI6K4w8pyAtSschY2geoW/j8cKSo7DElc+8d2NtUH8C6tE3QVLULep6vVio/bbsZHoUuDnWMI9BdtlZU24Pd9EPjZM9Q7G1inHYlWqo7ADY38f3sdeWAXqg9iI+Cgs4c3DqmQfFJFnwv0fxEahAzf151ibpCS+E7ApBudcA2rH6tKzil8Zd3bJy626sLRf9e3SqlsH615v13b84x3bl45r1apnpY0OaqeqveYw5dixlfMP/FS367CafeoqkgnG7T15Up279NdGRHZR1bkishO2e8gl2Id7vqJzBHCpqu5Qy+NTwMiaCi5E5E3galUdXcP39sZGOYNUdU3B7Z2xQpFLsNHSe6raI3xvV+AlVd1PRCZiI7jZ4XvTsWnMYcD7qvr3MHotVdXbwn1+im2PVopVcq5W1SsKnvsXWH/iyaE68+pw/4OxQpXXgF+q6msFjylV1fYF/11bXL2x9cyzsdHt29jWafsAJ6jqhWIns1+tqt+u6b3eHHWeW9RMvIn9ZuKN7M41oJW0aX9PxdDD7qkYyk4s+eKCkhGfnFL8nx07yOp9a7p/C2hx/MpVBx6/chUAo1u1mvRIpw4L/9umdbeyoqIeGzxARKZ2p/fU7sW9AdqU6YpDJuuYo8dVrvn6vI0Wyby+ua8pTHOuCFuLlWMJZyg2wmmPFW2MwA50renxxdgv2rsW3LYHVoyyPVYZWRJuPxtbD8yvT92Fjbj6iMipBc/xW+xzbAg2AuscRolnY6O4uaGwZXtglIjMwSo7O2FTjlcAFSLyGOHYHxFZg406L8PW/V6lajPpfNx/Bi4GDi+o5DwIS/7vYsUvLwEHisiDWJFLvgk+f40DsArYt0TkE6xisxM2DftDrE1iCVY48y6WZPsBJ4rICdjndkcReUxVC0+032yJGPGBn9Tg3Lb0dZkz8/KSZz87vmj07i2lfIOzAGvyWYuSmY927BC93K7tdsuLivrUte8nwC6LdeYxYytnHjpZW++wfpHMt/eePGmzZnlCo/UgVZ0pIu2wEd8OwDWq+kIYIV0G/EFV/1rD46/EpvqOyu8yEnZs+YeqPhkqRh9W1WEh8R2FTW2Wh9L897ACkvOx6cc12BZoZcDvwvrdq1gT/IFYgUq+cv0HWH/gm1hSmo01pX81+syP+LDT28dgI7FJYWpxAHBAKCA5KVx7EjYlO1pE9sEKW9ZhSWskNhV7JzZt2l9VFxWO+ETkA2yrtDbYumgaOzfv+yJyLdas/kGI5QzgdFX9qhJ/a4z4kpT4zsf2h3PObUMDZNInl5c8+8VA2y6t5r6/ahYXFS16smOHyc93aNdyfnHx/vXZ4aWkQtf2na6fHDdGF/eboUP3njxp5ebEKyKzsXWrCmxk9gRWqfgrbIqvBFvn6xKm/gorOrtj64H5bcTmAtdh5fznY7uq7ISNut7ENnD+Eba2tg6rUrwBq6LcDdsHsxhb4/u3qp4WRpTvYCOhA7D2hTOwUejvsBFaP6w9Il/5WFPiewtbQ2uFJfb2wJh8e4aILA0xtcFGpG9gFZRg26cNCzG+oKpnhJHyi1jF6S7htT+ArUvuhLVhHIwVxeyrdp7e6ViBS34z7dtU9dZqP48j8cS3eVKZXDfsh1WvajHnXMMqorJiSNEH4y4ueW7V3vL5/iIbHoZbk1UiK19s327CUx3bl3/aosU+Wncv3+vjzxq/WRtTw1eFLfnG7/tU9X6xk9FvDiO+K4HfqOoGLVIFBRkdCB/WIrIjoRgl3GdX4J+hGOXscP8vsErNK1R1VmgluA6bxmyLjZTuVtXbReQybOuuO6uNrCYAQ6qtow3EEtrEcP3l2Hrc22Lv43gsuc7G+gRbqup3RKRfuN/3Ctckw/TqKFV9LDzHg+G1PFPT+xbuU+N7F0bThQU3X607bm11TiU0F1E2PQf4KO44nEuqSoqKX6oceOAJa7OH9y57qOV16858b7bu+L4qazf2uLaq7U5bUTrwH3PmH/ZRNKvDnQu+GNt/9Zq3isMHfA1e2pz4RGQvERmLrbEVYZWH14nIT7Dk86jY9ly9YcOYxTa2XoKNvC4r+NZTwK4iMlFEhodra1jDy2AjoJ9gSeBhERmAnaqe38rsVWz6c/uQ3G4CzhKR5YRCH7Fm857Av0XkuVAMA2GfTqwBXrAE+4SIdFTVJVTtibkcW7ssF9u15k1gXxF5kfX3Qz0IuE1EpojI8eE2FZG/Ysm+JVaccpGIDArre22Ax8IIcseC9+5ubKT5vxBHft3zJhGZJSKlNf6gGkBiEl/wYtwBOOegjJatH64YcujhZX8a0Lfs/lV3lQ99e4m2H6vKRqegSqDk2FWr+/5t/sLBY6NZ3R+dO3/KcStXvdW6snJKwd02q3dPVaeoat8wJXgQlgD/jk1zDgvrdadi04vTa7jEYcDpWOHGN4GjQzHJSVjRxwFYz92Z2DTgBKxo5j/h8X8JzzsBWyvbGUt++VMb3g2xlGIN3e2AFiLyKbYO+RaWQKdiO8V0wqZsp6vqfuG1nRJiz+/I0wLrD5yBNdtPw44cUmwq9dDwNSIk6hRWgDME2/Wle3gtD4XXjKouxAqCBmBTnZeH0fGVIabpYj2G+S3a2oXnukZELsY+pwfU9nNqCElLfCPiDsA5t75ltO98W/lpR/Qru7/vEWv/OO+p8sFvrtaWU+vz2L5la/e6Y+GiwR/MnL1Xbtbc2acvX/H0+LPGT6n7kTUTkXYi0gE4BivaGIglrfzm1/kTCmqqF3gGq+a8GNt27HVVPUPt3Lg3sE2nW2KnFrygqpOwkVbeidiem6uwUdQOWEIoxvatfEVVc6r6NWwfz1HAKlXtoaqvYP14Z4Xbv0FVZWtxWBdEbFu5nsAMsYN8r8UayouwtoIHsF1itlPVFLZWWBZi64FVpp6KJbs5WDP9+1ivYln+PcSS4ARsjXFSiONVbD1zePj+c6raLTzPnVjhzl2qOkpV59X4A2ogiUp8UTY9hoJSXedc4zJbu+xybfn5R+5d9lCvdNlNn75a0e/Nci2q17/Z3crLu/9i8ZKaRmKboitWOPIkNrrJYZtE3yIi67Aeu39hFZGIyC4i8pJYBertWIN3bdd9GNvDcynWlA1VB60+Ef5+drj9G1himY41mp+hquUF1zsdK4wp9CA2dfgY1iSeCbfvAZSGqcN/YhWaX2ItEmuxhLknkFXVqVjCOjE89lSs+AWsJeFDbBT8CbaV2UOqWhFe39PYuX/vY5tG/ytc6yYRmYoV/bQJ710vLLm/LCIfUXXE0jaRqMQX+HSnc03ARN2jxznrfn5kj7JHu5299pqPP6rs8Xalypd1POzZLXlOVZ2BJaJKoK+q3oStP52jqi2wEUuvfE+bqs5VOynhQqyJfFa41JzCKkRVHYQVmbwC3K+qZeH2YVgp/w9V9ShVnRxuf1vtuKN9sR1bJuevJSItscT0dGGTeGh4Hxueo0d4LfOwY4HaYC0WbbF+ur7hPkdgxSWfquqT4VI/wdboPiTsKxqa7SU8z02q+nXsvf6o4H07AZtW3Te8b/lr7Y6Nmu/GClgUW887PLyfh4f3Zkw9f0xbLDFVnXmpTO5bbObit3MuXiWUr/tu8btjLigeUb6nzOsrQuH5fZ9x/bJaT4jYmHzTOtbC0B5b87oGm5Y7GPtQvhBLUsu04BRwEbkFm3psh23kvBab0lyINYRXYHtaZkTkLGxUt2O4vTTc90JsZDcJW2sDq578mYi8hU135h8zGdt4+puh7++v2PRrC2wnlSPCdGn+XMFJ2LTixaFCczq27tgZO3apBGs3mIit7e2P9dI9IyK9sBFhWbgPWJP86dgvBNdjG2mfig2k2qrqLgXvzcHY1OtpWK9h/lqdsZaQtuFa+2E9iftho8Yu2MkXdW5evjmSOOJ7HfufzTnXxJRT0uKZisEDjll7+zf6lD1YefO6095doJ1Hq1KBVU9uiaPUzrQbjU0j3oL1mE3FpvFuwTa7n5Z/gIh8A5umy6/HTcTWyZ7A9rfsj1U5HiYiaWxUlMsXm4RrFu5Yk09K/UPS2x2bFrxVVXtja26DsClEsINfH8GSdBtsxLWq4Hq3EYpnCtb3nsJGkqvC+trh4TWehCXlf4T7FwG/DM/dFxsZfokl1ynhWntjO9T0xg7MXVbw3hRjxy69jI0WC6/VD2svy295Nhj7bD6Wql7BrSZxiS/Kpsvw6U7nmryVtGl/b8XQwwaW3dP/4LJ7vry3/DuPbOk1xU6APw778FdsVHIu1nx+ANYgfl64b39sHa01NmprhY26lmEns7+BjQKfxRLE/dgo8A/h8Sdh612dsbXEh0MYhwPjQmvFc8AFqvpC+F4JVdumge1p+RpWaVkMfE9ExorIcBE5KHx/EDaqegZb33ulpuIRVY3Cjim7Y3uSTsaKWP4Wvj8RS/otsNMaLsI2oL4BO8j2PaCHiMwOu9tcgp1BeBjWuF94rfzG30XhcR+p6p9VNcKa2T8G2oZrXV/Lj2uzJWWvzurux7b2cc41A4voPOuCGx+bVPc9a6XY2phirQvLRORyqkYrRdia31ejkbDudaLYmXHzwv3uUtXfF9xngYgch62FHRvWwhCRi7Dy/lXYoavTxDa2nhhuX0RoNK8WZ8vwXPmWjXHA99ROajgZS7LHYP2Er2Ofc8dgI8iLa3zhlmwKN9OehI3Mnqnh7p2AH6jqyPA6HsOmMXti64uXhtfSDRv1Ho0V3Yys4Xp7YEU71dtPloR4F9UUb0NI3IgPIMqm36Rgsdg51+Q9XPddNuowVT2QguZrbA3rCrVz4K6gqhLzK2Ln6O2NtTt0w3r3BhV8vwSbNv1TPukBqOrdoUDkWmwKECyh7aaq/bDk90RYo9vYta4GBovIGGy6cA62rli92GaLiZ2ksR/2y0BeK2CNqvbH+hDze5f+Abg2VHzW91rbTCITX3Bf3AE45xrEOmx0sdlUdW74s7D5+izCehe2xldTU/VJWBFKqarm2wUOKfj+/cA0rXZYa4EnsYNpUdUyVV0c/v4h6zea13itUFV6ckiWvwi3LcOazi8ORTu3AWeKHYK7Jb6PFcmsK7htNlWVtM9hhTFga5tPhuc/BbhH7CDcjV1rm0ly4nsYq8ByzjVtuSib3uxpsYKm9erN13OxURRUK2op8Dk24ioRkRZUHcCKiNyITQ1eXu35ehb8Zzp/XRHpUlOjeR3X2lGqTrEYRhhxqeqPVHW3ULxyNfCIqmbYMj9gw97B57H3Buy1Tw3Pv4eqpsLzPwNcqKrP13GtbSaxiS/Kppew5VVgzrn4bek0Z1fgHREZx/rN1+cCt4fb1ytqEZEHwmOfwUZm47H1tnGq+qLYKQ2/wIpLPgoFJ+eEx1wc9u0ci01pnhVuHwR8HJ4vX4jyZR3XOhKYEhrEu2L7eG6UiNwidgLFesUjInJwuP1U4D6xw2Pzj0lh1ZtvVbtcFiuoGY9ttn0OdajtWiJyaXj+7uF9eGDDRzeMxPXxFUplcgOxHhPnXNM0E+gRZdPldd7TuSCxIz6AKJv+H9twtwDnXIO7w5Oe21SJTnyBF7k41zQtxprFndsknvis8XJF3EE45zbZ3VE2varuuzm3vsQnviibLsWSn3Ou6VgN/DnuIFzTlPjEF9wFGz8A0znXqPx1S1oYXLJ54gOibHoidsaUc67xq8DOvnNus3jiq3Id9g/KOde4PR1l05/FHYRrujzxBVE2PQU73sM517jdHHcArmnzxLe+32CHSDrnGqd/R9n02LiDcE2bJ74CUTY9E9th3DnXOPloz20xT3wbugnfvNq5xuj1KJt+Le4gXNPnia+aKJueh7U3OOcajwrgsriDcM2DJ76a3QwsjzsI59xXhkfZ9IS4g3DNgye+GkTZ9GLgzrjjcM4B8CXw67iDcM2HJ77a3YH9g3POxevXUTbt/xZdg/HEV4som14OXB93HM4l3HhgeNxBuObFE9/G3Q18GHcQziXYZVE27TsquQbliW8jomy6Ejgf38rMuTg8G2XTb8QdhGt+PPHVIcqmP8TbG5zb1tYAV8cdhGuePPHVzy+B2XEH4VyC3BZl01HcQbjmyRNfPYTDai+KOw7nEuIz4PdxB+GaL0989RRl0yOAJ+KOw7lmrgI4I8qmV8UdiGu+PPFtmkuABXEH4VwzdmOUTf837iBc8+aJbxOEJtqfxR2Hc83Ue8Bv4w7CNX+iqnHH0OSkMrkngB/EHYdzzchyoK+frO62BR/xbZ5L8CpP5xrSxZ703LbiiW8zhE2sT8FPa3euIfxflE0/GncQLjk88W2mKJv+H3Bp3HE418TNBC6IOwiXLJ74tkCUTd8H/C3uOJxrovKtC8viDsQliye+LXchvpG1c5vj91E2/U7cQbjk8cS3haJseg3wPWBx3LE414T8B/hN3EG4ZPLE1wCibHom8EOgMu5YnGsCZgAnR9l0edyBuGTyxNdAomz6FeDXccfhXCO3HPh2qIx2Lhae+BrW74ARcQfhXCNVAXw/yqYnxR2ISzZPfA0oyqYVOBOYFncszjVCV0bZ9MtxB+GcJ74GFkqzvwXMizsW5xqRP0bZ9J/iDsI58MS3VUTZ9HTgOLzS0zmAp4Er4w7CuTzfpHorSmVyBwGvAx3jjsW5mPwH+GaUTZfFHYhzeZ74trJUJnc48DLQNu5YnNvGJgKHR9n00rgDca6QT3VuZWFnipPwDa1dskTAEE96rjHyxLcNhB6/07Fybueau2nAoCib9qO7XKPkiW8bibLp54D/B/jcsmvOJmJJb1bcgThXG09821A4c+yiuONwbisZAxwZZdPz4w7EuY3xxLeNRdn0vcA1ccfhXAMbBRwdZdOL4g7Eubp4VWdMUpncT4HhQEncsTi3hd7C9t8sjTsQ5+rDE1+MUpncEKy5t33csTi3mV4Bvhtl06vjDsS5+vLEF7NUJtcPyAE7xx2Lc5toBLbptDenuybF1/hiFmXTY4BDgE/ijsW5TfB34BRPeq4p8sTXCETZ9OfAYcCbMYfiXF0U+C3wgyibXhd3MM5tDp/qbERSmVxL4G/Yae7ONTYrgDOjbPr5uANxbkv4iK8RibLptcAZQDbuWJyrZgowwJOeaw58xNdIpTK584E/Ay3ijsUl3gjgx1E2vTzuQJxrCD7ia6SibPo+YBDwedyxuMRS4DqsXcGTnms2fMTXyKUyue2Ah4ATYw7FJcsy4Iwomx4ZdyDONTRPfE1EKpO7Elv786lPt7VNwkZ5U+MOxLmtwRNfE5LK5AYAjwM94o7FNVuPARdG2fSKuANxbmvxxNfEpDK5dsAfgHPijsU1K3OAn/nUpksCT3xNVCqTGwr8BegSdyyuyXsAuDrKppfFHYhz24InviYslcl1xT60vh13LK5JioBzo2z61bgDcW5b8sTXDKQyuZOBO4Dd447FNQkK3AUMi7LplXEH49y25omvmUhlcm2ADHbIbeuYw3GN11Tgp1E2/U7cgTgXF098zUwqk9sDG/19N+5YXKNSAdwOXBdl02viDsa5OHnia6ZSmdzxwB+BveKOxcXuFeCaKJseF3cgzjUGnviasVQm1wK4HPg1fsp7Eo3DEt4rcQfiXGPiiS8BUpncLsCt+HFHSTEL+BXwaJRNV8YdjHONjSe+BEllcv2wAphT8A3Km6MF2LZ2w30dz7naeeJLoFQm1xOr/jwTaBlzOG7LLQJuAe6OsulVcQfjXGPniS/BUplcN+BK4Dx8DbApWgTcCfwpyqZL4w7GuabCE58jlcltD1wCXApsH3M4rm6jgLuBp6NsuizuYJxrajzxua/7rqQEAAACoElEQVSEDbDPA64CusUcjlvfauAJ4J4om/4o7mCca8o88bkNpDK5llgD/I+BIUBJvBEl2jTgXuChKJteEncwzjUHnvjcRqUyuZ2A07Ek2D/mcJKiEhiJTWf+O8qm/R+pcw3IE5+rt1Qm1xtLgD/CN8TeGsYAzwMPR9n0zLiDca658sTnNlkqkxNgEJYETwE6xRtRk1UO/Ad4AXjBk51z24YnPrdFUplcayANfAs4Hugeb0SNXinwMjayy/m6nXPbnic+16BSmdw+WAI8HhsVtok3okZhAfAiluxe811VnIuXJz631YTq0AHA4PD1DaBdrEFtfWuxzaHfL/ia4gUqzjUenvjcNpPK5EqAg4BDgH2BfcLXdnHGtQUUazcoTHJjvancucbNE5+LXSqT+xpVSbDwq0uccRVQYC7wGTADO8X8A+ADX6NzrunxxOcarVQmtyOWAPfAtlLb2FcnQDbh8uXYtORaYCUwH0tuc4F54c/ZWLKLfF3OuebDE59rFlKZXBE2Zbo90BZYR1ViK/xaB6z1NTfnkssTn3POuUTxw0idc84liic+55xzieKJzznnXKJ44nPOOZconvicc84liic+55xzieKJzznnXKJ44nPOOZconvicc84liic+55xzieKJzznnXKJ44nPOOZconvicc84liic+55xzieKJzznnXKJ44nPOOZconvicc84liic+55xzieKJzznnXKJ44nPOOZconvicc84liic+55xzieKJzznnXKJ44nPOOZconvicc84liic+55xzieKJzznnXKJ44nPOOZconvicc84liic+55xzieKJzznnXKJ44nPOOZconvicc84liic+55xzieKJzznnXKJ44nPOOZconvicc84liic+55xzifL/Ae/Dkw9OCtK8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['P008'].value_counts().plot(kind='pie', figsize=(6,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'No. P008')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = data['P008'].value_counts().plot(kind='bar', figsize=(14,6))\n",
    "ax.set_ylabel('No. P008')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Relationship between the columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>P001</th>\n",
       "      <th>P002</th>\n",
       "      <th>P003</th>\n",
       "      <th>P004</th>\n",
       "      <th>P005</th>\n",
       "      <th>P006</th>\n",
       "      <th>P007</th>\n",
       "      <th>P008</th>\n",
       "      <th>P009</th>\n",
       "      <th>P010</th>\n",
       "      <th>...</th>\n",
       "      <th>OTH7</th>\n",
       "      <th>OTH8</th>\n",
       "      <th>OTH9</th>\n",
       "      <th>비중</th>\n",
       "      <th>굴곡탄성률</th>\n",
       "      <th>굴곡강도</th>\n",
       "      <th>HDT</th>\n",
       "      <th>IZOD 충격강도</th>\n",
       "      <th>MI</th>\n",
       "      <th>인장강도</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>P001</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.010426</td>\n",
       "      <td>-0.011747</td>\n",
       "      <td>0.165040</td>\n",
       "      <td>-0.002419</td>\n",
       "      <td>-0.015579</td>\n",
       "      <td>-0.002419</td>\n",
       "      <td>-0.015714</td>\n",
       "      <td>-0.006620</td>\n",
       "      <td>-0.003229</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.026908</td>\n",
       "      <td>-0.023647</td>\n",
       "      <td>-0.003643</td>\n",
       "      <td>0.037091</td>\n",
       "      <td>0.073561</td>\n",
       "      <td>0.127760</td>\n",
       "      <td>0.093475</td>\n",
       "      <td>-0.033124</td>\n",
       "      <td>-0.075822</td>\n",
       "      <td>0.123250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P002</th>\n",
       "      <td>-0.010426</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.013780</td>\n",
       "      <td>0.013508</td>\n",
       "      <td>-0.005322</td>\n",
       "      <td>-0.034274</td>\n",
       "      <td>-0.005322</td>\n",
       "      <td>-0.034572</td>\n",
       "      <td>-0.014564</td>\n",
       "      <td>-0.007104</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.036558</td>\n",
       "      <td>-0.040541</td>\n",
       "      <td>-0.008014</td>\n",
       "      <td>0.085601</td>\n",
       "      <td>0.120227</td>\n",
       "      <td>0.191974</td>\n",
       "      <td>0.141578</td>\n",
       "      <td>-0.082996</td>\n",
       "      <td>-0.146186</td>\n",
       "      <td>0.194986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P003</th>\n",
       "      <td>-0.011747</td>\n",
       "      <td>0.013780</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.011306</td>\n",
       "      <td>-0.005996</td>\n",
       "      <td>-0.038615</td>\n",
       "      <td>-0.005996</td>\n",
       "      <td>0.009051</td>\n",
       "      <td>-0.016409</td>\n",
       "      <td>-0.008003</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.066697</td>\n",
       "      <td>-0.057061</td>\n",
       "      <td>-0.009029</td>\n",
       "      <td>0.170422</td>\n",
       "      <td>0.222353</td>\n",
       "      <td>0.238930</td>\n",
       "      <td>0.198083</td>\n",
       "      <td>-0.066535</td>\n",
       "      <td>-0.066942</td>\n",
       "      <td>0.210425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P004</th>\n",
       "      <td>0.165040</td>\n",
       "      <td>0.013508</td>\n",
       "      <td>0.011306</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.003236</td>\n",
       "      <td>-0.020841</td>\n",
       "      <td>-0.003236</td>\n",
       "      <td>-0.021022</td>\n",
       "      <td>-0.008856</td>\n",
       "      <td>-0.004319</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.035996</td>\n",
       "      <td>-0.031634</td>\n",
       "      <td>-0.004873</td>\n",
       "      <td>0.072198</td>\n",
       "      <td>0.089387</td>\n",
       "      <td>0.136393</td>\n",
       "      <td>0.118879</td>\n",
       "      <td>-0.027076</td>\n",
       "      <td>-0.086768</td>\n",
       "      <td>0.139209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P005</th>\n",
       "      <td>-0.002419</td>\n",
       "      <td>-0.005322</td>\n",
       "      <td>-0.005996</td>\n",
       "      <td>-0.003236</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.007951</td>\n",
       "      <td>-0.001235</td>\n",
       "      <td>-0.008020</td>\n",
       "      <td>-0.003379</td>\n",
       "      <td>-0.001648</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.013734</td>\n",
       "      <td>-0.012070</td>\n",
       "      <td>-0.001859</td>\n",
       "      <td>-0.047575</td>\n",
       "      <td>-0.022106</td>\n",
       "      <td>-0.010202</td>\n",
       "      <td>-0.037335</td>\n",
       "      <td>-0.022735</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.006824</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",
       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>굴곡강도</th>\n",
       "      <td>0.127760</td>\n",
       "      <td>0.191974</td>\n",
       "      <td>0.238930</td>\n",
       "      <td>0.136393</td>\n",
       "      <td>-0.010202</td>\n",
       "      <td>-0.055041</td>\n",
       "      <td>-0.007215</td>\n",
       "      <td>-0.062688</td>\n",
       "      <td>-0.029837</td>\n",
       "      <td>-0.009562</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.139990</td>\n",
       "      <td>-0.094476</td>\n",
       "      <td>-0.056622</td>\n",
       "      <td>0.728241</td>\n",
       "      <td>0.924009</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.722539</td>\n",
       "      <td>-0.170515</td>\n",
       "      <td>-0.215067</td>\n",
       "      <td>0.969047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HDT</th>\n",
       "      <td>0.093475</td>\n",
       "      <td>0.141578</td>\n",
       "      <td>0.198083</td>\n",
       "      <td>0.118879</td>\n",
       "      <td>-0.037335</td>\n",
       "      <td>-0.100120</td>\n",
       "      <td>-0.031139</td>\n",
       "      <td>-0.048556</td>\n",
       "      <td>-0.050007</td>\n",
       "      <td>-0.009748</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.152850</td>\n",
       "      <td>-0.045325</td>\n",
       "      <td>-0.119149</td>\n",
       "      <td>0.694633</td>\n",
       "      <td>0.736481</td>\n",
       "      <td>0.722539</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.302358</td>\n",
       "      <td>0.001357</td>\n",
       "      <td>0.677490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IZOD 충격강도</th>\n",
       "      <td>-0.033124</td>\n",
       "      <td>-0.082996</td>\n",
       "      <td>-0.066535</td>\n",
       "      <td>-0.027076</td>\n",
       "      <td>-0.022735</td>\n",
       "      <td>-0.076160</td>\n",
       "      <td>-0.023207</td>\n",
       "      <td>-0.057161</td>\n",
       "      <td>-0.031467</td>\n",
       "      <td>-0.026515</td>\n",
       "      <td>...</td>\n",
       "      <td>0.072659</td>\n",
       "      <td>-0.065952</td>\n",
       "      <td>-0.036192</td>\n",
       "      <td>-0.189979</td>\n",
       "      <td>-0.170063</td>\n",
       "      <td>-0.170515</td>\n",
       "      <td>-0.302358</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.259878</td>\n",
       "      <td>-0.116820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MI</th>\n",
       "      <td>-0.075822</td>\n",
       "      <td>-0.146186</td>\n",
       "      <td>-0.066942</td>\n",
       "      <td>-0.086768</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.191621</td>\n",
       "      <td>-0.046382</td>\n",
       "      <td>-0.002142</td>\n",
       "      <td>0.009246</td>\n",
       "      <td>0.019292</td>\n",
       "      <td>...</td>\n",
       "      <td>0.225019</td>\n",
       "      <td>0.181671</td>\n",
       "      <td>-0.121594</td>\n",
       "      <td>-0.119424</td>\n",
       "      <td>-0.210158</td>\n",
       "      <td>-0.215067</td>\n",
       "      <td>0.001357</td>\n",
       "      <td>-0.259878</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.269468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>인장강도</th>\n",
       "      <td>0.123250</td>\n",
       "      <td>0.194986</td>\n",
       "      <td>0.210425</td>\n",
       "      <td>0.139209</td>\n",
       "      <td>-0.006824</td>\n",
       "      <td>-0.050016</td>\n",
       "      <td>-0.005084</td>\n",
       "      <td>-0.086985</td>\n",
       "      <td>-0.029384</td>\n",
       "      <td>-0.011174</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.124182</td>\n",
       "      <td>-0.092936</td>\n",
       "      <td>-0.052665</td>\n",
       "      <td>0.689369</td>\n",
       "      <td>0.895452</td>\n",
       "      <td>0.969047</td>\n",
       "      <td>0.677490</td>\n",
       "      <td>-0.116820</td>\n",
       "      <td>-0.269468</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>97 rows × 97 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               P001      P002      P003      P004      P005      P006  \\\n",
       "P001       1.000000 -0.010426 -0.011747  0.165040 -0.002419 -0.015579   \n",
       "P002      -0.010426  1.000000  0.013780  0.013508 -0.005322 -0.034274   \n",
       "P003      -0.011747  0.013780  1.000000  0.011306 -0.005996 -0.038615   \n",
       "P004       0.165040  0.013508  0.011306  1.000000 -0.003236 -0.020841   \n",
       "P005      -0.002419 -0.005322 -0.005996 -0.003236  1.000000 -0.007951   \n",
       "...             ...       ...       ...       ...       ...       ...   \n",
       "굴곡강도       0.127760  0.191974  0.238930  0.136393 -0.010202 -0.055041   \n",
       "HDT        0.093475  0.141578  0.198083  0.118879 -0.037335 -0.100120   \n",
       "IZOD 충격강도 -0.033124 -0.082996 -0.066535 -0.027076 -0.022735 -0.076160   \n",
       "MI        -0.075822 -0.146186 -0.066942 -0.086768       NaN -0.191621   \n",
       "인장강도       0.123250  0.194986  0.210425  0.139209 -0.006824 -0.050016   \n",
       "\n",
       "               P007      P008      P009      P010  ...      OTH7      OTH8  \\\n",
       "P001      -0.002419 -0.015714 -0.006620 -0.003229  ... -0.026908 -0.023647   \n",
       "P002      -0.005322 -0.034572 -0.014564 -0.007104  ... -0.036558 -0.040541   \n",
       "P003      -0.005996  0.009051 -0.016409 -0.008003  ... -0.066697 -0.057061   \n",
       "P004      -0.003236 -0.021022 -0.008856 -0.004319  ... -0.035996 -0.031634   \n",
       "P005      -0.001235 -0.008020 -0.003379 -0.001648  ... -0.013734 -0.012070   \n",
       "...             ...       ...       ...       ...  ...       ...       ...   \n",
       "굴곡강도      -0.007215 -0.062688 -0.029837 -0.009562  ... -0.139990 -0.094476   \n",
       "HDT       -0.031139 -0.048556 -0.050007 -0.009748  ... -0.152850 -0.045325   \n",
       "IZOD 충격강도 -0.023207 -0.057161 -0.031467 -0.026515  ...  0.072659 -0.065952   \n",
       "MI        -0.046382 -0.002142  0.009246  0.019292  ...  0.225019  0.181671   \n",
       "인장강도      -0.005084 -0.086985 -0.029384 -0.011174  ... -0.124182 -0.092936   \n",
       "\n",
       "               OTH9        비중     굴곡탄성률      굴곡강도       HDT  IZOD 충격강도  \\\n",
       "P001      -0.003643  0.037091  0.073561  0.127760  0.093475  -0.033124   \n",
       "P002      -0.008014  0.085601  0.120227  0.191974  0.141578  -0.082996   \n",
       "P003      -0.009029  0.170422  0.222353  0.238930  0.198083  -0.066535   \n",
       "P004      -0.004873  0.072198  0.089387  0.136393  0.118879  -0.027076   \n",
       "P005      -0.001859 -0.047575 -0.022106 -0.010202 -0.037335  -0.022735   \n",
       "...             ...       ...       ...       ...       ...        ...   \n",
       "굴곡강도      -0.056622  0.728241  0.924009  1.000000  0.722539  -0.170515   \n",
       "HDT       -0.119149  0.694633  0.736481  0.722539  1.000000  -0.302358   \n",
       "IZOD 충격강도 -0.036192 -0.189979 -0.170063 -0.170515 -0.302358   1.000000   \n",
       "MI        -0.121594 -0.119424 -0.210158 -0.215067  0.001357  -0.259878   \n",
       "인장강도      -0.052665  0.689369  0.895452  0.969047  0.677490  -0.116820   \n",
       "\n",
       "                 MI      인장강도  \n",
       "P001      -0.075822  0.123250  \n",
       "P002      -0.146186  0.194986  \n",
       "P003      -0.066942  0.210425  \n",
       "P004      -0.086768  0.139209  \n",
       "P005            NaN -0.006824  \n",
       "...             ...       ...  \n",
       "굴곡강도      -0.215067  0.969047  \n",
       "HDT        0.001357  0.677490  \n",
       "IZOD 충격강도 -0.259878 -0.116820  \n",
       "MI         1.000000 -0.269468  \n",
       "인장강도      -0.269468  1.000000  \n",
       "\n",
       "[97 rows x 97 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr = data.corr()\n",
    "\n",
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16,16))\n",
    "plt.matshow(corr, cmap='RdBu', fignum=fig.number)\n",
    "plt.xticks(range(len(corr.columns)), corr.columns, rotation='vertical');\n",
    "plt.yticks(range(len(corr.columns)), corr.columns);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19ab9e41f60>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "colormap=plt.cm.PuBu\n",
    "f,ax=plt.subplots(figsize=(12,12))\n",
    "sns.heatmap(corr, vmax=8, square=True, linewidth=0.1, linecolor='white', cmap=colormap)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19ab9dcc7f0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(kind='scatter', x='P008', y='굴곡탄성률', figsize=(6,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19ab9ab7198>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(kind='scatter', x='P008', y='HDT', figsize=(6,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'HDT')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = data[['P008','HDT']].boxplot(by='P008', figsize=(10,6))\n",
    "ax.set_ylabel('HDT')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "P008       AxesSubplot(0.125,0.125;0.227941x0.755)\n",
       "P018    AxesSubplot(0.398529,0.125;0.227941x0.755)\n",
       "P028    AxesSubplot(0.672059,0.125;0.227941x0.755)\n",
       "dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "boxplot_cols = ['P008', 'P018', 'P028']\n",
    "data[boxplot_cols].plot(kind='box', subplots=True, layout=(1,3), figsize=(14,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>P001</th>\n",
       "      <th>P002</th>\n",
       "      <th>P003</th>\n",
       "      <th>P004</th>\n",
       "      <th>P005</th>\n",
       "      <th>P006</th>\n",
       "      <th>P007</th>\n",
       "      <th>P008</th>\n",
       "      <th>P009</th>\n",
       "      <th>P010</th>\n",
       "      <th>...</th>\n",
       "      <th>OTH7</th>\n",
       "      <th>OTH8</th>\n",
       "      <th>OTH9</th>\n",
       "      <th>비중</th>\n",
       "      <th>굴곡탄성률</th>\n",
       "      <th>굴곡강도</th>\n",
       "      <th>HDT</th>\n",
       "      <th>IZOD 충격강도</th>\n",
       "      <th>MI</th>\n",
       "      <th>인장강도</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.897</td>\n",
       "      <td>1256.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>79.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.03</td>\n",
       "      <td>26.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.894</td>\n",
       "      <td>1046.0</td>\n",
       "      <td>31.4</td>\n",
       "      <td>71.4</td>\n",
       "      <td>4.5</td>\n",
       "      <td>8.21</td>\n",
       "      <td>25.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 97 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     P001  P002  P003  P004  P005   P006  P007  P008  P009  P010  ...  OTH7  \\\n",
       "211   0.0   0.0   0.0   0.0   0.0  100.0     0   0.0   0.0   0.0  ...   0.0   \n",
       "395   0.0   0.0   0.0   0.0   0.0  100.0     0   0.0   0.0   0.0  ...   0.0   \n",
       "\n",
       "     OTH8  OTH9     비중   굴곡탄성률  굴곡강도   HDT  IZOD 충격강도    MI  인장강도  \n",
       "211   0.0   0.0  0.897  1256.0  34.0  79.8        NaN  8.03  26.8  \n",
       "395   0.0   0.0  0.894  1046.0  31.4  71.4        4.5  8.21  25.7  \n",
       "\n",
       "[2 rows x 97 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_P006_skip0dd = data.loc[data['P006'] == 100]\n",
    "data_P006_skip0dd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4      39.215686\n",
       "15     39.525692\n",
       "16     19.762846\n",
       "43     49.751244\n",
       "46     50.000000\n",
       "         ...    \n",
       "650    29.411765\n",
       "668     4.897160\n",
       "684     4.901961\n",
       "729     4.897160\n",
       "768    29.411765\n",
       "Name: F004, Length: 81, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_P006_skip0 = data.loc[data['F004'] != 0]\n",
    "data_P006_skip0['F004']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19abb4defd0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_P006_skip0.plot(kind='scatter', x='F004', y='굴곡탄성률', figsize=(14,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19abb8ba4a8>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_P006_skip0.plot(kind='scatter', x='P006', y='비중', figsize=(14,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19abb705518>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_P006_skip0.plot(kind='scatter', x='P006', y='HDT', figsize=(14,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>P001</th>\n",
       "      <th>P002</th>\n",
       "      <th>P003</th>\n",
       "      <th>P004</th>\n",
       "      <th>P005</th>\n",
       "      <th>P006</th>\n",
       "      <th>P007</th>\n",
       "      <th>P008</th>\n",
       "      <th>P009</th>\n",
       "      <th>P010</th>\n",
       "      <th>...</th>\n",
       "      <th>OTH7</th>\n",
       "      <th>OTH8</th>\n",
       "      <th>OTH9</th>\n",
       "      <th>비중</th>\n",
       "      <th>굴곡탄성률</th>\n",
       "      <th>굴곡강도</th>\n",
       "      <th>HDT</th>\n",
       "      <th>IZOD 충격강도</th>\n",
       "      <th>MI</th>\n",
       "      <th>인장강도</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>71.928072</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.076</td>\n",
       "      <td>4864.0</td>\n",
       "      <td>103.3</td>\n",
       "      <td>138.4</td>\n",
       "      <td>5.3</td>\n",
       "      <td>3.2</td>\n",
       "      <td>73.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>49.950050</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.978022</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.073</td>\n",
       "      <td>4810.0</td>\n",
       "      <td>103.7</td>\n",
       "      <td>137.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>73.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>59.940060</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.988012</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.073</td>\n",
       "      <td>4760.0</td>\n",
       "      <td>103.7</td>\n",
       "      <td>137.8</td>\n",
       "      <td>9.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>73.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>39.960040</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.968032</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.076</td>\n",
       "      <td>4844.0</td>\n",
       "      <td>104.4</td>\n",
       "      <td>139.1</td>\n",
       "      <td>9.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>73.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 97 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          P001  P002  P003       P004  P005  P006  P007  P008  P009  P010  \\\n",
       "403  71.928072   0.0   0.0   0.000000   0.0   0.0     0   0.0   0.0   0.0   \n",
       "404  49.950050   0.0   0.0  21.978022   0.0   0.0     0   0.0   0.0   0.0   \n",
       "405  59.940060   0.0   0.0  11.988012   0.0   0.0     0   0.0   0.0   0.0   \n",
       "406  39.960040   0.0   0.0  31.968032   0.0   0.0     0   0.0   0.0   0.0   \n",
       "\n",
       "     ...  OTH7  OTH8  OTH9     비중   굴곡탄성률   굴곡강도    HDT  IZOD 충격강도   MI  인장강도  \n",
       "403  ...   0.0   0.0   0.0  1.076  4864.0  103.3  138.4        5.3  3.2  73.5  \n",
       "404  ...   0.0   0.0   0.0  1.073  4810.0  103.7  137.0        9.4  NaN  73.6  \n",
       "405  ...   0.0   0.0   0.0  1.073  4760.0  103.7  137.8        9.2  NaN  73.2  \n",
       "406  ...   0.0   0.0   0.0  1.076  4844.0  104.4  139.1        9.7  NaN  73.2  \n",
       "\n",
       "[4 rows x 97 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_P001_skip0 = data.loc[data['P001'] != 0]\n",
    "data_P001_skip0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>P001</th>\n",
       "      <th>P002</th>\n",
       "      <th>P003</th>\n",
       "      <th>P004</th>\n",
       "      <th>P005</th>\n",
       "      <th>P006</th>\n",
       "      <th>P007</th>\n",
       "      <th>P008</th>\n",
       "      <th>P009</th>\n",
       "      <th>P010</th>\n",
       "      <th>...</th>\n",
       "      <th>OTH7</th>\n",
       "      <th>OTH8</th>\n",
       "      <th>OTH9</th>\n",
       "      <th>비중</th>\n",
       "      <th>굴곡탄성률</th>\n",
       "      <th>굴곡강도</th>\n",
       "      <th>HDT</th>\n",
       "      <th>IZOD 충격강도</th>\n",
       "      <th>MI</th>\n",
       "      <th>인장강도</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>0.0</td>\n",
       "      <td>68.627451</td>\n",
       "      <td>4.901961</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.040</td>\n",
       "      <td>2653.0</td>\n",
       "      <td>48.9</td>\n",
       "      <td>118.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>13.70</td>\n",
       "      <td>33.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>0.0</td>\n",
       "      <td>67.961165</td>\n",
       "      <td>9.708738</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.021</td>\n",
       "      <td>2580.0</td>\n",
       "      <td>51.2</td>\n",
       "      <td>119.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.00</td>\n",
       "      <td>36.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>0.0</td>\n",
       "      <td>14.084507</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.469484</td>\n",
       "      <td>2.816901</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.024</td>\n",
       "      <td>2154.0</td>\n",
       "      <td>33.7</td>\n",
       "      <td>106.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>0.0</td>\n",
       "      <td>13.145540</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.469484</td>\n",
       "      <td>2.816901</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.988</td>\n",
       "      <td>1787.0</td>\n",
       "      <td>30.9</td>\n",
       "      <td>93.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>0.0</td>\n",
       "      <td>76.470588</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.035</td>\n",
       "      <td>2944.0</td>\n",
       "      <td>53.2</td>\n",
       "      <td>118.6</td>\n",
       "      <td>1.9</td>\n",
       "      <td>13.15</td>\n",
       "      <td>36.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>0.0</td>\n",
       "      <td>67.961165</td>\n",
       "      <td>7.766990</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.047</td>\n",
       "      <td>3057.0</td>\n",
       "      <td>53.6</td>\n",
       "      <td>123.2</td>\n",
       "      <td>3.3</td>\n",
       "      <td>13.96</td>\n",
       "      <td>35.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>0.0</td>\n",
       "      <td>67.961165</td>\n",
       "      <td>9.708738</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.041</td>\n",
       "      <td>2943.0</td>\n",
       "      <td>53.2</td>\n",
       "      <td>68.1</td>\n",
       "      <td>2.2</td>\n",
       "      <td>14.32</td>\n",
       "      <td>35.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>0.0</td>\n",
       "      <td>98.039216</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>0.0</td>\n",
       "      <td>55.882353</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.223</td>\n",
       "      <td>8552.0</td>\n",
       "      <td>161.9</td>\n",
       "      <td>152.5</td>\n",
       "      <td>13.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>114.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>0.0</td>\n",
       "      <td>75.490196</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.036</td>\n",
       "      <td>4185.0</td>\n",
       "      <td>108.3</td>\n",
       "      <td>140.5</td>\n",
       "      <td>10.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>0.0</td>\n",
       "      <td>49.019608</td>\n",
       "      <td>22.549020</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.073</td>\n",
       "      <td>5037.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>146.4</td>\n",
       "      <td>11.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>119.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>0.0</td>\n",
       "      <td>66.933067</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.98002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.980</td>\n",
       "      <td>2532.0</td>\n",
       "      <td>52.6</td>\n",
       "      <td>110.9</td>\n",
       "      <td>2.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>0.0</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.122</td>\n",
       "      <td>5947.0</td>\n",
       "      <td>117.8</td>\n",
       "      <td>140.5</td>\n",
       "      <td>5.3</td>\n",
       "      <td>7.20</td>\n",
       "      <td>82.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>0.0</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.114</td>\n",
       "      <td>5999.0</td>\n",
       "      <td>139.2</td>\n",
       "      <td>160.1</td>\n",
       "      <td>12.5</td>\n",
       "      <td>4.80</td>\n",
       "      <td>101.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548</th>\n",
       "      <td>0.0</td>\n",
       "      <td>71.839080</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.340996</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.040</td>\n",
       "      <td>2543.0</td>\n",
       "      <td>44.3</td>\n",
       "      <td>109.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.51</td>\n",
       "      <td>31.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>622</th>\n",
       "      <td>0.0</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.124</td>\n",
       "      <td>5894.0</td>\n",
       "      <td>133.6</td>\n",
       "      <td>159.8</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.32</td>\n",
       "      <td>99.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635</th>\n",
       "      <td>0.0</td>\n",
       "      <td>67.647059</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.119</td>\n",
       "      <td>6172.0</td>\n",
       "      <td>139.3</td>\n",
       "      <td>148.9</td>\n",
       "      <td>11.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>667</th>\n",
       "      <td>0.0</td>\n",
       "      <td>87.169442</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.975</td>\n",
       "      <td>2424.0</td>\n",
       "      <td>50.7</td>\n",
       "      <td>112.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>11.00</td>\n",
       "      <td>37.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>768</th>\n",
       "      <td>0.0</td>\n",
       "      <td>67.647059</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.119</td>\n",
       "      <td>6203.0</td>\n",
       "      <td>139.8</td>\n",
       "      <td>150.1</td>\n",
       "      <td>9.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>798</th>\n",
       "      <td>0.0</td>\n",
       "      <td>97.991181</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.244</td>\n",
       "      <td>5379.0</td>\n",
       "      <td>55.4</td>\n",
       "      <td>131.2</td>\n",
       "      <td>2.1</td>\n",
       "      <td>15.07</td>\n",
       "      <td>33.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 97 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     P001       P002       P003      P004  P005  P006  P007  P008  P009  P010  \\\n",
       "98    0.0  68.627451   4.901961   0.00000   0.0   0.0     0   0.0   0.0   0.0   \n",
       "175   0.0  67.961165   9.708738   0.00000   0.0   0.0     0   0.0   0.0   0.0   \n",
       "214   0.0  14.084507   0.000000   0.00000   0.0   0.0     0   0.0   0.0   0.0   \n",
       "252   0.0  13.145540   0.000000   0.00000   0.0   0.0     0   0.0   0.0   0.0   \n",
       "260   0.0  76.470588   0.000000   0.00000   0.0   0.0     0   0.0   0.0   0.0   \n",
       "266   0.0  67.961165   7.766990   0.00000   0.0   0.0     0   0.0   0.0   0.0   \n",
       "267   0.0  67.961165   9.708738   0.00000   0.0   0.0     0   0.0   0.0   0.0   \n",
       "297   0.0  98.039216   0.000000   0.00000   0.0   0.0     0   0.0   0.0   0.0   \n",
       "318   0.0  55.882353   0.000000   0.00000   0.0   0.0     0   0.0   0.0   0.0   \n",
       "319   0.0  75.490196   0.000000   0.00000   0.0   0.0     0   0.0   0.0   0.0   \n",
       "335   0.0  49.019608  22.549020   0.00000   0.0   0.0     0   0.0   0.0   0.0   \n",
       "426   0.0  66.933067   0.000000  19.98002   0.0   0.0     0   0.0   0.0   0.0   \n",
       "488   0.0  68.000000   0.000000   0.00000   0.0   0.0     0   0.0   0.0   0.0   \n",
       "489   0.0  68.000000   0.000000   0.00000   0.0   0.0     0   0.0   0.0   0.0   \n",
       "548   0.0  71.839080   0.000000   0.00000   0.0   0.0     0   0.0   0.0   0.0   \n",
       "622   0.0  66.666667   0.000000   0.00000   0.0   0.0     0   0.0   0.0   0.0   \n",
       "635   0.0  67.647059   0.000000   0.00000   0.0   0.0     0   0.0   0.0   0.0   \n",
       "667   0.0  87.169442   0.000000   0.00000   0.0   0.0     0   0.0   0.0   0.0   \n",
       "768   0.0  67.647059   0.000000   0.00000   0.0   0.0     0   0.0   0.0   0.0   \n",
       "798   0.0  97.991181   0.000000   0.00000   0.0   0.0     0   0.0   0.0   0.0   \n",
       "\n",
       "     ...      OTH7      OTH8  OTH9     비중   굴곡탄성률   굴곡강도    HDT  IZOD 충격강도  \\\n",
       "98   ...  0.000000  0.000000   0.0  1.040  2653.0   48.9  118.6        1.5   \n",
       "175  ...  0.000000  0.000000   0.0  1.021  2580.0   51.2  119.9        NaN   \n",
       "214  ...  0.469484  2.816901   0.0  1.024  2154.0   33.7  106.1        5.1   \n",
       "252  ...  0.469484  2.816901   0.0  0.988  1787.0   30.9   93.5        4.0   \n",
       "260  ...  0.000000  0.000000   0.0  1.035  2944.0   53.2  118.6        1.9   \n",
       "266  ...  0.000000  0.000000   0.0  1.047  3057.0   53.6  123.2        3.3   \n",
       "267  ...  0.000000  0.000000   0.0  1.041  2943.0   53.2   68.1        2.2   \n",
       "297  ...  0.000000  0.000000   0.0    NaN     NaN    NaN    NaN        NaN   \n",
       "318  ...  0.000000  0.000000   0.0  1.223  8552.0  161.9  152.5       13.2   \n",
       "319  ...  0.000000  0.000000   0.0  1.036  4185.0  108.3  140.5       10.3   \n",
       "335  ...  0.000000  0.000000   0.0  1.073  5037.0  121.0  146.4       11.3   \n",
       "426  ...  0.000000  0.000000   0.0  0.980  2532.0   52.6  110.9        2.2   \n",
       "488  ...  0.000000  0.000000   0.0  1.122  5947.0  117.8  140.5        5.3   \n",
       "489  ...  0.000000  0.000000   0.0  1.114  5999.0  139.2  160.1       12.5   \n",
       "548  ...  1.340996  0.000000   0.0  1.040  2543.0   44.3  109.1        NaN   \n",
       "622  ...  0.000000  0.000000   0.0  1.124  5894.0  133.6  159.8       12.0   \n",
       "635  ...  0.000000  0.000000   0.0  1.119  6172.0  139.3  148.9       11.7   \n",
       "667  ...  0.000000  0.000000   0.0  0.975  2424.0   50.7  112.3        2.7   \n",
       "768  ...  0.000000  0.000000   0.0  1.119  6203.0  139.8  150.1        9.5   \n",
       "798  ...  0.000000  0.000000   0.0  1.244  5379.0   55.4  131.2        2.1   \n",
       "\n",
       "        MI   인장강도  \n",
       "98   13.70   33.5  \n",
       "175  14.00   36.4  \n",
       "214    NaN   61.8  \n",
       "252    NaN   22.2  \n",
       "260  13.15   36.1  \n",
       "266  13.96   35.6  \n",
       "267  14.32   35.1  \n",
       "297    NaN    NaN  \n",
       "318    NaN  114.0  \n",
       "319    NaN   78.9  \n",
       "335    NaN  119.0  \n",
       "426    NaN   36.5  \n",
       "488   7.20   82.9  \n",
       "489   4.80  101.0  \n",
       "548  15.51   31.4  \n",
       "622   3.32   99.4  \n",
       "635    NaN   98.5  \n",
       "667  11.00   37.1  \n",
       "768    NaN  100.7  \n",
       "798  15.07   33.9  \n",
       "\n",
       "[20 rows x 97 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_P002_skip0 = data.loc[data['P002'] != 0]\n",
    "data_P002_skip0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
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       "      <th>245</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.322</td>\n",
       "      <td>11206.0</td>\n",
       "      <td>118.2</td>\n",
       "      <td>159.9</td>\n",
       "      <td>10.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.330</td>\n",
       "      <td>11077.0</td>\n",
       "      <td>116.5</td>\n",
       "      <td>159.7</td>\n",
       "      <td>21.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.324</td>\n",
       "      <td>9873.0</td>\n",
       "      <td>120.9</td>\n",
       "      <td>160.1</td>\n",
       "      <td>8.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>0.0</td>\n",
       "      <td>67.961165</td>\n",
       "      <td>7.766990</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.047</td>\n",
       "      <td>3057.0</td>\n",
       "      <td>53.6</td>\n",
       "      <td>123.2</td>\n",
       "      <td>3.3</td>\n",
       "      <td>13.96</td>\n",
       "      <td>35.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>0.0</td>\n",
       "      <td>67.961165</td>\n",
       "      <td>9.708738</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.041</td>\n",
       "      <td>2943.0</td>\n",
       "      <td>53.2</td>\n",
       "      <td>68.1</td>\n",
       "      <td>2.2</td>\n",
       "      <td>14.32</td>\n",
       "      <td>35.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>75.728155</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.073</td>\n",
       "      <td>3237.0</td>\n",
       "      <td>52.5</td>\n",
       "      <td>120.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.607843</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>58.823529</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.026</td>\n",
       "      <td>2929.0</td>\n",
       "      <td>46.7</td>\n",
       "      <td>123.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>58.823529</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.027</td>\n",
       "      <td>2515.0</td>\n",
       "      <td>45.1</td>\n",
       "      <td>113.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.569472</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.036</td>\n",
       "      <td>2895.0</td>\n",
       "      <td>44.4</td>\n",
       "      <td>120.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.70</td>\n",
       "      <td>28.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>53.921569</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.030</td>\n",
       "      <td>2826.0</td>\n",
       "      <td>45.6</td>\n",
       "      <td>118.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.40</td>\n",
       "      <td>29.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.607843</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.036</td>\n",
       "      <td>2747.0</td>\n",
       "      <td>43.5</td>\n",
       "      <td>119.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.00</td>\n",
       "      <td>28.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>0.0</td>\n",
       "      <td>49.019608</td>\n",
       "      <td>22.549020</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.073</td>\n",
       "      <td>5037.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>146.4</td>\n",
       "      <td>11.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>119.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>24.975025</td>\n",
       "      <td>46.953047</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.076</td>\n",
       "      <td>4796.0</td>\n",
       "      <td>106.5</td>\n",
       "      <td>135.0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>74.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>593</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.059</td>\n",
       "      <td>3098.0</td>\n",
       "      <td>52.7</td>\n",
       "      <td>118.4</td>\n",
       "      <td>3.3</td>\n",
       "      <td>25.35</td>\n",
       "      <td>34.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>85.210578</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.964</td>\n",
       "      <td>2464.0</td>\n",
       "      <td>66.4</td>\n",
       "      <td>148.1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14.00</td>\n",
       "      <td>46.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>85.210578</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.974</td>\n",
       "      <td>2437.0</td>\n",
       "      <td>49.8</td>\n",
       "      <td>116.2</td>\n",
       "      <td>2.9</td>\n",
       "      <td>20.00</td>\n",
       "      <td>36.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>31 rows × 97 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     P001       P002       P003       P004  P005  P006  P007       P008  P009  \\\n",
       "0     0.0   0.000000  85.000000   0.000000   0.0   0.0     0   0.000000   0.0   \n",
       "5     0.0   0.000000  39.215686   0.000000   0.0   0.0     0  39.215686   0.0   \n",
       "6     0.0   0.000000  19.607843   0.000000   0.0   0.0     0  58.823529   0.0   \n",
       "7     0.0   0.000000  58.823529   0.000000   0.0   0.0     0  19.607843   0.0   \n",
       "8     0.0   0.000000  78.431373   0.000000   0.0   0.0     0   0.000000   0.0   \n",
       "61    0.0   0.000000  52.423343   0.000000   0.0   0.0     0   0.000000   0.0   \n",
       "62    0.0   0.000000  53.465347   0.000000   0.0   0.0     0   0.000000   0.0   \n",
       "63    0.0   0.000000  52.423343   0.000000   0.0   0.0     0   0.000000   0.0   \n",
       "98    0.0  68.627451   4.901961   0.000000   0.0   0.0     0   0.000000   0.0   \n",
       "110   0.0   0.000000  52.423343   0.000000   0.0   0.0     0   0.000000   0.0   \n",
       "111   0.0   0.000000  52.423343   0.000000   0.0   0.0     0   0.000000   0.0   \n",
       "112   0.0   0.000000  52.423343   0.000000   0.0   0.0     0   0.000000   0.0   \n",
       "174   0.0   0.000000   9.615385   0.000000   0.0   0.0     0   0.000000   0.0   \n",
       "175   0.0  67.961165   9.708738   0.000000   0.0   0.0     0   0.000000   0.0   \n",
       "244   0.0   0.000000  50.000000   0.000000   0.0   0.0     0   0.000000   0.0   \n",
       "245   0.0   0.000000  30.000000   0.000000   0.0   0.0     0   0.000000   0.0   \n",
       "246   0.0   0.000000  20.000000   0.000000   0.0   0.0     0   0.000000   0.0   \n",
       "247   0.0   0.000000  40.000000   0.000000   0.0   0.0     0   0.000000   0.0   \n",
       "266   0.0  67.961165   7.766990   0.000000   0.0   0.0     0   0.000000   0.0   \n",
       "267   0.0  67.961165   9.708738   0.000000   0.0   0.0     0   0.000000   0.0   \n",
       "283   0.0   0.000000  75.728155   0.000000   0.0   0.0     0   0.000000   0.0   \n",
       "294   0.0   0.000000  19.607843   0.000000   0.0   0.0     0  58.823529   0.0   \n",
       "296   0.0   0.000000  58.823529   0.000000   0.0   0.0     0   0.000000   0.0   \n",
       "309   0.0   0.000000  19.569472   0.000000   0.0   0.0     0   0.000000   0.0   \n",
       "311   0.0   0.000000  53.921569   0.000000   0.0   0.0     0   0.000000   0.0   \n",
       "312   0.0   0.000000  19.607843   0.000000   0.0   0.0     0   0.000000   0.0   \n",
       "335   0.0  49.019608  22.549020   0.000000   0.0   0.0     0   0.000000   0.0   \n",
       "415   0.0   0.000000  24.975025  46.953047   0.0   0.0     0   0.000000   0.0   \n",
       "593   0.0   0.000000  78.000000   0.000000   0.0   0.0     0   0.000000   0.0   \n",
       "729   0.0   0.000000  85.210578   0.000000   0.0   0.0     0   0.000000   0.0   \n",
       "730   0.0   0.000000  85.210578   0.000000   0.0   0.0     0   0.000000   0.0   \n",
       "\n",
       "     P010  ...  OTH7      OTH8  OTH9     비중    굴곡탄성률   굴곡강도    HDT  IZOD 충격강도  \\\n",
       "0     0.0  ...   0.0  0.000000   0.0  1.007   2607.0   53.4  120.2        NaN   \n",
       "5     0.0  ...   0.0  0.000000   0.0  1.036   2590.0   45.9  117.8        NaN   \n",
       "6     0.0  ...   0.0  0.000000   0.0  1.036   2463.0   43.0  112.1        NaN   \n",
       "7     0.0  ...   0.0  0.000000   0.0  1.037   2703.0   49.1  119.3        NaN   \n",
       "8     0.0  ...   0.0  0.000000   0.0  1.036   2909.0   52.8  122.6        NaN   \n",
       "61    0.0  ...   0.0  0.000000   0.0  1.277   9806.0  167.5  151.5       11.2   \n",
       "62    0.0  ...   0.0  0.000000   0.0  1.270   9911.0  165.9  153.1       12.0   \n",
       "63    0.0  ...   0.0  0.000000   0.0  1.276   9900.0  168.6  151.8       11.6   \n",
       "98    0.0  ...   0.0  0.000000   0.0  1.040   2653.0   48.9  118.6        1.5   \n",
       "110   0.0  ...   0.0  0.000000   0.0  1.281   9447.0  168.0  150.9       11.0   \n",
       "111   0.0  ...   0.0  0.000000   0.0  1.280   9170.0  167.0  150.8       12.4   \n",
       "112   0.0  ...   0.0  0.000000   0.0  1.277   9394.0  168.0  151.7       12.6   \n",
       "174   0.0  ...   0.0  0.961538   0.0  1.032   2975.0   54.9  125.9        NaN   \n",
       "175   0.0  ...   0.0  0.000000   0.0  1.021   2580.0   51.2  119.9        NaN   \n",
       "244   0.0  ...   0.0  0.000000   0.0  1.330  11127.0  121.9  160.1        7.7   \n",
       "245   0.0  ...   0.0  0.000000   0.0  1.322  11206.0  118.2  159.9       10.1   \n",
       "246   0.0  ...   0.0  0.000000   0.0  1.330  11077.0  116.5  159.7       21.3   \n",
       "247   0.0  ...   0.0  0.000000   0.0  1.324   9873.0  120.9  160.1        8.7   \n",
       "266   0.0  ...   0.0  0.000000   0.0  1.047   3057.0   53.6  123.2        3.3   \n",
       "267   0.0  ...   0.0  0.000000   0.0  1.041   2943.0   53.2   68.1        2.2   \n",
       "283   0.0  ...   0.0  0.000000   0.0  1.073   3237.0   52.5  120.9        2.0   \n",
       "294   0.0  ...   0.0  0.000000   0.0  1.026   2929.0   46.7  123.1        NaN   \n",
       "296   0.0  ...   0.0  0.000000   0.0  1.027   2515.0   45.1  113.7        NaN   \n",
       "309   0.0  ...   0.0  0.000000   0.0  1.036   2895.0   44.4  120.2        NaN   \n",
       "311   0.0  ...   0.0  0.000000   0.0  1.030   2826.0   45.6  118.0        NaN   \n",
       "312   0.0  ...   0.0  0.000000   0.0  1.036   2747.0   43.5  119.3        NaN   \n",
       "335   0.0  ...   0.0  0.000000   0.0  1.073   5037.0  121.0  146.4       11.3   \n",
       "415   0.0  ...   0.0  0.000000   0.0  1.076   4796.0  106.5  135.0        9.3   \n",
       "593   0.0  ...   0.0  0.000000   0.0  1.059   3098.0   52.7  118.4        3.3   \n",
       "729   0.0  ...   0.0  0.000000   0.0  0.964   2464.0   66.4  148.1        4.0   \n",
       "730   0.0  ...   0.0  0.000000   0.0  0.974   2437.0   49.8  116.2        2.9   \n",
       "\n",
       "        MI   인장강도  \n",
       "0    26.30   36.3  \n",
       "5    23.80   30.5  \n",
       "6    24.90   28.2  \n",
       "7    24.00   32.3  \n",
       "8    22.50   35.0  \n",
       "61    3.40  123.6  \n",
       "62    5.20  123.2  \n",
       "63    3.20  124.0  \n",
       "98   13.70   33.5  \n",
       "110    NaN  113.0  \n",
       "111    NaN  113.0  \n",
       "112    NaN  114.0  \n",
       "174  16.20   37.5  \n",
       "175  14.00   36.4  \n",
       "244    NaN   83.1  \n",
       "245    NaN   72.3  \n",
       "246    NaN   71.8  \n",
       "247    NaN   77.8  \n",
       "266  13.96   35.6  \n",
       "267  14.32   35.1  \n",
       "283    NaN    7.4  \n",
       "294    NaN   29.9  \n",
       "296    NaN   29.4  \n",
       "309  24.70   28.5  \n",
       "311  23.40   29.9  \n",
       "312  25.00   28.1  \n",
       "335    NaN  119.0  \n",
       "415    NaN   74.3  \n",
       "593  25.35   34.4  \n",
       "729  14.00   46.8  \n",
       "730  20.00   36.3  \n",
       "\n",
       "[31 rows x 97 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_P003_skip0 = data.loc[data['P003'] != 0]\n",
    "data_P003_skip0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
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       "         P001       P002       P003       P004  P005  P006  P007  P008  P009  \\\n",
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       "\n",
       "     P010  ...  OTH7  OTH8  OTH9     비중    굴곡탄성률   굴곡강도    HDT  IZOD 충격강도  \\\n",
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       "119   0.0  ...   0.0   0.0   0.0  1.001   3600.0  100.2  159.7       13.7   \n",
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       "404   0.0  ...   0.0   0.0   0.0  1.073   4810.0  103.7  137.0        9.4   \n",
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       "645   0.0  ...   0.0   0.0   0.0    NaN   1468.0   38.7   91.4       15.1   \n",
       "\n",
       "      MI   인장강도  \n",
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       "\n",
       "[9 rows x 97 columns]"
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     },
     "execution_count": 34,
     "metadata": {},
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    "data_P004_skip0"
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     "execution_count": 35,
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   "source": [
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  {
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   "source": [
    "# Modeling"
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  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>806</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.686275</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>807</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>808</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.686275</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>809</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.686275</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>810</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>811 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     P012      P013  P014  P015  P016  P017  P018  P019  P020  P021  ...  \\\n",
       "0     0.0  0.307615   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   \n",
       "1     0.0  0.307460   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   \n",
       "2     0.0  0.099502   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   \n",
       "3     0.0  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   \n",
       "4     0.0  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   \n",
       "..    ...       ...   ...   ...   ...   ...   ...   ...   ...   ...  ...   \n",
       "806   0.0  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   \n",
       "807   0.0  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   \n",
       "808   0.0  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   \n",
       "809   0.0  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   \n",
       "810   0.0  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   \n",
       "\n",
       "     P029      P030  P031  P032  P033  P034  P035  P036  P037  P038  \n",
       "0     0.0  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "1     0.0  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "2     0.0  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "3     0.0  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "4     0.0  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "..    ...       ...   ...   ...   ...   ...   ...   ...   ...   ...  \n",
       "806   0.0  0.686275   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "807   0.0  0.666667   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "808   0.0  0.686275   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "809   0.0  0.686275   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "810   0.0  0.666667   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "\n",
       "[811 rows x 26 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_P = data.iloc[:,:38]\n",
    "X_P = X_P.drop(['P023'], axis=1)\n",
    "X_P\n",
    "\n",
    "X_P_GS = X_P.iloc[:,:11]\n",
    "X_P_GS\n",
    "\n",
    "X_P_O = X_P.iloc[:,11:]\n",
    "X_P_O_scale = X_P_O/100\n",
    "X_P_O_scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['P001', 'P002', 'P003', 'P004', 'P005', 'P006', 'P007', 'P008', 'P009',\n",
       "       'P010', 'P011', 'P012', 'P013', 'P014', 'P015', 'P016', 'P017', 'P018',\n",
       "       'P019', 'P020', 'P021', 'P022', 'P023', 'P024', 'P025', 'P026', 'P027',\n",
       "       'P028', 'P029', 'P030', 'P031', 'P032', 'P033', 'P034', 'P035', 'P036',\n",
       "       'P037', 'P038', 'P039', 'P040', 'P041', 'F001', 'F002', 'F003', 'F004',\n",
       "       'F005', 'F006', 'F007', 'F008', 'F009', 'F010', 'F011', 'F012', 'F013',\n",
       "       'F014', 'F015', 'F016', 'F017', 'F018', 'R001', 'R002', 'R003', 'R004',\n",
       "       'R005', 'R006', 'R007', 'R008', 'R009', 'R010', 'R011', 'R012', 'R013',\n",
       "       'R014', 'R015', 'R016', 'R017', 'R018', 'R019', 'R020', 'R021', 'R022',\n",
       "       'OTH1', 'OTH2', 'OTH3', 'OTH4', 'OTH5', 'OTH6', 'OTH7', 'OTH8', 'OTH9',\n",
       "       '비중', '굴곡탄성률', '굴곡강도', 'HDT', 'IZOD 충격강도', 'MI', '인장강도'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4,\n",
       " 20,\n",
       " 31,\n",
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       " 59,\n",
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       " 3,\n",
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       " 2,\n",
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       " 106,\n",
       " 23,\n",
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       " 24,\n",
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       " 2,\n",
       " 126,\n",
       " 81,\n",
       " 20,\n",
       " 1,\n",
       " 7,\n",
       " 10,\n",
       " 1,\n",
       " 7,\n",
       " 2,\n",
       " 8,\n",
       " 2,\n",
       " 2,\n",
       " 121,\n",
       " 30,\n",
       " 6,\n",
       " 197,\n",
       " 29,\n",
       " 14,\n",
       " 47,\n",
       " 306,\n",
       " 44,\n",
       " 19,\n",
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       " 35,\n",
       " 8,\n",
       " 560,\n",
       " 9,\n",
       " 116,\n",
       " 1,\n",
       " 95,\n",
       " 75,\n",
       " 160,\n",
       " 92,\n",
       " 4]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_col = []\n",
    "for i in X.columns:\n",
    "    bit_mask = X[i] != 0\n",
    "    rows_nonzero = X.loc[bit_mask]\n",
    "    n = len(rows_nonzero.index)\n",
    "    no_col.append(n)\n",
    "no_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>P001</th>\n",
       "      <th>P002</th>\n",
       "      <th>P003</th>\n",
       "      <th>P004</th>\n",
       "      <th>P005</th>\n",
       "      <th>P006</th>\n",
       "      <th>P007</th>\n",
       "      <th>P008</th>\n",
       "      <th>P009</th>\n",
       "      <th>P010</th>\n",
       "      <th>...</th>\n",
       "      <th>OTH7</th>\n",
       "      <th>OTH8</th>\n",
       "      <th>OTH9</th>\n",
       "      <th>비중</th>\n",
       "      <th>굴곡탄성률</th>\n",
       "      <th>굴곡강도</th>\n",
       "      <th>HDT</th>\n",
       "      <th>IZOD 충격강도</th>\n",
       "      <th>MI</th>\n",
       "      <th>인장강도</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>31</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>160</td>\n",
       "      <td>92</td>\n",
       "      <td>4</td>\n",
       "      <td>811</td>\n",
       "      <td>811</td>\n",
       "      <td>811</td>\n",
       "      <td>811</td>\n",
       "      <td>811</td>\n",
       "      <td>811</td>\n",
       "      <td>811</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 97 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   P001  P002  P003  P004  P005  P006  P007  P008  P009  P010  ...  OTH7  \\\n",
       "0     4    20    31     9     1    59     1    44     9     2  ...   160   \n",
       "\n",
       "   OTH8  OTH9   비중  굴곡탄성률  굴곡강도  HDT  IZOD 충격강도   MI  인장강도  \n",
       "0    92     4  811    811   811  811        811  811   811  \n",
       "\n",
       "[1 rows x 97 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_df = pd.DataFrame(data=np.array([no_col]), columns=data.columns)\n",
    "no_df\n",
    "\n",
    "no_df.to_excel('F:\\\\Downloads\\\\GS Project Big leader\\\\재료사용횟수.xls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>811 rows × 18 columns</p>\n",
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       "\n",
       "     F010  F011  F012  F013  F014  F015  F016  F017  F018  \n",
       "0     0.0  15.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
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       "4     0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
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       "806   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "807   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "808   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "809   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "810   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "\n",
       "[811 rows x 18 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_F = data.iloc[:,41:59]\n",
    "X_F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
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       "4    -0.877384  0.266239  -1.715907   2.050595  \n",
       "..         ...       ...        ...        ...  \n",
       "806   1.823218  2.711849  -1.718879   0.515795  \n",
       "807   1.746058  2.641975  -1.718794   0.559646  \n",
       "808   1.823218  2.711849  -1.718879   0.515795  \n",
       "809   1.823218  2.711849  -1.718879   0.515795  \n",
       "810   1.746058  2.641975  -1.718794   0.559646  \n",
       "\n",
       "[811 rows x 10 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA(n_components=10)\n",
    "result = pca.fit_transform(X_P_O)\n",
    "\n",
    "result = pd.DataFrame(result, columns=['a', 'b', 'c', 'd','e','f','g','h','i','j'])\n",
    "result\n",
    "\n",
    "from sklearn import decomposition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x206884a8f28>"
      ]
     },
     "execution_count": 16,
     "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": [
    "result.plot(kind='scatter', x='a',y='b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x206884ba240>"
      ]
     },
     "execution_count": 19,
     "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": [
    "data.plot(kind='scatter', x='P012', y='P029')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "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>비중</th>\n",
       "      <th>굴곡탄성률</th>\n",
       "      <th>굴곡강도</th>\n",
       "      <th>HDT</th>\n",
       "      <th>IZOD 충격강도</th>\n",
       "      <th>MI</th>\n",
       "      <th>인장강도</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.007</td>\n",
       "      <td>2607.0</td>\n",
       "      <td>53.4</td>\n",
       "      <td>120.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.3</td>\n",
       "      <td>36.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.118</td>\n",
       "      <td>3289.0</td>\n",
       "      <td>43.5</td>\n",
       "      <td>117.5</td>\n",
       "      <td>3.4</td>\n",
       "      <td>20.7</td>\n",
       "      <td>27.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.032</td>\n",
       "      <td>2792.0</td>\n",
       "      <td>41.4</td>\n",
       "      <td>120.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>18.9</td>\n",
       "      <td>27.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.077</td>\n",
       "      <td>2979.0</td>\n",
       "      <td>43.1</td>\n",
       "      <td>127.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>21.7</td>\n",
       "      <td>27.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.219</td>\n",
       "      <td>8274.0</td>\n",
       "      <td>150.2</td>\n",
       "      <td>151.8</td>\n",
       "      <td>18.3</td>\n",
       "      <td>25.3</td>\n",
       "      <td>102.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>806</th>\n",
       "      <td>1.029</td>\n",
       "      <td>2385.0</td>\n",
       "      <td>36.8</td>\n",
       "      <td>117.1</td>\n",
       "      <td>6.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>807</th>\n",
       "      <td>1.032</td>\n",
       "      <td>2454.0</td>\n",
       "      <td>37.5</td>\n",
       "      <td>116.5</td>\n",
       "      <td>9.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>808</th>\n",
       "      <td>1.030</td>\n",
       "      <td>2542.0</td>\n",
       "      <td>38.3</td>\n",
       "      <td>113.5</td>\n",
       "      <td>8.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>809</th>\n",
       "      <td>1.028</td>\n",
       "      <td>2577.0</td>\n",
       "      <td>38.5</td>\n",
       "      <td>114.0</td>\n",
       "      <td>17.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>810</th>\n",
       "      <td>1.027</td>\n",
       "      <td>2567.0</td>\n",
       "      <td>38.8</td>\n",
       "      <td>116.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>811 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        비중   굴곡탄성률   굴곡강도    HDT  IZOD 충격강도    MI   인장강도\n",
       "0    1.007  2607.0   53.4  120.2        NaN  26.3   36.3\n",
       "1    1.118  3289.0   43.5  117.5        3.4  20.7   27.5\n",
       "2    1.032  2792.0   41.4  120.1        2.3  18.9   27.1\n",
       "3    1.077  2979.0   43.1  127.2        3.0  21.7   27.5\n",
       "4    1.219  8274.0  150.2  151.8       18.3  25.3  102.0\n",
       "..     ...     ...    ...    ...        ...   ...    ...\n",
       "806  1.029  2385.0   36.8  117.1        6.1   NaN   24.9\n",
       "807  1.032  2454.0   37.5  116.5        9.8   NaN   25.3\n",
       "808  1.030  2542.0   38.3  113.5        8.3   NaN   25.2\n",
       "809  1.028  2577.0   38.5  114.0       17.7   NaN   25.4\n",
       "810  1.027  2567.0   38.8  116.0       15.0   NaN   25.7\n",
       "\n",
       "[811 rows x 7 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = data.iloc[:,90:]\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>비중</th>\n",
       "      <th>굴곡탄성률</th>\n",
       "      <th>굴곡강도</th>\n",
       "      <th>HDT</th>\n",
       "      <th>IZOD 충격강도</th>\n",
       "      <th>MI</th>\n",
       "      <th>인장강도</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>비중</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.869586</td>\n",
       "      <td>0.728241</td>\n",
       "      <td>0.694633</td>\n",
       "      <td>-0.189979</td>\n",
       "      <td>-0.119424</td>\n",
       "      <td>0.689369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>굴곡탄성률</th>\n",
       "      <td>0.869586</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.924009</td>\n",
       "      <td>0.736481</td>\n",
       "      <td>-0.170063</td>\n",
       "      <td>-0.210158</td>\n",
       "      <td>0.895452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>굴곡강도</th>\n",
       "      <td>0.728241</td>\n",
       "      <td>0.924009</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.722539</td>\n",
       "      <td>-0.170515</td>\n",
       "      <td>-0.215067</td>\n",
       "      <td>0.969047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HDT</th>\n",
       "      <td>0.694633</td>\n",
       "      <td>0.736481</td>\n",
       "      <td>0.722539</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.302358</td>\n",
       "      <td>0.001357</td>\n",
       "      <td>0.677490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IZOD 충격강도</th>\n",
       "      <td>-0.189979</td>\n",
       "      <td>-0.170063</td>\n",
       "      <td>-0.170515</td>\n",
       "      <td>-0.302358</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.259878</td>\n",
       "      <td>-0.116820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MI</th>\n",
       "      <td>-0.119424</td>\n",
       "      <td>-0.210158</td>\n",
       "      <td>-0.215067</td>\n",
       "      <td>0.001357</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>-0.269468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>인장강도</th>\n",
       "      <td>0.689369</td>\n",
       "      <td>0.895452</td>\n",
       "      <td>0.969047</td>\n",
       "      <td>0.677490</td>\n",
       "      <td>-0.116820</td>\n",
       "      <td>-0.269468</td>\n",
       "      <td>1.000000</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 비중     굴곡탄성률      굴곡강도       HDT  IZOD 충격강도        MI  \\\n",
       "비중         1.000000  0.869586  0.728241  0.694633  -0.189979 -0.119424   \n",
       "굴곡탄성률      0.869586  1.000000  0.924009  0.736481  -0.170063 -0.210158   \n",
       "굴곡강도       0.728241  0.924009  1.000000  0.722539  -0.170515 -0.215067   \n",
       "HDT        0.694633  0.736481  0.722539  1.000000  -0.302358  0.001357   \n",
       "IZOD 충격강도 -0.189979 -0.170063 -0.170515 -0.302358   1.000000 -0.259878   \n",
       "MI        -0.119424 -0.210158 -0.215067  0.001357  -0.259878  1.000000   \n",
       "인장강도       0.689369  0.895452  0.969047  0.677490  -0.116820 -0.269468   \n",
       "\n",
       "               인장강도  \n",
       "비중         0.689369  \n",
       "굴곡탄성률      0.895452  \n",
       "굴곡강도       0.969047  \n",
       "HDT        0.677490  \n",
       "IZOD 충격강도 -0.116820  \n",
       "MI        -0.269468  \n",
       "인장강도       1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr = y.corr()\n",
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>비중</th>\n",
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       "      <th>HDT</th>\n",
       "      <th>IZOD 충격강도</th>\n",
       "      <th>MI</th>\n",
       "      <th>인장강도</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>1.000</td>\n",
       "      <td>1487.0</td>\n",
       "      <td>34.9</td>\n",
       "      <td>84.7</td>\n",
       "      <td>5.3</td>\n",
       "      <td>11.000</td>\n",
       "      <td>26.3</td>\n",
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       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>0.897</td>\n",
       "      <td>1086.0</td>\n",
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       "      <td>79.5</td>\n",
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       "      <td>22.7</td>\n",
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       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>1.010</td>\n",
       "      <td>1877.0</td>\n",
       "      <td>44.2</td>\n",
       "      <td>97.1</td>\n",
       "      <td>2.2</td>\n",
       "      <td>50.300</td>\n",
       "      <td>30.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>0.910</td>\n",
       "      <td>963.0</td>\n",
       "      <td>25.7</td>\n",
       "      <td>92.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>1.113</td>\n",
       "      <td>5553.0</td>\n",
       "      <td>122.6</td>\n",
       "      <td>149.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.400</td>\n",
       "      <td>89.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>0.910</td>\n",
       "      <td>1341.0</td>\n",
       "      <td>33.1</td>\n",
       "      <td>95.2</td>\n",
       "      <td>3.9</td>\n",
       "      <td>13.700</td>\n",
       "      <td>24.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>1.109</td>\n",
       "      <td>2709.0</td>\n",
       "      <td>31.9</td>\n",
       "      <td>120.2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>16.900</td>\n",
       "      <td>21.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>0.933</td>\n",
       "      <td>1262.0</td>\n",
       "      <td>27.5</td>\n",
       "      <td>86.2</td>\n",
       "      <td>12.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>0.883</td>\n",
       "      <td>434.0</td>\n",
       "      <td>11.1</td>\n",
       "      <td>53.4</td>\n",
       "      <td>84.7</td>\n",
       "      <td>1.057</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>1.021</td>\n",
       "      <td>2243.0</td>\n",
       "      <td>35.1</td>\n",
       "      <td>110.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>37.800</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>608 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        비중   굴곡탄성률   굴곡강도    HDT  IZOD 충격강도      MI  인장강도\n",
       "412  1.000  1487.0   34.9   84.7        5.3  11.000  26.3\n",
       "368  0.897  1086.0   28.7   79.5        5.5   7.490  22.7\n",
       "398  1.010  1877.0   44.2   97.1        2.2  50.300  30.3\n",
       "158  0.910   963.0   25.7   92.0        NaN     NaN  23.4\n",
       "69   1.113  5553.0  122.6  149.5        NaN  11.400  89.6\n",
       "..     ...     ...    ...    ...        ...     ...   ...\n",
       "71   0.910  1341.0   33.1   95.2        3.9  13.700  24.2\n",
       "106  1.109  2709.0   31.9  120.2        4.0  16.900  21.1\n",
       "270  0.933  1262.0   27.5   86.2       12.5     NaN  18.8\n",
       "435  0.883   434.0   11.1   53.4       84.7   1.057  14.0\n",
       "102  1.021  2243.0   35.1  110.4        3.7  37.800  23.0\n",
       "\n",
       "[608 rows x 7 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 811 entries, 0 to 810\n",
      "Data columns (total 7 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   비중         799 non-null    float64\n",
      " 1   굴곡탄성률      807 non-null    float64\n",
      " 2   굴곡강도       804 non-null    float64\n",
      " 3   HDT        807 non-null    float64\n",
      " 4   IZOD 충격강도  655 non-null    float64\n",
      " 5   MI         480 non-null    float64\n",
      " 6   인장강도       802 non-null    float64\n",
      "dtypes: float64(7)\n",
      "memory usage: 44.5 KB\n"
     ]
    }
   ],
   "source": [
    "y.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        비중  굴곡탄성률   굴곡강도    HDT  IZOD 충격강도     MI   인장강도\n",
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       "\n",
       "[811 rows x 7 columns]"
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     },
     "execution_count": 41,
     "metadata": {},
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  },
  {
   "cell_type": "code",
   "execution_count": 42,
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     "execution_count": 42,
     "metadata": {},
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   "source": [
    "y_MI_drop = y.drop(['MI'], axis = 1)\n",
    "y_MI_drop"
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  {
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   "execution_count": 43,
   "metadata": {},
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       "      <td>40.4</td>\n",
       "      <td>103.8</td>\n",
       "      <td>3.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87.97654</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.293255</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1081.0</td>\n",
       "      <td>27.9</td>\n",
       "      <td>77.1</td>\n",
       "      <td>43.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.483092</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>105.5</td>\n",
       "      <td>15.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1468.0</td>\n",
       "      <td>38.7</td>\n",
       "      <td>91.4</td>\n",
       "      <td>15.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>664</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.293255</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1415.0</td>\n",
       "      <td>35.8</td>\n",
       "      <td>94.1</td>\n",
       "      <td>31.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>665</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87.97654</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.293255</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>783.0</td>\n",
       "      <td>29.2</td>\n",
       "      <td>76.2</td>\n",
       "      <td>7.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>678</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87.97654</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.293255</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1123.0</td>\n",
       "      <td>29.3</td>\n",
       "      <td>79.2</td>\n",
       "      <td>6.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 97 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     P001       P002  P003  P004  P005      P006  P007       P008  P009  P010  \\\n",
       "297   0.0  98.039216   0.0   0.0   0.0   0.00000     0   0.000000   0.0   0.0   \n",
       "326   0.0   0.000000   0.0   0.0   0.0   0.00000     0  63.663075   0.0   0.0   \n",
       "327   0.0   0.000000   0.0   0.0   0.0   0.00000     0  63.663075   0.0   0.0   \n",
       "377   0.0   0.000000   0.0   0.0   0.0  87.97654     0   0.000000   0.0   0.0   \n",
       "471   0.0   0.000000   0.0   0.0   0.0   0.00000     0   0.000000   0.0   0.0   \n",
       "472   0.0   0.000000   0.0   0.0   0.0   0.00000     0   0.000000   0.0   0.0   \n",
       "507   0.0   0.000000   0.0   0.0   0.0  87.97654     0   0.000000   0.0   0.0   \n",
       "508   0.0   0.000000   0.0   0.0   0.0   0.00000     0   0.000000   0.0   0.0   \n",
       "645   0.0   0.000000   0.0  90.0   0.0   0.00000     0   0.000000   0.0   0.0   \n",
       "664   0.0   0.000000   0.0   0.0   0.0   0.00000     0   0.000000   0.0   0.0   \n",
       "665   0.0   0.000000   0.0   0.0   0.0  87.97654     0   0.000000   0.0   0.0   \n",
       "678   0.0   0.000000   0.0   0.0   0.0  87.97654     0   0.000000   0.0   0.0   \n",
       "\n",
       "     ...      OTH7      OTH8  OTH9  비중   굴곡탄성률  굴곡강도    HDT  IZOD 충격강도    MI  \\\n",
       "297  ...  0.000000  0.000000   0.0 NaN     NaN   NaN    NaN        NaN   NaN   \n",
       "326  ...  0.000000  0.000000   0.0 NaN  2775.0  43.3   84.9        2.5   NaN   \n",
       "327  ...  0.000000  0.000000   0.0 NaN  2579.0  42.4   86.5        2.4   NaN   \n",
       "377  ...  0.293255  0.000000   0.0 NaN  1093.0  28.7   80.4       13.0   NaN   \n",
       "471  ...  0.000000  0.000000   0.0 NaN  2059.0  31.3   98.2       10.0  34.3   \n",
       "472  ...  0.471698  1.886792   0.0 NaN  2396.0  40.4  103.8        3.7   NaN   \n",
       "507  ...  0.293255  0.000000   0.0 NaN  1081.0  27.9   77.1       43.6   NaN   \n",
       "508  ...  0.483092  0.000000   0.0 NaN  1966.0  26.0  105.5       15.3   NaN   \n",
       "645  ...  0.000000  0.000000   0.0 NaN  1468.0  38.7   91.4       15.1   NaN   \n",
       "664  ...  0.293255  0.000000   0.0 NaN  1415.0  35.8   94.1       31.7   NaN   \n",
       "665  ...  0.293255  0.000000   0.0 NaN   783.0  29.2   76.2        7.5   NaN   \n",
       "678  ...  0.293255  0.000000   0.0 NaN  1123.0  29.3   79.2        6.7   NaN   \n",
       "\n",
       "     인장강도  \n",
       "297   NaN  \n",
       "326  26.0  \n",
       "327  25.5  \n",
       "377  22.6  \n",
       "471   NaN  \n",
       "472   NaN  \n",
       "507  21.8  \n",
       "508  17.4  \n",
       "645  32.1  \n",
       "664  25.9  \n",
       "665  22.8  \n",
       "678  22.7  \n",
       "\n",
       "[12 rows x 97 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.isnull()['비중'] == True]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 0으로만 이루어진 열 제거"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "97"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col = data.columns\n",
    "col[0]\n",
    "len(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>P002</th>\n",
       "      <th>P003</th>\n",
       "      <th>P004</th>\n",
       "      <th>P016</th>\n",
       "      <th>P022</th>\n",
       "      <th>F001</th>\n",
       "      <th>F004</th>\n",
       "      <th>F007</th>\n",
       "      <th>F008</th>\n",
       "      <th>F011</th>\n",
       "      <th>...</th>\n",
       "      <th>OTH6</th>\n",
       "      <th>OTH7</th>\n",
       "      <th>OTH8</th>\n",
       "      <th>비중</th>\n",
       "      <th>굴곡탄성률</th>\n",
       "      <th>굴곡강도</th>\n",
       "      <th>HDT</th>\n",
       "      <th>IZOD 충격강도</th>\n",
       "      <th>MI</th>\n",
       "      <th>인장강도</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>68.627451</td>\n",
       "      <td>4.901961</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.588235</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.040</td>\n",
       "      <td>2653.0</td>\n",
       "      <td>48.9</td>\n",
       "      <td>118.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>13.70</td>\n",
       "      <td>33.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>67.961165</td>\n",
       "      <td>9.708738</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.417476</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.021</td>\n",
       "      <td>2580.0</td>\n",
       "      <td>51.2</td>\n",
       "      <td>119.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.00</td>\n",
       "      <td>36.4</td>\n",
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       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>14.084507</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.741784</td>\n",
       "      <td>14.084507</td>\n",
       "      <td>4.694836</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.084507</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.469484</td>\n",
       "      <td>2.816901</td>\n",
       "      <td>1.024</td>\n",
       "      <td>2154.0</td>\n",
       "      <td>33.7</td>\n",
       "      <td>106.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>13.145540</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.741784</td>\n",
       "      <td>14.084507</td>\n",
       "      <td>4.694836</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.084507</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.469484</td>\n",
       "      <td>2.816901</td>\n",
       "      <td>0.988</td>\n",
       "      <td>1787.0</td>\n",
       "      <td>30.9</td>\n",
       "      <td>93.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>76.470588</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.568627</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.035</td>\n",
       "      <td>2944.0</td>\n",
       "      <td>53.2</td>\n",
       "      <td>118.6</td>\n",
       "      <td>1.9</td>\n",
       "      <td>13.15</td>\n",
       "      <td>36.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>67.961165</td>\n",
       "      <td>7.766990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.047</td>\n",
       "      <td>3057.0</td>\n",
       "      <td>53.6</td>\n",
       "      <td>123.2</td>\n",
       "      <td>3.3</td>\n",
       "      <td>13.96</td>\n",
       "      <td>35.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>67.961165</td>\n",
       "      <td>9.708738</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.417476</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.041</td>\n",
       "      <td>2943.0</td>\n",
       "      <td>53.2</td>\n",
       "      <td>68.1</td>\n",
       "      <td>2.2</td>\n",
       "      <td>14.32</td>\n",
       "      <td>35.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>98.039216</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>55.882353</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.215686</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.223</td>\n",
       "      <td>8552.0</td>\n",
       "      <td>161.9</td>\n",
       "      <td>152.5</td>\n",
       "      <td>13.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>114.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>75.490196</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.607843</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.036</td>\n",
       "      <td>4185.0</td>\n",
       "      <td>108.3</td>\n",
       "      <td>140.5</td>\n",
       "      <td>10.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>49.019608</td>\n",
       "      <td>22.549020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.509804</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.073</td>\n",
       "      <td>5037.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>146.4</td>\n",
       "      <td>11.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>119.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>66.933067</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.98002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.980</td>\n",
       "      <td>2532.0</td>\n",
       "      <td>52.6</td>\n",
       "      <td>110.9</td>\n",
       "      <td>2.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>68.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.122</td>\n",
       "      <td>5947.0</td>\n",
       "      <td>117.8</td>\n",
       "      <td>140.5</td>\n",
       "      <td>5.3</td>\n",
       "      <td>7.20</td>\n",
       "      <td>82.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>68.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.114</td>\n",
       "      <td>5999.0</td>\n",
       "      <td>139.2</td>\n",
       "      <td>160.1</td>\n",
       "      <td>12.5</td>\n",
       "      <td>4.80</td>\n",
       "      <td>101.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548</th>\n",
       "      <td>71.839080</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.157088</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.340996</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.040</td>\n",
       "      <td>2543.0</td>\n",
       "      <td>44.3</td>\n",
       "      <td>109.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.51</td>\n",
       "      <td>31.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>622</th>\n",
       "      <td>66.666667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.392157</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.124</td>\n",
       "      <td>5894.0</td>\n",
       "      <td>133.6</td>\n",
       "      <td>159.8</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.32</td>\n",
       "      <td>99.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635</th>\n",
       "      <td>67.647059</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.411765</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.119</td>\n",
       "      <td>6172.0</td>\n",
       "      <td>139.3</td>\n",
       "      <td>148.9</td>\n",
       "      <td>11.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>667</th>\n",
       "      <td>87.169442</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.773751</td>\n",
       "      <td>...</td>\n",
       "      <td>0.097943</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.975</td>\n",
       "      <td>2424.0</td>\n",
       "      <td>50.7</td>\n",
       "      <td>112.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>11.00</td>\n",
       "      <td>37.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>768</th>\n",
       "      <td>67.647059</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.411765</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.119</td>\n",
       "      <td>6203.0</td>\n",
       "      <td>139.8</td>\n",
       "      <td>150.1</td>\n",
       "      <td>9.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>798</th>\n",
       "      <td>97.991181</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.048996</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.244</td>\n",
       "      <td>5379.0</td>\n",
       "      <td>55.4</td>\n",
       "      <td>131.2</td>\n",
       "      <td>2.1</td>\n",
       "      <td>15.07</td>\n",
       "      <td>33.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          P002       P003      P004       P016       P022      F001  \\\n",
       "98   68.627451   4.901961       NaN        NaN        NaN       NaN   \n",
       "175  67.961165   9.708738       NaN        NaN        NaN       NaN   \n",
       "214  14.084507        NaN       NaN  34.741784  14.084507  4.694836   \n",
       "252  13.145540        NaN       NaN  34.741784  14.084507  4.694836   \n",
       "260  76.470588        NaN       NaN        NaN        NaN       NaN   \n",
       "266  67.961165   7.766990       NaN        NaN        NaN       NaN   \n",
       "267  67.961165   9.708738       NaN        NaN        NaN       NaN   \n",
       "297  98.039216        NaN       NaN        NaN        NaN       NaN   \n",
       "318  55.882353        NaN       NaN        NaN        NaN       NaN   \n",
       "319  75.490196        NaN       NaN        NaN        NaN       NaN   \n",
       "335  49.019608  22.549020       NaN        NaN        NaN       NaN   \n",
       "426  66.933067        NaN  19.98002        NaN        NaN       NaN   \n",
       "488  68.000000        NaN       NaN        NaN        NaN       NaN   \n",
       "489  68.000000        NaN       NaN        NaN        NaN       NaN   \n",
       "548  71.839080        NaN       NaN        NaN        NaN       NaN   \n",
       "622  66.666667        NaN       NaN        NaN        NaN       NaN   \n",
       "635  67.647059        NaN       NaN        NaN        NaN       NaN   \n",
       "667  87.169442        NaN       NaN        NaN        NaN       NaN   \n",
       "768  67.647059        NaN       NaN        NaN        NaN       NaN   \n",
       "798  97.991181        NaN       NaN        NaN        NaN       NaN   \n",
       "\n",
       "          F004       F007       F008       F011  ...      OTH6      OTH7  \\\n",
       "98         NaN  20.588235        NaN        NaN  ...       NaN       NaN   \n",
       "175        NaN  19.417476        NaN        NaN  ...       NaN       NaN   \n",
       "214        NaN        NaN        NaN  14.084507  ...       NaN  0.469484   \n",
       "252        NaN        NaN        NaN  14.084507  ...       NaN  0.469484   \n",
       "260        NaN        NaN  21.568627        NaN  ...       NaN       NaN   \n",
       "266        NaN        NaN        NaN        NaN  ...       NaN       NaN   \n",
       "267        NaN  19.417476        NaN        NaN  ...       NaN       NaN   \n",
       "297        NaN        NaN        NaN        NaN  ...       NaN       NaN   \n",
       "318  39.215686        NaN        NaN        NaN  ...       NaN       NaN   \n",
       "319  19.607843        NaN        NaN        NaN  ...       NaN       NaN   \n",
       "335  24.509804        NaN        NaN        NaN  ...       NaN       NaN   \n",
       "426        NaN        NaN        NaN        NaN  ...       NaN       NaN   \n",
       "488  30.000000        NaN        NaN        NaN  ...       NaN       NaN   \n",
       "489  30.000000        NaN        NaN        NaN  ...       NaN       NaN   \n",
       "548        NaN        NaN        NaN  19.157088  ...       NaN  1.340996   \n",
       "622  30.392157        NaN        NaN        NaN  ...       NaN       NaN   \n",
       "635  29.411765        NaN        NaN        NaN  ...       NaN       NaN   \n",
       "667        NaN        NaN        NaN  10.773751  ...  0.097943       NaN   \n",
       "768  29.411765        NaN        NaN        NaN  ...       NaN       NaN   \n",
       "798        NaN        NaN        NaN        NaN  ...  0.048996       NaN   \n",
       "\n",
       "         OTH8     비중   굴곡탄성률   굴곡강도    HDT  IZOD 충격강도     MI   인장강도  \n",
       "98        NaN  1.040  2653.0   48.9  118.6        1.5  13.70   33.5  \n",
       "175       NaN  1.021  2580.0   51.2  119.9        NaN  14.00   36.4  \n",
       "214  2.816901  1.024  2154.0   33.7  106.1        5.1    NaN   61.8  \n",
       "252  2.816901  0.988  1787.0   30.9   93.5        4.0    NaN   22.2  \n",
       "260       NaN  1.035  2944.0   53.2  118.6        1.9  13.15   36.1  \n",
       "266       NaN  1.047  3057.0   53.6  123.2        3.3  13.96   35.6  \n",
       "267       NaN  1.041  2943.0   53.2   68.1        2.2  14.32   35.1  \n",
       "297       NaN    NaN     NaN    NaN    NaN        NaN    NaN    NaN  \n",
       "318       NaN  1.223  8552.0  161.9  152.5       13.2    NaN  114.0  \n",
       "319       NaN  1.036  4185.0  108.3  140.5       10.3    NaN   78.9  \n",
       "335       NaN  1.073  5037.0  121.0  146.4       11.3    NaN  119.0  \n",
       "426       NaN  0.980  2532.0   52.6  110.9        2.2    NaN   36.5  \n",
       "488       NaN  1.122  5947.0  117.8  140.5        5.3   7.20   82.9  \n",
       "489       NaN  1.114  5999.0  139.2  160.1       12.5   4.80  101.0  \n",
       "548       NaN  1.040  2543.0   44.3  109.1        NaN  15.51   31.4  \n",
       "622       NaN  1.124  5894.0  133.6  159.8       12.0   3.32   99.4  \n",
       "635       NaN  1.119  6172.0  139.3  148.9       11.7    NaN   98.5  \n",
       "667       NaN  0.975  2424.0   50.7  112.3        2.7  11.00   37.1  \n",
       "768       NaN  1.119  6203.0  139.8  150.1        9.5    NaN  100.7  \n",
       "798       NaN  1.244  5379.0   55.4  131.2        2.1  15.07   33.9  \n",
       "\n",
       "[20 rows x 27 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bit_mask = data['P002'] != 0\n",
    "rows_nonzero = data.loc[bit_mask]\n",
    "\n",
    "rows_nan = rows_nonzero.replace(0,np.nan)   # 0값 다 nan로 바꿔줌\n",
    "\n",
    "rows_nan.dropna(how='all', axis=1)   # 모두 0인 열 제거"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>P002</th>\n",
       "      <th>P003</th>\n",
       "      <th>P004</th>\n",
       "      <th>P016</th>\n",
       "      <th>P022</th>\n",
       "      <th>F001</th>\n",
       "      <th>F004</th>\n",
       "      <th>F007</th>\n",
       "      <th>F008</th>\n",
       "      <th>F011</th>\n",
       "      <th>F012</th>\n",
       "      <th>F014</th>\n",
       "      <th>R017</th>\n",
       "      <th>R018</th>\n",
       "      <th>OTH1</th>\n",
       "      <th>OTH3</th>\n",
       "      <th>OTH5</th>\n",
       "      <th>OTH6</th>\n",
       "      <th>OTH7</th>\n",
       "      <th>OTH8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>68.627451</td>\n",
       "      <td>4.901961</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.588235</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.921569</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.960784</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>67.961165</td>\n",
       "      <td>9.708738</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.417476</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.912621</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>14.084507</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.741784</td>\n",
       "      <td>14.084507</td>\n",
       "      <td>4.694836</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.084507</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.206573</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.816901</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.469484</td>\n",
       "      <td>2.816901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>13.145540</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.741784</td>\n",
       "      <td>14.084507</td>\n",
       "      <td>4.694836</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.084507</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.145540</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.816901</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.469484</td>\n",
       "      <td>2.816901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>76.470588</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.568627</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.960784</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>67.961165</td>\n",
       "      <td>7.766990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.359223</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.912621</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>67.961165</td>\n",
       "      <td>9.708738</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.417476</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.912621</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>98.039216</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.960784</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>55.882353</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.215686</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.960784</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.941176</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>75.490196</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.607843</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.960784</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.941176</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>49.019608</td>\n",
       "      <td>22.549020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.509804</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.960784</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.960784</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>66.933067</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.98002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.987013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>68.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>68.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548</th>\n",
       "      <td>71.839080</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.157088</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.789272</td>\n",
       "      <td>2.873563</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.340996</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>622</th>\n",
       "      <td>66.666667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.392157</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.960784</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.980392</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635</th>\n",
       "      <td>67.647059</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.411765</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.960784</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.980392</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>667</th>\n",
       "      <td>87.169442</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.773751</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.958864</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.097943</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>768</th>\n",
       "      <td>67.647059</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.411765</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.960784</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.980392</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>798</th>\n",
       "      <td>97.991181</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.959824</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.048996</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          P002       P003      P004       P016       P022      F001  \\\n",
       "98   68.627451   4.901961       NaN        NaN        NaN       NaN   \n",
       "175  67.961165   9.708738       NaN        NaN        NaN       NaN   \n",
       "214  14.084507        NaN       NaN  34.741784  14.084507  4.694836   \n",
       "252  13.145540        NaN       NaN  34.741784  14.084507  4.694836   \n",
       "260  76.470588        NaN       NaN        NaN        NaN       NaN   \n",
       "266  67.961165   7.766990       NaN        NaN        NaN       NaN   \n",
       "267  67.961165   9.708738       NaN        NaN        NaN       NaN   \n",
       "297  98.039216        NaN       NaN        NaN        NaN       NaN   \n",
       "318  55.882353        NaN       NaN        NaN        NaN       NaN   \n",
       "319  75.490196        NaN       NaN        NaN        NaN       NaN   \n",
       "335  49.019608  22.549020       NaN        NaN        NaN       NaN   \n",
       "426  66.933067        NaN  19.98002        NaN        NaN       NaN   \n",
       "488  68.000000        NaN       NaN        NaN        NaN       NaN   \n",
       "489  68.000000        NaN       NaN        NaN        NaN       NaN   \n",
       "548  71.839080        NaN       NaN        NaN        NaN       NaN   \n",
       "622  66.666667        NaN       NaN        NaN        NaN       NaN   \n",
       "635  67.647059        NaN       NaN        NaN        NaN       NaN   \n",
       "667  87.169442        NaN       NaN        NaN        NaN       NaN   \n",
       "768  67.647059        NaN       NaN        NaN        NaN       NaN   \n",
       "798  97.991181        NaN       NaN        NaN        NaN       NaN   \n",
       "\n",
       "          F004       F007       F008       F011       F012       F014  \\\n",
       "98         NaN  20.588235        NaN        NaN        NaN        NaN   \n",
       "175        NaN  19.417476        NaN        NaN        NaN        NaN   \n",
       "214        NaN        NaN        NaN  14.084507        NaN        NaN   \n",
       "252        NaN        NaN        NaN  14.084507        NaN        NaN   \n",
       "260        NaN        NaN  21.568627        NaN        NaN        NaN   \n",
       "266        NaN        NaN        NaN        NaN        NaN  21.359223   \n",
       "267        NaN  19.417476        NaN        NaN        NaN        NaN   \n",
       "297        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "318  39.215686        NaN        NaN        NaN        NaN        NaN   \n",
       "319  19.607843        NaN        NaN        NaN        NaN        NaN   \n",
       "335  24.509804        NaN        NaN        NaN        NaN        NaN   \n",
       "426        NaN        NaN        NaN        NaN  12.987013        NaN   \n",
       "488  30.000000        NaN        NaN        NaN        NaN        NaN   \n",
       "489  30.000000        NaN        NaN        NaN        NaN        NaN   \n",
       "548        NaN        NaN        NaN  19.157088        NaN        NaN   \n",
       "622  30.392157        NaN        NaN        NaN        NaN        NaN   \n",
       "635  29.411765        NaN        NaN        NaN        NaN        NaN   \n",
       "667        NaN        NaN        NaN  10.773751        NaN        NaN   \n",
       "768  29.411765        NaN        NaN        NaN        NaN        NaN   \n",
       "798        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "          R017      R018      OTH1    OTH3      OTH5      OTH6      OTH7  \\\n",
       "98    3.921569       NaN  1.960784     NaN       NaN       NaN       NaN   \n",
       "175        NaN       NaN  2.912621     NaN       NaN       NaN       NaN   \n",
       "214  12.206573       NaN  2.816901     NaN       NaN       NaN  0.469484   \n",
       "252  13.145540       NaN  2.816901     NaN       NaN       NaN  0.469484   \n",
       "260        NaN       NaN  1.960784     NaN       NaN       NaN       NaN   \n",
       "266        NaN       NaN  2.912621     NaN       NaN       NaN       NaN   \n",
       "267        NaN       NaN  2.912621     NaN       NaN       NaN       NaN   \n",
       "297        NaN       NaN  1.960784     NaN       NaN       NaN       NaN   \n",
       "318        NaN       NaN  1.960784     NaN  2.941176       NaN       NaN   \n",
       "319        NaN       NaN  1.960784     NaN  2.941176       NaN       NaN   \n",
       "335        NaN       NaN  1.960784     NaN  1.960784       NaN       NaN   \n",
       "426        NaN       NaN       NaN  0.0999       NaN       NaN       NaN   \n",
       "488        NaN       NaN       NaN     NaN  2.000000       NaN       NaN   \n",
       "489        NaN       NaN       NaN     NaN  2.000000       NaN       NaN   \n",
       "548        NaN  4.789272  2.873563     NaN       NaN       NaN  1.340996   \n",
       "622        NaN       NaN  1.960784     NaN  0.980392       NaN       NaN   \n",
       "635        NaN       NaN  1.960784     NaN  0.980392       NaN       NaN   \n",
       "667        NaN       NaN  1.958864     NaN       NaN  0.097943       NaN   \n",
       "768        NaN       NaN  1.960784     NaN  0.980392       NaN       NaN   \n",
       "798        NaN       NaN  1.959824     NaN       NaN  0.048996       NaN   \n",
       "\n",
       "         OTH8  \n",
       "98        NaN  \n",
       "175       NaN  \n",
       "214  2.816901  \n",
       "252  2.816901  \n",
       "260       NaN  \n",
       "266       NaN  \n",
       "267       NaN  \n",
       "297       NaN  \n",
       "318       NaN  \n",
       "319       NaN  \n",
       "335       NaN  \n",
       "426       NaN  \n",
       "488       NaN  \n",
       "489       NaN  \n",
       "548       NaN  \n",
       "622       NaN  \n",
       "635       NaN  \n",
       "667       NaN  \n",
       "768       NaN  \n",
       "798       NaN  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bit_mask = X['P006'] != 0\n",
    "rows_nonzero = X.loc[bit_mask]\n",
    "\n",
    "rows_nan = rows_nonzero.replace(0,np.nan)   # 0값 다 nan로 바꿔줌\n",
    "\n",
    "rows_nan.dropna(how='all', axis=1)   # 모두 0인 열 제거"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.912621</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>14.084507</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>34.741784</td>\n",
       "      <td>14.084507</td>\n",
       "      <td>4.694836</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.084507</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.206573</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.816901</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.469484</td>\n",
       "      <td>2.816901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>13.145540</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>34.741784</td>\n",
       "      <td>14.084507</td>\n",
       "      <td>4.694836</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.084507</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.145540</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.816901</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.469484</td>\n",
       "      <td>2.816901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>76.470588</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.568627</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.960784</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>67.961165</td>\n",
       "      <td>7.766990</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.359223</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.912621</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>67.961165</td>\n",
       "      <td>9.708738</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.417476</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.912621</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>98.039216</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.960784</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>55.882353</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>39.215686</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.960784</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2.941176</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>75.490196</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.607843</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.960784</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2.941176</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>49.019608</td>\n",
       "      <td>22.549020</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>24.509804</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.960784</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.960784</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>66.933067</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.98002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.987013</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0999</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>68.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>68.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548</th>\n",
       "      <td>71.839080</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.157088</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.789272</td>\n",
       "      <td>2.873563</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.340996</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>622</th>\n",
       "      <td>66.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.392157</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.960784</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.980392</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635</th>\n",
       "      <td>67.647059</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.411765</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.960784</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.980392</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>667</th>\n",
       "      <td>87.169442</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.773751</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.958864</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.097943</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>768</th>\n",
       "      <td>67.647059</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.411765</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.960784</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.980392</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>798</th>\n",
       "      <td>97.991181</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.959824</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.048996</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          P002       P003      P004       P016       P022      F001  \\\n",
       "98   68.627451   4.901961   0.00000   0.000000   0.000000  0.000000   \n",
       "175  67.961165   9.708738   0.00000   0.000000   0.000000  0.000000   \n",
       "214  14.084507   0.000000   0.00000  34.741784  14.084507  4.694836   \n",
       "252  13.145540   0.000000   0.00000  34.741784  14.084507  4.694836   \n",
       "260  76.470588   0.000000   0.00000   0.000000   0.000000  0.000000   \n",
       "266  67.961165   7.766990   0.00000   0.000000   0.000000  0.000000   \n",
       "267  67.961165   9.708738   0.00000   0.000000   0.000000  0.000000   \n",
       "297  98.039216   0.000000   0.00000   0.000000   0.000000  0.000000   \n",
       "318  55.882353   0.000000   0.00000   0.000000   0.000000  0.000000   \n",
       "319  75.490196   0.000000   0.00000   0.000000   0.000000  0.000000   \n",
       "335  49.019608  22.549020   0.00000   0.000000   0.000000  0.000000   \n",
       "426  66.933067   0.000000  19.98002   0.000000   0.000000  0.000000   \n",
       "488  68.000000   0.000000   0.00000   0.000000   0.000000  0.000000   \n",
       "489  68.000000   0.000000   0.00000   0.000000   0.000000  0.000000   \n",
       "548  71.839080   0.000000   0.00000   0.000000   0.000000  0.000000   \n",
       "622  66.666667   0.000000   0.00000   0.000000   0.000000  0.000000   \n",
       "635  67.647059   0.000000   0.00000   0.000000   0.000000  0.000000   \n",
       "667  87.169442   0.000000   0.00000   0.000000   0.000000  0.000000   \n",
       "768  67.647059   0.000000   0.00000   0.000000   0.000000  0.000000   \n",
       "798  97.991181   0.000000   0.00000   0.000000   0.000000  0.000000   \n",
       "\n",
       "          F004       F007       F008       F011       F012       F014  \\\n",
       "98    0.000000  20.588235   0.000000   0.000000   0.000000   0.000000   \n",
       "175   0.000000  19.417476   0.000000   0.000000   0.000000   0.000000   \n",
       "214   0.000000   0.000000   0.000000  14.084507   0.000000   0.000000   \n",
       "252   0.000000   0.000000   0.000000  14.084507   0.000000   0.000000   \n",
       "260   0.000000   0.000000  21.568627   0.000000   0.000000   0.000000   \n",
       "266   0.000000   0.000000   0.000000   0.000000   0.000000  21.359223   \n",
       "267   0.000000  19.417476   0.000000   0.000000   0.000000   0.000000   \n",
       "297   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000   \n",
       "318  39.215686   0.000000   0.000000   0.000000   0.000000   0.000000   \n",
       "319  19.607843   0.000000   0.000000   0.000000   0.000000   0.000000   \n",
       "335  24.509804   0.000000   0.000000   0.000000   0.000000   0.000000   \n",
       "426   0.000000   0.000000   0.000000   0.000000  12.987013   0.000000   \n",
       "488  30.000000   0.000000   0.000000   0.000000   0.000000   0.000000   \n",
       "489  30.000000   0.000000   0.000000   0.000000   0.000000   0.000000   \n",
       "548   0.000000   0.000000   0.000000  19.157088   0.000000   0.000000   \n",
       "622  30.392157   0.000000   0.000000   0.000000   0.000000   0.000000   \n",
       "635  29.411765   0.000000   0.000000   0.000000   0.000000   0.000000   \n",
       "667   0.000000   0.000000   0.000000  10.773751   0.000000   0.000000   \n",
       "768  29.411765   0.000000   0.000000   0.000000   0.000000   0.000000   \n",
       "798   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000   \n",
       "\n",
       "          R017      R018      OTH1    OTH3      OTH5      OTH6      OTH7  \\\n",
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       "175   0.000000  0.000000  2.912621  0.0000  0.000000  0.000000  0.000000   \n",
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       "252  13.145540  0.000000  2.816901  0.0000  0.000000  0.000000  0.469484   \n",
       "260   0.000000  0.000000  1.960784  0.0000  0.000000  0.000000  0.000000   \n",
       "266   0.000000  0.000000  2.912621  0.0000  0.000000  0.000000  0.000000   \n",
       "267   0.000000  0.000000  2.912621  0.0000  0.000000  0.000000  0.000000   \n",
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       "319   0.000000  0.000000  1.960784  0.0000  2.941176  0.000000  0.000000   \n",
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       "426   0.000000  0.000000  0.000000  0.0999  0.000000  0.000000  0.000000   \n",
       "488   0.000000  0.000000  0.000000  0.0000  2.000000  0.000000  0.000000   \n",
       "489   0.000000  0.000000  0.000000  0.0000  2.000000  0.000000  0.000000   \n",
       "548   0.000000  4.789272  2.873563  0.0000  0.000000  0.000000  1.340996   \n",
       "622   0.000000  0.000000  1.960784  0.0000  0.980392  0.000000  0.000000   \n",
       "635   0.000000  0.000000  1.960784  0.0000  0.980392  0.000000  0.000000   \n",
       "667   0.000000  0.000000  1.958864  0.0000  0.000000  0.097943  0.000000   \n",
       "768   0.000000  0.000000  1.960784  0.0000  0.980392  0.000000  0.000000   \n",
       "798   0.000000  0.000000  1.959824  0.0000  0.000000  0.048996  0.000000   \n",
       "\n",
       "         OTH8  \n",
       "98   0.000000  \n",
       "175  0.000000  \n",
       "214  2.816901  \n",
       "252  2.816901  \n",
       "260  0.000000  \n",
       "266  0.000000  \n",
       "267  0.000000  \n",
       "297  0.000000  \n",
       "318  0.000000  \n",
       "319  0.000000  \n",
       "335  0.000000  \n",
       "426  0.000000  \n",
       "488  0.000000  \n",
       "489  0.000000  \n",
       "548  0.000000  \n",
       "622  0.000000  \n",
       "635  0.000000  \n",
       "667  0.000000  \n",
       "768  0.000000  \n",
       "798  0.000000  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bit_mask = X['P002'] != 0\n",
    "rows_nonzero = X.loc[bit_mask]\n",
    "\n",
    "cols_drop = rows_nonzero.T[(rows_nonzero != 0).any()].T\n",
    "cols_drop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "PP = 'F012'\n",
    "\n",
    "bit_mask = data[PP] != 0\n",
    "rows_nonzero = data.loc[bit_mask]\n",
    "\n",
    "cols_drop = rows_nonzero.T[(rows_nonzero != 0).any()].T\n",
    "cols_drop\n",
    "\n",
    "cols_drop.to_excel('F:\\\\Downloads\\\\GS Project Big leader\\\\F012.xls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "PP = 'R017'\n",
    "\n",
    "bit_mask = data[PP] != 0\n",
    "rows_nonzero = data.loc[bit_mask]\n",
    "\n",
    "cols_drop = rows_nonzero.T[(rows_nonzero != 0).any()].T\n",
    "cols_drop\n",
    "\n",
    "ddddd =cols_drop.sort_values(by=['IZOD 충격강도'] ,ascending=False)\n",
    "\n",
    "P003_R017 = ddddd.loc[ddddd['P008'] != 0]\n",
    "\n",
    "P003_R017.to_excel('F:\\\\Downloads\\\\GS Project Big leader\\\\P008_R017.xls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "684   0.000000   0.000000   0.000000  29.411765  31.372549   4.901961   \n",
       "624   0.000000   0.000000  28.985507   0.000000  30.917874   0.000000   \n",
       "\n",
       "          F009       F011       F016      F017  ...      OTH5      OTH7  \\\n",
       "169   0.000000  32.512315   0.000000  0.000000  ...  0.000000  0.000000   \n",
       "100   0.000000   0.000000   0.000000  0.000000  ...  0.000000  0.000000   \n",
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       "700   0.000000  26.522593   0.000000  0.000000  ...  0.000000  0.294695   \n",
       "347   6.635071   0.000000   0.000000  0.000000  ...  0.000000  0.473934   \n",
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       "344   0.000000   0.000000  32.352941  0.000000  ...  0.000000  0.000000   \n",
       "345   0.000000   0.000000  32.352941  0.000000  ...  0.000000  0.000000   \n",
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       "684   0.000000   0.000000   4.901961  7.843137  ...  0.000000  0.000000   \n",
       "624   0.000000  19.323671   0.000000  0.000000  ...  0.000000  0.483092   \n",
       "\n",
       "         OTH8     비중   굴곡탄성률  굴곡강도    HDT  IZOD 충격강도     MI  인장강도  \n",
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       "100  2.912621  0.895   945.0  22.1   75.6      45.60    NaN  18.5  \n",
       "498  0.000000  1.144  2951.0  35.4  119.8      23.30  20.80  22.5  \n",
       "700  0.000000  1.078  2046.0   NaN  100.2      44.40  33.20  19.0  \n",
       "347  1.421801  0.936  1607.0  35.0  100.7       7.60  30.20  23.9  \n",
       "699  0.000000  1.085  1975.0   NaN  100.1       7.30  24.60  18.5  \n",
       "323  0.000000  1.136  2781.0  36.0  120.0       3.90    NaN  23.3  \n",
       "344  0.000000  1.136  3009.0  34.2  119.2       5.90    NaN  22.5  \n",
       "345  0.000000  1.124  2928.0  34.4  120.3       6.30    NaN  22.8  \n",
       "357  2.803738  0.993  2819.0  37.7  116.0       4.60  32.47  24.9  \n",
       "520  0.000000  1.061  2018.0  38.5  110.6      32.30    NaN  38.7  \n",
       "187  1.960784  0.995  1769.0  30.3  102.9      24.30  30.11  21.0  \n",
       "684  0.000000  0.994  2751.0  40.9  143.1      11.10    NaN  30.5  \n",
       "624  1.932367  1.009  1844.0  29.7  101.0       6.90  16.04  20.5  \n",
       "\n",
       "[14 rows x 29 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pp = cols_drop.loc[(cols_drop[PP] >= 30) & (cols_drop[PP] < 35)]\n",
    "pp\n",
    "\n",
    "pp_1 = pp.T[(pp != 0).any()].T\n",
    "pp_1\n",
    "\n",
    "pp_1.sort_values(by=[PP] ,ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols_drop.to_excel('F:\\\\Downloads\\\\GS Project Big leader\\\\P034.xls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "row = rows_nonzero.index\n",
    "row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rows_nonzero.loc[:,'P001']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "data100 = data == 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "data100.to_excel('F:\\\\Downloads\\\\GS Project Big leader\\\\percent100.xls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "\n",
       "       MI   인장강도  \n",
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       "\n",
       "[7 rows x 97 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_MI = data.loc[(data['비중'] >= ) & (data['비중'] < 4)]\n",
    "data_MI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "      <td>0.000000</td>\n",
       "      <td>2.997003</td>\n",
       "      <td>0.099900</td>\n",
       "      <td>1.076</td>\n",
       "      <td>4864.0</td>\n",
       "      <td>103.3</td>\n",
       "      <td>138.4</td>\n",
       "      <td>5.3</td>\n",
       "      <td>3.20</td>\n",
       "      <td>73.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>622</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.392157</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.960784</td>\n",
       "      <td>0.980392</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.124</td>\n",
       "      <td>5894.0</td>\n",
       "      <td>133.6</td>\n",
       "      <td>159.8</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.32</td>\n",
       "      <td>99.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>639</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.938</td>\n",
       "      <td>166.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>39.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.80</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>643</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.926</td>\n",
       "      <td>43.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>35.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.00</td>\n",
       "      <td>8.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 22 columns</p>\n",
       "</div>"
      ],
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       "          P001       P002       P003  P024  P026  F003       F004  R002  R003  \\\n",
       "46    0.000000   0.000000   0.000000   0.0  47.0   0.0  50.000000   0.0   0.0   \n",
       "61    0.000000   0.000000  52.423343   0.0   0.0   0.0  44.510386   0.0   0.0   \n",
       "63    0.000000   0.000000  52.423343   0.0   0.0   0.0  44.510386   0.0   0.0   \n",
       "403  71.928072   0.000000   0.000000   0.0   0.0   0.0  24.975025   0.0   0.0   \n",
       "622   0.000000  66.666667   0.000000   0.0   0.0   0.0  30.392157   0.0   0.0   \n",
       "639   0.000000   0.000000   0.000000  30.0   0.0   5.0   0.000000  25.0   0.0   \n",
       "643   0.000000   0.000000   0.000000  23.0   0.0   5.0   0.000000   0.0  15.0   \n",
       "\n",
       "     R005  ...      OTH1      OTH5      OTH6     비중    굴곡탄성률   굴곡강도    HDT  \\\n",
       "46    0.0  ...  0.000000  3.000000  0.000000  1.333  11105.0   98.9  163.8   \n",
       "61    0.0  ...  0.989120  1.978239  0.098912  1.277   9806.0  167.5  151.5   \n",
       "63    0.0  ...  0.989120  1.978239  0.098912  1.276   9900.0  168.6  151.8   \n",
       "403   0.0  ...  0.000000  2.997003  0.099900  1.076   4864.0  103.3  138.4   \n",
       "622   0.0  ...  1.960784  0.980392  0.000000  1.124   5894.0  133.6  159.8   \n",
       "639  20.0  ...  0.000000  0.000000  0.000000  0.938    166.0    3.7   39.4   \n",
       "643   0.0  ...  0.000000  0.000000  0.000000  0.926     43.0    2.4   35.2   \n",
       "\n",
       "     IZOD 충격강도    MI   인장강도  \n",
       "46        14.5  3.70  148.6  \n",
       "61        11.2  3.40  123.6  \n",
       "63        11.6  3.20  124.0  \n",
       "403        5.3  3.20   73.5  \n",
       "622       12.0  3.32   99.4  \n",
       "639        NaN  3.80    9.5  \n",
       "643        NaN  3.00    8.3  \n",
       "\n",
       "[7 rows x 22 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_MI_ed = data_MI.T[(data_MI != 0).any()].T\n",
    "data_MI_ed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_MI_ed.to_excel('F:\\\\Downloads\\\\GS Project Big leader\\\\data_MI_ed.xls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>P001</th>\n",
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       "      <th>HDT</th>\n",
       "      <th>IZOD 충격강도</th>\n",
       "      <th>MI</th>\n",
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       "      <td>43.1</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>21.7</td>\n",
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       "      <td>1.087</td>\n",
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       "      <td>25.1</td>\n",
       "      <td>104.2</td>\n",
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       "      <td>43.3</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>510 rows × 97 columns</p>\n",
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       "     P001  P002  P003  P004  P005       P006  P007       P008  P009  P010  \\\n",
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       "2     0.0   0.0   0.0   0.0   0.0  19.607843     0  58.823529   0.0   0.0   \n",
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       "10    0.0   0.0   0.0   0.0   0.0   0.000000     0   0.000000   0.0   0.0   \n",
       "..    ...   ...   ...   ...   ...        ...   ...        ...   ...   ...   \n",
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       "\n",
       "     ...  OTH7      OTH8  OTH9     비중   굴곡탄성률  굴곡강도    HDT  IZOD 충격강도    MI  \\\n",
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       "10   ...   0.0  0.000000   0.0  1.087  1731.0  24.7  107.1       19.6  42.3   \n",
       "..   ...   ...       ...   ...    ...     ...   ...    ...        ...   ...   \n",
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       "\n",
       "     인장강도  \n",
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       "\n",
       "[510 rows x 97 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddda = data.loc[y['굴곡탄성률']*0.0144+5.541 > y['굴곡강도']]\n",
    "ddda"
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  },
  {
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   "execution_count": 24,
   "metadata": {
    "scrolled": true
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   "outputs": [
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     "execution_count": 24,
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   "source": [
    "dddb = data.loc[y['굴곡탄성률']*0.0144+5.541 <= y['굴곡강도']]\n",
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  {
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   "execution_count": 27,
   "metadata": {},
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>113.0</td>\n",
       "      <td>147.2</td>\n",
       "      <td>7.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88.8</td>\n",
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       "    <tr>\n",
       "      <th>43</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.336</td>\n",
       "      <td>11321.0</td>\n",
       "      <td>169.9</td>\n",
       "      <td>163.6</td>\n",
       "      <td>12.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>122.9</td>\n",
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       "    <tr>\n",
       "      <th>61</th>\n",
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       "      <td>52.423343</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.277</td>\n",
       "      <td>9806.0</td>\n",
       "      <td>167.5</td>\n",
       "      <td>151.5</td>\n",
       "      <td>11.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>123.6</td>\n",
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       "      <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",
       "      <th>650</th>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.219</td>\n",
       "      <td>6672.0</td>\n",
       "      <td>148.6</td>\n",
       "      <td>162.7</td>\n",
       "      <td>11.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>106.0</td>\n",
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       "      <th>668</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.968</td>\n",
       "      <td>2514.0</td>\n",
       "      <td>69.8</td>\n",
       "      <td>153.4</td>\n",
       "      <td>4.1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>49.9</td>\n",
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       "    <tr>\n",
       "      <th>729</th>\n",
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       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.964</td>\n",
       "      <td>2464.0</td>\n",
       "      <td>66.4</td>\n",
       "      <td>148.1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>46.8</td>\n",
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       "    <tr>\n",
       "      <th>764</th>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.973</td>\n",
       "      <td>2971.0</td>\n",
       "      <td>60.6</td>\n",
       "      <td>124.2</td>\n",
       "      <td>3.6</td>\n",
       "      <td>13.0</td>\n",
       "      <td>39.1</td>\n",
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       "    <tr>\n",
       "      <th>768</th>\n",
       "      <td>0.0</td>\n",
       "      <td>67.647059</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.119</td>\n",
       "      <td>6203.0</td>\n",
       "      <td>139.8</td>\n",
       "      <td>150.1</td>\n",
       "      <td>9.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>64 rows × 97 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     P001       P002       P003       P004  P005  P006  P007  P008  P009  \\\n",
       "4     0.0   0.000000   0.000000   0.000000   0.0   0.0     0   0.0   0.0   \n",
       "15    0.0   0.000000   0.000000   0.000000   0.0   0.0     0   0.0   0.0   \n",
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       "61    0.0   0.000000  52.423343   0.000000   0.0   0.0     0   0.0   0.0   \n",
       "..    ...        ...        ...        ...   ...   ...   ...   ...   ...   \n",
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       "768   0.0  67.647059   0.000000   0.000000   0.0   0.0     0   0.0   0.0   \n",
       "\n",
       "     P010  ...  OTH7  OTH8  OTH9     비중    굴곡탄성률   굴곡강도    HDT  IZOD 충격강도  \\\n",
       "4     0.0  ...   0.0   0.0   0.0  1.219   8274.0  150.2  151.8       18.3   \n",
       "15    0.0  ...   0.0   0.0   0.0  1.216   8705.0  165.0  152.8       10.3   \n",
       "16    0.0  ...   0.0   0.0   0.0  1.043   4267.0  113.0  147.2        7.7   \n",
       "43    0.0  ...   0.0   0.0   0.0  1.336  11321.0  169.9  163.6       12.7   \n",
       "61    0.0  ...   0.0   0.0   0.0  1.277   9806.0  167.5  151.5       11.2   \n",
       "..    ...  ...   ...   ...   ...    ...      ...    ...    ...        ...   \n",
       "650   0.0  ...   0.0   0.0   0.0  1.219   6672.0  148.6  162.7       11.5   \n",
       "668   0.0  ...   0.0   0.0   0.0  0.968   2514.0   69.8  153.4        4.1   \n",
       "729   0.0  ...   0.0   0.0   0.0  0.964   2464.0   66.4  148.1        4.0   \n",
       "764   0.0  ...   0.0   0.0   0.0  0.973   2971.0   60.6  124.2        3.6   \n",
       "768   0.0  ...   0.0   0.0   0.0  1.119   6203.0  139.8  150.1        9.5   \n",
       "\n",
       "       MI   인장강도  \n",
       "4    25.3  102.0  \n",
       "15    NaN  112.0  \n",
       "16    NaN   88.8  \n",
       "43    0.3  122.9  \n",
       "61    3.4  123.6  \n",
       "..    ...    ...  \n",
       "650   NaN  106.0  \n",
       "668  20.0   49.9  \n",
       "729  14.0   46.8  \n",
       "764  13.0   39.1  \n",
       "768   NaN  100.7  \n",
       "\n",
       "[64 rows x 97 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddda_lim = data.loc[(y['굴곡탄성률']*0.0144+5.541 <= y['굴곡강도']) & (y['굴곡강도'] >= 60.6 )]\n",
    "ddda_lim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>2514.0</td>\n",
       "      <td>69.8</td>\n",
       "      <td>153.4</td>\n",
       "      <td>4.1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>49.9</td>\n",
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       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>85.210578</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.958864</td>\n",
       "      <td>0.097943</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.964</td>\n",
       "      <td>2464.0</td>\n",
       "      <td>66.4</td>\n",
       "      <td>148.1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>46.8</td>\n",
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       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.049480</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.973</td>\n",
       "      <td>2971.0</td>\n",
       "      <td>60.6</td>\n",
       "      <td>124.2</td>\n",
       "      <td>3.6</td>\n",
       "      <td>13.0</td>\n",
       "      <td>39.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>768</th>\n",
       "      <td>0.0</td>\n",
       "      <td>67.647059</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.980392</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.119</td>\n",
       "      <td>6203.0</td>\n",
       "      <td>139.8</td>\n",
       "      <td>150.1</td>\n",
       "      <td>9.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>64 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     P001       P002       P003       P004  P015       P025       P026  \\\n",
       "4     0.0   0.000000   0.000000   0.000000   0.0   0.000000   0.000000   \n",
       "15    0.0   0.000000   0.000000   0.000000   0.0   0.000000   0.000000   \n",
       "16    0.0   0.000000   0.000000   0.000000   0.0   0.000000   0.000000   \n",
       "43    0.0   0.000000   0.000000  48.258706   0.0   0.000000   0.000000   \n",
       "61    0.0   0.000000  52.423343   0.000000   0.0   0.000000   0.000000   \n",
       "..    ...        ...        ...        ...   ...        ...        ...   \n",
       "650   0.0   0.000000   0.000000   0.000000   0.0  39.215686  16.666667   \n",
       "668   0.0   0.000000   0.000000   0.000000   0.0   0.000000   0.000000   \n",
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       "768   0.0  67.647059   0.000000   0.000000   0.0   0.000000   0.000000   \n",
       "\n",
       "          P027  P029  P030  ...      OTH5      OTH6  OTH7     비중    굴곡탄성률  \\\n",
       "4     0.000000   0.0   0.0  ...  2.941176  0.000000   0.0  1.219   8274.0   \n",
       "15   56.324111   0.0   0.0  ...  2.964427  0.197628   0.0  1.216   8705.0   \n",
       "16   76.086957   0.0   0.0  ...  2.964427  0.197628   0.0  1.043   4267.0   \n",
       "43    0.000000   0.0   0.0  ...  1.492537  0.000000   0.0  1.336  11321.0   \n",
       "61    0.000000   0.0   0.0  ...  1.978239  0.098912   0.0  1.277   9806.0   \n",
       "..         ...   ...   ...  ...       ...       ...   ...    ...      ...   \n",
       "650   0.000000   0.0   0.0  ...  2.941176  0.000000   0.0  1.219   6672.0   \n",
       "668  85.210578   0.0   0.0  ...  1.958864  0.097943   0.0  0.968   2514.0   \n",
       "729   0.000000   0.0   0.0  ...  1.958864  0.097943   0.0  0.964   2464.0   \n",
       "764   0.000000   0.0   0.0  ...  0.000000  0.049480   0.0  0.973   2971.0   \n",
       "768   0.000000   0.0   0.0  ...  0.980392  0.000000   0.0  1.119   6203.0   \n",
       "\n",
       "      굴곡강도    HDT  IZOD 충격강도    MI   인장강도  \n",
       "4    150.2  151.8       18.3  25.3  102.0  \n",
       "15   165.0  152.8       10.3   NaN  112.0  \n",
       "16   113.0  147.2        7.7   NaN   88.8  \n",
       "43   169.9  163.6       12.7   0.3  122.9  \n",
       "61   167.5  151.5       11.2   3.4  123.6  \n",
       "..     ...    ...        ...   ...    ...  \n",
       "650  148.6  162.7       11.5   NaN  106.0  \n",
       "668   69.8  153.4        4.1  20.0   49.9  \n",
       "729   66.4  148.1        4.0  14.0   46.8  \n",
       "764   60.6  124.2        3.6  13.0   39.1  \n",
       "768  139.8  150.1        9.5   NaN  100.7  \n",
       "\n",
       "[64 rows x 31 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddda_lim_ed = ddda_lim.T[(ddda_lim != 0).any()].T\n",
    "ddda_lim_ed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>305</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.475</td>\n",
       "      <td>15692.0</td>\n",
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       "      <td>11.1</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>329</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>65.384615</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.138</td>\n",
       "      <td>5562.0</td>\n",
       "      <td>82.9</td>\n",
       "      <td>156.4</td>\n",
       "      <td>5.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>56.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.321</td>\n",
       "      <td>11175.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>159.7</td>\n",
       "      <td>6.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.327</td>\n",
       "      <td>11758.0</td>\n",
       "      <td>117.4</td>\n",
       "      <td>161.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.329</td>\n",
       "      <td>11981.0</td>\n",
       "      <td>112.1</td>\n",
       "      <td>161.9</td>\n",
       "      <td>7.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>74.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.478</td>\n",
       "      <td>14345.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>156.3</td>\n",
       "      <td>8.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>117.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.022</td>\n",
       "      <td>5322.0</td>\n",
       "      <td>57.2</td>\n",
       "      <td>145.8</td>\n",
       "      <td>1.9</td>\n",
       "      <td>16.87</td>\n",
       "      <td>38.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>675</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.132</td>\n",
       "      <td>3861.0</td>\n",
       "      <td>44.4</td>\n",
       "      <td>133.2</td>\n",
       "      <td>4.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>735</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.161</td>\n",
       "      <td>3702.0</td>\n",
       "      <td>47.3</td>\n",
       "      <td>133.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>5.00</td>\n",
       "      <td>29.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>736</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.183</td>\n",
       "      <td>4025.0</td>\n",
       "      <td>48.2</td>\n",
       "      <td>134.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.60</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>737</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.183</td>\n",
       "      <td>4152.0</td>\n",
       "      <td>49.1</td>\n",
       "      <td>137.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>5.40</td>\n",
       "      <td>30.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>738</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.181</td>\n",
       "      <td>3553.0</td>\n",
       "      <td>41.8</td>\n",
       "      <td>130.8</td>\n",
       "      <td>3.9</td>\n",
       "      <td>12.00</td>\n",
       "      <td>26.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>739</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.185</td>\n",
       "      <td>3980.0</td>\n",
       "      <td>49.1</td>\n",
       "      <td>135.8</td>\n",
       "      <td>5.3</td>\n",
       "      <td>5.60</td>\n",
       "      <td>30.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>740</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.176</td>\n",
       "      <td>3756.0</td>\n",
       "      <td>45.8</td>\n",
       "      <td>132.9</td>\n",
       "      <td>3.6</td>\n",
       "      <td>9.87</td>\n",
       "      <td>28.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>786</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.165</td>\n",
       "      <td>4260.0</td>\n",
       "      <td>47.1</td>\n",
       "      <td>132.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>12.39</td>\n",
       "      <td>29.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>787</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.185</td>\n",
       "      <td>4388.0</td>\n",
       "      <td>46.7</td>\n",
       "      <td>136.8</td>\n",
       "      <td>2.2</td>\n",
       "      <td>15.06</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>792</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.165</td>\n",
       "      <td>4316.0</td>\n",
       "      <td>46.2</td>\n",
       "      <td>131.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>21.59</td>\n",
       "      <td>28.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>798</th>\n",
       "      <td>0.0</td>\n",
       "      <td>97.991181</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.244</td>\n",
       "      <td>5379.0</td>\n",
       "      <td>55.4</td>\n",
       "      <td>131.2</td>\n",
       "      <td>2.1</td>\n",
       "      <td>15.07</td>\n",
       "      <td>33.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30 rows × 97 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     P001       P002  P003       P004  P005  P006  P007  P008  P009  P010  \\\n",
       "46    0.0   0.000000   0.0   0.000000   0.0   0.0     0   0.0   0.0   0.0   \n",
       "47    0.0   0.000000   0.0   0.000000   0.0   0.0     0   0.0   0.0   0.0   \n",
       "244   0.0   0.000000  50.0   0.000000   0.0   0.0     0   0.0   0.0   0.0   \n",
       "245   0.0   0.000000  30.0   0.000000   0.0   0.0     0   0.0   0.0   0.0   \n",
       "246   0.0   0.000000  20.0   0.000000   0.0   0.0     0   0.0   0.0   0.0   \n",
       "247   0.0   0.000000  40.0   0.000000   0.0   0.0     0   0.0   0.0   0.0   \n",
       "280   0.0   0.000000   0.0   0.000000   0.0   0.0     0   0.0   0.0   0.0   \n",
       "281   0.0   0.000000   0.0   0.000000   0.0   0.0     0   0.0   0.0   0.0   \n",
       "286   0.0   0.000000   0.0   0.000000   0.0   0.0     0   0.0   0.0   0.0   \n",
       "303   0.0   0.000000   0.0   0.000000   0.0   0.0     0   0.0   0.0   0.0   \n",
       "305   0.0   0.000000   0.0   0.000000   0.0   0.0     0   0.0   0.0   0.0   \n",
       "329   0.0   0.000000   0.0   0.000000   0.0   0.0     0   0.0   0.0   0.0   \n",
       "355   0.0   0.000000   0.0   0.000000   0.0   0.0     0   0.0   0.0   0.0   \n",
       "376   0.0   0.000000   0.0  65.384615   0.0   0.0     0   0.0   0.0   0.0   \n",
       "390   0.0   0.000000   0.0   0.000000   0.0   0.0     0   0.0   0.0   0.0   \n",
       "391   0.0   0.000000   0.0   0.000000   0.0   0.0     0   0.0   0.0   0.0   \n",
       "392   0.0   0.000000   0.0   0.000000   0.0   0.0     0   0.0   0.0   0.0   \n",
       "393   0.0   0.000000   0.0   0.000000   0.0   0.0     0   0.0   0.0   0.0   \n",
       "416   0.0   0.000000   0.0   0.000000   0.0   0.0     0   0.0   0.0   0.0   \n",
       "675   0.0   0.000000   0.0   0.000000   0.0   0.0     0   0.0   0.0   0.0   \n",
       "735   0.0   0.000000   0.0   0.000000   0.0   0.0     0   0.0   0.0   0.0   \n",
       "736   0.0   0.000000   0.0   0.000000   0.0   0.0     0   0.0   0.0   0.0   \n",
       "737   0.0   0.000000   0.0   0.000000   0.0   0.0     0   0.0   0.0   0.0   \n",
       "738   0.0   0.000000   0.0   0.000000   0.0   0.0     0   0.0   0.0   0.0   \n",
       "739   0.0   0.000000   0.0   0.000000   0.0   0.0     0   0.0   0.0   0.0   \n",
       "740   0.0   0.000000   0.0   0.000000   0.0   0.0     0   0.0   0.0   0.0   \n",
       "786   0.0   0.000000   0.0   0.000000   0.0   0.0     0   0.0   0.0   0.0   \n",
       "787   0.0   0.000000   0.0   0.000000   0.0   0.0     0   0.0   0.0   0.0   \n",
       "792   0.0   0.000000   0.0   0.000000   0.0   0.0     0   0.0   0.0   0.0   \n",
       "798   0.0  97.991181   0.0   0.000000   0.0   0.0     0   0.0   0.0   0.0   \n",
       "\n",
       "     ...  OTH7  OTH8  OTH9     비중    굴곡탄성률   굴곡강도    HDT  IZOD 충격강도     MI  \\\n",
       "46   ...   0.0   0.0   0.0  1.333  11105.0   98.9  163.8       14.5   3.70   \n",
       "47   ...   0.0   0.0   0.0  1.338  11046.0   95.9  163.3       13.9   4.80   \n",
       "244  ...   0.0   0.0   0.0  1.330  11127.0  121.9  160.1        7.7    NaN   \n",
       "245  ...   0.0   0.0   0.0  1.322  11206.0  118.2  159.9       10.1    NaN   \n",
       "246  ...   0.0   0.0   0.0  1.330  11077.0  116.5  159.7       21.3    NaN   \n",
       "247  ...   0.0   0.0   0.0  1.324   9873.0  120.9  160.1        8.7    NaN   \n",
       "280  ...   0.0   0.0   0.0  1.084   3901.0   52.8  133.6        2.3    NaN   \n",
       "281  ...   0.0   0.0   0.0  1.223   5787.0   60.7  143.9        2.3    NaN   \n",
       "286  ...   0.0   0.0   0.0  1.123   4560.0   60.8  137.1        2.6    NaN   \n",
       "303  ...   0.0   0.0   0.0  1.516  16294.0  185.6  157.9       10.2    NaN   \n",
       "305  ...   0.0   0.0   0.0  1.475  15692.0  198.7  157.3       11.1    NaN   \n",
       "329  ...   0.0   0.0   0.0  1.332  11070.0  135.7  156.6        6.8    NaN   \n",
       "355  ...   0.0   0.0   0.0  1.206   4113.0   48.7  132.5        6.0   9.44   \n",
       "376  ...   0.0   0.0   0.0  1.138   5562.0   82.9  156.4        5.2    NaN   \n",
       "390  ...   0.0   0.0   0.0  1.321  11175.0  113.0  159.7        6.8    NaN   \n",
       "391  ...   0.0   0.0   0.0  1.327  11758.0  117.4  161.0        6.5    NaN   \n",
       "392  ...   0.0   0.0   0.0  1.329  11981.0  112.1  161.9        7.9    NaN   \n",
       "393  ...   0.0   0.0   0.0  1.478  14345.0  174.0  156.3        8.8    NaN   \n",
       "416  ...   0.0   0.0   0.0  1.022   5322.0   57.2  145.8        1.9  16.87   \n",
       "675  ...   0.0   0.0   0.0  1.132   3861.0   44.4  133.2        4.5    NaN   \n",
       "735  ...   0.0   0.0   0.0  1.161   3702.0   47.3  133.4        2.3   5.00   \n",
       "736  ...   0.0   0.0   0.0  1.183   4025.0   48.2  134.8        2.7   5.60   \n",
       "737  ...   0.0   0.0   0.0  1.183   4152.0   49.1  137.0        2.2   5.40   \n",
       "738  ...   0.0   0.0   0.0  1.181   3553.0   41.8  130.8        3.9  12.00   \n",
       "739  ...   0.0   0.0   0.0  1.185   3980.0   49.1  135.8        5.3   5.60   \n",
       "740  ...   0.0   0.0   0.0  1.176   3756.0   45.8  132.9        3.6   9.87   \n",
       "786  ...   0.0   0.0   0.0  1.165   4260.0   47.1  132.8        4.8  12.39   \n",
       "787  ...   0.0   0.0   0.0  1.185   4388.0   46.7  136.8        2.2  15.06   \n",
       "792  ...   0.0   0.0   0.0  1.165   4316.0   46.2  131.0        4.0  21.59   \n",
       "798  ...   0.0   0.0   0.0  1.244   5379.0   55.4  131.2        2.1  15.07   \n",
       "\n",
       "      인장강도  \n",
       "46   148.6  \n",
       "47   145.3  \n",
       "244   83.1  \n",
       "245   72.3  \n",
       "246   71.8  \n",
       "247   77.8  \n",
       "280   33.3  \n",
       "281   37.6  \n",
       "286   37.8  \n",
       "303  115.6  \n",
       "305  130.0  \n",
       "329   98.2  \n",
       "355   29.1  \n",
       "376   56.7  \n",
       "390   72.8  \n",
       "391   75.0  \n",
       "392   74.5  \n",
       "393  117.4  \n",
       "416   38.2  \n",
       "675   28.2  \n",
       "735   29.6  \n",
       "736   30.0  \n",
       "737   30.3  \n",
       "738   26.4  \n",
       "739   30.6  \n",
       "740   28.6  \n",
       "786   29.1  \n",
       "787   29.0  \n",
       "792   28.7  \n",
       "798   33.9  \n",
       "\n",
       "[30 rows x 97 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dddb_lim = data.loc[(y['굴곡탄성률']*0.0144+5.541 >= y['굴곡강도']) & (y['굴곡탄성률'] >= 3553 )]\n",
    "dddb_lim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
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       "      <td>11758.0</td>\n",
       "      <td>117.4</td>\n",
       "      <td>161.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.329</td>\n",
       "      <td>11981.0</td>\n",
       "      <td>112.1</td>\n",
       "      <td>161.9</td>\n",
       "      <td>7.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>74.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.810945</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.990050</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.478</td>\n",
       "      <td>14345.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>156.3</td>\n",
       "      <td>8.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>117.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>88.235294</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.022</td>\n",
       "      <td>5322.0</td>\n",
       "      <td>57.2</td>\n",
       "      <td>145.8</td>\n",
       "      <td>1.9</td>\n",
       "      <td>16.87</td>\n",
       "      <td>38.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>675</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.132</td>\n",
       "      <td>3861.0</td>\n",
       "      <td>44.4</td>\n",
       "      <td>133.2</td>\n",
       "      <td>4.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>735</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.686275</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39.215686</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.980392</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.161</td>\n",
       "      <td>3702.0</td>\n",
       "      <td>47.3</td>\n",
       "      <td>133.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>5.00</td>\n",
       "      <td>29.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>736</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.450980</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.980392</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.183</td>\n",
       "      <td>4025.0</td>\n",
       "      <td>48.2</td>\n",
       "      <td>134.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.60</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>737</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.450980</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.980392</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.183</td>\n",
       "      <td>4152.0</td>\n",
       "      <td>49.1</td>\n",
       "      <td>137.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>5.40</td>\n",
       "      <td>30.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>738</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>24.509804</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.490196</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.980392</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.181</td>\n",
       "      <td>3553.0</td>\n",
       "      <td>41.8</td>\n",
       "      <td>130.8</td>\n",
       "      <td>3.9</td>\n",
       "      <td>12.00</td>\n",
       "      <td>26.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>739</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.861386</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39.603960</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.990099</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.185</td>\n",
       "      <td>3980.0</td>\n",
       "      <td>49.1</td>\n",
       "      <td>135.8</td>\n",
       "      <td>5.3</td>\n",
       "      <td>5.60</td>\n",
       "      <td>30.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>740</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>24.752475</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.752475</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.990099</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.176</td>\n",
       "      <td>3756.0</td>\n",
       "      <td>45.8</td>\n",
       "      <td>132.9</td>\n",
       "      <td>3.6</td>\n",
       "      <td>9.87</td>\n",
       "      <td>28.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>786</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.509804</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.411765</td>\n",
       "      <td>...</td>\n",
       "      <td>0.980392</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.165</td>\n",
       "      <td>4260.0</td>\n",
       "      <td>47.1</td>\n",
       "      <td>132.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>12.39</td>\n",
       "      <td>29.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>787</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.699321</td>\n",
       "      <td>...</td>\n",
       "      <td>0.096993</td>\n",
       "      <td>0.969932</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.185</td>\n",
       "      <td>4388.0</td>\n",
       "      <td>46.7</td>\n",
       "      <td>136.8</td>\n",
       "      <td>2.2</td>\n",
       "      <td>15.06</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>792</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.686275</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.450980</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.803922</td>\n",
       "      <td>...</td>\n",
       "      <td>0.980392</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.165</td>\n",
       "      <td>4316.0</td>\n",
       "      <td>46.2</td>\n",
       "      <td>131.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>21.59</td>\n",
       "      <td>28.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>798</th>\n",
       "      <td>97.991181</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.048996</td>\n",
       "      <td>1.244</td>\n",
       "      <td>5379.0</td>\n",
       "      <td>55.4</td>\n",
       "      <td>131.2</td>\n",
       "      <td>2.1</td>\n",
       "      <td>15.07</td>\n",
       "      <td>33.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          P002  P003       P004      P013       P019       P022  P025  \\\n",
       "46    0.000000   0.0   0.000000  0.000000   0.000000   0.000000   0.0   \n",
       "47    0.000000   0.0   0.000000  0.000000   0.000000   0.000000   0.0   \n",
       "244   0.000000  50.0   0.000000  0.000000   0.000000   0.000000   0.0   \n",
       "245   0.000000  30.0   0.000000  0.000000   0.000000   0.000000   0.0   \n",
       "246   0.000000  20.0   0.000000  0.000000   0.000000   0.000000   0.0   \n",
       "247   0.000000  40.0   0.000000  0.000000   0.000000   0.000000   0.0   \n",
       "280   0.000000   0.0   0.000000  0.000000   0.000000   0.000000   0.0   \n",
       "281   0.000000   0.0   0.000000  0.000000   0.000000   0.000000  45.0   \n",
       "286   0.000000   0.0   0.000000  0.000000   0.000000   0.000000  50.0   \n",
       "303   0.000000   0.0   0.000000  0.000000   0.000000   0.000000   0.0   \n",
       "305   0.000000   0.0   0.000000  9.950249   0.000000   0.000000   0.0   \n",
       "329   0.000000   0.0   0.000000  0.000000   0.000000   0.000000   0.0   \n",
       "355   0.000000   0.0   0.000000  0.000000   0.000000  23.300971   0.0   \n",
       "376   0.000000   0.0  65.384615  0.000000   0.000000   0.000000   0.0   \n",
       "390   0.000000   0.0   0.000000  0.000000   0.000000   0.000000   0.0   \n",
       "391   0.000000   0.0   0.000000  0.000000   0.000000   0.000000   0.0   \n",
       "392   0.000000   0.0   0.000000  0.000000   0.000000   0.000000  50.0   \n",
       "393   0.000000   0.0   0.000000  0.000000   0.000000   0.000000   0.0   \n",
       "416   0.000000   0.0   0.000000  0.000000  88.235294   0.000000   0.0   \n",
       "675   0.000000   0.0   0.000000  0.000000   0.000000   0.000000   0.0   \n",
       "735   0.000000   0.0   0.000000  0.000000   0.000000   0.000000   0.0   \n",
       "736   0.000000   0.0   0.000000  0.000000   0.000000   0.000000   0.0   \n",
       "737   0.000000   0.0   0.000000  0.000000   0.000000   0.000000   0.0   \n",
       "738   0.000000   0.0   0.000000  0.000000   0.000000  24.509804   0.0   \n",
       "739   0.000000   0.0   0.000000  0.000000   0.000000   0.000000   0.0   \n",
       "740   0.000000   0.0   0.000000  0.000000   0.000000  24.752475   0.0   \n",
       "786   0.000000   0.0   0.000000  0.000000   0.000000   0.000000   0.0   \n",
       "787   0.000000   0.0   0.000000  0.000000   0.000000   0.000000   0.0   \n",
       "792   0.000000   0.0   0.000000  0.000000   0.000000  15.686275   0.0   \n",
       "798  97.991181   0.0   0.000000  0.000000   0.000000   0.000000   0.0   \n",
       "\n",
       "          P026  P027       P029  ...      OTH3      OTH5      OTH6     비중  \\\n",
       "46   47.000000   0.0   0.000000  ...  0.000000  3.000000  0.000000  1.333   \n",
       "47   45.000000   0.0   0.000000  ...  0.000000  3.000000  0.000000  1.338   \n",
       "244   0.000000   0.0   0.000000  ...  0.000000  0.000000  0.000000  1.330   \n",
       "245   0.000000  20.0   0.000000  ...  0.000000  0.000000  0.000000  1.322   \n",
       "246   0.000000  30.0   0.000000  ...  0.000000  0.000000  0.000000  1.330   \n",
       "247   0.000000  10.0   0.000000  ...  0.000000  0.000000  0.000000  1.324   \n",
       "280   0.000000   0.0  60.000000  ...  0.000000  0.000000  0.000000  1.084   \n",
       "281   0.000000   0.0  15.000000  ...  0.000000  0.000000  0.000000  1.223   \n",
       "286   0.000000   0.0  20.000000  ...  0.000000  0.000000  0.000000  1.123   \n",
       "303  37.810945   0.0   0.000000  ...  0.000000  1.990050  0.000000  1.516   \n",
       "305  27.860697   0.0   0.000000  ...  0.000000  1.990050  0.000000  1.475   \n",
       "329  49.751244   0.0   0.000000  ...  0.000000  0.000000  0.000000  1.332   \n",
       "355  26.213592   0.0   0.000000  ...  0.000000  0.970874  0.000000  1.206   \n",
       "376   0.000000   0.0   0.000000  ...  0.000000  1.923077  0.000000  1.138   \n",
       "390   0.000000  50.0   0.000000  ...  0.000000  0.000000  0.000000  1.321   \n",
       "391  50.000000   0.0   0.000000  ...  0.000000  0.000000  0.000000  1.327   \n",
       "392   0.000000   0.0   0.000000  ...  0.000000  0.000000  0.000000  1.329   \n",
       "393  37.810945   0.0   0.000000  ...  0.000000  1.990050  0.000000  1.478   \n",
       "416   0.000000   0.0   0.000000  ...  0.000000  0.000000  0.000000  1.022   \n",
       "675   0.000000   0.0   0.000000  ...  0.000000  0.000000  0.000000  1.132   \n",
       "735  15.686275   0.0  39.215686  ...  0.000000  0.980392  0.000000  1.161   \n",
       "736   0.000000   0.0  27.450980  ...  0.000000  0.980392  0.000000  1.183   \n",
       "737   0.000000   0.0  27.450980  ...  0.000000  0.980392  0.000000  1.183   \n",
       "738  25.490196   0.0   0.000000  ...  0.000000  0.980392  0.000000  1.181   \n",
       "739  13.861386   0.0  39.603960  ...  0.000000  0.990099  0.000000  1.185   \n",
       "740  24.752475   0.0   0.000000  ...  0.000000  0.990099  0.000000  1.176   \n",
       "786  24.509804   0.0  29.411765  ...  0.980392  0.000000  0.000000  1.165   \n",
       "787   0.000000   0.0   9.699321  ...  0.096993  0.969932  0.000000  1.185   \n",
       "792  27.450980   0.0   9.803922  ...  0.980392  0.000000  0.000000  1.165   \n",
       "798   0.000000   0.0   0.000000  ...  0.000000  0.000000  0.048996  1.244   \n",
       "\n",
       "       굴곡탄성률   굴곡강도    HDT  IZOD 충격강도     MI   인장강도  \n",
       "46   11105.0   98.9  163.8       14.5   3.70  148.6  \n",
       "47   11046.0   95.9  163.3       13.9   4.80  145.3  \n",
       "244  11127.0  121.9  160.1        7.7    NaN   83.1  \n",
       "245  11206.0  118.2  159.9       10.1    NaN   72.3  \n",
       "246  11077.0  116.5  159.7       21.3    NaN   71.8  \n",
       "247   9873.0  120.9  160.1        8.7    NaN   77.8  \n",
       "280   3901.0   52.8  133.6        2.3    NaN   33.3  \n",
       "281   5787.0   60.7  143.9        2.3    NaN   37.6  \n",
       "286   4560.0   60.8  137.1        2.6    NaN   37.8  \n",
       "303  16294.0  185.6  157.9       10.2    NaN  115.6  \n",
       "305  15692.0  198.7  157.3       11.1    NaN  130.0  \n",
       "329  11070.0  135.7  156.6        6.8    NaN   98.2  \n",
       "355   4113.0   48.7  132.5        6.0   9.44   29.1  \n",
       "376   5562.0   82.9  156.4        5.2    NaN   56.7  \n",
       "390  11175.0  113.0  159.7        6.8    NaN   72.8  \n",
       "391  11758.0  117.4  161.0        6.5    NaN   75.0  \n",
       "392  11981.0  112.1  161.9        7.9    NaN   74.5  \n",
       "393  14345.0  174.0  156.3        8.8    NaN  117.4  \n",
       "416   5322.0   57.2  145.8        1.9  16.87   38.2  \n",
       "675   3861.0   44.4  133.2        4.5    NaN   28.2  \n",
       "735   3702.0   47.3  133.4        2.3   5.00   29.6  \n",
       "736   4025.0   48.2  134.8        2.7   5.60   30.0  \n",
       "737   4152.0   49.1  137.0        2.2   5.40   30.3  \n",
       "738   3553.0   41.8  130.8        3.9  12.00   26.4  \n",
       "739   3980.0   49.1  135.8        5.3   5.60   30.6  \n",
       "740   3756.0   45.8  132.9        3.6   9.87   28.6  \n",
       "786   4260.0   47.1  132.8        4.8  12.39   29.1  \n",
       "787   4388.0   46.7  136.8        2.2  15.06   29.0  \n",
       "792   4316.0   46.2  131.0        4.0  21.59   28.7  \n",
       "798   5379.0   55.4  131.2        2.1  15.07   33.9  \n",
       "\n",
       "[30 rows x 32 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dddb_lim_ed = dddb_lim.T[(dddb_lim != 0).any()].T\n",
    "dddb_lim_ed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "ddda_lim_ed.to_excel('F:\\\\Downloads\\\\GS Project Big leader\\\\굴곡탄성률강도above_lim.xls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "dddb_lim_ed.to_excel('F:\\\\Downloads\\\\GS Project Big leader\\\\굴곡탄성률강도below_lim.xls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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