overtpopping.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "pd.set_option(\"display.max_columns\", None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting xlrd\n",
      "  Downloading xlrd-2.0.1-py2.py3-none-any.whl (96 kB)\n",
      "Installing collected packages: xlrd\n",
      "Successfully installed xlrd-2.0.1\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "# %pip install xlrd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_excel('Database_20050101.xls', sheet_name ='20050101')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=data[1:]\n",
    "data=data[data.columns[~(data.columns.isin( [\"Pow\",\t\"Remark\",\"Reference\"]))]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=data[~((data.RF==4)|(data.CF==4))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9403, 30)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Hm0 deep</th>\n",
       "      <th>Tp deep</th>\n",
       "      <th>Tm deep</th>\n",
       "      <th>Tm-1,0 deep</th>\n",
       "      <th>h deep</th>\n",
       "      <th>m</th>\n",
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       "      <th>ht</th>\n",
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       "      <th>Rc</th>\n",
       "      <th>B</th>\n",
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       "      <th>tanaB</th>\n",
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       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>001-011</td>\n",
       "      <td>0.138125</td>\n",
       "      <td>2.165217</td>\n",
       "      <td>1.804348</td>\n",
       "      <td>1.968379</td>\n",
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       "      <td>1.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0.03125</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.000188</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>001-017</td>\n",
       "      <td>0.138125</td>\n",
       "      <td>2.165217</td>\n",
       "      <td>1.804348</td>\n",
       "      <td>1.968379</td>\n",
       "      <td>0.23625</td>\n",
       "      <td>42.857143</td>\n",
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       "      <td>0.085</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0.08375</td>\n",
       "      <td>0.431875</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.000406</td>\n",
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       "    <tr>\n",
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       "      <td>1.632091</td>\n",
       "      <td>1.780462</td>\n",
       "      <td>0.8</td>\n",
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       "      <td>0.184615</td>\n",
       "      <td>0.55</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.273846</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.215385</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
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       "      <td>1.38804</td>\n",
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       "      <td>1.38804</td>\n",
       "      <td>1.514225</td>\n",
       "      <td>0.218462</td>\n",
       "      <td>0.184615</td>\n",
       "      <td>0.55</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.273846</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.215385</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000203</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "       Name  Hm0 deep   Tp deep   Tm deep Tm-1,0 deep   h deep          m  b  \\\n",
       "5   001-005  0.138125  2.165217  1.804348    1.968379  0.23625  42.857143  0   \n",
       "11  001-011  0.138125  2.165217  1.804348    1.968379  0.23625  42.857143  0   \n",
       "17  001-017  0.138125  2.165217  1.804348    1.968379  0.23625  42.857143  0   \n",
       "25  002-001  0.081231  1.958509  1.632091    1.780462      0.8  66.000000  0   \n",
       "26  002-002  0.152308  1.665648   1.38804    1.514225      0.8  66.000000  0   \n",
       "\n",
       "           h   Hm0 toe    Tp toe    Tm toe Tm-1,0 toe        ht        Bt  \\\n",
       "5      0.085     0.063  2.165217  1.804348   1.968379     0.085         0   \n",
       "11     0.085     0.063  2.165217  1.804348   1.968379     0.085         0   \n",
       "17     0.085     0.063  2.165217  1.804348   1.968379     0.085         0   \n",
       "25  0.326154  0.081231  1.958509  1.632091   1.780462  0.218462  0.184615   \n",
       "26  0.326154  0.152308  1.665648   1.38804   1.514225  0.218462  0.184615   \n",
       "\n",
       "      gf     cotad     cotau  cotaexcl  cotaincl        Rc  B hb  tanaB Bh  \\\n",
       "5   1.00  0.000000  0.000000  0.000000  0.000000     0.025  0  0    0.0  0   \n",
       "11  1.00  0.000000  0.000000  0.000000  0.000000       0.1  0  0    0.0  0   \n",
       "17  1.00  0.000000  0.000000  0.000000  0.000000     0.025  0  0    0.0  0   \n",
       "25  0.55  1.333333  1.333333  1.333333  1.333333  0.273846  0  0    0.0  0   \n",
       "26  0.55  1.333333  1.333333  1.333333  1.333333  0.273846  0  0    0.0  0   \n",
       "\n",
       "          Ac        Gc   RF   CF         q  \n",
       "5    0.00875  0.431875  3.0  3.0  0.002063  \n",
       "11   0.03125  0.431875  3.0  3.0  0.000188  \n",
       "17   0.08375  0.431875  3.0  3.0  0.000406  \n",
       "25  0.215385  0.076923  2.0  2.0         0  \n",
       "26  0.215385  0.076923  2.0  2.0  0.000203  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name             0\n",
       "Hm0 deep       435\n",
       "Tp deep        379\n",
       "Tm deep        379\n",
       "Tm-1,0 deep    379\n",
       "h deep           1\n",
       "m               54\n",
       "b               24\n",
       "h                2\n",
       "Hm0 toe         35\n",
       "Tp toe          59\n",
       "Tm toe          59\n",
       "Tm-1,0 toe      25\n",
       "ht               1\n",
       "Bt               0\n",
       "gf              16\n",
       "cotad            0\n",
       "cotau            0\n",
       "cotaexcl        26\n",
       "cotaincl        26\n",
       "Rc               1\n",
       "B                0\n",
       "hb               0\n",
       "tanaB            0\n",
       "Bh               0\n",
       "Ac               0\n",
       "Gc               0\n",
       "RF               0\n",
       "CF               0\n",
       "q                7\n",
       "dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "not many missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>809.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>929.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>290.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94.0</td>\n",
       "      <td>954.0</td>\n",
       "      <td>203.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>7445.0</td>\n",
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       "      <td>1.156522</td>\n",
       "      <td>1.26166</td>\n",
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       "      <td>8260.0</td>\n",
       "      <td>410.00</td>\n",
       "      <td>284.000</td>\n",
       "      <td>270.000000</td>\n",
       "      <td>270.000000</td>\n",
       "      <td>270.00000</td>\n",
       "      <td>430.0</td>\n",
       "      <td>5994.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>758.0</td>\n",
       "      <td>7781.0</td>\n",
       "      <td>7258.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7781.0</td>\n",
       "      <td>797.0</td>\n",
       "      <td>4352.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>909.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\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>454.315906</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>0.69723</td>\n",
       "      <td>1.942951</td>\n",
       "      <td>1.859142</td>\n",
       "      <td>1.943630</td>\n",
       "      <td>2.157736</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.001759</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.941721</td>\n",
       "      <td>1.314155</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\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>464.917772</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>0.27427</td>\n",
       "      <td>1.446430</td>\n",
       "      <td>1.722511</td>\n",
       "      <td>1.493797</td>\n",
       "      <td>1.709688</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.010369</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.784344</td>\n",
       "      <td>0.627152</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\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>6.000000</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>0.33000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5.000000</td>\n",
       "      <td>-1.532567</td>\n",
       "      <td>-1.532567</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\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>40.473904</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>0.40000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>1.334259</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\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>100.000000</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>0.70000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\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>1000.000000</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>1.00000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.574703</td>\n",
       "      <td>2.807869</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\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>1000.000000</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>1.00000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>9.705882</td>\n",
       "      <td>8.144491</td>\n",
       "      <td>12.820513</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.100935</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Name  Hm0 deep      Tp deep      Tm deep  Tm-1,0 deep  h deep  \\\n",
       "count      9403  9028.000  9071.000000  9071.000000   9071.00000  9403.0   \n",
       "unique     9403  3244.000  1756.000000  2779.000000   2760.00000   620.0   \n",
       "top     001-005     0.129     1.387826     1.156522      1.26166     0.7   \n",
       "freq          1   289.000   270.000000   270.000000    270.00000   876.0   \n",
       "mean        NaN       NaN          NaN          NaN          NaN     NaN   \n",
       "std         NaN       NaN          NaN          NaN          NaN     NaN   \n",
       "min         NaN       NaN          NaN          NaN          NaN     NaN   \n",
       "25%         NaN       NaN          NaN          NaN          NaN     NaN   \n",
       "50%         NaN       NaN          NaN          NaN          NaN     NaN   \n",
       "75%         NaN       NaN          NaN          NaN          NaN     NaN   \n",
       "max         NaN       NaN          NaN          NaN          NaN     NaN   \n",
       "\n",
       "                  m       b        h   Hm0 toe       Tp toe       Tm toe  \\\n",
       "count   9403.000000  9403.0  9403.00  9403.000  9369.000000  9369.000000   \n",
       "unique          NaN    51.0   763.00  3764.000  1951.000000  2842.000000   \n",
       "top             NaN     0.0     0.16     0.129     1.387826     1.156522   \n",
       "freq            NaN  8260.0   410.00   284.000   270.000000   270.000000   \n",
       "mean     454.315906     NaN      NaN       NaN          NaN          NaN   \n",
       "std      464.917772     NaN      NaN       NaN          NaN          NaN   \n",
       "min        6.000000     NaN      NaN       NaN          NaN          NaN   \n",
       "25%       40.473904     NaN      NaN       NaN          NaN          NaN   \n",
       "50%      100.000000     NaN      NaN       NaN          NaN          NaN   \n",
       "75%     1000.000000     NaN      NaN       NaN          NaN          NaN   \n",
       "max     1000.000000     NaN      NaN       NaN          NaN          NaN   \n",
       "\n",
       "        Tm-1,0 toe      ht      Bt          gf        cotad        cotau  \\\n",
       "count   9403.00000  9403.0  9403.0  9403.00000  9403.000000  9403.000000   \n",
       "unique  2940.00000   809.0    96.0         NaN          NaN          NaN   \n",
       "top        1.26166     0.3     0.0         NaN          NaN          NaN   \n",
       "freq     270.00000   430.0  5994.0         NaN          NaN          NaN   \n",
       "mean           NaN     NaN     NaN     0.69723     1.942951     1.859142   \n",
       "std            NaN     NaN     NaN     0.27427     1.446430     1.722511   \n",
       "min            NaN     NaN     NaN     0.33000     0.000000    -5.000000   \n",
       "25%            NaN     NaN     NaN     0.40000     1.300000     1.000000   \n",
       "50%            NaN     NaN     NaN     0.70000     2.000000     2.000000   \n",
       "75%            NaN     NaN     NaN     1.00000     2.700000     2.500000   \n",
       "max            NaN     NaN     NaN     1.00000     7.000000     9.705882   \n",
       "\n",
       "           cotaexcl     cotaincl      Rc       B      hb        tanaB      Bh  \\\n",
       "count   9403.000000  9403.000000  9403.0  9403.0  9403.0  9403.000000  9403.0   \n",
       "unique          NaN          NaN   929.0    87.0   290.0          NaN    94.0   \n",
       "top             NaN          NaN     0.1     0.0     0.0          NaN     0.0   \n",
       "freq            NaN          NaN   758.0  7781.0  7258.0          NaN  7781.0   \n",
       "mean       1.943630     2.157736     NaN     NaN     NaN     0.001759     NaN   \n",
       "std        1.493797     1.709688     NaN     NaN     NaN     0.010369     NaN   \n",
       "min       -1.532567    -1.532567     NaN     NaN     NaN     0.000000     NaN   \n",
       "25%        1.300000     1.334259     NaN     NaN     NaN     0.000000     NaN   \n",
       "50%        2.000000     2.000000     NaN     NaN     NaN     0.000000     NaN   \n",
       "75%        2.574703     2.807869     NaN     NaN     NaN     0.000000     NaN   \n",
       "max        8.144491    12.820513     NaN     NaN     NaN     0.100935     NaN   \n",
       "\n",
       "            Ac      Gc           RF           CF       q  \n",
       "count   9403.0  9403.0  9403.000000  9403.000000  9403.0  \n",
       "unique   954.0   203.0          NaN          NaN  7445.0  \n",
       "top        0.1     0.0          NaN          NaN     0.0  \n",
       "freq     797.0  4352.0          NaN          NaN   909.0  \n",
       "mean       NaN     NaN     1.941721     1.314155     NaN  \n",
       "std        NaN     NaN     0.784344     0.627152     NaN  \n",
       "min        NaN     NaN     1.000000     1.000000     NaN  \n",
       "25%        NaN     NaN     1.000000     1.000000     NaN  \n",
       "50%        NaN     NaN     2.000000     1.000000     NaN  \n",
       "75%        NaN     NaN     3.000000     1.000000     NaN  \n",
       "max        NaN     NaN     3.000000     3.000000     NaN  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\PC\\AppData\\Local\\Temp\\ipykernel_14256\\445316826.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  data.var()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Hm0 deep            0.139663\n",
       "Tp deep             1.345692\n",
       "Tm deep             0.932754\n",
       "Tm-1,0 deep         1.092736\n",
       "h deep             81.995153\n",
       "m              216148.534336\n",
       "b                 125.850277\n",
       "h                   0.419136\n",
       "Hm0 toe             0.069089\n",
       "Tp toe              1.780507\n",
       "Tm toe              0.881480\n",
       "Tm-1,0 toe          1.121547\n",
       "ht                  0.368445\n",
       "Bt                  0.261468\n",
       "gf                  0.075224\n",
       "cotad               2.092160\n",
       "cotau               2.967045\n",
       "cotaexcl            2.231428\n",
       "cotaincl            2.923032\n",
       "Rc                  0.300582\n",
       "B                   0.087024\n",
       "hb                  0.009154\n",
       "tanaB               0.000108\n",
       "Bh                  0.073443\n",
       "Ac                  0.246414\n",
       "Gc                  0.229988\n",
       "RF                  0.615195\n",
       "CF                  0.393319\n",
       "q                   0.000033\n",
       "dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.var()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- high zero rate: b,Bt,B hb,tanaB,Bh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "96"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(data.Ac==0).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=scaler.fit_transform(data[data.columns[~(data.columns.isin( [\"Name\",'q']))]])\n",
    "df = pd.DataFrame(df,columns=data.columns[~(data.columns.isin( [\"Name\",'q']))]).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([df,pd.to_numeric(data.q.reset_index(drop=True))],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "import lightgbm as lgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import re\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df = df.rename(columns = lambda x:re.sub('[^A-Za-z0-9_]+', '', x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(df[df.columns[~df.columns.isin(['q'])]], df.q, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "lgb_train = lgb.Dataset(x_train, y_train,categorical_feature=['RF','CF'])\n",
    "lgb_eval = lgb.Dataset(x_test, y_test, reference=lgb_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.q.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\n",
    "    'task': 'train', \n",
    "    'boosting': 'gbdt',\n",
    "    'objective': 'regression',\n",
    "    'num_leaves': 10,\n",
    "    'learnnig_rage': 0.05,\n",
    "    'metric': {'l2','l1'},\n",
    "    'verbose': -1\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\tvalid_0's l2: 2.28308e-05\tvalid_0's l1: 0.000929883\n",
      "Training until validation scores don't improve for 30 rounds\n",
      "[2]\tvalid_0's l2: 1.99924e-05\tvalid_0's l1: 0.000872879\n",
      "[3]\tvalid_0's l2: 1.72755e-05\tvalid_0's l1: 0.000816288\n",
      "[4]\tvalid_0's l2: 1.53911e-05\tvalid_0's l1: 0.00077063\n",
      "[5]\tvalid_0's l2: 1.3863e-05\tvalid_0's l1: 0.00073182\n",
      "[6]\tvalid_0's l2: 1.24643e-05\tvalid_0's l1: 0.000696602\n",
      "[7]\tvalid_0's l2: 1.13507e-05\tvalid_0's l1: 0.000669321\n",
      "[8]\tvalid_0's l2: 1.00367e-05\tvalid_0's l1: 0.000641282\n",
      "[9]\tvalid_0's l2: 9.30811e-06\tvalid_0's l1: 0.000622918\n",
      "[10]\tvalid_0's l2: 8.474e-06\tvalid_0's l1: 0.000601214\n",
      "[11]\tvalid_0's l2: 8.01101e-06\tvalid_0's l1: 0.000584002\n",
      "[12]\tvalid_0's l2: 7.43236e-06\tvalid_0's l1: 0.000565236\n",
      "[13]\tvalid_0's l2: 7.02299e-06\tvalid_0's l1: 0.000551077\n",
      "[14]\tvalid_0's l2: 6.78923e-06\tvalid_0's l1: 0.000534871\n",
      "[15]\tvalid_0's l2: 6.52877e-06\tvalid_0's l1: 0.000522027\n",
      "[16]\tvalid_0's l2: 6.3999e-06\tvalid_0's l1: 0.00051163\n",
      "[17]\tvalid_0's l2: 6.25287e-06\tvalid_0's l1: 0.000505053\n",
      "[18]\tvalid_0's l2: 6.13591e-06\tvalid_0's l1: 0.000493317\n",
      "[19]\tvalid_0's l2: 5.99999e-06\tvalid_0's l1: 0.000484824\n",
      "[20]\tvalid_0's l2: 5.94851e-06\tvalid_0's l1: 0.000481348\n",
      "[21]\tvalid_0's l2: 5.90556e-06\tvalid_0's l1: 0.000478744\n",
      "[22]\tvalid_0's l2: 5.77154e-06\tvalid_0's l1: 0.000468394\n",
      "[23]\tvalid_0's l2: 5.76342e-06\tvalid_0's l1: 0.00046441\n",
      "[24]\tvalid_0's l2: 5.72339e-06\tvalid_0's l1: 0.000459943\n",
      "[25]\tvalid_0's l2: 5.70216e-06\tvalid_0's l1: 0.000457621\n",
      "[26]\tvalid_0's l2: 5.7294e-06\tvalid_0's l1: 0.000457848\n",
      "[27]\tvalid_0's l2: 5.74647e-06\tvalid_0's l1: 0.00045578\n",
      "[28]\tvalid_0's l2: 5.74805e-06\tvalid_0's l1: 0.00045445\n",
      "[29]\tvalid_0's l2: 5.76693e-06\tvalid_0's l1: 0.00045346\n",
      "[30]\tvalid_0's l2: 5.68888e-06\tvalid_0's l1: 0.00044686\n",
      "[31]\tvalid_0's l2: 5.73979e-06\tvalid_0's l1: 0.000447679\n",
      "[32]\tvalid_0's l2: 5.7587e-06\tvalid_0's l1: 0.000447203\n",
      "[33]\tvalid_0's l2: 5.78649e-06\tvalid_0's l1: 0.000445985\n",
      "[34]\tvalid_0's l2: 5.72903e-06\tvalid_0's l1: 0.000443265\n",
      "[35]\tvalid_0's l2: 5.68437e-06\tvalid_0's l1: 0.000441063\n",
      "[36]\tvalid_0's l2: 5.73064e-06\tvalid_0's l1: 0.000441186\n",
      "[37]\tvalid_0's l2: 5.77341e-06\tvalid_0's l1: 0.000438802\n",
      "[38]\tvalid_0's l2: 5.73738e-06\tvalid_0's l1: 0.000438012\n",
      "[39]\tvalid_0's l2: 5.76012e-06\tvalid_0's l1: 0.00043608\n",
      "[40]\tvalid_0's l2: 5.72939e-06\tvalid_0's l1: 0.000434653\n",
      "[41]\tvalid_0's l2: 5.70587e-06\tvalid_0's l1: 0.000433629\n",
      "[42]\tvalid_0's l2: 5.60973e-06\tvalid_0's l1: 0.000430398\n",
      "[43]\tvalid_0's l2: 5.65375e-06\tvalid_0's l1: 0.000430565\n",
      "[44]\tvalid_0's l2: 5.62163e-06\tvalid_0's l1: 0.000427092\n",
      "[45]\tvalid_0's l2: 5.60332e-06\tvalid_0's l1: 0.000425746\n",
      "[46]\tvalid_0's l2: 5.52745e-06\tvalid_0's l1: 0.000424368\n",
      "[47]\tvalid_0's l2: 5.568e-06\tvalid_0's l1: 0.000425747\n",
      "[48]\tvalid_0's l2: 5.54451e-06\tvalid_0's l1: 0.000425227\n",
      "[49]\tvalid_0's l2: 5.52916e-06\tvalid_0's l1: 0.000424459\n",
      "[50]\tvalid_0's l2: 5.50685e-06\tvalid_0's l1: 0.000423021\n",
      "[51]\tvalid_0's l2: 5.4888e-06\tvalid_0's l1: 0.000421721\n",
      "[52]\tvalid_0's l2: 5.42166e-06\tvalid_0's l1: 0.000420015\n",
      "[53]\tvalid_0's l2: 5.43232e-06\tvalid_0's l1: 0.000419603\n",
      "[54]\tvalid_0's l2: 5.4228e-06\tvalid_0's l1: 0.00041876\n",
      "[55]\tvalid_0's l2: 5.36953e-06\tvalid_0's l1: 0.000418411\n",
      "[56]\tvalid_0's l2: 5.36013e-06\tvalid_0's l1: 0.000417639\n",
      "[57]\tvalid_0's l2: 5.34014e-06\tvalid_0's l1: 0.000415351\n",
      "[58]\tvalid_0's l2: 5.34907e-06\tvalid_0's l1: 0.000414984\n",
      "[59]\tvalid_0's l2: 5.30202e-06\tvalid_0's l1: 0.000414177\n",
      "[60]\tvalid_0's l2: 5.2958e-06\tvalid_0's l1: 0.000412882\n",
      "[61]\tvalid_0's l2: 5.3197e-06\tvalid_0's l1: 0.000411095\n",
      "[62]\tvalid_0's l2: 5.30414e-06\tvalid_0's l1: 0.00040881\n",
      "[63]\tvalid_0's l2: 5.2893e-06\tvalid_0's l1: 0.000407686\n",
      "[64]\tvalid_0's l2: 5.22715e-06\tvalid_0's l1: 0.000405988\n",
      "[65]\tvalid_0's l2: 5.22172e-06\tvalid_0's l1: 0.000405647\n",
      "[66]\tvalid_0's l2: 5.17572e-06\tvalid_0's l1: 0.000405586\n",
      "[67]\tvalid_0's l2: 5.19584e-06\tvalid_0's l1: 0.000406379\n",
      "[68]\tvalid_0's l2: 5.16174e-06\tvalid_0's l1: 0.000406754\n",
      "[69]\tvalid_0's l2: 5.18575e-06\tvalid_0's l1: 0.000407816\n",
      "[70]\tvalid_0's l2: 5.17982e-06\tvalid_0's l1: 0.000406846\n",
      "[71]\tvalid_0's l2: 5.13931e-06\tvalid_0's l1: 0.000408242\n",
      "[72]\tvalid_0's l2: 5.0863e-06\tvalid_0's l1: 0.000406809\n",
      "[73]\tvalid_0's l2: 5.06704e-06\tvalid_0's l1: 0.000406031\n",
      "[74]\tvalid_0's l2: 5.06273e-06\tvalid_0's l1: 0.000405195\n",
      "[75]\tvalid_0's l2: 5.04932e-06\tvalid_0's l1: 0.000404404\n",
      "[76]\tvalid_0's l2: 5.01467e-06\tvalid_0's l1: 0.000404352\n",
      "[77]\tvalid_0's l2: 4.99419e-06\tvalid_0's l1: 0.000404454\n",
      "[78]\tvalid_0's l2: 4.99021e-06\tvalid_0's l1: 0.000403764\n",
      "[79]\tvalid_0's l2: 4.95368e-06\tvalid_0's l1: 0.000402285\n",
      "[80]\tvalid_0's l2: 4.94146e-06\tvalid_0's l1: 0.000401644\n",
      "[81]\tvalid_0's l2: 4.97667e-06\tvalid_0's l1: 0.000401929\n",
      "[82]\tvalid_0's l2: 4.97273e-06\tvalid_0's l1: 0.000400917\n",
      "[83]\tvalid_0's l2: 4.95452e-06\tvalid_0's l1: 0.000400199\n",
      "[84]\tvalid_0's l2: 4.9435e-06\tvalid_0's l1: 0.000398109\n",
      "[85]\tvalid_0's l2: 4.9261e-06\tvalid_0's l1: 0.000398496\n",
      "[86]\tvalid_0's l2: 4.90076e-06\tvalid_0's l1: 0.000397184\n",
      "[87]\tvalid_0's l2: 4.89956e-06\tvalid_0's l1: 0.000396672\n",
      "[88]\tvalid_0's l2: 4.89271e-06\tvalid_0's l1: 0.000395735\n",
      "[89]\tvalid_0's l2: 4.8963e-06\tvalid_0's l1: 0.000396492\n",
      "[90]\tvalid_0's l2: 4.87924e-06\tvalid_0's l1: 0.000396311\n",
      "[91]\tvalid_0's l2: 4.85268e-06\tvalid_0's l1: 0.000395229\n",
      "[92]\tvalid_0's l2: 4.84716e-06\tvalid_0's l1: 0.000394352\n",
      "[93]\tvalid_0's l2: 4.87728e-06\tvalid_0's l1: 0.000394002\n",
      "[94]\tvalid_0's l2: 4.85535e-06\tvalid_0's l1: 0.000393047\n",
      "[95]\tvalid_0's l2: 4.85366e-06\tvalid_0's l1: 0.000392615\n",
      "[96]\tvalid_0's l2: 4.8406e-06\tvalid_0's l1: 0.000392816\n",
      "[97]\tvalid_0's l2: 4.83214e-06\tvalid_0's l1: 0.000390469\n",
      "[98]\tvalid_0's l2: 4.80306e-06\tvalid_0's l1: 0.000389145\n",
      "[99]\tvalid_0's l2: 4.80114e-06\tvalid_0's l1: 0.000389489\n",
      "[100]\tvalid_0's l2: 4.82878e-06\tvalid_0's l1: 0.000389428\n",
      "Did not meet early stopping. Best iteration is:\n",
      "[99]\tvalid_0's l2: 4.80114e-06\tvalid_0's l1: 0.000389489\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\apps\\anaconda3\\envs\\all\\lib\\site-packages\\lightgbm\\engine.py:181: UserWarning: 'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. Pass 'early_stopping()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
      "d:\\apps\\anaconda3\\envs\\all\\lib\\site-packages\\lightgbm\\basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "d:\\apps\\anaconda3\\envs\\all\\lib\\site-packages\\lightgbm\\basic.py:2068: UserWarning: categorical_feature in Dataset is overridden.\n",
      "New categorical_feature is ['CF', 'RF']\n",
      "  _log_warning('categorical_feature in Dataset is overridden.\\n'\n",
      "d:\\apps\\anaconda3\\envs\\all\\lib\\site-packages\\lightgbm\\basic.py:1780: UserWarning: Overriding the parameters from Reference Dataset.\n",
      "  _log_warning('Overriding the parameters from Reference Dataset.')\n",
      "d:\\apps\\anaconda3\\envs\\all\\lib\\site-packages\\lightgbm\\basic.py:1513: UserWarning: categorical_column in param dict is overridden.\n",
      "  _log_warning(f'{cat_alias} in param dict is overridden.')\n"
     ]
    }
   ],
   "source": [
    "model = lgb.train(params,\n",
    "                 train_set=lgb_train,\n",
    "                 valid_sets=lgb_eval,\n",
    "                 early_stopping_rounds=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 0.00\n",
      "RMSE: 0.00\n"
     ]
    }
   ],
   "source": [
    "# prediction\n",
    "y_pred = model.predict(x_test)\n",
    "\n",
    "# accuracy check\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "rmse = mse**(0.5)\n",
    "print(\"MSE: %.2f\" % mse)\n",
    "print(\"RMSE: %.2f\" % rmse) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4.801138478885836e-06, 0.002191150035685789)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse,rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "residuals=y_pred-y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.scatter(residuals,y_test)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1881"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "c82311f1853cdc47af68c9dd3a7f51ce53cca1eb0533149c7be7da78de9a7cb6"
  },
  "kernelspec": {
   "display_name": "Python 3.9.7 ('all')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}