Cargo 2000 Freight Tracking and Tracing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Implementation of Multivariate Linear Regression and Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this dataset, I have implemented linear regression and two types of clustering.(K-Means and Hierarchical Clustering)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing the packages.\n",
    "from __future__ import print_function\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "import seaborn as sns\n",
    "import statsmodels.api as sm\n",
    "from sklearn import linear_model\n",
    "from statsmodels.formula.api import ols\n",
    "from sklearn.linear_model import LinearRegression, Ridge, Lasso\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.pipeline import make_pipeline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "d=pd.read_csv(\"freight.csv\", low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
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       "      <th>nr</th>\n",
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       "      <td>199.0</td>\n",
       "      <td>218.0</td>\n",
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       "      <td>215.0</td>\n",
       "      <td>609.0</td>\n",
       "      <td>935.0</td>\n",
       "      <td>736.0</td>\n",
       "      <td>256.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6523.0</td>\n",
       "      <td>844.0</td>\n",
       "      <td>584.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>1935.0</td>\n",
       "      <td>1415.0</td>\n",
       "      <td>431.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>?</td>\n",
       "      <td>3870.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>5878.0</td>\n",
       "      <td>4380.0</td>\n",
       "      <td>4119.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>456.0</td>\n",
       "      <td>905.0</td>\n",
       "      <td>547.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>550.0</td>\n",
       "      <td>1520.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1275.0</td>\n",
       "      <td>759.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>777.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>577.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>3780.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>8117.0</td>\n",
       "      <td>1597.0</td>\n",
       "      <td>1485.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>585.0</td>\n",
       "      <td>612.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>4140.0</td>\n",
       "      <td>4797.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.0</td>\n",
       "      <td>9889.0</td>\n",
       "      <td>181.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>815.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>223.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>3960.0</td>\n",
       "      <td>467.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3997.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>1267.0</td>\n",
       "      <td>815.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>391.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>2880.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.0</td>\n",
       "      <td>7125.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>232.0</td>\n",
       "      <td>783.0</td>\n",
       "      <td>255.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>815.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>3960.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.0</td>\n",
       "      <td>10481.0</td>\n",
       "      <td>387.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>514.0</td>\n",
       "      <td>815.0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>485.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>6680.0</td>\n",
       "      <td>2830.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9.0</td>\n",
       "      <td>3212.0</td>\n",
       "      <td>6145.0</td>\n",
       "      <td>6013.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>385.0</td>\n",
       "      <td>690.0</td>\n",
       "      <td>810.0</td>\n",
       "      <td>635.0</td>\n",
       "      <td>555.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>3960.0</td>\n",
       "      <td>218.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10.0</td>\n",
       "      <td>12343.0</td>\n",
       "      <td>748.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>758.0</td>\n",
       "      <td>281.0</td>\n",
       "      <td>395.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>3960.0</td>\n",
       "      <td>223.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11.0</td>\n",
       "      <td>8473.0</td>\n",
       "      <td>1415.0</td>\n",
       "      <td>1157.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>346.0</td>\n",
       "      <td>377.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>485.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>2880.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12.0</td>\n",
       "      <td>9931.0</td>\n",
       "      <td>898.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>965.0</td>\n",
       "      <td>281.0</td>\n",
       "      <td>415.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>815.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>5160.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13.0</td>\n",
       "      <td>12666.0</td>\n",
       "      <td>3140.0</td>\n",
       "      <td>3004.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>324.0</td>\n",
       "      <td>445.0</td>\n",
       "      <td>995.0</td>\n",
       "      <td>808.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>3960.0</td>\n",
       "      <td>934.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14.0</td>\n",
       "      <td>14132.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>456.0</td>\n",
       "      <td>1085.0</td>\n",
       "      <td>1093.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>6680.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15.0</td>\n",
       "      <td>13119.0</td>\n",
       "      <td>1081.0</td>\n",
       "      <td>413.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>859.0</td>\n",
       "      <td>753.0</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>928.0</td>\n",
       "      <td>511.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>3780.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16.0</td>\n",
       "      <td>13572.0</td>\n",
       "      <td>778.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>894.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>1410.0</td>\n",
       "      <td>2375.0</td>\n",
       "      <td>548.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>3960.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17.0</td>\n",
       "      <td>1388.0</td>\n",
       "      <td>27415.0</td>\n",
       "      <td>26485.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>963.0</td>\n",
       "      <td>815.0</td>\n",
       "      <td>1220.0</td>\n",
       "      <td>1027.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>3420.0</td>\n",
       "      <td>1681.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18.0</td>\n",
       "      <td>4697.0</td>\n",
       "      <td>671.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>713.0</td>\n",
       "      <td>815.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>381.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>3780.0</td>\n",
       "      <td>1115.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19.0</td>\n",
       "      <td>13297.0</td>\n",
       "      <td>5442.0</td>\n",
       "      <td>4568.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>934.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>815.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>550.0</td>\n",
       "      <td>492.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20.0</td>\n",
       "      <td>9481.0</td>\n",
       "      <td>327.0</td>\n",
       "      <td>167.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>278.0</td>\n",
       "      <td>579.0</td>\n",
       "      <td>1010.0</td>\n",
       "      <td>584.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>3960.0</td>\n",
       "      <td>7005.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21.0</td>\n",
       "      <td>5838.0</td>\n",
       "      <td>249.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>223.0</td>\n",
       "      <td>391.0</td>\n",
       "      <td>385.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>815.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>5160.0</td>\n",
       "      <td>432.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22.0</td>\n",
       "      <td>10613.0</td>\n",
       "      <td>2127.0</td>\n",
       "      <td>234.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>2158.0</td>\n",
       "      <td>332.0</td>\n",
       "      <td>705.0</td>\n",
       "      <td>783.0</td>\n",
       "      <td>815.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>5160.0</td>\n",
       "      <td>2060.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23.0</td>\n",
       "      <td>1363.0</td>\n",
       "      <td>287.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>746.0</td>\n",
       "      <td>870.0</td>\n",
       "      <td>828.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>1075.0</td>\n",
       "      <td>4090.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24.0</td>\n",
       "      <td>4038.0</td>\n",
       "      <td>295.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>382.0</td>\n",
       "      <td>815.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>391.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>5100.0</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25.0</td>\n",
       "      <td>3091.0</td>\n",
       "      <td>692.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>851.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>930.0</td>\n",
       "      <td>1062.0</td>\n",
       "      <td>456.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>4230.0</td>\n",
       "      <td>5859.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26.0</td>\n",
       "      <td>13510.0</td>\n",
       "      <td>1211.0</td>\n",
       "      <td>494.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>772.0</td>\n",
       "      <td>485.0</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>897.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>3780.0</td>\n",
       "      <td>1287.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27.0</td>\n",
       "      <td>4991.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>227.0</td>\n",
       "      <td>476.0</td>\n",
       "      <td>975.0</td>\n",
       "      <td>731.0</td>\n",
       "      <td>815.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>5100.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28.0</td>\n",
       "      <td>11674.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>596.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>1240.0</td>\n",
       "      <td>1048.0</td>\n",
       "      <td>413.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>4230.0</td>\n",
       "      <td>2649.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29.0</td>\n",
       "      <td>7108.0</td>\n",
       "      <td>342.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>583.0</td>\n",
       "      <td>753.0</td>\n",
       "      <td>510.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>4680.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</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>3913</th>\n",
       "      <td>3914.0</td>\n",
       "      <td>4773.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>189.0</td>\n",
       "      <td>815.0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>485.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>4740.0</td>\n",
       "      <td>4459.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3914</th>\n",
       "      <td>3915.0</td>\n",
       "      <td>10054.0</td>\n",
       "      <td>911.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>920.0</td>\n",
       "      <td>815.0</td>\n",
       "      <td>415.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>576.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>3960.0</td>\n",
       "      <td>8501.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3915</th>\n",
       "      <td>3916.0</td>\n",
       "      <td>10447.0</td>\n",
       "      <td>1412.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>1355.0</td>\n",
       "      <td>815.0</td>\n",
       "      <td>760.0</td>\n",
       "      <td>845.0</td>\n",
       "      <td>609.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>6680.0</td>\n",
       "      <td>17970.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3916</th>\n",
       "      <td>3917.0</td>\n",
       "      <td>10095.0</td>\n",
       "      <td>523.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>364.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>815.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>4230.0</td>\n",
       "      <td>1093.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3917</th>\n",
       "      <td>3918.0</td>\n",
       "      <td>9482.0</td>\n",
       "      <td>894.0</td>\n",
       "      <td>1260.0</td>\n",
       "      <td>780.0</td>\n",
       "      <td>363.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>1162.0</td>\n",
       "      <td>740.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>6990.0</td>\n",
       "      <td>3212.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3918</th>\n",
       "      <td>3919.0</td>\n",
       "      <td>6429.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>277.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>960.0</td>\n",
       "      <td>732.0</td>\n",
       "      <td>597.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>3960.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3919</th>\n",
       "      <td>3920.0</td>\n",
       "      <td>10793.0</td>\n",
       "      <td>246.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>815.0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>485.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>6960.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3920</th>\n",
       "      <td>3921.0</td>\n",
       "      <td>10328.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>815.0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>227.0</td>\n",
       "      <td>485.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>1140.0</td>\n",
       "      <td>5741.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3921</th>\n",
       "      <td>3922.0</td>\n",
       "      <td>8053.0</td>\n",
       "      <td>1775.0</td>\n",
       "      <td>738.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>1329.0</td>\n",
       "      <td>308.0</td>\n",
       "      <td>840.0</td>\n",
       "      <td>797.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>3960.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3922</th>\n",
       "      <td>3923.0</td>\n",
       "      <td>7722.0</td>\n",
       "      <td>2175.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>1907.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>805.0</td>\n",
       "      <td>568.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>3780.0</td>\n",
       "      <td>1114.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3923</th>\n",
       "      <td>3924.0</td>\n",
       "      <td>11701.0</td>\n",
       "      <td>2281.0</td>\n",
       "      <td>1465.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>1081.0</td>\n",
       "      <td>815.0</td>\n",
       "      <td>810.0</td>\n",
       "      <td>732.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>9480.0</td>\n",
       "      <td>27517.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3924</th>\n",
       "      <td>3925.0</td>\n",
       "      <td>10330.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>248.0</td>\n",
       "      <td>815.0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>227.0</td>\n",
       "      <td>485.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>4230.0</td>\n",
       "      <td>17454.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3925</th>\n",
       "      <td>3926.0</td>\n",
       "      <td>4322.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>815.0</td>\n",
       "      <td>295.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>485.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>6960.0</td>\n",
       "      <td>956.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3926</th>\n",
       "      <td>3927.0</td>\n",
       "      <td>2816.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>3960.0</td>\n",
       "      <td>1294.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3927</th>\n",
       "      <td>3928.0</td>\n",
       "      <td>14363.0</td>\n",
       "      <td>5844.0</td>\n",
       "      <td>2067.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>3980.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>750.0</td>\n",
       "      <td>671.0</td>\n",
       "      <td>798.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>5160.0</td>\n",
       "      <td>620.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3928</th>\n",
       "      <td>3929.0</td>\n",
       "      <td>13029.0</td>\n",
       "      <td>4614.0</td>\n",
       "      <td>1557.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>3192.0</td>\n",
       "      <td>441.0</td>\n",
       "      <td>670.0</td>\n",
       "      <td>714.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>584.0</td>\n",
       "      <td>408.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3929</th>\n",
       "      <td>3930.0</td>\n",
       "      <td>11958.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>812.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>783.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>2802.0</td>\n",
       "      <td>385.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3930</th>\n",
       "      <td>3931.0</td>\n",
       "      <td>1780.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>207.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>680.0</td>\n",
       "      <td>762.0</td>\n",
       "      <td>783.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>6984.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3931</th>\n",
       "      <td>3932.0</td>\n",
       "      <td>13334.0</td>\n",
       "      <td>1783.0</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>517.0</td>\n",
       "      <td>441.0</td>\n",
       "      <td>976.0</td>\n",
       "      <td>851.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>665.0</td>\n",
       "      <td>2906.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3932</th>\n",
       "      <td>3933.0</td>\n",
       "      <td>13957.0</td>\n",
       "      <td>318.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>780.0</td>\n",
       "      <td>859.0</td>\n",
       "      <td>556.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>281.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>3780.0</td>\n",
       "      <td>1130.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3933</th>\n",
       "      <td>3934.0</td>\n",
       "      <td>5987.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>727.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>...</td>\n",
       "      <td>545</td>\n",
       "      <td>495</td>\n",
       "      <td>411</td>\n",
       "      <td>780</td>\n",
       "      <td>2112</td>\n",
       "      <td>567</td>\n",
       "      <td>3900.0</td>\n",
       "      <td>5798.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3934</th>\n",
       "      <td>3935.0</td>\n",
       "      <td>4685.0</td>\n",
       "      <td>1362.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>1553.0</td>\n",
       "      <td>815.0</td>\n",
       "      <td>760.0</td>\n",
       "      <td>760.0</td>\n",
       "      <td>609.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>2880.0</td>\n",
       "      <td>8647.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3935</th>\n",
       "      <td>3936.0</td>\n",
       "      <td>9814.0</td>\n",
       "      <td>675.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>741.0</td>\n",
       "      <td>815.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>2820.0</td>\n",
       "      <td>1734.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3936</th>\n",
       "      <td>3937.0</td>\n",
       "      <td>2163.0</td>\n",
       "      <td>4772.0</td>\n",
       "      <td>3941.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>860.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>845.0</td>\n",
       "      <td>545.0</td>\n",
       "      <td>628.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>3960.0</td>\n",
       "      <td>571.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3937</th>\n",
       "      <td>3938.0</td>\n",
       "      <td>4166.0</td>\n",
       "      <td>703.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>493.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>908.0</td>\n",
       "      <td>815.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>300.0</td>\n",
       "      <td>2512.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3938</th>\n",
       "      <td>3939.0</td>\n",
       "      <td>4225.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>278.0</td>\n",
       "      <td>815.0</td>\n",
       "      <td>830.0</td>\n",
       "      <td>761.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>1665.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3939</th>\n",
       "      <td>3940.0</td>\n",
       "      <td>14017.0</td>\n",
       "      <td>2028.0</td>\n",
       "      <td>413.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>1825.0</td>\n",
       "      <td>605.0</td>\n",
       "      <td>2610.0</td>\n",
       "      <td>2535.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>3780.0</td>\n",
       "      <td>807.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3940</th>\n",
       "      <td>3941.0</td>\n",
       "      <td>4660.0</td>\n",
       "      <td>1356.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>1359.0</td>\n",
       "      <td>815.0</td>\n",
       "      <td>760.0</td>\n",
       "      <td>716.0</td>\n",
       "      <td>609.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>5100.0</td>\n",
       "      <td>4381.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3941</th>\n",
       "      <td>3942.0</td>\n",
       "      <td>6472.0</td>\n",
       "      <td>2692.0</td>\n",
       "      <td>1856.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>867.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>1060.0</td>\n",
       "      <td>1049.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>3780.0</td>\n",
       "      <td>945.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3942</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>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",
       "  </tbody>\n",
       "</table>\n",
       "<p>3943 rows × 98 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          nr  i1_legid  i1_rcs_p  i1_rcs_e  i1_dep_1_p  i1_dep_1_e  \\\n",
       "0        0.0    5182.0     199.0     218.0       210.0       215.0   \n",
       "1        1.0    6523.0     844.0     584.0        90.0       297.0   \n",
       "2        2.0    5878.0    4380.0    4119.0        90.0       280.0   \n",
       "3        3.0    1275.0     759.0     169.0       240.0       777.0   \n",
       "4        4.0    8117.0    1597.0    1485.0       150.0       241.0   \n",
       "5        5.0    9889.0     181.0      98.0       240.0       259.0   \n",
       "6        6.0    3997.0    1300.0     212.0       240.0      1267.0   \n",
       "7        7.0    7125.0     139.0      87.0       240.0       232.0   \n",
       "8        8.0   10481.0     387.0      68.0       240.0       514.0   \n",
       "9        9.0    3212.0    6145.0    6013.0       300.0       385.0   \n",
       "10      10.0   12343.0     748.0      96.0       150.0       758.0   \n",
       "11      11.0    8473.0    1415.0    1157.0       150.0       346.0   \n",
       "12      12.0    9931.0     898.0     112.0       240.0       965.0   \n",
       "13      13.0   12666.0    3140.0    3004.0       240.0       324.0   \n",
       "14      14.0   14132.0     142.0     136.0       240.0       182.0   \n",
       "15      15.0   13119.0    1081.0     413.0       180.0       859.0   \n",
       "16      16.0   13572.0     778.0     254.0       420.0       894.0   \n",
       "17      17.0    1388.0   27415.0   26485.0        90.0       963.0   \n",
       "18      18.0    4697.0     671.0     140.0       240.0       713.0   \n",
       "19      19.0   13297.0    5442.0    4568.0       120.0       934.0   \n",
       "20      20.0    9481.0     327.0     167.0        90.0       278.0   \n",
       "21      21.0    5838.0     249.0     116.0       150.0       223.0   \n",
       "22      22.0   10613.0    2127.0     234.0       300.0      2158.0   \n",
       "23      23.0    1363.0     287.0     172.0       150.0       179.0   \n",
       "24      24.0    4038.0     295.0      98.0       240.0       382.0   \n",
       "25      25.0    3091.0     692.0      72.0       270.0       851.0   \n",
       "26      26.0   13510.0    1211.0     494.0        90.0       772.0   \n",
       "27      27.0    4991.0     115.0      51.0       240.0       227.0   \n",
       "28      28.0   11674.0     123.0      48.0       420.0       596.0   \n",
       "29      29.0    7108.0     342.0       9.0       300.0       583.0   \n",
       "...      ...       ...       ...       ...         ...         ...   \n",
       "3913  3914.0    4773.0     187.0      76.0       135.0       189.0   \n",
       "3914  3915.0   10054.0     911.0     190.0       240.0       920.0   \n",
       "3915  3916.0   10447.0    1412.0     236.0       240.0      1355.0   \n",
       "3916  3917.0   10095.0     523.0     169.0       120.0       364.0   \n",
       "3917  3918.0    9482.0     894.0    1260.0       780.0       363.0   \n",
       "3918  3919.0    6429.0     152.0      71.0       240.0       277.0   \n",
       "3919  3920.0   10793.0     246.0      93.0       135.0       229.0   \n",
       "3920  3921.0   10328.0     188.0      32.0       135.0       250.0   \n",
       "3921  3922.0    8053.0    1775.0     738.0       300.0      1329.0   \n",
       "3922  3923.0    7722.0    2175.0     315.0        90.0      1907.0   \n",
       "3923  3924.0   11701.0    2281.0    1465.0       300.0      1081.0   \n",
       "3924  3925.0   10330.0     206.0      52.0       135.0       248.0   \n",
       "3925  3926.0    4322.0      88.0      57.0       135.0       150.0   \n",
       "3926  3927.0    2816.0     259.0     186.0       150.0       186.0   \n",
       "3927  3928.0   14363.0    5844.0    2067.0       240.0      3980.0   \n",
       "3928  3929.0   13029.0    4614.0    1557.0       195.0      3192.0   \n",
       "3929  3930.0   11958.0      55.0      15.0       150.0       140.0   \n",
       "3930  3931.0    1780.0      92.0       6.0       150.0       207.0   \n",
       "3931  3932.0   13334.0    1783.0    1460.0       240.0       517.0   \n",
       "3932  3933.0   13957.0     318.0     184.0       780.0       859.0   \n",
       "3933  3934.0    5987.0     145.0     151.0       120.0        49.0   \n",
       "3934  3935.0    4685.0    1362.0       8.0       240.0      1553.0   \n",
       "3935  3936.0    9814.0     675.0     113.0       240.0       741.0   \n",
       "3936  3937.0    2163.0    4772.0    3941.0        90.0       860.0   \n",
       "3937  3938.0    4166.0     703.0     360.0       210.0       493.0   \n",
       "3938  3939.0    4225.0     122.0      19.0       240.0       278.0   \n",
       "3939  3940.0   14017.0    2028.0     413.0       270.0      1825.0   \n",
       "3940  3941.0    4660.0    1356.0     178.0       240.0      1359.0   \n",
       "3941  3942.0    6472.0    2692.0    1856.0        90.0       867.0   \n",
       "3942     NaN       NaN       NaN       NaN         NaN         NaN   \n",
       "\n",
       "      i1_dep_1_place  i1_rcf_1_p  i1_rcf_1_e  i1_rcf_1_place ...  o_dep_3_p  \\\n",
       "0              609.0       935.0       736.0           256.0 ...          ?   \n",
       "1              700.0      1935.0      1415.0           431.0 ...          ?   \n",
       "2              456.0       905.0       547.0           700.0 ...          ?   \n",
       "3              173.0       340.0       577.0           349.0 ...          ?   \n",
       "4              411.0       585.0       612.0           128.0 ...          ?   \n",
       "5              815.0       215.0       223.0           128.0 ...          ?   \n",
       "6              815.0       270.0       135.0           391.0 ...          ?   \n",
       "7              783.0       255.0       154.0           815.0 ...          ?   \n",
       "8              815.0       285.0       219.0           485.0 ...          ?   \n",
       "9              690.0       810.0       635.0           555.0 ...          ?   \n",
       "10             281.0       395.0       152.0           128.0 ...          ?   \n",
       "11             377.0       280.0       179.0           485.0 ...          ?   \n",
       "12             281.0       415.0       186.0           815.0 ...          ?   \n",
       "13             445.0       995.0       808.0           128.0 ...          ?   \n",
       "14             456.0      1085.0      1093.0           349.0 ...          ?   \n",
       "15             753.0      1575.0       928.0           511.0 ...          ?   \n",
       "16             349.0      1410.0      2375.0           548.0 ...          ?   \n",
       "17             815.0      1220.0      1027.0           700.0 ...          ?   \n",
       "18             815.0       280.0       381.0           153.0 ...          ?   \n",
       "19             236.0       420.0       260.0           815.0 ...          ?   \n",
       "20             579.0      1010.0       584.0           128.0 ...          ?   \n",
       "21             391.0       385.0       155.0           815.0 ...          ?   \n",
       "22             332.0       705.0       783.0           815.0 ...          ?   \n",
       "23             746.0       870.0       828.0           349.0 ...          ?   \n",
       "24             815.0       270.0       143.0           391.0 ...          ?   \n",
       "25             349.0       930.0      1062.0           456.0 ...          ?   \n",
       "26             485.0      1250.0       897.0           700.0 ...          ?   \n",
       "27             476.0       975.0       731.0           815.0 ...          ?   \n",
       "28             349.0      1240.0      1048.0           413.0 ...          ?   \n",
       "29             753.0       510.0       322.0           174.0 ...          ?   \n",
       "...              ...         ...         ...             ... ...        ...   \n",
       "3913           815.0       285.0       212.0           485.0 ...          ?   \n",
       "3914           815.0       415.0        21.0           576.0 ...          ?   \n",
       "3915           815.0       760.0       845.0           609.0 ...          ?   \n",
       "3916           236.0        90.0        87.0           815.0 ...          ?   \n",
       "3917           349.0      1162.0       740.0           144.0 ...          ?   \n",
       "3918           128.0       960.0       732.0           597.0 ...          ?   \n",
       "3919           815.0       285.0       340.0           485.0 ...          ?   \n",
       "3920           815.0       285.0       227.0           485.0 ...          ?   \n",
       "3921           308.0       840.0       797.0           371.0 ...          ?   \n",
       "3922           206.0       805.0       568.0           700.0 ...          ?   \n",
       "3923           815.0       810.0       732.0           144.0 ...          ?   \n",
       "3924           815.0       285.0       227.0           485.0 ...          ?   \n",
       "3925           815.0       295.0       141.0           485.0 ...          ?   \n",
       "3926           166.0       130.0       158.0           128.0 ...          ?   \n",
       "3927           128.0       750.0       671.0           798.0 ...          ?   \n",
       "3928           441.0       670.0       714.0           349.0 ...          ?   \n",
       "3929           812.0       163.0        61.0           783.0 ...          ?   \n",
       "3930           128.0       680.0       762.0           783.0 ...          ?   \n",
       "3931           441.0       976.0       851.0           128.0 ...          ?   \n",
       "3932           556.0        60.0        63.0           281.0 ...          ?   \n",
       "3933           727.0       125.0       175.0           101.0 ...        545   \n",
       "3934           815.0       760.0       760.0           609.0 ...          ?   \n",
       "3935           815.0       260.0       197.0           134.0 ...          ?   \n",
       "3936           700.0       845.0       545.0           628.0 ...          ?   \n",
       "3937           233.0      1000.0       908.0           815.0 ...          ?   \n",
       "3938           815.0       830.0       761.0           174.0 ...          ?   \n",
       "3939           605.0      2610.0      2535.0           349.0 ...          ?   \n",
       "3940           815.0       760.0       716.0           609.0 ...          ?   \n",
       "3941           700.0      1060.0      1049.0           113.0 ...          ?   \n",
       "3942             NaN         NaN         NaN             NaN ...        NaN   \n",
       "\n",
       "     o_dep_3_e o_dep_3_place o_rcf_3_p o_rcf_3_e o_rcf_3_place o_dlv_p  \\\n",
       "0            ?             ?         ?         ?             ?   780.0   \n",
       "1            ?             ?         ?         ?             ?  3870.0   \n",
       "2            ?             ?         ?         ?             ?   550.0   \n",
       "3            ?             ?         ?         ?             ?  3780.0   \n",
       "4            ?             ?         ?         ?             ?  4140.0   \n",
       "5            ?             ?         ?         ?             ?  3960.0   \n",
       "6            ?             ?         ?         ?             ?  2880.0   \n",
       "7            ?             ?         ?         ?             ?  3960.0   \n",
       "8            ?             ?         ?         ?             ?  6680.0   \n",
       "9            ?             ?         ?         ?             ?  3960.0   \n",
       "10           ?             ?         ?         ?             ?  3960.0   \n",
       "11           ?             ?         ?         ?             ?  2880.0   \n",
       "12           ?             ?         ?         ?             ?  5160.0   \n",
       "13           ?             ?         ?         ?             ?  3960.0   \n",
       "14           ?             ?         ?         ?             ?  6680.0   \n",
       "15           ?             ?         ?         ?             ?  3780.0   \n",
       "16           ?             ?         ?         ?             ?  3960.0   \n",
       "17           ?             ?         ?         ?             ?  3420.0   \n",
       "18           ?             ?         ?         ?             ?  3780.0   \n",
       "19           ?             ?         ?         ?             ?   550.0   \n",
       "20           ?             ?         ?         ?             ?  3960.0   \n",
       "21           ?             ?         ?         ?             ?  5160.0   \n",
       "22           ?             ?         ?         ?             ?  5160.0   \n",
       "23           ?             ?         ?         ?             ?  1075.0   \n",
       "24           ?             ?         ?         ?             ?  5100.0   \n",
       "25           ?             ?         ?         ?             ?  4230.0   \n",
       "26           ?             ?         ?         ?             ?  3780.0   \n",
       "27           ?             ?         ?         ?             ?  5100.0   \n",
       "28           ?             ?         ?         ?             ?  4230.0   \n",
       "29           ?             ?         ?         ?             ?  4680.0   \n",
       "...        ...           ...       ...       ...           ...     ...   \n",
       "3913         ?             ?         ?         ?             ?  4740.0   \n",
       "3914         ?             ?         ?         ?             ?  3960.0   \n",
       "3915         ?             ?         ?         ?             ?  6680.0   \n",
       "3916         ?             ?         ?         ?             ?  4230.0   \n",
       "3917         ?             ?         ?         ?             ?  6990.0   \n",
       "3918         ?             ?         ?         ?             ?  3960.0   \n",
       "3919         ?             ?         ?         ?             ?  6960.0   \n",
       "3920         ?             ?         ?         ?             ?  1140.0   \n",
       "3921         ?             ?         ?         ?             ?  3960.0   \n",
       "3922         ?             ?         ?         ?             ?  3780.0   \n",
       "3923         ?             ?         ?         ?             ?  9480.0   \n",
       "3924         ?             ?         ?         ?             ?  4230.0   \n",
       "3925         ?             ?         ?         ?             ?  6960.0   \n",
       "3926         ?             ?         ?         ?             ?  3960.0   \n",
       "3927         ?             ?         ?         ?             ?  5160.0   \n",
       "3928         ?             ?         ?         ?             ?   584.0   \n",
       "3929         ?             ?         ?         ?             ?  2802.0   \n",
       "3930         ?             ?         ?         ?             ?  6000.0   \n",
       "3931         ?             ?         ?         ?             ?   665.0   \n",
       "3932         ?             ?         ?         ?             ?  3780.0   \n",
       "3933       495           411       780      2112           567  3900.0   \n",
       "3934         ?             ?         ?         ?             ?  2880.0   \n",
       "3935         ?             ?         ?         ?             ?  2820.0   \n",
       "3936         ?             ?         ?         ?             ?  3960.0   \n",
       "3937         ?             ?         ?         ?             ?   300.0   \n",
       "3938         ?             ?         ?         ?             ?  1665.0   \n",
       "3939         ?             ?         ?         ?             ?  3780.0   \n",
       "3940         ?             ?         ?         ?             ?  5100.0   \n",
       "3941         ?             ?         ?         ?             ?  3780.0   \n",
       "3942       NaN           NaN       NaN       NaN           NaN     NaN   \n",
       "\n",
       "      o_dlv_e o_hops legs  \n",
       "0       434.0    1.0  2.0  \n",
       "1       445.0    1.0  2.0  \n",
       "2      1520.0    1.0  1.0  \n",
       "3       159.0    1.0  1.0  \n",
       "4      4797.0    2.0  1.0  \n",
       "5       467.0    1.0  2.0  \n",
       "6       151.0    1.0  3.0  \n",
       "7       161.0    1.0  2.0  \n",
       "8      2830.0    1.0  1.0  \n",
       "9       218.0    1.0  1.0  \n",
       "10      223.0    1.0  2.0  \n",
       "11       20.0    1.0  1.0  \n",
       "12      140.0    1.0  3.0  \n",
       "13      934.0    1.0  2.0  \n",
       "14      164.0    1.0  3.0  \n",
       "15       11.0    1.0  1.0  \n",
       "16      108.0    1.0  2.0  \n",
       "17     1681.0    1.0  2.0  \n",
       "18     1115.0    2.0  1.0  \n",
       "19      492.0    1.0  2.0  \n",
       "20     7005.0    1.0  2.0  \n",
       "21      432.0    1.0  1.0  \n",
       "22     2060.0    1.0  3.0  \n",
       "23     4090.0    2.0  2.0  \n",
       "24     1966.0    2.0  3.0  \n",
       "25     5859.0    2.0  3.0  \n",
       "26     1287.0    1.0  1.0  \n",
       "27       24.0    1.0  1.0  \n",
       "28     2649.0    2.0  2.0  \n",
       "29        2.0    1.0  1.0  \n",
       "...       ...    ...  ...  \n",
       "3913   4459.0    2.0  3.0  \n",
       "3914   8501.0    2.0  2.0  \n",
       "3915  17970.0    1.0  2.0  \n",
       "3916   1093.0    2.0  3.0  \n",
       "3917   3212.0    2.0  2.0  \n",
       "3918   1467.0    2.0  2.0  \n",
       "3919     89.0    2.0  1.0  \n",
       "3920   5741.0    1.0  3.0  \n",
       "3921    280.0    2.0  3.0  \n",
       "3922   1114.0    2.0  1.0  \n",
       "3923  27517.0    1.0  3.0  \n",
       "3924  17454.0    2.0  1.0  \n",
       "3925    956.0    2.0  3.0  \n",
       "3926   1294.0    2.0  3.0  \n",
       "3927    620.0    2.0  2.0  \n",
       "3928    408.0    2.0  2.0  \n",
       "3929    385.0    1.0  1.0  \n",
       "3930   6984.0    2.0  1.0  \n",
       "3931   2906.0    2.0  2.0  \n",
       "3932   1130.0    2.0  3.0  \n",
       "3933   5798.0    3.0  1.0  \n",
       "3934   8647.0    2.0  3.0  \n",
       "3935   1734.0    1.0  2.0  \n",
       "3936    571.0    2.0  2.0  \n",
       "3937   2512.0    2.0  3.0  \n",
       "3938   1300.0    2.0  3.0  \n",
       "3939    807.0    2.0  1.0  \n",
       "3940   4381.0    2.0  1.0  \n",
       "3941    945.0    2.0  2.0  \n",
       "3942      NaN    NaN  NaN  \n",
       "\n",
       "[3943 rows x 98 columns]"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "d_replace = d.replace('?',np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nr</th>\n",
       "      <th>i1_legid</th>\n",
       "      <th>i1_rcs_p</th>\n",
       "      <th>i1_rcs_e</th>\n",
       "      <th>i1_dep_1_p</th>\n",
       "      <th>i1_dep_1_e</th>\n",
       "      <th>i1_dep_1_place</th>\n",
       "      <th>i1_rcf_1_p</th>\n",
       "      <th>i1_rcf_1_e</th>\n",
       "      <th>i1_rcf_1_place</th>\n",
       "      <th>...</th>\n",
       "      <th>o_dep_3_p</th>\n",
       "      <th>o_dep_3_e</th>\n",
       "      <th>o_dep_3_place</th>\n",
       "      <th>o_rcf_3_p</th>\n",
       "      <th>o_rcf_3_e</th>\n",
       "      <th>o_rcf_3_place</th>\n",
       "      <th>o_dlv_p</th>\n",
       "      <th>o_dlv_e</th>\n",
       "      <th>o_hops</th>\n",
       "      <th>legs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5182.0</td>\n",
       "      <td>199.0</td>\n",
       "      <td>218.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>609.0</td>\n",
       "      <td>935.0</td>\n",
       "      <td>736.0</td>\n",
       "      <td>256.0</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>780.0</td>\n",
       "      <td>434.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6523.0</td>\n",
       "      <td>844.0</td>\n",
       "      <td>584.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>1935.0</td>\n",
       "      <td>1415.0</td>\n",
       "      <td>431.0</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>3870.0</td>\n",
       "      <td>445.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>5878.0</td>\n",
       "      <td>4380.0</td>\n",
       "      <td>4119.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>456.0</td>\n",
       "      <td>905.0</td>\n",
       "      <td>547.0</td>\n",
       "      <td>700.0</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>550.0</td>\n",
       "      <td>1520.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1275.0</td>\n",
       "      <td>759.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>777.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>577.0</td>\n",
       "      <td>349.0</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>3780.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>8117.0</td>\n",
       "      <td>1597.0</td>\n",
       "      <td>1485.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>585.0</td>\n",
       "      <td>612.0</td>\n",
       "      <td>128.0</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>4140.0</td>\n",
       "      <td>4797.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.0</td>\n",
       "      <td>9889.0</td>\n",
       "      <td>181.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>815.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>223.0</td>\n",
       "      <td>128.0</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>3960.0</td>\n",
       "      <td>467.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3997.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>1267.0</td>\n",
       "      <td>815.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>391.0</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>2880.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.0</td>\n",
       "      <td>7125.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>232.0</td>\n",
       "      <td>783.0</td>\n",
       "      <td>255.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>815.0</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>3960.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.0</td>\n",
       "      <td>10481.0</td>\n",
       "      <td>387.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>514.0</td>\n",
       "      <td>815.0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>485.0</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>6680.0</td>\n",
       "      <td>2830.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9.0</td>\n",
       "      <td>3212.0</td>\n",
       "      <td>6145.0</td>\n",
       "      <td>6013.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>385.0</td>\n",
       "      <td>690.0</td>\n",
       "      <td>810.0</td>\n",
       "      <td>635.0</td>\n",
       "      <td>555.0</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>3960.0</td>\n",
       "      <td>218.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10.0</td>\n",
       "      <td>12343.0</td>\n",
       "      <td>748.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>758.0</td>\n",
       "      <td>281.0</td>\n",
       "      <td>395.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>128.0</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>3960.0</td>\n",
       "      <td>223.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11.0</td>\n",
       "      <td>8473.0</td>\n",
       "      <td>1415.0</td>\n",
       "      <td>1157.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>346.0</td>\n",
       "      <td>377.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>485.0</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>2880.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12.0</td>\n",
       "      <td>9931.0</td>\n",
       "      <td>898.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>965.0</td>\n",
       "      <td>281.0</td>\n",
       "      <td>415.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>815.0</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>5160.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13.0</td>\n",
       "      <td>12666.0</td>\n",
       "      <td>3140.0</td>\n",
       "      <td>3004.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>324.0</td>\n",
       "      <td>445.0</td>\n",
       "      <td>995.0</td>\n",
       "      <td>808.0</td>\n",
       "      <td>128.0</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>3960.0</td>\n",
       "      <td>934.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14.0</td>\n",
       "      <td>14132.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>456.0</td>\n",
       "      <td>1085.0</td>\n",
       "      <td>1093.0</td>\n",
       "      <td>349.0</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>6680.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15.0</td>\n",
       "      <td>13119.0</td>\n",
       "      <td>1081.0</td>\n",
       "      <td>413.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>859.0</td>\n",
       "      <td>753.0</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>928.0</td>\n",
       "      <td>511.0</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>3780.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16.0</td>\n",
       "      <td>13572.0</td>\n",
       "      <td>778.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>894.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>1410.0</td>\n",
       "      <td>2375.0</td>\n",
       "      <td>548.0</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>3960.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17.0</td>\n",
       "      <td>1388.0</td>\n",
       "      <td>27415.0</td>\n",
       "      <td>26485.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>963.0</td>\n",
       "      <td>815.0</td>\n",
       "      <td>1220.0</td>\n",
       "      <td>1027.0</td>\n",
       "      <td>700.0</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>3420.0</td>\n",
       "      <td>1681.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18.0</td>\n",
       "      <td>4697.0</td>\n",
       "      <td>671.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>713.0</td>\n",
       "      <td>815.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>381.0</td>\n",
       "      <td>153.0</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>3780.0</td>\n",
       "      <td>1115.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19.0</td>\n",
       "      <td>13297.0</td>\n",
       "      <td>5442.0</td>\n",
       "      <td>4568.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>934.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>815.0</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>550.0</td>\n",
       "      <td>492.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20.0</td>\n",
       "      <td>9481.0</td>\n",
       "      <td>327.0</td>\n",
       "      <td>167.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>278.0</td>\n",
       "      <td>579.0</td>\n",
       "      <td>1010.0</td>\n",
       "      <td>584.0</td>\n",
       "      <td>128.0</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>3960.0</td>\n",
       "      <td>7005.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21.0</td>\n",
       "      <td>5838.0</td>\n",
       "      <td>249.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>223.0</td>\n",
       "      <td>391.0</td>\n",
       "      <td>385.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>815.0</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>5160.0</td>\n",
       "      <td>432.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22.0</td>\n",
       "      <td>10613.0</td>\n",
       "      <td>2127.0</td>\n",
       "      <td>234.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>2158.0</td>\n",
       "      <td>332.0</td>\n",
       "      <td>705.0</td>\n",
       "      <td>783.0</td>\n",
       "      <td>815.0</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>5160.0</td>\n",
       "      <td>2060.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23.0</td>\n",
       "      <td>1363.0</td>\n",
       "      <td>287.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>746.0</td>\n",
       "      <td>870.0</td>\n",
       "      <td>828.0</td>\n",
       "      <td>349.0</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>1075.0</td>\n",
       "      <td>4090.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24.0</td>\n",
       "      <td>4038.0</td>\n",
       "      <td>295.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>382.0</td>\n",
       "      <td>815.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>391.0</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>5100.0</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25.0</td>\n",
       "      <td>3091.0</td>\n",
       "      <td>692.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>851.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>930.0</td>\n",
       "      <td>1062.0</td>\n",
       "      <td>456.0</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>4230.0</td>\n",
       "      <td>5859.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26.0</td>\n",
       "      <td>13510.0</td>\n",
       "      <td>1211.0</td>\n",
       "      <td>494.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>772.0</td>\n",
       "      <td>485.0</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>897.0</td>\n",
       "      <td>700.0</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>3780.0</td>\n",
       "      <td>1287.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27.0</td>\n",
       "      <td>4991.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>227.0</td>\n",
       "      <td>476.0</td>\n",
       "      <td>975.0</td>\n",
       "      <td>731.0</td>\n",
       "      <td>815.0</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>5100.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28.0</td>\n",
       "      <td>11674.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>596.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>1240.0</td>\n",
       "      <td>1048.0</td>\n",
       "      <td>413.0</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>4230.0</td>\n",
       "      <td>2649.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29.0</td>\n",
       "      <td>7108.0</td>\n",
       "      <td>342.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>583.0</td>\n",
       "      <td>753.0</td>\n",
       "      <td>510.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>174.0</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>4680.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</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>3913</th>\n",
       "      <td>3914.0</td>\n",
       "      <td>4773.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>189.0</td>\n",
       "      <td>815.0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>485.0</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>4740.0</td>\n",
       "      <td>4459.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3914</th>\n",
       "      <td>3915.0</td>\n",
       "      <td>10054.0</td>\n",
       "      <td>911.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>920.0</td>\n",
       "      <td>815.0</td>\n",
       "      <td>415.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>576.0</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>3960.0</td>\n",
       "      <td>8501.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3915</th>\n",
       "      <td>3916.0</td>\n",
       "      <td>10447.0</td>\n",
       "      <td>1412.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>1355.0</td>\n",
       "      <td>815.0</td>\n",
       "      <td>760.0</td>\n",
       "      <td>845.0</td>\n",
       "      <td>609.0</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>6680.0</td>\n",
       "      <td>17970.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3916</th>\n",
       "      <td>3917.0</td>\n",
       "      <td>10095.0</td>\n",
       "      <td>523.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>364.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>815.0</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>4230.0</td>\n",
       "      <td>1093.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3917</th>\n",
       "      <td>3918.0</td>\n",
       "      <td>9482.0</td>\n",
       "      <td>894.0</td>\n",
       "      <td>1260.0</td>\n",
       "      <td>780.0</td>\n",
       "      <td>363.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>1162.0</td>\n",
       "      <td>740.0</td>\n",
       "      <td>144.0</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>6990.0</td>\n",
       "      <td>3212.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3918</th>\n",
       "      <td>3919.0</td>\n",
       "      <td>6429.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>277.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>960.0</td>\n",
       "      <td>732.0</td>\n",
       "      <td>597.0</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>3960.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3919</th>\n",
       "      <td>3920.0</td>\n",
       "      <td>10793.0</td>\n",
       "      <td>246.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>815.0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>485.0</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>6960.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3920</th>\n",
       "      <td>3921.0</td>\n",
       "      <td>10328.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>815.0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>227.0</td>\n",
       "      <td>485.0</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>1140.0</td>\n",
       "      <td>5741.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3921</th>\n",
       "      <td>3922.0</td>\n",
       "      <td>8053.0</td>\n",
       "      <td>1775.0</td>\n",
       "      <td>738.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>1329.0</td>\n",
       "      <td>308.0</td>\n",
       "      <td>840.0</td>\n",
       "      <td>797.0</td>\n",
       "      <td>371.0</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>3960.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3922</th>\n",
       "      <td>3923.0</td>\n",
       "      <td>7722.0</td>\n",
       "      <td>2175.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>1907.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>805.0</td>\n",
       "      <td>568.0</td>\n",
       "      <td>700.0</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>3780.0</td>\n",
       "      <td>1114.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3923</th>\n",
       "      <td>3924.0</td>\n",
       "      <td>11701.0</td>\n",
       "      <td>2281.0</td>\n",
       "      <td>1465.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>1081.0</td>\n",
       "      <td>815.0</td>\n",
       "      <td>810.0</td>\n",
       "      <td>732.0</td>\n",
       "      <td>144.0</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>9480.0</td>\n",
       "      <td>27517.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3924</th>\n",
       "      <td>3925.0</td>\n",
       "      <td>10330.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>248.0</td>\n",
       "      <td>815.0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>227.0</td>\n",
       "      <td>485.0</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>4230.0</td>\n",
       "      <td>17454.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3925</th>\n",
       "      <td>3926.0</td>\n",
       "      <td>4322.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>815.0</td>\n",
       "      <td>295.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>485.0</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>6960.0</td>\n",
       "      <td>956.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3926</th>\n",
       "      <td>3927.0</td>\n",
       "      <td>2816.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>128.0</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>3960.0</td>\n",
       "      <td>1294.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3927</th>\n",
       "      <td>3928.0</td>\n",
       "      <td>14363.0</td>\n",
       "      <td>5844.0</td>\n",
       "      <td>2067.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>3980.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>750.0</td>\n",
       "      <td>671.0</td>\n",
       "      <td>798.0</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>5160.0</td>\n",
       "      <td>620.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3928</th>\n",
       "      <td>3929.0</td>\n",
       "      <td>13029.0</td>\n",
       "      <td>4614.0</td>\n",
       "      <td>1557.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>3192.0</td>\n",
       "      <td>441.0</td>\n",
       "      <td>670.0</td>\n",
       "      <td>714.0</td>\n",
       "      <td>349.0</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>584.0</td>\n",
       "      <td>408.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3929</th>\n",
       "      <td>3930.0</td>\n",
       "      <td>11958.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>812.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>783.0</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>2802.0</td>\n",
       "      <td>385.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3930</th>\n",
       "      <td>3931.0</td>\n",
       "      <td>1780.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>207.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>680.0</td>\n",
       "      <td>762.0</td>\n",
       "      <td>783.0</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>6000.0</td>\n",
       "      <td>6984.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3931</th>\n",
       "      <td>3932.0</td>\n",
       "      <td>13334.0</td>\n",
       "      <td>1783.0</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>517.0</td>\n",
       "      <td>441.0</td>\n",
       "      <td>976.0</td>\n",
       "      <td>851.0</td>\n",
       "      <td>128.0</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>665.0</td>\n",
       "      <td>2906.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3932</th>\n",
       "      <td>3933.0</td>\n",
       "      <td>13957.0</td>\n",
       "      <td>318.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>780.0</td>\n",
       "      <td>859.0</td>\n",
       "      <td>556.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>281.0</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>3780.0</td>\n",
       "      <td>1130.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3933</th>\n",
       "      <td>3934.0</td>\n",
       "      <td>5987.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>727.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>...</td>\n",
       "      <td>545</td>\n",
       "      <td>495</td>\n",
       "      <td>411</td>\n",
       "      <td>780</td>\n",
       "      <td>2112</td>\n",
       "      <td>567</td>\n",
       "      <td>3900.0</td>\n",
       "      <td>5798.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3934</th>\n",
       "      <td>3935.0</td>\n",
       "      <td>4685.0</td>\n",
       "      <td>1362.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>1553.0</td>\n",
       "      <td>815.0</td>\n",
       "      <td>760.0</td>\n",
       "      <td>760.0</td>\n",
       "      <td>609.0</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>2880.0</td>\n",
       "      <td>8647.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3935</th>\n",
       "      <td>3936.0</td>\n",
       "      <td>9814.0</td>\n",
       "      <td>675.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>741.0</td>\n",
       "      <td>815.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>134.0</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>2820.0</td>\n",
       "      <td>1734.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3936</th>\n",
       "      <td>3937.0</td>\n",
       "      <td>2163.0</td>\n",
       "      <td>4772.0</td>\n",
       "      <td>3941.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>860.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>845.0</td>\n",
       "      <td>545.0</td>\n",
       "      <td>628.0</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>3960.0</td>\n",
       "      <td>571.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3937</th>\n",
       "      <td>3938.0</td>\n",
       "      <td>4166.0</td>\n",
       "      <td>703.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>493.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>908.0</td>\n",
       "      <td>815.0</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>300.0</td>\n",
       "      <td>2512.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3938</th>\n",
       "      <td>3939.0</td>\n",
       "      <td>4225.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>278.0</td>\n",
       "      <td>815.0</td>\n",
       "      <td>830.0</td>\n",
       "      <td>761.0</td>\n",
       "      <td>174.0</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>1665.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3939</th>\n",
       "      <td>3940.0</td>\n",
       "      <td>14017.0</td>\n",
       "      <td>2028.0</td>\n",
       "      <td>413.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>1825.0</td>\n",
       "      <td>605.0</td>\n",
       "      <td>2610.0</td>\n",
       "      <td>2535.0</td>\n",
       "      <td>349.0</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>3780.0</td>\n",
       "      <td>807.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3940</th>\n",
       "      <td>3941.0</td>\n",
       "      <td>4660.0</td>\n",
       "      <td>1356.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>1359.0</td>\n",
       "      <td>815.0</td>\n",
       "      <td>760.0</td>\n",
       "      <td>716.0</td>\n",
       "      <td>609.0</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>5100.0</td>\n",
       "      <td>4381.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3941</th>\n",
       "      <td>3942.0</td>\n",
       "      <td>6472.0</td>\n",
       "      <td>2692.0</td>\n",
       "      <td>1856.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>867.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>1060.0</td>\n",
       "      <td>1049.0</td>\n",
       "      <td>113.0</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>3780.0</td>\n",
       "      <td>945.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3942</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>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",
       "  </tbody>\n",
       "</table>\n",
       "<p>3943 rows × 98 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          nr  i1_legid  i1_rcs_p  i1_rcs_e  i1_dep_1_p  i1_dep_1_e  \\\n",
       "0        0.0    5182.0     199.0     218.0       210.0       215.0   \n",
       "1        1.0    6523.0     844.0     584.0        90.0       297.0   \n",
       "2        2.0    5878.0    4380.0    4119.0        90.0       280.0   \n",
       "3        3.0    1275.0     759.0     169.0       240.0       777.0   \n",
       "4        4.0    8117.0    1597.0    1485.0       150.0       241.0   \n",
       "5        5.0    9889.0     181.0      98.0       240.0       259.0   \n",
       "6        6.0    3997.0    1300.0     212.0       240.0      1267.0   \n",
       "7        7.0    7125.0     139.0      87.0       240.0       232.0   \n",
       "8        8.0   10481.0     387.0      68.0       240.0       514.0   \n",
       "9        9.0    3212.0    6145.0    6013.0       300.0       385.0   \n",
       "10      10.0   12343.0     748.0      96.0       150.0       758.0   \n",
       "11      11.0    8473.0    1415.0    1157.0       150.0       346.0   \n",
       "12      12.0    9931.0     898.0     112.0       240.0       965.0   \n",
       "13      13.0   12666.0    3140.0    3004.0       240.0       324.0   \n",
       "14      14.0   14132.0     142.0     136.0       240.0       182.0   \n",
       "15      15.0   13119.0    1081.0     413.0       180.0       859.0   \n",
       "16      16.0   13572.0     778.0     254.0       420.0       894.0   \n",
       "17      17.0    1388.0   27415.0   26485.0        90.0       963.0   \n",
       "18      18.0    4697.0     671.0     140.0       240.0       713.0   \n",
       "19      19.0   13297.0    5442.0    4568.0       120.0       934.0   \n",
       "20      20.0    9481.0     327.0     167.0        90.0       278.0   \n",
       "21      21.0    5838.0     249.0     116.0       150.0       223.0   \n",
       "22      22.0   10613.0    2127.0     234.0       300.0      2158.0   \n",
       "23      23.0    1363.0     287.0     172.0       150.0       179.0   \n",
       "24      24.0    4038.0     295.0      98.0       240.0       382.0   \n",
       "25      25.0    3091.0     692.0      72.0       270.0       851.0   \n",
       "26      26.0   13510.0    1211.0     494.0        90.0       772.0   \n",
       "27      27.0    4991.0     115.0      51.0       240.0       227.0   \n",
       "28      28.0   11674.0     123.0      48.0       420.0       596.0   \n",
       "29      29.0    7108.0     342.0       9.0       300.0       583.0   \n",
       "...      ...       ...       ...       ...         ...         ...   \n",
       "3913  3914.0    4773.0     187.0      76.0       135.0       189.0   \n",
       "3914  3915.0   10054.0     911.0     190.0       240.0       920.0   \n",
       "3915  3916.0   10447.0    1412.0     236.0       240.0      1355.0   \n",
       "3916  3917.0   10095.0     523.0     169.0       120.0       364.0   \n",
       "3917  3918.0    9482.0     894.0    1260.0       780.0       363.0   \n",
       "3918  3919.0    6429.0     152.0      71.0       240.0       277.0   \n",
       "3919  3920.0   10793.0     246.0      93.0       135.0       229.0   \n",
       "3920  3921.0   10328.0     188.0      32.0       135.0       250.0   \n",
       "3921  3922.0    8053.0    1775.0     738.0       300.0      1329.0   \n",
       "3922  3923.0    7722.0    2175.0     315.0        90.0      1907.0   \n",
       "3923  3924.0   11701.0    2281.0    1465.0       300.0      1081.0   \n",
       "3924  3925.0   10330.0     206.0      52.0       135.0       248.0   \n",
       "3925  3926.0    4322.0      88.0      57.0       135.0       150.0   \n",
       "3926  3927.0    2816.0     259.0     186.0       150.0       186.0   \n",
       "3927  3928.0   14363.0    5844.0    2067.0       240.0      3980.0   \n",
       "3928  3929.0   13029.0    4614.0    1557.0       195.0      3192.0   \n",
       "3929  3930.0   11958.0      55.0      15.0       150.0       140.0   \n",
       "3930  3931.0    1780.0      92.0       6.0       150.0       207.0   \n",
       "3931  3932.0   13334.0    1783.0    1460.0       240.0       517.0   \n",
       "3932  3933.0   13957.0     318.0     184.0       780.0       859.0   \n",
       "3933  3934.0    5987.0     145.0     151.0       120.0        49.0   \n",
       "3934  3935.0    4685.0    1362.0       8.0       240.0      1553.0   \n",
       "3935  3936.0    9814.0     675.0     113.0       240.0       741.0   \n",
       "3936  3937.0    2163.0    4772.0    3941.0        90.0       860.0   \n",
       "3937  3938.0    4166.0     703.0     360.0       210.0       493.0   \n",
       "3938  3939.0    4225.0     122.0      19.0       240.0       278.0   \n",
       "3939  3940.0   14017.0    2028.0     413.0       270.0      1825.0   \n",
       "3940  3941.0    4660.0    1356.0     178.0       240.0      1359.0   \n",
       "3941  3942.0    6472.0    2692.0    1856.0        90.0       867.0   \n",
       "3942     NaN       NaN       NaN       NaN         NaN         NaN   \n",
       "\n",
       "      i1_dep_1_place  i1_rcf_1_p  i1_rcf_1_e  i1_rcf_1_place ...  o_dep_3_p  \\\n",
       "0              609.0       935.0       736.0           256.0 ...        NaN   \n",
       "1              700.0      1935.0      1415.0           431.0 ...        NaN   \n",
       "2              456.0       905.0       547.0           700.0 ...        NaN   \n",
       "3              173.0       340.0       577.0           349.0 ...        NaN   \n",
       "4              411.0       585.0       612.0           128.0 ...        NaN   \n",
       "5              815.0       215.0       223.0           128.0 ...        NaN   \n",
       "6              815.0       270.0       135.0           391.0 ...        NaN   \n",
       "7              783.0       255.0       154.0           815.0 ...        NaN   \n",
       "8              815.0       285.0       219.0           485.0 ...        NaN   \n",
       "9              690.0       810.0       635.0           555.0 ...        NaN   \n",
       "10             281.0       395.0       152.0           128.0 ...        NaN   \n",
       "11             377.0       280.0       179.0           485.0 ...        NaN   \n",
       "12             281.0       415.0       186.0           815.0 ...        NaN   \n",
       "13             445.0       995.0       808.0           128.0 ...        NaN   \n",
       "14             456.0      1085.0      1093.0           349.0 ...        NaN   \n",
       "15             753.0      1575.0       928.0           511.0 ...        NaN   \n",
       "16             349.0      1410.0      2375.0           548.0 ...        NaN   \n",
       "17             815.0      1220.0      1027.0           700.0 ...        NaN   \n",
       "18             815.0       280.0       381.0           153.0 ...        NaN   \n",
       "19             236.0       420.0       260.0           815.0 ...        NaN   \n",
       "20             579.0      1010.0       584.0           128.0 ...        NaN   \n",
       "21             391.0       385.0       155.0           815.0 ...        NaN   \n",
       "22             332.0       705.0       783.0           815.0 ...        NaN   \n",
       "23             746.0       870.0       828.0           349.0 ...        NaN   \n",
       "24             815.0       270.0       143.0           391.0 ...        NaN   \n",
       "25             349.0       930.0      1062.0           456.0 ...        NaN   \n",
       "26             485.0      1250.0       897.0           700.0 ...        NaN   \n",
       "27             476.0       975.0       731.0           815.0 ...        NaN   \n",
       "28             349.0      1240.0      1048.0           413.0 ...        NaN   \n",
       "29             753.0       510.0       322.0           174.0 ...        NaN   \n",
       "...              ...         ...         ...             ... ...        ...   \n",
       "3913           815.0       285.0       212.0           485.0 ...        NaN   \n",
       "3914           815.0       415.0        21.0           576.0 ...        NaN   \n",
       "3915           815.0       760.0       845.0           609.0 ...        NaN   \n",
       "3916           236.0        90.0        87.0           815.0 ...        NaN   \n",
       "3917           349.0      1162.0       740.0           144.0 ...        NaN   \n",
       "3918           128.0       960.0       732.0           597.0 ...        NaN   \n",
       "3919           815.0       285.0       340.0           485.0 ...        NaN   \n",
       "3920           815.0       285.0       227.0           485.0 ...        NaN   \n",
       "3921           308.0       840.0       797.0           371.0 ...        NaN   \n",
       "3922           206.0       805.0       568.0           700.0 ...        NaN   \n",
       "3923           815.0       810.0       732.0           144.0 ...        NaN   \n",
       "3924           815.0       285.0       227.0           485.0 ...        NaN   \n",
       "3925           815.0       295.0       141.0           485.0 ...        NaN   \n",
       "3926           166.0       130.0       158.0           128.0 ...        NaN   \n",
       "3927           128.0       750.0       671.0           798.0 ...        NaN   \n",
       "3928           441.0       670.0       714.0           349.0 ...        NaN   \n",
       "3929           812.0       163.0        61.0           783.0 ...        NaN   \n",
       "3930           128.0       680.0       762.0           783.0 ...        NaN   \n",
       "3931           441.0       976.0       851.0           128.0 ...        NaN   \n",
       "3932           556.0        60.0        63.0           281.0 ...        NaN   \n",
       "3933           727.0       125.0       175.0           101.0 ...        545   \n",
       "3934           815.0       760.0       760.0           609.0 ...        NaN   \n",
       "3935           815.0       260.0       197.0           134.0 ...        NaN   \n",
       "3936           700.0       845.0       545.0           628.0 ...        NaN   \n",
       "3937           233.0      1000.0       908.0           815.0 ...        NaN   \n",
       "3938           815.0       830.0       761.0           174.0 ...        NaN   \n",
       "3939           605.0      2610.0      2535.0           349.0 ...        NaN   \n",
       "3940           815.0       760.0       716.0           609.0 ...        NaN   \n",
       "3941           700.0      1060.0      1049.0           113.0 ...        NaN   \n",
       "3942             NaN         NaN         NaN             NaN ...        NaN   \n",
       "\n",
       "     o_dep_3_e o_dep_3_place o_rcf_3_p o_rcf_3_e o_rcf_3_place o_dlv_p  \\\n",
       "0          NaN           NaN       NaN       NaN           NaN   780.0   \n",
       "1          NaN           NaN       NaN       NaN           NaN  3870.0   \n",
       "2          NaN           NaN       NaN       NaN           NaN   550.0   \n",
       "3          NaN           NaN       NaN       NaN           NaN  3780.0   \n",
       "4          NaN           NaN       NaN       NaN           NaN  4140.0   \n",
       "5          NaN           NaN       NaN       NaN           NaN  3960.0   \n",
       "6          NaN           NaN       NaN       NaN           NaN  2880.0   \n",
       "7          NaN           NaN       NaN       NaN           NaN  3960.0   \n",
       "8          NaN           NaN       NaN       NaN           NaN  6680.0   \n",
       "9          NaN           NaN       NaN       NaN           NaN  3960.0   \n",
       "10         NaN           NaN       NaN       NaN           NaN  3960.0   \n",
       "11         NaN           NaN       NaN       NaN           NaN  2880.0   \n",
       "12         NaN           NaN       NaN       NaN           NaN  5160.0   \n",
       "13         NaN           NaN       NaN       NaN           NaN  3960.0   \n",
       "14         NaN           NaN       NaN       NaN           NaN  6680.0   \n",
       "15         NaN           NaN       NaN       NaN           NaN  3780.0   \n",
       "16         NaN           NaN       NaN       NaN           NaN  3960.0   \n",
       "17         NaN           NaN       NaN       NaN           NaN  3420.0   \n",
       "18         NaN           NaN       NaN       NaN           NaN  3780.0   \n",
       "19         NaN           NaN       NaN       NaN           NaN   550.0   \n",
       "20         NaN           NaN       NaN       NaN           NaN  3960.0   \n",
       "21         NaN           NaN       NaN       NaN           NaN  5160.0   \n",
       "22         NaN           NaN       NaN       NaN           NaN  5160.0   \n",
       "23         NaN           NaN       NaN       NaN           NaN  1075.0   \n",
       "24         NaN           NaN       NaN       NaN           NaN  5100.0   \n",
       "25         NaN           NaN       NaN       NaN           NaN  4230.0   \n",
       "26         NaN           NaN       NaN       NaN           NaN  3780.0   \n",
       "27         NaN           NaN       NaN       NaN           NaN  5100.0   \n",
       "28         NaN           NaN       NaN       NaN           NaN  4230.0   \n",
       "29         NaN           NaN       NaN       NaN           NaN  4680.0   \n",
       "...        ...           ...       ...       ...           ...     ...   \n",
       "3913       NaN           NaN       NaN       NaN           NaN  4740.0   \n",
       "3914       NaN           NaN       NaN       NaN           NaN  3960.0   \n",
       "3915       NaN           NaN       NaN       NaN           NaN  6680.0   \n",
       "3916       NaN           NaN       NaN       NaN           NaN  4230.0   \n",
       "3917       NaN           NaN       NaN       NaN           NaN  6990.0   \n",
       "3918       NaN           NaN       NaN       NaN           NaN  3960.0   \n",
       "3919       NaN           NaN       NaN       NaN           NaN  6960.0   \n",
       "3920       NaN           NaN       NaN       NaN           NaN  1140.0   \n",
       "3921       NaN           NaN       NaN       NaN           NaN  3960.0   \n",
       "3922       NaN           NaN       NaN       NaN           NaN  3780.0   \n",
       "3923       NaN           NaN       NaN       NaN           NaN  9480.0   \n",
       "3924       NaN           NaN       NaN       NaN           NaN  4230.0   \n",
       "3925       NaN           NaN       NaN       NaN           NaN  6960.0   \n",
       "3926       NaN           NaN       NaN       NaN           NaN  3960.0   \n",
       "3927       NaN           NaN       NaN       NaN           NaN  5160.0   \n",
       "3928       NaN           NaN       NaN       NaN           NaN   584.0   \n",
       "3929       NaN           NaN       NaN       NaN           NaN  2802.0   \n",
       "3930       NaN           NaN       NaN       NaN           NaN  6000.0   \n",
       "3931       NaN           NaN       NaN       NaN           NaN   665.0   \n",
       "3932       NaN           NaN       NaN       NaN           NaN  3780.0   \n",
       "3933       495           411       780      2112           567  3900.0   \n",
       "3934       NaN           NaN       NaN       NaN           NaN  2880.0   \n",
       "3935       NaN           NaN       NaN       NaN           NaN  2820.0   \n",
       "3936       NaN           NaN       NaN       NaN           NaN  3960.0   \n",
       "3937       NaN           NaN       NaN       NaN           NaN   300.0   \n",
       "3938       NaN           NaN       NaN       NaN           NaN  1665.0   \n",
       "3939       NaN           NaN       NaN       NaN           NaN  3780.0   \n",
       "3940       NaN           NaN       NaN       NaN           NaN  5100.0   \n",
       "3941       NaN           NaN       NaN       NaN           NaN  3780.0   \n",
       "3942       NaN           NaN       NaN       NaN           NaN     NaN   \n",
       "\n",
       "      o_dlv_e o_hops legs  \n",
       "0       434.0    1.0  2.0  \n",
       "1       445.0    1.0  2.0  \n",
       "2      1520.0    1.0  1.0  \n",
       "3       159.0    1.0  1.0  \n",
       "4      4797.0    2.0  1.0  \n",
       "5       467.0    1.0  2.0  \n",
       "6       151.0    1.0  3.0  \n",
       "7       161.0    1.0  2.0  \n",
       "8      2830.0    1.0  1.0  \n",
       "9       218.0    1.0  1.0  \n",
       "10      223.0    1.0  2.0  \n",
       "11       20.0    1.0  1.0  \n",
       "12      140.0    1.0  3.0  \n",
       "13      934.0    1.0  2.0  \n",
       "14      164.0    1.0  3.0  \n",
       "15       11.0    1.0  1.0  \n",
       "16      108.0    1.0  2.0  \n",
       "17     1681.0    1.0  2.0  \n",
       "18     1115.0    2.0  1.0  \n",
       "19      492.0    1.0  2.0  \n",
       "20     7005.0    1.0  2.0  \n",
       "21      432.0    1.0  1.0  \n",
       "22     2060.0    1.0  3.0  \n",
       "23     4090.0    2.0  2.0  \n",
       "24     1966.0    2.0  3.0  \n",
       "25     5859.0    2.0  3.0  \n",
       "26     1287.0    1.0  1.0  \n",
       "27       24.0    1.0  1.0  \n",
       "28     2649.0    2.0  2.0  \n",
       "29        2.0    1.0  1.0  \n",
       "...       ...    ...  ...  \n",
       "3913   4459.0    2.0  3.0  \n",
       "3914   8501.0    2.0  2.0  \n",
       "3915  17970.0    1.0  2.0  \n",
       "3916   1093.0    2.0  3.0  \n",
       "3917   3212.0    2.0  2.0  \n",
       "3918   1467.0    2.0  2.0  \n",
       "3919     89.0    2.0  1.0  \n",
       "3920   5741.0    1.0  3.0  \n",
       "3921    280.0    2.0  3.0  \n",
       "3922   1114.0    2.0  1.0  \n",
       "3923  27517.0    1.0  3.0  \n",
       "3924  17454.0    2.0  1.0  \n",
       "3925    956.0    2.0  3.0  \n",
       "3926   1294.0    2.0  3.0  \n",
       "3927    620.0    2.0  2.0  \n",
       "3928    408.0    2.0  2.0  \n",
       "3929    385.0    1.0  1.0  \n",
       "3930   6984.0    2.0  1.0  \n",
       "3931   2906.0    2.0  2.0  \n",
       "3932   1130.0    2.0  3.0  \n",
       "3933   5798.0    3.0  1.0  \n",
       "3934   8647.0    2.0  3.0  \n",
       "3935   1734.0    1.0  2.0  \n",
       "3936    571.0    2.0  2.0  \n",
       "3937   2512.0    2.0  3.0  \n",
       "3938   1300.0    2.0  3.0  \n",
       "3939    807.0    2.0  1.0  \n",
       "3940   4381.0    2.0  1.0  \n",
       "3941    945.0    2.0  2.0  \n",
       "3942      NaN    NaN  NaN  \n",
       "\n",
       "[3943 rows x 98 columns]"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_replace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nr                   1\n",
       "i1_legid             1\n",
       "i1_rcs_p             1\n",
       "i1_rcs_e             1\n",
       "i1_dep_1_p           1\n",
       "i1_dep_1_e           1\n",
       "i1_dep_1_place       1\n",
       "i1_rcf_1_p           1\n",
       "i1_rcf_1_e           1\n",
       "i1_rcf_1_place       1\n",
       "i1_dep_2_p        2748\n",
       "i1_dep_2_e        2748\n",
       "i1_dep_2_place    2748\n",
       "i1_rcf_2_p        2748\n",
       "i1_rcf_2_e        2748\n",
       "i1_rcf_2_place    2748\n",
       "i1_dep_3_p        3920\n",
       "i1_dep_3_e        3920\n",
       "i1_dep_3_place    3920\n",
       "i1_rcf_3_p        3920\n",
       "i1_rcf_3_e        3920\n",
       "i1_rcf_3_place    3920\n",
       "i1_dlv_p             1\n",
       "i1_dlv_e             1\n",
       "i1_hops              1\n",
       "i2_legid          1319\n",
       "i2_rcs_p          1319\n",
       "i2_rcs_e          1319\n",
       "i2_dep_1_p        1319\n",
       "i2_dep_1_e        1319\n",
       "                  ... \n",
       "i3_rcf_3_e        3935\n",
       "i3_rcf_3_place    3935\n",
       "i3_dlv_p          2577\n",
       "i3_dlv_e          2577\n",
       "i3_hops           2577\n",
       "o_legid              1\n",
       "o_rcs_p              1\n",
       "o_rcs_e              1\n",
       "o_dep_1_p            1\n",
       "o_dep_1_e            1\n",
       "o_dep_1_place        1\n",
       "o_rcf_1_p            1\n",
       "o_rcf_1_e            1\n",
       "o_rcf_1_place        1\n",
       "o_dep_2_p         2098\n",
       "o_dep_2_e         2098\n",
       "o_dep_2_place     2098\n",
       "o_rcf_2_p         2098\n",
       "o_rcf_2_e         2098\n",
       "o_rcf_2_place     2098\n",
       "o_dep_3_p         3917\n",
       "o_dep_3_e         3917\n",
       "o_dep_3_place     3917\n",
       "o_rcf_3_p         3917\n",
       "o_rcf_3_e         3917\n",
       "o_rcf_3_place     3917\n",
       "o_dlv_p              1\n",
       "o_dlv_e              1\n",
       "o_hops               1\n",
       "legs                 1\n",
       "Length: 98, dtype: int64"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_replace.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nr</th>\n",
       "      <th>i1_legid</th>\n",
       "      <th>i1_rcs_p</th>\n",
       "      <th>i1_rcs_e</th>\n",
       "      <th>i1_dep_1_p</th>\n",
       "      <th>i1_dep_1_e</th>\n",
       "      <th>i1_dep_1_place</th>\n",
       "      <th>i1_rcf_1_p</th>\n",
       "      <th>i1_rcf_1_e</th>\n",
       "      <th>i1_rcf_1_place</th>\n",
       "      <th>...</th>\n",
       "      <th>o_dep_3_p</th>\n",
       "      <th>o_dep_3_e</th>\n",
       "      <th>o_dep_3_place</th>\n",
       "      <th>o_rcf_3_p</th>\n",
       "      <th>o_rcf_3_e</th>\n",
       "      <th>o_rcf_3_place</th>\n",
       "      <th>o_dlv_p</th>\n",
       "      <th>o_dlv_e</th>\n",
       "      <th>o_hops</th>\n",
       "      <th>legs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>5182.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>218.00000</td>\n",
       "      <td>210.000000</td>\n",
       "      <td>215.000000</td>\n",
       "      <td>609.000000</td>\n",
       "      <td>935.00000</td>\n",
       "      <td>736.000000</td>\n",
       "      <td>256.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>780.000000</td>\n",
       "      <td>434.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>6523.000000</td>\n",
       "      <td>844.000000</td>\n",
       "      <td>584.00000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>1935.00000</td>\n",
       "      <td>1415.000000</td>\n",
       "      <td>431.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>3870.000000</td>\n",
       "      <td>445.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>5878.000000</td>\n",
       "      <td>4380.000000</td>\n",
       "      <td>4119.00000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>456.000000</td>\n",
       "      <td>905.00000</td>\n",
       "      <td>547.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>1520.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1275.000000</td>\n",
       "      <td>759.000000</td>\n",
       "      <td>169.00000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>777.000000</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>340.00000</td>\n",
       "      <td>577.000000</td>\n",
       "      <td>349.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>3780.000000</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>8117.000000</td>\n",
       "      <td>1597.000000</td>\n",
       "      <td>1485.00000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>241.000000</td>\n",
       "      <td>411.000000</td>\n",
       "      <td>585.00000</td>\n",
       "      <td>612.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>4140.000000</td>\n",
       "      <td>4797.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>9889.000000</td>\n",
       "      <td>181.000000</td>\n",
       "      <td>98.00000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>259.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>215.00000</td>\n",
       "      <td>223.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>3960.000000</td>\n",
       "      <td>467.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>3997.000000</td>\n",
       "      <td>1300.000000</td>\n",
       "      <td>212.00000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>1267.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>270.00000</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>391.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>2880.000000</td>\n",
       "      <td>151.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>7125.000000</td>\n",
       "      <td>139.000000</td>\n",
       "      <td>87.00000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>232.000000</td>\n",
       "      <td>783.000000</td>\n",
       "      <td>255.00000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>3960.000000</td>\n",
       "      <td>161.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>10481.000000</td>\n",
       "      <td>387.000000</td>\n",
       "      <td>68.00000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>514.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>285.00000</td>\n",
       "      <td>219.000000</td>\n",
       "      <td>485.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>6680.000000</td>\n",
       "      <td>2830.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>3212.000000</td>\n",
       "      <td>6145.000000</td>\n",
       "      <td>6013.00000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>385.000000</td>\n",
       "      <td>690.000000</td>\n",
       "      <td>810.00000</td>\n",
       "      <td>635.000000</td>\n",
       "      <td>555.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>3960.000000</td>\n",
       "      <td>218.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>12343.000000</td>\n",
       "      <td>748.000000</td>\n",
       "      <td>96.00000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>758.000000</td>\n",
       "      <td>281.000000</td>\n",
       "      <td>395.00000</td>\n",
       "      <td>152.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>3960.000000</td>\n",
       "      <td>223.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11.000000</td>\n",
       "      <td>8473.000000</td>\n",
       "      <td>1415.000000</td>\n",
       "      <td>1157.00000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>346.000000</td>\n",
       "      <td>377.000000</td>\n",
       "      <td>280.00000</td>\n",
       "      <td>179.000000</td>\n",
       "      <td>485.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>2880.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>9931.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>112.00000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>965.000000</td>\n",
       "      <td>281.000000</td>\n",
       "      <td>415.00000</td>\n",
       "      <td>186.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>5160.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13.000000</td>\n",
       "      <td>12666.000000</td>\n",
       "      <td>3140.000000</td>\n",
       "      <td>3004.00000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>324.000000</td>\n",
       "      <td>445.000000</td>\n",
       "      <td>995.00000</td>\n",
       "      <td>808.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>3960.000000</td>\n",
       "      <td>934.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14.000000</td>\n",
       "      <td>14132.000000</td>\n",
       "      <td>142.000000</td>\n",
       "      <td>136.00000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>182.000000</td>\n",
       "      <td>456.000000</td>\n",
       "      <td>1085.00000</td>\n",
       "      <td>1093.000000</td>\n",
       "      <td>349.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>6680.000000</td>\n",
       "      <td>164.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>13119.000000</td>\n",
       "      <td>1081.000000</td>\n",
       "      <td>413.00000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>859.000000</td>\n",
       "      <td>753.000000</td>\n",
       "      <td>1575.00000</td>\n",
       "      <td>928.000000</td>\n",
       "      <td>511.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>3780.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>13572.000000</td>\n",
       "      <td>778.000000</td>\n",
       "      <td>254.00000</td>\n",
       "      <td>420.000000</td>\n",
       "      <td>894.000000</td>\n",
       "      <td>349.000000</td>\n",
       "      <td>1410.00000</td>\n",
       "      <td>2375.000000</td>\n",
       "      <td>548.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>3960.000000</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>1388.000000</td>\n",
       "      <td>27415.000000</td>\n",
       "      <td>26485.00000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>963.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>1220.00000</td>\n",
       "      <td>1027.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>3420.000000</td>\n",
       "      <td>1681.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>4697.000000</td>\n",
       "      <td>671.000000</td>\n",
       "      <td>140.00000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>713.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>280.00000</td>\n",
       "      <td>381.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>3780.000000</td>\n",
       "      <td>1115.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>13297.000000</td>\n",
       "      <td>5442.000000</td>\n",
       "      <td>4568.00000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>934.000000</td>\n",
       "      <td>236.000000</td>\n",
       "      <td>420.00000</td>\n",
       "      <td>260.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>492.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>9481.000000</td>\n",
       "      <td>327.000000</td>\n",
       "      <td>167.00000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>278.000000</td>\n",
       "      <td>579.000000</td>\n",
       "      <td>1010.00000</td>\n",
       "      <td>584.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>3960.000000</td>\n",
       "      <td>7005.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>5838.000000</td>\n",
       "      <td>249.000000</td>\n",
       "      <td>116.00000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>223.000000</td>\n",
       "      <td>391.000000</td>\n",
       "      <td>385.00000</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>5160.000000</td>\n",
       "      <td>432.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22.000000</td>\n",
       "      <td>10613.000000</td>\n",
       "      <td>2127.000000</td>\n",
       "      <td>234.00000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>2158.000000</td>\n",
       "      <td>332.000000</td>\n",
       "      <td>705.00000</td>\n",
       "      <td>783.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>5160.000000</td>\n",
       "      <td>2060.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>1363.000000</td>\n",
       "      <td>287.000000</td>\n",
       "      <td>172.00000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>179.000000</td>\n",
       "      <td>746.000000</td>\n",
       "      <td>870.00000</td>\n",
       "      <td>828.000000</td>\n",
       "      <td>349.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>1075.000000</td>\n",
       "      <td>4090.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24.000000</td>\n",
       "      <td>4038.000000</td>\n",
       "      <td>295.000000</td>\n",
       "      <td>98.00000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>382.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>270.00000</td>\n",
       "      <td>143.000000</td>\n",
       "      <td>391.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>5100.000000</td>\n",
       "      <td>1966.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>3091.000000</td>\n",
       "      <td>692.000000</td>\n",
       "      <td>72.00000</td>\n",
       "      <td>270.000000</td>\n",
       "      <td>851.000000</td>\n",
       "      <td>349.000000</td>\n",
       "      <td>930.00000</td>\n",
       "      <td>1062.000000</td>\n",
       "      <td>456.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>4230.000000</td>\n",
       "      <td>5859.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26.000000</td>\n",
       "      <td>13510.000000</td>\n",
       "      <td>1211.000000</td>\n",
       "      <td>494.00000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>772.000000</td>\n",
       "      <td>485.000000</td>\n",
       "      <td>1250.00000</td>\n",
       "      <td>897.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>3780.000000</td>\n",
       "      <td>1287.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>4991.000000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>51.00000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>227.000000</td>\n",
       "      <td>476.000000</td>\n",
       "      <td>975.00000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>5100.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>11674.000000</td>\n",
       "      <td>123.000000</td>\n",
       "      <td>48.00000</td>\n",
       "      <td>420.000000</td>\n",
       "      <td>596.000000</td>\n",
       "      <td>349.000000</td>\n",
       "      <td>1240.00000</td>\n",
       "      <td>1048.000000</td>\n",
       "      <td>413.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>4230.000000</td>\n",
       "      <td>2649.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>7108.000000</td>\n",
       "      <td>342.000000</td>\n",
       "      <td>9.00000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>583.000000</td>\n",
       "      <td>753.000000</td>\n",
       "      <td>510.00000</td>\n",
       "      <td>322.000000</td>\n",
       "      <td>174.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>4680.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</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>3913</th>\n",
       "      <td>3914.000000</td>\n",
       "      <td>4773.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>76.00000</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>189.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>285.00000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>485.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>4740.000000</td>\n",
       "      <td>4459.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3914</th>\n",
       "      <td>3915.000000</td>\n",
       "      <td>10054.000000</td>\n",
       "      <td>911.000000</td>\n",
       "      <td>190.00000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>920.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>415.00000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>576.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>3960.000000</td>\n",
       "      <td>8501.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3915</th>\n",
       "      <td>3916.000000</td>\n",
       "      <td>10447.000000</td>\n",
       "      <td>1412.000000</td>\n",
       "      <td>236.00000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>1355.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>760.00000</td>\n",
       "      <td>845.000000</td>\n",
       "      <td>609.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>6680.000000</td>\n",
       "      <td>17970.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3916</th>\n",
       "      <td>3917.000000</td>\n",
       "      <td>10095.000000</td>\n",
       "      <td>523.000000</td>\n",
       "      <td>169.00000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>236.000000</td>\n",
       "      <td>90.00000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>4230.000000</td>\n",
       "      <td>1093.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3917</th>\n",
       "      <td>3918.000000</td>\n",
       "      <td>9482.000000</td>\n",
       "      <td>894.000000</td>\n",
       "      <td>1260.00000</td>\n",
       "      <td>780.000000</td>\n",
       "      <td>363.000000</td>\n",
       "      <td>349.000000</td>\n",
       "      <td>1162.00000</td>\n",
       "      <td>740.000000</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>6990.000000</td>\n",
       "      <td>3212.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3918</th>\n",
       "      <td>3919.000000</td>\n",
       "      <td>6429.000000</td>\n",
       "      <td>152.000000</td>\n",
       "      <td>71.00000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>277.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>960.00000</td>\n",
       "      <td>732.000000</td>\n",
       "      <td>597.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>3960.000000</td>\n",
       "      <td>1467.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3919</th>\n",
       "      <td>3920.000000</td>\n",
       "      <td>10793.000000</td>\n",
       "      <td>246.000000</td>\n",
       "      <td>93.00000</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>229.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>285.00000</td>\n",
       "      <td>340.000000</td>\n",
       "      <td>485.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>6960.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3920</th>\n",
       "      <td>3921.000000</td>\n",
       "      <td>10328.000000</td>\n",
       "      <td>188.000000</td>\n",
       "      <td>32.00000</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>285.00000</td>\n",
       "      <td>227.000000</td>\n",
       "      <td>485.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>1140.000000</td>\n",
       "      <td>5741.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3921</th>\n",
       "      <td>3922.000000</td>\n",
       "      <td>8053.000000</td>\n",
       "      <td>1775.000000</td>\n",
       "      <td>738.00000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>1329.000000</td>\n",
       "      <td>308.000000</td>\n",
       "      <td>840.00000</td>\n",
       "      <td>797.000000</td>\n",
       "      <td>371.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>3960.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3922</th>\n",
       "      <td>3923.000000</td>\n",
       "      <td>7722.000000</td>\n",
       "      <td>2175.000000</td>\n",
       "      <td>315.00000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>1907.000000</td>\n",
       "      <td>206.000000</td>\n",
       "      <td>805.00000</td>\n",
       "      <td>568.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>3780.000000</td>\n",
       "      <td>1114.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3923</th>\n",
       "      <td>3924.000000</td>\n",
       "      <td>11701.000000</td>\n",
       "      <td>2281.000000</td>\n",
       "      <td>1465.00000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>1081.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>810.00000</td>\n",
       "      <td>732.000000</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>9480.000000</td>\n",
       "      <td>27517.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3924</th>\n",
       "      <td>3925.000000</td>\n",
       "      <td>10330.000000</td>\n",
       "      <td>206.000000</td>\n",
       "      <td>52.00000</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>248.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>285.00000</td>\n",
       "      <td>227.000000</td>\n",
       "      <td>485.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>4230.000000</td>\n",
       "      <td>17454.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3925</th>\n",
       "      <td>3926.000000</td>\n",
       "      <td>4322.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>57.00000</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>295.00000</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>485.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>6960.000000</td>\n",
       "      <td>956.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3926</th>\n",
       "      <td>3927.000000</td>\n",
       "      <td>2816.000000</td>\n",
       "      <td>259.000000</td>\n",
       "      <td>186.00000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>186.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>130.00000</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>3960.000000</td>\n",
       "      <td>1294.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3927</th>\n",
       "      <td>3928.000000</td>\n",
       "      <td>14363.000000</td>\n",
       "      <td>5844.000000</td>\n",
       "      <td>2067.00000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>3980.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>750.00000</td>\n",
       "      <td>671.000000</td>\n",
       "      <td>798.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>5160.000000</td>\n",
       "      <td>620.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3928</th>\n",
       "      <td>3929.000000</td>\n",
       "      <td>13029.000000</td>\n",
       "      <td>4614.000000</td>\n",
       "      <td>1557.00000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>3192.000000</td>\n",
       "      <td>441.000000</td>\n",
       "      <td>670.00000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>349.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>584.000000</td>\n",
       "      <td>408.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3929</th>\n",
       "      <td>3930.000000</td>\n",
       "      <td>11958.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>15.00000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>812.000000</td>\n",
       "      <td>163.00000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>783.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>2802.000000</td>\n",
       "      <td>385.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3930</th>\n",
       "      <td>3931.000000</td>\n",
       "      <td>1780.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>6.00000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>207.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>680.00000</td>\n",
       "      <td>762.000000</td>\n",
       "      <td>783.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>6000.000000</td>\n",
       "      <td>6984.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3931</th>\n",
       "      <td>3932.000000</td>\n",
       "      <td>13334.000000</td>\n",
       "      <td>1783.000000</td>\n",
       "      <td>1460.00000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>517.000000</td>\n",
       "      <td>441.000000</td>\n",
       "      <td>976.00000</td>\n",
       "      <td>851.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>665.000000</td>\n",
       "      <td>2906.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3932</th>\n",
       "      <td>3933.000000</td>\n",
       "      <td>13957.000000</td>\n",
       "      <td>318.000000</td>\n",
       "      <td>184.00000</td>\n",
       "      <td>780.000000</td>\n",
       "      <td>859.000000</td>\n",
       "      <td>556.000000</td>\n",
       "      <td>60.00000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>281.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>3780.000000</td>\n",
       "      <td>1130.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3933</th>\n",
       "      <td>3934.000000</td>\n",
       "      <td>5987.000000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>151.00000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>727.000000</td>\n",
       "      <td>125.00000</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>545</td>\n",
       "      <td>495</td>\n",
       "      <td>411</td>\n",
       "      <td>780</td>\n",
       "      <td>2112</td>\n",
       "      <td>567</td>\n",
       "      <td>3900.000000</td>\n",
       "      <td>5798.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3934</th>\n",
       "      <td>3935.000000</td>\n",
       "      <td>4685.000000</td>\n",
       "      <td>1362.000000</td>\n",
       "      <td>8.00000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>1553.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>760.00000</td>\n",
       "      <td>760.000000</td>\n",
       "      <td>609.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>2880.000000</td>\n",
       "      <td>8647.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3935</th>\n",
       "      <td>3936.000000</td>\n",
       "      <td>9814.000000</td>\n",
       "      <td>675.000000</td>\n",
       "      <td>113.00000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>741.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>260.00000</td>\n",
       "      <td>197.000000</td>\n",
       "      <td>134.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>2820.000000</td>\n",
       "      <td>1734.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3936</th>\n",
       "      <td>3937.000000</td>\n",
       "      <td>2163.000000</td>\n",
       "      <td>4772.000000</td>\n",
       "      <td>3941.00000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>860.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>845.00000</td>\n",
       "      <td>545.000000</td>\n",
       "      <td>628.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>3960.000000</td>\n",
       "      <td>571.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3937</th>\n",
       "      <td>3938.000000</td>\n",
       "      <td>4166.000000</td>\n",
       "      <td>703.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>210.000000</td>\n",
       "      <td>493.000000</td>\n",
       "      <td>233.000000</td>\n",
       "      <td>1000.00000</td>\n",
       "      <td>908.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>2512.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3938</th>\n",
       "      <td>3939.000000</td>\n",
       "      <td>4225.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>19.00000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>278.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>830.00000</td>\n",
       "      <td>761.000000</td>\n",
       "      <td>174.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>1665.000000</td>\n",
       "      <td>1300.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3939</th>\n",
       "      <td>3940.000000</td>\n",
       "      <td>14017.000000</td>\n",
       "      <td>2028.000000</td>\n",
       "      <td>413.00000</td>\n",
       "      <td>270.000000</td>\n",
       "      <td>1825.000000</td>\n",
       "      <td>605.000000</td>\n",
       "      <td>2610.00000</td>\n",
       "      <td>2535.000000</td>\n",
       "      <td>349.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>3780.000000</td>\n",
       "      <td>807.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3940</th>\n",
       "      <td>3941.000000</td>\n",
       "      <td>4660.000000</td>\n",
       "      <td>1356.000000</td>\n",
       "      <td>178.00000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>1359.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>760.00000</td>\n",
       "      <td>716.000000</td>\n",
       "      <td>609.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>5100.000000</td>\n",
       "      <td>4381.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3941</th>\n",
       "      <td>3942.000000</td>\n",
       "      <td>6472.000000</td>\n",
       "      <td>2692.000000</td>\n",
       "      <td>1856.00000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>867.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>1060.00000</td>\n",
       "      <td>1049.000000</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>3780.000000</td>\n",
       "      <td>945.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3942</th>\n",
       "      <td>1971.342973</td>\n",
       "      <td>7304.492136</td>\n",
       "      <td>2203.533486</td>\n",
       "      <td>1653.32344</td>\n",
       "      <td>205.891933</td>\n",
       "      <td>711.408929</td>\n",
       "      <td>503.703957</td>\n",
       "      <td>796.00279</td>\n",
       "      <td>666.358447</td>\n",
       "      <td>466.677829</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>1400</td>\n",
       "      <td>411</td>\n",
       "      <td>2910</td>\n",
       "      <td>185</td>\n",
       "      <td>567</td>\n",
       "      <td>3573.239726</td>\n",
       "      <td>3698.563673</td>\n",
       "      <td>1.474632</td>\n",
       "      <td>2.012177</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3943 rows × 98 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               nr      i1_legid      i1_rcs_p     i1_rcs_e  i1_dep_1_p  \\\n",
       "0        0.000000   5182.000000    199.000000    218.00000  210.000000   \n",
       "1        1.000000   6523.000000    844.000000    584.00000   90.000000   \n",
       "2        2.000000   5878.000000   4380.000000   4119.00000   90.000000   \n",
       "3        3.000000   1275.000000    759.000000    169.00000  240.000000   \n",
       "4        4.000000   8117.000000   1597.000000   1485.00000  150.000000   \n",
       "5        5.000000   9889.000000    181.000000     98.00000  240.000000   \n",
       "6        6.000000   3997.000000   1300.000000    212.00000  240.000000   \n",
       "7        7.000000   7125.000000    139.000000     87.00000  240.000000   \n",
       "8        8.000000  10481.000000    387.000000     68.00000  240.000000   \n",
       "9        9.000000   3212.000000   6145.000000   6013.00000  300.000000   \n",
       "10      10.000000  12343.000000    748.000000     96.00000  150.000000   \n",
       "11      11.000000   8473.000000   1415.000000   1157.00000  150.000000   \n",
       "12      12.000000   9931.000000    898.000000    112.00000  240.000000   \n",
       "13      13.000000  12666.000000   3140.000000   3004.00000  240.000000   \n",
       "14      14.000000  14132.000000    142.000000    136.00000  240.000000   \n",
       "15      15.000000  13119.000000   1081.000000    413.00000  180.000000   \n",
       "16      16.000000  13572.000000    778.000000    254.00000  420.000000   \n",
       "17      17.000000   1388.000000  27415.000000  26485.00000   90.000000   \n",
       "18      18.000000   4697.000000    671.000000    140.00000  240.000000   \n",
       "19      19.000000  13297.000000   5442.000000   4568.00000  120.000000   \n",
       "20      20.000000   9481.000000    327.000000    167.00000   90.000000   \n",
       "21      21.000000   5838.000000    249.000000    116.00000  150.000000   \n",
       "22      22.000000  10613.000000   2127.000000    234.00000  300.000000   \n",
       "23      23.000000   1363.000000    287.000000    172.00000  150.000000   \n",
       "24      24.000000   4038.000000    295.000000     98.00000  240.000000   \n",
       "25      25.000000   3091.000000    692.000000     72.00000  270.000000   \n",
       "26      26.000000  13510.000000   1211.000000    494.00000   90.000000   \n",
       "27      27.000000   4991.000000    115.000000     51.00000  240.000000   \n",
       "28      28.000000  11674.000000    123.000000     48.00000  420.000000   \n",
       "29      29.000000   7108.000000    342.000000      9.00000  300.000000   \n",
       "...           ...           ...           ...          ...         ...   \n",
       "3913  3914.000000   4773.000000    187.000000     76.00000  135.000000   \n",
       "3914  3915.000000  10054.000000    911.000000    190.00000  240.000000   \n",
       "3915  3916.000000  10447.000000   1412.000000    236.00000  240.000000   \n",
       "3916  3917.000000  10095.000000    523.000000    169.00000  120.000000   \n",
       "3917  3918.000000   9482.000000    894.000000   1260.00000  780.000000   \n",
       "3918  3919.000000   6429.000000    152.000000     71.00000  240.000000   \n",
       "3919  3920.000000  10793.000000    246.000000     93.00000  135.000000   \n",
       "3920  3921.000000  10328.000000    188.000000     32.00000  135.000000   \n",
       "3921  3922.000000   8053.000000   1775.000000    738.00000  300.000000   \n",
       "3922  3923.000000   7722.000000   2175.000000    315.00000   90.000000   \n",
       "3923  3924.000000  11701.000000   2281.000000   1465.00000  300.000000   \n",
       "3924  3925.000000  10330.000000    206.000000     52.00000  135.000000   \n",
       "3925  3926.000000   4322.000000     88.000000     57.00000  135.000000   \n",
       "3926  3927.000000   2816.000000    259.000000    186.00000  150.000000   \n",
       "3927  3928.000000  14363.000000   5844.000000   2067.00000  240.000000   \n",
       "3928  3929.000000  13029.000000   4614.000000   1557.00000  195.000000   \n",
       "3929  3930.000000  11958.000000     55.000000     15.00000  150.000000   \n",
       "3930  3931.000000   1780.000000     92.000000      6.00000  150.000000   \n",
       "3931  3932.000000  13334.000000   1783.000000   1460.00000  240.000000   \n",
       "3932  3933.000000  13957.000000    318.000000    184.00000  780.000000   \n",
       "3933  3934.000000   5987.000000    145.000000    151.00000  120.000000   \n",
       "3934  3935.000000   4685.000000   1362.000000      8.00000  240.000000   \n",
       "3935  3936.000000   9814.000000    675.000000    113.00000  240.000000   \n",
       "3936  3937.000000   2163.000000   4772.000000   3941.00000   90.000000   \n",
       "3937  3938.000000   4166.000000    703.000000    360.00000  210.000000   \n",
       "3938  3939.000000   4225.000000    122.000000     19.00000  240.000000   \n",
       "3939  3940.000000  14017.000000   2028.000000    413.00000  270.000000   \n",
       "3940  3941.000000   4660.000000   1356.000000    178.00000  240.000000   \n",
       "3941  3942.000000   6472.000000   2692.000000   1856.00000   90.000000   \n",
       "3942  1971.342973   7304.492136   2203.533486   1653.32344  205.891933   \n",
       "\n",
       "       i1_dep_1_e  i1_dep_1_place  i1_rcf_1_p   i1_rcf_1_e  i1_rcf_1_place  \\\n",
       "0      215.000000      609.000000   935.00000   736.000000      256.000000   \n",
       "1      297.000000      700.000000  1935.00000  1415.000000      431.000000   \n",
       "2      280.000000      456.000000   905.00000   547.000000      700.000000   \n",
       "3      777.000000      173.000000   340.00000   577.000000      349.000000   \n",
       "4      241.000000      411.000000   585.00000   612.000000      128.000000   \n",
       "5      259.000000      815.000000   215.00000   223.000000      128.000000   \n",
       "6     1267.000000      815.000000   270.00000   135.000000      391.000000   \n",
       "7      232.000000      783.000000   255.00000   154.000000      815.000000   \n",
       "8      514.000000      815.000000   285.00000   219.000000      485.000000   \n",
       "9      385.000000      690.000000   810.00000   635.000000      555.000000   \n",
       "10     758.000000      281.000000   395.00000   152.000000      128.000000   \n",
       "11     346.000000      377.000000   280.00000   179.000000      485.000000   \n",
       "12     965.000000      281.000000   415.00000   186.000000      815.000000   \n",
       "13     324.000000      445.000000   995.00000   808.000000      128.000000   \n",
       "14     182.000000      456.000000  1085.00000  1093.000000      349.000000   \n",
       "15     859.000000      753.000000  1575.00000   928.000000      511.000000   \n",
       "16     894.000000      349.000000  1410.00000  2375.000000      548.000000   \n",
       "17     963.000000      815.000000  1220.00000  1027.000000      700.000000   \n",
       "18     713.000000      815.000000   280.00000   381.000000      153.000000   \n",
       "19     934.000000      236.000000   420.00000   260.000000      815.000000   \n",
       "20     278.000000      579.000000  1010.00000   584.000000      128.000000   \n",
       "21     223.000000      391.000000   385.00000   155.000000      815.000000   \n",
       "22    2158.000000      332.000000   705.00000   783.000000      815.000000   \n",
       "23     179.000000      746.000000   870.00000   828.000000      349.000000   \n",
       "24     382.000000      815.000000   270.00000   143.000000      391.000000   \n",
       "25     851.000000      349.000000   930.00000  1062.000000      456.000000   \n",
       "26     772.000000      485.000000  1250.00000   897.000000      700.000000   \n",
       "27     227.000000      476.000000   975.00000   731.000000      815.000000   \n",
       "28     596.000000      349.000000  1240.00000  1048.000000      413.000000   \n",
       "29     583.000000      753.000000   510.00000   322.000000      174.000000   \n",
       "...           ...             ...         ...          ...             ...   \n",
       "3913   189.000000      815.000000   285.00000   212.000000      485.000000   \n",
       "3914   920.000000      815.000000   415.00000    21.000000      576.000000   \n",
       "3915  1355.000000      815.000000   760.00000   845.000000      609.000000   \n",
       "3916   364.000000      236.000000    90.00000    87.000000      815.000000   \n",
       "3917   363.000000      349.000000  1162.00000   740.000000      144.000000   \n",
       "3918   277.000000      128.000000   960.00000   732.000000      597.000000   \n",
       "3919   229.000000      815.000000   285.00000   340.000000      485.000000   \n",
       "3920   250.000000      815.000000   285.00000   227.000000      485.000000   \n",
       "3921  1329.000000      308.000000   840.00000   797.000000      371.000000   \n",
       "3922  1907.000000      206.000000   805.00000   568.000000      700.000000   \n",
       "3923  1081.000000      815.000000   810.00000   732.000000      144.000000   \n",
       "3924   248.000000      815.000000   285.00000   227.000000      485.000000   \n",
       "3925   150.000000      815.000000   295.00000   141.000000      485.000000   \n",
       "3926   186.000000      166.000000   130.00000   158.000000      128.000000   \n",
       "3927  3980.000000      128.000000   750.00000   671.000000      798.000000   \n",
       "3928  3192.000000      441.000000   670.00000   714.000000      349.000000   \n",
       "3929   140.000000      812.000000   163.00000    61.000000      783.000000   \n",
       "3930   207.000000      128.000000   680.00000   762.000000      783.000000   \n",
       "3931   517.000000      441.000000   976.00000   851.000000      128.000000   \n",
       "3932   859.000000      556.000000    60.00000    63.000000      281.000000   \n",
       "3933    49.000000      727.000000   125.00000   175.000000      101.000000   \n",
       "3934  1553.000000      815.000000   760.00000   760.000000      609.000000   \n",
       "3935   741.000000      815.000000   260.00000   197.000000      134.000000   \n",
       "3936   860.000000      700.000000   845.00000   545.000000      628.000000   \n",
       "3937   493.000000      233.000000  1000.00000   908.000000      815.000000   \n",
       "3938   278.000000      815.000000   830.00000   761.000000      174.000000   \n",
       "3939  1825.000000      605.000000  2610.00000  2535.000000      349.000000   \n",
       "3940  1359.000000      815.000000   760.00000   716.000000      609.000000   \n",
       "3941   867.000000      700.000000  1060.00000  1049.000000      113.000000   \n",
       "3942   711.408929      503.703957   796.00279   666.358447      466.677829   \n",
       "\n",
       "        ...    o_dep_3_p o_dep_3_e o_dep_3_place o_rcf_3_p o_rcf_3_e  \\\n",
       "0       ...          170      1400           411      2910       185   \n",
       "1       ...          170      1400           411      2910       185   \n",
       "2       ...          170      1400           411      2910       185   \n",
       "3       ...          170      1400           411      2910       185   \n",
       "4       ...          170      1400           411      2910       185   \n",
       "5       ...          170      1400           411      2910       185   \n",
       "6       ...          170      1400           411      2910       185   \n",
       "7       ...          170      1400           411      2910       185   \n",
       "8       ...          170      1400           411      2910       185   \n",
       "9       ...          170      1400           411      2910       185   \n",
       "10      ...          170      1400           411      2910       185   \n",
       "11      ...          170      1400           411      2910       185   \n",
       "12      ...          170      1400           411      2910       185   \n",
       "13      ...          170      1400           411      2910       185   \n",
       "14      ...          170      1400           411      2910       185   \n",
       "15      ...          170      1400           411      2910       185   \n",
       "16      ...          170      1400           411      2910       185   \n",
       "17      ...          170      1400           411      2910       185   \n",
       "18      ...          170      1400           411      2910       185   \n",
       "19      ...          170      1400           411      2910       185   \n",
       "20      ...          170      1400           411      2910       185   \n",
       "21      ...          170      1400           411      2910       185   \n",
       "22      ...          170      1400           411      2910       185   \n",
       "23      ...          170      1400           411      2910       185   \n",
       "24      ...          170      1400           411      2910       185   \n",
       "25      ...          170      1400           411      2910       185   \n",
       "26      ...          170      1400           411      2910       185   \n",
       "27      ...          170      1400           411      2910       185   \n",
       "28      ...          170      1400           411      2910       185   \n",
       "29      ...          170      1400           411      2910       185   \n",
       "...     ...          ...       ...           ...       ...       ...   \n",
       "3913    ...          170      1400           411      2910       185   \n",
       "3914    ...          170      1400           411      2910       185   \n",
       "3915    ...          170      1400           411      2910       185   \n",
       "3916    ...          170      1400           411      2910       185   \n",
       "3917    ...          170      1400           411      2910       185   \n",
       "3918    ...          170      1400           411      2910       185   \n",
       "3919    ...          170      1400           411      2910       185   \n",
       "3920    ...          170      1400           411      2910       185   \n",
       "3921    ...          170      1400           411      2910       185   \n",
       "3922    ...          170      1400           411      2910       185   \n",
       "3923    ...          170      1400           411      2910       185   \n",
       "3924    ...          170      1400           411      2910       185   \n",
       "3925    ...          170      1400           411      2910       185   \n",
       "3926    ...          170      1400           411      2910       185   \n",
       "3927    ...          170      1400           411      2910       185   \n",
       "3928    ...          170      1400           411      2910       185   \n",
       "3929    ...          170      1400           411      2910       185   \n",
       "3930    ...          170      1400           411      2910       185   \n",
       "3931    ...          170      1400           411      2910       185   \n",
       "3932    ...          170      1400           411      2910       185   \n",
       "3933    ...          545       495           411       780      2112   \n",
       "3934    ...          170      1400           411      2910       185   \n",
       "3935    ...          170      1400           411      2910       185   \n",
       "3936    ...          170      1400           411      2910       185   \n",
       "3937    ...          170      1400           411      2910       185   \n",
       "3938    ...          170      1400           411      2910       185   \n",
       "3939    ...          170      1400           411      2910       185   \n",
       "3940    ...          170      1400           411      2910       185   \n",
       "3941    ...          170      1400           411      2910       185   \n",
       "3942    ...          170      1400           411      2910       185   \n",
       "\n",
       "     o_rcf_3_place      o_dlv_p       o_dlv_e    o_hops      legs  \n",
       "0              567   780.000000    434.000000  1.000000  2.000000  \n",
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       "11             567  2880.000000     20.000000  1.000000  1.000000  \n",
       "12             567  5160.000000    140.000000  1.000000  3.000000  \n",
       "13             567  3960.000000    934.000000  1.000000  2.000000  \n",
       "14             567  6680.000000    164.000000  1.000000  3.000000  \n",
       "15             567  3780.000000     11.000000  1.000000  1.000000  \n",
       "16             567  3960.000000    108.000000  1.000000  2.000000  \n",
       "17             567  3420.000000   1681.000000  1.000000  2.000000  \n",
       "18             567  3780.000000   1115.000000  2.000000  1.000000  \n",
       "19             567   550.000000    492.000000  1.000000  2.000000  \n",
       "20             567  3960.000000   7005.000000  1.000000  2.000000  \n",
       "21             567  5160.000000    432.000000  1.000000  1.000000  \n",
       "22             567  5160.000000   2060.000000  1.000000  3.000000  \n",
       "23             567  1075.000000   4090.000000  2.000000  2.000000  \n",
       "24             567  5100.000000   1966.000000  2.000000  3.000000  \n",
       "25             567  4230.000000   5859.000000  2.000000  3.000000  \n",
       "26             567  3780.000000   1287.000000  1.000000  1.000000  \n",
       "27             567  5100.000000     24.000000  1.000000  1.000000  \n",
       "28             567  4230.000000   2649.000000  2.000000  2.000000  \n",
       "29             567  4680.000000      2.000000  1.000000  1.000000  \n",
       "...            ...          ...           ...       ...       ...  \n",
       "3913           567  4740.000000   4459.000000  2.000000  3.000000  \n",
       "3914           567  3960.000000   8501.000000  2.000000  2.000000  \n",
       "3915           567  6680.000000  17970.000000  1.000000  2.000000  \n",
       "3916           567  4230.000000   1093.000000  2.000000  3.000000  \n",
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       "3918           567  3960.000000   1467.000000  2.000000  2.000000  \n",
       "3919           567  6960.000000     89.000000  2.000000  1.000000  \n",
       "3920           567  1140.000000   5741.000000  1.000000  3.000000  \n",
       "3921           567  3960.000000    280.000000  2.000000  3.000000  \n",
       "3922           567  3780.000000   1114.000000  2.000000  1.000000  \n",
       "3923           567  9480.000000  27517.000000  1.000000  3.000000  \n",
       "3924           567  4230.000000  17454.000000  2.000000  1.000000  \n",
       "3925           567  6960.000000    956.000000  2.000000  3.000000  \n",
       "3926           567  3960.000000   1294.000000  2.000000  3.000000  \n",
       "3927           567  5160.000000    620.000000  2.000000  2.000000  \n",
       "3928           567   584.000000    408.000000  2.000000  2.000000  \n",
       "3929           567  2802.000000    385.000000  1.000000  1.000000  \n",
       "3930           567  6000.000000   6984.000000  2.000000  1.000000  \n",
       "3931           567   665.000000   2906.000000  2.000000  2.000000  \n",
       "3932           567  3780.000000   1130.000000  2.000000  3.000000  \n",
       "3933           567  3900.000000   5798.000000  3.000000  1.000000  \n",
       "3934           567  2880.000000   8647.000000  2.000000  3.000000  \n",
       "3935           567  2820.000000   1734.000000  1.000000  2.000000  \n",
       "3936           567  3960.000000    571.000000  2.000000  2.000000  \n",
       "3937           567   300.000000   2512.000000  2.000000  3.000000  \n",
       "3938           567  1665.000000   1300.000000  2.000000  3.000000  \n",
       "3939           567  3780.000000    807.000000  2.000000  1.000000  \n",
       "3940           567  5100.000000   4381.000000  2.000000  1.000000  \n",
       "3941           567  3780.000000    945.000000  2.000000  2.000000  \n",
       "3942           567  3573.239726   3698.563673  1.474632  2.012177  \n",
       "\n",
       "[3943 rows x 98 columns]"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.base import TransformerMixin\n",
    "\n",
    "X = pd.DataFrame(d_replace)\n",
    "\n",
    "class DataFrameImputer(TransformerMixin):\n",
    "\n",
    "    def __init__(self):\n",
    "        \"\"\"Impute missing values.\n",
    "\n",
    "        Columns of dtype object are imputed with the most frequent value \n",
    "        in column.\n",
    "\n",
    "        Columns of other types are imputed with mean of column.\n",
    "\n",
    "        \"\"\"\n",
    "    def fit(self, X, y=None):\n",
    "\n",
    "        self.fill = pd.Series([X[c].value_counts().index[0]\n",
    "            if X[c].dtype == np.dtype('O') else X[c].mean() for c in X],\n",
    "            index=X.columns)\n",
    "\n",
    "        return self\n",
    "\n",
    "    def transform(self, X, y=None):\n",
    "        return X.fillna(self.fill)\n",
    "\n",
    "\n",
    "df = DataFrameImputer().fit_transform(d_replace)\n",
    "\n",
    "df\n",
    "# imputes missing values\n",
    "#credits-https://stackoverflow.com/questions/25239958/impute-categorical-missing-values-in-scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nr                0\n",
       "i1_legid          0\n",
       "i1_rcs_p          0\n",
       "i1_rcs_e          0\n",
       "i1_dep_1_p        0\n",
       "i1_dep_1_e        0\n",
       "i1_dep_1_place    0\n",
       "i1_rcf_1_p        0\n",
       "i1_rcf_1_e        0\n",
       "i1_rcf_1_place    0\n",
       "i1_dep_2_p        0\n",
       "i1_dep_2_e        0\n",
       "i1_dep_2_place    0\n",
       "i1_rcf_2_p        0\n",
       "i1_rcf_2_e        0\n",
       "i1_rcf_2_place    0\n",
       "i1_dep_3_p        0\n",
       "i1_dep_3_e        0\n",
       "i1_dep_3_place    0\n",
       "i1_rcf_3_p        0\n",
       "i1_rcf_3_e        0\n",
       "i1_rcf_3_place    0\n",
       "i1_dlv_p          0\n",
       "i1_dlv_e          0\n",
       "i1_hops           0\n",
       "i2_legid          0\n",
       "i2_rcs_p          0\n",
       "i2_rcs_e          0\n",
       "i2_dep_1_p        0\n",
       "i2_dep_1_e        0\n",
       "                 ..\n",
       "i3_rcf_3_e        0\n",
       "i3_rcf_3_place    0\n",
       "i3_dlv_p          0\n",
       "i3_dlv_e          0\n",
       "i3_hops           0\n",
       "o_legid           0\n",
       "o_rcs_p           0\n",
       "o_rcs_e           0\n",
       "o_dep_1_p         0\n",
       "o_dep_1_e         0\n",
       "o_dep_1_place     0\n",
       "o_rcf_1_p         0\n",
       "o_rcf_1_e         0\n",
       "o_rcf_1_place     0\n",
       "o_dep_2_p         0\n",
       "o_dep_2_e         0\n",
       "o_dep_2_place     0\n",
       "o_rcf_2_p         0\n",
       "o_rcf_2_e         0\n",
       "o_rcf_2_place     0\n",
       "o_dep_3_p         0\n",
       "o_dep_3_e         0\n",
       "o_dep_3_place     0\n",
       "o_rcf_3_p         0\n",
       "o_rcf_3_e         0\n",
       "o_rcf_3_place     0\n",
       "o_dlv_p           0\n",
       "o_dlv_e           0\n",
       "o_hops            0\n",
       "legs              0\n",
       "Length: 98, dtype: int64"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nr                float64\n",
       "i1_legid          float64\n",
       "i1_rcs_p          float64\n",
       "i1_rcs_e          float64\n",
       "i1_dep_1_p        float64\n",
       "i1_dep_1_e        float64\n",
       "i1_dep_1_place    float64\n",
       "i1_rcf_1_p        float64\n",
       "i1_rcf_1_e        float64\n",
       "i1_rcf_1_place    float64\n",
       "i1_dep_2_p         object\n",
       "i1_dep_2_e         object\n",
       "i1_dep_2_place     object\n",
       "i1_rcf_2_p         object\n",
       "i1_rcf_2_e         object\n",
       "i1_rcf_2_place     object\n",
       "i1_dep_3_p         object\n",
       "i1_dep_3_e         object\n",
       "i1_dep_3_place     object\n",
       "i1_rcf_3_p         object\n",
       "i1_rcf_3_e         object\n",
       "i1_rcf_3_place     object\n",
       "i1_dlv_p          float64\n",
       "i1_dlv_e          float64\n",
       "i1_hops           float64\n",
       "i2_legid           object\n",
       "i2_rcs_p           object\n",
       "i2_rcs_e           object\n",
       "i2_dep_1_p         object\n",
       "i2_dep_1_e         object\n",
       "                   ...   \n",
       "i3_rcf_3_e         object\n",
       "i3_rcf_3_place     object\n",
       "i3_dlv_p           object\n",
       "i3_dlv_e           object\n",
       "i3_hops            object\n",
       "o_legid           float64\n",
       "o_rcs_p           float64\n",
       "o_rcs_e           float64\n",
       "o_dep_1_p         float64\n",
       "o_dep_1_e         float64\n",
       "o_dep_1_place     float64\n",
       "o_rcf_1_p         float64\n",
       "o_rcf_1_e         float64\n",
       "o_rcf_1_place     float64\n",
       "o_dep_2_p          object\n",
       "o_dep_2_e          object\n",
       "o_dep_2_place      object\n",
       "o_rcf_2_p          object\n",
       "o_rcf_2_e          object\n",
       "o_rcf_2_place      object\n",
       "o_dep_3_p          object\n",
       "o_dep_3_e          object\n",
       "o_dep_3_place      object\n",
       "o_rcf_3_p          object\n",
       "o_rcf_3_e          object\n",
       "o_rcf_3_place      object\n",
       "o_dlv_p           float64\n",
       "o_dlv_e           float64\n",
       "o_hops            float64\n",
       "legs              float64\n",
       "Length: 98, dtype: object"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ds = df.astype(float) #Converts the whole dataset into type 'float' for ease in visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>...</th>\n",
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       "      <td>431.000000</td>\n",
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       "      <td>4380.000000</td>\n",
       "      <td>4119.00000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>456.000000</td>\n",
       "      <td>905.00000</td>\n",
       "      <td>547.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>1520.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>759.000000</td>\n",
       "      <td>169.00000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>777.000000</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>340.00000</td>\n",
       "      <td>577.000000</td>\n",
       "      <td>349.000000</td>\n",
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       "      <td>411.0</td>\n",
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       "      <td>1485.00000</td>\n",
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       "      <td>241.000000</td>\n",
       "      <td>411.000000</td>\n",
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       "      <td>612.000000</td>\n",
       "      <td>128.000000</td>\n",
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       "      <td>223.000000</td>\n",
       "      <td>128.000000</td>\n",
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       "      <td>467.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>212.00000</td>\n",
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       "      <td>815.000000</td>\n",
       "      <td>270.00000</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>391.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>2880.000000</td>\n",
       "      <td>151.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
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       "      <td>139.000000</td>\n",
       "      <td>87.00000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>232.000000</td>\n",
       "      <td>783.000000</td>\n",
       "      <td>255.00000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>3960.000000</td>\n",
       "      <td>161.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>10481.000000</td>\n",
       "      <td>387.000000</td>\n",
       "      <td>68.00000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>514.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>285.00000</td>\n",
       "      <td>219.000000</td>\n",
       "      <td>485.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>6680.000000</td>\n",
       "      <td>2830.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>3212.000000</td>\n",
       "      <td>6145.000000</td>\n",
       "      <td>6013.00000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>385.000000</td>\n",
       "      <td>690.000000</td>\n",
       "      <td>810.00000</td>\n",
       "      <td>635.000000</td>\n",
       "      <td>555.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>3960.000000</td>\n",
       "      <td>218.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>12343.000000</td>\n",
       "      <td>748.000000</td>\n",
       "      <td>96.00000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>758.000000</td>\n",
       "      <td>281.000000</td>\n",
       "      <td>395.00000</td>\n",
       "      <td>152.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>3960.000000</td>\n",
       "      <td>223.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11.000000</td>\n",
       "      <td>8473.000000</td>\n",
       "      <td>1415.000000</td>\n",
       "      <td>1157.00000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>346.000000</td>\n",
       "      <td>377.000000</td>\n",
       "      <td>280.00000</td>\n",
       "      <td>179.000000</td>\n",
       "      <td>485.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>2880.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>9931.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>112.00000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>965.000000</td>\n",
       "      <td>281.000000</td>\n",
       "      <td>415.00000</td>\n",
       "      <td>186.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>5160.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13.000000</td>\n",
       "      <td>12666.000000</td>\n",
       "      <td>3140.000000</td>\n",
       "      <td>3004.00000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>324.000000</td>\n",
       "      <td>445.000000</td>\n",
       "      <td>995.00000</td>\n",
       "      <td>808.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>3960.000000</td>\n",
       "      <td>934.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14.000000</td>\n",
       "      <td>14132.000000</td>\n",
       "      <td>142.000000</td>\n",
       "      <td>136.00000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>182.000000</td>\n",
       "      <td>456.000000</td>\n",
       "      <td>1085.00000</td>\n",
       "      <td>1093.000000</td>\n",
       "      <td>349.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>6680.000000</td>\n",
       "      <td>164.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>13119.000000</td>\n",
       "      <td>1081.000000</td>\n",
       "      <td>413.00000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>859.000000</td>\n",
       "      <td>753.000000</td>\n",
       "      <td>1575.00000</td>\n",
       "      <td>928.000000</td>\n",
       "      <td>511.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>3780.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>13572.000000</td>\n",
       "      <td>778.000000</td>\n",
       "      <td>254.00000</td>\n",
       "      <td>420.000000</td>\n",
       "      <td>894.000000</td>\n",
       "      <td>349.000000</td>\n",
       "      <td>1410.00000</td>\n",
       "      <td>2375.000000</td>\n",
       "      <td>548.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>3960.000000</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>1388.000000</td>\n",
       "      <td>27415.000000</td>\n",
       "      <td>26485.00000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>963.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>1220.00000</td>\n",
       "      <td>1027.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>3420.000000</td>\n",
       "      <td>1681.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>4697.000000</td>\n",
       "      <td>671.000000</td>\n",
       "      <td>140.00000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>713.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>280.00000</td>\n",
       "      <td>381.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>3780.000000</td>\n",
       "      <td>1115.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>13297.000000</td>\n",
       "      <td>5442.000000</td>\n",
       "      <td>4568.00000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>934.000000</td>\n",
       "      <td>236.000000</td>\n",
       "      <td>420.00000</td>\n",
       "      <td>260.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>492.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>9481.000000</td>\n",
       "      <td>327.000000</td>\n",
       "      <td>167.00000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>278.000000</td>\n",
       "      <td>579.000000</td>\n",
       "      <td>1010.00000</td>\n",
       "      <td>584.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>3960.000000</td>\n",
       "      <td>7005.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>5838.000000</td>\n",
       "      <td>249.000000</td>\n",
       "      <td>116.00000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>223.000000</td>\n",
       "      <td>391.000000</td>\n",
       "      <td>385.00000</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>5160.000000</td>\n",
       "      <td>432.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22.000000</td>\n",
       "      <td>10613.000000</td>\n",
       "      <td>2127.000000</td>\n",
       "      <td>234.00000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>2158.000000</td>\n",
       "      <td>332.000000</td>\n",
       "      <td>705.00000</td>\n",
       "      <td>783.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>5160.000000</td>\n",
       "      <td>2060.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>1363.000000</td>\n",
       "      <td>287.000000</td>\n",
       "      <td>172.00000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>179.000000</td>\n",
       "      <td>746.000000</td>\n",
       "      <td>870.00000</td>\n",
       "      <td>828.000000</td>\n",
       "      <td>349.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>1075.000000</td>\n",
       "      <td>4090.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24.000000</td>\n",
       "      <td>4038.000000</td>\n",
       "      <td>295.000000</td>\n",
       "      <td>98.00000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>382.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>270.00000</td>\n",
       "      <td>143.000000</td>\n",
       "      <td>391.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>5100.000000</td>\n",
       "      <td>1966.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>3091.000000</td>\n",
       "      <td>692.000000</td>\n",
       "      <td>72.00000</td>\n",
       "      <td>270.000000</td>\n",
       "      <td>851.000000</td>\n",
       "      <td>349.000000</td>\n",
       "      <td>930.00000</td>\n",
       "      <td>1062.000000</td>\n",
       "      <td>456.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>4230.000000</td>\n",
       "      <td>5859.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26.000000</td>\n",
       "      <td>13510.000000</td>\n",
       "      <td>1211.000000</td>\n",
       "      <td>494.00000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>772.000000</td>\n",
       "      <td>485.000000</td>\n",
       "      <td>1250.00000</td>\n",
       "      <td>897.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>3780.000000</td>\n",
       "      <td>1287.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>4991.000000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>51.00000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>227.000000</td>\n",
       "      <td>476.000000</td>\n",
       "      <td>975.00000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>5100.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>11674.000000</td>\n",
       "      <td>123.000000</td>\n",
       "      <td>48.00000</td>\n",
       "      <td>420.000000</td>\n",
       "      <td>596.000000</td>\n",
       "      <td>349.000000</td>\n",
       "      <td>1240.00000</td>\n",
       "      <td>1048.000000</td>\n",
       "      <td>413.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>4230.000000</td>\n",
       "      <td>2649.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>7108.000000</td>\n",
       "      <td>342.000000</td>\n",
       "      <td>9.00000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>583.000000</td>\n",
       "      <td>753.000000</td>\n",
       "      <td>510.00000</td>\n",
       "      <td>322.000000</td>\n",
       "      <td>174.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>4680.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</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>3913</th>\n",
       "      <td>3914.000000</td>\n",
       "      <td>4773.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>76.00000</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>189.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>285.00000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>485.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>4740.000000</td>\n",
       "      <td>4459.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3914</th>\n",
       "      <td>3915.000000</td>\n",
       "      <td>10054.000000</td>\n",
       "      <td>911.000000</td>\n",
       "      <td>190.00000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>920.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>415.00000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>576.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>3960.000000</td>\n",
       "      <td>8501.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3915</th>\n",
       "      <td>3916.000000</td>\n",
       "      <td>10447.000000</td>\n",
       "      <td>1412.000000</td>\n",
       "      <td>236.00000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>1355.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>760.00000</td>\n",
       "      <td>845.000000</td>\n",
       "      <td>609.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>6680.000000</td>\n",
       "      <td>17970.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3916</th>\n",
       "      <td>3917.000000</td>\n",
       "      <td>10095.000000</td>\n",
       "      <td>523.000000</td>\n",
       "      <td>169.00000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>236.000000</td>\n",
       "      <td>90.00000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>4230.000000</td>\n",
       "      <td>1093.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3917</th>\n",
       "      <td>3918.000000</td>\n",
       "      <td>9482.000000</td>\n",
       "      <td>894.000000</td>\n",
       "      <td>1260.00000</td>\n",
       "      <td>780.000000</td>\n",
       "      <td>363.000000</td>\n",
       "      <td>349.000000</td>\n",
       "      <td>1162.00000</td>\n",
       "      <td>740.000000</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>6990.000000</td>\n",
       "      <td>3212.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3918</th>\n",
       "      <td>3919.000000</td>\n",
       "      <td>6429.000000</td>\n",
       "      <td>152.000000</td>\n",
       "      <td>71.00000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>277.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>960.00000</td>\n",
       "      <td>732.000000</td>\n",
       "      <td>597.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>3960.000000</td>\n",
       "      <td>1467.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3919</th>\n",
       "      <td>3920.000000</td>\n",
       "      <td>10793.000000</td>\n",
       "      <td>246.000000</td>\n",
       "      <td>93.00000</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>229.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>285.00000</td>\n",
       "      <td>340.000000</td>\n",
       "      <td>485.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>6960.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3920</th>\n",
       "      <td>3921.000000</td>\n",
       "      <td>10328.000000</td>\n",
       "      <td>188.000000</td>\n",
       "      <td>32.00000</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>285.00000</td>\n",
       "      <td>227.000000</td>\n",
       "      <td>485.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>1140.000000</td>\n",
       "      <td>5741.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3921</th>\n",
       "      <td>3922.000000</td>\n",
       "      <td>8053.000000</td>\n",
       "      <td>1775.000000</td>\n",
       "      <td>738.00000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>1329.000000</td>\n",
       "      <td>308.000000</td>\n",
       "      <td>840.00000</td>\n",
       "      <td>797.000000</td>\n",
       "      <td>371.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>3960.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3922</th>\n",
       "      <td>3923.000000</td>\n",
       "      <td>7722.000000</td>\n",
       "      <td>2175.000000</td>\n",
       "      <td>315.00000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>1907.000000</td>\n",
       "      <td>206.000000</td>\n",
       "      <td>805.00000</td>\n",
       "      <td>568.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>3780.000000</td>\n",
       "      <td>1114.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3923</th>\n",
       "      <td>3924.000000</td>\n",
       "      <td>11701.000000</td>\n",
       "      <td>2281.000000</td>\n",
       "      <td>1465.00000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>1081.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>810.00000</td>\n",
       "      <td>732.000000</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>9480.000000</td>\n",
       "      <td>27517.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3924</th>\n",
       "      <td>3925.000000</td>\n",
       "      <td>10330.000000</td>\n",
       "      <td>206.000000</td>\n",
       "      <td>52.00000</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>248.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>285.00000</td>\n",
       "      <td>227.000000</td>\n",
       "      <td>485.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>4230.000000</td>\n",
       "      <td>17454.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3925</th>\n",
       "      <td>3926.000000</td>\n",
       "      <td>4322.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>57.00000</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>295.00000</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>485.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>6960.000000</td>\n",
       "      <td>956.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3926</th>\n",
       "      <td>3927.000000</td>\n",
       "      <td>2816.000000</td>\n",
       "      <td>259.000000</td>\n",
       "      <td>186.00000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>186.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>130.00000</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>3960.000000</td>\n",
       "      <td>1294.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3927</th>\n",
       "      <td>3928.000000</td>\n",
       "      <td>14363.000000</td>\n",
       "      <td>5844.000000</td>\n",
       "      <td>2067.00000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>3980.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>750.00000</td>\n",
       "      <td>671.000000</td>\n",
       "      <td>798.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>5160.000000</td>\n",
       "      <td>620.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3928</th>\n",
       "      <td>3929.000000</td>\n",
       "      <td>13029.000000</td>\n",
       "      <td>4614.000000</td>\n",
       "      <td>1557.00000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>3192.000000</td>\n",
       "      <td>441.000000</td>\n",
       "      <td>670.00000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>349.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>584.000000</td>\n",
       "      <td>408.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3929</th>\n",
       "      <td>3930.000000</td>\n",
       "      <td>11958.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>15.00000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>812.000000</td>\n",
       "      <td>163.00000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>783.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>2802.000000</td>\n",
       "      <td>385.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3930</th>\n",
       "      <td>3931.000000</td>\n",
       "      <td>1780.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>6.00000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>207.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>680.00000</td>\n",
       "      <td>762.000000</td>\n",
       "      <td>783.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>6000.000000</td>\n",
       "      <td>6984.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3931</th>\n",
       "      <td>3932.000000</td>\n",
       "      <td>13334.000000</td>\n",
       "      <td>1783.000000</td>\n",
       "      <td>1460.00000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>517.000000</td>\n",
       "      <td>441.000000</td>\n",
       "      <td>976.00000</td>\n",
       "      <td>851.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>665.000000</td>\n",
       "      <td>2906.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3932</th>\n",
       "      <td>3933.000000</td>\n",
       "      <td>13957.000000</td>\n",
       "      <td>318.000000</td>\n",
       "      <td>184.00000</td>\n",
       "      <td>780.000000</td>\n",
       "      <td>859.000000</td>\n",
       "      <td>556.000000</td>\n",
       "      <td>60.00000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>281.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>3780.000000</td>\n",
       "      <td>1130.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3933</th>\n",
       "      <td>3934.000000</td>\n",
       "      <td>5987.000000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>151.00000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>727.000000</td>\n",
       "      <td>125.00000</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>545.0</td>\n",
       "      <td>495.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>780.0</td>\n",
       "      <td>2112.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>3900.000000</td>\n",
       "      <td>5798.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3934</th>\n",
       "      <td>3935.000000</td>\n",
       "      <td>4685.000000</td>\n",
       "      <td>1362.000000</td>\n",
       "      <td>8.00000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>1553.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>760.00000</td>\n",
       "      <td>760.000000</td>\n",
       "      <td>609.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>2880.000000</td>\n",
       "      <td>8647.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3935</th>\n",
       "      <td>3936.000000</td>\n",
       "      <td>9814.000000</td>\n",
       "      <td>675.000000</td>\n",
       "      <td>113.00000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>741.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>260.00000</td>\n",
       "      <td>197.000000</td>\n",
       "      <td>134.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>2820.000000</td>\n",
       "      <td>1734.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3936</th>\n",
       "      <td>3937.000000</td>\n",
       "      <td>2163.000000</td>\n",
       "      <td>4772.000000</td>\n",
       "      <td>3941.00000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>860.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>845.00000</td>\n",
       "      <td>545.000000</td>\n",
       "      <td>628.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>3960.000000</td>\n",
       "      <td>571.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3937</th>\n",
       "      <td>3938.000000</td>\n",
       "      <td>4166.000000</td>\n",
       "      <td>703.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>210.000000</td>\n",
       "      <td>493.000000</td>\n",
       "      <td>233.000000</td>\n",
       "      <td>1000.00000</td>\n",
       "      <td>908.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>2512.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3938</th>\n",
       "      <td>3939.000000</td>\n",
       "      <td>4225.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>19.00000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>278.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>830.00000</td>\n",
       "      <td>761.000000</td>\n",
       "      <td>174.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>1665.000000</td>\n",
       "      <td>1300.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3939</th>\n",
       "      <td>3940.000000</td>\n",
       "      <td>14017.000000</td>\n",
       "      <td>2028.000000</td>\n",
       "      <td>413.00000</td>\n",
       "      <td>270.000000</td>\n",
       "      <td>1825.000000</td>\n",
       "      <td>605.000000</td>\n",
       "      <td>2610.00000</td>\n",
       "      <td>2535.000000</td>\n",
       "      <td>349.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>3780.000000</td>\n",
       "      <td>807.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3940</th>\n",
       "      <td>3941.000000</td>\n",
       "      <td>4660.000000</td>\n",
       "      <td>1356.000000</td>\n",
       "      <td>178.00000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>1359.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>760.00000</td>\n",
       "      <td>716.000000</td>\n",
       "      <td>609.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>5100.000000</td>\n",
       "      <td>4381.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3941</th>\n",
       "      <td>3942.000000</td>\n",
       "      <td>6472.000000</td>\n",
       "      <td>2692.000000</td>\n",
       "      <td>1856.00000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>867.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>1060.00000</td>\n",
       "      <td>1049.000000</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>3780.000000</td>\n",
       "      <td>945.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3942</th>\n",
       "      <td>1971.342973</td>\n",
       "      <td>7304.492136</td>\n",
       "      <td>2203.533486</td>\n",
       "      <td>1653.32344</td>\n",
       "      <td>205.891933</td>\n",
       "      <td>711.408929</td>\n",
       "      <td>503.703957</td>\n",
       "      <td>796.00279</td>\n",
       "      <td>666.358447</td>\n",
       "      <td>466.677829</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>3573.239726</td>\n",
       "      <td>3698.563673</td>\n",
       "      <td>1.474632</td>\n",
       "      <td>2.012177</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3943 rows × 98 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               nr      i1_legid      i1_rcs_p     i1_rcs_e  i1_dep_1_p  \\\n",
       "0        0.000000   5182.000000    199.000000    218.00000  210.000000   \n",
       "1        1.000000   6523.000000    844.000000    584.00000   90.000000   \n",
       "2        2.000000   5878.000000   4380.000000   4119.00000   90.000000   \n",
       "3        3.000000   1275.000000    759.000000    169.00000  240.000000   \n",
       "4        4.000000   8117.000000   1597.000000   1485.00000  150.000000   \n",
       "5        5.000000   9889.000000    181.000000     98.00000  240.000000   \n",
       "6        6.000000   3997.000000   1300.000000    212.00000  240.000000   \n",
       "7        7.000000   7125.000000    139.000000     87.00000  240.000000   \n",
       "8        8.000000  10481.000000    387.000000     68.00000  240.000000   \n",
       "9        9.000000   3212.000000   6145.000000   6013.00000  300.000000   \n",
       "10      10.000000  12343.000000    748.000000     96.00000  150.000000   \n",
       "11      11.000000   8473.000000   1415.000000   1157.00000  150.000000   \n",
       "12      12.000000   9931.000000    898.000000    112.00000  240.000000   \n",
       "13      13.000000  12666.000000   3140.000000   3004.00000  240.000000   \n",
       "14      14.000000  14132.000000    142.000000    136.00000  240.000000   \n",
       "15      15.000000  13119.000000   1081.000000    413.00000  180.000000   \n",
       "16      16.000000  13572.000000    778.000000    254.00000  420.000000   \n",
       "17      17.000000   1388.000000  27415.000000  26485.00000   90.000000   \n",
       "18      18.000000   4697.000000    671.000000    140.00000  240.000000   \n",
       "19      19.000000  13297.000000   5442.000000   4568.00000  120.000000   \n",
       "20      20.000000   9481.000000    327.000000    167.00000   90.000000   \n",
       "21      21.000000   5838.000000    249.000000    116.00000  150.000000   \n",
       "22      22.000000  10613.000000   2127.000000    234.00000  300.000000   \n",
       "23      23.000000   1363.000000    287.000000    172.00000  150.000000   \n",
       "24      24.000000   4038.000000    295.000000     98.00000  240.000000   \n",
       "25      25.000000   3091.000000    692.000000     72.00000  270.000000   \n",
       "26      26.000000  13510.000000   1211.000000    494.00000   90.000000   \n",
       "27      27.000000   4991.000000    115.000000     51.00000  240.000000   \n",
       "28      28.000000  11674.000000    123.000000     48.00000  420.000000   \n",
       "29      29.000000   7108.000000    342.000000      9.00000  300.000000   \n",
       "...           ...           ...           ...          ...         ...   \n",
       "3913  3914.000000   4773.000000    187.000000     76.00000  135.000000   \n",
       "3914  3915.000000  10054.000000    911.000000    190.00000  240.000000   \n",
       "3915  3916.000000  10447.000000   1412.000000    236.00000  240.000000   \n",
       "3916  3917.000000  10095.000000    523.000000    169.00000  120.000000   \n",
       "3917  3918.000000   9482.000000    894.000000   1260.00000  780.000000   \n",
       "3918  3919.000000   6429.000000    152.000000     71.00000  240.000000   \n",
       "3919  3920.000000  10793.000000    246.000000     93.00000  135.000000   \n",
       "3920  3921.000000  10328.000000    188.000000     32.00000  135.000000   \n",
       "3921  3922.000000   8053.000000   1775.000000    738.00000  300.000000   \n",
       "3922  3923.000000   7722.000000   2175.000000    315.00000   90.000000   \n",
       "3923  3924.000000  11701.000000   2281.000000   1465.00000  300.000000   \n",
       "3924  3925.000000  10330.000000    206.000000     52.00000  135.000000   \n",
       "3925  3926.000000   4322.000000     88.000000     57.00000  135.000000   \n",
       "3926  3927.000000   2816.000000    259.000000    186.00000  150.000000   \n",
       "3927  3928.000000  14363.000000   5844.000000   2067.00000  240.000000   \n",
       "3928  3929.000000  13029.000000   4614.000000   1557.00000  195.000000   \n",
       "3929  3930.000000  11958.000000     55.000000     15.00000  150.000000   \n",
       "3930  3931.000000   1780.000000     92.000000      6.00000  150.000000   \n",
       "3931  3932.000000  13334.000000   1783.000000   1460.00000  240.000000   \n",
       "3932  3933.000000  13957.000000    318.000000    184.00000  780.000000   \n",
       "3933  3934.000000   5987.000000    145.000000    151.00000  120.000000   \n",
       "3934  3935.000000   4685.000000   1362.000000      8.00000  240.000000   \n",
       "3935  3936.000000   9814.000000    675.000000    113.00000  240.000000   \n",
       "3936  3937.000000   2163.000000   4772.000000   3941.00000   90.000000   \n",
       "3937  3938.000000   4166.000000    703.000000    360.00000  210.000000   \n",
       "3938  3939.000000   4225.000000    122.000000     19.00000  240.000000   \n",
       "3939  3940.000000  14017.000000   2028.000000    413.00000  270.000000   \n",
       "3940  3941.000000   4660.000000   1356.000000    178.00000  240.000000   \n",
       "3941  3942.000000   6472.000000   2692.000000   1856.00000   90.000000   \n",
       "3942  1971.342973   7304.492136   2203.533486   1653.32344  205.891933   \n",
       "\n",
       "       i1_dep_1_e  i1_dep_1_place  i1_rcf_1_p   i1_rcf_1_e  i1_rcf_1_place  \\\n",
       "0      215.000000      609.000000   935.00000   736.000000      256.000000   \n",
       "1      297.000000      700.000000  1935.00000  1415.000000      431.000000   \n",
       "2      280.000000      456.000000   905.00000   547.000000      700.000000   \n",
       "3      777.000000      173.000000   340.00000   577.000000      349.000000   \n",
       "4      241.000000      411.000000   585.00000   612.000000      128.000000   \n",
       "5      259.000000      815.000000   215.00000   223.000000      128.000000   \n",
       "6     1267.000000      815.000000   270.00000   135.000000      391.000000   \n",
       "7      232.000000      783.000000   255.00000   154.000000      815.000000   \n",
       "8      514.000000      815.000000   285.00000   219.000000      485.000000   \n",
       "9      385.000000      690.000000   810.00000   635.000000      555.000000   \n",
       "10     758.000000      281.000000   395.00000   152.000000      128.000000   \n",
       "11     346.000000      377.000000   280.00000   179.000000      485.000000   \n",
       "12     965.000000      281.000000   415.00000   186.000000      815.000000   \n",
       "13     324.000000      445.000000   995.00000   808.000000      128.000000   \n",
       "14     182.000000      456.000000  1085.00000  1093.000000      349.000000   \n",
       "15     859.000000      753.000000  1575.00000   928.000000      511.000000   \n",
       "16     894.000000      349.000000  1410.00000  2375.000000      548.000000   \n",
       "17     963.000000      815.000000  1220.00000  1027.000000      700.000000   \n",
       "18     713.000000      815.000000   280.00000   381.000000      153.000000   \n",
       "19     934.000000      236.000000   420.00000   260.000000      815.000000   \n",
       "20     278.000000      579.000000  1010.00000   584.000000      128.000000   \n",
       "21     223.000000      391.000000   385.00000   155.000000      815.000000   \n",
       "22    2158.000000      332.000000   705.00000   783.000000      815.000000   \n",
       "23     179.000000      746.000000   870.00000   828.000000      349.000000   \n",
       "24     382.000000      815.000000   270.00000   143.000000      391.000000   \n",
       "25     851.000000      349.000000   930.00000  1062.000000      456.000000   \n",
       "26     772.000000      485.000000  1250.00000   897.000000      700.000000   \n",
       "27     227.000000      476.000000   975.00000   731.000000      815.000000   \n",
       "28     596.000000      349.000000  1240.00000  1048.000000      413.000000   \n",
       "29     583.000000      753.000000   510.00000   322.000000      174.000000   \n",
       "...           ...             ...         ...          ...             ...   \n",
       "3913   189.000000      815.000000   285.00000   212.000000      485.000000   \n",
       "3914   920.000000      815.000000   415.00000    21.000000      576.000000   \n",
       "3915  1355.000000      815.000000   760.00000   845.000000      609.000000   \n",
       "3916   364.000000      236.000000    90.00000    87.000000      815.000000   \n",
       "3917   363.000000      349.000000  1162.00000   740.000000      144.000000   \n",
       "3918   277.000000      128.000000   960.00000   732.000000      597.000000   \n",
       "3919   229.000000      815.000000   285.00000   340.000000      485.000000   \n",
       "3920   250.000000      815.000000   285.00000   227.000000      485.000000   \n",
       "3921  1329.000000      308.000000   840.00000   797.000000      371.000000   \n",
       "3922  1907.000000      206.000000   805.00000   568.000000      700.000000   \n",
       "3923  1081.000000      815.000000   810.00000   732.000000      144.000000   \n",
       "3924   248.000000      815.000000   285.00000   227.000000      485.000000   \n",
       "3925   150.000000      815.000000   295.00000   141.000000      485.000000   \n",
       "3926   186.000000      166.000000   130.00000   158.000000      128.000000   \n",
       "3927  3980.000000      128.000000   750.00000   671.000000      798.000000   \n",
       "3928  3192.000000      441.000000   670.00000   714.000000      349.000000   \n",
       "3929   140.000000      812.000000   163.00000    61.000000      783.000000   \n",
       "3930   207.000000      128.000000   680.00000   762.000000      783.000000   \n",
       "3931   517.000000      441.000000   976.00000   851.000000      128.000000   \n",
       "3932   859.000000      556.000000    60.00000    63.000000      281.000000   \n",
       "3933    49.000000      727.000000   125.00000   175.000000      101.000000   \n",
       "3934  1553.000000      815.000000   760.00000   760.000000      609.000000   \n",
       "3935   741.000000      815.000000   260.00000   197.000000      134.000000   \n",
       "3936   860.000000      700.000000   845.00000   545.000000      628.000000   \n",
       "3937   493.000000      233.000000  1000.00000   908.000000      815.000000   \n",
       "3938   278.000000      815.000000   830.00000   761.000000      174.000000   \n",
       "3939  1825.000000      605.000000  2610.00000  2535.000000      349.000000   \n",
       "3940  1359.000000      815.000000   760.00000   716.000000      609.000000   \n",
       "3941   867.000000      700.000000  1060.00000  1049.000000      113.000000   \n",
       "3942   711.408929      503.703957   796.00279   666.358447      466.677829   \n",
       "\n",
       "        ...     o_dep_3_p  o_dep_3_e  o_dep_3_place  o_rcf_3_p  o_rcf_3_e  \\\n",
       "0       ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "1       ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "2       ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "3       ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "4       ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "5       ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "6       ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "7       ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "8       ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "9       ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "10      ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "11      ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "12      ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "13      ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "14      ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "15      ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "16      ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "17      ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "18      ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "19      ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "20      ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "21      ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "22      ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "23      ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "24      ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "25      ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "26      ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "27      ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "28      ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "29      ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "...     ...           ...        ...            ...        ...        ...   \n",
       "3913    ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "3914    ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "3915    ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "3916    ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "3917    ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "3918    ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "3919    ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "3920    ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "3921    ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "3922    ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "3923    ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "3924    ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "3925    ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "3926    ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "3927    ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "3928    ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "3929    ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "3930    ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "3931    ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "3932    ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "3933    ...         545.0      495.0          411.0      780.0     2112.0   \n",
       "3934    ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "3935    ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "3936    ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "3937    ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "3938    ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "3939    ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "3940    ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "3941    ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "3942    ...         170.0     1400.0          411.0     2910.0      185.0   \n",
       "\n",
       "      o_rcf_3_place      o_dlv_p       o_dlv_e    o_hops      legs  \n",
       "0             567.0   780.000000    434.000000  1.000000  2.000000  \n",
       "1             567.0  3870.000000    445.000000  1.000000  2.000000  \n",
       "2             567.0   550.000000   1520.000000  1.000000  1.000000  \n",
       "3             567.0  3780.000000    159.000000  1.000000  1.000000  \n",
       "4             567.0  4140.000000   4797.000000  2.000000  1.000000  \n",
       "5             567.0  3960.000000    467.000000  1.000000  2.000000  \n",
       "6             567.0  2880.000000    151.000000  1.000000  3.000000  \n",
       "7             567.0  3960.000000    161.000000  1.000000  2.000000  \n",
       "8             567.0  6680.000000   2830.000000  1.000000  1.000000  \n",
       "9             567.0  3960.000000    218.000000  1.000000  1.000000  \n",
       "10            567.0  3960.000000    223.000000  1.000000  2.000000  \n",
       "11            567.0  2880.000000     20.000000  1.000000  1.000000  \n",
       "12            567.0  5160.000000    140.000000  1.000000  3.000000  \n",
       "13            567.0  3960.000000    934.000000  1.000000  2.000000  \n",
       "14            567.0  6680.000000    164.000000  1.000000  3.000000  \n",
       "15            567.0  3780.000000     11.000000  1.000000  1.000000  \n",
       "16            567.0  3960.000000    108.000000  1.000000  2.000000  \n",
       "17            567.0  3420.000000   1681.000000  1.000000  2.000000  \n",
       "18            567.0  3780.000000   1115.000000  2.000000  1.000000  \n",
       "19            567.0   550.000000    492.000000  1.000000  2.000000  \n",
       "20            567.0  3960.000000   7005.000000  1.000000  2.000000  \n",
       "21            567.0  5160.000000    432.000000  1.000000  1.000000  \n",
       "22            567.0  5160.000000   2060.000000  1.000000  3.000000  \n",
       "23            567.0  1075.000000   4090.000000  2.000000  2.000000  \n",
       "24            567.0  5100.000000   1966.000000  2.000000  3.000000  \n",
       "25            567.0  4230.000000   5859.000000  2.000000  3.000000  \n",
       "26            567.0  3780.000000   1287.000000  1.000000  1.000000  \n",
       "27            567.0  5100.000000     24.000000  1.000000  1.000000  \n",
       "28            567.0  4230.000000   2649.000000  2.000000  2.000000  \n",
       "29            567.0  4680.000000      2.000000  1.000000  1.000000  \n",
       "...             ...          ...           ...       ...       ...  \n",
       "3913          567.0  4740.000000   4459.000000  2.000000  3.000000  \n",
       "3914          567.0  3960.000000   8501.000000  2.000000  2.000000  \n",
       "3915          567.0  6680.000000  17970.000000  1.000000  2.000000  \n",
       "3916          567.0  4230.000000   1093.000000  2.000000  3.000000  \n",
       "3917          567.0  6990.000000   3212.000000  2.000000  2.000000  \n",
       "3918          567.0  3960.000000   1467.000000  2.000000  2.000000  \n",
       "3919          567.0  6960.000000     89.000000  2.000000  1.000000  \n",
       "3920          567.0  1140.000000   5741.000000  1.000000  3.000000  \n",
       "3921          567.0  3960.000000    280.000000  2.000000  3.000000  \n",
       "3922          567.0  3780.000000   1114.000000  2.000000  1.000000  \n",
       "3923          567.0  9480.000000  27517.000000  1.000000  3.000000  \n",
       "3924          567.0  4230.000000  17454.000000  2.000000  1.000000  \n",
       "3925          567.0  6960.000000    956.000000  2.000000  3.000000  \n",
       "3926          567.0  3960.000000   1294.000000  2.000000  3.000000  \n",
       "3927          567.0  5160.000000    620.000000  2.000000  2.000000  \n",
       "3928          567.0   584.000000    408.000000  2.000000  2.000000  \n",
       "3929          567.0  2802.000000    385.000000  1.000000  1.000000  \n",
       "3930          567.0  6000.000000   6984.000000  2.000000  1.000000  \n",
       "3931          567.0   665.000000   2906.000000  2.000000  2.000000  \n",
       "3932          567.0  3780.000000   1130.000000  2.000000  3.000000  \n",
       "3933          567.0  3900.000000   5798.000000  3.000000  1.000000  \n",
       "3934          567.0  2880.000000   8647.000000  2.000000  3.000000  \n",
       "3935          567.0  2820.000000   1734.000000  1.000000  2.000000  \n",
       "3936          567.0  3960.000000    571.000000  2.000000  2.000000  \n",
       "3937          567.0   300.000000   2512.000000  2.000000  3.000000  \n",
       "3938          567.0  1665.000000   1300.000000  2.000000  3.000000  \n",
       "3939          567.0  3780.000000    807.000000  2.000000  1.000000  \n",
       "3940          567.0  5100.000000   4381.000000  2.000000  1.000000  \n",
       "3941          567.0  3780.000000    945.000000  2.000000  2.000000  \n",
       "3942          567.0  3573.239726   3698.563673  1.474632  2.012177  \n",
       "\n",
       "[3943 rows x 98 columns]"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline \n",
    "np.random.seed(sum(map(ord, \"distributions\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>level_0</th>\n",
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       "      <th>99</th>\n",
       "      <td>i1_legid</td>\n",
       "      <td>i1_legid</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>i1_rcs_p</td>\n",
       "      <td>i1_rcs_p</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>i1_rcs_p</td>\n",
       "      <td>i1_rcs_e</td>\n",
       "      <td>0.995350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>i1_rcs_e</td>\n",
       "      <td>i1_rcs_p</td>\n",
       "      <td>0.995350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>i1_rcs_e</td>\n",
       "      <td>i1_rcs_e</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>i1_dep_1_p</td>\n",
       "      <td>i1_dep_1_p</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>i1_dep_1_e</td>\n",
       "      <td>i1_dep_1_e</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>594</th>\n",
       "      <td>i1_dep_1_place</td>\n",
       "      <td>i1_dep_1_place</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>693</th>\n",
       "      <td>i1_rcf_1_p</td>\n",
       "      <td>i1_rcf_1_p</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>792</th>\n",
       "      <td>i1_rcf_1_e</td>\n",
       "      <td>i1_rcf_1_e</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891</th>\n",
       "      <td>i1_rcf_1_place</td>\n",
       "      <td>i1_rcf_1_place</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>990</th>\n",
       "      <td>i1_dep_2_p</td>\n",
       "      <td>i1_dep_2_p</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991</th>\n",
       "      <td>i1_dep_2_p</td>\n",
       "      <td>i1_dep_2_e</td>\n",
       "      <td>0.987111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1088</th>\n",
       "      <td>i1_dep_2_e</td>\n",
       "      <td>i1_dep_2_p</td>\n",
       "      <td>0.987111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1089</th>\n",
       "      <td>i1_dep_2_e</td>\n",
       "      <td>i1_dep_2_e</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1188</th>\n",
       "      <td>i1_dep_2_place</td>\n",
       "      <td>i1_dep_2_place</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1200</th>\n",
       "      <td>i1_dep_2_place</td>\n",
       "      <td>i1_hops</td>\n",
       "      <td>0.864007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1287</th>\n",
       "      <td>i1_rcf_2_p</td>\n",
       "      <td>i1_rcf_2_p</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1288</th>\n",
       "      <td>i1_rcf_2_p</td>\n",
       "      <td>i1_rcf_2_e</td>\n",
       "      <td>0.971807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1298</th>\n",
       "      <td>i1_rcf_2_p</td>\n",
       "      <td>i1_hops</td>\n",
       "      <td>0.846204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1385</th>\n",
       "      <td>i1_rcf_2_e</td>\n",
       "      <td>i1_rcf_2_p</td>\n",
       "      <td>0.971807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1386</th>\n",
       "      <td>i1_rcf_2_e</td>\n",
       "      <td>i1_rcf_2_e</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1396</th>\n",
       "      <td>i1_rcf_2_e</td>\n",
       "      <td>i1_hops</td>\n",
       "      <td>0.895396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1485</th>\n",
       "      <td>i1_rcf_2_place</td>\n",
       "      <td>i1_rcf_2_place</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1584</th>\n",
       "      <td>i1_dep_3_p</td>\n",
       "      <td>i1_dep_3_p</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1585</th>\n",
       "      <td>i1_dep_3_p</td>\n",
       "      <td>i1_dep_3_e</td>\n",
       "      <td>0.952259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1682</th>\n",
       "      <td>i1_dep_3_e</td>\n",
       "      <td>i1_dep_3_p</td>\n",
       "      <td>0.952259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1683</th>\n",
       "      <td>i1_dep_3_e</td>\n",
       "      <td>i1_dep_3_e</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1782</th>\n",
       "      <td>i1_dep_3_place</td>\n",
       "      <td>i1_dep_3_place</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7821</th>\n",
       "      <td>o_rcf_1_p</td>\n",
       "      <td>o_rcf_1_p</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7822</th>\n",
       "      <td>o_rcf_1_p</td>\n",
       "      <td>o_rcf_1_e</td>\n",
       "      <td>0.923365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7919</th>\n",
       "      <td>o_rcf_1_e</td>\n",
       "      <td>o_rcf_1_p</td>\n",
       "      <td>0.923365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7920</th>\n",
       "      <td>o_rcf_1_e</td>\n",
       "      <td>o_rcf_1_e</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8019</th>\n",
       "      <td>o_rcf_1_place</td>\n",
       "      <td>o_rcf_1_place</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8118</th>\n",
       "      <td>o_dep_2_p</td>\n",
       "      <td>o_dep_2_p</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8119</th>\n",
       "      <td>o_dep_2_p</td>\n",
       "      <td>o_dep_2_e</td>\n",
       "      <td>0.996055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8216</th>\n",
       "      <td>o_dep_2_e</td>\n",
       "      <td>o_dep_2_p</td>\n",
       "      <td>0.996055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8217</th>\n",
       "      <td>o_dep_2_e</td>\n",
       "      <td>o_dep_2_e</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8316</th>\n",
       "      <td>o_dep_2_place</td>\n",
       "      <td>o_dep_2_place</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8415</th>\n",
       "      <td>o_rcf_2_p</td>\n",
       "      <td>o_rcf_2_p</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8514</th>\n",
       "      <td>o_rcf_2_e</td>\n",
       "      <td>o_rcf_2_e</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8613</th>\n",
       "      <td>o_rcf_2_place</td>\n",
       "      <td>o_rcf_2_place</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8712</th>\n",
       "      <td>o_dep_3_p</td>\n",
       "      <td>o_dep_3_p</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8714</th>\n",
       "      <td>o_dep_3_p</td>\n",
       "      <td>o_dep_3_place</td>\n",
       "      <td>0.815863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8811</th>\n",
       "      <td>o_dep_3_e</td>\n",
       "      <td>o_dep_3_e</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8908</th>\n",
       "      <td>o_dep_3_place</td>\n",
       "      <td>o_dep_3_p</td>\n",
       "      <td>0.815863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8910</th>\n",
       "      <td>o_dep_3_place</td>\n",
       "      <td>o_dep_3_place</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8912</th>\n",
       "      <td>o_dep_3_place</td>\n",
       "      <td>o_rcf_3_e</td>\n",
       "      <td>0.884144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9009</th>\n",
       "      <td>o_rcf_3_p</td>\n",
       "      <td>o_rcf_3_p</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9106</th>\n",
       "      <td>o_rcf_3_e</td>\n",
       "      <td>o_dep_3_place</td>\n",
       "      <td>0.884144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9108</th>\n",
       "      <td>o_rcf_3_e</td>\n",
       "      <td>o_rcf_3_e</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9207</th>\n",
       "      <td>o_rcf_3_place</td>\n",
       "      <td>o_rcf_3_place</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9306</th>\n",
       "      <td>o_dlv_p</td>\n",
       "      <td>o_dlv_p</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9405</th>\n",
       "      <td>o_dlv_e</td>\n",
       "      <td>o_dlv_e</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9504</th>\n",
       "      <td>o_hops</td>\n",
       "      <td>o_hops</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9558</th>\n",
       "      <td>legs</td>\n",
       "      <td>i3_dep_1_p</td>\n",
       "      <td>0.839225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9561</th>\n",
       "      <td>legs</td>\n",
       "      <td>i3_rcf_1_p</td>\n",
       "      <td>0.895488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9562</th>\n",
       "      <td>legs</td>\n",
       "      <td>i3_rcf_1_e</td>\n",
       "      <td>0.897506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9603</th>\n",
       "      <td>legs</td>\n",
       "      <td>legs</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>178 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             level_0         level_1         0\n",
       "0                 nr              nr  1.000000\n",
       "99          i1_legid        i1_legid  1.000000\n",
       "198         i1_rcs_p        i1_rcs_p  1.000000\n",
       "199         i1_rcs_p        i1_rcs_e  0.995350\n",
       "296         i1_rcs_e        i1_rcs_p  0.995350\n",
       "297         i1_rcs_e        i1_rcs_e  1.000000\n",
       "396       i1_dep_1_p      i1_dep_1_p  1.000000\n",
       "495       i1_dep_1_e      i1_dep_1_e  1.000000\n",
       "594   i1_dep_1_place  i1_dep_1_place  1.000000\n",
       "693       i1_rcf_1_p      i1_rcf_1_p  1.000000\n",
       "792       i1_rcf_1_e      i1_rcf_1_e  1.000000\n",
       "891   i1_rcf_1_place  i1_rcf_1_place  1.000000\n",
       "990       i1_dep_2_p      i1_dep_2_p  1.000000\n",
       "991       i1_dep_2_p      i1_dep_2_e  0.987111\n",
       "1088      i1_dep_2_e      i1_dep_2_p  0.987111\n",
       "1089      i1_dep_2_e      i1_dep_2_e  1.000000\n",
       "1188  i1_dep_2_place  i1_dep_2_place  1.000000\n",
       "1200  i1_dep_2_place         i1_hops  0.864007\n",
       "1287      i1_rcf_2_p      i1_rcf_2_p  1.000000\n",
       "1288      i1_rcf_2_p      i1_rcf_2_e  0.971807\n",
       "1298      i1_rcf_2_p         i1_hops  0.846204\n",
       "1385      i1_rcf_2_e      i1_rcf_2_p  0.971807\n",
       "1386      i1_rcf_2_e      i1_rcf_2_e  1.000000\n",
       "1396      i1_rcf_2_e         i1_hops  0.895396\n",
       "1485  i1_rcf_2_place  i1_rcf_2_place  1.000000\n",
       "1584      i1_dep_3_p      i1_dep_3_p  1.000000\n",
       "1585      i1_dep_3_p      i1_dep_3_e  0.952259\n",
       "1682      i1_dep_3_e      i1_dep_3_p  0.952259\n",
       "1683      i1_dep_3_e      i1_dep_3_e  1.000000\n",
       "1782  i1_dep_3_place  i1_dep_3_place  1.000000\n",
       "...              ...             ...       ...\n",
       "7821       o_rcf_1_p       o_rcf_1_p  1.000000\n",
       "7822       o_rcf_1_p       o_rcf_1_e  0.923365\n",
       "7919       o_rcf_1_e       o_rcf_1_p  0.923365\n",
       "7920       o_rcf_1_e       o_rcf_1_e  1.000000\n",
       "8019   o_rcf_1_place   o_rcf_1_place  1.000000\n",
       "8118       o_dep_2_p       o_dep_2_p  1.000000\n",
       "8119       o_dep_2_p       o_dep_2_e  0.996055\n",
       "8216       o_dep_2_e       o_dep_2_p  0.996055\n",
       "8217       o_dep_2_e       o_dep_2_e  1.000000\n",
       "8316   o_dep_2_place   o_dep_2_place  1.000000\n",
       "8415       o_rcf_2_p       o_rcf_2_p  1.000000\n",
       "8514       o_rcf_2_e       o_rcf_2_e  1.000000\n",
       "8613   o_rcf_2_place   o_rcf_2_place  1.000000\n",
       "8712       o_dep_3_p       o_dep_3_p  1.000000\n",
       "8714       o_dep_3_p   o_dep_3_place  0.815863\n",
       "8811       o_dep_3_e       o_dep_3_e  1.000000\n",
       "8908   o_dep_3_place       o_dep_3_p  0.815863\n",
       "8910   o_dep_3_place   o_dep_3_place  1.000000\n",
       "8912   o_dep_3_place       o_rcf_3_e  0.884144\n",
       "9009       o_rcf_3_p       o_rcf_3_p  1.000000\n",
       "9106       o_rcf_3_e   o_dep_3_place  0.884144\n",
       "9108       o_rcf_3_e       o_rcf_3_e  1.000000\n",
       "9207   o_rcf_3_place   o_rcf_3_place  1.000000\n",
       "9306         o_dlv_p         o_dlv_p  1.000000\n",
       "9405         o_dlv_e         o_dlv_e  1.000000\n",
       "9504          o_hops          o_hops  1.000000\n",
       "9558            legs      i3_dep_1_p  0.839225\n",
       "9561            legs      i3_rcf_1_p  0.895488\n",
       "9562            legs      i3_rcf_1_e  0.897506\n",
       "9603            legs            legs  1.000000\n",
       "\n",
       "[178 rows x 3 columns]"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displaying volues of correlation greater than a specific threshold\n",
    "\n",
    "corr_val=0.8\n",
    "dsp1 = dsp.corr().unstack().reset_index()\n",
    "dsp1[dsp1[0]>corr_val]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XkbXGmNnscPfWblhKPdiwvp9b8GLBg8MJVtYwC2IxrJpnci4HR1Lk3JnFF+Ad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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17e75c2da90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(y=\"o_rcf_1_p\", x=\"o_rcf_1_e\", data=ds)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AJR2bmyO9ugdW1Tfx/QfrefWdNgCmVpfywNWnUl2mn7Zk33ACeLuZneOcWwFg\nZucCOwb5jkjeSDpHIuE49cbHevqE40nHa9vbAZhVW8HdS05W+MqIGU4Afx74jZn9zH/fCHwm81US\n2X8zJ5azsakdcw4zSCQd8SQU+eN/m9q6+Ma9L7IzEiWRdBxWW8Fdi+cxvlzhKyNnSH3AZhYAjvdn\nu80B5jrn3uecey2rtRPZR99aOJuasjAGxBNJ4glH0ODAcaWYGYmkY3t7N4mkY+akcu5aPI8JFcW5\nrraMMUMKYOdcEviS/7rdOdeW1VqJ7KcFs2v50QVHc+z0Gg4cV0pRKMDUmlKqSsO0dcV4w19YJxQw\n7l58MhMVvpIDwxkF8YiZXWNm08xsfOqRtZqJZIjDG9nQHU/S3hXnjeYIDi98j55azaRKha/kxnD6\ngD/rP1+VVuYAza+UvNN3zYdEMsk7u7tJOm8f2VDAqK0q5sunHZbjmspYNuQAds4dMtDnZvZh59wj\n+18lkX2TPvNtd2eM8uIg40pLACgJh3rCNxgwjpo6jqtPm6XZapJTmdwR40ZAASw50bfF+3ZrF53R\nBMWhIAEzXm/uALzw/cs3P8hB1aU5rrFIlveE8/uLHzezV8xsvZl9xS8fb2aPmNlG/7nGLzczW2pm\nm8zsRTM7Lu1ci/zjN5rZorTy483sJf87S81Me9ONQX0XQS8OBcDg7dYuXm/uwLlUn+84ha/kjUwG\ncH97wsWBrzvnjsDbzv4qM5sDXAs86pybBTzqvwc4C5jlPxbjrUGMf7PveuAk4ETg+lRo+8csTvve\nwgxekxSIvhtpTqwoJukcXfEkSee1fGsri7n6tFk5rKVIb1ndE845t805t85/3Qa8AkzBW1P4Nv+w\n24Dz/NfnArc7z1NAtZkdCJwJPOKc2+mca8Hr6ljof1blnHvSX6v49rRzyRgyraaMzti7S5WEgkYy\n6b0OGhw1ZRz/9rH3qs9X8kom+4BfH+hDM5sBHAs8DUx2zm0DL6TNLPVfxRRgS9rXGv2ygcob+ynv\n+7cX47WSmT59+hAvR/JVf8tMLpk/k+tWrPcXQYfNzR04oLoszP1XncLBE8pzXW2RPexXAKePfHDO\nnT/AcRXA74GvOud2D9BN298Hbh/Kexc4dxNwE0BdXV1/XSVSIPrebHtlWyuX37YGA0rCQSqKAmzv\n8DbSHFca5r4vnsLm7R1c+/uXtEW85J397YK4ZbADzCyMF76/cc79wS9+x+8+wH9O7dfdCExL+/pU\nYOsg5VP7KZdRKv1m2zu7u9jV6bV4nYNoPEFTuxe+kyqKue+L7+ONHR3aIl7y1qABbGYr9vL4/4AJ\ng3zX8EL6Fefcf6Z9tAJIjWSPNwtkAAAaxUlEQVRYBNyfVn6pPxpiHtDqd1U8DJxhZjX+zbczgIf9\nz9rMbJ7/ty5NO5eMQuk325o7vNVRzbx/9sT9Pl8D7lw8j5mTKrRFvOS1oXRBvB9vH7j2PuWGNyJh\nIKfgrZj2kpk975f9A/AD4B4zuwJ4E7jQ/+xPwNnAJiACXA7gnNtpZt8D1vjH3eCc2+m//gJwK96O\nHQ/6DxllUv2+29u62dHezeTKEvz103F9OpXM4LDaCkBbxEt+G0oAPwVEnHN/7vuBmb060Bedc0/Q\nfz8twIf6Od7Re6pz+mfLgeX9lK8FjhyoHlLY0vt9D6gq5q1dXby1q9Nr+fYJ31CAXtvBa4t4yWeD\ndkE4585yzj2+l8/mZ75KIr2ldyNUlRYxtaaUUMD2uN0aCoDDuPLUd2fNa4t4yWdZHQcskgl9J1lU\nloSZVlNK+mCagHkt36+cdhhXn354T/mC2bU9e8C1dsaorSzhhnPmahSE5IVBuyDM7Ann3Klm1kbv\nNofh9RpUZa12IuzZjdAdS9Cwo4Okg6qSEL/93DyOnDJur9/XFvGSr4bSBXGq/1zpnKtKe1QqfGUk\npHcjdMUSvLajg4TzbqYNFr4i+SyTM+FEMi41+mFXJEp7d7xn5ENRMMDdSxS+UtgUwJIX+pteDHDd\nivXEEgkiaeFrQE15mJ3t0dxVWCQDFMCSc6lhZtF4grauOG+3drHuzRYmlYcpLgqxvS1GPO3uQ3Eo\nQEVxiGWrG9S3KwVNASw5t2x1A9F4gh3t3hbx3qw2R2NrN1PGQWcs2XNsUdBIOKfJFDIqKIAl57a0\nRGiJxIgnHYY3kw3nDbl5q7W757iiYAADwkHTZAoZFTQOWHJuWk0Z3ekLObDnknZBA4cjiaOyJKTJ\nFDIqKIAl53oFqdtzevGcA6uoKS+iNBxkXEmIQyZWaDKFjArqgpCcWzC7lqnjimls7d6j5TutuoQ/\nfeX9OamXSLYpgGVE9TfcbMHsWj5xwnR++uhGEmkJXFUS4nvnvTd3lRXJMgWwjJjUcLNYIkFrJMa2\n1k7WvdnC2UdO5pnXd2Fpy5uFA96iOupmkNFMASwj5rv3vchbad0MASBpjvue30YoYD2jIKZPKCMU\nMJ5s2MnVOayvSLYpgGVELF25gca0IWUASSDp9znE/Wlu08eXUVUSxjmncb4y6mkUhIyIm5/YPOgx\nB48vo8rfvULjfGUsUAtYRkRHNDHg56GAEQoazjk6YwmN85UxQS1gGRFFwb3tTOW5+rTDtGi6jDlq\nAUvGXbzsrzy5uaXn/ezJ5RSFAr3WdEhXVhTk6tMP1w03GXPUApaM6hu+APXvdNAVS1Aa2rMVHAwY\nn1dXg4xRCmDJqL7hm9Idd5j1/rmZsccebiJjibogZMREYt6NuKk1pRSHAtRWlih8ZUxTC1gy5mt3\nrRv0mCnVJRSHAhrlIIJawJJB97+wbcDPD51YTjSRpLaypGcNCJGxTAEsGbGqvqlnz7a9efSaBSNS\nF5FCoQCWfbJ05Qb+588NRGIJzCBsA4/znT25fIRqJlI4FMAybEtXbuAnj27safE6B9G+q6inqSgK\n8NDXFoxM5UQKiG7CybCsqm/ip49t6rVF/MBtX/jZJcdnu1oiBUkBLEP2tbvWcflta0ikdfY69ty/\nLcWAA8cVs2x1w0hUT6TgqAtChmTpyg3c9/zAoxzSBQwCZkwoL9aykiJ7oQCWIRnKcpLgtXpLwkGS\nzhEKaPt4kYEogGVQq+qb2N0VH/Lx8WQSHFSVhzXhQmQACmAZUGoft4Ax4Djf6tIw0XiCrniS0nCQ\nolCAGRMqNOFCZAAKYBnQstUNhIPGhPIitrdH+z0mHDSmjS8jEo0TDhg15cVsUb+vyKAUwLKHVfVN\n/ODBV9jcHKE7nqQ4FOCAqhJKQzE643s2g6tLQ0SicVo7YxgQSzqqS8M0tXVx3Yr13ABqBYv0QwEs\nvayqb+Kae19gZ0e0p8uhO57kjZ0RQubdZEtFcNB/v7srwWG1JRQFA0QTScqKvJ9VWVGIHe1dXH3X\nc1SVhplWU6YuCZE0CmDpZdnqBlrSwjddeuO3qiTE9PHe6IbWzhh3Lp7HqTc+RrW/qSZAW1eMHW1R\nHN5ux683t7Pk189SURzk8MlVCmMZ8zQRQ3osXbmBJxuaSQyyqE4qfM16DzObVlNGZ+zdzTe3t3WD\nQXEoQHt3nOb2GEnn6Iole7onVtU3ZfOSRPKaAlgAL3z/c+XGQY8zYGJFEQCRaLzXMLMl82cSSzgi\n0TjOObriXhhPrChme1s3ZhA06+mmCAdNs+RkTFMACzD0iRaH15Yzuaq0392LF8yu5YZz5vbsblxe\nFGJCeRFVpWGiiSRm3sI9RUHvZ1caDmqWnIxp6gMWANqGONHi22fPGbDfdsHs2p7PU2OIU8PTYn7H\n8sSKEgDNkpMxTwE8hi1duYGbn9hMe3d8rwvqpDvigMph3TRbMLuWG/Bu7LV2xmjrilNTFqayJLRH\n94XIWKQAHqOWrtzATx/bRMDvFhiKs448YNh/p2+LeNnqBhpbIkzVkDQRBfBYdfMTmwkYhAIBYonE\noMcb8GTDTq7ej7+ZHsYikuWbcGa23MyazOzltLLxZvaImW30n2v8cjOzpWa2ycxeNLPj0r6zyD9+\no5ktSis/3sxe8r+z1GyQfXGEVfVNLPzxn9nd5XUBpA8bG0hpOKAbZiIZlu1RELcCC/uUXQs86pyb\nBTzqvwc4C5jlPxYDvwAvsIHrgZOAE4HrU6HtH7M47Xt9/5akWbpyA5+9bQ3177QP6XhLex5XFtYN\nM5EMy2oAO+dWAzv7FJ8L3Oa/vg04L638dud5Cqg2swOBM4FHnHM7nXMtwCPAQv+zKufck845B9ye\ndi7pY1V9E//1+KZBdy5O5/DCt7osRDgY1A0zkQzLxTjgyc65bQD+c6pTcAqwJe24Rr9soPLGfsr3\nYGaLzWytma3dvn17Ri6i0Cxb3UBskCluBhw8vpSSUIBw0CgrChIMQCSapCysIeMimZZP/1X113/r\n9qF8z0LnbnLO1Tnn6iZNmrQfVSxcG5vaBj3GgFAwQG1VCV/+4GFMrCjm4AnlzKqtIJZ0mjoskmG5\nCOB3/O4D/OfUf9GNwLS046YCWwcpn9pPuaRJ3XTbsZe1fNM56Jnd9mTDTr8VHMLMNHVYJAtyEcAr\ngNRIhkXA/Wnll/qjIeYBrX4XxcPAGWZW4998OwN42P+szczm+aMfLk07l+CF75fvXDekm24GTKgo\n4s7F81gwu5YtLRFKw8Fex2jqsEhmZXUcsJndCSwAJppZI95ohh8A95jZFcCbwIX+4X8CzgY2ARHg\ncgDn3E4z+x6wxj/uBudc6sbeF/BGWpQCD/oPwQvfz92+hlhy8GNTN9pm1Vb2lE2rKaOpratnbV/w\npg6XFwW5+Kan2NIS0fq+IvvJ3FCnQY0SdXV1bu3atbmuRlalFlUfSrdDKnwrS4p6LayTWschHDRK\nw0E6Y4meHS+qSsM9ZbGE6/U9kVEs4/MM8ukmnGTId//40pDCF7x+3/buBBccN6VXiPZd2ay2soRJ\nFcVUlYbVLyySIZqKPMosXbmBxl1dgx6X2lrIgGDA+p1m3HfqcN8dL0D9wiL7Qy3gUWRVfRM/X/Xa\n0A72/zHlgETSDSlE++54AVpSUmR/KIBHgdRQs8tvW0N3fAh33dKkWsBDCdG+O15oSUmR/aMuiAK3\ndOUGlj62kWHmbs8SlAaEA8auSJRTb3xswJEN6ev7aklJkf2nURAFbFV9E5fdumbwA31B81q7UX9K\ncigAB1QW051wGtkgMjiNgpB3XXnb0MMXoCQc5D0HVHHopHJOnjmBTf/2EaZNqNDIBpEcURdEgVlV\n38SND9XzytuDr+3QV0Wx93/u9JELW1oiGtkgkiNqAReQVfVNfPE3z+5T+AK0d3sbb6aPXNDIBpHc\nUQAXkK/f8zyRocwt3ovueGKPkQsa2SCSOwrgArGqvonmSGy/zpFIQlEw0OsGW38z3nQDTmRkqA+4\nAKyqb+Lqu57br3OEA8bEyiI6onvuAafNMkVyQy3gPJfq993dFd/ncwQMptSUMrGiRCMcRPKIWsB5\nbFV9E1fevmbYkyzCQcOAaMIRDhoHjSulssQb6ZA+wmFVfRPLVjdoaUmRHFEA54m+YbiluZ3G1u5h\nn8fw1nYImL/IenkRVWnDzFIjHNKXm6wuDdPU1sV1K9ZzAyiERUaIAjiHUqG74Z3dtHcnGF8eZkJ5\nMeve3El3fPgzFA2v9euA6ePLKAoG6Ih6Ix/SZ7ktmT+TZasberYcAigrChGJxlm2ukEBLDJC1Aec\nI6kWaFNbF+3dCbrjSba1dvO3bbv3KXzh3ZXNgmbEEo6zjjyA8qIgjS2dbGxqJxywnhEO2nJIJPcU\nwDmSaoEmkq7XCmbJfVyaw/xHwsGkymIuOG4K9657i2giyazaCqbWlPYaQ6wJGCK5pwDOkVQLdHtb\nN5aBJT4cUBQ0ioJGU1s3Nz+xmVgisdc1HjQBQyT3FMA5kmqBRhNJLAML0hUHjVT7NpFMEokm2NEW\npa3r3ckb6V0MmoAhknu6CZcjS+bP5LoV68E59n1y8buiCUdRMAAGxcGAX5Zke1t3zxC0vl0MmoAh\nkltqAedIqgW6H0s79OIAh8P5fcATK4oB6Ion1MUgkqfUAs6hL/322Yydy4CAGQeMK+lp8XqL73jb\nyWdy9wpN4BDJDAVwjsy89n8z0vUAXv9vZUmIsuIwwYDhnKMzlqAoFOQH5x+V0XDUBA6RzFEXRA4s\n/PGqjIUvQE15EbMmV43ITbX0CRzaQUNk/6gFnGV9/7mOS1L/TkfGzm/A9vYoNWVRAO5cPC9j5+6P\ndtAQyRy1gLMofbZbNJbgyYZmntzcst/nTU26AG9jzanVJUQTSa5bsZ5V9U37ff6BaAKHSOaoBZxF\nqX+uv93a1e86vPujNBykqjTEpMqSnrJsruWQaslvbGqjrStOTVmYiRXFvdaXEJHhUQBn0camNnZF\nosNeTnIwXzt9Fvc82zhiXQHpN94OqCohHOxmZ0eMeCLJrMlVGgUhso8UwFmydOUGmjuiuAzMcksX\nNHiyYSfTaspoauvqWc0Mei81mclhYn1XTptYUUJZUYjaypKs9zmLjGbqA86Cr921jv9cuTHj4Qve\nYjsbm9r2upbDyTPH9/Q7pw8T25++Ya2cJpIdCuAMW7pyA/c9vy2rf6PN356ov2FnTzbszPgwMd14\nE8kOdUFk2P/8OfvjYWvKwixb3cCdi+ft0bXw3ftfznjfcGrdiv4WdheRfacAzrBILLOjHfr9G92x\nve7rVlHkBWR/fcP7asHsWm7A6wtubIlkdFqzyFimAM6AVAg+2dA8In9vd3eSuVP639dtd2eMVNdz\nJlurWjlNJPMUwPthVX0TNz5UT/3bbWThftuATp45vt993QCKggGqy4rUWhXJcwrgfbSqvolr7n2B\nHe3RnPz9m5/YTEc0QUkowMSK4p6dj0vDQVo7Yzz41fk5qZeIDJ1GQeyjHzz4Cs05Cl+Ajmic4qAR\nTSTZ2trJ7k5v5wuNThApHGoBD0P6dNxctXwBggEoCQWZVFnM1l1dOBw72rsJBU2jE0QKiAJ4iFI3\nvGKJRM5avgHADAIYkyqLqSwJc1A1NO3uoiuepLayRP29IgVEATxENz5UT9PuLrrjyRG/4QZQFg4y\noaKI1s4Y5cXBnl0vKkvCdMcTdHQn2NIS6ZlwoRAWyX/qAx6CVfVNbGhqpysH4Wt4S07+96eO4y/f\nOo2lFx1LOBjsmYK8o72LprYoZUXBjE09FpGRoRbwECxb3UAymYt2r7fZZtLBlbevIRgIMHNiORcc\nN4UnG3bS2BKhozvBpIqinmUpy4pCWV2WUkQyRy3gIdjSEhnxlm8AKAkFCJgXwIkkOOfY2NTOHU+9\nwZL5M/nLt06jqjTcswNyihbKESkMoyKAzWyhmb1qZpvM7NpMn7+xpTPTpxzU3CnjvA0208qCgQDB\ngNHWFe/p69VCOSKFq+AD2MyCwM+Bs4A5wMVmNidT559x7f9m6lTDFk0kSSWw2bvP8WSyp4W7t2Up\nNRRNJP8VfAADJwKbnHMNzrkocBdwbo7rtN8i0TjhtBZw0E9g5yAUCPS0cBfMrh2R3ZBFJPNGw024\nKcCWtPeNwEnpB5jZYmAxwPTp00euZvshFaixZIxEwmEGiWSSpPOWo0xv4WqhHJHCNBoC2Pop63XP\nzDl3E3ATQF1dXW6GMwxDwN7dXn5VfRM/ePAVNjd7XQ6zJpXzrYWzFbgio8BoCOBGYFra+6nA1hzV\nJSPOPfrAntdq3YqMXqOhD3gNMMvMDjGzIuAiYEWmTv7SP52RqVP1CAdg6rhiiv1hZinBgPGxYw7k\nxxcdl/G/KSL5p+BbwM65uJl9CXgYCALLnXPrM3Hu9u44i5Y/A3jdAv/5iWM479gpmTi1iEjhBzCA\nc+5PwJ8yec727jiXLX+GdW/uwgz+/cKjFb4iklGjoQsi4zq643z2V2tY+0YLZvDDjx/F+cdNzXW1\nRGSUUQD30RlNcMVta3jm9Z0A3Hj+UVxYN22Qb4mIDJ8COE0qfJ9q8ML3++e/l0+coPAVkexQAPu6\nYgk+d/ta/vqat7Px9847kotPLIxJGyJSmBTAvBu+T2zaAcAN587lM/MOznGtRGS0G/MB3B1P8Plf\nP8tfNnrhe/3fzeHSk2fktlIiMiaM6QDujif4wq/XserV7QB89yNHcPkph+S4ViIyVozZAI7Gk1z1\nm3U85m/d8w9nz+bK92sJRxEZOWMygKPxJFf9dh0rX/HC99qzZrN4/qE5rpWIjDVjLoAd8OU71/HI\n394B4BtnvofPf0DhKyIjb1RMRR6OLTsjNK/3wvfrHz6cqz54WI5rJCJj1ZhrAbd2xgD46umz+PKH\nZuW4NiIylo25AAa4+rTD+Orph+e6GiIyxplzeb9BREa958hjXP1Lz2HW30YaIiJ7lfHQGHMt4MqS\nkMJXRPLCmAtgEZF8oQAWEckRBbCISI4ogEVEckQBLCKSIwpgEZEcUQCLiOSIAlhEJEcUwCIiOaIA\nFhHJEQWwiEiOjLnFeMxsO/DGPn59IrAjg9XJJV1LftK15KeJQL1zbmEmTzrmAnh/mNla51xdruuR\nCbqW/KRryU/ZuhZ1QYiI5IgCWEQkRxTAw3NTriuQQbqW/KRryU9ZuRb1AYuI5IhawCIiOaIAFhHJ\nEQXwEJnZQjN71cw2mdm1ua5PipktN7MmM3s5rWy8mT1iZhv95xq/3MxsqX8NL5rZcWnfWeQfv9HM\nFqWVH29mL/nfWWpZ2lDPzKaZ2eNm9oqZrTezrxTwtZSY2TNm9oJ/Lf/slx9iZk/79brbzIr88mL/\n/Sb/8xlp5/q2X/6qmZ2ZVj6iv0czC5rZc2b2QCFfi5m97v8GnjeztX5Z7n5jzjk9BnkAQeA1YCZQ\nBLwAzMl1vfy6zQeOA15OK/shcK3/+lrgRv/12cCDeLu7zgOe9svHAw3+c43/usb/7BngZP87DwJn\nZek6DgSO819XAhuAOQV6LQZU+K/DwNN+He8BLvLL/wf4gv/6i8D/+K8vAu72X8/xf2vFwCH+bzCY\ni98j8PfAb4EH/PcFeS3A68DEPmU5+43lPEAK4eH/L/ThtPffBr6d63ql1WcGvQP4VeBA//WBwKv+\n62XAxX2PAy4GlqWVL/PLDsSb/ZMq73Vclq/pfuDDhX4tQBmwDjgJb1ZYqO9vCngYONl/HfKPs76/\ns9RxI/17BKYCjwKnAQ/4dSvUa3mdPQM4Z78xdUEMzRRgS9r7Rr8sX012zm0D8J9r/fK9XcdA5Y39\nlGeV/8/WY/FajgV5Lf4/2Z8HmoBH8Fp5u5xz8X7+fk+d/c9bgQkM/xqz5SfAN4Gk/34ChXstDvg/\nM3vWzBb7ZTn7jYX28SLGmv76cQpx/N7ermO45VljZhXA74GvOud2D9CFltfX4pxLAMeYWTVwH3DE\nAH9/uHXur+GUlWsxs48CTc65Z81sQap4gL+ft9fiO8U5t9XMaoFHzKx+gGOz/htTC3hoGoFpae+n\nAltzVJeheMfMDgTwn5v88r1dx0DlU/spzwozC+OF72+cc3/wiwvyWlKcc7uAVXh9iNVmlmr0pP/9\nnjr7n48DdjL8a8yGU4BzzOx14C68boifUJjXgnNuq//chPf/GE8kl7+xbPeBjYYH3r8UGvBuHqRu\nFMzNdb3S6jeD3n3AP6L3TYUf+q8/Qu+bCs/45eOBzXg3FGr81+P9z9b4x6ZuKpydpWsw4HbgJ33K\nC/FaJgHV/utS4C/AR4Hf0fvG1Rf911fR+8bVPf7rufS+cdWAd9MqJ79HYAHv3oQruGsByoHKtNd/\nBRbm8jeW8/AolAfeHdENeH1538l1fdLqdSewDYjh/X/gK/D63B4FNvrPqR+HAT/3r+EloC7tPJ8F\nNvmPy9PK64CX/e/8DH/2ZBau41S8f669CDzvP84u0Gs5CnjOv5aXgev88pl4d8k3+QFW7JeX+O83\n+Z/PTDvXd/z6vkraHfVc/B7pHcAFdy1+nV/wH+tTfyuXvzFNRRYRyRH1AYuI5IgCWEQkRxTAIiI5\nogAWEckRBbCISI4ogEVEckQBLAXNzP6a9vohM9uVWjJRJN9pHLCMGmb2IbzVx5Y45z46jO8Fnbd2\ng8iIUgtYCpqZtadeO+ceBdqG+L3Xzew6M3sCuNDMDjOzlf4i6uvM7FAzO9DMVvuLd79sZu8fqB5m\n9h/+dx81s0n7f3Uy2imAZSzrcs6d6py7C/gN8HPn3NHA+/Cmd1+Ct1btMcDReNOj96YcWOecOw74\nM3B9dqsuo4GWo5Sx7G4AM6sEpjjn7gNwznX55WuA5f4qbX90zg0UwMnU+YBfA38Y4FgRQC1gGds6\n/Od+Fx12zq3G2/LpLeAOM7t0GOfWzRUZlAJYxjzn3G6g0czOg56NJcvM7GC8xch/CdyCt/fe3gSA\nC/zXlwBPZLPOMjqoC0JGDTP7CzAbqDCzRuAK59zDQ/z6Z4BlZnYD3tKeFwLvB75hZjGgHRioBdwB\nzDWzZ/G24fnkPl6GjCEahiaSAWbW7pyryHU9pLCoC0JEJEfUBSGjmpndh7fdTbpvDaNrou/5nsbb\nVifdZ9T6lX2hLggRkRxRF4SISI4ogEVEckQBLCKSIwpgEZEc+X+w96mcOzJxEAAAAABJRU5ErkJg\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17e753fb5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(y=\"i1_rcs_e\", x=\"i1_rcs_p\", data=ds)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BZVOkJ10a8sGeCDc+vIHtbfZW9JeffhQfO/2otEkWXreLqdWBjAkaY50GYaVUwRzoidAV\n6p+G/PbBXq7/1XpauyO4BP71vfM5d8n0tM8PeN1MHUMpyLnQIKyUyrtMacivtxzi87/eSE8kTsDr\n4rYPLuKUuZPSnmMspiDnQoOwUiqvMqUhr9nSxp2PbyKWMNSVe7lz1RLmT61Ke46xmoKcCw3CSqm8\nyZSG/NBLLXx3zZsYoLGujLsuXsL0msE7IY/1FORcTJx3qpQqqHS7IVvG8N01b/Krl3cDsGhGNV+6\ncDE1ZYN3uii1XZBHiwZhpdSIReMW+zrD/TbjjMYtvvL4JtZuPQDA38ybwk3nLsCfJsj6PC6mVQfS\nLk8b7zQIK6VGJF0aclcoxi2PbmD97i4AVp00k39efnTaVQ7jKQU5FxqElVIZHalkZShqB+DULLh9\nnWFueHg9O9vtWhL/dGYTH3rHrLSrHGrKvEweZxlw2dIgrJTqkxp0q/we2noi1JR5+5WsvB07WSPd\nbsjb9ndz4yMbaO+N4nUL169YwHvSJHWICJMrfVQHSncX5NGiQVgpBRyuE+x1C7VlXra39hC3DBU+\nD+KzS1YGo3HuXdvMSUfVDUpDfnFHO7etfoNQLEGl38MdFyzihMbaQa/jEvsCXJlvYl2Ay0SDsFIK\n6F8nGCBhDC6xs96qndUMZV43bx/sHRSAn9iwj68/tZWEZWio8nPnqiXMnVIx6DXGewpyLjQIK6WA\nwXWCfW4XsYRFNGXJWXckTkPV4W3mjTH89Pmd/OgvOwBoqq/gzouWUF81eJ53IqQg50L/O1JKAXad\n4FBKkkV9lR/L2Ot3LcuiKxQlGre49J2NgJ2Y8R9PbesLwCfPruU/P3xi2gBcGfAwvUYDcDoahJVS\nwOA6wW6XUFfu5ai6MtqDMerK/Vz3nnmc0jSJUCzBLY9u4Lfr9wJwznEN3LlqCRVpMt0mVfhoqAqM\n+xoQudLpCKUUkL5O8M3nHsexM6r7pSF3BKPc9PAGtuy3t6L/yKmz+fi75wwKshMxBTkX+ukopfqk\n1glOpiGnBuCWjiDX/2o9ezvDuASuPXseK0+YMeg8HpeLhmr/hEtBzoUGYaXUIOnSkN/Y08VNj6yn\nKxzH73Fxy/nH8a6jpwx67kROQc6FBmGlVD/p0pD/sv0Ad/x2E9G4RU2Zl69ctJjjplcPeu5ET0HO\nhQZhpVSfdGnIv35lN99+djuWgRm1Ae5edTwz6waXodQU5NxoEFZKAQxKQ7aM4ft/eosHX9wFwIJp\nVXzlosXUlvcvtq4pyCNTtEkbEfmMiGwUkQ0i8oCIBERkrog8LyLbROTnIuJzjvU797c7j89JOc+N\nTvsWEXl/sd6PUmNZZyhGa1e4LwBH4xZ3/m5zXwA+vWkyX//bEwYFYJcI06oDGoBHoChBWERmAtcC\nS40xiwE3cClwN/ANY8w8oAO40nnKlUCHMeYY4BvOcYjIQud5i4AVwH+JiF6OVSoLHb3RfmnIPeE4\n//Kzl3l6cysAkyt8nL9kOmUDVjp43S5m1JZpDYgRKublSw9QJiIeoBzYC7wHeMh5/H7gQuf2Bc59\nnMfPFntR4gXAg8aYiDHmLWA7cMoo9V+pMe9AT6TfdvStXWH+8SfraD7QC8DkCi9lXhffXrOdF5rb\n+44LeN3MqC3TGhB5UJRP0BizG/h3YCd28O0EXgIOGWOS27O2ADOd2zOBXc5z487xk1Pb0zynHxG5\nSkTWici6tra2/L4hpcYYYwytXeF+29E3t/XwyQdeYX+XPSqeVuVncoWfcp8Hj0v6piY0BTm/ijUd\nUYc9ip0LzAAqgHPTHJq8RJvup22GaB/caMx9xpilxpil9fX12XdaqXHCsgz7u/pvR//Kzg6ue/BV\nDvREEWBmbaCvchpAwOtiX1dIU5ALoFirI84B3jLGtAGIyMPAu4BaEfE4o91ZwB7n+BagEWhxpi9q\ngPaU9qTU5yilBki3G/LTm/Zz9xNbiFuGyRU+Jlf4+hXyAQjHLGZPqhh0YU6NXLEmdHYCp4lIuTO3\nezbwBvAscIlzzOXAo87t1c59nMefMfZl3NXApc7qibnAPOCFUXoPSpWMNZtbuey+5zjj7me47L7n\nWONcVEsVT1jsORTqC8DGGB58cRdf/t1m4pbhqMnlfPvvTuLj755L3DKEYgkMhnDcPv5flh89qu9p\noijKSNgY87yIPAS8DMSBV4D7gN8CD4rIl5y2HzhP+QHwExHZjj0CvtQ5z0YR+QV2AI8D1xhj+v8X\nrtQ4N3BHjIHbEMHgNOSEZfjOs9v59av2H47Hz6rhjgsWURXwMrU6wHXM48EXd7G/K8RRkyv4pzOP\n7re3nMofSd0faqJYunSpWbduXbG7odSIJPeDe3lnByIwterwPG4wahdff+Cq0walIUdiCb70u038\nZftBAM46tp7rVywYtNKhwu+hvlJTkLOQ0welGXNKjUGpo9+EZeESYU9nCIDqMi9lXjctHcFBacid\nwRg3/3oDb+y1t6L/26WzuGpZE64BF9pqy31MqtD539GgQVipMSh1Pzi/x03cMog5vB9cKJZgek0Z\n+1Ky4PYcCnHDw+tp6QghwDVnHc2qk2f1O6+mII8+DcJKjUGp+8HVV/nZcygMGKLOzhiRmMWqk2b2\nBeAt+7q56ZH1dARjeN3Czecdx7L5/Zdqul1CQ5XugjzaNAgrNQY11pXT2h2m3OehKuBlRi3s6wxj\ngLpyH5ecPItTmiYB8FzzQW7/zRuE4xbVAQ9funAxi2fW9Duf7oJcPBqElRqDrl7WxK2rNxKMxinz\nuu1RbHWAz713PotnHQ6wv319L9/4w1YsA9OqA9y1agmzJ5f3O1eZz01DlWbAFYsGYaXGoIH7wc2s\nLePSdzb2BWBjDPf/39v8+Lm3AZjXUMmdq5YMuthWFfAypdKnGXBFpEFYqTEquR+cMXYacjBqpyHH\nExZff2orT27cD8Apc+q49YMLKff1/+c+ucJPTblegCs2DcJKjWED05CD0Ti3rX6DdW93AHDu4ml8\n5px5/fZ7czm7IKfbnl6NPv0pKDVGJXdDjiXsLLiDPRFufGQD21t7ALj89KP42OlH9Ztq8LhcTK3x\n4/foCohSoUFYqTEoGrfY33U4AL99sJcbHl7P/q4ILoHPvnc+5y2Z3u85fq+bqVV+3QW5xGgQVmqM\nGZiG/HrLIW55dCPd4TgBr4svfHAhp86d3O85FX57F2S9AFd6NAgrNYYMTEP+49Y2vvK7TcQShrpy\nL3euWsL8qVX9nqMpyKVt2EFYRCYDtwHvxi6c/mfgdmPMwcJ0TSmVauBuyA+91MJ317yJAWbVlXH3\nxUuYXnN4K3oRYUqljypNQS5p2UwOPQi0Ahdj1/RtA35eiE4ppfrrCh/eDdkyhu+ueZP/cgLwohnV\nfOuyk/oFYLdLmF4T0AA8BmQzHTHJGHNHyv0viciFGY9WSuXFoWCU9l57M85o3OKuxzezZqu9T+IZ\nx0zh5vMW4E/ZCdnrdjGtJoBXL8CNCdkE4WdF5FLgF879S7CLsCulCuRgT4ROZzPO7nCMWx7dyOst\nnQBceOIMrjnrmH7pxmU+N1OrAloDeAwZdlF3EenG3pAzuXOFG+h1bhtjTHX+u1cYWtRdlTpjDG09\nEXrCdhbc/q4wN/xqPW+3BwG7dsTfLp3Vb7WDpiAXXWGLuhtjqoZ6XEQWGWM25tIJpdRhA9OQt7f2\ncOPD6znYG8XrFq5fsYD3DNhqSFOQx658LlH7CXByHs+n1ISTsAz7u8KEnTTkdTvaue03bxCMJqjw\nu7njgsWc2Fjbd7xLhIZq/6C6EGrsyOdPTv8GUmoE4gmLfV1honE7C+73G/fxtd9vJWEZ6iv93HXx\nEuZOqeg7XlOQx4d8BuGJt2OoUnmSmoZsjOFnz+/kh3/ZAUBTfQV3XrSE+ip/3/Gagjx+6N8wShVZ\nJJ5gX6edhpywDP/59DYee30vACfPruW2lYuoTKl4Vun3UK8pyONGPoNwNI/nUmpCSE1DDsUS3PHY\nGzzX3A7AOcc18G/vP7bfel9NQR5/sklb9hpjYgPaphhjDgAYY07Ld+eUGs96I3FanTTkjmCUmx7Z\nwJZ93QBcdkojnzhjbt9oV1OQx68jTiiJyFki0gLsEZHfi8iclId/X6iOKTWedYVj7HfSkFs6gnzq\ngVfYsq8bl8B1Zx/DP/5NU18A1hTk8W04s/pfBd5vjKkH7gOeEpHkqFcnpZTK0qFglAPdEQDe2NPF\npx54lT2Hwvg9Lr64chEXnDiz71iv28WM2jICXl0BMV4NZzrCl0zCMMY8JCKbgIdF5AZ0RYRSWUlN\nQ/7L9gN86bebiMQtasq8fPnCxSyccTjxVFOQJ4bhBOGYiEwzxuwDMMZsFJGzgceAowvaO6XGiYFp\nyI++uodvPbMNy8CMWnsr+ll1h7eiry7zMrlCU5AnguEE4RuAqcC+ZIMxpkVEzgQ+WaiOKTVepKYh\nW8bwgz+/xQMv7ALg2GlVfOWixdSVH17xMLnST02Zzv9OFEcMwsaYP2Ro7wS+nLwvIr8yxlycx74p\nNealpiHHEhZfe3ILf9jUCsBpTZO45fyFlDnzvZqCPDHl86fdlMdzKTXmpaYh90TifGH1Rl7ZeQiA\n84+fznVnz+srQ+l1u2io1hTkiUjTlpUqgNQ05LbuCDc+sp7mNrvy65VnzOHvTpndN9/r97qZVh3o\nVxdYTRxFSzwXkVoReUhENovIJhE5XUQmichTIrLN+V7nHCsico+IbBeR10Xk5JTzXO4cv01ELi/W\n+1EqKRJPsLczRCxh8daBXj71wCs0t/Xidgk3rDiWj5x6VF8ArvR7mFFTmAC8ZnMrl933HGfc/QyX\n3fccaza35v011MjlMwhn+1v0n8ATxpgFwAnAJuyLgE8bY+YBTzv3Ac4F5jlfVwHfBRCRScAXgFOB\nU4AvJAO3UsUQiibYe8iuA/HqrkNc++ArtHZHKPe5ufOixbxv0bS+Y+vKfTRUBwqyAmLN5lZuXb2R\n1u4wtWVeWrvD3Lp6owbiEpRVEBYRn4gcLyJLRGRgAvv1WZynGlgG/ADAGBM1xhwCLgDudw67H0ju\nYXcB8GNjew6oFZHpwPuBp4wx7caYDuApYEU270mpfOmNxNnn1IF4ZnMr1//qdXojCSZX+Pjmh09k\n6ZxJgJ2C3FAdoK6ANSDuXduM1y2U+zyI2N+9buHetc0Fe02Vm2xqR3wA+B7wJvaod66IXG2MeRzA\nGJNNCnMT9m7NPxKRE4CXgOuAqcaYvc759opIcvuAmcCulOe3OG2Z2tP1/yrsUTSzZ8/OoqtK2dZs\nbuXetc3s6gjSWFfO1cuaWO7scNEVjnHAqQPxi3UtfcHuqEnl3HnxEqZVBwA7BXlqdaDgGXC7OoLU\nDljmVuZ109IRLOjrquxlMxL+OnCWMWa5MeZM4CzgGzm+rgd7F47vGmNOwt6r7oYhjk/395oZon1w\nozH3GWOWGmOW1tfXZ9tfNcEN9ed9Mg05YRm+/eybfQH4+Fk13HPZiX0BeDRTkBvrygnFEv3aQrFE\nv4QQVRqyCcKtxpjtKfebgVwnmFqAFmPM8879h7CD8n5nmgHne2vK8Y0pz58F7BmiXam8yvTn/bef\n3U57b5RILMEXf/MGj7yyG4Dl8+v56sXH9xXdKfd5mFlbNmrb0F+9rIlYwhCMxjHG/h5LGK5epitJ\nS002vxEbReR3IvIPziqE3wAvisgqEVmVzYs6KdC7RORYp+ls4A1gNZBc4XA58KhzezXwMWeVxGlA\npzNt8STwPhGpcy7Ivc9pUyqvdnUE+5IqwM6C87iElo4gnaEY//rL1/nz9gMAfOgds/j8+cfh89j/\nvKrLvEyrGd0aEMsXNHD7ykU0VAXoDMVoqApw+8pFfdMnqnRks+X9j4Z42BhjPp7VC4ucCHwf8GGP\nqq/A/k/hF8BsYCfwIWNMu9iXj7+NfdEtCFxhjFnnnOfjwE3Oab9sjBmqn4Buea+yd9l9z9HaHabc\n58EYQ9wy9EbiVPo9dEfitHSEEOCflx/NJe+Y1fc8TUGeUHL6X3bYQXg80SCsspWcE/a4wON2EYom\nCMUShGN2NpzXLdx03nGcOd++3qApyBNSTkF42NMRIjJfRJ4WkQ3O/eNF5PO5vKhSY0lyVURPOEpr\nd4S27ghel4uuUJyeSJyqgIevXXJ8XwBOXoDTAKyGI5s54f8GbgRiAMaY14FLC9EppUpFcgS8vyvE\nlMoAdeU+LGPY0d5LNGExtdoUYNPEAAAgAElEQVTPPZeeyPGzagEIeN3MqC3rmw9W6kiy+a+63Bjz\nwoDsnnie+6NUSbl3bXPfFIRx5oE7Q/av/TENldx50WImV9pb0VcGPNRX6i7IKjvZBOEDInI0zjpc\nEbkE2FuQXilVIna299oX4yzD/u4IXU5Rdp9b+OaHT+ibcqgr9xU0A06NX9kE4Wuw95hbICK7gbeA\njxakV0qVgN5InPqqAG3dYTqCMYJRO/mhwufmmPrKvjXD9VV+Kv06/6tyM+yJK2NMszHmHKAeWGCM\nOcMYs6NgPVOqiJK7IX9g8TRnVww7AFcHPFQFPFx2yuy+XZA1AKuROOJvj4h8NkM7AMaY/8hzn5Qq\nqkPBKO29UXYeDPKDv+wgbtnLOKsDHpqmVHLpOxs5Y/4UplUH8IxSBpwav4bzX3iV8/1Y4J3Y2WsA\nHwTWFqJTShVLcjfk9S2dfP7RDXSH4wQ8Lm794EJOa5oM2CnIDVV+3QVZ5cVw9pj7IoCI/B442RjT\n7dy/DfhlQXun1ChJ3Q157bY2vvzbTcQShrpyL1+5aAnHTrPHIjVl3r7VEErlQzaTWbOBaMr9KDAn\nr71RqghSd0N++OUWvvPsmxhgVl0Zd61awozaMkSEyZU+qgOagqzyK5sg/BPgBRF5BHuZ2kUcLsCu\n1JhkWYZ9XWGC0Tj3/rGZX77UAsDC6dV8+cLF1JR7cYldA7jMp5twqvwbdhA2xnxZRB4H/sZpusIY\n80rycRGpc3a3UKpohiq8PvCxT5wxl2OnV9ETjnP3E5t5dksbAGccM4Wbz1uA3+vG63YxtTqgGXCq\nYPJWwEdEXjbGnHzkI4tPC/iMT8kUY69bKPO6CcUSxBKG21cuAuj3WDAaJxyz+MQZTTz62m5ea+kE\n4MITZ3DNWcfgdgkBr5upuguyGr6cflHyucBRf1NVUaUWXgd7FUMwGu/b6SL5mGUMXrcdpL/65GbC\ncQuAq/5mLh9+ZyMioinIatTkMwhPvJqYqqQMta+aAWrLvFjGEEtYRKIJWrujJCy7OPv1K47l7OOm\nAjCpwkdtuaYgq9GhqT5q3GisK+8rvJ6Uuq/avq4QXreL3kicvZ1hLAMugbsvXsJJs+vsXZCr/FRo\nBpwaRVn9tjk7IycvzP3JGPNa6sN565UChr7IpAa7elkTt67eSDAa7zcnfPWyJkLRBLf/9g06gzHa\ngzHADsDXnjWPk2bX4XG5aKj2j8omnEqlyqao+3XAz4AG5+unIvKplEPOznPfJrShdvdV6WXaV+3E\n2bXMn1bJ8TNr+gJwwOvi+vcvYOVJM/B5XMyoLfw29Eqlk80ec68Dpxtjep37FcBfjTHHF7B/BTEW\nVkek7mmWFIzGaagK8MBVpxWxZ2PLwZ4I7b1R7nlmG795za68emJjLbdfsIhKv0dTkFU+FXx1hACJ\nlPuJXF9UHdlQF5nUkSXTkNu6I3zpsU38tfkgAOcc18C/vf9YvG6XpiCrkpBNEP4R8LyTMQdwIfCD\n/HdJwZEvMqnMkmnIuw8FufmRDWze1w3Ape9s5BN/Mxe3y6UpyKpkZFNP+D+wt6VvBzqwM+a+WaiO\nTXRXL2siljAEo3GMsb8nLzKpzCzLsLczzLb93XzqgVfYvK8bl8B1Zx/DVcua8LpdTKsOaABWJSOr\n1RHGmJeBlwvUF5Vi+YIGbsdOQGjpCDJLV0ccUTxhsa8rzGu7DnHTIxvoDMXweVx8/rzjOGPeFE1B\nViVJF0SWsOULGjToDlMsYbGvM8wft7Zyx2ObiMQtqgMevnLREhbOqNYUZFWyNAirMS8ST7CvM8wj\nr+zmnqe3YRmYXhPgrlVLaJxUrinIqqRpEFYlJZsqaFcva+K0oyez91CI//5TM//7wi4Ajp1axZcv\nWsykCp+mIKuSl7cqamPJWFgnPBFlUwUtFEsQiVv805lN/HHrAZ56Yz8ApzVN4pbzF/at/9UUZDWK\nil5FTakRGW4VNAC/x004luCux7fQE4kD8IEl0/n0OfPwe9xMrfHj92gGnCp9GoRVyRhOFTSAuGUR\njiZo64kSdcpQXvHuOXz01Nn4vW7dBVmNKRqEVUlYs7mVrlCMfZ1h/B4XUyr9VJd5+yWotHaH8bld\nBKNxdh8K921F///efywrFk+jwm9PQegFODWWaBBWRbdmcyufe+g1wrEEccuQiCbY1RGkIe7H53Fz\n9bImjDF8/tGNdIViHOyNYhl7Au6Kd81hxeJp1JbbF+GUGmv0bzZVdHc9volDwRiC4HXW8VoGOkMx\nbl+5iDOPrWfhzBqWzZvCgR47AHtcwifPOoa/f9ccplT5NQCrMauoI2ERcQPrgN3GmPNFZC7wIDAJ\nOzPv740xURHxAz8G3gEcBD5sjNnhnONG4ErsgkLXGmOeHP13okbizQO9JCxDfMDmLKGYxff++CYH\nesJsb+vlgRftJWhHTSrnzouXMLO2jIYq3QVZjW3FHglfB2xKuX838A1jzDzs+hRXOu1XAh3GmGOA\nbzjHISILgUuBRcAK4L+cwK7GiDWbW4klTMa9sba3dnPr6jf43h/tFRJLZtbwn5eeSGNdOTNqyzQA\nqzGvaEFYRGYBHwC+79wX4D3AQ84h92NXagO4wLmP8/jZzvEXAA8aYyLGmLeA7cApo/MOJq41m1u5\n7L7nOOPuZ7jsvudGVGj+3rXNeIZIJT7YGyMYtSuonjm/nq9dcjxTawLMqC3Dqysg1DhQzOmIbwL/\nD6hy7k8GDhlj4s79FmCmc3smsAvAGBMXkU7n+JnAcynnTH1OPyJyFXAVwOzZs/P3LiaY1ISK1B0/\nbncez3Y7pl0dQdwC8QyPJ0fI5T43t5x/HDVlPqZU+nQFhBo3ijKUEJHzgVZjzEupzWkONUd4bKjn\n9G805j5jzFJjzNL6+vqs+qsOS02oELG/e93C3U9szmk7psa6chJHSNqsLfMyv6GK+soA9boETY0z\nxfp77t3AShHZgX0h7j3YI+NaEUmOzmcBe5zbLUAjgPN4DXZd4772NM9RBbCrI0jZgL3Yyrxumg/0\npg3OyWy3TK5e1jRkZTMXUOZz809nNlFTrjWA1fhTlCBsjLnRGDPLGDMH+8LaM8aYjwDPApc4h10O\nPOrcXu3cx3n8GWMXvVgNXCoifmdlxTzghVF6G+PSkeZ7G+vKCcUS/dqS99MF55aO4JDnXL6ggWuW\nH02mEr9zJpdz+8pFrFgyPQ/vTqnSU/QCPiKyHPics0SticNL1F4BPmqMiYhIAPgJcBL2CPhSY0yz\n8/ybgY9jTyt+2hjz+JFeUwv4pDdUAZ3USmbpjin3uohZZtDGpJGYnV6csAx+j4uqgIe4Zaiv9NMd\nidNYV85HT53Ns1taeejl3X3PnVUT4DPvnc8FJ83UFGQ1VuQ0T1b0IFwMGoTTG+4Oz8mSkqk7fsDg\nKmedoRidIXuLebcIxkDcGIxl8HlcTK32s/dQiJh1eCL/mPpK7ly1mNmTKzQFWY01WkVNjcxwd3jO\ntONH6nZMlX4PwYg9SnYJiEtwuwQrZhfciVuG3R0hEhwOwD6Pi4+eOpujG6o0A26EhqrLrEqL/p2n\n+mSa7x3uDs/LFzTwwFWnsfSoWjbv6ybonMsy9vZDCctOyjDYQThhIPmHmAANlT5+u2GvBuARSk4Z\nZbtSRRWHBmHVJ5sdnjNdbLvnD1t55NW9g9YJGuxAPLAN7F9Cv0eoLvOyrzOc9/c10WRaRniklSqq\nODQIqz7LFzRw+8pFNFQF6AzFaKgK9LsolzTUSOv7f34r4/nTXX0QwOWChqoAkbg17FG3yizTMsKB\n00qqNOicsOpnODs8p9sB40BPmGsffIWu8OHcN5HD0w1Dtc2oCeD1uDKOulV2GuvKB11gzWZaSY0u\nHQmrrKWOtLrDMTbv62JvZ6RfAIbBwTbZ5hZhdl0Zs2oDeN2CQTKOulX2splWUsWnI2GVtca6cnYc\n7KGjN0Y4bh35CSncLqGxtoxyv4dIPME7jprUb/nbcOnV/8yWL2jot1Jlln4+JU2DsEprqCB3etMk\nXtjRTsLKfo15wjIkjEUknsh5dDZUESENNLbhTCup0qBBWA2SLsh97qHXqK/009YToSMYG1YAdom9\nPA2cC3DYlff3dIaZV1/JLR9YmFOgGGpXZg08aqzRIDzBpRvx3vX4Jlq7wyQsg8/totznpqM3yoGe\naFbnTg3ABjsAuwQaqvzUVfhzDpjDTSpRaizQIDyBZRrxtvdG8bgEtwjxhOFATzTjzhfDkfpcY6C1\nK0LsSPUrh6BX/9V4oqsjJrB0i/p7InGMARFBRHC5JOcAXOXvv1Y1mVifMBCMJAY/YZj06r8aTzQI\nT2DpFvUnLLvWgzFgGUPm3d+GNqXCO2hkmkxZBogmcg/Cw00qUWos0OmIIir2Mqt0f9a7nWmIaTUB\n2rojRBPZLUEDaKjyUV8VIBxL2BfkxAnAxk7WEMDtGtn//3r1X40XOhIuklIospL8s76tO0xzWw+b\n9nYRT1j4vS7cLmHulApmTyon22qS9ZV+wk6d4Vl1ZYgIXrcLv9eF1+1CRGiaUlGYN6XUGKNBuEhK\nocjK8gUNXHLyTDqCMcLxBC4MLhG6w3F2tQfZsq+bnQeDaTPfMnELdIXjfVMEd1ywmLpyLwLEExYC\n1JV7uX7FgkK9LaXGFJ2OKJJSWWb11+Z2ZtWVEU8Y9nSGcGFPCEdzWL0wrdrP3CmVgzLgvnbJCZq9\npVQGGoSLpFSWWSX/M3irsxcX9kqIbGaBk0kYDTV+vG532hUKOn+rVGY6HVEkpbLMKlnIPZqwEIG4\nld2FuEq/m7pKH3MmV+oKBaVyoCPhIilkkZVsVl1cvayJW1dvtLcesgzZloNY/8UVI+6vUhOZBuEi\nKsSf6dkWt0n+Z3DX45vY2tqT1WuV0hacxV7up1SudDpinBnOqouBWxMBPPGZM5lR7c/qtTwl8ttT\nCsv9lMqVjoTHmSOtukgGrFgiQWcwxu6OEH9tPpjTa9UMeJ1i0apqaizTIDzOpK666ArFONATIRK3\nKPe5+/5kjyUStHZFyLWGjt8t1FX4mDulMr+dz1GpLPdTKhcl8gelypfULLg9nSF71QNQ4Xfzbw+9\nxos7DrK3M/cADDBrUjk+T/rlaMWQXOGRSquqqbFCg3CJyrSl/JEki9sEowksY9cDnllXht/jdoqx\nj6xfXlfp7QdXKsv9lMqFTkeUoHxs3xOJW/1WL+w+FCKew3ZEqeorfXztkhNKJvgm6Z5qaizTIFyC\nhnOhKdOSrGQAFwGXCHHLsLM9mPX634HKfe6SDMBJmpWnxioNwiVouCsc0o2UkwG82u+hrSeacz3g\nVKU6AlZqPNAgXIKOVFfi3rXNROMJDvbEiSYsfG4XVQFP38jYLdAejOUh/EJ1wKMBWKkC0gtzI5Tr\nBbShHOlC09b9XRzsjRJPmL594A72Rtm2v4vGunL2doZHPP/rFnC74J5LT9IArFQBaRAegUJlaqVu\n37OvK0xbd4TeSIx71zazZnNr3yaZLtfhfeAAok6gzqUM5UCWgenVAQ3AShVYUYKwiDSKyLMisklE\nNorIdU77JBF5SkS2Od/rnHYRkXtEZLuIvC4iJ6ec63Ln+G0icvlovo9CFmZfvqCBq5c1Ue7zUO5z\nE45ZvLijnat/+pI9yk3ZA84y9n2fx8XrLYdG/Noel+B2CZV+na1SqtCKNRKOA/9qjDkOOA24RkQW\nAjcATxtj5gFPO/cBzgXmOV9XAd8FO2gDXwBOBU4BvpAM3KMh3UaZ+czU6pv7daYePC7BMoZQLEFV\nwIPHJSQsu31KlY8yr5tvPL0t59ez934T/B4XM2sD9EZz34xTKTU8RRnqGGP2Anud290isgmYCVwA\nLHcOux9YA1zvtP/YGGOA50SkVkSmO8c+ZYxpBxCRp4AVwAOj8T4KXZh9V0eQ7nAcF4enHNwC8YSh\nN5pwdsSw2N8VYX9XZMTL0BbPrOm7HYzaWxQppQqr6HPCIjIHOAl4HpjqBOhkoE5OSM4EdqU8rcVp\ny9Q+KgqdqdVYV24nXaRkXcSd+d5o3KKlI8Tb7SHCcWvEAdjjQjPOlCqCogZhEakEfgV82hjTNdSh\nadrMEO3pXusqEVknIuva2tqy72waqRfQOkOxvKfzXr2sCbdLSBhDPJEgFEsQt+yVvwY7Ky4fqvxu\nrn3PvIK9D6VUZkW78iIiXuwA/DNjzMNO834RmW6M2etMNySXGbQAjSlPnwXscdqXD2hfk+71jDH3\nAfcBLF26NB9LaIHDacT3rm1m6/4urn3wFXweF/MaqrJOnU2XBXfN8qP5xh+25WXNbzrlXjffuuxk\nli9o4NoCvYZSKrOiBGEREeAHwCZjzH+kPLQauBy4y/n+aEr7J0XkQeyLcJ1OoH4S+ErKxbj3ATeO\nxnsAO2je/cRmtrb22HO1lsHtEkLRBDsO9gy73kPqebxuYWqVn9buMJ976DUgw9B+hAQo87qYXOnT\nEa9SRVSskfC7gb8H1ovIq07bTdjB9xciciWwE/iQ89jvgPOA7UAQuALAGNMuIncALzrH3Z68SFdo\nfWuEu8K4BWIJe5rAbez1u12hONNqPEcsLD7wPMaCPZ32uuNDwdiIky4yMUBNuVfLPSpVZMVaHfFn\nMm9Rdnaa4w1wTYZz/RD4Yf56NzzJNcIJYxAOj1ZjlsEnduLEcJarpZ7H7RIEAQsO9kbxuAq3i5vb\nRcYt6pVSo6foqyPGquQaYbfIoAy1qJNOPJzlasnz+NwujHMaETtjLZaHzLdM/G63XnxTqgRoSlSO\nkmuEM7GMGdYyryq/h+2tPcQtQ8KZU06Oqws1Fzx7UhmWGX5tYqVU4WgQztHVy5rsDTMtC68L4tbh\noOl2ajocaaS5ZnMrbT0R4pbBJYCLgs0Bg52O7Pe48LhdQyZi6PbxSo0enY7IUXKNcIXPg0Eo97k5\nalI5S2bWMGdyOSfPrjti4Lp3bTM1ZV5m1pbhdbsQEXzuws0Di0BVwDPkCF23j1dqdOlIeASWL2jg\nnktP6iuwXuZ1HzHbLHWU2dYdYVq1n+oyH9VOEfet+7vJ50SES0DErjlRE/Awd0rlkCNb3T5eqdGl\nQXiEUvc327a/i2jC4PO4+iqppQaugTtiHOiJ8HZ7CCFUsGSMKRU+qsq8NFQFeOCq0/o9lm7aIVkU\nvrmtp69g/JRKn24fr1SBaBDO0sDAdXrTJP7a3M7W/V30RBKU+1yEogle3NHOyzs7uGb50Vx7znxg\n8CjTZQp3AS6pPRjDAm75wMJB7yPdFkkYw+7OCCKQsAy9iQS97SEa68oK2EulJi4NwlkYGLjeOtDD\nCzvaaajyEY5ZxBIWHUELj0vsMpPG8J01bwLw1+Z2XtjRTsDjYkqlHxEIxgsZfm2Cob7SP2gqIdO0\nw97uGMYYBpal2NsZYs3mVp2SUCrP9MJcFgYWce8Ox3EJdIXsvd6SLGMQsQujxxMW31nzJjsO9mBZ\ndgnKne1BWtpH58/7hIGtrT2Dtl7KVAs5krBwi30RT7DnlL0ugPwUq1dK9acj4SwM3AU5mrCc0o+J\nflMKloF4wsLlkr7bB3tieNz2fnAGKOQgOLm+wjh9qfC6+u3IvHxBQ8ZayBU+N73ROH6Py87eAyzL\n4HFLweeFdWmcmoh0JJyFxrpyQjF7t4muUIx4wvRbH5wqZhkicQuDPRqNJqy++hKjIfk6Akyp9A/a\neilTLeRPnDEXj8tFwjIYY7Asg4WhKuApaJ0JXRqnJioNwllIBq5d7b283R4ctYCardR+Tak8vPwt\nWcsiOeIMRuO0dUfY1xnqqyF87TnzuWb50bhEiDsj4MkVPnyewtaZKOR+fUqVMp2OyMLyBQ1c0nKI\nb/wh933cCiX5v6nbLbhEcItQXeahPiUzLjndkLy4OK06QCiW6FvXnPzT/9pz5nP8rFruXdtMS0eQ\nWaMwNTBwqgfyu1+fUqVKg3CW/trcXnIjYLfAvIZKbjj3uL5AmfzzPhiNU+Z19wVbn9s1rGSM5Qsa\nRnU+ttD79SlVqnQ6Iku7OoIZa3AWw7RqP6fMncwTnzlzUBBNt/VSdyRe0B2ic1Xo/fqUKlU6Es5S\nY105ezoKl+E2XB6BmXXlVAU8GQNoutFs49rSHHGmZh6O1hSIUqVAg3CWrl7WxLq327EKWOv3SPwe\nF/OnVgH2DsnZBNBk9beB0xSlMOIc7SkQpUqBTkdkIbmqIF7EAAz0LR/L5U/2Qu8QrZTKjo6Ehyk1\nZbmYXGIvQdu4pwuXS6jwudMWCxqKjjiVKh06Eh6m5DrWrlCsqPPBloEqvxuPW3AJ/XZ21sQGpcYe\nDcLDtKsjSDxhcaAnWrQ+OJtvEEsYXAgel6tvZ2dNbFBqbNIgPEyNdeXs744UdRTsckGZz000YSHO\nrIiInRJdCsvMlFLZ0yA8TMl1rMXkcbm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      "text/plain": [
       "<matplotlib.figure.Figure at 0x17e74f81c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(y=\"o_dep_2_p\", x=\"o_dep_2_e\", data=ds)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-465.41726904] [[ 0.96151963]]\n"
     ]
    }
   ],
   "source": [
    "# from sklearn import linear_model\n",
    "\n",
    "x = ds[[\"i1_rcs_p\"]]\n",
    "y = ds[[\"i1_rcs_e\"]]\n",
    "\n",
    "# Fit the linear model\n",
    "model = linear_model.LinearRegression()\n",
    "results = model.fit(x, y)\n",
    "\n",
    "# Print the coefficients\n",
    "print (results.intercept_, results.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>i1_rcs_e</td>     <th>  R-squared:         </th> <td>   0.973</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.973</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>1.413e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 14 Feb 2018</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>20:03:46</td>     <th>  Log-Likelihood:    </th> <td> -31942.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  3943</td>      <th>  AIC:               </th> <td>6.389e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  3942</td>      <th>  BIC:               </th> <td>6.389e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>        <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>i1_rcs_p</th> <td>    0.9232</td> <td>    0.002</td> <td>  375.929</td> <td> 0.000</td> <td>    0.918</td> <td>    0.928</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>2049.563</td> <th>  Durbin-Watson:     </th> <td>   1.580</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>44524.758</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td>-1.995</td>  <th>  Prob(JB):          </th> <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td>18.972</td>  <th>  Cond. No.          </th> <td>    1.00</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:               i1_rcs_e   R-squared:                       0.973\n",
       "Model:                            OLS   Adj. R-squared:                  0.973\n",
       "Method:                 Least Squares   F-statistic:                 1.413e+05\n",
       "Date:                Wed, 14 Feb 2018   Prob (F-statistic):               0.00\n",
       "Time:                        20:03:46   Log-Likelihood:                -31942.\n",
       "No. Observations:                3943   AIC:                         6.389e+04\n",
       "Df Residuals:                    3942   BIC:                         6.389e+04\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "i1_rcs_p       0.9232      0.002    375.929      0.000       0.918       0.928\n",
       "==============================================================================\n",
       "Omnibus:                     2049.563   Durbin-Watson:                   1.580\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            44524.758\n",
       "Skew:                          -1.995   Prob(JB):                         0.00\n",
       "Kurtosis:                      18.972   Cond. No.                         1.00\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import statsmodels.api as sm\n",
    "# Note the swap of x and y\n",
    "model = sm.OLS(y, x) # For calculating output w.r.t. the regression formula\n",
    "results = model.fit()\n",
    "# Statsmodels gives R-like statistical output\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Very good fit between the attributes ($R^{2}$ = 0.973). This means the timings between planned and estimated check-ins of Incoming Transport leg 1 is highly efficient!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>i1_dlv_p</td>     <th>  R-squared:         </th> <td>   0.605</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.605</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   2015.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 14 Feb 2018</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>20:03:46</td>     <th>  Log-Likelihood:    </th> <td> -36531.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  3943</td>      <th>  AIC:               </th> <td>7.307e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  3940</td>      <th>  BIC:               </th> <td>7.309e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>i1_rcs_p</th>   <td>    0.0144</td> <td>    0.009</td> <td>    1.609</td> <td> 0.108</td> <td>   -0.003</td> <td>    0.032</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>i1_dep_1_p</th> <td>    4.8312</td> <td>    0.240</td> <td>   20.164</td> <td> 0.000</td> <td>    4.361</td> <td>    5.301</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>i1_rcf_1_p</th> <td>    2.3550</td> <td>    0.071</td> <td>   33.161</td> <td> 0.000</td> <td>    2.216</td> <td>    2.494</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>338.911</td> <th>  Durbin-Watson:     </th> <td>   1.811</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 482.637</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.690</td>  <th>  Prob(JB):          </th> <td>1.57e-105</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.016</td>  <th>  Cond. No.          </th> <td>    31.2</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:               i1_dlv_p   R-squared:                       0.605\n",
       "Model:                            OLS   Adj. R-squared:                  0.605\n",
       "Method:                 Least Squares   F-statistic:                     2015.\n",
       "Date:                Wed, 14 Feb 2018   Prob (F-statistic):               0.00\n",
       "Time:                        20:03:46   Log-Likelihood:                -36531.\n",
       "No. Observations:                3943   AIC:                         7.307e+04\n",
       "Df Residuals:                    3940   BIC:                         7.309e+04\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "i1_rcs_p       0.0144      0.009      1.609      0.108      -0.003       0.032\n",
       "i1_dep_1_p     4.8312      0.240     20.164      0.000       4.361       5.301\n",
       "i1_rcf_1_p     2.3550      0.071     33.161      0.000       2.216       2.494\n",
       "==============================================================================\n",
       "Omnibus:                      338.911   Durbin-Watson:                   1.811\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              482.637\n",
       "Skew:                          -0.690   Prob(JB):                    1.57e-105\n",
       "Kurtosis:                       4.016   Cond. No.                         31.2\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leg_1_p = ols(\"i1_dlv_p ~ i1_rcs_p + i1_dep_1_p + i1_rcf_1_p -1\", data=ds).fit()\n",
    "leg_1_p.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# For estimated correlation factor of individual variables, we need its respective VIF values.\n",
    "# There is no function for calculating VIF values. \n",
    "# None of the pre-built libraries have this VIF calculation function\n",
    "# We may have to write our own function to calculate VIF values for each variable \n",
    "\n",
    "#Code for VIF Calculation\n",
    "\n",
    "#Writing a function to calculate the VIF values\n",
    "\n",
    "#Tried from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "\n",
    "def vif_cal(input_data, dependent_col):\n",
    "    x_vars=input_data.drop([dependent_col],axis=1)\n",
    "    xvar_names=x_vars.columns\n",
    "    for i in range(0,xvar_names.shape[0]):\n",
    "        y=x_vars[xvar_names[i]] \n",
    "        x=x_vars[xvar_names.drop(xvar_names[i])]\n",
    "        rsq= ols(formula=\"y~x\", data=x_vars).fit().rsquared  \n",
    "        vif=round(1/(1-rsq),2)                               # Formula for VIF\n",
    "        print (xvar_names[i], \" VIF = \" , vif)\n",
    "        \n",
    "#Source: https://statinfer.com/204-1-9-issue-of-multicollinearity-in-python/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "i1_rcs_p  VIF =  1.07\n",
      "i1_dep_1_p  VIF =  1.01\n",
      "i1_rcf_1_p  VIF =  1.06\n"
     ]
    }
   ],
   "source": [
    "#Calculating VIF values using that function\n",
    "\n",
    "ds_p = ds[[\"i1_rcs_p\",\"i1_dep_1_p\",\"i1_rcf_1_p\",\"i1_dlv_p\"]]\n",
    "\n",
    "\n",
    "\n",
    "vif_cal(input_data=ds_p, dependent_col=\"i1_dlv_p\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the individual attributes are having a range of 5-10, we consider all of them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cross-validation of linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AMAJ7pgKP/UYszWyumQ2Y2cDg4GCGh5SgDCWKuWxjGobjAh4CoJ2N7GIi3+H/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\nMj/BKPAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17e72373d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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McX8HeDiE922PwcCqZuPVRDD0mphZOTAaeDncStrleuDHQGPYheyDg4E6YGZs\nyuc2M+sRdlFt4e5rgOuAWmAdsNXdHwu3qnbr7+7rIGhwgANDridtHRbcZvZEbM6r5X+nNjunkuBH\n96qOet8MsxTHIrkMx8z2A+4FfuDu28Kupy3M7BRgg7vPC7uWfVQEHAlMd/fRwHYi9OM5QGwe+FTg\nIGAQ0MPMJoRbVf7psCfguPuJH/W6mZ0NnAKM9eisQVwNDG02HkIEfixsycyKCUK7yt3nhl1POxwP\nfNXMvgx0A3qZ2Wx3j1pgrAZWu3vTTzxziFhwAycCK9y9DsDM5gLHAbNDrap91pvZQHdfZ2YDgQ1h\nF5SuzlpVchJwKfBVd6/vjPfsIK8Ah5rZQWbWheBLmPtDrqlNzMwI5lQXu/u0sOtpD3e/zN2HuHs5\nwZ/BUxEMbdz9HWCVmX08dmgssCjEktqjFhhjZiWxv1tjidgXrM3cD5wd+/3ZwN9CrKVNOuuZkzcB\nXYHHgz9rXnL38zrpvdvN3RvM7ALgUYJvz+9w94Uhl9VWxwNnAm+Y2auxY//t7g+FWFM+uxCoijUC\ny4Fvh1xPm7j7y2Y2B5hPMO25gAjsQDSzu4ATgH5mthq4EvgVcI+ZnUvwD9I3wquwbbRzUkQkYrRz\nUkQkYhTcIiIRo+AWEYkYBbeISMQouEVEIkbBLSISMQpuEZGIUXCLiETM/we527LI3JGbzgAAAABJ\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17e75192320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = ds[['o_rcf_1_e']]\n",
    "y = ds[['o_rcf_1_p']]\n",
    "\n",
    "# Splitting the dataset into the Training set and Test set\n",
    "from sklearn.cross_validation import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 1/2,random_state = 0)\n",
    "\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc_X = StandardScaler()\n",
    "X_train = sc_X.fit_transform(X_train)\n",
    "X_test = sc_X.transform(X_test)\n",
    "sc_y = StandardScaler()\n",
    "y_train = sc_y.fit_transform(y_train)\n",
    "\n",
    "# Fitting Simple Linear Regression to the Training set\n",
    "from sklearn.linear_model import LinearRegression\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)\n",
    "\n",
    "# Predicting the Test set results\n",
    "y_pred = regressor.predict(X_test)\n",
    "\n",
    "# Visualising the Training set results\n",
    "plt.scatter(X_train, y_train, color = 'red')\n",
    "plt.plot(X_train, regressor.predict(X_train), color = 'blue')\n",
    "plt.show()\n",
    "\n",
    "# Visualising the Test set results\n",
    "plt.scatter(X_test, y_pred, color = 'red')\n",
    "plt.plot(X_train, regressor.predict(X_train), color = 'blue')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Cross-validation worked nearly perfect due to High $R^{2}$ value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Implementing multiple regression:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ds_xy = ds[[\"i1_rcs_p\",\"i1_dep_1_p\",\"i1_rcf_1_p\",\"i1_dlv_p\"]]\n",
    "X = ds[[\"i1_rcs_p\",\"i1_dep_1_p\",\"i1_rcf_1_p\"]]\n",
    "y = ds[[\"i1_dlv_p\"]]\n",
    "\n",
    "# Splitting the dataset into the Training set and Test set\n",
    "from sklearn.cross_validation import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.5,random_state = 0)\n",
    "\n",
    "\n",
    "# Fitting Simple Linear Regression to the Training set\n",
    "from sklearn.linear_model import LinearRegression\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)\n",
    "\n",
    "# Predicting the Test set results\n",
    "y_pred = regressor.predict(X_test)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4048.47050358],\n",
       "       [ 2781.58647107],\n",
       "       [ 3898.40687906],\n",
       "       ..., \n",
       "       [ 3897.47780742],\n",
       "       [ 2814.99153044],\n",
       "       [ 3856.30724222]])"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred # multiple variables in x predicted the value of y through multiple regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Implementation of K-Means clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5FCw1Jxw4goeuPoUzpu7Hdx5awAU3P8mS1ZtzXZaZdcKhYKkaXFnKDRceyffP\nP5xXVmzgAz98nP96bqkfXTXrpVINBUlnSlogaZGkr3Sw/jOSGiTNTV6XplmP5YYkPnLkGGZcdTKT\nRw7ki/c8zxV3Pce6ze4/yay3SXM4zmLgRuAsYApwoaQpHWx6T0Qckbx+mlY9lntjh1Vy92XH87/P\nmMSDL77NmT98jL8sWpXrsswsS0mKn30ssCgiXgeQdDdwDvByivu0Xq64SHz+/Qdy8kEjuPruuXzi\nlqc56cARHDZmMIeMGswhowYxblglknJdqlmflGYojAaWZM0vBd7XwXYfk3QK8CrwxYhY0n4DSZcB\nlwGMGzcuhVJtXztszBAeuPIkfjhzIY8uaODHj75Oc/J00sCKEqaMHNQWElNHD2ZiVX9Kin0LzCxt\nSuuGn6TzgDMi4tJk/pPAsRFxRdY2w4GNEdEo6XLg4xExvbPPnTZtWtTX16dSs+XO1m0tLHxnIy8u\nX8dLy9fx0vL1zF+xnq3bMj2wlpcUMXm/gRwyOhMUh4wazOT9BlJRWpzjys3yg6TZETGtq+3SPFNY\nCozNmh8D7DBsV0S8mzX7E+C6FOuxXqyitJhDxwzm0DGD25a1tAavN2zkpeXreWn5Ol5ctp4Hnl/O\nL55+C8hcijqwagCHjBrElOSMYsqoQQyqKM3VYZjlvTRD4VngIEkHAMuAC4CLsjeQNDIiViSzZwPz\nU6zH8kxxkTioZiAH1Qzkw0eOBiAiWLpmS9vZxEvL1/PEa6v49XPL2t43blhl22WnKaMGccioQVQP\nrMjVYZjlldRCISKaJX0BeAgoBn4WES9Juhaoj4j7gSslnQ00A6uBz6RVjxUGSYwdVsnYYZWcOXVk\n2/KGDY1ZQZH5+fsX325bXz2wvO2y0/bAGDO0n29om7WT2j2FtPiegnXX+q3beDk5m3hp+TpeXr6e\nhSs3tnW3MaiiJDmTyATFxKoBjBtWyZDKUoeFFZzecE/BLKcGVZRy3IThHDdheNuyrdtaWPD2Bl5a\nvj65qb2eO556k8asIUUHlpcwdlgl44ZVMm54JWOH9mubHz20H+UlvrlthcuhYH1KRWkxh48dwuFj\nh7Qta25pZfGqTbzx7mbeWr2ZJaszPxc1bGTWgpU7BIYEIwdVvBca24MjmR7ev8xnGZbXHArW55UU\nF7Xd0G6vtTVo2NjIW6s381a70Hj01QZWbthxlLnKsmLGDavcITTGDuvHuGGVjBla6UdorddzKJh1\noqhI1AyqoGZQBceMH7bT+i1NLSxdkx0WW9oC5M8LV7Fl246DDNUMKt8pNLa/qgaW+yzDcs6hYLYX\n+pUV7/IsIyJYtbFph7OL7a85S7IlAAAJ1klEQVQnX3uX/3puGdnPeVSUFjF6SD+G9S9jcL9SBvUr\nZXAXr0H9Sn32YT3KoWCWEklUDSynamA5R+8/dKf1W7e1sGztlvdC493NLF2zhbVbmli2divzV2xg\n3ZZtbGxs7nQ/FaVFHYZFRyEypHLH9b5pbu05FMxypKK0mIlVA5hYNaDT7ba1tLJ+yzbWtXt1tGzd\nlm1tgbJ2cxObuhgju7NAGVBeQmVZCQPKi+lfXpJ5lZXQv7yYAe3m3S9V4XAomPVypcVFDB9QzvAB\n5bv93j0JlJeXr2f91mY2NTXT3WZM5SVFmRApL6Z/Wcl7oZHM9y8vaVs/oC1MkvXlWduXZeZLHTI5\n41AwK2B7EygRwZZtLWxsbGZTYwubGpszr6ZmNja2sLmx+b11Tc1t6zc2trC5qZm1m5tYumbH9d0d\nprssCZl+pcVUlBZRXtLFz9JiKkoyP8tLdpzf6ecuPqe4yDf5waFgZrsgicqyzCUkdr6Pvtsigq3b\nWtnY2MzmpuYdw6bpvUDJnt/c1ELjtlYam1vYmvxctbF5h/nsn3ujtFhUlBRTnoREeWlR1nwRZSXF\nlBVvny6irDj52W6+w/VZ0+UlRZQVF+/6vcVFFOUwoBwKZrZPSKJfWTH9yoqB3T9z6UpE0NTS2hYS\n7cOkw5/bWtja3Erjtla2Ju9p/3P7Z63bso2m5laamltoamlNppNXSyvbWnquy6CSInUYKBcdO45L\nT57QY/vpcN+pfrqZ2T4iKfMNv6QY2Pfdp7e2ZkKpo8Boam6lsd18Zrqlbbqx/bp2840trYzYg8uA\nu8uhYGbWA4qKREVRcd63G/EtfjMza+NQMDOzNg4FMzNrk2ooSDpT0gJJiyR9pYP15ZLuSdY/LWl8\nmvWYmVnnUgsFScXAjcBZwBTgQklT2m12CbAmIg4Evg9cl1Y9ZmbWtTTPFI4FFkXE6xHRBNwNnNNu\nm3OA25Lpe4E6ue9gM7OcSTMURgNLsuaXJss63CYimoF1wPB22yDpMkn1kuobGhpSKtfMzNIMhY6+\n8bdv8tedbYiImyNiWkRMq6qq6pHizMxsZ2k2XlsKjM2aHwMs38U2SyWVAIOB1Z196OzZs1dJerMn\nC82BEcCqXBfRi/j3sSP/Pt7j38WO9ub3sX93NkozFJ4FDpJ0ALAMuAC4qN029wOfBp4EzgUejui8\ns96IyPtTBUn1ETEt13X0Fv597Mi/j/f4d7GjffH7SC0UIqJZ0heAh4Bi4GcR8ZKka4H6iLgfuAX4\nT0mLyJwhXJBWPWZm1rVU+z6KiBnAjHbLvp41vRU4L80azMys+9yiOTduznUBvYx/Hzvy7+M9/l3s\nKPXfh7q4hG9mZn2IzxTMzKyNQ8HMzNo4FPYhSWMlzZI0X9JLkq7KdU25JqlY0nOSHsh1LbkmaYik\neyW9kvw/cnyua8olSV9M/p28KOkuSRW5rmlfkvQzSSslvZi1bJikP0pamPwc2tP7dSjsW83AlyKi\nFjgO+HwHnQT2NVcB83NdRC/xQ+DBiJgMHE4f/r1IGg1cCUyLiKlkHmvva4+s3wqc2W7ZV4CZEXEQ\nMDOZ71EOhX0oIlZExJxkegOZf/Tt+4PqMySNAT4I/DTXteSapEHAKWTa7hARTRGxNrdV5VwJ0C/p\n7aCSnXtEKGgR8Rg79/CQ3YnobcCHe3q/DoUcScaOOBJ4OreV5NQPgC8DrbkupBeYADQAP08up/1U\nUv9cF5UrEbEM+C7wFrACWBcRf8htVb1CTUSsgMyXTKC6p3fgUMgBSQOA+4CrI2J9ruvJBUkfAlZG\nxOxc19JLlABHATdFxJHAJlK4NJAvkmvl5wAHAKOA/pIuzm1VfYNDYR+TVEomEO6MiF/nup4cOhE4\nW9IbZMbamC7pjtyWlFNLgaURsf3M8V4yIdFXnQ4sjoiGiNgG/Bo4Icc19QbvSBoJkPxc2dM7cCjs\nQ8kAQrcA8yPie7muJ5ci4qsRMSYixpO5gfhwRPTZb4IR8TawRNKkZFEd8HIOS8q1t4DjJFUm/27q\n6MM33rNs70SU5Odve3oHqfZ9ZDs5Efgk8IKkucmyryV9RJldAdwpqQx4HfhsjuvJmYh4WtK9wBwy\nT+09Rx/r8kLSXcBpwAhJS4F/Ar4F/FLSJWSCs8f7jnM3F2Zm1saXj8zMrI1DwczM2jgUzMysjUPB\nzMzaOBTMzKyNQ8F6HUkh6d+y5q+R9M899Nm3Sjq3Jz6ri/2cl/R0OivNuiSNl3TR7ldo1jGHgvVG\njcBHJY3IdSHZJBXvxuaXAH8bEe9Pq57EeGC3QmE3j8P6GIeC9UbNZBoqfbH9ivbfqCVtTH6eJulR\nSb+U9Kqkb0n6hKRnJL0gaWLWx5wu6fFkuw8l7y+W9B1Jz0qaJ+l/ZX3uLEm/AF7ooJ4Lk89/UdJ1\nybKvAycB/yHpOx2858vJe56X9K0O1r+xPRAlTZP0SDJ9qqS5yes5SQPJNGY6OVn2xe4eh6T+kv47\nqeFFSed35z+MFT63aLbe6kZgnqRv78Z7DgdqyXQ3/Drw04g4NhnM6Arg6mS78cCpwERglqQDgU+R\n6YnzGEnlwBOStvfKeSwwNSIWZ+9M0ijgOuBoYA3wB0kfjohrJU0HromI+nbvOYtMd8fvi4jNkobt\nxvFdA3w+Ip5IOlXcSqbTvGsiYnu4Xdad45D0MWB5RHwwed/g3ajDCpjPFKxXSnqPvZ3MQCvd9Wwy\nZkUj8Bqw/Y/hC2SCYLtfRkRrRCwkEx6Tgb8CPpV0P/I0MBw4KNn+mfaBkDgGeCTptK0ZuJPMmAid\nOR34eURsTo6zfX/5nXkC+J6kK4EhyT7b6+5xvEDmjOk6SSdHxLrdqMMKmEPBerMfkLk2nz2uQDPJ\n/7dJR2llWesas6Zbs+Zb2fGsuH3fLgEIuCIijkheB2T1379pF/WpuwfS7j1d9S3TdoxA2xCUEfEt\n4FKgH/CUpMm7+PwujyMiXiVzhvMC8K/JJS8zh4L1Xsm36F+SCYbt3iDzxwwy/e2X7sFHnyepKLnP\nMAFYADwEfC7p2hxJB3djkJungVMljUhu3l4IPNrFe/4A/I2kymQ/HV0+eoP3jvFj2xdKmhgRL0TE\ndUA9mTOcDcDArPd26ziSS1+bI+IOMoPZ9OVuui2L7ylYb/dvwBey5n8C/FbSM2TGqN3Vt/jOLCDz\nx7sGuDwitkr6KZlLTHOSM5AGuhjqMCJWSPoqMIvMN/QZEdFpV8YR8aCkI4B6SU3ADOBr7Tb7BnCL\npK+x48h8V0t6P9BCplvt35M5C2qW9DyZMX1/2M3jOBT4jqRWYBvwuc7qtr7DvaSamVkbXz4yM7M2\nDgUzM2vjUDAzszYOBTMza+NQMDOzNg4FMzNr41AwM7M2/x+XUdaJWjztgQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17e751a7710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Importing the dataset\n",
    "# as_matrix is used to convert from pandas ds to np.array\n",
    "X1 = ds.as_matrix(columns =[\"o_dep_2_e\",\"o_dep_2_p\"])\n",
    "# y = dataset.iloc[:, 3].values\n",
    "\n",
    "# Splitting the dataset into the Training set and Test set\n",
    "\"\"\"from sklearn.cross_validation import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)\"\"\"\n",
    "\n",
    "# Feature Scaling\n",
    "\"\"\"from sklearn.preprocessing import StandardScaler\n",
    "sc_X = StandardScaler()\n",
    "X_train = sc_X.fit_transform(X_train)\n",
    "X_test = sc_X.transform(X_test)\n",
    "sc_y = StandardScaler()\n",
    "y_train = sc_y.fit_transform(y_train)\"\"\"\n",
    "\n",
    "# Using the elbow method to find the optimal number of clusters\n",
    "from sklearn.cluster import KMeans\n",
    "wcss = []\n",
    "for i in range(1, 11):\n",
    "    kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 42)\n",
    "    kmeans.fit(X1)\n",
    "    wcss.append(kmeans.inertia_)\n",
    "plt.plot(range(1, 11), wcss)\n",
    "plt.title('The Elbow Method')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('WCSS')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, 4 clusters would be appropriate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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M9oT8Uo/N2s8UWQnXLyKzgauBQ4GD/JLSRQqbcbiwMKlQinOsNAwjNTrG+EsW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\nbl0e+8ljlIXKCBWlNnE0VBTi7JFnU9n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c0RbbTV4CXml8SJI8O5JmEm7ICyWZeCu2pM8J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\nGWCSJEmStvAENoo+uWF+EKUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17e74e87208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Fitting K-Means to the dataset\n",
    "kmeans = KMeans(n_clusters = 4, init = 'k-means++', random_state = 42)\n",
    "y_kmeans = kmeans.fit_predict(X1)\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc_X = StandardScaler()\n",
    "X = sc_X.fit_transform(X1)\n",
    "\n",
    "\n",
    "# Visualising the K-Means clusters\n",
    "plt.scatter(X1[y_kmeans == 0, 0], X1[y_kmeans == 0, 1], s = 100, c = 'red', label = 'Cluster 1')\n",
    "plt.scatter(X1[y_kmeans == 1, 0], X1[y_kmeans == 1, 1], s = 100, c = 'blue', label = 'Cluster 2')\n",
    "plt.scatter(X1[y_kmeans == 2, 0], X1[y_kmeans == 2, 1], s = 100, c = 'green', label = 'Cluster 3')\n",
    "plt.scatter(X1[y_kmeans == 3, 0], X1[y_kmeans == 3, 1], s = 100, c = 'cyan', label = 'Cluster 4')\n",
    "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s = 200, c = 'yellow', label = 'Centroids')\n",
    "plt.legend()\n",
    "plt.title('Clusters of time taken')\n",
    "plt.xlabel('Actual outgoing transport leg')\n",
    "plt.ylabel('Planned outgoing transport leg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Implementation of Hierarchical Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Fitting Hierarchical Clustering to the dataset\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "hc = AgglomerativeClustering(n_clusters = 4, affinity = 'euclidean', linkage = 'ward')\n",
    "y_hc = hc.fit_predict(X1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/ftCv47c/5TscPAic7XuZDQGGAi92I1/GmDTERjruqnCJ1Z02kiADLm30fv4I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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17e7359dba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualising the HC clusters\n",
    "plt.scatter(X1[y_hc == 0, 0], X1[y_hc == 0, 1], s = 100, c = 'red', label = 'Cluster 1')\n",
    "plt.scatter(X1[y_hc == 1, 0], X1[y_hc == 1, 1], s = 100, c = 'blue', label = 'Cluster 2')\n",
    "plt.scatter(X1[y_hc == 2, 0], X1[y_hc == 2, 1], s = 100, c = 'green', label = 'Cluster 3')\n",
    "plt.scatter(X1[y_hc == 3, 0], X1[y_hc == 3, 1], s = 100, c = 'cyan', label = 'Cluster 4')\n",
    "plt.title('Clusters of time taken')\n",
    "plt.xlabel('Actual outgoing transport leg')\n",
    "plt.ylabel('Planned outgoing transport leg')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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M9oT8Uo/N2s8UWQnXLyKzgauBQ4GD/JLSRQqbcbiwMKlQinOsNAwjNTrG+EsW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\nbl0e+8ljlIXKCBWlNnE0VBTi7JFnU9n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      "text/plain": [
       "<matplotlib.figure.Figure at 0x17e74e070b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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/ftCv47c/5TscPAic7XuZDQGGAi92I1/GmDTERjruqnCJ1Z02kiADLm30fv4I\n0gYzCzgHWAP0By6kG1PSq+oLuMb65cBKn4e5wKXAT0SkAdfGcrM/5GZgb5/+E+Ayf55XcHPUvAo8\nBkyxHmTGZF+qkY6700YSZMCljd7PH0FmtFyqqqNFpF5VK33a31T1q1nJYYbZjJbGZFb51eWBHlvF\nKpIi2n+V3u/BeBOPgc1iGbZ0ZrQMUoPZ7Ac9rvCh+6cBu6WVQ2NMj5NOg3p32kiiAy6rR1VT3rec\nIimy0ft5Kkgvsom4guhiYBquYf3MMDNljCkcQRveozLRRlIxoIJZp8yyicTyXMIajIgUA/+uqp+q\n6kZVvVJVf6KqDVnKnzEmz6Ua6djaSHqPhAWMbzQfGI0LZowxHQWNdFwiJRbhuJcJ8ohsLfBXP3r/\nk2iiql4XVqaMMYUjGuk4XsN7VP+S/pz/5fOZNmaaFS69SJAC5m2/FAG7+zQbrGiM2SHa8D5zyUwW\n1C9gU+smyvqUMbFyohUqvViQbspnqeqiZGmFwropG2NM6sLqpnx5wDRjjDFmhy4fkYnIeNwcLfuL\nyI0xm8qB5PEgjDHG9GqJ2mDeBpYC38CF049qwY2HMcYYY7rUZQGjqi8DL4vIf6tq8EBDxhhjDAHa\nYKxwMcYYk44gjfzGGGNMyqyAMcYYE4qkAy1F5CE6D6zcgOsA8CdV/TSMjBljjClsQWowbwCbgJv8\nshF4Fxjm140xxphOgoSK+bKqHhuz/pCIPKeqx4rIK2FlzBhjTGELUoMZKCIHRlf86338amsouTLG\nGFPwgtRgpgPPi0gjIMAQoEZEdgPmh5k5Y4wxhStpAaOqj4rIUOALuALm9ZiG/evDzJwxpudpbG5k\nxuIZLKxfuCPq8oTKCUw/crpFXe5hkkZTBhCRrwKDiSmQVPX28LIVHoumbEzu1K2piztvTKQoQqQ4\nQu1ZtYwfOj6HOTRdCSWasogsAK4FjgYO90tKFzHGpK6xuZGaR2oov7qcoiuLKL+6nJpHamhsbsx1\n1tLS2NxI1aIqNrdt7jQpWdv2Nja3baZqUVXB3p/pLEgj/2jgKFWtUdVL/PKj7lxURPYUkVoReV1E\nXhORI0VkgIg8ISJr/N+9/L4iIjeKSIOI1IvIYTHnmeT3XyMik7qTJ2PySd2aOirnVDJv+TxaWltQ\nlJbWFuYtn0flnErq1tTlOospm7F4Bm3tiSNPtbW3MXPJzCzlyIQtSAGzCviXDF/3BuAxVf0C8CXg\nNeAy4ElVHQo86dcBxgND/VIN/BFARAYAvwa+AhwB/DpaKBlTyHrqL/2F9QvjTqccq217GwvqF2Qp\nRyZsQQqYfYBXReRxEXkwuqQuywlTAAAgAElEQVR7QREpB44FbgZQ1VZV/Rg4nZ290uYDZ/jXpwO3\nq7ME2FNE9gVOAp5Q1WZV/Qh4Ajg53XwZky966i/9Ta2bMrqfyX9BuilfkeFrHgy8D9wqIl/CzTXz\nY+CzqroeQFXXi8hn/P77A+tijm/yaV2ldyIi1bjaDwceeGC8XYzJG6n80p91yqws5ar7yvqU0dLa\nEmg/0zMECdf/bLylG9csAQ4D/qiqXwY+YefjsHgkXrYSpHdOVJ2rqqNVdfTAgQNTza8xWdVTf+lP\nqJxApCiScJ9IUYSJlROzlCMTti4LGBF53v9tEZGNMUuLiGzsxjWbgCZVfcGv1+IKnHf9oy/83/di\n9j8g5vhBuNk2u0o3pmDVralD4/9O6qTQfulPP3I6keIkBUxxhGljbMLcnqLLAkZVj/Z/d1fV8phl\nd1UtT/eCqvoOsE5EPu+TTgBeBR4Eoj3BJgEP+NcPAt/zvcnGABv8o7THgXEispdv3B/n04wpSNHG\n/SAK8Zd+xYAKas+qpTRS2qkmEymKUBoppfasWhts2YMEaYPBt5Uc41efU9X6bl73EuAOEemDi9b8\nfVxhd4+IXAD8EzjL7/socArQAGz2+6KqzSLyG+Alv99VqtrczXwZkzNBGvejCvWX/vih46mfXM/M\nJTNZUL9gx0j+iZUTmTZmmhUuPUzSkfwi8mPgIuA+n/RNYK6q/lfIeQuFjeQ3+ar86vJAjeAAj373\nURvxbrIqnZH8QWowFwBfUdVP/EWuARYDBVnAGJOvgjbaC2KFiykIQcbBCNAes95O/B5cxphuCNpo\nv3vf3UPOiTGZEaSAuRV4QUSuEJErgCX4QZLGmMyxbrympwkyDuY6XMN6M/AR8H1VtTD9ptcKKwil\ndeM1PU2QRv4BcZJbVDVYd5c8Y438pjuChJsftvewtOc7sXD2Jl+l08gfpIBZixvQ+BGu7WVPYD1u\nIORFqrosrdzmiBUwJl2NzY1Uzqlkc9vmLvfpW9wXEaF9e3vaBURjc6N14zV5J6wCZg5wv6o+7tfH\n4YJK3gPcoKpfSTO/OWEFjElXzSM1zFs+L2mcsERKI6XUT663gsIUnFAmHANGRwsXAFX9C3Csj2zc\nN8U8mkxrbISaGigvh6Ii97emxqWbjAoShDKZQoyCbEy6ghQwzSJyqYgc5JefAR+LSDGwPeT8mUTq\n6qCyEubNg5YWUHV/581z6XWFNylVPstEcEmb78T0JkEKmO/iAkn+2S8HAGcDxcC3w8uaSaixEaqq\nYPNmaOvwq7qtzaVXVVlNJoMyFVyy0KIgG5OuIAXM1/w0yV/2yyU+rVVVG8LOoOnCjBmdC5aO2tpg\npj2OyZQg41SCKLQoyMakK0gBc3nANJNNCxcGK2AW2OOYTAkyTiUZGyhpepMuY5GJyHhcFOP9ReTG\nmE3lwLawM2aS2BTwMUvQ/UxS0XDzXY1TKSkqYbtuZ2v71i7PYQMlTW+SqAbzNrAU+BQ3rXF0eRA4\nKfysmYTKAj5mCbpfTxakp13A3njRcPPVo6op71tOkRRR3rec6lHVrPzhSu7/zv0234kxXpBxMCWq\n2mNqLD1mHExNjestlugxWSQC1dUwq3Dmbc+4ujrX2aGtbdf3KhJxS22tW0+2z/jgo+dtoKTpicIa\naPkmcea6V9WDU8tefugxBUxjo+uKvLnrUeWUlkJ9PVT00i+1IO9Rv34gAlu2dL1Pb38fjSHEgZbA\n4X45BrgRWJh69kxGVVS4X9alpe5XdqxIxKXX1vbeL8XGRjj99MSFC8DWrfDpp4n3sd54xqQlSDTl\nD2OWt3wk5eOzkDeTzPjx7pd1dfWubQfV1S49hcc6PUpdHYwYAa+8knxfVbckYr3xjElLkEdkh8Ws\nFuFqND9U1S+FmbGw9JhHZCa+xkYYPtzVTDKpqAja25PvZ0wPFdaUyTNiXm8D1mIj+E2++tWvMl+4\ngPXGMyYNQR6RfS1mOVFVL1LV1dnInOmhwgzQuWhR6sdIkhnAIxGYaIMjjUlV0gJGRPYQketEZKlf\nZojIHt29sIgUi8j/icjDfn2IiLwgImtE5G4R6ePT+/r1Br99cMw5Lvfpq0XExuYUgmQBOm+5pXuF\nT7LoBvEka4OJRGCaDY40JlVBepHdArTgHot9G9gI3JqBa/8YeC1m/RpgpqoOxU1udoFPvwD4SFU/\nB8z0+yEih+CCbg7HzU/zBx/h2eSrIAE6L7gAbropu9Ghi4uhpMR64xmTYUEKmApV/bWqvuGXK4Fu\njYERkUHAqcA8vy64nml+1BvzgTP869P9On77CX7/04G7VHWrqr4JNABHdCdfphuCPPYKEqATYFuH\ncb2x0aGfemrX65SVuR5jZWVuPR3t7W48jPXGMyajgjTybxGRo1X1eQAROQpIMCotkOuBnwG7+/W9\ngY9jIgY0Afv71/sD6wBUdZuIbPD77w8siTln7DG7EJFqoBrgwAMP7GbWTSfxRstHax7z5+8cCR8k\nQGcimzfDCSfsmvbJJ8G6Iwc596xZvTvqgTEZFuQn32RgtoisFZG1wCzgB+leUEROA95T1WWxyXF2\n1STbEh2za6LqXFUdraqjBw4cmFJ+TRKpzEuTz4E3rZdY6BqbG6l5pIbyq8spurKI8qvLqXmkhsZm\nm7Oop0pag1HVl4EviUi5X9/YzWseBXxDRE4B+uGiM18P7BkT92wQLtgmuJrJAUCTiJQAewDNMelR\nsceYbEllXpqyMlezyTfWSyx0dWvqOkWhbmltYd7yecx/eT61Z9Uyfqg9iuxpAj+0VtWNGShcUNXL\nVXWQqg7GNdI/parnAk8DVX63ScAD/vWDfh2//Sl1o0MfBM72vcyGAEOBF7ubP5OiVOalmTChc0N6\nPrBeYqFqbG6kalEVm9s27zLFAbgppDe3baZqUZXVZHqgNFtFQ3Ep8BMRacC1sdzs028G9vbpPwEu\nA1DVV4B7gFeBx4ApqmpDrbMtlXlppk/PvwKmXz/rJRayGYtn0Nae+EdIW3sbM5dYvLeeJmmomJ7G\nQsVkWHl5sMde5eWwYYPrEPCtbyUPMJkNp50G119vhUvIyq8up6U1+b+R8r7lbLhsQxZyZNKR0WjK\nIvKtREv3s2t6hKCPvbZscd2Lhw2DM89MPno+LOXlMGUKNDTAQw/FL1zCjDTQC21qDVbLDbqfKRxd\n1mBEJDqY8jPAV4Gn/PrXgGdUtSALGavBZFiQOVfyRbQWlUiQCcpsXExKrAbTM2S0BqOq31fV7+O6\n/h6iqmeq6pm4kfOmt4v+yv/yl3cWLrmqlQQRpKdYKl2uTWATKid0mkK6o0hRhImV1pOvpwnSyD9Y\nVdfHrL8LDAspP6YQdIwnFpXP7XlBeoql0uXaBDb9yOlEipMUMMURpo2xnnw9TZAC5hkReVxEzhOR\nScAjuC7FpjdK9Cs/H6USTyyVLtcmsIoBFdSeVUtppLRTTSZSFKE0UkrtWbVUDLDOFj1NkHD9FwNz\ngC8BI4G5qnpJ2Bkz5Gdjc9B4Yrkmkno8sVS6XJuUjB86nvrJ9VSPqqa8bzlFUkR533KqR1VTP7ne\nBln2UIG6KYvIQcBQVf0fESkFilU1D4dkJ1cwjfz52tgctFtyLj36aHrvTapdro3pRTLayB9z0otw\nUYz/5JP2B/6cevZMYPnc2FwIv97TLXiDdLm2sDLGBBakDWYKLn7YRgBVXYPrumzCks+NzT05KGSQ\nSAMWVsaYwIIUMFtVtTW64gNO5nF3oR4g243NqbT1TJiQmWuGZcSI9I+tqHCPHktLbfIxYzIgSAHz\nrIj8HOgvIicCi4CHws1WL5fNxuZkUxh3nEVy+vTuXzNMN9zQvePHj3edAmInHysrg89/3nUcOPXU\n/OhsYUwBCFLAXAa8D6zEzQPzKPD/wsxUrxf0MVR3H1el2tbz1FNw+undu2a6IhE3rXEiV14Jxx/f\n/WtVVLiJxzZsgIcfhu3bYfVqN7lZtqZxNqYHCNJNebuq3qSqZ6lqlX9tj8jClK3G5iBtPa2trq3n\nqqvcbJKZmD0yFbHdjV9/HZ58svNjsBEjXPqvfpXZa+dzZwtjCkDSbsp+iuQrgINwE5QJoKp6cOi5\nC0FBdFMOEt+rtNQ9yulOe0C+djkuKYE+fXIf96umxtVUEhXCkYgr/GyqZdPDhdJNGTcfy3XA0cDh\nwGj/14QlaGMzdG8gZr51OY7WVn7wg+CDI8NkI/uN6ZYgBcwGVa1T1fdU9cPoEnrOert4jc2xI9Mh\neON8V73E+vTJzb3FU1bm2jo2bHC1gXzoqWUj+43pliCPyH4PFAP3AVuj6aq6PNyshaMgHpElk8oj\ntGefdYVSe4fJPouLO6flSnExTJ6cf4+ZbGS/MTuE9YjsK7jHYr8DZvjl2tSz1ztkJXxY0IGYF14I\nF1wQvyDJl8IFoG/f/By8aCP7jekWmzI5g7IWPixfG+dTJQL9++e+Mb8r2epsYUwBSKcGk2RgAYhI\nX+BMYHDs/qp6VaoZ7Mlie7R2FC1wqqoy9F3UU575n3suXHFF/n45RztbJPvVkK/5NybHgjwiewA4\nHdgGfBKzmBhZDR9WyPHAiorcr/5HH3W9r/L9yzlZZ4t8rHkZkyeCNPKvUtVuBHjqdL4DgNuBfwG2\n4+aXuUFEBgB342pKa4Fvq+pHIiLADcApwGbgvGgHAz8BWjSqwG9VdX6y64f1iCyr7cE1NTBnTn7P\nIBlP//5w/vmuvSXfCxZjzC7CauT/m4gcmmae4tkGTFfVLwJjgCkicgguJM2TqjoUeNKvA4wHhvql\nGvgjgC+Qfo3rhHAE8GsR2SuD+UxJ0KdWGzd2o9E/2oPg9tsLq3A56CBoaHDPD/OlC7IxJnRBCpij\ngWUislpE6kVkpYjUp3tBVV0frYH4Sctew80xczoQrYHMB87wr08HbldnCbCniOwLnAQ8oarNqvoR\n8ARwcrr56q7S0uD7zp2bYhirxkY47TT43Ofgj390MbEKyZNPWqFiTC+UtJEfV4MIhYgMBr4MvAB8\nVlXXgyuERCQ658z+wLqYw5p8Wlfp8a5Tjav9cOCBB2buBry6Oti6Nfl+Ue3tO8NYJW30r6uDM8+E\nLVu6nc+cOO00K1yM6aWCBLv8h6r+A9iCmwcmunSLiJQB9wJTVXVjol3jZStBeudE1bmqOlpVRw8c\nODD1zCYQ7T22bVvqxyZt9I+evFALl3794Prrc50LY0yOBJky+RsisgZ4E3gW1wDfrRjlIhLBFS53\nqOp9Pvld/+gL//c9n94EHBBz+CDg7QTpWRWk91hXkoaxmjGDxtYDqGEW5XxMEe2U8zE1zKKRAog1\nKuLeIIs2bEyvFKQN5je4xvi/q+oQ4ATgr+le0PcKuxl4TVWvi9n0IDDJv56E6x4dTf+eOGNwsdHW\nA48D40RkL9+4P86nZVWQeIiJJOocUHfbu1RuW8Y8LqKFPVCKaGEP5nERldRTl7smp51KStyI9/79\nO2/bssXmTTGmFwtSwLT54JZFIlKkqk8DI7txzaOAicDxIrLCL6cAvwdO9LWlE/06uAnO3gAagJuA\nGgBVbcYVfi/55SqfllXdHfMYHdLSMcRMWRl8Y8tdbGY32tg1KGUbfdjMbpzCowjbOy1f5X+zU8Mp\nKXGRj6+4wtVW4rF5U4zptYKMg/kfXI+uq4F9cI+uDlfVr4afvczL9DiY7kRtiU4lcuqp8QeLd93U\nlIz7TH/Kf/KfXJpe5lIRibhGqET/lqKFUb4FtDTGBBLWOJjTcQ3804DHgEbgX1PPXs/0jW90/eM9\nmZISWLcOTjkl/qSJ6RUu0eOEa/m3XWo2e9DMQr6b5jkTaGtLPi5n2za4+ebMX9sYk7eC9CL7RFXb\nVXWbqs5X1RttPhinrg7uvTf9MY9tbfDQQ5nN065kl2UjezKRhUwkacCDcHz6KdxyS3avmZXw1saY\neLp8RCYiLez6jCa6Y3TK5PLws5d5mXpEFiTQbv5SFjCBCfx39i9dXAyrV2dnbEzWwlsb0/Nl9BGZ\nqu6uquX+b/R1dL0gC5dM6k735HzwY27IzYXb2zMU8TOJ2PDWHT8o63hgTFZ0WcCISD8RmSois0Sk\nWkSCjPrvNbrbPTm3hGb2zt3lszGHfVbDWxtj4knUBjMfN5PlSlwk4xlZyVGB6ClTsuRENt68IL8A\nko50NcZ0R6JaySGqeiiAiNwMvJidLBWGsrKeMalkTmRjPpughZj9UjAmNIlqMDt+/qlqGpG2erYJ\nE3Kdg+5Q+vJpbi5dUpKdOeyDFmKFPHmbMXkuUQHzJRHZ6JcWoDL6WkQSBafsFaqqcp2D7hCE7Zk/\nbZ8+wfaZNi3z1+5owgTXUyyRSCQ7hZ0xvVSiXmTFHXqOlVgvMqeuDv61wIeabqVfZk/Ypw+8+qob\nTFlc3Hl7SYmbNCdbc9hPnx6sgMlGYWdMLxVkJL+JEdv7NbCpuFgI2zssz0GugiIrRdRNfdyNElWF\nRx91BUDHL+UgYQoiEbjoIldwnH++G+cyZcqugxt/8IPszmFfUeEKs3j3FIlkt7AzprdS1V61jBo1\nSrvjhz9UjUSi38qqHKzKLFU+VqXd/53l08eqslGV7X7pmJntcZZPVPluzPlDXEpLVRsaYm6uoUF1\nyhTV8nLVoiL3d8IE1f79UzxRHol3T1Om5G9+jclTwFJN8fs2abDLnqa7I/l3CW55MnAf0Jdd64Id\n39JUQopFj13AzskLQnTuua5Hb0I2It6YXi+sYJcmxo5erQfjZqzpT+d3UTosqYgeMxE3u03Ij9Du\nuGPnVC1dhu0aNt493qqu3nVjdXV2H3sZYwqK1WBStKMGcx9uEoN0Ax4Hobi2mzNxcaxD0q8fzJ4N\nl1xilRRjTHxWg8mCHb1fv0G4hQv+/KXA/YRak9m61VVGLGyXMSaTrIBJ0fTpUHQq2X3n+gK/Du/0\nqi4GZSIWtssYkyorYFJVASwi/NpLLAG+k8XrxWFhu4wxqbICJkUzgNZcxJUOMEg+bBa2yxiTCitg\nUnQznXsh9xYWtssYkworYFLwFNCaq4uHeOHiYhfJJREL22WMSVXBFzAicrKIrBaRBhG5LMxrTQ7z\n5IkocE84p+7bF+bOTR6n0sJ2GWNSVdAFjIgUA7OB8cAhwDkickhY11sT1omTaQWuSP/wsjI3Yj+2\nEIlEXJfrV15x4cMsbJcxJtMKuoABjgAaVPUNVW0F7gJOz3GeMktxAzrfSO/wSAQmTXLhYLZu3RlA\nrLXV9QqLFhrjbbC+MSbDctEfKpP2B9bFrDcBX+m4k4hUA9UABx54YHZylgmKi0nWjVH8qTzaqqiA\nWbPcYowx3VXoNZh4o1E6dfJS1bmqOlpVRw8cODDti2X9zdoGXJneofZoyxiTa4VewDQBB8SsDwLe\nDutip4Z14ngU+AGBH40ddJBra7FHW8aYfFHoBcxLwFARGSIifYCzgQfDuthMIMkciZmhMOYBKL93\nZ4ExZQo8+WTnebymTIGGBli71gXhbG+HDRvcYy6ruRhjcqmg22BUdZuIXIwLbF8M3KKqr4R1vQpc\nhP5vAZ+GdI1iYK7A+WcAGzpvP/54ayMxxhSGQq/BoKqPquowVa1Q1X8P+3rjgVXAFKA8zXP8AGiI\nOUeR/zsFWA2c3/1sGmNMztl8MMYYY5Ky+WCMMcbkDStgjDHGhMIKGGOMMaGwAsYYY0worIAxxhgT\nCitgjDHGhMIKGGOMMaGwAsYYY0woet1ASxF5H/hHhk63D/BBhs6Va3Yv+cnuJX/1pPsJci8HqWpK\n4eh7XQGTSSKyNNWRrfnK7iU/2b3kr550P2Hdiz0iM8YYEworYIwxxoTCCpjumZvrDGSQ3Ut+snvJ\nXz3pfkK5F2uDMcYYEwqrwRhjjAmFFTDGGGNCYQVMmkTkZBFZLSINInJZrvPTkYgcICJPi8hrIvKK\niPzYpw8QkSdEZI3/u5dPFxG50d9PvYgcFnOuSX7/NSIyKYf3VCwi/yciD/v1ISLygs/X3SLSx6f3\n9esNfvvgmHNc7tNXi8hJubkTEJE9RaRWRF73n9GRhfrZiMg0/29slYjcKSL9CuWzEZFbROQ9EVkV\nk5axz0FERonISn/MjSIiWb6X//T/xupF5H4R2TNmW9z3u6vvtq4+04RU1ZYUF6AYaAQOBvoALwOH\n5DpfHfK4L3CYf7078HfgEOA/gMt8+mXANf71KUAdIMAY4AWfPgB4w//dy7/eK0f39BPgv4GH/fo9\nwNn+9Rzgh/51DTDHvz4buNu/PsR/Vn2BIf4zLM7RvcwHLvSv+wB7FuJnA+wPvAn0j/lMziuUzwY4\nFjgMWBWTlrHPAXgRONIfUweMz/K9jANK/OtrYu4l7vtNgu+2rj7ThHnK5j/GnrL4fzCPx6xfDlye\n63wlyfMDwInAamBfn7YvsNq//hNwTsz+q/32c4A/xaTvsl8W8z8IeBI4HnjY/4f9IOY/z47PBHgc\nONK/LvH7ScfPKXa/LN9LOe5LWTqkF9xngytg1vkv1xL/2ZxUSJ8NMLjDl3JGPge/7fWY9F32y8a9\ndNj2TeAO/zru+00X322J/r8lWuwRWXqi/6mimnxaXvKPIb4MvAB8VlXXA/i/n/G7dXVP+XKv1wM/\nA7b79b2Bj1V1W5x87ciz377B758v93Iw8D5wq3/kN09EdqMAPxtVfQu4FvgnsB73Xi+jcD8byNzn\nsL9/3TE9V87H1aIg9XtJ9P+tS1bApCfec9S87O8tImXAvcBUVd2YaNc4aZogPWtE5DTgPVVdFpsc\nZ1dNsi3n9+KV4B5l/FFVvwx8gnsU05W8vR/fPnE67jHLfsBuwPgE+crbewkg1bznzT2JyC+AbcAd\n0aQ4u2X8XqyASU8TcEDM+iDg7RzlpUsiEsEVLneo6n0++V0R2ddv3xd4z6d3dU/5cK9HAd8QkbXA\nXbjHZNcDe4pISZx87ciz374H0Ex+3As+H02q+oJfr8UVOIX42XwdeFNV31fVNuA+4KsU7mcDmfsc\nmvzrjulZ5TsdnAacq/75Fqnfywd0/Zl2yQqY9LwEDPW9KvrgGisfzHGeduF7q9wMvKaq18VsehCI\n9nKZhGubiaZ/z/eUGQNs8I8HHgfGiche/tfqOJ+WNap6uaoOUtXBuPf6KVU9F3gaqOriXqL3WOX3\nV59+tu/JNAQYimuEzSpVfQdYJyKf90knAK9SgJ8N7tHYGBEp9f/movdSkJ9NnDym/Tn4bS0iMsa/\nN9+LOVdWiMjJwKXAN1R1c8ymrt7vuN9t/jPq6jPtWjYa0XrigutR8ndcj4tf5Do/cfJ3NK4KWw+s\n8MspuGepTwJr/N8Bfn8BZvv7WQmMjjnX+UCDX76f4/say85eZAf7/xQNwCKgr0/v59cb/PaDY47/\nhb/H1YTYoyfAfYwElvrP58+43kcF+dkAVwKvA6uABbieSQXx2QB34tqO2nC/3i/I5OcAjPbvSyMw\niw4dO7JwLw24NpXod8CcZO83XXy3dfWZJlosVIwxxphQ2CMyY4wxobACxhhjTCisgDHGGBMKK2CM\nMcaEwgoYY4wxobACxmSViHxTRFREvhBg3/NEZL9uXGus+MjL3SUiP+/m8VeJyNfzIS/ZIiJTRaS0\ni23PiMjobOfJZJcVMCbbzgGexw3gSuY8XPiRfNCtL3VV/ZWq/k+YefEDAPPi/7SIFANTgbgFjOkd\n8uIfo+kdfFy0o3ADwM7usO1nft6Ml0Xk9yJShRukdoeIrBCR/iKyVkT28fuPFpFn/OsjRORvPnDk\n32JGyHeVj34icqu/3v+JyNd8+nkiMitmv4d9Lej3QH+fjzv8tl+Km2fjCXFzoPzUp48UkSWyc/6N\n6Fwit/l7wt/HlSKy3OfhCz59oD/fchH5k4j8I3q/MXnaJS8iMljcfDJ/AJYDB4jIH0Vkqbg5Wq6M\nObar6x7nz7fCvx+7+/t+zt/DqyIyJ1p4icg5/vhVInJNzPk3+ZraC7hBfPsBT4vI00k+j3Eistjn\na5H/d4KInOLf4+fFzaWSkdqoyaJcjPy1pXcuwATgZv/6b+ycr2a8Xy/169GR08+w62jptcA+/vVo\n4Bn/upydYcS/DtzrX4/Fj/rvkI/pwK3+9Rdw4U764WpMs2L2exgY619vikkfjRsV3R83184a4Kd+\nWz1wnH99FXC9f30bUBVzH5f41zXAPP96Fj6EOnAyLhLDPnHyH5uXwbgI02Ni0qLvX7F/DyuTXPch\n4Cj/ugwXjHMs8Clu9HYx8AQuTMh+/v0a6Pd7CjjDH6vAt+N9XnHu4Rn/Pu4DPAfs5tMvBX7lP491\nwBCffme8z9KW/F6sBmOy6RxcsEr833P866/jvvA3A6hqc4rn3QNYJG4mv5nA8CT7H40LaYKqvg78\nAxiWwvWOBh5Q1S2q2oL7gkZE9gD2VNVn/X7zcZNAxRMNProMV0hEz3uXz9djwEcB8/MPVV0Ss/5t\nEVkO/B/uvTgkyXX/ClwnIj/y+Y+GZH9RVd9Q1XbcF/zRwOG4gv19v98dMffYjguumooxPn9/FZEV\nuBhXB+EK/jdU9U2/350pntfkgZLkuxjTfSKyNy4K8ggRUdyvYhWRn+FiPAWJWbSNnY91+8Wk/wZ4\nWlW/KW7um2eSZSfA+TteI8jxqdjq/7az8/9huuf9JPpCXODCnwKHq+pHInIbu95Hp+uq6u9F5BFc\nDKolMZ0ROn4mXYVtj/rUF0apEOAJVT1nl0SRL6d4HpOHrAZjsqUKuF1VD1LVwap6AG5Wx6OBvwDn\nR3scicgAf0wL7hFU1FpglH99Zkz6HsBb/vV5AfLyHHCuv9Yw4EBcwL+1wEgRKRKRA4AjYo5pEzf9\nAbhOCv/q23LKgFMBVHUD8JGIHOP3mwg8S3DPA9/2+RqHC4AZT2xeOirHFTgbROSzxJ+bZRciUqGq\nK1X1GlwAzmgPvyPERdUtAr7j8/cCcJyI7COuIf8cur7Hjp9fPEuAo0Tkcz4vpf4zeR042P9gwF/f\nFBgrYEy2nAPc3yHtXqkleyAAAAFoSURBVOC7/nHQg8BS/5jkp377bcAc3/jcHxe19wYR+V/cL/Co\n/wCuFpG/4mpGyfwBKBaRlcDdwHmquhX3qOhNXKTca3GN5lFzgXoRuUNVX/L5fRn3yGkpbmZGcI94\n/lNE6nERk68KkJ+oK3Fh35fjCob1uC/pjnbkpeMGVX0Z92jsFeAWf0/JTPUN9i8DW9g56+Fi4Pe4\naMBvAverC0F/OS50+8vAclXtKmz7XKAuUSO/qr6P+1Fwp3/PlgBfUNUtuHaix0TkeeBddr7HpkBY\nNGVj0iAiZaq6yde6ngOqVXV5suOSnLMv0K6q20TkSNyMlyMzkd808jIW13HhtFxc3+ch+h5Hw+Sv\nUdWZucqPSZ21wRiTnrkicgiufWN+dwsX70DgHv9IqhW4KAPnLGQXiZuNsQ+uVvanHOfHpMhqMMYY\nY0JhbTDGGGNCYQWMMcaYUFgBY4wxJhRWwBhjjAmFFTDGGGNC8f8BHxS1eT7MOwAAAAAASUVORK5C\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17e74fe3588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# To evaluate these two clustering techniques\n",
    "\n",
    "# Visualising the K-Means clusters\n",
    "plt.scatter(X1[y_kmeans == 0, 0], X1[y_kmeans == 0, 1], s = 100, c = 'red', label = 'Cluster 1')\n",
    "plt.scatter(X1[y_kmeans == 1, 0], X1[y_kmeans == 1, 1], s = 100, c = 'blue', label = 'Cluster 2')\n",
    "plt.scatter(X1[y_kmeans == 2, 0], X1[y_kmeans == 2, 1], s = 100, c = 'green', label = 'Cluster 3')\n",
    "plt.scatter(X1[y_kmeans == 3, 0], X1[y_kmeans == 3, 1], s = 100, c = 'cyan', label = 'Cluster 4')\n",
    "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s = 200, c = 'yellow', label = 'Centroids')\n",
    "plt.legend()\n",
    "plt.title('Clusters of time taken')\n",
    "plt.xlabel('Actual outgoing transport leg')\n",
    "plt.ylabel('Planned outgoing transport leg')\n",
    "plt.show()\n",
    "\n",
    "# Visualising the HC clusters\n",
    "plt.scatter(X1[y_hc == 0, 0], X1[y_hc == 0, 1], s = 100, c = 'red', label = 'Cluster 1')\n",
    "plt.scatter(X1[y_hc == 1, 0], X1[y_hc == 1, 1], s = 100, c = 'blue', label = 'Cluster 2')\n",
    "plt.scatter(X1[y_hc == 2, 0], X1[y_hc == 2, 1], s = 100, c = 'green', label = 'Cluster 3')\n",
    "plt.scatter(X1[y_hc == 3, 0], X1[y_hc == 3, 1], s = 100, c = 'cyan', label = 'Cluster 4')\n",
    "plt.title('Clusters of time taken')\n",
    "plt.xlabel('Actual outgoing transport leg')\n",
    "plt.ylabel('Planned outgoing transport leg')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that for the same data points, both the clustering methods provide similar clusters with slight differences due to the properties of the algorithms."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "K-Means uses centroid method while Hierarchical clustering uses Euclidean distance for clustering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}