SyntheticAgentCreation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "exceptional-flour",
   "metadata": {},
   "source": [
    "## Creating Households Before"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 730,
   "id": "least-efficiency",
   "metadata": {},
   "outputs": [],
   "source": [
    "excelBuildingInformation = 'BuildingInSimulationAndStatsApril6.xlsx'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 731,
   "id": "filled-crown",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "micro-interface",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 732,
   "id": "regular-typing",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.max_rows=100\n",
    "pd.options.display.max_columns=100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 733,
   "id": "matched-evening",
   "metadata": {},
   "outputs": [],
   "source": [
    "buildingsDf = pd.read_excel(excelBuildingInformation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 734,
   "id": "arabic-arbor",
   "metadata": {},
   "outputs": [
    {
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       "      <th>max_living_till_2020</th>\n",
       "      <th>avg_living_till_2020</th>\n",
       "      <th>Min_age_2020</th>\n",
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       "      <th>after_app</th>\n",
       "      <th>Above_65</th>\n",
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       "      <th>Renter_sum</th>\n",
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       "      <td>0.050000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.750000</td>\n",
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       "      <td>210_14</td>\n",
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       "      <td>98.0</td>\n",
       "      <td>48.0</td>\n",
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       "      <td>16.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>25.806452</td>\n",
       "      <td>90.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
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       "      <td>16.0</td>\n",
       "      <td>0.750000</td>\n",
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       "      <td>0.250000</td>\n",
       "      <td>0.625000</td>\n",
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       "      <td>34.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>31.428571</td>\n",
       "      <td>90.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>7.0</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
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       "      <td>0.235294</td>\n",
       "      <td>0.588235</td>\n",
       "      <td>0.588235</td>\n",
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       "      <td>4</td>\n",
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       "      <td>43.0</td>\n",
       "      <td>14.0</td>\n",
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       "      <td>100.0</td>\n",
       "      <td>66.0</td>\n",
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       "      <td>0.153846</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>0.615385</td>\n",
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      "text/plain": [
       "   Unnamed: 0 BeforeBldgs AfterBldgs  ProjType ProjNumber  OrderA  OrderB  \\\n",
       "0           0      210_19   210_19_a         1   20180988       1       1   \n",
       "1           1      210_14   210_14_a         1   20100133       2       2   \n",
       "2           2      208_32   208_32_a         1   20180812       3       3   \n",
       "3           3       210_3    210_3_a         1   20181092       4       4   \n",
       "4           4      210_35   210_35_r         2   20171974       5      20   \n",
       "\n",
       "   OrderC  plan_num  Row Labels  min_living_till_2020  max_living_till_2020  \\\n",
       "0      17  20180988         1.0                   2.0                  35.0   \n",
       "1      18  20100133         1.0                   2.0                  57.0   \n",
       "2      19  20180812         1.0                   2.0                  33.0   \n",
       "3      20  20181092         1.0                   2.0                  54.0   \n",
       "4      13  20171974         1.0                   2.0                  43.0   \n",
       "\n",
       "   avg_living_till_2020  Min_age_2020  Max_age_2020  Average_age_2020  \\\n",
       "0                   9.0           5.0          82.0              45.0   \n",
       "1                  19.0           2.0          98.0              48.0   \n",
       "2                  10.0           5.0          89.0              46.0   \n",
       "3                  15.0           2.0          99.0              45.0   \n",
       "4                  10.0           3.0          90.0              46.0   \n",
       "\n",
       "   StdDev_age_2020  Before_app  after_app  Above_65  Under_65  \\\n",
       "0             19.0        20.0       41.0       8.0      47.0   \n",
       "1             24.0        16.0       26.0      16.0      46.0   \n",
       "2             23.0        17.0       34.0      11.0      24.0   \n",
       "3             26.0        16.0       30.0      17.0      42.0   \n",
       "4             23.0        13.0       43.0      14.0      39.0   \n",
       "\n",
       "   percent_above_65  Area_round_max  Area_round_mean  Area_round_min  \\\n",
       "0         14.545455            80.0             63.0            30.0   \n",
       "1         25.806452            90.0             77.0            70.0   \n",
       "2         31.428571            90.0             62.0            30.0   \n",
       "3         28.813559            80.0             80.0            80.0   \n",
       "4         26.415094           100.0             66.0            30.0   \n",
       "\n",
       "   Area_round_mode  Area_round_std  Discount_sum  High_discount_sum  \\\n",
       "0             70.0            16.0           5.0                4.0   \n",
       "1             70.0             8.0          12.0                8.0   \n",
       "2             50.0            17.0           7.0                4.0   \n",
       "3             80.0             0.0           7.0                6.0   \n",
       "4             60.0            16.0           5.0                3.0   \n",
       "\n",
       "   Low_Discount_35_sum  No_Discount_sum  Owner_sum  Renter_sum  \\\n",
       "0                  1.0             15.0       12.0         8.0   \n",
       "1                  4.0              4.0       10.0         6.0   \n",
       "2                  3.0             10.0       10.0         7.0   \n",
       "3                  1.0              9.0       10.0         6.0   \n",
       "4                  2.0              8.0        8.0         5.0   \n",
       "\n",
       "   discount_rate_mean  total_appartments  discount_percent  Low_discount  \\\n",
       "0            0.150829               20.0          0.250000      0.050000   \n",
       "1            0.480824               16.0          0.750000      0.250000   \n",
       "2            0.284133               17.0          0.411765      0.176471   \n",
       "3            0.285945               16.0          0.437500      0.062500   \n",
       "4            0.243069               13.0          0.384615      0.153846   \n",
       "\n",
       "   High_discount  No_discount  Owner_Percent  \n",
       "0       0.200000     0.750000       0.600000  \n",
       "1       0.500000     0.250000       0.625000  \n",
       "2       0.235294     0.588235       0.588235  \n",
       "3       0.375000     0.562500       0.625000  \n",
       "4       0.230769     0.615385       0.615385  "
      ]
     },
     "execution_count": 734,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "buildingsDf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 735,
   "id": "painful-roberts",
   "metadata": {},
   "outputs": [],
   "source": [
    "mainColumns = buildingsDf.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 736,
   "id": "frank-ordinary",
   "metadata": {},
   "outputs": [],
   "source": [
    "buildingsDf = buildingsDf.drop(columns=['plan_num'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 737,
   "id": "critical-campus",
   "metadata": {},
   "outputs": [
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       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.150829</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>210_14</td>\n",
       "      <td>210_14_a</td>\n",
       "      <td>1</td>\n",
       "      <td>20100133</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>25.806452</td>\n",
       "      <td>90.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.480824</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>208_32</td>\n",
       "      <td>208_32_a</td>\n",
       "      <td>1</td>\n",
       "      <td>20180812</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>31.428571</td>\n",
       "      <td>90.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.284133</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.411765</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.235294</td>\n",
       "      <td>0.588235</td>\n",
       "      <td>0.588235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>210_3</td>\n",
       "      <td>210_3_a</td>\n",
       "      <td>1</td>\n",
       "      <td>20181092</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>28.813559</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.285945</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.562500</td>\n",
       "      <td>0.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>210_35</td>\n",
       "      <td>210_35_r</td>\n",
       "      <td>2</td>\n",
       "      <td>20171974</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>13</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>26.415094</td>\n",
       "      <td>100.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.243069</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>0.615385</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 BeforeBldgs AfterBldgs  ProjType ProjNumber  OrderA  OrderB  \\\n",
       "0           0      210_19   210_19_a         1   20180988       1       1   \n",
       "1           1      210_14   210_14_a         1   20100133       2       2   \n",
       "2           2      208_32   208_32_a         1   20180812       3       3   \n",
       "3           3       210_3    210_3_a         1   20181092       4       4   \n",
       "4           4      210_35   210_35_r         2   20171974       5      20   \n",
       "\n",
       "   OrderC  Row Labels  min_living_till_2020  max_living_till_2020  \\\n",
       "0      17         1.0                   2.0                  35.0   \n",
       "1      18         1.0                   2.0                  57.0   \n",
       "2      19         1.0                   2.0                  33.0   \n",
       "3      20         1.0                   2.0                  54.0   \n",
       "4      13         1.0                   2.0                  43.0   \n",
       "\n",
       "   avg_living_till_2020  Min_age_2020  Max_age_2020  Average_age_2020  \\\n",
       "0                   9.0           5.0          82.0              45.0   \n",
       "1                  19.0           2.0          98.0              48.0   \n",
       "2                  10.0           5.0          89.0              46.0   \n",
       "3                  15.0           2.0          99.0              45.0   \n",
       "4                  10.0           3.0          90.0              46.0   \n",
       "\n",
       "   StdDev_age_2020  Before_app  after_app  Above_65  Under_65  \\\n",
       "0             19.0        20.0       41.0       8.0      47.0   \n",
       "1             24.0        16.0       26.0      16.0      46.0   \n",
       "2             23.0        17.0       34.0      11.0      24.0   \n",
       "3             26.0        16.0       30.0      17.0      42.0   \n",
       "4             23.0        13.0       43.0      14.0      39.0   \n",
       "\n",
       "   percent_above_65  Area_round_max  Area_round_mean  Area_round_min  \\\n",
       "0         14.545455            80.0             63.0            30.0   \n",
       "1         25.806452            90.0             77.0            70.0   \n",
       "2         31.428571            90.0             62.0            30.0   \n",
       "3         28.813559            80.0             80.0            80.0   \n",
       "4         26.415094           100.0             66.0            30.0   \n",
       "\n",
       "   Area_round_mode  Area_round_std  Discount_sum  High_discount_sum  \\\n",
       "0             70.0            16.0           5.0                4.0   \n",
       "1             70.0             8.0          12.0                8.0   \n",
       "2             50.0            17.0           7.0                4.0   \n",
       "3             80.0             0.0           7.0                6.0   \n",
       "4             60.0            16.0           5.0                3.0   \n",
       "\n",
       "   Low_Discount_35_sum  No_Discount_sum  Owner_sum  Renter_sum  \\\n",
       "0                  1.0             15.0       12.0         8.0   \n",
       "1                  4.0              4.0       10.0         6.0   \n",
       "2                  3.0             10.0       10.0         7.0   \n",
       "3                  1.0              9.0       10.0         6.0   \n",
       "4                  2.0              8.0        8.0         5.0   \n",
       "\n",
       "   discount_rate_mean  total_appartments  discount_percent  Low_discount  \\\n",
       "0            0.150829               20.0          0.250000      0.050000   \n",
       "1            0.480824               16.0          0.750000      0.250000   \n",
       "2            0.284133               17.0          0.411765      0.176471   \n",
       "3            0.285945               16.0          0.437500      0.062500   \n",
       "4            0.243069               13.0          0.384615      0.153846   \n",
       "\n",
       "   High_discount  No_discount  Owner_Percent  \n",
       "0       0.200000     0.750000       0.600000  \n",
       "1       0.500000     0.250000       0.625000  \n",
       "2       0.235294     0.588235       0.588235  \n",
       "3       0.375000     0.562500       0.625000  \n",
       "4       0.230769     0.615385       0.615385  "
      ]
     },
     "execution_count": 737,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "buildingsDf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 738,
   "id": "normal-medium",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47.22643726384274"
      ]
     },
     "execution_count": 738,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "buildingsDf['Average_age_2020'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 739,
   "id": "separate-defeat",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27.393879704706396"
      ]
     },
     "execution_count": 739,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "buildingsDf['percent_above_65'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 740,
   "id": "altered-revolution",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 740,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len([20,10,10,10,10,10,10,10,10,10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 741,
   "id": "general-spyware",
   "metadata": {},
   "outputs": [],
   "source": [
    "AgeDistribution = pd.DataFrame({'Age':[20,30,40,50,65,70,80,90,100],'Distribution':[0.14,0.18,0.23,0.14,0.09,0.08,0.07,0.06,0.01]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 742,
   "id": "premium-banana",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.31"
      ]
     },
     "execution_count": 742,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AgeDistribution[AgeDistribution['Age']>=65]['Distribution'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 743,
   "id": "normal-fluid",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6900000000000001"
      ]
     },
     "execution_count": 743,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AgeDistribution[AgeDistribution['Age']<65]['Distribution'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 744,
   "id": "korean-gather",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 744,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AgeDistribution['Distribution'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 745,
   "id": "consolidated-brisbane",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Age'>"
      ]
     },
     "execution_count": 745,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "AgeDistribution.plot(kind='bar',x='Age',y='Distribution')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 746,
   "id": "attempted-assessment",
   "metadata": {},
   "outputs": [],
   "source": [
    "mainColumns = ['BeforeBldgs','ProjNumber','min_living_till_2020','max_living_till_2020','Average_age_2020','StdDev_age_2020','Before_app','Above_65','Area_round_mode','High_discount_sum','Low_Discount_35_sum','Renter_sum']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 747,
   "id": "unable-responsibility",
   "metadata": {},
   "outputs": [],
   "source": [
    "ApartmentColums = ['bldCode','doorIndex','bldCodeDoorIndex','aprtmentSize','yearsInBldg','age','lowDiscount','highDiscount','noDiscount','income','rent','own','agentID']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 784,
   "id": "infinite-madagascar",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 0 entries\n",
      "Data columns (total 13 columns):\n",
      " #   Column            Non-Null Count  Dtype \n",
      "---  ------            --------------  ----- \n",
      " 0   bldCode           0 non-null      object\n",
      " 1   doorIndex         0 non-null      object\n",
      " 2   bldCodeDoorIndex  0 non-null      object\n",
      " 3   aprtmentSize      0 non-null      object\n",
      " 4   yearsInBldg       0 non-null      object\n",
      " 5   age               0 non-null      object\n",
      " 6   lowDiscount       0 non-null      object\n",
      " 7   highDiscount      0 non-null      object\n",
      " 8   noDiscount        0 non-null      object\n",
      " 9   income            0 non-null      object\n",
      " 10  rent              0 non-null      object\n",
      " 11  own               0 non-null      object\n",
      " 12  agentID           0 non-null      object\n",
      "dtypes: object(13)\n",
      "memory usage: 0.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "orginalAgentsDS = pd.DataFrame(columns=ApartmentColums)\n",
    "orginalAgentsDS.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 785,
   "id": "fuzzy-recording",
   "metadata": {},
   "outputs": [],
   "source": [
    "orginalAgentsDS['doorIndex'] = orginalAgentsDS['doorIndex'].astype('int')\n",
    "orginalAgentsDS['aprtmentSize'] = orginalAgentsDS['aprtmentSize'].astype('int')\n",
    "orginalAgentsDS['yearsInBldg'] = orginalAgentsDS['yearsInBldg'].astype('int')\n",
    "orginalAgentsDS['age'] = orginalAgentsDS['age'].astype('int')\n",
    "orginalAgentsDS['lowDiscount'] = orginalAgentsDS['lowDiscount'].astype('int')\n",
    "orginalAgentsDS['highDiscount'] = orginalAgentsDS['highDiscount'].astype('int')\n",
    "orginalAgentsDS['noDiscount'] = orginalAgentsDS['noDiscount'].astype('int')\n",
    "orginalAgentsDS['rent'] = orginalAgentsDS['rent'].astype('int')\n",
    "orginalAgentsDS['own'] = orginalAgentsDS['own'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 786,
   "id": "transsexual-reputation",
   "metadata": {},
   "outputs": [],
   "source": [
    "orginalAgentsDSJustHead = orginalAgentsDS.copy() # Just Leave Head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 787,
   "id": "together-matrix",
   "metadata": {},
   "outputs": [],
   "source": [
    "def createAgentAndApartment(bldgInfo,hedersonly):\n",
    "    bldgInfo['Above_65'] = bldgInfo['Above_65'].astype(int)\n",
    "    bldgInfo.numApartments = bldgInfo['Before_app']\n",
    "    bldgInfo.apartmentSize = bldgInfo['Area_round_mode'].astype('int')\n",
    "    bldgInfo.rent = bldgInfo['Renter_sum'].astype('int')\n",
    "    bldgInfo.High = bldgInfo['High_discount_sum'].astype('int')\n",
    "    bldgInfo.Low = bldgInfo['Low_Discount_35_sum'].astype('int')\n",
    "    bldgInfo.code = bldgInfo['BeforeBldgs'].split(',')\n",
    "    currentBldgloop = hedersonly.copy()\n",
    "    currentBldg = hedersonly.copy()\n",
    "    for bldItem in bldgInfo.code:\n",
    "        tempDataSet = pd.DataFrame({'doorIndex':[door for door in range(1,int(bldgInfo.numApartments)+1)],\n",
    "                                   'min_living_till_2020':bldgInfo['min_living_till_2020'],'max_living_till_2020':bldgInfo['max_living_till_2020'],\n",
    "                                   'Average_age_2020':bldgInfo['Average_age_2020'],'StdDev_age_2020':bldgInfo['StdDev_age_2020']})\n",
    "        #Age\n",
    "        AgeDistRandom = np.random.choice(a= AgeDistribution['Age'].values,size=bldgInfo.numApartments.astype(int),p=AgeDistribution['Distribution'].values)\n",
    "        AgeDistRandomNoise = [np.random.randint(age-5,age+5) for age in AgeDistRandom] #add Some Noise\n",
    "        tempDataSet['age_'] = AgeDistRandomNoise\n",
    "        tempDataSet['yearsLivingInBldg'] = tempDataSet.apply(lambda x: np.random.randint(x['min_living_till_2020'],x['max_living_till_2020']),axis=1)\n",
    "        tempDataSet = tempDataSet[['doorIndex','age_','yearsLivingInBldg']]\n",
    "\n",
    "        currentBldgloop['doorIndex'] = [door for door in range(1,int(bldgInfo.numApartments)+1)]\n",
    "        currentBldgloop['bldCode'] = bldItem # Coppy Clean Slate\n",
    "        currentBldgloop['bldCodeDoorIndex'] = currentBldgloop['bldCode']+\"_\"+currentBldgloop['doorIndex'].astype(str)\n",
    "        currentBldgloop['aprtmentSize'] = bldgInfo.apartmentSize\n",
    "        currentBldgloop = pd.merge(currentBldgloop,tempDataSet,on='doorIndex')\n",
    "        currentBldgloop['age'] =currentBldgloop['age_'] \n",
    "        currentBldgloop['yearsInBldg']=currentBldgloop['yearsLivingInBldg'] \n",
    "        currentBldgloop.drop(columns=['age_','yearsLivingInBldg'],inplace=True)\n",
    "        currentBldgloop.loc[currentBldgloop.sample(bldgInfo.High).index,'highDiscount']=1\n",
    "        \n",
    "        print('bld item',bldItem)\n",
    "        print('high',bldgInfo.High)\n",
    "        print('Low',bldgInfo.Low)\n",
    "        lowdiscountIndex = currentBldgloop.loc[currentBldgloop['highDiscount'].isna()].sample(n=bldgInfo.Low).index\n",
    "        currentBldgloop.loc[lowdiscountIndex,'lowDiscount']=1\n",
    "        currentBldgloop['lowDiscount'].fillna(0)\n",
    "        currentBldgloop['lowDiscount'] =currentBldgloop['lowDiscount'].fillna(0)\n",
    "        currentBldgloop['highDiscount'] =currentBldgloop['highDiscount'].fillna(0)\n",
    "        currentBldgloop.loc[currentBldgloop.query('highDiscount==0 and lowDiscount==0').index,'noDiscount']=1\n",
    "        currentBldg['noDiscount'] =currentBldgloop['noDiscount'].fillna(0)\n",
    "        # Rent Own\n",
    "        currentBldgloop['rent'] =0\n",
    "        currentBldgloop['own'] =0\n",
    "        currentBldgloop['rent'] = currentBldgloop['rent'].astype('int')\n",
    "        currentBldgloop['own'] = currentBldgloop['own'].astype('int')\n",
    "        rentIndex = currentBldgloop.sample(n=bldgInfo.rent).index\n",
    "        currentBldgloop.loc[rentIndex,'rent']=1\n",
    "        currentBldgloop.loc[currentBldgloop.query('rent==0').index,'own']=1\n",
    "        if bldItem==bldgInfo.code[0]:\n",
    "            currentBldg = currentBldgloop\n",
    "        else:\n",
    "            currentBldg = currentBldg.append(currentBldgloop)\n",
    "            currentBldg.reset_index(inplace=True,drop=True)\n",
    "        currentBldgloop = hedersonly.copy()\n",
    "    return currentBldg.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 788,
   "id": "crude-click",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bld item 210_19\n",
      "high 4\n",
      "Low 1\n",
      "bld item 210_14\n",
      "high 8\n",
      "Low 4\n",
      "bld item 208_32\n",
      "high 4\n",
      "Low 3\n",
      "bld item 210_3\n",
      "high 6\n",
      "Low 1\n",
      "bld item 210_35\n",
      "high 3\n",
      "Low 2\n",
      "bld item 210_12\n",
      "high 10\n",
      "Low 8\n",
      "bld item 210_10\n",
      "high 18\n",
      "Low 12\n",
      "bld item 211_19\n",
      "high 2\n",
      "Low 0\n",
      "bld item 211_20\n",
      "high 2\n",
      "Low 0\n",
      "bld item 211_19\n",
      "high 2\n",
      "Low 0\n",
      "bld item 224_3\n",
      "high 5\n",
      "Low 3\n",
      "bld item 220_7\n",
      "high 5\n",
      "Low 3\n",
      "bld item 220_3\n",
      "high 5\n",
      "Low 3\n",
      "bld item 210_34\n",
      "high 5\n",
      "Low 3\n",
      "bld item 210_36\n",
      "high 5\n",
      "Low 3\n",
      "bld item 220_5\n",
      "high 5\n",
      "Low 3\n",
      "bld item 210_32_210_30\n",
      "high 5\n",
      "Low 3\n",
      "bld item 210_32\n",
      "high 5\n",
      "Low 3\n",
      "bld item 224_6\n",
      "high 5\n",
      "Low 3\n",
      "bld item 224_7\n",
      "high 5\n",
      "Low 3\n",
      "bld item 220_7\n",
      "high 5\n",
      "Low 3\n",
      "bld item 215_6\n",
      "high 3\n",
      "Low 3\n",
      "bld item 216_4\n",
      "high 7\n",
      "Low 1\n",
      "bld item 209_6\n",
      "high 5\n",
      "Low 0\n",
      "bld item 215_22\n",
      "high 8\n",
      "Low 4\n",
      "bld item 210_11\n",
      "high 2\n",
      "Low 1\n",
      "bld item 216_3\n",
      "high 7\n",
      "Low 8\n",
      "bld item 209_8_209_10_209_12\n",
      "high 1\n",
      "Low 4\n",
      "bld item 208_12\n",
      "high 8\n",
      "Low 8\n",
      "bld item 220_4\n",
      "high 2\n",
      "Low 2\n",
      "bld item 215_18\n",
      "high 4\n",
      "Low 2\n",
      "bld item 215_24\n",
      "high 8\n",
      "Low 4\n",
      "bld item 217_5\n",
      "high 7\n",
      "Low 1\n",
      "bld item 212_53\n",
      "high 10\n",
      "Low 9\n",
      "bld item 209_29\n",
      "high 5\n",
      "Low 2\n",
      "bld item 212_61\n",
      "high 7\n",
      "Low 5\n",
      "bld item 212_61\n",
      "high 7\n",
      "Low 5\n",
      "bld item 212_61\n",
      "high 7\n",
      "Low 5\n",
      "bld item 216_8\n",
      "high 7\n",
      "Low 5\n",
      "bld item 209_30\n",
      "high 0\n",
      "Low 0\n",
      "bld item 209_32\n",
      "high 0\n",
      "Low 0\n",
      "bld item 209_30\n",
      "high 0\n",
      "Low 0\n",
      "bld item 209_32\n",
      "high 0\n",
      "Low 0\n",
      "bld item 215_12\n",
      "high 4\n",
      "Low 3\n",
      "bld item 215_14\n",
      "high 4\n",
      "Low 3\n",
      "bld item 102_3\n",
      "high 4\n",
      "Low 3\n",
      "bld item 215_12\n",
      "high 4\n",
      "Low 3\n",
      "bld item 212_63\n",
      "high 1\n",
      "Low 2\n",
      "bld item 201_7\n",
      "high 2\n",
      "Low 2\n",
      "bld item 201_9\n",
      "high 2\n",
      "Low 2\n",
      "bld item 201_9\n",
      "high 2\n",
      "Low 2\n",
      "bld item 201_9\n",
      "high 2\n",
      "Low 2\n",
      "bld item 220_4\n",
      "high 1\n",
      "Low 0\n",
      "bld item 225_8\n",
      "high 9\n",
      "Low 3\n",
      "bld item 225_6\n",
      "high 9\n",
      "Low 3\n",
      "bld item 210_8\n",
      "high 9\n",
      "Low 3\n",
      "bld item 225_6\n",
      "high 9\n",
      "Low 3\n",
      "bld item 211_9\n",
      "high 3\n",
      "Low 3\n",
      "bld item 210_18\n",
      "high 3\n",
      "Low 3\n",
      "bld item 210_16\n",
      "high 3\n",
      "Low 3\n",
      "bld item 211_11\n",
      "high 3\n",
      "Low 3\n",
      "bld item 211_7\n",
      "high 3\n",
      "Low 3\n",
      "bld item 221_30_211_26\n",
      "high 3\n",
      "Low 0\n",
      "bld item 211_25_211_27_211_29\n",
      "high 3\n",
      "Low 0\n",
      "bld item 221_22\n",
      "high 3\n",
      "Low 0\n",
      "bld item 221_4_221_2\n",
      "high 6\n",
      "Low 2\n",
      "bld item 221_6\n",
      "high 6\n",
      "Low 2\n",
      "bld item 209_4\n",
      "high 1\n",
      "Low 4\n",
      "bld item 201_75\n",
      "high 7\n",
      "Low 3\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "a must be greater than 0 unless no samples are taken",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-788-78fd37be03a5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mbuildingsDf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0mbldgInfo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuildingsDf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmainColumns\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mcurrentBldg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcreateAgentAndApartment\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbldgInfo\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0morginalAgentsDSJustHead\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m         \u001b[0morginalAgentsDS\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcurrentBldg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-787-dc3697f01593>\u001b[0m in \u001b[0;36mcreateAgentAndApartment\u001b[0;34m(bldgInfo, hedersonly)\u001b[0m\n\u001b[1;32m     33\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'high'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbldgInfo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mHigh\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Low'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbldgInfo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLow\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 35\u001b[0;31m         \u001b[0mlowdiscountIndex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcurrentBldgloop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcurrentBldgloop\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'highDiscount'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbldgInfo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLow\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     36\u001b[0m         \u001b[0mcurrentBldgloop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlowdiscountIndex\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'lowDiscount'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m         \u001b[0mcurrentBldgloop\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'lowDiscount'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfillna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/geo_env/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36msample\u001b[0;34m(self, n, frac, replace, weights, random_state, axis)\u001b[0m\n\u001b[1;32m   5345\u001b[0m             )\n\u001b[1;32m   5346\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5347\u001b[0;31m         \u001b[0mlocs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis_length\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mweights\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5348\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5349\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mmtrand.pyx\u001b[0m in \u001b[0;36mnumpy.random.mtrand.RandomState.choice\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: a must be greater than 0 unless no samples are taken"
     ]
    }
   ],
   "source": [
    "for i in buildingsDf.index:\n",
    "    bldgInfo = buildingsDf.loc[i,mainColumns]\n",
    "    currentBldg = createAgentAndApartment(bldgInfo,orginalAgentsDSJustHead.copy())\n",
    "    if (i==0):\n",
    "        orginalAgentsDS = currentBldg\n",
    "    else:\n",
    "        orginalAgentsDS = orginalAgentsDS.append(currentBldg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "removable-favor",
   "metadata": {},
   "outputs": [],
   "source": [
    "orginalAgentsDS.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "solar-clarity",
   "metadata": {},
   "outputs": [],
   "source": [
    "orginalAgentsDS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "labeled-logic",
   "metadata": {},
   "outputs": [],
   "source": [
    "discountTalbe = pd.DataFrame({'noDiscount':[9220,15430],'lowDiscount':[6514,9219],'highDiscount':[5011,6513]},index=['min_','max_'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "alien-homework",
   "metadata": {},
   "outputs": [],
   "source": [
    "discountTalbe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "moderate-rugby",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 789,
   "id": "universal-mentor",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lowDiscount\n",
      "highDiscount\n",
      "noDiscount\n"
     ]
    }
   ],
   "source": [
    "for item in ['lowDiscount','highDiscount','noDiscount']:\n",
    "    print(item)\n",
    "    con = orginalAgentsDS[item]==1\n",
    "    orginalAgentsDS.loc[con,'income'] = orginalAgentsDS.loc[con].apply(lambda x: np.random.randint(discountTalbe.loc['min_',item],discountTalbe.loc['max_',item]),axis=1)\n",
    "    #orginalAgentsDS['income'] == orginalAgentsDS[item].apply(lambda x: np.random.randint(discountTalbe.loc['min_',item],discountTalbe.loc['max_',item])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 790,
   "id": "shaped-cologne",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 790,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "orginalAgentsDS['income'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 791,
   "id": "silver-climate",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 791,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "orginalAgentsDS['age'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 792,
   "id": "liable-queens",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "392"
      ]
     },
     "execution_count": 792,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orginalAgentsDS['rent'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 793,
   "id": "curious-aberdeen",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "708"
      ]
     },
     "execution_count": 793,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orginalAgentsDS['own'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 794,
   "id": "hourly-newsletter",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "46.302727272727275"
      ]
     },
     "execution_count": 794,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orginalAgentsDS['age'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 795,
   "id": "adopted-elizabeth",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bldCode</th>\n",
       "      <th>doorIndex</th>\n",
       "      <th>bldCodeDoorIndex</th>\n",
       "      <th>aprtmentSize</th>\n",
       "      <th>yearsInBldg</th>\n",
       "      <th>age</th>\n",
       "      <th>lowDiscount</th>\n",
       "      <th>highDiscount</th>\n",
       "      <th>noDiscount</th>\n",
       "      <th>income</th>\n",
       "      <th>rent</th>\n",
       "      <th>own</th>\n",
       "      <th>agentID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>210_19</td>\n",
       "      <td>1</td>\n",
       "      <td>210_19_1</td>\n",
       "      <td>70</td>\n",
       "      <td>27</td>\n",
       "      <td>34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9495</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>210_19</td>\n",
       "      <td>2</td>\n",
       "      <td>210_19_2</td>\n",
       "      <td>70</td>\n",
       "      <td>29</td>\n",
       "      <td>78</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13758</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>210_19</td>\n",
       "      <td>3</td>\n",
       "      <td>210_19_3</td>\n",
       "      <td>70</td>\n",
       "      <td>8</td>\n",
       "      <td>42</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14378</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>210_19</td>\n",
       "      <td>4</td>\n",
       "      <td>210_19_4</td>\n",
       "      <td>70</td>\n",
       "      <td>17</td>\n",
       "      <td>73</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6354</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>210_19</td>\n",
       "      <td>5</td>\n",
       "      <td>210_19_5</td>\n",
       "      <td>70</td>\n",
       "      <td>4</td>\n",
       "      <td>50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9362</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>209_4</td>\n",
       "      <td>18</td>\n",
       "      <td>209_4_18</td>\n",
       "      <td>86</td>\n",
       "      <td>31</td>\n",
       "      <td>68</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11279</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>209_4</td>\n",
       "      <td>19</td>\n",
       "      <td>209_4_19</td>\n",
       "      <td>86</td>\n",
       "      <td>3</td>\n",
       "      <td>83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11055</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>209_4</td>\n",
       "      <td>20</td>\n",
       "      <td>209_4_20</td>\n",
       "      <td>86</td>\n",
       "      <td>26</td>\n",
       "      <td>48</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9520</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>209_4</td>\n",
       "      <td>21</td>\n",
       "      <td>209_4_21</td>\n",
       "      <td>86</td>\n",
       "      <td>23</td>\n",
       "      <td>21</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8255</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>209_4</td>\n",
       "      <td>22</td>\n",
       "      <td>209_4_22</td>\n",
       "      <td>86</td>\n",
       "      <td>13</td>\n",
       "      <td>75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6909</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1100 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   bldCode  doorIndex bldCodeDoorIndex  aprtmentSize  yearsInBldg  age  \\\n",
       "0   210_19          1         210_19_1            70           27   34   \n",
       "1   210_19          2         210_19_2            70           29   78   \n",
       "2   210_19          3         210_19_3            70            8   42   \n",
       "3   210_19          4         210_19_4            70           17   73   \n",
       "4   210_19          5         210_19_5            70            4   50   \n",
       "..     ...        ...              ...           ...          ...  ...   \n",
       "17   209_4         18         209_4_18            86           31   68   \n",
       "18   209_4         19         209_4_19            86            3   83   \n",
       "19   209_4         20         209_4_20            86           26   48   \n",
       "20   209_4         21         209_4_21            86           23   21   \n",
       "21   209_4         22         209_4_22            86           13   75   \n",
       "\n",
       "    lowDiscount  highDiscount  noDiscount income  rent  own agentID  \n",
       "0           0.0           0.0         1.0   9495     0    1     NaN  \n",
       "1           0.0           0.0         1.0  13758     0    1     NaN  \n",
       "2           0.0           0.0         1.0  14378     0    1     NaN  \n",
       "3           0.0           1.0         NaN   6354     1    0     NaN  \n",
       "4           0.0           0.0         1.0   9362     0    1     NaN  \n",
       "..          ...           ...         ...    ...   ...  ...     ...  \n",
       "17          0.0           0.0         1.0  11279     0    1     NaN  \n",
       "18          0.0           0.0         1.0  11055     1    0     NaN  \n",
       "19          0.0           0.0         1.0   9520     0    1     NaN  \n",
       "20          1.0           0.0         NaN   8255     0    1     NaN  \n",
       "21          1.0           0.0         NaN   6909     0    1     NaN  \n",
       "\n",
       "[1100 rows x 13 columns]"
      ]
     },
     "execution_count": 795,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orginalAgentsDS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 796,
   "id": "essential-discretion",
   "metadata": {},
   "outputs": [],
   "source": [
    "import uuid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 797,
   "id": "several-triumph",
   "metadata": {},
   "outputs": [],
   "source": [
    "orginalAgentsDS['agentID'] =orginalAgentsDS['agentID'].apply(lambda x:uuid.uuid1()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 798,
   "id": "assured-diesel",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bldCode</th>\n",
       "      <th>doorIndex</th>\n",
       "      <th>bldCodeDoorIndex</th>\n",
       "      <th>aprtmentSize</th>\n",
       "      <th>yearsInBldg</th>\n",
       "      <th>age</th>\n",
       "      <th>lowDiscount</th>\n",
       "      <th>highDiscount</th>\n",
       "      <th>noDiscount</th>\n",
       "      <th>income</th>\n",
       "      <th>rent</th>\n",
       "      <th>own</th>\n",
       "      <th>agentID</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <td>70</td>\n",
       "      <td>4</td>\n",
       "      <td>50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>209_4_19</td>\n",
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       "      <td>209_4_20</td>\n",
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       "      <td>209_4_21</td>\n",
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       "      <td>209_4_22</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6909</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>d39f6e9a-96aa-11eb-a97e-2a96f1f66eb5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1100 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   bldCode  doorIndex bldCodeDoorIndex  aprtmentSize  yearsInBldg  age  \\\n",
       "0   210_19          1         210_19_1            70           27   34   \n",
       "1   210_19          2         210_19_2            70           29   78   \n",
       "2   210_19          3         210_19_3            70            8   42   \n",
       "3   210_19          4         210_19_4            70           17   73   \n",
       "4   210_19          5         210_19_5            70            4   50   \n",
       "..     ...        ...              ...           ...          ...  ...   \n",
       "17   209_4         18         209_4_18            86           31   68   \n",
       "18   209_4         19         209_4_19            86            3   83   \n",
       "19   209_4         20         209_4_20            86           26   48   \n",
       "20   209_4         21         209_4_21            86           23   21   \n",
       "21   209_4         22         209_4_22            86           13   75   \n",
       "\n",
       "    lowDiscount  highDiscount  noDiscount income  rent  own  \\\n",
       "0           0.0           0.0         1.0   9495     0    1   \n",
       "1           0.0           0.0         1.0  13758     0    1   \n",
       "2           0.0           0.0         1.0  14378     0    1   \n",
       "3           0.0           1.0         NaN   6354     1    0   \n",
       "4           0.0           0.0         1.0   9362     0    1   \n",
       "..          ...           ...         ...    ...   ...  ...   \n",
       "17          0.0           0.0         1.0  11279     0    1   \n",
       "18          0.0           0.0         1.0  11055     1    0   \n",
       "19          0.0           0.0         1.0   9520     0    1   \n",
       "20          1.0           0.0         NaN   8255     0    1   \n",
       "21          1.0           0.0         NaN   6909     0    1   \n",
       "\n",
       "                                 agentID  \n",
       "0   d39efa5a-96aa-11eb-a97e-2a96f1f66eb5  \n",
       "1   d39efae6-96aa-11eb-a97e-2a96f1f66eb5  \n",
       "2   d39efb0e-96aa-11eb-a97e-2a96f1f66eb5  \n",
       "3   d39efb2c-96aa-11eb-a97e-2a96f1f66eb5  \n",
       "4   d39efb4a-96aa-11eb-a97e-2a96f1f66eb5  \n",
       "..                                   ...  \n",
       "17  d39f6e36-96aa-11eb-a97e-2a96f1f66eb5  \n",
       "18  d39f6e54-96aa-11eb-a97e-2a96f1f66eb5  \n",
       "19  d39f6e68-96aa-11eb-a97e-2a96f1f66eb5  \n",
       "20  d39f6e86-96aa-11eb-a97e-2a96f1f66eb5  \n",
       "21  d39f6e9a-96aa-11eb-a97e-2a96f1f66eb5  \n",
       "\n",
       "[1100 rows x 13 columns]"
      ]
     },
     "execution_count": 798,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orginalAgentsDS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 799,
   "id": "thousand-criterion",
   "metadata": {},
   "outputs": [],
   "source": [
    "orginalAgentsDS.to_excel('OrigianlAgentsApril6_2021.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 800,
   "id": "professional-mathematics",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['210_19', '210_14', '208_32', '210_3', '210_35', '210_12',\n",
       "       '210_10', '211_19', '224_3', '220_7', '220_3', '210_34', '210_36',\n",
       "       '220_5', '210_32_210_30', '210_32', '224_6', '224_7', '215_6',\n",
       "       '216_4', '209_6', '215_22', '210_11', '216_3',\n",
       "       '209_8_209_10_209_12', '208_12', '220_4', '215_18', '215_24',\n",
       "       '217_5', '212_53', '209_29', '212_61', '216_8', '209_30', '209_32',\n",
       "       '215_12', '212_63', '201_7', '201_9', '225_8', '225_6', '210_8',\n",
       "       '211_9', '210_18', '210_16', '211_11', '211_7', '221_30_211_26',\n",
       "       '211_25_211_27_211_29', '221_22', '221_4_221_2', '221_6', '209_4'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 800,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orginalAgentsDS['bldCode'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 801,
   "id": "varied-webmaster",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>OrderB</th>\n",
       "      <th>OrderC</th>\n",
       "      <th>Row Labels</th>\n",
       "      <th>min_living_till_2020</th>\n",
       "      <th>max_living_till_2020</th>\n",
       "      <th>avg_living_till_2020</th>\n",
       "      <th>Min_age_2020</th>\n",
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       "      <th>after_app</th>\n",
       "      <th>Above_65</th>\n",
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       "      <th>percent_above_65</th>\n",
       "      <th>Area_round_max</th>\n",
       "      <th>Area_round_mean</th>\n",
       "      <th>Area_round_min</th>\n",
       "      <th>Area_round_mode</th>\n",
       "      <th>Area_round_std</th>\n",
       "      <th>Discount_sum</th>\n",
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       "      <th>No_Discount_sum</th>\n",
       "      <th>Owner_sum</th>\n",
       "      <th>Renter_sum</th>\n",
       "      <th>discount_rate_mean</th>\n",
       "      <th>total_appartments</th>\n",
       "      <th>discount_percent</th>\n",
       "      <th>Low_discount</th>\n",
       "      <th>High_discount</th>\n",
       "      <th>No_discount</th>\n",
       "      <th>Owner_Percent</th>\n",
       "      <th>newApartmentSize</th>\n",
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       "      <th>0</th>\n",
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       "      <td>5.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>14.545455</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.150829</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>82.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>210_14</td>\n",
       "      <td>210_14_a</td>\n",
       "      <td>1</td>\n",
       "      <td>20100133</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>25.806452</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.480824</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>82.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>208_32</td>\n",
       "      <td>208_32_a</td>\n",
       "      <td>1</td>\n",
       "      <td>20180812</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>31.428571</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.284133</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>0.411765</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.235294</td>\n",
       "      <td>0.588235</td>\n",
       "      <td>0.588235</td>\n",
       "      <td>62.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>210_3</td>\n",
       "      <td>210_3_a</td>\n",
       "      <td>1</td>\n",
       "      <td>20181092</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>28.813559</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.285945</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.562500</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>92.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>210_35</td>\n",
       "      <td>210_35_r</td>\n",
       "      <td>2</td>\n",
       "      <td>20171974</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>13</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>26.415094</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.243069</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>85.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>210_12</td>\n",
       "      <td>210_12_a</td>\n",
       "      <td>1</td>\n",
       "      <td>20180345</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>21</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>38.775510</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.226707</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>0.461538</td>\n",
       "      <td>0.205128</td>\n",
       "      <td>0.256410</td>\n",
       "      <td>0.538462</td>\n",
       "      <td>0.692308</td>\n",
       "      <td>92.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>210_10</td>\n",
       "      <td>210_10_a</td>\n",
       "      <td>1</td>\n",
       "      <td>20140556</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>35.714286</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.296857</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>0.491803</td>\n",
       "      <td>0.196721</td>\n",
       "      <td>0.295082</td>\n",
       "      <td>0.508197</td>\n",
       "      <td>0.639344</td>\n",
       "      <td>82.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>211_19,211_20,211_19</td>\n",
       "      <td>211_22_r,211_20_r,211_19_r</td>\n",
       "      <td>2</td>\n",
       "      <td>20170076</td>\n",
       "      <td>8</td>\n",
       "      <td>21</td>\n",
       "      <td>14</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>75.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>224_3,220_7,220_3,210_34,210_36,220_5,210_32_2...</td>\n",
       "      <td>220_3_224_3_rr,210_30_210_32_rr,220_5_rr,220_7...</td>\n",
       "      <td>3</td>\n",
       "      <td>502-0196659</td>\n",
       "      <td>9</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.125000</td>\n",
       "      <td>48.875000</td>\n",
       "      <td>15.875000</td>\n",
       "      <td>4.375000</td>\n",
       "      <td>98.875000</td>\n",
       "      <td>47.875000</td>\n",
       "      <td>23.625000</td>\n",
       "      <td>20.250000</td>\n",
       "      <td>497.000000</td>\n",
       "      <td>18.375000</td>\n",
       "      <td>43.375000</td>\n",
       "      <td>29.204431</td>\n",
       "      <td>128.750000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>53.750000</td>\n",
       "      <td>73.750000</td>\n",
       "      <td>20.625000</td>\n",
       "      <td>8.750000</td>\n",
       "      <td>5.750000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>11.375000</td>\n",
       "      <td>10.375000</td>\n",
       "      <td>9.750000</td>\n",
       "      <td>0.258501</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.435034</td>\n",
       "      <td>0.160623</td>\n",
       "      <td>0.274411</td>\n",
       "      <td>0.564966</td>\n",
       "      <td>0.544690</td>\n",
       "      <td>98.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>215_6</td>\n",
       "      <td>215_6_r</td>\n",
       "      <td>2</td>\n",
       "      <td>20150857</td>\n",
       "      <td>10</td>\n",
       "      <td>22</td>\n",
       "      <td>15</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>28.571429</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.254921</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>115.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>216_4</td>\n",
       "      <td>216_4_a</td>\n",
       "      <td>1</td>\n",
       "      <td>20141086</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>23</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>27.272727</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.314753</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>92.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>209_6</td>\n",
       "      <td>209_6_r</td>\n",
       "      <td>2</td>\n",
       "      <td>20140231</td>\n",
       "      <td>13</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>14.285714</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.375612</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>115.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>215_22</td>\n",
       "      <td>215_22_a</td>\n",
       "      <td>1</td>\n",
       "      <td>20140882</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>24</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>23.287671</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.210411</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.781250</td>\n",
       "      <td>82.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>210_11</td>\n",
       "      <td>210_11_a</td>\n",
       "      <td>1</td>\n",
       "      <td>20150274</td>\n",
       "      <td>15</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>14.705882</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>92.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>216_3</td>\n",
       "      <td>216_3_a</td>\n",
       "      <td>1</td>\n",
       "      <td>20141190</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>26</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>29.090909</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.187275</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.228571</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>82.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>209_8_209_10_209_12</td>\n",
       "      <td>209_8_209_10_209_12_a</td>\n",
       "      <td>1</td>\n",
       "      <td>20120894</td>\n",
       "      <td>17</td>\n",
       "      <td>11</td>\n",
       "      <td>27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.073787</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.312500</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.687500</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>72.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>208_12</td>\n",
       "      <td>208_12_a</td>\n",
       "      <td>1</td>\n",
       "      <td>20181295</td>\n",
       "      <td>18</td>\n",
       "      <td>12</td>\n",
       "      <td>28</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>39.285714</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.348805</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.708333</td>\n",
       "      <td>92.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>220_4</td>\n",
       "      <td>220_4_a</td>\n",
       "      <td>1</td>\n",
       "      <td>20131419</td>\n",
       "      <td>19</td>\n",
       "      <td>13</td>\n",
       "      <td>29</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>19.607843</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.107741</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>102.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>215_18</td>\n",
       "      <td>215_18_a</td>\n",
       "      <td>1</td>\n",
       "      <td>20140060</td>\n",
       "      <td>20</td>\n",
       "      <td>14</td>\n",
       "      <td>30</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.218468</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>82.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>215_24</td>\n",
       "      <td>215_24_a</td>\n",
       "      <td>1</td>\n",
       "      <td>20110270</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>31</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>57.142857</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.363162</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>92.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>217_5</td>\n",
       "      <td>217_5_a</td>\n",
       "      <td>1</td>\n",
       "      <td>20140467</td>\n",
       "      <td>23</td>\n",
       "      <td>17</td>\n",
       "      <td>33</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>36.170213</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.344729</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>102.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>212_53</td>\n",
       "      <td>212_53_a</td>\n",
       "      <td>1</td>\n",
       "      <td>20160319</td>\n",
       "      <td>24</td>\n",
       "      <td>18</td>\n",
       "      <td>34</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>47.368421</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.318202</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>0.575758</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.303030</td>\n",
       "      <td>0.424242</td>\n",
       "      <td>0.787879</td>\n",
       "      <td>102.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>209_29</td>\n",
       "      <td>209_29_a</td>\n",
       "      <td>1</td>\n",
       "      <td>20141182</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>35</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>34.210526</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.308843</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>112.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>212_61,212_61,212_61,216_8</td>\n",
       "      <td>212_59_rr,212_61_1_rr,212_61_rr</td>\n",
       "      <td>3</td>\n",
       "      <td>502-0654715</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>50.666667</td>\n",
       "      <td>17.666667</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>104.666667</td>\n",
       "      <td>49.666667</td>\n",
       "      <td>25.666667</td>\n",
       "      <td>32.666667</td>\n",
       "      <td>284.000000</td>\n",
       "      <td>37.666667</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>33.289811</td>\n",
       "      <td>123.333333</td>\n",
       "      <td>91.666667</td>\n",
       "      <td>76.666667</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>12.666667</td>\n",
       "      <td>7.333333</td>\n",
       "      <td>5.333333</td>\n",
       "      <td>19.666667</td>\n",
       "      <td>21.666667</td>\n",
       "      <td>10.666667</td>\n",
       "      <td>0.175878</td>\n",
       "      <td>32.333333</td>\n",
       "      <td>0.443831</td>\n",
       "      <td>0.281385</td>\n",
       "      <td>0.162446</td>\n",
       "      <td>0.556169</td>\n",
       "      <td>0.607413</td>\n",
       "      <td>115.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>209_30,209_32,209_30,209_32</td>\n",
       "      <td>209_32_1_rr,209_32_rr</td>\n",
       "      <td>3</td>\n",
       "      <td>502-0316752</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>13.500000</td>\n",
       "      <td>59.500000</td>\n",
       "      <td>39.500000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>3.571429</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>97.500000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>215_12,215_14,102_3,215_12</td>\n",
       "      <td>215_14_rr,215_14_102_3_215_12_rr</td>\n",
       "      <td>3</td>\n",
       "      <td>502-0201483</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>9.666667</td>\n",
       "      <td>98.666667</td>\n",
       "      <td>51.666667</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>169.000000</td>\n",
       "      <td>17.666667</td>\n",
       "      <td>35.333333</td>\n",
       "      <td>33.559322</td>\n",
       "      <td>116.666667</td>\n",
       "      <td>85.666667</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>83.333333</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>11.333333</td>\n",
       "      <td>5.666667</td>\n",
       "      <td>0.223397</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>0.460752</td>\n",
       "      <td>0.184461</td>\n",
       "      <td>0.276291</td>\n",
       "      <td>0.539248</td>\n",
       "      <td>0.701153</td>\n",
       "      <td>108.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>212_63</td>\n",
       "      <td>212_63_rr,212_63_1_rr</td>\n",
       "      <td>3</td>\n",
       "      <td>502-0583948</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>18.518519</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.151063</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>125.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>201_7,201_9,201_9,201_9</td>\n",
       "      <td>201_7_rr,201_9_rr</td>\n",
       "      <td>3</td>\n",
       "      <td>502-0424911</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>33.333333</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.224987</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>105.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>220_4</td>\n",
       "      <td>215_2_rr,220_2_rr</td>\n",
       "      <td>3</td>\n",
       "      <td>502-0147678</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>32.453416</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.290009</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>115.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>225_8,225_6,210_8,225_6</td>\n",
       "      <td>225_6_rr,225_8_rr</td>\n",
       "      <td>3</td>\n",
       "      <td>502-0178285</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>10</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>54.666667</td>\n",
       "      <td>16.333333</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>89.333333</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>21.333333</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>331.000000</td>\n",
       "      <td>23.666667</td>\n",
       "      <td>57.666667</td>\n",
       "      <td>29.582747</td>\n",
       "      <td>96.666667</td>\n",
       "      <td>60.333333</td>\n",
       "      <td>43.333333</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>10.333333</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>9.666667</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.666667</td>\n",
       "      <td>12.333333</td>\n",
       "      <td>0.343599</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.473545</td>\n",
       "      <td>0.084656</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.526455</td>\n",
       "      <td>0.591270</td>\n",
       "      <td>85.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>30</td>\n",
       "      <td>211_9,210_18,210_16,211_11,211_7</td>\n",
       "      <td>210_18_rr,211_11_rr</td>\n",
       "      <td>3</td>\n",
       "      <td>502-0317495</td>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "      <td>11</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>11.333333</td>\n",
       "      <td>14.333333</td>\n",
       "      <td>72.666667</td>\n",
       "      <td>46.333333</td>\n",
       "      <td>17.666667</td>\n",
       "      <td>14.333333</td>\n",
       "      <td>210.000000</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>21.880342</td>\n",
       "      <td>83.333333</td>\n",
       "      <td>70.666667</td>\n",
       "      <td>46.666667</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>12.666667</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>7.666667</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>5.333333</td>\n",
       "      <td>0.239939</td>\n",
       "      <td>14.333333</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>0.236842</td>\n",
       "      <td>0.236842</td>\n",
       "      <td>0.526316</td>\n",
       "      <td>0.627193</td>\n",
       "      <td>91.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>31</td>\n",
       "      <td>221_30_211_26,211_25_211_27_211_29,221_22</td>\n",
       "      <td>221_22_221_29_rr,211_30_211_26_rr,211_27_rr</td>\n",
       "      <td>3</td>\n",
       "      <td>502-0189720</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>12</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.200000</td>\n",
       "      <td>31.200000</td>\n",
       "      <td>14.800000</td>\n",
       "      <td>11.200000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>48.200000</td>\n",
       "      <td>20.400000</td>\n",
       "      <td>9.200000</td>\n",
       "      <td>138.000000</td>\n",
       "      <td>5.400000</td>\n",
       "      <td>10.800000</td>\n",
       "      <td>22.200000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>5.400000</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>0.301113</td>\n",
       "      <td>9.200000</td>\n",
       "      <td>0.426667</td>\n",
       "      <td>0.047222</td>\n",
       "      <td>0.379444</td>\n",
       "      <td>0.573333</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>89.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>32</td>\n",
       "      <td>221_4_221_2,221_6</td>\n",
       "      <td>216_6_rr,140_1_rr,300_4_rr</td>\n",
       "      <td>3</td>\n",
       "      <td>502-0316588</td>\n",
       "      <td>11</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.034494</td>\n",
       "      <td>42.277741</td>\n",
       "      <td>12.496582</td>\n",
       "      <td>3.640933</td>\n",
       "      <td>85.216980</td>\n",
       "      <td>53.307495</td>\n",
       "      <td>25.205947</td>\n",
       "      <td>29.583756</td>\n",
       "      <td>21.112107</td>\n",
       "      <td>22.979817</td>\n",
       "      <td>56.453747</td>\n",
       "      <td>17.933179</td>\n",
       "      <td>138.284957</td>\n",
       "      <td>84.249697</td>\n",
       "      <td>59.275692</td>\n",
       "      <td>68.597426</td>\n",
       "      <td>15.739572</td>\n",
       "      <td>3.463875</td>\n",
       "      <td>6.346420</td>\n",
       "      <td>2.572850</td>\n",
       "      <td>11.297380</td>\n",
       "      <td>10.030725</td>\n",
       "      <td>4.499744</td>\n",
       "      <td>0.377727</td>\n",
       "      <td>19.463017</td>\n",
       "      <td>0.262629</td>\n",
       "      <td>0.112947</td>\n",
       "      <td>0.482004</td>\n",
       "      <td>0.689719</td>\n",
       "      <td>0.466555</td>\n",
       "      <td>93.597426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>33</td>\n",
       "      <td>209_4</td>\n",
       "      <td>209_4_a</td>\n",
       "      <td>1</td>\n",
       "      <td>20180845</td>\n",
       "      <td>22</td>\n",
       "      <td>16</td>\n",
       "      <td>32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.528627</td>\n",
       "      <td>32.646338</td>\n",
       "      <td>11.582384</td>\n",
       "      <td>6.844482</td>\n",
       "      <td>83.357566</td>\n",
       "      <td>50.328780</td>\n",
       "      <td>22.365085</td>\n",
       "      <td>22.882483</td>\n",
       "      <td>138.945675</td>\n",
       "      <td>15.860325</td>\n",
       "      <td>40.095408</td>\n",
       "      <td>20.586708</td>\n",
       "      <td>95.996652</td>\n",
       "      <td>86.267141</td>\n",
       "      <td>76.753637</td>\n",
       "      <td>86.430530</td>\n",
       "      <td>7.615565</td>\n",
       "      <td>10.405044</td>\n",
       "      <td>1.933758</td>\n",
       "      <td>4.400782</td>\n",
       "      <td>10.606053</td>\n",
       "      <td>18.928480</td>\n",
       "      <td>6.758559</td>\n",
       "      <td>0.261629</td>\n",
       "      <td>22.415725</td>\n",
       "      <td>0.527710</td>\n",
       "      <td>0.233112</td>\n",
       "      <td>0.204653</td>\n",
       "      <td>0.428229</td>\n",
       "      <td>0.589800</td>\n",
       "      <td>98.430530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>34</td>\n",
       "      <td>201_75</td>\n",
       "      <td>201_75_rr</td>\n",
       "      <td>3</td>\n",
       "      <td>502-0368860</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.507670</td>\n",
       "      <td>40.064271</td>\n",
       "      <td>13.656463</td>\n",
       "      <td>2.743948</td>\n",
       "      <td>93.555792</td>\n",
       "      <td>45.047362</td>\n",
       "      <td>22.626458</td>\n",
       "      <td>7.615784</td>\n",
       "      <td>65.473676</td>\n",
       "      <td>4.944792</td>\n",
       "      <td>24.857197</td>\n",
       "      <td>23.673719</td>\n",
       "      <td>81.792247</td>\n",
       "      <td>70.309373</td>\n",
       "      <td>69.610421</td>\n",
       "      <td>87.254877</td>\n",
       "      <td>8.187810</td>\n",
       "      <td>9.500606</td>\n",
       "      <td>7.756946</td>\n",
       "      <td>3.553614</td>\n",
       "      <td>3.419025</td>\n",
       "      <td>4.122822</td>\n",
       "      <td>4.718750</td>\n",
       "      <td>0.268917</td>\n",
       "      <td>29.487131</td>\n",
       "      <td>0.272619</td>\n",
       "      <td>0.138385</td>\n",
       "      <td>0.397476</td>\n",
       "      <td>0.536115</td>\n",
       "      <td>0.439184</td>\n",
       "      <td>112.254877</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0                                        BeforeBldgs  \\\n",
       "0            0                                             210_19   \n",
       "1            1                                             210_14   \n",
       "2            2                                             208_32   \n",
       "3            3                                              210_3   \n",
       "4            4                                             210_35   \n",
       "5            5                                             210_12   \n",
       "6            6                                             210_10   \n",
       "7            7                               211_19,211_20,211_19   \n",
       "8            8  224_3,220_7,220_3,210_34,210_36,220_5,210_32_2...   \n",
       "9            9                                              215_6   \n",
       "10          10                                              216_4   \n",
       "11          11                                              209_6   \n",
       "12          12                                             215_22   \n",
       "13          13                                             210_11   \n",
       "14          14                                              216_3   \n",
       "15          15                                209_8_209_10_209_12   \n",
       "16          16                                             208_12   \n",
       "17          17                                              220_4   \n",
       "18          18                                             215_18   \n",
       "19          19                                             215_24   \n",
       "20          20                                              217_5   \n",
       "21          21                                             212_53   \n",
       "22          22                                             209_29   \n",
       "23          23                         212_61,212_61,212_61,216_8   \n",
       "24          24                        209_30,209_32,209_30,209_32   \n",
       "25          25                         215_12,215_14,102_3,215_12   \n",
       "26          26                                             212_63   \n",
       "27          27                            201_7,201_9,201_9,201_9   \n",
       "28          28                                              220_4   \n",
       "29          29                            225_8,225_6,210_8,225_6   \n",
       "30          30                   211_9,210_18,210_16,211_11,211_7   \n",
       "31          31          221_30_211_26,211_25_211_27_211_29,221_22   \n",
       "32          32                                  221_4_221_2,221_6   \n",
       "33          33                                              209_4   \n",
       "34          34                                             201_75   \n",
       "\n",
       "                                           AfterBldgs  ProjType   ProjNumber  \\\n",
       "0                                            210_19_a         1     20180988   \n",
       "1                                            210_14_a         1     20100133   \n",
       "2                                            208_32_a         1     20180812   \n",
       "3                                             210_3_a         1     20181092   \n",
       "4                                            210_35_r         2     20171974   \n",
       "5                                            210_12_a         1     20180345   \n",
       "6                                            210_10_a         1     20140556   \n",
       "7                          211_22_r,211_20_r,211_19_r         2     20170076   \n",
       "8   220_3_224_3_rr,210_30_210_32_rr,220_5_rr,220_7...         3  502-0196659   \n",
       "9                                             215_6_r         2     20150857   \n",
       "10                                            216_4_a         1     20141086   \n",
       "11                                            209_6_r         2     20140231   \n",
       "12                                           215_22_a         1     20140882   \n",
       "13                                           210_11_a         1     20150274   \n",
       "14                                            216_3_a         1     20141190   \n",
       "15                              209_8_209_10_209_12_a         1     20120894   \n",
       "16                                           208_12_a         1     20181295   \n",
       "17                                            220_4_a         1     20131419   \n",
       "18                                           215_18_a         1     20140060   \n",
       "19                                           215_24_a         1     20110270   \n",
       "20                                            217_5_a         1     20140467   \n",
       "21                                           212_53_a         1     20160319   \n",
       "22                                           209_29_a         1     20141182   \n",
       "23                    212_59_rr,212_61_1_rr,212_61_rr         3  502-0654715   \n",
       "24                              209_32_1_rr,209_32_rr         3  502-0316752   \n",
       "25                   215_14_rr,215_14_102_3_215_12_rr         3  502-0201483   \n",
       "26                              212_63_rr,212_63_1_rr         3  502-0583948   \n",
       "27                                  201_7_rr,201_9_rr         3  502-0424911   \n",
       "28                                  215_2_rr,220_2_rr         3  502-0147678   \n",
       "29                                  225_6_rr,225_8_rr         3  502-0178285   \n",
       "30                                210_18_rr,211_11_rr         3  502-0317495   \n",
       "31        221_22_221_29_rr,211_30_211_26_rr,211_27_rr         3  502-0189720   \n",
       "32                         216_6_rr,140_1_rr,300_4_rr         3  502-0316588   \n",
       "33                                            209_4_a         1     20180845   \n",
       "34                                          201_75_rr         3  502-0368860   \n",
       "\n",
       "    OrderA  OrderB  OrderC  Row Labels  min_living_till_2020  \\\n",
       "0        1       1      17         1.0              2.000000   \n",
       "1        2       2      18         1.0              2.000000   \n",
       "2        3       3      19         1.0              2.000000   \n",
       "3        4       4      20         1.0              2.000000   \n",
       "4        5      20      13         1.0              2.000000   \n",
       "5        6       5      21         1.0              2.000000   \n",
       "6        7       6      22         1.0              2.000000   \n",
       "7        8      21      14         1.0              2.000000   \n",
       "8        9      24       1         8.0              2.125000   \n",
       "9       10      22      15         1.0              2.000000   \n",
       "10      12       7      23         1.0              2.000000   \n",
       "11      13      23      16         1.0              5.000000   \n",
       "12      14       8      24         1.0              2.000000   \n",
       "13      15       9      25         1.0              2.000000   \n",
       "14      16      10      26         1.0              2.000000   \n",
       "15      17      11      27         1.0              2.000000   \n",
       "16      18      12      28         1.0              2.000000   \n",
       "17      19      13      29         1.0              2.000000   \n",
       "18      20      14      30         1.0              2.000000   \n",
       "19      21      15      31         1.0              2.000000   \n",
       "20      23      17      33         1.0              2.000000   \n",
       "21      24      18      34         1.0              2.000000   \n",
       "22      25      19      35         1.0              2.000000   \n",
       "23      27      27       4         3.0              2.000000   \n",
       "24      28      28       5         2.0              5.500000   \n",
       "25      29      29       6         3.0              2.000000   \n",
       "26      30      30       7         1.0              2.000000   \n",
       "27      31      31       8         1.0              6.000000   \n",
       "28      32      32       9         2.0              3.500000   \n",
       "29      33      33      10         3.0              2.000000   \n",
       "30      34      34      11         3.0              2.333333   \n",
       "31      35      35      12         5.0              8.200000   \n",
       "32      11      25       2         NaN              3.034494   \n",
       "33      22      16      32         NaN              1.528627   \n",
       "34      26      26       3         NaN              1.507670   \n",
       "\n",
       "    max_living_till_2020  avg_living_till_2020  Min_age_2020  Max_age_2020  \\\n",
       "0              35.000000              9.000000      5.000000     82.000000   \n",
       "1              57.000000             19.000000      2.000000     98.000000   \n",
       "2              33.000000             10.000000      5.000000     89.000000   \n",
       "3              54.000000             15.000000      2.000000     99.000000   \n",
       "4              43.000000             10.000000      3.000000     90.000000   \n",
       "5              48.000000             17.000000      3.000000    104.000000   \n",
       "6              54.000000             15.000000      2.000000    109.000000   \n",
       "7              12.000000              6.000000      2.000000     72.000000   \n",
       "8              48.875000             15.875000      4.375000     98.875000   \n",
       "9              58.000000             14.000000      2.000000    104.000000   \n",
       "10             45.000000              9.000000      2.000000     97.000000   \n",
       "11             26.000000             13.000000      5.000000     94.000000   \n",
       "12             47.000000             12.000000      3.000000     97.000000   \n",
       "13             51.000000             17.000000      2.000000     89.000000   \n",
       "14             56.000000             19.000000      5.000000     94.000000   \n",
       "15             50.000000             10.000000      2.000000     94.000000   \n",
       "16             49.000000             16.000000      2.000000    109.000000   \n",
       "17             44.000000             14.000000     10.000000     98.000000   \n",
       "18             50.000000             13.000000      4.000000     96.000000   \n",
       "19             55.000000             21.000000     24.000000     90.000000   \n",
       "20             59.000000             19.000000     10.000000     92.000000   \n",
       "21             52.000000             18.000000     11.000000     97.000000   \n",
       "22             48.000000             17.000000      9.000000     96.000000   \n",
       "23             50.666667             17.666667      2.333333    104.666667   \n",
       "24             12.500000              7.500000     13.500000     59.500000   \n",
       "25             52.000000             18.000000      9.666667     98.666667   \n",
       "26             28.000000              8.000000      3.000000     88.000000   \n",
       "27             56.000000             20.000000      9.000000    102.000000   \n",
       "28             35.000000             17.500000     10.000000     99.000000   \n",
       "29             54.666667             16.333333      4.000000     89.333333   \n",
       "30             37.000000             11.333333     14.333333     72.666667   \n",
       "31             31.200000             14.800000     11.200000     79.000000   \n",
       "32             42.277741             12.496582      3.640933     85.216980   \n",
       "33             32.646338             11.582384      6.844482     83.357566   \n",
       "34             40.064271             13.656463      2.743948     93.555792   \n",
       "\n",
       "    Average_age_2020  StdDev_age_2020  Before_app   after_app   Above_65  \\\n",
       "0          45.000000        19.000000   20.000000   41.000000   8.000000   \n",
       "1          48.000000        24.000000   16.000000   26.000000  16.000000   \n",
       "2          46.000000        23.000000   17.000000   34.000000  11.000000   \n",
       "3          45.000000        26.000000   16.000000   30.000000  17.000000   \n",
       "4          46.000000        23.000000   13.000000   43.000000  14.000000   \n",
       "5          55.000000        20.000000   39.000000   48.000000  38.000000   \n",
       "6          50.000000        24.000000   61.000000   90.000000  55.000000   \n",
       "7          27.000000        26.000000    4.000000   55.000000   2.000000   \n",
       "8          47.875000        23.625000   20.250000  497.000000  18.375000   \n",
       "9          45.000000        26.000000   12.000000   39.000000  12.000000   \n",
       "10         45.000000        24.000000   18.000000   18.000000  15.000000   \n",
       "11         35.000000        24.000000    6.000000   28.000000   3.000000   \n",
       "12         45.000000        24.000000   32.000000   33.000000  17.000000   \n",
       "13         40.000000        23.000000   12.000000   20.000000   5.000000   \n",
       "14         49.000000        22.000000   36.000000   32.000000  32.000000   \n",
       "15         39.000000        23.000000   16.000000   24.000000   7.000000   \n",
       "16         53.000000        25.000000   24.000000   51.000000  22.000000   \n",
       "17         46.000000        19.000000   15.000000   29.000000  10.000000   \n",
       "18         40.000000        25.000000   15.000000   27.000000  12.000000   \n",
       "19         66.000000        17.000000   18.000000   33.000000  20.000000   \n",
       "20         50.000000        23.000000   16.000000   27.000000  17.000000   \n",
       "21         58.000000        21.000000   33.000000   38.000000  36.000000   \n",
       "22         55.000000        23.000000   12.000000   28.000000  13.000000   \n",
       "23         49.666667        25.666667   32.666667  284.000000  37.666667   \n",
       "24         39.500000        17.000000    1.500000   70.000000   0.500000   \n",
       "25         51.666667        23.000000   17.000000  169.000000  17.666667   \n",
       "26         42.000000        22.000000    9.000000   52.000000   5.000000   \n",
       "27         50.000000        29.000000    9.000000   69.000000   5.000000   \n",
       "28         53.000000        23.000000   12.000000   80.000000   6.500000   \n",
       "29         48.000000        21.333333   28.000000  331.000000  23.666667   \n",
       "30         46.333333        17.666667   14.333333  210.000000   6.666667   \n",
       "31         48.200000        20.400000    9.200000  138.000000   5.400000   \n",
       "32         53.307495        25.205947   29.583756   21.112107  22.979817   \n",
       "33         50.328780        22.365085   22.882483  138.945675  15.860325   \n",
       "34         45.047362        22.626458    7.615784   65.473676   4.944792   \n",
       "\n",
       "     Under_65  percent_above_65  Area_round_max  Area_round_mean  \\\n",
       "0   47.000000         14.545455       80.000000        63.000000   \n",
       "1   46.000000         25.806452       90.000000        77.000000   \n",
       "2   24.000000         31.428571       90.000000        62.000000   \n",
       "3   42.000000         28.813559       80.000000        80.000000   \n",
       "4   39.000000         26.415094      100.000000        66.000000   \n",
       "5   60.000000         38.775510       90.000000        69.000000   \n",
       "6   99.000000         35.714286       80.000000        62.000000   \n",
       "7    6.000000         25.000000       70.000000        57.000000   \n",
       "8   43.375000         29.204431      128.750000        77.000000   \n",
       "9   30.000000         28.571429      100.000000        88.000000   \n",
       "10  40.000000         27.272727      180.000000        94.000000   \n",
       "11  18.000000         14.285714       90.000000        90.000000   \n",
       "12  56.000000         23.287671      180.000000        83.000000   \n",
       "13  29.000000         14.705882       90.000000        79.000000   \n",
       "14  78.000000         29.090909      200.000000        77.000000   \n",
       "15  33.000000         17.500000       90.000000        70.000000   \n",
       "16  34.000000         39.285714      110.000000        83.000000   \n",
       "17  41.000000         19.607843       90.000000        78.000000   \n",
       "18  38.000000         24.000000      100.000000        82.000000   \n",
       "19  15.000000         57.142857       90.000000        74.000000   \n",
       "20  30.000000         36.170213       90.000000        77.000000   \n",
       "21  40.000000         47.368421      200.000000        84.000000   \n",
       "22  25.000000         34.210526      100.000000        87.000000   \n",
       "23  65.000000         33.289811      123.333333        91.666667   \n",
       "24   7.500000          3.571429      100.000000        97.500000   \n",
       "25  35.333333         33.559322      116.666667        85.666667   \n",
       "26  22.000000         18.518519      240.000000       121.000000   \n",
       "27  10.000000         33.333333      130.000000        80.000000   \n",
       "28  12.000000         32.453416      105.000000        94.000000   \n",
       "29  57.666667         29.582747       96.666667        60.333333   \n",
       "30  15.000000         21.880342       83.333333        70.666667   \n",
       "31  10.800000         22.200000       76.000000        66.000000   \n",
       "32  56.453747         17.933179      138.284957        84.249697   \n",
       "33  40.095408         20.586708       95.996652        86.267141   \n",
       "34  24.857197         23.673719       81.792247        70.309373   \n",
       "\n",
       "    Area_round_min  Area_round_mode  Area_round_std  Discount_sum  \\\n",
       "0        30.000000        70.000000       16.000000      5.000000   \n",
       "1        70.000000        70.000000        8.000000     12.000000   \n",
       "2        30.000000        50.000000       17.000000      7.000000   \n",
       "3        80.000000        80.000000        0.000000      7.000000   \n",
       "4        30.000000        60.000000       16.000000      5.000000   \n",
       "5        40.000000        80.000000       15.000000     18.000000   \n",
       "6        40.000000        70.000000        9.000000     30.000000   \n",
       "7        50.000000        50.000000        9.000000      2.000000   \n",
       "8        53.750000        73.750000       20.625000      8.750000   \n",
       "9        60.000000        90.000000       14.000000      6.000000   \n",
       "10       60.000000        80.000000       35.000000      8.000000   \n",
       "11       90.000000        90.000000        0.000000      5.000000   \n",
       "12       60.000000        70.000000       25.000000     12.000000   \n",
       "13       60.000000        80.000000        9.000000      3.000000   \n",
       "14       50.000000        70.000000       29.000000     15.000000   \n",
       "15       60.000000        60.000000       12.000000      5.000000   \n",
       "16       80.000000        80.000000       10.000000     16.000000   \n",
       "17       40.000000        90.000000       16.000000      4.000000   \n",
       "18       70.000000        70.000000       12.000000      6.000000   \n",
       "19       60.000000        80.000000       10.000000     12.000000   \n",
       "20       50.000000        90.000000       13.000000      8.000000   \n",
       "21       70.000000        90.000000       22.000000     19.000000   \n",
       "22       70.000000       100.000000       13.000000      7.000000   \n",
       "23       76.666667        90.000000       15.000000     12.666667   \n",
       "24       95.000000        95.000000        3.500000      0.000000   \n",
       "25       60.000000        83.333333       15.000000      8.000000   \n",
       "26      100.000000       100.000000       45.000000      3.000000   \n",
       "27       30.000000        80.000000       25.000000      4.000000   \n",
       "28       80.000000        90.000000       12.000000      1.500000   \n",
       "29       43.333333        60.000000       10.333333     13.000000   \n",
       "30       46.666667        66.666667       12.666667      6.666667   \n",
       "31       58.000000        64.000000        6.000000      3.800000   \n",
       "32       59.275692        68.597426       15.739572      3.463875   \n",
       "33       76.753637        86.430530        7.615565     10.405044   \n",
       "34       69.610421        87.254877        8.187810      9.500606   \n",
       "\n",
       "    High_discount_sum  Low_Discount_35_sum  No_Discount_sum  Owner_sum  \\\n",
       "0            4.000000             1.000000        15.000000  12.000000   \n",
       "1            8.000000             4.000000         4.000000  10.000000   \n",
       "2            4.000000             3.000000        10.000000  10.000000   \n",
       "3            6.000000             1.000000         9.000000  10.000000   \n",
       "4            3.000000             2.000000         8.000000   8.000000   \n",
       "5           10.000000             8.000000        21.000000  27.000000   \n",
       "6           18.000000            12.000000        31.000000  39.000000   \n",
       "7            2.000000             0.000000         2.000000   2.000000   \n",
       "8            5.750000             3.000000        11.375000  10.375000   \n",
       "9            3.000000             3.000000         6.000000   8.000000   \n",
       "10           7.000000             1.000000        10.000000   6.000000   \n",
       "11           5.000000             0.000000         1.000000   6.000000   \n",
       "12           8.000000             4.000000        20.000000  25.000000   \n",
       "13           2.000000             1.000000         9.000000   8.000000   \n",
       "14           7.000000             8.000000        20.000000  25.000000   \n",
       "15           1.000000             4.000000        11.000000  14.000000   \n",
       "16           8.000000             8.000000         8.000000  17.000000   \n",
       "17           2.000000             2.000000        11.000000  13.000000   \n",
       "18           4.000000             2.000000         9.000000   9.000000   \n",
       "19           8.000000             4.000000         6.000000  13.000000   \n",
       "20           7.000000             1.000000         8.000000  10.000000   \n",
       "21          10.000000             9.000000        14.000000  26.000000   \n",
       "22           5.000000             2.000000         5.000000   7.000000   \n",
       "23           7.333333             5.333333        19.666667  21.666667   \n",
       "24           0.000000             0.000000         1.500000   0.000000   \n",
       "25           4.666667             3.333333         9.000000  11.333333   \n",
       "26           1.000000             2.000000         6.000000   7.000000   \n",
       "27           2.000000             2.000000         5.000000   4.000000   \n",
       "28           1.500000             0.000000         2.000000   3.500000   \n",
       "29           9.666667             3.333333        15.000000  15.666667   \n",
       "30           3.333333             3.333333         7.666667   9.000000   \n",
       "31           3.000000             0.800000         5.400000   4.800000   \n",
       "32           6.346420             2.572850        11.297380  10.030725   \n",
       "33           1.933758             4.400782        10.606053  18.928480   \n",
       "34           7.756946             3.553614         3.419025   4.122822   \n",
       "\n",
       "    Renter_sum  discount_rate_mean  total_appartments  discount_percent  \\\n",
       "0     8.000000            0.150829          20.000000          0.250000   \n",
       "1     6.000000            0.480824          16.000000          0.750000   \n",
       "2     7.000000            0.284133          17.000000          0.411765   \n",
       "3     6.000000            0.285945          16.000000          0.437500   \n",
       "4     5.000000            0.243069          13.000000          0.384615   \n",
       "5    12.000000            0.226707          39.000000          0.461538   \n",
       "6    22.000000            0.296857          61.000000          0.491803   \n",
       "7     2.000000            0.500000           4.000000          0.500000   \n",
       "8     9.750000            0.258501          20.125000          0.435034   \n",
       "9     4.000000            0.254921          12.000000          0.500000   \n",
       "10   12.000000            0.314753          18.000000          0.444444   \n",
       "11    0.000000            0.375612           6.000000          0.833333   \n",
       "12    7.000000            0.210411          32.000000          0.375000   \n",
       "13    4.000000            0.187500          12.000000          0.250000   \n",
       "14   10.000000            0.187275          35.000000          0.428571   \n",
       "15    2.000000            0.073787          16.000000          0.312500   \n",
       "16    7.000000            0.348805          24.000000          0.666667   \n",
       "17    2.000000            0.107741          15.000000          0.266667   \n",
       "18    6.000000            0.218468          15.000000          0.400000   \n",
       "19    5.000000            0.363162          18.000000          0.666667   \n",
       "20    6.000000            0.344729          16.000000          0.500000   \n",
       "21    7.000000            0.318202          33.000000          0.575758   \n",
       "22    5.000000            0.308843          12.000000          0.583333   \n",
       "23   10.666667            0.175878          32.333333          0.443831   \n",
       "24    1.500000            0.000000           1.500000          0.000000   \n",
       "25    5.666667            0.223397          17.000000          0.460752   \n",
       "26    2.000000            0.151063           9.000000          0.333333   \n",
       "27    5.000000            0.224987           9.000000          0.444444   \n",
       "28    0.000000            0.290009           3.500000          0.300000   \n",
       "29   12.333333            0.343599          28.000000          0.473545   \n",
       "30    5.333333            0.239939          14.333333          0.473684   \n",
       "31    4.400000            0.301113           9.200000          0.426667   \n",
       "32    4.499744            0.377727          19.463017          0.262629   \n",
       "33    6.758559            0.261629          22.415725          0.527710   \n",
       "34    4.718750            0.268917          29.487131          0.272619   \n",
       "\n",
       "    Low_discount  High_discount  No_discount  Owner_Percent  newApartmentSize  \n",
       "0       0.050000       0.200000     0.750000       0.600000         82.000000  \n",
       "1       0.250000       0.500000     0.250000       0.625000         82.000000  \n",
       "2       0.176471       0.235294     0.588235       0.588235         62.000000  \n",
       "3       0.062500       0.375000     0.562500       0.625000         92.000000  \n",
       "4       0.153846       0.230769     0.615385       0.615385         85.000000  \n",
       "5       0.205128       0.256410     0.538462       0.692308         92.000000  \n",
       "6       0.196721       0.295082     0.508197       0.639344         82.000000  \n",
       "7       0.000000       0.500000     0.500000       0.500000         75.000000  \n",
       "8       0.160623       0.274411     0.564966       0.544690         98.750000  \n",
       "9       0.250000       0.250000     0.500000       0.666667        115.000000  \n",
       "10      0.055556       0.388889     0.555556       0.333333         92.000000  \n",
       "11      0.000000       0.833333     0.166667       1.000000        115.000000  \n",
       "12      0.125000       0.250000     0.625000       0.781250         82.000000  \n",
       "13      0.083333       0.166667     0.750000       0.666667         92.000000  \n",
       "14      0.228571       0.200000     0.571429       0.714286         82.000000  \n",
       "15      0.250000       0.062500     0.687500       0.875000         72.000000  \n",
       "16      0.333333       0.333333     0.333333       0.708333         92.000000  \n",
       "17      0.133333       0.133333     0.733333       0.866667        102.000000  \n",
       "18      0.133333       0.266667     0.600000       0.600000         82.000000  \n",
       "19      0.222222       0.444444     0.333333       0.722222         92.000000  \n",
       "20      0.062500       0.437500     0.500000       0.625000        102.000000  \n",
       "21      0.272727       0.303030     0.424242       0.787879        102.000000  \n",
       "22      0.166667       0.416667     0.416667       0.583333        112.000000  \n",
       "23      0.281385       0.162446     0.556169       0.607413        115.000000  \n",
       "24      0.000000       0.000000     1.000000       0.000000        120.000000  \n",
       "25      0.184461       0.276291     0.539248       0.701153        108.333333  \n",
       "26      0.222222       0.111111     0.666667       0.777778        125.000000  \n",
       "27      0.222222       0.222222     0.555556       0.444444        105.000000  \n",
       "28      0.000000       0.300000     0.700000       1.000000        115.000000  \n",
       "29      0.084656       0.388889     0.526455       0.591270         85.000000  \n",
       "30      0.236842       0.236842     0.526316       0.627193         91.666667  \n",
       "31      0.047222       0.379444     0.573333       0.480000         89.000000  \n",
       "32      0.112947       0.482004     0.689719       0.466555         93.597426  \n",
       "33      0.233112       0.204653     0.428229       0.589800         98.430530  \n",
       "34      0.138385       0.397476     0.536115       0.439184        112.254877  "
      ]
     },
     "execution_count": 801,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "buildingsDf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 802,
   "id": "ethical-oracle",
   "metadata": {},
   "outputs": [],
   "source": [
    "buildingsDf['newApartmentSize'] = buildingsDf['Area_round_mode'] + buildingsDf['ProjType'].apply(lambda x: 12 if x==1 else 25 )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "specific-campaign",
   "metadata": {},
   "source": [
    "## Need to add floors to the old and new buidlings\n",
    "## Need to add floors to the old and new buidlings Rent and House Price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 839,
   "id": "renewable-summit",
   "metadata": {},
   "outputs": [],
   "source": [
    "newApartmentColums = ['bldCode','doorIndex','bldCodeDoorIndex','aprtmentSize','prjectType','ProjNumber']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 840,
   "id": "electric-hollywood",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['bldCode',\n",
       " 'doorIndex',\n",
       " 'bldCodeDoorIndex',\n",
       " 'aprtmentSize',\n",
       " 'prjectType',\n",
       " 'ProjNumber']"
      ]
     },
     "execution_count": 840,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newApartmentColums"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 841,
   "id": "mental-fundamental",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 0 entries\n",
      "Data columns (total 6 columns):\n",
      " #   Column            Non-Null Count  Dtype \n",
      "---  ------            --------------  ----- \n",
      " 0   bldCode           0 non-null      object\n",
      " 1   doorIndex         0 non-null      object\n",
      " 2   bldCodeDoorIndex  0 non-null      object\n",
      " 3   aprtmentSize      0 non-null      object\n",
      " 4   prjectType        0 non-null      object\n",
      " 5   ProjNumber        0 non-null      object\n",
      "dtypes: object(6)\n",
      "memory usage: 0.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "newApartmentsDS = pd.DataFrame(columns=newApartmentColums)\n",
    "newApartmentsDS.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 842,
   "id": "hundred-replication",
   "metadata": {},
   "outputs": [],
   "source": [
    "newApartmentsDS['aprtmentSize'] = newApartmentsDS['aprtmentSize'].astype('int') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 843,
   "id": "copyrighted-munich",
   "metadata": {},
   "outputs": [],
   "source": [
    "newApartmentsDSDSJustHead = newApartmentsDS.copy() # Just Leave Head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 844,
   "id": "popular-implement",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bldCode</th>\n",
       "      <th>doorIndex</th>\n",
       "      <th>bldCodeDoorIndex</th>\n",
       "      <th>aprtmentSize</th>\n",
       "      <th>prjectType</th>\n",
       "      <th>ProjNumber</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [bldCode, doorIndex, bldCodeDoorIndex, aprtmentSize, prjectType, ProjNumber]\n",
       "Index: []"
      ]
     },
     "execution_count": 844,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newApartmentsDSDSJustHead"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 853,
   "id": "varied-message",
   "metadata": {},
   "outputs": [],
   "source": [
    "mainNewBldColumns = ['AfterBldgs','ProjType','after_app','newApartmentSize','ProjNumber']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 854,
   "id": "billion-symbol",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>BeforeBldgs</th>\n",
       "      <th>AfterBldgs</th>\n",
       "      <th>ProjType</th>\n",
       "      <th>ProjNumber</th>\n",
       "      <th>OrderA</th>\n",
       "      <th>OrderB</th>\n",
       "      <th>OrderC</th>\n",
       "      <th>Row Labels</th>\n",
       "      <th>min_living_till_2020</th>\n",
       "      <th>max_living_till_2020</th>\n",
       "      <th>avg_living_till_2020</th>\n",
       "      <th>Min_age_2020</th>\n",
       "      <th>Max_age_2020</th>\n",
       "      <th>Average_age_2020</th>\n",
       "      <th>StdDev_age_2020</th>\n",
       "      <th>Before_app</th>\n",
       "      <th>after_app</th>\n",
       "      <th>Above_65</th>\n",
       "      <th>Under_65</th>\n",
       "      <th>percent_above_65</th>\n",
       "      <th>Area_round_max</th>\n",
       "      <th>Area_round_mean</th>\n",
       "      <th>Area_round_min</th>\n",
       "      <th>Area_round_mode</th>\n",
       "      <th>Area_round_std</th>\n",
       "      <th>Discount_sum</th>\n",
       "      <th>High_discount_sum</th>\n",
       "      <th>Low_Discount_35_sum</th>\n",
       "      <th>No_Discount_sum</th>\n",
       "      <th>Owner_sum</th>\n",
       "      <th>Renter_sum</th>\n",
       "      <th>discount_rate_mean</th>\n",
       "      <th>total_appartments</th>\n",
       "      <th>discount_percent</th>\n",
       "      <th>Low_discount</th>\n",
       "      <th>High_discount</th>\n",
       "      <th>No_discount</th>\n",
       "      <th>Owner_Percent</th>\n",
       "      <th>newApartmentSize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>210_19</td>\n",
       "      <td>210_19_a</td>\n",
       "      <td>1</td>\n",
       "      <td>20180988</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>14.545455</td>\n",
       "      <td>80.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.150829</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>210_14</td>\n",
       "      <td>210_14_a</td>\n",
       "      <td>1</td>\n",
       "      <td>20100133</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>25.806452</td>\n",
       "      <td>90.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.480824</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>208_32</td>\n",
       "      <td>208_32_a</td>\n",
       "      <td>1</td>\n",
       "      <td>20180812</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>31.428571</td>\n",
       "      <td>90.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.284133</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.411765</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.235294</td>\n",
       "      <td>0.588235</td>\n",
       "      <td>0.588235</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>210_3</td>\n",
       "      <td>210_3_a</td>\n",
       "      <td>1</td>\n",
       "      <td>20181092</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>28.813559</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.285945</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.562500</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>210_35</td>\n",
       "      <td>210_35_r</td>\n",
       "      <td>2</td>\n",
       "      <td>20171974</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>13</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>26.415094</td>\n",
       "      <td>100.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.243069</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 BeforeBldgs AfterBldgs  ProjType ProjNumber  OrderA  OrderB  \\\n",
       "0           0      210_19   210_19_a         1   20180988       1       1   \n",
       "1           1      210_14   210_14_a         1   20100133       2       2   \n",
       "2           2      208_32   208_32_a         1   20180812       3       3   \n",
       "3           3       210_3    210_3_a         1   20181092       4       4   \n",
       "4           4      210_35   210_35_r         2   20171974       5      20   \n",
       "\n",
       "   OrderC  Row Labels  min_living_till_2020  max_living_till_2020  \\\n",
       "0      17         1.0                   2.0                  35.0   \n",
       "1      18         1.0                   2.0                  57.0   \n",
       "2      19         1.0                   2.0                  33.0   \n",
       "3      20         1.0                   2.0                  54.0   \n",
       "4      13         1.0                   2.0                  43.0   \n",
       "\n",
       "   avg_living_till_2020  Min_age_2020  Max_age_2020  Average_age_2020  \\\n",
       "0                   9.0           5.0          82.0              45.0   \n",
       "1                  19.0           2.0          98.0              48.0   \n",
       "2                  10.0           5.0          89.0              46.0   \n",
       "3                  15.0           2.0          99.0              45.0   \n",
       "4                  10.0           3.0          90.0              46.0   \n",
       "\n",
       "   StdDev_age_2020  Before_app  after_app  Above_65  Under_65  \\\n",
       "0             19.0        20.0       41.0       8.0      47.0   \n",
       "1             24.0        16.0       26.0      16.0      46.0   \n",
       "2             23.0        17.0       34.0      11.0      24.0   \n",
       "3             26.0        16.0       30.0      17.0      42.0   \n",
       "4             23.0        13.0       43.0      14.0      39.0   \n",
       "\n",
       "   percent_above_65  Area_round_max  Area_round_mean  Area_round_min  \\\n",
       "0         14.545455            80.0             63.0            30.0   \n",
       "1         25.806452            90.0             77.0            70.0   \n",
       "2         31.428571            90.0             62.0            30.0   \n",
       "3         28.813559            80.0             80.0            80.0   \n",
       "4         26.415094           100.0             66.0            30.0   \n",
       "\n",
       "   Area_round_mode  Area_round_std  Discount_sum  High_discount_sum  \\\n",
       "0             70.0            16.0           5.0                4.0   \n",
       "1             70.0             8.0          12.0                8.0   \n",
       "2             50.0            17.0           7.0                4.0   \n",
       "3             80.0             0.0           7.0                6.0   \n",
       "4             60.0            16.0           5.0                3.0   \n",
       "\n",
       "   Low_Discount_35_sum  No_Discount_sum  Owner_sum  Renter_sum  \\\n",
       "0                  1.0             15.0       12.0         8.0   \n",
       "1                  4.0              4.0       10.0         6.0   \n",
       "2                  3.0             10.0       10.0         7.0   \n",
       "3                  1.0              9.0       10.0         6.0   \n",
       "4                  2.0              8.0        8.0         5.0   \n",
       "\n",
       "   discount_rate_mean  total_appartments  discount_percent  Low_discount  \\\n",
       "0            0.150829               20.0          0.250000      0.050000   \n",
       "1            0.480824               16.0          0.750000      0.250000   \n",
       "2            0.284133               17.0          0.411765      0.176471   \n",
       "3            0.285945               16.0          0.437500      0.062500   \n",
       "4            0.243069               13.0          0.384615      0.153846   \n",
       "\n",
       "   High_discount  No_discount  Owner_Percent  newApartmentSize  \n",
       "0       0.200000     0.750000       0.600000              82.0  \n",
       "1       0.500000     0.250000       0.625000              82.0  \n",
       "2       0.235294     0.588235       0.588235              62.0  \n",
       "3       0.375000     0.562500       0.625000              92.0  \n",
       "4       0.230769     0.615385       0.615385              85.0  "
      ]
     },
     "execution_count": 854,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "buildingsDf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 858,
   "id": "fleet-strain",
   "metadata": {},
   "outputs": [],
   "source": [
    "def creatNewApartments(bldgInfo,hedersonly):\n",
    "    currentBldgloop = hedersonly.copy()\n",
    "    currentBldg = hedersonly.copy()\n",
    "    for bldItem in bldgInfo.code:\n",
    "        currentBldgloop['doorIndex'] = [door for door in range(1,int(bldgInfo.numApartments)+1)]\n",
    "        currentBldgloop['bldCode'] = bldItem # Coppy Clean Slate\n",
    "        currentBldgloop['bldCodeDoorIndex'] = currentBldgloop['bldCode']+\"_\"+currentBldgloop['doorIndex'].astype(str)\n",
    "        currentBldgloop['aprtmentSize'] = bldgInfo.apartmentSize\n",
    "        currentBldgloop['prjectType'] = bldgInfo.ProjType\n",
    "        currentBldgloop['ProjNumber'] = bldgInfo.ProjNumber\n",
    "        if bldItem==bldgInfo.code[0]:\n",
    "            currentBldg = currentBldgloop\n",
    "        else:\n",
    "            currentBldg = currentBldg.append(currentBldgloop)\n",
    "            currentBldg.reset_index(inplace=True,drop=True)\n",
    "        currentBldgloop = hedersonly.copy()\n",
    "    return currentBldg.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 860,
   "id": "hybrid-cowboy",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in buildingsDf.index:\n",
    "    bldgInfo = buildingsDf.loc[i,mainNewBldColumns]\n",
    "    bldgInfo.numApartments = bldgInfo['after_app'].astype('int')\n",
    "    bldgInfo.apartmentSize = bldgInfo['newApartmentSize'].astype('int')\n",
    "    bldgInfo.code = bldgInfo['AfterBldgs'].split(',')\n",
    "    bldgInfo.ProjType = bldgInfo['ProjType']\n",
    "    bldgInfo.ProjNumber = bldgInfo['ProjNumber']\n",
    "\n",
    "    newBldg = creatNewApartments(bldgInfo,newApartmentsDSDSJustHead.copy())\n",
    "    if (i==0):\n",
    "        newApartmentsDS = newBldg\n",
    "    else:\n",
    "        newApartmentsDS = newApartmentsDS.append(newBldg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 861,
   "id": "decreased-outdoors",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AfterBldgs            201_75_rr\n",
       "ProjType                      3\n",
       "after_app             65.473676\n",
       "newApartmentSize     112.254877\n",
       "ProjNumber          502-0368860\n",
       "Name: 34, dtype: object"
      ]
     },
     "execution_count": 861,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bldgInfo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 864,
   "id": "varied-terry",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bldCode</th>\n",
       "      <th>doorIndex</th>\n",
       "      <th>bldCodeDoorIndex</th>\n",
       "      <th>aprtmentSize</th>\n",
       "      <th>prjectType</th>\n",
       "      <th>ProjNumber</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>210_19_a</td>\n",
       "      <td>1</td>\n",
       "      <td>210_19_a_1</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>20180988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>210_19_a</td>\n",
       "      <td>2</td>\n",
       "      <td>210_19_a_2</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>20180988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>210_19_a</td>\n",
       "      <td>3</td>\n",
       "      <td>210_19_a_3</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>20180988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>210_19_a</td>\n",
       "      <td>4</td>\n",
       "      <td>210_19_a_4</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>20180988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>210_19_a</td>\n",
       "      <td>5</td>\n",
       "      <td>210_19_a_5</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>20180988</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>201_75_rr</td>\n",
       "      <td>61</td>\n",
       "      <td>201_75_rr_61</td>\n",
       "      <td>112</td>\n",
       "      <td>3</td>\n",
       "      <td>502-0368860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>201_75_rr</td>\n",
       "      <td>62</td>\n",
       "      <td>201_75_rr_62</td>\n",
       "      <td>112</td>\n",
       "      <td>3</td>\n",
       "      <td>502-0368860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>201_75_rr</td>\n",
       "      <td>63</td>\n",
       "      <td>201_75_rr_63</td>\n",
       "      <td>112</td>\n",
       "      <td>3</td>\n",
       "      <td>502-0368860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>201_75_rr</td>\n",
       "      <td>64</td>\n",
       "      <td>201_75_rr_64</td>\n",
       "      <td>112</td>\n",
       "      <td>3</td>\n",
       "      <td>502-0368860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>201_75_rr</td>\n",
       "      <td>65</td>\n",
       "      <td>201_75_rr_65</td>\n",
       "      <td>112</td>\n",
       "      <td>3</td>\n",
       "      <td>502-0368860</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7877 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      bldCode  doorIndex bldCodeDoorIndex  aprtmentSize  prjectType  \\\n",
       "0    210_19_a          1       210_19_a_1            82           1   \n",
       "1    210_19_a          2       210_19_a_2            82           1   \n",
       "2    210_19_a          3       210_19_a_3            82           1   \n",
       "3    210_19_a          4       210_19_a_4            82           1   \n",
       "4    210_19_a          5       210_19_a_5            82           1   \n",
       "..        ...        ...              ...           ...         ...   \n",
       "60  201_75_rr         61     201_75_rr_61           112           3   \n",
       "61  201_75_rr         62     201_75_rr_62           112           3   \n",
       "62  201_75_rr         63     201_75_rr_63           112           3   \n",
       "63  201_75_rr         64     201_75_rr_64           112           3   \n",
       "64  201_75_rr         65     201_75_rr_65           112           3   \n",
       "\n",
       "     ProjNumber  \n",
       "0      20180988  \n",
       "1      20180988  \n",
       "2      20180988  \n",
       "3      20180988  \n",
       "4      20180988  \n",
       "..          ...  \n",
       "60  502-0368860  \n",
       "61  502-0368860  \n",
       "62  502-0368860  \n",
       "63  502-0368860  \n",
       "64  502-0368860  \n",
       "\n",
       "[7877 rows x 6 columns]"
      ]
     },
     "execution_count": 864,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newApartmentsDS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 865,
   "id": "improved-transaction",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 865,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "newApartmentsDS['prjectType'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 866,
   "id": "false-bracelet",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 866,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "newApartmentsDS['aprtmentSize'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 867,
   "id": "comparative-legislation",
   "metadata": {},
   "outputs": [],
   "source": [
    "newApartmentsDS.to_excel('newApartmentsDataSetApril_6.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cutting-brass",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "geo_env",
   "language": "python",
   "name": "geo_env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}