34_EMA_tryouts.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Workbench imports\n",
    "import math\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from scipy.optimize import brentq\n",
    "\n",
    "from pathlib import Path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load results from EMA Workbench run (see notebook 30), inspect and clean the raw results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ginkel\\AppData\\Local\\Continuum\\anaconda3\\envs\\urban-SETP\\lib\\site-packages\\ema_workbench\\em_framework\\evaluators.py:21: UserWarning: ipyparallel not installed - IpyparalleEvaluator not available\n",
      "  warnings.warn(\n",
      "C:\\Users\\Ginkel\\AppData\\Local\\Continuum\\anaconda3\\envs\\urban-SETP\\lib\\site-packages\\ema_workbench\\em_framework\\optimization.py:48: ImportWarning: platypus based optimization not available\n",
      "  warnings.warn(\"platypus based optimization not available\", ImportWarning)\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Housing_market</th>\n",
       "      <th>SLR</th>\n",
       "      <th>implementation_time</th>\n",
       "      <th>transient</th>\n",
       "      <th>Mayor</th>\n",
       "      <th>scenario</th>\n",
       "      <th>policy</th>\n",
       "      <th>model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>395995</th>\n",
       "      <td>rational</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>SurgeHeight\\five_hundred\\five_hundred_430.csv</td>\n",
       "      <td>Lawkeeper</td>\n",
       "      <td>98995</td>\n",
       "      <td>3</td>\n",
       "      <td>Rotty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395996</th>\n",
       "      <td>rational</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>SurgeHeight\\five_hundred\\five_hundred_453.csv</td>\n",
       "      <td>Lawkeeper</td>\n",
       "      <td>98996</td>\n",
       "      <td>3</td>\n",
       "      <td>Rotty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395997</th>\n",
       "      <td>boundedly_rational</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>SurgeHeight\\five_hundred\\five_hundred_157.csv</td>\n",
       "      <td>Lawkeeper</td>\n",
       "      <td>98997</td>\n",
       "      <td>3</td>\n",
       "      <td>Rotty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395998</th>\n",
       "      <td>boundedly_rational</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>SurgeHeight\\five_hundred\\five_hundred_32.csv</td>\n",
       "      <td>Lawkeeper</td>\n",
       "      <td>98998</td>\n",
       "      <td>3</td>\n",
       "      <td>Rotty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395999</th>\n",
       "      <td>rational</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>SurgeHeight\\five_hundred\\five_hundred_308.csv</td>\n",
       "      <td>Lawkeeper</td>\n",
       "      <td>98999</td>\n",
       "      <td>3</td>\n",
       "      <td>Rotty</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Housing_market SLR implementation_time  \\\n",
       "395995            rational   1                   4   \n",
       "395996            rational   3                  13   \n",
       "395997  boundedly_rational   3                  10   \n",
       "395998  boundedly_rational   5                  11   \n",
       "395999            rational   6                   4   \n",
       "\n",
       "                                            transient      Mayor scenario  \\\n",
       "395995  SurgeHeight\\five_hundred\\five_hundred_430.csv  Lawkeeper    98995   \n",
       "395996  SurgeHeight\\five_hundred\\five_hundred_453.csv  Lawkeeper    98996   \n",
       "395997  SurgeHeight\\five_hundred\\five_hundred_157.csv  Lawkeeper    98997   \n",
       "395998   SurgeHeight\\five_hundred\\five_hundred_32.csv  Lawkeeper    98998   \n",
       "395999  SurgeHeight\\five_hundred\\five_hundred_308.csv  Lawkeeper    98999   \n",
       "\n",
       "       policy  model  \n",
       "395995      3  Rotty  \n",
       "395996      3  Rotty  \n",
       "395997      3  Rotty  \n",
       "395998      3  Rotty  \n",
       "395999      3  Rotty  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ema_workbench import load_results\n",
    "\n",
    "target = Path(\"temp\",\"experiments\")\n",
    "experiments, outcomes = load_results(target / \"2021_2_23_EMA_experiments_five_hundred.tar.gz\")\n",
    "experiments.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Housing_market</th>\n",
       "      <th>SLR</th>\n",
       "      <th>implementation_time</th>\n",
       "      <th>transient</th>\n",
       "      <th>Mayor</th>\n",
       "      <th>scenario</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>boundedly_rational</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>SurgeHeight\\five_hundred\\five_hundred_119.csv</td>\n",
       "      <td>R. Active</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>boundedly_rational</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>SurgeHeight\\five_hundred\\five_hundred_90.csv</td>\n",
       "      <td>R. Active</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>boundedly_rational</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>SurgeHeight\\five_hundred\\five_hundred_220.csv</td>\n",
       "      <td>R. Active</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>boundedly_rational</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>SurgeHeight\\five_hundred\\five_hundred_141.csv</td>\n",
       "      <td>R. Active</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>rational</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>SurgeHeight\\five_hundred\\five_hundred_7.csv</td>\n",
       "      <td>R. Active</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Housing_market SLR implementation_time  \\\n",
       "0  boundedly_rational   4                  12   \n",
       "1  boundedly_rational   4                  14   \n",
       "2  boundedly_rational   2                  10   \n",
       "3  boundedly_rational   9                   9   \n",
       "4            rational   5                  13   \n",
       "\n",
       "                                       transient      Mayor scenario  \n",
       "0  SurgeHeight\\five_hundred\\five_hundred_119.csv  R. Active        0  \n",
       "1   SurgeHeight\\five_hundred\\five_hundred_90.csv  R. Active        1  \n",
       "2  SurgeHeight\\five_hundred\\five_hundred_220.csv  R. Active        2  \n",
       "3  SurgeHeight\\five_hundred\\five_hundred_141.csv  R. Active        3  \n",
       "4    SurgeHeight\\five_hundred\\five_hundred_7.csv  R. Active        4  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "experiments2 = experiments.drop(['policy','model'],axis=1)\n",
    "experiments2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 396000 entries, 0 to 395999\n",
      "Data columns (total 6 columns):\n",
      " #   Column               Non-Null Count   Dtype   \n",
      "---  ------               --------------   -----   \n",
      " 0   Housing_market       396000 non-null  category\n",
      " 1   SLR                  396000 non-null  category\n",
      " 2   implementation_time  396000 non-null  category\n",
      " 3   transient            396000 non-null  category\n",
      " 4   Mayor                396000 non-null  category\n",
      " 5   scenario             396000 non-null  category\n",
      "dtypes: category(6)\n",
      "memory usage: 7.1 MB\n"
     ]
    }
   ],
   "source": [
    "experiments2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'HP_hp_2200': array([0, 0, 0, ..., 0, 0, 0], dtype=int64),\n",
       " 'CC_hp_2200': array([282357.9820158 , 286542.15584763,      0.        , ...,\n",
       "        350017.41980036, 350036.70600789, 350040.12775439]),\n",
       " 'HP_first_SETP': array([2081, 2080, 2040, ..., 2051, 2055, 9999], dtype=int64),\n",
       " 'CC_first_SETP': array([2096, 2125, 2106, ..., 9999, 9999, 9999], dtype=int64)}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcomes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiments2['CC_first_SETP'] = outcomes['CC_first_SETP']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Housing_market</th>\n",
       "      <th>SLR</th>\n",
       "      <th>implementation_time</th>\n",
       "      <th>transient</th>\n",
       "      <th>Mayor</th>\n",
       "      <th>scenario</th>\n",
       "      <th>CC_first_SETP</th>\n",
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       "      <th>0</th>\n",
       "      <td>boundedly_rational</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>SurgeHeight\\five_hundred\\five_hundred_119.csv</td>\n",
       "      <td>R. Active</td>\n",
       "      <td>0</td>\n",
       "      <td>2096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>boundedly_rational</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>SurgeHeight\\five_hundred\\five_hundred_90.csv</td>\n",
       "      <td>R. Active</td>\n",
       "      <td>1</td>\n",
       "      <td>2125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>boundedly_rational</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>SurgeHeight\\five_hundred\\five_hundred_220.csv</td>\n",
       "      <td>R. Active</td>\n",
       "      <td>2</td>\n",
       "      <td>2106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>boundedly_rational</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>SurgeHeight\\five_hundred\\five_hundred_141.csv</td>\n",
       "      <td>R. Active</td>\n",
       "      <td>3</td>\n",
       "      <td>9999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>rational</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>SurgeHeight\\five_hundred\\five_hundred_7.csv</td>\n",
       "      <td>R. Active</td>\n",
       "      <td>4</td>\n",
       "      <td>9999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Housing_market SLR implementation_time  \\\n",
       "0  boundedly_rational   4                  12   \n",
       "1  boundedly_rational   4                  14   \n",
       "2  boundedly_rational   2                  10   \n",
       "3  boundedly_rational   9                   9   \n",
       "4            rational   5                  13   \n",
       "\n",
       "                                       transient      Mayor scenario  \\\n",
       "0  SurgeHeight\\five_hundred\\five_hundred_119.csv  R. Active        0   \n",
       "1   SurgeHeight\\five_hundred\\five_hundred_90.csv  R. Active        1   \n",
       "2  SurgeHeight\\five_hundred\\five_hundred_220.csv  R. Active        2   \n",
       "3  SurgeHeight\\five_hundred\\five_hundred_141.csv  R. Active        3   \n",
       "4    SurgeHeight\\five_hundred\\five_hundred_7.csv  R. Active        4   \n",
       "\n",
       "   CC_first_SETP  \n",
       "0           2096  \n",
       "1           2125  \n",
       "2           2106  \n",
       "3           9999  \n",
       "4           9999  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "experiments2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# This is to explore sensitivity for storm surge scenarios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Housing_market</th>\n",
       "      <th>SLR</th>\n",
       "      <th>implementation_time</th>\n",
       "      <th>transient</th>\n",
       "      <th>Mayor</th>\n",
       "      <th>scenario</th>\n",
       "      <th>CC_first_SETP</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>boundedly_rational</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>65</td>\n",
       "      <td>R. Active</td>\n",
       "      <td>7</td>\n",
       "      <td>2100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>boundedly_rational</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>267</td>\n",
       "      <td>R. Active</td>\n",
       "      <td>19</td>\n",
       "      <td>2137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>boundedly_rational</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>430</td>\n",
       "      <td>R. Active</td>\n",
       "      <td>27</td>\n",
       "      <td>2190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>boundedly_rational</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>251</td>\n",
       "      <td>R. Active</td>\n",
       "      <td>55</td>\n",
       "      <td>2113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>boundedly_rational</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>83</td>\n",
       "      <td>R. Active</td>\n",
       "      <td>79</td>\n",
       "      <td>2097</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Housing_market SLR implementation_time transient      Mayor scenario  \\\n",
       "7   boundedly_rational   3                  12        65  R. Active        7   \n",
       "19  boundedly_rational   3                   9       267  R. Active       19   \n",
       "27  boundedly_rational   3                  11       430  R. Active       27   \n",
       "55  boundedly_rational   3                  14       251  R. Active       55   \n",
       "79  boundedly_rational   3                  12        83  R. Active       79   \n",
       "\n",
       "    CC_first_SETP  \n",
       "7            2100  \n",
       "19           2137  \n",
       "27           2190  \n",
       "55           2113  \n",
       "79           2097  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Select only BR housing market\n",
    "exp = experiments2.copy()\n",
    "exp['transient'] = exp['transient'].apply(lambda x : int(x.split('five_hundred_')[1].split('.')[0]))\n",
    "exp = exp.loc[(exp['Housing_market'] == 'boundedly_rational') & (exp['SLR'] == 3) & (exp['CC_first_SETP'] <= 2200)]\n",
    "exp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "130    56\n",
       "205    52\n",
       "490    47\n",
       "328    46\n",
       "477    45\n",
       "       ..\n",
       "238     6\n",
       "101     6\n",
       "326     6\n",
       "463     5\n",
       "287     4\n",
       "Name: transient, Length: 500, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp['transient'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2b3dc34ebe0>"
      ]
     },
     "execution_count": 49,
     "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": [
    "exp['transient'].value_counts().hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2b3dde839a0>"
      ]
     },
     "execution_count": 61,
     "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": [
    "#Select only BR housing market\n",
    "exp = experiments2.copy()\n",
    "exp['transient'] = exp['transient'].apply(lambda x : int(x.split('five_hundred_')[1].split('.')[0]))\n",
    "exp = exp.loc[(exp['Housing_market'] == 'boundedly_rational') &  (exp['SLR'] == 4) & (exp['CC_first_SETP'] <= 2150)]\n",
    "exp['transient'].value_counts().hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "494    47\n",
       "404    41\n",
       "282    39\n",
       "158    39\n",
       "48     37\n",
       "       ..\n",
       "169     0\n",
       "231     0\n",
       "170     0\n",
       "433     0\n",
       "19      0\n",
       "Name: transient, Length: 500, dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp['transient'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2b3ddf78be0>"
      ]
     },
     "execution_count": 64,
     "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": [
    "#Select only BR housing market\n",
    "exp = experiments2.copy()\n",
    "exp['transient'] = exp['transient'].apply(lambda x : int(x.split('five_hundred_')[1].split('.')[0]))\n",
    "exp = exp.loc[(exp['Housing_market'] == 'boundedly_rational') & (exp['Mayor'] == 'Sentiment') & (exp['SLR'] == 4) & (exp['CC_first_SETP'] <= 2200)]\n",
    "exp['transient'].value_counts().hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2b3ddfee640>"
      ]
     },
     "execution_count": 66,
     "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": [
    "#Select only BR housing market\n",
    "exp = experiments2.copy()\n",
    "exp['transient'] = exp['transient'].apply(lambda x : int(x.split('five_hundred_')[1].split('.')[0]))\n",
    "exp = exp.loc[(exp['CC_first_SETP'] <= 2200)]\n",
    "exp['transient'].value_counts().hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}