data preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# import covid data and get florida covid data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>county</th>\n",
       "      <th>state</th>\n",
       "      <th>fips</th>\n",
       "      <th>cases</th>\n",
       "      <th>deaths</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>2020-03-01</td>\n",
       "      <td>Hillsborough</td>\n",
       "      <td>Florida</td>\n",
       "      <td>12057.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>2020-03-01</td>\n",
       "      <td>Manatee</td>\n",
       "      <td>Florida</td>\n",
       "      <td>12081.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>Hillsborough</td>\n",
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       "      <td>12057.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>Manatee</td>\n",
       "      <td>Florida</td>\n",
       "      <td>12081.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>2020-03-03</td>\n",
       "      <td>Hillsborough</td>\n",
       "      <td>Florida</td>\n",
       "      <td>12057.0</td>\n",
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       "      <th>2138790</th>\n",
       "      <td>2022-01-22</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Florida</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22916</td>\n",
       "      <td>605.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2138791</th>\n",
       "      <td>2022-01-22</td>\n",
       "      <td>Volusia</td>\n",
       "      <td>Florida</td>\n",
       "      <td>12127.0</td>\n",
       "      <td>102609</td>\n",
       "      <td>1659.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2138792</th>\n",
       "      <td>2022-01-22</td>\n",
       "      <td>Wakulla</td>\n",
       "      <td>Florida</td>\n",
       "      <td>12129.0</td>\n",
       "      <td>8520</td>\n",
       "      <td>108.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2138793</th>\n",
       "      <td>2022-01-22</td>\n",
       "      <td>Walton</td>\n",
       "      <td>Florida</td>\n",
       "      <td>12131.0</td>\n",
       "      <td>14760</td>\n",
       "      <td>177.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2138794</th>\n",
       "      <td>2022-01-22</td>\n",
       "      <td>Washington</td>\n",
       "      <td>Florida</td>\n",
       "      <td>12133.0</td>\n",
       "      <td>5810</td>\n",
       "      <td>87.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>45814 rows × 6 columns</p>\n",
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      ],
      "text/plain": [
       "               date        county    state     fips   cases  deaths\n",
       "411      2020-03-01  Hillsborough  Florida  12057.0       1     0.0\n",
       "412      2020-03-01       Manatee  Florida  12081.0       1     0.0\n",
       "440      2020-03-02  Hillsborough  Florida  12057.0       1     0.0\n",
       "441      2020-03-02       Manatee  Florida  12081.0       1     0.0\n",
       "474      2020-03-03  Hillsborough  Florida  12057.0       2     0.0\n",
       "...             ...           ...      ...      ...     ...     ...\n",
       "2138790  2022-01-22       Unknown  Florida      NaN   22916   605.0\n",
       "2138791  2022-01-22       Volusia  Florida  12127.0  102609  1659.0\n",
       "2138792  2022-01-22       Wakulla  Florida  12129.0    8520   108.0\n",
       "2138793  2022-01-22        Walton  Florida  12131.0   14760   177.0\n",
       "2138794  2022-01-22    Washington  Florida  12133.0    5810    87.0\n",
       "\n",
       "[45814 rows x 6 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid=pd.read_csv('/Users/yoga/Documents/Projects/1_covid19_cre/72_update regression analysis 20220122/1_us-counties_covid20220122.csv')\n",
    "\n",
    "covid_fl=covid.loc[covid['state']=='Florida']\n",
    "covid_fl\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th>2067246</th>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Florida</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2067247</th>\n",
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       "      <td>Volusia</td>\n",
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       "      <th>2067248</th>\n",
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       "      <td>106.0</td>\n",
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       "      <th>2067249</th>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>Walton</td>\n",
       "      <td>Florida</td>\n",
       "      <td>12131.0</td>\n",
       "      <td>12464</td>\n",
       "      <td>173.0</td>\n",
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       "      <th>2067250</th>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>Washington</td>\n",
       "      <td>Florida</td>\n",
       "      <td>12133.0</td>\n",
       "      <td>4707</td>\n",
       "      <td>75.0</td>\n",
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      "text/plain": [
       "               date      county    state     fips  cases  deaths\n",
       "21976    2020-03-31     Alachua  Florida  12001.0     87     0.0\n",
       "21977    2020-03-31       Baker  Florida  12003.0      9     0.0\n",
       "21978    2020-03-31         Bay  Florida  12005.0      8     0.0\n",
       "21979    2020-03-31    Bradford  Florida  12007.0      2     0.0\n",
       "21980    2020-03-31     Brevard  Florida  12009.0     37     0.0\n",
       "...             ...         ...      ...      ...    ...     ...\n",
       "2067246  2021-12-31     Unknown  Florida      NaN      5   162.0\n",
       "2067247  2021-12-31     Volusia  Florida  12127.0  82954  1647.0\n",
       "2067248  2021-12-31     Wakulla  Florida  12129.0   6803   106.0\n",
       "2067249  2021-12-31      Walton  Florida  12131.0  12464   173.0\n",
       "2067250  2021-12-31  Washington  Florida  12133.0   4707    75.0\n",
       "\n",
       "[531 rows x 6 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time=['2020-03-31','2020-06-30','2020-09-30','2020-12-31','2021-03-31','2021-06-30','2021-09-30','2021-12-31']\n",
    "\n",
    "covid_fl_qtr=covid_fl.loc[covid_fl['date'].isin(time)]\n",
    "\n",
    "covid_fl_qtr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-20-6bf9af656386>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  covid_fl_qtr['quarter']=''\n",
      "<ipython-input-20-6bf9af656386>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  covid_fl_qtr['quarter'][covid_fl_qtr['date']=='2020-03-31']='2020 Q1'\n",
      "/Users/yoga/opt/anaconda3/lib/python3.8/site-packages/pandas/core/series.py:992: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._where(~key, value, inplace=True)\n",
      "<ipython-input-20-6bf9af656386>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  covid_fl_qtr['quarter'][covid_fl_qtr['date']=='2020-06-30']='2020 Q2'\n",
      "<ipython-input-20-6bf9af656386>:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  covid_fl_qtr['quarter'][covid_fl_qtr['date']=='2020-09-30']='2020 Q3'\n",
      "<ipython-input-20-6bf9af656386>:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  covid_fl_qtr['quarter'][covid_fl_qtr['date']=='2020-12-31']='2020 Q4'\n",
      "<ipython-input-20-6bf9af656386>:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  covid_fl_qtr['quarter'][covid_fl_qtr['date']=='2021-03-31']='2021 Q1'\n",
      "<ipython-input-20-6bf9af656386>:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  covid_fl_qtr['quarter'][covid_fl_qtr['date']=='2021-06-30']='2021 Q2'\n",
      "<ipython-input-20-6bf9af656386>:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  covid_fl_qtr['quarter'][covid_fl_qtr['date']=='2021-09-30']='2021 Q3'\n",
      "<ipython-input-20-6bf9af656386>:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  covid_fl_qtr['quarter'][covid_fl_qtr['date']=='2021-12-31']='2021 Q4'\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "covid_fl_qtr['quarter']=''\n",
    "covid_fl_qtr['quarter'][covid_fl_qtr['date']=='2020-03-31']='2020 Q1'\n",
    "covid_fl_qtr['quarter'][covid_fl_qtr['date']=='2020-06-30']='2020 Q2'\n",
    "covid_fl_qtr['quarter'][covid_fl_qtr['date']=='2020-09-30']='2020 Q3'\n",
    "covid_fl_qtr['quarter'][covid_fl_qtr['date']=='2020-12-31']='2020 Q4'\n",
    "\n",
    "covid_fl_qtr['quarter'][covid_fl_qtr['date']=='2021-03-31']='2021 Q1'\n",
    "covid_fl_qtr['quarter'][covid_fl_qtr['date']=='2021-06-30']='2021 Q2'\n",
    "covid_fl_qtr['quarter'][covid_fl_qtr['date']=='2021-09-30']='2021 Q3'\n",
    "covid_fl_qtr['quarter'][covid_fl_qtr['date']=='2021-12-31']='2021 Q4'\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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       "      <th>21976</th>\n",
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       "      <td>2</td>\n",
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       "      <th>21980</th>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>Brevard</td>\n",
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       "      <td>12009.0</td>\n",
       "      <td>37</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2020 Q1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2067246</th>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Florida</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>162.0</td>\n",
       "      <td>2021 Q4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2067247</th>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>Volusia</td>\n",
       "      <td>Florida</td>\n",
       "      <td>12127.0</td>\n",
       "      <td>82954</td>\n",
       "      <td>1647.0</td>\n",
       "      <td>2021 Q4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2067248</th>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>Wakulla</td>\n",
       "      <td>Florida</td>\n",
       "      <td>12129.0</td>\n",
       "      <td>6803</td>\n",
       "      <td>106.0</td>\n",
       "      <td>2021 Q4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2067249</th>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>Walton</td>\n",
       "      <td>Florida</td>\n",
       "      <td>12131.0</td>\n",
       "      <td>12464</td>\n",
       "      <td>173.0</td>\n",
       "      <td>2021 Q4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2067250</th>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>Washington</td>\n",
       "      <td>Florida</td>\n",
       "      <td>12133.0</td>\n",
       "      <td>4707</td>\n",
       "      <td>75.0</td>\n",
       "      <td>2021 Q4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>531 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               date      county    state     fips  cases  deaths  quarter\n",
       "21976    2020-03-31     Alachua  Florida  12001.0     87     0.0  2020 Q1\n",
       "21977    2020-03-31       Baker  Florida  12003.0      9     0.0  2020 Q1\n",
       "21978    2020-03-31         Bay  Florida  12005.0      8     0.0  2020 Q1\n",
       "21979    2020-03-31    Bradford  Florida  12007.0      2     0.0  2020 Q1\n",
       "21980    2020-03-31     Brevard  Florida  12009.0     37     0.0  2020 Q1\n",
       "...             ...         ...      ...      ...    ...     ...      ...\n",
       "2067246  2021-12-31     Unknown  Florida      NaN      5   162.0  2021 Q4\n",
       "2067247  2021-12-31     Volusia  Florida  12127.0  82954  1647.0  2021 Q4\n",
       "2067248  2021-12-31     Wakulla  Florida  12129.0   6803   106.0  2021 Q4\n",
       "2067249  2021-12-31      Walton  Florida  12131.0  12464   173.0  2021 Q4\n",
       "2067250  2021-12-31  Washington  Florida  12133.0   4707    75.0  2021 Q4\n",
       "\n",
       "[531 rows x 7 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_fl_qtr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>CBSAFP</th>\n",
       "      <th>CBSA NAME</th>\n",
       "      <th>County NAME</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23540</td>\n",
       "      <td>Gainesville, FL</td>\n",
       "      <td>Levy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23540</td>\n",
       "      <td>Gainesville, FL</td>\n",
       "      <td>Alachua</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23540</td>\n",
       "      <td>Gainesville, FL</td>\n",
       "      <td>Gilchrist</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18880</td>\n",
       "      <td>Crestview-Fort Walton Beach-Destin, FL</td>\n",
       "      <td>Walton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18880</td>\n",
       "      <td>Crestview-Fort Walton Beach-Destin, FL</td>\n",
       "      <td>Okaloosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>19660</td>\n",
       "      <td>Deltona-Daytona Beach-Ormond Beach, FL</td>\n",
       "      <td>Flagler</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>19660</td>\n",
       "      <td>Deltona-Daytona Beach-Ormond Beach, FL</td>\n",
       "      <td>Volusia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>15980</td>\n",
       "      <td>Cape Coral-Fort Myers, FL</td>\n",
       "      <td>Lee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>26140</td>\n",
       "      <td>Homosassa Springs, FL</td>\n",
       "      <td>Citrus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>27260</td>\n",
       "      <td>Jacksonville, FL</td>\n",
       "      <td>Clay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>27260</td>\n",
       "      <td>Jacksonville, FL</td>\n",
       "      <td>Nassau</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>27260</td>\n",
       "      <td>Jacksonville, FL</td>\n",
       "      <td>St. Johns</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>27260</td>\n",
       "      <td>Jacksonville, FL</td>\n",
       "      <td>Baker</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>27260</td>\n",
       "      <td>Jacksonville, FL</td>\n",
       "      <td>Duval</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>29460</td>\n",
       "      <td>Lakeland-Winter Haven, FL</td>\n",
       "      <td>Polk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>34940</td>\n",
       "      <td>Naples-Marco Island, FL</td>\n",
       "      <td>Collier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>35840</td>\n",
       "      <td>North Port-Sarasota-Bradenton, FL</td>\n",
       "      <td>Manatee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>35840</td>\n",
       "      <td>North Port-Sarasota-Bradenton, FL</td>\n",
       "      <td>Sarasota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>36100</td>\n",
       "      <td>Ocala, FL</td>\n",
       "      <td>Marion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>36740</td>\n",
       "      <td>Orlando-Kissimmee-Sanford, FL</td>\n",
       "      <td>Osceola</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>36740</td>\n",
       "      <td>Orlando-Kissimmee-Sanford, FL</td>\n",
       "      <td>Orange</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>36740</td>\n",
       "      <td>Orlando-Kissimmee-Sanford, FL</td>\n",
       "      <td>Lake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>36740</td>\n",
       "      <td>Orlando-Kissimmee-Sanford, FL</td>\n",
       "      <td>Seminole</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>37340</td>\n",
       "      <td>Palm Bay-Melbourne-Titusville, FL</td>\n",
       "      <td>Brevard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>37460</td>\n",
       "      <td>Panama City, FL</td>\n",
       "      <td>Bay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>37860</td>\n",
       "      <td>Pensacola-Ferry Pass-Brent, FL</td>\n",
       "      <td>Escambia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>37860</td>\n",
       "      <td>Pensacola-Ferry Pass-Brent, FL</td>\n",
       "      <td>Santa Rosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>38940</td>\n",
       "      <td>Port St. Lucie, FL</td>\n",
       "      <td>St. Lucie</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>38940</td>\n",
       "      <td>Port St. Lucie, FL</td>\n",
       "      <td>Martin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>39460</td>\n",
       "      <td>Punta Gorda, FL</td>\n",
       "      <td>Charlotte</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>42680</td>\n",
       "      <td>Sebastian-Vero Beach, FL</td>\n",
       "      <td>Indian River</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>42700</td>\n",
       "      <td>Sebring-Avon Park, FL</td>\n",
       "      <td>Highlands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>45220</td>\n",
       "      <td>Tallahassee, FL</td>\n",
       "      <td>Gadsden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>45220</td>\n",
       "      <td>Tallahassee, FL</td>\n",
       "      <td>Wakulla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>45220</td>\n",
       "      <td>Tallahassee, FL</td>\n",
       "      <td>Jefferson</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>45220</td>\n",
       "      <td>Tallahassee, FL</td>\n",
       "      <td>Leon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>45300</td>\n",
       "      <td>Tampa-St. Petersburg-Clearwater, FL</td>\n",
       "      <td>Hernando</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>45300</td>\n",
       "      <td>Tampa-St. Petersburg-Clearwater, FL</td>\n",
       "      <td>Pinellas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>45300</td>\n",
       "      <td>Tampa-St. Petersburg-Clearwater, FL</td>\n",
       "      <td>Pasco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>45300</td>\n",
       "      <td>Tampa-St. Petersburg-Clearwater, FL</td>\n",
       "      <td>Hillsborough</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>45540</td>\n",
       "      <td>The Villages, FL</td>\n",
       "      <td>Sumter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>22744</td>\n",
       "      <td>Fort Lauderdale, FL</td>\n",
       "      <td>Broward</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>33124</td>\n",
       "      <td>Miami, FL</td>\n",
       "      <td>Miami-Dade</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>48424</td>\n",
       "      <td>Palm Beach, FL</td>\n",
       "      <td>Palm Beach</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    CBSAFP                               CBSA NAME   County NAME\n",
       "0    23540                         Gainesville, FL          Levy\n",
       "1    23540                         Gainesville, FL       Alachua\n",
       "2    23540                         Gainesville, FL     Gilchrist\n",
       "3    18880  Crestview-Fort Walton Beach-Destin, FL        Walton\n",
       "4    18880  Crestview-Fort Walton Beach-Destin, FL      Okaloosa\n",
       "5    19660  Deltona-Daytona Beach-Ormond Beach, FL       Flagler\n",
       "6    19660  Deltona-Daytona Beach-Ormond Beach, FL       Volusia\n",
       "7    15980               Cape Coral-Fort Myers, FL           Lee\n",
       "8    26140                   Homosassa Springs, FL        Citrus\n",
       "9    27260                        Jacksonville, FL          Clay\n",
       "10   27260                        Jacksonville, FL        Nassau\n",
       "11   27260                        Jacksonville, FL     St. Johns\n",
       "12   27260                        Jacksonville, FL         Baker\n",
       "13   27260                        Jacksonville, FL         Duval\n",
       "14   29460               Lakeland-Winter Haven, FL          Polk\n",
       "15   34940                 Naples-Marco Island, FL       Collier\n",
       "16   35840       North Port-Sarasota-Bradenton, FL       Manatee\n",
       "17   35840       North Port-Sarasota-Bradenton, FL      Sarasota\n",
       "18   36100                               Ocala, FL        Marion\n",
       "19   36740           Orlando-Kissimmee-Sanford, FL       Osceola\n",
       "20   36740           Orlando-Kissimmee-Sanford, FL        Orange\n",
       "21   36740           Orlando-Kissimmee-Sanford, FL          Lake\n",
       "22   36740           Orlando-Kissimmee-Sanford, FL      Seminole\n",
       "23   37340       Palm Bay-Melbourne-Titusville, FL       Brevard\n",
       "24   37460                         Panama City, FL           Bay\n",
       "25   37860          Pensacola-Ferry Pass-Brent, FL      Escambia\n",
       "26   37860          Pensacola-Ferry Pass-Brent, FL    Santa Rosa\n",
       "27   38940                      Port St. Lucie, FL     St. Lucie\n",
       "28   38940                      Port St. Lucie, FL        Martin\n",
       "29   39460                         Punta Gorda, FL     Charlotte\n",
       "30   42680                Sebastian-Vero Beach, FL  Indian River\n",
       "31   42700                   Sebring-Avon Park, FL     Highlands\n",
       "32   45220                         Tallahassee, FL       Gadsden\n",
       "33   45220                         Tallahassee, FL       Wakulla\n",
       "34   45220                         Tallahassee, FL     Jefferson\n",
       "35   45220                         Tallahassee, FL          Leon\n",
       "36   45300     Tampa-St. Petersburg-Clearwater, FL      Hernando\n",
       "37   45300     Tampa-St. Petersburg-Clearwater, FL      Pinellas\n",
       "38   45300     Tampa-St. Petersburg-Clearwater, FL         Pasco\n",
       "39   45300     Tampa-St. Petersburg-Clearwater, FL  Hillsborough\n",
       "40   45540                        The Villages, FL        Sumter\n",
       "41   22744                     Fort Lauderdale, FL       Broward\n",
       "42   33124                               Miami, FL    Miami-Dade\n",
       "43   48424                          Palm Beach, FL    Palm Beach"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#这里缺少三个MSA,如下：33124  Miami - FL48424  Palm Beach - FL；22744 Fort Lauderdale - FL\n",
    "\n",
    "cbsa2coun=pd.read_excel('/Users/yoga/Dropbox/1 新冠和商业地产/101 CRE second time 202205/4-5 cbsa 2 county name.xlsx')\n",
    "cbsa2coun\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(cbsa2coun['CBSA NAME'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#try to merge\n",
    "covid2_merge=pd.merge(left=covid_fl_qtr,right=cbsa2coun,left_on='county',right_on='County NAME',how='left')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 531 entries, 0 to 530\n",
      "Data columns (total 10 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   date         531 non-null    object \n",
      " 1   county       531 non-null    object \n",
      " 2   state        531 non-null    object \n",
      " 3   fips         524 non-null    float64\n",
      " 4   cases        531 non-null    int64  \n",
      " 5   deaths       531 non-null    float64\n",
      " 6   quarter      531 non-null    object \n",
      " 7   CBSAFP       350 non-null    float64\n",
      " 8   CBSA NAME    350 non-null    object \n",
      " 9   County NAME  350 non-null    object \n",
      "dtypes: float64(3), int64(1), object(6)\n",
      "memory usage: 45.6+ KB\n"
     ]
    }
   ],
   "source": [
    "covid2_merge.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(covid2_merge['CBSAFP'].unique()) #多了一个nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(cbsa2coun['County NAME'].unique())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "covid2_merge2=covid2_merge.copy()\n",
    "covid2_merge2=covid2_merge2.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(covid2_merge2['County NAME'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>date</th>\n",
       "      <th>county</th>\n",
       "      <th>state</th>\n",
       "      <th>fips</th>\n",
       "      <th>cases</th>\n",
       "      <th>deaths</th>\n",
       "      <th>quarter</th>\n",
       "      <th>CBSAFP</th>\n",
       "      <th>CBSA NAME</th>\n",
       "      <th>County NAME</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>Alachua</td>\n",
       "      <td>Florida</td>\n",
       "      <td>12001.0</td>\n",
       "      <td>87</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>23540.0</td>\n",
       "      <td>Gainesville, FL</td>\n",
       "      <td>Alachua</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>Baker</td>\n",
       "      <td>Florida</td>\n",
       "      <td>12003.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>27260.0</td>\n",
       "      <td>Jacksonville, FL</td>\n",
       "      <td>Baker</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>Bay</td>\n",
       "      <td>Florida</td>\n",
       "      <td>12005.0</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>37460.0</td>\n",
       "      <td>Panama City, FL</td>\n",
       "      <td>Bay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>Brevard</td>\n",
       "      <td>Florida</td>\n",
       "      <td>12009.0</td>\n",
       "      <td>37</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>37340.0</td>\n",
       "      <td>Palm Bay-Melbourne-Titusville, FL</td>\n",
       "      <td>Brevard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>Broward</td>\n",
       "      <td>Florida</td>\n",
       "      <td>12011.0</td>\n",
       "      <td>1219</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>22744.0</td>\n",
       "      <td>Fort Lauderdale, FL</td>\n",
       "      <td>Broward</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>521</th>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>St. Lucie</td>\n",
       "      <td>Florida</td>\n",
       "      <td>12111.0</td>\n",
       "      <td>52135</td>\n",
       "      <td>1047.0</td>\n",
       "      <td>2021 Q4</td>\n",
       "      <td>38940.0</td>\n",
       "      <td>Port St. Lucie, FL</td>\n",
       "      <td>St. Lucie</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>Sumter</td>\n",
       "      <td>Florida</td>\n",
       "      <td>12119.0</td>\n",
       "      <td>15405</td>\n",
       "      <td>475.0</td>\n",
       "      <td>2021 Q4</td>\n",
       "      <td>45540.0</td>\n",
       "      <td>The Villages, FL</td>\n",
       "      <td>Sumter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527</th>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>Volusia</td>\n",
       "      <td>Florida</td>\n",
       "      <td>12127.0</td>\n",
       "      <td>82954</td>\n",
       "      <td>1647.0</td>\n",
       "      <td>2021 Q4</td>\n",
       "      <td>19660.0</td>\n",
       "      <td>Deltona-Daytona Beach-Ormond Beach, FL</td>\n",
       "      <td>Volusia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>528</th>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>Wakulla</td>\n",
       "      <td>Florida</td>\n",
       "      <td>12129.0</td>\n",
       "      <td>6803</td>\n",
       "      <td>106.0</td>\n",
       "      <td>2021 Q4</td>\n",
       "      <td>45220.0</td>\n",
       "      <td>Tallahassee, FL</td>\n",
       "      <td>Wakulla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>529</th>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>Walton</td>\n",
       "      <td>Florida</td>\n",
       "      <td>12131.0</td>\n",
       "      <td>12464</td>\n",
       "      <td>173.0</td>\n",
       "      <td>2021 Q4</td>\n",
       "      <td>18880.0</td>\n",
       "      <td>Crestview-Fort Walton Beach-Destin, FL</td>\n",
       "      <td>Walton</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>350 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date     county    state     fips  cases  deaths  quarter   CBSAFP  \\\n",
       "0    2020-03-31    Alachua  Florida  12001.0     87     0.0  2020 Q1  23540.0   \n",
       "1    2020-03-31      Baker  Florida  12003.0      9     0.0  2020 Q1  27260.0   \n",
       "2    2020-03-31        Bay  Florida  12005.0      8     0.0  2020 Q1  37460.0   \n",
       "4    2020-03-31    Brevard  Florida  12009.0     37     0.0  2020 Q1  37340.0   \n",
       "5    2020-03-31    Broward  Florida  12011.0   1219    12.0  2020 Q1  22744.0   \n",
       "..          ...        ...      ...      ...    ...     ...      ...      ...   \n",
       "521  2021-12-31  St. Lucie  Florida  12111.0  52135  1047.0  2021 Q4  38940.0   \n",
       "522  2021-12-31     Sumter  Florida  12119.0  15405   475.0  2021 Q4  45540.0   \n",
       "527  2021-12-31    Volusia  Florida  12127.0  82954  1647.0  2021 Q4  19660.0   \n",
       "528  2021-12-31    Wakulla  Florida  12129.0   6803   106.0  2021 Q4  45220.0   \n",
       "529  2021-12-31     Walton  Florida  12131.0  12464   173.0  2021 Q4  18880.0   \n",
       "\n",
       "                                  CBSA NAME County NAME  \n",
       "0                           Gainesville, FL     Alachua  \n",
       "1                          Jacksonville, FL       Baker  \n",
       "2                           Panama City, FL         Bay  \n",
       "4         Palm Bay-Melbourne-Titusville, FL     Brevard  \n",
       "5                       Fort Lauderdale, FL     Broward  \n",
       "..                                      ...         ...  \n",
       "521                      Port St. Lucie, FL   St. Lucie  \n",
       "522                        The Villages, FL      Sumter  \n",
       "527  Deltona-Daytona Beach-Ormond Beach, FL     Volusia  \n",
       "528                         Tallahassee, FL     Wakulla  \n",
       "529  Crestview-Fort Walton Beach-Destin, FL      Walton  \n",
       "\n",
       "[350 rows x 10 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid2_merge2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      2020 Q1&23540\n",
       "1      2020 Q1&27260\n",
       "2      2020 Q1&37460\n",
       "4      2020 Q1&37340\n",
       "5      2020 Q1&22744\n",
       "           ...      \n",
       "521    2021 Q4&38940\n",
       "522    2021 Q4&45540\n",
       "527    2021 Q4&19660\n",
       "528    2021 Q4&45220\n",
       "529    2021 Q4&18880\n",
       "Name: quarter_CBSAFP, Length: 350, dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid2_merge2['quarter_CBSAFP']=covid2_merge2['quarter'].astype(str)+\"&\"+covid2_merge2['CBSAFP'].astype(int).astype(str)\n",
    "covid2_merge2['quarter_CBSAFP']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>date</th>\n",
       "      <th>county</th>\n",
       "      <th>state</th>\n",
       "      <th>fips</th>\n",
       "      <th>cases</th>\n",
       "      <th>deaths</th>\n",
       "      <th>quarter</th>\n",
       "      <th>CBSAFP</th>\n",
       "      <th>CBSA NAME</th>\n",
       "      <th>County NAME</th>\n",
       "      <th>quarter_CBSAFP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>Martin</td>\n",
       "      <td>Florida</td>\n",
       "      <td>12085.0</td>\n",
       "      <td>22335</td>\n",
       "      <td>523.0</td>\n",
       "      <td>2021 Q4</td>\n",
       "      <td>38940.0</td>\n",
       "      <td>Port St. Lucie, FL</td>\n",
       "      <td>Martin</td>\n",
       "      <td>2021 Q4&amp;38940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>521</th>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>St. Lucie</td>\n",
       "      <td>Florida</td>\n",
       "      <td>12111.0</td>\n",
       "      <td>52135</td>\n",
       "      <td>1047.0</td>\n",
       "      <td>2021 Q4</td>\n",
       "      <td>38940.0</td>\n",
       "      <td>Port St. Lucie, FL</td>\n",
       "      <td>St. Lucie</td>\n",
       "      <td>2021 Q4&amp;38940</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           date     county    state     fips  cases  deaths  quarter   CBSAFP  \\\n",
       "504  2021-12-31     Martin  Florida  12085.0  22335   523.0  2021 Q4  38940.0   \n",
       "521  2021-12-31  St. Lucie  Florida  12111.0  52135  1047.0  2021 Q4  38940.0   \n",
       "\n",
       "              CBSA NAME County NAME quarter_CBSAFP  \n",
       "504  Port St. Lucie, FL      Martin  2021 Q4&38940  \n",
       "521  Port St. Lucie, FL   St. Lucie  2021 Q4&38940  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "covid2_merge2[covid2_merge2['quarter_CBSAFP']=='2021 Q4&38940']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Alachua'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid2_merge2.loc[i]['county']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create a dict to calcualte the cases and death.\n",
    "quarter_CBSAFP2case={}\n",
    "quarter_CBSAFP2death={}\n",
    "\n",
    "for i in covid2_merge2.index:\n",
    "    covid2_merge2i=covid2_merge2.loc[i]['quarter_CBSAFP']\n",
    "    \n",
    "    if covid2_merge2i in quarter_CBSAFP2case.keys():\n",
    "        quarter_CBSAFP2case[covid2_merge2i]=quarter_CBSAFP2case[covid2_merge2i]+covid2_merge2.loc[i]['cases']\n",
    "        quarter_CBSAFP2death[covid2_merge2i]=quarter_CBSAFP2death[covid2_merge2i]+covid2_merge2.loc[i]['deaths']   \n",
    "    else:      \n",
    "        quarter_CBSAFP2case[covid2_merge2i]=covid2_merge2.loc[i]['cases']\n",
    "        quarter_CBSAFP2death[covid2_merge2i]=covid2_merge2.loc[i]['deaths']\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "74470"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "quarter_CBSAFP2case['2021 Q4&38940']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "74470"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "22335+52135"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd_quarter_CBSAFP2case=pd.DataFrame(quarter_CBSAFP2case.items())\n",
    "pd_quarter_CBSAFP2death=pd.DataFrame(quarter_CBSAFP2death.items())\n",
    "\n",
    "pd_quarter_CBSAFP2death = pd_quarter_CBSAFP2death.rename({0:'quarter_cbsa', 1:'aggregate_death'}, axis='columns')\n",
    "pd_quarter_CBSAFP2death\n",
    "\n",
    "pd_quarter_CBSAFP2case = pd_quarter_CBSAFP2case.rename({0:'quarter_cbsa', 1:'aggregate_case'}, axis='columns')\n",
    "pd_quarter_CBSAFP2case\n",
    "\n",
    "pd_quarter_CBSAFP2case_death=pd_quarter_CBSAFP2case.merge(pd_quarter_CBSAFP2death,left_on='quarter_cbsa',right_on='quarter_cbsa',how='left')\n",
    "\n",
    "#pd_quarter_CBSAFP2case_death.to_csv('20_aggregate_death_case_covid_2020_2021.csv',index=0)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>quarter_cbsa</th>\n",
       "      <th>aggregate_case</th>\n",
       "      <th>aggregate_death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020 Q1&amp;23540</td>\n",
       "      <td>89</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020 Q1&amp;27260</td>\n",
       "      <td>346</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020 Q1&amp;37460</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020 Q1&amp;37340</td>\n",
       "      <td>37</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020 Q1&amp;22744</td>\n",
       "      <td>1219</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>2021 Q4&amp;33124</td>\n",
       "      <td>847746</td>\n",
       "      <td>9260.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>2021 Q4&amp;18880</td>\n",
       "      <td>48621</td>\n",
       "      <td>754.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>2021 Q4&amp;48424</td>\n",
       "      <td>268242</td>\n",
       "      <td>4322.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>2021 Q4&amp;29460</td>\n",
       "      <td>140400</td>\n",
       "      <td>2531.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>2021 Q4&amp;45540</td>\n",
       "      <td>15405</td>\n",
       "      <td>475.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>192 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      quarter_cbsa  aggregate_case  aggregate_death\n",
       "0    2020 Q1&23540              89              0.0\n",
       "1    2020 Q1&27260             346             11.0\n",
       "2    2020 Q1&37460               8              0.0\n",
       "3    2020 Q1&37340              37              0.0\n",
       "4    2020 Q1&22744            1219             12.0\n",
       "..             ...             ...              ...\n",
       "187  2021 Q4&33124          847746           9260.0\n",
       "188  2021 Q4&18880           48621            754.0\n",
       "189  2021 Q4&48424          268242           4322.0\n",
       "190  2021 Q4&29460          140400           2531.0\n",
       "191  2021 Q4&45540           15405            475.0\n",
       "\n",
       "[192 rows x 3 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd_quarter_CBSAFP2case_death"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## change aggregated number to number in current quarter\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "cbsa2deaths_current=dict()\n",
    "cbsa2cases_current=dict()\n",
    "\n",
    "quarter=['2020 Q1','2020 Q2','2020 Q3','2020 Q4','2021 Q1','2021 Q2','2021 Q3','2021 Q4']\n",
    "\n",
    "for i in pd_quarter_CBSAFP2case_death.index:\n",
    "    cbsa=pd_quarter_CBSAFP2case_death['quarter_cbsa'][i]\n",
    "    cbsa_period=cbsa.split('&')[0]\n",
    "    cbsa_code=cbsa.split('&')[1]\n",
    "    if cbsa_period=='2020 Q1':\n",
    "        cbsa2deaths_current[cbsa]=pd_quarter_CBSAFP2case_death['aggregate_death'][i]\n",
    "        cbsa2cases_current[cbsa]=pd_quarter_CBSAFP2case_death['aggregate_case'][i]\n",
    "    else:\n",
    "        if cbsa_period not in quarter:\n",
    "            print('Could not find ', cbsa_period)\n",
    "            break\n",
    "        cur_indx = quarter.index(cbsa_period)\n",
    "        pre_cbsa_period = quarter[cur_indx-1]\n",
    "        pre_cbsa_code=cbsa_code\n",
    "        pre_cbsa=pre_cbsa_period+'&'+str(pre_cbsa_code)\n",
    "        pre_case=pd_quarter_CBSAFP2case_death[pd_quarter_CBSAFP2case_death['quarter_cbsa']==pre_cbsa]['aggregate_case'].values[0]\n",
    "        pre_death=pd_quarter_CBSAFP2case_death[pd_quarter_CBSAFP2case_death['quarter_cbsa']==pre_cbsa]['aggregate_death'].values[0]\n",
    "       \n",
    "        cbsa2deaths_current[cbsa]=pd_quarter_CBSAFP2case_death['aggregate_death'][i]-pre_death\n",
    "        cbsa2cases_current[cbsa]=pd_quarter_CBSAFP2case_death['aggregate_case'][i]-pre_case\n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        #print(pre_cbsa_period, '->', cbsa_period)\n",
    "       \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd_quarter_CBSAFP2case_current=pd.DataFrame(cbsa2cases_current.items())\n",
    "pd_quarter_CBSAFP2death_current=pd.DataFrame(cbsa2deaths_current.items())\n",
    "\n",
    "pd_quarter_CBSAFP2death_current = pd_quarter_CBSAFP2death_current.rename({0:'quarter_cbsa', 1:'current_death'}, axis='columns')\n",
    "pd_quarter_CBSAFP2death\n",
    "\n",
    "pd_quarter_CBSAFP2case_current = pd_quarter_CBSAFP2case_current.rename({0:'quarter_cbsa', 1:'current_case'}, axis='columns')\n",
    "pd_quarter_CBSAFP2case\n",
    "\n",
    "pd_quarter_CBSAFP2case_death_current=pd_quarter_CBSAFP2case_current.merge(pd_quarter_CBSAFP2death_current,left_on='quarter_cbsa',right_on='quarter_cbsa',how='left')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pd_quarter_CBSAFP2case_death_current.to_csv('21_current_death_case_covid_2020_2021.csv',index=0)\n",
    "\n",
    "\n",
    "#pd_quarter_CBSAFP2case_death.to_csv('20_aggregate_death_case_covid_2020_2021.csv',index=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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>quarter_cbsa</th>\n",
       "      <th>aggregate_case</th>\n",
       "      <th>aggregate_death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020 Q1&amp;23540</td>\n",
       "      <td>89</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020 Q1&amp;27260</td>\n",
       "      <td>346</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020 Q1&amp;37460</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020 Q1&amp;37340</td>\n",
       "      <td>37</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020 Q1&amp;22744</td>\n",
       "      <td>1219</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>2021 Q4&amp;33124</td>\n",
       "      <td>847746</td>\n",
       "      <td>9260.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>2021 Q4&amp;18880</td>\n",
       "      <td>48621</td>\n",
       "      <td>754.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>2021 Q4&amp;48424</td>\n",
       "      <td>268242</td>\n",
       "      <td>4322.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>2021 Q4&amp;29460</td>\n",
       "      <td>140400</td>\n",
       "      <td>2531.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>2021 Q4&amp;45540</td>\n",
       "      <td>15405</td>\n",
       "      <td>475.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>192 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      quarter_cbsa  aggregate_case  aggregate_death\n",
       "0    2020 Q1&23540              89              0.0\n",
       "1    2020 Q1&27260             346             11.0\n",
       "2    2020 Q1&37460               8              0.0\n",
       "3    2020 Q1&37340              37              0.0\n",
       "4    2020 Q1&22744            1219             12.0\n",
       "..             ...             ...              ...\n",
       "187  2021 Q4&33124          847746           9260.0\n",
       "188  2021 Q4&18880           48621            754.0\n",
       "189  2021 Q4&48424          268242           4322.0\n",
       "190  2021 Q4&29460          140400           2531.0\n",
       "191  2021 Q4&45540           15405            475.0\n",
       "\n",
       "[192 rows x 3 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd_quarter_CBSAFP2case_death"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "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>quarter_cbsa</th>\n",
       "      <th>aggregate_case</th>\n",
       "      <th>aggregate_death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>2021 Q3&amp;45540</td>\n",
       "      <td>13990</td>\n",
       "      <td>428.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      quarter_cbsa  aggregate_case  aggregate_death\n",
       "167  2021 Q3&45540           13990            428.0"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd_quarter_CBSAFP2case_death[pd_quarter_CBSAFP2case_death['quarter_cbsa']=='2021 Q3&45540']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  import costar data and combine with costar data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "retail=pd.read_excel('/Users/yoga/Documents/Projects/1_covid19_cre/72_update regression analysis 20220122/Retail_01-18-2022.xlsx')\n",
    "office=pd.read_excel('/Users/yoga/Documents/Projects/1_covid19_cre/72_update regression analysis 20220122/Office_01-18-2022.xlsx')\n",
    "industry=pd.read_excel('/Users/yoga/Documents/Projects/1_covid19_cre/72_update regression analysis 20220122/Industrial_01-18-2022.xlsx')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df[df['ids'].str.contains(\"ball\")]\n",
    "retail2=retail[(retail['Period'].str.contains('2018 Q'))|(retail['Period'].str.contains('2019 Q'))|(retail['Period'].str.contains('2020 Q'))|(retail['Period'].str.contains('2021 Q'))]\n",
    "\n",
    "office2=office[(office['Period'].str.contains('2018 Q'))|(office['Period'].str.contains('2019 Q'))|(office['Period'].str.contains('2020 Q'))|(office['Period'].str.contains('2021 Q'))]\n",
    "\n",
    "industry2=industry[(industry['Period'].str.contains('2018 Q'))|(industry['Period'].str.contains('2019 Q'))|(industry['Period'].str.contains('2020 Q'))|(industry['Period'].str.contains('2021 Q'))]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>Property Class Name</th>\n",
       "      <th>Period</th>\n",
       "      <th>Slice</th>\n",
       "      <th>As Of</th>\n",
       "      <th>Geography Name</th>\n",
       "      <th>CBSA Code</th>\n",
       "      <th>Property Type</th>\n",
       "      <th>Forecast Scenario</th>\n",
       "      <th>Geography Type</th>\n",
       "      <th>Geography Code</th>\n",
       "      <th>...</th>\n",
       "      <th>Sold Building SF</th>\n",
       "      <th>Total Employment</th>\n",
       "      <th>Total Return</th>\n",
       "      <th>Total Sales Volume</th>\n",
       "      <th>Transaction Sale Price/SF</th>\n",
       "      <th>Under Construction Buildings</th>\n",
       "      <th>Under Construction SF</th>\n",
       "      <th>Vacancy Rate</th>\n",
       "      <th>Vacant Available %</th>\n",
       "      <th>Vacant Available SF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>2018 Q1</td>\n",
       "      <td>All</td>\n",
       "      <td>2022 Q1</td>\n",
       "      <td>Daytona Beach - FL</td>\n",
       "      <td>19660</td>\n",
       "      <td>Industrial</td>\n",
       "      <td>Base Case</td>\n",
       "      <td>Metro</td>\n",
       "      <td>G41649</td>\n",
       "      <td>...</td>\n",
       "      <td>227031.0</td>\n",
       "      <td>202634.593750</td>\n",
       "      <td>0.121489</td>\n",
       "      <td>16597775.0</td>\n",
       "      <td>82.98</td>\n",
       "      <td>4.0</td>\n",
       "      <td>54000.0</td>\n",
       "      <td>0.029080</td>\n",
       "      <td>0.026570</td>\n",
       "      <td>636770.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>2018 Q2</td>\n",
       "      <td>All</td>\n",
       "      <td>2022 Q1</td>\n",
       "      <td>Daytona Beach - FL</td>\n",
       "      <td>19660</td>\n",
       "      <td>Industrial</td>\n",
       "      <td>Base Case</td>\n",
       "      <td>Metro</td>\n",
       "      <td>G41649</td>\n",
       "      <td>...</td>\n",
       "      <td>623631.0</td>\n",
       "      <td>203406.906250</td>\n",
       "      <td>0.123515</td>\n",
       "      <td>41943208.0</td>\n",
       "      <td>71.42</td>\n",
       "      <td>3.0</td>\n",
       "      <td>36000.0</td>\n",
       "      <td>0.032372</td>\n",
       "      <td>0.028699</td>\n",
       "      <td>688304.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>2018 Q3</td>\n",
       "      <td>All</td>\n",
       "      <td>2022 Q1</td>\n",
       "      <td>Daytona Beach - FL</td>\n",
       "      <td>19660</td>\n",
       "      <td>Industrial</td>\n",
       "      <td>Base Case</td>\n",
       "      <td>Metro</td>\n",
       "      <td>G41649</td>\n",
       "      <td>...</td>\n",
       "      <td>171463.0</td>\n",
       "      <td>204901.390625</td>\n",
       "      <td>0.124135</td>\n",
       "      <td>5165600.0</td>\n",
       "      <td>44.95</td>\n",
       "      <td>4.0</td>\n",
       "      <td>37160.0</td>\n",
       "      <td>0.031292</td>\n",
       "      <td>0.030832</td>\n",
       "      <td>739480.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>2018 Q4</td>\n",
       "      <td>All</td>\n",
       "      <td>2022 Q1</td>\n",
       "      <td>Daytona Beach - FL</td>\n",
       "      <td>19660</td>\n",
       "      <td>Industrial</td>\n",
       "      <td>Base Case</td>\n",
       "      <td>Metro</td>\n",
       "      <td>G41649</td>\n",
       "      <td>...</td>\n",
       "      <td>192826.0</td>\n",
       "      <td>205294.500000</td>\n",
       "      <td>0.125630</td>\n",
       "      <td>5367000.0</td>\n",
       "      <td>46.90</td>\n",
       "      <td>5.0</td>\n",
       "      <td>93120.0</td>\n",
       "      <td>0.043182</td>\n",
       "      <td>0.043182</td>\n",
       "      <td>1035788.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>2019 Q1</td>\n",
       "      <td>All</td>\n",
       "      <td>2022 Q1</td>\n",
       "      <td>Daytona Beach - FL</td>\n",
       "      <td>19660</td>\n",
       "      <td>Industrial</td>\n",
       "      <td>Base Case</td>\n",
       "      <td>Metro</td>\n",
       "      <td>G41649</td>\n",
       "      <td>...</td>\n",
       "      <td>76136.0</td>\n",
       "      <td>205313.906250</td>\n",
       "      <td>0.125999</td>\n",
       "      <td>1646000.0</td>\n",
       "      <td>72.46</td>\n",
       "      <td>3.0</td>\n",
       "      <td>124960.0</td>\n",
       "      <td>0.046645</td>\n",
       "      <td>0.043040</td>\n",
       "      <td>1033872.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3858</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>2020 Q4</td>\n",
       "      <td>All</td>\n",
       "      <td>2022 Q1</td>\n",
       "      <td>The Villages - FL</td>\n",
       "      <td>45540</td>\n",
       "      <td>Industrial</td>\n",
       "      <td>Base Case</td>\n",
       "      <td>Metro</td>\n",
       "      <td>G39580</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31842.000000</td>\n",
       "      <td>0.138026</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>81600.0</td>\n",
       "      <td>0.021936</td>\n",
       "      <td>0.021936</td>\n",
       "      <td>34530.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3859</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>2021 Q1</td>\n",
       "      <td>All</td>\n",
       "      <td>2022 Q1</td>\n",
       "      <td>The Villages - FL</td>\n",
       "      <td>45540</td>\n",
       "      <td>Industrial</td>\n",
       "      <td>Base Case</td>\n",
       "      <td>Metro</td>\n",
       "      <td>G39580</td>\n",
       "      <td>...</td>\n",
       "      <td>13920.0</td>\n",
       "      <td>31496.189453</td>\n",
       "      <td>0.136862</td>\n",
       "      <td>1400000.0</td>\n",
       "      <td>100.57</td>\n",
       "      <td>7.0</td>\n",
       "      <td>81600.0</td>\n",
       "      <td>0.013023</td>\n",
       "      <td>0.013023</td>\n",
       "      <td>20500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3860</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>2021 Q2</td>\n",
       "      <td>All</td>\n",
       "      <td>2022 Q1</td>\n",
       "      <td>The Villages - FL</td>\n",
       "      <td>45540</td>\n",
       "      <td>Industrial</td>\n",
       "      <td>Base Case</td>\n",
       "      <td>Metro</td>\n",
       "      <td>G39580</td>\n",
       "      <td>...</td>\n",
       "      <td>72792.0</td>\n",
       "      <td>31540.269531</td>\n",
       "      <td>0.142301</td>\n",
       "      <td>5357195.0</td>\n",
       "      <td>73.60</td>\n",
       "      <td>7.0</td>\n",
       "      <td>81600.0</td>\n",
       "      <td>0.048391</td>\n",
       "      <td>0.048391</td>\n",
       "      <td>76172.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3861</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>2021 Q3</td>\n",
       "      <td>All</td>\n",
       "      <td>2022 Q1</td>\n",
       "      <td>The Villages - FL</td>\n",
       "      <td>45540</td>\n",
       "      <td>Industrial</td>\n",
       "      <td>Base Case</td>\n",
       "      <td>Metro</td>\n",
       "      <td>G39580</td>\n",
       "      <td>...</td>\n",
       "      <td>23266.0</td>\n",
       "      <td>31965.048828</td>\n",
       "      <td>0.156650</td>\n",
       "      <td>3750000.0</td>\n",
       "      <td>161.18</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21600.0</td>\n",
       "      <td>0.054643</td>\n",
       "      <td>0.054643</td>\n",
       "      <td>89292.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3862</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>2021 Q4</td>\n",
       "      <td>All</td>\n",
       "      <td>2022 Q1</td>\n",
       "      <td>The Villages - FL</td>\n",
       "      <td>45540</td>\n",
       "      <td>Industrial</td>\n",
       "      <td>Base Case</td>\n",
       "      <td>Metro</td>\n",
       "      <td>G39580</td>\n",
       "      <td>...</td>\n",
       "      <td>2896.0</td>\n",
       "      <td>32531.201172</td>\n",
       "      <td>0.168221</td>\n",
       "      <td>300000.0</td>\n",
       "      <td>103.59</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21600.0</td>\n",
       "      <td>0.050971</td>\n",
       "      <td>0.050971</td>\n",
       "      <td>83292.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>384 rows × 66 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Property Class Name   Period Slice    As Of      Geography Name  \\\n",
       "144           Industrial  2018 Q1   All  2022 Q1  Daytona Beach - FL   \n",
       "145           Industrial  2018 Q2   All  2022 Q1  Daytona Beach - FL   \n",
       "146           Industrial  2018 Q3   All  2022 Q1  Daytona Beach - FL   \n",
       "147           Industrial  2018 Q4   All  2022 Q1  Daytona Beach - FL   \n",
       "148           Industrial  2019 Q1   All  2022 Q1  Daytona Beach - FL   \n",
       "...                  ...      ...   ...      ...                 ...   \n",
       "3858          Industrial  2020 Q4   All  2022 Q1   The Villages - FL   \n",
       "3859          Industrial  2021 Q1   All  2022 Q1   The Villages - FL   \n",
       "3860          Industrial  2021 Q2   All  2022 Q1   The Villages - FL   \n",
       "3861          Industrial  2021 Q3   All  2022 Q1   The Villages - FL   \n",
       "3862          Industrial  2021 Q4   All  2022 Q1   The Villages - FL   \n",
       "\n",
       "      CBSA Code Property Type Forecast Scenario Geography Type Geography Code  \\\n",
       "144       19660    Industrial         Base Case          Metro         G41649   \n",
       "145       19660    Industrial         Base Case          Metro         G41649   \n",
       "146       19660    Industrial         Base Case          Metro         G41649   \n",
       "147       19660    Industrial         Base Case          Metro         G41649   \n",
       "148       19660    Industrial         Base Case          Metro         G41649   \n",
       "...         ...           ...               ...            ...            ...   \n",
       "3858      45540    Industrial         Base Case          Metro         G39580   \n",
       "3859      45540    Industrial         Base Case          Metro         G39580   \n",
       "3860      45540    Industrial         Base Case          Metro         G39580   \n",
       "3861      45540    Industrial         Base Case          Metro         G39580   \n",
       "3862      45540    Industrial         Base Case          Metro         G39580   \n",
       "\n",
       "      ...  Sold Building SF  Total Employment  Total Return  \\\n",
       "144   ...          227031.0     202634.593750      0.121489   \n",
       "145   ...          623631.0     203406.906250      0.123515   \n",
       "146   ...          171463.0     204901.390625      0.124135   \n",
       "147   ...          192826.0     205294.500000      0.125630   \n",
       "148   ...           76136.0     205313.906250      0.125999   \n",
       "...   ...               ...               ...           ...   \n",
       "3858  ...               NaN      31842.000000      0.138026   \n",
       "3859  ...           13920.0      31496.189453      0.136862   \n",
       "3860  ...           72792.0      31540.269531      0.142301   \n",
       "3861  ...           23266.0      31965.048828      0.156650   \n",
       "3862  ...            2896.0      32531.201172      0.168221   \n",
       "\n",
       "      Total Sales Volume  Transaction Sale Price/SF  \\\n",
       "144           16597775.0                      82.98   \n",
       "145           41943208.0                      71.42   \n",
       "146            5165600.0                      44.95   \n",
       "147            5367000.0                      46.90   \n",
       "148            1646000.0                      72.46   \n",
       "...                  ...                        ...   \n",
       "3858                 0.0                        NaN   \n",
       "3859           1400000.0                     100.57   \n",
       "3860           5357195.0                      73.60   \n",
       "3861           3750000.0                     161.18   \n",
       "3862            300000.0                     103.59   \n",
       "\n",
       "      Under Construction Buildings  Under Construction SF  Vacancy Rate  \\\n",
       "144                            4.0                54000.0      0.029080   \n",
       "145                            3.0                36000.0      0.032372   \n",
       "146                            4.0                37160.0      0.031292   \n",
       "147                            5.0                93120.0      0.043182   \n",
       "148                            3.0               124960.0      0.046645   \n",
       "...                            ...                    ...           ...   \n",
       "3858                           7.0                81600.0      0.021936   \n",
       "3859                           7.0                81600.0      0.013023   \n",
       "3860                           7.0                81600.0      0.048391   \n",
       "3861                           2.0                21600.0      0.054643   \n",
       "3862                           2.0                21600.0      0.050971   \n",
       "\n",
       "      Vacant Available %  Vacant Available SF  \n",
       "144             0.026570             636770.0  \n",
       "145             0.028699             688304.0  \n",
       "146             0.030832             739480.0  \n",
       "147             0.043182            1035788.0  \n",
       "148             0.043040            1033872.0  \n",
       "...                  ...                  ...  \n",
       "3858            0.021936              34530.0  \n",
       "3859            0.013023              20500.0  \n",
       "3860            0.048391              76172.0  \n",
       "3861            0.054643              89292.0  \n",
       "3862            0.050971              83292.0  \n",
       "\n",
       "[384 rows x 66 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "industry2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-56-c4f0b6e586eb>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  retail2['quarter_CBSAFP']=retail2['Period'].astype(str)+\"&\"+retail2['CBSA Code'].astype(str)\n",
      "<ipython-input-56-c4f0b6e586eb>:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  office2['quarter_CBSAFP']=office2['Period'].astype(str)+\"&\"+office2['CBSA Code'].astype(str)\n",
      "<ipython-input-56-c4f0b6e586eb>:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  industry2['quarter_CBSAFP']=industry2['Period'].astype(str)+\"&\"+industry2['CBSA Code'].astype(str)\n"
     ]
    }
   ],
   "source": [
    "#generate the key word for merge.\n",
    "\n",
    "retail2['quarter_CBSAFP']=retail2['Period'].astype(str)+\"&\"+retail2['CBSA Code'].astype(str)\n",
    "\n",
    "office2['quarter_CBSAFP']=office2['Period'].astype(str)+\"&\"+office2['CBSA Code'].astype(str)\n",
    "\n",
    "industry2['quarter_CBSAFP']=industry2['Period'].astype(str)+\"&\"+industry2['CBSA Code'].astype(str)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>Property Class Name</th>\n",
       "      <th>Period</th>\n",
       "      <th>Slice</th>\n",
       "      <th>As Of</th>\n",
       "      <th>Geography Name</th>\n",
       "      <th>CBSA Code</th>\n",
       "      <th>Property Type</th>\n",
       "      <th>Forecast Scenario</th>\n",
       "      <th>Geography Type</th>\n",
       "      <th>Geography Code</th>\n",
       "      <th>...</th>\n",
       "      <th>Total Employment</th>\n",
       "      <th>Total Return</th>\n",
       "      <th>Total Sales Volume</th>\n",
       "      <th>Transaction Sale Price/SF</th>\n",
       "      <th>Under Construction Buildings</th>\n",
       "      <th>Under Construction SF</th>\n",
       "      <th>Vacancy Rate</th>\n",
       "      <th>Vacant Available %</th>\n",
       "      <th>Vacant Available SF</th>\n",
       "      <th>quarter_CBSAFP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>2018 Q1</td>\n",
       "      <td>All</td>\n",
       "      <td>2022 Q1</td>\n",
       "      <td>Daytona Beach - FL</td>\n",
       "      <td>19660</td>\n",
       "      <td>Industrial</td>\n",
       "      <td>Base Case</td>\n",
       "      <td>Metro</td>\n",
       "      <td>G41649</td>\n",
       "      <td>...</td>\n",
       "      <td>202634.593750</td>\n",
       "      <td>0.121489</td>\n",
       "      <td>16597775.0</td>\n",
       "      <td>82.98</td>\n",
       "      <td>4.0</td>\n",
       "      <td>54000.0</td>\n",
       "      <td>0.029080</td>\n",
       "      <td>0.026570</td>\n",
       "      <td>636770.0</td>\n",
       "      <td>2018 Q1&amp;19660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>2018 Q2</td>\n",
       "      <td>All</td>\n",
       "      <td>2022 Q1</td>\n",
       "      <td>Daytona Beach - FL</td>\n",
       "      <td>19660</td>\n",
       "      <td>Industrial</td>\n",
       "      <td>Base Case</td>\n",
       "      <td>Metro</td>\n",
       "      <td>G41649</td>\n",
       "      <td>...</td>\n",
       "      <td>203406.906250</td>\n",
       "      <td>0.123515</td>\n",
       "      <td>41943208.0</td>\n",
       "      <td>71.42</td>\n",
       "      <td>3.0</td>\n",
       "      <td>36000.0</td>\n",
       "      <td>0.032372</td>\n",
       "      <td>0.028699</td>\n",
       "      <td>688304.0</td>\n",
       "      <td>2018 Q2&amp;19660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>2018 Q3</td>\n",
       "      <td>All</td>\n",
       "      <td>2022 Q1</td>\n",
       "      <td>Daytona Beach - FL</td>\n",
       "      <td>19660</td>\n",
       "      <td>Industrial</td>\n",
       "      <td>Base Case</td>\n",
       "      <td>Metro</td>\n",
       "      <td>G41649</td>\n",
       "      <td>...</td>\n",
       "      <td>204901.390625</td>\n",
       "      <td>0.124135</td>\n",
       "      <td>5165600.0</td>\n",
       "      <td>44.95</td>\n",
       "      <td>4.0</td>\n",
       "      <td>37160.0</td>\n",
       "      <td>0.031292</td>\n",
       "      <td>0.030832</td>\n",
       "      <td>739480.0</td>\n",
       "      <td>2018 Q3&amp;19660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>2018 Q4</td>\n",
       "      <td>All</td>\n",
       "      <td>2022 Q1</td>\n",
       "      <td>Daytona Beach - FL</td>\n",
       "      <td>19660</td>\n",
       "      <td>Industrial</td>\n",
       "      <td>Base Case</td>\n",
       "      <td>Metro</td>\n",
       "      <td>G41649</td>\n",
       "      <td>...</td>\n",
       "      <td>205294.500000</td>\n",
       "      <td>0.125630</td>\n",
       "      <td>5367000.0</td>\n",
       "      <td>46.90</td>\n",
       "      <td>5.0</td>\n",
       "      <td>93120.0</td>\n",
       "      <td>0.043182</td>\n",
       "      <td>0.043182</td>\n",
       "      <td>1035788.0</td>\n",
       "      <td>2018 Q4&amp;19660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>2019 Q1</td>\n",
       "      <td>All</td>\n",
       "      <td>2022 Q1</td>\n",
       "      <td>Daytona Beach - FL</td>\n",
       "      <td>19660</td>\n",
       "      <td>Industrial</td>\n",
       "      <td>Base Case</td>\n",
       "      <td>Metro</td>\n",
       "      <td>G41649</td>\n",
       "      <td>...</td>\n",
       "      <td>205313.906250</td>\n",
       "      <td>0.125999</td>\n",
       "      <td>1646000.0</td>\n",
       "      <td>72.46</td>\n",
       "      <td>3.0</td>\n",
       "      <td>124960.0</td>\n",
       "      <td>0.046645</td>\n",
       "      <td>0.043040</td>\n",
       "      <td>1033872.0</td>\n",
       "      <td>2019 Q1&amp;19660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3858</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>2020 Q4</td>\n",
       "      <td>All</td>\n",
       "      <td>2022 Q1</td>\n",
       "      <td>The Villages - FL</td>\n",
       "      <td>45540</td>\n",
       "      <td>Industrial</td>\n",
       "      <td>Base Case</td>\n",
       "      <td>Metro</td>\n",
       "      <td>G39580</td>\n",
       "      <td>...</td>\n",
       "      <td>31842.000000</td>\n",
       "      <td>0.138026</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>81600.0</td>\n",
       "      <td>0.021936</td>\n",
       "      <td>0.021936</td>\n",
       "      <td>34530.0</td>\n",
       "      <td>2020 Q4&amp;45540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3859</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>2021 Q1</td>\n",
       "      <td>All</td>\n",
       "      <td>2022 Q1</td>\n",
       "      <td>The Villages - FL</td>\n",
       "      <td>45540</td>\n",
       "      <td>Industrial</td>\n",
       "      <td>Base Case</td>\n",
       "      <td>Metro</td>\n",
       "      <td>G39580</td>\n",
       "      <td>...</td>\n",
       "      <td>31496.189453</td>\n",
       "      <td>0.136862</td>\n",
       "      <td>1400000.0</td>\n",
       "      <td>100.57</td>\n",
       "      <td>7.0</td>\n",
       "      <td>81600.0</td>\n",
       "      <td>0.013023</td>\n",
       "      <td>0.013023</td>\n",
       "      <td>20500.0</td>\n",
       "      <td>2021 Q1&amp;45540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3860</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>2021 Q2</td>\n",
       "      <td>All</td>\n",
       "      <td>2022 Q1</td>\n",
       "      <td>The Villages - FL</td>\n",
       "      <td>45540</td>\n",
       "      <td>Industrial</td>\n",
       "      <td>Base Case</td>\n",
       "      <td>Metro</td>\n",
       "      <td>G39580</td>\n",
       "      <td>...</td>\n",
       "      <td>31540.269531</td>\n",
       "      <td>0.142301</td>\n",
       "      <td>5357195.0</td>\n",
       "      <td>73.60</td>\n",
       "      <td>7.0</td>\n",
       "      <td>81600.0</td>\n",
       "      <td>0.048391</td>\n",
       "      <td>0.048391</td>\n",
       "      <td>76172.0</td>\n",
       "      <td>2021 Q2&amp;45540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3861</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>2021 Q3</td>\n",
       "      <td>All</td>\n",
       "      <td>2022 Q1</td>\n",
       "      <td>The Villages - FL</td>\n",
       "      <td>45540</td>\n",
       "      <td>Industrial</td>\n",
       "      <td>Base Case</td>\n",
       "      <td>Metro</td>\n",
       "      <td>G39580</td>\n",
       "      <td>...</td>\n",
       "      <td>31965.048828</td>\n",
       "      <td>0.156650</td>\n",
       "      <td>3750000.0</td>\n",
       "      <td>161.18</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21600.0</td>\n",
       "      <td>0.054643</td>\n",
       "      <td>0.054643</td>\n",
       "      <td>89292.0</td>\n",
       "      <td>2021 Q3&amp;45540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3862</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>2021 Q4</td>\n",
       "      <td>All</td>\n",
       "      <td>2022 Q1</td>\n",
       "      <td>The Villages - FL</td>\n",
       "      <td>45540</td>\n",
       "      <td>Industrial</td>\n",
       "      <td>Base Case</td>\n",
       "      <td>Metro</td>\n",
       "      <td>G39580</td>\n",
       "      <td>...</td>\n",
       "      <td>32531.201172</td>\n",
       "      <td>0.168221</td>\n",
       "      <td>300000.0</td>\n",
       "      <td>103.59</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21600.0</td>\n",
       "      <td>0.050971</td>\n",
       "      <td>0.050971</td>\n",
       "      <td>83292.0</td>\n",
       "      <td>2021 Q4&amp;45540</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>384 rows × 67 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Property Class Name   Period Slice    As Of      Geography Name  \\\n",
       "144           Industrial  2018 Q1   All  2022 Q1  Daytona Beach - FL   \n",
       "145           Industrial  2018 Q2   All  2022 Q1  Daytona Beach - FL   \n",
       "146           Industrial  2018 Q3   All  2022 Q1  Daytona Beach - FL   \n",
       "147           Industrial  2018 Q4   All  2022 Q1  Daytona Beach - FL   \n",
       "148           Industrial  2019 Q1   All  2022 Q1  Daytona Beach - FL   \n",
       "...                  ...      ...   ...      ...                 ...   \n",
       "3858          Industrial  2020 Q4   All  2022 Q1   The Villages - FL   \n",
       "3859          Industrial  2021 Q1   All  2022 Q1   The Villages - FL   \n",
       "3860          Industrial  2021 Q2   All  2022 Q1   The Villages - FL   \n",
       "3861          Industrial  2021 Q3   All  2022 Q1   The Villages - FL   \n",
       "3862          Industrial  2021 Q4   All  2022 Q1   The Villages - FL   \n",
       "\n",
       "      CBSA Code Property Type Forecast Scenario Geography Type Geography Code  \\\n",
       "144       19660    Industrial         Base Case          Metro         G41649   \n",
       "145       19660    Industrial         Base Case          Metro         G41649   \n",
       "146       19660    Industrial         Base Case          Metro         G41649   \n",
       "147       19660    Industrial         Base Case          Metro         G41649   \n",
       "148       19660    Industrial         Base Case          Metro         G41649   \n",
       "...         ...           ...               ...            ...            ...   \n",
       "3858      45540    Industrial         Base Case          Metro         G39580   \n",
       "3859      45540    Industrial         Base Case          Metro         G39580   \n",
       "3860      45540    Industrial         Base Case          Metro         G39580   \n",
       "3861      45540    Industrial         Base Case          Metro         G39580   \n",
       "3862      45540    Industrial         Base Case          Metro         G39580   \n",
       "\n",
       "      ...  Total Employment  Total Return  Total Sales Volume  \\\n",
       "144   ...     202634.593750      0.121489          16597775.0   \n",
       "145   ...     203406.906250      0.123515          41943208.0   \n",
       "146   ...     204901.390625      0.124135           5165600.0   \n",
       "147   ...     205294.500000      0.125630           5367000.0   \n",
       "148   ...     205313.906250      0.125999           1646000.0   \n",
       "...   ...               ...           ...                 ...   \n",
       "3858  ...      31842.000000      0.138026                 0.0   \n",
       "3859  ...      31496.189453      0.136862           1400000.0   \n",
       "3860  ...      31540.269531      0.142301           5357195.0   \n",
       "3861  ...      31965.048828      0.156650           3750000.0   \n",
       "3862  ...      32531.201172      0.168221            300000.0   \n",
       "\n",
       "      Transaction Sale Price/SF  Under Construction Buildings  \\\n",
       "144                       82.98                           4.0   \n",
       "145                       71.42                           3.0   \n",
       "146                       44.95                           4.0   \n",
       "147                       46.90                           5.0   \n",
       "148                       72.46                           3.0   \n",
       "...                         ...                           ...   \n",
       "3858                        NaN                           7.0   \n",
       "3859                     100.57                           7.0   \n",
       "3860                      73.60                           7.0   \n",
       "3861                     161.18                           2.0   \n",
       "3862                     103.59                           2.0   \n",
       "\n",
       "      Under Construction SF  Vacancy Rate  Vacant Available %  \\\n",
       "144                 54000.0      0.029080            0.026570   \n",
       "145                 36000.0      0.032372            0.028699   \n",
       "146                 37160.0      0.031292            0.030832   \n",
       "147                 93120.0      0.043182            0.043182   \n",
       "148                124960.0      0.046645            0.043040   \n",
       "...                     ...           ...                 ...   \n",
       "3858                81600.0      0.021936            0.021936   \n",
       "3859                81600.0      0.013023            0.013023   \n",
       "3860                81600.0      0.048391            0.048391   \n",
       "3861                21600.0      0.054643            0.054643   \n",
       "3862                21600.0      0.050971            0.050971   \n",
       "\n",
       "      Vacant Available SF  quarter_CBSAFP  \n",
       "144              636770.0   2018 Q1&19660  \n",
       "145              688304.0   2018 Q2&19660  \n",
       "146              739480.0   2018 Q3&19660  \n",
       "147             1035788.0   2018 Q4&19660  \n",
       "148             1033872.0   2019 Q1&19660  \n",
       "...                   ...             ...  \n",
       "3858              34530.0   2020 Q4&45540  \n",
       "3859              20500.0   2021 Q1&45540  \n",
       "3860              76172.0   2021 Q2&45540  \n",
       "3861              89292.0   2021 Q3&45540  \n",
       "3862              83292.0   2021 Q4&45540  \n",
       "\n",
       "[384 rows x 67 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "industry2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# merge case and death to costar.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "industry3=industry2.merge(pd_quarter_CBSAFP2case_death_current,left_on='quarter_CBSAFP',right_on='quarter_cbsa',how='left')\n",
    "\n",
    "retail3=retail2.merge(pd_quarter_CBSAFP2case_death_current,left_on='quarter_CBSAFP',right_on='quarter_cbsa',how='left')\n",
    "\n",
    "office3=office2.merge(pd_quarter_CBSAFP2case_death_current,left_on='quarter_CBSAFP',right_on='quarter_cbsa',how='left')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 384 entries, 0 to 383\n",
      "Data columns (total 70 columns):\n",
      " #   Column                        Non-Null Count  Dtype  \n",
      "---  ------                        --------------  -----  \n",
      " 0   Property Class Name           384 non-null    object \n",
      " 1   Period                        384 non-null    object \n",
      " 2   Slice                         384 non-null    object \n",
      " 3   As Of                         384 non-null    object \n",
      " 4   Geography Name                384 non-null    object \n",
      " 5   CBSA Code                     384 non-null    int64  \n",
      " 6   Property Type                 384 non-null    object \n",
      " 7   Forecast Scenario             384 non-null    object \n",
      " 8   Geography Type                384 non-null    object \n",
      " 9   Geography Code                384 non-null    object \n",
      " 10  Appreciation Return           384 non-null    float64\n",
      " 11  Asset Value                   384 non-null    float64\n",
      " 12  Availability Rate             384 non-null    float64\n",
      " 13  Available SF                  384 non-null    float64\n",
      " 14  Average Sale Price            381 non-null    float64\n",
      " 15  Cap Rate                      262 non-null    float64\n",
      " 16  Cap Rate Transactions         382 non-null    float64\n",
      " 17  Capital Value Index           384 non-null    float64\n",
      " 18  Demand SF                     384 non-null    float64\n",
      " 19  Demolished SF                 384 non-null    float64\n",
      " 20  Existing Buildings            384 non-null    float64\n",
      " 21  Gross Delivered Buildings     384 non-null    float64\n",
      " 22  Gross Delivered SF            384 non-null    float64\n",
      " 23  Households                    384 non-null    float64\n",
      " 24  Income Return                 384 non-null    float64\n",
      " 25  Industrial Employment         384 non-null    float64\n",
      " 26  Inventory SF                  384 non-null    int64  \n",
      " 27  Leasing Activity SF           380 non-null    float64\n",
      " 28  Market Cap Rate               384 non-null    float64\n",
      " 29  Market Rent Growth            384 non-null    float64\n",
      " 30  Market Rent Growth 12 Mo      384 non-null    float64\n",
      " 31  Market Rent Index             384 non-null    float64\n",
      " 32  Market Rent/SF                384 non-null    float64\n",
      " 33  Market Sale Price Growth      384 non-null    float64\n",
      " 34  Market Sale Price Index       384 non-null    float64\n",
      " 35  Market Sale Price Per SF      384 non-null    float64\n",
      " 36  Median Cap Rate               262 non-null    float64\n",
      " 37  Median Household Income       384 non-null    float64\n",
      " 38  Median Price/Bldg SF          381 non-null    float64\n",
      " 39  Net Absorption SF             384 non-null    float64\n",
      " 40  Net Absorption SF 12 Mo       384 non-null    float64\n",
      " 41  Net Absorption SF Direct      384 non-null    float64\n",
      " 42  Net Absorption SF Sublet      262 non-null    float64\n",
      " 43  Net Delivered SF              384 non-null    float64\n",
      " 44  Net Delivered SF 12 Mo        384 non-null    float64\n",
      " 45  NOI Index                     384 non-null    float64\n",
      " 46  Occupancy Rate                384 non-null    float64\n",
      " 47  Office Base Rent Direct       384 non-null    float64\n",
      " 48  Office Base Rent Overall      384 non-null    float64\n",
      " 49  Office Base Rent Sublet       309 non-null    float64\n",
      " 50  Office Employment             384 non-null    float64\n",
      " 51  Office Gross Rent Direct      384 non-null    float64\n",
      " 52  Office Gross Rent Overall     384 non-null    float64\n",
      " 53  Office Gross Rent Sublet      246 non-null    float64\n",
      " 54  Population                    384 non-null    float64\n",
      " 55  Sales Volume Transactions     382 non-null    float64\n",
      " 56  Sold Building SF              382 non-null    float64\n",
      " 57  Total Employment              384 non-null    float64\n",
      " 58  Total Return                  384 non-null    float64\n",
      " 59  Total Sales Volume            384 non-null    float64\n",
      " 60  Transaction Sale Price/SF     381 non-null    float64\n",
      " 61  Under Construction Buildings  384 non-null    float64\n",
      " 62  Under Construction SF         336 non-null    float64\n",
      " 63  Vacancy Rate                  384 non-null    float64\n",
      " 64  Vacant Available %            384 non-null    float64\n",
      " 65  Vacant Available SF           384 non-null    float64\n",
      " 66  quarter_CBSAFP                384 non-null    object \n",
      " 67  quarter_cbsa                  192 non-null    object \n",
      " 68  current_case                  192 non-null    float64\n",
      " 69  current_death                 192 non-null    float64\n",
      "dtypes: float64(57), int64(2), object(11)\n",
      "memory usage: 213.0+ KB\n"
     ]
    }
   ],
   "source": [
    "office3.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## get the log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yoga/opt/anaconda3/lib/python3.8/site-packages/pandas/core/arraylike.py:358: RuntimeWarning: invalid value encountered in log\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n",
      "<ipython-input-111-082dd0d23bd6>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  industry3['ln_cases_current'][(industry3.current_case.notnull())&(industry3['ln_cases_current']!=0)]=np.log(industry3['current_case'][(industry3.current_case.notnull())&(industry3['ln_cases_current']!=0)])\n",
      "<ipython-input-111-082dd0d23bd6>:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  industry3['ln_death_current'][(industry3.current_death.notnull())&(industry3['ln_death_current']!=0)]=np.log(industry3['current_death'][(industry3.current_death.notnull())&(industry3['ln_death_current']!=0)])\n",
      "/Users/yoga/opt/anaconda3/lib/python3.8/site-packages/pandas/core/arraylike.py:358: RuntimeWarning: invalid value encountered in log\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n",
      "<ipython-input-111-082dd0d23bd6>:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  retail3['ln_cases_current'][(retail3.current_case.notnull())&(retail3['ln_cases_current']!=0)]=np.log(retail3['current_case'][(retail3.current_case.notnull())&(retail3['ln_cases_current']!=0)])\n",
      "<ipython-input-111-082dd0d23bd6>:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  retail3['ln_death_current'][(retail3.current_death.notnull())&(retail3['ln_death_current']!=0)]=np.log(retail3['current_death'][(retail3.current_death.notnull())&(retail3['ln_death_current']!=0)])\n",
      "/Users/yoga/opt/anaconda3/lib/python3.8/site-packages/pandas/core/arraylike.py:358: RuntimeWarning: invalid value encountered in log\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n",
      "<ipython-input-111-082dd0d23bd6>:20: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  office3['ln_cases_current'][(office3.current_case.notnull())&(office3['ln_cases_current']!=0)]=np.log(office3['current_case'][(office3.current_case.notnull())&(office3['ln_cases_current']!=0)])\n",
      "<ipython-input-111-082dd0d23bd6>:24: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  office3['ln_death_current'][(office3.current_death.notnull())&(office3['ln_death_current']!=0)]=np.log(office3['current_death'][(office3.current_death.notnull())&(office3['ln_death_current']!=0)])\n"
     ]
    }
   ],
   "source": [
    "#取对数 industry\n",
    "industry3['ln_cases_current']=industry3['current_case']\n",
    "industry3['ln_cases_current'][(industry3.current_case.notnull())&(industry3['ln_cases_current']!=0)]=np.log(industry3['current_case'][(industry3.current_case.notnull())&(industry3['ln_cases_current']!=0)])\n",
    "\n",
    "\n",
    "industry3['ln_death_current']=industry3['current_death']\n",
    "industry3['ln_death_current'][(industry3.current_death.notnull())&(industry3['ln_death_current']!=0)]=np.log(industry3['current_death'][(industry3.current_death.notnull())&(industry3['ln_death_current']!=0)])\n",
    "\n",
    "\n",
    "#取对数 retail\n",
    "retail3['ln_cases_current']=retail3['current_case']\n",
    "retail3['ln_cases_current'][(retail3.current_case.notnull())&(retail3['ln_cases_current']!=0)]=np.log(retail3['current_case'][(retail3.current_case.notnull())&(retail3['ln_cases_current']!=0)])\n",
    "\n",
    "\n",
    "retail3['ln_death_current']=retail3['current_death']\n",
    "retail3['ln_death_current'][(retail3.current_death.notnull())&(retail3['ln_death_current']!=0)]=np.log(retail3['current_death'][(retail3.current_death.notnull())&(retail3['ln_death_current']!=0)])\n",
    "\n",
    "#取对数 office\n",
    "office3['ln_cases_current']=office3['current_case']\n",
    "office3['ln_cases_current'][(office3.current_case.notnull())&(office3['ln_cases_current']!=0)]=np.log(office3['current_case'][(office3.current_case.notnull())&(office3['ln_cases_current']!=0)])\n",
    "\n",
    "\n",
    "office3['ln_death_current']=office3['current_death']\n",
    "office3['ln_death_current'][(office3.current_death.notnull())&(office3['ln_death_current']!=0)]=np.log(office3['current_death'][(office3.current_death.notnull())&(office3['ln_death_current']!=0)])\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "office3[['ln_cases_current','ln_death_current']] = office3[['ln_cases_current','ln_death_current']].fillna(0)\n",
    "retail3[['ln_cases_current','ln_death_current']] = retail3[['ln_cases_current','ln_death_current']].fillna(0)\n",
    "industry3[['ln_cases_current','ln_death_current']] = industry3[['ln_cases_current','ln_death_current']].fillna(0)\n",
    "\n",
    "\n",
    "#office3.to_csv('31_office_covid_2018-2021.csv',index=0)\n",
    "\n",
    "#retail3.to_csv('32_retail_covid_2018-2021.csv',index=0)\n",
    "\n",
    "#industry3.to_csv('33_industry_covid_2018-2021.csv',index=0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## fixed effect regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##目标处理数据，然后用stata进行fixed effect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "office3=pd.read_csv('/Users/yoga/Dropbox/1 新冠和商业地产/101 CRE second time 202205/31_office_covid_2018-2021.csv')\n",
    "retail3=pd.read_csv('/Users/yoga/Dropbox/1 新冠和商业地产/101 CRE second time 202205/32_retail_covid_2018-2021.csv')\n",
    "industry3=pd.read_csv('/Users/yoga/Dropbox/1 新冠和商业地产/101 CRE second time 202205/33_industry_covid_2018-2021.csv')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "period=office3['Period'].unique().tolist()\n",
    "period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Period</th>\n",
       "      <th>TimeIndex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018 Q1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018 Q2</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018 Q3</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018 Q4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019 Q1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019 Q2</td>\n",
       "      <td>6</td>\n",
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       "      <th>6</th>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2019 Q4</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020 Q2</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2020 Q3</td>\n",
       "      <td>11</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2020 Q4</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2021 Q1</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2021 Q2</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2021 Q3</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2021 Q4</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Period  TimeIndex\n",
       "0   2018 Q1          1\n",
       "1   2018 Q2          2\n",
       "2   2018 Q3          3\n",
       "3   2018 Q4          4\n",
       "4   2019 Q1          5\n",
       "5   2019 Q2          6\n",
       "6   2019 Q3          7\n",
       "7   2019 Q4          8\n",
       "8   2020 Q1          9\n",
       "9   2020 Q2         10\n",
       "10  2020 Q3         11\n",
       "11  2020 Q4         12\n",
       "12  2021 Q1         13\n",
       "13  2021 Q2         14\n",
       "14  2021 Q3         15\n",
       "15  2021 Q4         16"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t=list(range(1,17))\n",
    "period2t=zip(period, t)\n",
    "period2t\n",
    "dict_period2t = dict(period2t)\n",
    "dict_period2t\n",
    "new_data = {'Period':list(dict_period2t.keys()), 'TimeIndex': list(dict_period2t.values())}\n",
    "\n",
    "df = pd.DataFrame.from_dict(new_data)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "office4=office3.merge(df, left_on='Period',right_on='Period', how ='left')\n",
    "retail4=retail3.merge(df, left_on='Period',right_on='Period', how ='left')\n",
    "industry4=industry3.merge(df, left_on='Period',right_on='Period', how ='left')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "#office4.to_csv('31_2_office_covid_2018-2021.csv',index=0)\n",
    "\n",
    "#retail4.to_csv('32_2_retail_covid_2018-2021.csv',index=0)\n",
    "\n",
    "#industry4.to_csv('33_2_industry_covid_2018-2021.csv',index=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# add facial mask and stay at home\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## merge data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask=pd.read_excel('/Users/yoga/Dropbox/1 新冠和商业地产/101 CRE second time 202205/91_old_data_used for extracting facial mask and stay at home20220507.xlsx')\n",
    "office4=pd.read_csv('/Users/yoga/Dropbox/1 新冠和商业地产/101 CRE second time 202205/31_2_office_covid_2018-2021.csv')\n",
    "retail4=pd.read_csv('/Users/yoga/Dropbox/1 新冠和商业地产/101 CRE second time 202205/32_2_retail_covid_2018-2021.csv')\n",
    "industry4=pd.read_csv('/Users/yoga/Dropbox/1 新冠和商业地产/101 CRE second time 202205/33_2_industry_covid_2018-2021.csv')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Property Class Name',\n",
       " 'Period',\n",
       " 'Slice',\n",
       " 'As Of',\n",
       " 'Geography Name',\n",
       " 'CBSA Code',\n",
       " 'Property Type',\n",
       " 'Forecast Scenario',\n",
       " 'Geography Type',\n",
       " 'Geography Code',\n",
       " 'Appreciation Return',\n",
       " 'Asset Value',\n",
       " 'Availability Rate',\n",
       " 'Available SF',\n",
       " 'Average Sale Price',\n",
       " 'Cap Rate',\n",
       " 'Cap Rate Transactions',\n",
       " 'Capital Value Index',\n",
       " 'Demand SF',\n",
       " 'Demolished SF',\n",
       " 'Existing Buildings',\n",
       " 'Gross Delivered Buildings',\n",
       " 'Gross Delivered SF',\n",
       " 'Households',\n",
       " 'Income Return',\n",
       " 'Industrial Employment',\n",
       " 'Inventory SF',\n",
       " 'Leasing Activity SF',\n",
       " 'Market Cap Rate',\n",
       " 'Market Rent Growth',\n",
       " 'Market Rent Growth 12 Mo',\n",
       " 'Market Rent Index',\n",
       " 'Market Rent/SF',\n",
       " 'Market Sale Price Growth',\n",
       " 'Market Sale Price Index',\n",
       " 'Market Sale Price Per SF',\n",
       " 'Median Cap Rate',\n",
       " 'Median Household Income',\n",
       " 'Median Price/Bldg SF',\n",
       " 'Net Absorption SF',\n",
       " 'Net Absorption SF 12 Mo',\n",
       " 'Net Absorption SF Direct',\n",
       " 'Net Absorption SF Sublet',\n",
       " 'Net Delivered SF',\n",
       " 'Net Delivered SF 12 Mo',\n",
       " 'NOI Index',\n",
       " 'Occupancy Rate',\n",
       " 'Office Base Rent Direct',\n",
       " 'Office Base Rent Overall',\n",
       " 'Office Base Rent Sublet',\n",
       " 'Office Employment',\n",
       " 'Office Gross Rent Direct',\n",
       " 'Office Gross Rent Overall',\n",
       " 'Office Gross Rent Sublet',\n",
       " 'Population',\n",
       " 'Sales Volume Transactions',\n",
       " 'Sold Building SF',\n",
       " 'Total Employment',\n",
       " 'Total Return',\n",
       " 'Total Sales Volume',\n",
       " 'Transaction Sale Price/SF',\n",
       " 'Under Construction Buildings',\n",
       " 'Under Construction SF',\n",
       " 'Vacancy Rate',\n",
       " 'Vacant Available %',\n",
       " 'Vacant Available SF',\n",
       " 'Property Class Name.1',\n",
       " 'Period_x',\n",
       " 'Geography Name_x',\n",
       " 'CBSA Code.1',\n",
       " 'Households.1',\n",
       " 'Population.1',\n",
       " 'Industrial Employment.1',\n",
       " 'Office Employment.1',\n",
       " 'Total Employment.1',\n",
       " 'Market Rent Growth.1',\n",
       " 'Market Sale Price Growth.1',\n",
       " 'Sales Volume Transactions.1',\n",
       " 'Median Household Income.1',\n",
       " '2020 percentage of vote for trump',\n",
       " '2016 percentage of vote for trump',\n",
       " 'case in current quarter',\n",
       " 'death in current quarter',\n",
       " 'case in last quarter',\n",
       " 'death in last quarter',\n",
       " '2018 CBSA GDP',\n",
       " 'ln 2018 CBSA GDP',\n",
       " 'CBSA_Population2010',\n",
       " 'ln CBSA_Population2010',\n",
       " 'Overall_CBSA_Population_density2010',\n",
       " 'Non-HispanicWhite alone:Percent of population 2010 CBSA',\n",
       " 'Hispanic or Latino:Percent of population 2010 CBSA',\n",
       " 'Black orAfrican AmericanOne race Percent of population  2010 CBSA',\n",
       " 'Asian_One race:Percent of population 2010 CBSA',\n",
       " 'male population Percent in 2010 CBSA',\n",
       " 'Median age, 2010 CBSA',\n",
       " 'Q2',\n",
       " 'Q3',\n",
       " 'Q4',\n",
       " 'percentage_stay at home',\n",
       " 'percentage_facial mask',\n",
       " 'lnCOVID-19 case current quarter',\n",
       " 'lnCOVID-19 death current quarter',\n",
       " 'lnCOVID-19 case last quarter',\n",
       " 'lnCOVID-19 death last quarter',\n",
       " 'ln Households',\n",
       " 'ln Industrial Employment',\n",
       " 'ln Office Employment',\n",
       " 'ln Total Employment',\n",
       " 'ln Median Household Income']"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-239-444b926b4fdb>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mask2['quarter_CBSAFP']=mask2['Period'].astype(str)+\"&\"+mask2['CBSA Code'].astype(int).astype(str)\n"
     ]
    },
    {
     "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>Period</th>\n",
       "      <th>CBSA Code</th>\n",
       "      <th>percentage_stay at home</th>\n",
       "      <th>percentage_facial mask</th>\n",
       "      <th>quarter_CBSAFP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019 Q1</td>\n",
       "      <td>19660</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019 Q1&amp;19660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019 Q2</td>\n",
       "      <td>19660</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019 Q2&amp;19660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019 Q3</td>\n",
       "      <td>19660</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019 Q3&amp;19660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019 Q4</td>\n",
       "      <td>19660</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019 Q4&amp;19660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>19660</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2020 Q1&amp;19660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>2019 Q4</td>\n",
       "      <td>45540</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019 Q4&amp;45540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>45540</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2020 Q1&amp;45540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>2020 Q2</td>\n",
       "      <td>45540</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2020 Q2&amp;45540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>2020 Q3</td>\n",
       "      <td>45540</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2020 Q3&amp;45540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>2020 Q4</td>\n",
       "      <td>45540</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2020 Q4&amp;45540</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>192 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Period  CBSA Code  percentage_stay at home  percentage_facial mask  \\\n",
       "0    2019 Q1      19660                      NaN                     NaN   \n",
       "1    2019 Q2      19660                      NaN                     NaN   \n",
       "2    2019 Q3      19660                      NaN                     NaN   \n",
       "3    2019 Q4      19660                      NaN                     NaN   \n",
       "4    2020 Q1      19660                      0.0                     0.0   \n",
       "..       ...        ...                      ...                     ...   \n",
       "187  2019 Q4      45540                      NaN                     NaN   \n",
       "188  2020 Q1      45540                      0.0                     0.0   \n",
       "189  2020 Q2      45540                      0.0                     0.0   \n",
       "190  2020 Q3      45540                      0.0                     0.0   \n",
       "191  2020 Q4      45540                      0.0                     0.0   \n",
       "\n",
       "    quarter_CBSAFP  \n",
       "0    2019 Q1&19660  \n",
       "1    2019 Q2&19660  \n",
       "2    2019 Q3&19660  \n",
       "3    2019 Q4&19660  \n",
       "4    2020 Q1&19660  \n",
       "..             ...  \n",
       "187  2019 Q4&45540  \n",
       "188  2020 Q1&45540  \n",
       "189  2020 Q2&45540  \n",
       "190  2020 Q3&45540  \n",
       "191  2020 Q4&45540  \n",
       "\n",
       "[192 rows x 5 columns]"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask2=mask[['Period','CBSA Code','percentage_stay at home','percentage_facial mask']]\n",
    "\n",
    "mask2['quarter_CBSAFP']=mask2['Period'].astype(str)+\"&\"+mask2['CBSA Code'].astype(int).astype(str)\n",
    "\n",
    "mask2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>percentage_stay at home</th>\n",
       "      <th>percentage_facial mask</th>\n",
       "      <th>quarter_CBSAFP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>3</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <th>187</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>2019 Q4&amp;45540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2020 Q1&amp;45540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2020 Q2&amp;45540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2020 Q3&amp;45540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2020 Q4&amp;45540</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>192 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     percentage_stay at home  percentage_facial mask quarter_CBSAFP\n",
       "0                        NaN                     NaN  2019 Q1&19660\n",
       "1                        NaN                     NaN  2019 Q2&19660\n",
       "2                        NaN                     NaN  2019 Q3&19660\n",
       "3                        NaN                     NaN  2019 Q4&19660\n",
       "4                        0.0                     0.0  2020 Q1&19660\n",
       "..                       ...                     ...            ...\n",
       "187                      NaN                     NaN  2019 Q4&45540\n",
       "188                      0.0                     0.0  2020 Q1&45540\n",
       "189                      0.0                     0.0  2020 Q2&45540\n",
       "190                      0.0                     0.0  2020 Q3&45540\n",
       "191                      0.0                     0.0  2020 Q4&45540\n",
       "\n",
       "[192 rows x 3 columns]"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask3=mask2[['percentage_stay at home','percentage_facial mask','quarter_CBSAFP']]\n",
    "mask3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>percentage_stay at home</th>\n",
       "      <th>percentage_facial mask</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>96.000000</td>\n",
       "      <td>96.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>19.763423</td>\n",
       "      <td>26.148626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>38.725825</td>\n",
       "      <td>40.760137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.868132</td>\n",
       "      <td>47.009348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       percentage_stay at home  percentage_facial mask\n",
       "count                96.000000               96.000000\n",
       "mean                 19.763423               26.148626\n",
       "std                  38.725825               40.760137\n",
       "min                   0.000000                0.000000\n",
       "25%                   0.000000                0.000000\n",
       "50%                   0.000000                0.000000\n",
       "75%                   6.868132               47.009348\n",
       "max                 100.000000              100.000000"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask3.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [],
   "source": [
    "#merge mask into office retail and industry\n",
    "\n",
    "office5=office4.merge(mask3, left_on='quarter_CBSAFP',right_on='quarter_CBSAFP', how ='left')\n",
    "retail5=retail4.merge(mask3, left_on='quarter_CBSAFP',right_on='quarter_CBSAFP', how ='left')\n",
    "industry5=industry4.merge(mask3, left_on='quarter_CBSAFP',right_on='quarter_CBSAFP', how ='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#fill nan with 0\n",
    "office5.update(office5[['percentage_stay at home','percentage_facial mask']].fillna(0))\n",
    "retail5.update(retail5[['percentage_stay at home','percentage_facial mask']].fillna(0))\n",
    "industry5.update(industry5[['percentage_stay at home','percentage_facial mask']].fillna(0))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# add quarterly dummy variable\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#add quarter dummy variables\n",
    "dummies=pd.get_dummies(office5['Period']).rename(columns=lambda x: 'Period'+str(x))\n",
    "\n",
    "office6=pd.concat([office5,dummies],axis=1)\n",
    "\n",
    "#plan to add quarterly column\n",
    "office6.loc[:,'Q1']=office6.loc[:,'Period2018 Q1'].add(office6.loc[:,'Period2019 Q1']).add(office6.loc[:,'Period2020 Q1']).add(office6.loc[:,'Period2021 Q1'])\n",
    "\n",
    "office6.loc[:,'Q2']=office6.loc[:,'Period2018 Q2'].add(office6.loc[:,'Period2019 Q2']).add(office6.loc[:,'Period2020 Q2']).add(office6.loc[:,'Period2021 Q2'])\n",
    "\n",
    "office6.loc[:,'Q3']=office6.loc[:,'Period2018 Q3'].add(office6.loc[:,'Period2019 Q3']).add(office6.loc[:,'Period2020 Q3']).add(office6.loc[:,'Period2021 Q3'])\n",
    "\n",
    "office6.loc[:,'Q4']=office6.loc[:,'Period2018 Q4'].add(office6.loc[:,'Period2019 Q4']).add(office6.loc[:,'Period2020 Q4']).add(office6.loc[:,'Period2021 Q4'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [],
   "source": [
    "#same to retail\n",
    "#add quarter dummy variables\n",
    "dummies=pd.get_dummies(retail5['Period']).rename(columns=lambda x: 'Period'+str(x))\n",
    "\n",
    "retail6=pd.concat([retail5,dummies],axis=1)\n",
    "\n",
    "#plan to add quarterly column\n",
    "retail6.loc[:,'Q1']=retail6.loc[:,'Period2018 Q1'].add(retail6.loc[:,'Period2019 Q1']).add(retail6.loc[:,'Period2020 Q1']).add(retail6.loc[:,'Period2021 Q1'])\n",
    "\n",
    "retail6.loc[:,'Q2']=retail6.loc[:,'Period2018 Q2'].add(retail6.loc[:,'Period2019 Q2']).add(retail6.loc[:,'Period2020 Q2']).add(retail6.loc[:,'Period2021 Q2'])\n",
    "\n",
    "retail6.loc[:,'Q3']=retail6.loc[:,'Period2018 Q3'].add(retail6.loc[:,'Period2019 Q3']).add(retail6.loc[:,'Period2020 Q3']).add(retail6.loc[:,'Period2021 Q3'])\n",
    "\n",
    "retail6.loc[:,'Q4']=retail6.loc[:,'Period2018 Q4'].add(retail6.loc[:,'Period2019 Q4']).add(retail6.loc[:,'Period2020 Q4']).add(retail6.loc[:,'Period2021 Q4'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [],
   "source": [
    "#same to industry\n",
    "\n",
    "dummies=pd.get_dummies(industry5['Period']).rename(columns=lambda x: 'Period'+str(x))\n",
    "\n",
    "industry6=pd.concat([industry5,dummies],axis=1)\n",
    "\n",
    "#plan to add quarterly column\n",
    "industry6.loc[:,'Q1']=industry6.loc[:,'Period2018 Q1'].add(industry6.loc[:,'Period2019 Q1']).add(industry6.loc[:,'Period2020 Q1']).add(industry6.loc[:,'Period2021 Q1'])\n",
    "\n",
    "industry6.loc[:,'Q2']=industry6.loc[:,'Period2018 Q2'].add(industry6.loc[:,'Period2019 Q2']).add(industry6.loc[:,'Period2020 Q2']).add(industry6.loc[:,'Period2021 Q2'])\n",
    "\n",
    "industry6.loc[:,'Q3']=industry6.loc[:,'Period2018 Q3'].add(industry6.loc[:,'Period2019 Q3']).add(industry6.loc[:,'Period2020 Q3']).add(industry6.loc[:,'Period2021 Q3'])\n",
    "\n",
    "industry6.loc[:,'Q4']=industry6.loc[:,'Period2018 Q4'].add(industry6.loc[:,'Period2019 Q4']).add(industry6.loc[:,'Period2020 Q4']).add(industry6.loc[:,'Period2021 Q4'])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [],
   "source": [
    "#office6.to_csv('31_3_office_covid_2018-2021.csv',index=0)\n",
    "\n",
    "#retail6.to_csv('32_3_retail_covid_2018-2021.csv',index=0)\n",
    "\n",
    "#industry6.to_csv('33_3_industry_covid_2018-2021.csv',index=0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# add the one quarter lag covid data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "office6=pd.read_csv('/Users/yoga/Dropbox/1 新冠和商业地产/101 CRE second time 202205/31_3_office_covid_2018-2021.csv')\n",
    "retail6=pd.read_csv('/Users/yoga/Dropbox/1 新冠和商业地产/101 CRE second time 202205/32_3_retail_covid_2018-2021.csv')\n",
    "industry6=pd.read_csv('/Users/yoga/Dropbox/1 新冠和商业地产/101 CRE second time 202205/33_3_industry_covid_2018-2021.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Property Class Name',\n",
       " 'Period',\n",
       " 'Slice',\n",
       " 'As Of',\n",
       " 'Geography Name',\n",
       " 'CBSA Code',\n",
       " 'Property Type',\n",
       " 'Forecast Scenario',\n",
       " 'Geography Type',\n",
       " 'Geography Code',\n",
       " 'Appreciation Return',\n",
       " 'Asset Value',\n",
       " 'Availability Rate',\n",
       " 'Available SF',\n",
       " 'Average Sale Price',\n",
       " 'Cap Rate',\n",
       " 'Cap Rate Transactions',\n",
       " 'Capital Value Index',\n",
       " 'Demand SF',\n",
       " 'Demolished SF',\n",
       " 'Existing Buildings',\n",
       " 'Gross Delivered Buildings',\n",
       " 'Gross Delivered SF',\n",
       " 'Households',\n",
       " 'Income Return',\n",
       " 'Industrial Employment',\n",
       " 'Inventory SF',\n",
       " 'Leasing Activity SF',\n",
       " 'Market Cap Rate',\n",
       " 'Market Rent Growth',\n",
       " 'Market Rent Growth 12 Mo',\n",
       " 'Market Rent Index',\n",
       " 'Market Rent/SF',\n",
       " 'Market Sale Price Growth',\n",
       " 'Market Sale Price Index',\n",
       " 'Market Sale Price Per SF',\n",
       " 'Median Cap Rate',\n",
       " 'Median Household Income',\n",
       " 'Median Price/Bldg SF',\n",
       " 'Net Absorption SF',\n",
       " 'Net Absorption SF 12 Mo',\n",
       " 'Net Absorption SF Direct',\n",
       " 'Net Absorption SF Sublet',\n",
       " 'Net Delivered SF',\n",
       " 'Net Delivered SF 12 Mo',\n",
       " 'NOI Index',\n",
       " 'Occupancy Rate',\n",
       " 'Office Base Rent Direct',\n",
       " 'Office Base Rent Overall',\n",
       " 'Office Base Rent Sublet',\n",
       " 'Office Employment',\n",
       " 'Office Gross Rent Direct',\n",
       " 'Office Gross Rent Overall',\n",
       " 'Office Gross Rent Sublet',\n",
       " 'Population',\n",
       " 'Sales Volume Transactions',\n",
       " 'Sold Building SF',\n",
       " 'Total Employment',\n",
       " 'Total Return',\n",
       " 'Total Sales Volume',\n",
       " 'Transaction Sale Price/SF',\n",
       " 'Under Construction Buildings',\n",
       " 'Under Construction SF',\n",
       " 'Vacancy Rate',\n",
       " 'Vacant Available %',\n",
       " 'Vacant Available SF',\n",
       " 'quarter_CBSAFP',\n",
       " 'quarter_cbsa',\n",
       " 'current_case',\n",
       " 'current_death',\n",
       " 'ln_cases_current',\n",
       " 'ln_death_current',\n",
       " 'TimeIndex',\n",
       " 'percentage_stay at home',\n",
       " 'percentage_facial mask',\n",
       " 'Period2018 Q1',\n",
       " 'Period2018 Q2',\n",
       " 'Period2018 Q3',\n",
       " 'Period2018 Q4',\n",
       " 'Period2019 Q1',\n",
       " 'Period2019 Q2',\n",
       " 'Period2019 Q3',\n",
       " 'Period2019 Q4',\n",
       " 'Period2020 Q1',\n",
       " 'Period2020 Q2',\n",
       " 'Period2020 Q3',\n",
       " 'Period2020 Q4',\n",
       " 'Period2021 Q1',\n",
       " 'Period2021 Q2',\n",
       " 'Period2021 Q3',\n",
       " 'Period2021 Q4',\n",
       " 'Q1',\n",
       " 'Q2',\n",
       " 'Q3',\n",
       " 'Q4']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "office6.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>Period</th>\n",
       "      <th>CBSA Code</th>\n",
       "      <th>ln_cases_current</th>\n",
       "      <th>ln_death_current</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018 Q1</td>\n",
       "      <td>19660</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018 Q2</td>\n",
       "      <td>19660</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018 Q3</td>\n",
       "      <td>19660</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018 Q4</td>\n",
       "      <td>19660</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019 Q1</td>\n",
       "      <td>19660</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>2020 Q4</td>\n",
       "      <td>45540</td>\n",
       "      <td>7.927324</td>\n",
       "      <td>3.912023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>2021 Q1</td>\n",
       "      <td>45540</td>\n",
       "      <td>8.217439</td>\n",
       "      <td>4.919981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>2021 Q2</td>\n",
       "      <td>45540</td>\n",
       "      <td>6.586172</td>\n",
       "      <td>3.295837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>2021 Q3</td>\n",
       "      <td>45540</td>\n",
       "      <td>8.406262</td>\n",
       "      <td>4.969813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>2021 Q4</td>\n",
       "      <td>45540</td>\n",
       "      <td>7.254885</td>\n",
       "      <td>3.850148</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>384 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Period  CBSA Code  ln_cases_current  ln_death_current\n",
       "0    2018 Q1      19660          0.000000          0.000000\n",
       "1    2018 Q2      19660          0.000000          0.000000\n",
       "2    2018 Q3      19660          0.000000          0.000000\n",
       "3    2018 Q4      19660          0.000000          0.000000\n",
       "4    2019 Q1      19660          0.000000          0.000000\n",
       "..       ...        ...               ...               ...\n",
       "379  2020 Q4      45540          7.927324          3.912023\n",
       "380  2021 Q1      45540          8.217439          4.919981\n",
       "381  2021 Q2      45540          6.586172          3.295837\n",
       "382  2021 Q3      45540          8.406262          4.969813\n",
       "383  2021 Q4      45540          7.254885          3.850148\n",
       "\n",
       "[384 rows x 4 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "lncovid=office6[['Period','CBSA Code','ln_cases_current','ln_death_current']]\n",
    "lncovid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['2018 Q1',\n",
       " '2018 Q2',\n",
       " '2018 Q3',\n",
       " '2018 Q4',\n",
       " '2019 Q1',\n",
       " '2019 Q2',\n",
       " '2019 Q3',\n",
       " '2019 Q4',\n",
       " '2020 Q1',\n",
       " '2020 Q2',\n",
       " '2020 Q3',\n",
       " '2020 Q4',\n",
       " '2021 Q1',\n",
       " '2021 Q2',\n",
       " '2021 Q3',\n",
       " '2021 Q4']"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#目标是产生下一期的时间序列，然后合并到cbsa code, 然后和表格进行merge，就可以了。\n",
    "period=office6['Period'].unique().tolist()\n",
    "period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2'"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "period[1][-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "period2next={}\n",
    "for i in range(len(period)):\n",
    "    qi=period[i][-1]\n",
    "    if qi!=\"4\":\n",
    "        period2next[period[i]]=period[i][:6]+str(int(qi)+1)\n",
    "    else:\n",
    "        period2next[period[i]]='20'+str(int(period[i][2:4])+1)+\" Q1\"\n",
    "        \n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'2018 Q1': '2018 Q2',\n",
       " '2018 Q2': '2018 Q3',\n",
       " '2018 Q3': '2018 Q4',\n",
       " '2018 Q4': '2019 Q1',\n",
       " '2019 Q1': '2019 Q2',\n",
       " '2019 Q2': '2019 Q3',\n",
       " '2019 Q3': '2019 Q4',\n",
       " '2019 Q4': '2020 Q1',\n",
       " '2020 Q1': '2020 Q2',\n",
       " '2020 Q2': '2020 Q3',\n",
       " '2020 Q3': '2020 Q4',\n",
       " '2020 Q4': '2021 Q1',\n",
       " '2021 Q1': '2021 Q2',\n",
       " '2021 Q2': '2021 Q3',\n",
       " '2021 Q3': '2021 Q4',\n",
       " '2021 Q4': '2022 Q1'}"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "period2next"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd_period2next = pd.DataFrame(list(period2next.items()),columns = ['current', 'next'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>Period</th>\n",
       "      <th>CBSA Code</th>\n",
       "      <th>ln_cases_current</th>\n",
       "      <th>ln_death_current</th>\n",
       "      <th>current</th>\n",
       "      <th>next</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018 Q1</td>\n",
       "      <td>19660</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2018 Q1</td>\n",
       "      <td>2018 Q2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018 Q2</td>\n",
       "      <td>19660</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2018 Q2</td>\n",
       "      <td>2018 Q3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018 Q3</td>\n",
       "      <td>19660</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2018 Q3</td>\n",
       "      <td>2018 Q4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018 Q4</td>\n",
       "      <td>19660</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2018 Q4</td>\n",
       "      <td>2019 Q1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019 Q1</td>\n",
       "      <td>19660</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2019 Q1</td>\n",
       "      <td>2019 Q2</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>379</th>\n",
       "      <td>2020 Q4</td>\n",
       "      <td>45540</td>\n",
       "      <td>7.927324</td>\n",
       "      <td>3.912023</td>\n",
       "      <td>2020 Q4</td>\n",
       "      <td>2021 Q1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>2021 Q1</td>\n",
       "      <td>45540</td>\n",
       "      <td>8.217439</td>\n",
       "      <td>4.919981</td>\n",
       "      <td>2021 Q1</td>\n",
       "      <td>2021 Q2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>2021 Q2</td>\n",
       "      <td>45540</td>\n",
       "      <td>6.586172</td>\n",
       "      <td>3.295837</td>\n",
       "      <td>2021 Q2</td>\n",
       "      <td>2021 Q3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>2021 Q3</td>\n",
       "      <td>45540</td>\n",
       "      <td>8.406262</td>\n",
       "      <td>4.969813</td>\n",
       "      <td>2021 Q3</td>\n",
       "      <td>2021 Q4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>2021 Q4</td>\n",
       "      <td>45540</td>\n",
       "      <td>7.254885</td>\n",
       "      <td>3.850148</td>\n",
       "      <td>2021 Q4</td>\n",
       "      <td>2022 Q1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>384 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Period  CBSA Code  ln_cases_current  ln_death_current  current     next\n",
       "0    2018 Q1      19660          0.000000          0.000000  2018 Q1  2018 Q2\n",
       "1    2018 Q2      19660          0.000000          0.000000  2018 Q2  2018 Q3\n",
       "2    2018 Q3      19660          0.000000          0.000000  2018 Q3  2018 Q4\n",
       "3    2018 Q4      19660          0.000000          0.000000  2018 Q4  2019 Q1\n",
       "4    2019 Q1      19660          0.000000          0.000000  2019 Q1  2019 Q2\n",
       "..       ...        ...               ...               ...      ...      ...\n",
       "379  2020 Q4      45540          7.927324          3.912023  2020 Q4  2021 Q1\n",
       "380  2021 Q1      45540          8.217439          4.919981  2021 Q1  2021 Q2\n",
       "381  2021 Q2      45540          6.586172          3.295837  2021 Q2  2021 Q3\n",
       "382  2021 Q3      45540          8.406262          4.969813  2021 Q3  2021 Q4\n",
       "383  2021 Q4      45540          7.254885          3.850148  2021 Q4  2022 Q1\n",
       "\n",
       "[384 rows x 6 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lncovid2=lncovid.merge(pd_period2next,left_on='Period',right_on='current',how='left')\n",
    "lncovid2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Period</th>\n",
       "      <th>CBSA Code</th>\n",
       "      <th>ln_cases_current</th>\n",
       "      <th>ln_death_current</th>\n",
       "      <th>current</th>\n",
       "      <th>next</th>\n",
       "      <th>nest_quarter_CBSAFP</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018 Q1</td>\n",
       "      <td>19660</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2018 Q1</td>\n",
       "      <td>2018 Q2</td>\n",
       "      <td>2018 Q2&amp;19660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018 Q2</td>\n",
       "      <td>19660</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2018 Q2</td>\n",
       "      <td>2018 Q3</td>\n",
       "      <td>2018 Q3&amp;19660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018 Q3</td>\n",
       "      <td>19660</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2018 Q3</td>\n",
       "      <td>2018 Q4</td>\n",
       "      <td>2018 Q4&amp;19660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018 Q4</td>\n",
       "      <td>19660</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2018 Q4</td>\n",
       "      <td>2019 Q1</td>\n",
       "      <td>2019 Q1&amp;19660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019 Q1</td>\n",
       "      <td>19660</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2019 Q1</td>\n",
       "      <td>2019 Q2</td>\n",
       "      <td>2019 Q2&amp;19660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>2020 Q4</td>\n",
       "      <td>45540</td>\n",
       "      <td>7.927324</td>\n",
       "      <td>3.912023</td>\n",
       "      <td>2020 Q4</td>\n",
       "      <td>2021 Q1</td>\n",
       "      <td>2021 Q1&amp;45540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>2021 Q1</td>\n",
       "      <td>45540</td>\n",
       "      <td>8.217439</td>\n",
       "      <td>4.919981</td>\n",
       "      <td>2021 Q1</td>\n",
       "      <td>2021 Q2</td>\n",
       "      <td>2021 Q2&amp;45540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>2021 Q2</td>\n",
       "      <td>45540</td>\n",
       "      <td>6.586172</td>\n",
       "      <td>3.295837</td>\n",
       "      <td>2021 Q2</td>\n",
       "      <td>2021 Q3</td>\n",
       "      <td>2021 Q3&amp;45540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>2021 Q3</td>\n",
       "      <td>45540</td>\n",
       "      <td>8.406262</td>\n",
       "      <td>4.969813</td>\n",
       "      <td>2021 Q3</td>\n",
       "      <td>2021 Q4</td>\n",
       "      <td>2021 Q4&amp;45540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>2021 Q4</td>\n",
       "      <td>45540</td>\n",
       "      <td>7.254885</td>\n",
       "      <td>3.850148</td>\n",
       "      <td>2021 Q4</td>\n",
       "      <td>2022 Q1</td>\n",
       "      <td>2022 Q1&amp;45540</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>384 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Period  CBSA Code  ln_cases_current  ln_death_current  current     next  \\\n",
       "0    2018 Q1      19660          0.000000          0.000000  2018 Q1  2018 Q2   \n",
       "1    2018 Q2      19660          0.000000          0.000000  2018 Q2  2018 Q3   \n",
       "2    2018 Q3      19660          0.000000          0.000000  2018 Q3  2018 Q4   \n",
       "3    2018 Q4      19660          0.000000          0.000000  2018 Q4  2019 Q1   \n",
       "4    2019 Q1      19660          0.000000          0.000000  2019 Q1  2019 Q2   \n",
       "..       ...        ...               ...               ...      ...      ...   \n",
       "379  2020 Q4      45540          7.927324          3.912023  2020 Q4  2021 Q1   \n",
       "380  2021 Q1      45540          8.217439          4.919981  2021 Q1  2021 Q2   \n",
       "381  2021 Q2      45540          6.586172          3.295837  2021 Q2  2021 Q3   \n",
       "382  2021 Q3      45540          8.406262          4.969813  2021 Q3  2021 Q4   \n",
       "383  2021 Q4      45540          7.254885          3.850148  2021 Q4  2022 Q1   \n",
       "\n",
       "    nest_quarter_CBSAFP  \n",
       "0         2018 Q2&19660  \n",
       "1         2018 Q3&19660  \n",
       "2         2018 Q4&19660  \n",
       "3         2019 Q1&19660  \n",
       "4         2019 Q2&19660  \n",
       "..                  ...  \n",
       "379       2021 Q1&45540  \n",
       "380       2021 Q2&45540  \n",
       "381       2021 Q3&45540  \n",
       "382       2021 Q4&45540  \n",
       "383       2022 Q1&45540  \n",
       "\n",
       "[384 rows x 7 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lncovid2['nest_quarter_CBSAFP']=lncovid2['next'].astype(str)+\"&\"+lncovid2['CBSA Code'].astype(int).astype(str)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nest_quarter_CBSAFP</th>\n",
       "      <th>ln_cases_prior</th>\n",
       "      <th>ln_death_prior</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018 Q2&amp;19660</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018 Q3&amp;19660</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018 Q4&amp;19660</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019 Q1&amp;19660</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019 Q2&amp;19660</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>2021 Q1&amp;45540</td>\n",
       "      <td>7.927324</td>\n",
       "      <td>3.912023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>2021 Q2&amp;45540</td>\n",
       "      <td>8.217439</td>\n",
       "      <td>4.919981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>2021 Q3&amp;45540</td>\n",
       "      <td>6.586172</td>\n",
       "      <td>3.295837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>2021 Q4&amp;45540</td>\n",
       "      <td>8.406262</td>\n",
       "      <td>4.969813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>2022 Q1&amp;45540</td>\n",
       "      <td>7.254885</td>\n",
       "      <td>3.850148</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>384 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    nest_quarter_CBSAFP  ln_cases_prior  ln_death_prior\n",
       "0         2018 Q2&19660        0.000000        0.000000\n",
       "1         2018 Q3&19660        0.000000        0.000000\n",
       "2         2018 Q4&19660        0.000000        0.000000\n",
       "3         2019 Q1&19660        0.000000        0.000000\n",
       "4         2019 Q2&19660        0.000000        0.000000\n",
       "..                  ...             ...             ...\n",
       "379       2021 Q1&45540        7.927324        3.912023\n",
       "380       2021 Q2&45540        8.217439        4.919981\n",
       "381       2021 Q3&45540        6.586172        3.295837\n",
       "382       2021 Q4&45540        8.406262        4.969813\n",
       "383       2022 Q1&45540        7.254885        3.850148\n",
       "\n",
       "[384 rows x 3 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lncovid3=lncovid2[['nest_quarter_CBSAFP','ln_cases_current','ln_death_current']]\n",
    "\n",
    "lncovid3=lncovid3.rename(columns={\"ln_cases_current\": \"ln_cases_prior\", \"ln_death_current\": \"ln_death_prior\"})\n",
    "\n",
    "lncovid3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "#然后用next quarter cbsa fp作为key merge 到原来的数据里。\n",
    "office7=office6.merge(lncovid3,left_on='quarter_CBSAFP',right_on='nest_quarter_CBSAFP',how='left')\n",
    "\n",
    "retail7=retail6.merge(lncovid3,left_on='quarter_CBSAFP',right_on='nest_quarter_CBSAFP',how='left')\n",
    "\n",
    "industry7=industry6.merge(lncovid3,left_on='quarter_CBSAFP',right_on='nest_quarter_CBSAFP',how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>Period</th>\n",
       "      <th>Geography Name</th>\n",
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       "      <th>ln_death_prior</th>\n",
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       "      <th>374</th>\n",
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       "      <td>45540</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th>375</th>\n",
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       "      <td>The Villages - FL</td>\n",
       "      <td>45540</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>The Villages - FL</td>\n",
       "      <td>45540</td>\n",
       "      <td>3.931826</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>2020 Q2</td>\n",
       "      <td>The Villages - FL</td>\n",
       "      <td>45540</td>\n",
       "      <td>5.683580</td>\n",
       "      <td>3.931826</td>\n",
       "      <td>2.833213</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>2020 Q3</td>\n",
       "      <td>The Villages - FL</td>\n",
       "      <td>45540</td>\n",
       "      <td>7.584773</td>\n",
       "      <td>5.683580</td>\n",
       "      <td>3.970292</td>\n",
       "      <td>2.833213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>2020 Q4</td>\n",
       "      <td>The Villages - FL</td>\n",
       "      <td>45540</td>\n",
       "      <td>7.927324</td>\n",
       "      <td>7.584773</td>\n",
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       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>2021 Q1</td>\n",
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       "      <td>45540</td>\n",
       "      <td>8.217439</td>\n",
       "      <td>7.927324</td>\n",
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       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>2021 Q2</td>\n",
       "      <td>The Villages - FL</td>\n",
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       "      <td>6.586172</td>\n",
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       "      <td>3.295837</td>\n",
       "      <td>4.919981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>2021 Q3</td>\n",
       "      <td>The Villages - FL</td>\n",
       "      <td>45540</td>\n",
       "      <td>8.406262</td>\n",
       "      <td>6.586172</td>\n",
       "      <td>4.969813</td>\n",
       "      <td>3.295837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>2021 Q4</td>\n",
       "      <td>The Villages - FL</td>\n",
       "      <td>45540</td>\n",
       "      <td>7.254885</td>\n",
       "      <td>8.406262</td>\n",
       "      <td>3.850148</td>\n",
       "      <td>4.969813</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Period     Geography Name  CBSA Code  ln_cases_current  ln_cases_prior  \\\n",
       "374  2019 Q3  The Villages - FL      45540          0.000000        0.000000   \n",
       "375  2019 Q4  The Villages - FL      45540          0.000000        0.000000   \n",
       "376  2020 Q1  The Villages - FL      45540          3.931826        0.000000   \n",
       "377  2020 Q2  The Villages - FL      45540          5.683580        3.931826   \n",
       "378  2020 Q3  The Villages - FL      45540          7.584773        5.683580   \n",
       "379  2020 Q4  The Villages - FL      45540          7.927324        7.584773   \n",
       "380  2021 Q1  The Villages - FL      45540          8.217439        7.927324   \n",
       "381  2021 Q2  The Villages - FL      45540          6.586172        8.217439   \n",
       "382  2021 Q3  The Villages - FL      45540          8.406262        6.586172   \n",
       "383  2021 Q4  The Villages - FL      45540          7.254885        8.406262   \n",
       "\n",
       "     ln_death_current  ln_death_prior  \n",
       "374          0.000000        0.000000  \n",
       "375          0.000000        0.000000  \n",
       "376          0.000000        0.000000  \n",
       "377          2.833213        0.000000  \n",
       "378          3.970292        2.833213  \n",
       "379          3.912023        3.970292  \n",
       "380          4.919981        3.912023  \n",
       "381          3.295837        4.919981  \n",
       "382          4.969813        3.295837  \n",
       "383          3.850148        4.969813  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#检查通过没有问题\n",
    "#industry7 retail7 office7\n",
    "retail7[['Period',\n",
    " 'Geography Name',\n",
    " 'CBSA Code','ln_cases_current','ln_cases_prior','ln_death_current','ln_death_prior']].tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "industry7[['ln_cases_prior','ln_death_prior']] = industry7[['ln_cases_prior','ln_death_prior']].fillna(0)\n",
    "\n",
    "retail7[['ln_cases_prior','ln_death_prior']] = retail7[['ln_cases_prior','ln_death_prior']].fillna(0)\n",
    "\n",
    "office7[['ln_cases_prior','ln_death_prior']] = office7[['ln_cases_prior','ln_death_prior']].fillna(0)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "#office7.to_csv('31_4_office_covid_2018-2021.csv',index=0)\n",
    "\n",
    "#retail7.to_csv('32_4_retail_covid_2018-2021.csv',index=0)\n",
    "\n",
    "#industry7.to_csv('33_4_industry_covid_2018-2021.csv',index=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# calculate correlation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "retail=pd.read_csv('/Users/yoga/Dropbox/1 新冠和商业地产/101 CRE second time 202205/32_4_retail_covid_2018-2021.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Property Class Name', 'Period', 'Slice', 'As Of', 'Geography Name',\n",
       "       'CBSA Code', 'Property Type', 'Forecast Scenario', 'Geography Type',\n",
       "       'Geography Code', 'All Service Type Rent Direct',\n",
       "       'All Service Type Rent Overall', 'All Service Type Rent Sublet',\n",
       "       'Appreciation Return', 'Asset Value', 'Availability Rate',\n",
       "       'Available SF', 'Average Sale Price', 'Cap Rate',\n",
       "       'Cap Rate Transactions', 'Capital Value Index', 'Demand SF',\n",
       "       'Demolished SF', 'Existing Buildings', 'Existing Centers',\n",
       "       'Gross Delivered Buildings', 'Gross Delivered SF', 'Households',\n",
       "       'Income Return', 'Industrial Employment', 'Inventory SF',\n",
       "       'Leasing Activity SF', 'Market Cap Rate', 'Market Rent Growth',\n",
       "       'Market Rent Growth 12 Mo', 'Market Rent Index', 'Market Rent/SF',\n",
       "       'Market Sale Price Growth', 'Market Sale Price Index',\n",
       "       'Market Sale Price Per SF', 'Median Cap Rate',\n",
       "       'Median Household Income', 'Median Price/Bldg SF', 'Net Absorption SF',\n",
       "       'Net Absorption SF 12 Mo', 'Net Absorption SF Direct',\n",
       "       'Net Absorption SF Sublet', 'Net Delivered SF',\n",
       "       'Net Delivered SF 12 Mo', 'NNN Rent Direct', 'NNN Rent Overall',\n",
       "       'NNN Rent Sublet', 'NOI Index', 'Occupancy Rate', 'Office Employment',\n",
       "       'Population', 'Sales Volume Transactions', 'Sold Building SF',\n",
       "       'Total Employment', 'Total Return', 'Total Sales Volume',\n",
       "       'Transaction Sale Price/SF', 'Under Construction Buildings',\n",
       "       'Under Construction SF', 'Vacancy Rate', 'Vacant Available %',\n",
       "       'Vacant Available SF', 'quarter_CBSAFP', 'quarter_cbsa', 'current_case',\n",
       "       'current_death', 'ln_cases_current', 'ln_death_current', 'TimeIndex',\n",
       "       'percentage_stay at home', 'percentage_facial mask', 'Period2018 Q1',\n",
       "       'Period2018 Q2', 'Period2018 Q3', 'Period2018 Q4', 'Period2019 Q1',\n",
       "       'Period2019 Q2', 'Period2019 Q3', 'Period2019 Q4', 'Period2020 Q1',\n",
       "       'Period2020 Q2', 'Period2020 Q3', 'Period2020 Q4', 'Period2021 Q1',\n",
       "       'Period2021 Q2', 'Period2021 Q3', 'Period2021 Q4', 'Q1', 'Q2', 'Q3',\n",
       "       'Q4', 'nest_quarter_CBSAFP', 'ln_cases_prior', 'ln_death_prior'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "retail.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "retail_cor=retail[['ln_cases_current', 'ln_cases_prior', 'ln_death_current','ln_death_prior',\n",
    "                   'Total Employment','percentage_stay at home', 'percentage_facial mask']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>ln_cases_current</th>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>380</th>\n",
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       "      <td>3.295837</td>\n",
       "      <td>4.919981</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>382</th>\n",
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       "<p>384 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     ln_cases_current  ln_cases_prior  ln_death_current  ln_death_prior  \\\n",
       "0            0.000000        0.000000          0.000000        0.000000   \n",
       "1            0.000000        0.000000          0.000000        0.000000   \n",
       "2            0.000000        0.000000          0.000000        0.000000   \n",
       "3            0.000000        0.000000          0.000000        0.000000   \n",
       "4            0.000000        0.000000          0.000000        0.000000   \n",
       "..                ...             ...               ...             ...   \n",
       "379          7.927324        7.584773          3.912023        3.970292   \n",
       "380          8.217439        7.927324          4.919981        3.912023   \n",
       "381          6.586172        8.217439          3.295837        4.919981   \n",
       "382          8.406262        6.586172          4.969813        3.295837   \n",
       "383          7.254885        8.406262          3.850148        4.969813   \n",
       "\n",
       "     Total Employment  percentage_stay at home  percentage_facial mask  \n",
       "0       202634.593750                      0.0                     0.0  \n",
       "1       203406.906250                      0.0                     0.0  \n",
       "2       204901.390625                      0.0                     0.0  \n",
       "3       205294.500000                      0.0                     0.0  \n",
       "4       205313.906250                      0.0                     0.0  \n",
       "..                ...                      ...                     ...  \n",
       "379      31842.000000                      0.0                     0.0  \n",
       "380      31496.189453                      0.0                     0.0  \n",
       "381      31540.269531                      0.0                     0.0  \n",
       "382      31965.048828                      0.0                     0.0  \n",
       "383      32531.201172                      0.0                     0.0  \n",
       "\n",
       "[384 rows x 7 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "retail_cor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "corrMatrix = retail_cor.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>ln_cases_current</th>\n",
       "      <th>ln_cases_prior</th>\n",
       "      <th>ln_death_current</th>\n",
       "      <th>ln_death_prior</th>\n",
       "      <th>Total Employment</th>\n",
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       "      <th>percentage_facial mask</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>1.000000</td>\n",
       "      <td>0.929994</td>\n",
       "      <td>0.973075</td>\n",
       "      <td>0.876552</td>\n",
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       "      <td>0.343981</td>\n",
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       "    <tr>\n",
       "      <th>ln_cases_prior</th>\n",
       "      <td>0.929994</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.956173</td>\n",
       "      <td>0.972408</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>ln_death_current</th>\n",
       "      <td>0.973075</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>0.913185</td>\n",
       "      <td>0.137557</td>\n",
       "      <td>0.267302</td>\n",
       "      <td>0.347986</td>\n",
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       "    <tr>\n",
       "      <th>ln_death_prior</th>\n",
       "      <td>0.876552</td>\n",
       "      <td>0.972408</td>\n",
       "      <td>0.913185</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.129157</td>\n",
       "      <td>0.159154</td>\n",
       "      <td>0.280199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Employment</th>\n",
       "      <td>0.106457</td>\n",
       "      <td>0.092816</td>\n",
       "      <td>0.137557</td>\n",
       "      <td>0.129157</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.182648</td>\n",
       "      <td>0.161612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>percentage_stay at home</th>\n",
       "      <td>0.279194</td>\n",
       "      <td>0.216451</td>\n",
       "      <td>0.267302</td>\n",
       "      <td>0.159154</td>\n",
       "      <td>0.182648</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.651636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>percentage_facial mask</th>\n",
       "      <td>0.343981</td>\n",
       "      <td>0.315155</td>\n",
       "      <td>0.347986</td>\n",
       "      <td>0.280199</td>\n",
       "      <td>0.161612</td>\n",
       "      <td>0.651636</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         ln_cases_current  ln_cases_prior  ln_death_current  \\\n",
       "ln_cases_current                 1.000000        0.929994          0.973075   \n",
       "ln_cases_prior                   0.929994        1.000000          0.956173   \n",
       "ln_death_current                 0.973075        0.956173          1.000000   \n",
       "ln_death_prior                   0.876552        0.972408          0.913185   \n",
       "Total Employment                 0.106457        0.092816          0.137557   \n",
       "percentage_stay at home          0.279194        0.216451          0.267302   \n",
       "percentage_facial mask           0.343981        0.315155          0.347986   \n",
       "\n",
       "                         ln_death_prior  Total Employment  \\\n",
       "ln_cases_current               0.876552          0.106457   \n",
       "ln_cases_prior                 0.972408          0.092816   \n",
       "ln_death_current               0.913185          0.137557   \n",
       "ln_death_prior                 1.000000          0.129157   \n",
       "Total Employment               0.129157          1.000000   \n",
       "percentage_stay at home        0.159154          0.182648   \n",
       "percentage_facial mask         0.280199          0.161612   \n",
       "\n",
       "                         percentage_stay at home  percentage_facial mask  \n",
       "ln_cases_current                        0.279194                0.343981  \n",
       "ln_cases_prior                          0.216451                0.315155  \n",
       "ln_death_current                        0.267302                0.347986  \n",
       "ln_death_prior                          0.159154                0.280199  \n",
       "Total Employment                        0.182648                0.161612  \n",
       "percentage_stay at home                 1.000000                0.651636  \n",
       "percentage_facial mask                  0.651636                1.000000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corrMatrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#corrMatrix.to_csv('399_correlation_appendix_in_paper.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-23-a8b10cdac90f>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  retail_cor['Million_emp']=retail_cor['Total Employment']/1000000\n"
     ]
    }
   ],
   "source": [
    "retail_cor['Million_emp']=retail_cor['Total Employment']/1000000\n",
    "describe=retail_cor.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#describe.to_csv('398_correlation_describe.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ln_cases_current</th>\n",
       "      <th>ln_cases_prior</th>\n",
       "      <th>ln_death_current</th>\n",
       "      <th>ln_death_prior</th>\n",
       "      <th>Total Employment</th>\n",
       "      <th>percentage_stay at home</th>\n",
       "      <th>percentage_facial mask</th>\n",
       "      <th>Million_emp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ln_cases_current</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.929994</td>\n",
       "      <td>0.973075</td>\n",
       "      <td>0.876552</td>\n",
       "      <td>0.106457</td>\n",
       "      <td>0.279194</td>\n",
       "      <td>0.343981</td>\n",
       "      <td>0.106457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ln_cases_prior</th>\n",
       "      <td>0.929994</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.956173</td>\n",
       "      <td>0.972408</td>\n",
       "      <td>0.092816</td>\n",
       "      <td>0.216451</td>\n",
       "      <td>0.315155</td>\n",
       "      <td>0.092816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ln_death_current</th>\n",
       "      <td>0.973075</td>\n",
       "      <td>0.956173</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.913185</td>\n",
       "      <td>0.137557</td>\n",
       "      <td>0.267302</td>\n",
       "      <td>0.347986</td>\n",
       "      <td>0.137557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ln_death_prior</th>\n",
       "      <td>0.876552</td>\n",
       "      <td>0.972408</td>\n",
       "      <td>0.913185</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.129157</td>\n",
       "      <td>0.159154</td>\n",
       "      <td>0.280199</td>\n",
       "      <td>0.129157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Employment</th>\n",
       "      <td>0.106457</td>\n",
       "      <td>0.092816</td>\n",
       "      <td>0.137557</td>\n",
       "      <td>0.129157</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.182648</td>\n",
       "      <td>0.161612</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>percentage_stay at home</th>\n",
       "      <td>0.279194</td>\n",
       "      <td>0.216451</td>\n",
       "      <td>0.267302</td>\n",
       "      <td>0.159154</td>\n",
       "      <td>0.182648</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.651636</td>\n",
       "      <td>0.182648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>percentage_facial mask</th>\n",
       "      <td>0.343981</td>\n",
       "      <td>0.315155</td>\n",
       "      <td>0.347986</td>\n",
       "      <td>0.280199</td>\n",
       "      <td>0.161612</td>\n",
       "      <td>0.651636</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.161612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Million_emp</th>\n",
       "      <td>0.106457</td>\n",
       "      <td>0.092816</td>\n",
       "      <td>0.137557</td>\n",
       "      <td>0.129157</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.182648</td>\n",
       "      <td>0.161612</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         ln_cases_current  ln_cases_prior  ln_death_current  \\\n",
       "ln_cases_current                 1.000000        0.929994          0.973075   \n",
       "ln_cases_prior                   0.929994        1.000000          0.956173   \n",
       "ln_death_current                 0.973075        0.956173          1.000000   \n",
       "ln_death_prior                   0.876552        0.972408          0.913185   \n",
       "Total Employment                 0.106457        0.092816          0.137557   \n",
       "percentage_stay at home          0.279194        0.216451          0.267302   \n",
       "percentage_facial mask           0.343981        0.315155          0.347986   \n",
       "Million_emp                      0.106457        0.092816          0.137557   \n",
       "\n",
       "                         ln_death_prior  Total Employment  \\\n",
       "ln_cases_current               0.876552          0.106457   \n",
       "ln_cases_prior                 0.972408          0.092816   \n",
       "ln_death_current               0.913185          0.137557   \n",
       "ln_death_prior                 1.000000          0.129157   \n",
       "Total Employment               0.129157          1.000000   \n",
       "percentage_stay at home        0.159154          0.182648   \n",
       "percentage_facial mask         0.280199          0.161612   \n",
       "Million_emp                    0.129157          1.000000   \n",
       "\n",
       "                         percentage_stay at home  percentage_facial mask  \\\n",
       "ln_cases_current                        0.279194                0.343981   \n",
       "ln_cases_prior                          0.216451                0.315155   \n",
       "ln_death_current                        0.267302                0.347986   \n",
       "ln_death_prior                          0.159154                0.280199   \n",
       "Total Employment                        0.182648                0.161612   \n",
       "percentage_stay at home                 1.000000                0.651636   \n",
       "percentage_facial mask                  0.651636                1.000000   \n",
       "Million_emp                             0.182648                0.161612   \n",
       "\n",
       "                         Million_emp  \n",
       "ln_cases_current            0.106457  \n",
       "ln_cases_prior              0.092816  \n",
       "ln_death_current            0.137557  \n",
       "ln_death_prior              0.129157  \n",
       "Total Employment            1.000000  \n",
       "percentage_stay at home     0.182648  \n",
       "percentage_facial mask      0.161612  \n",
       "Million_emp                 1.000000  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "retail_cor.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>ln_cases_current</th>\n",
       "      <th>ln_cases_prior</th>\n",
       "      <th>ln_death_current</th>\n",
       "      <th>ln_death_prior</th>\n",
       "      <th>Total Employment</th>\n",
       "      <th>percentage_stay at home</th>\n",
       "      <th>percentage_facial mask</th>\n",
       "      <th>Million_emp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>384.000000</td>\n",
       "      <td>384.000000</td>\n",
       "      <td>384.000000</td>\n",
       "      <td>384.000000</td>\n",
       "      <td>3.840000e+02</td>\n",
       "      <td>384.000000</td>\n",
       "      <td>384.000000</td>\n",
       "      <td>384.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.286741</td>\n",
       "      <td>3.712482</td>\n",
       "      <td>2.335957</td>\n",
       "      <td>1.996656</td>\n",
       "      <td>3.571100e+05</td>\n",
       "      <td>4.940856</td>\n",
       "      <td>6.537156</td>\n",
       "      <td>0.357110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.560674</td>\n",
       "      <td>4.471949</td>\n",
       "      <td>2.694712</td>\n",
       "      <td>2.615021</td>\n",
       "      <td>4.048860e+05</td>\n",
       "      <td>21.104811</td>\n",
       "      <td>23.251490</td>\n",
       "      <td>0.404886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.402740e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.024027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.797862e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.097979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.844490e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.184449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.054283</td>\n",
       "      <td>8.657080</td>\n",
       "      <td>5.051362</td>\n",
       "      <td>4.738382</td>\n",
       "      <td>3.835281e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.383528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>12.159678</td>\n",
       "      <td>12.118852</td>\n",
       "      <td>7.796058</td>\n",
       "      <td>7.796058</td>\n",
       "      <td>1.415920e+06</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>1.415920</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ln_cases_current  ln_cases_prior  ln_death_current  ln_death_prior  \\\n",
       "count        384.000000      384.000000        384.000000      384.000000   \n",
       "mean           4.286741        3.712482          2.335957        1.996656   \n",
       "std            4.560674        4.471949          2.694712        2.615021   \n",
       "min            0.000000        0.000000          0.000000        0.000000   \n",
       "25%            0.000000        0.000000          0.000000        0.000000   \n",
       "50%            0.000000        0.000000          0.000000        0.000000   \n",
       "75%            9.054283        8.657080          5.051362        4.738382   \n",
       "max           12.159678       12.118852          7.796058        7.796058   \n",
       "\n",
       "       Total Employment  percentage_stay at home  percentage_facial mask  \\\n",
       "count      3.840000e+02               384.000000              384.000000   \n",
       "mean       3.571100e+05                 4.940856                6.537156   \n",
       "std        4.048860e+05                21.104811               23.251490   \n",
       "min        2.402740e+04                 0.000000                0.000000   \n",
       "25%        9.797862e+04                 0.000000                0.000000   \n",
       "50%        1.844490e+05                 0.000000                0.000000   \n",
       "75%        3.835281e+05                 0.000000                0.000000   \n",
       "max        1.415920e+06               100.000000              100.000000   \n",
       "\n",
       "       Million_emp  \n",
       "count   384.000000  \n",
       "mean      0.357110  \n",
       "std       0.404886  \n",
       "min       0.024027  \n",
       "25%       0.097979  \n",
       "50%       0.184449  \n",
       "75%       0.383528  \n",
       "max       1.415920  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# number used in the paper"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## It has caused 824k deaths as of December 2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "covid=pd.read_csv('/Users/yoga/Documents/Projects/1_covid19_cre/72_update regression analysis 20220122/1_us-counties_covid20220122.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "covid2021=covid[covid['date']=='2021-12-31']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "824336.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid2021['deaths'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Florida is a state with the third largest accumulated number of COVID cases ( 4.17 million) in the U.S. in 2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "covid2021fl=covid2021.loc[covid2021['state']=='Florida']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4166392"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid2021fl['cases'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The asset value of the commercial real estate industry reached 4 billion in the fourth quarter of 2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "office=pd.read_excel('/Users/yoga/Documents/Projects/1_covid19_cre/73_costar data 20220118/reupdatecostardatatoincludethewholeyearof2021/Office_01-18-2022.xlsx')\n",
    "industry=pd.read_excel('/Users/yoga/Documents/Projects/1_covid19_cre/73_costar data 20220118/reupdatecostardatatoincludethewholeyearof2021/Industrial_01-18-2022.xlsx')\n",
    "retail=pd.read_excel('/Users/yoga/Documents/Projects/1_covid19_cre/73_costar data 20220118/reupdatecostardatatoincludethewholeyearof2021/Retail_01-18-2022.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "280633485696.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "retail1=retail[['Geography Name','Period','Asset Value']]\n",
    "retail2=retail1[retail1['Period']=='2021 Q4']\n",
    "\n",
    "retail3=retail2['Asset Value'].sum()\n",
    "retail3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "162607961792.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "office1=office[['Geography Name','Period','Asset Value']]\n",
    "office2=office1[office1['Period']=='2021 Q4']\n",
    "\n",
    "office3=office2['Asset Value'].sum()\n",
    "office3\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "180730644904.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "industry1=industry[['Geography Name','Period','Asset Value']]\n",
    "industry2=industry1[industry1['Period']=='2021 Q4']\n",
    "\n",
    "industry3=industry2['Asset Value'].sum()\n",
    "industry3\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "total=industry3+office3+retail3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "623.972092392"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total/1000000000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Every one percent increase in COVID cases causes the retail vacancy rate to grow by 0.3 percent each quarter – about 352 thousand square feet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3864 entries, 0 to 3863\n",
      "Data columns (total 67 columns):\n",
      " #   Column                         Non-Null Count  Dtype  \n",
      "---  ------                         --------------  -----  \n",
      " 0   Property Class Name            3864 non-null   object \n",
      " 1   Period                         3864 non-null   object \n",
      " 2   Slice                          3864 non-null   object \n",
      " 3   As Of                          3864 non-null   object \n",
      " 4   Geography Name                 3864 non-null   object \n",
      " 5   CBSA Code                      3864 non-null   int64  \n",
      " 6   Property Type                  3864 non-null   object \n",
      " 7   Forecast Scenario              3864 non-null   object \n",
      " 8   Geography Type                 3864 non-null   object \n",
      " 9   Geography Code                 3864 non-null   object \n",
      " 10  All Service Type Rent Direct   1487 non-null   float64\n",
      " 11  All Service Type Rent Overall  1487 non-null   float64\n",
      " 12  All Service Type Rent Sublet   1176 non-null   float64\n",
      " 13  Appreciation Return            3047 non-null   float64\n",
      " 14  Asset Value                    3143 non-null   float64\n",
      " 15  Availability Rate              1463 non-null   float64\n",
      " 16  Available SF                   1487 non-null   float64\n",
      " 17  Average Sale Price             1459 non-null   float64\n",
      " 18  Cap Rate                       1218 non-null   float64\n",
      " 19  Cap Rate Transactions          1465 non-null   float64\n",
      " 20  Capital Value Index            3143 non-null   float64\n",
      " 21  Demand SF                      2063 non-null   float64\n",
      " 22  Demolished SF                  1484 non-null   float64\n",
      " 23  Existing Buildings             1487 non-null   float64\n",
      " 24  Existing Centers               1463 non-null   float64\n",
      " 25  Gross Delivered Buildings      1463 non-null   float64\n",
      " 26  Gross Delivered SF             1484 non-null   float64\n",
      " 27  Households                     3864 non-null   float64\n",
      " 28  Income Return                  3143 non-null   float64\n",
      " 29  Industrial Employment          3096 non-null   float64\n",
      " 30  Inventory SF                   3864 non-null   int64  \n",
      " 31  Leasing Activity SF            1464 non-null   float64\n",
      " 32  Market Cap Rate                3143 non-null   float64\n",
      " 33  Market Rent Growth             1883 non-null   float64\n",
      " 34  Market Rent Growth 12 Mo       1811 non-null   float64\n",
      " 35  Market Rent Index              1907 non-null   float64\n",
      " 36  Market Rent/SF                 1907 non-null   float64\n",
      " 37  Market Sale Price Growth       1391 non-null   float64\n",
      " 38  Market Sale Price Index        3143 non-null   float64\n",
      " 39  Market Sale Price Per SF       1487 non-null   float64\n",
      " 40  Median Cap Rate                0 non-null      float64\n",
      " 41  Median Household Income        1584 non-null   float64\n",
      " 42  Median Price/Bldg SF           0 non-null      float64\n",
      " 43  Net Absorption SF              2038 non-null   float64\n",
      " 44  Net Absorption SF 12 Mo        1967 non-null   float64\n",
      " 45  Net Absorption SF Direct       1462 non-null   float64\n",
      " 46  Net Absorption SF Sublet       970 non-null    float64\n",
      " 47  Net Delivered SF               3840 non-null   float64\n",
      " 48  Net Delivered SF 12 Mo         3768 non-null   float64\n",
      " 49  NNN Rent Direct                1487 non-null   float64\n",
      " 50  NNN Rent Overall               1487 non-null   float64\n",
      " 51  NNN Rent Sublet                1090 non-null   float64\n",
      " 52  NOI Index                      3143 non-null   float64\n",
      " 53  Occupancy Rate                 2063 non-null   float64\n",
      " 54  Office Employment              3096 non-null   float64\n",
      " 55  Population                     3864 non-null   float64\n",
      " 56  Sales Volume Transactions      1465 non-null   float64\n",
      " 57  Sold Building SF               1465 non-null   float64\n",
      " 58  Total Employment               3096 non-null   float64\n",
      " 59  Total Return                   3047 non-null   float64\n",
      " 60  Total Sales Volume             1487 non-null   float64\n",
      " 61  Transaction Sale Price/SF      1459 non-null   float64\n",
      " 62  Under Construction Buildings   1463 non-null   float64\n",
      " 63  Under Construction SF          1424 non-null   float64\n",
      " 64  Vacancy Rate                   2063 non-null   float64\n",
      " 65  Vacant Available %             1463 non-null   float64\n",
      " 66  Vacant Available SF            1463 non-null   float64\n",
      "dtypes: float64(56), int64(2), object(9)\n",
      "memory usage: 2.0+ MB\n"
     ]
    }
   ],
   "source": [
    "retail.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
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       "    .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>Property Class Name</th>\n",
       "      <th>Period</th>\n",
       "      <th>Slice</th>\n",
       "      <th>As Of</th>\n",
       "      <th>Geography Name</th>\n",
       "      <th>CBSA Code</th>\n",
       "      <th>Property Type</th>\n",
       "      <th>Forecast Scenario</th>\n",
       "      <th>Geography Type</th>\n",
       "      <th>Geography Code</th>\n",
       "      <th>...</th>\n",
       "      <th>Sold Building SF</th>\n",
       "      <th>Total Employment</th>\n",
       "      <th>Total Return</th>\n",
       "      <th>Total Sales Volume</th>\n",
       "      <th>Transaction Sale Price/SF</th>\n",
       "      <th>Under Construction Buildings</th>\n",
       "      <th>Under Construction SF</th>\n",
       "      <th>Vacancy Rate</th>\n",
       "      <th>Vacant Available %</th>\n",
       "      <th>Vacant Available SF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>Retail</td>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>All</td>\n",
       "      <td>2022 Q1</td>\n",
       "      <td>Daytona Beach - FL</td>\n",
       "      <td>19660</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Base Case</td>\n",
       "      <td>Metro</td>\n",
       "      <td>G41649</td>\n",
       "      <td>...</td>\n",
       "      <td>567655.0</td>\n",
       "      <td>2.076436e+05</td>\n",
       "      <td>0.092421</td>\n",
       "      <td>86575872.0</td>\n",
       "      <td>161.15</td>\n",
       "      <td>7.0</td>\n",
       "      <td>148897.0</td>\n",
       "      <td>0.043260</td>\n",
       "      <td>0.042572</td>\n",
       "      <td>1691699.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>Retail</td>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>All</td>\n",
       "      <td>2022 Q1</td>\n",
       "      <td>Fort Lauderdale - FL</td>\n",
       "      <td>22744</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Base Case</td>\n",
       "      <td>Metro</td>\n",
       "      <td>G41715</td>\n",
       "      <td>...</td>\n",
       "      <td>721566.0</td>\n",
       "      <td>8.713660e+05</td>\n",
       "      <td>0.069432</td>\n",
       "      <td>162616624.0</td>\n",
       "      <td>278.63</td>\n",
       "      <td>45.0</td>\n",
       "      <td>913417.0</td>\n",
       "      <td>0.042585</td>\n",
       "      <td>0.039103</td>\n",
       "      <td>4275266.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>Retail</td>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>All</td>\n",
       "      <td>2022 Q1</td>\n",
       "      <td>Fort Myers - FL</td>\n",
       "      <td>15980</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Base Case</td>\n",
       "      <td>Metro</td>\n",
       "      <td>G41609</td>\n",
       "      <td>...</td>\n",
       "      <td>857931.0</td>\n",
       "      <td>2.806959e+05</td>\n",
       "      <td>0.072605</td>\n",
       "      <td>91193224.0</td>\n",
       "      <td>111.94</td>\n",
       "      <td>25.0</td>\n",
       "      <td>298268.0</td>\n",
       "      <td>0.055938</td>\n",
       "      <td>0.052618</td>\n",
       "      <td>2532393.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635</th>\n",
       "      <td>Retail</td>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>All</td>\n",
       "      <td>2022 Q1</td>\n",
       "      <td>Ft Walton Beach - FL</td>\n",
       "      <td>18880</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Base Case</td>\n",
       "      <td>Metro</td>\n",
       "      <td>G41639</td>\n",
       "      <td>...</td>\n",
       "      <td>247256.0</td>\n",
       "      <td>1.196070e+05</td>\n",
       "      <td>0.069466</td>\n",
       "      <td>8625300.0</td>\n",
       "      <td>126.14</td>\n",
       "      <td>9.0</td>\n",
       "      <td>178006.0</td>\n",
       "      <td>0.025671</td>\n",
       "      <td>0.024909</td>\n",
       "      <td>489583.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>796</th>\n",
       "      <td>Retail</td>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>All</td>\n",
       "      <td>2022 Q1</td>\n",
       "      <td>Gainesville - FL</td>\n",
       "      <td>23540</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Base Case</td>\n",
       "      <td>Metro</td>\n",
       "      <td>G41743</td>\n",
       "      <td>...</td>\n",
       "      <td>190624.0</td>\n",
       "      <td>1.486744e+05</td>\n",
       "      <td>0.057596</td>\n",
       "      <td>12316000.0</td>\n",
       "      <td>104.60</td>\n",
       "      <td>7.0</td>\n",
       "      <td>65443.0</td>\n",
       "      <td>0.019187</td>\n",
       "      <td>0.018826</td>\n",
       "      <td>365530.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>957</th>\n",
       "      <td>Retail</td>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>All</td>\n",
       "      <td>2022 Q1</td>\n",
       "      <td>Homosassa Springs - FL</td>\n",
       "      <td>26140</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Base Case</td>\n",
       "      <td>Metro</td>\n",
       "      <td>G41804</td>\n",
       "      <td>...</td>\n",
       "      <td>77264.0</td>\n",
       "      <td>3.320562e+04</td>\n",
       "      <td>0.075378</td>\n",
       "      <td>6597843.0</td>\n",
       "      <td>109.20</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6665.0</td>\n",
       "      <td>0.065337</td>\n",
       "      <td>0.051881</td>\n",
       "      <td>514747.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1118</th>\n",
       "      <td>Retail</td>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>All</td>\n",
       "      <td>2022 Q1</td>\n",
       "      <td>Jacksonville - FL</td>\n",
       "      <td>27260</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Base Case</td>\n",
       "      <td>Metro</td>\n",
       "      <td>G41817</td>\n",
       "      <td>...</td>\n",
       "      <td>787557.0</td>\n",
       "      <td>7.319252e+05</td>\n",
       "      <td>0.090172</td>\n",
       "      <td>126094592.0</td>\n",
       "      <td>165.75</td>\n",
       "      <td>66.0</td>\n",
       "      <td>1247215.0</td>\n",
       "      <td>0.045949</td>\n",
       "      <td>0.042167</td>\n",
       "      <td>4068671.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1279</th>\n",
       "      <td>Retail</td>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>All</td>\n",
       "      <td>2022 Q1</td>\n",
       "      <td>Lakeland - FL</td>\n",
       "      <td>29460</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Base Case</td>\n",
       "      <td>Metro</td>\n",
       "      <td>G39424</td>\n",
       "      <td>...</td>\n",
       "      <td>410747.0</td>\n",
       "      <td>2.413164e+05</td>\n",
       "      <td>0.077058</td>\n",
       "      <td>48159796.0</td>\n",
       "      <td>131.83</td>\n",
       "      <td>21.0</td>\n",
       "      <td>207633.0</td>\n",
       "      <td>0.035479</td>\n",
       "      <td>0.029721</td>\n",
       "      <td>933814.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1440</th>\n",
       "      <td>Retail</td>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>All</td>\n",
       "      <td>2022 Q1</td>\n",
       "      <td>Melbourne - FL</td>\n",
       "      <td>37340</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Base Case</td>\n",
       "      <td>Metro</td>\n",
       "      <td>G39499</td>\n",
       "      <td>...</td>\n",
       "      <td>255452.0</td>\n",
       "      <td>2.339077e+05</td>\n",
       "      <td>0.084913</td>\n",
       "      <td>41828464.0</td>\n",
       "      <td>193.30</td>\n",
       "      <td>10.0</td>\n",
       "      <td>164677.0</td>\n",
       "      <td>0.054184</td>\n",
       "      <td>0.052177</td>\n",
       "      <td>1810976.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1601</th>\n",
       "      <td>Retail</td>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>All</td>\n",
       "      <td>2022 Q1</td>\n",
       "      <td>Miami - FL</td>\n",
       "      <td>33124</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Base Case</td>\n",
       "      <td>Metro</td>\n",
       "      <td>G39457</td>\n",
       "      <td>...</td>\n",
       "      <td>774827.0</td>\n",
       "      <td>1.227054e+06</td>\n",
       "      <td>0.061674</td>\n",
       "      <td>175010416.0</td>\n",
       "      <td>261.18</td>\n",
       "      <td>47.0</td>\n",
       "      <td>2184584.0</td>\n",
       "      <td>0.039201</td>\n",
       "      <td>0.035548</td>\n",
       "      <td>4911815.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1762</th>\n",
       "      <td>Retail</td>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>All</td>\n",
       "      <td>2022 Q1</td>\n",
       "      <td>Naples - FL</td>\n",
       "      <td>34940</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Base Case</td>\n",
       "      <td>Metro</td>\n",
       "      <td>G39476</td>\n",
       "      <td>...</td>\n",
       "      <td>161809.0</td>\n",
       "      <td>1.566170e+05</td>\n",
       "      <td>0.048812</td>\n",
       "      <td>51608832.0</td>\n",
       "      <td>336.57</td>\n",
       "      <td>8.0</td>\n",
       "      <td>159173.0</td>\n",
       "      <td>0.049864</td>\n",
       "      <td>0.047395</td>\n",
       "      <td>1112639.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1923</th>\n",
       "      <td>Retail</td>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>All</td>\n",
       "      <td>2022 Q1</td>\n",
       "      <td>Ocala - FL</td>\n",
       "      <td>36100</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Base Case</td>\n",
       "      <td>Metro</td>\n",
       "      <td>G39488</td>\n",
       "      <td>...</td>\n",
       "      <td>240810.0</td>\n",
       "      <td>1.094951e+05</td>\n",
       "      <td>0.060625</td>\n",
       "      <td>34945712.0</td>\n",
       "      <td>149.97</td>\n",
       "      <td>4.0</td>\n",
       "      <td>27270.0</td>\n",
       "      <td>0.045249</td>\n",
       "      <td>0.039248</td>\n",
       "      <td>799364.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2084</th>\n",
       "      <td>Retail</td>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>All</td>\n",
       "      <td>2022 Q1</td>\n",
       "      <td>Orlando - FL</td>\n",
       "      <td>36740</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Base Case</td>\n",
       "      <td>Metro</td>\n",
       "      <td>G39495</td>\n",
       "      <td>...</td>\n",
       "      <td>1885823.0</td>\n",
       "      <td>1.340572e+06</td>\n",
       "      <td>0.091379</td>\n",
       "      <td>202806128.0</td>\n",
       "      <td>186.48</td>\n",
       "      <td>81.0</td>\n",
       "      <td>1182669.0</td>\n",
       "      <td>0.044527</td>\n",
       "      <td>0.040057</td>\n",
       "      <td>5954751.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2245</th>\n",
       "      <td>Retail</td>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>All</td>\n",
       "      <td>2022 Q1</td>\n",
       "      <td>Palm Beach - FL</td>\n",
       "      <td>48424</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Base Case</td>\n",
       "      <td>Metro</td>\n",
       "      <td>G39605</td>\n",
       "      <td>...</td>\n",
       "      <td>667428.0</td>\n",
       "      <td>6.462659e+05</td>\n",
       "      <td>0.073972</td>\n",
       "      <td>187616736.0</td>\n",
       "      <td>306.55</td>\n",
       "      <td>21.0</td>\n",
       "      <td>701771.0</td>\n",
       "      <td>0.042726</td>\n",
       "      <td>0.039443</td>\n",
       "      <td>3121896.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2406</th>\n",
       "      <td>Retail</td>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>All</td>\n",
       "      <td>2022 Q1</td>\n",
       "      <td>Panama City - FL</td>\n",
       "      <td>37460</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Base Case</td>\n",
       "      <td>Metro</td>\n",
       "      <td>G39500</td>\n",
       "      <td>...</td>\n",
       "      <td>157774.0</td>\n",
       "      <td>8.192945e+04</td>\n",
       "      <td>0.051401</td>\n",
       "      <td>12240200.0</td>\n",
       "      <td>103.49</td>\n",
       "      <td>11.0</td>\n",
       "      <td>200044.0</td>\n",
       "      <td>0.020073</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>346126.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2567</th>\n",
       "      <td>Retail</td>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>All</td>\n",
       "      <td>2022 Q1</td>\n",
       "      <td>Pensacola - FL</td>\n",
       "      <td>37860</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Base Case</td>\n",
       "      <td>Metro</td>\n",
       "      <td>G39502</td>\n",
       "      <td>...</td>\n",
       "      <td>299672.0</td>\n",
       "      <td>1.884878e+05</td>\n",
       "      <td>0.057898</td>\n",
       "      <td>33108394.0</td>\n",
       "      <td>141.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>140213.0</td>\n",
       "      <td>0.033185</td>\n",
       "      <td>0.031656</td>\n",
       "      <td>962101.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2728</th>\n",
       "      <td>Retail</td>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>All</td>\n",
       "      <td>2022 Q1</td>\n",
       "      <td>Port St. Lucie - FL</td>\n",
       "      <td>38940</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Base Case</td>\n",
       "      <td>Metro</td>\n",
       "      <td>G39512</td>\n",
       "      <td>...</td>\n",
       "      <td>352956.0</td>\n",
       "      <td>1.575515e+05</td>\n",
       "      <td>0.065455</td>\n",
       "      <td>58138720.0</td>\n",
       "      <td>181.17</td>\n",
       "      <td>8.0</td>\n",
       "      <td>110316.0</td>\n",
       "      <td>0.061643</td>\n",
       "      <td>0.058468</td>\n",
       "      <td>1480257.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2889</th>\n",
       "      <td>Retail</td>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>All</td>\n",
       "      <td>2022 Q1</td>\n",
       "      <td>Punta Gorda - FL</td>\n",
       "      <td>39460</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Base Case</td>\n",
       "      <td>Metro</td>\n",
       "      <td>G39517</td>\n",
       "      <td>...</td>\n",
       "      <td>184650.0</td>\n",
       "      <td>5.035907e+04</td>\n",
       "      <td>0.091477</td>\n",
       "      <td>27836470.0</td>\n",
       "      <td>160.99</td>\n",
       "      <td>5.0</td>\n",
       "      <td>31245.0</td>\n",
       "      <td>0.032150</td>\n",
       "      <td>0.027396</td>\n",
       "      <td>294539.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3050</th>\n",
       "      <td>Retail</td>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>All</td>\n",
       "      <td>2022 Q1</td>\n",
       "      <td>Sarasota - FL</td>\n",
       "      <td>35840</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Base Case</td>\n",
       "      <td>Metro</td>\n",
       "      <td>G39485</td>\n",
       "      <td>...</td>\n",
       "      <td>687464.0</td>\n",
       "      <td>3.143020e+05</td>\n",
       "      <td>0.061450</td>\n",
       "      <td>63475836.0</td>\n",
       "      <td>103.47</td>\n",
       "      <td>36.0</td>\n",
       "      <td>545152.0</td>\n",
       "      <td>0.049458</td>\n",
       "      <td>0.037692</td>\n",
       "      <td>1954465.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3211</th>\n",
       "      <td>Retail</td>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>All</td>\n",
       "      <td>2022 Q1</td>\n",
       "      <td>Sebastian-Vero Beach - FL</td>\n",
       "      <td>42680</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Base Case</td>\n",
       "      <td>Metro</td>\n",
       "      <td>G39556</td>\n",
       "      <td>...</td>\n",
       "      <td>80337.0</td>\n",
       "      <td>5.473087e+04</td>\n",
       "      <td>0.041938</td>\n",
       "      <td>12885092.0</td>\n",
       "      <td>175.13</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10640.0</td>\n",
       "      <td>0.053124</td>\n",
       "      <td>0.052894</td>\n",
       "      <td>463651.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3372</th>\n",
       "      <td>Retail</td>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>All</td>\n",
       "      <td>2022 Q1</td>\n",
       "      <td>Sebring - FL</td>\n",
       "      <td>42700</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Base Case</td>\n",
       "      <td>Metro</td>\n",
       "      <td>G39557</td>\n",
       "      <td>...</td>\n",
       "      <td>107727.0</td>\n",
       "      <td>2.576355e+04</td>\n",
       "      <td>0.071466</td>\n",
       "      <td>3572000.0</td>\n",
       "      <td>41.87</td>\n",
       "      <td>5.0</td>\n",
       "      <td>40490.0</td>\n",
       "      <td>0.038602</td>\n",
       "      <td>0.038602</td>\n",
       "      <td>317610.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3533</th>\n",
       "      <td>Retail</td>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>All</td>\n",
       "      <td>2022 Q1</td>\n",
       "      <td>Tallahassee - FL</td>\n",
       "      <td>45220</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Base Case</td>\n",
       "      <td>Metro</td>\n",
       "      <td>G39576</td>\n",
       "      <td>...</td>\n",
       "      <td>144227.0</td>\n",
       "      <td>1.868191e+05</td>\n",
       "      <td>0.057665</td>\n",
       "      <td>29208400.0</td>\n",
       "      <td>237.18</td>\n",
       "      <td>5.0</td>\n",
       "      <td>21585.0</td>\n",
       "      <td>0.057943</td>\n",
       "      <td>0.056881</td>\n",
       "      <td>1421346.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3694</th>\n",
       "      <td>Retail</td>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>All</td>\n",
       "      <td>2022 Q1</td>\n",
       "      <td>Tampa - FL</td>\n",
       "      <td>45300</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Base Case</td>\n",
       "      <td>Metro</td>\n",
       "      <td>G39577</td>\n",
       "      <td>...</td>\n",
       "      <td>1950506.0</td>\n",
       "      <td>1.404281e+06</td>\n",
       "      <td>0.108211</td>\n",
       "      <td>313957856.0</td>\n",
       "      <td>183.41</td>\n",
       "      <td>90.0</td>\n",
       "      <td>1050855.0</td>\n",
       "      <td>0.041709</td>\n",
       "      <td>0.037409</td>\n",
       "      <td>6370966.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3855</th>\n",
       "      <td>Retail</td>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>All</td>\n",
       "      <td>2022 Q1</td>\n",
       "      <td>The Villages - FL</td>\n",
       "      <td>45540</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Base Case</td>\n",
       "      <td>Metro</td>\n",
       "      <td>G39580</td>\n",
       "      <td>...</td>\n",
       "      <td>23961.0</td>\n",
       "      <td>3.280515e+04</td>\n",
       "      <td>0.061790</td>\n",
       "      <td>3020050.0</td>\n",
       "      <td>147.59</td>\n",
       "      <td>9.0</td>\n",
       "      <td>166459.0</td>\n",
       "      <td>0.030096</td>\n",
       "      <td>0.029783</td>\n",
       "      <td>143304.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24 rows × 67 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Property Class Name   Period Slice    As Of             Geography Name  \\\n",
       "152               Retail  2020 Q1   All  2022 Q1         Daytona Beach - FL   \n",
       "313               Retail  2020 Q1   All  2022 Q1       Fort Lauderdale - FL   \n",
       "474               Retail  2020 Q1   All  2022 Q1            Fort Myers - FL   \n",
       "635               Retail  2020 Q1   All  2022 Q1       Ft Walton Beach - FL   \n",
       "796               Retail  2020 Q1   All  2022 Q1           Gainesville - FL   \n",
       "957               Retail  2020 Q1   All  2022 Q1     Homosassa Springs - FL   \n",
       "1118              Retail  2020 Q1   All  2022 Q1          Jacksonville - FL   \n",
       "1279              Retail  2020 Q1   All  2022 Q1              Lakeland - FL   \n",
       "1440              Retail  2020 Q1   All  2022 Q1             Melbourne - FL   \n",
       "1601              Retail  2020 Q1   All  2022 Q1                 Miami - FL   \n",
       "1762              Retail  2020 Q1   All  2022 Q1                Naples - FL   \n",
       "1923              Retail  2020 Q1   All  2022 Q1                 Ocala - FL   \n",
       "2084              Retail  2020 Q1   All  2022 Q1               Orlando - FL   \n",
       "2245              Retail  2020 Q1   All  2022 Q1            Palm Beach - FL   \n",
       "2406              Retail  2020 Q1   All  2022 Q1           Panama City - FL   \n",
       "2567              Retail  2020 Q1   All  2022 Q1             Pensacola - FL   \n",
       "2728              Retail  2020 Q1   All  2022 Q1        Port St. Lucie - FL   \n",
       "2889              Retail  2020 Q1   All  2022 Q1           Punta Gorda - FL   \n",
       "3050              Retail  2020 Q1   All  2022 Q1              Sarasota - FL   \n",
       "3211              Retail  2020 Q1   All  2022 Q1  Sebastian-Vero Beach - FL   \n",
       "3372              Retail  2020 Q1   All  2022 Q1               Sebring - FL   \n",
       "3533              Retail  2020 Q1   All  2022 Q1           Tallahassee - FL   \n",
       "3694              Retail  2020 Q1   All  2022 Q1                 Tampa - FL   \n",
       "3855              Retail  2020 Q1   All  2022 Q1          The Villages - FL   \n",
       "\n",
       "      CBSA Code Property Type Forecast Scenario Geography Type Geography Code  \\\n",
       "152       19660        Retail         Base Case          Metro         G41649   \n",
       "313       22744        Retail         Base Case          Metro         G41715   \n",
       "474       15980        Retail         Base Case          Metro         G41609   \n",
       "635       18880        Retail         Base Case          Metro         G41639   \n",
       "796       23540        Retail         Base Case          Metro         G41743   \n",
       "957       26140        Retail         Base Case          Metro         G41804   \n",
       "1118      27260        Retail         Base Case          Metro         G41817   \n",
       "1279      29460        Retail         Base Case          Metro         G39424   \n",
       "1440      37340        Retail         Base Case          Metro         G39499   \n",
       "1601      33124        Retail         Base Case          Metro         G39457   \n",
       "1762      34940        Retail         Base Case          Metro         G39476   \n",
       "1923      36100        Retail         Base Case          Metro         G39488   \n",
       "2084      36740        Retail         Base Case          Metro         G39495   \n",
       "2245      48424        Retail         Base Case          Metro         G39605   \n",
       "2406      37460        Retail         Base Case          Metro         G39500   \n",
       "2567      37860        Retail         Base Case          Metro         G39502   \n",
       "2728      38940        Retail         Base Case          Metro         G39512   \n",
       "2889      39460        Retail         Base Case          Metro         G39517   \n",
       "3050      35840        Retail         Base Case          Metro         G39485   \n",
       "3211      42680        Retail         Base Case          Metro         G39556   \n",
       "3372      42700        Retail         Base Case          Metro         G39557   \n",
       "3533      45220        Retail         Base Case          Metro         G39576   \n",
       "3694      45300        Retail         Base Case          Metro         G39577   \n",
       "3855      45540        Retail         Base Case          Metro         G39580   \n",
       "\n",
       "      ...  Sold Building SF  Total Employment  Total Return  \\\n",
       "152   ...          567655.0      2.076436e+05      0.092421   \n",
       "313   ...          721566.0      8.713660e+05      0.069432   \n",
       "474   ...          857931.0      2.806959e+05      0.072605   \n",
       "635   ...          247256.0      1.196070e+05      0.069466   \n",
       "796   ...          190624.0      1.486744e+05      0.057596   \n",
       "957   ...           77264.0      3.320562e+04      0.075378   \n",
       "1118  ...          787557.0      7.319252e+05      0.090172   \n",
       "1279  ...          410747.0      2.413164e+05      0.077058   \n",
       "1440  ...          255452.0      2.339077e+05      0.084913   \n",
       "1601  ...          774827.0      1.227054e+06      0.061674   \n",
       "1762  ...          161809.0      1.566170e+05      0.048812   \n",
       "1923  ...          240810.0      1.094951e+05      0.060625   \n",
       "2084  ...         1885823.0      1.340572e+06      0.091379   \n",
       "2245  ...          667428.0      6.462659e+05      0.073972   \n",
       "2406  ...          157774.0      8.192945e+04      0.051401   \n",
       "2567  ...          299672.0      1.884878e+05      0.057898   \n",
       "2728  ...          352956.0      1.575515e+05      0.065455   \n",
       "2889  ...          184650.0      5.035907e+04      0.091477   \n",
       "3050  ...          687464.0      3.143020e+05      0.061450   \n",
       "3211  ...           80337.0      5.473087e+04      0.041938   \n",
       "3372  ...          107727.0      2.576355e+04      0.071466   \n",
       "3533  ...          144227.0      1.868191e+05      0.057665   \n",
       "3694  ...         1950506.0      1.404281e+06      0.108211   \n",
       "3855  ...           23961.0      3.280515e+04      0.061790   \n",
       "\n",
       "      Total Sales Volume  Transaction Sale Price/SF  \\\n",
       "152           86575872.0                     161.15   \n",
       "313          162616624.0                     278.63   \n",
       "474           91193224.0                     111.94   \n",
       "635            8625300.0                     126.14   \n",
       "796           12316000.0                     104.60   \n",
       "957            6597843.0                     109.20   \n",
       "1118         126094592.0                     165.75   \n",
       "1279          48159796.0                     131.83   \n",
       "1440          41828464.0                     193.30   \n",
       "1601         175010416.0                     261.18   \n",
       "1762          51608832.0                     336.57   \n",
       "1923          34945712.0                     149.97   \n",
       "2084         202806128.0                     186.48   \n",
       "2245         187616736.0                     306.55   \n",
       "2406          12240200.0                     103.49   \n",
       "2567          33108394.0                     141.40   \n",
       "2728          58138720.0                     181.17   \n",
       "2889          27836470.0                     160.99   \n",
       "3050          63475836.0                     103.47   \n",
       "3211          12885092.0                     175.13   \n",
       "3372           3572000.0                      41.87   \n",
       "3533          29208400.0                     237.18   \n",
       "3694         313957856.0                     183.41   \n",
       "3855           3020050.0                     147.59   \n",
       "\n",
       "      Under Construction Buildings  Under Construction SF  Vacancy Rate  \\\n",
       "152                            7.0               148897.0      0.043260   \n",
       "313                           45.0               913417.0      0.042585   \n",
       "474                           25.0               298268.0      0.055938   \n",
       "635                            9.0               178006.0      0.025671   \n",
       "796                            7.0                65443.0      0.019187   \n",
       "957                            2.0                 6665.0      0.065337   \n",
       "1118                          66.0              1247215.0      0.045949   \n",
       "1279                          21.0               207633.0      0.035479   \n",
       "1440                          10.0               164677.0      0.054184   \n",
       "1601                          47.0              2184584.0      0.039201   \n",
       "1762                           8.0               159173.0      0.049864   \n",
       "1923                           4.0                27270.0      0.045249   \n",
       "2084                          81.0              1182669.0      0.044527   \n",
       "2245                          21.0               701771.0      0.042726   \n",
       "2406                          11.0               200044.0      0.020073   \n",
       "2567                          10.0               140213.0      0.033185   \n",
       "2728                           8.0               110316.0      0.061643   \n",
       "2889                           5.0                31245.0      0.032150   \n",
       "3050                          36.0               545152.0      0.049458   \n",
       "3211                           3.0                10640.0      0.053124   \n",
       "3372                           5.0                40490.0      0.038602   \n",
       "3533                           5.0                21585.0      0.057943   \n",
       "3694                          90.0              1050855.0      0.041709   \n",
       "3855                           9.0               166459.0      0.030096   \n",
       "\n",
       "      Vacant Available %  Vacant Available SF  \n",
       "152             0.042572            1691699.0  \n",
       "313             0.039103            4275266.0  \n",
       "474             0.052618            2532393.0  \n",
       "635             0.024909             489583.0  \n",
       "796             0.018826             365530.0  \n",
       "957             0.051881             514747.0  \n",
       "1118            0.042167            4068671.0  \n",
       "1279            0.029721             933814.0  \n",
       "1440            0.052177            1810976.0  \n",
       "1601            0.035548            4911815.0  \n",
       "1762            0.047395            1112639.0  \n",
       "1923            0.039248             799364.0  \n",
       "2084            0.040057            5954751.0  \n",
       "2245            0.039443            3121896.0  \n",
       "2406            0.019278             346126.0  \n",
       "2567            0.031656             962101.0  \n",
       "2728            0.058468            1480257.0  \n",
       "2889            0.027396             294539.0  \n",
       "3050            0.037692            1954465.0  \n",
       "3211            0.052894             463651.0  \n",
       "3372            0.038602             317610.0  \n",
       "3533            0.056881            1421346.0  \n",
       "3694            0.037409            6370966.0  \n",
       "3855            0.029783             143304.0  \n",
       "\n",
       "[24 rows x 67 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get the 2020 Q1 data\n",
    "retail2=retail[retail['Period'].str.contains('2020 Q1')]\n",
    "retail3=retail[retail['Period'].str.contains('2020 Q2')]\n",
    "retail2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Period</th>\n",
       "      <th>Geography Name</th>\n",
       "      <th>Inventory SF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>Daytona Beach - FL</td>\n",
       "      <td>39737253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>Fort Lauderdale - FL</td>\n",
       "      <td>109331087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>Fort Myers - FL</td>\n",
       "      <td>48127459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635</th>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>Ft Walton Beach - FL</td>\n",
       "      <td>19654258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>796</th>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>Gainesville - FL</td>\n",
       "      <td>19415898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>957</th>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>Homosassa Springs - FL</td>\n",
       "      <td>9921534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1118</th>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>Jacksonville - FL</td>\n",
       "      <td>96488785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1279</th>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>Lakeland - FL</td>\n",
       "      <td>31419307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1440</th>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>Melbourne - FL</td>\n",
       "      <td>34707799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1601</th>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>Miami - FL</td>\n",
       "      <td>138173131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1762</th>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>Naples - FL</td>\n",
       "      <td>23475586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1923</th>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>Ocala - FL</td>\n",
       "      <td>20366721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2084</th>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>Orlando - FL</td>\n",
       "      <td>148654414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2245</th>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>Palm Beach - FL</td>\n",
       "      <td>79148622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2406</th>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>Panama City - FL</td>\n",
       "      <td>17954123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2567</th>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>Pensacola - FL</td>\n",
       "      <td>30391534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2728</th>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>Port St. Lucie - FL</td>\n",
       "      <td>25316986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2889</th>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>Punta Gorda - FL</td>\n",
       "      <td>10750978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3050</th>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>Sarasota - FL</td>\n",
       "      <td>51852829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3211</th>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>Sebastian-Vero Beach - FL</td>\n",
       "      <td>8765565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3372</th>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>Sebring - FL</td>\n",
       "      <td>8227747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3533</th>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>Tallahassee - FL</td>\n",
       "      <td>24987649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3694</th>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>Tampa - FL</td>\n",
       "      <td>170305492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3855</th>\n",
       "      <td>2020 Q1</td>\n",
       "      <td>The Villages - FL</td>\n",
       "      <td>4811451</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Period             Geography Name  Inventory SF\n",
       "152   2020 Q1         Daytona Beach - FL      39737253\n",
       "313   2020 Q1       Fort Lauderdale - FL     109331087\n",
       "474   2020 Q1            Fort Myers - FL      48127459\n",
       "635   2020 Q1       Ft Walton Beach - FL      19654258\n",
       "796   2020 Q1           Gainesville - FL      19415898\n",
       "957   2020 Q1     Homosassa Springs - FL       9921534\n",
       "1118  2020 Q1          Jacksonville - FL      96488785\n",
       "1279  2020 Q1              Lakeland - FL      31419307\n",
       "1440  2020 Q1             Melbourne - FL      34707799\n",
       "1601  2020 Q1                 Miami - FL     138173131\n",
       "1762  2020 Q1                Naples - FL      23475586\n",
       "1923  2020 Q1                 Ocala - FL      20366721\n",
       "2084  2020 Q1               Orlando - FL     148654414\n",
       "2245  2020 Q1            Palm Beach - FL      79148622\n",
       "2406  2020 Q1           Panama City - FL      17954123\n",
       "2567  2020 Q1             Pensacola - FL      30391534\n",
       "2728  2020 Q1        Port St. Lucie - FL      25316986\n",
       "2889  2020 Q1           Punta Gorda - FL      10750978\n",
       "3050  2020 Q1              Sarasota - FL      51852829\n",
       "3211  2020 Q1  Sebastian-Vero Beach - FL       8765565\n",
       "3372  2020 Q1               Sebring - FL       8227747\n",
       "3533  2020 Q1           Tallahassee - FL      24987649\n",
       "3694  2020 Q1                 Tampa - FL     170305492\n",
       "3855  2020 Q1          The Villages - FL       4811451"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "retail21=retail2[['Period','Geography Name','Inventory SF']]\n",
    "retail21"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 24 entries, 152 to 3855\n",
      "Data columns (total 3 columns):\n",
      " #   Column          Non-Null Count  Dtype \n",
      "---  ------          --------------  ----- \n",
      " 0   Period          24 non-null     object\n",
      " 1   Geography Name  24 non-null     object\n",
      " 2   Inventory SF    24 non-null     int64 \n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 768.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "#用2020 q1的数据作为baseline 进行计算\n",
    "retail21.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1171986208"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "retail21['Inventory SF'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "351595.8624"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1171986208*0.0003"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "585993.104"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1171986208*0.0005"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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