Referral .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline  \n",
    "%pylab inline\n",
    "\n",
    "pylab.rcParams['figure.figsize'] = (10, 6)\n",
    "\n",
    "import igraph\n",
    "import pandas as pd\n",
    "import csv\n",
    "pd.set_option('display.max_rows', 500)\n",
    "import itertools\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_table('/Users/mueric35/Desktop/Margolis_Center/physician-shared-patient-patterns-2014-days30.txt',header= None, sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.columns = ['referer','receiver','shared_Count','Unique_Beneficiaries','Number_Same_Day_Visits']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>referer</th>\n",
       "      <th>receiver</th>\n",
       "      <th>shared_Count</th>\n",
       "      <th>Unique_Beneficiaries</th>\n",
       "      <th>Number_Same_Day_Visits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000000004</td>\n",
       "      <td>1548295421</td>\n",
       "      <td>25</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000000004</td>\n",
       "      <td>1790775229</td>\n",
       "      <td>60</td>\n",
       "      <td>22</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000026017</td>\n",
       "      <td>1598773715</td>\n",
       "      <td>35</td>\n",
       "      <td>15</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      referer    receiver  shared_Count  Unique_Beneficiaries  \\\n",
       "0  1000000004  1548295421            25                    11   \n",
       "1  1000000004  1790775229            60                    22   \n",
       "2  1000026017  1598773715            35                    15   \n",
       "\n",
       "   Number_Same_Day_Visits  \n",
       "0                       8  \n",
       "1                      30  \n",
       "2                      18  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "edge = data[['referer','receiver']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>receiver</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>referer</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1700865094</th>\n",
       "      <td>98028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1538144910</th>\n",
       "      <td>84583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1063497451</th>\n",
       "      <td>65019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1932184322</th>\n",
       "      <td>49632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1346233277</th>\n",
       "      <td>48464</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            receiver\n",
       "referer             \n",
       "1700865094     98028\n",
       "1538144910     84583\n",
       "1063497451     65019\n",
       "1932184322     49632\n",
       "1346233277     48464"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sort_provider = edge.groupby('referer').count().sort_values('receiver',ascending = False)\n",
    "sort_provider.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#subgraph = data[data.provider == a.iloc[0,:].name]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "npi = pd.read_csv('/Users/mueric35/Desktop/Margolis_Center/npi_medicarexw.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>npi</th>\n",
       "      <th>seq</th>\n",
       "      <th>othpid</th>\n",
       "      <th>othpidty</th>\n",
       "      <th>othpidst</th>\n",
       "      <th>othpidiss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1003000142</td>\n",
       "      <td>2</td>\n",
       "      <td>H007320</td>\n",
       "      <td>8</td>\n",
       "      <td>OH</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1003000159</td>\n",
       "      <td>1</td>\n",
       "      <td>P099265277</td>\n",
       "      <td>8</td>\n",
       "      <td>SC</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1003000159</td>\n",
       "      <td>4</td>\n",
       "      <td>9295</td>\n",
       "      <td>8</td>\n",
       "      <td>SC</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1003000159</td>\n",
       "      <td>5</td>\n",
       "      <td>P099269295</td>\n",
       "      <td>2</td>\n",
       "      <td>SC</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1003000274</td>\n",
       "      <td>2</td>\n",
       "      <td>4J980BC64</td>\n",
       "      <td>8</td>\n",
       "      <td>LA</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          npi  seq      othpid  othpidty othpidst othpidiss\n",
       "0  1003000142    2     H007320         8       OH       NaN\n",
       "1  1003000159    1  P099265277         8       SC       NaN\n",
       "2  1003000159    4        9295         8       SC       NaN\n",
       "3  1003000159    5  P099269295         2       SC       NaN\n",
       "4  1003000274    2   4J980BC64         8       LA       NaN"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "npi.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read Taxonomy Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "taxo = pd.read_csv('/Users/mueric35/Desktop/Margolis_Center/tax_csv.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Code</th>\n",
       "      <th>Grouping</th>\n",
       "      <th>Classification</th>\n",
       "      <th>Specialization</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>163WH1000X</td>\n",
       "      <td>Nursing Service Providers</td>\n",
       "      <td>Registered Nurse</td>\n",
       "      <td>Hospice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>207LH0002X</td>\n",
       "      <td>Allopathic &amp; Osteopathic Physicians</td>\n",
       "      <td>Anesthesiology</td>\n",
       "      <td>Hospice and Palliative Medicine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>207PH0002X</td>\n",
       "      <td>Allopathic &amp; Osteopathic Physicians</td>\n",
       "      <td>Emergency Medicine</td>\n",
       "      <td>Hospice and Palliative Medicine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>207QH0002X</td>\n",
       "      <td>Allopathic &amp; Osteopathic Physicians</td>\n",
       "      <td>Family Medicine</td>\n",
       "      <td>Hospice and Palliative Medicine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>207RH0002X</td>\n",
       "      <td>Allopathic &amp; Osteopathic Physicians</td>\n",
       "      <td>Internal Medicine</td>\n",
       "      <td>Hospice and Palliative Medicine</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Code                             Grouping      Classification  \\\n",
       "0  163WH1000X            Nursing Service Providers    Registered Nurse   \n",
       "1  207LH0002X  Allopathic & Osteopathic Physicians      Anesthesiology   \n",
       "2  207PH0002X  Allopathic & Osteopathic Physicians  Emergency Medicine   \n",
       "3  207QH0002X  Allopathic & Osteopathic Physicians     Family Medicine   \n",
       "4  207RH0002X  Allopathic & Osteopathic Physicians   Internal Medicine   \n",
       "\n",
       "                    Specialization  \n",
       "0                          Hospice  \n",
       "1  Hospice and Palliative Medicine  \n",
       "2  Hospice and Palliative Medicine  \n",
       "3  Hospice and Palliative Medicine  \n",
       "4  Hospice and Palliative Medicine  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "taxo = taxo.rename(columns = {'Code' :'Healthcare Provider Taxonomy Code_1'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Healthcare Provider Taxonomy Code_1</th>\n",
       "      <th>Grouping</th>\n",
       "      <th>Classification</th>\n",
       "      <th>Specialization</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>163WH1000X</td>\n",
       "      <td>Nursing Service Providers</td>\n",
       "      <td>Registered Nurse</td>\n",
       "      <td>Hospice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>207LH0002X</td>\n",
       "      <td>Allopathic &amp; Osteopathic Physicians</td>\n",
       "      <td>Anesthesiology</td>\n",
       "      <td>Hospice and Palliative Medicine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>207PH0002X</td>\n",
       "      <td>Allopathic &amp; Osteopathic Physicians</td>\n",
       "      <td>Emergency Medicine</td>\n",
       "      <td>Hospice and Palliative Medicine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>207QH0002X</td>\n",
       "      <td>Allopathic &amp; Osteopathic Physicians</td>\n",
       "      <td>Family Medicine</td>\n",
       "      <td>Hospice and Palliative Medicine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>207RH0002X</td>\n",
       "      <td>Allopathic &amp; Osteopathic Physicians</td>\n",
       "      <td>Internal Medicine</td>\n",
       "      <td>Hospice and Palliative Medicine</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Healthcare Provider Taxonomy Code_1                             Grouping  \\\n",
       "0                          163WH1000X            Nursing Service Providers   \n",
       "1                          207LH0002X  Allopathic & Osteopathic Physicians   \n",
       "2                          207PH0002X  Allopathic & Osteopathic Physicians   \n",
       "3                          207QH0002X  Allopathic & Osteopathic Physicians   \n",
       "4                          207RH0002X  Allopathic & Osteopathic Physicians   \n",
       "\n",
       "       Classification                   Specialization  \n",
       "0    Registered Nurse                          Hospice  \n",
       "1      Anesthesiology  Hospice and Palliative Medicine  \n",
       "2  Emergency Medicine  Hospice and Palliative Medicine  \n",
       "3     Family Medicine  Hospice and Palliative Medicine  \n",
       "4   Internal Medicine  Hospice and Palliative Medicine  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxo.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read 2005-2017 data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Read Data by Generator\n",
    "df = pd.read_csv('/Users/mueric35/Desktop/Margolis_Center/npidata_20050523-20170312.csv', iterator = True, chunksize=1000)\n",
    "node = pd.concat([i[['NPI','Entity Type Code','Provider Business Practice Location Address State Name','Healthcare Provider Taxonomy Code_1']] for i in df])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NPI</th>\n",
       "      <th>Entity Type Code</th>\n",
       "      <th>Provider Business Practice Location Address State Name</th>\n",
       "      <th>Healthcare Provider Taxonomy Code_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1679576722</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NE</td>\n",
       "      <td>207X00000X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1588667638</td>\n",
       "      <td>1.0</td>\n",
       "      <td>FL</td>\n",
       "      <td>207RC0000X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1497758544</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NC</td>\n",
       "      <td>251G00000X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1306849450</td>\n",
       "      <td>1.0</td>\n",
       "      <td>TX</td>\n",
       "      <td>2085R0202X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1215930367</td>\n",
       "      <td>1.0</td>\n",
       "      <td>TX</td>\n",
       "      <td>174400000X</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          NPI  Entity Type Code  \\\n",
       "0  1679576722               1.0   \n",
       "1  1588667638               1.0   \n",
       "2  1497758544               2.0   \n",
       "3  1306849450               1.0   \n",
       "4  1215930367               1.0   \n",
       "\n",
       "  Provider Business Practice Location Address State Name  \\\n",
       "0                                                 NE       \n",
       "1                                                 FL       \n",
       "2                                                 NC       \n",
       "3                                                 TX       \n",
       "4                                                 TX       \n",
       "\n",
       "  Healthcare Provider Taxonomy Code_1  \n",
       "0                          207X00000X  \n",
       "1                          207RC0000X  \n",
       "2                          251G00000X  \n",
       "3                          2085R0202X  \n",
       "4                          174400000X  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "node.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "node.columns = ['NPI', 'Type',\n",
    "       'State',\n",
    "       'Healthcare Provider Taxonomy Code_1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NPI</th>\n",
       "      <th>Type</th>\n",
       "      <th>State</th>\n",
       "      <th>Healthcare Provider Taxonomy Code_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1679576722</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NE</td>\n",
       "      <td>207X00000X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1588667638</td>\n",
       "      <td>1.0</td>\n",
       "      <td>FL</td>\n",
       "      <td>207RC0000X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1497758544</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NC</td>\n",
       "      <td>251G00000X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1306849450</td>\n",
       "      <td>1.0</td>\n",
       "      <td>TX</td>\n",
       "      <td>2085R0202X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1215930367</td>\n",
       "      <td>1.0</td>\n",
       "      <td>TX</td>\n",
       "      <td>174400000X</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          NPI  Type State Healthcare Provider Taxonomy Code_1\n",
       "0  1679576722   1.0    NE                          207X00000X\n",
       "1  1588667638   1.0    FL                          207RC0000X\n",
       "2  1497758544   2.0    NC                          251G00000X\n",
       "3  1306849450   1.0    TX                          2085R0202X\n",
       "4  1215930367   1.0    TX                          174400000X"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "node.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filter Hospice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_hospice = node.loc[node['Healthcare Provider Taxonomy Code_1'].isin(taxo['Healthcare Provider Taxonomy Code_1'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_hospice = pd.merge(node_hospice,taxo, on = 'Healthcare Provider Taxonomy Code_1',how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NPI</th>\n",
       "      <th>Type</th>\n",
       "      <th>State</th>\n",
       "      <th>Healthcare Provider Taxonomy Code_1</th>\n",
       "      <th>Grouping</th>\n",
       "      <th>Classification</th>\n",
       "      <th>Specialization</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1861495954</td>\n",
       "      <td>2.0</td>\n",
       "      <td>KY</td>\n",
       "      <td>207QH0002X</td>\n",
       "      <td>Allopathic &amp; Osteopathic Physicians</td>\n",
       "      <td>Family Medicine</td>\n",
       "      <td>Hospice and Palliative Medicine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1144223231</td>\n",
       "      <td>1.0</td>\n",
       "      <td>OH</td>\n",
       "      <td>207RH0002X</td>\n",
       "      <td>Allopathic &amp; Osteopathic Physicians</td>\n",
       "      <td>Internal Medicine</td>\n",
       "      <td>Hospice and Palliative Medicine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1477556934</td>\n",
       "      <td>2.0</td>\n",
       "      <td>CO</td>\n",
       "      <td>207RH0002X</td>\n",
       "      <td>Allopathic &amp; Osteopathic Physicians</td>\n",
       "      <td>Internal Medicine</td>\n",
       "      <td>Hospice and Palliative Medicine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1467455709</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NY</td>\n",
       "      <td>207QH0002X</td>\n",
       "      <td>Allopathic &amp; Osteopathic Physicians</td>\n",
       "      <td>Family Medicine</td>\n",
       "      <td>Hospice and Palliative Medicine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1669474854</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NY</td>\n",
       "      <td>207RH0002X</td>\n",
       "      <td>Allopathic &amp; Osteopathic Physicians</td>\n",
       "      <td>Internal Medicine</td>\n",
       "      <td>Hospice and Palliative Medicine</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          NPI  Type State Healthcare Provider Taxonomy Code_1  \\\n",
       "0  1861495954   2.0    KY                          207QH0002X   \n",
       "1  1144223231   1.0    OH                          207RH0002X   \n",
       "2  1477556934   2.0    CO                          207RH0002X   \n",
       "3  1467455709   1.0    NY                          207QH0002X   \n",
       "4  1669474854   1.0    NY                          207RH0002X   \n",
       "\n",
       "                              Grouping     Classification  \\\n",
       "0  Allopathic & Osteopathic Physicians    Family Medicine   \n",
       "1  Allopathic & Osteopathic Physicians  Internal Medicine   \n",
       "2  Allopathic & Osteopathic Physicians  Internal Medicine   \n",
       "3  Allopathic & Osteopathic Physicians    Family Medicine   \n",
       "4  Allopathic & Osteopathic Physicians  Internal Medicine   \n",
       "\n",
       "                    Specialization  \n",
       "0  Hospice and Palliative Medicine  \n",
       "1  Hospice and Palliative Medicine  \n",
       "2  Hospice and Palliative Medicine  \n",
       "3  Hospice and Palliative Medicine  \n",
       "4  Hospice and Palliative Medicine  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "node_hospice.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Save Node Info\n",
    "pd.DataFrame.to_csv(node_hospice,'/Users/mueric35/Desktop/Margolis_Center/node.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_hospice.columns = ['NPI','Type','State','Taxonomy_Code','Grouping',\"Classification\",'Specialization']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NPI</th>\n",
       "      <th>Type</th>\n",
       "      <th>State</th>\n",
       "      <th>Taxonomy_Code</th>\n",
       "      <th>Grouping</th>\n",
       "      <th>Classification</th>\n",
       "      <th>Specialization</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1861495954</td>\n",
       "      <td>2.0</td>\n",
       "      <td>KY</td>\n",
       "      <td>207QH0002X</td>\n",
       "      <td>Allopathic &amp; Osteopathic Physicians</td>\n",
       "      <td>Family Medicine</td>\n",
       "      <td>Hospice and Palliative Medicine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1144223231</td>\n",
       "      <td>1.0</td>\n",
       "      <td>OH</td>\n",
       "      <td>207RH0002X</td>\n",
       "      <td>Allopathic &amp; Osteopathic Physicians</td>\n",
       "      <td>Internal Medicine</td>\n",
       "      <td>Hospice and Palliative Medicine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1477556934</td>\n",
       "      <td>2.0</td>\n",
       "      <td>CO</td>\n",
       "      <td>207RH0002X</td>\n",
       "      <td>Allopathic &amp; Osteopathic Physicians</td>\n",
       "      <td>Internal Medicine</td>\n",
       "      <td>Hospice and Palliative Medicine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1467455709</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NY</td>\n",
       "      <td>207QH0002X</td>\n",
       "      <td>Allopathic &amp; Osteopathic Physicians</td>\n",
       "      <td>Family Medicine</td>\n",
       "      <td>Hospice and Palliative Medicine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1669474854</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NY</td>\n",
       "      <td>207RH0002X</td>\n",
       "      <td>Allopathic &amp; Osteopathic Physicians</td>\n",
       "      <td>Internal Medicine</td>\n",
       "      <td>Hospice and Palliative Medicine</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          NPI  Type State Taxonomy_Code                             Grouping  \\\n",
       "0  1861495954   2.0    KY    207QH0002X  Allopathic & Osteopathic Physicians   \n",
       "1  1144223231   1.0    OH    207RH0002X  Allopathic & Osteopathic Physicians   \n",
       "2  1477556934   2.0    CO    207RH0002X  Allopathic & Osteopathic Physicians   \n",
       "3  1467455709   1.0    NY    207QH0002X  Allopathic & Osteopathic Physicians   \n",
       "4  1669474854   1.0    NY    207RH0002X  Allopathic & Osteopathic Physicians   \n",
       "\n",
       "      Classification                   Specialization  \n",
       "0    Family Medicine  Hospice and Palliative Medicine  \n",
       "1  Internal Medicine  Hospice and Palliative Medicine  \n",
       "2  Internal Medicine  Hospice and Palliative Medicine  \n",
       "3    Family Medicine  Hospice and Palliative Medicine  \n",
       "4  Internal Medicine  Hospice and Palliative Medicine  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "node_hospice.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Patients refered to Hospice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### keep the reveiver to be only the hospice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_hospice = data.loc[data['receiver'].isin(node_hospice['NPI'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27751, 5)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_hospice.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_referer = node[['NPI','Type','State']]\n",
    "node_referer.columns = ['referer', 'Type', 'State']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = data_hospice.merge(node_referer, on = 'referer', how = 'left')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = a.rename(columns = {'State':'State_referer', 'Type': 'Type_referer'})\n",
    "node_hospice.columns = ['receiver', 'Type', 'State', 'Taxonomy_Code', 'Grouping', 'Classification',\n",
    "       'Specialization']\n",
    "b = a.merge(node_hospice)\n",
    "full_data = b.rename(columns = {'State':'State_receiver', 'Type': 'Type_receiver'})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27751, 13)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pd.DataFrame.to_csv(full_data,'/Users/mueric35/Desktop/Margolis_Center/data_node.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Count Group by State "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "#full_data = pd.read_csv('/Users/mueric35/Desktop/Margolis_Center/data_node.csv')\n",
    "count_data = full_data[['State_referer','State_receiver']]\n",
    "\n",
    "#del full_data['Unnamed: 0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['CO', 'MN', 'OH', 'CT', 'NY', 'OR', 'GA', 'NJ', 'NH', 'TX', 'MD',\n",
       "       'CA', 'NV', 'MO', 'FL', 'AL', 'AZ', 'LA', 'IL', 'KS', 'MI', 'WI',\n",
       "       'IN', 'NE', 'UT', nan, 'DC', 'VT', 'MA', 'PA', 'NC', 'MT', 'SC',\n",
       "       'WY', 'VA', 'PR', 'TN', 'IA', 'KY', 'WV', 'MS', 'WA', 'ME', 'RI',\n",
       "       'DE', 'AR', 'SD', 'NM', 'OK', 'ND', 'GU', 'ID', 'PW', 'AK', 'AP',\n",
       "       'AE', 'HI', 'FORT BEND'], dtype=object)"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_data.State_referer.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "#count_data.loc[count_data['State_provider'] == 'SELECT REGION']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State_referer</th>\n",
       "      <th>State_receiver</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CO</td>\n",
       "      <td>CO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CO</td>\n",
       "      <td>CO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CO</td>\n",
       "      <td>CO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CO</td>\n",
       "      <td>CO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CO</td>\n",
       "      <td>CO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  State_referer State_receiver\n",
       "0            CO             CO\n",
       "1            CO             CO\n",
       "2            CO             CO\n",
       "3            CO             CO\n",
       "4            CO             CO"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State_referer</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State_receiver</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>2493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA</th>\n",
       "      <td>2329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MD</th>\n",
       "      <td>2242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FL</th>\n",
       "      <td>2185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC</th>\n",
       "      <td>1681</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                State_referer\n",
       "State_receiver               \n",
       "NY                       2493\n",
       "CA                       2329\n",
       "MD                       2242\n",
       "FL                       2185\n",
       "NC                       1681"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_data_receiver = count_data.groupby(['State_receiver']).count().sort_values('State_referer',ascending = False)\n",
    "group_data_receiver.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "group_data_receiver = group_data_receiver.reset_index()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State_receiver</th>\n",
       "      <th>State_referer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NY</td>\n",
       "      <td>2493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CA</td>\n",
       "      <td>2329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MD</td>\n",
       "      <td>2242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FL</td>\n",
       "      <td>2185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NC</td>\n",
       "      <td>1681</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  State_receiver  State_referer\n",
       "0             NY           2493\n",
       "1             CA           2329\n",
       "2             MD           2242\n",
       "3             FL           2185\n",
       "4             NC           1681"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_data_receiver.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>receiver_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NY</td>\n",
       "      <td>2493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CA</td>\n",
       "      <td>2329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MD</td>\n",
       "      <td>2242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FL</td>\n",
       "      <td>2185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NC</td>\n",
       "      <td>1681</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  region  receiver_count\n",
       "0     NY            2493\n",
       "1     CA            2329\n",
       "2     MD            2242\n",
       "3     FL            2185\n",
       "4     NC            1681"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_data_receiver = group_data_receiver.rename(columns = {'State_receiver':'region', 'State_referer': 'receiver_count'})\n",
    "group_data_receiver.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "group_data_referer = count_data.groupby(['State_referer']).count().sort_values('State_receiver',ascending = False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State_referer</th>\n",
       "      <th>State_receiver</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NY</td>\n",
       "      <td>2454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CA</td>\n",
       "      <td>2327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FL</td>\n",
       "      <td>2183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MD</td>\n",
       "      <td>2119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NC</td>\n",
       "      <td>1671</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  State_referer  State_receiver\n",
       "0            NY            2454\n",
       "1            CA            2327\n",
       "2            FL            2183\n",
       "3            MD            2119\n",
       "4            NC            1671"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_data_referer = group_data_referer.reset_index()\n",
    "group_data_referer.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>referer_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NY</td>\n",
       "      <td>2454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CA</td>\n",
       "      <td>2327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FL</td>\n",
       "      <td>2183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MD</td>\n",
       "      <td>2119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NC</td>\n",
       "      <td>1671</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  region  referer_count\n",
       "0     NY           2454\n",
       "1     CA           2327\n",
       "2     FL           2183\n",
       "3     MD           2119\n",
       "4     NC           1671"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_data_referer = group_data_referer.rename(columns = {'State_referer':'region', 'State_receiver': 'referer_count'})\n",
    "group_data_referer.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>receiver_count</th>\n",
       "      <th>referer_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NY</td>\n",
       "      <td>2493</td>\n",
       "      <td>2454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CA</td>\n",
       "      <td>2329</td>\n",
       "      <td>2327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MD</td>\n",
       "      <td>2242</td>\n",
       "      <td>2119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FL</td>\n",
       "      <td>2185</td>\n",
       "      <td>2183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NC</td>\n",
       "      <td>1681</td>\n",
       "      <td>1671</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  region  receiver_count  referer_count\n",
       "0     NY            2493           2454\n",
       "1     CA            2329           2327\n",
       "2     MD            2242           2119\n",
       "3     FL            2185           2183\n",
       "4     NC            1681           1671"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Count receiver and provider group by state \n",
    "group_state = group_data_receiver.merge(group_data_referer)\n",
    "group_state.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "group_state_US = group_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change name for plotting on map\n",
    "us_state_abbrev = {\n",
    "    'Alabama': 'AL',\n",
    "    'Alaska': 'AK',\n",
    "    'Arizona': 'AZ',\n",
    "    'Arkansas': 'AR',\n",
    "    'California': 'CA',\n",
    "    'Colorado': 'CO',\n",
    "    'Connecticut': 'CT',\n",
    "    'Delaware': 'DE',\n",
    "    'Florida': 'FL',\n",
    "    'Georgia': 'GA',\n",
    "    'Hawaii': 'HI',\n",
    "    'Idaho': 'ID',\n",
    "    'Illinois': 'IL',\n",
    "    'Indiana': 'IN',\n",
    "    'Iowa': 'IA',\n",
    "    'Kansas': 'KS',\n",
    "    'Kentucky': 'KY',\n",
    "    'Louisiana': 'LA',\n",
    "    'Maine': 'ME',\n",
    "    'Maryland': 'MD',\n",
    "    'Massachusetts': 'MA',\n",
    "    'Michigan': 'MI',\n",
    "    'Minnesota': 'MN',\n",
    "    'Mississippi': 'MS',\n",
    "    'Missouri': 'MO',\n",
    "    'Montana': 'MT',\n",
    "    'Nebraska': 'NE',\n",
    "    'Nevada': 'NV',\n",
    "    'New Hampshire': 'NH',\n",
    "    'New Jersey': 'NJ',\n",
    "    'New Mexico': 'NM',\n",
    "    'New York': 'NY',\n",
    "    'North Carolina': 'NC',\n",
    "    'North Dakota': 'ND',\n",
    "    'Ohio': 'OH',\n",
    "    'Oklahoma': 'OK',\n",
    "    'Oregon': 'OR',\n",
    "    'Pennsylvania': 'PA',\n",
    "    'Rhode Island': 'RI',\n",
    "    'South Carolina': 'SC',\n",
    "    'South Dakota': 'SD',\n",
    "    'Tennessee': 'TN',\n",
    "    'Texas': 'TX',\n",
    "    'Utah': 'UT',\n",
    "    'Vermont': 'VT',\n",
    "    'Virginia': 'VA',\n",
    "    'Washington': 'WA',\n",
    "    'West Virginia': 'WV',\n",
    "    'Wisconsin': 'WI',\n",
    "    'Wyoming': 'WY',\n",
    "}\n",
    "us_state = {v: k.lower() for k, v in us_state_abbrev.items()}\n",
    "Us_state = {v: k for k, v in us_state_abbrev.items()}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "group_state_US = group_state_US.replace(us_state)\n",
    "group_state_US.head()\n",
    "pd.DataFrame.to_csv(group_state_US,'/Users/mueric35/Desktop/Margolis_Center/region_count.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>receiver_count</th>\n",
       "      <th>referer_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>new york</td>\n",
       "      <td>2493</td>\n",
       "      <td>2454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>california</td>\n",
       "      <td>2329</td>\n",
       "      <td>2327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>maryland</td>\n",
       "      <td>2242</td>\n",
       "      <td>2119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>florida</td>\n",
       "      <td>2185</td>\n",
       "      <td>2183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>north carolina</td>\n",
       "      <td>1681</td>\n",
       "      <td>1671</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           region  receiver_count  referer_count\n",
       "0        new york            2493           2454\n",
       "1      california            2329           2327\n",
       "2        maryland            2242           2119\n",
       "3         florida            2185           2183\n",
       "4  north carolina            1681           1671"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_state_US.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#group_state_US_upper = group_state_US.replace(Us_state)\n",
    "#pd.DataFrame.to_csv(group_state_US_upper,'/Users/mueric35/Desktop/Margolis_Center/region_count_upper.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#group_state_US_upper.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The rpy2.ipython extension is already loaded. To reload it, use:\n",
      "  %reload_ext rpy2.ipython\n"
     ]
    }
   ],
   "source": [
    "import rpy2\n",
    "%load_ext rpy2.ipython\n",
    "rpy2.robjects.packages.importr('ggmap')\n",
    "rpy2.robjects.packages.importr('maps')\n",
    "rpy2.robjects.packages.importr('tidyr')\n",
    "rpy2.robjects.packages.importr('DT')\n",
    "rpy2.robjects.packages.importr('ggplot2')\n",
    "\n",
    "%R all_states <- map_data(\"state\")\n",
    "%R -o all_states\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "map_data = group_state_US.merge(all_states)\n",
    "pd.DataFrame.to_csv(map_data,'/Users/mueric35/Desktop/Margolis_Center/Provide_map.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The rpy2.ipython extension is already loaded. To reload it, use:\n",
      "  %reload_ext rpy2.ipython\n"
     ]
    }
   ],
   "source": [
    "%load_ext rpy2.ipython\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ggplot import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qj6czgYCe9Ai4jgHv5EEnm70/d7IjUwQ/h7jeHPNs19csuMZhcT7UwHpDiFg1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i3TVw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\nAABJRU5ErkJggg==\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -i map_data\n",
    "library('ggplot2')\n",
    "library('maps')\n",
    "states <- data.frame(state.center, state.abb)\n",
    "\n",
    "\n",
    "ggplot(aes(x=long, y=lat, group = group), data = map_data) + \n",
    "scale_fill_continuous(high = \"skyblue\", low = \"white\", guide=\"colorbar\" ) + \n",
    "geom_polygon(aes(fill = receiver_count),colour=\"white\") + \n",
    "scale_y_continuous(breaks=c()) + \n",
    "scale_x_continuous(breaks=c()) + \n",
    "geom_text(data = states, aes(x = x, y = y, label = state.abb, group = NULL), size = 2) + \n",
    "labs(title=\"Receiver by State\") + \n",
    "theme(plot.title = element_text(hjust = 0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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S7e99fX3i9/uDx/UXjUChIFBXVyv8lJaWFsqU9Tw1AhqBHCCQcwJsmqZcdNFFsmTJErX8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\ntRrgPqP4ef9rVeKLPviSU49bixCa2GJKiTEgFVXwy0YpYUz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05ByvHbsaaO2b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rKknO5/pYue+7CbLj6yjN7YGqAT5huqLxVTOkqdhJc4Q03gx6RXTiGtbWPC4jlfgMMIRjnTNI1L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\n/Xe5jZzDsY8AAAAASUVORK5CYII=\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -i map_data\n",
    "library('ggplot2')\n",
    "library('maps')\n",
    "states <- data.frame(state.center, state.abb)\n",
    "\n",
    "\n",
    "ggplot(aes(x=long, y=lat, group = group), data = map_data) + \n",
    "scale_fill_continuous(low = \"white\", high = \"orange\", guide=\"colorbar\" ) + \n",
    "geom_polygon(aes(fill = referer_count),colour=\"white\") + \n",
    "scale_y_continuous(breaks=c()) + \n",
    "scale_x_continuous(breaks=c()) + \n",
    "geom_text(data = states, aes(x = x, y = y, label = state.abb, group = NULL), size = 2) + \n",
    "labs(title=\"Referer by State\") + \n",
    "theme(plot.title = element_text(hjust = 0.5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## State to State"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "state_edge = full_data[['State_referer','State_receiver']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "#select state name\n",
    "state_name = list(group_state['region'])[:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State_referer</th>\n",
       "      <th>State_receiver</th>\n",
       "      <th>counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AE</td>\n",
       "      <td>CA</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AK</td>\n",
       "      <td>AK</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AK</td>\n",
       "      <td>MD</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  State_referer State_receiver  counts\n",
       "0            AE             CA       1\n",
       "1            AK             AK      14\n",
       "2            AK             MD       1"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_to_state = pd.DataFrame(state_edge.groupby(['State_referer','State_receiver']).size().rename('counts'))\n",
    "st_to_st_df = state_to_state.reset_index()\n",
    "st_to_st_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "st_st_us = st_to_st_df[st_to_st_df['State_referer'].isin(state_name) & st_to_st_df['State_receiver'].isin(state_name)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "st_st_group = st_st_us.groupby(['State_referer','State_receiver']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "g = st_st_group.groupby(level=0, group_keys=False)\n",
    "st_st_group = g.apply(lambda x: x.sort_values('counts',ascending=False))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>counts</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State_referer</th>\n",
       "      <th>State_receiver</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">AK</th>\n",
       "      <th>AK</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NH</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MD</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MI</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL</th>\n",
       "      <th>AL</th>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              counts\n",
       "State_referer State_receiver        \n",
       "AK            AK                  14\n",
       "              NH                   4\n",
       "              MD                   1\n",
       "              MI                   1\n",
       "AL            AL                  63"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st_st_group.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27751, 13)"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The rpy2.ipython extension is already loaded. To reload it, use:\n",
      "  %reload_ext rpy2.ipython\n"
     ]
    }
   ],
   "source": [
    "%load_ext rpy2.ipython\n",
    "%R -i full_data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>referer</th>\n",
       "      <th>receiver</th>\n",
       "      <th>shared_Count</th>\n",
       "      <th>Unique_Beneficiaries</th>\n",
       "      <th>Number_Same_Day_Visits</th>\n",
       "      <th>Type_referer</th>\n",
       "      <th>State_referer</th>\n",
       "      <th>Type_receiver</th>\n",
       "      <th>State_receiver</th>\n",
       "      <th>Taxonomy_Code</th>\n",
       "      <th>Grouping</th>\n",
       "      <th>Classification</th>\n",
       "      <th>Specialization</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1003004771</td>\n",
       "      <td>1649494980</td>\n",
       "      <td>119</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>CO</td>\n",
       "      <td>1.0</td>\n",
       "      <td>CO</td>\n",
       "      <td>2081H0002X</td>\n",
       "      <td>Allopathic &amp; Osteopathic Physicians</td>\n",
       "      <td>Physical Medicine &amp; Rehabilitation</td>\n",
       "      <td>Hospice and Palliative Medicine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1003878448</td>\n",
       "      <td>1649494980</td>\n",
       "      <td>59</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>CO</td>\n",
       "      <td>1.0</td>\n",
       "      <td>CO</td>\n",
       "      <td>2081H0002X</td>\n",
       "      <td>Allopathic &amp; Osteopathic Physicians</td>\n",
       "      <td>Physical Medicine &amp; Rehabilitation</td>\n",
       "      <td>Hospice and Palliative Medicine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1013063601</td>\n",
       "      <td>1649494980</td>\n",
       "      <td>99</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>CO</td>\n",
       "      <td>1.0</td>\n",
       "      <td>CO</td>\n",
       "      <td>2081H0002X</td>\n",
       "      <td>Allopathic &amp; Osteopathic Physicians</td>\n",
       "      <td>Physical Medicine &amp; Rehabilitation</td>\n",
       "      <td>Hospice and Palliative Medicine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1013989342</td>\n",
       "      <td>1649494980</td>\n",
       "      <td>166</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>CO</td>\n",
       "      <td>1.0</td>\n",
       "      <td>CO</td>\n",
       "      <td>2081H0002X</td>\n",
       "      <td>Allopathic &amp; Osteopathic Physicians</td>\n",
       "      <td>Physical Medicine &amp; Rehabilitation</td>\n",
       "      <td>Hospice and Palliative Medicine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1033195052</td>\n",
       "      <td>1649494980</td>\n",
       "      <td>28</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>CO</td>\n",
       "      <td>1.0</td>\n",
       "      <td>CO</td>\n",
       "      <td>2081H0002X</td>\n",
       "      <td>Allopathic &amp; Osteopathic Physicians</td>\n",
       "      <td>Physical Medicine &amp; Rehabilitation</td>\n",
       "      <td>Hospice and Palliative Medicine</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      referer    receiver  shared_Count  Unique_Beneficiaries  \\\n",
       "0  1003004771  1649494980           119                    30   \n",
       "1  1003878448  1649494980            59                    21   \n",
       "2  1013063601  1649494980            99                    20   \n",
       "3  1013989342  1649494980           166                    16   \n",
       "4  1033195052  1649494980            28                    13   \n",
       "\n",
       "   Number_Same_Day_Visits  Type_referer State_referer  Type_receiver  \\\n",
       "0                       2           1.0            CO            1.0   \n",
       "1                       1           1.0            CO            1.0   \n",
       "2                       8           1.0            CO            1.0   \n",
       "3                       2           1.0            CO            1.0   \n",
       "4                       0           1.0            CO            1.0   \n",
       "\n",
       "  State_receiver Taxonomy_Code                             Grouping  \\\n",
       "0             CO    2081H0002X  Allopathic & Osteopathic Physicians   \n",
       "1             CO    2081H0002X  Allopathic & Osteopathic Physicians   \n",
       "2             CO    2081H0002X  Allopathic & Osteopathic Physicians   \n",
       "3             CO    2081H0002X  Allopathic & Osteopathic Physicians   \n",
       "4             CO    2081H0002X  Allopathic & Osteopathic Physicians   \n",
       "\n",
       "                       Classification                   Specialization  \n",
       "0  Physical Medicine & Rehabilitation  Hospice and Palliative Medicine  \n",
       "1  Physical Medicine & Rehabilitation  Hospice and Palliative Medicine  \n",
       "2  Physical Medicine & Rehabilitation  Hospice and Palliative Medicine  \n",
       "3  Physical Medicine & Rehabilitation  Hospice and Palliative Medicine  \n",
       "4  Physical Medicine & Rehabilitation  Hospice and Palliative Medicine  "
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp = full_data.groupby('receiver').count().sort_values('provider',ascending = False).reset_index()\n",
    "count_by_hospice = tmp[['receiver','provider']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "count_by_hospice.columns = ['Hospice','Count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hospice</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1780773408</td>\n",
       "      <td>1151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1639147051</td>\n",
       "      <td>678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1912962697</td>\n",
       "      <td>549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1861538209</td>\n",
       "      <td>518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1538339726</td>\n",
       "      <td>401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1437374683</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1538168950</td>\n",
       "      <td>305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1225252273</td>\n",
       "      <td>271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1033328232</td>\n",
       "      <td>254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1295170686</td>\n",
       "      <td>245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1801893151</td>\n",
       "      <td>218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1548375199</td>\n",
       "      <td>211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1679782668</td>\n",
       "      <td>201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1447367057</td>\n",
       "      <td>198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1912177288</td>\n",
       "      <td>196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1740442946</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1083812531</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1720295637</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1689933392</td>\n",
       "      <td>173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1558521203</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1598982753</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1053368266</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1396966016</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1093953168</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1679683486</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1699840736</td>\n",
       "      <td>169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1932406907</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1932311883</td>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1922240480</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1740232578</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1629251715</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1497756761</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1073836680</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1952557209</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1558522425</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1164639068</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1114949328</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1770501405</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1841499472</td>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1871899393</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1376870337</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>1609083666</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>1902952153</td>\n",
       "      <td>143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>1699848499</td>\n",
       "      <td>143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>1184839755</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1811935240</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1548294655</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>1023079456</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>1437259157</td>\n",
       "      <td>139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>1376804054</td>\n",
       "      <td>138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>1487870259</td>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>1902836042</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>1639336886</td>\n",
       "      <td>134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>1568679710</td>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>1093894164</td>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>1043444649</td>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>1679842967</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>1518194711</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>1972540516</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>1497764112</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>1679810188</td>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>1073775607</td>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>1710984281</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>1982828828</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>1487884656</td>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>1649487547</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>1992924104</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>1649494980</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>1033198676</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>1477556934</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>1588697882</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>1548219405</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>1457570384</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>1508040361</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>1568635449</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>1306038930</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>1316168164</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>1174505366</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>1225063092</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>1376785337</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>1689603565</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>1841402252</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>1033217138</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>1346359528</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>1538397716</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>1114954708</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>1003850108</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>1780947226</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>1760505309</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>1790946028</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>1750318309</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>1366603250</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>1477630762</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>1063401776</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>1861495954</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>1770506446</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>1588890115</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>1104879105</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>1356414395</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>1376741975</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>1730399627</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>1669662318</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>1053650507</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>1558498279</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>1790713139</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>1790784312</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>1831364454</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>1326343732</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>1740367903</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>1336216134</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>1407174295</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>1780847574</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>1720023674</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>1790901072</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>1881838001</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>1184844979</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>1639497969</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>1659402576</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>1164469755</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>1902073802</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>1669442927</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>1386636397</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>1184823411</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>1184888760</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>1720147150</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>1669622098</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>1336383298</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>1871709659</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>1003031683</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>1972710432</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>1073728218</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>1285846568</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>1578718292</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>1578770004</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>1245339662</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>1730353731</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>1992917009</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>1639382708</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>1013033455</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>1760599633</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>1366418121</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>1487743852</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>1326092925</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>1114039153</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>1427249184</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>1033319314</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>1194755660</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>1912128695</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>1952371353</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>1003028093</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>1154544377</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>1063672905</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>1154581726</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>1619137064</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>1194949792</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>1942236765</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>1053311514</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>1316943335</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>1255454872</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>1144244831</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>1356470785</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>1801878665</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>1033313523</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>1508092610</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>1164681995</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>1184673808</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>1497076400</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>1700043742</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>1417957465</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>1225118631</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>1922056407</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>1497911846</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>1417198011</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>1912104209</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>1821171570</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>1932479953</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>1225250236</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>1689893455</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>1841212909</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>1659585420</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>1346336807</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>1902072531</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>1932124153</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>1073615209</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>1104030956</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>1124070602</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>1144223231</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>1457337768</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>1205866407</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>1356596787</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>1629282330</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>1780909531</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>1851661862</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>1447241229</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>1598727273</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>1174565204</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>1922245042</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>1447286109</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>1043290810</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>1164719779</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>1558524074</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>1851341531</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>1972709160</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>1871741769</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>1588870950</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>1578646832</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>1407871486</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>1376607028</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>1427216944</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>1699709857</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>1780892075</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>1720394067</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>1639155757</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>1578523486</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>1184855066</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>1467455709</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>1073741450</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>1245394196</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>1073773818</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>1124243431</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>1235177932</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>1497973416</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>1255326591</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>1598730533</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>1295900819</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>1033368642</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>1477751139</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>1669474854</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>1689897019</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>1386851251</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>1558692707</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>1407076052</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>1417070715</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>1598855207</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>1700834140</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>1891786802</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>1447486550</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>1003044728</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>1427060318</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>1649497405</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>1588971014</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>1609028893</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>1780867531</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>1154399434</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>1376500645</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>1316055585</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>1235251836</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>1538228879</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>1447473640</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>1255453924</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>1568687135</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>1689680134</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>1326207366</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>1306047550</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>1619292570</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>1245551332</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>1043491061</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>1134101983</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>1891752226</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>1265434781</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>1063630762</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>1144541475</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>1407061336</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>1922049600</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>1346223906</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>1639430861</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>1205153012</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>1215909445</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>1821050444</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>1902125511</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>1346467388</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>1053526285</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>1306060371</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>1881628857</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>1518273945</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>1669624664</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>1750505442</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>1962721118</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>1225083660</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>1801008099</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>1952537565</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>1528242724</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>1407026099</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>1164680609</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>1386853224</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>1093711160</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>1871580530</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>1710299037</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>1942250642</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>1134243157</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>1679877211</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>1396774998</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>1184786246</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>1194932244</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>1083913693</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>1356558522</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>1104042795</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>1619929916</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>1407039415</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>1154527158</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>1881811719</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>1649229972</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>1487678835</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>1093906026</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>1144298118</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>1154542686</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>1174718472</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>1427385020</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>1548501000</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>1033323639</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>1033326152</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>1588693550</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>1710957428</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>1619298171</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>1437182052</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>1871520437</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>1205152543</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>1356508709</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>1104137140</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>1659592624</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>1720199615</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>1528273240</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>1740416445</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>1538114426</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>1346378015</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>1316947351</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>1992880033</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>1164623039</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>1518906841</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>1336113034</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>1730313685</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>1730464033</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>1417114414</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>1629041041</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>1336341312</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>1235442773</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>1225247935</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>1821290172</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>1912130634</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>1548467905</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>1487828414</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>1013994938</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>1407817877</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>1073683454</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>1396969267</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>1598771735</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>1023234507</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>1699869479</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>1902121890</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>1316157191</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>1295783561</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>1518930247</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>1306811948</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>1134449424</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>1386843530</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>1255548400</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>1174665392</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>1851518351</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>1376572255</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>1386865038</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>1710109905</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>1679724496</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>1962771196</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>1437277092</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>1790930733</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>1801007190</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>1215198601</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>1730286394</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>1043203201</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>1477553550</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>1144547548</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371</th>\n",
       "      <td>1144489584</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>1578671970</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>1306843354</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>1962628107</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>1821259003</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>1194723437</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>1326157710</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>1558538777</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>1598033110</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>1669765178</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>1376765180</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>1225242597</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>1447473103</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>1851519813</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385</th>\n",
       "      <td>1508091125</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>1780849380</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>1053537852</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>1053424382</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>1891723359</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>1760641997</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>1245443803</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>1790988756</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>1902035215</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>1669626891</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>1982670105</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>1881771913</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>1205978152</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>1285612267</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>1154682987</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>1457568354</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>1750508776</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>1750596961</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>1548486756</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>1326215815</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>1336318518</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>1750431219</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>1467671578</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>1255478251</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>1811230436</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>1467405282</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>1811104938</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>1265650766</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>1164649554</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1841344595</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>1003023813</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>1437312220</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>1417070707</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>1356669394</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>1376665794</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>1710900287</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>1558362467</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>1407065675</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>1326259250</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>1750464665</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425</th>\n",
       "      <td>1326118522</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>1780747923</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427</th>\n",
       "      <td>1902872054</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>1497963227</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429</th>\n",
       "      <td>1164729034</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>430</th>\n",
       "      <td>1942424486</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>1194926899</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>1093939902</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>1497978589</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>434</th>\n",
       "      <td>1861753519</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>1215913736</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>1003099912</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>1902989734</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>1457646887</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>1710202841</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>1285998401</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>1285814400</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>442</th>\n",
       "      <td>1295744167</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443</th>\n",
       "      <td>1730276015</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444</th>\n",
       "      <td>1831422070</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>1306974845</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>1356607311</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>447</th>\n",
       "      <td>1346253713</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448</th>\n",
       "      <td>1336456532</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>1053533257</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>1427260884</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>451</th>\n",
       "      <td>1730408360</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452</th>\n",
       "      <td>1316169287</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>1528289873</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>1093072225</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>1952418287</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>1710906524</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>1558376988</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458</th>\n",
       "      <td>1689711962</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>1023200177</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>460</th>\n",
       "      <td>1588871297</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>461</th>\n",
       "      <td>1568455954</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>462</th>\n",
       "      <td>1013126432</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>463</th>\n",
       "      <td>1013075605</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464</th>\n",
       "      <td>1376512574</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>465</th>\n",
       "      <td>1982828455</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466</th>\n",
       "      <td>1992755706</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>467</th>\n",
       "      <td>1568555837</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>468</th>\n",
       "      <td>1003058074</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>1518191089</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>470</th>\n",
       "      <td>1114139334</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>1508125105</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>1104801455</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>1235429481</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>1841391208</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>1659588721</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>1457352783</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>1205020450</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>1245443688</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>1477768521</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480</th>\n",
       "      <td>1831284207</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>1477847630</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>1003024175</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>1811946569</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>1437363983</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>1285945030</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>1164623997</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>1487745865</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>1427262609</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>1689627226</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>1508028481</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>1427261171</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>1639332257</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>1508094707</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>1639121098</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>1225258965</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>1720298912</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Hospice  Count\n",
       "0    1780773408   1151\n",
       "1    1639147051    678\n",
       "2    1912962697    549\n",
       "3    1861538209    518\n",
       "4    1538339726    401\n",
       "5    1437374683    333\n",
       "6    1538168950    305\n",
       "7    1225252273    271\n",
       "8    1033328232    254\n",
       "9    1295170686    245\n",
       "10   1801893151    218\n",
       "11   1548375199    211\n",
       "12   1679782668    201\n",
       "13   1447367057    198\n",
       "14   1912177288    196\n",
       "15   1740442946    188\n",
       "16   1083812531    184\n",
       "17   1720295637    180\n",
       "18   1689933392    173\n",
       "19   1558521203    172\n",
       "20   1598982753    171\n",
       "21   1053368266    171\n",
       "22   1396966016    171\n",
       "23   1093953168    171\n",
       "24   1679683486    170\n",
       "25   1699840736    169\n",
       "26   1932406907    168\n",
       "27   1932311883    166\n",
       "28   1922240480    162\n",
       "29   1740232578    162\n",
       "30   1629251715    160\n",
       "31   1497756761    158\n",
       "32   1073836680    157\n",
       "33   1952557209    156\n",
       "34   1558522425    155\n",
       "35   1164639068    153\n",
       "36   1114949328    151\n",
       "37   1770501405    150\n",
       "38   1841499472    148\n",
       "39   1871899393    145\n",
       "40   1376870337    144\n",
       "41   1609083666    144\n",
       "42   1902952153    143\n",
       "43   1699848499    143\n",
       "44   1184839755    142\n",
       "45   1811935240    142\n",
       "46   1548294655    142\n",
       "47   1023079456    141\n",
       "48   1437259157    139\n",
       "49   1376804054    138\n",
       "50   1487870259    137\n",
       "51   1902836042    135\n",
       "52   1639336886    134\n",
       "53   1568679710    133\n",
       "54   1093894164    133\n",
       "55   1043444649    133\n",
       "56   1679842967    132\n",
       "57   1518194711    132\n",
       "58   1972540516    130\n",
       "59   1497764112    129\n",
       "60   1679810188    127\n",
       "61   1073775607    127\n",
       "62   1710984281    126\n",
       "63   1982828828    125\n",
       "64   1487884656    123\n",
       "65   1649487547    122\n",
       "66   1992924104    122\n",
       "67   1649494980    121\n",
       "68   1033198676    121\n",
       "69   1477556934    120\n",
       "70   1588697882    119\n",
       "71   1548219405    118\n",
       "72   1457570384    115\n",
       "73   1508040361    114\n",
       "74   1568635449    114\n",
       "75   1306038930    113\n",
       "76   1316168164    113\n",
       "77   1174505366    113\n",
       "78   1225063092    112\n",
       "79   1376785337    112\n",
       "80   1689603565    111\n",
       "81   1841402252    110\n",
       "82   1033217138    110\n",
       "83   1346359528    110\n",
       "84   1538397716    109\n",
       "85   1114954708    108\n",
       "86   1003850108    106\n",
       "87   1780947226    105\n",
       "88   1760505309    105\n",
       "89   1790946028    103\n",
       "90   1750318309    102\n",
       "91   1366603250    101\n",
       "92   1477630762    101\n",
       "93   1063401776    100\n",
       "94   1861495954    100\n",
       "95   1770506446     99\n",
       "96   1588890115     97\n",
       "97   1104879105     97\n",
       "98   1356414395     97\n",
       "99   1376741975     96\n",
       "100  1730399627     96\n",
       "101  1669662318     96\n",
       "102  1053650507     95\n",
       "103  1558498279     95\n",
       "104  1790713139     93\n",
       "105  1790784312     93\n",
       "106  1831364454     91\n",
       "107  1326343732     89\n",
       "108  1740367903     88\n",
       "109  1336216134     87\n",
       "110  1407174295     87\n",
       "111  1780847574     86\n",
       "112  1720023674     86\n",
       "113  1790901072     85\n",
       "114  1881838001     85\n",
       "115  1184844979     85\n",
       "116  1639497969     84\n",
       "117  1659402576     82\n",
       "118  1164469755     82\n",
       "119  1902073802     81\n",
       "120  1669442927     81\n",
       "121  1386636397     80\n",
       "122  1184823411     79\n",
       "123  1184888760     79\n",
       "124  1720147150     78\n",
       "125  1669622098     78\n",
       "126  1336383298     78\n",
       "127  1871709659     77\n",
       "128  1003031683     77\n",
       "129  1972710432     77\n",
       "130  1073728218     76\n",
       "131  1285846568     76\n",
       "132  1578718292     75\n",
       "133  1578770004     75\n",
       "134  1245339662     74\n",
       "135  1730353731     73\n",
       "136  1992917009     72\n",
       "137  1639382708     71\n",
       "138  1013033455     71\n",
       "139  1760599633     71\n",
       "140  1366418121     70\n",
       "141  1487743852     70\n",
       "142  1326092925     69\n",
       "143  1114039153     69\n",
       "144  1427249184     68\n",
       "145  1033319314     67\n",
       "146  1194755660     67\n",
       "147  1912128695     67\n",
       "148  1952371353     67\n",
       "149  1003028093     66\n",
       "150  1154544377     64\n",
       "151  1063672905     63\n",
       "152  1154581726     63\n",
       "153  1619137064     61\n",
       "154  1194949792     61\n",
       "155  1942236765     61\n",
       "156  1053311514     61\n",
       "157  1316943335     61\n",
       "158  1255454872     60\n",
       "159  1144244831     60\n",
       "160  1356470785     60\n",
       "161  1801878665     59\n",
       "162  1033313523     59\n",
       "163  1508092610     59\n",
       "164  1164681995     59\n",
       "165  1184673808     58\n",
       "166  1497076400     58\n",
       "167  1700043742     58\n",
       "168  1417957465     57\n",
       "169  1225118631     57\n",
       "170  1922056407     56\n",
       "171  1497911846     56\n",
       "172  1417198011     56\n",
       "173  1912104209     56\n",
       "174  1821171570     56\n",
       "175  1932479953     56\n",
       "176  1225250236     55\n",
       "177  1689893455     55\n",
       "178  1841212909     53\n",
       "179  1659585420     52\n",
       "180  1346336807     52\n",
       "181  1902072531     51\n",
       "182  1932124153     51\n",
       "183  1073615209     49\n",
       "184  1104030956     49\n",
       "185  1124070602     49\n",
       "186  1144223231     49\n",
       "187  1457337768     49\n",
       "188  1205866407     49\n",
       "189  1356596787     48\n",
       "190  1629282330     47\n",
       "191  1780909531     46\n",
       "192  1851661862     46\n",
       "193  1447241229     45\n",
       "194  1598727273     45\n",
       "195  1174565204     45\n",
       "196  1922245042     45\n",
       "197  1447286109     45\n",
       "198  1043290810     44\n",
       "199  1164719779     43\n",
       "200  1558524074     43\n",
       "201  1851341531     43\n",
       "202  1972709160     42\n",
       "203  1871741769     41\n",
       "204  1588870950     41\n",
       "205  1578646832     41\n",
       "206  1407871486     41\n",
       "207  1376607028     40\n",
       "208  1427216944     40\n",
       "209  1699709857     40\n",
       "210  1780892075     39\n",
       "211  1720394067     39\n",
       "212  1639155757     39\n",
       "213  1578523486     38\n",
       "214  1184855066     38\n",
       "215  1467455709     38\n",
       "216  1073741450     38\n",
       "217  1245394196     38\n",
       "218  1073773818     38\n",
       "219  1124243431     37\n",
       "220  1235177932     37\n",
       "221  1497973416     37\n",
       "222  1255326591     37\n",
       "223  1598730533     37\n",
       "224  1295900819     36\n",
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       "232  1417070715     35\n",
       "233  1598855207     34\n",
       "234  1700834140     33\n",
       "235  1891786802     33\n",
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       "237  1003044728     32\n",
       "238  1427060318     32\n",
       "239  1649497405     31\n",
       "240  1588971014     31\n",
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       "242  1780867531     31\n",
       "243  1154399434     31\n",
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       "245  1316055585     31\n",
       "246  1235251836     30\n",
       "247  1538228879     30\n",
       "248  1447473640     30\n",
       "249  1255453924     30\n",
       "250  1568687135     29\n",
       "251  1689680134     29\n",
       "252  1326207366     28\n",
       "253  1306047550     28\n",
       "254  1619292570     28\n",
       "255  1245551332     28\n",
       "256  1043491061     28\n",
       "257  1134101983     28\n",
       "258  1891752226     27\n",
       "259  1265434781     27\n",
       "260  1063630762     27\n",
       "261  1144541475     27\n",
       "262  1407061336     27\n",
       "263  1922049600     26\n",
       "264  1346223906     25\n",
       "265  1639430861     25\n",
       "266  1205153012     25\n",
       "267  1215909445     25\n",
       "268  1821050444     24\n",
       "269  1902125511     24\n",
       "270  1346467388     23\n",
       "271  1053526285     23\n",
       "272  1306060371     23\n",
       "273  1881628857     23\n",
       "274  1518273945     22\n",
       "275  1669624664     22\n",
       "276  1750505442     21\n",
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       "279  1801008099     21\n",
       "280  1952537565     21\n",
       "281  1528242724     21\n",
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       "284  1386853224     20\n",
       "285  1093711160     20\n",
       "286  1871580530     20\n",
       "287  1710299037     20\n",
       "288  1942250642     19\n",
       "289  1134243157     19\n",
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       "291  1396774998     19\n",
       "292  1184786246     19\n",
       "293  1194932244     18\n",
       "294  1083913693     18\n",
       "295  1356558522     18\n",
       "296  1104042795     18\n",
       "297  1619929916     17\n",
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       "339  1548467905     11\n",
       "340  1487828414     10\n",
       "341  1013994938     10\n",
       "342  1407817877     10\n",
       "343  1073683454     10\n",
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       "346  1023234507     10\n",
       "347  1699869479      9\n",
       "348  1902121890      9\n",
       "349  1316157191      9\n",
       "350  1295783561      9\n",
       "351  1518930247      8\n",
       "352  1306811948      8\n",
       "353  1134449424      8\n",
       "354  1386843530      8\n",
       "355  1255548400      8\n",
       "356  1174665392      8\n",
       "357  1851518351      8\n",
       "358  1376572255      8\n",
       "359  1386865038      7\n",
       "360  1710109905      7\n",
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       "393  1902035215      5\n",
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       "397  1205978152      4\n",
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