4. topic_modeling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import re\n",
    "import spacy\n",
    "import string\n",
    "import nltk\n",
    "from nltk.stem.porter import PorterStemmer\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer, ENGLISH_STOP_WORDS\n",
    "from sklearn.decomposition import LatentDirichletAllocation\n",
    "import pyLDAvis\n",
    "import pyLDAvis.sklearn\n",
    "pyLDAvis.enable_notebook()\n",
    "\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"city_SanJose_Minutes.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.iloc[:, 1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='mainID', ylabel='Count'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEGCAYAAABiq/5QAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAARlUlEQVR4nO3df7DldV3H8ecLNlQUFeNKtOy6YEQ5jhPOtR+YVqKFZqBlAqlRUktTmmaWGFM600xTaqZZo26IkBJSCqP9sCBFnYKwywKyBIogygKxV/0DGxtt5d0f57vT5Xbv7tnlfr/fw/08HzNnzjmf873fz5vP+fLaz/3e8/2cVBWSpHYcNHYBkqRhGfyS1BiDX5IaY/BLUmMMfklqzIaxC5jGEUccUVu2bBm7DEl6SLn22mu/XFVzy9sfEsG/ZcsWFhYWxi5Dkh5SknxxpXZP9UhSYwx+SWqMwS9JjTH4JakxBr8kNcbgl6TG9Bb8Sc5PsivJjhVee22SSnJEX/1LklbW54z/AuDk5Y1JNgHPAb7UY9+SpFX0FvxV9Sngqyu89CfAbwN+EYAkjWDQc/xJTgHuqqobpth2a5KFJAuLi4sDVCe1beOmzSQZ/bZx0+axh2LdG2zJhiSHAucCPz7N9lW1DdgGMD8/728HUs/u3nknp737qrHL4JKzTxy7hHVvyBn/E4FjgBuS3AEcDWxP8h0D1iBJzRtsxl9VNwKP3/O8C//5qvryUDVIkvr9OOfFwNXA8Ul2Jjmrr74kSdPrbcZfVWfs4/UtffUtSVqdV+5KUmMMfklqjMEvSY0x+CWpMQa/JDXG4Jekxhj8ktQYg1+SGmPwS1JjDH5JaozBL0mNMfglqTEGvyQ1xuCXpMYY/JLUGINfkhpj8EtSYwx+SWqMwS9JjTH4JakxvQV/kvOT7EqyY0nbm5PckuQzSS5L8ti++pckrazPGf8FwMnL2q4AnlxVTwE+B7y+x/4lSSvoLfir6lPAV5e1XV5Vu7un/wYc3Vf/kqSVjXmO/+XAR1d7McnWJAtJFhYXFwcsS5LWt1GCP8m5wG7gotW2qaptVTVfVfNzc3PDFSdJ69yGoTtMcibwfOCkqqqh+5ek1g0a/ElOBl4H/EhVfX3IviVJE31+nPNi4Grg+CQ7k5wF/BlwGHBFkuuTvKuv/iVJK+ttxl9VZ6zQ/J6++pMkTccrdyWpMQa/JDXG4Jekxhj8ktQYg1+SGmPwS1JjDH5JaozBL0mNMfglqTGDL9ImSXt10AaSjF0F33n0Ju6680tjl9ELg1/SbLl/N6e9+6qxq+CSs08cu4TeeKpHkhpj8EtSYwx+SWqMwS9JjTH4JakxBr8kNcbgl6TGGPyS1BiDX5IaY/BLUmN6C/4k5yfZlWTHkrbHJbkiya3d/eF99S9JWlmfM/4LgJOXtZ0DfKyqjgM+1j2XJA2ot+Cvqk8BX13WfCpwYff4QuAFffUvSVrZ0Of4j6yqewC6+8evtmGSrUkWkiwsLi4OVqAkrXcz+8fdqtpWVfNVNT83Nzd2OZK0bgwd/PcmOQqgu981cP+S1Lyhg/8jwJnd4zOBDw/cvyQ1r8+Pc14MXA0cn2RnkrOAPwSek+RW4Dndc0nSgHr76sWqOmOVl07qq09J0r7N7B93JUn9MPglqTEGvyQ1xuCXpMYY/JLUGINfkhpj8EtSYwx+SWqMwS9JjTH4JakxBr8kNcbgl6TGGPyS1BiDX5IaY/BLUmMMfklqjMEvSY0x+CWpMQa/JDXG4JekxowS/El+I8lNSXYkuTjJw8eoQ5JaNFXwJ3n6NG1T7msj8OvAfFU9GTgYOP1A9iVJ2n/TzvjfMWXbtDYAj0iyATgUuPtB7EuStB827O3FJD8EnAjMJXnNkpcezWSmvt+q6q4kbwG+BPw3cHlVXb5C31uBrQCbN28+kK4kSSvY14z/EOBRTP6BOGzJ7T7gRQfSYZLDgVOBY4DvBB6Z5KXLt6uqbVU1X1Xzc3NzB9KVJGkFe53xV9UngU8muaCqvrhGfT4b+EJVLQIkuZTJbxXvX6P9S5L2Yq/Bv8TDkmwDtiz9map61gH0+SXgB5McyuRUz0nAwgHsR5J0AKYN/r8B3gWcB3zrwXRYVdck+SCwHdgNXAdsezD7lCRNb9rg311V71yrTqvqDcAb1mp/kqTpTftxzr9N8qtJjkryuD23XiuTJPVi2hn/md39by1pK+DYtS1HktS3qYK/qo7puxBJ0jCmCv4kP79Se1X95dqWI0nq27Snep625PHDmXwEcztg8EvSQ8y0p3peufR5kscA7+ulIklSrw50WeavA8etZSGSpGFMe47/b5l8igcmi7N9L/DXfRUlSerPtOf437Lk8W7gi1W1s4d6JEk9m+pUT7dY2y1MVuY8HPhmn0VJkvoz7TdwvRj4NPCzwIuBa5Ic0LLMkqRxTXuq51zgaVW1CyDJHPDPwAf7KkyS1I9pP9Vz0J7Q73xlP35WkjRDpp3x/2OSfwIu7p6fBvxDPyVJkvq0r+/c/S7gyKr6rSQ/DfwwEOBq4KIB6pMkrbF9na55G/A1gKq6tKpeU1W/wWS2/7Z+S5Mk9WFfwb+lqj6zvLGqFph8DaMk6SFmX8H/8L289oi1LESSNIx9Bf+/J/nl5Y1JzgKu7ackSVKf9vWpnlcDlyV5Cf8X9PPAIcALe6xLktSTvQZ/Vd0LnJjkx4And81/X1Uf770ySVIvpl2P/0rgyrXqNMljgfOY/GNSwMur6uq12r8kaXXTXsC11t4O/GNVvSjJIcChI9UhSc0ZPPiTPBp4JvALAFX1TVztU5IGM8Z6O8cCi8B7k1yX5Lwkj1y+UZKtSRaSLCwuLg5fpTSQjZs2k2T0m5Y5aMPo70kSNm7avOb/aWOc6tkAPBV4ZVVdk+TtwDnA7y7dqKq2AdsA5ufn6//tRVon7t55J6e9+6qxy+CSs08cu4TZcv/udfu+jDHj3wnsrKpruucfZPIPgSRpAIMHf1X9J3BnkuO7ppOA/xi6Dklq1Vif6nklcFH3iZ7bgV8cqQ5Jas4owV9V1zO5AliSNDC/RUuSGmPwS1JjDH5JaozBL0mNMfglqTEGvyQ1xuCXpMYY/JLUGINfkhqz7oN/Vpa87WNpVUk6EGOt1TMYl7yVpAda9zN+SdIDGfyS1BiDX5IaY/BLUmMMfklqjMEvSY0x+CWpMQa/JDXG4JekxowW/EkOTnJdkr8bqwZJatGYM/5XATeP2L8kNWmU4E9yNPCTwHlj9C9JLRtrxv824LeB+0fqX5KaNXjwJ3k+sKuqrt3HdluTLCRZWFxcHKg6SVr/xpjxPx04JckdwAeAZyV5//KNqmpbVc1X1fzc3NzQNUrSujV48FfV66vq6KraApwOfLyqXjp0HZLUKj/HL0mNGfUbuKrqE8AnxqxBklrjjF+SGmPwS1JjDH5JaozBL0mNMfglqTEGvyQ1xuCXpMYY/JLUGINfkhoz6pW70pg2btrM3TvvHLsMaXAGv5p19847Oe3dV41dBpecfeLYJagxnuqRpMYY/JLUGINfkhpj8EtSYwx+SWqMwS9JjTH4JakxBr8kNcbgl6TGGPyS1JjBgz/JpiRXJrk5yU1JXjV0DZLUsjHW6tkN/GZVbU9yGHBtkiuq6j9GqEWSmjP4jL+q7qmq7d3jrwE3AxuHrkOSWjXqOf4kW4ATgGtWeG1rkoUkC4uLi4PXJknr1WjBn+RRwIeAV1fVfctfr6ptVTVfVfNzc3PDFyhJ69QowZ/k25iE/kVVdekYNUhSq8b4VE+A9wA3V9Vbh+5fklo3xoz/6cDLgGclub67PW+EOiSpSYN/nLOq/gXI0P1Kkia8cleSGmPwS1JjDH5JaozBL0mNMfglqTEGvyQ1xuCXpMYY/JLUGINfkhpj8EtSYwx+SWqMwS9JjTH4JakxBr8kNcbgl6TGGPyS1BiDX5IaY/BLUmMMfklqjMEvSY0x+CWpMaMEf5KTk3w2yeeTnDNGDZLUqsGDP8nBwJ8DzwWeBJyR5ElD1yFJrRpjxv/9wOer6vaq+ibwAeDUEeqQpCalqobtMHkRcHJV/VL3/GXAD1TVK5ZttxXY2j09HvjsAXZ5BPDlA/zZvs1qbda1/2a1tlmtC2a3tlmtC/a/tidU1dzyxg1rV8/UskLb//vXp6q2AdsedGfJQlXNP9j99GFWa7Ou/Tertc1qXTC7tc1qXbB2tY1xqmcnsGnJ86OBu0eoQ5KaNEbw/ztwXJJjkhwCnA58ZIQ6JKlJg5/qqardSV4B/BNwMHB+Vd3UY5cP+nRRj2a1Nuvaf7Na26zWBbNb26zWBWtU2+B/3JUkjcsrdyWpMQa/JDVmXQf/rCwNkWRTkiuT3JzkpiSv6trfmOSuJNd3t+eNUNsdSW7s+l/o2h6X5Iokt3b3h49Q1/FLxuX6JPclefUYY5bk/CS7kuxY0rbqGCV5fXfMfTbJT4xQ25uT3JLkM0kuS/LYrn1Lkv9eMnbvGriuVd+7GRizS5bUdUeS67v2IcdstZxY+2Otqtbljckfjm8DjgUOAW4AnjRSLUcBT+0eHwZ8jslyFW8EXjvyON0BHLGs7U3AOd3jc4A/moH38j+BJ4wxZsAzgacCO/Y1Rt37egPwMOCY7hg8eODafhzY0D3+oyW1bVm63QhjtuJ7Nwtjtuz1PwZ+b4QxWy0n1vxYW88z/plZGqKq7qmq7d3jrwE3AxvHqGVKpwIXdo8vBF4wXikAnATcVlVfHKPzqvoU8NVlzauN0anAB6rqG1X1BeDzTI7FwWqrqsuranf39N+YXCszqFXGbDWjj9keSQK8GLi4r/5Xs5ecWPNjbT0H/0bgziXPdzIDYZtkC3ACcE3X9IruV/LzxzilwuSq6cuTXNstkwFwZFXdA5ODEXj8CHUtdToP/B9x7DGD1cdo1o67lwMfXfL8mCTXJflkkmeMUM9K790sjdkzgHur6tYlbYOP2bKcWPNjbT0H/1RLQwwpyaOADwGvrqr7gHcCTwS+D7iHya+YQ3t6VT2VyWqpv5bkmSPUsKruIr9TgL/pmmZhzPZmZo67JOcCu4GLuqZ7gM1VdQLwGuCvkjx6wJJWe+9mZsyAM3jgJGPwMVshJ1bddIW2qcZtPQf/TC0NkeTbmLyZF1XVpQBVdW9Vfauq7gf+gh5/vV1NVd3d3e8CLutquDfJUV3dRwG7hq5riecC26vqXpiNMeusNkYzcdwlORN4PvCS6k4Id6cEvtI9vpbJOeHvHqqmvbx3szJmG4CfBi7Z0zb0mK2UE/RwrK3n4J+ZpSG684bvAW6uqrcuaT9qyWYvBHYs/9me63pkksP2PGbyR8EdTMbpzG6zM4EPD1nXMg+YgY09ZkusNkYfAU5P8rAkxwDHAZ8esrAkJwOvA06pqq8vaZ/L5PswSHJsV9vtA9a12ns3+ph1ng3cUlU79zQMOWar5QR9HGtD/LV6rBvwPCZ/Gb8NOHfEOn6Yya9gnwGu727PA94H3Ni1fwQ4auC6jmXyqYAbgJv2jBHw7cDHgFu7+8eNNG6HAl8BHrOkbfAxY/IPzz3A/zCZZZ21tzECzu2Ouc8Czx2hts8zOfe751h7V7ftz3Tv8w3AduCnBq5r1fdu7DHr2i8AfmXZtkOO2Wo5sebHmks2SFJj1vOpHknSCgx+SWqMwS9JjTH4JakxBr8kNcbglw5AkvkkfzrFdv/V3e9Z5fG6bvXFT3cXWUmDG/yrF6X1oKoWgIX9/LHbanLp/56LgS5NclBVvXfNC5T2whm/mtXNwm9Jcl6SHUkuSvLsJP/arX3+/d3tqm6mflWS47uf/dEkf9c9fmO36Ngnktye5Nf31XdV3c5k7Zd9biutNYNfrfsu4O3AU4DvAX6OyRWUrwV+B7gFeGY3U/894A9W2c/3AD/BZP2ZN3RrruzL9u7npEF5qket+0JV3QiQ5CbgY1VVSW5k8iUcjwEuTHIck8vpVwv0v6+qbwDfSLILOJLJcgB7s9LqilLvnPGrdd9Y8vj+Jc/vZzIx+n3gyqp6MvBTwMOn2M+3mG5SdQKTL9uQBmXwS3v3GOCu7vEvrNVOuy/aeAvwjrXapzQtT/VIe/cmJqd6XgN8/EHu64lJrmPyW8PXgHf4iR6NwdU5JakxnuqRpMYY/JLUGINfkhpj8EtSYwx+SWqMwS9JjTH4Jakx/wuELMNyC4Pn6wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df.groupby('artID').sum('content')['mainID'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1576, 7)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "art_df = pd.DataFrame(df.groupby('artID').sum('content')['subID'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "art_df = art_df[art_df.subID != 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.merge(art_df, on='artID', how = 'inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1569 entries, 0 to 1568\n",
      "Data columns (total 8 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   content     1569 non-null   object\n",
      " 1   subID_x     1569 non-null   int64 \n",
      " 2   mainID      1569 non-null   int64 \n",
      " 3   artID       1569 non-null   int64 \n",
      " 4   hasDollar   1569 non-null   bool  \n",
      " 5   hasItem     1569 non-null   bool  \n",
      " 6   hasSection  1569 non-null   bool  \n",
      " 7   subID_y     1569 non-null   int64 \n",
      "dtypes: bool(3), int64(4), object(1)\n",
      "memory usage: 78.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "mainID_df = pd.DataFrame(df.groupby(by = ['mainID', 'subID_x']).sum('content'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='subID_x', ylabel='Count'>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df.subID_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.3 Approval of Council Committee Minutes.\n",
      "None provided.\n",
      "\n",
      "City of San Jose Page 3 January 7,2020\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in df[df['subID_x']!=0]['content'][2:3]:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_subID = df[df['subID_x']!=0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>content</th>\n",
       "      <th>subID_x</th>\n",
       "      <th>mainID</th>\n",
       "      <th>artID</th>\n",
       "      <th>hasDollar</th>\n",
       "      <th>hasItem</th>\n",
       "      <th>hasSection</th>\n",
       "      <th>subID_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>2.1 Approval of City Council Minutes.None prov...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>2.2 Final Adoption of Ordinances.20-056 Final ...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>2.3 Approval of Council Committee Minutes.\\nNo...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>2.4 20-005 Mayor and Council Excused Absence R...</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>2.5 City Council Travel Reports.\\nNone provided.</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1126</th>\n",
       "      <td>1559</td>\n",
       "      <td>3.9 20-1635 Actions Related to Food Distributi...</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>45</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1127</th>\n",
       "      <td>1560</td>\n",
       "      <td>3.10 20-1684 JPA-issued Bonds for Moderate-Inc...</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>45</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1128</th>\n",
       "      <td>1561</td>\n",
       "      <td>3.11 20-1703 Fee Cap on Food Delivery Services...</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>45</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1129</th>\n",
       "      <td>1562</td>\n",
       "      <td>3.12 20-1702 Inter-Department Transfer of Dist...</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>45</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1130</th>\n",
       "      <td>1564</td>\n",
       "      <td>4.1 20-1638 Update on Fine Amounts for Illegal...</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>269</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1131 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      index                                            content  subID_x  \\\n",
       "0         3  2.1 Approval of City Council Minutes.None prov...        1   \n",
       "1         4  2.2 Final Adoption of Ordinances.20-056 Final ...        2   \n",
       "2         5  2.3 Approval of Council Committee Minutes.\\nNo...        3   \n",
       "3         6  2.4 20-005 Mayor and Council Excused Absence R...        4   \n",
       "4         7   2.5 City Council Travel Reports.\\nNone provided.        5   \n",
       "...     ...                                                ...      ...   \n",
       "1126   1559  3.9 20-1635 Actions Related to Food Distributi...        9   \n",
       "1127   1560  3.10 20-1684 JPA-issued Bonds for Moderate-Inc...       10   \n",
       "1128   1561  3.11 20-1703 Fee Cap on Food Delivery Services...       11   \n",
       "1129   1562  3.12 20-1702 Inter-Department Transfer of Dist...       12   \n",
       "1130   1564  4.1 20-1638 Update on Fine Amounts for Illegal...        1   \n",
       "\n",
       "      mainID  artID  hasDollar  hasItem  hasSection  subID_y  \n",
       "0          2      1      False    False       False       45  \n",
       "1          2      1      False    False        True       45  \n",
       "2          2      1      False    False       False       45  \n",
       "3          2      1      False     True       False       45  \n",
       "4          2      1      False    False       False       45  \n",
       "...      ...    ...        ...      ...         ...      ...  \n",
       "1126       3     45       True     True       False      269  \n",
       "1127       3     45      False     True       False      269  \n",
       "1128       3     45      False     True       False      269  \n",
       "1129       3     45       True     True       False      269  \n",
       "1130       4     45      False     True        True      269  \n",
       "\n",
       "[1131 rows x 9 columns]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_subID = df_subID.reset_index()\n",
    "df_subID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_text(text):\n",
    "    #text = text.decode(\"UTF-8\")\n",
    "    text = text.replace('\\n',\" \")\n",
    "    text = text.replace('\\x0c',\" \")\n",
    "    text = re.sub(r\"-\", \" \", text) # Split the words with \"-\" (for example：pre-processing ==> pre processing）\n",
    "    text = re.sub(r\"\\d+/\\d+/\\d+\", \"\", text) # Take out the dates\n",
    "    text = re.sub(r\"[0-2]?[0-9]:[0-6][0-9]\", \"\", text) # Take out the time\n",
    "    text = re.sub(r\"[\\w]+@[\\.\\w]+\", \"\", text) # Take out the emails\n",
    "    text = re.sub(r\"/[a-zA-Z]*[:\\//\\]*[A-Za-z0-9\\-_]+\\.+[A-Za-z0-9\\.\\/%&=\\?\\-_]+/i\", \"\", text) # Take out the websites\n",
    "    pure_text = ''\n",
    "    # Validate to check if there are any non-text content \n",
    "    for letter in text:\n",
    "        # Keep only letters and spaces\n",
    "        if letter.isalpha() or letter==' ':\n",
    "            pure_text += letter\n",
    "    # Join the words are not stand-alone letters\n",
    "    text = ' '.join(word for word in pure_text.split() if len(word)>1)\n",
    "    return text\n",
    "\n",
    "# Create our list of punctuation marks\n",
    "punctuations = string.punctuation\n",
    "# Load English tokenizer, tagger, parser, NER and word vectors\n",
    "parser = spacy.load('en_core_web_sm')\n",
    "# Create our list of stopwords\n",
    "stop_words = spacy.lang.en.stop_words.STOP_WORDS\n",
    "# Creating our tokenizer function\n",
    "def spacy_tokenizer(sentence):\n",
    "    # Creating our token object, which is used to create documents with linguistic annotations.\n",
    "    mytokens = parser(sentence)\n",
    "    # Lemmatizing each token and converting each token into lowercase\n",
    "    mytokens = [ word.lemma_.lower().strip() if word.lemma_ != \"-PRON-\" else word.lower_ for word in mytokens ]\n",
    "    # Removing stop words\n",
    "    mytokens = [ word for word in mytokens if word not in stop_words and word not in punctuations ]\n",
    "    # return preprocessed list of tokens\n",
    "    return ' '.join(mytokens)\n",
    "\n",
    "def nltk_stemmer(text):\n",
    "    porter_stemmer = PorterStemmer()\n",
    "    word_data = ' '.join(text)\n",
    "    # First Word tokenization\n",
    "    nltk_tokens = nltk.word_tokenize(word_data)\n",
    "    #Next find the roots of the word\n",
    "    t = []\n",
    "    for w in nltk_tokens:       \n",
    "        t.append(porter_stemmer.stem(w))\n",
    "    return t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [],
   "source": [
    "text = df_subID['content']\n",
    "\n",
    "text = text.apply(lambda x: clean_text(x))\n",
    "\n",
    "text = text.apply(lambda x: spacy_tokenizer(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [],
   "source": [
    "t_list =[]\n",
    "for i in text:\n",
    "    t_list.append(clean_text(i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [],
   "source": [
    "spacy_list =[]\n",
    "for i in t_list:\n",
    "    spacy_list.append(spacy_tokenizer(i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [],
   "source": [
    "nltk_list =[]\n",
    "for i in spacy_list:\n",
    "    nltk_list.append(nltk_stemmer(i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [],
   "source": [
    "#flat_list = [item for sublist in spacy_list for item in sublist]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf_vectorizer = TfidfVectorizer(min_df=0.085, stop_words=ENGLISH_STOP_WORDS)\n",
    "\n",
    "word_matrix = tfidf_vectorizer.fit_transform(text)\n",
    "\n",
    "vocab = tfidf_vectorizer.get_feature_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['absent',\n",
       " 'accept',\n",
       " 'action',\n",
       " 'additional',\n",
       " 'administration',\n",
       " 'administrative',\n",
       " 'adopt',\n",
       " 'agreement',\n",
       " 'agreementscontracts',\n",
       " 'allow',\n",
       " 'amend',\n",
       " 'amendment',\n",
       " 'annual',\n",
       " 'appropriation',\n",
       " 'approval',\n",
       " 'approve',\n",
       " 'arenas',\n",
       " 'assessment',\n",
       " 'august',\n",
       " 'authorize',\n",
       " 'beekman',\n",
       " 'blair',\n",
       " 'building',\n",
       " 'business',\n",
       " 'california',\n",
       " 'carrasco',\n",
       " 'carry',\n",
       " 'ceqa',\n",
       " 'change',\n",
       " 'chappie',\n",
       " 'city',\n",
       " 'citys',\n",
       " 'clara',\n",
       " 'code',\n",
       " 'comment',\n",
       " 'comments',\n",
       " 'commitment',\n",
       " 'committee',\n",
       " 'community',\n",
       " 'compensation',\n",
       " 'construction',\n",
       " 'contd',\n",
       " 'continue',\n",
       " 'contract',\n",
       " 'council',\n",
       " 'councilmember',\n",
       " 'county',\n",
       " 'covid',\n",
       " 'davis',\n",
       " 'december',\n",
       " 'defer',\n",
       " 'department',\n",
       " 'deputy',\n",
       " 'dev',\n",
       " 'development',\n",
       " 'direct',\n",
       " 'director',\n",
       " 'district',\n",
       " 'draft',\n",
       " 'emergency',\n",
       " 'end',\n",
       " 'enforcement',\n",
       " 'environment',\n",
       " 'environmental',\n",
       " 'esparza',\n",
       " 'event',\n",
       " 'exceed',\n",
       " 'execute',\n",
       " 'exempt',\n",
       " 'extend',\n",
       " 'file',\n",
       " 'final',\n",
       " 'finance',\n",
       " 'fiscal',\n",
       " 'foley',\n",
       " 'follow',\n",
       " 'fund',\n",
       " 'funding',\n",
       " 'funds',\n",
       " 'general',\n",
       " 'government',\n",
       " 'group',\n",
       " 'guidelines',\n",
       " 'hear',\n",
       " 'housing',\n",
       " 'impact',\n",
       " 'include',\n",
       " 'increase',\n",
       " 'individual',\n",
       " 'informational',\n",
       " 'involve',\n",
       " 'item',\n",
       " 'jimenez',\n",
       " 'jones',\n",
       " 'josé',\n",
       " 'june',\n",
       " 'khamis',\n",
       " 'liccardo',\n",
       " 'manager',\n",
       " 'march',\n",
       " 'maya',\n",
       " 'mayor',\n",
       " 'meeting',\n",
       " 'memorandum',\n",
       " 'memos',\n",
       " 'minutes',\n",
       " 'minutessynopsis',\n",
       " 'motion',\n",
       " 'municipal',\n",
       " 'need',\n",
       " 'negotiate',\n",
       " 'new',\n",
       " 'offer',\n",
       " 'office',\n",
       " 'open',\n",
       " 'order',\n",
       " 'ordinance',\n",
       " 'page',\n",
       " 'pam',\n",
       " 'peralez',\n",
       " 'physical',\n",
       " 'plan',\n",
       " 'police',\n",
       " 'policy',\n",
       " 'pp',\n",
       " 'present',\n",
       " 'presentation',\n",
       " 'program',\n",
       " 'project',\n",
       " 'property',\n",
       " 'provide',\n",
       " 'public',\n",
       " 'question',\n",
       " 'recommend',\n",
       " 'recommendation',\n",
       " 'referral',\n",
       " 'regard',\n",
       " 'relate',\n",
       " 'report',\n",
       " 'reports',\n",
       " 'require',\n",
       " 'resolution',\n",
       " 'respond',\n",
       " 'result',\n",
       " 'rules',\n",
       " 'sam',\n",
       " 'san',\n",
       " 'santa',\n",
       " 'second',\n",
       " 'section',\n",
       " 'service',\n",
       " 'services',\n",
       " 'significant',\n",
       " 'special',\n",
       " 'specific',\n",
       " 'staff',\n",
       " 'state',\n",
       " 'subject',\n",
       " 'support',\n",
       " 'term',\n",
       " 'unanimously',\n",
       " 'update',\n",
       " 'use',\n",
       " 'vice',\n",
       " 'work',\n",
       " 'year']"
      ]
     },
     "execution_count": 365,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1131, 166)"
      ]
     },
     "execution_count": 373,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_matrix.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import LatentDirichletAllocation\n",
    "import pyLDAvis\n",
    "import pyLDAvis.sklearn\n",
    "pyLDAvis.enable_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 463,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LatentDirichletAllocation(n_components=9, random_state=0)"
      ]
     },
     "execution_count": 463,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda_tfidf = LatentDirichletAllocation(n_components=9, random_state=0)\n",
    "lda_tfidf.fit(word_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 464,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
       "\n",
       "\n",
       "<div id=\"ldavis_el585531406104006839528524960628\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "var ldavis_el585531406104006839528524960628_data = {\"mdsDat\": {\"x\": [0.08233007554624286, 0.15860808386174838, 0.13405827431519435, 0.000276681944471914, 0.043571121615411545, -0.27911154108922337, -0.25534220154816367, 0.15012884657293826, -0.03451934121862029], \"y\": [0.04279112696584861, -0.014512350073064199, 0.030703312661302287, -0.1714952143805508, -0.0930987715574421, 0.06257866711341827, -0.09849385044442735, -0.0031369870735057136, 0.24466406678842081], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9], \"cluster\": [1, 1, 1, 1, 1, 1, 1, 1, 1], \"Freq\": [36.976544213073765, 17.445434622561496, 11.351074362619885, 8.797307290937766, 7.597234812204451, 5.884779223681889, 4.447253188741569, 3.913458973998928, 3.5869133121802586]}, \"tinfo\": {\"Term\": [\"provide\", \"report\", \"minutes\", \"council\", \"event\", \"approval\", \"mayor\", \"update\", \"committee\", \"final\", \"present\", \"city\", \"ordinance\", \"property\", \"liccardo\", \"sam\", \"councilmember\", 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\"offer\", \"offer\", \"office\", \"office\", \"office\", \"office\", \"open\", \"open\", \"open\", \"open\", \"open\", \"order\", \"order\", \"order\", \"ordinance\", \"ordinance\", \"ordinance\", \"ordinance\", \"page\", \"page\", \"page\", \"page\", \"page\", \"page\", \"page\", \"page\", \"page\", \"pam\", \"pam\", \"pam\", \"peralez\", \"peralez\", \"peralez\", \"peralez\", \"physical\", \"physical\", \"physical\", \"physical\", \"plan\", \"plan\", \"plan\", \"plan\", \"plan\", \"police\", \"police\", \"police\", \"policy\", \"policy\", \"policy\", \"policy\", \"policy\", \"pp\", \"pp\", \"pp\", \"pp\", \"pp\", \"pp\", \"present\", \"present\", \"presentation\", \"presentation\", \"presentation\", \"presentation\", \"project\", \"project\", \"project\", \"project\", \"project\", \"project\", \"property\", \"property\", \"property\", \"property\", \"provide\", \"provide\", \"provide\", \"provide\", \"provide\", \"public\", \"public\", \"public\", \"public\", \"public\", \"question\", \"question\", \"recommend\", \"recommend\", \"recommend\", \"recommend\", \"recommendation\", \"recommendation\", \"recommendation\", \"recommendation\", \"recommendation\", \"recommendation\", \"referral\", \"referral\", \"referral\", \"referral\", \"regard\", \"regard\", \"regard\", \"regard\", \"relate\", \"relate\", \"relate\", \"relate\", \"report\", \"report\", \"report\", \"report\", \"report\", \"reports\", \"reports\", \"reports\", \"require\", \"require\", \"require\", \"resolution\", \"resolution\", \"resolution\", \"resolution\", \"resolution\", \"respond\", \"respond\", \"result\", \"result\", \"result\", \"result\", \"rules\", \"rules\", \"rules\", \"rules\", \"rules\", \"sam\", \"sam\", \"san\", \"san\", \"san\", \"san\", \"san\", \"san\", \"san\", \"san\", \"san\", \"santa\", \"santa\", \"santa\", \"santa\", \"second\", \"second\", \"second\", \"section\", \"section\", \"section\", \"service\", \"service\", \"service\", \"service\", \"services\", \"services\", \"services\", \"services\", \"services\", \"significant\", \"significant\", \"significant\", \"special\", \"special\", \"special\", \"special\", \"specific\", \"staff\", \"staff\", \"staff\", \"state\", \"state\", \"state\", \"state\", \"subject\", \"subject\", \"subject\", \"support\", \"support\", \"support\", \"support\", \"support\", \"term\", \"term\", \"term\", \"unanimously\", \"unanimously\", \"update\", \"update\", \"update\", \"use\", \"use\", \"use\", \"vice\", \"vice\", \"vice\", \"work\", \"work\", \"work\", \"work\", \"year\", \"year\", \"year\", \"year\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [8, 7, 4, 9, 3, 5, 2, 1, 6]};\n",
       "\n",
       "function LDAvis_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(LDAvis) !== \"undefined\"){\n",
       "   // already loaded: just create the visualization\n",
       "   !function(LDAvis){\n",
       "       new LDAvis(\"#\" + \"ldavis_el585531406104006839528524960628\", ldavis_el585531406104006839528524960628_data);\n",
       "   }(LDAvis);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/LDAvis\n",
       "   require.config({paths: {d3: \"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "        new LDAvis(\"#\" + \"ldavis_el585531406104006839528524960628\", ldavis_el585531406104006839528524960628_data);\n",
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       "    // require.js not available: dynamically load d3 & LDAvis\n",
       "    LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n",
       "         LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
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       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
       "topic                                                \n",
       "7      0.082330  0.042791       1        1  36.976544\n",
       "6      0.158608 -0.014512       2        1  17.445435\n",
       "3      0.134058  0.030703       3        1  11.351074\n",
       "8      0.000277 -0.171495       4        1   8.797307\n",
       "2      0.043571 -0.093099       5        1   7.597235\n",
       "4     -0.279112  0.062579       6        1   5.884779\n",
       "1     -0.255342 -0.098494       7        1   4.447253\n",
       "0      0.150129 -0.003137       8        1   3.913459\n",
       "5     -0.034519  0.244664       9        1   3.586913, topic_info=        Term        Freq       Total Category  logprob  loglift\n",
       "130  provide  100.000000  100.000000  Default  30.0000  30.0000\n",
       "138   report  107.000000  107.000000  Default  29.0000  29.0000\n",
       "105  minutes   60.000000   60.000000  Default  28.0000  28.0000\n",
       "44   council  125.000000  125.000000  Default  27.0000  27.0000\n",
       "65     event   50.000000   50.000000  Default  26.0000  26.0000\n",
       "..       ...         ...         ...      ...      ...      ...\n",
       "146      san    3.626998   88.577127   Topic9  -3.8718   0.1324\n",
       "94      josé    2.687528   78.933731   Topic9  -4.1715  -0.0521\n",
       "30      city    3.409632  153.736892   Topic9  -3.9336  -0.4808\n",
       "44   council    2.177549  125.197007   Topic9  -4.3820  -0.7238\n",
       "165     year    1.242539   31.384793   Topic9  -4.9430   0.0987\n",
       "\n",
       "[387 rows x 6 columns], token_table=      Topic      Freq    Term\n",
       "term                         \n",
       "0         1  0.250759  absent\n",
       "0         2  0.200608  absent\n",
       "0         3  0.200608  absent\n",
       "0         4  0.150456  absent\n",
       "0         5  0.100304  absent\n",
       "...     ...       ...     ...\n",
       "164       9  0.071716    work\n",
       "165       1  0.350488    year\n",
       "165       2  0.318626    year\n",
       "165       5  0.223038    year\n",
       "165       9  0.031863    year\n",
       "\n",
       "[657 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[8, 7, 4, 9, 3, 5, 2, 1, 6])"
      ]
     },
     "execution_count": 464,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyLDAvis.sklearn.prepare(lda_tfidf, word_matrix, tfidf_vectorizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 465,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>topic_0</th>\n",
       "      <th>topic_1</th>\n",
       "      <th>topic_2</th>\n",
       "      <th>topic_3</th>\n",
       "      <th>topic_4</th>\n",
       "      <th>topic_5</th>\n",
       "      <th>topic_6</th>\n",
       "      <th>topic_7</th>\n",
       "      <th>topic_8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.037584</td>\n",
       "      <td>0.699270</td>\n",
       "      <td>0.037585</td>\n",
       "      <td>0.037584</td>\n",
       "      <td>0.037635</td>\n",
       "      <td>0.037583</td>\n",
       "      <td>0.037584</td>\n",
       "      <td>0.037586</td>\n",
       "      <td>0.037588</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.028495</td>\n",
       "      <td>0.028454</td>\n",
       "      <td>0.028456</td>\n",
       "      <td>0.772283</td>\n",
       "      <td>0.028455</td>\n",
       "      <td>0.028453</td>\n",
       "      <td>0.028474</td>\n",
       "      <td>0.028463</td>\n",
       "      <td>0.028467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.030042</td>\n",
       "      <td>0.759653</td>\n",
       "      <td>0.030034</td>\n",
       "      <td>0.030047</td>\n",
       "      <td>0.030074</td>\n",
       "      <td>0.030036</td>\n",
       "      <td>0.030041</td>\n",
       "      <td>0.030039</td>\n",
       "      <td>0.030034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.022741</td>\n",
       "      <td>0.022744</td>\n",
       "      <td>0.022747</td>\n",
       "      <td>0.022745</td>\n",
       "      <td>0.022756</td>\n",
       "      <td>0.022772</td>\n",
       "      <td>0.022757</td>\n",
       "      <td>0.022752</td>\n",
       "      <td>0.817985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.037637</td>\n",
       "      <td>0.037653</td>\n",
       "      <td>0.037639</td>\n",
       "      <td>0.037637</td>\n",
       "      <td>0.698886</td>\n",
       "      <td>0.037636</td>\n",
       "      <td>0.037636</td>\n",
       "      <td>0.037637</td>\n",
       "      <td>0.037639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1126</th>\n",
       "      <td>0.017564</td>\n",
       "      <td>0.017420</td>\n",
       "      <td>0.017467</td>\n",
       "      <td>0.017443</td>\n",
       "      <td>0.017699</td>\n",
       "      <td>0.017514</td>\n",
       "      <td>0.842588</td>\n",
       "      <td>0.034818</td>\n",
       "      <td>0.017487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1127</th>\n",
       "      <td>0.017029</td>\n",
       "      <td>0.017038</td>\n",
       "      <td>0.017043</td>\n",
       "      <td>0.017032</td>\n",
       "      <td>0.017054</td>\n",
       "      <td>0.017031</td>\n",
       "      <td>0.017041</td>\n",
       "      <td>0.863688</td>\n",
       "      <td>0.017045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1128</th>\n",
       "      <td>0.016002</td>\n",
       "      <td>0.016015</td>\n",
       "      <td>0.016005</td>\n",
       "      <td>0.016005</td>\n",
       "      <td>0.016008</td>\n",
       "      <td>0.016028</td>\n",
       "      <td>0.245659</td>\n",
       "      <td>0.642258</td>\n",
       "      <td>0.016018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1129</th>\n",
       "      <td>0.015496</td>\n",
       "      <td>0.015496</td>\n",
       "      <td>0.015496</td>\n",
       "      <td>0.015499</td>\n",
       "      <td>0.015500</td>\n",
       "      <td>0.015488</td>\n",
       "      <td>0.015502</td>\n",
       "      <td>0.876016</td>\n",
       "      <td>0.015505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1130</th>\n",
       "      <td>0.019209</td>\n",
       "      <td>0.019204</td>\n",
       "      <td>0.019248</td>\n",
       "      <td>0.243949</td>\n",
       "      <td>0.108335</td>\n",
       "      <td>0.019205</td>\n",
       "      <td>0.532380</td>\n",
       "      <td>0.019223</td>\n",
       "      <td>0.019248</td>\n",
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       "  </tbody>\n",
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       "<p>1131 rows × 9 columns</p>\n",
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      ],
      "text/plain": [
       "       topic_0   topic_1   topic_2   topic_3   topic_4   topic_5   topic_6  \\\n",
       "0     0.037584  0.699270  0.037585  0.037584  0.037635  0.037583  0.037584   \n",
       "1     0.028495  0.028454  0.028456  0.772283  0.028455  0.028453  0.028474   \n",
       "2     0.030042  0.759653  0.030034  0.030047  0.030074  0.030036  0.030041   \n",
       "3     0.022741  0.022744  0.022747  0.022745  0.022756  0.022772  0.022757   \n",
       "4     0.037637  0.037653  0.037639  0.037637  0.698886  0.037636  0.037636   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "1126  0.017564  0.017420  0.017467  0.017443  0.017699  0.017514  0.842588   \n",
       "1127  0.017029  0.017038  0.017043  0.017032  0.017054  0.017031  0.017041   \n",
       "1128  0.016002  0.016015  0.016005  0.016005  0.016008  0.016028  0.245659   \n",
       "1129  0.015496  0.015496  0.015496  0.015499  0.015500  0.015488  0.015502   \n",
       "1130  0.019209  0.019204  0.019248  0.243949  0.108335  0.019205  0.532380   \n",
       "\n",
       "       topic_7   topic_8  \n",
       "0     0.037586  0.037588  \n",
       "1     0.028463  0.028467  \n",
       "2     0.030039  0.030034  \n",
       "3     0.022752  0.817985  \n",
       "4     0.037637  0.037639  \n",
       "...        ...       ...  \n",
       "1126  0.034818  0.017487  \n",
       "1127  0.863688  0.017045  \n",
       "1128  0.642258  0.016018  \n",
       "1129  0.876016  0.015505  \n",
       "1130  0.019223  0.019248  \n",
       "\n",
       "[1131 rows x 9 columns]"
      ]
     },
     "execution_count": 465,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topic_matrix = lda_tfidf.transform(word_matrix)\n",
    "\n",
    "topic_matrix_df = pd.DataFrame(topic_matrix).add_prefix('topic_')\n",
    "\n",
    "topic_matrix_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 543,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>topic_6</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>absent</th>\n",
       "      <td>5.181872</td>\n",
       "      <td>0.438389</td>\n",
       "      <td>2.890074</td>\n",
       "      <td>6.659691</td>\n",
       "      <td>0.176757</td>\n",
       "      <td>4.977626</td>\n",
       "      <td>0.388314</td>\n",
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       "    <tr>\n",
       "      <th>accept</th>\n",
       "      <td>6.256920</td>\n",
       "      <td>0.145770</td>\n",
       "      <td>21.275473</td>\n",
       "      <td>2.278497</td>\n",
       "      <td>3.504920</td>\n",
       "      <td>4.046185</td>\n",
       "      <td>0.143216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>action</th>\n",
       "      <td>13.690499</td>\n",
       "      <td>1.656012</td>\n",
       "      <td>11.367443</td>\n",
       "      <td>12.617551</td>\n",
       "      <td>2.817550</td>\n",
       "      <td>18.408643</td>\n",
       "      <td>1.712941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>additional</th>\n",
       "      <td>3.461466</td>\n",
       "      <td>0.142859</td>\n",
       "      <td>0.142955</td>\n",
       "      <td>1.047934</td>\n",
       "      <td>0.937825</td>\n",
       "      <td>6.936801</td>\n",
       "      <td>1.398887</td>\n",
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       "    <tr>\n",
       "      <th>administration</th>\n",
       "      <td>6.466205</td>\n",
       "      <td>0.142859</td>\n",
       "      <td>2.066764</td>\n",
       "      <td>3.266709</td>\n",
       "      <td>0.143004</td>\n",
       "      <td>8.060259</td>\n",
       "      <td>0.142859</td>\n",
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       "    <tr>\n",
       "      <th>administrative</th>\n",
       "      <td>15.945058</td>\n",
       "      <td>0.145643</td>\n",
       "      <td>0.142924</td>\n",
       "      <td>1.117882</td>\n",
       "      <td>0.142930</td>\n",
       "      <td>1.679234</td>\n",
       "      <td>0.142866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adopt</th>\n",
       "      <td>11.912387</td>\n",
       "      <td>0.142860</td>\n",
       "      <td>0.219651</td>\n",
       "      <td>21.804514</td>\n",
       "      <td>0.147224</td>\n",
       "      <td>32.145350</td>\n",
       "      <td>1.954869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>agreement</th>\n",
       "      <td>1.371235</td>\n",
       "      <td>0.142859</td>\n",
       "      <td>0.142865</td>\n",
       "      <td>3.047422</td>\n",
       "      <td>0.142999</td>\n",
       "      <td>57.852695</td>\n",
       "      <td>0.142863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>agreementscontracts</th>\n",
       "      <td>0.468789</td>\n",
       "      <td>0.142859</td>\n",
       "      <td>0.142864</td>\n",
       "      <td>0.144250</td>\n",
       "      <td>0.142873</td>\n",
       "      <td>18.311015</td>\n",
       "      <td>0.843831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>allow</th>\n",
       "      <td>0.145084</td>\n",
       "      <td>0.142860</td>\n",
       "      <td>0.142905</td>\n",
       "      <td>6.449004</td>\n",
       "      <td>1.778196</td>\n",
       "      <td>4.059337</td>\n",
       "      <td>0.142860</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       topic_0   topic_1    topic_2    topic_3   topic_4  \\\n",
       "absent                5.181872  0.438389   2.890074   6.659691  0.176757   \n",
       "accept                6.256920  0.145770  21.275473   2.278497  3.504920   \n",
       "action               13.690499  1.656012  11.367443  12.617551  2.817550   \n",
       "additional            3.461466  0.142859   0.142955   1.047934  0.937825   \n",
       "administration        6.466205  0.142859   2.066764   3.266709  0.143004   \n",
       "administrative       15.945058  0.145643   0.142924   1.117882  0.142930   \n",
       "adopt                11.912387  0.142860   0.219651  21.804514  0.147224   \n",
       "agreement             1.371235  0.142859   0.142865   3.047422  0.142999   \n",
       "agreementscontracts   0.468789  0.142859   0.142864   0.144250  0.142873   \n",
       "allow                 0.145084  0.142860   0.142905   6.449004  1.778196   \n",
       "\n",
       "                       topic_5   topic_6  \n",
       "absent                4.977626  0.388314  \n",
       "accept                4.046185  0.143216  \n",
       "action               18.408643  1.712941  \n",
       "additional            6.936801  1.398887  \n",
       "administration        8.060259  0.142859  \n",
       "administrative        1.679234  0.142866  \n",
       "adopt                32.145350  1.954869  \n",
       "agreement            57.852695  0.142863  \n",
       "agreementscontracts  18.311015  0.843831  \n",
       "allow                 4.059337  0.142860  "
      ]
     },
     "execution_count": 543,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_topic_matrix_df = pd.DataFrame(lda_tfidf.components_, columns=vocab).T.add_prefix('topic_')\n",
    "word_topic_matrix_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K-means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import TruncatedSVD, NMF\n",
    "from sklearn.metrics.pairwise import cosine_similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import scale\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.datasets import make_blobs\n",
    "from sklearn.utils import shuffle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_cluster(X, km=[], num_clusters=0):\n",
    "    #color = 'brgcmyk'\n",
    "    color = 'cmy'\n",
    "    alpha = 0.5\n",
    "    s = 20\n",
    "    \n",
    "    if num_clusters == 0:\n",
    "        plt.scatter(X[:,0], X[:,1], c=color[0], alpha=alpha, s=s)\n",
    "        \n",
    "    else:\n",
    "        for i in range(num_clusters):\n",
    "            plt.scatter(X[km.labels_==i,0],X[km.labels_==i,1],c = color[i],alpha = alpha,s=s)\n",
    "            plt.scatter(km.cluster_centers_[i][0],km.cluster_centers_[i][1],c = color[i], marker = 'x', s = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_clusters = 3\n",
    "km = KMeans(n_clusters=num_clusters)\n",
    "km.fit(topic_matrix)\n",
    "display_cluster(topic_matrix,km,num_clusters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 482,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The within cluster sum of squares for 1 clusters is 482.77\n",
      "The within cluster sum of squares for 2 clusters is 335.37\n",
      "The within cluster sum of squares for 3 clusters is 250.73\n",
      "The within cluster sum of squares for 4 clusters is 195.21\n",
      "The within cluster sum of squares for 5 clusters is 145.25\n",
      "The within cluster sum of squares for 6 clusters is 102.10\n",
      "The within cluster sum of squares for 7 clusters is 76.01\n",
      "The within cluster sum of squares for 8 clusters is 55.77\n",
      "The within cluster sum of squares for 9 clusters is 42.20\n"
     ]
    }
   ],
   "source": [
    "wcss={} # Within cluster sum of squares\n",
    "for i in range(1,10):\n",
    "    kmeans=KMeans(n_clusters=i, random_state=0)\n",
    "    kmeans.fit(topic_matrix)\n",
    "    wcss[i]=(kmeans.inertia_) #Sum of squared distances of samples to their closest cluster center.\n",
    "    print(f'The within cluster sum of squares for {i} clusters is {wcss[i]:.2f}') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 483,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yKa/yyswwfjmkG2u27uHJCYuiLkdEBIjfUmjj7rPCx9cAo9x9CHASQTjIUerbpj7nH9eEP49fxOotu6MuR0Qkbijsj3l8JkEHM+6+HdCsMWXk7nM74w4PvD8v6lJEROKGwgozuzmcfrMX8D6AmVUHqpRHcemgeb0crh/Qljc/W830ZZuiLkdE0ly8ULiO4EK1q4HL3X1LuPwk4LnklpVefjCwHcfUrsav3p5Dfr5OURWR6Bw2FNx9nbt/392Huvt/YpaPdfffl0956SEnO4s7z+3EFyu38vqMVVGXIyJpLKFTUiX5hh7fjB4t6vK79+exY29e1OWISJpSKKSIjAzjl0O6smHHXm746zRduyAikYh3ncID4f2l5VdOeuvZsh4PXXY8nyzayHdfnKpgEJFyF6+lcJ6ZVQHuLq9iBC7u2ZyHw2C47gUFg4iUr3ih8D6wATjOzLaZ2fbY+3KqLy0VBMOkxQoGESlf8c4+usPd6wDvunttd68Ve1+ONaali3s25yEFg4iUs0Q6moeb2QVmdr6Zab7mchS0GHowafFGrn1ewSAiyRevo7m2mb0KjCYY6+i7wBgz+4eZqaVQTi7q2YyHL+vB5CVBMOzap9NVRSR54rUU/gDMATq4+yXufjHQjmC01MfKozgJXNSzGY9cHgTDdc9PUzCISNLEC4X+7n6vuxcOfueB+4B+yS9NYg3tcTAY1GIQkWSJFwpWblVIQgqCYcqSTQoGEUmKeKHwsZn9wswOCQcz+zkwKbllyeEoGEQkmeKFws3AscBCM/unmb1mZosI5mq+qVyqk2LFBsM1zykYRKTsZB1uhbtvAy41s3ZAV4LDSXe6u+aOTAFDezTDzBg+cgbXPDeV567pQ072Yf86RUQSUuJ1Cu6+yN3fdve3jiQQzKyamU0xs8/NbLaZ/SpcXt/MRpnZgvC+Xsw+d5vZQjObb2Znl+4jpY8Lj2/KiCt6MnXpJq5Wi0FEykAyR0ndC5zh7scDPYBzzOwk4C5gjLt3AMaEzzGzrsAVBBP7nAM8YWaZSayvUrjw+KY8ekVPpoXBsFPDbovIUUhaKISnr+4In1YJbw4MBV4Il78AXBQ+HgqMdPe97r4EWAj0TVZ9lcmQMBimL9vMNc8rGESk9BIKBTOrZ2bHmVmvgluC+2Wa2WfAOmCUu08GGrv7GoDwvlG4eTNgRczuK8NlRV/zejObZmbT1q9fn0gZaWHI8U0ZcXmPIBjUYhCRUiqxZ9LM/pdgnuZFBL/0Ce/PKGlfdz8A9DCzusAbZtY93lsV9xLFvOZTwFMAvXv31oTGMYYc3xQzuHXkZ4WdzzWqqvNZRBKXyDfGZUA7d99X2jdx9y1mNo6gr2CtmTVx9zVm1oSgFQFBy6BFzG7NgdWlfc90dcFxTQEFg4iUTiKHj2YBdY/0hc0sN2whYGbVgUHAPOAtYFi42TDgzfDxW8AVZlY1HI21AzDlSN9XgmD4wxU9mb58M1c/N0WHkkQkYYn8hPwNMMPMZhGcUQSAu19Ywn5NgBfCM4gygFfd/R0zmwi8ambXAcuBS8PXmx2OyjoHyANuDA8/SSmcf1wTAG4ZOYOrn5vCc9f0paZaDCJSAnOPf1jezGYDTxKMjho7ON745JZWst69e/u0adOiLiOlvfvFGm4ZOYNeLesqGEQEADOb7u69i1uXyDfEBnf/QxnXJOUktsVwjVoMIlKCRPoUppvZb8ys35Gekiqp4fzjmvDHK3vy6fItXP3sFHaoj0FEDiORn4w9w/uTYpYldEqqpI7zjg1aDDe/MoOrn53C89eqxSAiX1fit4K7n14ehUjynXdsEwy4ScEgIoeRyMVrvyhueTgDm1Qw5x7bhMcIWgzDnp3CCwoGEYmRSJ/CzpjbAeBcoHUSa5IkO/fYoI/h8xVbGPbsFLbv2R91SSKSIhIZOvuhmNv9wECKGZNIKpZzj23CY98MguHq56YqGEQEKN0oqTlA27IuRMrfOd0PBoNaDCICCYSCmc00sy/C22xgPvBo8kuT8hAEQy++WLlVwSAiCbUULgCGhLfBQFN3fyypVUm5Oqf7MQoGEQHihIKZ5ZhZFXdf5u7LgGoEI6YOKbfqpNzEBsN3FAwiaSteS+F9wrOMzKw9MJGgL+EmM/tt8kuT8nZO92N4/Fu9mBkGwzYFg0jaiRcK9dx9Qfh4GPCKu99McErq+UmvTCJxdreDwTBMwSCSduKFQuzwqWcAowDCyXbyi91DKoWzux3DEwUthmcUDCLpJF4ofGFmvzez24D2wH8ACibOkcptcBgMs1crGETSSbxQ+B6wgaBfYbC77wqXdwV+n+S6JAUM7nYMj3/zYDBs3lnqGVlFpIIocZKdVKZJdsrHqDlr+eHL06lTPZv/HdqNc8MRV0WkYoo3yU5prmiWNHNW18a8eeMpNK5dlR+8/Ck/fHk667fvLXlHEalwFAqSkK5Na/OvG/tzx9mdGD1nHWc9Mp43ZqykIrc0ReTr4oaCmWWa2YPlVYyktiqZGdx4enveu/UU2jaswW1//5zrXpjGmq27oy5NRMpI3FBw9wPACWZm5VSPVADtG9XiH98/mZ9f0JVPFm1g8MMTeGXKcrUaRCqBRA4fzQDeNLOrzOySgluyC5PUlplhXHdKGz4YPoDuzepw9+sz+fYzk1mxaVfJO4tIykokFOoDGwkuYCsYGO+CZBYlFUerBjV4+bsn8n8XH8vnK7Yy+JEJPPfxEvLz1WoQqYh0SqqUmdVbdvPTN2Yybv56ereqxwPfOI52uTWjLktEijiqU1LNrKOZjTGzWeHz48zsnrIuUiq+pnWr89zVfXj4suNZsG4H5z76EX8at4i8AxoVRaSiSOTw0V+Au4H9AO7+BXBFMouSisvMuKRXc0b9aABndGrEA+/P4+InPmHumm1RlyYiCUgkFHLcfUqRZXnJKEYqj0a1qvHnq07giW/1Ys3W3Qz54395ZNSX7MtTq0EklSUSChvMrB3hqKlm9g1gTVKrkkrjvGObMOq20xhyfFMeHbOAIX/8L5+v2BJ1WSJyGImEwo3Ak0BnM1sFDAe+n8yipHKpVyObRy7vwTPDerN1934ufuJjfvPeXPbsPxB1aSJSRCKh4O4+CMgFOrv7KQnuJ3KIM7s05j8/GsDlfVrw5ITFnPvoR0xduinqskQkRiJf7v8EcPed7r49XPZa8kqSyqx2tSr85pLjePm7J7L/QD6XPTmRX745i5171U0lkgqyDrfCzDoD3YA6Ra5grg1US3ZhUrn1b9+QD4YP4MEP5vPCxKWMmbeO315yHKd0aBh1aSJpLV5LoRPBlct1OXgl8xCgF8EEPCJHpUbVLO69sBv/uKEf2ZkZfPuZydz52hds3a1Z3kSiUuIVzWbWz90nllM9R0RXNFcee/YfYMToBTw1YRG5tapy/0XHMqhr46jLEqmUjnaSnYvNrLaZVQmvbN5gZt8u4xolzVWrksld53bmXzf2p15ONt99cRq3jpzBJk0BKlKuEgmFwe6+jeBQ0kqgI3BHUquStHVc87q8ddMpDB/UgfdmruGsh8fzzherNSy3SDlJJBSqhPfnAa+4e0LnEJpZCzMba2ZzzWy2md0aLq9vZqPMbEF4Xy9mn7vNbKGZzTezs4/400ilkJ2VwfBBHXn75lNoVq86N/1tBt9/aTrrtu2JujSRSi+RUHjbzOYBvYExZpYLJPK/Mw+43d27ACcBN5pZV+AuYIy7dwDGhM8J111BcMbTOcATZpZ5pB9IKo/Ox9Tm9R+czF3ndmbs/PUMeng8r03XFKAiyVRiKLj7XUA/oLe77wd2AkMT2G+Nu38aPt4OzAWahfu+EG72AnBR+HgoMNLd97r7EmAh0PeIPo1UOlmZGXz/tHb8+9ZT6di4Fj/+x+dc/dxUVm3RFKAiyZDI0NnfITgV9Vvh428Ag4/kTcysNdATmAw0dvc1EAQH0CjcrBmwIma3leGyoq91vZlNM7Np69evP5IypAJrl1uTV2/ox68u7MbUpZsY/PB4Xpq0TJP5iJSxRA4f9Ym5nQrcC1yY6BuYWU2Cq6KHhx3Wh920mGVf+x/v7k+5e293752bm5toGVIJZGQYw05uzQfDB9CjZV3u+dcsrvzLJBav3xF1aSKVRiKHj26OuX2P4Bd/diIvbmZVCALhZXd/PVy81syahOubAOvC5SuBFjG7NwdWJ/YxJJ20qJ/DS9edyAP/cyxz1mzj7BET+M2/57JDQ2WIHLXSDGy3C+hQ0kZmZsAzwFx3fzhm1VvAsPDxMODNmOVXmFlVM2sTvkfReRxEgGAyn8v7tOTD2wcytEcznhy/mDN+P443ZqgjWuRoJHJF89scPIyTAXQFXg07oOPtdwrwETATKJhZ5acE/QqvAi2B5cClBae5mtnPgGsJzlwa7u7/jvceuqJZCsxYvpl735rN5yu30rtVPe69sBvdm9WJuiyRlBTviuZEQuG0mKd5wDJ3X1mG9ZWaQkFi5ec7/5i+gt+9P59Nu/bxzb4t+fHgTtSrkdDRTpG0cVShkMoUClKcrbv388ioL/nrpGXUrJrFjwd35JsntiIzo7hzGUTST6lCwcy2U8zZPwRnCbm71y67EktHoSDxzP9qO798axaTFm+iS5Pa/OrCbvRtUz/qskQiV6oB8dy9lrvXLuZWKxUCQaQknY6pxSvfO4nHv9mLrbv2cdmTE7l15Ay+2qrhMkQO57ChYGZ9zOzcYpYPMbMTkluWSNkwM84/rgmjbz+Nm89oz79nfcUZD43jiXEL2ZunOaJFiop3SuqDBENTFDU3XCdSYeRkZ3H74E6Mvu00+rdvyO/en885Iz5i7Lx1Je8skkbihUIDd19adKG7LwQaJK0ikSRq2SCHv3ynN89f0wcDrnl+Ktc9P5WlG3ZGXZpISogXCtXjrKtR1oWIlKeBnRrx/vAB3H1uZyYt3sjgRybw4Afz2LVPV0VLeosXCqPN7P7wyuRCZvYr4MPkliWSfNlZGdxwWjs+/PFAzj+uCY+PXcSZD43n7c81qY+kr3ihcDvQFlhoZv8MbwuBTsCPyqU6kXLQuHY1Hrm8B699vx/1crK5+ZUZXPHUJOauiTd+o0jllMgVzW0JJr4BmO3ui5NeVYJ0nYKUtQP5zsipy3nwg/ls272fq05qxY/O6kSdnCol7yxSQeiKZpEjtGXXPh76z5e8PHkZdXOyuePsTlzWu4WuipZKoVQXr4mks7o52fzvRd15++ZTaJ9bk7tfn8lFj3/M9GWboy5NJKkUCiJxdGtah7/fcBKPXtGDddv38D9/+oQfvfoZ67brqmipnLIS2cjMMoHGsdu7+/JkFSWSSsyMoT2aMahLYx4bu5CnP1rMf2av5dYzOzDs5NZkZ+m3lVQeiXQ03wz8EljLwXkR3N2PS3JtJVKfgkRhyYad3Pf2bMbOX0+73Brce2E3Tu2gqWGl4jja+RQWAie6+8ZkFHc0FAoSpTFz13LfO3NYtnEXZ3drzD3nd6VF/ZyoyxIp0dF2NK8AtpZtSSIV35ldGvPB8AHccXYnJny5gUEPj+fhUV+ye58G2pOKK5GWwjMEF6y9C+wtWF5k3uVIqKUgqWL1lt3833tzeeeLNTSrW517zu/COd2PociAACIp4WhbCsuBUUA2UCvmJiKhpnWr89g3ezHy+pOoVS2LH7z8KZf+eSKfLNygITOkQtHFayJlLO9APiOnruCxDxfy1bY9nNimPj86qyMnttXgwpIaSjsd5wh3H25mb1PMtJzufmHZlnnkFAqSyvbsP8DIKct5fNwi1m/fyyntG3LbWR05oVW9qEuTNFfaUDjB3aeb2WnFrXf38WVYY6koFKQi2LP/AC9NWsafxi1i4859nNYxl9vO6kiPFnWjLk3SlMY+EkkBu/bl8eLEZTw5fhGbd+1nUJdGDB/Uke7N6kRdmqSZo71OoT9wL9CK4IpmI7h4rW0Z13nEFApSEe3Ym8fzHy/hqQmL2bYnj7O7Nea2szrS+ZjaUZcmaeJoQ2EecBswHSg8ATsVLmZTKEhFtm3Pfp797xKe+WgJ2/fmcf5xTRh+Zgc6NNbJfZJcRxsKk939xKRUdpQUClIZbNm1j6c/WsJzHy9h1/4DXHh8U249swNtc2tGXZpUUqXtaO4VPrwMyARe59CL1z4t4zqPmEJBKpNNO/fx5IRFvPjJMvbmHeDins255cz2tGqgKdGlbJU2FMbGeU139zPKorijoVCQymj99r08OX4Rf520jLx85xu9mnPTGe01rpKUmaM9fNS26BScxS2LgkJBKrN12/bwxLhF/G3ychznst4tuPH09jStWz3q0qSCO9pQ+NTdexVZNt3dTyjDGktFoSDpYM3W3Tw+diF/n7oCw7iybwt+eHp7GteuFnVpUkHFC4XDTrJjZp2BbkAdM7skZlVtQP8aRcpJkzrV+fVFx3LDgHY8PnYhL01ezsipK/j2Sa34/mntyK1VNeoSpRKJ16cwFLgIuBB4K2bVdmCku3+S9OpKoJaCpKNlG3fyxw8X8vqnK8nOymBYv9bccFo76tfIjro0qSCO9vBRP3efmJTKjpJCQdLZ4vU7+MOYBbz5+WpyqmRydf/WfO/UttTNUThIfKU9++gn7v47M/sjxQ+Id0vZlnnkFAoisHDddkaMXsA7X6yhVtUsrj2lDdee0oY61atEXZqkqFL1KQBzw3t964qksPaNavHYN3tx0xnbGDFqAY+OWcBzHy/he6e25er+ralVTeEgiYvXUhgOfAzMcPe88iwqUWopiHzdrFVbGTF6AaPnrqVuThVuGNCO7/RrRY2q8X4DSjop7cxrzYFHgXVmNs7M/s/Mzjez+gm+6bNmts7MZsUsq29mo8xsQXhfL2bd3Wa20Mzmm9nZCX42ESmie7M6PD2sN2/e2J8eLerywPvzGPC7sfxlwmLNHy0lSqSjORvoDZwM9AtvW9y9awn7DQB2AC+6e/dw2e+ATe7+WzO7C6jn7neaWVfgFaAv0BQYDXR097j/gtVSECnZ9GWbGTH6Sz5asIGGNatyWe/mnNW1Mcc3r0tGhuaQTkel7VMoUJ3g2oQ64W01MLOkndx9gpm1LrJ4KDAwfPwCMA64M1w+0t33AkvMbCFBQKTkWU8iFckJrerx1+tOZPLijTw+bhFPTljME+MW0ahWVc7s0pjBXRvTr10DqlXJjLpUSQHxLl57iuDite3AZOAT4GF333wU79fY3dcAuPsaM2sULm8GTIrZbmW4rLi6rgeuB2jZsuVRlCKSXk5s24AT2zZgy659jJ2/jtFz1vHWZ6t4ZcpycrIzGdAhl7O6NuaMzo2op2se0la8lkJLoCqwAFhF8EW9JUl1FNeGLfa4lrs/BTwFweGjJNUjUmnVzcnm4p7Nubhnc/bmHWDioo2MmrOW0XPX8v7sr8gw6N26PoO7Nuasro01SmuaOWwouPs5ZmYErYWTgduB7ma2CZjo7r8sxfutNbMmYSuhCbAuXL4SaBGzXXOCw1QikkRVszIZ2KkRAzs14tcXdWfmqq2MmrOWUXPW8ut35/Lrd+fSsXFNzuramEFd1A+RDhKao9nMmgP9CcLhAqCBu9dNYL/WwDsxHc0PAhtjOprru/tPzKwb8DcOdjSPATqoo1kkOis27SoMiClLN3Eg39UPUUmU9ormWwhCoD+wn+CahYnh/Ux3zy/hTV8h6FRuCKwFfgn8C3iV4NDUcuBSd98Ubv8z4FogDxju7v8u6YMpFETKR0E/xKg5axk/fz079x1QP0QFVtpQeJigc/njgs7hVKNQECl/Rfsh1m7bq36ICuaoBsRLZQoFkWjl5zszV21l9NzgMNO8r7YDqB8ixSkURKRcLN+4i1Fz1zJa/RApTaEgIuVO/RCpS6EgIpFSP0RqUSiISMoo6IcoCIiCfoiW9XPo07o+fdvUo0/r+rRpWIPgUikpawoFEUlZyzfuYvTctUxavJFpyzazaec+ABrWrEqf1vXo26Y+fVrXp0uT2mSqw7pMKBREpEJwdxat38GUJZuZunQTU5ZsYtWW3QDUqppFr1YHQ+K45nXUaV1KCgURqbBWbdnN1CWbmLJ0E1OXbGLBuh0AZGdmcHyLOvRpXZ8+bepzQqt61NYscwlRKIhIpbFp5z6mLd0UtCSWbmbWqq0cyHcyDDofU5u+beoXtiZya1WNutyUpFAQkUpr1748ZizfwpQlQVB8unwze/YHo/C0aViDPq3rhR3Y9WlZP0ed1xz9JDsiIikrJzuL/u0b0r99QwD25eUza/VWpoYh8cHstbw6bSUAjWpVpU+b+vRtHbQkOh9TS1dbF6GWgohUavn5zoJ1Owr7JKYu3cSarXsAqF0ti95hQPRtU49jm9UlOyve1PWVg1oKIpK2MjKMTsfUotMxtbjqpFa4Oys372ZqQb/Ekk18OC+Y2qVqVgY9WtQt7JPo1aoeNaum19dken1aEUl7ZkaL+jm0qJ/DJb2aA7Bhx16mLd1UeCrs42MXku+QmWG0aViD9rk1ad/o4K1tbg1ysivn12fl/FQiIkegYc2qnNO9Ced0bwLAjr15fLpsM9OWbmLuV9v5cu12Rs1dy4H8g4fbm9WtfkhQtG9Uk/a5NSv8eE4KBRGRImpWzWJAx1wGdMwtXLY37wDLNu5i4bodh9wmL9lYeLYTQIMa2bSLCYmCwGhSp1qFOPNJoSAikoCqWZl0bFyLjo1rHbI8P99ZtWX3oWGxfgfvfrGGrbv3F25XIzszCIvcmgdDo1FNWtXPISszdTq3FQoiIkchI+NgH8XpnRsVLnd3NuzYVxgSi8LA+GTRRl6fsapwuyqZRusGNQ45DNUuN7hVzy7/YTwUCiIiSWBm5NaqSm6tqvRr1+CQddv37GfR+p2HtC7mfbWdD2Z/RUy3RST9FgoFEZFyVqtaFXq0qEuPFnUPWb437wBLN+w65DDUwnU7mLR4I3vzDu23uLhnM+65oGuZ16ZQEBFJEVWzMguvqYh1IN9ZtXk3C9dvLwyMJnWrJ6UGhYKISIrLzDBaNsihZYMczujcOKnvlTpd3iIiEjmFgoiIFFIoiIhIIYWCiIgUUiiIiEghhYKIiBRSKIiISCGFgoiIFKrQ03Ga2Xpg2VG8RENgQxmVU5ZU15FRXUdGdR2ZylhXK3fPLW5FhQ6Fo2Vm0w43T2mUVNeRUV1HRnUdmXSrS4ePRESkkEJBREQKpXsoPBV1AYehuo6M6joyquvIpFVdad2nICIih0r3loKIiMRQKIiISKG0CwUze9bM1pnZrKhriWVmLcxsrJnNNbPZZnZr1DUBmFk1M5tiZp+Hdf0q6ppimVmmmc0ws3eirqWAmS01s5lm9pmZTYu6ngJmVtfMXjOzeeG/s34pUFOn8M+p4LbNzIZHXReAmd0W/pufZWavmFm1qGsCMLNbw5pmJ+PPKu36FMxsALADeNHdu0ddTwEzawI0cfdPzawWMB24yN3nRFyXATXcfYeZVQH+C9zq7pOirKuAmf0I6A3UdvcLoq4HglAAert7Sl3wZGYvAB+5+9Nmlg3kuPuWiMsqZGaZwCrgRHc/motSy6KWZgT/1ru6+24zexV4z92fj7iu7sBIoC+wD3gf+IG7Lyir90i7loK7TwA2RV1HUe6+xt0/DR9vB+YCzaKtCjywI3xaJbylxC8JM2sOnA88HXUtqc7MagMDgGcA3H1fKgVC6ExgUdSBECMLqG5mWUAOsDriegC6AJPcfZe75wHjgYvL8g3SLhQqAjNrDfQEJkdcClB4iOYzYB0wyt1Toi5gBPATID/iOopy4D9mNt3Mro+6mFBbYD3wXHi47WkzqxF1UUVcAbwSdREA7r4K+D2wHFgDbHX3/0RbFQCzgAFm1sDMcoDzgBZl+QYKhRRjZjWBfwLD3X1b1PUAuPsBd+8BNAf6hk3YSJnZBcA6d58edS3F6O/uvYBzgRvDQ5ZRywJ6AX9y957ATuCuaEs6KDycdSHwj6hrATCzesBQoA3QFKhhZt+Otipw97nAA8AogkNHnwN5ZfkeCoUUEh6z/yfwsru/HnU9RYWHG8YB50RbCQD9gQvD4/cjgTPM7KVoSwq4++rwfh3wBsHx36itBFbGtPJeIwiJVHEu8Km7r426kNAgYIm7r3f3/cDrwMkR1wSAuz/j7r3cfQDBofAy608AhULKCDt0nwHmuvvDUddTwMxyzaxu+Lg6wX+WeZEWBbj73e7e3N1bExx2+NDdI/8lZ2Y1whMFCA/PDCZo8kfK3b8CVphZp3DRmUCkJzEUcSUpcugotBw4ycxywv+bZxL080XOzBqF9y2BSyjjP7essnyxisDMXgEGAg3NbCXwS3d/JtqqgOCX71XAzPD4PcBP3f296EoCoAnwQnhmSAbwqrunzOmfKagx8EbwPUIW8Dd3fz/akgrdDLwcHqpZDFwTcT0AhMfGzwJuiLqWAu4+2cxeAz4lODwzg9QZ7uKfZtYA2A/c6O6by/LF0+6UVBEROTwdPhIRkUIKBRERKaRQEBGRQgoFEREppFAQEZFCCgVJWWbmZvZQzPMfm9m9ZfTaz5vZN8ritUp4n0vDEUnHFrOuo5m9Z2YLw21eNbPGZjawtKO+mtnw8BRPkVJRKEgq2wtcYmYNoy4kVnjNRqKuA37o7qcXeY1qwLsEw060d/cuwJ+A3KMsbzjB4G0JO8LPI5WcQkFSWR7BBUO3FV1R9Je+me0I7wea2fjwV/eXZvZbM/tWOCfETDNrF/Myg8zso3C7C8L9M83sQTObamZfmNkNMa871sz+Bswspp4rw9efZWYPhMt+AZwC/NnMHiyyyzeBie7+dsECdx/r7odc/Wxm95rZj2OezzKz1uGV0+9aMM/FLDO73MxuIRinZ2xBy8TMBpvZRDP71Mz+EY6tVTDnwy/M7L/ApWZ2i5nNCT/zyBL+XqQSS7srmqXCeRz4wsx+dwT7HE8wxPAmgit3n3b3vhZMXHQzwa9pgNbAaUA7gi/S9sB3CEbE7GNmVYGPzaxgdMy+QHd3XxL7ZmbWlGCQshOAzQQjpF7k7veZ2RnAj9296GQ73QnmzCitc4DV7n5+WEMdd99qwfwSp7v7hrCFdQ8wyN13mtmdwI+A+8LX2OPup4T7rwbauPvegmFNJD2ppSApLRwp9kXgliPYbWo4P8VeYBFQ8KU+kyAICrzq7vnhBCWLgc4EYxV9JxxqZDLQAOgQbj+laCCE+gDjwsHT8oCXCeYuSKaZBC2dB8zsVHffWsw2JwFdCYLtM2AY0Cpm/d9jHn9BMATGtynjUTelYlEoSEUwguDYfOz4/3mE/37DAcuyY9btjXmcH/M8n0Nbx0XHeHHAgJvdvUd4axMzjv7Ow9RnCX6OWLMJWhYlKfycoWoA7v5luP9M4Dfhoari6hoV81m6uvt1MetjP8/5BK2yE4DpFkwsI2lIoSApz903Aa8SBEOBpRz8Uh1KMCPckbrUzDLCfoa2wHzgA+AHFgxjXnCGUEmT0UwGTjOzhmGn7ZUEM2LF8zfgZDM7v2CBmZ1jZscW2W4p4RDXZtaLYHz/gkNWu9z9JYLJYAqGwd4O1AofTwL6h4fFsGDEz45FCzGzDKCFu48lmLSoLlCzhPqlktKvAakoHgJuinn+F+BNM5sCjOHwv+LjmU/w5d0Y+L677zGzpwkOMX0atkDWAxfFexF3X2NmdwNjCX6dv+fub5awz+6wc3uEmY0gGPHyC+BWgkNWBf7JwcNZU4Evw+XHAg+aWX647w/C5U8B/zazNe5+upldDbwS9o9A0MdQ8BoFMoGXzKxOWP8jKThVp5QTjZIqIiKFdPhIREQKKRRERKSQQkFERAopFEREpJBCQURECikURESkkEJBREQK/T+XNozlbMs7dQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(list(wcss.keys()),list(wcss.values()))\n",
    "plt.title('Elbow Method')\n",
    "plt.xlabel('Number of Clusters')\n",
    "plt.ylabel('Within Cluster Sum Of Squares')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import silhouette_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 484,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The silhouette score for 2 clusters is 0.352\n",
      "The silhouette score for 3 clusters is 0.452\n",
      "The silhouette score for 4 clusters is 0.518\n",
      "The silhouette score for 5 clusters is 0.590\n",
      "The silhouette score for 6 clusters is 0.656\n",
      "The silhouette score for 7 clusters is 0.691\n",
      "The silhouette score for 8 clusters is 0.725\n",
      "The silhouette score for 9 clusters is 0.748\n"
     ]
    }
   ],
   "source": [
    "s_score={} \n",
    "for i in range(2,10): # Note that the minimum number of clusters is 2\n",
    "    kmeans=KMeans(n_clusters=i, random_state=0)\n",
    "    kmeans.fit(topic_matrix)\n",
    "    classes=kmeans.predict(topic_matrix)\n",
    "    s_score[i]=(silhouette_score(topic_matrix, classes)) \n",
    "    print(f'The silhouette score for {i} clusters is {s_score[i]:.3f}') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.cluster.hierarchy as hier\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "from sklearn.mixture import GaussianMixture\n",
    "from sklearn.cluster import DBSCAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,12))\n",
    "heirachical_graph=hier.dendrogram(hier.linkage(topic_matrix, method='ward')) #method='ward' uses the Ward variance minimization algorithm\n",
    "plt.title('Hierachical Clustering Graph')\n",
    "plt.xlabel('Flowers')\n",
    "plt.ylabel('Euclidean Distance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 485,
   "metadata": {},
   "outputs": [],
   "source": [
    "topic_matrix_df[\"topic\"] = topic_matrix_df.iloc[:,:].idxmax(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 487,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_df = pd.concat([df_subID,topic_matrix_df], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 536,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>content</th>\n",
       "      <th>subID_x</th>\n",
       "      <th>mainID</th>\n",
       "      <th>artID</th>\n",
       "      <th>hasDollar</th>\n",
       "      <th>hasItem</th>\n",
       "      <th>hasSection</th>\n",
       "      <th>subID_y</th>\n",
       "      <th>topic_0</th>\n",
       "      <th>topic_1</th>\n",
       "      <th>topic_2</th>\n",
       "      <th>topic_3</th>\n",
       "      <th>topic_4</th>\n",
       "      <th>topic_5</th>\n",
       "      <th>topic_6</th>\n",
       "      <th>topic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>2.1 Approval of City Council Minutes.None prov...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>45</td>\n",
       "      <td>0.048355</td>\n",
       "      <td>0.709539</td>\n",
       "      <td>0.048374</td>\n",
       "      <td>0.048354</td>\n",
       "      <td>0.048558</td>\n",
       "      <td>0.048350</td>\n",
       "      <td>0.048470</td>\n",
       "      <td>topic_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>2.2 Final Adoption of Ordinances.20-056 Final ...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>45</td>\n",
       "      <td>0.036673</td>\n",
       "      <td>0.036595</td>\n",
       "      <td>0.036623</td>\n",
       "      <td>0.780106</td>\n",
       "      <td>0.036597</td>\n",
       "      <td>0.036727</td>\n",
       "      <td>0.036677</td>\n",
       "      <td>topic_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>2.3 Approval of Council Committee Minutes.\\nNo...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>45</td>\n",
       "      <td>0.038681</td>\n",
       "      <td>0.767677</td>\n",
       "      <td>0.038668</td>\n",
       "      <td>0.038736</td>\n",
       "      <td>0.038722</td>\n",
       "      <td>0.038696</td>\n",
       "      <td>0.038819</td>\n",
       "      <td>topic_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>2.4 20-005 Mayor and Council Excused Absence R...</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>45</td>\n",
       "      <td>0.824019</td>\n",
       "      <td>0.029332</td>\n",
       "      <td>0.029358</td>\n",
       "      <td>0.029315</td>\n",
       "      <td>0.029344</td>\n",
       "      <td>0.029360</td>\n",
       "      <td>0.029272</td>\n",
       "      <td>topic_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>2.5 City Council Travel Reports.\\nNone provided.</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>45</td>\n",
       "      <td>0.048419</td>\n",
       "      <td>0.048561</td>\n",
       "      <td>0.048446</td>\n",
       "      <td>0.048424</td>\n",
       "      <td>0.709228</td>\n",
       "      <td>0.048416</td>\n",
       "      <td>0.048505</td>\n",
       "      <td>topic_4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1126</th>\n",
       "      <td>1559</td>\n",
       "      <td>3.9 20-1635 Actions Related to Food Distributi...</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>45</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>269</td>\n",
       "      <td>0.022475</td>\n",
       "      <td>0.022427</td>\n",
       "      <td>0.022507</td>\n",
       "      <td>0.022457</td>\n",
       "      <td>0.022565</td>\n",
       "      <td>0.864876</td>\n",
       "      <td>0.022692</td>\n",
       "      <td>topic_5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1127</th>\n",
       "      <td>1560</td>\n",
       "      <td>3.10 20-1684 JPA-issued Bonds for Moderate-Inc...</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>45</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>269</td>\n",
       "      <td>0.867572</td>\n",
       "      <td>0.021996</td>\n",
       "      <td>0.022027</td>\n",
       "      <td>0.021975</td>\n",
       "      <td>0.022210</td>\n",
       "      <td>0.022084</td>\n",
       "      <td>0.022136</td>\n",
       "      <td>topic_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1128</th>\n",
       "      <td>1561</td>\n",
       "      <td>3.11 20-1703 Fee Cap on Food Delivery Services...</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>45</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>269</td>\n",
       "      <td>0.632834</td>\n",
       "      <td>0.020659</td>\n",
       "      <td>0.020629</td>\n",
       "      <td>0.020690</td>\n",
       "      <td>0.066310</td>\n",
       "      <td>0.218015</td>\n",
       "      <td>0.020862</td>\n",
       "      <td>topic_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1129</th>\n",
       "      <td>1562</td>\n",
       "      <td>3.12 20-1702 Inter-Department Transfer of Dist...</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>45</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>269</td>\n",
       "      <td>0.878907</td>\n",
       "      <td>0.019999</td>\n",
       "      <td>0.020048</td>\n",
       "      <td>0.020874</td>\n",
       "      <td>0.020043</td>\n",
       "      <td>0.020077</td>\n",
       "      <td>0.020051</td>\n",
       "      <td>topic_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1130</th>\n",
       "      <td>1564</td>\n",
       "      <td>4.1 20-1638 Update on Fine Amounts for Illegal...</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>269</td>\n",
       "      <td>0.758815</td>\n",
       "      <td>0.024704</td>\n",
       "      <td>0.024777</td>\n",
       "      <td>0.024952</td>\n",
       "      <td>0.024730</td>\n",
       "      <td>0.024843</td>\n",
       "      <td>0.117178</td>\n",
       "      <td>topic_0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1131 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      index                                            content  subID_x  \\\n",
       "0         3  2.1 Approval of City Council Minutes.None prov...        1   \n",
       "1         4  2.2 Final Adoption of Ordinances.20-056 Final ...        2   \n",
       "2         5  2.3 Approval of Council Committee Minutes.\\nNo...        3   \n",
       "3         6  2.4 20-005 Mayor and Council Excused Absence R...        4   \n",
       "4         7   2.5 City Council Travel Reports.\\nNone provided.        5   \n",
       "...     ...                                                ...      ...   \n",
       "1126   1559  3.9 20-1635 Actions Related to Food Distributi...        9   \n",
       "1127   1560  3.10 20-1684 JPA-issued Bonds for Moderate-Inc...       10   \n",
       "1128   1561  3.11 20-1703 Fee Cap on Food Delivery Services...       11   \n",
       "1129   1562  3.12 20-1702 Inter-Department Transfer of Dist...       12   \n",
       "1130   1564  4.1 20-1638 Update on Fine Amounts for Illegal...        1   \n",
       "\n",
       "      mainID  artID  hasDollar  hasItem  hasSection  subID_y   topic_0  \\\n",
       "0          2      1      False    False       False       45  0.048355   \n",
       "1          2      1      False    False        True       45  0.036673   \n",
       "2          2      1      False    False       False       45  0.038681   \n",
       "3          2      1      False     True       False       45  0.824019   \n",
       "4          2      1      False    False       False       45  0.048419   \n",
       "...      ...    ...        ...      ...         ...      ...       ...   \n",
       "1126       3     45       True     True       False      269  0.022475   \n",
       "1127       3     45      False     True       False      269  0.867572   \n",
       "1128       3     45      False     True       False      269  0.632834   \n",
       "1129       3     45       True     True       False      269  0.878907   \n",
       "1130       4     45      False     True        True      269  0.758815   \n",
       "\n",
       "       topic_1   topic_2   topic_3   topic_4   topic_5   topic_6    topic  \n",
       "0     0.709539  0.048374  0.048354  0.048558  0.048350  0.048470  topic_1  \n",
       "1     0.036595  0.036623  0.780106  0.036597  0.036727  0.036677  topic_3  \n",
       "2     0.767677  0.038668  0.038736  0.038722  0.038696  0.038819  topic_1  \n",
       "3     0.029332  0.029358  0.029315  0.029344  0.029360  0.029272  topic_0  \n",
       "4     0.048561  0.048446  0.048424  0.709228  0.048416  0.048505  topic_4  \n",
       "...        ...       ...       ...       ...       ...       ...      ...  \n",
       "1126  0.022427  0.022507  0.022457  0.022565  0.864876  0.022692  topic_5  \n",
       "1127  0.021996  0.022027  0.021975  0.022210  0.022084  0.022136  topic_0  \n",
       "1128  0.020659  0.020629  0.020690  0.066310  0.218015  0.020862  topic_0  \n",
       "1129  0.019999  0.020048  0.020874  0.020043  0.020077  0.020051  topic_0  \n",
       "1130  0.024704  0.024777  0.024952  0.024730  0.024843  0.117178  topic_0  \n",
       "\n",
       "[1131 rows x 17 columns]"
      ]
     },
     "execution_count": 536,
     "metadata": {},
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    }
   ],
   "source": [
    "all_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 480,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.6 20-181 Agreement with Valley Water for the Sale of Pond A-18 for the Shoreline  \n",
      " Levee Project – Deferred. \n",
      "  Adopt a resolution authorizing the City Manager to negotiate and  \n",
      "\n",
      " execute an agreement with Valley Water for the sale of Pond A-18 and  \n",
      "\n",
      " related easements to support the Shoreline Levee Project consistent  \n",
      "\n",
      " with the criteria directed by the Treatment Plant Advisory Committee  \n",
      "\n",
      " and Council in October of 2015 and 2017. \n",
      "\n",
      " CEQA: Determination of Consistency with the San Jose/Santa Clara  \n",
      "\n",
      " Water Pollution Control Plant Master Plan Final EIR (Resolution No.  \n",
      "\n",
      " 76858) and all addenda thereto. File No. PP20-006. Council District 4.  \n",
      "\n",
      " (Environmental Services/Economic Development) \n",
      "\n",
      " \n",
      "\n",
      "Action:  Deferred to 3/3/2020 per Administration. \n",
      "\n",
      " \n",
      "\n",
      " \n",
      " \n",
      "\n",
      " \n",
      "\n",
      "\n",
      "\n",
      " City of San José Page 18  February 25, 2020 \n",
      "\n",
      "==============================================\n",
      "6.1 20-261 Agreement with Valley Water for the Sale of Pond A-18 for the Shoreline Levee Project. - DEFERRED \n",
      "\n",
      "Adopt a resolution authorizing the City Manager to negotiate and  \n",
      "\n",
      "execute an agreement with Valley Water for the sale of Pond A-18 and  \n",
      "\n",
      "related easements to support the Shoreline Levee Project consistent  \n",
      "\n",
      "with the criteria directed by the Treatment Plant Advisory Committee  \n",
      "\n",
      "and Council in October of 2015 and 2017. \n",
      "\n",
      "CEQA: Determination of Consistency with the San José/Santa Clara  \n",
      "\n",
      "Water Pollution Control Plant Master Plan Final EIR (Resolution No.  \n",
      "\n",
      "76858) and all addenda thereto, File No. PP20-006. Council District 4.  \n",
      "\n",
      "(Environmental Services/Economic Development) \n",
      "\n",
      "[Deferred from 2/25/20 - Item 6.6 (20-181) and 3/3/20 - Item 6.2  \n",
      "\n",
      "(20-208)] \n",
      "\n",
      "Deferred to 3/24/20 per Administration \n",
      "\n",
      "==============================================\n",
      "6.1 20-395 Agreement with Valley Water for the Sale of Pond A-18 for the Shoreline Levee  Project.  \n",
      " \n",
      "\n",
      " Recommendation: Adopt a resolution authorizing the City Manager to negotiate an agreement with Valley \n",
      " Water for the sale of Pond A-18 and related easements to support the Shoreline Levee  \n",
      " Project consistent with the criteria directed by the Treatment Plant Advisory Committee  \n",
      "\n",
      " and Council in October of 2015 and 2017. \n",
      "\n",
      " CEQA: Determination of Consistency with the San José/Santa Clara Water Pollution  \n",
      " Control Plant Master Plan Final EIR (Resolution No. 76858) and all addenda thereto,  \n",
      "\n",
      " File No. PP20-006. Council District 4. \n",
      "\n",
      " (Environmental Services/Economic Development) \n",
      " [Deferred from 2/25/2020; 3/3/2020; 3/17/2020; and 3/24/2020 Item 6.1 (20-359)] \n",
      " \n",
      "\n",
      "   Action: Deferred to 4/14/20 per Administration. \n",
      " \n",
      "\n",
      "==============================================\n",
      "6.1 20-359 Agreement with Valley Water for the Sale of Pond A-18 for the  \n",
      " Shoreline Levee Project. - DEFERRED \n",
      "  Adopt a resolution authorizing the City Manager to negotiate and  \n",
      " execute an agreement with Valley Water for the sale of Pond A-18 and  \n",
      " related easements to support the Shoreline Levee Project consistent  \n",
      " with the criteria directed by the Treatment Plant Advisory Committee  \n",
      " and Council in October of 2015 and 2017. \n",
      " CEQA: Determination of Consistency with the San José/Santa Clara  \n",
      " Water Pollution Control Plant Master Plan Final EIR (Resolution No.  \n",
      " 76858) and all addenda thereto, File No. PP20-006. Council District 4.  \n",
      " (Environmental Services/Economic Development) \n",
      " [Deferred from 2/25/2020 Item 6.6 (20-181); 3/3/2020 Item 6.2  \n",
      " (20-208); and 3/17/2020 Item 6.1 (20-261)] \n",
      " Deferred to 4/7/2020 Per Administration \n",
      " \n",
      "\n",
      " \n",
      "\n",
      "==============================================\n",
      "6.2 20-208 Agreement with Valley Water for the Sale of Pond A-18 for the Shoreline   Levee Project. - DEFERRED \n",
      "  Adopt a resolution authorizing the City Manager to negotiate and  \n",
      " execute an agreement with Valley Water for the sale of Pond A-18 and  \n",
      " related easements to support the Shoreline Levee Project consistent  \n",
      " with the criteria directed by the Treatment Plant Advisory Committee  \n",
      " and Council in October of 2015 and 2017. \n",
      " CEQA: Determination of Consistency with the San José/Santa Clara  \n",
      " Water Pollution Control Plant Master Plan Final EIR (Resolution No.  \n",
      " 76858) and all addenda thereto. File No. PP20-006. Council District 4.  \n",
      " (Environmental Services/Economic Development) \n",
      " [Deferred from 2/25/2020 - Item 6.6 (20-181)] \n",
      " Deferred to 3/17/2020 per Administration \n",
      " \n",
      " \n",
      "==============================================\n",
      "2.14 20-247 Pond A-18 Surplus Property. - DEFERRED \n",
      "Adopt a resolution declaring the City’s Pond A-18 property (Assessor’s \n",
      "\n",
      "Parcel Numbers 015-32-042 and 015-32-043) as exempt surplus land  \n",
      "\n",
      "under Cal. Gov. Code §54221 as the land is surplus to the needs of the  \n",
      "\n",
      "City. \n",
      "\n",
      "CEQA: Determination of Consistency with the San José/Santa Clara  \n",
      "\n",
      "Water Pollution Control Plant Master Plan Final EIR (Resolution No.  \n",
      "\n",
      "76858), File No. PP20-006. Council District 4. (Environmental  \n",
      "\n",
      "Services/Economic Development) \n",
      "\n",
      "[Deferred from 2/25/20 - Item 2.8 (20-168); and 3/3/20 - Item 2.9  \n",
      "\n",
      "(20-222)] \n",
      "\n",
      "Deferred to 3/24/20 per Administration \n",
      "\n",
      "\n",
      "\n",
      " City of San José Page 7  March 17, 2020 \n",
      "\n",
      "==============================================\n",
      "2.14 20-355 Pond A-18 Surplus Property. - DEFERRED \n",
      "Adopt a resolution declaring the City’s Pond A-18 property (Assessor’s \n",
      "\n",
      "Parcel Numbers 015-32-042 and 015-32-043) as exempt surplus land  \n",
      "\n",
      "under Cal. Gov. Code §54221 as the land is surplus to the needs of the  \n",
      "\n",
      "City. \n",
      "\n",
      "CEQA: Determination of Consistency with the San José/Santa Clara  \n",
      "\n",
      "Water Pollution Control Plant Master Plan Final EIR (Resolution No.  \n",
      "\n",
      "76858), File No. PP20-006. Council District 4. (Environmental  \n",
      "\n",
      "Services/Economic Development) \n",
      "\n",
      "[Deferred from 2/25/2020 Item 2.8 (20-168); 3/3/2020 Item 2.9  \n",
      "\n",
      "(20-222); and 3/17/2020 Item 2.14 (20-247)] \n",
      "\n",
      "Deferred to 4/7/20 per Administration. \n",
      "\n",
      "\n",
      "\n",
      " \n",
      "\n",
      " City of San José Page 7  March 24, 2020 \n",
      "\n",
      "==============================================\n",
      "2.11 20-386 Pond A-18 Surplus Property. -   \n",
      "\n",
      " Recommendation: Adopt a resolution declaring the City’s Pond A-18 property (Assessor’s Parcel Numbers  \n",
      "\n",
      "  015-32-042 and 015-32-043) as exempt surplus land under Cal. Gov. Code §54221 as  \n",
      "\n",
      "  the land is surplus to the needs of the City. \n",
      "\n",
      " CEQA: Determination of Consistency with the San José/Santa Clara Water Pollution  \n",
      "\n",
      " Control Plant Master Plan Final EIR (Resolution No. 76858), File No. PP20-006.  \n",
      "\n",
      " Council District 4. (Environmental Services/Economic Development) \n",
      "\n",
      " [Deferred from 2/25/2020; 3/3/2020; 3/17/2020; and 3/24/2020 Item 2.14 (20-355)] \n",
      " \n",
      "\n",
      "   Action: Deferred to 4/14/20 per Administration. \n",
      " \n",
      "\n",
      "==============================================\n",
      "2.9 20-222 Pond A-18 Surplus Property. - DEFERRED  \n",
      "  Adopt a resolution declaring the City’s Pond A-18 property (Assessor’s  \n",
      " Parcel Numbers 015-32-042 and 015-32-043) as exempt surplus land  \n",
      " under Cal. Gov. Code §54221 as the land is surplus to the needs of the  \n",
      " City. \n",
      " CEQA: Determination of Consistency with the San José/Santa Clara  \n",
      " Water Pollution Control Plant Master Plan Final EIR (Resolution no.  \n",
      " 76858), File No. PP20-006. Council District 4. (Environmental  \n",
      " Services/Economic Development) \n",
      " [(Deferred from 2/25/20 - Item 2.8 (20-168)] \n",
      " Deferred to 3/17/2020 per Administration \n",
      " \n",
      "==============================================\n",
      "2.8 20-168 Pond A-18 Surplus Property – Deferred. \n",
      "Adopt a resolution declaring the City’s Pond A-18 property (Assessor’s \n",
      "\n",
      "Parcel Numbers 015-32-042 and 015-32-043) as exempt surplus land  \n",
      "\n",
      "under Cal. Gov. Code §54221 as the land is surplus to the needs of the  \n",
      "\n",
      "City. \n",
      "\n",
      "CEQA: Determination of Consistency with the San José/Santa Clara  \n",
      "\n",
      "Water Pollution Control Plant Master Plan Final EIR (Resolution no.  \n",
      "\n",
      "76858), File No. PP20-006. Council District 4. (Environmental  \n",
      "\n",
      "Services/Economic Development) \n",
      "\n",
      "Action:  Deferred to 3/3/2020 per Administration. \n",
      "\n",
      "==============================================\n"
     ]
    }
   ],
   "source": [
    "for i in all_df.sort_values('topic_0', ascending = False).head(10).content:\n",
    "    print(i)\n",
    "    print(\"==============================================\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 497,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>topic</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mainID</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        topic\n",
       "mainID       \n",
       "1           1\n",
       "2           9\n",
       "3           7\n",
       "4           3\n",
       "5           4\n",
       "6           4\n",
       "7           4\n",
       "8           4\n",
       "9           1\n",
       "10          2"
      ]
     },
     "execution_count": 497,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_df[['mainID','topic']].groupby('mainID').nunique('topic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 542,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>topic</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mainID</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        topic\n",
       "mainID       \n",
       "1           2\n",
       "2           7\n",
       "3           6\n",
       "4           4\n",
       "5           4\n",
       "6           3\n",
       "7           4\n",
       "8           4\n",
       "9           1\n",
       "10          2"
      ]
     },
     "execution_count": 542,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda_tfidf = LatentDirichletAllocation(n_components=7, random_state=0)\n",
    "lda_tfidf.fit(word_matrix)\n",
    "topic_matrix = lda_tfidf.transform(word_matrix)\n",
    "topic_matrix_df = pd.DataFrame(topic_matrix).add_prefix('topic_')\n",
    "topic_matrix_df[\"topic\"] = topic_matrix_df.iloc[:,:].idxmax(axis=1)\n",
    "all_df = pd.concat([df_subID,topic_matrix_df], axis=1)\n",
    "all_df[['mainID','topic']].groupby('mainID').nunique('topic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 528,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 topic    17\n",
      "dtype: int64\n",
      "===============================\n",
      "3 topic    17\n",
      "dtype: int64\n",
      "===============================\n",
      "4 topic    22\n",
      "dtype: int64\n",
      "===============================\n",
      "5 topic    22\n",
      "dtype: int64\n",
      "===============================\n",
      "6 topic    26\n",
      "dtype: int64\n",
      "===============================\n",
      "7 topic    30\n",
      "dtype: int64\n",
      "===============================\n",
      "8 topic    30\n",
      "dtype: int64\n",
      "===============================\n",
      "9 topic    30\n",
      "dtype: int64\n",
      "===============================\n",
      "10 topic    31\n",
      "dtype: int64\n",
      "===============================\n"
     ]
    }
   ],
   "source": [
    "for i in range(2,11):\n",
    "    lda_tfidf = LatentDirichletAllocation(n_components=i, random_state=0)\n",
    "    lda_tfidf.fit(word_matrix)\n",
    "    topic_matrix = lda_tfidf.transform(word_matrix)\n",
    "    topic_matrix_df = pd.DataFrame(topic_matrix).add_prefix('topic_')\n",
    "    topic_matrix_df[\"topic\"] = topic_matrix_df.iloc[:,:].idxmax(axis=1)\n",
    "    all_df = pd.concat([df_subID,topic_matrix_df], axis=1)\n",
    "    print(i, all_df[['mainID','topic']].groupby('mainID').nunique('topic').sum()-i)\n",
    "    print(\"===============================\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 517,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/metis/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning:\n",
      "\n",
      "Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(range(1,10),[19,20,26,27,32,37,38,39,41]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gensim\n",
    "from gensim import corpora, models, similarities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-04-27 23:24:13,387 - INFO     - collecting all words and their counts\n",
      "2021-04-27 23:24:13,389 - INFO     - PROGRESS: at sentence #0, processed 0 words, keeping 0 word types\n",
      "2021-04-27 23:24:13,421 - INFO     - collected 7363 word types from a corpus of 146919 raw words and 1131 sentences\n",
      "2021-04-27 23:24:13,421 - INFO     - Loading a fresh vocabulary\n",
      "2021-04-27 23:24:13,436 - INFO     - effective_min_count=1 retains 7363 unique words (100% of original 7363, drops 0)\n",
      "2021-04-27 23:24:13,436 - INFO     - effective_min_count=1 leaves 146919 word corpus (100% of original 146919, drops 0)\n",
      "2021-04-27 23:24:13,452 - INFO     - deleting the raw counts dictionary of 7363 items\n",
      "2021-04-27 23:24:13,453 - INFO     - sample=0.001 downsamples 57 most-common words\n",
      "2021-04-27 23:24:13,453 - INFO     - downsampling leaves estimated 128150 word corpus (87.2% of prior 146919)\n",
      "2021-04-27 23:24:13,463 - INFO     - estimated required memory for 7363 words and 100 dimensions: 9571900 bytes\n",
      "2021-04-27 23:24:13,463 - INFO     - resetting layer weights\n",
      "2021-04-27 23:24:14,482 - INFO     - training model with 2 workers on 7363 vocabulary and 100 features, using sg=1 hs=0 sample=0.001 negative=5 window=5\n",
      "2021-04-27 23:24:14,744 - INFO     - worker thread finished; awaiting finish of 1 more threads\n",
      "2021-04-27 23:24:14,775 - INFO     - worker thread finished; awaiting finish of 0 more threads\n",
      "2021-04-27 23:24:14,775 - INFO     - EPOCH - 1 : training on 146919 raw words (128098 effective words) took 0.3s, 438208 effective words/s\n",
      "2021-04-27 23:24:15,038 - INFO     - worker thread finished; awaiting finish of 1 more threads\n",
      "2021-04-27 23:24:15,069 - INFO     - worker thread finished; awaiting finish of 0 more threads\n",
      "2021-04-27 23:24:15,069 - INFO     - EPOCH - 2 : training on 146919 raw words (128277 effective words) took 0.3s, 440535 effective words/s\n",
      "2021-04-27 23:24:15,331 - INFO     - worker thread finished; awaiting finish of 1 more threads\n",
      "2021-04-27 23:24:15,361 - INFO     - worker thread finished; awaiting finish of 0 more threads\n",
      "2021-04-27 23:24:15,361 - INFO     - EPOCH - 3 : training on 146919 raw words (127961 effective words) took 0.3s, 439794 effective words/s\n",
      "2021-04-27 23:24:15,635 - INFO     - worker thread finished; awaiting finish of 1 more threads\n",
      "2021-04-27 23:24:15,668 - INFO     - worker thread finished; awaiting finish of 0 more threads\n",
      "2021-04-27 23:24:15,668 - INFO     - EPOCH - 4 : training on 146919 raw words (128201 effective words) took 0.3s, 419557 effective words/s\n",
      "2021-04-27 23:24:15,939 - INFO     - worker thread finished; awaiting finish of 1 more threads\n",
      "2021-04-27 23:24:15,971 - INFO     - worker thread finished; awaiting finish of 0 more threads\n",
      "2021-04-27 23:24:15,971 - INFO     - EPOCH - 5 : training on 146919 raw words (127980 effective words) took 0.3s, 425936 effective words/s\n",
      "2021-04-27 23:24:15,972 - INFO     - training on a 734595 raw words (640517 effective words) took 1.5s, 430063 effective words/s\n"
     ]
    }
   ],
   "source": [
    "w2v = gensim.models.Word2Vec(spacy_list, size=100, window=5, min_count=1, workers=2, sg=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('approval', <gensim.models.keyedvectors.Vocab at 0x7fe2d90c9310>),\n",
       " ('city', <gensim.models.keyedvectors.Vocab at 0x7fe2cc5c7f70>),\n",
       " ('council', <gensim.models.keyedvectors.Vocab at 0x7fe26b158a90>),\n",
       " ('minutesnone', <gensim.models.keyedvectors.Vocab at 0x7fe26b158df0>),\n",
       " ('provide', <gensim.models.keyedvectors.Vocab at 0x7fe26b158fa0>),\n",
       " ('final', <gensim.models.keyedvectors.Vocab at 0x7fe26b158fd0>),\n",
       " ('adoption', <gensim.models.keyedvectors.Vocab at 0x7fe26b158f70>),\n",
       " ('ordinances', <gensim.models.keyedvectors.Vocab at 0x7fe26b158c70>),\n",
       " ('ord', <gensim.models.keyedvectors.Vocab at 0x7fe26b158bb0>),\n",
       " ('ordinance', <gensim.models.keyedvectors.Vocab at 0x7fe26b158dc0>),\n",
       " ('san', <gensim.models.keyedvectors.Vocab at 0x7fe26b158e80>),\n",
       " ('jose', <gensim.models.keyedvectors.Vocab at 0x7fe27837b0d0>),\n",
       " ('rezone', <gensim.models.keyedvectors.Vocab at 0x7fe27837b160>),\n",
       " ('certain', <gensim.models.keyedvectors.Vocab at 0x7fe27837b040>),\n",
       " ('real', <gensim.models.keyedvectors.Vocab at 0x7fe27837bc40>),\n",
       " ('property', <gensim.models.keyedvectors.Vocab at 0x7fe27837b3a0>),\n",
       " ('approximately', <gensim.models.keyedvectors.Vocab at 0x7fe27837b430>),\n",
       " ('gross', <gensim.models.keyedvectors.Vocab at 0x7fe27837b310>),\n",
       " ('acre', <gensim.models.keyedvectors.Vocab at 0x7fe27837b4f0>),\n",
       " ('site', <gensim.models.keyedvectors.Vocab at 0x7fe27837bbb0>),\n",
       " ('situate', <gensim.models.keyedvectors.Vocab at 0x7fe27837b250>),\n",
       " ('southeast', <gensim.models.keyedvectors.Vocab at 0x7fe27837ba30>),\n",
       " ('corner', <gensim.models.keyedvectors.Vocab at 0x7fe27837b2b0>),\n",
       " ('oakland', <gensim.models.keyedvectors.Vocab at 0x7fe27837b910>),\n",
       " ('road', <gensim.models.keyedvectors.Vocab at 0x7fe27837b400>),\n",
       " ('corie', <gensim.models.keyedvectors.Vocab at 0x7fe27837b820>),\n",
       " ('court', <gensim.models.keyedvectors.Vocab at 0x7fe27837b970>),\n",
       " ('lipd', <gensim.models.keyedvectors.Vocab at 0x7fe27837b940>),\n",
       " ('light', <gensim.models.keyedvectors.Vocab at 0x7fe27837b880>),\n",
       " ('industrial', <gensim.models.keyedvectors.Vocab at 0x7fe27837b790>),\n",
       " ('planned', <gensim.models.keyedvectors.Vocab at 0x7fe27837b5e0>),\n",
       " ('development', <gensim.models.keyedvectors.Vocab at 0x7fe27837b610>),\n",
       " ('zoning', <gensim.models.keyedvectors.Vocab at 0x7fe27837b520>),\n",
       " ('district', <gensim.models.keyedvectors.Vocab at 0x7fe27837b640>),\n",
       " ('cic', <gensim.models.keyedvectors.Vocab at 0x7fe27837b4c0>),\n",
       " ('combined', <gensim.models.keyedvectors.Vocab at 0x7fe27837b730>),\n",
       " ('commercial', <gensim.models.keyedvectors.Vocab at 0x7fe27837b1f0>),\n",
       " ('pass', <gensim.models.keyedvectors.Vocab at 0x7fe27837b7c0>),\n",
       " ('publication', <gensim.models.keyedvectors.Vocab at 0x7fe27837bd90>),\n",
       " ('item', <gensim.models.keyedvectors.Vocab at 0x7fe27837b550>),\n",
       " ('amending', <gensim.models.keyedvectors.Vocab at 0x7fe27837bf10>),\n",
       " ('title', <gensim.models.keyedvectors.Vocab at 0x7fe27837beb0>),\n",
       " ('code', <gensim.models.keyedvectors.Vocab at 0x7fe27837be20>),\n",
       " ('municipal', <gensim.models.keyedvectors.Vocab at 0x7fe27837bf70>),\n",
       " ('amend', <gensim.models.keyedvectors.Vocab at 0x7fe27837bd00>),\n",
       " ('section', <gensim.models.keyedvectors.Vocab at 0x7fe27837bcd0>),\n",
       " ('repeal', <gensim.models.keyedvectors.Vocab at 0x7fe269c4ae80>),\n",
       " ('add', <gensim.models.keyedvectors.Vocab at 0x7fe26b16b7f0>),\n",
       " ('chapter', <gensim.models.keyedvectors.Vocab at 0x7fe26b16b2e0>),\n",
       " ('modify', <gensim.models.keyedvectors.Vocab at 0x7fe26b16b4f0>),\n",
       " ('current', <gensim.models.keyedvectors.Vocab at 0x7fe26b16b520>),\n",
       " ('accessory', <gensim.models.keyedvectors.Vocab at 0x7fe26b16b670>),\n",
       " ('dwell', <gensim.models.keyedvectors.Vocab at 0x7fe26b16bca0>),\n",
       " ('unit', <gensim.models.keyedvectors.Vocab at 0x7fe26b16b640>),\n",
       " ('adu', <gensim.models.keyedvectors.Vocab at 0x7fe26b16b760>),\n",
       " ('provisions', <gensim.models.keyedvectors.Vocab at 0x7fe26b16b5e0>),\n",
       " ('changes', <gensim.models.keyedvectors.Vocab at 0x7fe26b16ba30>),\n",
       " ('omit', <gensim.models.keyedvectors.Vocab at 0x7fe26b16b6d0>),\n",
       " ('minimum', <gensim.models.keyedvectors.Vocab at 0x7fe26b16b880>),\n",
       " ('lot', <gensim.models.keyedvectors.Vocab at 0x7fe26b16bbe0>),\n",
       " ('size', <gensim.models.keyedvectors.Vocab at 0x7fe26b16b370>),\n",
       " ('coverage', <gensim.models.keyedvectors.Vocab at 0x7fe26b16b4c0>),\n",
       " ('requirements', <gensim.models.keyedvectors.Vocab at 0x7fe26b16b430>),\n",
       " ('technical', <gensim.models.keyedvectors.Vocab at 0x7fe26b16b850>),\n",
       " ('non', <gensim.models.keyedvectors.Vocab at 0x7fe26b16bc10>),\n",
       " ('substantive', <gensim.models.keyedvectors.Vocab at 0x7fe26b16ba60>),\n",
       " ('format', <gensim.models.keyedvectors.Vocab at 0x7fe26b16bac0>),\n",
       " ('change', <gensim.models.keyedvectors.Vocab at 0x7fe26b16bb20>),\n",
       " ('action', <gensim.models.keyedvectors.Vocab at 0x7fe26b16b040>),\n",
       " ('numbers', <gensim.models.keyedvectors.Vocab at 0x7fe26b16bf70>),\n",
       " ('adopt', <gensim.models.keyedvectors.Vocab at 0x7fe26b16beb0>),\n",
       " ('committee', <gensim.models.keyedvectors.Vocab at 0x7fe26b16bfa0>),\n",
       " ('minutes', <gensim.models.keyedvectors.Vocab at 0x7fe26b16bee0>),\n",
       " ('page', <gensim.models.keyedvectors.Vocab at 0x7fe26b16bbb0>),\n",
       " ('january', <gensim.models.keyedvectors.Vocab at 0x7fe26b16bd90>),\n",
       " ('mayor', <gensim.models.keyedvectors.Vocab at 0x7fe26b16b970>),\n",
       " ('excused', <gensim.models.keyedvectors.Vocab at 0x7fe26b16ba00>),\n",
       " ('absence', <gensim.models.keyedvectors.Vocab at 0x7fe26b16bd00>),\n",
       " ('requests', <gensim.models.keyedvectors.Vocab at 0x7fe26b16b790>),\n",
       " ('request', <gensim.models.keyedvectors.Vocab at 0x7fe26b16b5b0>),\n",
       " ('excuse', <gensim.models.keyedvectors.Vocab at 0x7fe26b16bf40>),\n",
       " ('councilmember', <gensim.models.keyedvectors.Vocab at 0x7fe26b16b1f0>),\n",
       " ('jimenez', <gensim.models.keyedvectors.Vocab at 0x7fe26b16bfd0>),\n",
       " ('neighborhood', <gensim.models.keyedvectors.Vocab at 0x7fe2693baca0>),\n",
       " ('services', <gensim.models.keyedvectors.Vocab at 0x7fe2d853f430>),\n",
       " ('education', <gensim.models.keyedvectors.Vocab at 0x7fe28801cb80>),\n",
       " ('december', <gensim.models.keyedvectors.Vocab at 0x7fe2ba75eaf0>),\n",
       " ('illness', <gensim.models.keyedvectors.Vocab at 0x7fe26b158eb0>),\n",
       " ('ceqa', <gensim.models.keyedvectors.Vocab at 0x7fe2ba5053d0>),\n",
       " ('project', <gensim.models.keyedvectors.Vocab at 0x7fe2cce1b5b0>),\n",
       " ('file', <gensim.models.keyedvectors.Vocab at 0x7fe2cce1b640>),\n",
       " ('pp', <gensim.models.keyedvectors.Vocab at 0x7fe2cce1b6d0>),\n",
       " ('organizational', <gensim.models.keyedvectors.Vocab at 0x7fe2cce1b820>),\n",
       " ('administrative', <gensim.models.keyedvectors.Vocab at 0x7fe2cce1b880>),\n",
       " ('activities', <gensim.models.keyedvectors.Vocab at 0x7fe2cce1b940>),\n",
       " ('result', <gensim.models.keyedvectors.Vocab at 0x7fe2cce1b8e0>),\n",
       " ('physical', <gensim.models.keyedvectors.Vocab at 0x7fe2cce1b8b0>),\n",
       " ('environment', <gensim.models.keyedvectors.Vocab at 0x7fe2cce1bfd0>),\n",
       " ('approve', <gensim.models.keyedvectors.Vocab at 0x7fe2cce1bfa0>),\n",
       " ('travel', <gensim.models.keyedvectors.Vocab at 0x7fe2cce1bdc0>),\n",
       " ('report', <gensim.models.keyedvectors.Vocab at 0x7fe2cce1be50>),\n",
       " ('liaison', <gensim.models.keyedvectors.Vocab at 0x7fe2cce1bd90>),\n",
       " ('retirement', <gensim.models.keyedvectors.Vocab at 0x7fe2cce1b9a0>),\n",
       " ('boards', <gensim.models.keyedvectors.Vocab at 0x7fe2cce1bd60>),\n",
       " ('dev', <gensim.models.keyedvectors.Vocab at 0x7fe2cce1bbe0>),\n",
       " ('davis', <gensim.models.keyedvectors.Vocab at 0x7fe2cce1bc40>),\n",
       " ('pull', <gensim.models.keyedvectors.Vocab at 0x7fe2cce1bc70>),\n",
       " ('federated', <gensim.models.keyedvectors.Vocab at 0x7fe2cce1bb80>),\n",
       " ('board', <gensim.models.keyedvectors.Vocab at 0x7fe2cce1ba60>),\n",
       " ('pam', <gensim.models.keyedvectors.Vocab at 0x7fe2cce1ba30>),\n",
       " ('foley', <gensim.models.keyedvectors.Vocab at 0x7fe2cce1bb50>),\n",
       " ('police', <gensim.models.keyedvectors.Vocab at 0x7fe2cce1b9d0>),\n",
       " ('fire', <gensim.models.keyedvectors.Vocab at 0x7fe2cce1b7c0>),\n",
       " ('motion', <gensim.models.keyedvectors.Vocab at 0x7fe2cce1b670>),\n",
       " ('second', <gensim.models.keyedvectors.Vocab at 0x7fe2cce1b550>),\n",
       " ('carry', <gensim.models.keyedvectors.Vocab at 0x7fe2cce1b4f0>),\n",
       " ('unanimously', <gensim.models.keyedvectors.Vocab at 0x7fe2cce1b490>),\n",
       " ('accept', <gensim.models.keyedvectors.Vocab at 0x7fe2cce1b430>),\n",
       " ('amendment', <gensim.models.keyedvectors.Vocab at 0x7fe2cce1b3d0>),\n",
       " ('agreement', <gensim.models.keyedvectors.Vocab at 0x7fe2cce1b370>),\n",
       " ('cruz', <gensim.models.keyedvectors.Vocab at 0x7fe2cce1b310>),\n",
       " ('strategies', <gensim.models.keyedvectors.Vocab at 0x7fe2cce1b2b0>),\n",
       " ('llc', <gensim.models.keyedvectors.Vocab at 0x7fe2cce1b250>),\n",
       " ('state', <gensim.models.keyedvectors.Vocab at 0x7fe2cce1b1f0>),\n",
       " ('legislativeadvocacy', <gensim.models.keyedvectors.Vocab at 0x7fe2cce1b190>),\n",
       " ('resolution', <gensim.models.keyedvectors.Vocab at 0x7fe2cce1b130>),\n",
       " ('authorize', <gensim.models.keyedvectors.Vocab at 0x7fe2cce1b0d0>),\n",
       " ('manager', <gensim.models.keyedvectors.Vocab at 0x7fe2cce1b070>),\n",
       " ('execute', <gensim.models.keyedvectors.Vocab at 0x7fe2cc81bbe0>),\n",
       " ('consultant', <gensim.models.keyedvectors.Vocab at 0x7fe2cc81bbb0>),\n",
       " ('include', <gensim.models.keyedvectors.Vocab at 0x7fe2ce4db040>),\n",
       " ('additional', <gensim.models.keyedvectors.Vocab at 0x7fe2ce4dbfa0>),\n",
       " ('lobbying', <gensim.models.keyedvectors.Vocab at 0x7fe2cd200610>),\n",
       " ('service', <gensim.models.keyedvectors.Vocab at 0x7fe2ce356af0>),\n",
       " ('obligation', <gensim.models.keyedvectors.Vocab at 0x7fe2ce356a00>),\n",
       " ('relate', <gensim.models.keyedvectors.Vocab at 0x7fe2ce356b50>),\n",
       " ('public', <gensim.models.keyedvectors.Vocab at 0x7fe2ce356b20>),\n",
       " ('safety', <gensim.models.keyedvectors.Vocab at 0x7fe2ce356a60>),\n",
       " ('power', <gensim.models.keyedvectors.Vocab at 0x7fe2ce356970>),\n",
       " ('shutoff', <gensim.models.keyedvectors.Vocab at 0x7fe2ce356880>),\n",
       " ('event', <gensim.models.keyedvectors.Vocab at 0x7fe2ce356850>),\n",
       " ('advance', <gensim.models.keyedvectors.Vocab at 0x7fe2ce356760>),\n",
       " ('energy', <gensim.models.keyedvectors.Vocab at 0x7fe2ce356730>),\n",
       " ('resiliency', <gensim.models.keyedvectors.Vocab at 0x7fe2ce3566a0>),\n",
       " ('increase', <gensim.models.keyedvectors.Vocab at 0x7fe2ce356640>),\n",
       " ('total', <gensim.models.keyedvectors.Vocab at 0x7fe2ce356550>),\n",
       " ('compensation', <gensim.models.keyedvectors.Vocab at 0x7fe2ce356580>),\n",
       " ('pay', <gensim.models.keyedvectors.Vocab at 0x7fe2ce356520>),\n",
       " ('legislative', <gensim.models.keyedvectors.Vocab at 0x7fe2ce356340>),\n",
       " ('advocacy', <gensim.models.keyedvectors.Vocab at 0x7fe2ce356490>),\n",
       " ('period', <gensim.models.keyedvectors.Vocab at 0x7fe2ce3563d0>),\n",
       " ('july', <gensim.models.keyedvectors.Vocab at 0x7fe2ce356250>),\n",
       " ('june', <gensim.models.keyedvectors.Vocab at 0x7fe2ce3562b0>),\n",
       " ('month', <gensim.models.keyedvectors.Vocab at 0x7fe2ce3562e0>),\n",
       " ('remain', <gensim.models.keyedvectors.Vocab at 0x7fe2ce356220>),\n",
       " ('contract', <gensim.models.keyedvectors.Vocab at 0x7fe2ce356070>),\n",
       " ('subject', <gensim.models.keyedvectors.Vocab at 0x7fe2ce356040>),\n",
       " ('appropriation', <gensim.models.keyedvectors.Vocab at 0x7fe2ce356160>),\n",
       " ('fund', <gensim.models.keyedvectors.Vocab at 0x7fe2ce3568e0>),\n",
       " ('year', <gensim.models.keyedvectors.Vocab at 0x7fe2ce3568b0>),\n",
       " ('option', <gensim.models.keyedvectors.Vocab at 0x7fe2ce356c10>),\n",
       " ('extend', <gensim.models.keyedvectors.Vocab at 0x7fe2ce356bb0>),\n",
       " ('ongoing', <gensim.models.keyedvectors.Vocab at 0x7fe2ce356d00>),\n",
       " ('design', <gensim.models.keyedvectors.Vocab at 0x7fe2ce356dc0>),\n",
       " ('study', <gensim.models.keyedvectors.Vocab at 0x7fe2ce356df0>),\n",
       " ('inspection', <gensim.models.keyedvectors.Vocab at 0x7fe2ce356e80>),\n",
       " ('professional', <gensim.models.keyedvectors.Vocab at 0x7fe2ce356eb0>),\n",
       " ('commitment', <gensim.models.keyedvectors.Vocab at 0x7fe2ce356ee0>),\n",
       " ('future', <gensim.models.keyedvectors.Vocab at 0x7fe2ce356e50>),\n",
       " ('retroactive', <gensim.models.keyedvectors.Vocab at 0x7fe2ce356f40>),\n",
       " ('eastridge', <gensim.models.keyedvectors.Vocab at 0x7fe2983e48b0>),\n",
       " ('fall', <gensim.models.keyedvectors.Vocab at 0x7fe2983e40d0>),\n",
       " ('fiesta', <gensim.models.keyedvectors.Vocab at 0x7fe2983e43d0>),\n",
       " ('sponsor', <gensim.models.keyedvectors.Vocab at 0x7fe2983e47f0>),\n",
       " ('sponsored', <gensim.models.keyedvectors.Vocab at 0x7fe2983e4160>),\n",
       " ('special', <gensim.models.keyedvectors.Vocab at 0x7fe2983e46d0>),\n",
       " ('expend', <gensim.models.keyedvectors.Vocab at 0x7fe2983e48e0>),\n",
       " ('funds', <gensim.models.keyedvectors.Vocab at 0x7fe2983e4370>),\n",
       " ('donation', <gensim.models.keyedvectors.Vocab at 0x7fe2983e4460>),\n",
       " ('materials', <gensim.models.keyedvectors.Vocab at 0x7fe2983e4730>),\n",
       " ('retroactively', <gensim.models.keyedvectors.Vocab at 0x7fe2983e4700>),\n",
       " ('place', <gensim.models.keyedvectors.Vocab at 0x7fe2983e4640>),\n",
       " ('thursday', <gensim.models.keyedvectors.Vocab at 0x7fe2983e4850>),\n",
       " ('october', <gensim.models.keyedvectors.Vocab at 0x7fe2983e47c0>),\n",
       " ('expenditure', <gensim.models.keyedvectors.Vocab at 0x7fe2983e44c0>),\n",
       " ('individual', <gensim.models.keyedvectors.Vocab at 0x7fe2983e4130>),\n",
       " ('business', <gensim.models.keyedvectors.Vocab at 0x7fe2983e4190>),\n",
       " ('community', <gensim.models.keyedvectors.Vocab at 0x7fe2983e40a0>),\n",
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       " ('sykes', <gensim.models.keyedvectors.Vocab at 0x7fe2983e4520>),\n",
       " ('verbal', <gensim.models.keyedvectors.Vocab at 0x7fe2983e45b0>),\n",
       " ('labor', <gensim.models.keyedvectors.Vocab at 0x7fe2983e4820>),\n",
       " ('negotiation', <gensim.models.keyedvectors.Vocab at 0x7fe2983e41c0>),\n",
       " ('update', <gensim.models.keyedvectors.Vocab at 0x7fe2983e4fd0>),\n",
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       " ('fi', <gensim.models.keyedvectors.Vocab at 0x7fe2983e4f10>),\n",
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       " ('improvements', <gensim.models.keyedvectors.Vocab at 0x7fe2983e4eb0>),\n",
       " ('november', <gensim.models.keyedvectors.Vocab at 0x7fe2983e4cd0>),\n",
       " ('citywide', <gensim.models.keyedvectors.Vocab at 0x7fe2983e4a60>),\n",
       " ('approach', <gensim.models.keyedvectors.Vocab at 0x7fe2983e4d60>),\n",
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       " ('minimize', <gensim.models.keyedvectors.Vocab at 0x7fe2cce21730>),\n",
       " ('digital', <gensim.models.keyedvectors.Vocab at 0x7fe2cce216d0>),\n",
       " ('divide', <gensim.models.keyedvectors.Vocab at 0x7fe2cce215e0>),\n",
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       " ('assessment', <gensim.models.keyedvectors.Vocab at 0x7fe2cce215b0>),\n",
       " ('annual', <gensim.models.keyedvectors.Vocab at 0x7fe2cce213d0>),\n",
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       " ('involve', <gensim.models.keyedvectors.Vocab at 0x7fe2cce21310>),\n",
       " ('referral', <gensim.models.keyedvectors.Vocab at 0x7fe2cce213a0>),\n",
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       " ('establish', <gensim.models.keyedvectors.Vocab at 0x7fe2cce21b20>),\n",
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       " ('determine', <gensim.models.keyedvectors.Vocab at 0x7fe2cce219d0>),\n",
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       " ('rosalynn', <gensim.models.keyedvectors.Vocab at 0x7fe2cce1fcd0>),\n",
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       " ('magdalena', <gensim.models.keyedvectors.Vocab at 0x7fe2cce1f1f0>),\n",
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       " ('government', <gensim.models.keyedvectors.Vocab at 0x7fe2cce33be0>),\n",
       " ('conference', <gensim.models.keyedvectors.Vocab at 0x7fe2cce33c70>),\n",
       " ('legal', <gensim.models.keyedvectors.Vocab at 0x7fe2cce33bb0>),\n",
       " ('counsel', <gensim.models.keyedvectors.Vocab at 0x7fe2cce335b0>),\n",
       " ('anticipate', <gensim.models.keyedvectors.Vocab at 0x7fe2cce33b80>),\n",
       " ('litigation', <gensim.models.keyedvectors.Vocab at 0x7fe2cce337f0>),\n",
       " ('significant', <gensim.models.keyedvectors.Vocab at 0x7fe2cce33910>),\n",
       " ('exposure', <gensim.models.keyedvectors.Vocab at 0x7fe2cce33ac0>),\n",
       " ('number', <gensim.models.keyedvectors.Vocab at 0x7fe2cce337c0>),\n",
       " ('matter', <gensim.models.keyedvectors.Vocab at 0x7fe2cce334f0>),\n",
       " ('discuss', <gensim.models.keyedvectors.Vocab at 0x7fe2cce33430>),\n",
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       " ('case', <gensim.models.keyedvectors.Vocab at 0x7fe2cce33580>),\n",
       " ('estate', <gensim.models.keyedvectors.Vocab at 0x7fe2cce33310>),\n",
       " ('nunez', <gensim.models.keyedvectors.Vocab at 0x7fe2cce33fd0>),\n",
       " ('et', <gensim.models.keyedvectors.Vocab at 0x7fe2cce33f70>),\n",
       " ('al', <gensim.models.keyedvectors.Vocab at 0x7fe2cec64760>),\n",
       " ('names', <gensim.models.keyedvectors.Vocab at 0x7fe2cec64820>),\n",
       " ('partiess', <gensim.models.keyedvectors.Vocab at 0x7fe2cec647c0>),\n",
       " ('michael', <gensim.models.keyedvectors.Vocab at 0x7fe2cec646a0>),\n",
       " ('santos', <gensim.models.keyedvectors.Vocab at 0x7fe2cec64670>),\n",
       " ('anthony', <gensim.models.keyedvectors.Vocab at 0x7fe2cec64580>),\n",
       " ('vizzusi', <gensim.models.keyedvectors.Vocab at 0x7fe2cec64550>),\n",
       " ('tony', <gensim.models.keyedvectors.Vocab at 0x7fe2cec644c0>),\n",
       " ('individually', <gensim.models.keyedvectors.Vocab at 0x7fe2cec64460>),\n",
       " ('successor', <gensim.models.keyedvectors.Vocab at 0x7fe2cec64370>),\n",
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       " ('northern', <gensim.models.keyedvectors.Vocab at 0x7fe2cec64160>),\n",
       " ('division', <gensim.models.keyedvectors.Vocab at 0x7fe2cec642b0>),\n",
       " ('cv', <gensim.models.keyedvectors.Vocab at 0x7fe2cec641f0>),\n",
       " ('lhk', <gensim.models.keyedvectors.Vocab at 0x7fe2cec64040>),\n",
       " ('money', <gensim.models.keyedvectors.Vocab at 0x7fe2cec640a0>),\n",
       " ('relief', <gensim.models.keyedvectors.Vocab at 0x7fe2cec64100>),\n",
       " ('seek', <gensim.models.keyedvectors.Vocab at 0x7fe2cec64700>),\n",
       " ('damage', <gensim.models.keyedvectors.Vocab at 0x7fe2cec646d0>),\n",
       " ('accord', <gensim.models.keyedvectors.Vocab at 0x7fe2cec64880>),\n",
       " ('proof', <gensim.models.keyedvectors.Vocab at 0x7fe2cec648b0>),\n",
       " ('amerisourcebergen', <gensim.models.keyedvectors.Vocab at 0x7fe2cec64940>),\n",
       " ('drug', <gensim.models.keyedvectors.Vocab at 0x7fe2cec649a0>),\n",
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       " ('cardinal', <gensim.models.keyedvectors.Vocab at 0x7fe26c5455e0>),\n",
       " ('inc', <gensim.models.keyedvectors.Vocab at 0x7fe26c545790>),\n",
       " ('mckesson', <gensim.models.keyedvectors.Vocab at 0x7fe26c5456d0>),\n",
       " ('purdue', <gensim.models.keyedvectors.Vocab at 0x7fe26d6871f0>),\n",
       " ('pharma', <gensim.models.keyedvectors.Vocab at 0x7fe26d6877c0>),\n",
       " ('lp', <gensim.models.keyedvectors.Vocab at 0x7fe26c5305e0>),\n",
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       " ('company', <gensim.models.keyedvectors.Vocab at 0x7fe26c5308e0>),\n",
       " ('teva', <gensim.models.keyedvectors.Vocab at 0x7fe2a91901c0>),\n",
       " ('pharmaceutical', <gensim.models.keyedvectors.Vocab at 0x7fe2a9190fa0>),\n",
       " ('industries', <gensim.models.keyedvectors.Vocab at 0x7fe2a9190f40>),\n",
       " ('ltd', <gensim.models.keyedvectors.Vocab at 0x7fe2a9190ee0>),\n",
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       " ('janssen', <gensim.models.keyedvectors.Vocab at 0x7fe2a9190d00>),\n",
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       " ('mcneil', <gensim.models.keyedvectors.Vocab at 0x7fe2a9190c40>),\n",
       " ('jans', <gensim.models.keyedvectors.Vocab at 0x7fe2a9190be0>),\n",
       " ('sen', <gensim.models.keyedvectors.Vocab at 0x7fe2a9190b80>),\n",
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       " ('noramco', <gensim.models.keyedvectors.Vocab at 0x7fe2a9190a60>),\n",
       " ('endo', <gensim.models.keyedvectors.Vocab at 0x7fe2a9190730>),\n",
       " ('solutions', <gensim.models.keyedvectors.Vocab at 0x7fe2a91909d0>),\n",
       " ('allergan', <gensim.models.keyedvectors.Vocab at 0x7fe2a9190970>),\n",
       " ('plc', <gensim.models.keyedvectors.Vocab at 0x7fe2a9190910>),\n",
       " ('fka', <gensim.models.keyedvectors.Vocab at 0x7fe2a91908b0>),\n",
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       " ('laboratories', <gensim.models.keyedvectors.Vocab at 0x7fe2a9190700>),\n",
       " ('mallinckrodt', <gensim.models.keyedvectors.Vocab at 0x7fe2a9190670>),\n",
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       " ('del', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f43f70>),\n",
       " ('roble', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f43fd0>),\n",
       " ('beautification', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f45070>),\n",
       " ('khamisrules', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f450d0>),\n",
       " ('gb', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f45130>),\n",
       " ('clean', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f45190>),\n",
       " ('councils', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f451f0>),\n",
       " ('superseding', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f45250>),\n",
       " ('standing', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f452b0>),\n",
       " ('delete', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f45310>),\n",
       " ('area', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f45370>),\n",
       " ('representative', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f453d0>),\n",
       " ('fees', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f45430>),\n",
       " ('charge', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f45490>),\n",
       " ('reduce', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f454f0>),\n",
       " ('review', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f45550>),\n",
       " ('qualify', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f455b0>),\n",
       " ('forth', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f45610>),\n",
       " ('sections', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f45670>),\n",
       " ('parkland', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f456d0>),\n",
       " ('adjustment', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f45730>),\n",
       " ('rates', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f45790>),\n",
       " ('fare', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f457f0>),\n",
       " ('expansion', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f45850>),\n",
       " ('enforcementparks', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f458b0>),\n",
       " ('recreation', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f45910>),\n",
       " ('negotiations', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f45970>),\n",
       " ('technology', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f459d0>),\n",
       " ('deployment', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f45a30>),\n",
       " ('audit', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f45a90>),\n",
       " ('auditor', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f45af0>),\n",
       " ('appeals', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f45b50>),\n",
       " ('hearing', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f45bb0>),\n",
       " ('interviews', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f45c10>),\n",
       " ('interview', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f45c70>),\n",
       " ('applicant', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f45cd0>),\n",
       " ('member', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f45d30>),\n",
       " ('seat', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f45d90>),\n",
       " ('vacancy', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f45df0>),\n",
       " ('recruitment', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f45e50>),\n",
       " ('bring', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f45eb0>),\n",
       " ('forward', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f45f10>),\n",
       " ('consideration', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f45f70>),\n",
       " ('afternoon', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f45fd0>),\n",
       " ('hook', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f46070>),\n",
       " ('timothy', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f460d0>),\n",
       " ('kenny', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f46130>),\n",
       " ('jorge', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f46190>),\n",
       " ('antoni', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f461f0>),\n",
       " ('garcia', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f46250>),\n",
       " ('vice', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f462b0>),\n",
       " ('chappie', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f46310>),\n",
       " ('sylvia', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f46370>),\n",
       " ('clarification', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f463d0>),\n",
       " ('apply', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f46430>),\n",
       " ('tenure', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f46490>),\n",
       " ('bonds', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f464f0>),\n",
       " ('joint', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f46550>),\n",
       " ('powers', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f465b0>),\n",
       " ('moderate', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f46610>),\n",
       " ('rental', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f46670>),\n",
       " ('housing', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f466d0>),\n",
       " ('agency', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f46730>),\n",
       " ('bond', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f46790>),\n",
       " ('creation', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f467f0>),\n",
       " ('acquisition', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f46850>),\n",
       " ('potential', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f468b0>),\n",
       " ('product', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f46910>),\n",
       " ('restrict', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f46970>),\n",
       " ('develop', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f469d0>),\n",
       " ('parameter', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f46a30>),\n",
       " ('authorization', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f46a90>),\n",
       " ('join', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f46af0>),\n",
       " ('related', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f46b50>),\n",
       " ('preliminary', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f46bb0>),\n",
       " ('direction', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f46c10>),\n",
       " ('eventual', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f46c70>),\n",
       " ('decision', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f46cd0>),\n",
       " ('body', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f46d30>),\n",
       " ('drop', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f46d90>),\n",
       " ('loan', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f46df0>),\n",
       " ('affirmed', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f46e50>),\n",
       " ('alum', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f46eb0>),\n",
       " ('rock', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f46f10>),\n",
       " ('locate', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f46f70>),\n",
       " ('avenue', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f46fd0>),\n",
       " ('asset', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f48070>),\n",
       " ('permanent', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f480d0>),\n",
       " ('affiliated', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f48130>),\n",
       " ('entity', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f48190>),\n",
       " ('capitalization', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f481f0>),\n",
       " ('apartments', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f48250>),\n",
       " ('rent', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f482b0>),\n",
       " ('restricted', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f48310>),\n",
       " ('apartment', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f48370>),\n",
       " ('unrestricted', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f483d0>),\n",
       " ('document', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f48430>),\n",
       " ('financing', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f48490>),\n",
       " ('approving', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f484f0>),\n",
       " ('value', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f48550>),\n",
       " ('ratio', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f485b0>),\n",
       " ('great', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f48610>),\n",
       " ('allow', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f48670>),\n",
       " ('developments', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f486d0>),\n",
       " ('median', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f48730>),\n",
       " ('new', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f48790>),\n",
       " ('tenant', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f487f0>),\n",
       " ('expiration', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f48850>),\n",
       " ('termination', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f488b0>),\n",
       " ('subsidy', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f48910>),\n",
       " ('foreclosure', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f48970>),\n",
       " ('extent', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f489d0>),\n",
       " ('need', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f48a30>),\n",
       " ('feasibility', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f48a90>),\n",
       " ('fiscal', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f48af0>),\n",
       " ('amendments', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f48b50>),\n",
       " ('decrease', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f48bb0>),\n",
       " ('statutorily', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f48c10>),\n",
       " ('senate', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f48c70>),\n",
       " ('bill', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f48cd0>),\n",
       " ('streamline', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f48d30>),\n",
       " ('wiener', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f48d90>),\n",
       " ('zone', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f48df0>),\n",
       " ('housingcity', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f48e50>),\n",
       " ('jackie', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f48eb0>),\n",
       " ('morales', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f48f10>),\n",
       " ('ferrand', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f48f70>),\n",
       " ('deputy', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f48fd0>),\n",
       " ('rachel', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4a070>),\n",
       " ('vanderveen', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4a0d0>),\n",
       " ('actions', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4a130>),\n",
       " ('robert', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4a190>),\n",
       " ('stromberg', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4a1f0>),\n",
       " ('destination', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4a250>),\n",
       " ('ray', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4a2b0>),\n",
       " ('bramston', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4a310>),\n",
       " ('chris', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4a370>),\n",
       " ('dart', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4a3d0>),\n",
       " ('danco', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4a430>),\n",
       " ('communities', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4a490>),\n",
       " ('hear', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4a4f0>),\n",
       " ('jointly', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4a550>),\n",
       " ('items', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4a5b0>),\n",
       " ('affiliate', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4a610>),\n",
       " ('west', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4a670>),\n",
       " ('carlos', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4a6d0>),\n",
       " ('supportive', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4a730>),\n",
       " ('extremely', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4a790>),\n",
       " ('unrestricte', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4a7f0>),\n",
       " ('threshold', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4a850>),\n",
       " ('loans', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4a8b0>),\n",
       " ('grants', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4a910>),\n",
       " ('resources', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4a970>),\n",
       " ('ministerial', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4a9d0>),\n",
       " ('ground', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4aa30>),\n",
       " ('leasing', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4aa90>),\n",
       " ('owned', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4aaf0>),\n",
       " ('arya', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4ab50>),\n",
       " ('located', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4abb0>),\n",
       " ('almaden', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4ac10>),\n",
       " ('boulevard', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4ac70>),\n",
       " ('hold', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4acd0>),\n",
       " ('finding', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4ad30>),\n",
       " ('disposition', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4ad90>),\n",
       " ('balbach', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4adf0>),\n",
       " ('ave', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4ae50>),\n",
       " ('lease', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4aeb0>),\n",
       " ('satellite', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4af10>),\n",
       " ('associates', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4af70>),\n",
       " ('leasehold', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4afd0>),\n",
       " ('restriction', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4b070>),\n",
       " ('extension', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4b0d0>),\n",
       " ('connection', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4b130>),\n",
       " ('rider', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4b190>),\n",
       " ('credit', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4b1f0>),\n",
       " ('allocation', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4b250>),\n",
       " ('designee', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4b2b0>),\n",
       " ('application', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4b310>),\n",
       " ('infill', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4b370>),\n",
       " ('grant', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4b3d0>),\n",
       " ('qualifying', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4b430>),\n",
       " ('areas', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4b490>),\n",
       " ('addendum', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4b4f0>),\n",
       " ('downtown', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4b550>),\n",
       " ('environmental', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4b5b0>),\n",
       " ('eir', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4b610>),\n",
       " ('envision', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4b670>),\n",
       " ('supplemental', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4b6d0>),\n",
       " ('addenda', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4b730>),\n",
       " ('thereto', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4b790>),\n",
       " ('sp', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4b7f0>),\n",
       " ('airport', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4b850>),\n",
       " ('master', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4b8b0>),\n",
       " ('upcome', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4b910>),\n",
       " ('norman', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4b970>),\n",
       " ('mineta', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4b9d0>),\n",
       " ('international', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4ba30>),\n",
       " ('aiken', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4ba90>),\n",
       " ('judy', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4baf0>),\n",
       " ('ross', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4bb50>),\n",
       " ('scott', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4bbb0>),\n",
       " ('mace', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4bc10>),\n",
       " ('liccardos', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4bc70>),\n",
       " ('memo', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4bcd0>),\n",
       " ('partially', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4bd30>),\n",
       " ('mitigate', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4bd90>),\n",
       " ('greenhouse', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4bdf0>),\n",
       " ('gas', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4be50>),\n",
       " ('emission', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4beb0>),\n",
       " ('air', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4bf10>),\n",
       " ('traffic', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4bf70>),\n",
       " ('sjc', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4bfd0>),\n",
       " ('proposal', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4d070>),\n",
       " ('reduction', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4d0d0>),\n",
       " ('airline', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4d130>),\n",
       " ('hybrid', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4d190>),\n",
       " ('electric', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4d1f0>),\n",
       " ('fuel', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4d250>),\n",
       " ('airplane', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4d2b0>),\n",
       " ('vehicle', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4d310>),\n",
       " ('mobility', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4d370>),\n",
       " ('citys', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4d3d0>),\n",
       " ('roadmap', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4d430>),\n",
       " ('jessica', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4d490>),\n",
       " ('ramses', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4d4f0>),\n",
       " ('emerging', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4d550>),\n",
       " ('lead', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4d5b0>),\n",
       " ('laura', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4d610>),\n",
       " ('stuchinsky', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4d670>),\n",
       " ('draft', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4d6d0>),\n",
       " ('carshare', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4d730>),\n",
       " ('flexibility', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4d790>),\n",
       " ('residential', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4d7f0>),\n",
       " ('parking', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4d850>),\n",
       " ('structure', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4d8b0>),\n",
       " ('vendor', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4d910>),\n",
       " ('requirement', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4d970>),\n",
       " ('like', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4d9d0>),\n",
       " ('serve', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4da30>),\n",
       " ('comply', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4da90>),\n",
       " ('discount', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4daf0>),\n",
       " ('membership', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4db50>),\n",
       " ('federal', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4dbb0>),\n",
       " ('concern', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4dc10>),\n",
       " ('iii', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4dc70>),\n",
       " ('outreach', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4dcd0>),\n",
       " ('marketing', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4dd30>),\n",
       " ('material', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4dd90>),\n",
       " ('spanish', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4ddf0>),\n",
       " ('vietnamese', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4de50>),\n",
       " ('multi', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4deb0>),\n",
       " ('explore', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4df10>),\n",
       " ('incentivize', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4df70>),\n",
       " ('ev', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4dfd0>),\n",
       " ('thing', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4f070>),\n",
       " ('ensure', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4f0d0>),\n",
       " ('access', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4f130>),\n",
       " ('charging', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4f190>),\n",
       " ('identify', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4f1f0>),\n",
       " ('pursue', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4f250>),\n",
       " ('pilot', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4f2b0>),\n",
       " ('manufacturer', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4f310>),\n",
       " ('developer', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4f370>),\n",
       " ('operate', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4f3d0>),\n",
       " ('carsharing', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4f430>),\n",
       " ('green', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4f490>),\n",
       " ('fleet', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4f4f0>),\n",
       " ('electrification', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4f550>),\n",
       " ('emergency', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4f5b0>),\n",
       " ('align', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4f610>),\n",
       " ('climate', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4f670>),\n",
       " ('goal', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4f6d0>),\n",
       " ('electrify', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4f730>),\n",
       " ('technologically', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4f790>),\n",
       " ('feasible', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4f7f0>),\n",
       " ('lifecycle', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4f850>),\n",
       " ('replace', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4f8b0>),\n",
       " ('type', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4f910>),\n",
       " ('commercially', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4f970>),\n",
       " ('available', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4f9d0>),\n",
       " ('battery', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4fa30>),\n",
       " ('school', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4fa90>),\n",
       " ('bus', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4faf0>),\n",
       " ('demonstration', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4fb50>),\n",
       " ('partnership', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4fbb0>),\n",
       " ('prospect', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4fc10>),\n",
       " ('dot', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4fc70>),\n",
       " ('backup', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4fcd0>),\n",
       " ('critical', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4fd30>),\n",
       " ('facility', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4fd90>),\n",
       " ('relevant', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4fdf0>),\n",
       " ('culturally', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4fe50>),\n",
       " ('competent', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4feb0>),\n",
       " ('specific', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4ff10>),\n",
       " ('focus', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4ff70>),\n",
       " ('justice', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f4ffd0>),\n",
       " ('color', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f50070>),\n",
       " ('monolingual', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f500d0>),\n",
       " ('leverage', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f50130>),\n",
       " ('underway', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f50190>),\n",
       " ('counseling', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f501f0>),\n",
       " ('base', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f50250>),\n",
       " ('promote', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f502b0>),\n",
       " ('empowerment', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f50310>),\n",
       " ('hate', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f50370>),\n",
       " ('crime', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f503d0>),\n",
       " ('statistics', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f50430>),\n",
       " ('statistic', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f50490>),\n",
       " ('departments', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f504f0>),\n",
       " ('address', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f50550>),\n",
       " ('chief', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f505b0>),\n",
       " ('eddie', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f50610>),\n",
       " ('lt', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f50670>),\n",
       " ('brian', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f506d0>),\n",
       " ('shab', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f50730>),\n",
       " ('represent', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f50790>),\n",
       " ('horseshoe', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f507f0>),\n",
       " ('steven', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f50850>),\n",
       " ('romero', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f508b0>),\n",
       " ('murder', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f50910>),\n",
       " ('cycle', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f50970>),\n",
       " ('marketsan', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f509d0>),\n",
       " ('pedro', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f50a30>),\n",
       " ('square', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f50a90>),\n",
       " ('garage', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f50af0>),\n",
       " ('certify', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f50b50>),\n",
       " ('hotel', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f50bb0>),\n",
       " ('associated', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f50c10>),\n",
       " ('mitigation', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f50c70>),\n",
       " ('monitoring', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f50cd0>),\n",
       " ('reporting', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f50d30>),\n",
       " ('accordance', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f50d90>),\n",
       " ('quality', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f50df0>),\n",
       " ('act', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f50e50>),\n",
       " ('story', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f50eb0>),\n",
       " ('guest', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f50f10>),\n",
       " ('foot', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f50f70>),\n",
       " ('ancillary', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f50fd0>),\n",
       " ('space', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f52070>),\n",
       " ('arrangement', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f520d0>),\n",
       " ('thirty', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f52130>),\n",
       " ('rosalyn', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f52190>),\n",
       " ('planner', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f521f0>),\n",
       " ('patrick', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f52250>),\n",
       " ('kelly', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f522b0>),\n",
       " ('works', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f52310>),\n",
       " ('doan', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f52370>),\n",
       " ('mark', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f523d0>),\n",
       " ('tersini', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f52430>),\n",
       " ('kt', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f52490>),\n",
       " ('urban', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f524f0>),\n",
       " ('almadencorner', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f52550>),\n",
       " ('walter', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f525b0>),\n",
       " ('soellner', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f52610>),\n",
       " ('pac', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f52670>),\n",
       " ('ernest', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f526d0>),\n",
       " ('madison', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f52730>),\n",
       " ('de', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f52790>),\n",
       " ('anza', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f527f0>),\n",
       " ('mitchell', <gensim.models.keyedvectors.Vocab at 0x7fe2a9f52850>),\n",
       " ...]"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(w2v.wv.vocab.items())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 569,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 569,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'action' in [i for j in spacy_list for i in j]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = list(w2v.wv.vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 563,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('actions', 0.8337326049804688),\n",
       " ('sheltering', 0.8117275238037109),\n",
       " ('find', 0.8074434995651245),\n",
       " ('commencement', 0.8002893924713135),\n",
       " ('preventioncontd', 0.7777178883552551),\n",
       " ('budgetary', 0.777370810508728),\n",
       " ('decline', 0.7691840529441833),\n",
       " ('impacts', 0.7663147449493408),\n",
       " ('constitute', 0.7649955749511719),\n",
       " ('shutoff', 0.7595281600952148),\n",
       " ('guidance', 0.7592676877975464),\n",
       " ('impose', 0.7575991153717041),\n",
       " ('pandemic', 0.7572484612464905),\n",
       " ('evictions', 0.7572098970413208),\n",
       " ('collective', 0.7570220232009888),\n",
       " ('following', 0.7520774602890015),\n",
       " ('deadline', 0.7490081191062927),\n",
       " ('expire', 0.7479731440544128),\n",
       " ('feedback', 0.7459443211555481),\n",
       " ('shelters', 0.7457842826843262)]"
      ]
     },
     "execution_count": 563,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w2v.wv.most_similar(['covid','action'] ,topn=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3490647"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w2v.wv.similarity('covid','action')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 565,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "topic_0    13.690499\n",
       "topic_1     1.656012\n",
       "topic_2    11.367443\n",
       "topic_3    12.617551\n",
       "topic_4     2.817550\n",
       "topic_5    18.408643\n",
       "topic_6     1.712941\n",
       "Name: action, dtype: float64"
      ]
     },
     "execution_count": 565,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_topic_matrix_df.loc['action']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 549,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.1 20-544 Report of the City Manager, David Sykes.  \n",
      "\n",
      " Recommendation: (a) City Manager’s COVID-19 Update (Verbal Report). \n",
      "\n",
      " (b) City Manager’s Report on Other City Matters (Verbal Report) \n",
      "\n",
      " Heard before Consent \n",
      " \n",
      "\n",
      "   City Manager David Sykes provided updates regarding the City’s response to  \n",
      "\n",
      "   COVID-19. The City Manager recognized an extra-ordinary individual, Reggie Roberts,  \n",
      "\n",
      "   from Public Works Department. Reggie has worked for the City of San Jose since 1999,  \n",
      "\n",
      "   when he started his career in the City's warehouse and transitioned into the Mailroom. In  \n",
      "\n",
      "   addition to always having a good attitude and great sense of humor, Reggie’s work in  \n",
      "\n",
      "   processing and receiving the City's mail has been vital to organizational operations, in  \n",
      "\n",
      "   terms of keeping the City functions going. Furthermore, Reggie has undertaken  \n",
      "\n",
      "   additional duties such as taking on the bids that come in from vendors so the City can  \n",
      "\n",
      "   accept and process those bids. \n",
      " \n",
      "\n",
      "   Deputy City Manager Kip Harkness, and the leadership team from the City’s  \n",
      "\n",
      "   Emergency Operations Center provided updates, including efforts to expand testing and  \n",
      "\n",
      "   tracing of COVID-19 cases, the City’s road to financial recovery, and progress in  \n",
      "\n",
      "   supporting development in the City. The key objective is to create a one-team approach. \n",
      " \n",
      "\n",
      "(Item Continued on the Next Page) \n",
      "\n",
      "\n",
      "\n",
      " \n",
      " City Council Draft Minutes/Synopsis May 19, 2020 \n",
      "\n",
      " City of San José Page 9  May 19, 2020 \n",
      "\n",
      " \n",
      "3.1 20-544 Report of the City Manager, David Sykes. (Cont’d)  \n",
      "\n",
      "   Free COVID-19 testing is now available for all residents, asymptomatic or otherwise, at  \n",
      "\n",
      "   PAL Stadium in East San José and the Santa Clara County Fairgrounds. Testing sites  \n",
      "\n",
      "   are open Monday through Friday from 9:00 AM. to 3:00 PM. at both locations and  \n",
      "\n",
      "   residents will need to schedule an appointment to get tested. \n",
      " \n",
      "\n",
      "   Public Comments: Offering comments on the Report of the City Manager were:  Jeff  \n",
      "\n",
      "   Buchannan, (Working Partnerships USA); Blair Beekman, Last four digits of telephone  \n",
      "\n",
      "   #: 2896, (Mike Mattingly); Last four digits of telephone #: 6137, Un-named female  \n",
      "\n",
      "   speaker, Robert Aguirre, and Amy Alvarez,  \n",
      " \n",
      "\n",
      "   The City Manager’s updates and public comments inspired widespread interests. \n",
      " \n",
      "\n",
      "   Action: The motion by Councilmember Maya Esparza to not limit question time for  \n",
      "\n",
      "   Councilmembers on corvid-19 items while the public health order is in place, seconded  \n",
      "\n",
      "   by Councilmember Sylvia Arenas, was not unanimously carried.  \n",
      "\n",
      "   (5-0-6. Noes: Liccardo, Jones, Diep, Davis, Foley, Khamis). \n",
      " \n",
      "\n",
      "   Action: The substitute-motion by Councilmember Dev Davis, to limit Council  \n",
      "\n",
      "   discussion to 10 minutes per Councilmember, seconded by Councilmember Pam Foley, \n",
      "\n",
      "    was unanimously adopted. \n",
      "\n",
      "   (6-0-5. Noes: Jimenez, Peralez, Carrasco, Esparza, Arenas). \n",
      " \n",
      "\n",
      "   Council subsequently had a productive and extensive debate.  \n",
      " \n",
      "\n",
      "   Action: No motion was required. Council accepted the verbal report.  \n",
      " \n",
      "\n",
      "===============================\n",
      "3.1  Report of the City Manager, David Sykes (Verbal Report)  \n",
      "\n",
      " 20-737 (a) City Manager’s COVID-19 Update (Verbal Report)  \n",
      "\n",
      " (b) City Manager’s Report on Other City Matters (Verbal Report) \n",
      "\n",
      " Heard Before Consent \n",
      "\n",
      " \n",
      "\n",
      "Mayor Sam Liccardo asked that Council discussion be limited to 15 minutes per speaker. \n",
      "\n",
      " \n",
      "\n",
      "Motion: Councilmember Maya Esparza moved that no time limit be enforced, but that Council be as \n",
      "\n",
      "brief as they can. Councilmember Sylvia Arenas seconded the motion. \n",
      "\n",
      " \n",
      "\n",
      "Action: On a call for the question, the motion carried, with no time limit determined for Item 3.1.   \n",
      "\n",
      "(6-5. Noes: Diep, Davis Foley, Khamis, Liccardo.) \n",
      "\n",
      " \n",
      "\n",
      "City Manager, David Sykes and Chief of Staff, Office of the City Manager, Emergency Operations \n",
      "\n",
      "Center Director, Lee Wilcox, offered opening comments.  Director of Intergovernmental Relations, \n",
      "\n",
      "Bena Chang offered a presentation on financial recovery; Deputy City Manager/EOC Operations \n",
      "\n",
      "Section Coordinator Jim Ortbal spoke on sheltering the homeless and the BeautifySJ response.  Director \n",
      "\n",
      "of Housing Jacky Morales-Ferrand and Deputy Director, Housing, Ragan Henninger, spoke on the \n",
      "\n",
      "shelter crisis response and the use of trailers for sheltering high-risk, vulnerable individuals in the Kelly \n",
      "\n",
      "Park lot.  \n",
      "\n",
      " \n",
      "\n",
      "Public comments: Twenty-two members of the public recognized a variety of concerns, including \n",
      "\n",
      "addressing the failure of using trailers at the Kelly Park lot, the need for better control of natural \n",
      "\n",
      "disasters at a local level, illegal dumping, concerns regarding abating the homeless, deficiencies of \n",
      "\n",
      "using the COVID hotline number, and the need for greater communication and outreach.  \n",
      "\n",
      " \n",
      "\n",
      "Council recessed at 12:05 p.m., and returned at 1 p.m. with all Council members present.  \n",
      "\n",
      " \n",
      "\n",
      "Council discussion continued, and staff responded to questions. \n",
      "\n",
      " \n",
      "\n",
      "Action: No action required. \n",
      "\n",
      " \n",
      "\n",
      " \n",
      "\n",
      "===============================\n",
      "3.1  Report of the City Manager, David Sykes (Verbal Report)  \n",
      "\n",
      " 20-737 (a) City Manager’s COVID-19 Update (Verbal Report)  \n",
      "\n",
      " (b) City Manager’s Report on Other City Matters (Verbal Report) \n",
      "\n",
      " Heard Before Consent \n",
      "\n",
      " \n",
      "\n",
      "Mayor Sam Liccardo asked that Council discussion be limited to 15 minutes per speaker. \n",
      "\n",
      " \n",
      "\n",
      "Motion: Councilmember Maya Esparza moved that no time limit be enforced, but that Council be as \n",
      "\n",
      "brief as they can. Councilmember Sylvia Arenas seconded the motion. \n",
      "\n",
      " \n",
      "\n",
      "Action: On a call for the question, the motion carried, with no time limit determined for Item 3.1.   \n",
      "\n",
      "(6-5. Noes: Diep, Davis Foley, Khamis, Liccardo.) \n",
      "\n",
      " \n",
      "\n",
      "City Manager, David Sykes and Chief of Staff, Office of the City Manager, Emergency Operations \n",
      "\n",
      "Center Director, Lee Wilcox, offered opening comments.  Director of Intergovernmental Relations, \n",
      "\n",
      "Bena Chang offered a presentation on financial recovery; Deputy City Manager/EOC Operations \n",
      "\n",
      "Section Coordinator Jim Ortbal spoke on sheltering the homeless and the BeautifySJ response.  Director \n",
      "\n",
      "of Housing Jacky Morales-Ferrand and Deputy Director, Housing, Ragan Henninger, spoke on the \n",
      "\n",
      "shelter crisis response and the use of trailers for sheltering high-risk, vulnerable individuals in the Kelly \n",
      "\n",
      "Park lot.  \n",
      "\n",
      " \n",
      "\n",
      "Public comments: Twenty-two members of the public recognized a variety of concerns, including \n",
      "\n",
      "addressing the failure of using trailers at the Kelly Park lot, the need for better control of natural \n",
      "\n",
      "disasters at a local level, illegal dumping, concerns regarding abating the homeless, deficiencies of \n",
      "\n",
      "using the COVID hotline number, and the need for greater communication and outreach.  \n",
      "\n",
      " \n",
      "\n",
      "Council recessed at 12:05 p.m., and returned at 1 p.m. with all Council members present.  \n",
      "\n",
      " \n",
      "\n",
      "Council discussion continued, and staff responded to questions. \n",
      "\n",
      " \n",
      "\n",
      "Action: No action required. \n",
      "\n",
      " \n",
      "\n",
      " \n",
      "\n",
      "===============================\n",
      "3.1 20-497 Report of the City Manager, David Sykes.  \n",
      " Recommendation: (a) City Manager’s COVID-19 Update (Verbal Report). \n",
      " (b) City Manager’s Report on Other City Matters (Verbal Report) \n",
      " Heard before Consent \n",
      " \n",
      "   Mayor Sam Liccardo noted that we’re going have Public Comments on Item 3.1,  \n",
      "   starting today \n",
      " \n",
      "   City Manager David Sykes provided updates regarding the City’s response to  \n",
      "   COVID-19, and its community impacts.  \n",
      " \n",
      "   Deputy City Manager Kip Harkness, and the leadership team from the City’s  \n",
      "   Emergency Operations Center provided updates on the City’s Roadmap through the  \n",
      "   epidemic, City Response Highest Priority Actions, including efforts to support people  \n",
      "   experiencing homelessness during the crisis and provide food and household supplies to  \n",
      "   those in need. The report highlighted the County’s revised health order and gave an  \n",
      "   overview of the resumption of construction activities and in-person inspection services  \n",
      "   in San José. \n",
      " \n",
      "   Public Comments: Offering comments on the Report of the City Manager were: Blair  \n",
      "   Beekman, and Wendi Mahaney, (Early Learning Deputy Chief at FIRST 5 Santa Clara  \n",
      "   County).  \n",
      " \n",
      "   Action: No motion was required. Council accepted the verbal report.  \n",
      " \n",
      "===============================\n",
      "3.1 20-392 Report of the City Manager, David Sykes.  \n",
      "\n",
      " Recommendation: (a) City Manager’s COVID-19 Update (Verbal Report). \n",
      "\n",
      " (b) City Manager’s Report on Other City Matters (Verbal Report) \n",
      "\n",
      " Heard before Consent \n",
      " \n",
      "\n",
      "   City Manager David Sykes provided updates on the impacts of COVID-19 and its  \n",
      "\n",
      "   budget impacts. The community will receive an invitation to a virtual town hall in the  \n",
      "\n",
      "   next week where additional updates will be provided. The City Manager will field  \n",
      "\n",
      "   questions from community members. Input from community stakeholders are welcome. \n",
      " \n",
      "\n",
      "   Deputy City Manager Kip Harkness, and the leadership team from the City’s  \n",
      "\n",
      "   Emergency Operations Center provided updates on the impacts of COVID-19, including  \n",
      "\n",
      "   information on the City’s Roadmap for the duration of the pandemic, local assistance  \n",
      "\n",
      "   programs, State, and Federal funding resources, as well as a synopsis of the City’s  \n",
      "\n",
      "   communications strategy and public education campaign. \n",
      " \n",
      "\n",
      "   Action: No motion was required. Council accepted the verbal report. \n",
      "\n",
      "\n",
      "\n",
      " \n",
      " City Council Draft Minutes/Synopsis April 7, 2020 \n",
      "\n",
      " City of San José Page 10  April 7, 2020 \n",
      "\n",
      " \n",
      "\n",
      "===============================\n",
      "2.5  City Council Travel Reports. \n",
      "Mayor Sam Liccardo reported on his attendance at a meeting in Sacramento with the Governor and other \n",
      "\n",
      "legislative leaders where, among other things, they advocated for funding for the unhoused and resistance \n",
      "\n",
      "to certain expressions of the Governor’s budget message on allocation of City funds. \n",
      "\n",
      "Action:  Upon motion by Councilmember Johnny Khamis, seconded by Councilmember Dev Davis and \n",
      "\n",
      "carried unanimously, the report was accepted. (11-0.)   \n",
      "\n",
      "===============================\n",
      "3.1  Report of the City Manager, David Sykes (Verbal Report)  \n",
      "\n",
      " 20-1438 (a) City Manager’s COVID 19 Update (Verbal Report)  \n",
      "\n",
      " (b) City Manager’s Report on Other City Matters (Verbal Report) \n",
      "\n",
      " \n",
      "\n",
      "Deputy City Manager/Director of Economic Development Kim Walesh honored members of the Census \n",
      "\n",
      "Team as “Unsung Heroes” for their amazing efforts and record-breaking results during the 2020 Census. \n",
      "\n",
      " \n",
      "\n",
      "Chief of Staff, Office of the City Manager, Lee Wilcox, offered the EOC update, and Police Lt. Steve \n",
      "\n",
      "Donohue provided crime statistics.  Colin Heyne, Emergency Public Information Officer; Chelsea \n",
      "\n",
      "Palacio, Emergency Public Information Officer - Creative Manager; and Carolina Camarena, \n",
      "\n",
      "Emergency Public Information Officer provided the Strategic Communications Plan. The EOC Budget \n",
      "\n",
      "Rebalancing Update and 2021 Funding Strategies were provided by Assistant Director of Finance, Luz \n",
      "\n",
      "Confresi-Howe and Budget Director Jim Shannon.  City Manager, David Sykes concluded with a \n",
      "\n",
      "discussion of the City’s Road Map, indicating a special meeting could be convened in February 2021 to \n",
      "\n",
      "further define these priorities. \n",
      "\n",
      "Extensive Council discussion followed, and staff responded to questions. \n",
      "\n",
      " \n",
      "\n",
      "===============================\n",
      "3.1 20-530 Report of the City Manager, David Sykes (Verbal Report)  (a) City Manager’s COVID-19 Update (Verbal Report)  \n",
      "\n",
      " (b) City Manager’s Report on Other City Matters (Verbal Report) \n",
      "\n",
      " \n",
      "\n",
      "Opening comments were provided by Chief of Staff, Office of the City Manager, Lee Wilcox; Deputy \n",
      "\n",
      "City Manager, Kip Harkness, presented an update of COVID-19 developments; Director of the Office of \n",
      "\n",
      "Emergency Management, Ray Riordan spoke on funding and cost recovery.  Assistant Director of \n",
      "\n",
      "Finance, Luz Cofresi-Howe continued with ‘waterfall of funding’ information.  Staff responded to an \n",
      "\n",
      "extensive line of questioning.  \n",
      "\n",
      " \n",
      "\n",
      "The Council Meeting was recessed at 12:30 p.m., and reconvened at 1:30 p.m. with all members present. \n",
      "\n",
      " \n",
      "===============================\n",
      "3.1 20-453 Report of the City Manager, David Sykes (Verbal Report).  \n",
      "\n",
      " Recommendation: (a) City Manager’s COVID-19 Update (Verbal Report). \n",
      "\n",
      " (b) City Manager’s Report on Other City Matters (Verbal Report) \n",
      "\n",
      " Heard before Consent \n",
      " \n",
      "\n",
      "   City Manager David Sykes provided updates regarding the City’s response to  \n",
      "\n",
      "   COVID-19 and emergency housing, food distribution, and the Local Assistance Center.  \n",
      "\n",
      "   The City Manager is in the process of holding a series of virtual town hall meetings with  \n",
      "\n",
      "   City employees. The first session will be held on April 22, 2020. \n",
      " \n",
      "\n",
      "   Each of these virtual town hall sessions will provide updates about the City’s response  \n",
      "\n",
      "   to COVID-19; discuss the City’s current, and future budget, as well as take questions  \n",
      "\n",
      "   from attendees. City staff with any questions for the City Manager to answer at these  \n",
      "\n",
      "   town halls are encouraged to email them to:  \n",
      "\n",
      "   SJTownHallwebinar@sanjoseca.gov. \n",
      " \n",
      "\n",
      "   Deputy City Manager Kip Harkness, and the leadership team from the City’s  \n",
      "\n",
      "   Emergency Operations Center, (EOC), provided update on the City’s roadmap, and  \n",
      "\n",
      "   efforts to provide food and emergency shelter to those in need, the City’s new Virtual  \n",
      "\n",
      "   Local Assistance Center for residents, businesses, and nonprofits, as well as highlighted  \n",
      "\n",
      "   staffs’ hard work throughout the organization. \n",
      " \n",
      "\n",
      "   Action: No motion was required. Council accepted the verbal report. \n",
      "\n",
      "mailto:SJTownHallwebinar@sanjoseca.gov\n",
      "\n",
      "\n",
      " \n",
      " City Council Minutes/Synopsis April 21, 2020 \n",
      "\n",
      " City of San José Page 9  April 21, 2020 \n",
      "\n",
      " \n",
      "\n",
      "===============================\n",
      "3.1  Report of the City Manager, David Sykes (Verbal Report) \n",
      " \n",
      "\n",
      "      City Manager, David Sykes spoke on the Unsung Heroes: Business Process Automation Team, gave  \n",
      "\n",
      "      a verbal report of COVID-19 updates, and the move to Stage 8 / Tier 3.  \n",
      "\n",
      " \n",
      "\n",
      "      (Item Continued on the Next Page)  \n",
      "\n",
      " \n",
      "\n",
      "\n",
      "\n",
      " \n",
      " \n",
      "\n",
      " City of San José Page 8 December 8, 2020 \n",
      "\n",
      "   3.1 (Cont’d.)   \n",
      " \n",
      "\n",
      "       Deputy City Manager, Emergency Operations Center Director, Kip Harkness and Chief of Staff,    \n",
      "\n",
      "      Emergency Operations Center Director, Lee Wilcox presented the EOC Update, Regional Stay-at-Home  \n",
      "\n",
      "      Order, and Efforts and Strategies for Distribution of COVID-19 Vaccine. Community and Economic  \n",
      "\n",
      "      Recovery Task Force Director, Andrea Flores Shelton, Community and Economic Recovery Task Force  \n",
      "\n",
      "      Assistant Director, Michelle McGurk, and Office of Economic Development  Deputy Director, Chris  \n",
      "\n",
      "      Burton; presented the Community and Economic Recovery Update.   \n",
      "\n",
      " \n",
      "\n",
      "===============================\n"
     ]
    }
   ],
   "source": [
    "for i in all_df.sort_values('topic_4', ascending = False).head(10).content:\n",
    "    print(i)\n",
    "    print('===============================')"
   ]
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