Climate_Change_and_its_Impact_on _Health_Code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Climate Change and its Impact on Health</h1>\n",
    "<h3>Project Team Members:</h3>\n",
    "<ul><li>Ameya Shanbhag (avs431)</li>\n",
    "<li>Titash Mandal (tm2761)</li>\n",
    "<li>Vikram Sunil Bajaj (vsb259)</li></ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<h3> Loading the data</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pyspark\n",
    "from pyspark.sql import SQLContext\n",
    "from pyspark import SparkContext\n",
    "sc =SparkContext.getOrCreate()\n",
    "sqlContext = SQLContext(sc)\n",
    "df = sqlContext.read.csv(\"pollution_us_2000_2016.csv\", header=True)\n",
    "# df.printSchema()\n",
    "df.registerTempTable(\"Pollution\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pyspark.sql.dataframe.DataFrame"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Changing the data type of certains columns to allow querying.</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "from pyspark.sql.functions import col, udf\n",
    "from pyspark.sql.types import DateType\n",
    "# converting the date from string to DateTime format using UDF\n",
    "func =  udf (lambda x: datetime.strptime(x, '%Y-%m-%d'), DateType())\n",
    "df_new = df.withColumn('Date', func(col('Date Local')))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pyspark.sql.types import DoubleType\n",
    "df_new = df_new.withColumn(\"N02AQI\", df_new[\"NO2 AQI\"].cast(DoubleType()))\n",
    "df_new = df_new.withColumn(\"SO2 AQI\", df_new[\"SO2 AQI\"].cast(DoubleType()))\n",
    "df_new = df_new.withColumn(\"CO AQI\", df_new[\"CO AQI\"].cast(DoubleType()))\n",
    "df_new = df_new.withColumn(\"O3 AQI\", df_new[\"O3 AQI\"].cast(DoubleType()))\n",
    "df_new.registerTempTable(\"Pollution_Data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Each day has various NO2 AQI levels. <p>We select the daily Maximum NO2 AQI and SO2 AQI for every day of every  month with years ranging from 2000-2016</p></h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "q2 = sqlContext.sql(\"SELECT MAX(`NO2 AQI`) as Max_NO2 , MAX(`SO2 AQI`) as SO2_AQI, City,Year(Date) as Yearly FROM Pollution_Data GROUP BY Day(Date),City,Year(Date) ORDER BY City,YEAR(Date)\")\n",
    "q2.registerTempTable('q2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Select the maximum yearly AQI values for every city. </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "q2_sub = sqlContext.sql(\"SELECT MAX(Max_NO2) as x, MAX(SO2_AQI) as SO2_AQI, City, Yearly FROM q2 GROUP BY Yearly, City ORDER BY Yearly,City\")\n",
    "q2_sub.registerTempTable(\"q2_sub\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> We filter out the AQI values lesser than 80 as an AQI of 80 is concerned as a dangerous level for pollutants in the air </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------+-----------+\n",
      "|Yearly|count(City)|\n",
      "+------+-----------+\n",
      "|  2000|         52|\n",
      "|  2001|         41|\n",
      "|  2002|         42|\n",
      "|  2003|         41|\n",
      "|  2004|         35|\n",
      "|  2005|         34|\n",
      "|  2006|         39|\n",
      "|  2007|         34|\n",
      "|  2008|         29|\n",
      "|  2009|         20|\n",
      "|  2010|         12|\n",
      "|  2011|         14|\n",
      "|  2012|         13|\n",
      "|  2013|          7|\n",
      "|  2014|         13|\n",
      "|  2015|         10|\n",
      "|  2016|          3|\n",
      "+------+-----------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "sqlContext.sql(\"SELECT Yearly, count(City) FROM q2_sub WHERE x >80 OR SO2_AQI>80 GROUP BY Yearly ORDER BY Yearly\").show(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------------+------+\n",
      "|                City|Yearly|\n",
      "+--------------------+------+\n",
      "|          Alexandria|  2000|\n",
      "|             Ashland|  2000|\n",
      "|         Baton Rouge|  2000|\n",
      "|        Beaver Falls|  2000|\n",
      "|             Bristol|  2000|\n",
      "|Calumet City (PU ...|  2000|\n",
      "|              Camden|  2000|\n",
      "|           Charleroi|  2000|\n",
      "|           Charlotte|  2000|\n",
      "|             Chicago|  2000|\n",
      "|             Detroit|  2000|\n",
      "|            Ferguson|  2000|\n",
      "|          Greensburg|  2000|\n",
      "|           Hawthorne|  2000|\n",
      "|           Henderson|  2000|\n",
      "|          Holtsville|  2000|\n",
      "|             Houston|  2000|\n",
      "|Indianapolis (Rem...|  2000|\n",
      "|           Johnstown|  2000|\n",
      "|         Kansas City|  2000|\n",
      "+--------------------+------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "sqlContext.sql(\"SELECT City, Yearly FROM q2_sub WHERE Yearly=2016 AND x >80 OR SO2_AQI>80\").show(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> We wanted to check for any city which has atleast 3 pollutants having AQI >80. \n",
    "We found out that Houston and Kansas City have a high pollution level with 3 gases in the higher AQI level. \n",
    "No other combination of gases gave any output for a city.</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------+-----------+-----------+-----------+------+-------+------+\n",
      "| State|       City|Max_NO2_AQI|MAX_SO2_AQI|O3_AQI|NO2_AQI|Yearly|\n",
      "+------+-----------+-----------+-----------+------+-------+------+\n",
      "| Texas|    Houston|        101|       86.0| 150.0|    101|  2000|\n",
      "|Kansas|Kansas City|        103|       97.0| 200.0|    103|  2005|\n",
      "+------+-----------+-----------+-----------+------+-------+------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "sqlContext.sql(\" SELECT State, City, MAX(`NO2 AQI`) as Max_NO2_AQI , MAX(`SO2 AQI`) AS MAX_SO2_AQI, MAX(`O3 AQI`) AS O3_AQI, MAX(`NO2 AQI`) AS NO2_AQI, YEAR(Date) as Yearly FROM Pollution_Data WHERE `O3 AQI` > 80 AND `SO2 AQI`>80 AND `NO2 AQI`>80 GROUP BY YEAR(Date),State,City ORDER BY  YEAR(Date)\").show(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----+----+-----------+----------+------+-------+------+\n",
      "|State|City|Max_SO2_AQI|MAX_O3_AQI|CO_AQI|NO2_AQI|Yearly|\n",
      "+-----+----+-----------+----------+------+-------+------+\n",
      "+-----+----+-----------+----------+------+-------+------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "sqlContext.sql(\" SELECT State, City, MAX(`SO2 AQI`) as Max_SO2_AQI , MAX(`O3 AQI`) AS MAX_O3_AQI, MAX(`CO AQI`) AS CO_AQI, MAX(`NO2 AQI`) AS NO2_AQI, YEAR(Date) as Yearly FROM Pollution_Data WHERE `O3 AQI` > 80 AND `SO2 AQI`>80 AND `CO AQI`>80 GROUP BY YEAR(Date),State,City ORDER BY  YEAR(Date)\").show(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "most_polluted_cities_yearly =sqlContext.sql(\"SELECT State, City, MAX(`N02AQI`) as Max_NO2_AQI , YEAR(Date) as Yearly FROM Pollution_Data WHERE `N02AQI`>80 GROUP BY YEAR(Date),City,State ORDER BY  YEAR(Date),MAX(`N02AQI`) DESC\")\n",
    "most_polluted_cities_yearly.registerTempTable(\"most_polluted_cities_yearly\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Yearly most polluted cities ordered in Desceding order by AQI Levels with years ranging from 2000-2016.</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------------+--------------------+-----------+-----------+----------+------+------+\n",
      "|               State|                City|Max_NO2_AQI|Max_SO2_AQI|MAX_O3_AQI|CO_AQI|Yearly|\n",
      "+--------------------+--------------------+-----------+-----------+----------+------+------+\n",
      "|            Missouri|           St. Louis|       80.0|      103.0|     140.0|  52.0|  2000|\n",
      "|          California|       Not in a city|       84.0|       21.0|     154.0|  35.0|  2000|\n",
      "|        Pennsylvania|             Reading|       57.0|      135.0|     172.0|  22.0|  2000|\n",
      "|            Virginia|          Alexandria|       74.0|      109.0|     150.0|  25.0|  2000|\n",
      "|          California|          Long Beach|      108.0|       65.0|     114.0|  63.0|  2000|\n",
      "|            Michigan|             Detroit|       93.0|      134.0|     114.0|  51.0|  2000|\n",
      "|          California|           Davenport|       33.0|       33.0|      47.0|   9.0|  2000|\n",
      "|          California|              Goleta|       54.0|       13.0|      74.0|  18.0|  2000|\n",
      "|          California|       Bethel Island|       41.0|       26.0|     124.0|  17.0|  2000|\n",
      "|        Pennsylvania|           Lancaster|       55.0|       79.0|     177.0|  19.0|  2000|\n",
      "|              Kansas|         Kansas City|       74.0|      101.0|     132.0|  57.0|  2000|\n",
      "|            Illinois|              Cicero|       84.0|       79.0|      87.0|  38.0|  2000|\n",
      "|            Illinois|Calumet City (PU ...|       58.0|      136.0|     101.0|  24.0|  2000|\n",
      "|          California|           San Pablo|       64.0|       39.0|      74.0|  22.0|  2000|\n",
      "|            Missouri|             St. Ann|       84.0|       91.0|     142.0|  22.0|  2000|\n",
      "|          California|Vandenberg Air Fo...|       31.0|        6.0|      80.0|   6.0|  2000|\n",
      "|          California|       San Francisco|       72.0|       27.0|      36.0|  36.0|  2000|\n",
      "|        Pennsylvania|          Pittsburgh|       74.0|      117.0|     169.0|  18.0|  2000|\n",
      "|        Pennsylvania|          Norristown|       64.0|       44.0|     207.0|  16.0|  2000|\n",
      "|             Indiana|Indianapolis (Rem...|       58.0|       89.0|     142.0|  33.0|  2000|\n",
      "|District Of Columbia|          Washington|      109.0|      104.0|     164.0|  58.0|  2000|\n",
      "|          California|             Concord|       72.0|       29.0|     147.0|  31.0|  2000|\n",
      "|        Pennsylvania|          Greensburg|       49.0|      110.0|     145.0|  17.0|  2000|\n",
      "|            Illinois|             Chicago|       97.0|      102.0|      42.0|  26.0|  2000|\n",
      "|            New York|          Holtsville|       67.0|       86.0|     203.0|  32.0|  2000|\n",
      "|          California|            Rubidoux|       94.0|      115.0|     192.0|  48.0|  2000|\n",
      "|          California|         Victorville|       94.0|       36.0|     179.0|  18.0|  2000|\n",
      "|               Texas|             Houston|      101.0|      172.0|     203.0|  42.0|  2000|\n",
      "|            Virginia|       Seven Corners|       87.0|       89.0|     161.0|  32.0|  2000|\n",
      "|        Pennsylvania|        Freemansburg|       44.0|       59.0|     201.0|  26.0|  2000|\n",
      "+--------------------+--------------------+-----------+-----------+----------+------+------+\n",
      "only showing top 30 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "sqlContext.sql(\"SELECT State,City, MAX(`N02AQI`) as Max_NO2_AQI , MAX(`SO2 AQI`) as Max_SO2_AQI , MAX(`O3 AQI`) AS MAX_O3_AQI, MAX(`CO AQI`) AS CO_AQI, YEAR(Date) as Yearly FROM Pollution_Data GROUP BY YEAR(Date),City,State ORDER BY  YEAR(Date)\").show(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "t1=sqlContext.sql(\"SELECT State,City, MAX(`N02AQI`) as Max_NO2_AQI , MAX(`SO2 AQI`) as Max_SO2_AQI , MAX(`O3 AQI`) AS MAX_O3_AQI, MAX(`CO AQI`) AS Max_CO_AQI, YEAR(Date) as Yearly FROM Pollution_Data GROUP BY YEAR(Date),City,State ORDER BY  YEAR(Date)\")\n",
    "t1.registerTempTable(\"Entire_Pollution\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> We wanted to check which cities have constantly featured as having a higher pollution level as compared to other cities. <p>We ranked every city based on their pollution level and found the top polluted cities.\n",
    "These cities have constantly shown up in many consecutive years.<p> </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------------+------------+-----+\n",
      "|               State|        City|Count|\n",
      "+--------------------+------------+-----+\n",
      "|          California|    Calexico|   16|\n",
      "|          California| Los Angeles|   15|\n",
      "|          California|  Long Beach|   14|\n",
      "|            New York|    New York|   13|\n",
      "|          California|   San Diego|   11|\n",
      "|            Colorado|       Welby|   11|\n",
      "|District Of Columbia|  Washington|   11|\n",
      "|          California|     Burbank|   11|\n",
      "|               Texas|     El Paso|   10|\n",
      "|        Pennsylvania|Philadelphia|   10|\n",
      "+--------------------+------------+-----+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "Top_polluted_cities=sqlContext.sql(\"SELECT State,City,COUNT(Yearly) as Count  FROM most_polluted_cities_yearly GROUP BY City,State ORDER BY COUNT(Yearly) DESC LIMIT 10\")\n",
    "Top_polluted_cities.registerTempTable(\"Top_polluted_cities\")\n",
    "Top_polluted_cities.show(20)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "table_state=sqlContext.sql(\"SELECT Top_polluted_cities.City as City, Top_polluted_cities.State as State, most_polluted_cities_yearly.Max_NO2_AQI, most_polluted_cities_yearly.Yearly  as Yearly FROM Top_polluted_cities JOIN most_polluted_cities_yearly ON Top_polluted_cities.City=most_polluted_cities_yearly.City GROUP BY Top_polluted_cities.`City`,most_polluted_cities_yearly.Max_NO2_AQI,most_polluted_cities_yearly.Yearly ,Top_polluted_cities.`State` ORDER BY Top_polluted_cities.`City`,most_polluted_cities_yearly.Yearly  \")\n",
    "table_state.toPandas().to_csv(\"State_Polluted_Data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------+-----------+------+\n",
      "|    City|Max_NO2_AQI|Yearly|\n",
      "+--------+-----------+------+\n",
      "| Burbank|      113.0|  2000|\n",
      "| Burbank|      129.0|  2001|\n",
      "| Burbank|      131.0|  2002|\n",
      "| Burbank|      108.0|  2003|\n",
      "| Burbank|      105.0|  2004|\n",
      "| Burbank|       88.0|  2005|\n",
      "| Burbank|      101.0|  2006|\n",
      "| Burbank|       86.0|  2007|\n",
      "| Burbank|      102.0|  2008|\n",
      "| Burbank|       87.0|  2009|\n",
      "| Burbank|       81.0|  2010|\n",
      "|Calexico|      118.0|  2000|\n",
      "|Calexico|      108.0|  2001|\n",
      "|Calexico|      108.0|  2002|\n",
      "|Calexico|      111.0|  2003|\n",
      "|Calexico|      102.0|  2004|\n",
      "|Calexico|      107.0|  2005|\n",
      "|Calexico|      101.0|  2006|\n",
      "|Calexico|      102.0|  2007|\n",
      "|Calexico|      110.0|  2008|\n",
      "+--------+-----------+------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "table=sqlContext.sql(\"SELECT Top_polluted_cities.City as City, most_polluted_cities_yearly.Max_NO2_AQI, most_polluted_cities_yearly.Yearly  as Yearly FROM Top_polluted_cities JOIN most_polluted_cities_yearly ON Top_polluted_cities.City=most_polluted_cities_yearly.City GROUP BY Top_polluted_cities.`City`,most_polluted_cities_yearly.Max_NO2_AQI,most_polluted_cities_yearly.Yearly  ORDER BY Top_polluted_cities.`City`,most_polluted_cities_yearly.Yearly  \")\n",
    "table.show()\n",
    "table.registerTempTable(\"Final_Table\")\n",
    "table.toPandas().to_csv(\"Non_Pivoted_Table.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "table=table.toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(table)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> To plot the table on a graph using R, we pivoted the tabled based on the cities, with their yearly AQI values as rows. </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        text-align: right;\n",
       "    }\n",
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>City</th>\n",
       "      <th>Burbank</th>\n",
       "      <th>Calexico</th>\n",
       "      <th>El Paso</th>\n",
       "      <th>Long Beach</th>\n",
       "      <th>Los Angeles</th>\n",
       "      <th>New York</th>\n",
       "      <th>Philadelphia</th>\n",
       "      <th>San Diego</th>\n",
       "      <th>Washington</th>\n",
       "      <th>Welby</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yearly</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>113.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>109.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>129.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>102.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>131.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>108.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>108.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>105.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>103.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>88.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>101.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>86.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>103.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>102.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>105.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>87.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>103.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>83.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>81.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>104.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>NaN</td>\n",
       "      <td>106.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>102.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>NaN</td>\n",
       "      <td>90.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>NaN</td>\n",
       "      <td>111.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>87.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>NaN</td>\n",
       "      <td>93.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>107.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>102.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>NaN</td>\n",
       "      <td>82.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>NaN</td>\n",
       "      <td>83.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "City    Burbank  Calexico  El Paso  Long Beach  Los Angeles  New York  \\\n",
       "Yearly                                                                  \n",
       "2000      113.0     118.0     99.0       108.0        111.0     101.0   \n",
       "2001      129.0     108.0    101.0       105.0        112.0     102.0   \n",
       "2002      131.0     108.0    111.0       106.0        112.0     102.0   \n",
       "2003      108.0     111.0    100.0       107.0        113.0     105.0   \n",
       "2004      105.0     102.0     81.0       105.0        112.0     109.0   \n",
       "2005       88.0     107.0     82.0       108.0        106.0      91.0   \n",
       "2006      101.0     101.0    103.0       101.0        103.0      91.0   \n",
       "2007       86.0     102.0     89.0       102.0        102.0     101.0   \n",
       "2008      102.0     110.0     88.0       106.0        105.0      82.0   \n",
       "2009       87.0     101.0      NaN       103.0        104.0      95.0   \n",
       "2010       81.0       NaN      NaN       104.0         88.0     102.0   \n",
       "2011        NaN     106.0      NaN       102.0        103.0       NaN   \n",
       "2012        NaN      90.0      NaN        97.0          NaN       NaN   \n",
       "2013        NaN     111.0      NaN         NaN         89.0       NaN   \n",
       "2014        NaN      93.0      NaN       107.0         86.0       NaN   \n",
       "2015        NaN      82.0     93.0         NaN         86.0      97.0   \n",
       "2016        NaN      83.0      NaN         NaN          NaN      88.0   \n",
       "\n",
       "City    Philadelphia  San Diego  Washington  Welby  \n",
       "Yearly                                              \n",
       "2000            95.0      104.0       109.0  109.0  \n",
       "2001            87.0      110.0        88.0  102.0  \n",
       "2002            85.0      106.0       101.0    NaN  \n",
       "2003           100.0      110.0       101.0  108.0  \n",
       "2004           101.0      106.0       104.0  103.0  \n",
       "2005            85.0      103.0       104.0   96.0  \n",
       "2006           102.0       97.0       104.0   89.0  \n",
       "2007            87.0      101.0        81.0  103.0  \n",
       "2008             NaN      105.0       101.0   84.0  \n",
       "2009             NaN       90.0        82.0   83.0  \n",
       "2010           106.0       82.0        85.0    NaN  \n",
       "2011            86.0        NaN         NaN    NaN  \n",
       "2012             NaN        NaN         NaN    NaN  \n",
       "2013             NaN        NaN         NaN   87.0  \n",
       "2014             NaN        NaN         NaN  102.0  \n",
       "2015             NaN        NaN         NaN    NaN  \n",
       "2016             NaN        NaN         NaN    NaN  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table=table.pivot(index= 'Yearly',columns='City', values='Max_NO2_AQI')\n",
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "for city in list(table):\n",
    "    means=np.nanmean(table[city])\n",
    "    table[city].fillna(means,inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>City</th>\n",
       "      <th>Burbank</th>\n",
       "      <th>Calexico</th>\n",
       "      <th>El Paso</th>\n",
       "      <th>Long Beach</th>\n",
       "      <th>Los Angeles</th>\n",
       "      <th>New York</th>\n",
       "      <th>Philadelphia</th>\n",
       "      <th>San Diego</th>\n",
       "      <th>Washington</th>\n",
       "      <th>Welby</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yearly</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>113.000000</td>\n",
       "      <td>118.0000</td>\n",
       "      <td>99.0</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>111.000000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>95.0</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>109.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>129.000000</td>\n",
       "      <td>108.0000</td>\n",
       "      <td>101.0</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>87.0</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>102.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>131.000000</td>\n",
       "      <td>108.0000</td>\n",
       "      <td>111.0</td>\n",
       "      <td>106.000000</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>85.0</td>\n",
       "      <td>106.000000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>96.909091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>108.000000</td>\n",
       "      <td>111.0000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>107.000000</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>108.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>105.000000</td>\n",
       "      <td>102.0000</td>\n",
       "      <td>81.0</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>101.0</td>\n",
       "      <td>106.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>103.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>88.000000</td>\n",
       "      <td>107.0000</td>\n",
       "      <td>82.0</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>106.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>85.0</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>96.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>101.000000</td>\n",
       "      <td>101.0000</td>\n",
       "      <td>103.0</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>102.0</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>89.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>86.000000</td>\n",
       "      <td>102.0000</td>\n",
       "      <td>89.0</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>87.0</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>103.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>102.000000</td>\n",
       "      <td>110.0000</td>\n",
       "      <td>88.0</td>\n",
       "      <td>106.000000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>93.4</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>84.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>87.000000</td>\n",
       "      <td>101.0000</td>\n",
       "      <td>94.7</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>93.4</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>83.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>81.000000</td>\n",
       "      <td>102.0625</td>\n",
       "      <td>94.7</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>106.0</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>96.909091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>102.818182</td>\n",
       "      <td>106.0000</td>\n",
       "      <td>94.7</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>97.384615</td>\n",
       "      <td>86.0</td>\n",
       "      <td>101.272727</td>\n",
       "      <td>96.363636</td>\n",
       "      <td>96.909091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>102.818182</td>\n",
       "      <td>90.0000</td>\n",
       "      <td>94.7</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>102.133333</td>\n",
       "      <td>97.384615</td>\n",
       "      <td>93.4</td>\n",
       "      <td>101.272727</td>\n",
       "      <td>96.363636</td>\n",
       "      <td>96.909091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>102.818182</td>\n",
       "      <td>111.0000</td>\n",
       "      <td>94.7</td>\n",
       "      <td>104.357143</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>97.384615</td>\n",
       "      <td>93.4</td>\n",
       "      <td>101.272727</td>\n",
       "      <td>96.363636</td>\n",
       "      <td>87.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>102.818182</td>\n",
       "      <td>93.0000</td>\n",
       "      <td>94.7</td>\n",
       "      <td>107.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>97.384615</td>\n",
       "      <td>93.4</td>\n",
       "      <td>101.272727</td>\n",
       "      <td>96.363636</td>\n",
       "      <td>102.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>102.818182</td>\n",
       "      <td>82.0000</td>\n",
       "      <td>93.0</td>\n",
       "      <td>104.357143</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>93.4</td>\n",
       "      <td>101.272727</td>\n",
       "      <td>96.363636</td>\n",
       "      <td>96.909091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>102.818182</td>\n",
       "      <td>83.0000</td>\n",
       "      <td>94.7</td>\n",
       "      <td>104.357143</td>\n",
       "      <td>102.133333</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>93.4</td>\n",
       "      <td>101.272727</td>\n",
       "      <td>96.363636</td>\n",
       "      <td>96.909091</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "City       Burbank  Calexico  El Paso  Long Beach  Los Angeles    New York  \\\n",
       "Yearly                                                                       \n",
       "2000    113.000000  118.0000     99.0  108.000000   111.000000  101.000000   \n",
       "2001    129.000000  108.0000    101.0  105.000000   112.000000  102.000000   \n",
       "2002    131.000000  108.0000    111.0  106.000000   112.000000  102.000000   \n",
       "2003    108.000000  111.0000    100.0  107.000000   113.000000  105.000000   \n",
       "2004    105.000000  102.0000     81.0  105.000000   112.000000  109.000000   \n",
       "2005     88.000000  107.0000     82.0  108.000000   106.000000   91.000000   \n",
       "2006    101.000000  101.0000    103.0  101.000000   103.000000   91.000000   \n",
       "2007     86.000000  102.0000     89.0  102.000000   102.000000  101.000000   \n",
       "2008    102.000000  110.0000     88.0  106.000000   105.000000   82.000000   \n",
       "2009     87.000000  101.0000     94.7  103.000000   104.000000   95.000000   \n",
       "2010     81.000000  102.0625     94.7  104.000000    88.000000  102.000000   \n",
       "2011    102.818182  106.0000     94.7  102.000000   103.000000   97.384615   \n",
       "2012    102.818182   90.0000     94.7   97.000000   102.133333   97.384615   \n",
       "2013    102.818182  111.0000     94.7  104.357143    89.000000   97.384615   \n",
       "2014    102.818182   93.0000     94.7  107.000000    86.000000   97.384615   \n",
       "2015    102.818182   82.0000     93.0  104.357143    86.000000   97.000000   \n",
       "2016    102.818182   83.0000     94.7  104.357143   102.133333   88.000000   \n",
       "\n",
       "City    Philadelphia   San Diego  Washington       Welby  \n",
       "Yearly                                                    \n",
       "2000            95.0  104.000000  109.000000  109.000000  \n",
       "2001            87.0  110.000000   88.000000  102.000000  \n",
       "2002            85.0  106.000000  101.000000   96.909091  \n",
       "2003           100.0  110.000000  101.000000  108.000000  \n",
       "2004           101.0  106.000000  104.000000  103.000000  \n",
       "2005            85.0  103.000000  104.000000   96.000000  \n",
       "2006           102.0   97.000000  104.000000   89.000000  \n",
       "2007            87.0  101.000000   81.000000  103.000000  \n",
       "2008            93.4  105.000000  101.000000   84.000000  \n",
       "2009            93.4   90.000000   82.000000   83.000000  \n",
       "2010           106.0   82.000000   85.000000   96.909091  \n",
       "2011            86.0  101.272727   96.363636   96.909091  \n",
       "2012            93.4  101.272727   96.363636   96.909091  \n",
       "2013            93.4  101.272727   96.363636   87.000000  \n",
       "2014            93.4  101.272727   96.363636  102.000000  \n",
       "2015            93.4  101.272727   96.363636   96.909091  \n",
       "2016            93.4  101.272727   96.363636   96.909091  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table\n",
    "table.astype('float64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "table.to_csv(\"Polluted_cities_NO2AQI.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataFrame[_c0: string, State Code: string, County Code: string, Site Num: string, Address: string, State: string, County: string, City: string, Date Local: string, NO2 Units: string, NO2 Mean: string, NO2 1st Max Value: string, NO2 1st Max Hour: string, NO2 AQI: string, O3 Units: string, O3 Mean: string, O3 1st Max Value: string, O3 1st Max Hour: string, O3 AQI: double, SO2 Units: string, SO2 Mean: string, SO2 1st Max Value: string, SO2 1st Max Hour: string, SO2 AQI: double, CO Units: string, CO Mean: string, CO 1st Max Value: string, CO 1st Max Hour: string, CO AQI: double, Date: date, N02AQI: double]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> <font color='green'> Linking the Pollution Dataset with the Land Temperature Data set.</font> </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sc =SparkContext.getOrCreate()\n",
    "sqlContext = SQLContext(sc)\n",
    "df = sqlContext.read.csv(\"GlobalLandTemperaturesByCity.csv\", header=True)\n",
    "# df.printSchema()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataFrame[summary: string, dt: string, AverageTemperature: string, AverageTemperatureUncertainty: string, City: string, Country: string, Latitude: string, Longitude: string]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# converting the date from string to DateTime format using UDF\n",
    "func =  udf (lambda x: datetime.strptime(x, '%Y-%m-%d'), DateType())\n",
    "df = df.withColumn('Dt', func(col('dt')))\n",
    "df.registerTempTable(\"City_Data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------+------------------+-----------------------------+-----+-------+--------+---------+\n",
      "|        Dt|AverageTemperature|AverageTemperatureUncertainty| City|Country|Latitude|Longitude|\n",
      "+----------+------------------+-----------------------------+-----+-------+--------+---------+\n",
      "|1743-11-01|             6.068|           1.7369999999999999|Århus|Denmark|  57.05N|   10.33E|\n",
      "|1743-12-01|              null|                         null|Århus|Denmark|  57.05N|   10.33E|\n",
      "|1744-01-01|              null|                         null|Århus|Denmark|  57.05N|   10.33E|\n",
      "|1744-02-01|              null|                         null|Århus|Denmark|  57.05N|   10.33E|\n",
      "|1744-03-01|              null|                         null|Århus|Denmark|  57.05N|   10.33E|\n",
      "|1744-04-01|5.7879999999999985|           3.6239999999999997|Århus|Denmark|  57.05N|   10.33E|\n",
      "|1744-05-01|            10.644|           1.2830000000000001|Århus|Denmark|  57.05N|   10.33E|\n",
      "|1744-06-01|14.050999999999998|                        1.347|Århus|Denmark|  57.05N|   10.33E|\n",
      "|1744-07-01|            16.082|                        1.396|Århus|Denmark|  57.05N|   10.33E|\n",
      "|1744-08-01|              null|                         null|Århus|Denmark|  57.05N|   10.33E|\n",
      "+----------+------------------+-----------------------------+-----+-------+--------+---------+\n",
      "only showing top 10 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df.show(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "table34=sqlContext.sql(\"SELECT  City, MAX(`N02AQI`) as Max_NO2_AQI , MAX(`SO2 AQI`) as Max_SO2_AQI, MAX(`CO AQI`) as Max_CO_AQI, MAX(`O3 AQI`) as Max_O3_AQI , first(Date) AS Year FROM Pollution_Data  GROUP BY YEAR(Date) , City ORDER BY YEAR(Date), City\")\n",
    "table34.registerTempTable(\"Polls\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Each city has yearly max pollutant values. </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+-----------+-----------+----------+----------+----------+\n",
      "|   City|Max_NO2_AQI|Max_SO2_AQI|Max_CO_AQI|Max_O3_AQI|      Year|\n",
      "+-------+-----------+-----------+----------+----------+----------+\n",
      "|Burbank|      113.0|       14.0|      68.0|     202.0|2000-01-01|\n",
      "|Burbank|      129.0|       19.0|      56.0|     172.0|2001-01-01|\n",
      "|Burbank|      131.0|       19.0|      51.0|     150.0|2002-01-01|\n",
      "|Burbank|      108.0|       19.0|      51.0|     177.0|2003-01-01|\n",
      "|Burbank|      105.0|       34.0|      39.0|     172.0|2004-01-14|\n",
      "|Burbank|       88.0|       19.0|      39.0|     182.0|2005-01-01|\n",
      "|Burbank|      101.0|       13.0|      39.0|     206.0|2006-01-01|\n",
      "|Burbank|       86.0|       11.0|      32.0|     151.0|2007-01-01|\n",
      "|Burbank|      102.0|       10.0|      28.0|     185.0|2008-01-01|\n",
      "|Burbank|       87.0|       19.0|      33.0|     151.0|2009-01-01|\n",
      "|Burbank|       81.0|       20.0|      26.0|     122.0|2010-01-01|\n",
      "|Burbank|       65.0|        3.0|      27.0|     122.0|2011-01-01|\n",
      "|Burbank|       78.0|        4.0|      27.0|     132.0|2012-01-01|\n",
      "|Burbank|       70.0|       16.0|      27.0|     143.0|2013-01-01|\n",
      "|Burbank|       71.0|        6.0|      34.0|     129.0|2014-01-01|\n",
      "+-------+-----------+-----------+----------+----------+----------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "sqlContext.sql(\"SELECT * FROM Polls where City=='Burbank'\").show(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Calculating the average yearly temperature for each city. </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------+---------+------------------+\n",
      "|year(Dt)|     City|   Yearly_Avg_Temp|\n",
      "+--------+---------+------------------+\n",
      "|    1767|    Århus| 6.946416666666668|\n",
      "|    1781|    Århus| 8.528916666666667|\n",
      "|    1766|    Çorlu|          13.39575|\n",
      "|    1868|    Çorlu|           13.0665|\n",
      "|    1938|  Öskemen|1.3139999999999992|\n",
      "|    1988|  Öskemen|2.1085000000000007|\n",
      "|    1994| A Coruña|13.764333333333335|\n",
      "|    1840|   Aachen|7.8524166666666675|\n",
      "|    1840|  Aalborg| 6.725416666666667|\n",
      "|    1863|  Aalborg| 8.568416666666666|\n",
      "|    1880|      Aba|25.996166666666667|\n",
      "|    1888|      Aba|25.647000000000002|\n",
      "|    1900|      Aba|26.418833333333335|\n",
      "|    1917|      Aba| 26.94966666666667|\n",
      "|    1932|      Aba|26.749749999999995|\n",
      "|    1914|Abakaliki|26.909999999999997|\n",
      "|    1838|   Abakan|-1.885833333333333|\n",
      "|    1914|   Abakan|0.2978333333333338|\n",
      "|    1979|   Abakan|0.6380833333333328|\n",
      "|    1860| Aberdeen| 6.903583333333333|\n",
      "+--------+---------+------------------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "sqlContext.sql(\" SELECT YEAR(Dt),City, AVG(AverageTemperature) AS Yearly_Avg_Temp FROM City_Data GROUP BY YEAR(Dt), City\").show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "table123=sqlContext.sql(\" SELECT YEAR(Dt) as Years,City, AVG(AverageTemperature) AS Yearly_Avg_Temp FROM City_Data GROUP BY YEAR(Dt), City\")\n",
    "table123.registerTempTable(\"City_Temp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------+----------------+----------------+----------+----------+----------+\n",
      "|         City|max(Max_NO2_AQI)|max(Max_SO2_AQI)|Max_CO_AQI|Max_O3_AQI|      Year|\n",
      "+-------------+----------------+----------------+----------+----------+----------+\n",
      "|  Baton Rouge|            94.0|            99.0|      48.0|     187.0|2000-01-01|\n",
      "|      Burbank|           113.0|            14.0|      68.0|     202.0|2000-01-01|\n",
      "|  Chula Vista|            70.0|            62.0|      35.0|     104.0|2000-01-01|\n",
      "|      Concord|            72.0|            29.0|      31.0|     147.0|2000-01-01|\n",
      "|   Costa Mesa|           102.0|            29.0|      69.0|     127.0|2000-01-01|\n",
      "|       Dallas|            97.0|            19.0|      25.0|     177.0|2000-01-01|\n",
      "|      Houston|           101.0|           172.0|      42.0|     203.0|2000-01-01|\n",
      "|  Kansas City|            74.0|           101.0|      57.0|     132.0|2000-01-01|\n",
      "|   Long Beach|           108.0|            65.0|      63.0|     114.0|2000-01-01|\n",
      "| Philadelphia|            95.0|            89.0|      51.0|     140.0|2000-01-01|\n",
      "|      Phoenix|           105.0|            41.0|      59.0|     132.0|2000-01-01|\n",
      "|    San Diego|           104.0|            79.0|      65.0|     109.0|2000-01-01|\n",
      "|San Francisco|            72.0|            27.0|      36.0|      36.0|2000-01-01|\n",
      "|    San Pablo|            64.0|            39.0|      22.0|      74.0|2000-01-01|\n",
      "|   Scottsdale|           132.0|            31.0|      38.0|     106.0|2000-01-01|\n",
      "|       Tucson|            73.0|            27.0|      31.0|     109.0|2000-01-01|\n",
      "|      Vallejo|            62.0|            24.0|      57.0|      47.0|2000-01-01|\n",
      "|   Washington|           109.0|           104.0|      58.0|     164.0|2000-01-01|\n",
      "|      Chicago|            97.0|           102.0|      26.0|      42.0|2000-01-06|\n",
      "|      El Paso|            99.0|            50.0|      49.0|     154.0|2000-01-06|\n",
      "|    Henderson|            71.0|           136.0|      17.0|     122.0|2000-02-29|\n",
      "|  Los Angeles|           111.0|           100.0|      66.0|     169.0|2000-03-01|\n",
      "|   Louisville|            58.0|           128.0|      20.0|     129.0|2000-03-21|\n",
      "|   Alexandria|            74.0|           109.0|      25.0|     150.0|2000-03-31|\n",
      "|      Bristol|            64.0|            82.0|      19.0|     207.0|2000-03-31|\n",
      "|    Charleroi|            43.0|           110.0|      11.0|     169.0|2000-03-31|\n",
      "|    Charlotte|            74.0|            92.0|      42.0|     205.0|2000-03-31|\n",
      "|      Detroit|            93.0|           134.0|      51.0|     114.0|2000-03-31|\n",
      "|    Lancaster|            55.0|            79.0|      19.0|     177.0|2000-03-31|\n",
      "|   Pittsburgh|            74.0|           117.0|      18.0|     169.0|2000-03-31|\n",
      "|      Reading|            57.0|           135.0|      22.0|     172.0|2000-03-31|\n",
      "|         York|            63.0|           104.0|      22.0|     177.0|2000-03-31|\n",
      "|     New York|           101.0|           105.0|      55.0|     201.0|2000-05-05|\n",
      "|  Bakersfield|            88.0|            17.0|      43.0|      40.0|2000-11-01|\n",
      "|  Bakersfield|           104.0|            43.0|      39.0|      80.0|2001-01-01|\n",
      "|  Baton Rouge|            73.0|           111.0|      62.0|     150.0|2001-01-01|\n",
      "|      Burbank|           129.0|            19.0|      56.0|     172.0|2001-01-01|\n",
      "|  Chula Vista|            69.0|            67.0|      53.0|     109.0|2001-01-01|\n",
      "|      Concord|            63.0|            33.0|      31.0|     129.0|2001-01-01|\n",
      "|   Costa Mesa|            81.0|            19.0|      52.0|      93.0|2001-01-01|\n",
      "|       Dallas|            72.0|            21.0|      28.0|     192.0|2001-01-01|\n",
      "|      El Paso|           101.0|            33.0|      59.0|     137.0|2001-01-01|\n",
      "|      Houston|           106.0|           135.0|      28.0|     204.0|2001-01-01|\n",
      "|  Kansas City|            71.0|            79.0|      47.0|     119.0|2001-01-01|\n",
      "|   Long Beach|           105.0|            65.0|      53.0|      84.0|2001-01-01|\n",
      "|        Miami|            44.0|            70.0|       3.0|     127.0|2001-01-01|\n",
      "|     New York|           102.0|           105.0|      42.0|     166.0|2001-01-01|\n",
      "| Philadelphia|            87.0|            72.0|      49.0|     106.0|2001-01-01|\n",
      "|      Phoenix|            94.0|            26.0|      49.0|     104.0|2001-01-01|\n",
      "|    San Diego|           110.0|            81.0|      55.0|      97.0|2001-01-01|\n",
      "|San Francisco|            71.0|            36.0|      38.0|      46.0|2001-01-01|\n",
      "|    San Pablo|            58.0|            19.0|      16.0|     100.0|2001-01-01|\n",
      "|   Scottsdale|            76.0|            13.0|      36.0|      77.0|2001-01-01|\n",
      "|       Tucson|            57.0|            14.0|      22.0|     100.0|2001-01-01|\n",
      "|      Vallejo|            54.0|            23.0|      47.0|      80.0|2001-01-01|\n",
      "|   Washington|            88.0|           122.0|      54.0|     164.0|2001-01-01|\n",
      "|    Henderson|            70.0|           126.0|      15.0|     104.0|2001-02-28|\n",
      "|   Louisville|            57.0|           137.0|      18.0|     127.0|2001-02-28|\n",
      "|  Los Angeles|           112.0|            36.0|      62.0|     159.0|2001-03-03|\n",
      "|      Bristol|            71.0|            51.0|      23.0|     202.0|2001-03-31|\n",
      "|    Charleroi|            42.0|           122.0|       5.0|     137.0|2001-03-31|\n",
      "|    Charlotte|            67.0|           132.0|      27.0|     177.0|2001-03-31|\n",
      "|      Detroit|            72.0|           161.0|      28.0|     142.0|2001-03-31|\n",
      "|    Lancaster|            50.0|            54.0|      16.0|     174.0|2001-03-31|\n",
      "|   Pittsburgh|           102.0|           109.0|      23.0|     172.0|2001-03-31|\n",
      "|      Reading|            58.0|           125.0|      16.0|     177.0|2001-03-31|\n",
      "|         York|           117.0|            66.0|      18.0|     145.0|2001-03-31|\n",
      "|       Boston|            72.0|            62.0|      35.0|     145.0|2001-04-01|\n",
      "|       Newark|           101.0|           141.0|      34.0|      67.0|2001-08-16|\n",
      "|      Oakland|            60.0|            11.0|      36.0|      29.0|2001-12-05|\n",
      "|  Baton Rouge|            71.0|           109.0|      55.0|     169.0|2002-01-01|\n",
      "|       Boston|            78.0|            56.0|      31.0|     142.0|2002-01-01|\n",
      "|      Burbank|           131.0|            19.0|      51.0|     150.0|2002-01-01|\n",
      "|  Chula Vista|            93.0|            61.0|      30.0|      93.0|2002-01-01|\n",
      "|      Concord|            61.0|            41.0|      26.0|     135.0|2002-01-01|\n",
      "|   Costa Mesa|           102.0|            39.0|      49.0|      84.0|2002-01-01|\n",
      "|      El Paso|           111.0|            47.0|      42.0|     164.0|2002-01-01|\n",
      "|  Kansas City|            68.0|            66.0|      32.0|     127.0|2002-01-01|\n",
      "|   Long Beach|           106.0|            43.0|      52.0|      64.0|2002-01-01|\n",
      "|  Los Angeles|           112.0|            23.0|      61.0|     109.0|2002-01-01|\n",
      "|     New York|           102.0|           176.0|      33.0|     174.0|2002-01-01|\n",
      "|       Newark|           101.0|           105.0|      36.0|     161.0|2002-01-01|\n",
      "|      Oakland|            79.0|            29.0|      57.0|      47.0|2002-01-01|\n",
      "| Philadelphia|            85.0|            85.0|      39.0|     122.0|2002-01-01|\n",
      "|      Phoenix|            86.0|            30.0|      50.0|     122.0|2002-01-01|\n",
      "|    San Diego|           106.0|            40.0|      53.0|     129.0|2002-01-01|\n",
      "|San Francisco|            73.0|            72.0|      30.0|      42.0|2002-01-01|\n",
      "|    San Pablo|            51.0|            21.0|      20.0|      45.0|2002-01-01|\n",
      "|   Scottsdale|            64.0|            19.0|      34.0|      67.0|2002-01-01|\n",
      "|       Tucson|            61.0|            39.0|      23.0|     124.0|2002-01-01|\n",
      "|      Vallejo|            48.0|            24.0|      44.0|      87.0|2002-01-01|\n",
      "|   Washington|           101.0|            96.0|      55.0|     206.0|2002-01-01|\n",
      "|       Dallas|            77.0|            27.0|      30.0|     202.0|2002-01-10|\n",
      "|   Louisville|            40.0|           120.0|      19.0|     151.0|2002-02-28|\n",
      "|        Miami|            38.0|            21.0|       6.0|     109.0|2002-02-28|\n",
      "|      Bristol|            54.0|            80.0|      10.0|     204.0|2002-03-31|\n",
      "|    Charleroi|            55.0|           105.0|       8.0|     182.0|2002-03-31|\n",
      "|    Charlotte|            68.0|            92.0|      18.0|     182.0|2002-03-31|\n",
      "|      Detroit|           128.0|           115.0|      26.0|     156.0|2002-03-31|\n",
      "| Grand Rapids|            97.0|           105.0|      18.0|     154.0|2002-03-31|\n",
      "+-------------+----------------+----------------+----------+----------+----------+\n",
      "only showing top 100 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "sqlContext.sql(\"SELECT Polls.City, Max(Polls.Max_NO2_AQI) ,Max(Polls.Max_SO2_AQI),Polls.Max_CO_AQI,Polls.Max_O3_AQI, Polls.Year FROM Polls JOIN City_Temp ON YEAR(Polls.Year)==City_Temp.Years AND Polls.City==City_Temp.City Group by City_Temp.Years, Polls.City, Polls.Max_NO2_AQI, Polls.Max_SO2_AQI, Polls.Max_CO_AQI,Polls.Max_O3_AQI, Polls.Year ORDER BY Polls.Year, Polls.City \").show(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "temp_pollution_yearly=sqlContext.sql(\"SELECT Polls.City, Max(Polls.Max_NO2_AQI) ,Max(Polls.Max_SO2_AQI),Polls.Max_CO_AQI,Polls.Max_O3_AQI, Polls.Year FROM Polls JOIN City_Temp ON YEAR(Polls.Year)==City_Temp.Years AND Polls.City==City_Temp.City Group by City_Temp.Years, Polls.City, Polls.Max_NO2_AQI, Polls.Max_SO2_AQI, Polls.Max_CO_AQI,Polls.Max_O3_AQI, Polls.Year ORDER BY Polls.Year, Polls.City \")\n",
    "temp_pollution_yearly.toPandas().to_csv(\"temp_pollution.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> <font color='blue'><p>Joining the two tables - Global Land Temperature Data set, Pollution Data Set.</p>\n",
    "<p> We want the joined table to display all the pollution levels caused by different air pollutants vs their temperature for each city. </p> </font></h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "temp_pollution_yearly=sqlContext.sql(\"SELECT Polls.City, City_Temp.Yearly_Avg_Temp , Max(Polls.Max_NO2_AQI) AS max_NO2 ,Max(Polls.Max_SO2_AQI) AS MAX_SO2,Polls.Max_CO_AQI AS MAX_CO,Polls.Max_O3_AQI as MAX_03, Polls.Year FROM Polls JOIN City_Temp ON YEAR(Polls.Year)==City_Temp.Years AND Polls.City==City_Temp.City Group by City_Temp.Years, Polls.City, Polls.Max_NO2_AQI, Polls.Max_SO2_AQI, Polls.Max_CO_AQI,Polls.Max_O3_AQI, Polls.Year,City_Temp.Yearly_Avg_Temp ORDER BY Polls.Year, Polls.City \")\n",
    "temp_pollution_yearly.toPandas().to_csv(\"temperature_pollution_Rerun.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "temp_pollution_yearly.registerTempTable(\"Temperature_Pollution_Dataset\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------+------------------+-------+-------+------+------+----------+\n",
      "|         City|   Yearly_Avg_Temp|max_NO2|MAX_SO2|MAX_CO|MAX_03|      Year|\n",
      "+-------------+------------------+-------+-------+------+------+----------+\n",
      "|    Charleroi|            11.368|   43.0|  110.0|  11.0| 169.0|2000-03-31|\n",
      "|    Lancaster|16.643333333333334|   55.0|   79.0|  19.0| 177.0|2000-03-31|\n",
      "|      Reading|11.226416666666667|   57.0|  135.0|  22.0| 172.0|2000-03-31|\n",
      "|   Louisville|13.499749999999999|   58.0|  128.0|  20.0| 129.0|2000-03-21|\n",
      "|      Vallejo|15.022000000000004|   62.0|   24.0|  57.0|  47.0|2000-01-01|\n",
      "|         York| 9.526916666666667|   63.0|  104.0|  22.0| 177.0|2000-03-31|\n",
      "|    San Pablo|26.966749999999994|   64.0|   39.0|  22.0|  74.0|2000-01-01|\n",
      "|      Bristol| 9.970083333333335|   64.0|   82.0|  19.0| 207.0|2000-03-31|\n",
      "|  Chula Vista|16.900083333333335|   70.0|   62.0|  35.0| 104.0|2000-01-01|\n",
      "|    Henderson|            18.509|   71.0|  136.0|  17.0| 122.0|2000-02-29|\n",
      "|San Francisco|15.022000000000004|   72.0|   27.0|  36.0|  36.0|2000-01-01|\n",
      "|      Concord|15.022000000000004|   72.0|   29.0|  31.0| 147.0|2000-01-01|\n",
      "|       Tucson|          18.66575|   73.0|   27.0|  31.0| 109.0|2000-01-01|\n",
      "|    Charlotte|16.428583333333332|   74.0|   92.0|  42.0| 205.0|2000-03-31|\n",
      "|  Kansas City|12.726750000000003|   74.0|  101.0|  57.0| 132.0|2000-01-01|\n",
      "|   Alexandria|         16.511125|   74.0|  109.0|  25.0| 150.0|2000-03-31|\n",
      "|   Pittsburgh|10.225166666666665|   74.0|  117.0|  18.0| 169.0|2000-03-31|\n",
      "|  Bakersfield|16.504083333333337|   88.0|   17.0|  43.0|  40.0|2000-11-01|\n",
      "|      Detroit| 9.344166666666666|   93.0|  134.0|  51.0| 114.0|2000-03-31|\n",
      "|  Baton Rouge|21.092500000000005|   94.0|   99.0|  48.0| 187.0|2000-01-01|\n",
      "| Philadelphia|12.311666666666667|   95.0|   89.0|  51.0| 140.0|2000-01-01|\n",
      "|       Dallas| 18.89666666666667|   97.0|   19.0|  25.0| 177.0|2000-01-01|\n",
      "|      Chicago|11.089666666666666|   97.0|  102.0|  26.0|  42.0|2000-01-06|\n",
      "|      El Paso| 16.88958333333333|   99.0|   50.0|  49.0| 154.0|2000-01-06|\n",
      "|     New York| 9.969083333333334|  101.0|  105.0|  55.0| 201.0|2000-05-05|\n",
      "|      Houston|21.342000000000002|  101.0|  172.0|  42.0| 203.0|2000-01-01|\n",
      "|   Costa Mesa|16.900083333333335|  102.0|   29.0|  69.0| 127.0|2000-01-01|\n",
      "|    San Diego|16.900083333333335|  104.0|   79.0|  65.0| 109.0|2000-01-01|\n",
      "|      Phoenix|22.003250000000005|  105.0|   41.0|  59.0| 132.0|2000-01-01|\n",
      "|   Long Beach|16.900083333333335|  108.0|   65.0|  63.0| 114.0|2000-01-01|\n",
      "|   Washington|12.277750000000003|  109.0|  104.0|  58.0| 164.0|2000-01-01|\n",
      "|  Los Angeles|14.816250000000004|  111.0|  100.0|  66.0| 169.0|2000-03-01|\n",
      "|      Burbank|16.643333333333334|  113.0|   14.0|  68.0| 202.0|2000-01-01|\n",
      "|   Scottsdale|22.003250000000005|  132.0|   31.0|  38.0| 106.0|2000-01-01|\n",
      "|    Charleroi|10.938249999999998|   42.0|  122.0|   5.0| 137.0|2001-03-31|\n",
      "|        Miami| 23.70183333333333|   44.0|   70.0|   3.0| 127.0|2001-01-01|\n",
      "|    Lancaster|          16.46625|   50.0|   54.0|  16.0| 174.0|2001-03-31|\n",
      "|      Vallejo| 15.24691666666667|   54.0|   23.0|  47.0|  80.0|2001-01-01|\n",
      "|       Tucson|18.269916666666663|   57.0|   14.0|  22.0| 100.0|2001-01-01|\n",
      "|   Louisville|14.103999999999997|   57.0|  137.0|  18.0| 127.0|2001-02-28|\n",
      "+-------------+------------------+-------+-------+------+------+----------+\n",
      "only showing top 40 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "sqlContext.sql(\"SELECT * FROM Temperature_Pollution_Dataset ORDER BY Year(Year),max_NO2,MAX_SO2,MAX_CO,MAX_03  \").show(40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "temperature_table=sqlContext.sql(\"SELECT * FROM Temperature_Pollution_Dataset ORDER BY Year(Year),max_NO2,MAX_SO2,MAX_CO,MAX_03  \")\n",
    "temperature_table.toPandas().to_csv(\"Temperature_Values.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Using a Decision Tree Model to predict the future temperature values of a city based on the pollution levels. </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "table11 = sqlContext.sql(\"SELECT Yearly_Avg_Temp,max_NO2, MAX_SO2, MAX_CO,MAX_03 FROM Temperature_Pollution_Dataset\")\n",
    "table11.registerTempTable(\"Model_Dataset\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "table11.toPandas().to_csv(\"Model_Data.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataFrame=sc.textFile(\"Model_Data.csv\")\n",
    "header = dataFrame.first()\n",
    "dataFrame = dataFrame.filter(lambda x: x!=header)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pyspark.rdd.PipelinedRDD"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(dataFrame)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pyspark.rdd.RDD"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFrame = sc.parallelize(dataFrame.collect())\n",
    "type(dataFrame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PythonRDD[542] at RDD at PythonRDD.scala:48"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFrame=dataFrame.map(lambda line: line.split(','))\n",
    "dataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['0', '21.092500000000005', '94.0', '99.0', '48.0', '187.0'],\n",
       " ['1', '16.643333333333334', '113.0', '14.0', '68.0', '202.0'],\n",
       " ['2', '16.900083333333335', '70.0', '62.0', '35.0', '104.0'],\n",
       " ['3', '15.022000000000004', '72.0', '29.0', '31.0', '147.0'],\n",
       " ['4', '16.900083333333335', '102.0', '29.0', '69.0', '127.0'],\n",
       " ['5', '18.89666666666667', '97.0', '19.0', '25.0', '177.0'],\n",
       " ['6', '21.342000000000002', '101.0', '172.0', '42.0', '203.0'],\n",
       " ['7', '12.726750000000003', '74.0', '101.0', '57.0', '132.0'],\n",
       " ['8', '16.900083333333335', '108.0', '65.0', '63.0', '114.0'],\n",
       " ['9', '12.311666666666667', '95.0', '89.0', '51.0', '140.0']]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFrame.take(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1><font color='red'> Decision Tree Model using MLLIB</font> </h1>\n",
    "<p><b> A decision tree is a flowchart-like structure in which each internal node represents a \"test\" on an attribute (e.g. whether a coin flip comes up heads or tails), each branch represents the outcome of the test, and each leaf node represents a class label (decision taken after computing all attributes). \n",
    "The paths from root to leaf represent classification rules.</p></b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Mean Squared Error = 16.65643623052192\n",
      "Learned regression tree model:\n",
      "DecisionTreeModel regressor of depth 5 with 59 nodes\n",
      "  If (feature 1 <= 30.0)\n",
      "   If (feature 1 <= 10.0)\n",
      "    If (feature 3 <= 100.0)\n",
      "     If (feature 0 <= 38.0)\n",
      "      Predict: 21.556083333333333\n",
      "     Else (feature 0 > 38.0)\n",
      "      If (feature 1 <= 7.0)\n",
      "       Predict: 10.955869047619046\n",
      "      Else (feature 1 > 7.0)\n",
      "       Predict: 14.403327380952382\n",
      "    Else (feature 3 > 100.0)\n",
      "     If (feature 1 <= 9.0)\n",
      "      If (feature 3 <= 112.0)\n",
      "       Predict: 15.372856481481483\n",
      "      Else (feature 3 > 112.0)\n",
      "       Predict: 17.64037962962963\n",
      "     Else (feature 1 > 9.0)\n",
      "      If (feature 0 <= 38.0)\n",
      "       Predict: 18.695500000000003\n",
      "      Else (feature 0 > 38.0)\n",
      "       Predict: 20.45077777777778\n",
      "   Else (feature 1 > 10.0)\n",
      "    If (feature 2 <= 20.0)\n",
      "     If (feature 3 <= 112.0)\n",
      "      If (feature 2 <= 19.0)\n",
      "       Predict: 21.12206216931217\n",
      "      Else (feature 2 > 19.0)\n",
      "       Predict: 27.01897222222222\n",
      "     Else (feature 3 > 112.0)\n",
      "      If (feature 2 <= 7.0)\n",
      "       Predict: 22.0095\n",
      "      Else (feature 2 > 7.0)\n",
      "       Predict: 15.190347222222222\n",
      "    Else (feature 2 > 20.0)\n",
      "     If (feature 2 <= 23.0)\n",
      "      If (feature 1 <= 27.0)\n",
      "       Predict: 16.286875\n",
      "      Else (feature 1 > 27.0)\n",
      "       Predict: 10.064666666666653\n",
      "     Else (feature 2 > 23.0)\n",
      "      If (feature 0 <= 55.0)\n",
      "       Predict: 15.21513095238095\n",
      "      Else (feature 0 > 55.0)\n",
      "       Predict: 17.757499158249153\n",
      "  Else (feature 1 > 30.0)\n",
      "   If (feature 2 <= 18.0)\n",
      "    If (feature 0 <= 57.0)\n",
      "     If (feature 1 <= 107.0)\n",
      "      If (feature 2 <= 7.0)\n",
      "       Predict: 23.70183333333333\n",
      "      Else (feature 2 > 7.0)\n",
      "       Predict: 14.57955521680217\n",
      "     Else (feature 1 > 107.0)\n",
      "      If (feature 0 <= 54.0)\n",
      "       Predict: 11.303794642857143\n",
      "      Else (feature 0 > 54.0)\n",
      "       Predict: 14.022708333333327\n",
      "    Else (feature 0 > 57.0)\n",
      "     If (feature 1 <= 49.0)\n",
      "      If (feature 3 <= 116.0)\n",
      "       Predict: 8.720555555555555\n",
      "      Else (feature 3 > 116.0)\n",
      "       Predict: 10.968729166666666\n",
      "     Else (feature 1 > 49.0)\n",
      "      If (feature 1 <= 52.0)\n",
      "       Predict: 21.857166666666668\n",
      "      Else (feature 1 > 52.0)\n",
      "       Predict: 11.686128205128202\n",
      "   Else (feature 2 > 18.0)\n",
      "    If (feature 0 <= 51.0)\n",
      "     If (feature 3 <= 43.0)\n",
      "      Predict: 15.051583333333332\n",
      "     Else (feature 3 > 43.0)\n",
      "      If (feature 1 <= 92.0)\n",
      "       Predict: 10.028607142857142\n",
      "      Else (feature 1 > 92.0)\n",
      "       Predict: 14.289416666666668\n",
      "    Else (feature 0 > 51.0)\n",
      "     If (feature 2 <= 53.0)\n",
      "      If (feature 0 <= 71.0)\n",
      "       Predict: 16.12488131313131\n",
      "      Else (feature 0 > 71.0)\n",
      "       Predict: 14.56948568376068\n",
      "     Else (feature 2 > 53.0)\n",
      "      If (feature 1 <= 104.0)\n",
      "       Predict: 16.694398437500002\n",
      "      Else (feature 1 > 104.0)\n",
      "       Predict: 20.869041666666675\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from pyspark.mllib.tree import DecisionTree, DecisionTreeModel\n",
    "from pyspark.mllib.util import MLUtils\n",
    "from pyspark.mllib.regression import LabeledPoint\n",
    "\n",
    "\n",
    "(trainingData, testData) = dataFrame.randomSplit([0.7, 0.3])\n",
    "def parsePoint(line):\n",
    "    values=[x for x in line]\n",
    "    return LabeledPoint(values[1], values[2:])\n",
    "\n",
    "\n",
    "TrainParseData = trainingData.map(parsePoint)\n",
    "TestParseData=testData.map(parsePoint)\n",
    "\n",
    "# Train a DecisionTree model.\n",
    "#  Empty categoricalFeaturesInfo indicates all features are continuous.\n",
    "model = DecisionTree.trainRegressor(TrainParseData, categoricalFeaturesInfo={},impurity='variance', maxDepth=5, maxBins=32)\n",
    "\n",
    "# Evaluate model on test instances and compute test error\n",
    "predictions = model.predict(TestParseData.map(lambda x: x.features))\n",
    "labelsAndPredictions = TestParseData.map(lambda lp: lp.label).zip(predictions)\n",
    "testMSE = labelsAndPredictions.map(lambda lp: (lp[0] - lp[1]) * (lp[0] - lp[1])).sum()/float(TestParseData.count())\n",
    "print('Test Mean Squared Error = ' + str(testMSE))\n",
    "print('Learned regression tree model:')\n",
    "print(model.toDebugString())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_table=sqlContext.sql(\"SELECT  Max_NO2_AQI,Max_SO2_AQI,Max_CO_AQI, Max_O3_AQI from Polls where YEAR(Year)>2013\")\n",
    "test_table.toPandas().to_csv(\"test_data.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------+-------------+-----------+-----------+----------+----------+\n",
      "|      Year|         City|Max_NO2_AQI|Max_SO2_AQI|Max_CO_AQI|Max_O3_AQI|\n",
      "+----------+-------------+-----------+-----------+----------+----------+\n",
      "|2014-01-01|  Albuquerque|       43.0|       92.0|      15.0|      87.0|\n",
      "|2014-01-01| Arden-Arcade|       41.0|        7.0|      19.0|     122.0|\n",
      "|2014-01-02|       Austin|       34.0|       10.0|       5.0|      97.0|\n",
      "|2014-01-01|  Baton Rouge|       55.0|       54.0|      47.0|     129.0|\n",
      "|2014-01-01|   Beltsville|       40.0|       20.0|      10.0|     101.0|\n",
      "|2014-01-01|Bethel Island|       31.0|       14.0|       8.0|     101.0|\n",
      "|2014-01-01|   Birmingham|       82.0|       97.0|      11.0|      90.0|\n",
      "|2014-05-02|       Blaine|       37.0|       11.0|       8.0|      93.0|\n",
      "|2014-01-01|       Boston|       60.0|       40.0|      13.0|      54.0|\n",
      "|2014-01-01|      Bristol|       50.0|       20.0|      16.0|      84.0|\n",
      "+----------+-------------+-----------+-----------+----------+----------+\n",
      "only showing top 10 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "all=sqlContext.sql(\"SELECT  Year,City,Max_NO2_AQI,Max_SO2_AQI,Max_CO_AQI, Max_O3_AQI from Polls where YEAR(Year)>2013\").show(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "all_data=sqlContext.sql(\"SELECT  Year,City,Max_NO2_AQI,Max_SO2_AQI,Max_CO_AQI, Max_O3_AQI from Polls where YEAR(Year)>2013\")\n",
    "all_data=all_data.toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_table.registerTempTable(\"Table\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Panda_temp_table=test_table.toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataFrame[summary: string, Max_NO2_AQI: string, Max_SO2_AQI: string, Max_CO_AQI: string, Max_O3_AQI: string]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_table.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pyspark.sql.dataframe.DataFrame"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(test_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_table1=test_table.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "92.0"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_table.collect()[0].Max_SO2_AQI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "218"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_table.collect())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3><b> Using test data set to predict the values of temperature based on pollution level of each city. </h3></b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[13.949487037037038, 15.797569444444443, 19.923519841269844, 11.556602272727275, 26.339145833333333, 19.923519841269844, 11.067762820512817, 19.923519841269844, 13.949487037037038, 18.436983333333334, 18.67566666666667, 17.036499507947113, 18.436983333333334, 14.4626, 11.756817401960785, 18.436983333333334, 11.756817401960785, 11.067762820512817, 13.949487037037038, 19.923519841269844, 17.036499507947113, 13.808333333333332, 26.339145833333333, 12.32964814814815, 17.036499507947113, 10.745023809523806, 17.294455555555558, 18.066970238095237, 12.32964814814815, 13.949487037037038, 15.797569444444443, 17.036499507947113, 12.32964814814815, 13.949487037037038, 10.745023809523806, 12.870636666666666, 17.294455555555558, 18.67566666666667, 14.4626, 14.4626, 9.701924382716049, 13.949487037037038, 13.949487037037038, 11.067762820512817, 15.797569444444443, 17.036499507947113, 17.036499507947113, 11.756817401960785, 18.436983333333334, 18.436983333333334, 17.036499507947113, 18.436983333333334, 11.756817401960785, 17.036499507947113, 17.036499507947113, 19.923519841269844, 18.436983333333334, 17.036499507947113, 13.949487037037038, 12.32964814814815, 14.26758888888889, 18.066970238095237, 15.797569444444443, 14.26758888888889, 18.67566666666667, 18.436983333333334, 13.808333333333332, 18.436983333333334, 14.26758888888889, 26.339145833333333, 13.949487037037038, 15.051583333333326, 18.066970238095237, 17.036499507947113, 26.339145833333333, 17.294455555555558, 14.26758888888889, 19.923519841269844, 11.556602272727275, 12.870636666666666, 18.436983333333334, 12.32964814814815, 20.738208333333333, 15.797569444444443, 17.036499507947113, 11.556602272727275, 18.066970238095237, 15.051583333333326, 11.756817401960785, 18.436983333333334, 15.051583333333326, 17.036499507947113, 18.436983333333334, 14.4626, 13.949487037037038, 12.32964814814815, 11.756817401960785, 13.949487037037038, 15.797569444444443, 9.701924382716049, 18.67566666666667, 18.67566666666667, 18.436983333333334, 18.436983333333334, 12.870636666666666, 14.26758888888889, 17.294455555555558, 18.066970238095237, 12.32964814814815, 13.949487037037038, 10.745023809523806, 17.036499507947113, 11.756817401960785, 11.556602272727275, 10.745023809523806, 15.600605654761905, 17.294455555555558, 18.066970238095237, 14.4626, 14.26758888888889, 18.436983333333334, 13.949487037037038, 11.067762820512817, 13.949487037037038, 10.745023809523806, 12.870636666666666, 18.436983333333334, 12.32964814814815, 18.436983333333334, 18.436983333333334, 17.036499507947113, 11.756817401960785, 26.339145833333333, 15.789712962962959, 17.036499507947113, 18.422666666666668, 17.036499507947113, 17.036499507947113, 14.26758888888889, 18.066970238095237, 18.436983333333334, 14.26758888888889, 18.67566666666667, 18.436983333333334, 18.67566666666667, 18.066970238095237, 15.051583333333326, 15.797569444444443, 13.949487037037038, 10.745023809523806, 18.436983333333334, 18.422666666666668, 14.26758888888889, 15.600605654761905, 14.26758888888889, 19.923519841269844, 18.436983333333334, 17.036499507947113, 18.066970238095237, 10.745023809523806, 9.701924382716049, 10.745023809523806, 18.436983333333334, 15.051583333333326, 14.26758888888889, 11.556602272727275, 10.745023809523806, 13.808333333333332, 14.26758888888889, 10.745023809523806, 15.051583333333326, 9.701924382716049, 14.26758888888889, 10.745023809523806, 14.26758888888889, 18.066970238095237, 14.26758888888889, 18.436983333333334, 14.26758888888889, 10.745023809523806, 10.745023809523806, 11.756817401960785, 17.036499507947113, 19.923519841269844, 18.436983333333334, 10.745023809523806, 13.808333333333332, 19.923519841269844, 10.745023809523806, 18.436983333333334, 14.4626, 26.339145833333333, 14.26758888888889, 18.436983333333334, 10.745023809523806, 14.26758888888889, 18.436983333333334, 12.32964814814815, 13.808333333333332, 12.32964814814815, 10.745023809523806, 10.745023809523806, 10.745023809523806, 10.745023809523806, 12.32964814814815, 14.26758888888889, 10.745023809523806, 14.26758888888889, 18.066970238095237, 18.422666666666668, 14.26758888888889, 10.745023809523806, 19.923519841269844, 15.051583333333326, 18.436983333333334, 18.436983333333334, 10.745023809523806, 18.436983333333334]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "list_predict=[]\n",
    "for i in range(len(test_table.collect())):\n",
    "    list_new=[test_table.collect()[i].Max_NO2_AQI,test_table.collect()[i].Max_SO2_AQI,test_table.collect()[i].Max_CO_AQI,test_table.collect()[i].Max_O3_AQI]\n",
    "    list_predict.append(model.predict(list_new))\n",
    "\n",
    "print(list_predict)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "all_data['Temperature_Predicted']=list_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>City</th>\n",
       "      <th>Max_NO2_AQI</th>\n",
       "      <th>Max_SO2_AQI</th>\n",
       "      <th>Max_CO_AQI</th>\n",
       "      <th>Max_O3_AQI</th>\n",
       "      <th>Temperature_Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>Albuquerque</td>\n",
       "      <td>43.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>13.949487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>Arden-Arcade</td>\n",
       "      <td>41.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>15.797569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>Austin</td>\n",
       "      <td>34.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>19.923520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>Baton Rouge</td>\n",
       "      <td>55.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>11.556602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>Beltsville</td>\n",
       "      <td>40.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>26.339146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>Bethel Island</td>\n",
       "      <td>31.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>19.923520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>82.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>11.067763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2014-05-02</td>\n",
       "      <td>Blaine</td>\n",
       "      <td>37.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>19.923520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>Boston</td>\n",
       "      <td>60.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>13.949487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>Bristol</td>\n",
       "      <td>50.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>18.436983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>Burbank</td>\n",
       "      <td>71.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>18.675667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>Calexico</td>\n",
       "      <td>93.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>17.036500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>Camden</td>\n",
       "      <td>62.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>18.436983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>Capitan</td>\n",
       "      <td>12.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>14.462600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>Charleroi</td>\n",
       "      <td>42.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>11.756817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>Charlotte</td>\n",
       "      <td>48.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>18.436983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>Cincinnati</td>\n",
       "      <td>47.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>11.756817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>Cleveland</td>\n",
       "      <td>64.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>11.067763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>Concord</td>\n",
       "      <td>45.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>13.949487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>Cornwall</td>\n",
       "      <td>37.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>19.923520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>Costa Mesa</td>\n",
       "      <td>57.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>17.036500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>54.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>13.808333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>Davenport</td>\n",
       "      <td>42.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>26.339146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>Deer Park</td>\n",
       "      <td>39.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>12.329648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>Denver</td>\n",
       "      <td>108.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>17.036500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>Des Moines</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>10.745024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>East Hartford</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>17.294456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>East Highland Park</td>\n",
       "      <td>41.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>18.066970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>East Providence</td>\n",
       "      <td>40.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>12.329648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>East Saint Louis</td>\n",
       "      <td>49.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>13.949487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>Houston</td>\n",
       "      <td>54.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>10.745024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>Indianapolis (Remainder)</td>\n",
       "      <td>54.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>18.436983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>Johnstown</td>\n",
       "      <td>42.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>14.462600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>Kansas City</td>\n",
       "      <td>42.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>26.339146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>Lompoc</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>14.267589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>New York</td>\n",
       "      <td>88.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>18.436983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>North Little Rock</td>\n",
       "      <td>41.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>10.745024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>Northbrook</td>\n",
       "      <td>37.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>14.267589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>Not in a city</td>\n",
       "      <td>65.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>18.436983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>Oakland</td>\n",
       "      <td>45.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>12.329648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>63.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>13.808333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>Portland</td>\n",
       "      <td>44.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>12.329648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>Raleigh</td>\n",
       "      <td>42.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>10.745024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>Reno</td>\n",
       "      <td>45.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>10.745024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>Salt Lake City</td>\n",
       "      <td>57.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>10.745024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>10.745024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>San Pablo</td>\n",
       "      <td>36.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>12.329648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>41.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>14.267589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>St. Louis</td>\n",
       "      <td>40.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>10.745024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>Tucson</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>14.267589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>Tulsa</td>\n",
       "      <td>42.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>18.066970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>Vallejo</td>\n",
       "      <td>36.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>18.422667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>Vandenberg Air Force Base</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>14.267589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>Victorville</td>\n",
       "      <td>51.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>10.745024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>Waco</td>\n",
       "      <td>41.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>19.923520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>2016-03-04</td>\n",
       "      <td>Washington</td>\n",
       "      <td>55.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>15.051583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>Welby</td>\n",
       "      <td>62.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>18.436983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>Wilmington</td>\n",
       "      <td>58.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>18.436983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>Winter Park</td>\n",
       "      <td>38.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>10.745024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>York</td>\n",
       "      <td>56.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>18.436983</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>218 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Year                       City  Max_NO2_AQI  Max_SO2_AQI  \\\n",
       "0    2014-01-01                Albuquerque         43.0         92.0   \n",
       "1    2014-01-01               Arden-Arcade         41.0          7.0   \n",
       "2    2014-01-02                     Austin         34.0         10.0   \n",
       "3    2014-01-01                Baton Rouge         55.0         54.0   \n",
       "4    2014-01-01                 Beltsville         40.0         20.0   \n",
       "5    2014-01-01              Bethel Island         31.0         14.0   \n",
       "6    2014-01-01                 Birmingham         82.0         97.0   \n",
       "7    2014-05-02                     Blaine         37.0         11.0   \n",
       "8    2014-01-01                     Boston         60.0         40.0   \n",
       "9    2014-01-01                    Bristol         50.0         20.0   \n",
       "10   2014-01-01                    Burbank         71.0          6.0   \n",
       "11   2014-01-01                   Calexico         93.0         16.0   \n",
       "12   2014-01-01                     Camden         62.0         14.0   \n",
       "13   2014-01-01                    Capitan         12.0        117.0   \n",
       "14   2014-01-01                  Charleroi         42.0         82.0   \n",
       "15   2014-01-01                  Charlotte         48.0         11.0   \n",
       "16   2014-01-01                 Cincinnati         47.0        122.0   \n",
       "17   2014-01-01                  Cleveland         64.0         95.0   \n",
       "18   2014-01-01                    Concord         45.0         41.0   \n",
       "19   2014-01-01                   Cornwall         37.0         13.0   \n",
       "20   2014-01-01                 Costa Mesa         57.0         11.0   \n",
       "21   2014-01-01                     Dallas         54.0          9.0   \n",
       "22   2014-01-01                  Davenport         42.0         19.0   \n",
       "23   2014-01-01                  Deer Park         39.0         19.0   \n",
       "24   2014-01-01                     Denver        108.0         27.0   \n",
       "25   2014-03-31                 Des Moines         44.0          1.0   \n",
       "26   2014-03-31              East Hartford         33.0          6.0   \n",
       "27   2014-01-01         East Highland Park         41.0         14.0   \n",
       "28   2014-01-01            East Providence         40.0         20.0   \n",
       "29   2014-01-01           East Saint Louis         49.0         43.0   \n",
       "..          ...                        ...          ...          ...   \n",
       "188  2016-01-01                    Houston         54.0          9.0   \n",
       "189  2016-01-01   Indianapolis (Remainder)         54.0         13.0   \n",
       "190  2016-01-01                  Johnstown         42.0         81.0   \n",
       "191  2016-01-01                Kansas City         42.0         19.0   \n",
       "192  2016-01-01                     Lompoc         28.0          1.0   \n",
       "193  2016-01-01                   New York         88.0         14.0   \n",
       "194  2016-01-01          North Little Rock         41.0          7.0   \n",
       "195  2016-01-01                 Northbrook         37.0          7.0   \n",
       "196  2016-01-01              Not in a city         65.0         21.0   \n",
       "197  2016-01-01                    Oakland         45.0         30.0   \n",
       "198  2016-01-04                    Phoenix         63.0          9.0   \n",
       "199  2016-01-01                   Portland         44.0         17.0   \n",
       "200  2016-01-01                    Raleigh         42.0          6.0   \n",
       "201  2016-01-01                       Reno         45.0          7.0   \n",
       "202  2016-01-01             Salt Lake City         57.0          4.0   \n",
       "203  2016-01-01                   San Jose         48.0          1.0   \n",
       "204  2016-01-01                  San Pablo         36.0         17.0   \n",
       "205  2016-01-01                    Seattle         41.0          3.0   \n",
       "206  2016-01-01                  St. Louis         40.0          7.0   \n",
       "207  2016-01-01                     Tucson         42.0          1.0   \n",
       "208  2016-01-01                      Tulsa         42.0         13.0   \n",
       "209  2016-01-01                    Vallejo         36.0          9.0   \n",
       "210  2016-01-01  Vandenberg Air Force Base          7.0          4.0   \n",
       "211  2016-01-02                Victorville         51.0          3.0   \n",
       "212  2016-01-01                       Waco         41.0         13.0   \n",
       "213  2016-03-04                 Washington         55.0         11.0   \n",
       "214  2016-01-01                      Welby         62.0         14.0   \n",
       "215  2016-01-01                 Wilmington         58.0         13.0   \n",
       "216  2016-01-01                Winter Park         38.0          7.0   \n",
       "217  2016-01-01                       York         56.0         26.0   \n",
       "\n",
       "     Max_CO_AQI  Max_O3_AQI  Temperature_Predicted  \n",
       "0          15.0        87.0              13.949487  \n",
       "1          19.0       122.0              15.797569  \n",
       "2           5.0        97.0              19.923520  \n",
       "3          47.0       129.0              11.556602  \n",
       "4          10.0       101.0              26.339146  \n",
       "5           8.0       101.0              19.923520  \n",
       "6          11.0        90.0              11.067763  \n",
       "7           8.0        93.0              19.923520  \n",
       "8          13.0        54.0              13.949487  \n",
       "9          16.0        84.0              18.436983  \n",
       "10         34.0       129.0              18.675667  \n",
       "11         43.0       151.0              17.036500  \n",
       "12         17.0       115.0              18.436983  \n",
       "13          5.0       151.0              14.462600  \n",
       "14          9.0       100.0              11.756817  \n",
       "15         17.0       105.0              18.436983  \n",
       "16          9.0       126.0              11.756817  \n",
       "17         15.0        97.0              11.067763  \n",
       "18         13.0       133.0              13.949487  \n",
       "19          7.0       129.0              19.923520  \n",
       "20         22.0       129.0              17.036500  \n",
       "21         17.0       100.0              13.808333  \n",
       "22          8.0        71.0              26.339146  \n",
       "23         13.0       151.0              12.329648  \n",
       "24         25.0        93.0              17.036500  \n",
       "25          9.0        93.0              10.745024  \n",
       "26          7.0       143.0              17.294456  \n",
       "27         14.0        84.0              18.066970  \n",
       "28         15.0       108.0              12.329648  \n",
       "29         16.0       112.0              13.949487  \n",
       "..          ...         ...                    ...  \n",
       "188        15.0        61.0              10.745024  \n",
       "189        16.0       105.0              18.436983  \n",
       "190         8.0        64.0              14.462600  \n",
       "191         7.0        49.0              26.339146  \n",
       "192         5.0        48.0              14.267589  \n",
       "193        14.0        61.0              18.436983  \n",
       "194        10.0        46.0              10.745024  \n",
       "195         7.0        44.0              14.267589  \n",
       "196        20.0        74.0              18.436983  \n",
       "197        16.0        45.0              12.329648  \n",
       "198        18.0        67.0              13.808333  \n",
       "199        15.0        51.0              12.329648  \n",
       "200        10.0        54.0              10.745024  \n",
       "201        13.0        47.0              10.745024  \n",
       "202        15.0        36.0              10.745024  \n",
       "203        15.0        67.0              10.745024  \n",
       "204        11.0        61.0              12.329648  \n",
       "205         8.0        35.0              14.267589  \n",
       "206        11.0        43.0              10.745024  \n",
       "207         8.0        58.0              14.267589  \n",
       "208        14.0        74.0              18.066970  \n",
       "209        20.0        84.0              18.422667  \n",
       "210         5.0        51.0              14.267589  \n",
       "211        10.0        84.0              10.745024  \n",
       "212         3.0        64.0              19.923520  \n",
       "213         9.0        93.0              15.051583  \n",
       "214        18.0        49.0              18.436983  \n",
       "215        16.0        61.0              18.436983  \n",
       "216        15.0        67.0              10.745024  \n",
       "217        11.0        64.0              18.436983  \n",
       "\n",
       "[218 rows x 7 columns]"
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "all_data.to_csv(\"Predicted_temperatures_DT.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "list_of_data=[]\n",
    "for i in range(len(test_table.collect())):\n",
    "    list_of_data.append([list_predict[i],test_table.collect()[i].Max_NO2_AQI,test_table.collect()[i].Max_SO2_AQI,test_table.collect()[i].Max_CO_AQI,test_table.collect()[i].Max_O3_AQI])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[13.949487037037038, 43.0, 92.0, 15.0, 87.0],\n",
       " [15.797569444444443, 41.0, 7.0, 19.0, 122.0],\n",
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       " [13.949487037037038, 39.0, 79.0, 17.0, 101.0],\n",
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       " [15.051583333333326, 46.0, 14.0, 9.0, 84.0],\n",
       " [9.701924382716049, 61.0, 55.0, 11.0, 54.0],\n",
       " [14.26758888888889, 27.0, 4.0, 8.0, 97.0],\n",
       " [10.745023809523806, 74.0, 4.0, 9.0, 54.0],\n",
       " [14.26758888888889, 33.0, 4.0, 6.0, 84.0],\n",
       " [18.066970238095237, 37.0, 10.0, 14.0, 74.0],\n",
       " [14.26758888888889, 37.0, 4.0, 7.0, 47.0],\n",
       " [18.436983333333334, 85.0, 11.0, 20.0, 46.0],\n",
       " [14.26758888888889, 32.0, 0.0, 6.0, 80.0],\n",
       " [10.745023809523806, 42.0, 7.0, 11.0, 44.0],\n",
       " [10.745023809523806, 45.0, 6.0, 10.0, 80.0],\n",
       " [11.756817401960785, 37.0, 40.0, 11.0, 67.0],\n",
       " [17.036499507947113, 77.0, 21.0, 23.0, 64.0],\n",
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       " [10.745023809523806, 22.0, 1.0, 10.0, 38.0],\n",
       " [13.808333333333332, 50.0, 0.0, 19.0, 50.0],\n",
       " [19.923519841269844, 22.0, 11.0, 3.0, 61.0],\n",
       " [10.745023809523806, 54.0, 9.0, 15.0, 61.0],\n",
       " [18.436983333333334, 54.0, 13.0, 16.0, 105.0],\n",
       " [14.4626, 42.0, 81.0, 8.0, 64.0],\n",
       " [26.339145833333333, 42.0, 19.0, 7.0, 49.0],\n",
       " [14.26758888888889, 28.0, 1.0, 5.0, 48.0],\n",
       " [18.436983333333334, 88.0, 14.0, 14.0, 61.0],\n",
       " [10.745023809523806, 41.0, 7.0, 10.0, 46.0],\n",
       " [14.26758888888889, 37.0, 7.0, 7.0, 44.0],\n",
       " [18.436983333333334, 65.0, 21.0, 20.0, 74.0],\n",
       " [12.32964814814815, 45.0, 30.0, 16.0, 45.0],\n",
       " [13.808333333333332, 63.0, 9.0, 18.0, 67.0],\n",
       " [12.32964814814815, 44.0, 17.0, 15.0, 51.0],\n",
       " [10.745023809523806, 42.0, 6.0, 10.0, 54.0],\n",
       " [10.745023809523806, 45.0, 7.0, 13.0, 47.0],\n",
       " [10.745023809523806, 57.0, 4.0, 15.0, 36.0],\n",
       " [10.745023809523806, 48.0, 1.0, 15.0, 67.0],\n",
       " [12.32964814814815, 36.0, 17.0, 11.0, 61.0],\n",
       " [14.26758888888889, 41.0, 3.0, 8.0, 35.0],\n",
       " [10.745023809523806, 40.0, 7.0, 11.0, 43.0],\n",
       " [14.26758888888889, 42.0, 1.0, 8.0, 58.0],\n",
       " [18.066970238095237, 42.0, 13.0, 14.0, 74.0],\n",
       " [18.422666666666668, 36.0, 9.0, 20.0, 84.0],\n",
       " [14.26758888888889, 7.0, 4.0, 5.0, 51.0],\n",
       " [10.745023809523806, 51.0, 3.0, 10.0, 84.0],\n",
       " [19.923519841269844, 41.0, 13.0, 3.0, 64.0],\n",
       " [15.051583333333326, 55.0, 11.0, 9.0, 93.0],\n",
       " [18.436983333333334, 62.0, 14.0, 18.0, 49.0],\n",
       " [18.436983333333334, 58.0, 13.0, 16.0, 61.0],\n",
       " [10.745023809523806, 38.0, 7.0, 15.0, 67.0],\n",
       " [18.436983333333334, 56.0, 26.0, 11.0, 64.0]]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_of_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_target=[]\n",
    "list_features=[]\n",
    "for i in range(len(list_of_data)-200):\n",
    "    list_target.append(list_of_data[i][0])\n",
    "    list_features.append(list_of_data[i][1:])\n",
    "len(list_target)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13.949487037037038"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_target[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/v++R+iyKIrb2qizPn37hz5rMZZifnvIGmiRJ0hgKf/xjANc4SpIkPSMLNKXa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iW88CPU7Qe7P4XDCwrdTRAE/HX+BIYGFu0efIpwt3RneN3hbL+1nVOhpwpsr+1ixZXQBPQl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OkJXzHyFFEKBmR5gVClVyF+xSY+H8BljTHpY1hMPzILYY8ZF5+PaFfmsg5DT8UDYiWm0X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zlTdhcuRis/annbEhqbSlicYf67SUi8ycT8vpKUkydxHjcWpYcHAKlXrxI5cyYWrVph/f77ep5Q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4uWUR0i2XTiIn/H3T4S9F4lb/NmshYuwmfIEPK3bcNw/DjBH36A2t/f3qNJJPc+SpXYyNB3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\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hqOxBk0jqC4pajd+703Hs14+k998nZeEPlh5JUsNIAU1iNgqLCtkUs4mu/l3Ra6xjp2JN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nlODhnvvMORgYM4PXMmnj17ELPoe6I+mIWxY0cA7GYLiqHhXqOtp09jN5vRR0U22DWb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\n6UATomEKDzTw2cNJ3NQpkldXHuaZxfsoszSdHQZ16cFOD+Lv5c+03dMufcx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QooYkQBP1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RNm9oWvnoDCLE9HJy4wREdj7NSJguWuSaBpg4IIumUY+d98g72oqFr3KHo9xnbtJIEmhBAV\naEBPkISoWKwplqP5R68enu4C0sJRCOEqJj89H0zszvheLZm17igPz9tBsaVxzB3IL7FSancQHlCz\nBBpAv+b96Na0G+/+8i7nredrtX+vZr2wOqzsyNpRq/vhshloDaWFI4DBD25+ATJ3we6FdV+vWRfQ\n+VaaQOvYLIgQP32VbRzLEmiZ+bVPWgohqkdRFF4Z0YkSq51Xl+0r9/W/DGtPVoGF9zdWMRfYJwBS\nP4VuE9XEzBcPgU0qSetFYCTc961a9Tc/FTK2wYRvwNRCTaodW1/+fU4n7PoUZvVX52De9RHcNh0M\n/m4J22E2o/j6VnldabqaQJMKNPex5agHQXWhMgOtpkp+/ZX0RyaTNmo053/6ibDHHyd+7RrCp0xB\nGxzs6fA8R2eAXpNhyk7oORl2zVfno637F5TW7nu5cK2glBTMe/ZQ6qIZZCGpY3GeP0/+0qXVvseY\nmIh53z6cdmndLoQQvycJNOEV4kxxFFmLOFtS9cyWmipLoEkLRyGEK+i1Gl6+PZEXb+3A2v1ZjJ65\nmVN53p+AKKuIiAiq3syUyymKwtPdnibHnMOH+z6s1f5dI7pi0BjqNAft4gy0htLCsUynO6FZV1j7\nEpQW120tnQGie1TaElKjUegdH8aGw2cqPZgSaVIfrp7Olwo0IdwhPiKAR25szZJdp8qtEL2hVSiD\nOzTl3R+OcK6oioSYVgfD31AT9Hs+V2dzleTWU+TXOP8mMOFraHY9fDYBjv2otk4Lbgmf3qX++nLm\nfFj8ACyZrFabTd4IHW53a8hOsxmNsToVaGpLaqlAcx97rvrfqTb4GqqUqqOSX37hxKRJpN15F+d3\n7iT8ySlq4uzxx9CaTJ4Or+HwC4WUv8NjWyF+IHz/Crx1A/yyQCqVPSwoZQgABStWumQ9306JGDt1\nInf+/GofQjcmJuI4f57StDSXxCCEEI2JJNCEV4g1xQJUaw6a3VGzEzNBPkGAVKAJIVxHURTu6xPL\n+/d1JyPnPLe/vZFd6d6dpM8uS6DVcAZamevCryO5ZTJz98zlXEnF87kqYtQZ6dK0C5sza59Asze0\nGWhlNBpImQaFp2DTjLqv16ovZO2B8zkVXtIvPoysAguHsytu7RLgoyPQR0emJNCEcJvHboonpokf\nf12yB7P16u+0U1PaUWK1M+O7w1UvpijQ7xm4Y45aGfXeEMg9Xg9RC3yD4Z4vIaa3WvF3cIVaiRYc\nA5/cBUfXqded2Kq2UNu7BAb+VU28mVq4PVyHxYLiU/WBmNL0DDT+/td29Y6b2XPVz26ZgVa18z/v\n5MQDD5I2JhXz7l8Jf/pp4teuIWzyZLSBgZ4Or+Fq0hrGzFNb0AZEwJcPw/9uct08XlFj+ubN8b3u\nOgpWLHfZmiGpqZQePkLJ9u3Vut43sSOAtHEUQohySAJNeIU4UxxQvQTagZwDACSEJFRr7UCD+uW6\nyFq9/tBCCFFdA9pG8MWjvTHqNYyZtZmvfznl6ZBqLbtQTaKE1zKBBjCl6xQsdguzd8+u1f1JUUkc\nyj1U62pk24UEWoOrQANo2Qs6jICN/4WCOv49iekDOOFExcnGvglhAFW2cYw0GaUCTQg3Muq1vHR7\nIsfOFjNz3ZGrXo+PCGBM92jmbTlO2tlqVqx2vlNN7hSdhvcGqXNwhOv5BMLdn0HrgeqMob1fqAmy\n0Fj4ZDR8MAw+GKpee/8K6P8n0Gg9EqqzpASNb9UJNGt6OvroaBSlAX5uNlK2HDWBJi0cK3Z++3aO\nT5zI8bvvxrxvHxF//AOt16wh7OFJaAMCPB2e94jpDQ9+B3f8D4rPwtxhsGCcOptRuF3g0BQs+/a7\nrAIsaNhQNEFB5M5fUK3rDXFxKL6+lOzZ65L9hRCiMZEEmvAKEX4R+Ov9OZZfxcwHYOvprQD0jOxZ\nrbU1ivxnIISoPwlNA1nyaB86tzDxxPydvLnmYL3Mc6xvZ+pYgQZqNfHIhJF8dvAz0gvSa3x/UrMk\nALZkbqnV/pcq0Bro+/6g/wcOG6x9uW7rNO8GOmOlJ4lbhPgRG+bPhnLaxF0u0mQks0ASaEK4041t\nwhneOYp3vj/CsXKSZE8lJ2DQafjXqt+qv2irvnD/KtAa4INb4OAqF0YsLjL4wdj50G44LJ8Ku+bB\niHfAXqrOpvQLhUc2qK12Paj6FWjpGKLdXyF3LbPnqh0LpPXg1Yq3buP4vRM4Pv4eLAcPETF1KvFr\n19DkwQfRBrhnfmCjo9FA57vgie0w8G9w9Ad4uweseLbSTgbC9YJSUgAoWLHCJetpfH0JHjmCgtWr\nsZ2t+vChotVi7NAB86+/umR/IYRoTBroEyQhrqQoCrFBsdWqQNt2ehsAPSI9+4OpEEKUaRLgw7wH\nezKqawveXHOIKQt2lduaqyHLLrDgq9cS4KOr0zqTr5uMTtExY1fNWxW2C21HsE9wreegNegKNICQ\nVuqQ918+rVuFiN4ILbrD8Q2VXtY3PowtR89Raqt47kVkkJHT+d4/w08Ib/P88A746DT8bcmeqw5d\nRAQaeahfHMt2Z9asPXBEO3hwjdq+a34qbP/AxVELAHQ+cOdcSBwFa16E2QMuvWYphIzqtdOqT9WZ\ngeZ0OLBmZKBvIfPP3Mmek4PGZELR6z0dSoPgdDop3ryZ4+Pv4cSECViOHaXps/9H/JrVNLl/Iho/\nP0+H2DjofaH/H+GJn6HLeNg6E6Z3gc3vgK3U09FdE/SRkfh27UrBt65r4xg8JhWsVvIWf1Gt630T\nEzHv34/TZnNZDEII0RhIAk14jbjgOI7lVV2Bti1TTaB1i+xW3yEJIUS1+ei0vH5nZ/6c0o5vdp8i\ndfaWi20RvUF2oYXwQJ86t3GK8Ivgng73sPzYcvad21ejezWKhp5RPdlyakutqvjsFwakN7gZaJfr\n9wfwC4OVz0FdKhVj+sDpX8Fc8XzPvglhnC+1s/NEboXXRJmMZBdasNpluLwQ7hQRZORPKW3ZcPgs\nX5XT/veh/nGEBRiY9u3+mr0fBkaqc29aD4RvnoK1L9XtvUaUz16qVgKXaTcc/nQUmiTA/LFweI3H\nQnPa7ThLS1GMvpVeZztzFqfFgqGlJNDcyZ6Xi05mzuF0OinasJHj48ZzYuL9lJ44QdPnniN+9WpC\nJ0xA41v5319RS4FN4db/wiMb1Y4GK5+Fd3rC/q/ls8INgoYOxXLwIJYjrmmj6RMXi1+vXuQtXIjT\nXvXhTWNiIk6LxWX7CyFEYyEJNOE1Yk2xZJdkU1Ra+awyq8MKQJAhyB1hCSFEtSmKwuQBrZk5vhu/\nnS5kxFsb2XeqwNNhVcuZQkud2jdebmLiREw+Jt7c8WaN702KSiK7JLtaFcm/Z7VfaOGobcAJNKMJ\nbvqL2upr/9e1X6dVH3A64ETF7S6TWjdBq1EqbeMYafLF6bzUwlMI4T7jesbQuYWJl7/ZT36J9YrX\nAnx0PJnchq3HcvjuQHbNFvYJgLELoOu9sP7f8MUkqTBwpVO7YFZ/2PUp9H0aut0HB76B71+Fe5dA\neBuYfzcc8kwSzWlR38+rqkCzZqitlqUCzb1sOblor+H5Z06nk6IffyQtNZX0Bx/EeuoUTf/2V1qv\nXkXoPePRGKtuPSpcoGkHuOcLGLcYtD6wcLw6x/Hkz56OrFELHDwYFMVlbRwBQlJTsZ46RdH69VVe\na0zsCIB5zx6X7S+EEI2BJNCE14g1xQJUaw6aEEI0ZEM6RrLokSQcThg9cxOr92V5OqQqZReaiQhy\nTQIt0BDIpE6T2Jy5mU2nNtXo3rrMQWvwM9DKdJ0A4e1h9fNgq2XiqkV3ddZRWsU/LAcZ9VzXwsT6\nQxUn0KJM6oOq0zIHTQi302oU/j6yEznFFv618sBVr6d2jyYuzJ/Xlh/AVtMqUa0Obp2uzrz59TOY\ndweU1KAdpLiawwGbZsCcZCg9DxO+guQXYfib0HsKbH9PfV8f/yWEt4UFY+HQaveHeSGBVtUMtNJ0\nNYEmM9Dcy56bizYkxNNhuJ3T6aTw++9Ju2sM6ZMexnbmDJEvvkDrVSsJHTcOjY9rvoOKGkpIVuc2\nDn8Tzh2C/92kHrrIz/B0ZI2SvmkEft26UbDcdW0cA28eiC48nLz5C6q81hATgyYggBJJoAkhxBUa\n+BMkIS6JM8UB1KrqQAghGprE5iaWPt6H+IgAJn28nVnrjtSqLaG7ZBdaCA9w3cOL1HapNPNvxps7\n3sThrP6D32YBzYgJiqnVHLQGPwOtjFYHQ16B3GOwbXbt1tD7QvMbIG1jpZf1TQhnd0Ye+eet5b4e\nWZZAy5cEmhCekNjcxITerfhk64mr2q3qtRqmprTlUHYRi3+uxcNMRVFn3oycrVarvp8Ceekuivwa\nU5gFn4yCVX+FNkNg8kaI7a++pigw6CUY8Czs+gSWT4XxiyGiPSy4Gw6ucmuozhJ1rqVSVQVaegYo\nCvpmzdwRlrjAnpODNvTaSaA5nU4K164lbdRoMiY/ij0nh8iX/h/xK1YQkpqKxmDwdIhCq4MbJqrz\n0fo+A3uXwIxusPZlda6jcKnAYUMpPXwEy6FDLllP0esJvvNOin78kdKMyr8rKBoNxo4dMe/Z65K9\nhRCisZAEmvAaLQJboFN0lVagmW3ygE8I4T2aBhlZOCmJYZ2imLb8AFM/302preHNmjJb7RSabUQE\nua5tjkFr4PEuj7M/Zz8r01bW6N5eUb346fRPF1v2Vpfd7gUz0MrEJ0P8IFj3Lyg+V7s1WvWBzF8q\nfbjRLyEMhxM2Hy2/Cq2sAi1TEmhCeMwzg9oQEejDc1/uuarSbEjHSLq2DOY/qw9yvtRWuw2uG6O2\n6io4pVZPZf7igqivIQdXwru94fhmGP4GjJkHfr9rwacoMOD/1ETa3i/g66fg7kUQ0QEWjoPfXNeu\nqyoOc1kLx8pnSFkz0tFFRaJIAsNtnE4ntrw8dCGNv4Wj0+GgYNUqjo28g4zHHsdeWEjUq6/QesVy\nQu66S/7eNUTGIEh+AZ7YAe1vg/Wvw/SusGMuOKqeryWqJ2jwYNBoXFqFFnzXnaDRkLfwsyqvNSZ2\nxHLgAM5Sae0shBBlJIEmvIZeo6dlUMtKK9B+OaP+wN81oqu7whJCiDrxNWiZkdqFKTcnsGhHBuPf\n20pOccP6gaVs/lW4i2aglRkWO4w2IW2Y/vN0rPbqJ8OSopI4bzvP7jO7a7TfxQq0hjwD7XKDX4HS\nIvhhWu3uj+kDTjuc2FrhJddHBxPgo6uwjaPJV49Rr+F0fkntYhBC1FmgUc8Lt3ZkX2YBczelXfGa\noij8ZVh7sgosvL+hDm3OY/vD/StAo1Pn3HhoPpdXsZrh26nw6V0QGAmTfoAb7leTZRXp8yQMex1+\nWwZLHlFn0TXtqM4X+s11D0sr47SoByKqqkArPZGOQeafuZWjqAis1kbdwtHpcFCwYgXHRozk5JQn\ncZScJ2raNFov/5bgUaNQ9HpPhyiqEhwNo/4HD34HTVrD10/CzH5weK2nI2sUdGFh+PXoQcHyFS7r\nTqKPjCTgpgHkLV6Mo4rEmG9iIk6rFbOLKuCEEKIxkASa8CpxprhKK9C2ZqoPCXtE9XBXSEIIUWca\njcIzg9rw39Tr2ZWex8h3NnI4u8jTYV2UXag+bItwcQJNq9HyVNenyCjKYNHBRdW+r3tUdzSKpsZt\nHC/NQPOSBFpEO7Vlzvb34cxvNb8/uof6MLySOWh6rYZecaFsOFx+Ak1RFKJMvlKBJoSHDU2MZEDb\ncP6z+iCn8q5MaN/QKpTBHZoyc91RzhXVcm4iQNMO8OAaCI1Vk0I/f1THqBux7P3wv4GwbRb0nAwP\nrlXfs6ujx0Nw21tw5HtY/IBasRbZCRbeAwe+rd+4AUeJ+n6uMVYxAy0jHb3MP3Mre04OQKNs4ei0\n28lftoyjt93Gyaeexmm10uxf/6T1smUEjxyBotN5OkRRUy26wcTlcNdHYC1WZ2nOG62+P4o6CUpJ\nofTYMSy/1eL7fwVCUsdiz8mhcFXlszeNiYkA0sZRCCEuIwk04VViTbGkF6ZXWKmw7fQ2AHpESgJN\nCOF9br++OfMf6kWxxcbIdzby48Ezng4JqL8KNIC+zfvSPbI7s3bPothaXK17ggxBJIYlsiVzS432\n8poZaJcb8CwYAtS5OjVl8Ifm3eB4FXPQ4sM4fu486Tnny309MsgoM9CE8DBFUXj59kTsDicvfb3v\nqtenprSjxGpnxneH67ZRUJT6QDRuAHz1BHz3KjTg+Zxu53TCT3Ng9gAoylLbMA59DfQ1bHHc9R4Y\nNUedPffZBLhzLkR1hs/uhQPL6iPyi8oq0CpLoDlKSrCfOYshumW9xiKuZM9V5xzqGlEFmtNmI//r\nrzl6622c+sMfAWj279eJ++ZrTLfeKokzb6co0OF2eGwbDH4VMrapLW2/eRqKGsbPMd4ocPAg0Gop\nWO669r7+vZPQt2xJ7oL5lV6nb9ECjcmEec8el+0thBDeThJowqvEmmKxO+2cKDxR7utlLRw7h3d2\nZ1hCCOEy3WJCWPJYH5oH+zJx7k98tDnN0yGRfSGBFhHouhloZRRF4emuT5NjzuHDvR9W+76kqCT2\nnN1DYWn1h5dfqkDzoq8//mHQ/49waFXtWuPE9IFTO6G04uRk34RwgArbOEaajFKBJkQDEB3qx5Sb\nE1ix9zRr92dd8Vp8RACp3aOZt+U4aWerdxihQj6BcPdC6DIefvwnfPkI2BpWa2GPKD4HC8bBsj9A\nTG+YvAnaDK79ep1Gq5Ubp3erc9BGzYGo69Qk2v6vXRf37zjMZS0cK/5Mt2ZkAEgFmpvZctQEmjbU\n+2egOW028pYs4egtwzn1p6koWi3N33yDuK++wnTLLSharadDFK6k84Hej8OUXdBjklrBPL0LrP83\nWKUNeE3pQkPx79mTghXLXdbGUdFoCBkzhpLtOzAfPFjxdYqCb8eOlOyVBJoQQpTxiidIiqK8ryhK\ntqIoey77vX8pinJAUZTdiqJ8qShKsCdjFO4RFxwHUGkbRwAfreurJIQQwl1ahPjx+eTeDGgTzvNL\n9/LC0j3Y7A6PxZNdYEGrUQj1r5+B7p3COzEoZhBz987lbEn5SZzfS2qWhN1pv1h5XB1lFWjeVIAG\nQM+HIaSVWoVmt9Xs3lZ9wGGD9IrnoLUO9yfKZGTD4fJPCkeajGQVmHE4pApFCE97qF8cCREBPL90\nL+dLr3w/eDI5AYNOw79WuaDlk1avthm86TnYvQA+GQ3m/Lqv662OroOZfdTDDEP+DuMWQ2DTuq/b\nfjiMnQ9nD8H8sTByJjTrAovug31f1X39cjgt6qEYxafin5dK09UEmiFaZqC5U1kFmjfPQHNareQt\n/oIjw24h8/+eRfH1pfn0/xK7dAlBKSko3nSISdScXygM/Qc8ulWdrbn2JXirO/z6uVQz11Dg0BSs\nx09g3nd11Xltme4YiWIwkLdgYaXXGRMTsRw8hMNSh7bQQgjRiHjLt5e5QMrvfm81kOh0OjsDB4Fn\n3R2UcL/YoFgAjuYf9XAkQghRvwJ8dMy+9wYe6hfLh5uPc/+H2ykwl9++tr5lF5pp4m+o19aHU7pM\nodReyuzds6t1feewzvjqfGs0B81md6DXKiiKl2XQdD4w6CXI3gc7P67ZvdE9QdFCWsVtHBVFoW98\nGBsPn7tYpXe5KJMRm8PJ2WL5IVoITzPoNLwyIpGTeSVMX3tlu8aIQCMP9Ytj2e5Mdp7IrftmigI3\nToUR76qtYN9PgfyMuq/rTexWWP0CfHS72k73obWQ9Bi4MgkQnwzjPoe8dHX23K3ToVnXC0m0pa7b\n54KLM9B8fSu8xpqRDoBeEmhuZc9VZ6B5YwtHZ2kpuYsWcWToMDKfew5NgD8t3n6L2C8WEzR4sCTO\nrjVh8TD2U5jwDfiGqPMe59ystq0V1RKYnAw6HYUrXNfGURcSQtDQFPKXLsVRXHG1ujGxI9hsLp3B\nJoQQ3swrvsU4nc4fgZzf/d4qp9NZduxyCyD9Ja4Bfno/Iv0jXZ5A02v0AFjs8nBQCNFwaDUKz93S\ngX+M6sSmw2e5451NHD9Xx9ZctXCm0EJEUP1W9rYytWJUwigW/baI9IL0Kq/Xa/V0j+xeozlodofT\nu+afXa79bdCyN3z/KpgLqn+fTyA0ux7SNlR6Wd+EMPJLrOw5eXWFSWSQ2uYrK18+I4VoCHrGNeHO\nbi2Ys/4ov52+so3tQ/3jCAvwYdryAy5r+8T1d8P4xWrybE4ynP7VNes2dOeOwHuDYeOb0PVeeHid\n2mKxPsT2g3uXqm0iP70LbnkdWtwAiybC3i9dulXZDLSqKtA0/v5og6XJizvZcnJRfHxQ/Pw8HUq1\nOUpLyV2wkCMpQzn9t+fRBgfT4t13iF28mMCbb5bE2bUuth9MWgcjZkJBJrw/RG1TmyMHoquiCwnB\nPymJgm9d18YRIGTsWBzFxeR//U2F1/gmJgJQInPQhBAC8JIEWjXcDyyv6EVFUSYpirJdUZTtZ87I\nIFNvFxsUW24Lx3yL+tCvNu0bTT6mK9YQQoiGZEz3lnz8QE/OFlkY8fZGth4959b9swst9TL/7Pce\nue4R9Fo9M3bOqNb1SVFJHC84zqmiU9W63uZwetf8s8spCgx5FYrPqPMkaqJVXzi5A0rPV3hJn/gw\nADYcvrqFZpRJrVLIzJcZFkI0FM8Oa0+AUcdzX/56RXvVAB8dTyYnsO1YDt8dyHbdhnED4P4VgALv\nD63dTEZv4XTCrk9hVn/IOQJ3fgi3TQeDf/3uG90dJnylzqz85C4YMg1adIfPH4A9X7hsm7IZaJrK\nZqCdOIE+Otr7Kra9nD03F21IiFf8uTssFnI+/ZQjQ1I4/eKLaMPDiJ49i1aLPiPwppu84t9BuIlG\nA9ePhSd2qG2BD62Bt3rAyuegJM/T0TVoQUOHYj15ErMLE1nG667Dp317chcsqDAxp4uKQhsainnP\nXpftK4QQ3sxLnyJdoijKc4AN+KSia5xO52yn03mD0+m8ITw83H3BiXoRFxzHsfxjOJxXzgPafno7\nAD0ie9R4zWAf9XRlnkW+wAkhGqak1k1Y8mgfQvwNjH9vK4u2V12l5SrZhRbCA+p/tmS4Xzj3dLiH\n5WnL2Xuu6h/YkpolAVS7Cs2rK9AAmneF68bClncgN63698X0BYcVMn6q8JKwAB86RAWx/tDVB40i\nTepD1tMF5ppGLISoJ6H+Bv4ytD3bj+eyaMeVnwep3aOJC/PnteUHXDs/s2lHeHANhMSoVVI757lu\n7YbCnA+LH4Qlk9Vqs8mboOMI9+3f7HqY+C3ghE/vhOQX1Va8ix+EPYtdsoWzGgm00owMDNHS4MXd\n7Dk5aEMbdvtGh9lMzsfzODJoMFkvvYw+MpLoOXNotWABAf37S+JMVMzgp7YFnvIzXJcKm9+G6V1g\n6yy1Xa64SuDNA0Gvp2C569o4KopCSGoqlgMHKNm1q8JrjIkdXZq4E0IIb+bVCTRFUSYAw4FxTlfW\nNIsGLc4UR4mthOzzV56q3Xp6KwA9o3rWeE2pQBNCeINWYf58ObkPPWOb8KfPdzNt+f4rKg/qg93h\n5FxR/bdwLDOx40SCfYJ5c8ebVV4bZ4ojwjei2nPQbA4HOm9OoAEM/Js602zNi9W/p2UvUDTqDKNK\n9EsIY8fxXM6X2q74/Sb+BvRahcx8SaAJ0ZCM7taCHq1Cmbb8AOeKLrVY1Ws1TE1px6HsIj7f4eKZ\nZabmMHE5tOoHSx+D76epFVuNQfo2mNlXbZl4019hwtdg8kASKaK9+mes84X5Y2DA/6nv44sfhF8/\nr/PyDrNZrQjR68t93elwYM3IQB/dss57iZqx5eWiC26YCTRHSQk5H37I4UGDyHr1VfQto2n5wfvE\nfPoJAX37SOJMVF9gJNz+FjyyHiI7wfKp8E4vOPBt4/k8cRGtyURA794UrHBtG0fT8FvQ+PuTt2BB\nhdf4JiZiOXwYx/mKO1gIIcS1wmsTaIqipAB/Bm5zOp3yjn4NiTXFAnA078q+2dsytwG1q0At4+34\nAAAgAElEQVQzGSSBJoTwDiY/PR9M7M74Xi2Zte4oD8/bQbHFVvWNtXSu2ILDCRGB7kmgBRgCeLjz\nw2zJ3MKmU5sqvVZRFHo168XWzK1XVSWXx+sr0EB9eN3nSfUB74mt1bvHGASRnas1B81qd7L12BVj\nZ9FoFJoGGTktCTQhGhSNRuGVkYkUmW38/dsDV7w2pGNTusWE8Maag1clxevMGATjFsH142Dda2oi\nzZurBxx2WPdPeD9F/fX9K+DGP4FG67mYmrRWK9F8Q2DB3dD3aXUO5hcPwe5FdVraabagGI0VJjxs\nZ87itFikAs0D7Dm5aENDPR3GFRznz3Pu/Q84PGgwWdNewyc2jpZz5xLz8cf4JyVJ4kzUXmQndfbj\n3Z+pB70WjIUPb4VT5VdFXauChg3FdioT8y+/uGxNjb8/pttvp2D5Cmy5ueVeY0zsBA4H5gMHyn1d\nCCGuJV6RQFMUZT6wGWirKEqGoigPAG8BgcBqRVF2KYoy06NBCre5mEDLvzKBdiT/CABtQ9vWeM1g\no7RwFEJ4D71Ww8u3J/LirR1Yuz+L0TM3cyqvfuZTZReoVQ3hbkqgAdzV9i6aBzTnzR1vVpkY6xXV\ni1xLLgdyqv7hzmZ3en8FGkCfKRAYBSufBUc127O16gsZ28FacRKse6tQDDoNGw6VNwfNKDPQhGiA\n2jQN5KH+cSz+OYPNRy7Nx1QUhWeHtiOrwML7G66eHVxnWj3c/jbc+H+w6xP45E4wF7h+n/qWlw5z\nh8P3r0LiHfDIBoiu+WG8ehESo1aiBTWDhfdAr8kQ0we+nAS7P6v1sg5zCRqfij/TrRlqS1B9i+ha\n7yFqp2wGWkPgKC7m3Jw5HE4eRPY//4lPQjwxH39EzEcf4t+rpyTOhGsoCrQZorbLHfY6ZO+D2QPg\ny8lQUL0Zx41dwMCBKHo9BcuXu3TdkLGpOEtLyf/iy3JfN3bsCCBtHIUQAi9JoDmdzrFOpzPK6XTq\nnU5nC6fT+Z7T6Yx3Op3RTqfz+gv/e8TTcQr3aGJsQpAhiGP55T8M0Cg1/2stLRyFEN5GURTu6xPL\n+/d1JyPnPLe/vZFd6a4/BHCmqCyBVvGsFFczaA083uVx9ufsZ8Wxynv+12QOmt3hRKttBA98DP5w\n8/Nwckf1Z+K06gt2C5zcXuElRr2WHq1Cy02gSQWaEA3XlIEJtAjx5a9LfqXUdimpfkOrUIZ0bMrM\ndUc5e1mLR5dRFLjpWTWRlrYePhgK+Sddv0992bsEZvaB07th5CwYNQeMJk9HdaWgZnDft2pF2ucT\nodt9F5JoD8MvFbfeqozTbEHxrWT+WbqaQJMKNPdylJbiKCpC5+EZaPaiIs7Oms3hm5PJfv3fGNu3\nJ+bTT4j54AP8unf3aGyiEdPqocdDMGWnelBsz+cwoxt8/3ewFHk6Oo/SBgbi378/BStW4qzuwblq\n8ElIwPeGbuQuXFjuuvqmEegiIiiRBJoQQnhHAk2IyymKQqwp9qoKtLqQFo5CCG81oG0EXzzaG6Ne\nw5hZm/n6F9ee1jxzoQLNXS0cywyLHUbbkLZM3zkdayWtwcJ8w0gISajWHDSrw4lO00i++nROhajr\n1FlopdXoZN0yCVAgrfI5aH0Twvgtq5DsgiuTZWoFmtml8xeEEK7ha9Dy8u2JHDlTzOwfj1zx2tSU\ndpRY7cxYe6j+AugyXm3BlXsc5iTD6Qb+sK20GJY+DosmQJN4dQ7PdamejqpiAeHqPLamiWri7Pq7\n1UMRXz4Cu+bXeDmHxYzGp+IEmjU9AxQFfbNmdYla1JD9Qhs1bYhnWjjaCws5++67HL45mTNvvIGx\ncydi5n9Ky/fm4Ne1q0diEtcgowkGvQSP/wRtUmDdP9RE2s8fq+12r1FBKSnYsrIo2eXa9pYhqWOx\nnjhB8abyf44yJiZi3rPXpXsKIYQ3aiRPkcS1Js4U59oE2oUKNGnhKITwRglNA1nyaB86tzDxxPyd\nvLnmoMsSHdmFaiLFnS0cQa0mfrrb05wsOslnBytvVZUUlcTPWT9jtlVeIWV3OBpHC0cAjQaGTIOC\nDNjydtXX+wZDZCIcr2IOWnwYABsOX1mFFmnyxWJzkHfei+ccCdGI3dQugqGJkcz47jDHzxVf/P3W\n4QGkdo/mk60nSDtbXMkKdRR/M9y/HHCqlWhHvq+/veri1C6Y1R92zoO+z8D9KyE0ztNRVc0vVJ0V\n1KK7OnOuwwiIuxGWTIZdn9ZoKWeJGcVYSQItIx1dVCSKwVDXqEUNXEqgubcCzV5QwJm33lYTZ/+d\njl+XLrRa9BktZ8/Gr0sXt8YixEUhreDOD+CB1RAcDV89DrNuhKM/eDoyjwi46SYUHx8KvnVtG8fA\nwYPQNmlC7vzyD2MYEztSeuwY9qJruwpQCCEkgSa8UpwpjhxzzsWKsVNFasVFE2OTWq0X7KPOQJMK\nNCGEt2oS4MO8B3syqmsL3lxziCkLdmG21v2k5plCC0FGHUa91gVR1kzvZr3pGdmTWb/Moqi04h/c\nekX1otRRys/ZP1e6ns3uRNtYEmgArfpA+1th/RtQeLrq62P6Qvo2sFXcyq1DVBBN/A1XtXGMMqkP\nWzOljaMQDdYLt3ZEp1F4funeKw5RPJmcgEGn4V8rf6vfACI7wYNrwNQCPhld48ROvXI4YNMMtUKu\n9DxM+AqSX1DbhnkLYxCMXwyx/WHZM5AwGOIGwJJH1YRgNTksZjSVJNBKT6RjkPlnbmfPyQFwWwtH\ne14eZ6ZP5/DAmzn71lv4de9Oq8WfEz3zXXw7dXJLDEJUKbqHmkQb/QFY8uGj2+HTMXDmoKcjcytt\ngD8B/ftTuHIlTrvrKvE0BgPBo0ZR9P33WDMzr3rdNzERnE7M+/a5bE8hhPBGkkATXinWFAtwcQ7a\nttPbAOgRVbuh31KBJoRoDHx0Wl6/szN/TmnHN7tPkTp7y8UKstrKLrQQEeS++WeXUxSFp7o9Ra4l\nlw/3fVjhdd2adkOv0bPlVOVz0OwOJ7rGMAPtcsn/D+yl8N0rVV/bqi/YzHCy4kSjRqPQOz6MDYfP\nXvEAPvJCAi2rQBJoQjRUkSYjfxjclnUHz7Ds10sPwiICjTzUL45lv2ay80Ru/QZhagH3r1DndC2Z\nDOv+CZ5u/VqYBZ+MglV/hTZDYPJGNQnljQz+MHah2tps5V/U9/XWN6ktKX/+uFpLOM0WFGPFVeWl\nGenoW0oCzd1sbqpAs+Xmkv3Gmxy+OZmz77yLf1ISsV9+QfTbb+HbsWO97i1ErSgKJN4Bj/2ktnc8\nvgne6QXL/gjFV8/tbayChqZgO3OG8zt2uHTd4LvuAqeTvEWLrnrNeOE9Qdo4CiGudZJAE14pzqS2\nWilr47gtU02g9YzsWav1yhJoUoEmhPB2iqIweUBr3h3Xjd9OFzLirY3sO1VQ6/WyCy1un392ucSw\nRIa0GsKHez/kbEn5PyT76f24PuJ6tmRWnkCzOZxoG8sMtDJNWkPPh9Xqg8zdlV8b01v9ZxVtHPvF\nh5FdaOFg1qWqP6lAE8I73JsUQ8dmQbz09T4KzJdarj7UP46wAB+mLT9Q/7MMjSYY9zlcNxa+fxW+\negIqmWVZrw6uhHd7w/HNMPwNGDNPbYfozfRGuOtjtY3jdy9Ds67QeqDa4mxHxYdNyjjMFc9Ac5SU\nYD9zVirQPMCecyGBFlo/fz9tOTlk//vfHL45mXOzZ+Pfrx+xS5fSYsZ0jO3b18ueQriU3gh9noQp\nO+GG+2H7+zC9C2z8L1gb//fTgAEDUIxGClescOm6hhbNCejfn7xFn+O0XvlZrWvSBF2zKMx7Gvhs\nUyGEqGeN7CmSuFY0C2iGQWNwWQXaxRaOpZJAE0I0DimJkSx6JAmHE0bP3MTqfVm1WudMocXt889+\n74kuT2C1W5n5y8wKr0mKSmJ/zn5yzDkVXmN3OBvPDLTL9f8T+Iao1QiVPRj3C4WIjpC2sdLl+iao\nc9DWHzpz8ffCA3zQKHA6v8QlIQsh6odOq+HVkZ04U2ThP6sutbgK8NHxVHIC247lsHZ/thsCMcCI\nd6H/VNj5sdpyy1JY//uWsZrh26nw6V0QGAmTflAfuCqN5DNAZ4BR70HnVFj/OoS3hfhk+HoK7Jhb\n6a1OsxnFt/wEmjUjAwB9dAtXRyyqYM/NBUVBazK5dF3b2bNk/fNfauJsznsEDhhA3FdLafHmGxjb\ntnHpXkK4hX8Y3PI6PLpZPRy2+nl4uzvs+cLzFc/1SOPnR8CAARSsXOXSNo4AwWNTsZ05Q+Ha7656\nzbdjIiV7JYEmhLi2SQJNeCWtRkuMKeZiBVrWefXBcIuA2v2wJy0chRCNUWJzE0sf70N8RACTPt7O\nrHVHalR54HQ6yS40e7QCDSAmKIZRbUax+OBijhccL/eapGZJAGzN3FrhOjaHo3HNQCvjGww3/QXS\n1sNvVQwXb9UH0rdWWg3SLNiXuHB/Nhy+VPGn02qICDRKBZoQXuD66GDu6RXDR5vT2J1x6bvtmO7R\nxIX589qKA9jsjvoPRFFg4HNw2ww4+gN8MBQKrp6x4nLZ++F/A2HbLOg5GR5cCxHt6n9fd9Pq1CRl\nt4mw5R21fWZ8Mnz9JGz/oMLbHJaKK9BK09UEmiFaKtDczZabg9ZkQtG6ZuasNTubrGmvcTh5EDlz\n5xKYnEzcsm9o/p9/45OQ4JI9hPCo8LZw90K4dyn4mODzifDeYEj/ydOR1ZuglBTs585x/ifX/jsG\n9OuHvlkzchcsuOo1Y2Ii1uMnsOfLYXMhxLVLEmjCa8WZ4i5WoJVRanmq1EerPhy2OWx1jksIIRqS\npkFGFk5KYlinKKYtP8CfF++m1Fa9B6eFFhtmq4OIQM/MQLvcI9c9gl6rZ8bOGeW+3j60PUGGIDaf\n2lzhGo22Ag2g230Q1kad8WMrrfi6Vn3Beh5O7ax0uX7xYWw9moPFdumEa6TJyGmZgSaEV/jjkLY0\nCfDhuS/3YHeoByf0Wg1TU9pxOLuIz3dkuC+YrvfCuM8g5xjMSYasffWzj9MJP82B2QOgKAvuXgRD\nX1PbfjVWGo3amrLXY2rlmV+YmkT75in46b1yb6lsBpo1Ix0AvSTQ3M6ek+uS9o3WrCxOv/IqR5IH\nkTNvHkFDhqiJs3/9E5+4OBdEKkQDEzcAHl4Ht70FeSfgvWRYNBFyyz90580CbuyP4udHwXLXtnFU\ntFqCx4zh/JYtWI5e+YzNmHhhDtq+evrsFkIILyAJNOG1Yk2xnCw6icVu8XQoQgjRoPkatMxI7cKU\nmxP4bHsG97y3ldziSpIsF2QXqO+vnm7hCBDmG8a9He5lZdpK9py9uo2IVqOlZ1RPNmdurrDKTp2B\n1kgTaFo9DH4Vco7A9vIfmgIQ00f9Z1rlc9D6JoRTYrXz8/FL1StRJqlAE8JbBBn1/G14B349mc/H\nm9Mu/v6Qjk3pFhPCf1Yf5HypGw+OxSfDxG/BYYP3U+DoOteuX3wOFoyDZX9QW3pN3gRtBrt2j4ZK\nUWDIq2o7390LwOCv/nkve0ZNKP6Ow2xGY/Qtd6nS9Aw0/v5og4PrO2rxO/bcXLQhIbW+35qZyemX\nXuJI8iBy588naPhwWn+7jGb/eA2f2FgXRipEA6TRQtd74IkdcOOf1Y4Mb3VX2zuaG0/llMbXl8AB\nAyhctQqnzbWf4cGjR4FeT97CK6vQfDuqCbQSmYMmhLiGSQJNeK04UxwOp4O0/DRPhyKEEA2eRqPw\nzKA2/Df1enam5zHinY0czi6q9J4zhWoCzdMtHMvc1/E+QnxCeGPHG+UmyXpF9eJ08ekK2zza7I24\nAg0gYRC0Hgg/vAbnK5gF5x8G4e3geOVz0HrFhaLVKGw4fGkOWtMgI6clgSaE17i1cxT9EsJ4fdVB\nsi5UjyqKwl+GtSO70MJ7649VsYKLRV0HD66BoGYwbxT8stA16x5dBzP7wKFVMOTvMG4xBDZ1zdre\n4v+zd9/hURVYH8e/02fSe5+EEEJJIQMJJXTpoNKjYAMsoK51V11dy+qqa33VtYMdV1FBxEYRUFEk\ngAQSCL0nEwIklBTIZCaZef+4CYGVIpBkUs7neXjQeyczBzRl7u+ec1QqGPgIDHoMNn8Nam1NiPY3\nWPPOyYe5XC5lB9rZOtDy8tCZzRc91UNcvOqjR9AGXHiA5igooPDxx9k5dBhHv5iD75jRxC1aSMS/\nn0YfE9MAlQrRhBm8lLHmd62D5Anw26vwahfl62B1y5g25DNyBNVHj3J89dnH1l8MbWAgPkOHcuyr\n+Tgr6nYea/z80JnN2HI31evrCSFEcyIBmmi22voqIyh+2PcDAG182rixGiGEaB5GWyKZfUtPjldW\nMfbN3/hle9FZH3uoTLngGuLTNAI0L70X01Oms+bAGlbuX/mH87V70DILzzzGscrpQqtpwT/6qFRK\nF1plKSx//uyPi+kNeavOeSHB26iji9mPFTvq9qCF+xopr6yizHb2/WlCiKZDpVLx5Ogk7NVO/vVt\n3eil1JgAhiWGMuOX3RSXN/IkBz8z3LgIonvCV9PglxeU0YsXo9oBS/4Js0aD3gtuWQbpf1HGGrZW\nff8Gw5+F7YuUv5+4gbDgPlg9UznvcIDTidp4lh1oVqvsP3OTqqPH0Pj9+QDNbrVS+Ohj7Bw+gmNf\nzsNv3DjaLV5E+JNPyn9DIXwiYMybMO1nCElQvg6+1Qu2L7747zlNhGffvqg9PChbVL9jHAH8J03E\nWVpK6YIFpx03JiVikw40IUQr1orfXYjmLsYnBhUqPt+m3L3aPay7mysSQojmITXGn/l/6U2kn4mp\nH/5+2nivU9V2oAV7NZ39MRntM4j0iuTlrJdxuk7f5Wb2NhPlFXXWPWjVTmfL7kADCE2ArpPh93eg\neMeZH9OmN9jLoTDnnE/VJz6IDQUlHDuhjPsM81X+P5AuNCGajzZBntxxWTu+31jIT9sOnTz+wPCO\nVDiqeW3ZWb5ONCSTH1z3JSRfBT8+Bd/efeGdAYd3wXtD4bdXlB1r05crHW4Cet4GV7wCe34BR4Wy\nH2jh/bDqbZy2mk7EMwRoLqcTh9Uq+8/cwOV0KiMc/8QONHteHvsffphdw0dQMn8+/hkTaPfDYsKf\neBxdZGQjVCtEMxJhgcnfwsTZ4KqGT6+Cj8fAgY3uruyiqQ0GvAYNouyHJbgc9XtTmyk1FUN8O47O\n/p8xjklJOAoKqDp6tF5fTwghmgsJ0ESzZdQaifCKoKRSmWndPVwCNCGE+LOi/D2Ye1svBrQP5tGv\nN/HPr3Opqj49kCoqq0SvVeNj0rqpyj/Sa/Tc2eVOth3dxoI9C/5wvmdET34/8DtVzj9ejG3RO9BO\nddk/QGtS9j6cSUwf5fd959mD1i4IlwtW7joMQLivsjPnQKkEaEI0J9P7t6VtsCePfZ2LzVENQFyw\nF5O6m/lkdR57io83flFaA4ybqXRMrfsIZk+EynOPFQaUzoHsT2FGP2XnY8ZHMOpVZe+XqJM2FcbO\ngPzVyv6f2H6w6O84f5sBcMYOtKqiYlyVlejNUY1dbavnLCuD6upzjnC0793L/gcfYteIkZR++x3+\nEycSt+QHwh57DF14eCNWK0Qzo1JBx5Fw+yoY8bxyA9nbfeHrv0DZAXdXd1F8RoyguqSE46tW1evz\nqlQq/CZOxJabS8XGuo4zY1IygHShCSFaLQnQRLMW61u3ELlbWDc3ViKEEM2Pl0HLzBvSuKVvLB9l\n7uPGj9ZSesp4vkNllYR4G5rcLpQRsSPoGNCR19e/jr3aftq59PB0yh3l5Bb/8Q1etbOF70Cr5RUC\n/f4G2xYou4H+l3coBMbD3nPvQUsx++Fl0PJrzRjH8JoOtELpQBOiWTFoNTw1Jon8IxW89mNdx9nd\ng9qj16p5cfE29xSmUik7u654BXb9CB+OPPfFTFsJfHkzzL9N6Ta7bSUkjmm8epublKthwgdKp8WJ\nI9CmL65lzwCgMvwxQHNY8wHQRUkHWmOrOqLsLdX4/zFAq9y9m4IHHmDXyMspXbiQgOuuJW7JEsIe\neRhdWFhjlypE86XRQY/pcNd6Zdxvzufwaldl7Ln9hLuruyCefXqj9vamdMHCen9u39GjUXl4cPSz\n2SePGRMTAAnQhBCtlwRoolmr3YMGEGA8/8gLIYQQp9OoVTx8eQLPjU9m5c5ixr25kn2HlW6EQ2U2\ngr2bxv6zU6lVau7tei8F5QV8se2L0871CO+BCtUZ96BVVbvQtJbdOD1uA79oWPwwOKv/eL5Nb8jL\nPPO5GjqNmp5tA1mxU9mTV7sLT0Y4CtH89IoLYlyXSGb+spsdB8sACPY2MK1fW77fWMj6PDeOZUqb\nCpM+g+Kd8O5gOLT1j4/JXwNv94FNX8FljygjuXylU+q8EsfAxE+Vkb7lB3EGdwFAve+nPzzUnq8E\naNKB1viqa8aiafzr3s9W7txJwd/uY/flV1C2ZCkBkyfTbukSQh96CF1oiLtKFaL5M/nDsKfhjjUQ\nPxh+ehpeS4Xs2eB0nv/jmwC1Xo/3oEGULV2Ky24//wdcAI2XF75XXEHp9wuoLik5eUwfG0tF7qZ6\nfS0hhGguWslVJNFSnRqgCSGEuHhXd4vm45t6UFxeyZg3fmPNniMU1XSgNUXpEen0CO/BjA0zKLOX\nnTzua/AlMTCRVfv/ONKk1XSgAeiMMPgJOLgRsj/54/mYPlBZet4dEH3jg8g/UsG+w8cxaDUEeeml\nA02IZuofl3fCQ6/l4fm5uFwuAG7p25YgLwPPLNh68phbtB8KUxdAtV3ZbbbnV+W4s1rpDnh/uPLv\nNy6C/veDWuO+Wpub9sPg2i+gxIqrVNmDp9o0G3579bSHOfKtoFKhi4hwR5WtWl2A5o9t23as997L\n7itHUfbTTwTedKMSnP39AbTBwW6uVIgWJKAtXDULpi4Cn3CYfyu8M6Du+08T5zNiOM6yMsp/O/dE\niYvhP2kiLpuNkq+/PnnMmJQkHWhCiFZLAjTRrJ06wlEIIcSlSY8LZP7tvfH31HPtu6vYW3yCEO8/\njnlqClQqFfd2vZdjlcf4cNOHp51Lj0hnQ9EGjjtO3+tT5XSh0bSSAA0gcSyYe8CyJ6Gy7PRzbXor\nv+89zx60+CCAk2Mcw3yNHCipqPdShRANL8jLwIMjOrJmzxG+XFcAgKdByz2D41mz9wjLthxyb4ER\nFrhpCXiHwX/HweqZ8OEVSndA0ji4dQWYZefxRWk7AK6bh6u8FAB1aDwseRR++8/Jh9jz89CGh6HS\n691TYytWO8Lx0PPPs2f0aI4v/4XAW26h3bKlhNx3H9rAQDdXKEQLFpMONy2F8e8p424/ugJmX6N0\nRTdhnunpqH19KVu0qN6f29ipE6aUFI7O/uzkzTWmpESqDh7EccjNPysIIYQbSIAmmjXpQBNCiPrV\nJsiTr27rTY/YQOzVzibbgQaQGJTI8DbD+XjzxxSdKDp5vGd4T6pcVfx+4PfTHu9yufhtZzGfrcmj\n7JRdby2WSgXD/g3HD8GKV04/5xOh3Hm779x3rbYN8iTC18iK2gDNxyQdaEI0Y1enmeka7ce/F2zh\n6HFl7NPV3cy0Dfbk2UVbqap28/gq/xi4aTFEdYeF98OBDTB2Box7B4y+7q2tuYtJxzngnwCoKosg\nrDMseQxWvAwoHWh6c7Q7K2y1nGXlANg2bybwtltp9+MyQv56L9oz7EQTQjQAtRraD4eRL4JPJGz7\nHt7sAQeb1shCl9NJ5e7dHPtyHgeefApcLsp/PsO+43rgf80k7Hv2cGL1akDpQAOwbWpafydCCNEY\nJEATzZqf0Q9vnTcAH2/+2M3VCCFEy+DroeODqd14ckwSE9Ka9i6UO7vciaPawds5b588ZgmxYNKa\nyNx/+h60v4/oiE6j5sF5G+n29FLu/TyblTuLcTrdOLasoUWlQXIGZL4Ox/JPPxfTG/atPOe+B5VK\nRZ/4IFbuKqba6SLM18CBUgnQhGiu1GoVT49NpqTCwbMLlV1jOo2avw/vyM5D5czJsrq5QpT9NNfP\ngxHPw62/QspE5YYAccmcXmYA1BonlFghJBGWPg6//h92az462X/mFj6XjyTsiSeUjrO770bj5+fu\nkoRo+Y4fhi3fKfuCZ14Gz0bD7KuhrFC5waD7dLfv2qwuK6N8xW8UvfEGebdMY3t6L3aPvJzChx+m\ndNEiTJ07E3zP3Q3y2t7Dh6Px8+Po7M8AMHbsCGo1NtmDJoRohbTuLkCIS/XLxF+4f/n9PP/783jp\nvBgbP9bdJQkhRLOn06i5vmeMu8s4r2ifaCa0n8Cc7XO4PuF62vi2Qa/R0zW0K6sKT9+DdlWamYzU\nKLLzjzE3y8o3Ofv5an0BkX4mxqdGkZEahTnAw01/kgY06J+w5VtY9gSMf7fueJs+sP5jOLQJwpLP\n+uF94oP5Yq2VDdZjhPuaOHbCQYW9GpNedhAJ0Rx1Cvfh5j6xzPhlNxPSoujWJoChCaGkxvjz8pLt\njLZE4KF389tErQF6THdvDS2Qy1YJgCpjJvx8uxKiBcbjXPwk1UXh6KPMbq6wddKFhuJ/9VXuLkOI\nlsvlgmN5kJep3DyWlwnF25VzGoNyw1mfeyC6lzIq2OjT+CU6ndh37aIiJ4cT2dlUZGdj37VbqV2l\nwtCuHT5Dh2KypGCyWNDHxqJSN1xPhNpgwHfcOI7MmoXj0CF0ISEY4trKHjQhRKskAZpo9rRqLc/1\ne447lt3B45mP4633ZnDM4It+PqfLiVolzZlCCNFcTE+Zzte7vubV9a/y0oCXAEgPT+fFtS9y4PgB\nwjzDTj5WpVLRJdqfLtH+PHpFAos3HWBulpXXftzBq8t20CM2gIw0MyOTw9x/Abm++Jmh153wywvQ\n41blIgEoHWig7EE7R4DWO07ZvbJiRzERfiYADpTaiA3ybNCyhRAN5+7B8Xy3oZCHv9rId3f2Ra9V\n84+RHRn/Vibv/bqHOwfFu7tE0QCcNmWHpTq8I0xdCLNGQel+HBozUIWuPNu9BQohRPrekg4AACAA\nSURBVH1wOqFoS01YtkoJzEqV3Z8YfCG6B6RMgpheENFFuWmjkVWXlFCxYQMV67OpyMmhYsMGnGXK\nzmK1ry+mlM74jByJh8WCMTkZjbd3o9fof/VVHHn/fY7NnUvw7bdjTEyifMUKXC4XKukMF0K0Ii3k\nypBo7fQaPa9c9grTlkzjgV8e4PVBr9MrotcFPYe3zpsyRxll9jJ8DbJjQQghmosgUxBTEqfwVs5b\nbCjaQOfgzqRHpAOwqnAVY9qNOePHGXUaRlsiGW2JZP+xCuatszI3y8p9c3L459e5jEwOJyPNTLc2\n/s3/TWLve2DdLFj0ENz0gzIOzc8MfjFKgNbztrN+aKCXgcQIH37dWcw9NRfVD5RIgCZEc+ah1/L4\nqERumbWW91bs4bYBcaTGBDA8MYy3l+9iUo9ogrya7g5McXFOdqCZTOAfUROijca+6SDgjX7fHFge\nC/0fcG+hQghxIarsUJhd112Wtwpsx5Rz3uEQna6EZdE9ISQB1I07RcFVXU3lzl1U5GRTkZ2jdJft\n3q2cVKsxxMfjM3IkppTa7rI2TeK9hz4mBs/evTn2xRyCpk3DmJREyfz5VB08iC4s7PxPIIQQLYQE\naKLF8NB58MagN7hx8Y3c89M9zBwyE0uI5U9/vK/BlzJHGSWVJRKgCSFEMzM5cTKfb/ucl7Ne5v1h\n7xPvF0+gMZDM/ZlnDdBOFeFn4o6B8fzlsnas3XeUOWvz+X5DIXOyrMQEejChaxTjU6NOdmA1OwYv\nGPgofHMHbJoHSeOV4236wLaFyp265xgD0yc+iPdX7MHHpAPgQGlFY1QthGhAQxJCGZIQyn+WbeeK\nzuGYAzy4f3gHlmw5yGvLdvDE6CR3lyjqmatS2WGpNtSEo75RMGUBjr+NACrQBfvCT0+DywkDHnRf\noUIIcS6VZZC/pmYkYyYUrIWqmh29gfHQ6cqawCwd/Ns0+h7N6mPHTo5itOXkUJGzAefx4wBo/Pww\nWSz4jroSk8WCMSkZjVfTvSnNf9JErHfcSfny5ZiSEgGw5eZKgCaEaFUkQBMtiq/BlxlDZjB54WRu\nX3Y7Hwz7gA4BHf70x1rLrRyrPEY00Q1cqRBCiPrkqfNkeufpPLPmGVYUrKBvVF96RvQkc3/mBY3m\nValUdGsTQLc2ATw+KpGFGw8wJyuf/1uynZeWbqdPuyAmpEYxLDEMo66Z7QCzXANrZsCSx6HD5aAz\nKmMcsz+Boq0QmnDWD+3bLpgZy3ez7/AJAApLbI1UtBCiIT0+KpEhLy3nn99s4r3JacQFezGpu5lP\nVucxpXesdJq2MM7aDjSjse6gdyj2iMtR679E4zwKBh/4+Rll785lD7mpUiGEOEV5UU1nWc0OswMb\nwVUNKjWEdYa0G5WwLDodvIIbtTRXdTWVO3ac7CyryM7GvnevclKtxtChAz6jrsTDYsGUkoIuJqZJ\ndJf9WV4DBqANC+Po7M+Iev010GioyM3Fe/DFr00RQojmRgI00eIEmYKYOXQmNyy8gelLpvPRiI+I\n8Yk578f5GfwAKKksaegShRBCNICM9hl8vPljXl73Mr0iepEens73u79nx9Edf/pmilN56LWMT1U6\nz/KPnGBulpUv11m5+7NsvA1arkiJICMtii5mv+bxRlitgaFPKztvVr0Jff8KbWr2oO377ZwBWlob\nfwxaNWv3HcHXpOOABGhCtAiRfibuHdyepxdsYfGmAwxPCufuQe2Zt66AFxZv5c1rU91doqhHLlsF\nKr0e1f90HDsKi9DFtkMVFQnW30FjgOXPAi4Y8FCjd28IIVoxlwuO7q0Ly/Iy4fBO5ZzWCFHdoO/f\nlHGM5u5gaNzdYFVHj9YEZUpgZtu4EecJ5QYzTUCA0l02diwmiwVTUiJqz+Z9I4pKq8UvYwLFr71O\n1aFDGOLjseVucndZQgjRqCRAEy1SpFck7wx5hymLpjDth2l8NOIjwjzP3WJeO7axxC4BmhBCNEc6\njY67ut7FA788wII9C+gZ3hNQ9qBdTIB2KnOAB/cOac/dg+JZtecwc7OszF9fwOw1ecQFezIh1cy4\nrpGE+hjP/2Tu1LY/dBgJv74EXa5TdqD5RMHeX6H7LWf9MKNOQ/fYAFbsKCbc1ygdaEK0IFN6t+HL\ndVYe/2YzfeKDCfY2MK1fW15ZuoP1eUfpEu3v7hJFPXHaKk/vPqtht1oxtG0L1/8bZk9UvicALH9O\nGed42cMSogkhGoazGg5tVkYx5q1U9peVFSrnjH5KUNblemUkY7gFtPpGK81VVUXl9u0nRzGeyM7G\nsS9POanRYOzQAd8xYzB1qekuM5ubx011F8hvQgbFb73N0c8+x5iUSPnSZbhcrhb5ZxVCiDORAE20\nWG392vLWkLe4afFNTFsyjQ+Hf0iAMeCsjz8ZoEkHmhBCNFvD2gzjg9wPeG39a3w79lvifOPI3J/J\n5MTJ9fL8arWKXnFB9IoL4olRDhZsLGRulpXnFm3lhcVb6d8+mAmpZgYnhGDQNtERj0OehDd7KHtu\nrvyPsgdt1zLljt9zvBHu0y6IZxZupWOYt3SgCdGC6DRq/j0umfFvreTlJdt59IoEbunblv+uyuOZ\nBVv5fHpPuUjWQrgqbXX7z2qPOZ04rFa8+vdX9mVeOwc+vw52LlUe8MsLSog28FEJ0YQQl66qEvav\nr+suy1sNtddgfCKV8eIx6RDdC4I7nnNHb72XdvgwFTk5VKxXRjFW5ObiqlD2/mqCgjBZUvCbMAEP\niwVjUhJqUzPdjXyBdKEheA8aRMm8eQTddislc7/EUbAffVSku0sTQohGIQGaaNESAxN5beBr3Lb0\nNm5behvvDX0PL73XGR9bO8LxWOWxxixRCCFEPVKr1Nybei/Tlkzj862f0zOiJ19u/5LK6koMGsP5\nn+ACeBt1XN0tmqu7RbOn+Dhzs/KZt66Av3y6Dj8PHaNTIpiQaiYp0qdpXXwOagfdblH2oXWfpoxx\n3PAZFG+H4LN36vWJD4KFsKf4ON5GXSMWLIRoaF2j/ZnUPZoPftvD2C6RJEX6cu+QeB7+KpelWw4x\nJCHU3SWKeuCssKH6nwu+VUXFuCor0ZujlAM6E0z8FObeCFu/U479+n/KTRaDHpMQTQhxYWylkL9G\n6S7blwkFWVCt7GMkqAMkja3bX+YX3WhfY1wOB7Zt25WgLEcZx+jIz1dOarUYO3XCb/x4ZRSjJQVd\nZGTT+nm+kflPmkjZ4sXYCwoAsOXmSoAmhGg1JEATLV63sG68NOAl7v7xbu748Q7eHvw2Ru0fR5dI\nB5oQQrQM6RHppIenM3PjTB7s/iCfbPmE7EPZ9Ajv0WCvGRvkyf3DOvLXIR34bWcxc7KszP49n48y\n99ExzJsJqVGM6RJJkFf9hngXrf8DkDMbFv8DLn9JObZ3xTkDtE5hPgR66jl83E5leSX2Kid6bePd\nFSyEaFh/H9aRHzYd4OGvNjLv9t5cnWbmvRV7eG7RVi7rEIxWI5/vzd2ZOtAcVuWCsS7KXHdQa4CM\nD+GrWyF3rnJsxUtKJ9rgxyVEE0KcXdnBulGM+1bCwVzla4dKA+Epysjw6HRlNKNnUKOVVVVUdDIo\nO5GdjS13Ey6bMlFBGxyMyWLBf+JETJYUjImJqM8w7rY18+jRA31sLBVZ61DpdNhyN+IzfJi7yxJC\niEYhAZpoFfpF9ePpPk/z4K8P8rflf+OVy15Bpz797vnaAE060IQQovm7J/Uerv7uajYWbUSr0rKq\ncFWDBmi1NGoV/doH0699MCUVDr7N2c+cLCtPfb+FZxdu5bKOIUxIjWJgxxB07rwY7REAAx6ERQ8q\ni9m9w5UArdtNZ/0QtVpF73ZBfJOzH4CDpTbMAR6NVbEQooH5euh45PIE7vk8m0/X5HF9zxj+Prwj\n0z/OYk6WlUndo91dorhEZ9qBZq/puDjZgVZLo4NxM5WOtPUfK8d+ewVwweAnJEQTQiidqUd2K6MY\na3eYHdmtnNN5QFQa9HtAGckYmaaMiW2Msux2bNu21Y1izMnBUdM5hU6HMaETfldl4GGxYLJY0IaH\nt+rusj9DpVLhP/FqDj7zLCqDgYrcTe4uSQghGo0EaKLVGNl2JOWOcp5c9SQPr3iYZ/o8g0Zdt5/G\nVy8daEII0VIkBCYwInYE83bMI9wrnMz9mdzd9e5GrcHXpOO6njFc1zOGHQfLmJtlZd76ApZsPkig\np54xXSKZkBpFp3CfRq3rpG43w+/vwg+PgLkH7Pvt/HvQ4iVAE6IlG22J4Iu1+Ty/aCvDEkMZmhBK\nWow/Ly3ZzmhLBB56efvYnLkqKv7YgZaXD2o1uoiIP36AWgNXvqpcCF8zQzn223+UbpIhT0qIJkRr\n46xWOspqw7K8VVB+UDln8lc6y1KnQkwvpdtM0zgjvx0HD502itG2aROuSmVMpDY0VOkuu+46TCkp\nGBMT/vB1UPw5vmPGcOjlV3DZbMrfsdOJqhF31AkhhLvIOyDRqlzV4SpK7aX8Z91/8NZ580jPR07e\naVS7A00CNCGEaBnu7HInS/YtIb8sHxUqjtmO4Wf0c0st8aHePDSyE/cP68AvO4qYs9bKrMy9vLdi\nD0mRPmSkmhmVEoG/p77xitLoYOhTMHsiaPTKBZDDu5QdaWfRN75u1E5hia0xqhRCNCKVSsWTY5IY\n8cqvPPXdFl6d1IWHRnZk/FuZvPfrHu4cFO/uEsUlcFZWovE7/fug3ZqPLiwMlf4s33/UahjxnNKJ\n9tsryrGVryk3XAx9SkI0IVoyh03ZWZaXqfzKXwOVpco5XzPE9le6y6J7QVB75etFA3Pa7VRu2XJy\nFGNFTg5V+wsBUOl0GBMTlVGMXZTuMl1YWIPX1FpofH3xGTmSknnzcJaV4cjLQ9+mjbvLEkKIBicB\nmmh1bk6+mVJ7KR/kfoCPwedkR0JtgCYjHIUQomUwe5u5qv1VfLr1U1y4WH1gNcPauHdWv1ajZmDH\nUAZ2DOXIcTvfZBcwJ8vKP7/ZxNPfb2FwQggZqWb6xgc1zr6h9sMhth/s+UX5930rzhmghfuaiAv2\nZFfRcQ5IgCZEixQX7MWtA+J4ddkOMtKi6BsfzPDEMN5evotJPaKbzi5HccFcNhtq4/90oOVb0ZnN\nZ/mIGiqVsvtM7wk/Pa0cy3xdCdGGPS0hmhAtRcUxJSTLW6l0me1fB9V25VxwJ0ieoIRl0T3B7zxf\nN+qJ48ABpbssu6a7bPNmXHalJm14OCZLCh6TJ2NKScGQkID6bDcDiHrhP2kiJfPmAVCRu0kCNCFE\nqyABmmiV7u16L2X2Mt7d+C7eem9uTLoRH4MyQks60IQQouWY1nka83fO50TVCVYVrnJ7gHaqAE89\nU3rHMqV3LJv3lzI3y8r87AIWbDxAiLeBsV0jyUiNol2Id8MVoVLBsH/D230BF+z9DVKnnPND+sYH\ns6vouHSgCdGC3T4gjm+yC3h0fi6L7unHA8M7sGTLQV5dtoN/jU5yd3niIjltNlSG/9mBZs3Hq3//\n83+wSgX9H1A60X54RDm26g1lnOPwZyREE6I5Ki2sC8vyVinjGXGBWgvhFugxvS4w8who8HKclZXY\nNm8+GZZV5ORQdeAAACq9HmNS0slRjCZLCrrQ0AavSZzOlJyMMSkJW24uttxcfK+43N0lCSFEg5MA\nTbRKKpWKR3o8Qrm9nJezXsZb7834+PEAlDvK3VydEEKI+hJoCmRK0hTezH6TzP2Z7i7nrBIifHgs\nIoEHR3Tkp22HmLPWyru/7mHG8t1YzH5kpEVxRecIfE0NsEsiLBm6XAfrP4a9K86/B61dEB+u3MuB\n0or6r0UI0SQYdRqeHJPE9e+t4c2fd/HXIe2Z1N3Mp6vzmNo7ltggT3eXKC6Cy2ZDbaoL0JwVFVQX\nFaOPuoBOkl53gtYIC+5T/n31W4ALhj8rIZoQTZnLpYzqPhmYrYSje5VzOk8wd4MBDykjGSPTQN+w\ne25dLhdVhYWnjWKs3LwFl8OhlBQZiUfXrpgsFkxdLBg7dDj7qFnRqPwnTaTw4Uew5ea6uxQhhGgU\nEqCJVkuj1vDvPv+m3FHOk5lP4q1rwDv8hRBCuM3khMl8tvUzCsoLyC/Nx+zTOCNnLoZeq2ZYYhjD\nEsMoKqvk6+wC5qy18vBXufzr280MSwwjIy2KXnFBaNT1eKFy4KOQOw/K9sPRPRDQ9qwP7RkXiFat\nkg40IVq4vvHBjEqJ4O2fdzHaEsHdg9ozb10BLyzeypvXprq7PHERnJWVp3WgOaxWAHTmqAt7ou63\ngM4DvrlD6UBb/bby+4jnJUQToqmoroKDG+vCsrxVcLxIOecRCNHp0O0WJTAL66zsxm1ATpsN26ZN\ndd1l2dlUFSn1qIxGjEmJBEy+AWNKCqaUFHQhIQ1aj7h4PiNHcvC555VxmtXVqDQad5ckhBANSgI0\n0arpNDpeGvASty65lYd+fcjd5QghhGgAHjoPbku5jadXP01mYWaTDtBOFext4Oa+bbmpTywbC0qY\nm2Xl6+z9fJOzn3BfI+O7RjE+Nap+OkG8Q6HvvfDjU8oYx3MEaF4GLV2i/bAelQ40IVq6R67oxE/b\nDvHo/Fw+ubkH0/vF8fLS7azLO0rXaH93lycukKui4rQdaPZ8JUDTn28H2pl0uRZ0Rpg3DZxVsGam\n0uEy8gUJ0YRwB0cFWNdCXqbyK38N2Gum6/jFQNwgJSyL7gVB8Q36eepyuXAU7D8ZlFXk5GDbsgWq\nqgDQmc149OihdJdZLBg7tEela9gAT9QftcmE75jRHJ31Mfa9ezHExbm7JCGEaFASoIlWz6Q18fqg\n17lp8U1sObLF3eUIIYRoAOPbj+eHfT9g1BrP/+AmRqVS0TnKj85RfvxjZCeWbjnI3Cwrb/68k9d/\n2km3Nv5kpJoZ2TkcL8Ml/GiXfgfsWAL68wdyV3eL5puc/Rf/WkKIZiHE28gDwzvy6Pxc5mcXcHPf\nWD5etY9nF2zl8+k9UUlQ0my4qqtxORyojKaTxxz5eYByMfuiJI0HrQnmTIZqO/z+jtKJNvJFUKvr\no2whxNlUHIW81XUjGfevB6cDUEFIAqRMVLrMotPBN7JBS3FWVGDLzT05irEiO4fq4mIAVCYTpqQk\nAqdOxWRRusu0QUENWo9oeAHXXcfxlStRaeWyshCi5VO5XC5319Co0tLSXGvXrnV3GaIJOmI7Qv/P\nlQXan1/xOQmBCW6uSAghhDi7AyU25q23MjfLyu6i45h0GkYkh5GRaqZHbADq+hzxKIRotaqdLsa9\ntRLrkRP8+LcBfLdxPw9/lcs7N6QxJCHU3eWJP8l54gTbuqYSct/fCLz5ZgAOPPU0JfPn0/73NZcW\nhu5cBp9dC1U1nclpN8LI/5MQTYj6VFKgdJbtW6n8fmizclytg8iudWFZdA8wNVyHsMvlwpGfrwRl\n65UOM9u2bVBdDYAuJhoPiwVjSgoeFguG9u0lZBFCCNEkqVSqLJfLlXa+x8l3MSFqBBgDTv7zrUtu\n5cMRH9LW9+wjrIQQQgh3CvM1cvuAdtzWP451eceYm5XPdzmFzFtXgDnApIx47BqFOaBhl8ALIVo2\njVrF02OSGPX6Cp5bvJV/jUrkvRV7eHbhFi7rEIxWIyFJc+CsrAQ4fQdafj46s/nSOwnbDYLr5sKn\nVysj49a+r3SiXf6yhGhCXAyXC4q31wRmNTvMjikdo+i9wNwdEscpIxkjU0FnOvfzXQLniRNUbMw9\nOYqxIieH6sOHAVB5eGBKTibw5pvrussCAs7zjEIIIUTzIgGaEGegUqmY9sM0Zo2YRYRXhLvLEUII\nIc5KpVKRGuNPaow/j12RyOJNB5ibZeU/y3bwytIdpLcNJCMtihFJ4Zj0suRbCHHhkiJ9mdo7lvdW\n7GF81yj+Prwj0z/OYk6WlUndo91dnvgTXBVKd5jaVBeg2a1WDG3r6YbBNn3g+vnwyXiwlUDWh0oI\ncMUrEqIJcT7VVXAgpyYsq/l1Qgmp8AxWOst63q78HpoEmoa5lOdyuXDs20dFTo4yjjE7h8rt2092\nl+nbtMGrb18lLLNYMMTHo9LIz5ZCCCFaNgnQhDiDmUNmMnXRVKYtmcaHwz8kyCQzuoUQQjR9Jr2G\nMV0iGdMlkoJjFczLsjJ3nZW/fpHDY19v4vLkcDLSokiN8ZfdRUKIC3LvkPZ8v6GQh7/ayLd39iEt\nxp+XlmxntCUCD728rWzqnLbTO9BcTicOqxWv/v3r70XM3WDyt/DxWOXi/7qPABdc8R8J0YQ4lf0E\nWH+vG8loXQuO48o5/1hoPxyie0J0LwiMgwb6ma26/Di23I1Kd1l2TXfZ0aMAqD09MXZOJnDaLcpI\nxs6d0fo33GhIIYQQoqmSdzpCnEGHgA68MfgNpi+Zzq1LbuX94e/jo/dxd1lCCCHEnxbpZ+LOQfHc\nMbAda/YcYW6WlW837OfztfnEBnkyITWKcV0jCfdtuLE/QoiWw8ug5fFRCdz633V8+NteHhrZifFv\nreTdX/dw16B4d5cnzsNVaQNAZTQAUFVUjKuyEr05qn5fKDwFpiyAWaOg/CCsm6WMc7zyNQnRROt1\n4gjkrVJGMe7LhMJscFYBKqWjrMu1dTvMfMIbpASXy4V9z966UYzZ2VTu2AFOJwD6tm3xuuwyTCk1\n3WXt4qS7TAghhEACNCHOqktIF14e8DJ3/HgHf1n6F2YMmYGHTvbICCGEaF5UKhU92gbSo20gj49K\nZMHGQuZmWXlh8TZe/GEbfdoFkZFmZmhCKEadXCgRQpzdsMQwBnUM4eWl21ny1/4MTwxjxvJdXNMj\nmiAvg7vLE+fgrFACNLVR6UBzWPMB0EWZ6//FQjrC1IXw0SgotcL6/yrjHEe9Bmr5PiNagWP5dd1l\neZlQtFU5rtErO8t63QUxvSCqG5j8GqSE6vJybBs21IxizMaWs4HqkhIA1F5emFJS8B40CFMXC6bk\nZDR+DVOHEEII0dxJgCbEOfSO7M1zfZ/j/l/u596f7+W1ga+h1+jdXZYQQghxUTwNWjLSzGSkmdl3\n+DhfZln5cl0Bd81ej49Ry5UpEWSkmUmJ8pURj0KIP1CpVDw+KpEhLy/n8W828dCIjizZcpBXl+3g\nX6OT3F2eOIfaDrTaAM2epwRo9d6BViswDm6sCdGO7oHsT5QQbfTrEqKJlsXphOJtNWHZKiUwK1E+\nvzD4gLkHJGcogVlEV9AZz/18F8HldGLfs6duFGN2NpU7dyqfc4C+XRxeQwbjYbFgSklBHxeHSjpC\nhRBCiD9FAjQhzmNom6EcdxznsZWP8eCvD/JCvxfQyJs+IYQQzVxMoCd/HdqBewa3J3P3YeaszWdu\nlpVPVucRH+LFhNQoxnaNJMS7/i/0CCGaL3OAB3cPas9zi7aSkRrFNd2j+XR1HlN7xxIb5Onu8sRZ\nOG21IxxrOtDy80GtRhcR0XAv6hetdKLNGq0EDDmfAi4Y/YaEaKL5qnZAYU5dd1leJlQoe8PwClXG\nMPa6U/k9NLFB/l+vLi2lYkPt7rJsKjZswFlaCoDax0fpLhs2DJPFgqlzMhofWUchhBBCXCwJ0IT4\nE8bGj6XUXsqLa1/kicwneKLXE3JnvhBCiBZBrVbRu10QvdsF8S+bg+83FDJnbT7PLNzK84u3MaB9\nMBNSoxjUKRS9Vu5WFkLAzX1j+Wq9lce/2cRn09KZt87KC4u38ua1qe4uTZyFqzZAMyijNu3WfHRh\nYaj0DTxdwyccpi6AWWPg4EbIma10xYx5U0I00TxUloP197qRjNa1UFWhnAuIg46XQ3QviO4JAW2h\nnq8TuJxO7Lt2KaMYa3aX2XftVj6PVCoM8fH41IZllhT0sbHSXSaEEELUIwnQhPiTJidOptReyswN\nM/HWe3Nf2n0SogkhhGhRfIw6JnWPZlL3aHYVlTM3y8q8dVaWbT2Ev4eO0ZZIJqRGkRTp6+5ShRBu\npNOoeXpsMhlvZ/Lf1fuY1i+Ol5duZ13eUbpG+7u7PHEGTlslAGqTCQBHvhWduQH2n52JZxBM+Rb+\nOx4KsmDDZ+Bywti3JUQTTc/x4prOslVKYFaYA65qUKkhNAlSJyvdZdHp4B1a7y9fXVJSE5Tl1HWX\nlZcDoPH1xWhJwWfkSDwsFoydO6Px8qr3GoQQQghRRwI0IU6hU+twOB3YqmwYtX8cWXWH5Q7K7GXM\n2jwLH70P01Omu6FKIYQQouHFBXvx9+EduW9oB37dUcScLCufrs7jw5V76RTuw4TUKMZYIgj0Mri7\nVCGEG3RrE8DVaWbeW7GHL6an89/V+3hmwRa+mJ4uN5k1QbU70E7tQPPq37/xCjD5w/Xz4dOrIW8l\nbPwCcMGYt0EjlyWEm7hccCyvrrssLxOKtyvnNAaISoM+90JMOkR1B2P9jkJ0VVdTuXNX3SjGnBzs\nu3crJ9VqDO3b43P55Up3WUoK+tg28vVVCCGEaGTyk6oQp/A1+FJcUUxJZckZAzSVSsWD3R+k3F7O\n69mv46335ppO17ihUiGEEKJxaNQqBnQIYUCHEI6dsPNtzn7mZFl58rvNPLNgCwM7hpCRZmZAh2B0\nGhkZJERr8uCIjizZcpCnv9/MXYPieXR+Lku3HGJIQv13ZYhL46xQAjS10YizooLqomL0UY3UgVbL\n6APXfQmfXQO7f4KNc2o60WZKiCYah9MJRVtO2V+2CkoLlHNGXzD3BMs1SndZRBfQ1u9NQlVHjyrd\nZTWjGG0bNuI8fhwAjZ8fJosF31GjMFlSMCYlo/GSvZJCCCGEu8lPqUKcws/gpwRo9hJCPc/8xl+t\nUvNE7ycoc5TxzJpn8NZ7c2XclY1cqRBCCNH4/Dz0XJ/ehuvT27DtQBlzs/L5an0BP2w+SJCXnjGW\nSDLSzHQI83Z3qUKIRuDvqecfIztx35wcxnaNom2wJ88u3MJlHYLRSqDepNR2oKmNRuz79gGgM0c1\nfiF6D5j0GcyZAtsXQu6XShfQuHckRBP1r8oOhdmnB2a2Y8o573AlKIvppfwe0X++4wAAIABJREFU\nkgD1uDvMVVVF5c6dSmfZ+prusr17lZMaDYYO7fEZdSUeFgsmiwVddLR0lwkhhBBNkPyEKsQpfA3K\nTpeSypJzPk6n1vFi/xe5fentPPrbo3jqPBkYPbAxShRCCCGahA5h3jx8eQIPDO/I8m1FzMnK58OV\ne3l3xR46R/kyITWKUSkR+Hno3V2qEKIBje8ayZy1+by4eBsPjujIQ/M28sVaK9f0iHZ3aeIUTptN\nCQd0Ouz5+QDoG2sH2v/SGeHqj2HeLbDpK9g0T+lEG/8uaHTuqUm0DJVlkL+mZiRjJhSshSolPCYw\nHhJGQXQvZSSjXwzUY2BVdeSIsresprusYuNGXCdOAKAJCFC6y8aNw5SSgik5CbWHR729thBCCCEa\njgRoQpzCV68EaMcqj533sQaNgVcHvsotP9zC/cvv583Bb9IjvEdDlyiEEEI0KTqNmsEJoQxOCOVw\neSVfZysjHh/7ehNPfbeFIYmhTEiNol98MBq13FktREujUql4emwSI/7zK2v2HCEtxp+Xl25nTJcI\nPPTydrOpcFXYUBmNqFQqHDUBms5dARooQdn490BrgpxPYfN8wKUckxBN/FnlRcpOvX2ZSmh2YIMS\nxqo0EN4Z0m5Uusui08EruN5e1lVVReX27Zw4ZXeZY1+eclKjwdixI35jx2KypCjdZVFR0l0mhBBC\nNFPyjkaIU/gZ/YDzd6DV8tR58uagN5m6eCp3/ngn7w19j+Tg5IYsUQghhGiyAr0M3Ngnlhv7xJJb\nUMLcLCtfZxfw/YZCQn0MjOsaxYTUKOKCvdxdqhCiHrUL8WZ6vzhe/2kn9w/rwAuLt/Hur3u4a1C8\nu0sTNZyVNtRGZcezPd+K2ssLjZ+fe4tSa2D0G0pH2tr3YfPXyjjHCe9LiCb+yOWCo3trustqRjIe\n3qmc0xohqhv0vU/pLovqBob6GydddfiwEpRlZytdZrm5uCoqANAEBWGypOCfkYHJYsGYmIjaZKq3\n1xZCCCGEe0mAJsQp/uwIx1P5Gf2YMWQGNyy8gduW3cYHwz4g3l8uFgghhGjdkiJ9SYr05aGRHflx\nyyHmZlmZ+ctu3vp5F12j/ZiQauaKlHB8jHKRVIiW4I6B7fgmZz9fZlkZ1DGEGct3cU2PaIK8DO4u\nTQAuWyUqo/LfwpGfj85sbhodMWo1XP4S6Dwg83XY8g3MnQoTPpAQrbVzVsOhzTXdZSuV/WVlhco5\no5/SVdb1BmUkY3gKaOtnZLTL4cC2dVvdKMbsbBxWq3JSq8XYqRN+EyYooxgtFnSREU3jc0kIIYQQ\nDUICNCFOUTvC8UICNIAQjxDeGfoOkxdOZvqS6Xw04iPM3m4ciSKEEEI0EQathhHJ4YxIDudQqY2v\n1hcwJ8vKP77ayL++28TwxDAmpJrpFReIWkY8CtFsGXUa/jU6kSkf/E6K2Q9blZNXl+3gX6OT3F2a\noKYDzVDTgWa1Ymjb1s0VnUKlgqFPKSHaL8/Dlm9hzhQlRKunUEQ0A1WVULBO6SzLy4S81VD7vtwn\nCtr0geieSmAW3FEJX+vjZYuKThvFaMvdhMum7E3TBgdjsljwnzQJUxcLxoSEk52cQgghhGgdJEAT\n4hR+BmWMyZ/Zgfa/zN5mZgyZwZRFU7jlh1uYNWIWIR4h9V2iEEII0WyF+BiZ3j+Oaf3akmMtYW5W\nPt9k72d+9n4i/UyM7xrJ+NQoYgI93V2qEOIiDOgQwuXJ4Xy/sZBecYF8ujqPKb3a0FbGtrqdq8KG\nymTE5XTisFrx6t/f3SWdTqWCgQ+DzgTLnoCt3ykhWsaHEqK1VLZSyF9Tt8OsIAuqK5VzQR0gaawS\nlsWkg190vbyky27HtnWrMoaxtrts/37lpE6HMaET/ldfdbK7TBseLt1lQgghRCsnAZoQp6gN0C60\nA61WvH88bw9+m5t/uJnpS6bzwbAPTu5VE0IIIYRCpVJhMfthMfvxyOUJLNl8kDlZVl77aSev/riT\n7rEBZKRGMTI5HE+D/LgqRHPy2JUJLN9eRHG5HYNWzQuLt/HWdanuLqvVq+1AqyoqxlVZid4c5e6S\nzqzvX5VOtEV/h23fw5zJkPGRhGgtQdnBurAsbyUc3AQuJ6i1ygjG7rdATC8w9wTPwHp5ScfBQ3W7\ny3JysG3ahKtSCem0YWFKd9n112OypCjdZQYZOSuEEEKI08kVCSFO4WPwAS6uA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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d55f080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure(figsize=[30,30])\n",
    "\n",
    "y_values = [x for x in list_target]\n",
    "x_values = [x for x in list_features]\n",
    "plt.xlabel(\"AQI Values\",fontsize=30)\n",
    "plt.ylabel(\"Temperature\",fontsize=30)\n",
    "plt.plot(x_values,y_values)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "causes_of_death=pd.read_csv(\"NCHS_-_Leading_Causes_of_Death__United_States.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "not_wanted_diseases_list=[\n",
    "                          'All Causes',\n",
    "                          'Assault (homicide) (*U01-*U02,X85-Y09,Y87.1)',\n",
    "                          'Assault (homicide) by discharge of firearms (*U01.4,X93-X95)',\n",
    "                          'Assault (homicide) by other and unspecified means and their sequelae (*U01.0-*U01.3,*U01.5-*U01.9,*U02,X85-X92,X96-Y09,Y87.1)'\n",
    "                          'Legal intervention (Y35,Y89.0)',\n",
    "                          'Events of undetermined intent (Y10-Y34,Y87.2,Y89.9)',\n",
    "                          'Discharge of firearms, undetermined intent (Y22-Y24)',\n",
    "                          'Other and unspecified events of undetermined intent and their sequelae (Y10-Y21,Y25-Y34,Y87.2,Y89.9)',\n",
    "                          'Operations of war and their sequelae (Y36,Y89.1)',\n",
    "                          'Complications of medical and surgical care (Y40-Y84,Y88)',\n",
    "                          'Other land transport accidents (V01,V05-V06,V09.1,V09.3-V09.9, V10-V11,V15-V18,V19.3,V19.8-V19.9,V80.0-V80.2,V80.6-V80.9,V81.2-V81.9,V82.2-V82.9,V87.9,V88.9,V89.1,V89.3,V89.9)',\n",
    "                          'Water, air and space, and other and unspecified transport accidents and theirsequelae (V90-V99,Y85)',\n",
    "                          'Nontransport accidents (W00-X59,Y86)',\n",
    "                          'Falls (W00-W19)',\n",
    "                          'Accidental discharge of firearms (W32-W34)',\n",
    "                          'Accidental drowning and submersion (W65-W74)',\n",
    "                          'Accidental exposure to smoke, fire and flames (X00-X09)',\n",
    "                          'Accidental poisoning and exposure to noxious substances (X40-X49)',\n",
    "                          'Other and unspecified nontransport accidents and their sequelae (W20-W31,W35-W64,W75-W99,X10-X39,X50-X59,Y86)',\n",
    "                          'Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)',\n",
    "                          'Intentional self-harm (suicide) by discharge of firearms (X72-X74)',\n",
    "                          'Intentional self-harm (suicide) by other and unspecified means and their sequelae (*U03,X60-X71,X75-X84,Y87.0)',\n",
    "                          'Accidents (unintentional injuries) (V01-X59,Y85-Y86)',\n",
    "                          'Transport accidents (V01-V99,Y85)',\n",
    "                          'Motor vehicle accidents (V02-V04,V09.0,V09.2,V12-V14,V19.0-V19.2,V19.4-V19.6,V20-V79,V80.3-V80.5,V81.0-V81.1,V82.0-V82.1,V83-V86,V87.0-V87.8,V88.0-V88.8,V89.0,V89.2)',\n",
    "                          'All other diseases (Residual) (D65-E07,E15-E34,E65-F99,G04-G14,G23-G25,G31-H93,K00-K22,K29-K31,K50-K66,K71-K72,K75-K76,K83-M99, N13.0-N13.5,N13.7-N13.9,N14,N15.0,N15.8-N15.9,N20-N23,N28-N39,N41-N64,N80-N98)'\n",
    "                                                 \n",
    "                         ]\n",
    "causes_of_death = causes_of_death[~causes_of_death['113 Cause Name'].isin(not_wanted_diseases_list)]\n",
    "causes_of_death.shape\n",
    "causes_of_death.to_csv(\"Leading_Causes_of_death.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "from pyspark.sql.functions import col, udf\n",
    "from pyspark.sql.types import DateType\n",
    "# converting the date from string to DateTime format using UDF\n",
    "func =  udf (lambda x: datetime.strptime(x, '%Y'), DateType())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- _c0: string (nullable = true)\n",
      " |-- Year: date (nullable = true)\n",
      " |-- 113 Cause Name: string (nullable = true)\n",
      " |-- Cause Name: string (nullable = true)\n",
      " |-- State: string (nullable = true)\n",
      " |-- Deaths: string (nullable = true)\n",
      " |-- Age-adjusted Death Rate: string (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import pyspark\n",
    "from pyspark.sql import SQLContext\n",
    "from pyspark import SparkContext\n",
    "sc =SparkContext.getOrCreate()\n",
    "sqlContext = SQLContext(sc)\n",
    "df = sqlContext.read.csv(\"Leading_Causes_of_death.csv\", header=True)\n",
    "df = df.withColumn('Year', func(col('Year')))\n",
    "df.printSchema()\n",
    "df.registerTempTable(\"Death_data_Set\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "death=sqlContext.sql(\"SELECT YEAR(Year) AS Years,`113 Cause Name`,State, Deaths FROM Death_data_Set \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----+--------------------+--------------------+------+\n",
      "|Years|      113 Cause Name|               State|Deaths|\n",
      "+-----+--------------------+--------------------+------+\n",
      "| 1999|Alzheimer's disea...|             Alabama| 772.0|\n",
      "| 1999|Alzheimer's disea...|              Alaska|  24.0|\n",
      "| 1999|Alzheimer's disea...|             Arizona| 963.0|\n",
      "| 1999|Alzheimer's disea...|            Arkansas| 434.0|\n",
      "| 1999|Alzheimer's disea...|          California|4532.0|\n",
      "| 1999|Alzheimer's disea...|            Colorado| 756.0|\n",
      "| 1999|Alzheimer's disea...|         Connecticut| 449.0|\n",
      "| 1999|Alzheimer's disea...|            Delaware| 107.0|\n",
      "| 1999|Alzheimer's disea...|District of Columbia|  53.0|\n",
      "| 1999|Alzheimer's disea...|             Florida|3059.0|\n",
      "| 1999|Alzheimer's disea...|             Georgia|1080.0|\n",
      "| 1999|Alzheimer's disea...|              Hawaii| 109.0|\n",
      "| 1999|Alzheimer's disea...|               Idaho| 243.0|\n",
      "| 1999|Alzheimer's disea...|            Illinois|1908.0|\n",
      "| 1999|Alzheimer's disea...|             Indiana|1106.0|\n",
      "| 1999|Alzheimer's disea...|                Iowa| 706.0|\n",
      "| 1999|Alzheimer's disea...|              Kansas| 511.0|\n",
      "| 1999|Alzheimer's disea...|            Kentucky| 728.0|\n",
      "| 1999|Alzheimer's disea...|           Louisiana| 683.0|\n",
      "| 1999|Alzheimer's disea...|               Maine| 429.0|\n",
      "+-----+--------------------+--------------------+------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "death.show()\n",
    "death.registerTempTable(\"Death_dataset\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pyspark.sql.types import IntegerType\n",
    "death=death.withColumn(\"Years\", death[\"Years\"].cast(IntegerType()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataFrame[summary: string, Years: string, 113 Cause Name: string, State: string, Deaths: string]"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "death.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>The average number of deaths each year for each disease for each State<h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "D=sqlContext.sql(\"SELECT  Years as Years,avg(CAST(Death_dataset.`Deaths` AS DOUBLE)) AS Deaths,State,`113 Cause Name` FROM Death_dataset GROUP BY  State,`113 Cause Name`, Years ORDER BY Years,State,Deaths\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataFrame[summary: string, Years: string, Deaths: string, State: string, 113 Cause Name: string]"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D.registerTempTable(\"Disease\")\n",
    "D.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pyspark.sql.types import DoubleType\n",
    "\n",
    "D = D.withColumn(\"Deaths\", D[\"Deaths\"].cast(DoubleType()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----+--------+--------------+--------------------+\n",
      "|Years|  Deaths|         State|      113 Cause Name|\n",
      "+-----+--------+--------------+--------------------+\n",
      "| 1999|725192.0| United States|Diseases of heart...|\n",
      "| 1999|549838.0| United States|Malignant neoplas...|\n",
      "| 1999|167366.0| United States|Cerebrovascular d...|\n",
      "| 1999|124181.0| United States|Chronic lower res...|\n",
      "| 1999| 71930.0|    California|Diseases of heart...|\n",
      "| 1999| 68399.0| United States|Diabetes mellitus...|\n",
      "| 1999| 63730.0| United States|Influenza and pne...|\n",
      "| 1999| 58987.0|      New York|Diseases of heart...|\n",
      "| 1999| 53067.0|    California|Malignant neoplas...|\n",
      "| 1999| 51434.0|       Florida|Diseases of heart...|\n",
      "| 1999| 44536.0| United States|Alzheimer's disea...|\n",
      "| 1999| 43418.0|         Texas|Diseases of heart...|\n",
      "| 1999| 41707.0|  Pennsylvania|Diseases of heart...|\n",
      "| 1999| 38478.0|       Florida|Malignant neoplas...|\n",
      "| 1999| 37609.0|      New York|Malignant neoplas...|\n",
      "| 1999| 35525.0| United States|Nephritis, nephro...|\n",
      "| 1999| 33387.0|      Illinois|Diseases of heart...|\n",
      "| 1999| 33192.0|          Ohio|Diseases of heart...|\n",
      "| 1999| 32755.0|         Texas|Malignant neoplas...|\n",
      "| 1999| 30680.0| United States|Septicemia (A40-A41)|\n",
      "| 1999| 30312.0|  Pennsylvania|Malignant neoplas...|\n",
      "| 1999| 27693.0|      Michigan|Diseases of heart...|\n",
      "| 1999| 26259.0| United States|Chronic liver dis...|\n",
      "| 1999| 25233.0|          Ohio|Malignant neoplas...|\n",
      "| 1999| 25024.0|      Illinois|Malignant neoplas...|\n",
      "| 1999| 23493.0|    New Jersey|Diseases of heart...|\n",
      "| 1999| 19744.0|      Michigan|Malignant neoplas...|\n",
      "| 1999| 19191.0|North Carolina|Diseases of heart...|\n",
      "| 1999| 18178.0|    New Jersey|Malignant neoplas...|\n",
      "| 1999| 17974.0|      Missouri|Diseases of heart...|\n",
      "| 1999| 17962.0|    California|Cerebrovascular d...|\n",
      "| 1999| 17597.0|       Georgia|Diseases of heart...|\n",
      "| 1999| 16968.0| United States|Essential hyperte...|\n",
      "| 1999| 16661.0|       Indiana|Diseases of heart...|\n",
      "| 1999| 16280.0|     Tennessee|Diseases of heart...|\n",
      "| 1999| 15871.0| Massachusetts|Diseases of heart...|\n",
      "| 1999| 15815.0|North Carolina|Malignant neoplas...|\n",
      "| 1999| 15329.0|      Virginia|Diseases of heart...|\n",
      "| 1999| 15268.0| United States|Pneumonitis due t...|\n",
      "| 1999| 14593.0| United States|Parkinson's disea...|\n",
      "| 1999| 13853.0| Massachusetts|Malignant neoplas...|\n",
      "| 1999| 13827.0|     Wisconsin|Diseases of heart...|\n",
      "| 1999| 13419.0|       Alabama|Diseases of heart...|\n",
      "| 1999| 13365.0|      Virginia|Malignant neoplas...|\n",
      "| 1999| 13225.0|       Georgia|Malignant neoplas...|\n",
      "| 1999| 13165.0|    California|Chronic lower res...|\n",
      "| 1999| 12898.0|       Indiana|Malignant neoplas...|\n",
      "| 1999| 12186.0|      Missouri|Malignant neoplas...|\n",
      "| 1999| 12098.0|      Kentucky|Diseases of heart...|\n",
      "| 1999| 12080.0|      Maryland|Diseases of heart...|\n",
      "| 1999| 12007.0|     Louisiana|Diseases of heart...|\n",
      "| 1999| 11943.0|     Tennessee|Malignant neoplas...|\n",
      "| 1999| 11515.0|    Washington|Diseases of heart...|\n",
      "| 1999| 11263.0|      Oklahoma|Diseases of heart...|\n",
      "| 1999| 10800.0|       Arizona|Diseases of heart...|\n",
      "| 1999| 10755.0|     Wisconsin|Malignant neoplas...|\n",
      "| 1999| 10653.0|    Washington|Malignant neoplas...|\n",
      "| 1999| 10560.0|       Florida|Cerebrovascular d...|\n",
      "| 1999| 10414.0|         Texas|Cerebrovascular d...|\n",
      "| 1999| 10143.0|      Maryland|Malignant neoplas...|\n",
      "| 1999|  9981.0|South Carolina|Diseases of heart...|\n",
      "| 1999|  9533.0|     Minnesota|Diseases of heart...|\n",
      "| 1999|  9506.0|       Alabama|Malignant neoplas...|\n",
      "| 1999|  9412.0|     Louisiana|Malignant neoplas...|\n",
      "| 1999|  9336.0|   Mississippi|Diseases of heart...|\n",
      "| 1999|  9131.0|       Florida|Chronic lower res...|\n",
      "| 1999|  9127.0|   Connecticut|Diseases of heart...|\n",
      "| 1999|  9006.0|       Arizona|Malignant neoplas...|\n",
      "| 1999|  8925.0|      Kentucky|Malignant neoplas...|\n",
      "| 1999|  8892.0|     Minnesota|Malignant neoplas...|\n",
      "| 1999|  8699.0|          Iowa|Diseases of heart...|\n",
      "| 1999|  8600.0|  Pennsylvania|Cerebrovascular d...|\n",
      "| 1999|  8315.0|      Arkansas|Diseases of heart...|\n",
      "| 1999|  8124.0|      New York|Cerebrovascular d...|\n",
      "| 1999|  8089.0|South Carolina|Malignant neoplas...|\n",
      "| 1999|  7714.0|      Illinois|Cerebrovascular d...|\n",
      "| 1999|  7518.0|         Texas|Chronic lower res...|\n",
      "| 1999|  7312.0|      Oklahoma|Malignant neoplas...|\n",
      "| 1999|  7263.0|        Oregon|Diseases of heart...|\n",
      "| 1999|  7235.0|          Ohio|Cerebrovascular d...|\n",
      "| 1999|  7086.0|      New York|Chronic lower res...|\n",
      "| 1999|  7054.0|   Connecticut|Malignant neoplas...|\n",
      "| 1999|  6975.0|        Kansas|Diseases of heart...|\n",
      "| 1999|  6905.0|        Oregon|Malignant neoplas...|\n",
      "| 1999|  6822.0| West Virginia|Diseases of heart...|\n",
      "| 1999|  6420.0|      Colorado|Diseases of heart...|\n",
      "| 1999|  6401.0|    California|Diabetes mellitus...|\n",
      "| 1999|  6346.0|          Iowa|Malignant neoplas...|\n",
      "| 1999|  6143.0|   Mississippi|Malignant neoplas...|\n",
      "| 1999|  6141.0|  Pennsylvania|Chronic lower res...|\n",
      "| 1999|  6137.0|      Arkansas|Malignant neoplas...|\n",
      "| 1999|  6041.0|      Michigan|Cerebrovascular d...|\n",
      "| 1999|  5863.0|      Colorado|Malignant neoplas...|\n",
      "| 1999|  5858.0|          Ohio|Chronic lower res...|\n",
      "| 1999|  5626.0|North Carolina|Cerebrovascular d...|\n",
      "| 1999|  5482.0|      New York|Influenza and pne...|\n",
      "| 1999|  5334.0|        Kansas|Malignant neoplas...|\n",
      "| 1999|  5155.0|      Illinois|Chronic lower res...|\n",
      "| 1999|  4931.0|         Texas|Diabetes mellitus...|\n",
      "| 1999|  4762.0| West Virginia|Malignant neoplas...|\n",
      "+-----+--------+--------------+--------------------+\n",
      "only showing top 100 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "sqlContext.sql(\"SELECT * FROM Disease WHERE Deaths IS NOT NULL  GROUP BY `113 Cause Name`, Years,Deaths,State ORDER BY  Years, Deaths DESC\").show(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "table=sqlContext.sql(\"SELECT * FROM Disease WHERE Deaths IS NOT NULL ORDER BY Deaths DESC\")\n",
    "table.registerTempTable(\"Death_Data_Set\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------+--------------------+-----+------------+\n",
      "|Max_Deaths|              Causes|Years|       State|\n",
      "+----------+--------------------+-----+------------+\n",
      "|   71930.0|Diseases of heart...| 1999|  California|\n",
      "|   58987.0|Diseases of heart...| 1999|    New York|\n",
      "|   53067.0|Malignant neoplas...| 1999|  California|\n",
      "|   51434.0|Diseases of heart...| 1999|     Florida|\n",
      "|   43418.0|Diseases of heart...| 1999|       Texas|\n",
      "|   41707.0|Diseases of heart...| 1999|Pennsylvania|\n",
      "|   38478.0|Malignant neoplas...| 1999|     Florida|\n",
      "|   37609.0|Malignant neoplas...| 1999|    New York|\n",
      "|   33387.0|Diseases of heart...| 1999|    Illinois|\n",
      "|   33192.0|Diseases of heart...| 1999|        Ohio|\n",
      "+----------+--------------------+-----+------------+\n",
      "only showing top 10 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "sqlContext.sql(\"SELECT MAX(Deaths) AS Max_Deaths,`113 Cause Name` as Causes, Years, State From Death_Data_Set WHERE State != 'United States' GROUP BY  Years, `113 Cause Name`, State ORDER BY Years,MAX(Deaths) DESC\").show(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "result=sqlContext.sql(\"SELECT MAX(Deaths) AS Max_Deaths,`113 Cause Name` as Causes, Years as Yearly, State From Death_Data_Set WHERE State != 'United States' GROUP BY  Years, `113 Cause Name`, State ORDER BY Years,MAX(Deaths) DESC\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pyspark.sql.types import IntegerType\n",
    "result=result.withColumn(\"Yearly\", result[\"Yearly\"].cast(IntegerType()))\n",
    "result.registerTempTable(\"max_city_wise_death\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- Max_Deaths: double (nullable = true)\n",
      " |-- Causes: string (nullable = true)\n",
      " |-- Yearly: integer (nullable = true)\n",
      " |-- State: string (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "result.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- State: string (nullable = true)\n",
      " |-- City: string (nullable = true)\n",
      " |-- Max_NO2_AQI: double (nullable = true)\n",
      " |-- Max_SO2_AQI: double (nullable = true)\n",
      " |-- MAX_O3_AQI: double (nullable = true)\n",
      " |-- Max_CO_AQI: double (nullable = true)\n",
      " |-- Yearly: integer (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "t1.printSchema()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Joining the Death Data set with the pollution data set to check how the pollution effects death in different cities. </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------+----------+--------------------+----------+--------------------+-----------+-----------+----------+----------+\n",
      "|Yearly|Max_Deaths|              Causes|     State|                City|Max_NO2_AQI|Max_SO2_AQI|Max_CO_AQI|MAX_O3_AQI|\n",
      "+------+----------+--------------------+----------+--------------------+-----------+-----------+----------+----------+\n",
      "|  2000|   68426.0|Diseases of heart...|California|       Bethel Island|       41.0|       26.0|      17.0|     124.0|\n",
      "|  2000|   68426.0|Diseases of heart...|California|             Vallejo|       62.0|       24.0|      57.0|      47.0|\n",
      "|  2000|   68426.0|Diseases of heart...|California|          Long Beach|      108.0|       65.0|      63.0|     114.0|\n",
      "|  2000|   68426.0|Diseases of heart...|California|         Victorville|       94.0|       36.0|      18.0|     179.0|\n",
      "|  2000|   68426.0|Diseases of heart...|California|       Not in a city|       84.0|       21.0|      35.0|     154.0|\n",
      "|  2000|   68426.0|Diseases of heart...|California|Vandenberg Air Fo...|       31.0|        6.0|       6.0|      80.0|\n",
      "|  2000|   68426.0|Diseases of heart...|California|           San Pablo|       64.0|       39.0|      22.0|      74.0|\n",
      "|  2000|   68426.0|Diseases of heart...|California|       San Francisco|       72.0|       27.0|      36.0|      36.0|\n",
      "|  2000|   68426.0|Diseases of heart...|California|           Davenport|       33.0|       33.0|       9.0|      47.0|\n",
      "|  2000|   68426.0|Diseases of heart...|California|           Pittsburg|       51.0|       40.0|      28.0|     111.0|\n",
      "+------+----------+--------------------+----------+--------------------+-----------+-----------+----------+----------+\n",
      "only showing top 10 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "sqlContext.sql(\"SELECT Entire_Pollution.Yearly,max_city_wise_death.Max_Deaths, max_city_wise_death.Causes,max_city_wise_death.State,Entire_Pollution.City,Entire_Pollution.Max_NO2_AQI,Entire_Pollution.Max_SO2_AQI, Entire_Pollution.Max_CO_AQI,Entire_Pollution.MAX_O3_AQI FROM Entire_Pollution JOIN max_city_wise_death ON max_city_wise_death.State==Entire_Pollution.State AND Entire_Pollution.Yearly=max_city_wise_death.Yearly GROUP BY Entire_Pollution.Yearly,max_city_wise_death.State,Entire_Pollution.City,max_city_wise_death.Causes,max_city_wise_death.Max_Deaths, Entire_Pollution.Max_NO2_AQI,Entire_Pollution.Max_SO2_AQI, Entire_Pollution.Max_CO_AQI,Entire_Pollution.MAX_O3_AQI ORDER BY Entire_Pollution.Yearly ,max_city_wise_death.Max_Deaths DESC\").show(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Pollution_wise_Death=sqlContext.sql(\"SELECT Entire_Pollution.Yearly,max_city_wise_death.Max_Deaths, max_city_wise_death.Causes,max_city_wise_death.State,Entire_Pollution.City,Entire_Pollution.Max_NO2_AQI,Entire_Pollution.Max_SO2_AQI, Entire_Pollution.Max_CO_AQI,Entire_Pollution.MAX_O3_AQI FROM Entire_Pollution JOIN max_city_wise_death ON max_city_wise_death.State==Entire_Pollution.State AND Entire_Pollution.Yearly=max_city_wise_death.Yearly GROUP BY Entire_Pollution.Yearly,max_city_wise_death.State,Entire_Pollution.City,max_city_wise_death.Causes,max_city_wise_death.Max_Deaths, Entire_Pollution.Max_NO2_AQI,Entire_Pollution.Max_SO2_AQI, Entire_Pollution.Max_CO_AQI,Entire_Pollution.MAX_O3_AQI ORDER BY Entire_Pollution.Yearly ,max_city_wise_death.Max_Deaths DESC\")\n",
    "Pollution_wise_Death.toPandas().to_csv(\"Pollution_wise_Death.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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