Storing and Analysing Storm Data with PostgreSQL on AWS.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Table of Contents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## I) <a href='#first_part'> Creating a PostgreSQL DB Instance on Amazon RDS</a>\n",
    "## II) <a href='#second_part'>Creating a Table in the PostgreSQL DB</a>\n",
    "## III) <a href='#third_part'>DB Management</a>\n",
    "## IV) <a href='#fourth_part'>Loading the Storm Data into the PostgreSQL DB</a>\n",
    "## V) <a href='#fifth_part'>Updating the PostgreSQL DB</a>\n",
    "## VI) <a href='#sixth_part'>Analysing the Storm Data on Amazon RDS</a>\n",
    "### A) <a href='#subpart_one'>Average Wind Speed, Atmospheric Pressure and Shape Length by Storm Category</a>\n",
    "### B) <a href='#subpart_two'>2005 : Record-Breaking Storm Season</a>\n",
    "#### 1) <a href='#first_query'>Year with the Maximum Number of Storms in North Atlantic</a>\n",
    "#### 2) <a href='#second_query'>Number of Hurricanes in North Atlantic in 2005</a>\n",
    "#### 3) <a href='#third_query'>Number of Category 5 Hurricanes in North Atlantic in 2005</a>\n",
    "#### 4) <a href='#fourth_query'>Storm Duration in North Atlantic in 2005</a>\n",
    "#### 5) <a href='#fifth_query'>Storm with the Lowest Barometric Pressure in North Atlantic</a>\n",
    "#### 6) <a href='#sixth_query'>Total Number of Days with a Named Storm in North Atlantic during the 2005 season</a>\n",
    "#### 7) <a href='#seventh_query'>Hurricane Katrina</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# I) <a id='first_part'> Creating a PostgreSQL DB Instance on Amazon RDS</a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will import the following library to use PostgreSQL : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import psycopg2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After setting up our instance on Amazon RDS, we can create a new PostgreSQL database, on which we will store the storm data : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conn = psycopg2.connect(dbname = 'mydatabase', host ='mydbinstance.cv2rhdnhy2zb.eu-west-3.rds.amazonaws.com', \n",
    "port = '5432', user= 'clementmsika', password = 'xxxx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cur = conn.cursor()\n",
    "conn.autocommit = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cur.execute(\"CREATE DATABASE storm_database ;\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conn.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# II) <a id='#second_part'>Creating a Table in the PostgreSQL DB</a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before creating our table, we need to know the appropriate data types with which we will store the storm data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at the first rows of the CSV file : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ï»¿FID', 'YEAR', 'MONTH', 'DAY', 'AD_TIME', 'BTID', 'NAME', 'LAT', 'LONG', 'WIND_KTS', 'PRESSURE', 'CAT', 'BASIN', 'Shape_Leng']\n",
      "['2001', '1957', '8', '8', '1800Z', '63', 'NOTNAMED', '22.5', '-140', '50', '0', 'TS', 'Eastern Pacific', '1.140175']\n",
      "['2002', '1961', '10', '3', '1200Z', '116', 'PAULINE', '22.1', '-140.2', '45', '0', 'TS', 'Eastern Pacific', '1.16619']\n",
      "['2003', '1962', '8', '29', '0600Z', '124', 'C', '18', '-140', '45', '0', 'TS', 'Eastern Pacific', '2.10238']\n",
      "['2004', '1967', '7', '14', '0600Z', '168', 'DENISE', '16.6', '-139.5', '45', '0', 'TS', 'Eastern Pacific', '2.12132']\n",
      "['2005', '1972', '8', '16', '1200Z', '251', 'DIANA', '18.5', '-139.8', '70', '0', 'H1', 'Eastern Pacific', '1.702939']\n",
      "['2006', '1976', '7', '22', '0000Z', '312', 'DIANA', '18.6', '-139.8', '30', '0', 'TD', 'Eastern Pacific', '1.6']\n",
      "['2007', '1978', '8', '26', '1200Z', '342', 'KRISTY', '21.4', '-140.2', '45', '0', 'TS', 'Eastern Pacific', '1.30384']\n",
      "['2008', '1980', '9', '24', '1800Z', '371', 'KAY', '20.5', '-140.2', '75', '0', 'H1', 'Eastern Pacific', '1.220656']\n",
      "['2009', '1970', '8', '23', '0000Z', '223', 'MAGGIE', '14.9', '-139.4', '45', '0', 'TS', 'Eastern Pacific', '0.921954']\n"
     ]
    }
   ],
   "source": [
    "import io\n",
    "from urllib import request\n",
    "import csv\n",
    "\n",
    "response = request.urlopen('https://dq-content.s3.amazonaws.com/251/storm_data.csv')\n",
    "reader = csv.reader(io.TextIOWrapper(response))\n",
    "\n",
    "count = 0\n",
    "\n",
    "for line in reader:\n",
    "    if count < 10 : \n",
    "        count += 1\n",
    "        print(line)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will find here the appropriate data type for the column \"NAME\" : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9\n"
     ]
    }
   ],
   "source": [
    "response = request.urlopen('https://dq-content.s3.amazonaws.com/251/storm_data.csv')\n",
    "reader = csv.reader(io.TextIOWrapper(response))\n",
    "max_length = 0\n",
    "unique_storm_name = set([\n",
    "        row[6] for row in reader])\n",
    "for i in unique_storm_name :\n",
    "    if len(i) > max_length : \n",
    "        max_length = len(i)\n",
    "print(max_length)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The maximum length for the column name is 9. We will assign to the column \"NAME\" the data type VARCHAR(9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will find here the appropriate data type for the column \"BASIN\" : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15\n"
     ]
    }
   ],
   "source": [
    "response = request.urlopen('https://dq-content.s3.amazonaws.com/251/storm_data.csv')\n",
    "reader = csv.reader(io.TextIOWrapper(response))\n",
    "max_length = 0\n",
    "unique_basin_name = set([\n",
    "        row[12] for row in reader])\n",
    "for i in unique_basin_name :\n",
    "    if len(i) > max_length : \n",
    "        max_length = len(i)\n",
    "print(max_length)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The maximum length for the column \"BASIN\" is 15. We will assign to the column \"BASIN\" the data type VARCHAR(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will find here the appropriate data type for the column \"Shape_Leng\" : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    }
   ],
   "source": [
    "response = request.urlopen('https://dq-content.s3.amazonaws.com/251/storm_data.csv')\n",
    "reader = csv.reader(io.TextIOWrapper(response))\n",
    "max_length = 0\n",
    "unique_basin_name = set([\n",
    "        row[-1] for row in reader])\n",
    "for i in unique_basin_name :\n",
    "    if len(i) > max_length : \n",
    "        max_length = len(i)\n",
    "print(max_length)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The maximum length for the column \"Shape_Leng\" is 10. We will assign to the column \"Shape_Leng\" the data type DECIMAL(10,8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now create a table with the appropriate data types on our new PostgreSQL DB : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conn = psycopg2.connect(dbname = 'storm_database', host ='mydbinstance.cv2rhdnhy2zb.eu-west-3.rds.amazonaws.com', \n",
    "port = '5432', user = 'clementmsika', password = 'xxxx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cur = conn.cursor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cur.execute('''\n",
    "\n",
    "CREATE TABLE IF NOT EXISTS storm_table (\n",
    "             \n",
    "fid INTEGER PRIMARY KEY,\n",
    "\n",
    "year SMALLINT,\n",
    "\n",
    "month SMALLINT,\n",
    "\n",
    "day SMALLINT,\n",
    "\n",
    "ad_time TIME, \n",
    "\n",
    "btid SMALLINT,\n",
    "\n",
    "name VARCHAR(9),\n",
    "\n",
    "lat DECIMAL(4,1),\n",
    "\n",
    "long DECIMAL(4,1),\n",
    "\n",
    "wind_kts SMALLINT,\n",
    "\n",
    "pressure SMALLINT,\n",
    "\n",
    "cat VARCHAR(2),\n",
    "\n",
    "basin VARCHAR(15),\n",
    "\n",
    "shape_leng DECIMAL(10,8)\n",
    "\n",
    ")\n",
    "           \n",
    ";\n",
    "           \n",
    "''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conn.commit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conn.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# III) <a id='#third_part'>DB Management</a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before loading our data into our database, we will manage the privileges on the newly created table."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Firstly, we will create a user \"tornado\" who can select, insert and update the data in the table : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conn = psycopg2.connect(dbname = 'storm_database', host = 'mydbinstance.cv2rhdnhy2zb.eu-west-3.rds.amazonaws.com', \n",
    "port = '5432', user = 'clementmsika', password = 'xxxx')\n",
    "cur = conn.cursor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cur.execute(\"CREATE USER tornado WITH PASSWORD 'i_love_storms' ;\")\n",
    "\n",
    "cur.execute(\"REVOKE ALL ON storm_table FROM tornado;\")\n",
    "\n",
    "cur.execute(\"GRANT SELECT, INSERT, UPDATE ON storm_table TO tornado ;\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Secondly, we will create the group \"analysts\" who can only read the data in storm_table : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cur.execute(\"CREATE GROUP analysts NOLOGIN ;\")\n",
    "\n",
    "cur.execute(\"REVOKE ALL ON storm_table FROM analysts ;\")\n",
    "\n",
    "cur.execute(\"GRANT SELECT ON storm_table TO analysts ;\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thirdly, we will create the user \"first_analyst\" and assign the group \"analysts\" to this user : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cur.execute(\"CREATE USER first_analyst WITH PASSWORD 'i_analyse_storm_data' ;\")\n",
    "\n",
    "cur.execute(\"REVOKE ALL ON storm_table FROM first_analyst ;\")\n",
    "\n",
    "cur.execute(\"GRANT analysts TO first_analyst ;\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we will create the user \"second_analyst\" and assign the group \"analysts\" to this user : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cur.execute(\"CREATE USER second_analyst WITH PASSWORD 'i_predict_storms' ;\")\n",
    "\n",
    "cur.execute(\"REVOKE ALL ON storm_table FROM second_analyst ;\")\n",
    "\n",
    "cur.execute(\"GRANT analysts TO second_analyst ;\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conn.commit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conn.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IV) <a id='#fourth_part'>Loading the Storm Data into the PostgreSQL DB</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can connect to the DB as the user \"tornado\" and load the storm data into the DB :  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conn = psycopg2.connect(dbname = 'storm_database', host ='mydbinstance.cv2rhdnhy2zb.eu-west-3.rds.amazonaws.com', \n",
    "port = '5432', user = 'tornado', password = 'i_love_storms')\n",
    "cur = conn.cursor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open(\"storm_data.csv\", \"r\") as f:\n",
    "    reader = csv.reader(f)\n",
    "    next(reader)\n",
    "    cur.copy_from(f, \"storm_table\", sep = \",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conn.commit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conn.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# V) <a id='#fifth_part'>Updating the PostgreSQL DB</a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will here create a single column \"date\", which concatenates the information included in the columns \"year\", \"month\", \"day\" and \"ad_time\" : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conn = psycopg2.connect(dbname = 'storm_database', host ='mydbinstance.cv2rhdnhy2zb.eu-west-3.rds.amazonaws.com', \n",
    "port = '5432', user = 'clementmsika', password = 'xxxx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cur = conn.cursor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cur.execute(\"ALTER TABLE storm_table ADD COLUMN date TIMESTAMP ;\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cur.execute(\"UPDATE storm_table SET date = to_timestamp(year || '-' || month || '-' || day || ' ' || ad_time, 'YYYY-MM-DD HH24:MI:SS') ; \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cur.execute('''\n",
    "\n",
    "ALTER TABLE storm_table \n",
    "\n",
    "DROP COLUMN year,\n",
    "\n",
    "DROP COLUMN month,\n",
    "\n",
    "DROP COLUMN day,\n",
    "\n",
    "DROP COLUMN ad_time\n",
    "\n",
    ";\n",
    "\n",
    "''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conn.commit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conn.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# VI) <a id='#sixth_part'>Analysing the Storm Data on Amazon RDS</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conn = psycopg2.connect(dbname = 'storm_database', host ='mydbinstance.cv2rhdnhy2zb.eu-west-3.rds.amazonaws.com', \n",
    "port = '5432', user = 'first_analyst', password = 'i_analyse_storm_data')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset is limited to storms located in the North Atlantic and Eastern Pacific basins between 1851 and 2008. \n",
    "\n",
    "The storm data contained in the csv file can be found on the National Hurricane Center website (www.nhc.noaa.gov/).\n",
    "\n",
    "Here is the glossary of the National Hurricane Center (NHC) terms : https://www.nhc.noaa.gov/aboutgloss.shtml"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A) <a id='#subpart_one'>Average Wind Speed, Atmospheric Pressure and Shape Length by Storm Category</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's calculate the average wind speed (in knots per second), atmospheric pressure and shape length for the 7 storm categories : Tropical Depression (TD), Tropical Storm (TS), H1, H2, H3, H4 and H5 . We will remove the data where we have the value 0 mbar as atmospheric pressure : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "query_average_wind_speed_pressure_shape_length = '''\n",
    "\n",
    "SELECT\n",
    "\n",
    "cat category,\n",
    "\n",
    "AVG(wind_kts) average_wind_speed,\n",
    "\n",
    "AVG(pressure) average_atmospheric_pressure,\n",
    "\n",
    "AVG(shape_leng) average_shape_length\n",
    "\n",
    "FROM storm_table\n",
    "\n",
    "WHERE pressure > 0\n",
    "\n",
    "GROUP BY cat\n",
    "\n",
    "HAVING cat = 'TD' OR cat = 'TS' OR cat = 'H1' OR cat = 'H2' OR cat = 'H3' OR cat = 'H4' OR cat = 'H5'\n",
    "\n",
    "ORDER BY 2\n",
    "\n",
    ";\n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>category</th>\n",
       "      <th>average_wind_speed</th>\n",
       "      <th>average_atmospheric_pressure</th>\n",
       "      <th>average_shape_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TD</td>\n",
       "      <td>27.460480</td>\n",
       "      <td>1007.437539</td>\n",
       "      <td>1.014611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TS</td>\n",
       "      <td>45.717167</td>\n",
       "      <td>999.049912</td>\n",
       "      <td>1.116699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>H1</td>\n",
       "      <td>71.064881</td>\n",
       "      <td>982.780655</td>\n",
       "      <td>1.245807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>H2</td>\n",
       "      <td>89.546277</td>\n",
       "      <td>969.295755</td>\n",
       "      <td>1.201254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>H3</td>\n",
       "      <td>104.322421</td>\n",
       "      <td>957.001984</td>\n",
       "      <td>1.166742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>H4</td>\n",
       "      <td>121.521480</td>\n",
       "      <td>941.684964</td>\n",
       "      <td>1.131484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>H5</td>\n",
       "      <td>144.349593</td>\n",
       "      <td>918.317073</td>\n",
       "      <td>1.112514</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  category  average_wind_speed  average_atmospheric_pressure  \\\n",
       "0       TD           27.460480                   1007.437539   \n",
       "1       TS           45.717167                    999.049912   \n",
       "2       H1           71.064881                    982.780655   \n",
       "3       H2           89.546277                    969.295755   \n",
       "4       H3          104.322421                    957.001984   \n",
       "5       H4          121.521480                    941.684964   \n",
       "6       H5          144.349593                    918.317073   \n",
       "\n",
       "   average_shape_length  \n",
       "0              1.014611  \n",
       "1              1.116699  \n",
       "2              1.245807  \n",
       "3              1.201254  \n",
       "4              1.166742  \n",
       "5              1.131484  \n",
       "6              1.112514  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "storm_data_by_category = pd.read_sql_query(query_average_wind_speed_pressure_shape_length,con=conn)\n",
    "storm_data_by_category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HSR6cZO8kzxvuH3qlnwUAACzIcx98n9Vuwqr6i/9462o3AbZq3iB7QmvtzUlS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\nJEmtYiIrSZIkSWqVn70yE7AT9s5LAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d74aac1fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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nPY+9W644Rt1Ow+3slR6/kmSHqtqxtbYhA74Dm4jW2sVZd1gGfgWtta9n3eE4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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d74ad67fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams.update({'font.size': 15})\n",
    "\n",
    "\n",
    "ax1 = storm_data_by_category.plot.bar(x='category',\n",
    "                                y='average_wind_speed',\n",
    "                                figsize=(16,8),\n",
    "                                title=\"Average Wind Speed (kts) by Storm Category\"\n",
    "                                )\n",
    "plt.xticks(rotation=360)\n",
    "\n",
    "ax2 = storm_data_by_category.plot.bar(x='category',\n",
    "                                y='average_atmospheric_pressure',\n",
    "                                figsize=(16,8),\n",
    "                                title=\"Average Atmospheric Pressure (mbar) by Storm Category\"\n",
    "                                )\n",
    "\n",
    "ax1.legend().set_visible(False)\n",
    "ax2.legend().set_visible(False)\n",
    "\n",
    "plt.xticks(rotation=360)\n",
    "\n",
    "ax1.xaxis.set_label_text(\"\")\n",
    "ax2.xaxis.set_label_text(\"\")\n",
    "\n",
    "plt.ylim(900, 1020)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can compare these results with the descriptions of the tropical cyclone categories here : https://en.wikipedia.org/wiki/Tropical_cyclone_scales"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is another interesting article on storm measures : https://www.wired.com/2012/11/what-is-the-true-measure-of-a-storm/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## B) <a id='#subpart_two'>2005 : Record-Breaking Storm Season</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With 8 queries, we will analyse the year 2005 in the North Atlantic basin. Our analysis is inspired by this article : https://weather.com/storms/hurricane/news/2005-hurricane-season-by-the-numbers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1) <a id='#first_query'>Year with the Maximum Number of Storms in North Atlantic</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "query_maximum_number_of_storms_north_atlantic = '''\n",
    "\n",
    "WITH number_of_storms_by_year_north_atlantic_table AS \n",
    "\n",
    "(\n",
    "\n",
    "SELECT \n",
    "\n",
    "EXTRACT(YEAR from date) \"year\",\n",
    "\n",
    "COUNT(DISTINCT(name)) number_of_storms\n",
    "\n",
    "FROM storm_table\n",
    "\n",
    "WHERE basin = 'North Atlantic'\n",
    "\n",
    "GROUP BY 1\n",
    "\n",
    ")\n",
    "\n",
    "SELECT \n",
    "\n",
    "year,\n",
    "\n",
    "number_of_storms maximum_number_of_storms_north_atlantic\n",
    "\n",
    "FROM \n",
    "\n",
    "number_of_storms_by_year_north_atlantic_table\n",
    "\n",
    "WHERE number_of_storms = \n",
    "\n",
    "(\n",
    "\n",
    "SELECT \n",
    "\n",
    "MAX(number_of_storms)\n",
    "\n",
    "FROM \n",
    "\n",
    "number_of_storms_by_year_north_atlantic_table\n",
    "\n",
    ")\n",
    "\n",
    ";\n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>maximum_number_of_storms_north_atlantic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2005.0</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     year  maximum_number_of_storms_north_atlantic\n",
       "0  2005.0                                       28"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql_query(query_maximum_number_of_storms_north_atlantic,con=conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2) <a id='#second_query'>Number of Hurricanes in North Atlantic in 2005</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "query_number_of_hurricanes_north_atlantic_2005 = '''\n",
    "\n",
    "SELECT \n",
    "\n",
    "COUNT(DISTINCT(name)) number_of_hurricanes_north_atlantic_2005\n",
    "\n",
    "FROM storm_table\n",
    "\n",
    "WHERE basin = 'North Atlantic'\n",
    "\n",
    "AND (cat = 'H1' OR cat = 'H2' OR cat = 'H3' OR cat = 'H4' OR cat = 'H5')\n",
    "\n",
    "AND EXTRACT(YEAR from date) = 2005\n",
    "\n",
    ";\n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>number_of_hurricanes_north_atlantic_2005</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   number_of_hurricanes_north_atlantic_2005\n",
       "0                                        15"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql_query(query_number_of_hurricanes_north_atlantic_2005,con=conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3) <a id='#third_query'>Number of Category 5 Hurricanes in North Atlantic in 2005</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "query_number_of_h5_hurricanes_north_atlantic_2005 = '''\n",
    "\n",
    "SELECT \n",
    "\n",
    "COUNT(DISTINCT(name)) number_of_category_5_hurricanes_north_atlantic_2005\n",
    "\n",
    "FROM storm_table\n",
    "\n",
    "WHERE basin = 'North Atlantic'\n",
    "\n",
    "AND cat = 'H5'\n",
    "\n",
    "AND EXTRACT(YEAR from date) = 2005\n",
    "\n",
    ";\n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>number_of_category_5_hurricanes_north_atlantic_2005</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   number_of_category_5_hurricanes_north_atlantic_2005\n",
       "0                                                  4  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql_query(query_number_of_h5_hurricanes_north_atlantic_2005,con=conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4) <a id='#fourth_query'>Storm Duration in North Atlantic in 2005</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To calculate the storm duration in North Atlantic in 2005, we will remove two outliers : the storms Zeta and Jose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "query_storm_duration_north_atlantic_2005 = '''\n",
    "\n",
    "WITH north_atlantic_2005_table AS\n",
    "\n",
    "(\n",
    "\n",
    "SELECT \n",
    "\n",
    "*\n",
    "\n",
    "FROM storm_table\n",
    "\n",
    "WHERE basin = 'North Atlantic'\n",
    "\n",
    "AND EXTRACT(YEAR FROM date) = 2005\n",
    "\n",
    "ORDER BY date\n",
    "\n",
    ")\n",
    "\n",
    "SELECT \n",
    "\n",
    "name,\n",
    "\n",
    "MIN(date) starting_date,\n",
    "\n",
    "MAX(date) ending_date,\n",
    "\n",
    "AGE(MAX(date), MIN(date)) storm_duration,\n",
    "\n",
    "EXTRACT(DAY FROM AGE(MAX(date), MIN(date))) number_of_days\n",
    "\n",
    "FROM north_atlantic_2005_table\n",
    "\n",
    "WHERE name != 'ZETA' AND name != 'JOSE'\n",
    "\n",
    "GROUP BY name\n",
    "\n",
    "ORDER BY storm_duration DESC\n",
    "\n",
    ";\n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>starting_date</th>\n",
       "      <th>ending_date</th>\n",
       "      <th>storm_duration</th>\n",
       "      <th>number_of_days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>OPHELIA</td>\n",
       "      <td>2005-09-06 06:00:00</td>\n",
       "      <td>2005-09-22 18:00:00</td>\n",
       "      <td>16 days 12:00:00</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IRENE</td>\n",
       "      <td>2005-08-04 18:00:00</td>\n",
       "      <td>2005-08-18 06:00:00</td>\n",
       "      <td>13 days 12:00:00</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DENNIS</td>\n",
       "      <td>2005-07-04 18:00:00</td>\n",
       "      <td>2005-07-18 00:00:00</td>\n",
       "      <td>13 days 06:00:00</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MARIA</td>\n",
       "      <td>2005-09-01 12:00:00</td>\n",
       "      <td>2005-09-13 18:00:00</td>\n",
       "      <td>12 days 06:00:00</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HARVEY</td>\n",
       "      <td>2005-08-02 18:00:00</td>\n",
       "      <td>2005-08-13 18:00:00</td>\n",
       "      <td>11 days 00:00:00</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>WILMA</td>\n",
       "      <td>2005-10-15 18:00:00</td>\n",
       "      <td>2005-10-26 12:00:00</td>\n",
       "      <td>10 days 18:00:00</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>EMILY</td>\n",
       "      <td>2005-07-11 00:00:00</td>\n",
       "      <td>2005-07-21 06:00:00</td>\n",
       "      <td>10 days 06:00:00</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>EPSILON</td>\n",
       "      <td>2005-11-29 06:00:00</td>\n",
       "      <td>2005-12-09 12:00:00</td>\n",
       "      <td>10 days 06:00:00</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>DELTA</td>\n",
       "      <td>2005-11-19 12:00:00</td>\n",
       "      <td>2005-11-29 12:00:00</td>\n",
       "      <td>10 days 00:00:00</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>FRANKLIN</td>\n",
       "      <td>2005-07-21 18:00:00</td>\n",
       "      <td>2005-07-30 18:00:00</td>\n",
       "      <td>9 days 00:00:00</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>RITA</td>\n",
       "      <td>2005-09-18 00:00:00</td>\n",
       "      <td>2005-09-26 00:00:00</td>\n",
       "      <td>8 days 00:00:00</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>GAMMA</td>\n",
       "      <td>2005-11-14 00:00:00</td>\n",
       "      <td>2005-11-21 18:00:00</td>\n",
       "      <td>7 days 18:00:00</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>KATRINA</td>\n",
       "      <td>2005-08-23 18:00:00</td>\n",
       "      <td>2005-08-31 00:00:00</td>\n",
       "      <td>7 days 06:00:00</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>CINDY</td>\n",
       "      <td>2005-07-03 18:00:00</td>\n",
       "      <td>2005-07-11 00:00:00</td>\n",
       "      <td>7 days 06:00:00</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>NATE</td>\n",
       "      <td>2005-09-05 18:00:00</td>\n",
       "      <td>2005-09-12 12:00:00</td>\n",
       "      <td>6 days 18:00:00</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>PHILIPPE</td>\n",
       "      <td>2005-09-17 12:00:00</td>\n",
       "      <td>2005-09-24 00:00:00</td>\n",
       "      <td>6 days 12:00:00</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>LEE</td>\n",
       "      <td>2005-08-28 12:00:00</td>\n",
       "      <td>2005-09-03 12:00:00</td>\n",
       "      <td>6 days 00:00:00</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>ARLENE</td>\n",
       "      <td>2005-06-08 18:00:00</td>\n",
       "      <td>2005-06-14 00:00:00</td>\n",
       "      <td>5 days 06:00:00</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>BETA</td>\n",
       "      <td>2005-10-26 18:00:00</td>\n",
       "      <td>2005-10-30 18:00:00</td>\n",
       "      <td>4 days 00:00:00</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>STAN</td>\n",
       "      <td>2005-10-01 12:00:00</td>\n",
       "      <td>2005-10-05 00:00:00</td>\n",
       "      <td>3 days 12:00:00</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>VINCE</td>\n",
       "      <td>2005-10-08 06:00:00</td>\n",
       "      <td>2005-10-11 06:00:00</td>\n",
       "      <td>3 days 00:00:00</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>ALPHA</td>\n",
       "      <td>2005-10-22 12:00:00</td>\n",
       "      <td>2005-10-24 12:00:00</td>\n",
       "      <td>2 days 00:00:00</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>GERT</td>\n",
       "      <td>2005-07-23 18:00:00</td>\n",
       "      <td>2005-07-25 12:00:00</td>\n",
       "      <td>1 days 18:00:00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>TAMMY</td>\n",
       "      <td>2005-10-05 06:00:00</td>\n",
       "      <td>2005-10-06 18:00:00</td>\n",
       "      <td>1 days 12:00:00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>SUBTROP</td>\n",
       "      <td>2005-10-04 00:00:00</td>\n",
       "      <td>2005-10-05 06:00:00</td>\n",
       "      <td>1 days 06:00:00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>BRET</td>\n",
       "      <td>2005-06-28 18:00:00</td>\n",
       "      <td>2005-06-29 18:00:00</td>\n",
       "      <td>1 days 00:00:00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name       starting_date         ending_date   storm_duration  \\\n",
       "0    OPHELIA 2005-09-06 06:00:00 2005-09-22 18:00:00 16 days 12:00:00   \n",
       "1      IRENE 2005-08-04 18:00:00 2005-08-18 06:00:00 13 days 12:00:00   \n",
       "2     DENNIS 2005-07-04 18:00:00 2005-07-18 00:00:00 13 days 06:00:00   \n",
       "3      MARIA 2005-09-01 12:00:00 2005-09-13 18:00:00 12 days 06:00:00   \n",
       "4     HARVEY 2005-08-02 18:00:00 2005-08-13 18:00:00 11 days 00:00:00   \n",
       "5      WILMA 2005-10-15 18:00:00 2005-10-26 12:00:00 10 days 18:00:00   \n",
       "6      EMILY 2005-07-11 00:00:00 2005-07-21 06:00:00 10 days 06:00:00   \n",
       "7    EPSILON 2005-11-29 06:00:00 2005-12-09 12:00:00 10 days 06:00:00   \n",
       "8      DELTA 2005-11-19 12:00:00 2005-11-29 12:00:00 10 days 00:00:00   \n",
       "9   FRANKLIN 2005-07-21 18:00:00 2005-07-30 18:00:00  9 days 00:00:00   \n",
       "10      RITA 2005-09-18 00:00:00 2005-09-26 00:00:00  8 days 00:00:00   \n",
       "11     GAMMA 2005-11-14 00:00:00 2005-11-21 18:00:00  7 days 18:00:00   \n",
       "12   KATRINA 2005-08-23 18:00:00 2005-08-31 00:00:00  7 days 06:00:00   \n",
       "13     CINDY 2005-07-03 18:00:00 2005-07-11 00:00:00  7 days 06:00:00   \n",
       "14      NATE 2005-09-05 18:00:00 2005-09-12 12:00:00  6 days 18:00:00   \n",
       "15  PHILIPPE 2005-09-17 12:00:00 2005-09-24 00:00:00  6 days 12:00:00   \n",
       "16       LEE 2005-08-28 12:00:00 2005-09-03 12:00:00  6 days 00:00:00   \n",
       "17    ARLENE 2005-06-08 18:00:00 2005-06-14 00:00:00  5 days 06:00:00   \n",
       "18      BETA 2005-10-26 18:00:00 2005-10-30 18:00:00  4 days 00:00:00   \n",
       "19      STAN 2005-10-01 12:00:00 2005-10-05 00:00:00  3 days 12:00:00   \n",
       "20     VINCE 2005-10-08 06:00:00 2005-10-11 06:00:00  3 days 00:00:00   \n",
       "21     ALPHA 2005-10-22 12:00:00 2005-10-24 12:00:00  2 days 00:00:00   \n",
       "22      GERT 2005-07-23 18:00:00 2005-07-25 12:00:00  1 days 18:00:00   \n",
       "23     TAMMY 2005-10-05 06:00:00 2005-10-06 18:00:00  1 days 12:00:00   \n",
       "24   SUBTROP 2005-10-04 00:00:00 2005-10-05 06:00:00  1 days 06:00:00   \n",
       "25      BRET 2005-06-28 18:00:00 2005-06-29 18:00:00  1 days 00:00:00   \n",
       "\n",
       "    number_of_days  \n",
       "0             16.0  \n",
       "1             13.0  \n",
       "2             13.0  \n",
       "3             12.0  \n",
       "4             11.0  \n",
       "5             10.0  \n",
       "6             10.0  \n",
       "7             10.0  \n",
       "8             10.0  \n",
       "9              9.0  \n",
       "10             8.0  \n",
       "11             7.0  \n",
       "12             7.0  \n",
       "13             7.0  \n",
       "14             6.0  \n",
       "15             6.0  \n",
       "16             6.0  \n",
       "17             5.0  \n",
       "18             4.0  \n",
       "19             3.0  \n",
       "20             3.0  \n",
       "21             2.0  \n",
       "22             1.0  \n",
       "23             1.0  \n",
       "24             1.0  \n",
       "25             1.0  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "storm_duration_north_atlantic_2005_dataframe = pd.read_sql_query(query_storm_duration_north_atlantic_2005,con=conn)\n",
    "storm_duration_north_atlantic_2005_dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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V9mOju7sk+UVr7eJ5jn14ksOTZMOGDfMFZQAAALYhE4Xd/jq6/5FkpyRXSnJu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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d74adf9940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams.update({'font.size': 15})\n",
    "\n",
    "\n",
    "ax = storm_duration_north_atlantic_2005_dataframe.plot.bar(x='name',\n",
    "                                y='number_of_days',\n",
    "                                figsize=(16,8),\n",
    "                                title=\"Storm Duration in North Atlantic in 2005\"\n",
    "                                )\n",
    "\n",
    "ax.legend().set_visible(False)\n",
    "\n",
    "ax.xaxis.set_label_text(\"\")\n",
    "ax.yaxis.set_label_text(\"number of days\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now calculate the average storm duration in North Atlantic in 2005 : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "query_average_storm_duration_north_atlantic_2005 = '''\n",
    "\n",
    "WITH north_atlantic_2005_table AS\n",
    "\n",
    "(\n",
    "\n",
    "SELECT \n",
    "\n",
    "*\n",
    "\n",
    "FROM storm_table\n",
    "\n",
    "WHERE basin = 'North Atlantic'\n",
    "\n",
    "AND EXTRACT(YEAR FROM date) = 2005\n",
    "\n",
    "ORDER BY date\n",
    "\n",
    "),\n",
    "\n",
    "storm_duration_north_atlantic_2005_table AS \n",
    "\n",
    "(\n",
    "\n",
    "SELECT \n",
    "\n",
    "name,\n",
    "\n",
    "MIN(date) starting_date,\n",
    "\n",
    "MAX(date) ending_date,\n",
    "\n",
    "AGE(MAX(date), MIN(date)) storm_duration,\n",
    "\n",
    "EXTRACT(DAY FROM AGE(MAX(date), MIN(date))) number_of_days\n",
    "\n",
    "FROM north_atlantic_2005_table\n",
    "\n",
    "WHERE name != 'ZETA' AND name != 'JOSE'\n",
    "\n",
    "GROUP BY name\n",
    "\n",
    "ORDER BY storm_duration DESC\n",
    "\n",
    ")\n",
    "\n",
    "SELECT \n",
    "\n",
    "AVG(number_of_days) average_storm_duration_in_number_of_days\n",
    "\n",
    "FROM storm_duration_north_atlantic_2005_table\n",
    "\n",
    ";\n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>average_storm_duration_in_number_of_days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   average_storm_duration_in_number_of_days\n",
       "0                                       7.0"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql_query(query_average_storm_duration_north_atlantic_2005,con=conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see here that the storm Katrina matches the average storm duration of 7 days."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5) <a id='#fifth_query'>Storm with the Lowest Barometric Pressure in North Atlantic</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "query_lowest_barometric_pressure_north_atlantic = '''\n",
    "\n",
    "WITH north_atlantic_table AS\n",
    "\n",
    "(\n",
    "\n",
    "SELECT \n",
    "\n",
    "*\n",
    "\n",
    "FROM storm_table\n",
    "\n",
    "WHERE basin = 'North Atlantic'\n",
    "\n",
    "AND pressure > 0\n",
    "\n",
    ")\n",
    "\n",
    "SELECT \n",
    "\n",
    "name,\n",
    "\n",
    "cat category,\n",
    "\n",
    "pressure,\n",
    "\n",
    "date\n",
    "\n",
    "FROM north_atlantic_table\n",
    "\n",
    "WHERE pressure = \n",
    "\n",
    "(\n",
    "\n",
    "SELECT \n",
    "\n",
    "MIN(pressure)\n",
    "\n",
    "FROM north_atlantic_table\n",
    "\n",
    ")\n",
    "\n",
    ";\n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>category</th>\n",
       "      <th>pressure</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WILMA</td>\n",
       "      <td>H5</td>\n",
       "      <td>882</td>\n",
       "      <td>2005-10-19 12:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name category  pressure                date\n",
       "0  WILMA       H5       882 2005-10-19 12:00:00"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql_query(query_lowest_barometric_pressure_north_atlantic,con=conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6) <a id='#sixth_query'>Total Number of Days with a Named Storm in North Atlantic during the 2005 season</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's find the total number of days with a named storm in the Atlantic basin during the 2005 season. The Atlantic season starts in June and runs until end of November. We will remove the categories E, W, L, SS, and SD from our analysis to keep only the relevant categories TD, TS, H1, H2, H3, H4, H5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "total_number_of_days_with_named_storm_north_atlantic_2005 = '''\n",
    "\n",
    "WITH north_atlantic_2005_table AS\n",
    "\n",
    "(\n",
    "\n",
    "SELECT \n",
    "\n",
    "*\n",
    "\n",
    "FROM storm_table\n",
    "\n",
    "WHERE basin = 'North Atlantic'\n",
    "\n",
    "AND EXTRACT(YEAR from date) = 2005\n",
    "\n",
    "ORDER BY date\n",
    "\n",
    ")\n",
    "\n",
    "SELECT \n",
    "\n",
    "COUNT(DISTINCT(TO_CHAR(date, 'YYYY-MM-DD'))) total_number_of_days_with_named_storm__North_Altantic_2005\n",
    "\n",
    "FROM \n",
    "\n",
    "north_atlantic_2005_table\n",
    "\n",
    "WHERE date >= timestamp '2005-06-01 00:00:00' AND date < timestamp '2005-12-01 00:00:00'\n",
    "\n",
    "AND cat != 'E'\n",
    "\n",
    "AND cat != 'W'\n",
    "\n",
    "AND cat != 'L'\n",
    "\n",
    "AND cat != 'SS'\n",
    "\n",
    "AND cat != 'SD'\n",
    "\n",
    ";\n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_number_of_days_with_named_storm__north_altantic_2005</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_number_of_days_with_named_storm__north_altantic_2005\n",
       "0                                                128         "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql_query(total_number_of_days_with_named_storm_north_atlantic_2005,con=conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7) <a id='#seventh_query'>Hurricane Katrina </a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "query_katrina = '''\n",
    "\n",
    "WITH north_atlantic_2005_table AS\n",
    "\n",
    "(\n",
    "\n",
    "SELECT \n",
    "\n",
    "*\n",
    "\n",
    "FROM storm_table\n",
    "\n",
    "WHERE basin = 'North Atlantic'\n",
    "\n",
    "AND EXTRACT(YEAR from date) = 2005\n",
    "\n",
    "ORDER BY date\n",
    "\n",
    ")\n",
    "\n",
    "SELECT \n",
    "\n",
    "wind_kts,\n",
    "\n",
    "pressure,\n",
    "\n",
    "cat category,\n",
    "\n",
    "shape_leng shape_length,\n",
    "\n",
    "date\n",
    "\n",
    "FROM north_atlantic_2005_table\n",
    "\n",
    "WHERE name = 'KATRINA'\n",
    "\n",
    ";\n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>wind_kts</th>\n",
       "      <th>pressure</th>\n",
       "      <th>category</th>\n",
       "      <th>shape_length</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
       "      <td>1008</td>\n",
       "      <td>TD</td>\n",
       "      <td>0.670820</td>\n",
       "      <td>2005-08-23 18:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30</td>\n",
       "      <td>1007</td>\n",
       "      <td>TD</td>\n",
       "      <td>0.640312</td>\n",
       "      <td>2005-08-24 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>1007</td>\n",
       "      <td>TD</td>\n",
       "      <td>0.761577</td>\n",
       "      <td>2005-08-24 06:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35</td>\n",
       "      <td>1006</td>\n",
       "      <td>TS</td>\n",
       "      <td>0.984886</td>\n",
       "      <td>2005-08-24 12:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40</td>\n",
       "      <td>1003</td>\n",
       "      <td>TS</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2005-08-24 18:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>45</td>\n",
       "      <td>1000</td>\n",
       "      <td>TS</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>2005-08-25 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>50</td>\n",
       "      <td>997</td>\n",
       "      <td>TS</td>\n",
       "      <td>0.608276</td>\n",
       "      <td>2005-08-25 06:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>55</td>\n",
       "      <td>994</td>\n",
       "      <td>TS</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>2005-08-25 12:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>60</td>\n",
       "      <td>988</td>\n",
       "      <td>TS</td>\n",
       "      <td>0.761577</td>\n",
       "      <td>2005-08-25 18:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>70</td>\n",
       "      <td>983</td>\n",
       "      <td>H1</td>\n",
       "      <td>1.118034</td>\n",
       "      <td>2005-08-26 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>65</td>\n",
       "      <td>987</td>\n",
       "      <td>H1</td>\n",
       "      <td>0.761577</td>\n",
       "      <td>2005-08-26 06:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>75</td>\n",
       "      <td>979</td>\n",
       "      <td>H1</td>\n",
       "      <td>0.632456</td>\n",
       "      <td>2005-08-26 12:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>85</td>\n",
       "      <td>968</td>\n",
       "      <td>H2</td>\n",
       "      <td>0.761577</td>\n",
       "      <td>2005-08-26 18:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>90</td>\n",
       "      <td>959</td>\n",
       "      <td>H2</td>\n",
       "      <td>0.728011</td>\n",
       "      <td>2005-08-27 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>95</td>\n",
       "      <td>950</td>\n",
       "      <td>H2</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>2005-08-27 06:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>100</td>\n",
       "      <td>942</td>\n",
       "      <td>H3</td>\n",
       "      <td>0.608276</td>\n",
       "      <td>2005-08-27 12:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>100</td>\n",
       "      <td>948</td>\n",
       "      <td>H3</td>\n",
       "      <td>0.670820</td>\n",
       "      <td>2005-08-27 18:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>100</td>\n",
       "      <td>941</td>\n",
       "      <td>H3</td>\n",
       "      <td>0.894427</td>\n",
       "      <td>2005-08-28 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>125</td>\n",
       "      <td>930</td>\n",
       "      <td>H4</td>\n",
       "      <td>1.118034</td>\n",
       "      <td>2005-08-28 06:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>145</td>\n",
       "      <td>909</td>\n",
       "      <td>H5</td>\n",
       "      <td>1.081665</td>\n",
       "      <td>2005-08-28 12:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>150</td>\n",
       "      <td>902</td>\n",
       "      <td>H5</td>\n",
       "      <td>1.081665</td>\n",
       "      <td>2005-08-28 18:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>140</td>\n",
       "      <td>905</td>\n",
       "      <td>H5</td>\n",
       "      <td>1.077033</td>\n",
       "      <td>2005-08-29 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>125</td>\n",
       "      <td>913</td>\n",
       "      <td>H4</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>2005-08-29 06:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>110</td>\n",
       "      <td>923</td>\n",
       "      <td>H3</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>2005-08-29 12:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>80</td>\n",
       "      <td>948</td>\n",
       "      <td>H1</td>\n",
       "      <td>1.581139</td>\n",
       "      <td>2005-08-29 18:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>50</td>\n",
       "      <td>961</td>\n",
       "      <td>TS</td>\n",
       "      <td>1.581139</td>\n",
       "      <td>2005-08-30 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>40</td>\n",
       "      <td>978</td>\n",
       "      <td>TS</td>\n",
       "      <td>1.615549</td>\n",
       "      <td>2005-08-30 06:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>30</td>\n",
       "      <td>985</td>\n",
       "      <td>TD</td>\n",
       "      <td>1.720465</td>\n",
       "      <td>2005-08-30 12:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>30</td>\n",
       "      <td>990</td>\n",
       "      <td>TD</td>\n",
       "      <td>2.334524</td>\n",
       "      <td>2005-08-30 18:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>994</td>\n",
       "      <td>E</td>\n",
       "      <td>2.830194</td>\n",
       "      <td>2005-08-31 00:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    wind_kts  pressure category  shape_length                date\n",
       "0         30      1008       TD      0.670820 2005-08-23 18:00:00\n",
       "1         30      1007       TD      0.640312 2005-08-24 00:00:00\n",
       "2         30      1007       TD      0.761577 2005-08-24 06:00:00\n",
       "3         35      1006       TS      0.984886 2005-08-24 12:00:00\n",
       "4         40      1003       TS      1.000000 2005-08-24 18:00:00\n",
       "5         45      1000       TS      0.707107 2005-08-25 00:00:00\n",
       "6         50       997       TS      0.608276 2005-08-25 06:00:00\n",
       "7         55       994       TS      0.600000 2005-08-25 12:00:00\n",
       "8         60       988       TS      0.761577 2005-08-25 18:00:00\n",
       "9         70       983       H1      1.118034 2005-08-26 00:00:00\n",
       "10        65       987       H1      0.761577 2005-08-26 06:00:00\n",
       "11        75       979       H1      0.632456 2005-08-26 12:00:00\n",
       "12        85       968       H2      0.761577 2005-08-26 18:00:00\n",
       "13        90       959       H2      0.728011 2005-08-27 00:00:00\n",
       "14        95       950       H2      0.700000 2005-08-27 06:00:00\n",
       "15       100       942       H3      0.608276 2005-08-27 12:00:00\n",
       "16       100       948       H3      0.670820 2005-08-27 18:00:00\n",
       "17       100       941       H3      0.894427 2005-08-28 00:00:00\n",
       "18       125       930       H4      1.118034 2005-08-28 06:00:00\n",
       "19       145       909       H5      1.081665 2005-08-28 12:00:00\n",
       "20       150       902       H5      1.081665 2005-08-28 18:00:00\n",
       "21       140       905       H5      1.077033 2005-08-29 00:00:00\n",
       "22       125       913       H4      1.300000 2005-08-29 06:00:00\n",
       "23       110       923       H3      1.600000 2005-08-29 12:00:00\n",
       "24        80       948       H1      1.581139 2005-08-29 18:00:00\n",
       "25        50       961       TS      1.581139 2005-08-30 00:00:00\n",
       "26        40       978       TS      1.615549 2005-08-30 06:00:00\n",
       "27        30       985       TD      1.720465 2005-08-30 12:00:00\n",
       "28        30       990       TD      2.334524 2005-08-30 18:00:00\n",
       "29        30       994        E      2.830194 2005-08-31 00:00:00"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "katrina_dataframe = pd.read_sql_query(query_katrina,con=conn)\n",
    "katrina_dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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tODg0ctCI0VoJDI+OMzg0Mn+dkiRJkiRJUteZc1Rz5uWPXgvAu78+RO0gy2VR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\neHpm3tSi3E+Be3XYhiRJkiRJkiTNmU5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xKsUO748BOoA7gesy80stqVCSJEmSJEmS5kGl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\nbIAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d74ae9a048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.pyplot import figure\n",
    "figure(figsize=(16, 6), dpi=100)\n",
    "plt.scatter(x = \"wind_kts\", y = \"pressure\", data = katrina_dataframe)\n",
    "plt.title(\"Statistics on Hurricane Katrina (2005)\")\n",
    "plt.xlabel(\"wind speed (kts)\")\n",
    "plt.ylabel(\"atmospheric pressure (mbar)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conn.close()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}