Create A View And Find The Difference Between Nukes and Quakes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating A VIEW and using pandas to plot charts.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Tony McDonald](https://www.youtube.com/Dreamazium)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'24-07-2019'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "datetime.today().strftime('%d-%m-%Y')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "License: The original dataset was provided by Copyright (c) 2019 Socratica https://github.com/socratica/data/blob/master/LICENSE <br>\n",
    "The license allows me to freely download, use, manipulate and distribute. \n",
    "## Task\n",
    "\n",
    "#### Investigate differences in the data between the causes. \n",
    "<ul>\n",
    "    <li>Create View</li>\n",
    "    <li>Update View</li>\n",
    "    <li>Plot View</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Before getting started, remember that this database has already been created"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## There is a need to start the Postgres server"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Start the postgres server. \n",
    "!./serverStart.command"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Stop the postgres server. \n",
    "!./serverStop.command"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Restart the postgres server. \n",
    "!./serverRestart.command"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Is the server running? \n",
    "!./serverStatus.command"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Connect to a database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "DB_ENGINE='postgresql' \n",
    "DB_USER='postgres'  \n",
    "DB_PWD='postgres' \n",
    "DB_ADDR='localhost:5432' \n",
    "DB_NAME='earthquake' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "DB_CONNECTION = '{engine}://{user}:{pwd}@{addr}/{name}'.format(engine=DB_ENGINE,\n",
    "                                                               user=DB_USER,\n",
    "                                                               pwd=DB_PWD,\n",
    "                                                               addr=DB_ADDR,\n",
    "                                                               name=DB_NAME)\n",
    "DB_CONNECTION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext sql"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Connected: postgres@earthquake'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%sql $DB_CONNECTION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://postgres:***@localhost:5432/earthquake\n",
      "3 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <th>earthquake_id</th>\n",
       "        <th>occurred_on</th>\n",
       "        <th>latitude</th>\n",
       "        <th>longitude</th>\n",
       "        <th>depth</th>\n",
       "        <th>magnitude</th>\n",
       "        <th>calculation_method</th>\n",
       "        <th>network_id</th>\n",
       "        <th>place</th>\n",
       "        <th>cause</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1</td>\n",
       "        <td>1969-01-01 09:07:06</td>\n",
       "        <td>51.096</td>\n",
       "        <td>-179.392</td>\n",
       "        <td>45</td>\n",
       "        <td>5.6</td>\n",
       "        <td>mw</td>\n",
       "        <td>iscgem812771</td>\n",
       "        <td>Andreanof Islands, Aleutian Islands, Alaska</td>\n",
       "        <td>earthquake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>2</td>\n",
       "        <td>1969-01-02 17:50:48</td>\n",
       "        <td>-56.096</td>\n",
       "        <td>-27.842</td>\n",
       "        <td>80.1</td>\n",
       "        <td>6</td>\n",
       "        <td>mw</td>\n",
       "        <td>iscgemsup812819</td>\n",
       "        <td>South Sandwich Islands region</td>\n",
       "        <td>earthquake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>3</td>\n",
       "        <td>1969-01-03 03:16:40</td>\n",
       "        <td>37.14</td>\n",
       "        <td>57.899</td>\n",
       "        <td>10</td>\n",
       "        <td>5.5</td>\n",
       "        <td>mw</td>\n",
       "        <td>iscgem812826</td>\n",
       "        <td>Turkmenistan-Iran border region</td>\n",
       "        <td>earthquake</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "[(1, datetime.datetime(1969, 1, 1, 9, 7, 6), Decimal('51.096'), Decimal('-179.392'), Decimal('45'), Decimal('5.6'), 'mw', 'iscgem812771', 'Andreanof Islands, Aleutian Islands, Alaska', 'earthquake'),\n",
       " (2, datetime.datetime(1969, 1, 2, 17, 50, 48), Decimal('-56.096'), Decimal('-27.842'), Decimal('80.1'), Decimal('6'), 'mw', 'iscgemsup812819', 'South Sandwich Islands region', 'earthquake'),\n",
       " (3, datetime.datetime(1969, 1, 3, 3, 16, 40), Decimal('37.14'), Decimal('57.899'), Decimal('10'), Decimal('5.5'), 'mw', 'iscgem812826', 'Turkmenistan-Iran border region', 'earthquake')]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "SELECT *\n",
    "FROM earthquake\n",
    "LIMIT 3;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create View"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://postgres:***@localhost:5432/earthquake\n",
      "Done.\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "DROP VIEW IF EXISTS \"Cause_Data_VIEW\" CASCADE;\n",
    "CREATE VIEW \"Cause_Data_VIEW\" AS\n",
    "SELECT cause AS \"Cause\", COUNT(cause) AS \"Number Of Occurencies\", \n",
    "    ROUND(AVG(magnitude),2) AS \"Average Magnitude\",\n",
    "    MAX(magnitude), MIN(magnitude), ROUND(AVG(depth), 2) AS \"Average Depth\",\n",
    "    MAX(depth) AS \"MAX Depth\", MIN(depth) AS \"MIN Depth\"\n",
    "\n",
    "FROM earthquake\n",
    "GROUP BY cause;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# I can now select from this view as if it was a real table. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://postgres:***@localhost:5432/earthquake\n",
      "3 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <th>Cause</th>\n",
       "        <th>Number Of Occurencies</th>\n",
       "        <th>Average Magnitude</th>\n",
       "        <th>max</th>\n",
       "        <th>min</th>\n",
       "        <th>Average Depth</th>\n",
       "        <th>MAX Depth</th>\n",
       "        <th>MIN Depth</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>explosion</td>\n",
       "        <td>4</td>\n",
       "        <td>5.85</td>\n",
       "        <td>6.4</td>\n",
       "        <td>5.6</td>\n",
       "        <td>0.00</td>\n",
       "        <td>0</td>\n",
       "        <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>nuclear explosion</td>\n",
       "        <td>173</td>\n",
       "        <td>5.86</td>\n",
       "        <td>6.9</td>\n",
       "        <td>5.5</td>\n",
       "        <td>0.28</td>\n",
       "        <td>33</td>\n",
       "        <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>earthquake</td>\n",
       "        <td>22942</td>\n",
       "        <td>5.87</td>\n",
       "        <td>9.1</td>\n",
       "        <td>5.5</td>\n",
       "        <td>71.71</td>\n",
       "        <td>700</td>\n",
       "        <td>-1.1</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "[('explosion', 4, Decimal('5.85'), Decimal('6.4'), Decimal('5.6'), Decimal('0.00'), Decimal('0'), Decimal('0')),\n",
       " ('nuclear explosion', 173, Decimal('5.86'), Decimal('6.9'), Decimal('5.5'), Decimal('0.28'), Decimal('33'), Decimal('0')),\n",
       " ('earthquake', 22942, Decimal('5.87'), Decimal('9.1'), Decimal('5.5'), Decimal('71.71'), Decimal('700'), Decimal('-1.1'))]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "    SELECT *\n",
    "    FROM \"Cause_Data_VIEW\"\n",
    "    ;\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://postgres:***@localhost:5432/earthquake\n",
      "3 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <th>Cause</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>explosion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>nuclear explosion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>earthquake</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "[('explosion',), ('nuclear explosion',), ('earthquake',)]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "SELECT \"Cause\"\n",
    "FROM \"Cause_Data_VIEW\"\n",
    ";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The above table needs the rows 'explosion' and 'nuclear explosion' merged into one.  In order to do this without altering the earthquake database, i will make a view and then alter that."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# View 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a copy of earthquake as a VIEW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://postgres:***@localhost:5432/earthquake\n",
      "Done.\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "DROP VIEW IF EXISTS \"earthquake_VIEW\" CASCADE;\n",
    "\n",
    "CREATE VIEW \"earthquake_VIEW\" AS\n",
    "SELECT * \n",
    "FROM \"earthquake\"\n",
    "\n",
    "\n",
    ";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### UPDATE the copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://postgres:***@localhost:5432/earthquake\n",
      "4 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "UPDATE \"earthquake_VIEW\" \n",
    "SET cause = 'nuclear explosion'\n",
    "WHERE cause = 'explosion'\n",
    ";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GROUP BY cause and create new columns for aggregation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://postgres:***@localhost:5432/earthquake\n",
      "Done.\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "DROP VIEW IF EXISTS \"Cause_Data_VIEW_2\" CASCADE;\n",
    "CREATE VIEW \"Cause_Data_VIEW_2\" AS\n",
    "SELECT cause AS \"Cause\", COUNT(cause) AS \"Number Of Occurencies\", \n",
    "    ROUND(AVG(magnitude),2) AS \"Average Magnitude\",\n",
    "    MAX(magnitude), MIN(magnitude), ROUND(AVG(depth), 2) AS \"Average Depth\",\n",
    "    MAX(depth) AS \"MAX Depth\", MIN(depth) AS \"MIN Depth\"\n",
    "\n",
    "FROM \"earthquake_VIEW\" --The \"\" keep the uppercasing \n",
    "GROUP BY cause;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <font color=\"red\">Note! </font>In the above SQL  FROM \"earthquake_VIEW\".  This must be in \"\" not ''.  While \"\" = keep case, '' = accept whitespace."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confirm the changes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://postgres:***@localhost:5432/earthquake\n",
      "2 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <th>Cause</th>\n",
       "        <th>Number Of Occurencies</th>\n",
       "        <th>Average Magnitude</th>\n",
       "        <th>max</th>\n",
       "        <th>min</th>\n",
       "        <th>Average Depth</th>\n",
       "        <th>MAX Depth</th>\n",
       "        <th>MIN Depth</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>earthquake</td>\n",
       "        <td>22942</td>\n",
       "        <td>5.87</td>\n",
       "        <td>9.1</td>\n",
       "        <td>5.5</td>\n",
       "        <td>71.71</td>\n",
       "        <td>700</td>\n",
       "        <td>-1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>nuclear explosion</td>\n",
       "        <td>177</td>\n",
       "        <td>5.86</td>\n",
       "        <td>6.9</td>\n",
       "        <td>5.5</td>\n",
       "        <td>0.28</td>\n",
       "        <td>33</td>\n",
       "        <td>0</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "[('earthquake', 22942, Decimal('5.87'), Decimal('9.1'), Decimal('5.5'), Decimal('71.71'), Decimal('700'), Decimal('-1.1')),\n",
       " ('nuclear explosion', 177, Decimal('5.86'), Decimal('6.9'), Decimal('5.5'), Decimal('0.28'), Decimal('33'), Decimal('0'))]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "    SELECT *\n",
    "    FROM \"Cause_Data_VIEW_2\"\n",
    "    ;\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://postgres:***@localhost:5432/earthquake\n",
      "2 rows affected.\n",
      "Returning data to local variable cause_plot\n"
     ]
    }
   ],
   "source": [
    "%%sql cause_plot <<\n",
    "    SELECT *\n",
    "    FROM \"Cause_Data_VIEW_2\"\n",
    "    ;\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <th>Cause</th>\n",
       "        <th>Number Of Occurencies</th>\n",
       "        <th>Average Magnitude</th>\n",
       "        <th>max</th>\n",
       "        <th>min</th>\n",
       "        <th>Average Depth</th>\n",
       "        <th>MAX Depth</th>\n",
       "        <th>MIN Depth</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>earthquake</td>\n",
       "        <td>22942</td>\n",
       "        <td>5.87</td>\n",
       "        <td>9.1</td>\n",
       "        <td>5.5</td>\n",
       "        <td>71.71</td>\n",
       "        <td>700</td>\n",
       "        <td>-1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>nuclear explosion</td>\n",
       "        <td>177</td>\n",
       "        <td>5.86</td>\n",
       "        <td>6.9</td>\n",
       "        <td>5.5</td>\n",
       "        <td>0.28</td>\n",
       "        <td>33</td>\n",
       "        <td>0</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "[('earthquake', 22942, Decimal('5.87'), Decimal('9.1'), Decimal('5.5'), Decimal('71.71'), Decimal('700'), Decimal('-1.1')),\n",
       " ('nuclear explosion', 177, Decimal('5.86'), Decimal('6.9'), Decimal('5.5'), Decimal('0.28'), Decimal('33'), Decimal('0'))]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cause_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "#import matplotlib.patches as mpatches\n",
    "import pandas as pd\n",
    "from pandas import DataFrame "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convert to dataFrame, add headers for columns. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "ColHeaders = ['Cause', 'Number Of Occurrences', 'Average Magnitude', 'MAX Magnitude', 'MIN Magnitude', 'Average Depth', 'MAX Depth', 'MIN Depth']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "cp_df = DataFrame(cause_plot)\n",
    "cp_df.columns=ColHeaders"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check types and change if needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cause                    object\n",
       "Number Of Occurrences     int64\n",
       "Average Magnitude        object\n",
       "MAX Magnitude            object\n",
       "MIN Magnitude            object\n",
       "Average Depth            object\n",
       "MAX Depth                object\n",
       "MIN Depth                object\n",
       "dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cp_df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Change data types ready for plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "cp_df[['Average Magnitude','MAX Magnitude', 'MIN Magnitude', 'Average Depth', \n",
    "       'MAX Depth','MIN Depth' ]] = cp_df[['Average Magnitude','MAX Magnitude', \n",
    "                                           'MIN Magnitude', 'Average Depth', 'MAX Depth',\n",
    "                                           'MIN Depth' ]].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cause                     object\n",
       "Number Of Occurrences      int64\n",
       "Average Magnitude        float64\n",
       "MAX Magnitude            float64\n",
       "MIN Magnitude            float64\n",
       "Average Depth            float64\n",
       "MAX Depth                float64\n",
       "MIN Depth                float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cp_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11bc74128>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cp_df.plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## _'Number Of Occurrences'_ is getting in the way because it is vastly different."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "cp_df = cp_df.drop(['Number Of Occurrences'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cp_df = cp_df.set_index('Cause')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Cause</th>\n",
       "      <th>Average Magnitude</th>\n",
       "      <th>MAX Magnitude</th>\n",
       "      <th>MIN Magnitude</th>\n",
       "      <th>Average Depth</th>\n",
       "      <th>MAX Depth</th>\n",
       "      <th>MIN Depth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>earthquake</td>\n",
       "      <td>5.87</td>\n",
       "      <td>9.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>71.71</td>\n",
       "      <td>700.0</td>\n",
       "      <td>-1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>nuclear explosion</td>\n",
       "      <td>5.86</td>\n",
       "      <td>6.9</td>\n",
       "      <td>5.5</td>\n",
       "      <td>0.28</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Cause  Average Magnitude  MAX Magnitude  MIN Magnitude  \\\n",
       "0         earthquake               5.87            9.1            5.5   \n",
       "1  nuclear explosion               5.86            6.9            5.5   \n",
       "\n",
       "   Average Depth  MAX Depth  MIN Depth  \n",
       "0          71.71      700.0       -1.1  \n",
       "1           0.28       33.0        0.0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cp_df.plot.barh(x=\"Cause\")\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## I am just going to include the columns i am interested in. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "cp_one = cp_df[['Cause', 'Average Magnitude', 'MAX Magnitude', 'MIN Magnitude', 'Average Depth']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Cause</th>\n",
       "      <th>Average Magnitude</th>\n",
       "      <th>MAX Magnitude</th>\n",
       "      <th>MIN Magnitude</th>\n",
       "      <th>Average Depth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>earthquake</td>\n",
       "      <td>5.87</td>\n",
       "      <td>9.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>71.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>nuclear explosion</td>\n",
       "      <td>5.86</td>\n",
       "      <td>6.9</td>\n",
       "      <td>5.5</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Cause  Average Magnitude  MAX Magnitude  MIN Magnitude  \\\n",
       "0         earthquake               5.87            9.1            5.5   \n",
       "1  nuclear explosion               5.86            6.9            5.5   \n",
       "\n",
       "   Average Depth  \n",
       "0          71.71  \n",
       "1           0.28  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cp_one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cp_one.plot.barh(x='Cause')\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The above plot is still not as i want it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "cp_one = cp_one.set_index('Cause')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reshape the table using `transpose()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "cp_one = cp_one.transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>Cause</th>\n",
       "      <th>earthquake</th>\n",
       "      <th>nuclear explosion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Average Magnitude</th>\n",
       "      <td>5.87</td>\n",
       "      <td>5.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAX Magnitude</th>\n",
       "      <td>9.10</td>\n",
       "      <td>6.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIN Magnitude</th>\n",
       "      <td>5.50</td>\n",
       "      <td>5.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Average Depth</th>\n",
       "      <td>71.71</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Cause              earthquake  nuclear explosion\n",
       "Average Magnitude        5.87               5.86\n",
       "MAX Magnitude            9.10               6.90\n",
       "MIN Magnitude            5.50               5.50\n",
       "Average Depth           71.71               0.28"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cp_one"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Much better. Now i can plot it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cp_one.plot.barh(title = 'Comparison of Earthquakes and Nuclear Explosions\\n',\n",
    "             figsize=(10,5), color=['blue','orange']);\n",
    "plt.ylabel('\\nMeasurement')\n",
    "plt.xlabel('\\nScale. Note: depth is in kilometers ') \n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "<ul>\n",
    "    <li>From the plot above i can tell that depth is the only real difference.</li> \n",
    "    <li>Although Max magnitude is a little more for earthquakes, the average is more or less the same</li>\n",
    "</ul>\n",
    "The only real give a way is the depth, so if it is near the surface it is more likely to be a nuclear explosion."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[View Full Report](https://www.sciencetony.com/2019/07/nuclear-test-map-for-1969-to-2017.html)<br>\n",
    "[Sciencetony.com](https://www.sciencetony.com/)<br>\n",
    "[View My LinkedIn CV](https://www.linkedin.com/in/tony-mcdonald-sciencetony/)<br>\n",
    "[My Twitter](https://twitter.com/ScienceTony)<br>\n",
    "[My YouTube](https://www.youtube.com/Dreamazium)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}