EDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Weather Disasters in the USA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first dataset comes from the [U.S. Government’s open data](https://www.data.gov/). Here is the official description of the dataset (can be found [here](https://catalog.data.gov/dataset/disaster-declaration)):\n",
    "<center>The FEMA Disaster Declarations Summary is a summarized dataset describing all <b>federally declared disasters</b>, starting with the first disaster declaration in <b>1953</b>, featuring all three disaster declaration types: major disaster, emergency and fire management assistance. </center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        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>Disaster Number</th>\n",
       "      <th>State</th>\n",
       "      <th>Declaration Date</th>\n",
       "      <th>Disaster Type</th>\n",
       "      <th>Incident Type</th>\n",
       "      <th>Title</th>\n",
       "      <th>Incident Begin Date</th>\n",
       "      <th>Incident End Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4419</td>\n",
       "      <td>AL</td>\n",
       "      <td>2019-03-05 17:33:00</td>\n",
       "      <td>DR</td>\n",
       "      <td>Tornado</td>\n",
       "      <td>SEVERE STORMS, STRAIGHT-LINE WINDS, AND TORNADOES</td>\n",
       "      <td>2019-03-03 00:01:00</td>\n",
       "      <td>2019-03-03 23:59:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4418</td>\n",
       "      <td>WA</td>\n",
       "      <td>2019-03-04 14:38:00</td>\n",
       "      <td>DR</td>\n",
       "      <td>Severe Storm(s)</td>\n",
       "      <td>SEVERE WINTER STORMS, STRAIGHT-LINE WINDS, FLO...</td>\n",
       "      <td>2018-12-10 00:00:00</td>\n",
       "      <td>2018-12-24 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4418</td>\n",
       "      <td>WA</td>\n",
       "      <td>2019-03-04 14:38:00</td>\n",
       "      <td>DR</td>\n",
       "      <td>Severe Storm(s)</td>\n",
       "      <td>SEVERE WINTER STORMS, STRAIGHT-LINE WINDS, FLO...</td>\n",
       "      <td>2018-12-10 00:00:00</td>\n",
       "      <td>2018-12-24 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4418</td>\n",
       "      <td>WA</td>\n",
       "      <td>2019-03-04 14:38:00</td>\n",
       "      <td>DR</td>\n",
       "      <td>Severe Storm(s)</td>\n",
       "      <td>SEVERE WINTER STORMS, STRAIGHT-LINE WINDS, FLO...</td>\n",
       "      <td>2018-12-10 00:00:00</td>\n",
       "      <td>2018-12-24 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4418</td>\n",
       "      <td>WA</td>\n",
       "      <td>2019-03-04 14:38:00</td>\n",
       "      <td>DR</td>\n",
       "      <td>Severe Storm(s)</td>\n",
       "      <td>SEVERE WINTER STORMS, STRAIGHT-LINE WINDS, FLO...</td>\n",
       "      <td>2018-12-10 00:00:00</td>\n",
       "      <td>2018-12-24 00:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Disaster Number State    Declaration Date Disaster Type    Incident Type  \\\n",
       "0             4419    AL 2019-03-05 17:33:00            DR          Tornado   \n",
       "1             4418    WA 2019-03-04 14:38:00            DR  Severe Storm(s)   \n",
       "2             4418    WA 2019-03-04 14:38:00            DR  Severe Storm(s)   \n",
       "3             4418    WA 2019-03-04 14:38:00            DR  Severe Storm(s)   \n",
       "4             4418    WA 2019-03-04 14:38:00            DR  Severe Storm(s)   \n",
       "\n",
       "                                               Title Incident Begin Date  \\\n",
       "0  SEVERE STORMS, STRAIGHT-LINE WINDS, AND TORNADOES 2019-03-03 00:01:00   \n",
       "1  SEVERE WINTER STORMS, STRAIGHT-LINE WINDS, FLO... 2018-12-10 00:00:00   \n",
       "2  SEVERE WINTER STORMS, STRAIGHT-LINE WINDS, FLO... 2018-12-10 00:00:00   \n",
       "3  SEVERE WINTER STORMS, STRAIGHT-LINE WINDS, FLO... 2018-12-10 00:00:00   \n",
       "4  SEVERE WINTER STORMS, STRAIGHT-LINE WINDS, FLO... 2018-12-10 00:00:00   \n",
       "\n",
       "    Incident End Date  \n",
       "0 2019-03-03 23:59:00  \n",
       "1 2018-12-24 00:00:00  \n",
       "2 2018-12-24 00:00:00  \n",
       "3 2018-12-24 00:00:00  \n",
       "4 2018-12-24 00:00:00  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel(\"resources/data.gov.FEMADeclarations.3.15.19.xlsx\",\n",
    "                   sheet_name=\"FEMA Declarations\",\n",
    "                   usecols=[\"Declaration Date\",\n",
    "                            \"Disaster Number\", \n",
    "                            \"State \", \n",
    "                            \"Disaster Type\", \n",
    "                            \"Incident Type\", \n",
    "                            \"Title\", \n",
    "                            \"Incident Begin Date\", \n",
    "                            \"Incident End Date\"],\n",
    "                   header=2)\n",
    "df.rename({\"State \" : \"State\"}, inplace=True, axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 48555 entries, 0 to 48554\n",
      "Data columns (total 8 columns):\n",
      "Disaster Number        48555 non-null int64\n",
      "State                  48555 non-null object\n",
      "Declaration Date       48555 non-null datetime64[ns]\n",
      "Disaster Type          48555 non-null object\n",
      "Incident Type          48555 non-null object\n",
      "Title                  48555 non-null object\n",
      "Incident Begin Date    48555 non-null datetime64[ns]\n",
      "Incident End Date      48216 non-null datetime64[ns]\n",
      "dtypes: datetime64[ns](3), int64(1), object(4)\n",
      "memory usage: 2.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to the MetaData sheet into the xlsx file, the **Disaster Number** column is a number used to designate an event declared as a disaster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1239    257\n",
       "3261    255\n",
       "3216    254\n",
       "1624    254\n",
       "1606    254\n",
       "       ... \n",
       "2706      1\n",
       "2957      1\n",
       "2674      1\n",
       "2424      1\n",
       "1495      1\n",
       "Name: Disaster Number, Length: 3568, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "disaster_number_counts = df[\"Disaster Number\"].value_counts()\n",
    "disaster_number_counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that this number isn't unique. Let's analyse a sample for a non-unique Disaster Number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Disaster Number</th>\n",
       "      <th>State</th>\n",
       "      <th>Declaration Date</th>\n",
       "      <th>Disaster Type</th>\n",
       "      <th>Incident Type</th>\n",
       "      <th>Title</th>\n",
       "      <th>Incident Begin Date</th>\n",
       "      <th>Incident End Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30915</th>\n",
       "      <td>1239</td>\n",
       "      <td>TX</td>\n",
       "      <td>1998-08-26 15:50:08</td>\n",
       "      <td>DR</td>\n",
       "      <td>Severe Storm(s)</td>\n",
       "      <td>TROPICAL STORM CHARLEY</td>\n",
       "      <td>1998-08-22 13:55:00</td>\n",
       "      <td>1998-08-31 12:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31035</th>\n",
       "      <td>1239</td>\n",
       "      <td>TX</td>\n",
       "      <td>1998-08-26 15:50:08</td>\n",
       "      <td>DR</td>\n",
       "      <td>Severe Storm(s)</td>\n",
       "      <td>TROPICAL STORM CHARLEY</td>\n",
       "      <td>1998-08-22 13:55:00</td>\n",
       "      <td>1998-08-31 12:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31058</th>\n",
       "      <td>1239</td>\n",
       "      <td>TX</td>\n",
       "      <td>1998-08-26 15:50:08</td>\n",
       "      <td>DR</td>\n",
       "      <td>Severe Storm(s)</td>\n",
       "      <td>TROPICAL STORM CHARLEY</td>\n",
       "      <td>1998-08-22 13:55:00</td>\n",
       "      <td>1998-08-31 12:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30988</th>\n",
       "      <td>1239</td>\n",
       "      <td>TX</td>\n",
       "      <td>1998-08-26 15:50:08</td>\n",
       "      <td>DR</td>\n",
       "      <td>Severe Storm(s)</td>\n",
       "      <td>TROPICAL STORM CHARLEY</td>\n",
       "      <td>1998-08-22 13:55:00</td>\n",
       "      <td>1998-08-31 12:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Disaster Number State    Declaration Date Disaster Type  \\\n",
       "30915             1239    TX 1998-08-26 15:50:08            DR   \n",
       "31035             1239    TX 1998-08-26 15:50:08            DR   \n",
       "31058             1239    TX 1998-08-26 15:50:08            DR   \n",
       "30988             1239    TX 1998-08-26 15:50:08            DR   \n",
       "\n",
       "         Incident Type                   Title Incident Begin Date  \\\n",
       "30915  Severe Storm(s)  TROPICAL STORM CHARLEY 1998-08-22 13:55:00   \n",
       "31035  Severe Storm(s)  TROPICAL STORM CHARLEY 1998-08-22 13:55:00   \n",
       "31058  Severe Storm(s)  TROPICAL STORM CHARLEY 1998-08-22 13:55:00   \n",
       "30988  Severe Storm(s)  TROPICAL STORM CHARLEY 1998-08-22 13:55:00   \n",
       "\n",
       "        Incident End Date  \n",
       "30915 1998-08-31 12:00:00  \n",
       "31035 1998-08-31 12:00:00  \n",
       "31058 1998-08-31 12:00:00  \n",
       "30988 1998-08-31 12:00:00  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index = 0\n",
    "most_occured_disaster_number = df.loc[disaster_number_counts.index[index] == df[\"Disaster Number\"]]\n",
    "most_occured_disaster_number.sample(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like these rows are redundant. Let's verify it for each column for this given index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Disaster Number contains only 1 element : True\n",
      "State contains only 1 element : True\n",
      "Declaration Date contains only 1 element : True\n",
      "Disaster Type contains only 1 element : True\n",
      "Incident Type contains only 1 element : True\n",
      "Title contains only 1 element : True\n",
      "Incident Begin Date contains only 1 element : True\n",
      "Incident End Date contains only 1 element : True\n"
     ]
    }
   ],
   "source": [
    "most_occured_disaster_number.apply(lambda col:print(f'{col.name} contains only 1 element : {col.value_counts(normalize=True).iloc[0] == 1.0}'));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All these lines refer to the same event. Consequently, we can drop duplicated without considering a subset of columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3570, 8)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop_duplicates(subset=None, keep='first', inplace=True)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Way better ! We can then look after remaining duplicated **Disaster Number**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1110    2\n",
       "572     2\n",
       "Name: Disaster Number, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "disaster_number_counts = df[\"Disaster Number\"].value_counts()\n",
    "remaining_duplicates = disaster_number_counts[disaster_number_counts != 1]\n",
    "remaining_duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Disaster Number</th>\n",
       "      <th>State</th>\n",
       "      <th>Declaration Date</th>\n",
       "      <th>Disaster Type</th>\n",
       "      <th>Incident Type</th>\n",
       "      <th>Title</th>\n",
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       "      <th>33521</th>\n",
       "      <td>1110</td>\n",
       "      <td>AR</td>\n",
       "      <td>1996-04-23</td>\n",
       "      <td>DR</td>\n",
       "      <td>Severe Storm(s)</td>\n",
       "      <td>SEVERE STORMS AND TORNADOES</td>\n",
       "      <td>1996-04-18</td>\n",
       "      <td>1996-04-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33523</th>\n",
       "      <td>1110</td>\n",
       "      <td>IL</td>\n",
       "      <td>1996-04-23</td>\n",
       "      <td>DR</td>\n",
       "      <td>Severe Storm(s)</td>\n",
       "      <td>SEVERE STORMS AND TORNADOES</td>\n",
       "      <td>1996-04-18</td>\n",
       "      <td>1996-04-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41759</th>\n",
       "      <td>572</td>\n",
       "      <td>MH</td>\n",
       "      <td>1979-02-12</td>\n",
       "      <td>DR</td>\n",
       "      <td>Typhoon</td>\n",
       "      <td>TYPHOON ALICE</td>\n",
       "      <td>1979-02-12</td>\n",
       "      <td>1979-02-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41767</th>\n",
       "      <td>572</td>\n",
       "      <td>PW</td>\n",
       "      <td>1979-02-12</td>\n",
       "      <td>DR</td>\n",
       "      <td>Typhoon</td>\n",
       "      <td>TYPHOON ALICE</td>\n",
       "      <td>1979-02-12</td>\n",
       "      <td>1979-02-12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Disaster Number State Declaration Date Disaster Type    Incident Type  \\\n",
       "33521             1110    AR       1996-04-23            DR  Severe Storm(s)   \n",
       "33523             1110    IL       1996-04-23            DR  Severe Storm(s)   \n",
       "41759              572    MH       1979-02-12            DR          Typhoon   \n",
       "41767              572    PW       1979-02-12            DR          Typhoon   \n",
       "\n",
       "                             Title Incident Begin Date Incident End Date  \n",
       "33521  SEVERE STORMS AND TORNADOES          1996-04-18        1996-04-19  \n",
       "33523  SEVERE STORMS AND TORNADOES          1996-04-18        1996-04-19  \n",
       "41759                TYPHOON ALICE          1979-02-12        1979-02-12  \n",
       "41767                TYPHOON ALICE          1979-02-12        1979-02-12  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"Disaster Number\"].isin(remaining_duplicates.index)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Only 2 numbers remaining ! Great ! The only difference is the **State** column. With the help of [wikipedia](https://en.wikipedia.org/wiki/List_of_U.S._state_abbreviations), we can add a column containing the full name of the state instead of this 2 letters code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fullname</th>\n",
       "      <th>Code</th>\n",
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       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>WY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Florida</td>\n",
       "      <td>FL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>South Carolina</td>\n",
       "      <td>SC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Maine</td>\n",
       "      <td>ME</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>New Jersey</td>\n",
       "      <td>NJ</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Fullname Code\n",
       "50         Wyoming   WY\n",
       "9          Florida   FL\n",
       "40  South Carolina   SC\n",
       "19           Maine   ME\n",
       "30      New Jersey   NJ"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states_df = pd.read_html(\"https://en.wikipedia.org/wiki/List_of_U.S._state_abbreviations\", \n",
    "                         attrs = {'class': 'wikitable sortable'},\n",
    "                         header = 11)[0]\n",
    "states_df = states_df.rename({\"United States of America\" : \"Fullname\", \"US\" : \"Code\"}, axis=1)[[\"Fullname\", \"Code\"]]\n",
    "states_df.dropna(inplace=True)\n",
    "states_df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60, 2)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we can now merge our 2 datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Disaster Number</th>\n",
       "      <th>State</th>\n",
       "      <th>Declaration Date</th>\n",
       "      <th>Disaster Type</th>\n",
       "      <th>Incident Type</th>\n",
       "      <th>Title</th>\n",
       "      <th>Incident Begin Date</th>\n",
       "      <th>Incident End Date</th>\n",
       "      <th>State name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>3400</td>\n",
       "      <td>SC</td>\n",
       "      <td>2018-09-10 19:48:00</td>\n",
       "      <td>EM</td>\n",
       "      <td>Hurricane</td>\n",
       "      <td>HURRICANE FLORENCE</td>\n",
       "      <td>2018-09-08 00:01:00</td>\n",
       "      <td>2018-10-08 23:59:00</td>\n",
       "      <td>South Carolina</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1077</th>\n",
       "      <td>2737</td>\n",
       "      <td>CA</td>\n",
       "      <td>2007-10-22 16:22:00</td>\n",
       "      <td>FM</td>\n",
       "      <td>Fire</td>\n",
       "      <td>SANTIAGO FIRE</td>\n",
       "      <td>2007-10-21 20:55:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>2974</td>\n",
       "      <td>NV</td>\n",
       "      <td>2012-01-19 18:02:00</td>\n",
       "      <td>FM</td>\n",
       "      <td>Fire</td>\n",
       "      <td>WASHOE FIRE</td>\n",
       "      <td>2012-01-19 12:30:00</td>\n",
       "      <td>2012-01-21 08:00:00</td>\n",
       "      <td>Nevada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3223</th>\n",
       "      <td>348</td>\n",
       "      <td>IA</td>\n",
       "      <td>1972-08-18 00:00:00</td>\n",
       "      <td>DR</td>\n",
       "      <td>Flood</td>\n",
       "      <td>SEVERE STORMS &amp; FLOODING</td>\n",
       "      <td>1972-08-18 00:00:00</td>\n",
       "      <td>1972-08-18 00:00:00</td>\n",
       "      <td>Iowa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2601</th>\n",
       "      <td>876</td>\n",
       "      <td>NH</td>\n",
       "      <td>1990-08-29 00:00:00</td>\n",
       "      <td>DR</td>\n",
       "      <td>Flood</td>\n",
       "      <td>SEVERE STORMS &amp; FLOODING</td>\n",
       "      <td>1990-08-07 00:00:00</td>\n",
       "      <td>1990-08-11 00:00:00</td>\n",
       "      <td>New Hampshire</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Disaster Number State    Declaration Date Disaster Type Incident Type  \\\n",
       "33               3400    SC 2018-09-10 19:48:00            EM     Hurricane   \n",
       "1077             2737    CA 2007-10-22 16:22:00            FM          Fire   \n",
       "454              2974    NV 2012-01-19 18:02:00            FM          Fire   \n",
       "3223              348    IA 1972-08-18 00:00:00            DR         Flood   \n",
       "2601              876    NH 1990-08-29 00:00:00            DR         Flood   \n",
       "\n",
       "                         Title Incident Begin Date   Incident End Date  \\\n",
       "33          HURRICANE FLORENCE 2018-09-08 00:01:00 2018-10-08 23:59:00   \n",
       "1077             SANTIAGO FIRE 2007-10-21 20:55:00                 NaT   \n",
       "454                WASHOE FIRE 2012-01-19 12:30:00 2012-01-21 08:00:00   \n",
       "3223  SEVERE STORMS & FLOODING 1972-08-18 00:00:00 1972-08-18 00:00:00   \n",
       "2601  SEVERE STORMS & FLOODING 1990-08-07 00:00:00 1990-08-11 00:00:00   \n",
       "\n",
       "          State name  \n",
       "33    South Carolina  \n",
       "1077      California  \n",
       "454           Nevada  \n",
       "3223            Iowa  \n",
       "2601   New Hampshire  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_with_states = (df.merge(states_df, how='left',left_on='State', right_on=\"Code\")\n",
    "                  .drop([\"Code\"], axis=1)\n",
    "                  .rename({\"Fullname\":\"State name\"}, axis=1))\n",
    "df_with_states.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To be sure, I want to verify we have a **State name** for each disaster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_with_states[\"State name\"].isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look again to our remaining duplicated **Disaster Number**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Disaster Number</th>\n",
       "      <th>State</th>\n",
       "      <th>Declaration Date</th>\n",
       "      <th>Disaster Type</th>\n",
       "      <th>Incident Type</th>\n",
       "      <th>Title</th>\n",
       "      <th>Incident Begin Date</th>\n",
       "      <th>Incident End Date</th>\n",
       "      <th>State name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2341</th>\n",
       "      <td>1110</td>\n",
       "      <td>AR</td>\n",
       "      <td>1996-04-23</td>\n",
       "      <td>DR</td>\n",
       "      <td>Severe Storm(s)</td>\n",
       "      <td>SEVERE STORMS AND TORNADOES</td>\n",
       "      <td>1996-04-18</td>\n",
       "      <td>1996-04-19</td>\n",
       "      <td>Arkansas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2342</th>\n",
       "      <td>1110</td>\n",
       "      <td>IL</td>\n",
       "      <td>1996-04-23</td>\n",
       "      <td>DR</td>\n",
       "      <td>Severe Storm(s)</td>\n",
       "      <td>SEVERE STORMS AND TORNADOES</td>\n",
       "      <td>1996-04-18</td>\n",
       "      <td>1996-04-19</td>\n",
       "      <td>Illinois</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2929</th>\n",
       "      <td>572</td>\n",
       "      <td>MH</td>\n",
       "      <td>1979-02-12</td>\n",
       "      <td>DR</td>\n",
       "      <td>Typhoon</td>\n",
       "      <td>TYPHOON ALICE</td>\n",
       "      <td>1979-02-12</td>\n",
       "      <td>1979-02-12</td>\n",
       "      <td>Marshall Islands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2930</th>\n",
       "      <td>572</td>\n",
       "      <td>PW</td>\n",
       "      <td>1979-02-12</td>\n",
       "      <td>DR</td>\n",
       "      <td>Typhoon</td>\n",
       "      <td>TYPHOON ALICE</td>\n",
       "      <td>1979-02-12</td>\n",
       "      <td>1979-02-12</td>\n",
       "      <td>Palau</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Disaster Number State Declaration Date Disaster Type    Incident Type  \\\n",
       "2341             1110    AR       1996-04-23            DR  Severe Storm(s)   \n",
       "2342             1110    IL       1996-04-23            DR  Severe Storm(s)   \n",
       "2929              572    MH       1979-02-12            DR          Typhoon   \n",
       "2930              572    PW       1979-02-12            DR          Typhoon   \n",
       "\n",
       "                            Title Incident Begin Date Incident End Date  \\\n",
       "2341  SEVERE STORMS AND TORNADOES          1996-04-18        1996-04-19   \n",
       "2342  SEVERE STORMS AND TORNADOES          1996-04-18        1996-04-19   \n",
       "2929                TYPHOON ALICE          1979-02-12        1979-02-12   \n",
       "2930                TYPHOON ALICE          1979-02-12        1979-02-12   \n",
       "\n",
       "            State name  \n",
       "2341          Arkansas  \n",
       "2342          Illinois  \n",
       "2929  Marshall Islands  \n",
       "2930             Palau  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_with_states[df_with_states[\"Disaster Number\"].isin(remaining_duplicates.index)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the [following map](https://images-na.ssl-images-amazon.com/images/I/81r8Hxz5lQL._SL1500_.jpg), we can suppose it is because these events are spread on several states. Indeed, **Arkansas** and **Illinois** are in the same region. The same hypothesis can be done considering [**Palau**](https://en.wikipedia.org/wiki/Palau) & [**Marshall Islands**](https://en.wikipedia.org/wiki/Marshall_Islands)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img width=\"600px\" src=\"https://images-na.ssl-images-amazon.com/images/I/81r8Hxz5lQL._SL1500_.jpg\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now consider the **Incident Type** column in order to only consider natural disasters caused by weather."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Severe Storm(s)     941\n",
       "Fire                829\n",
       "Flood               805\n",
       "Hurricane           353\n",
       "Tornado             166\n",
       "Snow                165\n",
       "Severe Ice Storm     62\n",
       "Typhoon              61\n",
       "Drought              46\n",
       "Earthquake           31\n",
       "Coastal Storm        27\n",
       "Other                26\n",
       "Freezing             18\n",
       "Toxic Substances      8\n",
       "Mud/Landslide         7\n",
       "Fishing Losses        6\n",
       "Volcano               6\n",
       "Dam/Levee Break       4\n",
       "Human Cause           3\n",
       "Tsunami               3\n",
       "Terrorist             2\n",
       "Chemical              1\n",
       "Name: Incident Type, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_with_states[\"Incident Type\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3473, 9)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_disasters = [\"Severe Storm(s)\", \n",
    "                     \"Fire\", \"Flood\", \n",
    "                     \"Hurricane\", \"Tornado\", \n",
    "                     \"Snow\", \"Severe Ice Storm\", \n",
    "                     \"Typhoon\", \"Drought\", \n",
    "                     \"Coastal Storm\", \"Freezing\"]\n",
    "df_weather_disasters = df_with_states[df_with_states[\"Incident Type\"].isin(weather_disasters)]\n",
    "df_weather_disasters.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Disaster Number</th>\n",
       "      <th>State</th>\n",
       "      <th>Declaration Date</th>\n",
       "      <th>Disaster Type</th>\n",
       "      <th>Incident Type</th>\n",
       "      <th>Title</th>\n",
       "      <th>Incident Begin Date</th>\n",
       "      <th>Incident End Date</th>\n",
       "      <th>State name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3492</th>\n",
       "      <td>79</td>\n",
       "      <td>ND</td>\n",
       "      <td>1957-06-22 00:00:00</td>\n",
       "      <td>DR</td>\n",
       "      <td>Tornado</td>\n",
       "      <td>TORNADO</td>\n",
       "      <td>1957-06-22 00:00:00</td>\n",
       "      <td>1957-06-22 00:00:00</td>\n",
       "      <td>North Dakota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3045</th>\n",
       "      <td>511</td>\n",
       "      <td>SD</td>\n",
       "      <td>1976-06-25 00:00:00</td>\n",
       "      <td>DR</td>\n",
       "      <td>Flood</td>\n",
       "      <td>FLASH FLOODING &amp; MUDSLIDES</td>\n",
       "      <td>1976-06-25 00:00:00</td>\n",
       "      <td>1976-06-25 00:00:00</td>\n",
       "      <td>South Dakota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>4217</td>\n",
       "      <td>KY</td>\n",
       "      <td>2015-05-01 17:37:00</td>\n",
       "      <td>DR</td>\n",
       "      <td>Flood</td>\n",
       "      <td>SEVERE STORMS, TORNADOES, FLOODING, LANDSLIDES...</td>\n",
       "      <td>2015-04-02 12:00:00</td>\n",
       "      <td>2015-04-17 23:59:00</td>\n",
       "      <td>Kentucky</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2187</th>\n",
       "      <td>3132</td>\n",
       "      <td>MS</td>\n",
       "      <td>1998-09-28 00:00:00</td>\n",
       "      <td>EM</td>\n",
       "      <td>Hurricane</td>\n",
       "      <td>HURRICANE GEORGES</td>\n",
       "      <td>1998-09-28 00:00:00</td>\n",
       "      <td>1998-10-05 00:00:00</td>\n",
       "      <td>Mississippi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2386</th>\n",
       "      <td>1070</td>\n",
       "      <td>AL</td>\n",
       "      <td>1995-10-04 00:00:00</td>\n",
       "      <td>DR</td>\n",
       "      <td>Hurricane</td>\n",
       "      <td>HURRICANE OPAL</td>\n",
       "      <td>1995-10-04 00:00:00</td>\n",
       "      <td>1995-10-08 00:00:00</td>\n",
       "      <td>Alabama</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Disaster Number State    Declaration Date Disaster Type Incident Type  \\\n",
       "3492               79    ND 1957-06-22 00:00:00            DR       Tornado   \n",
       "3045              511    SD 1976-06-25 00:00:00            DR         Flood   \n",
       "236              4217    KY 2015-05-01 17:37:00            DR         Flood   \n",
       "2187             3132    MS 1998-09-28 00:00:00            EM     Hurricane   \n",
       "2386             1070    AL 1995-10-04 00:00:00            DR     Hurricane   \n",
       "\n",
       "                                                  Title Incident Begin Date  \\\n",
       "3492                                            TORNADO 1957-06-22 00:00:00   \n",
       "3045                         FLASH FLOODING & MUDSLIDES 1976-06-25 00:00:00   \n",
       "236   SEVERE STORMS, TORNADOES, FLOODING, LANDSLIDES... 2015-04-02 12:00:00   \n",
       "2187                                  HURRICANE GEORGES 1998-09-28 00:00:00   \n",
       "2386                                     HURRICANE OPAL 1995-10-04 00:00:00   \n",
       "\n",
       "       Incident End Date    State name  \n",
       "3492 1957-06-22 00:00:00  North Dakota  \n",
       "3045 1976-06-25 00:00:00  South Dakota  \n",
       "236  2015-04-17 23:59:00      Kentucky  \n",
       "2187 1998-10-05 00:00:00   Mississippi  \n",
       "2386 1995-10-08 00:00:00       Alabama  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_weather_disasters.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first interesting thing is to consider the number of weather disasters by year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": [
    "final_df = df_weather_disasters.set_index(\"Declaration Date\").to_period(\"Y\")\n",
    "disasters_by_year = final_df.groupby(level=0).count()\n",
    "# Let's create the plot\n",
    "ax = disasters_by_year.rename({\"Title\":\"# Disasters\"}, axis=1).plot(y='# Disasters', use_index=True);\n",
    "ax.set_title(\"# of weather disasters by year\");\n",
    "ax.set_xlabel(\"Year\");\n",
    "ax.set_ylabel(\"# of Disasters\");\n",
    "ax.legend().set_visible(False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another plot is the number of weather disasters by state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": [
    "ax = df_weather_disasters.groupby([\"State name\"]).count().sort_values('Title', ascending=False).plot.bar(y='Title', use_index=True, width=1);\n",
    "ax.legend().set_visible(False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "My final plot consider the number of disasters by state and by incident."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Incident Type</th>\n",
       "      <th>Coastal Storm</th>\n",
       "      <th>Drought</th>\n",
       "      <th>Fire</th>\n",
       "      <th>Flood</th>\n",
       "      <th>Freezing</th>\n",
       "      <th>Hurricane</th>\n",
       "      <th>Severe Ice Storm</th>\n",
       "      <th>Severe Storm(s)</th>\n",
       "      <th>Snow</th>\n",
       "      <th>Tornado</th>\n",
       "      <th>Typhoon</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>152</td>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>147</td>\n",
       "      <td>37</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oklahoma</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>76</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Washington</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>43</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Incident Type  Coastal Storm  Drought  Fire  Flood  Freezing  Hurricane  \\\n",
       "State name                                                                \n",
       "Texas                      2        1   152     38         2         21   \n",
       "California                 2        1   147     37         3          1   \n",
       "Oklahoma                   0        0    76     22         0          1   \n",
       "Florida                    2        0    42     13         6         40   \n",
       "Washington                 1        1    43     30         0          0   \n",
       "\n",
       "Incident Type  Severe Ice Storm  Severe Storm(s)  Snow  Tornado  Typhoon  \\\n",
       "State name                                                                 \n",
       "Texas                         1               20     0       15        0   \n",
       "California                    0               12     0        0        0   \n",
       "Oklahoma                      9               41     1       11        0   \n",
       "Florida                       0               22     0        1        0   \n",
       "Washington                    1               17     0        0        0   \n",
       "\n",
       "Incident Type  Total  \n",
       "State name            \n",
       "Texas            252  \n",
       "California       203  \n",
       "Oklahoma         161  \n",
       "Florida          126  \n",
       "Washington        93  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_by_state_incident = (df_weather_disasters[[\"State name\", \"Incident Type\", \"Title\"]]\n",
    "                        .groupby([\"State name\", \"Incident Type\"])\n",
    "                        .count()\n",
    "                        .unstack(\"Incident Type\").fillna(0).astype(int))\n",
    "df_by_state_incident.loc[:,(\"Title\",'Total')] = df_by_state_incident.sum(axis=1)\n",
    "df_by_state_incident.columns = df_by_state_incident.columns.droplevel()\n",
    "df_by_state_incident.sort_values(by=[\"Total\"], inplace=True, ascending=False)\n",
    "df_by_state_incident.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_by_state_incident.drop(\"Total\",axis=1).plot(kind='bar', stacked=True, width=1)\n",
    "ax.get_figure().set_size_inches(20,10)\n",
    "ax.set_ylabel(\"# of Disasters\");\n",
    "ax.set_title(\"# of weather disasters by state and incident type in the USA\");"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv",
   "language": "python",
   "name": "venv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}