FinalProject_group014.ipynb

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    "# FireCode Compliant\n",
    "\n",
    "## Authors:\n",
    "    1)Vincent Bensawan XXXXXXXXX\n",
    "    2)Kyle Gillaspy XXXXXXXXX\n",
    "    3)Oscar Jimenez XXXXXXXXX\n",
    "    4)Sayaka Kuwayama XXXXXXXXX\n",
    "    5)Ritaj Mangush XXXXXXXXX\n",
    "    6)Nancy Nguyen XXXXXXXXX\n",
    "## Introduction and Background\n",
    "1) **REASEARCH QUESTION:**\n",
    "\n",
    "Is it possible to analyse wildfires in order to efficiently allocate firefighting resources to mitigate future damages?\n",
    "\n",
    "2) **BACKGROUND:**\n",
    "\n",
    "Wildfires can burn acres of land within minutes, they become uncontrolled when fueled by weather, wind, and dry underbrush. Such wildfires cause devastating ecological and economic impacts.There has been significant discussion of what role forest management can and can’t play in preventing wildfires. Naturally occurring wildfires play an integral role in nature but are very destructive to humans. They return nutrients to the soil by burning dead or decaying matter.  It becomes important to employ mechanisms to better predict wildfires and fire behaviour once it started. \n",
    "\n",
    "In 2018, California experienced one of the deadliest wildfires on record that devastated hundred thousand acres of land and affected the air quality of the Northern California regions. Several articles pointed out that the wildfire is caused by numerous factors such bad climate conditions and human activities. It was also discussed that one of the challenges in putting out the wildfire is the lack of personnel.\n",
    "\n",
    "We wanted to find out whether it’s possible to assess areas with risk levels according to how the area is vulnerable from catching fire. We are aiming to try and incorporate this data with wind data, time since there has been a fire in this area, human visitation statistics, and lightning fire data, to help assess possibly overlooked areas. By determining the risk levels of different areas, we hope to be able to map the best way to allocate firefighting resources to improve firefighting efficiency. Similar projects have looked at fire data in relation to its economic effects, as a UC Davis Project team used fire boundary data to provide wineries in California with the most up-to-date risk assessments for wildfires (https://github.com/UCDavisLibrary/ava).\n",
    "The goal is to have communities with emergency plans and preparations ready in these “high risk” areas.\n",
    "\n",
    "\n",
    "3) **Hypothesis:**  \n",
    "We hypothesize that counties with more acres burned and more fatalities to require more resources."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Graph of Fire Frequencies by County"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![basicmap.jpg](attachment: basicmap.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='basicmap.jpg') \n",
    "\n",
    "#To view the interactive version of the map uncomment below\n",
    "#IFrame(\".\\map.html\", width='100%', height='800')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### More Context :\n",
    "\n",
    "Here we can see that our map seems to have no apprent patterns at first glance, we hope to be able to make sense of this map to better predict wildfires to allocate firefighting resources more effectively!\n",
    "\n",
    "Our analysis continues after we show our Data Gathering and Data Wrangling proccesses . . ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DATASETS\n",
    "\n",
    "We will derive our datasets from publicly available resources specifically on the federal level. We would like to use both tabular and GIS data in order to drive more complex analysis and data visualization. We chose these particular datasets because they are both public and backed by the federal government. Although government datasets are often messy, their data collection methods are effective and trusted especially for the hands-on nature of the hypothesis. \n",
    "\n",
    "1) **GEOJSON**\n",
    "    This GeoJSON file contains the boundries for all California Counties as drawn by CALFIRE's Fire and Resource Assessment Program (FRAP). This dataset will help in determining boundries for map vizualizations.\n",
    "\n",
    "Data Source: https://data.ca.gov/dataset/california-counties\n",
    "\n",
    "2) **SQL Database**\n",
    "    SQL Lite database obtained from Kaggle contains 1.88 million wildfires throughout the US and US Territories. For the purposes of our analysis we will only use fires that are located in California. This dataset contains a myriad of information such as different ID types, latitude and longitude measurements, fire size, discovery date, and containment date. This dataset will be used in conjunction with the GeoJSON file to make the map vizualizations\n",
    "\n",
    "Data Source: https://www.kaggle.com/rtatman/188-million-us-wildfires\n",
    "\n",
    "3) **PDF SCRAPING**\n",
    "    Unfortunately there is not any good datasets for wildfires that are in a nice format. For this reason, we have decided to collect the data ourselves the messy way from... PDF files. The PDF format was not meant to be read by a computer but by using the Tabula package, an open source PDF scraper, we are able to read pdf tables directly to pandas DataFrames. The tool was far from perfect, combined with inconsistencies in government data collection, required each pdf to be scraped individually page by page. To further complicate things, the DataFrames read are very messy. Data cleaning/data wrangling will be covered in the next section. It is important to note that the 2012 data is missing due to a broken link.\n",
    "\n",
    "Link to example pdf: http://cdfdata.fire.ca.gov/pub/cdf/images/incidentstatsevents_272.pdf\n",
    "\n",
    "Data Source: http://cdfdata.fire.ca.gov/incidents/incidents_statsevents\n",
    "\n",
    "\n",
    "## Special Steps:\n",
    "Our dataset is created by merging cleaned data from a variety of places, utilizing SQL, Tabula, and GEOJSON datasets. This extends our data wrangling and cleaning processes, however it is well worth the effort!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **SETUP**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IMPORTS\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#there will be more packages added as needed in following cells\n",
    "#standard datascience packages\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "#packages for reading geojson file\n",
    "import json\n",
    "import os\n",
    "\n",
    "#package for reading sql lite database\n",
    "import sqlite3\n",
    "\n",
    "#package for map making\n",
    "import folium\n",
    "\n",
    "#package for accurate date/time manipulations\n",
    "import datetime\n",
    "\n",
    "#note this package is NOT native to Anaconda, follow tabula\n",
    "#documentation for steps on installation\n",
    "#package for PDF scraping\n",
    "import tabula\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "#Formatting, warnings are ignored\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "#Used for predictive modeling to identify most afflicted counties\n",
    "import sklearn\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.compose import ColumnTransformer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DATA GATHERING"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1) GEOJSON\n",
    "\n",
    "This GeoJSON file contains the boundries for all California Counties as drawn by CALFIRE's Fire and Resource Assessment Program (FRAP). This dataset will help in determining boundries for map vizualizations.\n",
    "\n",
    "**Data Source:** https://data.ca.gov/dataset/california-counties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "ca_counties = json.load(open('California_Counties.geojson'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2) SQL Database\n",
    "\n",
    "SQL Lite database obtained from Kaggle contains 1.88 million wildfires throughout the US and US Territories. For the purposes of our analysis we will only use fires that are located in California. This dataset contains a myriad of information such as different ID types, latitude and longitude measurements, fire size, discovery date, and containment date. This dataset will be used in conjunction with the GeoJSON file to make the map vizualizations\n",
    "\n",
    "**Data Source:** https://www.kaggle.com/rtatman/188-million-us-wildfires"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>OBJECTID</th>\n",
       "      <th>FOD_ID</th>\n",
       "      <th>FPA_ID</th>\n",
       "      <th>SOURCE_SYSTEM_TYPE</th>\n",
       "      <th>SOURCE_SYSTEM</th>\n",
       "      <th>NWCG_REPORTING_AGENCY</th>\n",
       "      <th>NWCG_REPORTING_UNIT_ID</th>\n",
       "      <th>NWCG_REPORTING_UNIT_NAME</th>\n",
       "      <th>SOURCE_REPORTING_UNIT</th>\n",
       "      <th>SOURCE_REPORTING_UNIT_NAME</th>\n",
       "      <th>...</th>\n",
       "      <th>FIRE_SIZE_CLASS</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>OWNER_CODE</th>\n",
       "      <th>OWNER_DESCR</th>\n",
       "      <th>STATE</th>\n",
       "      <th>COUNTY</th>\n",
       "      <th>FIPS_CODE</th>\n",
       "      <th>FIPS_NAME</th>\n",
       "      <th>Shape</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>FS-1418826</td>\n",
       "      <td>FED</td>\n",
       "      <td>FS-FIRESTAT</td>\n",
       "      <td>FS</td>\n",
       "      <td>USCAPNF</td>\n",
       "      <td>Plumas National Forest</td>\n",
       "      <td>0511</td>\n",
       "      <td>Plumas National Forest</td>\n",
       "      <td>...</td>\n",
       "      <td>A</td>\n",
       "      <td>40.036944</td>\n",
       "      <td>-121.005833</td>\n",
       "      <td>5.0</td>\n",
       "      <td>USFS</td>\n",
       "      <td>CA</td>\n",
       "      <td>63</td>\n",
       "      <td>063</td>\n",
       "      <td>Plumas</td>\n",
       "      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00\\xe8d\\xc2\\x92_@^\\xc0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>FS-1418827</td>\n",
       "      <td>FED</td>\n",
       "      <td>FS-FIRESTAT</td>\n",
       "      <td>FS</td>\n",
       "      <td>USCAENF</td>\n",
       "      <td>Eldorado National Forest</td>\n",
       "      <td>0503</td>\n",
       "      <td>Eldorado National Forest</td>\n",
       "      <td>...</td>\n",
       "      <td>A</td>\n",
       "      <td>38.933056</td>\n",
       "      <td>-120.404444</td>\n",
       "      <td>5.0</td>\n",
       "      <td>USFS</td>\n",
       "      <td>CA</td>\n",
       "      <td>61</td>\n",
       "      <td>061</td>\n",
       "      <td>Placer</td>\n",
       "      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00T\\xb6\\xeej\\xe2\\x19^\\...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>FS-1418835</td>\n",
       "      <td>FED</td>\n",
       "      <td>FS-FIRESTAT</td>\n",
       "      <td>FS</td>\n",
       "      <td>USCAENF</td>\n",
       "      <td>Eldorado National Forest</td>\n",
       "      <td>0503</td>\n",
       "      <td>Eldorado National Forest</td>\n",
       "      <td>...</td>\n",
       "      <td>A</td>\n",
       "      <td>38.984167</td>\n",
       "      <td>-120.735556</td>\n",
       "      <td>13.0</td>\n",
       "      <td>STATE OR PRIVATE</td>\n",
       "      <td>CA</td>\n",
       "      <td>17</td>\n",
       "      <td>017</td>\n",
       "      <td>El Dorado</td>\n",
       "      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00\\xd0\\xa5\\xa0W\\x13/^\\...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>FS-1418845</td>\n",
       "      <td>FED</td>\n",
       "      <td>FS-FIRESTAT</td>\n",
       "      <td>FS</td>\n",
       "      <td>USCAENF</td>\n",
       "      <td>Eldorado National Forest</td>\n",
       "      <td>0503</td>\n",
       "      <td>Eldorado National Forest</td>\n",
       "      <td>...</td>\n",
       "      <td>A</td>\n",
       "      <td>38.559167</td>\n",
       "      <td>-119.913333</td>\n",
       "      <td>5.0</td>\n",
       "      <td>USFS</td>\n",
       "      <td>CA</td>\n",
       "      <td>3</td>\n",
       "      <td>003</td>\n",
       "      <td>Alpine</td>\n",
       "      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00\\x94\\xac\\xa3\\rt\\xfa]...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>FS-1418847</td>\n",
       "      <td>FED</td>\n",
       "      <td>FS-FIRESTAT</td>\n",
       "      <td>FS</td>\n",
       "      <td>USCAENF</td>\n",
       "      <td>Eldorado National Forest</td>\n",
       "      <td>0503</td>\n",
       "      <td>Eldorado National Forest</td>\n",
       "      <td>...</td>\n",
       "      <td>A</td>\n",
       "      <td>38.559167</td>\n",
       "      <td>-119.933056</td>\n",
       "      <td>5.0</td>\n",
       "      <td>USFS</td>\n",
       "      <td>CA</td>\n",
       "      <td>3</td>\n",
       "      <td>003</td>\n",
       "      <td>Alpine</td>\n",
       "      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00@\\xe3\\xaa.\\xb7\\xfb]\\...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   OBJECTID  FOD_ID      FPA_ID SOURCE_SYSTEM_TYPE SOURCE_SYSTEM  \\\n",
       "0         1       1  FS-1418826                FED   FS-FIRESTAT   \n",
       "1         2       2  FS-1418827                FED   FS-FIRESTAT   \n",
       "2         3       3  FS-1418835                FED   FS-FIRESTAT   \n",
       "3         4       4  FS-1418845                FED   FS-FIRESTAT   \n",
       "4         5       5  FS-1418847                FED   FS-FIRESTAT   \n",
       "\n",
       "  NWCG_REPORTING_AGENCY NWCG_REPORTING_UNIT_ID  NWCG_REPORTING_UNIT_NAME  \\\n",
       "0                    FS                USCAPNF    Plumas National Forest   \n",
       "1                    FS                USCAENF  Eldorado National Forest   \n",
       "2                    FS                USCAENF  Eldorado National Forest   \n",
       "3                    FS                USCAENF  Eldorado National Forest   \n",
       "4                    FS                USCAENF  Eldorado National Forest   \n",
       "\n",
       "  SOURCE_REPORTING_UNIT SOURCE_REPORTING_UNIT_NAME  \\\n",
       "0                  0511     Plumas National Forest   \n",
       "1                  0503   Eldorado National Forest   \n",
       "2                  0503   Eldorado National Forest   \n",
       "3                  0503   Eldorado National Forest   \n",
       "4                  0503   Eldorado National Forest   \n",
       "\n",
       "                         ...                         FIRE_SIZE_CLASS  \\\n",
       "0                        ...                                       A   \n",
       "1                        ...                                       A   \n",
       "2                        ...                                       A   \n",
       "3                        ...                                       A   \n",
       "4                        ...                                       A   \n",
       "\n",
       "    LATITUDE   LONGITUDE OWNER_CODE       OWNER_DESCR STATE COUNTY FIPS_CODE  \\\n",
       "0  40.036944 -121.005833        5.0              USFS    CA     63       063   \n",
       "1  38.933056 -120.404444        5.0              USFS    CA     61       061   \n",
       "2  38.984167 -120.735556       13.0  STATE OR PRIVATE    CA     17       017   \n",
       "3  38.559167 -119.913333        5.0              USFS    CA      3       003   \n",
       "4  38.559167 -119.933056        5.0              USFS    CA      3       003   \n",
       "\n",
       "   FIPS_NAME                                              Shape  \n",
       "0     Plumas  b'\\x00\\x01\\xad\\x10\\x00\\x00\\xe8d\\xc2\\x92_@^\\xc0...  \n",
       "1     Placer  b'\\x00\\x01\\xad\\x10\\x00\\x00T\\xb6\\xeej\\xe2\\x19^\\...  \n",
       "2  El Dorado  b'\\x00\\x01\\xad\\x10\\x00\\x00\\xd0\\xa5\\xa0W\\x13/^\\...  \n",
       "3     Alpine  b'\\x00\\x01\\xad\\x10\\x00\\x00\\x94\\xac\\xa3\\rt\\xfa]...  \n",
       "4     Alpine  b'\\x00\\x01\\xad\\x10\\x00\\x00@\\xe3\\xaa.\\xb7\\xfb]\\...  \n",
       "\n",
       "[5 rows x 39 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conn = sqlite3.connect(\"fires_sql.sqlite\")\n",
    "fires = pd.read_sql_query(\"SELECT * FROM fires\", conn)\n",
    "fires.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PDF SCRAPING\n",
    "\n",
    "Unfortunately there is not any good datasets for wildfires that are in a nice format. For this reason, we have decided to collect the data ourselves the messy way from... PDF files. The PDF format was not meant to be read by a computer but by using the `Tabula` package, an open source PDF scraper, we are able to read pdf tables directly to pandas DataFrames. The tool was far from perfect, combined with inconsistencies in government data collection, required each pdf to be scraped individually page by page. To further complicate things, the DataFrames read are very messy. Data cleaning/data wrangling will be covered in the next section.\n",
    "\n",
    "**Link to example pdf:** http://cdfdata.fire.ca.gov/pub/cdf/images/incidentstatsevents_272.pdf\n",
    "\n",
    "**Data Source:** http://cdfdata.fire.ca.gov/incidents/incidents_statsevents\n",
    "\n",
    "**WARNING: This may take a few minutes**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#we will scrape some data directly from pdf using tabula open source pdf reader.\n",
    "\n",
    "#variable format largest_[year]_[page]\n",
    "#2012 link is broken\n",
    "raw_fire_array = []\n",
    "#2000 2 pages\n",
    "largest_2000_1 = tabula.read_pdf('largest_2000.pdf', pages=1, area=[60, 105, 1004, 800])\n",
    "raw_fire_array.append(largest_2000_1)\n",
    "largest_2000_2 = tabula.read_pdf('largest_2000.pdf', pages=2, area=[60, 51, 1004, 650])\n",
    "raw_fire_array.append(largest_2000_2)\n",
    "#2001 2 pages\n",
    "largest_2001_1 = tabula.read_pdf('largest_2001.pdf', pages=1, area=[100, 88, 952, 792])\n",
    "raw_fire_array.append(largest_2001_1)\n",
    "largest_2001_2 = tabula.read_pdf('largest_2001.pdf', pages=2, area=[100, 46, 952, 623])\n",
    "raw_fire_array.append(largest_2001_2)\n",
    "#2002 3 pages\n",
    "largest_2002_1 = tabula.read_pdf('largest_2002.pdf', pages=1, area=[76, 104, 1010, 797])\n",
    "raw_fire_array.append(largest_2002_1)\n",
    "largest_2002_2 = tabula.read_pdf('largest_2002.pdf', pages=2, area=[76, 50, 1010, 786])\n",
    "raw_fire_array.append(largest_2002_2)\n",
    "largest_2002_3 = tabula.read_pdf('largest_2002.pdf', pages=3, area=[76, 50, 1010, 681])\n",
    "raw_fire_array.append(largest_2002_3)\n",
    "#2003 3 pages\n",
    "largest_2003_1 = tabula.read_pdf('largest_2003.pdf', pages=1, area=[58, 81, 1015, 787])\n",
    "raw_fire_array.append(largest_2003_1)\n",
    "largest_2003_2 = tabula.read_pdf('largest_2003.pdf', pages=2, area=[58, 33, 1015, 784])\n",
    "raw_fire_array.append(largest_2003_2)\n",
    "largest_2003_3 = tabula.read_pdf('largest_2003.pdf', pages=3, area=[58, 33, 1015, 169])\n",
    "raw_fire_array.append(largest_2003_3)\n",
    "#end(largest_2016_3)\n",
    "#45 total pages!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2004 2 pages\n",
    "largest_2004_1 = tabula.read_pdf('largest_2004.pdf', pages=1, area=[81, 125, 1017, 797])\n",
    "raw_fire_array.append(largest_2004_1)\n",
    "largest_2004_2 = tabula.read_pdf('largest_2004.pdf', pages=2, area=[81, 76, 1017, 646])\n",
    "raw_fire_array.append(largest_2004_2)\n",
    "#2005 3 pages\n",
    "largest_2005_1 = tabula.read_pdf('largest_2005.pdf', pages=1, area=[110, 86, 958, 793])\n",
    "raw_fire_array.append(largest_2005_1)\n",
    "largest_2005_2 = tabula.read_pdf('largest_2005.pdf', pages=2, area=[110, 86, 958, 793])\n",
    "raw_fire_array.append(largest_2005_2)\n",
    "largest_2005_3 = tabula.read_pdf('largest_2005.pdf', pages=3, area=[110, 86, 958, 385])\n",
    "raw_fire_array.append(largest_2005_3)\n",
    "#2006 4 pages\n",
    "largest_2006_1 = tabula.read_pdf('largest_2006.pdf', pages=1, area=[64, 90, 1005, 806])\n",
    "raw_fire_array.append(largest_2006_1)\n",
    "largest_2006_2 = tabula.read_pdf('largest_2006.pdf', pages=2, area=[64, 90, 1005, 806])\n",
    "raw_fire_array.append(largest_2006_2)\n",
    "largest_2006_3 = tabula.read_pdf('largest_2006.pdf', pages=3, area=[64, 90, 1005, 806])\n",
    "raw_fire_array.append(largest_2006_3)\n",
    "largest_2006_4 = tabula.read_pdf('largest_2006.pdf', pages=4, area=[64, 90, 1005, 633])\n",
    "raw_fire_array.append(largest_2006_4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2007 3 pages\n",
    "largest_2007_1 = tabula.read_pdf('largest_2007.pdf', pages=1, area=[97, 86, 973, 793])\n",
    "raw_fire_array.append(largest_2007_1)\n",
    "largest_2007_2 = tabula.read_pdf('largest_2007.pdf', pages=2, area=[97, 86, 973, 793])\n",
    "raw_fire_array.append(largest_2007_2)\n",
    "largest_2007_3 = tabula.read_pdf('largest_2007.pdf', pages=3, area=[97, 86, 973, 634])\n",
    "raw_fire_array.append(largest_2007_3)\n",
    "#2008 6 pages\n",
    "largest_2008_1 = tabula.read_pdf('largest_2008.pdf', pages=1, area=[69, 86, 1034, 804])\n",
    "raw_fire_array.append(largest_2008_1)\n",
    "largest_2008_2 = tabula.read_pdf('largest_2008.pdf', pages=2, area=[69, 86, 1034, 804])\n",
    "raw_fire_array.append(largest_2008_2)\n",
    "largest_2008_3 = tabula.read_pdf('largest_2008.pdf', pages=3, area=[69, 86, 1034, 804])\n",
    "raw_fire_array.append(largest_2008_3)\n",
    "largest_2008_4 = tabula.read_pdf('largest_2008.pdf', pages=4, area=[69, 86, 1034, 804])\n",
    "raw_fire_array.append(largest_2008_4)\n",
    "largest_2008_5 = tabula.read_pdf('largest_2008.pdf', pages=5, area=[69, 86, 1034, 804])\n",
    "raw_fire_array.append(largest_2008_5)\n",
    "largest_2008_6 = tabula.read_pdf('largest_2008.pdf', pages=1, area=[69, 86, 1034, 260])\n",
    "raw_fire_array.append(largest_2008_6)\n",
    "#2009 3 pages\n",
    "largest_2009_1 = tabula.read_pdf('largest_2009.pdf', pages=1, area=[72, 199, 1052, 780])\n",
    "raw_fire_array.append(largest_2009_1)\n",
    "largest_2009_2 = tabula.read_pdf('largest_2009.pdf', pages=2, area=[72, 89, 1052, 788])\n",
    "raw_fire_array.append(largest_2009_2)\n",
    "largest_2009_3 = tabula.read_pdf('largest_2009.pdf', pages=3, area=[72, 89, 1052, 390])\n",
    "raw_fire_array.append(largest_2009_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2010 2 pages\n",
    "largest_2010_1 = tabula.read_pdf('largest_2010.pdf', pages=1, area=[69, 435, 1052, 780])\n",
    "raw_fire_array.append(largest_2010_1)\n",
    "largest_2010_2 = tabula.read_pdf('largest_2010.pdf', pages=2, area=[69, 88, 1052, 738])\n",
    "raw_fire_array.append(largest_2010_2)\n",
    "#2011 2 pages\n",
    "largest_2011_1 = tabula.read_pdf('largest_2011.pdf', pages=1, area=[86, 117, 1037, 815])\n",
    "raw_fire_array.append(largest_2011_1)\n",
    "largest_2011_2 = tabula.read_pdf('largest_2011.pdf', pages=2, area=[86, 121, 1037, 731])\n",
    "raw_fire_array.append(largest_2011_2)\n",
    "#2013 2 pages (remember 2012 link is broken)\n",
    "largest_2013_1 = tabula.read_pdf('largest_2013.pdf', pages=1, area=[102, 90, 960, 785])\n",
    "raw_fire_array.append(largest_2013_1)\n",
    "largest_2013_2 = tabula.read_pdf('largest_2013.pdf', pages=2, area=[102, 50, 960, 417])\n",
    "raw_fire_array.append(largest_2013_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2014 2 pages\n",
    "largest_2014_1 = tabula.read_pdf('largest_2014.pdf', pages=1, area=[220, 155, 1658, 1332])\n",
    "raw_fire_array.append(largest_2014_1)\n",
    "largest_2014_2 = tabula.read_pdf('largest_2014.pdf', pages=2, area=[220, 155, 1658, 818])\n",
    "raw_fire_array.append(largest_2014_2)\n",
    "#2015 3 pages\n",
    "largest_2015_1 = tabula.read_pdf('largest_2015.pdf', pages=1, area=[43, 93, 1058, 717])\n",
    "raw_fire_array.append(largest_2015_1)\n",
    "largest_2015_2 = tabula.read_pdf('largest_2015.pdf', pages=2, area=[43, 125, 1058, 670])\n",
    "raw_fire_array.append(largest_2015_2)\n",
    "largest_2015_3 = tabula.read_pdf('largest_2015.pdf', pages=3, area=[43, 107, 1058, 346])\n",
    "raw_fire_array.append(largest_2015_3)\n",
    "#2016 3 pages\n",
    "largest_2016_1 = tabula.read_pdf('largest_2016.pdf', pages=1, area=[38, 162, 1061, 792])\n",
    "raw_fire_array.append(largest_2016_1)\n",
    "largest_2016_2 = tabula.read_pdf('largest_2016.pdf', pages=2, area=[38, 177, 1061, 786])\n",
    "raw_fire_array.append(largest_2016_2)\n",
    "largest_2016_3 = tabula.read_pdf('largest_2016.pdf', pages=3, area=[38, 100, 1061, 372])\n",
    "raw_fire_array.append(largest_2016_3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Messy DataFrame Example\n",
    "\n",
    "The table is practically unreadable, many problems to fix such as combined columns, NaN/missing columns, useless rows, and incorrect/missing column names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Unnamed: 0</th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>DATE</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "      <th>ACRES BURNED</th>\n",
       "      <th>VEG.</th>\n",
       "      <th>Unnamed: 7</th>\n",
       "      <th>STRUCTURESFATALITIES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>INC. # COUNTY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FIRE NAME</td>\n",
       "      <td>START CONT. DPA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CDF OTHER TOTAL</td>\n",
       "      <td>TYPE</td>\n",
       "      <td>CAUSE</td>\n",
       "      <td>DEST. DAM. FIRE CIVIL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BDF-3917 SAN BERNARDINO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UPPER SANTA ANA</td>\n",
       "      <td>03/25/04 03/26/04 USFS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350 350</td>\n",
       "      <td>BT</td>\n",
       "      <td>ESCAPE</td>\n",
       "      <td>1 0 0 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SHF-000443 TEHAMA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SHF ASSIST (HAMILTON)</td>\n",
       "      <td>04/09/04 04/12/04 USFS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>301 301</td>\n",
       "      <td>T</td>\n",
       "      <td>HUMAN</td>\n",
       "      <td>0 0 0 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RRU-032913 RIVERSIDE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PLEASURE FIRE</td>\n",
       "      <td>04/25/04 04/26/04 CDF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2,464 2,464</td>\n",
       "      <td>GB</td>\n",
       "      <td>VEHICLE</td>\n",
       "      <td>9 0 0 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MVU-003337 SAN DIEGO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ASSIST #31 (INDIA FIRE)</td>\n",
       "      <td>05/02/04 05/03/04 MIL.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2,040 2,040</td>\n",
       "      <td>BT</td>\n",
       "      <td>UI</td>\n",
       "      <td>0 0 0 0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Unnamed: 0  Unnamed: 1               Unnamed: 2  \\\n",
       "0            INC. # COUNTY         NaN                FIRE NAME   \n",
       "1  BDF-3917 SAN BERNARDINO         NaN          UPPER SANTA ANA   \n",
       "2        SHF-000443 TEHAMA         NaN    SHF ASSIST (HAMILTON)   \n",
       "3     RRU-032913 RIVERSIDE         NaN            PLEASURE FIRE   \n",
       "4     MVU-003337 SAN DIEGO         NaN  ASSIST #31 (INDIA FIRE)   \n",
       "\n",
       "                     DATE  Unnamed: 4     ACRES BURNED  VEG. Unnamed: 7  \\\n",
       "0         START CONT. DPA         NaN  CDF OTHER TOTAL  TYPE      CAUSE   \n",
       "1  03/25/04 03/26/04 USFS         NaN          350 350    BT     ESCAPE   \n",
       "2  04/09/04 04/12/04 USFS         NaN          301 301     T      HUMAN   \n",
       "3   04/25/04 04/26/04 CDF         NaN      2,464 2,464    GB    VEHICLE   \n",
       "4  05/02/04 05/03/04 MIL.         NaN      2,040 2,040    BT         UI   \n",
       "\n",
       "    STRUCTURESFATALITIES  \n",
       "0  DEST. DAM. FIRE CIVIL  \n",
       "1                1 0 0 0  \n",
       "2                0 0 0 0  \n",
       "3                9 0 0 0  \n",
       "4                0 0 0 0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This is an example of our dataframe before cleaning\n",
    "largest_2004_1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning/Wrangling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### UNIVERSAL VARIABLES\n",
    "Here we standardize our column names to be easily identifiable and recognizable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Row names for cleaned tables\n",
    "ROWS_N = ['INCIDENT #', 'COUNTY/UNIT', 'FIRE_NAME', 'START_DATE', \n",
    "          'CONT_DATE', 'ORIGIN_DPT', 'BURNED_TOTAL', 'VEG_TYPE', \n",
    "          'CAUSE', 'STRUCT_DEST', 'STRUCT_DAM', 'FATALITY_FIRE', \n",
    "          'FATALITY_CIVIL']\n",
    "STRUCT_ROW = ['STRUCT_DEST', 'STRUCT_DAM']\n",
    "FATAL_ROW = ['FATALITY_FIRE', 'FATALITY_CIVIL']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### HELPER FUNCTIONS\n",
    "Our data is very unorganized, so we make use of several helper functions detailed below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "NOTE: this only works for fatality and structural columns\n",
    "\n",
    "column_separator(df, col, new_names)\n",
    "\n",
    "Separates one column into two (if values are separated by spaces)\n",
    "\n",
    "params:\n",
    "df : DataFrame to change\n",
    "col : column name to split\n",
    "new_names : array of 2 new names for columns\n",
    "\n",
    "returns:\n",
    "pandas DataFrame with changes applied\n",
    "\"\"\"\n",
    "def column_separator(df, col, new_names):\n",
    "    column = df[col].astype(str).apply(lambda x : x.split(' '))\n",
    "    left_arr = []\n",
    "    right_arr = []\n",
    "    #uncomment below to see actual array values\n",
    "    #display(column)\n",
    "    \n",
    "    #iterate through values to separate them\n",
    "    for val in column:\n",
    "        #edge case array is len 1\n",
    "        if len(val) == 1:\n",
    "            left_arr.append(np.nan)\n",
    "            right_arr.append(np.nan)\n",
    "        #normal case array len 2\n",
    "        elif len(val) == 2:\n",
    "            left_arr.append(val[0])\n",
    "            right_arr.append(val[1])\n",
    "        #edge case array is bigger than expected\n",
    "        else:\n",
    "            left_arr.append('error')\n",
    "            right_arr.append('error')\n",
    "    #now make new column names and remove the old one\n",
    "    #display(left_arr)\n",
    "    #display(right_arr)\n",
    "    ans_df = df.drop(col, axis=1)\n",
    "    ans_df[new_names[0]] = left_arr\n",
    "    ans_df[new_names[1]] = right_arr\n",
    "    return ans_df\n",
    "\"\"\"\n",
    "Separates one column into two columns, values must be separated by spaces; drops the original column at iloc index\n",
    "\n",
    "params:\n",
    "\n",
    "df : the dataframe to modify\n",
    "index : the index of the column as given by iloc indexing (int)\n",
    "new_names : the new names for the two new columns created (array size 2)\n",
    "front : determines whether to separate the value from the front or from the back (boolean, optional):\n",
    "\n",
    "Example:\n",
    "Consider the following string:\n",
    "Apple Cinnamon Bannana\n",
    "\n",
    "front = True will give\n",
    "[Apple] [Cinnamon Bannana]\n",
    "\n",
    "front = False will give\n",
    "[Apple Cinnamon] [Bannana]\n",
    "\n",
    "returns:\n",
    "pandas DataFrame with changes applied\n",
    "\"\"\"\n",
    "\n",
    "def robust_col_sep(df, index, new_names, front=True):\n",
    "    column = df.iloc[:, index].astype(str).apply(lambda x : x.split(' '))\n",
    "    left_arr = []\n",
    "    right_arr = []\n",
    "    \n",
    "    #display(column)\n",
    "    #iterate through column vals\n",
    "    for val in column:\n",
    "        if front:\n",
    "            left_arr.append(val[0])\n",
    "            right_arr.append(' '.join(val[1:]))\n",
    "        #if not front\n",
    "        else:\n",
    "            left_arr.append(' '.join(val[:-1]))\n",
    "            right_arr.append(val[-1])\n",
    "    #make new cols and drop old\n",
    "    ans_df = df.drop(df.columns[index], axis=1)\n",
    "    #add right column first\n",
    "    ans_df.insert(index, new_names[1], right_arr)\n",
    "    ans_df.insert(index, new_names[0], left_arr)\n",
    "    return ans_df\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "Cleans the cause column and consolidates labels\n",
    "\n",
    "params:\n",
    "\n",
    "val: the string value for the cause\n",
    "\n",
    "Example:\n",
    "Consider the following strings:\n",
    "\"Playing with fire\", \"p/w/f\", \"play/w/fire\"\n",
    "These all point to the same cause, however we want to create a standarddized cause\n",
    "to perform analysis\n",
    "\n",
    "We then group all of these unique values and change them to a standard string:\n",
    "\"Playing with fire\"\n",
    "\n",
    "returns:\n",
    "new standardized cause\n",
    "\"\"\"\n",
    "def cause_cleaner(val):\n",
    "    string = str(val).lower().strip(' ').strip('.')\n",
    "    #nan\n",
    "    nan_str = ['na', 'nan', 'n/a', '']\n",
    "    #under investigation\n",
    "    ui_str = ['under invest', 'ui']\n",
    "    #undetermined\n",
    "    undet_str = ['undetermined', 'undet']\n",
    "    #equipment\n",
    "    eqt_str = ['equipment', 'equip', 'equip. use', 'welding', 'weadeater']\n",
    "    #miscellaneous\n",
    "    misc_str = ['misc', 'miscellaneous', 'other', 'human/other']\n",
    "    #electrical\n",
    "    elec_str = ['elec. power', 'elec. pow', 'powerlines', 'powerline', 'power equip', 'electrical power']\n",
    "    #shooter\n",
    "    shoot_str = ['shooter', 'shooting', 'munition', 'ammunition', 'shooters']\n",
    "    #playing with fire\n",
    "    pwf_str = ['play/w/fire', 'p/w/f', 'playing with fire']\n",
    "    #transportation\n",
    "    transport_str = ['railroad', 'vehicle']\n",
    "    #control burned\n",
    "    control_str = ['escape control', 'escape', 'control burn', 'prescribed']\n",
    "    #debris\n",
    "    debris_str = ['debris', 'debris burning']\n",
    "    #campfire\n",
    "    camp_str = ['campfire', 'rekindle']\n",
    "    if string in nan_str:\n",
    "        return np.nan\n",
    "    elif string in ui_str:\n",
    "        return 'Under Investigation'\n",
    "    elif string in undet_str:\n",
    "        return 'Undetermined'\n",
    "    elif string in eqt_str:\n",
    "        return 'Equipment'\n",
    "    elif string in misc_str:\n",
    "        return 'Miscellaneous'\n",
    "    elif string in elec_str:\n",
    "        return 'Electrical'\n",
    "    elif string in shoot_str:\n",
    "        return 'Guns'\n",
    "    elif string in pwf_str:\n",
    "        return 'Playing With Fire'\n",
    "    elif string in transport_str:\n",
    "        return 'Transportation'\n",
    "    elif string in control_str:\n",
    "        return 'Controlled Burn'\n",
    "    elif string in debris_str:\n",
    "        return 'Debris Burning'\n",
    "    elif string in camp_str:\n",
    "        return 'Camping'\n",
    "    else:\n",
    "        return string.title()\n",
    "\n",
    "\"\"\"\n",
    "Extracts the value from a certain cell and moves it left or right, deleting the value that was in the original cell\n",
    "\n",
    "params:\n",
    "df: dataframe to edit\n",
    "r: row containing value using iloc\n",
    "c: column containing value using iloc\n",
    "item: the item to transpose\n",
    "d: direction to move data \"left\" \"right\"\n",
    "\n",
    "returns:\n",
    "dataframe object with changes applied\n",
    "\"\"\"\n",
    "def transposer(df, r, c, item, d):\n",
    "    frame = df.copy()\n",
    "    frame.iloc[r,c] = frame.iloc[r,c].replace(item, '')\n",
    "    if frame.iloc[r,c] == df.iloc[r,c]:\n",
    "        frame.iloc[r,c] = df.iloc[r,c]\n",
    "        return frame\n",
    "    else:\n",
    "        if 'left' in d:\n",
    "            frame.iloc[r,c-1] = item\n",
    "        elif 'right' in d:\n",
    "            frame.iloc[r,c+1] = item\n",
    "        else:\n",
    "            raise Exception('please use left or right')\n",
    "        return frame\n",
    "\n",
    "############################################################################\n",
    "#                           APPLY HELPER FUNCTIONS                         #\n",
    "############################################################################\n",
    "\n",
    "'''\n",
    "APPLY HELPER FUNCTION (used in Series.apply() only)\n",
    "\n",
    "Takes in an array with year and DOY (Day Of Year) and converts it to a datetime object.\n",
    "This function is needed due to odd dating format in SQL table\n",
    "'''\n",
    "def datetime_convert_sql(year_day_array):\n",
    "    year = int(year_day_array[0])\n",
    "    #issue with nan values not converting to int values\n",
    "    try:\n",
    "        day = int(year_day_array[1].split('.')[0])\n",
    "        ans = datetime.datetime(year, 1, 1) + datetime.timedelta(days=day-1)\n",
    "    except ValueError:\n",
    "        ans = np.nan\n",
    "    return ans\n",
    "'''\n",
    "APPLY HELPER FUNCTION (used in Series.apply() only)\n",
    "\n",
    "Takes in a string in the format MM/DD/YY and converts it to a datetime object\n",
    "This function is needed due to YY format and NaN error handling\n",
    "'''\n",
    "def datetime_convert_pdf(date):\n",
    "    date_arr = date.split('/')\n",
    "    if len(date_arr) == 3:\n",
    "        try:\n",
    "            return datetime.datetime(int('20'+date_arr[2].split('.')[0]), int(date_arr[0]), int(date_arr[1]))\n",
    "        except ValueError:\n",
    "            return np.nan\n",
    "    else:\n",
    "        return np.nan\n",
    "'''\n",
    "APPLY HELPER FUNCTION (used in Series.apply() only)\n",
    "\n",
    "Takes in a string value and converts to integer value\n",
    "This function is needed for NaN value handleing to join PDF table and SQL table\n",
    "'''\n",
    "def int_convert(val):\n",
    "    try:\n",
    "        ans = int(val.replace(\",\", \"\"))\n",
    "    except:\n",
    "        ans = 0\n",
    "    return ans\n",
    "\n",
    "'''\n",
    "APPLY HELPER FUNCTION (used in Series.apply() only)\n",
    "\n",
    "Takes in a string and list of counties and checks if string c contains any of the counties in c_lst\n",
    "'''\n",
    "def county_cleaner(c, c_lst):\n",
    "    #remove any dashes\n",
    "    string = c.split(\"-\")[0]\n",
    "    #remove any slashes\n",
    "    string = c.split(\"/\")[0]\n",
    "    #standardize\n",
    "    string = string.title().strip(\" \")\n",
    "    #use n_grams to match to potential couny\n",
    "    string_split = string.split(\" \")\n",
    "    for i in np.arange(len(string_split)+1):\n",
    "        n_gram = \" \".join(string_split[0:i]) + \" County\"\n",
    "        if n_gram in c_lst:\n",
    "            return n_gram\n",
    "    return np.nan\n",
    "\n",
    "'''\n",
    "APPLY HELPER FUNCTION (used in Series.apply() only)\n",
    "\n",
    "Takes in a string veg and cleans out invalid strings\n",
    "'''\n",
    "def veg_cleaner(veg):\n",
    "    string = str(veg).lower().replace(' ', '').replace(',', '').replace('.', '')\n",
    "    #check null\n",
    "    veg_str = ['na', 'n/a', 'nan', '']\n",
    "    if string in veg_str:\n",
    "        return np.nan\n",
    "    try:\n",
    "        int(veg)\n",
    "        return np.nan\n",
    "    except ValueError:\n",
    "        if len(veg) > 4:\n",
    "            return np.nan\n",
    "        \n",
    "    return string.upper()\n",
    "\n",
    "'''\n",
    "APPLY HELPER FUNCTION (used in Series.apply() only)\n",
    "\n",
    "Categorizes the burned total into 3 categories\n",
    "'''\n",
    "def burn_cat(val):\n",
    "    if val < 100:\n",
    "        return 0\n",
    "    elif val < 5000:\n",
    "        return 1\n",
    "    else:\n",
    "        return 2\n",
    "\n",
    "'''\n",
    "APPLY HELPER FUNCTION (used in Series.apply() only)\n",
    "\n",
    "Removes numbers from county column \n",
    "'''\n",
    "def resolve_county(s):\n",
    "    #check if string is a number\n",
    "    try:\n",
    "        s_int = int(s)\n",
    "        c_nm = number_county_dict[s_int]\n",
    "        return c_nm\n",
    "    #not a number\n",
    "    except ValueError:\n",
    "        c_nm = s.split('/')[0]\n",
    "        c_nm = c_nm.title() + ' County'\n",
    "        return c_nm\n",
    "\n",
    "'''\n",
    "APPLY HELPER FUNCTION (used in Series.apply() only)\n",
    "\n",
    "Resolves different null value types and changes values from string to int\n",
    "'''\n",
    "def fatality_cleaner(val):\n",
    "    string = str(val).split('.')[0]\n",
    "    null_str = ['nan', 'na', 'Unnamed: 14','Unnamed: 15', 'n/a']\n",
    "    if string in null_str:\n",
    "        return np.nan\n",
    "    return int(string)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SQL TABLE CLEANING\n",
    "\n",
    "The SQL table is largely clean already, we will just drop a few extra columns and convert the strange dates into datetime objects as well as clean up the `FIRE_SIZE` column into `int` format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "fires = fires[fires['STATE'] == 'CA']\n",
    "fires = fires.drop(columns=fires.columns[[1, 2, 3, 4, 5, 6, 7, 8, 9, 12, 14, 15, 16, 17, 18, 24, 25, 32, 33, 36, 37, 38]])\n",
    "#preparing for date conversion\n",
    "fires['START_DATE'] = fires[['FIRE_YEAR', 'DISCOVERY_DOY']].astype(str).apply(lambda x: ' '.join(x).split(' '), axis=1)\n",
    "fires['END_DATE'] = fires[['FIRE_YEAR', 'CONT_DOY']].astype(str).apply(lambda x: ' '.join(x).split(' '), axis=1)\n",
    "#date conversion\n",
    "fires['START_DATE'] = fires.START_DATE.apply(datetime_convert_sql)\n",
    "fires['END_DATE'] = fires.END_DATE.apply(datetime_convert_sql)\n",
    "fires = fires.drop(['FIRE_YEAR', 'DISCOVERY_DATE', 'DISCOVERY_DOY', \n",
    "                    'CONT_DOY'], axis=1)\n",
    "fires.FIRE_SIZE = fires.FIRE_SIZE.astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PDF TABLE CLEANING\n",
    "\n",
    "Mostly cleaned datasets anyways, largest issue is the combination of `County/Unit` and `Fire Name`. Normally, I would fix these issues by splitting on spaces (' ') but some departments and fire names contain more than one word so this method will not work. There are two ideas I have to solve this issues but it requires to add all the datasets in first\n",
    "\n",
    "This issue does not happen with all years which means some will have correct separation between `County/Unit` and `Fire Name` which means we can do one of two:\n",
    "1. Once we have all datasets combined, we can make a list of all counties/units and search for them in each grouped string\n",
    "2. There seems to be some correlation between the first three letters of Incident # and the county/unit, we can map the county from the Incident # (Incident # is fairly complete and easy to extract)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2000\n",
    "#page 1\n",
    "#columns are rows, add correct columns, superfluous col\n",
    "clean_2000_1 = largest_2000_1.append(largest_2000_1.columns.to_series(), ignore_index=True)\n",
    "clean_2000_1 = clean_2000_1.drop(['100%', 'Unnamed: 7', '400'], axis=1)\n",
    "clean_2000_1.columns = ROWS_N\n",
    "clean_2000_1.loc[22, 'CONT_DATE'] = clean_2000_1.loc[22, 'CONT_DATE'].split('.')[0]\n",
    "clean_2000_1.iloc[22,7] = np.nan\n",
    "clean_2000_1.iloc[22,9] = np.nan\n",
    "clean_2000_1.iloc[22,10] = np.nan\n",
    "clean_2000_1.iloc[22,11] = np.nan\n",
    "clean_2000_1.iloc[22,12] = np.nan\n",
    "#page 2\n",
    "#columns are a row, add correct cols, structural cols connected\n",
    "clean_2000_2 = largest_2000_2.dropna(subset=['08/27/00'])\n",
    "clean_2000_2 = clean_2000_2.drop(['Unnamed: 13', 'Unnamed: 7', '8,084'], axis=1)\n",
    "clean_2000_2 = clean_2000_2.append(clean_2000_2.columns.to_series(), ignore_index=True)\n",
    "clean_2000_2['x'] = np.nan\n",
    "clean_2000_2['y'] = np.nan\n",
    "clean_2000_2.columns = ROWS_N\n",
    "clean_2000_2['INCIDENT #'] = clean_2000_2['INCIDENT #'].apply(lambda x: x.split(' ')[0])\n",
    "\n",
    "#2001\n",
    "#page 1\n",
    "# columns and row 0 useless, col 11 useless\n",
    "# structure and fatality rows are stuck\n",
    "# drop acres burned broken (except total)\n",
    "clean_2001_1 = largest_2001_1.drop(0).drop(['Unnamed: 11', '%', 'ACRES BURNED'], axis=1)\n",
    "clean_2001_1 = column_separator(clean_2001_1, 'STRUCTURES', STRUCT_ROW)\n",
    "clean_2001_1 = column_separator(clean_2001_1, 'FATALITIES', FATAL_ROW)\n",
    "clean_2001_1.columns = ROWS_N\n",
    "comb = clean_2001_1.iloc[21, 4].split(' ')\n",
    "clean_2001_1.iloc[21, 4] = comb[0]\n",
    "clean_2001_1.iloc[21, 5] = comb[1]\n",
    "# page 2\n",
    "#columns and row 0 useless, % col 3, 13 useless\n",
    "#drop nan start date\n",
    "#connected columns : unit/name structures fatalities\n",
    "clean_2001_2 = largest_2001_2.drop(0).drop(['%', 'Unnamed: 3', 'ACRES BURNED', 'Unnamed: 13'], axis = 1)\n",
    "clean_2001_2 = clean_2001_2.dropna(subset=['DATE'])\n",
    "clean_2001_2.insert(2, 'placeholder', -1)\n",
    "clean_2001_2 = column_separator(clean_2001_2, 'STRUCTURES', STRUCT_ROW)\n",
    "clean_2001_2 = column_separator(clean_2001_2, 'FATALITIES', FATAL_ROW)\n",
    "clean_2001_2.columns = ROWS_N\n",
    "clean_2001_2\n",
    "\n",
    "#2002\n",
    "#page 1\n",
    "#column is row\n",
    "#connected columns: unit/name\n",
    "clean_2002_1 = largest_2002_1.drop(['100%', '320', 'Unnamed: 8'], axis=1)\n",
    "clean_2002_1 = clean_2002_1.append(clean_2002_1.columns.to_series(), ignore_index=True)\n",
    "clean_2002_1.columns = ROWS_N\n",
    "clean_2002_1.loc[20,'CONT_DATE'] = clean_2002_1.loc[20,'CONT_DATE'].split('.')[0]\n",
    "\n",
    "#page 2\n",
    "#column is row\n",
    "clean_2002_2 = largest_2002_2.drop(['100%', 'Unnamed: 7', '670'], axis=1)\n",
    "clean_2002_2 = clean_2002_2.append(clean_2002_2.columns.to_series(), ignore_index=True)\n",
    "clean_2002_2.columns = ROWS_N\n",
    "clean_2002_2\n",
    "#page 3\n",
    "#column is row\n",
    "#drop na dates\n",
    "#combined columns: name/date structure fatality\n",
    "clean_2002_3 = largest_2002_3.drop(['Unnamed: 6', '1,200'], axis=1)\n",
    "clean_2002_3 = column_separator(clean_2002_3, 'Unnamed: 11', STRUCT_ROW)\n",
    "clean_2002_3 = column_separator(clean_2002_3, 'Unnamed: 12', FATAL_ROW)\n",
    "clean_2002_3 = clean_2002_3.dropna(subset=['Mountain 09/09/02'])\n",
    "clean_2002_3.columns = ROWS_N\n",
    "#procedure for separating firename and date\n",
    "clean_2002_3.START_DATE = clean_2002_3.FIRE_NAME.apply(lambda x: x.split(' ')[-1])\n",
    "clean_2002_3.FIRE_NAME = clean_2002_3.FIRE_NAME.apply(lambda x: ' '.join(x.split(' ')[:-1]))\n",
    "\n",
    "#2003\n",
    "#page 1\n",
    "largest_2003_1\n",
    "clean_2003_1 = robust_col_sep(largest_2003_1, 0, ['apples', 'bannanas'])\n",
    "clean_2003_1 = robust_col_sep(clean_2003_1, 4, ['oranges', 'strawberries'])\n",
    "clean_2003_1 = clean_2003_1.drop(clean_2003_1.columns[[2, 6, 8, 9, 13]], axis=1)\n",
    "clean_2003_1 = clean_2003_1.drop(0)\n",
    "clean_2003_1 = robust_col_sep(clean_2003_1, 9, ['mangos', 'peaches'])\n",
    "clean_2003_1 = robust_col_sep(clean_2003_1, 11, ['grapefruit', 'lemons'])\n",
    "#clean_2003_1 = clean_2003_1.drop(clean_2003_1.columns[[1]], axis=1)\n",
    "clean_2003_1.columns = ROWS_N\n",
    "clean_2003_1 = transposer(clean_2003_1, 26, 0, \"Merced\", 'right')\n",
    "clean_2003_1 = transposer(clean_2003_1, 32, 0, 'Mariposa', 'right')\n",
    "#page 2\n",
    "clean_2003_2 = robust_col_sep(largest_2003_2, 0, ['1', '2']) \n",
    "clean_2003_2 = robust_col_sep(clean_2003_2, 4, ['3', '4'])\n",
    "clean_2003_2 = robust_col_sep(clean_2003_2, 13, ['13', '14'])\n",
    "clean_2003_2 = robust_col_sep(clean_2003_2, 15, ['15', '16'])\n",
    "clean_2003_2 = robust_col_sep(clean_2003_2, 11, ['11', '12'])\n",
    "clean_2003_2 = clean_2003_2.drop(0)\n",
    "clean_2003_2 = clean_2003_2.drop(columns=clean_2003_2.columns[[3, 6, 8, 9, 13]])\n",
    "clean_2003_2.columns = ROWS_N\n",
    "clean_2003_2 = transposer(clean_2003_2, 6, 3, '10/03/03', 'right')\n",
    "clean_2003_2 = transposer(clean_2003_2, 6, 2, '07/20/03', 'right')\n",
    "clean_2003_2 = transposer(clean_2003_2, 46, 6, 'Human', 'right')\n",
    "clean_2003_2 = transposer(clean_2003_2, 46, 7, 'Human', 'right')\n",
    "clean_2003_2 = transposer(clean_2003_2, 46, 6, 'GWAP', 'right')\n",
    "#page 3\n",
    "clean_2003_3 = largest_2003_3.drop(range(7,12))\n",
    "clean_2003_3 = robust_col_sep(clean_2003_3, 0, [0, 1])\n",
    "clean_2003_3 = robust_col_sep(clean_2003_3, 5, [5, 6])\n",
    "clean_2003_3 = robust_col_sep(clean_2003_3, 9, [9, 10])\n",
    "clean_2003_3 = robust_col_sep(clean_2003_3, 14, [14, 15])\n",
    "clean_2003_3 = robust_col_sep(clean_2003_3, 16, [16, 17])\n",
    "clean_2003_3 = clean_2003_3.drop(columns=clean_2003_3.columns[[2, 4, 8, 10, 11]])\n",
    "clean_2003_3 = clean_2003_3.drop(0)\n",
    "clean_2003_3.columns = ROWS_N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2004\n",
    "#page1\n",
    "clean_2004_1 = robust_col_sep(largest_2004_1, 0, ['0','1'])\n",
    "clean_2004_1 = robust_col_sep(clean_2004_1, 4, ['4','5'])\n",
    "clean_2004_1 = robust_col_sep(clean_2004_1, 5, ['5','6'])\n",
    "clean_2004_1 = robust_col_sep(clean_2004_1, 8, ['8','9'], front=False)\n",
    "abomination = clean_2004_1.STRUCTURESFATALITIES\n",
    "clean_2004_1['12'] = abomination.apply(lambda x: x.split(' ')[0])\n",
    "clean_2004_1['13'] = abomination.apply(lambda x: x.split(' ')[1])\n",
    "clean_2004_1['14'] = abomination.apply(lambda x: x.split(' ')[2])\n",
    "clean_2004_1['15'] = abomination.apply(lambda x: x.split(' ')[3])\n",
    "clean_2004_1 = clean_2004_1.drop(['STRUCTURESFATALITIES'], axis=1)\n",
    "clean_2004_1 = clean_2004_1.drop(0)\n",
    "clean_2004_1 = clean_2004_1.drop(columns=clean_2004_1.columns[[2, 7, 8]])\n",
    "clean_2004_1.columns = ROWS_N\n",
    "clean_2004_1 = transposer(clean_2004_1, 23, 4, 'USFS', 'right')\n",
    "clean_2004_1 = transposer(clean_2004_1, 23, 3, '06/26/04', 'right')\n",
    "clean_2004_1 = transposer(clean_2004_1, 23, 2, '06/26/04', 'right')\n",
    "clean_2004_1 = transposer(clean_2004_1, 25, 0, 'MARIPOSA', 'right')\n",
    "clean_2004_1 = transposer(clean_2004_1, 40, 4, 'CC/USFS', 'right')\n",
    "clean_2004_1 = transposer(clean_2004_1, 44, 4, 'USFS', 'right')\n",
    "clean_2004_1 = transposer(clean_2004_1, 44, 3, '07/23/04', 'right')\n",
    "clean_2004_1 = transposer(clean_2004_1, 44, 2, '07/20/04', 'right')\n",
    "#page2\n",
    "clean_2004_2 = robust_col_sep(largest_2004_2, 0, ['0', '1'])\n",
    "clean_2004_2 = robust_col_sep(clean_2004_2, 4, ['4', '5'])\n",
    "clean_2004_2 = robust_col_sep(clean_2004_2, 5, ['5', '6'])\n",
    "clean_2004_2 = robust_col_sep(clean_2004_2, 8, ['8', '9'])\n",
    "abomination = clean_2004_2.STRUCTURESFATALITIE\n",
    "clean_2004_2['12'] = abomination.apply(lambda x: x.split(' ')[0])\n",
    "clean_2004_2['13'] = abomination.apply(lambda x: x.split(' ')[1])\n",
    "clean_2004_2['14'] = abomination.apply(lambda x: x.split(' ')[2])\n",
    "clean_2004_2['15'] = 0\n",
    "clean_2004_2 = clean_2004_2.drop(['STRUCTURESFATALITIE'], axis=1)\n",
    "clean_2004_2 = clean_2004_2.drop([0, 38])\n",
    "clean_2004_2 = clean_2004_2.drop(columns=clean_2004_2.columns[[3, 7, 8]])\n",
    "clean_2004_2.columns = ROWS_N\n",
    "#37 pages left\n",
    "\n",
    "#2005\n",
    "#page1\n",
    "clean_2005_1 = robust_col_sep(largest_2005_1, 7, [7,8], front=False)\n",
    "clean_2005_1 = robust_col_sep(clean_2005_1, 9, [9,10])\n",
    "clean_2005_1 = robust_col_sep(clean_2005_1, 12, [12,13])\n",
    "clean_2005_1 = robust_col_sep(clean_2005_1, 14, [14,15])\n",
    "clean_2005_1 = clean_2005_1.drop(columns=clean_2005_1.columns[[1, 7, 11]])\n",
    "clean_2005_1 = clean_2005_1.drop(0)\n",
    "clean_2005_1.columns = ROWS_N\n",
    "#page2\n",
    "clean_2005_2 = robust_col_sep(largest_2005_2, 8, [8,9], front=False)\n",
    "clean_2005_2 = robust_col_sep(clean_2005_2, 10, [10,11])\n",
    "clean_2005_2 = robust_col_sep(clean_2005_2, 13, [13,14])\n",
    "clean_2005_2 = robust_col_sep(clean_2005_2, 15, [15,16])\n",
    "clean_2005_2 = clean_2005_2.drop(columns=clean_2005_2.columns[[1, 4, 8, 12]])\n",
    "clean_2005_2 = clean_2005_2.drop(0)\n",
    "clean_2005_2.columns = ROWS_N\n",
    "#page3\n",
    "clean_2005_3 = robust_col_sep(largest_2005_3, 7, [7,8], front=False)\n",
    "clean_2005_3 = robust_col_sep(clean_2005_3, 9, [9,10])\n",
    "clean_2005_3 = robust_col_sep(clean_2005_3, 13, [13,14])\n",
    "clean_2005_3 = robust_col_sep(clean_2005_3, 15, [15,16])\n",
    "clean_2005_3 = clean_2005_3.drop(columns=clean_2005_3.columns[[1, 7, 11, 12]])\n",
    "clean_2005_3 = clean_2005_3.drop(clean_2005_3.index[10:])\n",
    "clean_2005_3 = clean_2005_3.drop(0)\n",
    "clean_2005_3.columns = ROWS_N\n",
    "\n",
    "#2006\n",
    "#Special Case VEG TYPE and CAUSE\n",
    "#page1\n",
    "clean_2006_1 = robust_col_sep(largest_2006_1, 7, [7,8])\n",
    "clean_2006_1 = robust_col_sep(clean_2006_1, 8, [8,9])\n",
    "clean_2006_1 = robust_col_sep(clean_2006_1, 9, [9,10])\n",
    "clean_2006_1 = robust_col_sep(clean_2006_1, 13, [13,14])\n",
    "clean_2006_1 = robust_col_sep(clean_2006_1, 15, [15,16])\n",
    "clean_2006_1 = clean_2006_1.drop(columns=clean_2006_1.columns[[1, 7, 11, 12]])\n",
    "clean_2006_1 = clean_2006_1.drop(0)\n",
    "clean_2006_1.columns = ROWS_N\n",
    "#page2\n",
    "#Special case: COUNTY/UNIT and FIRE NAME\n",
    "clean_2006_2 = robust_col_sep(largest_2006_2, 8, [8,9], front=False)\n",
    "clean_2006_2 = robust_col_sep(clean_2006_2, 10, [10,11])\n",
    "clean_2006_2 = robust_col_sep(clean_2006_2, 13, [13,14])\n",
    "clean_2006_2 = robust_col_sep(clean_2006_2, 15, [15,16])\n",
    "clean_2006_2 = clean_2006_2.drop(columns=clean_2006_2.columns[[1, 4, 8, 12]])\n",
    "clean_2006_2 = clean_2006_2.drop(0)\n",
    "clean_2006_2.columns = ROWS_N\n",
    "#page3\n",
    "#Special case COUNTY/UNIT and FIRE NAME\n",
    "clean_2006_3 = robust_col_sep(largest_2006_3, 7, [7,8], front=False)\n",
    "clean_2006_3 = robust_col_sep(clean_2006_3, 9, [9,10])\n",
    "clean_2006_3 = robust_col_sep(clean_2006_3, 12, [12,13])\n",
    "clean_2006_3 = robust_col_sep(clean_2006_3, 14, [14,15])\n",
    "clean_2006_3 = clean_2006_3.drop(columns=clean_2006_3.columns[[1, 7, 11]])\n",
    "clean_2006_3 = clean_2006_3.drop(0)\n",
    "clean_2006_3.columns = ROWS_N\n",
    "#page4\n",
    "clean_2006_4 = robust_col_sep(largest_2006_4, 7, [7,8])\n",
    "clean_2006_4 = robust_col_sep(clean_2006_4, 8, [8,9])\n",
    "clean_2006_4 = robust_col_sep(clean_2006_4, 9, [9,10])\n",
    "clean_2006_4 = robust_col_sep(clean_2006_4, 14, [14,15])\n",
    "clean_2006_4 = robust_col_sep(clean_2006_4, 16, [16,17])\n",
    "clean_2006_4 = clean_2006_4.drop(columns=clean_2006_4.columns[[1,7,11,12,13]])\n",
    "clean_2006_4 = clean_2006_4.drop(clean_2006_4.index[[0,21,22]])\n",
    "clean_2006_4.columns = ROWS_N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_2007_1 = largest_2007_1.drop(11)\n",
    "clean_2007_1 = robust_col_sep(clean_2007_1,6, [6,7])\n",
    "clean_2007_1 = robust_col_sep(clean_2007_1,10, [10,11])\n",
    "clean_2007_1 = clean_2007_1.drop(index=0)\n",
    "clean_2007_1 = clean_2007_1.drop(columns='Unnamed: 11')\n",
    "clean_2007_1.columns = ROWS_N\n",
    "#display(clean_2007_1)\n",
    "#clean_2007_2\n",
    "clean_2007_2 = largest_2007_2\n",
    "clean_2007_2 = clean_2007_2.drop(columns='Unnamed: 9')\n",
    "clean_2007_2 = robust_col_sep(clean_2007_2,6, [6,7])\n",
    "clean_2007_2 = robust_col_sep(clean_2007_2,10, [10,11])\n",
    "clean_2007_2 = clean_2007_2.drop(columns=11)\n",
    "clean_2007_2 = clean_2007_2.drop(index=0)\n",
    "clean_2007_2.columns = ROWS_N\n",
    "#display(clean_2007_2)\n",
    "#clean_2007_3\n",
    "clean_2007_3 = largest_2007_3\n",
    "clean_2007_3 = clean_2007_3.drop(columns='Unnamed: 3')\n",
    "clean_2007_3 = clean_2007_3.drop(index=0)\n",
    "clean_2007_3 = robust_col_sep(clean_2007_3,6, [6,7])\n",
    "clean_2007_3 = clean_2007_3.drop(columns=7)\n",
    "clean_2007_3 = robust_col_sep(clean_2007_3,9, [9,10])\n",
    "clean_2007_3.columns = ROWS_N\n",
    "#display(clean_2007_3)\n",
    "#clean_2008_1\n",
    "clean_2008_1 = largest_2008_1\n",
    "clean_2008_1 = clean_2008_1.drop(index=0)\n",
    "clean_2008_1 = clean_2008_1.drop(columns='Unnamed: 3')\n",
    "clean_2008_1 = clean_2008_1.drop(index=12)\n",
    "clean_2008_1 = clean_2008_1.drop(index=32)\n",
    "clean_2008_1 = robust_col_sep(clean_2008_1,10, [10,11])\n",
    "clean_2008_1 = clean_2008_1.drop(columns='Unnamed: 8')\n",
    "clean_2008_1.columns = ROWS_N\n",
    "#display(clean_2008_1)\n",
    "#clean_2008_2\n",
    "clean_2008_2 = largest_2008_2\n",
    "clean_2008_2 = clean_2008_2.drop(columns=\"CON-\")\n",
    "clean_2008_2 = clean_2008_2.drop(index=0)\n",
    "clean_2008_2 = robust_col_sep(clean_2008_2, 1, [\"COUNTY/UNIT\", \"FIRE_NAME\"])\n",
    "clean_2008_2 = clean_2008_2.drop(columns=\"Unnamed: 3\")\n",
    "\n",
    "clean_2008_2.iloc[3, clean_2008_2.columns.get_loc('COUNTY/UNIT')] = \"SAN BENITO\"\n",
    "clean_2008_2.iloc[3, clean_2008_2.columns.get_loc('FIRE_NAME')] = \"BROWN COMPLEX\"\n",
    "\n",
    "clean_2008_2.iloc[13, clean_2008_2.columns.get_loc('COUNTY/UNIT')] = \"SHASTA/LASSEN\"\n",
    "clean_2008_2.iloc[13, clean_2008_2.columns.get_loc('FIRE_NAME')] = \"PETERSON COMPLEX\"\n",
    "\n",
    "clean_2008_2.iloc[27, clean_2008_2.columns.get_loc('COUNTY/UNIT')] = \"LAKE, MENDOCINO/TEHAMA\"\n",
    "clean_2008_2.iloc[27, clean_2008_2.columns.get_loc('FIRE_NAME')] = \"(MERGED WITH YOLLA BOLLY COMPLEX)\"\n",
    "\n",
    "clean_2008_2.iloc[30, clean_2008_2.columns.get_loc('COUNTY/UNIT')] = \"SANTA CLARA\"\n",
    "clean_2008_2.iloc[30, clean_2008_2.columns.get_loc('FIRE_NAME')] = \"HUMMINGBIRD\"\n",
    "\n",
    "clean_2008_2.iloc[32, clean_2008_2.columns.get_loc('COUNTY/UNIT')] = \"TRINITY\"\n",
    "clean_2008_2.iloc[32, clean_2008_2.columns.get_loc('FIRE_NAME')] = \"IRON COMPLEX & ALPS COMPLEX\"\n",
    "\n",
    "clean_2008_2 = clean_2008_2.drop(index=27)\n",
    "clean_2008_2 = clean_2008_2.drop(index=32)\n",
    "\n",
    "clean_2008_2.columns = ROWS_N\n",
    "\n",
    "clean_2008_2[\"BURNED_TOTAL\"] = clean_2008_2[\"VEG_TYPE\"]\n",
    "clean_2008_2[\"ORIGIN_DPT\"] = clean_2008_2[\"CONT_DATE\"]\n",
    "clean_2008_2['CONT_DATE'] = clean_2008_2['START_DATE'].apply(lambda x: ' '.join(x.split(' ')[1:]))\n",
    "clean_2008_2['START_DATE'] = clean_2008_2['START_DATE'].apply(lambda x: ''.join(x.split(' ')[0]))\n",
    "\n",
    "clean_2008_2.iloc[26, clean_2008_2.columns.get_loc('CAUSE')] = \"LIGHTNING\"\n",
    "\n",
    "clean_2008_2[\"VEG_TYPE\"] = clean_2008_2[\"CAUSE\"].apply(lambda x: ''.join(x.split(' ')[0]))\n",
    "clean_2008_2[\"CAUSE\"] = clean_2008_2[\"CAUSE\"].apply(lambda x: ''.join(x.split(' ')[1:]))\n",
    "clean_2008_2.iloc[26, clean_2008_2.columns.get_loc('VEG_TYPE')] = \"BGWT\"\n",
    "clean_2008_2.iloc[26, clean_2008_2.columns.get_loc('BURNED_TOTAL')] = 3000\n",
    "clean_2008_2.iloc[26, clean_2008_2.columns.get_loc('CAUSE')] = \"LIGHTNING\"\n",
    "\n",
    "clean_2008_2[\"STRUCT_DEST\"] = clean_2008_2[\"STRUCT_DAM\"].apply(lambda x: ''.join(x.split(' ')[0]))\n",
    "clean_2008_2[\"STRUCT_DAM\"] = clean_2008_2[\"STRUCT_DAM\"].apply(lambda x: ''.join(x.split(' ')[1:]))\n",
    "#display(clean_2008_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_2008_3 = largest_2008_3.drop(columns=largest_2008_1.columns[[1, 6, 7]])\n",
    "clean_2008_3 = clean_2008_3.drop(0)\n",
    "clean_2008_3 = robust_col_sep(clean_2008_3, 3, [3,4])\n",
    "clean_2008_3 = robust_col_sep(clean_2008_3, 9, [9,10])\n",
    "clean_2008_3.columns = ROWS_N\n",
    "#display(clean_2008_3)\n",
    "\n",
    "clean_2008_4 = largest_2008_4.drop(columns=largest_2008_4.columns[[1, 6, 7]])\n",
    "clean_2008_4 = clean_2008_4.drop(0)\n",
    "clean_2008_4 = robust_col_sep(clean_2008_4, 3, [3,4])\n",
    "clean_2008_4 = robust_col_sep(clean_2008_4, 9, [9,10])\n",
    "clean_2008_4.columns = ROWS_N\n",
    "#display(clean_2008_4)\n",
    "\n",
    "clean_2008_5 = largest_2008_5.drop(columns=largest_2008_5.columns[[1,4,7,8,]])\n",
    "clean_2008_5 = clean_2008_5.drop(0)\n",
    "clean_2008_5 = robust_col_sep(clean_2008_5, 3, [3,4])\n",
    "clean_2008_5 = robust_col_sep(clean_2008_5, 9, [9,10])\n",
    "clean_2008_5.columns = ROWS_N\n",
    "\n",
    "#do clean pg 6 after\n",
    "clean_2008_6 = largest_2008_6.drop(columns=largest_2008_6.columns[[1, 6, 7]])\n",
    "clean_2008_6 = clean_2008_6.drop([0,32,33])\n",
    "clean_2008_6 = robust_col_sep(clean_2008_6, 3, [3,4])\n",
    "clean_2008_6 = robust_col_sep(clean_2008_6, 9, [9,10])\n",
    "clean_2008_6.columns = ROWS_N\n",
    "#display(clean_2008_6)\n",
    "clean_2009_1 = largest_2009_1\n",
    "clean_2009_1 = clean_2009_1.drop(index=0)\n",
    "clean_2009_1 = clean_2009_1.drop(columns='Unnamed: 6')\n",
    "clean_2009_1 = clean_2009_1.drop(columns='ACRES BURNED')\n",
    "clean_2009_1 = robust_col_sep(clean_2009_1,4, [4,5])\n",
    "clean_2009_1 = clean_2009_1.drop(columns='Unnamed: 10')\n",
    "clean_2009_1 = robust_col_sep(clean_2009_1,8, [8,9])\n",
    "clean_2009_1 = clean_2009_1.drop(columns='Unnamed: 3')\n",
    "clean_2009_1 = clean_2009_1.drop([1,2,3,14])\n",
    "clean_2009_1 = robust_col_sep(clean_2009_1,9, [45,10])\n",
    "clean_2009_1 = robust_col_sep(clean_2009_1, 1, [1,2])\n",
    "clean_2009_1 = clean_2009_1.drop(columns=clean_2009_1.columns[1])\n",
    "clean_2009_1.columns = ROWS_N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_2009_2 = robust_col_sep(largest_2009_2, 1, [1,2 ])\n",
    "clean_2009_2 = robust_col_sep(clean_2009_2, 6, [6,7])\n",
    "clean_2009_2 = robust_col_sep(clean_2009_2, 14, [14,15])\n",
    "clean_2009_2 = clean_2009_2.drop(clean_2009_2.columns[[1,3, 5,9, 10]], axis=1)\n",
    "clean_2009_2 = clean_2009_2.drop(0)\n",
    "clean_2009_2.columns = ROWS_N\n",
    "#display (clean_2009_2)\n",
    "#cleaning 2009_3\n",
    "clean_2009_3 = robust_col_sep(largest_2009_3, 1, [1,2 ])\n",
    "clean_2009_3 = robust_col_sep(clean_2009_3, 4, [4,5])\n",
    "clean_2009_3 = robust_col_sep(clean_2009_3, 13, [13,14])\n",
    "clean_2009_3 = clean_2009_3.drop(clean_2009_3.columns[[1,7,8,12]], axis=1)\n",
    "clean_2009_3 = clean_2009_3.drop(0)\n",
    "clean_2009_3.columns = ROWS_N\n",
    "#display (clean_2009_3)\n",
    "#cleaning 2010_1\n",
    "clean_2010_1 = robust_col_sep(largest_2010_1, 1, [1,2 ])\n",
    "clean_2010_1 = robust_col_sep(clean_2010_1, 6, [6,7])\n",
    "clean_2010_1 = robust_col_sep(clean_2010_1, 14, [14,15])\n",
    "clean_2010_1 = clean_2010_1.drop(clean_2010_1.columns[[1,3,5,9,11]], axis=1)\n",
    "clean_2010_1 = clean_2010_1.drop(0)\n",
    "clean_2010_1.columns = ROWS_N\n",
    "#display(clean_2010_1)\n",
    "#cleaning 2010_2\n",
    "clean_2010_2 = robust_col_sep(largest_2010_2, 1, [1,2 ])\n",
    "clean_2010_2 = robust_col_sep(clean_2010_2, 4, [4,5])\n",
    "clean_2010_2 = robust_col_sep(clean_2010_2, 11, [11,12])\n",
    "clean_2010_2 = robust_col_sep(clean_2010_2, 14, [14,15])\n",
    "clean_2010_2 = clean_2010_2.drop(clean_2010_2.columns[[1,7,11]], axis=1)\n",
    "clean_2010_2 = clean_2010_2.drop(0)\n",
    "clean_2010_2.columns = ROWS_N\n",
    "#display(clean_2010_2)\n",
    "clean_2011_1 = robust_col_sep(largest_2011_1, 0, [0,1])\n",
    "clean_2011_1 = robust_col_sep(clean_2011_1, 1, [1,2])\n",
    "clean_2011_1 = clean_2011_1.drop([0, 1, 2, 9])\n",
    "clean_2011_1 = clean_2011_1.drop(columns = clean_2011_1.columns[[1, 7, 15]])\n",
    "clean_2011_1.columns = ROWS_N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Sayaka's Section 2011_2, 2013_1, 2013_2, 2014_1, 2014_2\n",
    "\n",
    "#cleaning 2011_2\n",
    "clean_2011_2 = robust_col_sep(largest_2011_2, 1,[1,2])\n",
    "clean_2011_2 = robust_col_sep(clean_2011_2, 9, [9, 10])\n",
    "clean_2011_2 = robust_col_sep(clean_2011_2, 15, [15, 16])\n",
    "clean_2011_2 = robust_col_sep(clean_2011_2, 18, [18, 19])\n",
    "clean_2011_2 = clean_2011_2.drop(columns=clean_2011_2.columns[[1, 4, 7, 9, 10,14,17]])\n",
    "clean_2011_2 = clean_2011_2.drop(0)\n",
    "clean_2011_2 = clean_2011_2.drop(1)\n",
    "clean_2011_2 = clean_2011_2.drop((range(34,39)))\n",
    "clean_2011_2.columns = ROWS_N\n",
    "#display(clean_2011_2)\n",
    "\n",
    "#cleaning 2013_1\n",
    "clean_2013_1 = robust_col_sep(largest_2013_1, 0,[0,1])\n",
    "clean_2013_1 = robust_col_sep(clean_2013_1, 5, [5, 6])\n",
    "clean_2013_1 = robust_col_sep(clean_2013_1, 8, [8, 9])\n",
    "clean_2013_1 = robust_col_sep(clean_2013_1, 13, [13, 14])\n",
    "clean_2013_1 = clean_2013_1.drop(columns=clean_2013_1.columns[[2, 4, 8, 9]])\n",
    "clean_2013_1 = clean_2013_1.drop(0)\n",
    "clean_2013_1 = clean_2013_1.drop(1)\n",
    "clean_2013_1 = clean_2013_1.drop(32)\n",
    "clean_2013_1 = clean_2013_1.drop(33)\n",
    "clean_2013_1.columns = ROWS_N\n",
    "#display(clean_2013_1)\n",
    "\n",
    "#cleaning 2013_2\n",
    "clean_2013_2 = largest_2013_2.drop(18)\n",
    "clean_2013_2 = clean_2013_2.drop(19)\n",
    "clean_2013_2 = clean_2013_2.drop(columns=clean_2013_2.columns[[7,8]])\n",
    "clean_2013_2.columns = ROWS_N\n",
    "#display(clean_2013_2)\n",
    "\n",
    "#cleaning 2014_1\n",
    "clean_2014_1 = robust_col_sep(largest_2014_1, 0,[0,1])\n",
    "clean_2014_1 = robust_col_sep(clean_2014_1, 5, [5, 6])\n",
    "clean_2014_1 = robust_col_sep(clean_2014_1, 8, [8, 9])\n",
    "clean_2014_1 = robust_col_sep(clean_2014_1, 13, [13, 14])\n",
    "clean_2014_1 = clean_2014_1.drop(columns=clean_2014_1.columns[[2, 4, 8, 9]])\n",
    "clean_2014_1 = clean_2014_1.drop(0)\n",
    "clean_2014_1 = clean_2014_1.drop(1)\n",
    "clean_2014_1 = clean_2014_1.drop(26)\n",
    "clean_2014_1.columns = ROWS_N\n",
    "#display(clean_2014_1)\n",
    "\n",
    "#cleaning 2014_2\n",
    "clean_2014_2 = robust_col_sep(largest_2014_2, 3,[3,4])\n",
    "clean_2014_2 = robust_col_sep(clean_2014_2, 6, [6, 7])\n",
    "clean_2014_2 = robust_col_sep(clean_2014_2, 11, [11, 12])\n",
    "clean_2014_2 = robust_col_sep(clean_2014_2, 13, [13, 14])\n",
    "clean_2014_2 = clean_2014_2.drop(columns=clean_2014_2.columns[[6,7,15]])\n",
    "clean_2014_2 = clean_2014_2.drop(0)\n",
    "clean_2014_2 = clean_2014_2.drop(19)\n",
    "clean_2014_2.columns = ROWS_N\n",
    "#display(clean_2014_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2015\n",
    "# Clean the 2015_page_1\n",
    "clean_2015_1 = largest_2015_1\n",
    "clean_2015_1 = clean_2015_1.drop(0)\n",
    "clean_2015_1.columns = ROWS_N\n",
    "clean_2015_1['ORIGIN_DPT'] = clean_2015_1['BURNED_TOTAL']\n",
    "clean_2015_1['BURNED_TOTAL'] = clean_2015_1['VEG_TYPE']\n",
    "clean_2015_1['VEG_TYPE'] = clean_2015_1['STRUCT_DEST']\n",
    "clean_2015_1['CAUSE'] = clean_2015_1['STRUCT_DAM']\n",
    "clean_2015_1 = column_separator(clean_2015_1, \"FATALITY_FIRE\", STRUCT_ROW)\n",
    "clean_2015_1['FATALITY_FIRE'] = np.nan\n",
    "# Reorder the columns\n",
    "clean_2015_1 = clean_2015_1.reindex(columns=ROWS_N)\n",
    "# Refill missing data (Some values lost during column separation)\n",
    "clean_2015_1.iloc[3, clean_2015_1.columns.get_loc('STRUCT_DEST')] = 2\n",
    "clean_2015_1.iloc[11, clean_2015_1.columns.get_loc('STRUCT_DEST')] = 16\n",
    "clean_2015_1.iloc[13, clean_2015_1.columns.get_loc('STRUCT_DEST')] = 7\n",
    "clean_2015_1.iloc[15, clean_2015_1.columns.get_loc('STRUCT_DEST')] = 27\n",
    "clean_2015_1.iloc[17, clean_2015_1.columns.get_loc('STRUCT_DEST')] = 1\n",
    "clean_2015_1.iloc[18, clean_2015_1.columns.get_loc('STRUCT_DEST')] = 1\n",
    "clean_2015_1.iloc[20, clean_2015_1.columns.get_loc('STRUCT_DEST')] = 965\n",
    "\n",
    "# Clean the 2015_page_2 \n",
    "clean_2015_2 = largest_2015_2.drop(0)\n",
    "clean_2015_2.columns = ROWS_N\n",
    "clean_2015_2['ORIGIN_DPT'] = clean_2015_2['BURNED_TOTAL']\n",
    "clean_2015_2['BURNED_TOTAL'] = clean_2015_2['VEG_TYPE']\n",
    "clean_2015_2['VEG_TYPE'] = clean_2015_2['STRUCT_DEST']\n",
    "clean_2015_2['CAUSE'] = clean_2015_2['STRUCT_DAM']\n",
    "clean_2015_2 = column_separator(clean_2015_2, \"FATALITY_FIRE\", STRUCT_ROW)\n",
    "clean_2015_2['FATALITY_FIRE'] = clean_2015_2['FATALITY_CIVIL']\n",
    "# Reorder the columns\n",
    "clean_2015_2 = clean_2015_2.reindex(columns=ROWS_N)\n",
    "# Crosschecking dataframe data manually\n",
    "clean_2015_2['FATALITY_CIVIL'] = np.nan\n",
    "clean_2015_2.iloc[6, clean_2015_2.columns.get_loc('STRUCT_DEST')] = 4\n",
    "clean_2015_2.iloc[8, clean_2015_2.columns.get_loc('STRUCT_DAM')] = 1\n",
    "# Fill columns 14-18 with right values for STRUCT_DEST column\n",
    "clean_2015_2.iloc[14:19, clean_2015_2.columns.get_loc('STRUCT_DEST')] = largest_2015_2['Structures'][15:20]\n",
    "\n",
    "# Clean the 2015_page_3\n",
    "# Note do we keep TOTAL?\n",
    "clean_2015_3 = largest_2015_3.drop(0)\n",
    "# Drop the total row\n",
    "clean_2015_3 = clean_2015_3.drop(8)\n",
    "clean_2015_3.columns = ROWS_N\n",
    "clean_2015_3['ORIGIN_DPT'] = clean_2015_3['BURNED_TOTAL']\n",
    "clean_2015_3['BURNED_TOTAL'] = clean_2015_3['VEG_TYPE']\n",
    "clean_2015_3['VEG_TYPE'] = clean_2015_3['STRUCT_DEST']\n",
    "clean_2015_3['CAUSE'] = clean_2015_3['STRUCT_DAM']\n",
    "clean_2015_3 = column_separator(clean_2015_3, \"FATALITY_FIRE\", STRUCT_ROW)\n",
    "clean_2015_3['FATALITY_FIRE'] = np.nan\n",
    "# Reorder the columns\n",
    "clean_2015_3 = clean_2015_3.reindex(columns=ROWS_N)\n",
    "clean_2015_3.iloc[1, clean_2015_3.columns.get_loc('STRUCT_DEST')] = 3\n",
    "\n",
    "# 2016\n",
    "clean_2016_1 = largest_2016_1.drop(0)\n",
    "clean_2016_1 = clean_2016_1.drop(1)\n",
    "clean_2016_1.columns = ROWS_N\n",
    "# Split the incident number and COUNTY/UNIT\n",
    "clean_2016_1['COUNTY/UNIT'] = clean_2016_1['INCIDENT #'].apply(lambda x: ' '.join(x.split(' ')[1:]))\n",
    "clean_2016_1['INCIDENT #'] = clean_2016_1['INCIDENT #'].apply(lambda x: x.split(' ')[0])\n",
    "# Drop NAN row\n",
    "clean_2016_1 = clean_2016_1.drop(31)\n",
    "# Separate the date\n",
    "clean_2016_1['START_DATE'] = clean_2016_1['CONT_DATE'].apply(lambda x: x.split(' ')[0])\n",
    "clean_2016_1['CONT_DATE'] = clean_2016_1['CONT_DATE'].apply(lambda x: ' '.join(x.split(' ')[1:]))\n",
    "# Reassign columns\n",
    "clean_2016_1['BURNED_TOTAL'] = clean_2016_1['VEG_TYPE']\n",
    "clean_2016_1['VEG_TYPE'] = clean_2016_1['CAUSE']\n",
    "clean_2016_1['CAUSE'] = clean_2016_1['STRUCT_DEST']\n",
    "# Split structure data\n",
    "clean_2016_1 = column_separator(clean_2016_1, \"STRUCT_DAM\", [\"STRUCT_DEST\",\"TEMP_DAM\"])\n",
    "clean_2016_1['STRUCT_DAM'] = clean_2016_1['TEMP_DAM']\n",
    "clean_2016_1 = clean_2016_1.drop(columns=[\"TEMP_DAM\"], axis=1)\n",
    "# Restructure data\n",
    "clean_2016_1 = clean_2016_1.reindex(columns=ROWS_N)\n",
    "\n",
    "# Fill data after cross-checking\n",
    "# -2 since 2 columnds dropped\n",
    "clean_2016_1.iloc[24, clean_2016_1.columns.get_loc('STRUCT_DEST')] = 7\n",
    "clean_2016_1.iloc[22, clean_2016_1.columns.get_loc('STRUCT_DEST')] = 3\n",
    "clean_2016_1.iloc[21, clean_2016_1.columns.get_loc('STRUCT_DEST')] = 1\n",
    "clean_2016_1.iloc[20, clean_2016_1.columns.get_loc('STRUCT_DAM')] = 1\n",
    "clean_2016_1.iloc[18, clean_2016_1.columns.get_loc('STRUCT_DEST')] = 3\n",
    "clean_2016_1.iloc[14, clean_2016_1.columns.get_loc('STRUCT_DEST')] = 2\n",
    "clean_2016_1.iloc[13, clean_2016_1.columns.get_loc('STRUCT_DEST')] = 2\n",
    "clean_2016_1.iloc[9, clean_2016_1.columns.get_loc('STRUCT_DEST')] = 2\n",
    "clean_2016_1.iloc[7, clean_2016_1.columns.get_loc('STRUCT_DEST')] = 1\n",
    "\n",
    "# Page 2 for 2016 dataset\n",
    "# Drop first two columns\n",
    "clean_2016_2 = largest_2016_2.drop(0)\n",
    "clean_2016_2 = clean_2016_2.drop(1)\n",
    "clean_2016_2['TEMP'] = np.nan\n",
    "# Drop last column: SUBTOTAL COLUMN (3 columns dropped)\n",
    "clean_2016_2.columns = ROWS_N\n",
    "\n",
    "# Split the INCIDENT NUM to extract COUNTY/UNIT\n",
    "clean_2016_2['COUNTY/UNIT'] = clean_2016_2['INCIDENT #'].apply(lambda x: ' '.join(x.split(' ')[1:]))\n",
    "clean_2016_2['INCIDENT #'] = clean_2016_2['INCIDENT #'].apply(lambda x: x.split(' ')[0])\n",
    "# MOVE CONT_DATE TO TEMP\n",
    "clean_2016_2['TEMP'] = clean_2016_2['CONT_DATE']\n",
    "# Split the DATES (stuck at START_DATE COLUMNN)\n",
    "clean_2016_2['CONT_DATE'] = clean_2016_2['START_DATE'].apply(lambda x: ' '.join(x.split(' ')[1:]))\n",
    "clean_2016_2['START_DATE'] = clean_2016_2['START_DATE'].apply(lambda x: x.split(' ')[0])\n",
    "# Set ORIGIN_DPT with its values held at TEMP \n",
    "clean_2016_2['ORIGIN_DPT'] = clean_2016_2['TEMP']\n",
    "# Split the STRUCT_DEST\n",
    "clean_2016_2 = column_separator(clean_2016_2, \"STRUCT_DEST\", [\"TEMP\", \"STRUCT_DAM\"])\n",
    "# Swap FATAL_FIRE and FATAL_CIVIL\n",
    "clean_2016_2[\"FATALITY_CIVIL\"] = clean_2016_2[\"FATALITY_FIRE\"]\n",
    "clean_2016_2[\"FATALITY_FIRE\"] = np.nan\n",
    "clean_2016_2 = clean_2016_2.rename(index=str, columns={\"TEMP\": \"STRUCT_DEST\"})\n",
    "# Reorder the columns\n",
    "clean_2016_2 = clean_2016_2.reindex(columns=ROWS_N)\n",
    "# Crosscheck data and fill in (-2 cols)\n",
    "clean_2016_2.iloc[28, clean_2016_1.columns.get_loc('STRUCT_DEST')] = 14\n",
    "clean_2016_2.iloc[24, clean_2016_1.columns.get_loc('STRUCT_DEST')] = 5\n",
    "clean_2016_2.iloc[22, clean_2016_1.columns.get_loc('STRUCT_DEST')] = 6\n",
    "clean_2016_2.iloc[16, clean_2016_1.columns.get_loc('STRUCT_DAM')] = 2\n",
    "clean_2016_2.iloc[11, clean_2016_1.columns.get_loc('STRUCT_DEST')] = 1\n",
    "clean_2016_2.iloc[7, clean_2016_1.columns.get_loc('STRUCT_DEST')] = 5\n",
    "clean_2016_2.iloc[5, clean_2016_1.columns.get_loc('STRUCT_DEST')] = 1\n",
    "\n",
    "# Clean 2016_page_3\n",
    "# Drop unwanted columns\n",
    "clean_2016_3 = largest_2016_3.drop(0)\n",
    "clean_2016_3 = clean_2016_3.drop(12)\n",
    "clean_2016_3 = clean_2016_3.drop(13)\n",
    "clean_2016_3.columns = ROWS_N\n",
    "\n",
    "# Split the dates (COMBINED ON CONT_DATE)\n",
    "clean_2016_3['START_DATE'] = clean_2016_3['CONT_DATE'].apply(lambda x: x.split(' ')[0])\n",
    "clean_2016_3['CONT_DATE'] = clean_2016_3['CONT_DATE'].apply(lambda x: ' '.join(x.split(' ')[1:]))\n",
    "# Reassign column values\n",
    "clean_2016_3['VEG_TYPE'] = clean_2016_3['CAUSE']\n",
    "clean_2016_3['CAUSE'] = clean_2016_3['STRUCT_DEST']\n",
    "clean_2016_3['STRUCT_DEST'] = clean_2016_3['STRUCT_DAM']\n",
    "clean_2016_3['STRUCT_DAM'] = np.nan\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## COMBINING DATA\n",
    "Here we complete our final fire dataset of cleaned values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#up to pdf book 10\n",
    "pdf_book = pdf_book = [clean_2000_1, clean_2000_2,\n",
    "            clean_2001_1, clean_2001_2,\n",
    "            clean_2002_1, clean_2002_2, clean_2002_3,\n",
    "            clean_2003_1, clean_2003_2, clean_2003_3,\n",
    "            clean_2004_1, clean_2004_2,\n",
    "            clean_2005_1, clean_2005_2, clean_2005_3,\n",
    "            clean_2006_1, clean_2006_2, clean_2006_3, clean_2006_4,\n",
    "            clean_2007_1, clean_2007_2, clean_2007_3,\n",
    "            clean_2008_1, clean_2008_2, clean_2008_3, clean_2008_4, clean_2008_5, clean_2008_6,\n",
    "            clean_2009_1, clean_2009_2, clean_2009_3,\n",
    "            clean_2010_1, clean_2010_2,\n",
    "            clean_2011_1, clean_2011_2,\n",
    "            clean_2013_1, clean_2013_2,\n",
    "            clean_2014_1, clean_2014_2,\n",
    "            clean_2015_1, clean_2015_2, clean_2015_3,\n",
    "            clean_2016_1, clean_2016_2, clean_2016_3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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></th>\n",
       "      <th>INCIDENT #</th>\n",
       "      <th>COUNTY/UNIT</th>\n",
       "      <th>FIRE_NAME</th>\n",
       "      <th>START_DATE</th>\n",
       "      <th>CONT_DATE</th>\n",
       "      <th>ORIGIN_DPT</th>\n",
       "      <th>BURNED_TOTAL</th>\n",
       "      <th>VEG_TYPE</th>\n",
       "      <th>CAUSE</th>\n",
       "      <th>STRUCT_DEST</th>\n",
       "      <th>STRUCT_DAM</th>\n",
       "      <th>FATALITY_FIRE</th>\n",
       "      <th>FATALITY_CIVIL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TUU-5954</td>\n",
       "      <td>Tulare</td>\n",
       "      <td>Richgrove</td>\n",
       "      <td>2000-06-28</td>\n",
       "      <td>2000-06-28</td>\n",
       "      <td>CDF</td>\n",
       "      <td>450</td>\n",
       "      <td>G</td>\n",
       "      <td>Electrical</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MVU-4431</td>\n",
       "      <td>San Diego (exp002 in)</td>\n",
       "      <td>Barrett</td>\n",
       "      <td>2000-06-29</td>\n",
       "      <td>2000-06-30</td>\n",
       "      <td>CDF</td>\n",
       "      <td>2000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Under Investigation</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MMU-9447</td>\n",
       "      <td>Mariposa</td>\n",
       "      <td>Granite</td>\n",
       "      <td>2000-07-02</td>\n",
       "      <td>2000-07-04</td>\n",
       "      <td>CDF</td>\n",
       "      <td>2000</td>\n",
       "      <td>WB</td>\n",
       "      <td>Guns</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BDU-5166</td>\n",
       "      <td>San Bernardino</td>\n",
       "      <td>Citrus</td>\n",
       "      <td>2000-07-03</td>\n",
       "      <td>2000-07-04</td>\n",
       "      <td>CDF</td>\n",
       "      <td>600</td>\n",
       "      <td>G</td>\n",
       "      <td>Under Investigation</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BDU-5318</td>\n",
       "      <td>San Bernardino</td>\n",
       "      <td>Assist (Morgan)</td>\n",
       "      <td>2000-07-07</td>\n",
       "      <td>2000-07-08</td>\n",
       "      <td>USFS</td>\n",
       "      <td>300</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Under Investigation</td>\n",
       "      <td>na</td>\n",
       "      <td>na</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  INCIDENT #            COUNTY/UNIT        FIRE_NAME START_DATE  CONT_DATE  \\\n",
       "0   TUU-5954                 Tulare        Richgrove 2000-06-28 2000-06-28   \n",
       "1   MVU-4431  San Diego (exp002 in)          Barrett 2000-06-29 2000-06-30   \n",
       "2   MMU-9447               Mariposa          Granite 2000-07-02 2000-07-04   \n",
       "3   BDU-5166         San Bernardino           Citrus 2000-07-03 2000-07-04   \n",
       "4   BDU-5318         San Bernardino  Assist (Morgan) 2000-07-07 2000-07-08   \n",
       "\n",
       "  ORIGIN_DPT  BURNED_TOTAL VEG_TYPE                CAUSE STRUCT_DEST  \\\n",
       "0        CDF           450        G           Electrical           0   \n",
       "1        CDF          2000      NaN  Under Investigation         NaN   \n",
       "2        CDF          2000       WB                 Guns           0   \n",
       "3        CDF           600        G  Under Investigation           0   \n",
       "4       USFS           300      NaN  Under Investigation          na   \n",
       "\n",
       "  STRUCT_DAM  FATALITY_FIRE  FATALITY_CIVIL  \n",
       "0          0              0               0  \n",
       "1        NaN              0               0  \n",
       "2          0              0               0  \n",
       "3          0              0               0  \n",
       "4         na              0               0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#combined dataset, this will grow as more datasets are cleaned\n",
    "clean_df = pd.concat(pdf_book, ignore_index=True, sort=False)\n",
    "clean_df['START_DATE'] = clean_df.START_DATE.astype(str).apply(datetime_convert_pdf)\n",
    "clean_df['CONT_DATE'] = clean_df.CONT_DATE.astype(str).apply(datetime_convert_pdf)\n",
    "clean_df['BURNED_TOTAL'] = clean_df.BURNED_TOTAL.apply(int_convert)\n",
    "clean_df['CAUSE'] = clean_df.CAUSE.apply(cause_cleaner)\n",
    "clean_df['VEG_TYPE'] = clean_df.VEG_TYPE.apply(veg_cleaner)\n",
    "clean_df = clean_df.dropna(subset=['INCIDENT #', 'START_DATE'])\n",
    "clean_df['FATALITY_FIRE'] = clean_df.FATALITY_FIRE.apply(fatality_cleaner).fillna(0).astype(int)\n",
    "clean_df['FATALITY_CIVIL'] = clean_df.FATALITY_FIRE.apply(fatality_cleaner).fillna(0)\n",
    "clean_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>INCIDENT #</th>\n",
       "      <th>COUNTY/UNIT</th>\n",
       "      <th>FIRE_NAME_x</th>\n",
       "      <th>START_DATE</th>\n",
       "      <th>CONT_DATE</th>\n",
       "      <th>ORIGIN_DPT</th>\n",
       "      <th>BURNED_TOTAL</th>\n",
       "      <th>VEG_TYPE</th>\n",
       "      <th>CAUSE</th>\n",
       "      <th>STRUCT_DEST</th>\n",
       "      <th>...</th>\n",
       "      <th>DISCOVERY_TIME</th>\n",
       "      <th>STAT_CAUSE_CODE</th>\n",
       "      <th>CONT_TIME</th>\n",
       "      <th>FIRE_SIZE</th>\n",
       "      <th>FIRE_SIZE_CLASS</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>STATE</th>\n",
       "      <th>COUNTY</th>\n",
       "      <th>END_DATE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TUU-5954</td>\n",
       "      <td>Tulare</td>\n",
       "      <td>Richgrove</td>\n",
       "      <td>2000-06-28</td>\n",
       "      <td>2000-06-28</td>\n",
       "      <td>CDF</td>\n",
       "      <td>450</td>\n",
       "      <td>G</td>\n",
       "      <td>Electrical</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0330</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0600</td>\n",
       "      <td>0</td>\n",
       "      <td>B</td>\n",
       "      <td>41.753056</td>\n",
       "      <td>-121.304722</td>\n",
       "      <td>CA</td>\n",
       "      <td>None</td>\n",
       "      <td>2000-06-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TUU-5954</td>\n",
       "      <td>Tulare</td>\n",
       "      <td>Richgrove</td>\n",
       "      <td>2000-06-28</td>\n",
       "      <td>2000-06-28</td>\n",
       "      <td>CDF</td>\n",
       "      <td>450</td>\n",
       "      <td>G</td>\n",
       "      <td>Electrical</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0800</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1430</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>38.650278</td>\n",
       "      <td>-120.168889</td>\n",
       "      <td>CA</td>\n",
       "      <td>None</td>\n",
       "      <td>2000-06-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TUU-5954</td>\n",
       "      <td>Tulare</td>\n",
       "      <td>Richgrove</td>\n",
       "      <td>2000-06-28</td>\n",
       "      <td>2000-06-28</td>\n",
       "      <td>CDF</td>\n",
       "      <td>450</td>\n",
       "      <td>G</td>\n",
       "      <td>Electrical</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1416</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2300</td>\n",
       "      <td>127</td>\n",
       "      <td>D</td>\n",
       "      <td>34.787500</td>\n",
       "      <td>-120.107500</td>\n",
       "      <td>CA</td>\n",
       "      <td>None</td>\n",
       "      <td>2000-06-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TUU-5954</td>\n",
       "      <td>Tulare</td>\n",
       "      <td>Richgrove</td>\n",
       "      <td>2000-06-28</td>\n",
       "      <td>2000-06-28</td>\n",
       "      <td>CDF</td>\n",
       "      <td>450</td>\n",
       "      <td>G</td>\n",
       "      <td>Electrical</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1200</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1210</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>37.416667</td>\n",
       "      <td>-119.000000</td>\n",
       "      <td>CA</td>\n",
       "      <td>None</td>\n",
       "      <td>2000-06-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TUU-5954</td>\n",
       "      <td>Tulare</td>\n",
       "      <td>Richgrove</td>\n",
       "      <td>2000-06-28</td>\n",
       "      <td>2000-06-28</td>\n",
       "      <td>CDF</td>\n",
       "      <td>450</td>\n",
       "      <td>G</td>\n",
       "      <td>Electrical</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1300</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1400</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>37.482778</td>\n",
       "      <td>-119.035556</td>\n",
       "      <td>CA</td>\n",
       "      <td>None</td>\n",
       "      <td>2000-06-28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  INCIDENT # COUNTY/UNIT FIRE_NAME_x START_DATE  CONT_DATE ORIGIN_DPT  \\\n",
       "0   TUU-5954      Tulare   Richgrove 2000-06-28 2000-06-28        CDF   \n",
       "1   TUU-5954      Tulare   Richgrove 2000-06-28 2000-06-28        CDF   \n",
       "2   TUU-5954      Tulare   Richgrove 2000-06-28 2000-06-28        CDF   \n",
       "3   TUU-5954      Tulare   Richgrove 2000-06-28 2000-06-28        CDF   \n",
       "4   TUU-5954      Tulare   Richgrove 2000-06-28 2000-06-28        CDF   \n",
       "\n",
       "   BURNED_TOTAL VEG_TYPE       CAUSE STRUCT_DEST    ...     DISCOVERY_TIME  \\\n",
       "0           450        G  Electrical           0    ...               0330   \n",
       "1           450        G  Electrical           0    ...               0800   \n",
       "2           450        G  Electrical           0    ...               1416   \n",
       "3           450        G  Electrical           0    ...               1200   \n",
       "4           450        G  Electrical           0    ...               1300   \n",
       "\n",
       "   STAT_CAUSE_CODE  CONT_TIME  FIRE_SIZE FIRE_SIZE_CLASS   LATITUDE  \\\n",
       "0              7.0       0600          0               B  41.753056   \n",
       "1              1.0       1430          0               A  38.650278   \n",
       "2              9.0       2300        127               D  34.787500   \n",
       "3              1.0       1210          0               A  37.416667   \n",
       "4              1.0       1400          0               A  37.482778   \n",
       "\n",
       "    LONGITUDE STATE  COUNTY   END_DATE  \n",
       "0 -121.304722    CA    None 2000-06-28  \n",
       "1 -120.168889    CA    None 2000-06-28  \n",
       "2 -120.107500    CA    None 2000-06-28  \n",
       "3 -119.000000    CA    None 2000-06-28  \n",
       "4 -119.035556    CA    None 2000-06-28  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Merge the SQL database and the cdfdata Tabula database\n",
    "joined = clean_df.merge(fires, left_on=['START_DATE', 'CONT_DATE'], right_on=['START_DATE', 'END_DATE'])\n",
    "joined.COUNTY = joined.COUNTY.astype(str)\n",
    "joined.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": false
   },
   "source": [
    "## GEOJSON AND JOINED\n",
    "This continues the data cleaning by merging our datasets together to create a singular dataframe named:\n",
    "     joined\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'OBJECTID': 5,\n",
       " 'COUNTY_NAME': 'Calaveras County',\n",
       " 'COUNTY_ABBREV': 'CAL',\n",
       " 'COUNTY_NUM': 5,\n",
       " 'COUNTY_CODE': '05',\n",
       " 'COUNTY_FIPS': '009'}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ca_counties['features'][4]['properties']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['None', 'San Bernardino', 'Butte', 'Kern', 'Placer', 'El Dorado',\n",
       "       'Madera', 'Nevada', 'Riverside', '17', 'Calaveras', 'Amador',\n",
       "       'Mariposa', 'Yolo', 'Mendocino', '57', 'Shasta', 'Tulare',\n",
       "       'San Luis Obispo', 'San Diego', 'Lassen', 'Humboldt', 'Tuolumne',\n",
       "       'Fresno', 'Modoc', 'Trinity', 'Colusa', 'Lake', 'Tehama',\n",
       "       'Del Norte', 'Sonoma', '61', 'Napa', 'Plumas', '91', 'Mono',\n",
       "       'Los Angeles', 'Monterey', 'Siskiyou', 'Alpine', 'Inyo', 'ORANGE',\n",
       "       'Sacramento', 'SACRAMENTO/AMADOR', 'Yuba', 'Contra Costa', 'Glenn',\n",
       "       '71', 'KERN', 'San Benito', 'Santa Barbara', '63', 'Alameda', '5',\n",
       "       '93', 'SANTA CLARA', '51', 'Imperial', '3', '7', '59', '65', '37',\n",
       "       '29', '19', '49', '109', '23', '105', '89', '73', '35', '9',\n",
       "       'SAN BERNARDINO', '27', '11', 'Stanislaus', '15', '53', '115',\n",
       "       '39', '107', '111', '83', 'Kings', 'Solano', '103', '33', '43',\n",
       "       'Santa Cruz', 'TEHAMA ', 'SAN DIEGO', '45', '21', '071', '037',\n",
       "       '063', '053', '065', '089', '091', '073', '019', 'Orange', '049',\n",
       "       '057', '051', 'Ventura', '003', '093', 'BUTTE', 'TUOLUMNE',\n",
       "       'SANTA CRUZ', 'MADERA', 'EL DORADO', 'NAPA', 'SAN LUIS OBISPO',\n",
       "       'PLACER', 'FRESNO', 'MERCED', 'SHASTA', 'CONTRA COSTA', 'AMADOR',\n",
       "       'SISKIYOU', 'CALAVERAS', 'NEVADA', 'TULARE', 'SONOMA', 'ALAMEDA',\n",
       "       'SACRAMENTO', 'LOS ANGELES', 'TEHAMA', 'SANTA BARBARA', 'LAKE',\n",
       "       'RIVERSIDE', 'HUMBOLDT', 'MENDOCINO', '061', '029', 'DEL NORTE',\n",
       "       'MARIPOSA', 'YUBA', 'SAN JOAQUIN', 'SOLANO', 'LASSEN', '023',\n",
       "       '027', '033', 'Sierra', 'TRINITY', 'YOLO', 'MONTEREY', 'MODOC',\n",
       "       '035', '009', 'SAN BENITO', '017'], dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joined.COUNTY.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "number_county_dict = {}\n",
    "for raw_county in ca_counties['features']:\n",
    "    county = raw_county['properties']\n",
    "    number_county_dict[int(county['COUNTY_FIPS'])] = county['COUNTY_NAME']\n",
    "#number_county_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "joined.COUNTY = joined.COUNTY.apply(resolve_county)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "count_joined = joined[['COUNTY', 'INCIDENT #']].groupby('COUNTY').count().drop('None County').reset_index()\n",
    "count_joined.columns = ['COUNTY', 'COUNT']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_df['COUNTY/UNIT'] = clean_df['COUNTY/UNIT'].astype(str).apply(county_cleaner, args=(count_joined.COUNTY.values,))\n",
    "#clean_df['COUNTY/UNIT'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DATA VISUALIZATION\n",
    "\n",
    "This section contains a summary of the dataset, things like: number of variables, number of observations, proportional distribution in variables as well as some graphs used to explore our data as is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>INCIDENT #</th>\n",
       "      <th>COUNTY/UNIT</th>\n",
       "      <th>FIRE_NAME</th>\n",
       "      <th>START_DATE</th>\n",
       "      <th>CONT_DATE</th>\n",
       "      <th>ORIGIN_DPT</th>\n",
       "      <th>BURNED_TOTAL</th>\n",
       "      <th>VEG_TYPE</th>\n",
       "      <th>CAUSE</th>\n",
       "      <th>STRUCT_DEST</th>\n",
       "      <th>STRUCT_DAM</th>\n",
       "      <th>FATALITY_FIRE</th>\n",
       "      <th>FATALITY_CIVIL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TUU-5954</td>\n",
       "      <td>Tulare County</td>\n",
       "      <td>Richgrove</td>\n",
       "      <td>2000-06-28</td>\n",
       "      <td>2000-06-28</td>\n",
       "      <td>CDF</td>\n",
       "      <td>450</td>\n",
       "      <td>G</td>\n",
       "      <td>Electrical</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MVU-4431</td>\n",
       "      <td>San Diego County</td>\n",
       "      <td>Barrett</td>\n",
       "      <td>2000-06-29</td>\n",
       "      <td>2000-06-30</td>\n",
       "      <td>CDF</td>\n",
       "      <td>2000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Under Investigation</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MMU-9447</td>\n",
       "      <td>Mariposa County</td>\n",
       "      <td>Granite</td>\n",
       "      <td>2000-07-02</td>\n",
       "      <td>2000-07-04</td>\n",
       "      <td>CDF</td>\n",
       "      <td>2000</td>\n",
       "      <td>WB</td>\n",
       "      <td>Guns</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BDU-5166</td>\n",
       "      <td>San Bernardino County</td>\n",
       "      <td>Citrus</td>\n",
       "      <td>2000-07-03</td>\n",
       "      <td>2000-07-04</td>\n",
       "      <td>CDF</td>\n",
       "      <td>600</td>\n",
       "      <td>G</td>\n",
       "      <td>Under Investigation</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BDU-5318</td>\n",
       "      <td>San Bernardino County</td>\n",
       "      <td>Assist (Morgan)</td>\n",
       "      <td>2000-07-07</td>\n",
       "      <td>2000-07-08</td>\n",
       "      <td>USFS</td>\n",
       "      <td>300</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Under Investigation</td>\n",
       "      <td>na</td>\n",
       "      <td>na</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  INCIDENT #            COUNTY/UNIT        FIRE_NAME START_DATE  CONT_DATE  \\\n",
       "0   TUU-5954          Tulare County        Richgrove 2000-06-28 2000-06-28   \n",
       "1   MVU-4431       San Diego County          Barrett 2000-06-29 2000-06-30   \n",
       "2   MMU-9447        Mariposa County          Granite 2000-07-02 2000-07-04   \n",
       "3   BDU-5166  San Bernardino County           Citrus 2000-07-03 2000-07-04   \n",
       "4   BDU-5318  San Bernardino County  Assist (Morgan) 2000-07-07 2000-07-08   \n",
       "\n",
       "  ORIGIN_DPT  BURNED_TOTAL VEG_TYPE                CAUSE STRUCT_DEST  \\\n",
       "0        CDF           450        G           Electrical           0   \n",
       "1        CDF          2000      NaN  Under Investigation         NaN   \n",
       "2        CDF          2000       WB                 Guns           0   \n",
       "3        CDF           600        G  Under Investigation           0   \n",
       "4       USFS           300      NaN  Under Investigation          na   \n",
       "\n",
       "  STRUCT_DAM  FATALITY_FIRE  FATALITY_CIVIL  \n",
       "0          0              0               0  \n",
       "1        NaN              0               0  \n",
       "2          0              0               0  \n",
       "3          0              0               0  \n",
       "4         na              0               0  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_df.shape\n",
    "clean_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['INCIDENT #', 'COUNTY/UNIT', 'FIRE_NAME', 'START_DATE', 'CONT_DATE',\n",
       "       'ORIGIN_DPT', 'BURNED_TOTAL', 'VEG_TYPE', 'CAUSE', 'STRUCT_DEST',\n",
       "       'STRUCT_DAM', 'FATALITY_FIRE', 'FATALITY_CIVIL'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_df.columns"
   ]
  },
  {
   "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></th>\n",
       "      <th>BURNED_TOTAL</th>\n",
       "      <th>FATALITY_FIRE</th>\n",
       "      <th>FATALITY_CIVIL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1212.000000</td>\n",
       "      <td>1212.000000</td>\n",
       "      <td>1212.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7060.708746</td>\n",
       "      <td>0.063531</td>\n",
       "      <td>0.063531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>22550.149926</td>\n",
       "      <td>1.066767</td>\n",
       "      <td>1.066767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>420.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>978.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3344.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>273246.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>35.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        BURNED_TOTAL  FATALITY_FIRE  FATALITY_CIVIL\n",
       "count    1212.000000    1212.000000     1212.000000\n",
       "mean     7060.708746       0.063531        0.063531\n",
       "std     22550.149926       1.066767        1.066767\n",
       "min         0.000000       0.000000        0.000000\n",
       "25%       420.000000       0.000000        0.000000\n",
       "50%       978.000000       0.000000        0.000000\n",
       "75%      3344.000000       0.000000        0.000000\n",
       "max    273246.000000      35.000000       35.000000"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8194, 27)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joined.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>BURNED_TOTAL</th>\n",
       "      <th>FATALITY_FIRE</th>\n",
       "      <th>FATALITY_CIVIL</th>\n",
       "      <th>OBJECTID</th>\n",
       "      <th>STAT_CAUSE_CODE</th>\n",
       "      <th>FIRE_SIZE</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8194.000000</td>\n",
       "      <td>8194.000000</td>\n",
       "      <td>8194.000000</td>\n",
       "      <td>8.194000e+03</td>\n",
       "      <td>8194.000000</td>\n",
       "      <td>8194.000000</td>\n",
       "      <td>8194.000000</td>\n",
       "      <td>8194.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4662.342568</td>\n",
       "      <td>0.030144</td>\n",
       "      <td>0.030144</td>\n",
       "      <td>4.569777e+05</td>\n",
       "      <td>4.347449</td>\n",
       "      <td>270.088845</td>\n",
       "      <td>38.128509</td>\n",
       "      <td>-120.464326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>15173.351924</td>\n",
       "      <td>0.170994</td>\n",
       "      <td>0.170994</td>\n",
       "      <td>5.036986e+05</td>\n",
       "      <td>3.635031</td>\n",
       "      <td>5396.636610</td>\n",
       "      <td>2.625038</td>\n",
       "      <td>2.155154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>32.560038</td>\n",
       "      <td>-124.280000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>430.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.405098e+05</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>36.026475</td>\n",
       "      <td>-122.202792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>750.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.120895e+05</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>38.745139</td>\n",
       "      <td>-120.628969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1830.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.688285e+05</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>40.383333</td>\n",
       "      <td>-119.001389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>273246.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.880412e+06</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>280059.000000</td>\n",
       "      <td>41.998662</td>\n",
       "      <td>-114.418000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        BURNED_TOTAL  FATALITY_FIRE  FATALITY_CIVIL      OBJECTID  \\\n",
       "count    8194.000000    8194.000000     8194.000000  8.194000e+03   \n",
       "mean     4662.342568       0.030144        0.030144  4.569777e+05   \n",
       "std     15173.351924       0.170994        0.170994  5.036986e+05   \n",
       "min         0.000000       0.000000        0.000000  6.000000e+00   \n",
       "25%       430.000000       0.000000        0.000000  1.405098e+05   \n",
       "50%       750.000000       0.000000        0.000000  3.120895e+05   \n",
       "75%      1830.000000       0.000000        0.000000  3.688285e+05   \n",
       "max    273246.000000       1.000000        1.000000  1.880412e+06   \n",
       "\n",
       "       STAT_CAUSE_CODE      FIRE_SIZE     LATITUDE    LONGITUDE  \n",
       "count      8194.000000    8194.000000  8194.000000  8194.000000  \n",
       "mean          4.347449     270.088845    38.128509  -120.464326  \n",
       "std           3.635031    5396.636610     2.625038     2.155154  \n",
       "min           1.000000       0.000000    32.560038  -124.280000  \n",
       "25%           1.000000       0.000000    36.026475  -122.202792  \n",
       "50%           2.000000       0.000000    38.745139  -120.628969  \n",
       "75%           9.000000       1.000000    40.383333  -119.001389  \n",
       "max          13.000000  280059.000000    41.998662  -114.418000  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joined.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['INCIDENT #', 'COUNTY/UNIT', 'FIRE_NAME_x', 'START_DATE', 'CONT_DATE',\n",
       "       'ORIGIN_DPT', 'BURNED_TOTAL', 'VEG_TYPE', 'CAUSE', 'STRUCT_DEST',\n",
       "       'STRUCT_DAM', 'FATALITY_FIRE', 'FATALITY_CIVIL', 'OBJECTID',\n",
       "       'LOCAL_FIRE_REPORT_ID', 'LOCAL_INCIDENT_ID', 'FIRE_NAME_y',\n",
       "       'DISCOVERY_TIME', 'STAT_CAUSE_CODE', 'CONT_TIME', 'FIRE_SIZE',\n",
       "       'FIRE_SIZE_CLASS', 'LATITUDE', 'LONGITUDE', 'STATE', 'COUNTY',\n",
       "       'END_DATE'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joined.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2a658f98>"
      ]
     },
     "execution_count": 36,
     "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": [
    "cdf = clean_df\n",
    "\n",
    "length = cdf['CONT_DATE'] - cdf['START_DATE']\n",
    "#display(length)\n",
    "cdf = cdf[['START_DATE', 'BURNED_TOTAL']]\n",
    "cdf['START_DATE']= length\n",
    "cdf.columns=['LENGTH', 'BURNED']\n",
    "\n",
    "\n",
    "len_df = cdf[['LENGTH', 'BURNED']]\n",
    "aggregation_functions = {'BURNED': 'sum'}\n",
    "len_df = len_df.groupby('LENGTH').mean().reset_index()\n",
    "len_df['LENGTH'] = len_df['LENGTH'].apply(lambda x: x.days)\n",
    "#len_df['BURNED'] = len_df['BURNED'].apply(lambda x: x/)\n",
    "len_df = len_df.drop([0,1,2])\n",
    "#normalizing the data to better view the kde plot\n",
    "len_df = (len_df - len_df.mean()) / (len_df.max() - len_df.min())\n",
    "len_df.plot(kind='kde')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Explanation:**  \n",
    "Based on the numbers so far, nothing really jumps out. This lead to the idea of normalization. The following graph is   a normalized kde plot of the length of a fire and the total acres burned. We can see that the plots align, signaling   we may want to be looking more into fire frequency rather than length, since a there is a higher probability that a fire's burntime does not correlate to its total acres burned, we may want to look elsewhere to test our hypothesis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2beeb208>"
      ]
     },
     "execution_count": 37,
     "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": [
    "##This section plots the total acres burned based on YEAR the fire BEGAN\n",
    "year_df = clean_df\n",
    "dates = year_df['START_DATE'].apply(lambda x: float(x.year))\n",
    "year_df = year_df.dropna(subset=['START_DATE'])\n",
    "year_df['START_DATE'] = dates\n",
    "#display(year_df)\n",
    "\n",
    "year_df = year_df[['START_DATE', 'BURNED_TOTAL']]\n",
    "aggregation_functions = {'BURNED_TOTAL': 'sum'}\n",
    "df_new = year_df.groupby(year_df['START_DATE']).aggregate(aggregation_functions).reset_index()\n",
    "df_new.columns = ['YEAR', 'BURNED_TOTAL']\n",
    "df_new = df_new[df_new['YEAR'] < 2017]\n",
    "#display(df_new)\n",
    "df_new.plot('YEAR', 'BURNED_TOTAL', kind='bar', color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a90c34e80>"
      ]
     },
     "execution_count": 38,
     "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"
    },
    {
     "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": [
    "##This section plots the total acres burned based on DAY the fire BEGAN and ENDED\n",
    "day_df = clean_df\n",
    "dates = day_df['START_DATE'].apply(lambda x: float(x.day))\n",
    "day_df = day_df.dropna(subset=['START_DATE'])\n",
    "day_df['START_DATE'] = dates\n",
    "\n",
    "day_df = day_df[['START_DATE', 'BURNED_TOTAL']]\n",
    "aggregation_functions = {'BURNED_TOTAL': 'sum'}\n",
    "df_new = day_df.groupby(day_df['START_DATE']).aggregate(aggregation_functions).reset_index()\n",
    "df_new.columns = ['DAY_BEGAN', 'BURNED_TOTAL']\n",
    "#display(df_new)\n",
    "df_new.plot('DAY_BEGAN', 'BURNED_TOTAL', kind='bar', color='red')\n",
    "\n",
    "\n",
    "\n",
    "dayb_df = clean_df\n",
    "dates = dayb_df['CONT_DATE'].apply(lambda x: float(x.day))\n",
    "dayb_df = dayb_df.dropna(subset=['CONT_DATE'])\n",
    "dayb_df['CONT_DATE'] = dates\n",
    "\n",
    "dayb_df = dayb_df[['CONT_DATE', 'BURNED_TOTAL']]\n",
    "aggregation_functions = {'BURNED_TOTAL': 'sum'}\n",
    "dfb_new = dayb_df.groupby(dayb_df['CONT_DATE']).aggregate(aggregation_functions).reset_index()\n",
    "dfb_new.columns = ['DAY_ENDED', 'BURNED_TOTAL']\n",
    "#display(df_new)\n",
    "dfb_new.plot('DAY_ENDED', 'BURNED_TOTAL', kind='bar', color='blue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Explanation:**  \n",
    "Here we move past the kde plot and visualize the total acres burned based on when the fires began, first by year, then by day. There are some interesting finds on this data, however we need to move to something more concrete, perhaps occurrance per county per year?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DATA ANALYSIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "ans_df = joined.copy()\n",
    "#display(an_df)\n",
    "dates = ans_df['START_DATE'].apply(lambda x: float(x.year))\n",
    "ans_df = ans_df.dropna(subset=[\"START_DATE\"])\n",
    "ans_df['START_DATE'] = dates\n",
    "ans_df = ans_df[ans_df['START_DATE'] < 2017.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "count = ans_df.START_DATE.value_counts().values\n",
    "#print(ans_df.START_DATE.value_counts())\n",
    "year = ans_df.START_DATE.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1abe85d9b0>"
      ]
     },
     "execution_count": 41,
     "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": [
    "df = pd.DataFrame({\n",
    "     'YEAR' : year,\n",
    "     'COUNT' : count,\n",
    " })\n",
    "import seaborn as sns\n",
    "sns.kdeplot(df.YEAR, df.COUNT)\n",
    "#This seemingly shows that the number of fires is on the decline, however\n",
    "#if we look to the total number of acres burned, we see that fire frequency\n",
    "#does not tell the whole story"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ac59f3978>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context(\"poster\", font_scale = .5, rc={\"grid.linewidth\": 0.6})\n",
    "years = clean_df.START_DATE.apply(lambda x: x.year)\n",
    "def burn_cat(val):\n",
    "    if val < 1000:\n",
    "        return 0\n",
    "    elif val < 25000:\n",
    "        return 1\n",
    "    else:\n",
    "        return 2\n",
    "cats = clean_df.BURNED_TOTAL.apply(burn_cat)\n",
    "new_df = pd.DataFrame({'years': years,\n",
    "                       'cats': cats})\n",
    "new_df['count'] = 0\n",
    "new_df = new_df.groupby(['years', 'cats']).count().reset_index()\n",
    "new_df = new_df[new_df['years'] < 2017.0]\n",
    "sns.barplot(x='years',y='count', hue='cats', data=new_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "heat = joined.copy()\n",
    "heat = heat[['LATITUDE', 'LONGITUDE']]\n",
    "from folium import plugins\n",
    "m = folium.Map(location = [36, -119])\n",
    "stationArr = heat[['LATITUDE', 'LONGITUDE']].as_matrix()\n",
    "# plot heatmap\n",
    "m.add_children(plugins.HeatMap(stationArr, radius=15))\n",
    "m.save(\"heat.html\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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WvJT9n8vx+/8AQw9pV9to+um+1u1vxuS3viTxJBpsurPBpOn2i7mSzv2dLh1HqcgKxxwMHqKz/Ftzf64nha4tFshaXtxE6Q3UbP8AvCjH58HBXHbrmqNlpiHw7PZSeD7u48QPE6zXd5bhlMhBzIJmJz6gLz/OtcWN4+ieB1Fnch7W4iM6GFgYQIyCWGPlGfWtrQhJNef5ev4nNzVKkXGTdml9915L5ol0iePSPFniW4vBFGlrZ2zyiFcIMISdo9PSpota8X3OmJq9tpenNaygPFZNI4nKHoS/3c85xjp3pkuiXGp+IPFsEkUkMN9ZwxRTshCMdjDg98HrisO20zTLSwitr/whr8moRKEkEE05icgcsriTbg9f0xU2hLV6vT8vVFuVSHurRXl9/M/J/d1Op1z/AJDU/wDwH/0EVn139Fc6q2VrHZLD3bdzgKK7+ij23kT9V8zgKK7+ij23kH1XzOAorv6KPbeQfVfM4Ciu/oo9t5B9V8zgKK7+ij23kH1XzOAorv6KPbeQfVfM4Ciu/oo9t5B9V8zgKK7+ij23kH1XzOAorv6KPbeQfVfM4Ciu/oo9t5B9V8zgKK7+ij23kH1XzOAorv6KPbeQfVfM4Ciu/oo9t5B9V8zgKK7+ij23kH1XzOAorv6KPbeQfVfM4Ciu/oo9t5B9V8zgKK7+ij23kH1XzOAorv6KPbeQfVfM4Ciu/oo9t5B9V8zgKK7+ij23kH1XzOAorv6KPbeQfVfM4Ciu/oo9t5B9V8zgKK7+ij23kH1XzOAorv6KPbeQfVfM4Ciu/oo9t5B9V8zgKK7+ij23kH1XzOAorv6KPbeQfVfM4Ciu/oo9t5B9V8zgKK7+ij23kH1XzOAorv6KPbeQfVfM4Ciu/oo9t5B9V8zgKK7+ij23kH1XzOAorv6KPbeQfVfM4Ciu/oo9t5B9V8zgKK7+ij23kH1XzOAorv6KPbeQfVfM4Ciu/oo9t5B9V8zgKK7+ij23kH1XzOAorv6KPbeQfVfM4Ciu/oo9t5B9V8zgKK7+ij23kH1XzOAJwCTWHcSte3YRemeK9boqo17dDWnRVN3ued28IghCrUtd/RS9t5Gbwzbu2cBRXf0UvbeQvqvmcBRXf0Ue28g+q+ZwFFd/RR7byD6r5nAYzTWiRxhlFeg0U/beQfVrfaPMLzT0aPdEMEVTsro27mOXp/KvW6KtYjSzRuqb5eWTueZSXMOMlxVKW7g5y1etUVUcUo9PxMvq0e54tPeQdmzWbPdx5OK97orqhmXL9n8f+AV9XgfPDXah/u5qWXUI4I/mTA9a+gqK2/tf+5+P/ANIUoR6Hzo+tx44Uj8OtMj1Od2DRQZPb5q+jqKl5s/5Px/4B0fuv5D51lTUroZ+zYx6VAl5d2rlJlbHoe1fSNFZPMpX0j+P/ANPbRcbOJ8/GC4uIQ8RJz70NoVw+N8nPpmvoGiksyne7iZ+0drI+eZNH+zjLyfSmQxeWwO7n1xX0TRTlmcpfZ/EyklJe8eBBnVNw5pY5LiVS4Plr275r3yiiOY2esfx/wCAcrwtN9DwpJ70fd5x3qzYXTXDF7nnb0Fe2UUSzBP7H4/8Axnl9OcWv0PLIbyMDG0flT5Ps10BvXBHQ16hRXJUrUqnxQ/r7jzZZDBu6nb5f8E8rFgv8Nxj8KSWGWAZEu9fpXqtFcjpYZu/syf7BV9an4f8E8ps7+3lkMb/AHv51YeeKP7nFenUV006lCn8NP8AH/gD/sCle7l+H/BPJZdVSF3dsAgctWbBfjVt00y7VB+UjvXtlFdf16H8n4/8A6IZLRirX/A8kjnjMQjPMfpU0bWaDyhFkP1ya9VorOeLhNaw/H/gESySlLr+B5jFb2SYKJinNZWryiTHzDvXplFcc44ae9P+vuOZ8PRe1T8P+CeYyafaSvvcc+1SGG2CheoHtXpVFRGnhY/8u/x/4ALh6K/5efh/wTy+RLMdY8/Q1Wle2UEKh/OvWaK7aeIpU/hp/j/wDpp5JCG87/16nid0qOfkZlH1NU/sd3vP2a6KKexGa94orqWZpbQ/H/gHq0cNGkrI8Ea01KFvMkH2gL3HFaVnq8KqBNEwbvwa9porCpjpT1St8/8AgHoKcLWlH7tDy611exbALbfwrYt7qzcAiZa7miuSVepLZibpdI/j/wAA5WLyXHysCKnWEEZGCK6Ois+eXUzduhzhg9qia39q6iiqVRoRyL2wPaoHtB6V2tFaKvJCscC9mPSq72IPavRqK1WLkugWPNfsQHalWzHpXpNFV9dl2FynnqWntVuK2x2rt6Kzlim+g7Hn2p3boy2NkN1xLxkfw1o2GmRaTYnaN8z/AHnPU12FFc85uSsdHtbQUEtOvmYdtAIogMDd1JqxjIIPQjBrUorJRsrIycm3c5VPDGno8reWf3hzwelX7KxisIPKgGB3PrW3RRymkq9SatJ3M2itKijlMrmbRWlRRyhczaK0qKOULmbRWlRRyhczD0OOtZb/ANp7jt6dq6eiqj7pE483U5f/AImlPibUVcGRdw9K6Wiq5vJEKnbqzMUkqCwwfSlrSorPlNrmbVPUNOjvkz9yVfuuO1b1FHKVGbi7o5e01KW2mFpqQ2t0ST+9Wt16c1pUUcpU5xk7pWM2kyPUfnWnRRymdzLMiAcuv/fVV5NStIs75enoM1uUUcpSlHqjmptX090KNK3/AHyaWDWrIxDfKc/7prpKKdtLFc1K9+V/f/wDk9V1Czn05xGxLqQV4qxa63aS2qM8m1sYIxXSUVHs/e5jT2tNw5OX8f8AgHPSapYtC4eTKkYPy1z+karNZ3Eun2lubgA5jycZzXoNFNxuyVUgvs/icr9n1W7/ANZN9lU9VwDUsOhWiHfMDLJ/eJP8q6WinyjeIntHT0MtY0SPYiKFHQYqslmBNuIG30xW7RS5UZc8u5km3hP/ACzX8qhuraEWrlYxnjtW5RUypJpoqNWSd7mLFaQCMDywfwp32O3/AOeYrYopqmkrCdWbe5iSWFu68RgGkXT7YD5owTW5RR7NB7WfcxP7Otf+eQpradaAZMVbtFHs49h+2n3Zz6adblt3l4HpVtIo4xhEUD6Vq0UciWwnUm92ZQiUMxKqc+1VJ9ItZQSimOQ/xA10FFDpqW4Rqzg7xZzBt9StFAt5vPQfw4xT11lUk2XsJgPr1rpKKj2TXws1deMvjjf8DIhuYbhd0MgI9+KlrSorRR7mDavoZtMkQuuA2K1aKfKK5mjp+FV7b/WSmtqiodO7T7FKVk0cHrcwmvRDF0zyR3q7olmLe6l3feAFdfRW03zRSXQ0Va0WrGbRWlRUcpjczaK0qKOULmdQa0aKnkHczaUVo0U3G4rmdWTqQ2ajZYP96unoqZU+ZWNadTklexzjFjGrZ3Y7etIBv3H7jV0lFZ+w8x+28jnJH3RsAMkUoIcoxH0roqKPYeYe28jnWJMYU/ITSFWdwd2AByvrXR0Uew8x+28jmkH7on/VvnnvSl1Xe7N6duldJRR7DzD23kc7ny1UlsjvUq/Nyfwrdoqo0uXqS6vkYZOKQDPJrdorTkF7TyMMnFIBnk1u0Ucge08jDJxSAZ5NbtFHIHtPIwycUgGeTW7RRyB7TyMMnFWbLJ3k+1adFCiJzurFekqzRVWIuV6SrNFFguV6SrNFFguV6SrNFFguf//Z\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import IFrame\n",
    "Image(filename='heatmap.jpg') \n",
    "#To view the entire interactive heatmap, uncommnt below\n",
    "#IFrame(\"./heat.html\", width=1000, height=500)\n",
    "#This section from the full heatmap focuses on Southern CA"
   ]
  },
  {
   "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>COUNTY/UNIT</th>\n",
       "      <th>BURNED_TOTAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Ventura County</td>\n",
       "      <td>34102.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Trinity County</td>\n",
       "      <td>22483.291667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Santa Barbara County</td>\n",
       "      <td>22410.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Siskiyou County</td>\n",
       "      <td>19931.837838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Monterey County</td>\n",
       "      <td>17271.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Plumas County</td>\n",
       "      <td>16887.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Del Norte County</td>\n",
       "      <td>16873.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>San Diego County</td>\n",
       "      <td>16748.982456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Tuolumne County</td>\n",
       "      <td>15476.368421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Amador County</td>\n",
       "      <td>11547.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Lake County</td>\n",
       "      <td>10419.909091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>El Dorado County</td>\n",
       "      <td>9241.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Los Angeles County</td>\n",
       "      <td>9169.684211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>San Bernardino County</td>\n",
       "      <td>8258.517241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Humboldt County</td>\n",
       "      <td>8180.611111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Santa Clara County</td>\n",
       "      <td>7826.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Fresno County</td>\n",
       "      <td>6339.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Kings County</td>\n",
       "      <td>5644.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Orange County</td>\n",
       "      <td>5481.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Shasta County</td>\n",
       "      <td>5250.695652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Modoc County</td>\n",
       "      <td>5099.954545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Tulare County</td>\n",
       "      <td>4894.797101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Lassen County</td>\n",
       "      <td>4577.314286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Nevada County</td>\n",
       "      <td>4086.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Inyo County</td>\n",
       "      <td>3611.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Yolo County</td>\n",
       "      <td>3606.411765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>San Luis Obispo County</td>\n",
       "      <td>3600.904762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Butte County</td>\n",
       "      <td>3570.828571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Stanislaus County</td>\n",
       "      <td>3434.466667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Mariposa County</td>\n",
       "      <td>3307.424242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Riverside County</td>\n",
       "      <td>3259.012346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>San Joaquin County</td>\n",
       "      <td>3180.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Tehama County</td>\n",
       "      <td>2975.535714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Sierra County</td>\n",
       "      <td>2879.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Placer County</td>\n",
       "      <td>2852.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Kern County</td>\n",
       "      <td>2825.439394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Mendocino County</td>\n",
       "      <td>2618.608696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alameda County</td>\n",
       "      <td>2417.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Glenn County</td>\n",
       "      <td>2341.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Napa County</td>\n",
       "      <td>2046.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Santa Cruz County</td>\n",
       "      <td>2030.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Sonoma County</td>\n",
       "      <td>2013.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Mono County</td>\n",
       "      <td>2002.411765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Calaveras County</td>\n",
       "      <td>1914.529412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Madera County</td>\n",
       "      <td>1673.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Merced County</td>\n",
       "      <td>1652.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Sacramento County</td>\n",
       "      <td>1444.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Imperial County</td>\n",
       "      <td>1138.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>San Benito County</td>\n",
       "      <td>1026.777778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Yuba County</td>\n",
       "      <td>896.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Contra Costa County</td>\n",
       "      <td>700.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alpine County</td>\n",
       "      <td>655.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Solano County</td>\n",
       "      <td>188.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               COUNTY/UNIT  BURNED_TOTAL\n",
       "50          Ventura County  34102.166667\n",
       "47          Trinity County  22483.291667\n",
       "37    Santa Barbara County  22410.700000\n",
       "42         Siskiyou County  19931.837838\n",
       "24         Monterey County  17271.000000\n",
       "29           Plumas County  16887.166667\n",
       "6         Del Norte County  16873.000000\n",
       "34        San Diego County  16748.982456\n",
       "49         Tuolumne County  15476.368421\n",
       "2            Amador County  11547.250000\n",
       "15             Lake County  10419.909091\n",
       "7         El Dorado County   9241.571429\n",
       "17      Los Angeles County   9169.684211\n",
       "33   San Bernardino County   8258.517241\n",
       "10         Humboldt County   8180.611111\n",
       "38      Santa Clara County   7826.750000\n",
       "8            Fresno County   6339.375000\n",
       "14            Kings County   5644.000000\n",
       "27           Orange County   5481.500000\n",
       "40           Shasta County   5250.695652\n",
       "22            Modoc County   5099.954545\n",
       "48           Tulare County   4894.797101\n",
       "16           Lassen County   4577.314286\n",
       "26           Nevada County   4086.142857\n",
       "12             Inyo County   3611.900000\n",
       "51             Yolo County   3606.411765\n",
       "36  San Luis Obispo County   3600.904762\n",
       "3             Butte County   3570.828571\n",
       "45       Stanislaus County   3434.466667\n",
       "19         Mariposa County   3307.424242\n",
       "30        Riverside County   3259.012346\n",
       "35      San Joaquin County   3180.714286\n",
       "46           Tehama County   2975.535714\n",
       "41           Sierra County   2879.333333\n",
       "28           Placer County   2852.000000\n",
       "13             Kern County   2825.439394\n",
       "20        Mendocino County   2618.608696\n",
       "0           Alameda County   2417.714286\n",
       "9             Glenn County   2341.000000\n",
       "25             Napa County   2046.000000\n",
       "39       Santa Cruz County   2030.400000\n",
       "44           Sonoma County   2013.666667\n",
       "23             Mono County   2002.411765\n",
       "4         Calaveras County   1914.529412\n",
       "18           Madera County   1673.000000\n",
       "21           Merced County   1652.416667\n",
       "31       Sacramento County   1444.000000\n",
       "11         Imperial County   1138.500000\n",
       "32       San Benito County   1026.777778\n",
       "52             Yuba County    896.142857\n",
       "5      Contra Costa County    700.125000\n",
       "1            Alpine County    655.000000\n",
       "43           Solano County    188.500000"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "burned_total_df = clean_df[['COUNTY/UNIT', 'BURNED_TOTAL']].groupby('COUNTY/UNIT').mean().reset_index()\n",
    "burned_total_df.sort_values('BURNED_TOTAL', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "#folium map, this map can be found at the beginning of our report, and can \n",
    "#also be viewed by opening the html file called 'map.html'\n",
    "from IPython.display import HTML, display, IFrame\n",
    "m = folium.Map(location = [36, -119])\n",
    "folium.Choropleth(\n",
    "    geo_data=ca_counties,\n",
    "    name='fire_counts',\n",
    "    data=count_joined,\n",
    "    columns=['COUNTY', 'COUNT'],\n",
    "    key_on='feature.properties.COUNTY_NAME',\n",
    "    fill_color='Reds'\n",
    "    ).add_to(m)\n",
    "m.save('map.html')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# IFrame(src='./map.html', width=700, height=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The 5 Counties with the largest slopes:'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'County Name: Lake County, Slope: 708.1906969309462'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'County Name: Amador County, Slope: 1205.1355498721227'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'County Name: Siskiyou County, Slope: 1507.1474531116799'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'County Name: Tuolumne County, Slope: 1701.6643222506393'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'County Name: Fresno County, Slope: 2880.167471227621'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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": [
    "from scipy.stats import linregress\n",
    "# Goal: FIND THE MOST PROBABLE COUNTIES THAT IS SUSCEPTIBLE TO FIRE |\n",
    "# clean_df = clean_df.drop(494)\n",
    "unique_counties = clean_df[\"COUNTY/UNIT\"].dropna().unique()\n",
    "years = clean_df[\"START_DATE\"].apply(lambda x: x.year).unique()\n",
    "# Removed the abnormal year data\n",
    "years = np.delete(years, 10)\n",
    "years = np.delete(years, 7)\n",
    "res_tuples = []\n",
    "\n",
    "for county in unique_counties:\n",
    "    # Get the count of fires per year\n",
    "    county_df = clean_df[clean_df[\"COUNTY/UNIT\"] == county]\n",
    "    county_df[\"YEAR\"] = county_df[\"START_DATE\"].apply(lambda x: x.year)\n",
    "    years_total = county_df.groupby('YEAR').mean().BURNED_TOTAL\n",
    "    for year in years:\n",
    "        try:\n",
    "            years_total[year]\n",
    "        except KeyError:\n",
    "            years_total[year] = 0\n",
    "    \n",
    "    res = linregress(years_total.index, years_total.values)\n",
    "    slope = res[0]\n",
    "    intercept = res[1]\n",
    "    r_squared = res[2]\n",
    "    p_val = res[3]\n",
    "    res_tuples.append((county, slope, intercept, r_squared, p_val))\n",
    "\n",
    "res_tuples.sort(key=lambda tup: tup[1])\n",
    "# Extract the top-5\n",
    "best_tuples = res_tuples[-5:]\n",
    "for tup in best_tuples:\n",
    "    slope = tup[1]\n",
    "    intercept = tup[2]\n",
    "    y_pts = []\n",
    "    for year in years:\n",
    "        y_pts.append( slope * year + intercept)  \n",
    "    county_fireplot = sns.lineplot(years, y_pts, label=tup[0])\n",
    "    \n",
    "county_fireplot.set(xlabel=\"Year\", ylabel=\"Predicted Average Burned Total Fire\", title=\"Top 5 Slopes of County & Burned_Total\")\n",
    "\n",
    "display(\"The 5 Counties with the largest slopes:\")\n",
    "for tuple in best_tuples:\n",
    "    res_str = \"County Name: \" + str(tuple[0]) + \", Slope: \" + str(tuple[1]) \n",
    "    display(res_str)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explanation\n",
    "In the graph above, we tried to examine the relationship between burned total acres and year and then we try to predict the average burned total acres for every county. We took the average of the total acres burned for every county every year and did a linear regression on those data. The graph shown above is made from the 5 counties whose linear regression is within the Top 5 of other slopes.  \n",
    "\n",
    "The graph above produces positive slopes, which seems to suggest that the 5 counties presented above are increasingly more vulnerable to forest fires as the year goes. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categorization of Deadly Fires\n",
    "\n",
    "### Split Training Data and Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "fatality_df = clean_df[['ORIGIN_DPT', 'BURNED_TOTAL', 'VEG_TYPE', 'CAUSE', 'FATALITY_FIRE', 'FATALITY_CIVIL']]\n",
    "fatality_df['FATAL'] = fatality_df[['FATALITY_FIRE', 'FATALITY_CIVIL']].apply(lambda x: 1 if x.any() else 0, axis=1)\n",
    "\n",
    "#fixing massive class imbalance using bootstrap\n",
    "bootstrap_dpt = np.random.choice(fatality_df.ORIGIN_DPT.values, 900, replace=True)\n",
    "bootstrap_burned_total = np.random.choice(fatality_df.ORIGIN_DPT.values, 900, replace=True)\n",
    "bootstrap_veg_type = np.random.choice(fatality_df.ORIGIN_DPT.values, 900, replace=True)\n",
    "bootstrap_cause = np.random.choice(fatality_df.ORIGIN_DPT.values, 900, replace=True)\n",
    "bootstrap_fatal = [1] * 900\n",
    "\n",
    "bootstrap_df = pd.DataFrame({'ORIGIN_DPT' : bootstrap_dpt,\n",
    "                             'BURNED_TOTAL' : bootstrap_burned_total,\n",
    "                             'VEG_TYPE' : bootstrap_veg_type,\n",
    "                             'CAUSE' : bootstrap_cause,\n",
    "                             'FATALITY_FIRE' : [0] * 900,\n",
    "                             'FATALITY_CIVIL' : [0] * 900,\n",
    "                             'FATAL' : bootstrap_fatal})\n",
    "\n",
    "balance_df = pd.concat([fatality_df, bootstrap_df])\n",
    "\n",
    "X = balance_df[['ORIGIN_DPT', 'BURNED_TOTAL', 'VEG_TYPE', 'CAUSE']]\n",
    "y = balance_df['FATAL']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Pipelines and Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "#train_test_split\n",
    "X_train, X_test, y_train, y_yest = train_test_split(X, y)\n",
    "#deal with class imbalance\n",
    "\n",
    "###\n",
    "bayes = GaussianNB()\n",
    "knn = KNeighborsClassifier()\n",
    "tree = DecisionTreeClassifier()\n",
    "forest = RandomForestClassifier() \n",
    "#fit\n",
    "#column_transformer into pipeline\n",
    "#col_transformer\n",
    "categorical_cols = [\"ORIGIN_DPT\", \"VEG_TYPE\", \"CAUSE\"]\n",
    "col_trans = ColumnTransformer([(\"hottie\", sklearn.preprocessing.OneHotEncoder(handle_unknown= \"ignore\", sparse=False), categorical_cols)])\n",
    "\n",
    "#data pipelines\n",
    "#naive bayes\n",
    "pipe1 = sklearn.pipeline.Pipeline([('preproc', col_trans), ('classifier', bayes)]).fit(X_train.fillna(\"None\"), y_train)\n",
    "guess_bayes = pipe1.predict(X_test.fillna(\"None\"))\n",
    "#knn\n",
    "pipe2 = sklearn.pipeline.Pipeline([('preproc', col_trans), ('classifier', knn)]).fit(X_train.fillna(\"None\"), y_train)\n",
    "guess_knn = pipe2.predict(X_test.fillna(\"None\"))\n",
    "#descision tree\n",
    "pipe3 = sklearn.pipeline.Pipeline([('preproc', col_trans), ('classifier', tree)]).fit(X_train.fillna(\"None\"), y_train)\n",
    "guess_tree = pipe3.predict(X_test.fillna(\"None\"))\n",
    "#random forest\n",
    "pipe4 = sklearn.pipeline.Pipeline([('preproc', col_trans), ('classifier', forest)]).fit(X_train.fillna(\"None\"), y_train)\n",
    "guess_forest = pipe4.predict(X_test.fillna(\"None\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Scoring\n",
    "\n",
    "Due to the relative rarity of our event happening, we want to place more emphasis on the *recall* score of our function. Nevertheless we don't want to ignore precision either so we will use the `F2` score to grade our models. Since `sklearn` doesn't have an `F2` score, we will make our own using the *confusion matrix* which contains **True Positives (TP)**, **False Positives (FP)**, **True Negatives (TN)**, and **False Negatives (FN)**. \n",
    "\n",
    "The formula for `F2` score is as follows:\n",
    "\n",
    "<img src=\"http://bit.ly/317jgjQ\" align=\"center\" border=\"0\" alt=\"F_{2} = \\frac{5}{4} *  \\frac{precision * recall}{precision + recall}   \" width=\"348\" height=\"71\" />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def f2_score(cm):\n",
    "    tn = cm[0][0]\n",
    "    fn = cm[1][0]\n",
    "    tp = cm[1][1]\n",
    "    fp = cm[0][1]\n",
    "    precision = tp / (tp + fp)\n",
    "    recall = tp / (tp + fn)\n",
    "    f1 = precision * recall / (precision + recall)\n",
    "    return (5 / 4) * f1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'F2 results found: '"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Bayes score: 0.6153846153846153, KNN score: 0.6180709534368071Tree score: 0.6109865470852017, Forest score: 0.6138392857142858'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bayes_score = f2_score(sklearn.metrics.confusion_matrix(guess_bayes, y_yest))\n",
    "knn_score = f2_score(sklearn.metrics.confusion_matrix(guess_knn, y_yest))\n",
    "tree_score = f2_score(sklearn.metrics.confusion_matrix(guess_tree, y_yest))\n",
    "forest_score = f2_score(sklearn.metrics.confusion_matrix(guess_forest, y_yest))\n",
    "\n",
    "display(\"F2 results found: \")\n",
    "display(\"Bayes score: \" + str(bayes_score) + \", KNN score: \" + str(knn_score) + \"Tree score: \" + str(tree_score) + \", Forest score: \" + str(forest_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **ETHICAL & PRIVACY CONSIDERATIONS: **\n",
    "\n",
    "The datasets to be used for our project are easily accessible by the public and can be found on Federal.gov websites or on kaggle via Fire Program Analysis data sourced from the US Government, this means there is not a concern regarding privacy issues. Moreover, we believe the outcome of our research would not produce an ethical issue since it is concerned with improving public readiness to combat and avoid wildfires. Since our data is also based on objective statistics, there is no bias regarding the data we will be using since the data is compiled using satellite imaging.\n",
    "\n",
    "\n",
    "This also follows the safe harbor method, as although the dataset includes longitude and latitude, the data is not personal in nature.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **RESULTS AND CONCLUSION**\n",
    "\n",
    "With an f2 score of .61, we can safely assume that while our hypothesis seemed to be heading in the right direction, we need more data to conclude our hypothesis is true. While this f2 score is better than our initial predictive models which used economic data and attempted to connect acres burned to dollar amount of damage, we still cannot make any prediction about where to definitively allocate resources to mitigate future fires and damages. However, with our data we were able to eliminate many preconceived notions that the public may have about fires. For example, there was no correlation found between vegetation type and fire frequency, nor fire length and fire damage in terms of acres burned and structural damage. If we were to create an ideal dataset on which to conduct this research, as opposed to resorting to PDF scraping using Tabula, it would include all fires instead of only the largest fires per year, as well as data for resources used by county and departments per county. Overall we believe that our findings are very interesting and with the right data set, our predictive model could be slightly changed to effectively allocate firefighting resources."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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