FinalProjectGroup093_Fa21.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3d15SeXPt5L5"
   },
   "source": [
    "# COGS 108 - Wildfires and Environmental Variables\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7XrrarEFPxCh"
   },
   "source": [
    "# Permissions\n",
    "\n",
    "*   [X] YES - make available\n",
    "*   [ ] NO - keep private\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "K7r1EbnoNBH8"
   },
   "source": [
    "# Overview\n",
    "\n",
    "As California residents, we've witnessed the prevalence of wildfires all across the state, and decided to look at which factors contribute to and might predict the occurrence of a wildfire. Specifically, we looked at 20 counties in Northern California, and wildfires occurring rom 2018 to 2020, as they seem to be getting more intense and common in recent years.\n",
    "\n",
    "Through our visualizations and analysis of the data, we found a statistically significant correlation between precipitation and temperature in wildfire occurrences, meaning that wildfires were most likely to occur during months with especially high temperatures and low levels of precipitation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cr_mnVdzK3zq"
   },
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mDoMau1Pt5L8"
   },
   "source": [
    "# Names\n",
    "\n",
    "- Kairi Sageshima\n",
    "- Brandon Wang\n",
    "- Marisol Jimenez\n",
    "- Ashley Chu\n",
    "- Daniel Milton"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GRFsCwcDt5L9"
   },
   "source": [
    "<a id='research_question'></a>\n",
    "# Research Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "J4XoRMG0t5L9"
   },
   "source": [
    "*Which factor is most influential in predicting the likelihood of a forest fire in the Northern Region (Department of Forestry and Fire Protection) of California: temperature or precipitation in the region? Can we use such variables to predict and serve as an early warning system for wildfires in California?*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Rsk045GHQRFB"
   },
   "source": [
    "# Background and Prior Work\n",
    "\n",
    "An increase in wildfires in California have been an issue in recent years, and geologists have pointed to a number of factors that have been attributed to this increase. Factors such as drought season, combustible material, ambient environmental variables such as temperature, humidity, and vapor pressure deficit, grass coverage, weather patterns, and distance to roads have been some factors identified. To concentrate on easily quantifiable data, we decided to use temperature and humidity levels of the area to serve as predictors of wildfires, as other factors will require extensive coding and wrangling to make into quantitative variables. Hopefully, we will be able to build a model that can reliably predict areas of high wildfire probability to serve as an early warning system.\n",
    "\n",
    "### References (include links):\n",
    "\n",
    "**Source 1:** https://earthobservatory.nasa.gov/images/148908/whats-behind-californias-surge-of-large-fires\n",
    "\n",
    "1) The number of wildfires in California has been proliferating in recent years. “Eight of the state’s ten largest fires on record—and twelve of the top twenty—have happened within the past five years.” Jon Keeley, a U.S. Geological Survey scientist, claims that many factors may be responsible for this increase. The primary reasons seem to be a particularly harsh drought season and rising temperatures due to global warming. Decades of fire suppression have also allowed highly combustible material to pile up and create hotspots for forest fires.\n",
    "\n",
    "**Source 2:** https://www.nature.com/articles/s41598-021-88131-9\n",
    "\n",
    "2)  As seen on the news, wildfires have become more and more of a common occurrence, especially in California. “The wildfire season has lengthened and the peak months have been advanced from August to July. In terms of the variables related to the risk of wildfire occurrence, the temperature, vapor pressure deficit, grass cover, and the distance to roads are crucial.” These are some additional variables that previous projects have been aware of in the past. The increase of temperature does have a direct correlation with occurrence of wildfires, but there are also many other factors in the mix.\n",
    "\n",
    "**Source 3:** https://www.predictiveservices.nifc.gov/outlooks/monthly_seasonal_outlook.pdf\n",
    "\n",
    "3) Common predictors of wildfires in Northern California as researched and identified by the National Interagency Fire Center are: drought conditions (below average levels of precipitation, above average temperatures), live and dead fuel moisture contents, and enhanced windy periods. These weather patterns are influenced by sea temperatures, El Niño and the time of year.\n",
    "Live fuel moisture content refers to the amount of moisture in living plants/vegetation that could be consumed as fuel in a forest fire.\n",
    "In Southern California, upper level high pressure over the Desert Southwest causes higher than average temperatures during the late summer/early fall months. Thunderstorms also start fires (such as the Windy and KNP Complex Fires in southern central California).\n",
    "Upper level highs occur where there is especially high pressure in the upper atmosphere of a particular region, causing “comparatively warm dry air with light winds over a large area” — National Wildfire Coordinating Group. Upper level highs are caused by above normal sea surface temperatures.\n",
    "\n",
    "Other similar projects on predicting wildfires focused on weather events as well, but also location, such as this one: Wildfires in California Prediction. In this project, they learned how to create a model that could essentially predict that 2019 was not a “bad” year for wildfires.\n",
    "\n",
    "If we can, we will try to account for location using humidity, and the amount of live and dead moisture content (availability of combustible fuel), as they would point to locations such as forests where fires commonly happen.\n",
    "Hypothesis\n",
    "\n",
    "We predict that an increase in temperature and a decrease in humidity will lead to higher chance of fires, and a decrease in temperatures or an increase in humidity will predict a lower chance of fires.\n",
    "Data\n",
    "\n",
    "The variables needed for this analysis are: Count and location of fires in california. Average temperature of region during the time period of the fire, Humidity or lack thereof at location of fire during the same time period. The amount of combustible materials at the fire location. \n",
    "\n",
    "This data of fire frequency, location and date has been collected by the California fire department . As for the climate data it can be accessed through https://climatetoolbox.org/ and https://www.usclimatedata.com/  which provides records of temperature, vegetation, water and drought data for any region within the US. These data frames would have to be imported, cleaned and combined to make the ideal data set to answer the research question. \n",
    " \n",
    "We will collect data by collecting the temperature and humidity data from each record of a fire in California in the last year. We can go further back in years if needed to expand our statistical validity with more observations, but for now to limit the scope of the data collected we will focus on the year of 2021. \n",
    "\n",
    "Data will be stored as a CSV, with columns for timestamp, location, temperature, humidity, drought, and vegetation presence. Data wrangling will ensure that the temperature and humidity variables are stored as floats in decimal notation, while location will be used as a categorical variable. Timestamps will he converted into an appropriate time object (or coded integer) for parsing, and drought and vegetation will be stored as coded indexes for meaningful comparisons.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0G1-Eim9RR9x"
   },
   "source": [
    "# Hypothesis\n",
    "\n",
    "We predict that an increase in temperature and a decrease in precipitation will lead to higher chance of fires, and a decrease in temperatures or an increase in precipitation will predict a lower chance of fires.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "H3ldMWH3Re9h"
   },
   "source": [
    "# Dataset(s)\n",
    "\n",
    "**Wildfire Incident Database**\n",
    "\n",
    "Link to the dataset: https://gis.data.ca.gov/datasets/e3802d2abf8741a187e73a9db49d68fe_0/explore?showTable=true\n",
    "Number of observations: 21,318 observations\n",
    "This database logs wildfire incidents by county/forest in California, and also records the cause, timestamp recorded, and the timestamp it was contained. \n",
    "\n",
    "**Weather Database**\n",
    "\n",
    "Link to the dataset: https://www.ncdc.noaa.gov/cag/county/mapping/4/tavg/201802/1/value\n",
    "Number of observations: 88,219 observations\n",
    "This database holds the average temperature for each county of California in each month from 2018-2020.\n",
    "\n",
    "We plan to use the wildfire incident database to gather observations of wildfires in our selected counties by timestamp and location. We then plan to match the timestamp and location from the fire to the environmental variables from the weather database, so we can see the temperature at the given location and time. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ic4iZBhwt5L-"
   },
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FwtNXYs4t5L-",
    "outputId": "e6d21f3f-6d59-4e2d-951a-84afb8ebbaad"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "# import matplotlib\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.style as style\n",
    "\n",
    "# Statmodels & patsy\n",
    "import patsy\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 684
    },
    "id": "GznlUSjYt5L_",
    "outputId": "098e63b4-94a5-4753-8037-5c1872474924"
   },
   "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>YEAR_</th>\n",
       "      <th>STATE</th>\n",
       "      <th>AGENCY</th>\n",
       "      <th>UNIT_ID</th>\n",
       "      <th>FIRE_NAME</th>\n",
       "      <th>INC_NUM</th>\n",
       "      <th>ALARM_DATE</th>\n",
       "      <th>CONT_DATE</th>\n",
       "      <th>CAUSE</th>\n",
       "      <th>COMMENTS</th>\n",
       "      <th>REPORT_AC</th>\n",
       "      <th>GIS_ACRES</th>\n",
       "      <th>C_METHOD</th>\n",
       "      <th>OBJECTIVE</th>\n",
       "      <th>FIRE_NUM</th>\n",
       "      <th>SHAPE_Length</th>\n",
       "      <th>SHAPE_Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21440</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>CA</td>\n",
       "      <td>CDF</td>\n",
       "      <td>NEU</td>\n",
       "      <td>NELSON</td>\n",
       "      <td>00013212</td>\n",
       "      <td>2020/06/18 00:00:00+00</td>\n",
       "      <td>2020/06/23 00:00:00+00</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>110.0</td>\n",
       "      <td>109.602500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4179.743142</td>\n",
       "      <td>-7.331347e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21441</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>CA</td>\n",
       "      <td>CDF</td>\n",
       "      <td>NEU</td>\n",
       "      <td>AMORUSO</td>\n",
       "      <td>00011799</td>\n",
       "      <td>2020/06/01 00:00:00+00</td>\n",
       "      <td>2020/06/04 00:00:00+00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>670.0</td>\n",
       "      <td>685.585020</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12399.375391</td>\n",
       "      <td>-4.578172e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21442</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>CA</td>\n",
       "      <td>CDF</td>\n",
       "      <td>NEU</td>\n",
       "      <td>ATHENS</td>\n",
       "      <td>00018493</td>\n",
       "      <td>2020/08/10 00:00:00+00</td>\n",
       "      <td>2020/03/01 00:00:00+00</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.0</td>\n",
       "      <td>27.300480</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2119.194120</td>\n",
       "      <td>-1.823876e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21443</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>CA</td>\n",
       "      <td>CDF</td>\n",
       "      <td>NEU</td>\n",
       "      <td>FLEMING</td>\n",
       "      <td>00007619</td>\n",
       "      <td>2020/03/31 00:00:00+00</td>\n",
       "      <td>2020/04/01 00:00:00+00</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0</td>\n",
       "      <td>12.931550</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2029.524881</td>\n",
       "      <td>-8.667942e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21444</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>CA</td>\n",
       "      <td>CDF</td>\n",
       "      <td>NEU</td>\n",
       "      <td>MELANESE</td>\n",
       "      <td>00008471</td>\n",
       "      <td>2020/04/14 00:00:00+00</td>\n",
       "      <td>2020/04/19 00:00:00+00</td>\n",
       "      <td>18.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.3</td>\n",
       "      <td>10.315960</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1342.742903</td>\n",
       "      <td>-7.017912e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21313</th>\n",
       "      <td>42760</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>CA</td>\n",
       "      <td>CCO</td>\n",
       "      <td>LAC</td>\n",
       "      <td>MUREAU</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019/10/30 00:00:00+00</td>\n",
       "      <td>2019/10/30 00:00:00+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.297734</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>897.323534</td>\n",
       "      <td>-3.730642e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21314</th>\n",
       "      <td>42761</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>CA</td>\n",
       "      <td>LRA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OAK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019/10/28 00:00:00+00</td>\n",
       "      <td>2019/10/28 00:00:00+00</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NPS#596 Rapid ROS in light grassy 1yr old fuels</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.482671</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1215.382952</td>\n",
       "      <td>-5.024716e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21315</th>\n",
       "      <td>42762</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>CA</td>\n",
       "      <td>LRA</td>\n",
       "      <td>LDF</td>\n",
       "      <td>BARHAM</td>\n",
       "      <td>00000845</td>\n",
       "      <td>2019/11/09 00:00:00+00</td>\n",
       "      <td>2019/11/10 00:00:00+00</td>\n",
       "      <td>14.0</td>\n",
       "      <td>LACFD 0845</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.888229</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4093.657796</td>\n",
       "      <td>-3.843117e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21316</th>\n",
       "      <td>42763</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>CA</td>\n",
       "      <td>NPS</td>\n",
       "      <td>MNP</td>\n",
       "      <td>STAR</td>\n",
       "      <td>00013598</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66.587181</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4777.042672</td>\n",
       "      <td>-4.051741e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21317</th>\n",
       "      <td>42764</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>CA</td>\n",
       "      <td>LRA</td>\n",
       "      <td>LDF</td>\n",
       "      <td>SADDLE RIDGE</td>\n",
       "      <td>00001582</td>\n",
       "      <td>2019/10/10 00:00:00+00</td>\n",
       "      <td>2019/10/31 00:00:00+00</td>\n",
       "      <td>14.0</td>\n",
       "      <td>shape from GEOMAC, dat from CAL FIRE web</td>\n",
       "      <td>8799.0</td>\n",
       "      <td>8799.325200</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86547.407832</td>\n",
       "      <td>-5.233553e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21318 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       OBJECTID   YEAR_ STATE AGENCY UNIT_ID     FIRE_NAME   INC_NUM  \\\n",
       "0         21440  2020.0    CA    CDF     NEU        NELSON  00013212   \n",
       "1         21441  2020.0    CA    CDF     NEU       AMORUSO  00011799   \n",
       "2         21442  2020.0    CA    CDF     NEU        ATHENS  00018493   \n",
       "3         21443  2020.0    CA    CDF     NEU       FLEMING  00007619   \n",
       "4         21444  2020.0    CA    CDF     NEU      MELANESE  00008471   \n",
       "...         ...     ...   ...    ...     ...           ...       ...   \n",
       "21313     42760  2019.0    CA    CCO     LAC        MUREAU       NaN   \n",
       "21314     42761  2019.0    CA    LRA     NaN           OAK       NaN   \n",
       "21315     42762  2019.0    CA    LRA     LDF        BARHAM  00000845   \n",
       "21316     42763  2019.0    CA    NPS     MNP          STAR  00013598   \n",
       "21317     42764  2019.0    CA    LRA     LDF  SADDLE RIDGE  00001582   \n",
       "\n",
       "                   ALARM_DATE               CONT_DATE  CAUSE  \\\n",
       "0      2020/06/18 00:00:00+00  2020/06/23 00:00:00+00   11.0   \n",
       "1      2020/06/01 00:00:00+00  2020/06/04 00:00:00+00    2.0   \n",
       "2      2020/08/10 00:00:00+00  2020/03/01 00:00:00+00   14.0   \n",
       "3      2020/03/31 00:00:00+00  2020/04/01 00:00:00+00    9.0   \n",
       "4      2020/04/14 00:00:00+00  2020/04/19 00:00:00+00   18.0   \n",
       "...                       ...                     ...    ...   \n",
       "21313  2019/10/30 00:00:00+00  2019/10/30 00:00:00+00    NaN   \n",
       "21314  2019/10/28 00:00:00+00  2019/10/28 00:00:00+00   14.0   \n",
       "21315  2019/11/09 00:00:00+00  2019/11/10 00:00:00+00   14.0   \n",
       "21316                     NaN                     NaN   14.0   \n",
       "21317  2019/10/10 00:00:00+00  2019/10/31 00:00:00+00   14.0   \n",
       "\n",
       "                                              COMMENTS  REPORT_AC  \\\n",
       "0                                                  NaN      110.0   \n",
       "1                                                  NaN      670.0   \n",
       "2                                                  NaN       26.0   \n",
       "3                                                  NaN       13.0   \n",
       "4                                                  NaN       10.3   \n",
       "...                                                ...        ...   \n",
       "21313                                              NaN        NaN   \n",
       "21314  NPS#596 Rapid ROS in light grassy 1yr old fuels        NaN   \n",
       "21315                                       LACFD 0845        NaN   \n",
       "21316                                              NaN        NaN   \n",
       "21317         shape from GEOMAC, dat from CAL FIRE web     8799.0   \n",
       "\n",
       "         GIS_ACRES  C_METHOD  OBJECTIVE FIRE_NUM  SHAPE_Length    SHAPE_Area  \n",
       "0       109.602500       1.0        1.0      NaN   4179.743142 -7.331347e+05  \n",
       "1       685.585020       1.0        1.0      NaN  12399.375391 -4.578172e+06  \n",
       "2        27.300480       1.0        1.0      NaN   2119.194120 -1.823876e+05  \n",
       "3        12.931550       1.0        1.0      NaN   2029.524881 -8.667942e+04  \n",
       "4        10.315960       1.0        1.0      NaN   1342.742903 -7.017912e+04  \n",
       "...            ...       ...        ...      ...           ...           ...  \n",
       "21313     6.297734       NaN        1.0      NaN    897.323534 -3.730642e+04  \n",
       "21314     8.482671       8.0        1.0      NaN   1215.382952 -5.024716e+04  \n",
       "21315    64.888229       8.0        1.0      NaN   4093.657796 -3.843117e+05  \n",
       "21316    66.587181       8.0        1.0      NaN   4777.042672 -4.051741e+05  \n",
       "21317  8799.325200       7.0        1.0      NaN  86547.407832 -5.233553e+07  \n",
       "\n",
       "[21318 rows x 18 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "  ## Import the wildfire data csv\n",
    "df = pd.read_csv('fire.csv')\n",
    "## Import the temperature data csv\n",
    "dfw = pd.read_csv('temp.csv')\n",
    "## Import the precipitation data csv\n",
    "dfp = pd.read_csv('precipitation.csv')\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NbSJPEldt5MB"
   },
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BBoO45Xm-Kk4"
   },
   "source": [
    "When choosing our data set we decided on using data from California where most wildfires occurances have increased in the past couple of years. This motivated us to focus on data from the years 2018 to 2020, the period with the most drastic increase in fires. Because California has a high variety of climates and biomes a generalization of the whole state would have caused many confounds and unwanted generalizations. Therefore to better focus our hypothesis we decided to focus on only northern California counties. To facilitate isolation of interested counties we made a county dictionary with all relevant counties and their abreviation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "xquwycmKt5MC"
   },
   "outputs": [],
   "source": [
    "# County Dictionary\n",
    "county_dict = {\n",
    "    'Yuba County': 'NEU',\n",
    "    'Nevada County': 'NEU',\n",
    "    'Placer County': 'NEU',\n",
    "    'Butte County' : 'BTU',\n",
    "    'San Mateo County' : 'CZU',\n",
    "    'Santa Cruz County' : 'CZU',\n",
    "    'Mendocino County' : 'MEU',\n",
    "    'Humboldt County' : 'HUU',\n",
    "    'Del Norte County' : 'HUU',\n",
    "    'Tehama County' : 'TGU',\n",
    "    'Glenn County' : 'TGU',\n",
    "    'Lassen County' : 'LMU',\n",
    "    'Modoc County' : 'LMU',\n",
    "    'Siskiyou County' : 'SKU',\n",
    "    'Shasta County' : 'SHU',\n",
    "    'Trinity County' : 'SHU',\n",
    "    'Santa Clara County' : 'SCU',\n",
    "    'Sonoma County' : 'LNU',\n",
    "    'Lake County' : 'LNU',\n",
    "    'Napa County' : 'LNU',\n",
    "    'Marin County' : 'MRN',\n",
    "    'El Dorado County' : 'AEU',\n",
    "    'Amador County' : 'AEU'\n",
    "}\n",
    "\n",
    "# Counties we are interested in\n",
    "# counties = ['SKU', 'HUU', 'SHU', 'LMU', 'TGU', 'MEU', 'BTU', 'NEU', 'AEU', 'LNU', 'SHF', 'TNF', 'PNF', 'HIA', 'LNF',\n",
    "#            'KNF', 'MNF', 'SRF', 'BNP', 'RNP']\n",
    "counties = ['SKU', 'HUU', 'SHU', 'LMU', 'TGU', 'MEU', 'BTU', 'NEU', 'AEU', 'LNU']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "52hufCPJt5MD"
   },
   "source": [
    "## Cleaning Fire Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "deSwPuB1_l30"
   },
   "source": [
    "Cleaning the Fire dataset included using the county dictionary to isolate the fires only in the norther counties. Unessesary columns were dropped to focus the data set on only the columns which provided information about the location, date and area the fire affected. We dropped the unnecessary columns, changed column names and made all columns lower case to make the header the the dataset uniform. To make the merge of the fire and weather datasets easier the fire dates were changed from DateTime objects to integers including only the month and day. Finally we dropped all columns with empty cells to make sure future coding ran smoothly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "16Mk5hpat5MD"
   },
   "outputs": [],
   "source": [
    "## Cleaning fire data set\n",
    "# Rename columns\n",
    "df = df.rename(columns = {'UNIT_ID' : 'county'})\n",
    "# Drop unused columns\n",
    "df = df.drop(columns = ['OBJECTID', 'AGENCY', 'INC_NUM', 'COMMENTS', 'REPORT_AC', 'SHAPE_Length', 'SHAPE_Area', 'FIRE_NUM', 'OBJECTIVE', 'C_METHOD'])\n",
    "df = df.reset_index()\n",
    "# Change to lowercase\n",
    "df.columns = df.columns.str.lower()\n",
    "# Only want the counties interested in\n",
    "df = df[df['county'].isin(counties)]\n",
    "# Convert dates into ints\n",
    "df['alarm_date'] = pd.to_datetime(df['alarm_date'])\n",
    "df['date_conv'] = (df['alarm_date'].dt.year.fillna(0).astype(int)).astype(str) + (df['alarm_date'].dt.month.fillna(0).astype(int)).astype(str)\n",
    "df['date_conv'] = df['date_conv'].astype(int)\n",
    "df = df.drop(columns = ['year_', 'alarm_date', 'cont_date'])\n",
    "# Only want 2018 - 2020\n",
    "df = df[df.date_conv > 201800]\n",
    "df = df[df.date_conv < 202100]\n",
    "# Drop NA values\n",
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Jd6G7KNnt5MD"
   },
   "source": [
    "## Cleaning Temperature Dataset\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DAB1lCoe__K7"
   },
   "source": [
    "Cleaning the temperature data set involved dropping unused columns, then renaming certain columns. We then created a region column for us to use later. We made a function that allowed us to convert county names to its corresponding county ID. We used this function and applied it to the county column and assigned it to the region column we created earlier. We then filtered the dates to only include years between 2018 and 2020. Finally, we then dropped all NA values in the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "6JbGnMpPt5ME"
   },
   "outputs": [],
   "source": [
    "# Drop unused columns\n",
    "dfw = dfw.drop(columns = ['Location ID', 'Rank', 'Anomaly (1901-2000 base period)', '1901-2000 Mean']) \n",
    "# Rename columns\n",
    "dfw = dfw.rename(columns = {'Location':'county', 'Value':'temperature'})\n",
    "# Create ‘region’ column\n",
    "dfw.assign(region = '')\n",
    "# Change to lowercase\n",
    "dfw.columns = dfw.columns.str.lower()\n",
    "\n",
    "# Function to change County full name to county ID in temp.csv\n",
    "def County_toID(county):\n",
    "       if county in county_dict:\n",
    "            return county.replace(county, county_dict[county])\n",
    "\n",
    "# Convert counties to regions\n",
    "new_county = dfw['county'].apply(County_toID)\n",
    "dfw['region'] = new_county\n",
    "\n",
    "# Filter out dates\n",
    "dfw = dfw[dfw.date > 201800]\n",
    "dfw = dfw[dfw.date < 202100]\n",
    "\n",
    "# Drop NA values\n",
    "dfw = dfw.dropna()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "vUWvnTT5t5ME",
    "outputId": "eab81034-84b4-4ff5-a985-f544b790f48d"
   },
   "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>index</th>\n",
       "      <th>state</th>\n",
       "      <th>county</th>\n",
       "      <th>fire_name</th>\n",
       "      <th>cause</th>\n",
       "      <th>gis_acres</th>\n",
       "      <th>date_conv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>CA</td>\n",
       "      <td>NEU</td>\n",
       "      <td>FIELDS</td>\n",
       "      <td>5.0</td>\n",
       "      <td>55.328430</td>\n",
       "      <td>202010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>CA</td>\n",
       "      <td>HUU</td>\n",
       "      <td>REDWOOD</td>\n",
       "      <td>2.0</td>\n",
       "      <td>101.400300</td>\n",
       "      <td>202010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>34</td>\n",
       "      <td>CA</td>\n",
       "      <td>HUU</td>\n",
       "      <td>MINE</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.074130</td>\n",
       "      <td>202012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>37</td>\n",
       "      <td>CA</td>\n",
       "      <td>BTU</td>\n",
       "      <td>GRAND</td>\n",
       "      <td>14.0</td>\n",
       "      <td>31.061090</td>\n",
       "      <td>202010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>45</td>\n",
       "      <td>CA</td>\n",
       "      <td>NEU</td>\n",
       "      <td>SIMPSON</td>\n",
       "      <td>14.0</td>\n",
       "      <td>28.574560</td>\n",
       "      <td>202010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>47</td>\n",
       "      <td>CA</td>\n",
       "      <td>NEU</td>\n",
       "      <td>LOCUST</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15.859510</td>\n",
       "      <td>202012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>120</td>\n",
       "      <td>CA</td>\n",
       "      <td>SHU</td>\n",
       "      <td>POINT</td>\n",
       "      <td>9.0</td>\n",
       "      <td>48.147720</td>\n",
       "      <td>202010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>127</td>\n",
       "      <td>CA</td>\n",
       "      <td>SHU</td>\n",
       "      <td>DERSCH</td>\n",
       "      <td>7.0</td>\n",
       "      <td>133.299500</td>\n",
       "      <td>202010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>147</td>\n",
       "      <td>CA</td>\n",
       "      <td>AEU</td>\n",
       "      <td>CAMERON FIRE</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.426010</td>\n",
       "      <td>202010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>149</td>\n",
       "      <td>CA</td>\n",
       "      <td>AEU</td>\n",
       "      <td>LAMBERT FIRE</td>\n",
       "      <td>9.0</td>\n",
       "      <td>21.816271</td>\n",
       "      <td>202010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>211</td>\n",
       "      <td>CA</td>\n",
       "      <td>LNU</td>\n",
       "      <td>POPE</td>\n",
       "      <td>10.0</td>\n",
       "      <td>61.800121</td>\n",
       "      <td>202010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>212</td>\n",
       "      <td>CA</td>\n",
       "      <td>LNU</td>\n",
       "      <td>LYONS</td>\n",
       "      <td>10.0</td>\n",
       "      <td>25.585110</td>\n",
       "      <td>202010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>213</td>\n",
       "      <td>CA</td>\n",
       "      <td>LNU</td>\n",
       "      <td>SALT</td>\n",
       "      <td>10.0</td>\n",
       "      <td>86.663361</td>\n",
       "      <td>202010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>453</td>\n",
       "      <td>CA</td>\n",
       "      <td>LNU</td>\n",
       "      <td>HUBCAP</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.017772</td>\n",
       "      <td>202010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>465</th>\n",
       "      <td>465</td>\n",
       "      <td>CA</td>\n",
       "      <td>LNU</td>\n",
       "      <td>LYTTON</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.931224</td>\n",
       "      <td>202010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>471</td>\n",
       "      <td>CA</td>\n",
       "      <td>LNU</td>\n",
       "      <td>CHABLIS</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.331930</td>\n",
       "      <td>202011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>487</td>\n",
       "      <td>CA</td>\n",
       "      <td>LNU</td>\n",
       "      <td>TWIN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.042918</td>\n",
       "      <td>202011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20607</th>\n",
       "      <td>20607</td>\n",
       "      <td>CA</td>\n",
       "      <td>BTU</td>\n",
       "      <td>JUNES</td>\n",
       "      <td>5.0</td>\n",
       "      <td>450.773010</td>\n",
       "      <td>201810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20608</th>\n",
       "      <td>20608</td>\n",
       "      <td>CA</td>\n",
       "      <td>BTU</td>\n",
       "      <td>CAMP</td>\n",
       "      <td>11.0</td>\n",
       "      <td>153335.560000</td>\n",
       "      <td>201811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20652</th>\n",
       "      <td>20652</td>\n",
       "      <td>CA</td>\n",
       "      <td>NEU</td>\n",
       "      <td>BUTTE_2</td>\n",
       "      <td>14.0</td>\n",
       "      <td>40.303265</td>\n",
       "      <td>201811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20672</th>\n",
       "      <td>20672</td>\n",
       "      <td>CA</td>\n",
       "      <td>TGU</td>\n",
       "      <td>SUN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3866.887700</td>\n",
       "      <td>201810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20673</th>\n",
       "      <td>20673</td>\n",
       "      <td>CA</td>\n",
       "      <td>TGU</td>\n",
       "      <td>ADAMS</td>\n",
       "      <td>11.0</td>\n",
       "      <td>55.531456</td>\n",
       "      <td>201811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20883</th>\n",
       "      <td>20883</td>\n",
       "      <td>CA</td>\n",
       "      <td>SKU</td>\n",
       "      <td>IRON GATE</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.111814</td>\n",
       "      <td>201810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21030</th>\n",
       "      <td>21030</td>\n",
       "      <td>CA</td>\n",
       "      <td>LNU</td>\n",
       "      <td>EAGLE</td>\n",
       "      <td>14.0</td>\n",
       "      <td>73.550552</td>\n",
       "      <td>201911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21031</th>\n",
       "      <td>21031</td>\n",
       "      <td>CA</td>\n",
       "      <td>LNU</td>\n",
       "      <td>KINCADE</td>\n",
       "      <td>11.0</td>\n",
       "      <td>77762.141000</td>\n",
       "      <td>201910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21032</th>\n",
       "      <td>21032</td>\n",
       "      <td>CA</td>\n",
       "      <td>LNU</td>\n",
       "      <td>AMERICAN</td>\n",
       "      <td>14.0</td>\n",
       "      <td>526.857850</td>\n",
       "      <td>201910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21108</th>\n",
       "      <td>21108</td>\n",
       "      <td>CA</td>\n",
       "      <td>SHU</td>\n",
       "      <td>PLAINS</td>\n",
       "      <td>10.0</td>\n",
       "      <td>42.175037</td>\n",
       "      <td>201910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21117</th>\n",
       "      <td>21117</td>\n",
       "      <td>CA</td>\n",
       "      <td>TGU</td>\n",
       "      <td>RAWSON</td>\n",
       "      <td>10.0</td>\n",
       "      <td>605.489750</td>\n",
       "      <td>201910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21118</th>\n",
       "      <td>21118</td>\n",
       "      <td>CA</td>\n",
       "      <td>TGU</td>\n",
       "      <td>RANCH</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2534.210900</td>\n",
       "      <td>201911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21164</th>\n",
       "      <td>21164</td>\n",
       "      <td>CA</td>\n",
       "      <td>NEU</td>\n",
       "      <td>FOOTHILLS</td>\n",
       "      <td>7.0</td>\n",
       "      <td>24.590448</td>\n",
       "      <td>201911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21165</th>\n",
       "      <td>21165</td>\n",
       "      <td>CA</td>\n",
       "      <td>NEU</td>\n",
       "      <td>LOMA</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.965376</td>\n",
       "      <td>201910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21174</th>\n",
       "      <td>21174</td>\n",
       "      <td>CA</td>\n",
       "      <td>MEU</td>\n",
       "      <td>BURRIS</td>\n",
       "      <td>9.0</td>\n",
       "      <td>703.355710</td>\n",
       "      <td>201910</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       index state county     fire_name  cause      gis_acres  date_conv\n",
       "12        12    CA    NEU        FIELDS    5.0      55.328430     202010\n",
       "28        28    CA    HUU       REDWOOD    2.0     101.400300     202010\n",
       "34        34    CA    HUU          MINE    5.0      11.074130     202012\n",
       "37        37    CA    BTU         GRAND   14.0      31.061090     202010\n",
       "45        45    CA    NEU       SIMPSON   14.0      28.574560     202010\n",
       "47        47    CA    NEU        LOCUST    5.0      15.859510     202012\n",
       "120      120    CA    SHU         POINT    9.0      48.147720     202010\n",
       "127      127    CA    SHU        DERSCH    7.0     133.299500     202010\n",
       "147      147    CA    AEU  CAMERON FIRE   10.0      14.426010     202010\n",
       "149      149    CA    AEU  LAMBERT FIRE    9.0      21.816271     202010\n",
       "211      211    CA    LNU          POPE   10.0      61.800121     202010\n",
       "212      212    CA    LNU         LYONS   10.0      25.585110     202010\n",
       "213      213    CA    LNU          SALT   10.0      86.663361     202010\n",
       "453      453    CA    LNU        HUBCAP    2.0       2.017772     202010\n",
       "465      465    CA    LNU        LYTTON    7.0       1.931224     202010\n",
       "471      471    CA    LNU       CHABLIS    8.0       1.331930     202011\n",
       "487      487    CA    LNU          TWIN    5.0       1.042918     202011\n",
       "20607  20607    CA    BTU         JUNES    5.0     450.773010     201810\n",
       "20608  20608    CA    BTU          CAMP   11.0  153335.560000     201811\n",
       "20652  20652    CA    NEU       BUTTE_2   14.0      40.303265     201811\n",
       "20672  20672    CA    TGU           SUN   10.0    3866.887700     201810\n",
       "20673  20673    CA    TGU         ADAMS   11.0      55.531456     201811\n",
       "20883  20883    CA    SKU     IRON GATE    2.0      15.111814     201810\n",
       "21030  21030    CA    LNU         EAGLE   14.0      73.550552     201911\n",
       "21031  21031    CA    LNU       KINCADE   11.0   77762.141000     201910\n",
       "21032  21032    CA    LNU      AMERICAN   14.0     526.857850     201910\n",
       "21108  21108    CA    SHU       PLAINS    10.0      42.175037     201910\n",
       "21117  21117    CA    TGU        RAWSON   10.0     605.489750     201910\n",
       "21118  21118    CA    TGU         RANCH   14.0    2534.210900     201911\n",
       "21164  21164    CA    NEU     FOOTHILLS    7.0      24.590448     201911\n",
       "21165  21165    CA    NEU          LOMA    2.0       0.965376     201910\n",
       "21174  21174    CA    MEU        BURRIS    9.0     703.355710     201910"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DBLnVXrQt5MF",
    "outputId": "cc2aded6-33a6-452a-94d2-fe50899c08ce"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['AEU', 'BTU', 'HUU', 'TGU', 'LNU', 'LMU', 'MRN', 'MEU', 'NEU',\n",
       "       'CZU', 'SCU', 'SHU', 'SKU'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfw['region'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "Db8EEVckt5MF"
   },
   "outputs": [],
   "source": [
    "## Create a temp column for wildfires and grab data from temp dataset based on region\n",
    "\n",
    "# Create ‘temp’ column\n",
    "df.assign(temp = '')\n",
    "\n",
    "temp_region = dfw.groupby(['region', 'date']).mean()\n",
    "temp_region.loc['BTU', 201805]\n",
    "\n",
    "# Function to get averages\n",
    "# @param region\n",
    "# @param date int\n",
    "def getTempForRegion(region, date):\n",
    "    return temp_region.loc[region, date]\n",
    "        \n",
    "\n",
    "# Apply function to temp column\n",
    "df['temp'] = df.apply(lambda x: getTempForRegion(x['county'], x['date_conv']), axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "viteWYMht5MF",
    "outputId": "5b4da904-00a8-4532-9a85-00c0f78ec895"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "temperature    59.3\n",
       "Name: (AEU, 201805), dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_region.loc['AEU', 201805]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "oQem78LRt5MG",
    "outputId": "6adc523e-c01a-4559-8537-e04139d60e22"
   },
   "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>index</th>\n",
       "      <th>state</th>\n",
       "      <th>county</th>\n",
       "      <th>fire_name</th>\n",
       "      <th>cause</th>\n",
       "      <th>gis_acres</th>\n",
       "      <th>date_conv</th>\n",
       "      <th>temp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>CA</td>\n",
       "      <td>NEU</td>\n",
       "      <td>FIELDS</td>\n",
       "      <td>5.0</td>\n",
       "      <td>55.328430</td>\n",
       "      <td>202010</td>\n",
       "      <td>64.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>CA</td>\n",
       "      <td>HUU</td>\n",
       "      <td>REDWOOD</td>\n",
       "      <td>2.0</td>\n",
       "      <td>101.400300</td>\n",
       "      <td>202010</td>\n",
       "      <td>60.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>34</td>\n",
       "      <td>CA</td>\n",
       "      <td>HUU</td>\n",
       "      <td>MINE</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.074130</td>\n",
       "      <td>202012</td>\n",
       "      <td>43.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>37</td>\n",
       "      <td>CA</td>\n",
       "      <td>BTU</td>\n",
       "      <td>GRAND</td>\n",
       "      <td>14.0</td>\n",
       "      <td>31.061090</td>\n",
       "      <td>202010</td>\n",
       "      <td>68.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>45</td>\n",
       "      <td>CA</td>\n",
       "      <td>NEU</td>\n",
       "      <td>SIMPSON</td>\n",
       "      <td>14.0</td>\n",
       "      <td>28.574560</td>\n",
       "      <td>202010</td>\n",
       "      <td>64.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>47</td>\n",
       "      <td>CA</td>\n",
       "      <td>NEU</td>\n",
       "      <td>LOCUST</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15.859510</td>\n",
       "      <td>202012</td>\n",
       "      <td>43.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>120</td>\n",
       "      <td>CA</td>\n",
       "      <td>SHU</td>\n",
       "      <td>POINT</td>\n",
       "      <td>9.0</td>\n",
       "      <td>48.147720</td>\n",
       "      <td>202010</td>\n",
       "      <td>61.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>127</td>\n",
       "      <td>CA</td>\n",
       "      <td>SHU</td>\n",
       "      <td>DERSCH</td>\n",
       "      <td>7.0</td>\n",
       "      <td>133.299500</td>\n",
       "      <td>202010</td>\n",
       "      <td>61.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>147</td>\n",
       "      <td>CA</td>\n",
       "      <td>AEU</td>\n",
       "      <td>CAMERON FIRE</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.426010</td>\n",
       "      <td>202010</td>\n",
       "      <td>66.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>149</td>\n",
       "      <td>CA</td>\n",
       "      <td>AEU</td>\n",
       "      <td>LAMBERT FIRE</td>\n",
       "      <td>9.0</td>\n",
       "      <td>21.816271</td>\n",
       "      <td>202010</td>\n",
       "      <td>66.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>211</td>\n",
       "      <td>CA</td>\n",
       "      <td>LNU</td>\n",
       "      <td>POPE</td>\n",
       "      <td>10.0</td>\n",
       "      <td>61.800121</td>\n",
       "      <td>202010</td>\n",
       "      <td>68.133333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>212</td>\n",
       "      <td>CA</td>\n",
       "      <td>LNU</td>\n",
       "      <td>LYONS</td>\n",
       "      <td>10.0</td>\n",
       "      <td>25.585110</td>\n",
       "      <td>202010</td>\n",
       "      <td>68.133333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>213</td>\n",
       "      <td>CA</td>\n",
       "      <td>LNU</td>\n",
       "      <td>SALT</td>\n",
       "      <td>10.0</td>\n",
       "      <td>86.663361</td>\n",
       "      <td>202010</td>\n",
       "      <td>68.133333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>453</td>\n",
       "      <td>CA</td>\n",
       "      <td>LNU</td>\n",
       "      <td>HUBCAP</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.017772</td>\n",
       "      <td>202010</td>\n",
       "      <td>68.133333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>465</th>\n",
       "      <td>465</td>\n",
       "      <td>CA</td>\n",
       "      <td>LNU</td>\n",
       "      <td>LYTTON</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.931224</td>\n",
       "      <td>202010</td>\n",
       "      <td>68.133333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>471</td>\n",
       "      <td>CA</td>\n",
       "      <td>LNU</td>\n",
       "      <td>CHABLIS</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.331930</td>\n",
       "      <td>202011</td>\n",
       "      <td>51.866667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>487</td>\n",
       "      <td>CA</td>\n",
       "      <td>LNU</td>\n",
       "      <td>TWIN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.042918</td>\n",
       "      <td>202011</td>\n",
       "      <td>51.866667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20607</th>\n",
       "      <td>20607</td>\n",
       "      <td>CA</td>\n",
       "      <td>BTU</td>\n",
       "      <td>JUNES</td>\n",
       "      <td>5.0</td>\n",
       "      <td>450.773010</td>\n",
       "      <td>201810</td>\n",
       "      <td>63.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20608</th>\n",
       "      <td>20608</td>\n",
       "      <td>CA</td>\n",
       "      <td>BTU</td>\n",
       "      <td>CAMP</td>\n",
       "      <td>11.0</td>\n",
       "      <td>153335.560000</td>\n",
       "      <td>201811</td>\n",
       "      <td>53.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20652</th>\n",
       "      <td>20652</td>\n",
       "      <td>CA</td>\n",
       "      <td>NEU</td>\n",
       "      <td>BUTTE_2</td>\n",
       "      <td>14.0</td>\n",
       "      <td>40.303265</td>\n",
       "      <td>201811</td>\n",
       "      <td>50.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20672</th>\n",
       "      <td>20672</td>\n",
       "      <td>CA</td>\n",
       "      <td>TGU</td>\n",
       "      <td>SUN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3866.887700</td>\n",
       "      <td>201810</td>\n",
       "      <td>62.950000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20673</th>\n",
       "      <td>20673</td>\n",
       "      <td>CA</td>\n",
       "      <td>TGU</td>\n",
       "      <td>ADAMS</td>\n",
       "      <td>11.0</td>\n",
       "      <td>55.531456</td>\n",
       "      <td>201811</td>\n",
       "      <td>53.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20883</th>\n",
       "      <td>20883</td>\n",
       "      <td>CA</td>\n",
       "      <td>SKU</td>\n",
       "      <td>IRON GATE</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.111814</td>\n",
       "      <td>201810</td>\n",
       "      <td>51.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21030</th>\n",
       "      <td>21030</td>\n",
       "      <td>CA</td>\n",
       "      <td>LNU</td>\n",
       "      <td>EAGLE</td>\n",
       "      <td>14.0</td>\n",
       "      <td>73.550552</td>\n",
       "      <td>201911</td>\n",
       "      <td>54.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21031</th>\n",
       "      <td>21031</td>\n",
       "      <td>CA</td>\n",
       "      <td>LNU</td>\n",
       "      <td>KINCADE</td>\n",
       "      <td>11.0</td>\n",
       "      <td>77762.141000</td>\n",
       "      <td>201910</td>\n",
       "      <td>60.766667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21032</th>\n",
       "      <td>21032</td>\n",
       "      <td>CA</td>\n",
       "      <td>LNU</td>\n",
       "      <td>AMERICAN</td>\n",
       "      <td>14.0</td>\n",
       "      <td>526.857850</td>\n",
       "      <td>201910</td>\n",
       "      <td>60.766667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21108</th>\n",
       "      <td>21108</td>\n",
       "      <td>CA</td>\n",
       "      <td>SHU</td>\n",
       "      <td>PLAINS</td>\n",
       "      <td>10.0</td>\n",
       "      <td>42.175037</td>\n",
       "      <td>201910</td>\n",
       "      <td>53.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21117</th>\n",
       "      <td>21117</td>\n",
       "      <td>CA</td>\n",
       "      <td>TGU</td>\n",
       "      <td>RAWSON</td>\n",
       "      <td>10.0</td>\n",
       "      <td>605.489750</td>\n",
       "      <td>201910</td>\n",
       "      <td>60.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21118</th>\n",
       "      <td>21118</td>\n",
       "      <td>CA</td>\n",
       "      <td>TGU</td>\n",
       "      <td>RANCH</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2534.210900</td>\n",
       "      <td>201911</td>\n",
       "      <td>55.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21164</th>\n",
       "      <td>21164</td>\n",
       "      <td>CA</td>\n",
       "      <td>NEU</td>\n",
       "      <td>FOOTHILLS</td>\n",
       "      <td>7.0</td>\n",
       "      <td>24.590448</td>\n",
       "      <td>201911</td>\n",
       "      <td>51.233333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21165</th>\n",
       "      <td>21165</td>\n",
       "      <td>CA</td>\n",
       "      <td>NEU</td>\n",
       "      <td>LOMA</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.965376</td>\n",
       "      <td>201910</td>\n",
       "      <td>55.966667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21174</th>\n",
       "      <td>21174</td>\n",
       "      <td>CA</td>\n",
       "      <td>MEU</td>\n",
       "      <td>BURRIS</td>\n",
       "      <td>9.0</td>\n",
       "      <td>703.355710</td>\n",
       "      <td>201910</td>\n",
       "      <td>56.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       index state county     fire_name  cause      gis_acres  date_conv  \\\n",
       "12        12    CA    NEU        FIELDS    5.0      55.328430     202010   \n",
       "28        28    CA    HUU       REDWOOD    2.0     101.400300     202010   \n",
       "34        34    CA    HUU          MINE    5.0      11.074130     202012   \n",
       "37        37    CA    BTU         GRAND   14.0      31.061090     202010   \n",
       "45        45    CA    NEU       SIMPSON   14.0      28.574560     202010   \n",
       "47        47    CA    NEU        LOCUST    5.0      15.859510     202012   \n",
       "120      120    CA    SHU         POINT    9.0      48.147720     202010   \n",
       "127      127    CA    SHU        DERSCH    7.0     133.299500     202010   \n",
       "147      147    CA    AEU  CAMERON FIRE   10.0      14.426010     202010   \n",
       "149      149    CA    AEU  LAMBERT FIRE    9.0      21.816271     202010   \n",
       "211      211    CA    LNU          POPE   10.0      61.800121     202010   \n",
       "212      212    CA    LNU         LYONS   10.0      25.585110     202010   \n",
       "213      213    CA    LNU          SALT   10.0      86.663361     202010   \n",
       "453      453    CA    LNU        HUBCAP    2.0       2.017772     202010   \n",
       "465      465    CA    LNU        LYTTON    7.0       1.931224     202010   \n",
       "471      471    CA    LNU       CHABLIS    8.0       1.331930     202011   \n",
       "487      487    CA    LNU          TWIN    5.0       1.042918     202011   \n",
       "20607  20607    CA    BTU         JUNES    5.0     450.773010     201810   \n",
       "20608  20608    CA    BTU          CAMP   11.0  153335.560000     201811   \n",
       "20652  20652    CA    NEU       BUTTE_2   14.0      40.303265     201811   \n",
       "20672  20672    CA    TGU           SUN   10.0    3866.887700     201810   \n",
       "20673  20673    CA    TGU         ADAMS   11.0      55.531456     201811   \n",
       "20883  20883    CA    SKU     IRON GATE    2.0      15.111814     201810   \n",
       "21030  21030    CA    LNU         EAGLE   14.0      73.550552     201911   \n",
       "21031  21031    CA    LNU       KINCADE   11.0   77762.141000     201910   \n",
       "21032  21032    CA    LNU      AMERICAN   14.0     526.857850     201910   \n",
       "21108  21108    CA    SHU       PLAINS    10.0      42.175037     201910   \n",
       "21117  21117    CA    TGU        RAWSON   10.0     605.489750     201910   \n",
       "21118  21118    CA    TGU         RANCH   14.0    2534.210900     201911   \n",
       "21164  21164    CA    NEU     FOOTHILLS    7.0      24.590448     201911   \n",
       "21165  21165    CA    NEU          LOMA    2.0       0.965376     201910   \n",
       "21174  21174    CA    MEU        BURRIS    9.0     703.355710     201910   \n",
       "\n",
       "            temp  \n",
       "12     64.600000  \n",
       "28     60.300000  \n",
       "34     43.450000  \n",
       "37     68.400000  \n",
       "45     64.600000  \n",
       "47     43.800000  \n",
       "120    61.800000  \n",
       "127    61.800000  \n",
       "147    66.200000  \n",
       "149    66.200000  \n",
       "211    68.133333  \n",
       "212    68.133333  \n",
       "213    68.133333  \n",
       "453    68.133333  \n",
       "465    68.133333  \n",
       "471    51.866667  \n",
       "487    51.866667  \n",
       "20607  63.700000  \n",
       "20608  53.200000  \n",
       "20652  50.800000  \n",
       "20672  62.950000  \n",
       "20673  53.450000  \n",
       "20883  51.600000  \n",
       "21030  54.666667  \n",
       "21031  60.766667  \n",
       "21032  60.766667  \n",
       "21108  53.350000  \n",
       "21117  60.300000  \n",
       "21118  55.200000  \n",
       "21164  51.233333  \n",
       "21165  55.966667  \n",
       "21174  56.800000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PqsyIzFWt5MG"
   },
   "source": [
    "## Cleaning the precipitation dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "btWE3nic58wt"
   },
   "source": [
    "The cleaning of our precipitation dataset was very similar to the temperature clean up, we focused only on the date, location and time. The columns names were changed accordingly. All observation not during the years of 2018 and 2020 were drops along with the observation not in the counties of interest. Finally based on the region the data observations that matched with those in the fire dataset (based on dates) were them pulled from the precipitation data set and added to the fire data set in a new column \"precipitation\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "mGo_G1N2t5MG",
    "outputId": "44b6dc74-90be-432e-8df4-e3d16b2480ee"
   },
   "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>Location</th>\n",
       "      <th>Date</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alameda County</td>\n",
       "      <td>189501</td>\n",
       "      <td>8.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alameda County</td>\n",
       "      <td>189502</td>\n",
       "      <td>2.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alameda County</td>\n",
       "      <td>189503</td>\n",
       "      <td>1.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alameda County</td>\n",
       "      <td>189504</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alameda County</td>\n",
       "      <td>189505</td>\n",
       "      <td>1.20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Location    Date  Value\n",
       "0  Alameda County  189501   8.43\n",
       "1  Alameda County  189502   2.09\n",
       "2  Alameda County  189503   1.97\n",
       "3  Alameda County  189504   1.75\n",
       "4  Alameda County  189505   1.20"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cleaning  and renaming columns \n",
    "dfp = dfp.drop(columns = ['Location ID', 'Rank','Anomaly (1901-2000 base period)', '1901-2000 Mean'])\n",
    "dfp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "_1bAguEdt5MG",
    "outputId": "ba0f05f9-8d9f-4a0a-a60a-7f5c109188b8"
   },
   "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</th>\n",
       "      <th>date</th>\n",
       "      <th>precipitation (inches)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alameda County</td>\n",
       "      <td>189501</td>\n",
       "      <td>8.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alameda County</td>\n",
       "      <td>189502</td>\n",
       "      <td>2.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alameda County</td>\n",
       "      <td>189503</td>\n",
       "      <td>1.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alameda County</td>\n",
       "      <td>189504</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alameda County</td>\n",
       "      <td>189505</td>\n",
       "      <td>1.20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           county    date  precipitation (inches)\n",
       "0  Alameda County  189501                    8.43\n",
       "1  Alameda County  189502                    2.09\n",
       "2  Alameda County  189503                    1.97\n",
       "3  Alameda County  189504                    1.75\n",
       "4  Alameda County  189505                    1.20"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rename columns\n",
    "dfp = dfp.rename(columns = {'Location':'county', 'Value':'precipitation (inches)'})\n",
    "# Create ‘region’ column\n",
    "dfp.assign(region = '')\n",
    "\n",
    "# Change to lowercase\n",
    "dfp.columns = dfp.columns.str.lower()\n",
    "dfp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "5wbRPYdHt5MH"
   },
   "outputs": [],
   "source": [
    "# Function to change County full name to county ID in temp.csv\n",
    "def County_toID(county):\n",
    "       if county in county_dict:\n",
    "            return county.replace(county, county_dict[county])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cA3apvsUt5MH",
    "outputId": "c4b9fe3e-89eb-48fb-9cc4-5039f74867f6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        None\n",
       "1        None\n",
       "2        None\n",
       "3        None\n",
       "4        None\n",
       "         ... \n",
       "88271     NEU\n",
       "88272     NEU\n",
       "88273     NEU\n",
       "88274     NEU\n",
       "88275     NEU\n",
       "Name: county, Length: 88276, dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfp['county'].apply(County_toID)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "cT7iLMbTt5MH",
    "outputId": "c7523616-41a5-4844-e6a1-f591d2b4ea95"
   },
   "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</th>\n",
       "      <th>date</th>\n",
       "      <th>precipitation (inches)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alameda County</td>\n",
       "      <td>189501</td>\n",
       "      <td>8.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alameda County</td>\n",
       "      <td>189502</td>\n",
       "      <td>2.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alameda County</td>\n",
       "      <td>189503</td>\n",
       "      <td>1.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alameda County</td>\n",
       "      <td>189504</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alameda County</td>\n",
       "      <td>189505</td>\n",
       "      <td>1.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88271</th>\n",
       "      <td>Yuba County</td>\n",
       "      <td>202106</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88272</th>\n",
       "      <td>Yuba County</td>\n",
       "      <td>202107</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88273</th>\n",
       "      <td>Yuba County</td>\n",
       "      <td>202108</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88274</th>\n",
       "      <td>Yuba County</td>\n",
       "      <td>202109</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88275</th>\n",
       "      <td>Yuba County</td>\n",
       "      <td>202110</td>\n",
       "      <td>10.64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>88276 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               county    date  precipitation (inches)\n",
       "0      Alameda County  189501                    8.43\n",
       "1      Alameda County  189502                    2.09\n",
       "2      Alameda County  189503                    1.97\n",
       "3      Alameda County  189504                    1.75\n",
       "4      Alameda County  189505                    1.20\n",
       "...               ...     ...                     ...\n",
       "88271     Yuba County  202106                    0.02\n",
       "88272     Yuba County  202107                    0.01\n",
       "88273     Yuba County  202108                    0.00\n",
       "88274     Yuba County  202109                    0.23\n",
       "88275     Yuba County  202110                   10.64\n",
       "\n",
       "[88276 rows x 3 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "2moLPGplt5MH"
   },
   "outputs": [],
   "source": [
    "# Convert counties to regions\n",
    "new_county = dfp['county'].apply(County_toID)\n",
    "dfp['region'] = new_county\n",
    "\n",
    "# Filter out dates\n",
    "dfp = dfp[dfp.date > 201800]\n",
    "dfp = dfp[dfp.date < 202100]\n",
    "\n",
    "# Drop NA values\n",
    "dfp = dfp.dropna()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-MdR3pJmt5MI",
    "outputId": "53a80177-27d8-49da-e619-531fb75e6c6e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "precipitation (inches)    0.52\n",
       "Name: (BTU, 201805), dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Create a precipitaion column for wildfires and grab data from precipitation dataset based on region\n",
    "# Create ‘precipitation’ column\n",
    "df.assign(precipitation = '')\n",
    "\n",
    "prec_region = dfp.groupby(['region', 'date']).mean()\n",
    "prec_region.loc['BTU', 201805]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "HhbB2Wd6t5MI"
   },
   "outputs": [],
   "source": [
    "# Function to get averages\n",
    "#@param region\n",
    "#@param date int\n",
    "def getPrecForRegion(region, date):\n",
    "    return prec_region.loc[region, date]\n",
    "        \n",
    "\n",
    "# Apply function to temp column\n",
    "df['precipitation'] = df.apply(lambda x: getPrecForRegion(x['county'], x['date_conv']), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ugPxcNart5MI",
    "outputId": "d6392b86-81a3-4138-aa8f-9232cb02f12d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fs8_18P7t5MI"
   },
   "source": [
    "\n",
    "\n",
    "# Data Analysis & Results (EDA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "cK_joFANt5MI",
    "outputId": "a889f6e7-ed06-4d3b-962e-7666afe47e12"
   },
   "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>index</th>\n",
       "      <th>state</th>\n",
       "      <th>county</th>\n",
       "      <th>fire_name</th>\n",
       "      <th>cause</th>\n",
       "      <th>gis_acres</th>\n",
       "      <th>date_conv</th>\n",
       "      <th>temp</th>\n",
       "      <th>precipitation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>CA</td>\n",
       "      <td>NEU</td>\n",
       "      <td>FIELDS</td>\n",
       "      <td>5.0</td>\n",
       "      <td>55.328430</td>\n",
       "      <td>202010</td>\n",
       "      <td>64.600000</td>\n",
       "      <td>0.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>CA</td>\n",
       "      <td>HUU</td>\n",
       "      <td>REDWOOD</td>\n",
       "      <td>2.0</td>\n",
       "      <td>101.400300</td>\n",
       "      <td>202010</td>\n",
       "      <td>60.300000</td>\n",
       "      <td>0.940000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>34</td>\n",
       "      <td>CA</td>\n",
       "      <td>HUU</td>\n",
       "      <td>MINE</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.074130</td>\n",
       "      <td>202012</td>\n",
       "      <td>43.450000</td>\n",
       "      <td>8.435000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>37</td>\n",
       "      <td>CA</td>\n",
       "      <td>BTU</td>\n",
       "      <td>GRAND</td>\n",
       "      <td>14.0</td>\n",
       "      <td>31.061090</td>\n",
       "      <td>202010</td>\n",
       "      <td>68.400000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>45</td>\n",
       "      <td>CA</td>\n",
       "      <td>NEU</td>\n",
       "      <td>SIMPSON</td>\n",
       "      <td>14.0</td>\n",
       "      <td>28.574560</td>\n",
       "      <td>202010</td>\n",
       "      <td>64.600000</td>\n",
       "      <td>0.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>47</td>\n",
       "      <td>CA</td>\n",
       "      <td>NEU</td>\n",
       "      <td>LOCUST</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15.859510</td>\n",
       "      <td>202012</td>\n",
       "      <td>43.800000</td>\n",
       "      <td>3.676667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>120</td>\n",
       "      <td>CA</td>\n",
       "      <td>SHU</td>\n",
       "      <td>POINT</td>\n",
       "      <td>9.0</td>\n",
       "      <td>48.147720</td>\n",
       "      <td>202010</td>\n",
       "      <td>61.800000</td>\n",
       "      <td>0.105000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>127</td>\n",
       "      <td>CA</td>\n",
       "      <td>SHU</td>\n",
       "      <td>DERSCH</td>\n",
       "      <td>7.0</td>\n",
       "      <td>133.299500</td>\n",
       "      <td>202010</td>\n",
       "      <td>61.800000</td>\n",
       "      <td>0.105000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>147</td>\n",
       "      <td>CA</td>\n",
       "      <td>AEU</td>\n",
       "      <td>CAMERON FIRE</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.426010</td>\n",
       "      <td>202010</td>\n",
       "      <td>66.200000</td>\n",
       "      <td>0.015000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>149</td>\n",
       "      <td>CA</td>\n",
       "      <td>AEU</td>\n",
       "      <td>LAMBERT FIRE</td>\n",
       "      <td>9.0</td>\n",
       "      <td>21.816271</td>\n",
       "      <td>202010</td>\n",
       "      <td>66.200000</td>\n",
       "      <td>0.015000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>211</td>\n",
       "      <td>CA</td>\n",
       "      <td>LNU</td>\n",
       "      <td>POPE</td>\n",
       "      <td>10.0</td>\n",
       "      <td>61.800121</td>\n",
       "      <td>202010</td>\n",
       "      <td>68.133333</td>\n",
       "      <td>0.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>212</td>\n",
       "      <td>CA</td>\n",
       "      <td>LNU</td>\n",
       "      <td>LYONS</td>\n",
       "      <td>10.0</td>\n",
       "      <td>25.585110</td>\n",
       "      <td>202010</td>\n",
       "      <td>68.133333</td>\n",
       "      <td>0.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>213</td>\n",
       "      <td>CA</td>\n",
       "      <td>LNU</td>\n",
       "      <td>SALT</td>\n",
       "      <td>10.0</td>\n",
       "      <td>86.663361</td>\n",
       "      <td>202010</td>\n",
       "      <td>68.133333</td>\n",
       "      <td>0.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>453</td>\n",
       "      <td>CA</td>\n",
       "      <td>LNU</td>\n",
       "      <td>HUBCAP</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.017772</td>\n",
       "      <td>202010</td>\n",
       "      <td>68.133333</td>\n",
       "      <td>0.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>465</th>\n",
       "      <td>465</td>\n",
       "      <td>CA</td>\n",
       "      <td>LNU</td>\n",
       "      <td>LYTTON</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.931224</td>\n",
       "      <td>202010</td>\n",
       "      <td>68.133333</td>\n",
       "      <td>0.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>471</td>\n",
       "      <td>CA</td>\n",
       "      <td>LNU</td>\n",
       "      <td>CHABLIS</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.331930</td>\n",
       "      <td>202011</td>\n",
       "      <td>51.866667</td>\n",
       "      <td>1.630000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>487</td>\n",
       "      <td>CA</td>\n",
       "      <td>LNU</td>\n",
       "      <td>TWIN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.042918</td>\n",
       "      <td>202011</td>\n",
       "      <td>51.866667</td>\n",
       "      <td>1.630000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20607</th>\n",
       "      <td>20607</td>\n",
       "      <td>CA</td>\n",
       "      <td>BTU</td>\n",
       "      <td>JUNES</td>\n",
       "      <td>5.0</td>\n",
       "      <td>450.773010</td>\n",
       "      <td>201810</td>\n",
       "      <td>63.700000</td>\n",
       "      <td>0.990000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20608</th>\n",
       "      <td>20608</td>\n",
       "      <td>CA</td>\n",
       "      <td>BTU</td>\n",
       "      <td>CAMP</td>\n",
       "      <td>11.0</td>\n",
       "      <td>153335.560000</td>\n",
       "      <td>201811</td>\n",
       "      <td>53.200000</td>\n",
       "      <td>6.080000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20652</th>\n",
       "      <td>20652</td>\n",
       "      <td>CA</td>\n",
       "      <td>NEU</td>\n",
       "      <td>BUTTE_2</td>\n",
       "      <td>14.0</td>\n",
       "      <td>40.303265</td>\n",
       "      <td>201811</td>\n",
       "      <td>50.800000</td>\n",
       "      <td>6.390000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20672</th>\n",
       "      <td>20672</td>\n",
       "      <td>CA</td>\n",
       "      <td>TGU</td>\n",
       "      <td>SUN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3866.887700</td>\n",
       "      <td>201810</td>\n",
       "      <td>62.950000</td>\n",
       "      <td>0.980000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20673</th>\n",
       "      <td>20673</td>\n",
       "      <td>CA</td>\n",
       "      <td>TGU</td>\n",
       "      <td>ADAMS</td>\n",
       "      <td>11.0</td>\n",
       "      <td>55.531456</td>\n",
       "      <td>201811</td>\n",
       "      <td>53.450000</td>\n",
       "      <td>5.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20883</th>\n",
       "      <td>20883</td>\n",
       "      <td>CA</td>\n",
       "      <td>SKU</td>\n",
       "      <td>IRON GATE</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.111814</td>\n",
       "      <td>201810</td>\n",
       "      <td>51.600000</td>\n",
       "      <td>0.970000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21030</th>\n",
       "      <td>21030</td>\n",
       "      <td>CA</td>\n",
       "      <td>LNU</td>\n",
       "      <td>EAGLE</td>\n",
       "      <td>14.0</td>\n",
       "      <td>73.550552</td>\n",
       "      <td>201911</td>\n",
       "      <td>54.666667</td>\n",
       "      <td>1.426667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21031</th>\n",
       "      <td>21031</td>\n",
       "      <td>CA</td>\n",
       "      <td>LNU</td>\n",
       "      <td>KINCADE</td>\n",
       "      <td>11.0</td>\n",
       "      <td>77762.141000</td>\n",
       "      <td>201910</td>\n",
       "      <td>60.766667</td>\n",
       "      <td>0.040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21032</th>\n",
       "      <td>21032</td>\n",
       "      <td>CA</td>\n",
       "      <td>LNU</td>\n",
       "      <td>AMERICAN</td>\n",
       "      <td>14.0</td>\n",
       "      <td>526.857850</td>\n",
       "      <td>201910</td>\n",
       "      <td>60.766667</td>\n",
       "      <td>0.040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21108</th>\n",
       "      <td>21108</td>\n",
       "      <td>CA</td>\n",
       "      <td>SHU</td>\n",
       "      <td>PLAINS</td>\n",
       "      <td>10.0</td>\n",
       "      <td>42.175037</td>\n",
       "      <td>201910</td>\n",
       "      <td>53.350000</td>\n",
       "      <td>0.465000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21117</th>\n",
       "      <td>21117</td>\n",
       "      <td>CA</td>\n",
       "      <td>TGU</td>\n",
       "      <td>RAWSON</td>\n",
       "      <td>10.0</td>\n",
       "      <td>605.489750</td>\n",
       "      <td>201910</td>\n",
       "      <td>60.300000</td>\n",
       "      <td>0.090000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21118</th>\n",
       "      <td>21118</td>\n",
       "      <td>CA</td>\n",
       "      <td>TGU</td>\n",
       "      <td>RANCH</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2534.210900</td>\n",
       "      <td>201911</td>\n",
       "      <td>55.200000</td>\n",
       "      <td>1.205000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21164</th>\n",
       "      <td>21164</td>\n",
       "      <td>CA</td>\n",
       "      <td>NEU</td>\n",
       "      <td>FOOTHILLS</td>\n",
       "      <td>7.0</td>\n",
       "      <td>24.590448</td>\n",
       "      <td>201911</td>\n",
       "      <td>51.233333</td>\n",
       "      <td>1.986667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21165</th>\n",
       "      <td>21165</td>\n",
       "      <td>CA</td>\n",
       "      <td>NEU</td>\n",
       "      <td>LOMA</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.965376</td>\n",
       "      <td>201910</td>\n",
       "      <td>55.966667</td>\n",
       "      <td>0.106667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21174</th>\n",
       "      <td>21174</td>\n",
       "      <td>CA</td>\n",
       "      <td>MEU</td>\n",
       "      <td>BURRIS</td>\n",
       "      <td>9.0</td>\n",
       "      <td>703.355710</td>\n",
       "      <td>201910</td>\n",
       "      <td>56.800000</td>\n",
       "      <td>0.370000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       index state county     fire_name  cause      gis_acres  date_conv  \\\n",
       "12        12    CA    NEU        FIELDS    5.0      55.328430     202010   \n",
       "28        28    CA    HUU       REDWOOD    2.0     101.400300     202010   \n",
       "34        34    CA    HUU          MINE    5.0      11.074130     202012   \n",
       "37        37    CA    BTU         GRAND   14.0      31.061090     202010   \n",
       "45        45    CA    NEU       SIMPSON   14.0      28.574560     202010   \n",
       "47        47    CA    NEU        LOCUST    5.0      15.859510     202012   \n",
       "120      120    CA    SHU         POINT    9.0      48.147720     202010   \n",
       "127      127    CA    SHU        DERSCH    7.0     133.299500     202010   \n",
       "147      147    CA    AEU  CAMERON FIRE   10.0      14.426010     202010   \n",
       "149      149    CA    AEU  LAMBERT FIRE    9.0      21.816271     202010   \n",
       "211      211    CA    LNU          POPE   10.0      61.800121     202010   \n",
       "212      212    CA    LNU         LYONS   10.0      25.585110     202010   \n",
       "213      213    CA    LNU          SALT   10.0      86.663361     202010   \n",
       "453      453    CA    LNU        HUBCAP    2.0       2.017772     202010   \n",
       "465      465    CA    LNU        LYTTON    7.0       1.931224     202010   \n",
       "471      471    CA    LNU       CHABLIS    8.0       1.331930     202011   \n",
       "487      487    CA    LNU          TWIN    5.0       1.042918     202011   \n",
       "20607  20607    CA    BTU         JUNES    5.0     450.773010     201810   \n",
       "20608  20608    CA    BTU          CAMP   11.0  153335.560000     201811   \n",
       "20652  20652    CA    NEU       BUTTE_2   14.0      40.303265     201811   \n",
       "20672  20672    CA    TGU           SUN   10.0    3866.887700     201810   \n",
       "20673  20673    CA    TGU         ADAMS   11.0      55.531456     201811   \n",
       "20883  20883    CA    SKU     IRON GATE    2.0      15.111814     201810   \n",
       "21030  21030    CA    LNU         EAGLE   14.0      73.550552     201911   \n",
       "21031  21031    CA    LNU       KINCADE   11.0   77762.141000     201910   \n",
       "21032  21032    CA    LNU      AMERICAN   14.0     526.857850     201910   \n",
       "21108  21108    CA    SHU       PLAINS    10.0      42.175037     201910   \n",
       "21117  21117    CA    TGU        RAWSON   10.0     605.489750     201910   \n",
       "21118  21118    CA    TGU         RANCH   14.0    2534.210900     201911   \n",
       "21164  21164    CA    NEU     FOOTHILLS    7.0      24.590448     201911   \n",
       "21165  21165    CA    NEU          LOMA    2.0       0.965376     201910   \n",
       "21174  21174    CA    MEU        BURRIS    9.0     703.355710     201910   \n",
       "\n",
       "            temp  precipitation  \n",
       "12     64.600000       0.010000  \n",
       "28     60.300000       0.940000  \n",
       "34     43.450000       8.435000  \n",
       "37     68.400000       0.000000  \n",
       "45     64.600000       0.010000  \n",
       "47     43.800000       3.676667  \n",
       "120    61.800000       0.105000  \n",
       "127    61.800000       0.105000  \n",
       "147    66.200000       0.015000  \n",
       "149    66.200000       0.015000  \n",
       "211    68.133333       0.016667  \n",
       "212    68.133333       0.016667  \n",
       "213    68.133333       0.016667  \n",
       "453    68.133333       0.016667  \n",
       "465    68.133333       0.016667  \n",
       "471    51.866667       1.630000  \n",
       "487    51.866667       1.630000  \n",
       "20607  63.700000       0.990000  \n",
       "20608  53.200000       6.080000  \n",
       "20652  50.800000       6.390000  \n",
       "20672  62.950000       0.980000  \n",
       "20673  53.450000       5.125000  \n",
       "20883  51.600000       0.970000  \n",
       "21030  54.666667       1.426667  \n",
       "21031  60.766667       0.040000  \n",
       "21032  60.766667       0.040000  \n",
       "21108  53.350000       0.465000  \n",
       "21117  60.300000       0.090000  \n",
       "21118  55.200000       1.205000  \n",
       "21164  51.233333       1.986667  \n",
       "21165  55.966667       0.106667  \n",
       "21174  56.800000       0.370000  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KywWv8iGt5MJ"
   },
   "source": [
    "### Structure\n",
    "For our data, we combined three different tabular CSV datasets: forest fire, temperature, and precipitation rates for regions in Northern California. The forest fire data set provided data for fire districts per the Department of Forestry and Fire Protection, while the temperature and precipitation data was per California county. In order to combine the data consistently into a dataset, we used the average temperature and precipitation rates between the counties in each district. For example, the ‘NEU’ region represents Nevada, Yuba, and Placer county, so we created an average monthly temperature/precipitation value between the 3 counties. In doing so, we were able to ensure that each observation in our dataset is in a separate row.\n",
    "The columns in our dataset show data on the region, date, temperature, and precipitation. The region column stores an object data type, the date column stores an integer data type, and the temperature and precipitation columns store float data types. These are all the variables that we need in order to answer our research question of whether temperature or precipitation rates are a better predictor of forest fires in Northern California. We joined the data according to the region IDs of the forest fire dataset, so all the other variables line up in accordance with this.\n",
    "\n",
    "### Granularity\n",
    "Each observation in our Dataset is an individual-level record, where each one represents a single wildfire incident in specified Northern California counties. All records in the dataset are uniformly individual, no wildfires have been grouped together so each record is its own wildfire incident.\n",
    "The temperature column for each wildfire is the mean temperature in the region during that given month. This was taken from a temperature dataset that held group-level records that were summarized by counties. The precipitation column for each wildfire is the average inches of rainfall in that region during that given month, and was taken from a dataset that held group-level observations for specified counties.. Therefore, the granularity for temperature and precipitation are group-level and are constrained to the region of the wildfire, while the granularity of the wildfire records themselves are all individual-level. \n",
    "\n",
    "### Scope"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "r0QTJoADt5MJ"
   },
   "source": [
    "For our dataset, we focused on Northern California counties, specifically:\n",
    "- SKU: Siskiyou County\n",
    "- HUU: Humboldt, Del Norte Counties\n",
    "- SHU: Shasta, Trinity Counties\n",
    "- LMU: Modoc County\n",
    "- TGU: Tehama, Glenn Counties\n",
    "- MEU: Mendocino County\n",
    "- BTU: Butte County\n",
    "- NEU: Yuba, Nevada, Placer Counties\n",
    "- AEU: El Dorado, Amador Counties\n",
    "- LNU: Sonoma, Lake, Napa Counties\n",
    "\n",
    "For temperature and precipitation data, we use mean data for the wildfire's given month, as it was the most accessible data that we could find. Ideally, it would be more accurate to get each wildfire's temperature and precipitation for the given time and location, but we were unable to find datasets with this level of detail. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xmStuVUQt5MJ"
   },
   "source": [
    "##### Distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "id": "gnj_TDRLt5MJ",
    "outputId": "8bf8ee75-af2c-44eb-9bb5-4368727a503d"
   },
   "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>index</th>\n",
       "      <th>cause</th>\n",
       "      <th>gis_acres</th>\n",
       "      <th>date_conv</th>\n",
       "      <th>temp</th>\n",
       "      <th>precipitation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9915.375000</td>\n",
       "      <td>8.625000</td>\n",
       "      <td>7521.214495</td>\n",
       "      <td>201944.750000</td>\n",
       "      <td>59.071875</td>\n",
       "      <td>1.341250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10519.168552</td>\n",
       "      <td>3.824792</td>\n",
       "      <td>29930.740114</td>\n",
       "      <td>78.693772</td>\n",
       "      <td>7.262886</td>\n",
       "      <td>2.185566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.965376</td>\n",
       "      <td>201810.000000</td>\n",
       "      <td>43.450000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>142.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>15.672586</td>\n",
       "      <td>201910.000000</td>\n",
       "      <td>53.312500</td>\n",
       "      <td>0.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>479.000000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>45.161378</td>\n",
       "      <td>202010.000000</td>\n",
       "      <td>60.533333</td>\n",
       "      <td>0.238333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>21030.250000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>212.667878</td>\n",
       "      <td>202010.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>1.477500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>21174.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>153335.560000</td>\n",
       "      <td>202012.000000</td>\n",
       "      <td>68.400000</td>\n",
       "      <td>8.435000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              index      cause      gis_acres      date_conv       temp  \\\n",
       "count     32.000000  32.000000      32.000000      32.000000  32.000000   \n",
       "mean    9915.375000   8.625000    7521.214495  201944.750000  59.071875   \n",
       "std    10519.168552   3.824792   29930.740114      78.693772   7.262886   \n",
       "min       12.000000   2.000000       0.965376  201810.000000  43.450000   \n",
       "25%      142.000000   5.000000      15.672586  201910.000000  53.312500   \n",
       "50%      479.000000   9.500000      45.161378  202010.000000  60.533333   \n",
       "75%    21030.250000  11.000000     212.667878  202010.000000  65.000000   \n",
       "max    21174.000000  14.000000  153335.560000  202012.000000  68.400000   \n",
       "\n",
       "       precipitation  \n",
       "count      32.000000  \n",
       "mean        1.341250  \n",
       "std         2.185566  \n",
       "min         0.000000  \n",
       "25%         0.016667  \n",
       "50%         0.238333  \n",
       "75%         1.477500  \n",
       "max         8.435000  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8vTxffKqt5MK"
   },
   "source": [
    "From the descriptive table, we see that after our limitation on Northern California counties, we have 32 records left to work with. We also found that there is a mean temperature of 59 degrees Fahrenheit in our regions, and the mean inches of rainfall is about 1.34 inches. We also see that the dates covered are between the start of 2018 and the end of 2020. \n",
    "\n",
    "Temperature ranges from 43.45 < t < 68.4\n",
    "Precipitation ranges from 0 < p < 8.435"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 312
    },
    "id": "ENd_ddu-t5MK",
    "outputId": "cb94bf25-6a97-4d37-c949-a134d26c223c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Count of Fires at Mean Inches of Rainfall per Month')"
      ]
     },
     "execution_count": 23,
     "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": [
    "# Find distribution of precipitation variable\n",
    "ax2 = sns.histplot(data = df , x = 'precipitation', color = 'blue')\n",
    "ax2.set_xlabel('Precipitation (Mean inches per month)')\n",
    "ax2.set_ylabel('Number of Fires')\n",
    "\n",
    "ax2.set_title('Count of Fires at Mean Inches of Rainfall per Month')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AtoIG9jzt5MK"
   },
   "source": [
    "The precipitation histogram shows a higher concentration of wildfires occuring in low precipitation, with fewer cases occuring as precipitation increases. Precipitation is coded in terms of mean inches of rainfall per month in the area of the wildfire, so it makes sense that the more rainfall the fewer cases of wildfires. There does not seem to be any extreme cases of outliers in the precipitation distribution, though the wildfire case with a precipitation of 8 seems odd."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 312
    },
    "id": "vVpvnV1ft5MK",
    "outputId": "4f8fdd2a-8d87-479e-9e1f-e2186de5c85a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Count of Fires at Average Temperatures per Month')"
      ]
     },
     "execution_count": 24,
     "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": [
    "# Find distribution of temp variable\n",
    "ax2 = sns.histplot(data = df , x = 'temp', color = 'red')\n",
    "ax2.set_xlabel('Average Temperature (F)')\n",
    "ax2.set_ylabel('Number of Fires')\n",
    "\n",
    "ax2.set_title('Count of Fires at Average Temperatures per Month')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Q-_BVAlst5MK"
   },
   "source": [
    "The temperature histogram shows a higher concentration of wildfires occuring in higher temperatures. The count of wildfire cases seems to gradually slope off as temperature gets lower. Temperature is coded in terms of the monthly average temperature in the area of the wildfire, so it makes sense that the higher the temperatures the higher amount of wildfire cases. There does not seem to be any extreme cases of outliers in the precipitation distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "x491oempt5MK"
   },
   "source": [
    "### Relationships"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 312
    },
    "id": "tM6gFYrNt5MK",
    "outputId": "dafb45ec-678f-4d5f-9c51-bb21bdb6672a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of Fires at Different Temperatures and Precipitation Levels ')"
      ]
     },
     "execution_count": 25,
     "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": [
    "# Find relationship between temp and precipitation\n",
    "ax = sns.scatterplot(data=df, x='temp', y='precipitation')\n",
    "ax.set_xlabel('Temperature')\n",
    "ax.set_ylabel('Precipitation')\n",
    "\n",
    "ax.set_title('Distribution of Fires at Different Temperatures and Precipitation Levels ')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jffMogSVt5ML"
   },
   "source": [
    "In a temperature vs precipitation scatter plot, there seems to be an inverse linear relationship between the two variables. Most of the cases are concentrated where temperatures are higher (50 < t < 70), and precipiation is lower (p < 2). There are a few wildfire cases that occur with lower temps and higher precipitations. There is also an extreme outlier, where a wildfire occured at both the lowest temperature and the highest precipitation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 458
    },
    "id": "H7G-W9ast5ML",
    "outputId": "882aa889-aff1-4290-bf78-31c280d0259d"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# See where most wildfires occur on a temp vs precipitation plot\n",
    "p = sns.jointplot(data=df, x='precipitation', y='temp', kind=\"hex\", color=\"#4CB391\")\n",
    "p.fig.suptitle(\"Correlation of Temperature and Precipitation & Their Individual Distributions\")\n",
    "p.fig.subplots_adjust(top=0.95)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wFBtJOXXt5ML"
   },
   "source": [
    "Similar to the scatterplot above, this hex plot shows that the highest concentration of wildfires occured at the lowest range of precipitation and the highest range of temperatures, and drops off steeply as precipiation increases and temperature lowers. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kgeslYlHt5ML"
   },
   "source": [
    "### Temporality\n",
    "The temporality of our data is represented by the year and month the alarm was called for each fire. Every datetime data point is from the Pacific Standard Time as all the data points are from northern California. These dates are represented in the dataframe as integers beginning with the four numbers of the year and ending with the last two digits of the specific month such that a data point with the date of  January of the year 2019 would look like (201901). \n",
    "In our dataset there were no null responses in any of the columns and therefore we did not need to set a date for null entries. We did begin with different dates in each of the data frames, for the fire data set the starting dates were represented by the year- month - day and time yet in the average precipitation and average temperature data sets the date were only provided as averages for each month thus to consolidate the data frames to only include the data points we wanted we changes the fire data set date format to match those in the precipitation and temperature data and used these dates to find the averages needed for each fire alarm date. \n",
    "In this data set the variables are stationary meaning that as time goes on the mean or average does not change because the dates simply represent the start of the fire and they do not influence any other variable within the dataset and thus should not influence the future mean and average predictions. \n",
    "\n",
    "### Faithfulness\n",
    "Our data values are reasonable and what we would expect, as the dates are valid and drawn from counties in Northern California. Although we have some slight outliers, most of the data is clustered together and the outliers are still relatively plausible since our measurements of temperature are averages of temperatures per month, as opposed to the exact temperature of the start of the fire.\n",
    "There are no inconsistencies across tables, as it makes sense that the higher the temperature, the less likely it is to rain or have precipitation, and no values are stored or counted more than once.\n",
    "From our inspection, the data does not have inconsistencies or signs of falsification; our source (the Global Historical Climatology Network daily) also screens the data (for impossible values and extreme outliers such as those outside of 6 standard deviations of the surrounding data) before integrating it into their database which we used.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_AptAxjLA-z6"
   },
   "source": [
    "# Data Analysis and Results\n",
    "\n",
    "We will use linear regression to see how well temperature can predict precipitation for these wildfire cases. While we are not interested in predicting precipitation, we will run an OLS analysis to run a statistical test and find the p-value of the datapoints to see if there is statistical power of temperature on precipitation in the case of the wildfires. In other words, how likely are the chances that we would see these datapoints (wildfire cases) or something more extreme from the linear regression model. \n",
    "\n",
    "### Lets see a visualization of the linear regression line and run OLS analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 530
    },
    "id": "C0RqSGXxBCln",
    "outputId": "c94c52e7-0291-4db9-fd7f-5a05d55610e2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'Line of best fit for temperature vs. precipitation')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 806.4x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p2 = sns.lmplot(\n",
    "    x = 'temp',\n",
    "    y = 'precipitation',\n",
    "    data = df,\n",
    "    fit_reg = True,\n",
    "    scatter_kws = {'color':'red'},\n",
    "    line_kws = {'color':'blue'},\n",
    "    aspect = 1.6,\n",
    "    height = 7\n",
    ")\n",
    "p2.fig.suptitle(\"Line of best fit for temperature vs. precipitation\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "stOV37fOOGaw",
    "outputId": "6801e665-6570-4f4a-fafa-4809b466e983"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                   temp   R-squared:                       0.520\n",
      "Model:                            OLS   Adj. R-squared:                  0.504\n",
      "Method:                 Least Squares   F-statistic:                     32.54\n",
      "Date:                Fri, 10 Dec 2021   Prob (F-statistic):           3.19e-06\n",
      "Time:                        23:29:54   Log-Likelihood:                -96.593\n",
      "No. Observations:                  32   AIC:                             197.2\n",
      "Df Residuals:                      30   BIC:                             200.1\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=================================================================================\n",
      "                    coef    std err          t      P>|t|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------\n",
      "Intercept        62.2869      1.065     58.472      0.000      60.111      64.462\n",
      "precipitation    -2.3970      0.420     -5.704      0.000      -3.255      -1.539\n",
      "==============================================================================\n",
      "Omnibus:                        7.105   Durbin-Watson:                   1.042\n",
      "Prob(Omnibus):                  0.029   Jarque-Bera (JB):                2.742\n",
      "Skew:                          -0.388   Prob(JB):                        0.254\n",
      "Kurtosis:                       1.794   Cond. No.                         3.13\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "df_linreg = df[['temp','precipitation']]\n",
    "outcome, predictors = patsy.dmatrices('temp ~ precipitation', df)\n",
    "mod_log = sm.OLS(outcome, predictors)\n",
    "fit_res = mod_log.fit()\n",
    "\n",
    "print(fit_res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KMKb6j8BOM5G"
   },
   "source": [
    "We see that the linear regression model has the equation of y = -2.3970x + 62.2869\n",
    "\n",
    "We also see the p-value is approx. 0, which in the case of our statistical test p = approx. 0 < 0.05, so this means that there is a statistically significant correlation between temperature and precipitation in wildfire cases. While this cannot be used to predict wildfires, this at least tells us that there is a correlation between temperature and precipitation in wildfire cases, and that these factors could be factors that influences the likelihood of a wildfire. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DxBTouRrvIzY"
   },
   "source": [
    "# Ethics & Privacy\n",
    "\n",
    "Some biases that may arise in this project is the multitude of covariant factors affecting the probability of wildfires in California. While temperature is surely one of the leading proponents of wildfires in California, there are a multitude of other factors such as people starting fires, fire suppression creating a buildup of flammable materials, the Santa Ana winds, lightning and much more. Furthermore, these factors often occur with and affect one another, which is important to note as we find the factor that correlates most closely with wildfire occurrence. That’s why we will look at multiple factors and try to see if there is covariance. Although our data set was small there should not be biases within the data used as they come from official government sites such as the California State Geoportal and the National Centers for Environmental Information.\n",
    "\n",
    "Privacy concerns won’t be a huge problem regarding the study of wildfires in California. The only area where privacy will be an issue is the cases of wildfires that were started by people. In these instances, we can make sure to keep their personal information from our study and not leave any type of subtle information regarding their identity. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NZhGy-VH3n8w"
   },
   "source": [
    "# Conclusion & Discussion\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "40eQdmrzBOyc"
   },
   "source": [
    "The results of our analysis made it clear that the focus of our hypothesis did not give us enough data points to make very accurate predictions of fires based on temperature and precipitation individually. We can see a positive linear trend between increasing temperature and the occurance of fires as most fires in our dataset occured at an average temperature of 55 degrees Farenheight. On the other hand a negative linear trend is seen between precipitation and wildfires, as most wildfires orccured in areas with low precipitation. Although each individual variable could not significantly provide a good correlation, the combination of precipitation and temperature showed a statistically significant correlation with wildfires. A confound which may have influenced this outcome is that in the biomes, which make up the counties included in our analysis, precipitation tends to be at a low during high temperatures and higher at lower temperatures thus creating a correlation. \n",
    "\n",
    "  The statistical outcome of this correlation could potentially mean a future predictability of temperature and precipitation for wildfires, yet in order to confidently make this claim a bigger dataset and more controlled tests need to be done. With more recorces and time we would have loved to continue this analysis, and would love to have the model necessary to predict the percentage chance that a wildfire can occur in a region given temperature and precipitation. While our current analysis and dataset was insufficient for such a model, we hope to continue this project outside the scope of this class. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Nyru5ZFrSi-H"
   },
   "source": [
    "# Team Contributions\n",
    "\n",
    "Brandon - Linear / OLS analysis, EDA Granularity + Distribution Visualizations, helped with data cleaning\n",
    "\n",
    "Daniel - Ethics and Privacy, Data Wrangling, Data Cleaning\n",
    "\n",
    "Kairi - Background Research, Data Wrangling, Structure, Video\n",
    "\n",
    "Marisol - EDA Temporality, Data Wrangling, Visualizations, Conclusion & Discussion \n",
    "\n",
    "Ashley - EDA Faithfulness, Data Wrangling, Data Cleaning, Visualizations, Background Research"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "FinalProjectGroup093-Fa21.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}