Project.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Project.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "idn5r7_W0YMf",
        "colab_type": "text"
      },
      "source": [
        "Imports:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VKd567uW0bOe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import sqlite3\n",
        "import geopy\n",
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uIjAARfkgEDh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "b334bd8b-9954-4f4a-d7cd-108e6de36297"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3e9WqFYe0dQZ",
        "colab_type": "text"
      },
      "source": [
        "Loading Fires Data:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EzX_NlYq0gzv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 460
        },
        "outputId": "ee40682a-4ce6-4f61-9f0f-1bffd06c1c00"
      },
      "source": [
        "cnx = sqlite3.connect('/content/drive/My Drive/Data 301 Project/FPA_FOD_20170508.sqlite')\n",
        "fires = pd.read_sql_query(\"SELECT * FROM Fires\", cnx)\n",
        "fires"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>OBJECTID</th>\n",
              "      <th>FOD_ID</th>\n",
              "      <th>FPA_ID</th>\n",
              "      <th>SOURCE_SYSTEM_TYPE</th>\n",
              "      <th>SOURCE_SYSTEM</th>\n",
              "      <th>NWCG_REPORTING_AGENCY</th>\n",
              "      <th>NWCG_REPORTING_UNIT_ID</th>\n",
              "      <th>NWCG_REPORTING_UNIT_NAME</th>\n",
              "      <th>SOURCE_REPORTING_UNIT</th>\n",
              "      <th>SOURCE_REPORTING_UNIT_NAME</th>\n",
              "      <th>LOCAL_FIRE_REPORT_ID</th>\n",
              "      <th>LOCAL_INCIDENT_ID</th>\n",
              "      <th>FIRE_CODE</th>\n",
              "      <th>FIRE_NAME</th>\n",
              "      <th>ICS_209_INCIDENT_NUMBER</th>\n",
              "      <th>ICS_209_NAME</th>\n",
              "      <th>MTBS_ID</th>\n",
              "      <th>MTBS_FIRE_NAME</th>\n",
              "      <th>COMPLEX_NAME</th>\n",
              "      <th>FIRE_YEAR</th>\n",
              "      <th>DISCOVERY_DATE</th>\n",
              "      <th>DISCOVERY_DOY</th>\n",
              "      <th>DISCOVERY_TIME</th>\n",
              "      <th>STAT_CAUSE_CODE</th>\n",
              "      <th>STAT_CAUSE_DESCR</th>\n",
              "      <th>CONT_DATE</th>\n",
              "      <th>CONT_DOY</th>\n",
              "      <th>CONT_TIME</th>\n",
              "      <th>FIRE_SIZE</th>\n",
              "      <th>FIRE_SIZE_CLASS</th>\n",
              "      <th>LATITUDE</th>\n",
              "      <th>LONGITUDE</th>\n",
              "      <th>OWNER_CODE</th>\n",
              "      <th>OWNER_DESCR</th>\n",
              "      <th>STATE</th>\n",
              "      <th>COUNTY</th>\n",
              "      <th>FIPS_CODE</th>\n",
              "      <th>FIPS_NAME</th>\n",
              "      <th>Shape</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>FS-1418826</td>\n",
              "      <td>FED</td>\n",
              "      <td>FS-FIRESTAT</td>\n",
              "      <td>FS</td>\n",
              "      <td>USCAPNF</td>\n",
              "      <td>Plumas National Forest</td>\n",
              "      <td>0511</td>\n",
              "      <td>Plumas National Forest</td>\n",
              "      <td>1</td>\n",
              "      <td>PNF-47</td>\n",
              "      <td>BJ8K</td>\n",
              "      <td>FOUNTAIN</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>2005</td>\n",
              "      <td>2453403.5</td>\n",
              "      <td>33</td>\n",
              "      <td>1300</td>\n",
              "      <td>9.0</td>\n",
              "      <td>Miscellaneous</td>\n",
              "      <td>2453403.5</td>\n",
              "      <td>33.0</td>\n",
              "      <td>1730</td>\n",
              "      <td>0.10</td>\n",
              "      <td>A</td>\n",
              "      <td>40.036944</td>\n",
              "      <td>-121.005833</td>\n",
              "      <td>5.0</td>\n",
              "      <td>USFS</td>\n",
              "      <td>CA</td>\n",
              "      <td>63</td>\n",
              "      <td>063</td>\n",
              "      <td>Plumas</td>\n",
              "      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00\\xe8d\\xc2\\x92_@^\\xc0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>FS-1418827</td>\n",
              "      <td>FED</td>\n",
              "      <td>FS-FIRESTAT</td>\n",
              "      <td>FS</td>\n",
              "      <td>USCAENF</td>\n",
              "      <td>Eldorado National Forest</td>\n",
              "      <td>0503</td>\n",
              "      <td>Eldorado National Forest</td>\n",
              "      <td>13</td>\n",
              "      <td>13</td>\n",
              "      <td>AAC0</td>\n",
              "      <td>PIGEON</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>2004</td>\n",
              "      <td>2453137.5</td>\n",
              "      <td>133</td>\n",
              "      <td>0845</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Lightning</td>\n",
              "      <td>2453137.5</td>\n",
              "      <td>133.0</td>\n",
              "      <td>1530</td>\n",
              "      <td>0.25</td>\n",
              "      <td>A</td>\n",
              "      <td>38.933056</td>\n",
              "      <td>-120.404444</td>\n",
              "      <td>5.0</td>\n",
              "      <td>USFS</td>\n",
              "      <td>CA</td>\n",
              "      <td>61</td>\n",
              "      <td>061</td>\n",
              "      <td>Placer</td>\n",
              "      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00T\\xb6\\xeej\\xe2\\x19^\\...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>FS-1418835</td>\n",
              "      <td>FED</td>\n",
              "      <td>FS-FIRESTAT</td>\n",
              "      <td>FS</td>\n",
              "      <td>USCAENF</td>\n",
              "      <td>Eldorado National Forest</td>\n",
              "      <td>0503</td>\n",
              "      <td>Eldorado National Forest</td>\n",
              "      <td>27</td>\n",
              "      <td>021</td>\n",
              "      <td>A32W</td>\n",
              "      <td>SLACK</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>2004</td>\n",
              "      <td>2453156.5</td>\n",
              "      <td>152</td>\n",
              "      <td>1921</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Debris Burning</td>\n",
              "      <td>2453156.5</td>\n",
              "      <td>152.0</td>\n",
              "      <td>2024</td>\n",
              "      <td>0.10</td>\n",
              "      <td>A</td>\n",
              "      <td>38.984167</td>\n",
              "      <td>-120.735556</td>\n",
              "      <td>13.0</td>\n",
              "      <td>STATE OR PRIVATE</td>\n",
              "      <td>CA</td>\n",
              "      <td>17</td>\n",
              "      <td>017</td>\n",
              "      <td>El Dorado</td>\n",
              "      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00\\xd0\\xa5\\xa0W\\x13/^\\...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>FS-1418845</td>\n",
              "      <td>FED</td>\n",
              "      <td>FS-FIRESTAT</td>\n",
              "      <td>FS</td>\n",
              "      <td>USCAENF</td>\n",
              "      <td>Eldorado National Forest</td>\n",
              "      <td>0503</td>\n",
              "      <td>Eldorado National Forest</td>\n",
              "      <td>43</td>\n",
              "      <td>6</td>\n",
              "      <td>None</td>\n",
              "      <td>DEER</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>2004</td>\n",
              "      <td>2453184.5</td>\n",
              "      <td>180</td>\n",
              "      <td>1600</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Lightning</td>\n",
              "      <td>2453189.5</td>\n",
              "      <td>185.0</td>\n",
              "      <td>1400</td>\n",
              "      <td>0.10</td>\n",
              "      <td>A</td>\n",
              "      <td>38.559167</td>\n",
              "      <td>-119.913333</td>\n",
              "      <td>5.0</td>\n",
              "      <td>USFS</td>\n",
              "      <td>CA</td>\n",
              "      <td>3</td>\n",
              "      <td>003</td>\n",
              "      <td>Alpine</td>\n",
              "      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00\\x94\\xac\\xa3\\rt\\xfa]...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>FS-1418847</td>\n",
              "      <td>FED</td>\n",
              "      <td>FS-FIRESTAT</td>\n",
              "      <td>FS</td>\n",
              "      <td>USCAENF</td>\n",
              "      <td>Eldorado National Forest</td>\n",
              "      <td>0503</td>\n",
              "      <td>Eldorado National Forest</td>\n",
              "      <td>44</td>\n",
              "      <td>7</td>\n",
              "      <td>None</td>\n",
              "      <td>STEVENOT</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>2004</td>\n",
              "      <td>2453184.5</td>\n",
              "      <td>180</td>\n",
              "      <td>1600</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Lightning</td>\n",
              "      <td>2453189.5</td>\n",
              "      <td>185.0</td>\n",
              "      <td>1200</td>\n",
              "      <td>0.10</td>\n",
              "      <td>A</td>\n",
              "      <td>38.559167</td>\n",
              "      <td>-119.933056</td>\n",
              "      <td>5.0</td>\n",
              "      <td>USFS</td>\n",
              "      <td>CA</td>\n",
              "      <td>3</td>\n",
              "      <td>003</td>\n",
              "      <td>Alpine</td>\n",
              "      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00@\\xe3\\xaa.\\xb7\\xfb]\\...</td>\n",
              "    </tr>\n",
              "    <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",
              "      <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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1880460</th>\n",
              "      <td>1880461</td>\n",
              "      <td>300348363</td>\n",
              "      <td>2015CAIRS29019636</td>\n",
              "      <td>NONFED</td>\n",
              "      <td>ST-CACDF</td>\n",
              "      <td>ST/C&amp;L</td>\n",
              "      <td>USCASHU</td>\n",
              "      <td>Shasta-Trinity Unit</td>\n",
              "      <td>CASHU</td>\n",
              "      <td>Shasta-Trinity Unit</td>\n",
              "      <td>591814</td>\n",
              "      <td>009371</td>\n",
              "      <td>None</td>\n",
              "      <td>ODESSA 2</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>2015</td>\n",
              "      <td>2457291.5</td>\n",
              "      <td>269</td>\n",
              "      <td>1726</td>\n",
              "      <td>13.0</td>\n",
              "      <td>Missing/Undefined</td>\n",
              "      <td>2457291.5</td>\n",
              "      <td>269.0</td>\n",
              "      <td>1843</td>\n",
              "      <td>0.01</td>\n",
              "      <td>A</td>\n",
              "      <td>40.481637</td>\n",
              "      <td>-122.389375</td>\n",
              "      <td>13.0</td>\n",
              "      <td>STATE OR PRIVATE</td>\n",
              "      <td>CA</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00P\\xb8\\x1e\\x85\\xeb\\x9...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1880461</th>\n",
              "      <td>1880462</td>\n",
              "      <td>300348373</td>\n",
              "      <td>2015CAIRS29217935</td>\n",
              "      <td>NONFED</td>\n",
              "      <td>ST-CACDF</td>\n",
              "      <td>ST/C&amp;L</td>\n",
              "      <td>USCATCU</td>\n",
              "      <td>Tuolumne-Calaveras Unit</td>\n",
              "      <td>CATCU</td>\n",
              "      <td>Tuolumne-Calaveras Unit</td>\n",
              "      <td>569419</td>\n",
              "      <td>000366</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>2015</td>\n",
              "      <td>2457300.5</td>\n",
              "      <td>278</td>\n",
              "      <td>0126</td>\n",
              "      <td>9.0</td>\n",
              "      <td>Miscellaneous</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>0.20</td>\n",
              "      <td>A</td>\n",
              "      <td>37.617619</td>\n",
              "      <td>-120.938570</td>\n",
              "      <td>12.0</td>\n",
              "      <td>MUNICIPAL/LOCAL</td>\n",
              "      <td>CA</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00\\x00\\x80\\xbe\\x88\\x11...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1880462</th>\n",
              "      <td>1880463</td>\n",
              "      <td>300348375</td>\n",
              "      <td>2015CAIRS28364460</td>\n",
              "      <td>NONFED</td>\n",
              "      <td>ST-CACDF</td>\n",
              "      <td>ST/C&amp;L</td>\n",
              "      <td>USCATCU</td>\n",
              "      <td>Tuolumne-Calaveras Unit</td>\n",
              "      <td>CATCU</td>\n",
              "      <td>Tuolumne-Calaveras Unit</td>\n",
              "      <td>574245</td>\n",
              "      <td>000158</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>2015</td>\n",
              "      <td>2457144.5</td>\n",
              "      <td>122</td>\n",
              "      <td>2052</td>\n",
              "      <td>13.0</td>\n",
              "      <td>Missing/Undefined</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>0.10</td>\n",
              "      <td>A</td>\n",
              "      <td>37.617619</td>\n",
              "      <td>-120.938570</td>\n",
              "      <td>12.0</td>\n",
              "      <td>MUNICIPAL/LOCAL</td>\n",
              "      <td>CA</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00\\x00\\x80\\xbe\\x88\\x11...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1880463</th>\n",
              "      <td>1880464</td>\n",
              "      <td>300348377</td>\n",
              "      <td>2015CAIRS29218079</td>\n",
              "      <td>NONFED</td>\n",
              "      <td>ST-CACDF</td>\n",
              "      <td>ST/C&amp;L</td>\n",
              "      <td>USCATCU</td>\n",
              "      <td>Tuolumne-Calaveras Unit</td>\n",
              "      <td>CATCU</td>\n",
              "      <td>Tuolumne-Calaveras Unit</td>\n",
              "      <td>570462</td>\n",
              "      <td>000380</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>2015</td>\n",
              "      <td>2457309.5</td>\n",
              "      <td>287</td>\n",
              "      <td>2309</td>\n",
              "      <td>13.0</td>\n",
              "      <td>Missing/Undefined</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>2.00</td>\n",
              "      <td>B</td>\n",
              "      <td>37.672235</td>\n",
              "      <td>-120.898356</td>\n",
              "      <td>12.0</td>\n",
              "      <td>MUNICIPAL/LOCAL</td>\n",
              "      <td>CA</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00x\\xba_\\xaa~9^\\xc0\\xb...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1880464</th>\n",
              "      <td>1880465</td>\n",
              "      <td>300348399</td>\n",
              "      <td>2015CAIRS26733926</td>\n",
              "      <td>NONFED</td>\n",
              "      <td>ST-CACDF</td>\n",
              "      <td>ST/C&amp;L</td>\n",
              "      <td>USCABDU</td>\n",
              "      <td>San Bernardino Unit</td>\n",
              "      <td>CABDU</td>\n",
              "      <td>CDF - San Bernardino Unit</td>\n",
              "      <td>535436</td>\n",
              "      <td>003225</td>\n",
              "      <td>None</td>\n",
              "      <td>BARKER BL  BIG_BEAR_LAKE_</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>2015</td>\n",
              "      <td>2457095.5</td>\n",
              "      <td>73</td>\n",
              "      <td>2128</td>\n",
              "      <td>9.0</td>\n",
              "      <td>Miscellaneous</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>0.10</td>\n",
              "      <td>A</td>\n",
              "      <td>34.263217</td>\n",
              "      <td>-116.830950</td>\n",
              "      <td>13.0</td>\n",
              "      <td>STATE OR PRIVATE</td>\n",
              "      <td>CA</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00\\x1c\\xa7\\xe8H.5]\\xc0...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1880465 rows × 39 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         OBJECTID  ...                                              Shape\n",
              "0               1  ...  b'\\x00\\x01\\xad\\x10\\x00\\x00\\xe8d\\xc2\\x92_@^\\xc0...\n",
              "1               2  ...  b'\\x00\\x01\\xad\\x10\\x00\\x00T\\xb6\\xeej\\xe2\\x19^\\...\n",
              "2               3  ...  b'\\x00\\x01\\xad\\x10\\x00\\x00\\xd0\\xa5\\xa0W\\x13/^\\...\n",
              "3               4  ...  b'\\x00\\x01\\xad\\x10\\x00\\x00\\x94\\xac\\xa3\\rt\\xfa]...\n",
              "4               5  ...  b'\\x00\\x01\\xad\\x10\\x00\\x00@\\xe3\\xaa.\\xb7\\xfb]\\...\n",
              "...           ...  ...                                                ...\n",
              "1880460   1880461  ...  b'\\x00\\x01\\xad\\x10\\x00\\x00P\\xb8\\x1e\\x85\\xeb\\x9...\n",
              "1880461   1880462  ...  b'\\x00\\x01\\xad\\x10\\x00\\x00\\x00\\x80\\xbe\\x88\\x11...\n",
              "1880462   1880463  ...  b'\\x00\\x01\\xad\\x10\\x00\\x00\\x00\\x80\\xbe\\x88\\x11...\n",
              "1880463   1880464  ...  b'\\x00\\x01\\xad\\x10\\x00\\x00x\\xba_\\xaa~9^\\xc0\\xb...\n",
              "1880464   1880465  ...  b'\\x00\\x01\\xad\\x10\\x00\\x00\\x1c\\xa7\\xe8H.5]\\xc0...\n",
              "\n",
              "[1880465 rows x 39 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 156
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "huCpf0e_07FA",
        "colab_type": "text"
      },
      "source": [
        "Cleaning Fires Data:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EN21lcEi09YT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "fires = fires[fires['FIRE_YEAR'] >= 2012]\n",
        "fires = fires[fires['STATE'] == 'CA']\n",
        "fires = fires[['FIRE_YEAR', 'DISCOVERY_DOY', 'CONT_DOY', 'FIRE_SIZE', 'LATITUDE', 'LONGITUDE', 'FIPS_NAME']]\n",
        "fires = fires.dropna(subset = ['CONT_DOY'])\n",
        "\n",
        "months = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December']\n",
        "daysnl = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]\n",
        "daysl = [31, 29, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]\n",
        "def doy_to_month(doy, year):\n",
        "  month = \"\"\n",
        "  counter = 0\n",
        "  while doy >= 1:\n",
        "    if year == 2012:\n",
        "      doy -= daysl[counter]\n",
        "    else:\n",
        "      doy -= daysnl[counter]\n",
        "    month = months[counter]\n",
        "    counter += 1\n",
        "  return month\n",
        "def doy_to_dom(doy, year, month):\n",
        "  monthindex = months.index(month)\n",
        "  for i in range(0, monthindex):\n",
        "    if year == 2012:\n",
        "      doy -= daysl[i]\n",
        "    else:\n",
        "      doy -= daysnl[i]\n",
        "  return doy\n",
        "def month_to_num_rep(month):\n",
        "  return months.index(month) + 1\n",
        "fires['MONTH'] = np.vectorize(doy_to_month)(fires['DISCOVERY_DOY'], fires['FIRE_YEAR'])\n",
        "fires['MONTH_AS_NUM'] = np.vectorize(month_to_num_rep)(fires['MONTH'])\n",
        "fires['DOM'] = np.vectorize(doy_to_dom)(fires['DISCOVERY_DOY'], fires['FIRE_YEAR'], fires['MONTH'])\n",
        "fires['DURATION'] = fires['CONT_DOY'] - fires['DISCOVERY_DOY']\n",
        "fires = fires[['MONTH', 'FIRE_YEAR', 'DOM', 'FIRE_SIZE', 'DURATION', 'LATITUDE', 'LONGITUDE', 'FIPS_NAME', 'MONTH_AS_NUM']]\n",
        "fires.columns = ['MONTH', 'YEAR', 'DAY_OF_MONTH', 'SIZE', 'DURATION', 'LATITUDE', 'LONGITUDE', 'COUNTY', 'MONTH_AS_NUM']\n",
        "fires = fires.sort_values(by = ['LATITUDE', 'LONGITUDE'])\n",
        "fires = fires.fillna(method = \"ffill\", axis = \"rows\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oIISQ8BW1A1c",
        "colab_type": "text"
      },
      "source": [
        "Loading Weather Month Data:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hruypjRq1DvZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ca_weather = pd.read_csv('/content/drive/My Drive/Data 301 Project/4-tavg.csv', delimiter=',')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xmNYMCd41GMz",
        "colab_type": "text"
      },
      "source": [
        "Cleaning Weather Month Data:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wVkX1v5M1IoX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ca_weather['Date'] = ca_weather['Date'].astype(str)\n",
        "ca_weather['Date'] = pd.to_datetime(ca_weather['Date'], format = '%Y%m')\n",
        "ca_weather['Location'] = ca_weather['Location'].str.replace(\"County\", \" \").str.strip()\n",
        "\n",
        "def strip_year(date):\n",
        "  date = pd.to_datetime(str(date))\n",
        "  return date.strftime('%Y')\n",
        "def strip_month(date):\n",
        "  date = pd.to_datetime(str(date))\n",
        "  return date.strftime('%B')\n",
        "  \n",
        "ca_weather['Year'] = np.vectorize(strip_year)(ca_weather['Date'])\n",
        "ca_weather['Month'] = np.vectorize(strip_month)(ca_weather['Date'])\n",
        "ca_weather['Year'] = ca_weather['Year'].astype(int)\n",
        "ca_weather = ca_weather[ca_weather['Year'] >= 2012]\n",
        "ca_weather = ca_weather[ca_weather['Year'] <= 2015]\n",
        "ca_weather = ca_weather[['Location', 'Year', 'Month', 'Value']]\n",
        "ca_weather.columns = ['COUNTY', 'YEAR', 'MONTH', 'VALUE']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m_xNqY_B1LcZ",
        "colab_type": "text"
      },
      "source": [
        "Merging The Two Data:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xjjxGv5g1ObR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.merge(fires, ca_weather, on = ['YEAR', 'MONTH', 'COUNTY'])\n",
        "df = df.sort_values(by = ['COUNTY', 'YEAR'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-cHyut4irogi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "outputId": "edd3867e-e073-41aa-c58a-1aa41dd00398"
      },
      "source": [
        "df.columns = [\"Month\", \"Year\", \"Day\", \"Size\", \"Duration\", \"Lat\", \"Long\", \"County\", \"Month_n\", \"Month_Temp\"]\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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",
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              "    }\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>Month</th>\n",
              "      <th>Year</th>\n",
              "      <th>Day</th>\n",
              "      <th>Size</th>\n",
              "      <th>Duration</th>\n",
              "      <th>Lat</th>\n",
              "      <th>Long</th>\n",
              "      <th>County</th>\n",
              "      <th>Month_n</th>\n",
              "      <th>Month_Temp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>9723</th>\n",
              "      <td>August</td>\n",
              "      <td>2012</td>\n",
              "      <td>17</td>\n",
              "      <td>2.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>37.595600</td>\n",
              "      <td>-119.595500</td>\n",
              "      <td>Alameda</td>\n",
              "      <td>8</td>\n",
              "      <td>71.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9724</th>\n",
              "      <td>August</td>\n",
              "      <td>2012</td>\n",
              "      <td>18</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37.630010</td>\n",
              "      <td>-119.573650</td>\n",
              "      <td>Alameda</td>\n",
              "      <td>8</td>\n",
              "      <td>71.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9725</th>\n",
              "      <td>August</td>\n",
              "      <td>2012</td>\n",
              "      <td>11</td>\n",
              "      <td>1.00</td>\n",
              "      <td>3.0</td>\n",
              "      <td>37.728840</td>\n",
              "      <td>-119.544040</td>\n",
              "      <td>Alameda</td>\n",
              "      <td>8</td>\n",
              "      <td>71.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9726</th>\n",
              "      <td>August</td>\n",
              "      <td>2012</td>\n",
              "      <td>31</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37.742200</td>\n",
              "      <td>-119.627200</td>\n",
              "      <td>Alameda</td>\n",
              "      <td>8</td>\n",
              "      <td>71.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9727</th>\n",
              "      <td>August</td>\n",
              "      <td>2012</td>\n",
              "      <td>28</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37.742630</td>\n",
              "      <td>-119.577490</td>\n",
              "      <td>Alameda</td>\n",
              "      <td>8</td>\n",
              "      <td>71.0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14970</th>\n",
              "      <td>February</td>\n",
              "      <td>2015</td>\n",
              "      <td>26</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>39.470636</td>\n",
              "      <td>-121.318982</td>\n",
              "      <td>Yuba</td>\n",
              "      <td>2</td>\n",
              "      <td>55.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14989</th>\n",
              "      <td>August</td>\n",
              "      <td>2015</td>\n",
              "      <td>21</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.0</td>\n",
              "      <td>39.243640</td>\n",
              "      <td>-121.354048</td>\n",
              "      <td>Yuba</td>\n",
              "      <td>8</td>\n",
              "      <td>76.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14990</th>\n",
              "      <td>August</td>\n",
              "      <td>2015</td>\n",
              "      <td>7</td>\n",
              "      <td>0.10</td>\n",
              "      <td>1.0</td>\n",
              "      <td>39.294017</td>\n",
              "      <td>-120.411083</td>\n",
              "      <td>Yuba</td>\n",
              "      <td>8</td>\n",
              "      <td>76.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14991</th>\n",
              "      <td>August</td>\n",
              "      <td>2015</td>\n",
              "      <td>13</td>\n",
              "      <td>5.20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>39.300720</td>\n",
              "      <td>-121.376961</td>\n",
              "      <td>Yuba</td>\n",
              "      <td>8</td>\n",
              "      <td>76.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14992</th>\n",
              "      <td>August</td>\n",
              "      <td>2015</td>\n",
              "      <td>21</td>\n",
              "      <td>2.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>39.488914</td>\n",
              "      <td>-121.489618</td>\n",
              "      <td>Yuba</td>\n",
              "      <td>8</td>\n",
              "      <td>76.7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>21634 rows × 10 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          Month  Year  Day  Size  ...        Long   County  Month_n Month_Temp\n",
              "9723     August  2012   17  2.00  ... -119.595500  Alameda        8       71.0\n",
              "9724     August  2012   18  0.10  ... -119.573650  Alameda        8       71.0\n",
              "9725     August  2012   11  1.00  ... -119.544040  Alameda        8       71.0\n",
              "9726     August  2012   31  0.10  ... -119.627200  Alameda        8       71.0\n",
              "9727     August  2012   28  0.10  ... -119.577490  Alameda        8       71.0\n",
              "...         ...   ...  ...   ...  ...         ...      ...      ...        ...\n",
              "14970  February  2015   26  1.00  ... -121.318982     Yuba        2       55.4\n",
              "14989    August  2015   21  0.02  ... -121.354048     Yuba        8       76.7\n",
              "14990    August  2015    7  0.10  ... -120.411083     Yuba        8       76.7\n",
              "14991    August  2015   13  5.20  ... -121.376961     Yuba        8       76.7\n",
              "14992    August  2015   21  2.00  ... -121.489618     Yuba        8       76.7\n",
              "\n",
              "[21634 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 161
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sscC843x62P3",
        "colab_type": "text"
      },
      "source": [
        "Removing irrelevant Couties"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i-Ulrrv97A8W",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "outputId": "8ba85f5a-26b2-427a-95f3-a173b0a9c433"
      },
      "source": [
        "reduced = df.query(\"County == 'San Diego' | County == 'Los Angeles' | County == 'San Mateo' | County == 'Santa Clara' | County == 'Alameda' | County == 'Contra Coasta'\")\n",
        "reduced"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "    }\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>Month</th>\n",
              "      <th>Year</th>\n",
              "      <th>Day</th>\n",
              "      <th>Size</th>\n",
              "      <th>Duration</th>\n",
              "      <th>Lat</th>\n",
              "      <th>Long</th>\n",
              "      <th>County</th>\n",
              "      <th>Month_n</th>\n",
              "      <th>Month_Temp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>9723</th>\n",
              "      <td>August</td>\n",
              "      <td>2012</td>\n",
              "      <td>17</td>\n",
              "      <td>2.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>37.595600</td>\n",
              "      <td>-119.595500</td>\n",
              "      <td>Alameda</td>\n",
              "      <td>8</td>\n",
              "      <td>71.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9724</th>\n",
              "      <td>August</td>\n",
              "      <td>2012</td>\n",
              "      <td>18</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37.630010</td>\n",
              "      <td>-119.573650</td>\n",
              "      <td>Alameda</td>\n",
              "      <td>8</td>\n",
              "      <td>71.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9725</th>\n",
              "      <td>August</td>\n",
              "      <td>2012</td>\n",
              "      <td>11</td>\n",
              "      <td>1.00</td>\n",
              "      <td>3.0</td>\n",
              "      <td>37.728840</td>\n",
              "      <td>-119.544040</td>\n",
              "      <td>Alameda</td>\n",
              "      <td>8</td>\n",
              "      <td>71.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9726</th>\n",
              "      <td>August</td>\n",
              "      <td>2012</td>\n",
              "      <td>31</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37.742200</td>\n",
              "      <td>-119.627200</td>\n",
              "      <td>Alameda</td>\n",
              "      <td>8</td>\n",
              "      <td>71.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9727</th>\n",
              "      <td>August</td>\n",
              "      <td>2012</td>\n",
              "      <td>28</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37.742630</td>\n",
              "      <td>-119.577490</td>\n",
              "      <td>Alameda</td>\n",
              "      <td>8</td>\n",
              "      <td>71.0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8924</th>\n",
              "      <td>June</td>\n",
              "      <td>2015</td>\n",
              "      <td>24</td>\n",
              "      <td>2.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37.220500</td>\n",
              "      <td>-121.734233</td>\n",
              "      <td>Santa Clara</td>\n",
              "      <td>6</td>\n",
              "      <td>69.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8925</th>\n",
              "      <td>June</td>\n",
              "      <td>2015</td>\n",
              "      <td>18</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37.256350</td>\n",
              "      <td>-121.694917</td>\n",
              "      <td>Santa Clara</td>\n",
              "      <td>6</td>\n",
              "      <td>69.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9086</th>\n",
              "      <td>March</td>\n",
              "      <td>2015</td>\n",
              "      <td>10</td>\n",
              "      <td>1.30</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37.159317</td>\n",
              "      <td>-118.295533</td>\n",
              "      <td>Santa Clara</td>\n",
              "      <td>3</td>\n",
              "      <td>58.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9087</th>\n",
              "      <td>March</td>\n",
              "      <td>2015</td>\n",
              "      <td>31</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37.205503</td>\n",
              "      <td>-119.724582</td>\n",
              "      <td>Santa Clara</td>\n",
              "      <td>3</td>\n",
              "      <td>58.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9088</th>\n",
              "      <td>March</td>\n",
              "      <td>2015</td>\n",
              "      <td>30</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37.211944</td>\n",
              "      <td>-119.560942</td>\n",
              "      <td>Santa Clara</td>\n",
              "      <td>3</td>\n",
              "      <td>58.4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1804 rows × 10 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       Month  Year  Day  Size  ...        Long       County  Month_n Month_Temp\n",
              "9723  August  2012   17  2.00  ... -119.595500      Alameda        8       71.0\n",
              "9724  August  2012   18  0.10  ... -119.573650      Alameda        8       71.0\n",
              "9725  August  2012   11  1.00  ... -119.544040      Alameda        8       71.0\n",
              "9726  August  2012   31  0.10  ... -119.627200      Alameda        8       71.0\n",
              "9727  August  2012   28  0.10  ... -119.577490      Alameda        8       71.0\n",
              "...      ...   ...  ...   ...  ...         ...          ...      ...        ...\n",
              "8924    June  2015   24  2.00  ... -121.734233  Santa Clara        6       69.0\n",
              "8925    June  2015   18  1.00  ... -121.694917  Santa Clara        6       69.0\n",
              "9086   March  2015   10  1.30  ... -118.295533  Santa Clara        3       58.4\n",
              "9087   March  2015   31  0.08  ... -119.724582  Santa Clara        3       58.4\n",
              "9088   March  2015   30  0.10  ... -119.560942  Santa Clara        3       58.4\n",
              "\n",
              "[1804 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 162
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u9DGy-xdEaMc",
        "colab_type": "text"
      },
      "source": [
        "Creating datetime column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "unwobsoR_Njp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "24fdda67-9e3d-4bec-a5c8-ed133d007f14"
      },
      "source": [
        "from datetime import datetime\n",
        "\n",
        "def convert_to_dt(i_year, i_month, i_day):\n",
        "  return datetime(year = i_year, month = i_month, day = i_day)\n",
        "\n",
        "reduced[\"datetime\"] = np.vectorize(convert_to_dt)(reduced['Year'], reduced['Month_n'], reduced[\"Day\"])\n",
        "reduced"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "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>Month</th>\n",
              "      <th>Year</th>\n",
              "      <th>Day</th>\n",
              "      <th>Size</th>\n",
              "      <th>Duration</th>\n",
              "      <th>Lat</th>\n",
              "      <th>Long</th>\n",
              "      <th>County</th>\n",
              "      <th>Month_n</th>\n",
              "      <th>Month_Temp</th>\n",
              "      <th>datetime</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>9723</th>\n",
              "      <td>August</td>\n",
              "      <td>2012</td>\n",
              "      <td>17</td>\n",
              "      <td>2.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>37.595600</td>\n",
              "      <td>-119.595500</td>\n",
              "      <td>Alameda</td>\n",
              "      <td>8</td>\n",
              "      <td>71.0</td>\n",
              "      <td>2012-08-17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9724</th>\n",
              "      <td>August</td>\n",
              "      <td>2012</td>\n",
              "      <td>18</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37.630010</td>\n",
              "      <td>-119.573650</td>\n",
              "      <td>Alameda</td>\n",
              "      <td>8</td>\n",
              "      <td>71.0</td>\n",
              "      <td>2012-08-18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9725</th>\n",
              "      <td>August</td>\n",
              "      <td>2012</td>\n",
              "      <td>11</td>\n",
              "      <td>1.00</td>\n",
              "      <td>3.0</td>\n",
              "      <td>37.728840</td>\n",
              "      <td>-119.544040</td>\n",
              "      <td>Alameda</td>\n",
              "      <td>8</td>\n",
              "      <td>71.0</td>\n",
              "      <td>2012-08-11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9726</th>\n",
              "      <td>August</td>\n",
              "      <td>2012</td>\n",
              "      <td>31</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37.742200</td>\n",
              "      <td>-119.627200</td>\n",
              "      <td>Alameda</td>\n",
              "      <td>8</td>\n",
              "      <td>71.0</td>\n",
              "      <td>2012-08-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9727</th>\n",
              "      <td>August</td>\n",
              "      <td>2012</td>\n",
              "      <td>28</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37.742630</td>\n",
              "      <td>-119.577490</td>\n",
              "      <td>Alameda</td>\n",
              "      <td>8</td>\n",
              "      <td>71.0</td>\n",
              "      <td>2012-08-28</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8924</th>\n",
              "      <td>June</td>\n",
              "      <td>2015</td>\n",
              "      <td>24</td>\n",
              "      <td>2.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37.220500</td>\n",
              "      <td>-121.734233</td>\n",
              "      <td>Santa Clara</td>\n",
              "      <td>6</td>\n",
              "      <td>69.0</td>\n",
              "      <td>2015-06-24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8925</th>\n",
              "      <td>June</td>\n",
              "      <td>2015</td>\n",
              "      <td>18</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37.256350</td>\n",
              "      <td>-121.694917</td>\n",
              "      <td>Santa Clara</td>\n",
              "      <td>6</td>\n",
              "      <td>69.0</td>\n",
              "      <td>2015-06-18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9086</th>\n",
              "      <td>March</td>\n",
              "      <td>2015</td>\n",
              "      <td>10</td>\n",
              "      <td>1.30</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37.159317</td>\n",
              "      <td>-118.295533</td>\n",
              "      <td>Santa Clara</td>\n",
              "      <td>3</td>\n",
              "      <td>58.4</td>\n",
              "      <td>2015-03-10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9087</th>\n",
              "      <td>March</td>\n",
              "      <td>2015</td>\n",
              "      <td>31</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37.205503</td>\n",
              "      <td>-119.724582</td>\n",
              "      <td>Santa Clara</td>\n",
              "      <td>3</td>\n",
              "      <td>58.4</td>\n",
              "      <td>2015-03-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9088</th>\n",
              "      <td>March</td>\n",
              "      <td>2015</td>\n",
              "      <td>30</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37.211944</td>\n",
              "      <td>-119.560942</td>\n",
              "      <td>Santa Clara</td>\n",
              "      <td>3</td>\n",
              "      <td>58.4</td>\n",
              "      <td>2015-03-30</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1804 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       Month  Year  Day  Size  ...       County  Month_n  Month_Temp   datetime\n",
              "9723  August  2012   17  2.00  ...      Alameda        8        71.0 2012-08-17\n",
              "9724  August  2012   18  0.10  ...      Alameda        8        71.0 2012-08-18\n",
              "9725  August  2012   11  1.00  ...      Alameda        8        71.0 2012-08-11\n",
              "9726  August  2012   31  0.10  ...      Alameda        8        71.0 2012-08-31\n",
              "9727  August  2012   28  0.10  ...      Alameda        8        71.0 2012-08-28\n",
              "...      ...   ...  ...   ...  ...          ...      ...         ...        ...\n",
              "8924    June  2015   24  2.00  ...  Santa Clara        6        69.0 2015-06-24\n",
              "8925    June  2015   18  1.00  ...  Santa Clara        6        69.0 2015-06-18\n",
              "9086   March  2015   10  1.30  ...  Santa Clara        3        58.4 2015-03-10\n",
              "9087   March  2015   31  0.08  ...  Santa Clara        3        58.4 2015-03-31\n",
              "9088   March  2015   30  0.10  ...  Santa Clara        3        58.4 2015-03-30\n",
              "\n",
              "[1804 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 163
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZXnNwLRREe4f",
        "colab_type": "text"
      },
      "source": [
        "removing unnecessary data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HkE5nvBgA_nc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "1920f387-5560-4b1f-d724-91d65ec62447"
      },
      "source": [
        "from datetime import datetime\n",
        "\n",
        "def convert_name(name):\n",
        "  if name == \"Los Angeles\" or name == \"San Diego\":\n",
        "    return name\n",
        "  return \"San Francisco\"\n",
        "\n",
        "reduced[\"City\"] = np.vectorize(convert_name)(reduced['County'])\n",
        "reduced = reduced.drop([\"County\", \"Month\", \"Year\", \"Day\", \"Month_n\", \"Lat\", \"Long\"], axis = 1)\n",
        "reduced"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "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>Size</th>\n",
              "      <th>Duration</th>\n",
              "      <th>Month_Temp</th>\n",
              "      <th>datetime</th>\n",
              "      <th>City</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>9723</th>\n",
              "      <td>2.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>71.0</td>\n",
              "      <td>2012-08-17</td>\n",
              "      <td>San Francisco</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9724</th>\n",
              "      <td>0.10</td>\n",
              "      <td>0.0</td>\n",
              "      <td>71.0</td>\n",
              "      <td>2012-08-18</td>\n",
              "      <td>San Francisco</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9725</th>\n",
              "      <td>1.00</td>\n",
              "      <td>3.0</td>\n",
              "      <td>71.0</td>\n",
              "      <td>2012-08-11</td>\n",
              "      <td>San Francisco</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9726</th>\n",
              "      <td>0.10</td>\n",
              "      <td>0.0</td>\n",
              "      <td>71.0</td>\n",
              "      <td>2012-08-31</td>\n",
              "      <td>San Francisco</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9727</th>\n",
              "      <td>0.10</td>\n",
              "      <td>0.0</td>\n",
              "      <td>71.0</td>\n",
              "      <td>2012-08-28</td>\n",
              "      <td>San Francisco</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8924</th>\n",
              "      <td>2.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>2015-06-24</td>\n",
              "      <td>San Francisco</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8925</th>\n",
              "      <td>1.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>2015-06-18</td>\n",
              "      <td>San Francisco</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9086</th>\n",
              "      <td>1.30</td>\n",
              "      <td>0.0</td>\n",
              "      <td>58.4</td>\n",
              "      <td>2015-03-10</td>\n",
              "      <td>San Francisco</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9087</th>\n",
              "      <td>0.08</td>\n",
              "      <td>0.0</td>\n",
              "      <td>58.4</td>\n",
              "      <td>2015-03-31</td>\n",
              "      <td>San Francisco</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9088</th>\n",
              "      <td>0.10</td>\n",
              "      <td>0.0</td>\n",
              "      <td>58.4</td>\n",
              "      <td>2015-03-30</td>\n",
              "      <td>San Francisco</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1804 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Size  Duration  Month_Temp   datetime           City\n",
              "9723  2.00       1.0        71.0 2012-08-17  San Francisco\n",
              "9724  0.10       0.0        71.0 2012-08-18  San Francisco\n",
              "9725  1.00       3.0        71.0 2012-08-11  San Francisco\n",
              "9726  0.10       0.0        71.0 2012-08-31  San Francisco\n",
              "9727  0.10       0.0        71.0 2012-08-28  San Francisco\n",
              "...    ...       ...         ...        ...            ...\n",
              "8924  2.00       0.0        69.0 2015-06-24  San Francisco\n",
              "8925  1.00       0.0        69.0 2015-06-18  San Francisco\n",
              "9086  1.30       0.0        58.4 2015-03-10  San Francisco\n",
              "9087  0.08       0.0        58.4 2015-03-31  San Francisco\n",
              "9088  0.10       0.0        58.4 2015-03-30  San Francisco\n",
              "\n",
              "[1804 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 164
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xjD8zpRbE9h5",
        "colab_type": "text"
      },
      "source": [
        "getting the daily weather conditions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fhnPLfjTOnoQ",
        "colab_type": "text"
      },
      "source": [
        "definitions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rPjoOaoZOnGW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def convert_to_dt(time):\n",
        "    time = time.split(\" \")[0]\n",
        "    time = time.split(\"-\")\n",
        "    return datetime(year = int(time[0]), month = int(time[1]), day = int(time[2]))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rxjl7IRINdgM",
        "colab_type": "text"
      },
      "source": [
        "Humidity"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GcJE0IIBE9Ah",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "humidity = pd.read_csv('/content/drive/My Drive/Data 301 Project/humidity.csv', delimiter = ',', engine = 'python')\n",
        "humidity = humidity[[\"datetime\", \"Los Angeles\", \"San Diego\", \"San Francisco\"]]\n",
        "humidity = humidity.fillna(method = \"ffill\", axis = \"rows\").dropna()\n",
        "humidity = humidity.set_index(['datetime'])\n",
        "humidity = pd.DataFrame(humidity.stack()).reset_index()\n",
        "humidity.columns = [\"datetime\", \"City\", \"Humidity\"]\n",
        "humidity[\"datetime\"] = np.vectorize(convert_to_dt)(humidity[\"datetime\"])\n",
        "humidity = humidity.groupby(['datetime', \"City\"]).mean().reset_index()\n",
        "data = pd.merge(reduced, humidity, left_on = [\"datetime\", \"City\"], right_on = [\"datetime\", \"City\"])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y3tmP-0iNgn6",
        "colab_type": "text"
      },
      "source": [
        "Pressure 4"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GEgVpmpsOJdy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pressure = pd.read_csv('/content/drive/My Drive/Data 301 Project/pressure.csv', delimiter = ',', engine = 'python')\n",
        "pressure = pressure[[\"datetime\", \"Los Angeles\", \"San Diego\", \"San Francisco\"]]\n",
        "pressure = pressure.fillna(method = \"ffill\", axis = \"rows\").dropna()\n",
        "pressure = pressure.set_index(['datetime'])\n",
        "pressure = pd.DataFrame(pressure.stack()).reset_index()\n",
        "pressure.columns = [\"datetime\", \"City\", \"Pressure\"]\n",
        "pressure[\"datetime\"] = np.vectorize(convert_to_dt)(pressure[\"datetime\"])\n",
        "pressure = pressure.groupby(['datetime', \"City\"]).mean().reset_index()\n",
        "data = pd.merge(data, pressure, left_on = [\"datetime\", \"City\"], right_on = [\"datetime\", \"City\"])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5-S24sgVOIl6",
        "colab_type": "text"
      },
      "source": [
        "Temperature 2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z-AcEuHSOJDk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "temperature = pd.read_csv('/content/drive/My Drive/Data 301 Project/temperature.csv', delimiter = ',', engine = 'python')\n",
        "temperature = temperature[[\"datetime\", \"Los Angeles\", \"San Diego\", \"San Francisco\"]]\n",
        "temperature = temperature.fillna(method = \"ffill\", axis = \"rows\").dropna()\n",
        "temperature = temperature.set_index(['datetime'])\n",
        "temperature = pd.DataFrame(temperature.stack()).reset_index()\n",
        "temperature.columns = [\"datetime\", \"City\", \"Temperature\"]\n",
        "temperature[\"datetime\"] = np.vectorize(convert_to_dt)(temperature[\"datetime\"])\n",
        "temperature = temperature.groupby(['datetime', \"City\"]).mean().reset_index()\n",
        "temperature.columns = [\"datetime\", \"City\", \"Day_Temp\"]\n",
        "data = pd.merge(data, temperature, left_on = [\"datetime\", \"City\"], right_on = [\"datetime\", \"City\"])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B5y5vVCvOMNw",
        "colab_type": "text"
      },
      "source": [
        "Wind Speed 1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oHdP7eY2OMWY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "wind_speed = pd.read_csv('/content/drive/My Drive/Data 301 Project/wind_speed.csv', delimiter = ',', engine = 'python')\n",
        "wind_speed = wind_speed[[\"datetime\", \"Los Angeles\", \"San Diego\", \"San Francisco\"]]\n",
        "wind_speed = wind_speed.fillna(method = \"ffill\", axis = \"rows\").dropna()\n",
        "wind_speed = wind_speed.set_index(['datetime'])\n",
        "wind_speed = pd.DataFrame(wind_speed.stack()).reset_index()\n",
        "wind_speed.columns = [\"datetime\", \"City\", \"Wind Speed\"]\n",
        "wind_speed[\"datetime\"] = np.vectorize(convert_to_dt)(wind_speed[\"datetime\"])\n",
        "wind_speed = wind_speed.groupby(['datetime', \"City\"]).mean().reset_index()\n",
        "data = pd.merge(data, wind_speed, left_on = [\"datetime\", \"City\"], right_on = [\"datetime\", \"City\"])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p0PfSqHsONvS",
        "colab_type": "text"
      },
      "source": [
        "Weather Descr. 3"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IjeTeLjhON8I",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "7904fe36-e472-4005-d54a-35d551d12e2c"
      },
      "source": [
        "weather_descr = pd.read_csv('/content/drive/My Drive/Data 301 Project/weather_description.csv', delimiter = ',', engine = 'python')\n",
        "weather_descr = weather_descr[[\"datetime\", \"Los Angeles\", \"San Diego\", \"San Francisco\"]]\n",
        "weather_descr = weather_descr.fillna(method = \"ffill\", axis = \"rows\").dropna()\n",
        "weather_descr = weather_descr.set_index(['datetime'])\n",
        "weather_descr = pd.DataFrame(weather_descr.stack()).reset_index()\n",
        "weather_descr.columns = [\"datetime\", \"City\", \"Weather Description\"]\n",
        "weather_descr[\"datetime\"] = np.vectorize(convert_to_dt)(weather_descr[\"datetime\"])\n",
        "fancy_index = weather_descr[\"Weather Description\"]  == \"light rain\"\n",
        "fancy_index += weather_descr[\"Weather Description\"] == \"light intensity drizzle'\"\n",
        "fancy_index += weather_descr[\"Weather Description\"] == 'moderate rain'\n",
        "fancy_index += weather_descr[\"Weather Description\"] == 'thunderstorm with rain'\n",
        "fancy_index += weather_descr[\"Weather Description\"] == 'shower rain'\n",
        "fancy_index += weather_descr[\"Weather Description\"] == 'heavy intensity rain'\n",
        "fancy_index += weather_descr[\"Weather Description\"] == 'thunderstorm with heavy rain'\n",
        "fancy_index += weather_descr[\"Weather Description\"] == 'drizzle'\n",
        "fancy_index += weather_descr[\"Weather Description\"] == 'proximity shower rain'\n",
        "fancy_index += weather_descr[\"Weather Description\"] == 'heavy intensity drizzle'\n",
        "fancy_index += weather_descr[\"Weather Description\"] == 'light intensity shower rain'\n",
        "fancy_index += weather_descr[\"Weather Description\"] == 'thunderstorm with light rain'\n",
        "fancy_index += weather_descr[\"Weather Description\"] == 'very heavy rain'\n",
        "fancy_index += weather_descr[\"Weather Description\"] == 'proximity thunderstorm with rain'\n",
        "\n",
        "weather_descr = weather_descr[fancy_index]\n",
        "\n",
        "weather_descr = weather_descr.groupby(['datetime', \"City\"]).count().reset_index() # we need to change this to boolean did it rain and is it sunny.\n",
        "weather_descr[\"Weather Description\"] = 1\n",
        "weather_descr.columns = [\"datetime\", \"City\", \"hasRained\"]\n",
        "data = pd.merge(data, weather_descr, left_on = [\"datetime\", \"City\"], right_on = [\"datetime\", \"City\"], how = \"left\")\n",
        "data = data.fillna(0)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/core/computation/expressions.py:178: UserWarning: evaluating in Python space because the '+' operator is not supported by numexpr for the bool dtype, use '|' instead\n",
            "  f\"evaluating in Python space because the {repr(op_str)} \"\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JD8RfPt1Jxsm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "outputId": "f81a0541-9a5c-48bf-832c-64a5c11c6916"
      },
      "source": [
        "data = data[['Size', 'Month_Temp', 'datetime', 'City', 'Humidity', 'Pressure', 'Day_Temp', 'Wind Speed', 'hasRained']]\n",
        "data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Size</th>\n",
              "      <th>Month_Temp</th>\n",
              "      <th>datetime</th>\n",
              "      <th>City</th>\n",
              "      <th>Humidity</th>\n",
              "      <th>Pressure</th>\n",
              "      <th>Day_Temp</th>\n",
              "      <th>Wind Speed</th>\n",
              "      <th>hasRained</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.00</td>\n",
              "      <td>63.2</td>\n",
              "      <td>2012-10-31</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>84.416667</td>\n",
              "      <td>1017.875000</td>\n",
              "      <td>287.403750</td>\n",
              "      <td>3.750000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.10</td>\n",
              "      <td>68.3</td>\n",
              "      <td>2013-06-30</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>79.583333</td>\n",
              "      <td>1013.000000</td>\n",
              "      <td>298.631042</td>\n",
              "      <td>1.166667</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.10</td>\n",
              "      <td>68.3</td>\n",
              "      <td>2013-06-30</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>79.583333</td>\n",
              "      <td>1013.000000</td>\n",
              "      <td>298.631042</td>\n",
              "      <td>1.166667</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.40</td>\n",
              "      <td>67.4</td>\n",
              "      <td>2013-06-30</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>79.583333</td>\n",
              "      <td>1013.000000</td>\n",
              "      <td>298.631042</td>\n",
              "      <td>1.166667</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6.25</td>\n",
              "      <td>68.3</td>\n",
              "      <td>2013-06-29</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>77.958333</td>\n",
              "      <td>1015.333333</td>\n",
              "      <td>296.238333</td>\n",
              "      <td>1.375000</td>\n",
              "      <td>0.0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1526</th>\n",
              "      <td>0.10</td>\n",
              "      <td>69.0</td>\n",
              "      <td>2015-06-30</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>76.500000</td>\n",
              "      <td>1011.416667</td>\n",
              "      <td>295.748177</td>\n",
              "      <td>4.041667</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1527</th>\n",
              "      <td>1.50</td>\n",
              "      <td>69.0</td>\n",
              "      <td>2015-06-13</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>84.791667</td>\n",
              "      <td>1019.041667</td>\n",
              "      <td>289.147883</td>\n",
              "      <td>1.916667</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1528</th>\n",
              "      <td>1.30</td>\n",
              "      <td>58.4</td>\n",
              "      <td>2015-03-10</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>95.041667</td>\n",
              "      <td>1027.041667</td>\n",
              "      <td>284.899854</td>\n",
              "      <td>1.041667</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1529</th>\n",
              "      <td>0.08</td>\n",
              "      <td>58.4</td>\n",
              "      <td>2015-03-31</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>81.291667</td>\n",
              "      <td>1032.125000</td>\n",
              "      <td>287.667604</td>\n",
              "      <td>4.541667</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1530</th>\n",
              "      <td>0.10</td>\n",
              "      <td>58.4</td>\n",
              "      <td>2015-03-30</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>86.500000</td>\n",
              "      <td>1030.166667</td>\n",
              "      <td>286.452417</td>\n",
              "      <td>1.333333</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1531 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Size  Month_Temp   datetime  ...    Day_Temp  Wind Speed  hasRained\n",
              "0     1.00        63.2 2012-10-31  ...  287.403750    3.750000        0.0\n",
              "1     0.10        68.3 2013-06-30  ...  298.631042    1.166667        0.0\n",
              "2     0.10        68.3 2013-06-30  ...  298.631042    1.166667        0.0\n",
              "3     0.40        67.4 2013-06-30  ...  298.631042    1.166667        0.0\n",
              "4     6.25        68.3 2013-06-29  ...  296.238333    1.375000        0.0\n",
              "...    ...         ...        ...  ...         ...         ...        ...\n",
              "1526  0.10        69.0 2015-06-30  ...  295.748177    4.041667        0.0\n",
              "1527  1.50        69.0 2015-06-13  ...  289.147883    1.916667        1.0\n",
              "1528  1.30        58.4 2015-03-10  ...  284.899854    1.041667        0.0\n",
              "1529  0.08        58.4 2015-03-31  ...  287.667604    4.541667        0.0\n",
              "1530  0.10        58.4 2015-03-30  ...  286.452417    1.333333        0.0\n",
              "\n",
              "[1531 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 171
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6M9kIcp4XQAz",
        "colab_type": "text"
      },
      "source": [
        "Splitting Data into Training and Testing Sets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "npEHe8ciXTMl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import random\n",
        "import math\n",
        "from numpy.random import permutation\n",
        "random_indices = permutation(data.index)\n",
        "cutoff = math.floor(len(data) / 10)\n",
        "cutoff2 = cutoff * 2\n",
        "testing = data.loc[random_indices[:cutoff]]\n",
        "verification = data.loc[random_indices[cutoff:cutoff2]]\n",
        "training = data.loc[random_indices[cutoff2:]]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EpMvkt8wT2ll",
        "colab_type": "text"
      },
      "source": [
        "Feature Extraction - Predicting Size w/ Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ng2Def3jCEre",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cols = training.columns[[False, True, False, False, True, True, True, True, True]]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "67Z6JokOT2KY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 115
        },
        "outputId": "8eceddae-575b-47df-8973-211ca019fb75"
      },
      "source": [
        "correlations = []\n",
        "for i in cols:\n",
        "  correlation =  training[\"Size\"].corr(training[i])\n",
        "  correlations.append([i, correlation])\n",
        "\n",
        "training1 = training.copy()\n",
        "\n",
        "training1[\"Humidity\"] = training1[\"Humidity\"].mul(-1)\n",
        "training1['Pressure'] = training1['Pressure'].mul(-1)\n",
        "training1['Wind Speed'] = training1['Wind Speed'].mul(-1)\n",
        "training1['hasRained'] = training1['hasRained'].mul(-1)\n",
        "\n",
        "verification1 = verification.copy()\n",
        "\n",
        "verification1[\"Humidity\"] = verification1[\"Humidity\"].mul(-1)\n",
        "verification1['Pressure'] = verification1['Pressure'].mul(-1)\n",
        "verification1['Wind Speed'] = verification1['Wind Speed'].mul(-1)\n",
        "verification1['hasRained'] = verification1['hasRained'].mul(-1)\n",
        "\n",
        "testing1 = testing.copy()\n",
        "\n",
        "testing1[\"Humidity\"] = testing1[\"Humidity\"].mul(-1)\n",
        "testing1['Pressure'] = testing1['Pressure'].mul(-1)\n",
        "testing1['Wind Speed'] = testing1['Wind Speed'].mul(-1)\n",
        "testing1['hasRained'] = testing1['hasRained'].mul(-1)\n",
        "correlations"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[['Month_Temp', 0.042510404925172975],\n",
              " ['Humidity', 0.0037820128209751698],\n",
              " ['Pressure', -0.06174543661454112],\n",
              " ['Day_Temp', 0.034113545993064306],\n",
              " ['Wind Speed', 0.014963645908227347],\n",
              " ['hasRained', -0.03798800605387105]]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 206
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9KRW9Q_eXWR4",
        "colab_type": "text"
      },
      "source": [
        "Linear Regression to Predict Size"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kbvnx0uNX-Df",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def normalize(col):\n",
        "  mean = col.mean()\n",
        "  std = col.std()\n",
        "  return (col - mean) / std"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QqZCJZfcb9An",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "from sklearn.pipeline import make_pipeline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z3K-rQttXZYs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "ba7ac407-94a6-4c71-fa9a-de4b7471ec4f"
      },
      "source": [
        "#training\n",
        "training1_x = training1[[\"Month_Temp\", \"Humidity\", \"Pressure\", \"Day_Temp\", \"Wind Speed\", \"hasRained\"]]\n",
        "training1_y = training1[['Size']]\n",
        "\n",
        "\n",
        "training1_x[\"Month_Temp\"] = np.apply_along_axis(normalize, 0, training1_x[\"Month_Temp\"])\n",
        "training1_x[\"Humidity\"] = np.apply_along_axis(normalize, 0, training1_x[\"Humidity\"])\n",
        "training1_x[\"Pressure\"] = np.apply_along_axis(normalize, 0, training1_x[\"Pressure\"])\n",
        "training1_x[\"Day_Temp\"] = np.apply_along_axis(normalize, 0, training1_x[\"Day_Temp\"])\n",
        "training1_x[\"Wind Speed\"] = np.apply_along_axis(normalize, 0, training1_x[\"Wind Speed\"])\n",
        "\n",
        "#verification\n",
        "verification1_x = verification[[\"Month_Temp\", \"Humidity\", \"Pressure\", \"Day_Temp\", \"Wind Speed\", \"hasRained\"]]\n",
        "verification1_y = verification[['Size']]\n",
        "\n",
        "verification1_x[\"Month_Temp\"] = np.apply_along_axis(normalize, 0, verification1_x[\"Month_Temp\"])\n",
        "verification1_x[\"Humidity\"] = np.apply_along_axis(normalize, 0, verification1_x[\"Humidity\"])\n",
        "verification1_x[\"Pressure\"] = np.apply_along_axis(normalize, 0, verification1_x[\"Pressure\"])\n",
        "verification1_x[\"Day_Temp\"] = np.apply_along_axis(normalize, 0, verification1_x[\"Day_Temp\"])\n",
        "verification1_x[\"Wind Speed\"] = np.apply_along_axis(normalize, 0, verification1_x[\"Wind Speed\"])\n",
        "\n",
        "\n",
        "poly_model = make_pipeline(PolynomialFeatures(2), LinearRegression(fit_intercept=False))\n",
        "poly_model.fit(training1_x, training1_y)\n",
        "\n",
        "verification1_y['predictions'] = math.e ** (abs(poly_model.predict(verification1_x)) / 100) / 10\n",
        "\n",
        "#mean_diff = (abs(verification1_y['predictions'] - verification1_y['Size'])).mean()\n",
        "#display(verification1_y)\n",
        "#print(mean_diff)\n",
        "\n",
        "#testing\n",
        "\n",
        "testing1_x = testing1[[\"Month_Temp\", \"Humidity\", \"Pressure\", \"Day_Temp\", \"Wind Speed\", \"hasRained\"]]\n",
        "testing1_y = testing1[['Size']]\n",
        "\n",
        "\n",
        "testing1_x[\"Month_Temp\"] = np.apply_along_axis(normalize, 0, testing1_x[\"Month_Temp\"])\n",
        "testing1_x[\"Humidity\"] = np.apply_along_axis(normalize, 0, testing1_x[\"Humidity\"])\n",
        "testing1_x[\"Pressure\"] = np.apply_along_axis(normalize, 0, testing1_x[\"Pressure\"])\n",
        "testing1_x[\"Day_Temp\"] = np.apply_along_axis(normalize, 0, testing1_x[\"Day_Temp\"])\n",
        "testing1_x[\"Wind Speed\"] = np.apply_along_axis(normalize, 0, testing1_x[\"Wind Speed\"])\n",
        "\n",
        "testing1_y['predictions'] = math.e ** (abs(poly_model.predict(testing1_x)) / 100) / 10\n",
        "display(testing1_y)\n",
        "print((abs(testing1_y['predictions'] - testing1_y['Size'])).mean())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  import sys\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  if __name__ == '__main__':\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:10: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  # Remove the CWD from sys.path while we load stuff.\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:16: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  app.launch_new_instance()\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:17: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:18: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:19: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:20: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:26: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:38: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:39: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:40: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:41: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:42: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:44: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "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>Size</th>\n",
              "      <th>predictions</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>853</th>\n",
              "      <td>2.00</td>\n",
              "      <td>0.101333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1256</th>\n",
              "      <td>0.10</td>\n",
              "      <td>0.114338</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>302</th>\n",
              "      <td>0.10</td>\n",
              "      <td>0.167046</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>2.00</td>\n",
              "      <td>0.150090</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>241</th>\n",
              "      <td>0.75</td>\n",
              "      <td>0.109346</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1262</th>\n",
              "      <td>0.01</td>\n",
              "      <td>0.100718</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1138</th>\n",
              "      <td>1.00</td>\n",
              "      <td>0.103275</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1352</th>\n",
              "      <td>0.10</td>\n",
              "      <td>0.115684</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>960</th>\n",
              "      <td>1.00</td>\n",
              "      <td>0.107576</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>915</th>\n",
              "      <td>0.10</td>\n",
              "      <td>0.106377</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>153 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Size  predictions\n",
              "853   2.00     0.101333\n",
              "1256  0.10     0.114338\n",
              "302   0.10     0.167046\n",
              "22    2.00     0.150090\n",
              "241   0.75     0.109346\n",
              "...    ...          ...\n",
              "1262  0.01     0.100718\n",
              "1138  1.00     0.103275\n",
              "1352  0.10     0.115684\n",
              "960   1.00     0.107576\n",
              "915   0.10     0.106377\n",
              "\n",
              "[153 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "3.5060943351693763\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HwLNpSGVW-y4",
        "colab_type": "text"
      },
      "source": [
        "Visualization:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-l9Q3_sQXAmn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "8b241252-0b85-4235-e171-b935a8c8605f"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "plt.style.use('Solarize_Light2')\n",
        "testing1_y.plot.scatter(x = 'predictions', y = 'Size')\n",
        "plt.title('Scatter of Predictions vs. Actual Fire Size')\n",
        "plt.xlabel('Predicted Fire Size')\n",
        "plt.ylabel('Actual Fire Size')\n",
        "plt.savefig('reg_v1.png')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tUii9U9cYazj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "68e526f3-40db-403e-d967-fdd177b21148"
      },
      "source": [
        "testing1_y.plot.scatter(x = 'predictions', y = 'Size')\n",
        "plt.title('Predictions vs. Actual Fire Size (No Outliers)')\n",
        "plt.xlabel('Predicted Fire Size')\n",
        "plt.ylabel('Actual Fire Size')\n",
        "plt.xlim(0, 2)\n",
        "plt.ylim(0, 5)\n",
        "plt.savefig('reg_v2.png')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ouSo47v-ec6n",
        "colab_type": "text"
      },
      "source": [
        "Neural Network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MiVjIbf6fJsT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.python.keras import models\n",
        "from tensorflow.python.keras import layers"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PH3JisBrefWj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "1aa046eb-4c47-4463-e161-2430ed4547b3"
      },
      "source": [
        "verification2_y = testing1[['Size']]\n",
        "testing2_y = testing1[['Size']]\n",
        "model = models.Sequential()\n",
        "model.add(layers.Dense(8, activation = 'relu', input_shape = (6, )))\n",
        "model.add(layers.Dense(8, activation = 'relu'))\n",
        "model.add(layers.Dense(1, activation = 'softmax'))\n",
        "model.compile(optimizer = 'rmsprop', loss = 'mean_squared_logarithmic_error', metrics = ['accuracy'])\n",
        "# training\n",
        "vis = model.fit(training1_x, training1_y, epochs = 4, batch_size = 16, validation_data=(verification1_x, verification2_y))\n",
        "# testing\n",
        "test_results = model.evaluate(testing1_x, testing2_y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/4\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 1.2988 - accuracy: 0.1078 - val_loss: 0.9614 - val_accuracy: 0.1176\n",
            "Epoch 2/4\n",
            "77/77 [==============================] - 0s 2ms/step - loss: 1.2988 - accuracy: 0.1078 - val_loss: 0.9614 - val_accuracy: 0.1176\n",
            "Epoch 3/4\n",
            "77/77 [==============================] - 0s 3ms/step - loss: 1.2988 - accuracy: 0.1078 - val_loss: 0.9614 - val_accuracy: 0.1176\n",
            "Epoch 4/4\n",
            "77/77 [==============================] - 0s 2ms/step - loss: 1.2988 - accuracy: 0.1078 - val_loss: 0.9614 - val_accuracy: 0.1176\n",
            "5/5 [==============================] - 0s 1ms/step - loss: 0.9614 - accuracy: 0.1176\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-wJMP-IegJYs",
        "colab_type": "text"
      },
      "source": [
        "Visualization:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qAqkVncPgLbn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "outputId": "ba89afac-b97c-44b8-fc18-1dc260053387"
      },
      "source": [
        "acc_train = np.array(vis.history['accuracy'])\n",
        "acc_val = np.array(vis.history['val_accuracy'])\n",
        "epochs = np.arange(4,8)\n",
        "plt.plot(epochs, acc_train, 'g', label='Training Accuracy')\n",
        "plt.plot(epochs, acc_val, 'b', label='Validation Accuracy')\n",
        "plt.title('Training vs. Validation Accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.ylim(0.08, 0.15)\n",
        "plt.legend()\n",
        "plt.show()\n",
        "plt.savefig('nn.png')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}