marcus_nb.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d7de7b48-4a35-41b0-9337-a43f51802353",
   "metadata": {},
   "source": [
    "# Project 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "ba2d1a32-1398-4c64-bf32-97592bf79314",
   "metadata": {},
   "outputs": [],
   "source": [
    "#imports\n",
    "import pandas as pd\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "25d508a9-50b3-43bb-b43d-eb3dbd138e95",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_accident = pd.read_csv('./data/accident.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b5836718-d5d9-4154-bd5d-5f978d9c3a60",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_accident.columns = df_accident.columns.str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "34ae9f3f-af76-480c-a98b-cf3c7de97c53",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>statename</th>\n",
       "      <th>st_case</th>\n",
       "      <th>ve_total</th>\n",
       "      <th>ve_forms</th>\n",
       "      <th>pvh_invl</th>\n",
       "      <th>peds</th>\n",
       "      <th>persons</th>\n",
       "      <th>permvit</th>\n",
       "      <th>pernotmvit</th>\n",
       "      <th>...</th>\n",
       "      <th>arr_hour</th>\n",
       "      <th>arr_hourname</th>\n",
       "      <th>arr_min</th>\n",
       "      <th>arr_minname</th>\n",
       "      <th>hosp_hr</th>\n",
       "      <th>hosp_hrname</th>\n",
       "      <th>hosp_mn</th>\n",
       "      <th>hosp_mnname</th>\n",
       "      <th>fatals</th>\n",
       "      <th>drunk_dr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>10001</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3:00am-3:59am</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Hospital Arrival Time</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>10002</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>17</td>\n",
       "      <td>5:00pm-5:59pm</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Hospital Arrival Time</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>10003</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>15</td>\n",
       "      <td>3:00pm-3:59pm</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Hospital Arrival Time</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>10004</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Hour</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Minutes</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Hospital Arrival Time</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>10005</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0:00am-0:59am</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>88</td>\n",
       "      <td>Not Applicable (Not Transported)</td>\n",
       "      <td>88</td>\n",
       "      <td>Not Applicable (Not Transported)</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   state statename  st_case  ve_total  ve_forms  pvh_invl  peds  persons  \\\n",
       "0      1   Alabama    10001         1         1         0     0        4   \n",
       "1      1   Alabama    10002         4         4         0     0        6   \n",
       "2      1   Alabama    10003         2         2         0     0        2   \n",
       "3      1   Alabama    10004         1         1         0     0        5   \n",
       "4      1   Alabama    10005         1         1         0     0        1   \n",
       "\n",
       "   permvit  pernotmvit  ...  arr_hour                    arr_hourname  \\\n",
       "0        4           0  ...         3                   3:00am-3:59am   \n",
       "1        6           0  ...        17                   5:00pm-5:59pm   \n",
       "2        2           0  ...        15                   3:00pm-3:59pm   \n",
       "3        5           0  ...        99  Unknown EMS Scene Arrival Hour   \n",
       "4        1           0  ...         0                   0:00am-0:59am   \n",
       "\n",
       "   arr_min                        arr_minname  hosp_hr  \\\n",
       "0       10                                 10       99   \n",
       "1       26                                 26       99   \n",
       "2       15                                 15       99   \n",
       "3       99  Unknown EMS Scene Arrival Minutes       99   \n",
       "4       55                                 55       88   \n",
       "\n",
       "                        hosp_hrname  hosp_mn  \\\n",
       "0                           Unknown       99   \n",
       "1                           Unknown       99   \n",
       "2                           Unknown       99   \n",
       "3                           Unknown       99   \n",
       "4  Not Applicable (Not Transported)       88   \n",
       "\n",
       "                         hosp_mnname  fatals  drunk_dr  \n",
       "0  Unknown EMS Hospital Arrival Time       3         1  \n",
       "1  Unknown EMS Hospital Arrival Time       1         0  \n",
       "2  Unknown EMS Hospital Arrival Time       1         0  \n",
       "3  Unknown EMS Hospital Arrival Time       1         0  \n",
       "4   Not Applicable (Not Transported)       1         0  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_accident.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "f508a0ab-cead-485d-928a-67ac78fd6e28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2, 10,  5, 99, 12,  4, 98, 11,  3,  6,  7,  8], dtype=int64)"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_accident['weather'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "08b76bcc-446f-404b-9c4c-bc239d60858d",
   "metadata": {},
   "outputs": [
    {
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       "      <th>35764</th>\n",
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       "<p>35766 rows × 2 columns</p>\n",
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      ],
      "text/plain": [
       "       harm_ev  arr_hour\n",
       "0           42         3\n",
       "1           12        17\n",
       "2           34        15\n",
       "3           42        99\n",
       "4           42         0\n",
       "...        ...       ...\n",
       "35761       12        16\n",
       "35762       12        18\n",
       "35763       12        17\n",
       "35764       12        20\n",
       "35765        1         9\n",
       "\n",
       "[35766 rows x 2 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#c19\n",
    "df_accident[['harm_ev', 'arr_hour']]\n",
    "#lets take a look at the particular hour and how thw vehicle crashed(the type of collision/harm). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "edc14061-f22a-4ab9-8f93-31d34aa1ad6b",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>harm_ev</th>\n",
       "      <th>arr_hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>harm_ev</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.018754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arr_hour</th>\n",
       "      <td>-0.018754</td>\n",
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      ],
      "text/plain": [
       "           harm_ev  arr_hour\n",
       "harm_ev   1.000000 -0.018754\n",
       "arr_hour -0.018754  1.000000"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_accident[['harm_ev', 'arr_hour']].corr()\n",
    "#The correlation between these two features seems to be pretty weak as I thought there would be more to unpack here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2fd11102-fbb3-42ee-8710-65af7e0d2f8a",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>wrk_zone</th>\n",
       "      <th>lgt_cond</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>wrk_zone</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.000052</td>\n",
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      "text/plain": [
       "          wrk_zone  lgt_cond\n",
       "wrk_zone  1.000000 -0.000052\n",
       "lgt_cond -0.000052  1.000000"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df_accident[['wrk_zone', 'lgt_cond']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "1b279dc5-f10f-43e2-9376-239f123edbee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 1, 4, 3], dtype=int64)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_accident['wrk_zone'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "e26d4728-cab2-405f-b21a-f06e6f8c337c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3, 1, 5, 4, 9, 6, 8, 7], dtype=int64)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_accident['lgt_cond'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "5fb7caa7-fee2-47fc-b7f9-7d6e5a7aff0e",
   "metadata": {},
   "outputs": [
    {
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       "      <th>fatals</th>\n",
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       "      <th rowspan=\"8\" valign=\"top\">lgt_cond</th>\n",
       "      <th>count</th>\n",
       "      <td>33226.000000</td>\n",
       "      <td>2154.000000</td>\n",
       "      <td>289.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.0</td>\n",
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       "      <th>mean</th>\n",
       "      <td>1.967525</td>\n",
       "      <td>1.861653</td>\n",
       "      <td>1.951557</td>\n",
       "      <td>2.056338</td>\n",
       "      <td>1.650000</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.229484</td>\n",
       "      <td>1.111267</td>\n",
       "      <td>1.144616</td>\n",
       "      <td>1.297110</td>\n",
       "      <td>0.933302</td>\n",
       "      <td>1.258306</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">wrk_zone</th>\n",
       "      <th>count</th>\n",
       "      <td>33226.000000</td>\n",
       "      <td>2154.000000</td>\n",
       "      <td>289.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.046741</td>\n",
       "      <td>0.059424</td>\n",
       "      <td>0.038062</td>\n",
       "      <td>0.014085</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.379156</td>\n",
       "      <td>0.421438</td>\n",
       "      <td>0.303803</td>\n",
       "      <td>0.118678</td>\n",
       "      <td>0.910465</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "fatals                     1            2           3          4          5  \\\n",
       "lgt_cond count  33226.000000  2154.000000  289.000000  71.000000  20.000000   \n",
       "         mean       1.967525     1.861653    1.951557   2.056338   1.650000   \n",
       "         std        1.229484     1.111267    1.144616   1.297110   0.933302   \n",
       "         min        1.000000     1.000000    1.000000   1.000000   1.000000   \n",
       "         25%        1.000000     1.000000    1.000000   1.000000   1.000000   \n",
       "         50%        2.000000     2.000000    2.000000   2.000000   1.000000   \n",
       "         75%        3.000000     2.000000    3.000000   3.000000   2.000000   \n",
       "         max        9.000000     9.000000    8.000000   9.000000   4.000000   \n",
       "wrk_zone count  33226.000000  2154.000000  289.000000  71.000000  20.000000   \n",
       "         mean       0.046741     0.059424    0.038062   0.014085   0.250000   \n",
       "         std        0.379156     0.421438    0.303803   0.118678   0.910465   \n",
       "         min        0.000000     0.000000    0.000000   0.000000   0.000000   \n",
       "         25%        0.000000     0.000000    0.000000   0.000000   0.000000   \n",
       "         50%        0.000000     0.000000    0.000000   0.000000   0.000000   \n",
       "         75%        0.000000     0.000000    0.000000   0.000000   0.000000   \n",
       "         max        4.000000     4.000000    4.000000   1.000000   4.000000   \n",
       "\n",
       "fatals                 6    7    8  \n",
       "lgt_cond count  4.000000  1.0  1.0  \n",
       "         mean   2.250000  3.0  1.0  \n",
       "         std    1.258306  NaN  NaN  \n",
       "         min    1.000000  3.0  1.0  \n",
       "         25%    1.750000  3.0  1.0  \n",
       "         50%    2.000000  3.0  1.0  \n",
       "         75%    2.500000  3.0  1.0  \n",
       "         max    4.000000  3.0  1.0  \n",
       "wrk_zone count  4.000000  1.0  1.0  \n",
       "         mean   0.000000  0.0  0.0  \n",
       "         std    0.000000  NaN  NaN  \n",
       "         min    0.000000  0.0  0.0  \n",
       "         25%    0.000000  0.0  0.0  \n",
       "         50%    0.000000  0.0  0.0  \n",
       "         75%    0.000000  0.0  0.0  \n",
       "         max    0.000000  0.0  0.0  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_accident.groupby('fatals')[['lgt_cond', 'wrk_zone']].describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "1b6ed97e-bce1-427a-847a-8cef268a12a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 1, 2, 4, 8, 6, 5, 7], dtype=int64)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_accident['fatals'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "a4e6ddb4-ceeb-4943-bb7c-9714c28bde00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4,  6,  2,  5,  1,  3,  7, 21, 13,  8,  9,  0, 11, 10, 17, 19, 15,\n",
       "       16, 14, 24, 12, 26, 22, 28, 61, 20, 31], dtype=int64)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_accident['persons'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "d50b5da5-cd1c-4891-a0a9-f4c4add563b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "fatals_chart = df_accident.sort_values(by= 'fatals', ascending=False)[:12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "ea4e380c-61cb-4b8c-a8cd-d0f47c68859a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
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       "    .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>state</th>\n",
       "      <th>statename</th>\n",
       "      <th>st_case</th>\n",
       "      <th>ve_total</th>\n",
       "      <th>ve_forms</th>\n",
       "      <th>pvh_invl</th>\n",
       "      <th>peds</th>\n",
       "      <th>persons</th>\n",
       "      <th>permvit</th>\n",
       "      <th>pernotmvit</th>\n",
       "      <th>...</th>\n",
       "      <th>arr_hour</th>\n",
       "      <th>arr_hourname</th>\n",
       "      <th>arr_min</th>\n",
       "      <th>arr_minname</th>\n",
       "      <th>hosp_hr</th>\n",
       "      <th>hosp_hrname</th>\n",
       "      <th>hosp_mn</th>\n",
       "      <th>hosp_mnname</th>\n",
       "      <th>fatals</th>\n",
       "      <th>drunk_dr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6236</th>\n",
       "      <td>8</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>80224</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Hour</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Minutes</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Hospital Arrival Time</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31654</th>\n",
       "      <td>48</td>\n",
       "      <td>Texas</td>\n",
       "      <td>482030</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Hour</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Minutes</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Hospital Arrival Time</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32850</th>\n",
       "      <td>48</td>\n",
       "      <td>Texas</td>\n",
       "      <td>483256</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Hour</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Minutes</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Hospital Arrival Time</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9671</th>\n",
       "      <td>12</td>\n",
       "      <td>Florida</td>\n",
       "      <td>122861</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Hour</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Minutes</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Hospital Arrival Time</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10319</th>\n",
       "      <td>13</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>130222</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2:00am-2:59am</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>88</td>\n",
       "      <td>Not Applicable (Not Transported)</td>\n",
       "      <td>88</td>\n",
       "      <td>Not Applicable (Not Transported)</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14279</th>\n",
       "      <td>20</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>200181</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>7:00pm-7:59pm</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>8:00pm-8:59pm</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27982</th>\n",
       "      <td>45</td>\n",
       "      <td>South Carolina</td>\n",
       "      <td>450518</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Hour</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Minutes</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Hospital Arrival Time</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31348</th>\n",
       "      <td>48</td>\n",
       "      <td>Texas</td>\n",
       "      <td>481716</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Hour</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Minutes</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Hospital Arrival Time</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20002</th>\n",
       "      <td>30</td>\n",
       "      <td>Montana</td>\n",
       "      <td>300050</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>8:00pm-8:59pm</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>21</td>\n",
       "      <td>9:00pm-9:59pm</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22015</th>\n",
       "      <td>36</td>\n",
       "      <td>New York</td>\n",
       "      <td>360377</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>17</td>\n",
       "      <td>5:00pm-5:59pm</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Hospital Arrival Time</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31566</th>\n",
       "      <td>48</td>\n",
       "      <td>Texas</td>\n",
       "      <td>481940</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Hour</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Minutes</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Hospital Arrival Time</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29566</th>\n",
       "      <td>47</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>471035</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>10:00am-10:59am</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>88</td>\n",
       "      <td>Not Applicable (Not Transported)</td>\n",
       "      <td>88</td>\n",
       "      <td>Not Applicable (Not Transported)</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       state       statename  st_case  ve_total  ve_forms  pvh_invl  peds  \\\n",
       "6236       8        Colorado    80224         2         2         0     0   \n",
       "31654     48           Texas   482030         1         1         0     0   \n",
       "32850     48           Texas   483256         2         2         0     0   \n",
       "9671      12         Florida   122861         2         2         0     0   \n",
       "10319     13         Georgia   130222         2         2         0     0   \n",
       "14279     20          Kansas   200181         2         2         0     0   \n",
       "27982     45  South Carolina   450518         3         3         0     0   \n",
       "31348     48           Texas   481716         2         2         0     0   \n",
       "20002     30         Montana   300050         2         2         0     0   \n",
       "22015     36        New York   360377         2         2         0     0   \n",
       "31566     48           Texas   481940         2         2         0     0   \n",
       "29566     47       Tennessee   471035         2         1         1     0   \n",
       "\n",
       "       persons  permvit  pernotmvit  ...  arr_hour  \\\n",
       "6236         8        8           0  ...        99   \n",
       "31654       10       10           0  ...        99   \n",
       "32850        9        9           0  ...        99   \n",
       "9671         6        6           0  ...        99   \n",
       "10319        6        6           0  ...         2   \n",
       "14279        8        8           0  ...        19   \n",
       "27982        9        9           0  ...        99   \n",
       "31348        8        8           0  ...        99   \n",
       "20002        5        5           0  ...        20   \n",
       "22015        7        7           0  ...        17   \n",
       "31566       10       10           0  ...        99   \n",
       "29566        6        5           1  ...        10   \n",
       "\n",
       "                         arr_hourname  arr_min  \\\n",
       "6236   Unknown EMS Scene Arrival Hour       99   \n",
       "31654  Unknown EMS Scene Arrival Hour       99   \n",
       "32850  Unknown EMS Scene Arrival Hour       99   \n",
       "9671   Unknown EMS Scene Arrival Hour       99   \n",
       "10319                   2:00am-2:59am       15   \n",
       "14279                   7:00pm-7:59pm        1   \n",
       "27982  Unknown EMS Scene Arrival Hour       99   \n",
       "31348  Unknown EMS Scene Arrival Hour       99   \n",
       "20002                   8:00pm-8:59pm       30   \n",
       "22015                   5:00pm-5:59pm       35   \n",
       "31566  Unknown EMS Scene Arrival Hour       99   \n",
       "29566                 10:00am-10:59am       32   \n",
       "\n",
       "                             arr_minname  hosp_hr  \\\n",
       "6236   Unknown EMS Scene Arrival Minutes       99   \n",
       "31654  Unknown EMS Scene Arrival Minutes       99   \n",
       "32850  Unknown EMS Scene Arrival Minutes       99   \n",
       "9671   Unknown EMS Scene Arrival Minutes       99   \n",
       "10319                                 15       88   \n",
       "14279                                  1       20   \n",
       "27982  Unknown EMS Scene Arrival Minutes       99   \n",
       "31348  Unknown EMS Scene Arrival Minutes       99   \n",
       "20002                                 30       21   \n",
       "22015                                 35       99   \n",
       "31566  Unknown EMS Scene Arrival Minutes       99   \n",
       "29566                                 32       88   \n",
       "\n",
       "                            hosp_hrname  hosp_mn  \\\n",
       "6236                            Unknown       99   \n",
       "31654                           Unknown       99   \n",
       "32850                           Unknown       99   \n",
       "9671                            Unknown       99   \n",
       "10319  Not Applicable (Not Transported)       88   \n",
       "14279                     8:00pm-8:59pm        8   \n",
       "27982                           Unknown       99   \n",
       "31348                           Unknown       99   \n",
       "20002                     9:00pm-9:59pm        9   \n",
       "22015                           Unknown       99   \n",
       "31566                           Unknown       99   \n",
       "29566  Not Applicable (Not Transported)       88   \n",
       "\n",
       "                             hosp_mnname  fatals  drunk_dr  \n",
       "6236   Unknown EMS Hospital Arrival Time       8         0  \n",
       "31654  Unknown EMS Hospital Arrival Time       7         1  \n",
       "32850  Unknown EMS Hospital Arrival Time       6         0  \n",
       "9671   Unknown EMS Hospital Arrival Time       6         0  \n",
       "10319   Not Applicable (Not Transported)       6         0  \n",
       "14279                                  8       6         0  \n",
       "27982  Unknown EMS Hospital Arrival Time       5         0  \n",
       "31348  Unknown EMS Hospital Arrival Time       5         0  \n",
       "20002                                  9       5         2  \n",
       "22015  Unknown EMS Hospital Arrival Time       5         0  \n",
       "31566  Unknown EMS Hospital Arrival Time       5         0  \n",
       "29566   Not Applicable (Not Transported)       5         0  \n",
       "\n",
       "[12 rows x 81 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fatals_chart.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "c1756b51-a0d5-4ea7-bc2a-764a25d439fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6, 11,  2,  7], dtype=int64)"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fatals_chart['month'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "fa8ecdc0-3793-4a93-9b63-d7ca8e960f5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Chart of fatalities & month')"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Fatals\n",
    "plt.figure(figsize=(15,10))\n",
    "plt.bar(fatals_chart['month'],\n",
    "       fatals_chart['fatals'])\n",
    "plt.xticks(rotation = 10)\n",
    "plt.ylabel('fatalities')\n",
    "plt.xlabel('month')\n",
    "plt.title('Chart of fatalities & month')\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "10fa7264-a0fc-4764-8547-09bb8bb0ac00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(35766, 81)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_accident.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "0ccd834e-0d33-4869-8113-ed4b00350219",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day_week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   day_week\n",
       "0         4\n",
       "1         5\n",
       "2         5\n",
       "3         6\n",
       "4         7"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_accident[['day_week']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "0782693a-1ba4-4a17-8268-264d49cbf8de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 11, 10, 12], dtype=int64)"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_accident['month'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "d2e399bc-bba1-4233-b097-adfb8bdafada",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  7,  8,  9, 10, 11, 13, 14, 15, 16, 17, 18, 12,  6,\n",
       "        5, 28, 19, 21, 22, 23, 26, 25, 27, 29, 30, 31, 20, 24],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_accident['day'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "dded6b21-94e0-4cdb-941e-1a8d8508b559",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 5, 6, 7, 3, 2, 1], dtype=int64)"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_accident['day_week'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fbb28b7e-ba3e-421d-acb2-0fa5a3faa279",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 35766 entries, 0 to 35765\n",
      "Data columns (total 81 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   state         35766 non-null  int64  \n",
      " 1   statename     35766 non-null  object \n",
      " 2   st_case       35766 non-null  int64  \n",
      " 3   ve_total      35766 non-null  int64  \n",
      " 4   ve_forms      35766 non-null  int64  \n",
      " 5   pvh_invl      35766 non-null  int64  \n",
      " 6   peds          35766 non-null  int64  \n",
      " 7   persons       35766 non-null  int64  \n",
      " 8   permvit       35766 non-null  int64  \n",
      " 9   pernotmvit    35766 non-null  int64  \n",
      " 10  county        35766 non-null  int64  \n",
      " 11  countyname    35766 non-null  object \n",
      " 12  city          35766 non-null  int64  \n",
      " 13  cityname      35766 non-null  object \n",
      " 14  day           35766 non-null  int64  \n",
      " 15  dayname       35766 non-null  int64  \n",
      " 16  month         35766 non-null  int64  \n",
      " 17  monthname     35766 non-null  object \n",
      " 18  year          35766 non-null  int64  \n",
      " 19  day_week      35766 non-null  int64  \n",
      " 20  day_weekname  35766 non-null  object \n",
      " 21  hour          35766 non-null  int64  \n",
      " 22  hourname      35766 non-null  object \n",
      " 23  minute        35766 non-null  int64  \n",
      " 24  minutename    35766 non-null  object \n",
      " 25  nhs           35766 non-null  int64  \n",
      " 26  nhsname       35766 non-null  object \n",
      " 27  route         35766 non-null  int64  \n",
      " 28  routename     35766 non-null  object \n",
      " 29  tway_id       35766 non-null  object \n",
      " 30  tway_id2      8769 non-null   object \n",
      " 31  rur_urb       35766 non-null  int64  \n",
      " 32  rur_urbname   35766 non-null  object \n",
      " 33  func_sys      35766 non-null  int64  \n",
      " 34  func_sysname  35766 non-null  object \n",
      " 35  rd_owner      35766 non-null  int64  \n",
      " 36  rd_ownername  35766 non-null  object \n",
      " 37  milept        35766 non-null  int64  \n",
      " 38  mileptname    35766 non-null  object \n",
      " 39  latitude      35766 non-null  float64\n",
      " 40  latitudename  35766 non-null  object \n",
      " 41  longitud      35766 non-null  float64\n",
      " 42  longitudname  35766 non-null  object \n",
      " 43  sp_jur        35766 non-null  int64  \n",
      " 44  sp_jurname    35766 non-null  object \n",
      " 45  harm_ev       35766 non-null  int64  \n",
      " 46  harm_evname   35766 non-null  object \n",
      " 47  man_coll      35766 non-null  int64  \n",
      " 48  man_collname  35766 non-null  object \n",
      " 49  reljct1       35766 non-null  int64  \n",
      " 50  reljct1name   35766 non-null  object \n",
      " 51  reljct2       35766 non-null  int64  \n",
      " 52  reljct2name   35766 non-null  object \n",
      " 53  typ_int       35766 non-null  int64  \n",
      " 54  typ_intname   35766 non-null  object \n",
      " 55  wrk_zone      35766 non-null  int64  \n",
      " 56  wrk_zonename  35766 non-null  object \n",
      " 57  rel_road      35766 non-null  int64  \n",
      " 58  rel_roadname  35766 non-null  object \n",
      " 59  lgt_cond      35766 non-null  int64  \n",
      " 60  lgt_condname  35766 non-null  object \n",
      " 61  weather       35766 non-null  int64  \n",
      " 62  weathername   35766 non-null  object \n",
      " 63  sch_bus       35766 non-null  int64  \n",
      " 64  sch_busname   35766 non-null  object \n",
      " 65  rail          35766 non-null  object \n",
      " 66  railname      35766 non-null  object \n",
      " 67  not_hour      35766 non-null  int64  \n",
      " 68  not_hourname  35766 non-null  object \n",
      " 69  not_min       35766 non-null  int64  \n",
      " 70  not_minname   35766 non-null  object \n",
      " 71  arr_hour      35766 non-null  int64  \n",
      " 72  arr_hourname  35766 non-null  object \n",
      " 73  arr_min       35766 non-null  int64  \n",
      " 74  arr_minname   35766 non-null  object \n",
      " 75  hosp_hr       35766 non-null  int64  \n",
      " 76  hosp_hrname   35766 non-null  object \n",
      " 77  hosp_mn       35766 non-null  int64  \n",
      " 78  hosp_mnname   35766 non-null  object \n",
      " 79  fatals        35766 non-null  int64  \n",
      " 80  drunk_dr      35766 non-null  int64  \n",
      "dtypes: float64(2), int64(43), object(36)\n",
      "memory usage: 22.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df_accident.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9e2e2fdf-25c4-49fe-99e6-92cef8fdcc05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state          0\n",
       "statename      0\n",
       "st_case        0\n",
       "ve_total       0\n",
       "ve_forms       0\n",
       "              ..\n",
       "hosp_hrname    0\n",
       "hosp_mn        0\n",
       "hosp_mnname    0\n",
       "fatals         0\n",
       "drunk_dr       0\n",
       "Length: 81, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_accident.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "f8c333df-f72c-4ed4-8265-b5bdd723272c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_accident.drop(columns='tway_id2', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "36a8d301-2cba-4b25-9fb4-588278723ebc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 2, 3, 4], dtype=int64)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_accident['drunk_dr'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "60cdab1f-c72d-4728-884f-fc3a9e99f533",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>drunk_dr</th>\n",
       "      <th>fatals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>drunk_dr</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.048722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fatals</th>\n",
       "      <td>0.048722</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          drunk_dr    fatals\n",
       "drunk_dr  1.000000  0.048722\n",
       "fatals    0.048722  1.000000"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_accident[['drunk_dr', 'fatals']].corr()\n",
    "#the correlation between fatality and drunk driving is low"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "fb7f5d6e-6f01-4592-a29a-a970727bf5e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# deleted this column for data cleaning purposes\n",
    "#df_accident['tway_id2'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "294d43b8-7cdd-483e-b09d-5603360f94b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>st_case</th>\n",
       "      <th>ve_total</th>\n",
       "      <th>ve_forms</th>\n",
       "      <th>pvh_invl</th>\n",
       "      <th>peds</th>\n",
       "      <th>persons</th>\n",
       "      <th>permvit</th>\n",
       "      <th>pernotmvit</th>\n",
       "      <th>county</th>\n",
       "      <th>...</th>\n",
       "      <th>weather</th>\n",
       "      <th>sch_bus</th>\n",
       "      <th>not_hour</th>\n",
       "      <th>not_min</th>\n",
       "      <th>arr_hour</th>\n",
       "      <th>arr_min</th>\n",
       "      <th>hosp_hr</th>\n",
       "      <th>hosp_mn</th>\n",
       "      <th>fatals</th>\n",
       "      <th>drunk_dr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>35766.000000</td>\n",
       "      <td>35766.000000</td>\n",
       "      <td>35766.000000</td>\n",
       "      <td>35766.000000</td>\n",
       "      <td>35766.000000</td>\n",
       "      <td>35766.000000</td>\n",
       "      <td>35766.000000</td>\n",
       "      <td>35766.000000</td>\n",
       "      <td>35766.000000</td>\n",
       "      <td>35766.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>35766.000000</td>\n",
       "      <td>35766.000000</td>\n",
       "      <td>35766.000000</td>\n",
       "      <td>35766.000000</td>\n",
       "      <td>35766.000000</td>\n",
       "      <td>35766.000000</td>\n",
       "      <td>35766.000000</td>\n",
       "      <td>35766.000000</td>\n",
       "      <td>35766.000000</td>\n",
       "      <td>35766.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>27.158307</td>\n",
       "      <td>272387.092937</td>\n",
       "      <td>1.560113</td>\n",
       "      <td>1.517419</td>\n",
       "      <td>0.042694</td>\n",
       "      <td>0.228485</td>\n",
       "      <td>2.172818</td>\n",
       "      <td>2.162585</td>\n",
       "      <td>0.238718</td>\n",
       "      <td>93.060421</td>\n",
       "      <td>...</td>\n",
       "      <td>9.724683</td>\n",
       "      <td>0.001426</td>\n",
       "      <td>61.393642</td>\n",
       "      <td>68.456439</td>\n",
       "      <td>61.884387</td>\n",
       "      <td>68.736901</td>\n",
       "      <td>77.587765</td>\n",
       "      <td>80.760583</td>\n",
       "      <td>1.085500</td>\n",
       "      <td>0.266426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>16.354713</td>\n",
       "      <td>163438.330390</td>\n",
       "      <td>0.782449</td>\n",
       "      <td>0.736814</td>\n",
       "      <td>0.292877</td>\n",
       "      <td>0.470604</td>\n",
       "      <td>1.593437</td>\n",
       "      <td>1.589051</td>\n",
       "      <td>0.494223</td>\n",
       "      <td>98.691960</td>\n",
       "      <td>...</td>\n",
       "      <td>25.543251</td>\n",
       "      <td>0.037735</td>\n",
       "      <td>42.613391</td>\n",
       "      <td>36.188582</td>\n",
       "      <td>42.549598</td>\n",
       "      <td>36.144762</td>\n",
       "      <td>32.738110</td>\n",
       "      <td>27.561744</td>\n",
       "      <td>0.342381</td>\n",
       "      <td>0.459707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>10001.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>122077.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>26.000000</td>\n",
       "      <td>260916.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>42.000000</td>\n",
       "      <td>420476.750000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>56.000000</td>\n",
       "      <td>560115.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              state        st_case      ve_total      ve_forms      pvh_invl  \\\n",
       "count  35766.000000   35766.000000  35766.000000  35766.000000  35766.000000   \n",
       "mean      27.158307  272387.092937      1.560113      1.517419      0.042694   \n",
       "std       16.354713  163438.330390      0.782449      0.736814      0.292877   \n",
       "min        1.000000   10001.000000      1.000000      1.000000      0.000000   \n",
       "25%       12.000000  122077.500000      1.000000      1.000000      0.000000   \n",
       "50%       26.000000  260916.500000      1.000000      1.000000      0.000000   \n",
       "75%       42.000000  420476.750000      2.000000      2.000000      0.000000   \n",
       "max       56.000000  560115.000000     15.000000     15.000000     10.000000   \n",
       "\n",
       "               peds       persons       permvit    pernotmvit        county  \\\n",
       "count  35766.000000  35766.000000  35766.000000  35766.000000  35766.000000   \n",
       "mean       0.228485      2.172818      2.162585      0.238718     93.060421   \n",
       "std        0.470604      1.593437      1.589051      0.494223     98.691960   \n",
       "min        0.000000      0.000000      0.000000      0.000000      1.000000   \n",
       "25%        0.000000      1.000000      1.000000      0.000000     31.000000   \n",
       "50%        0.000000      2.000000      2.000000      0.000000     71.000000   \n",
       "75%        0.000000      3.000000      3.000000      0.000000    117.000000   \n",
       "max        8.000000     61.000000     61.000000      9.000000    999.000000   \n",
       "\n",
       "       ...       weather       sch_bus      not_hour       not_min  \\\n",
       "count  ...  35766.000000  35766.000000  35766.000000  35766.000000   \n",
       "mean   ...      9.724683      0.001426     61.393642     68.456439   \n",
       "std    ...     25.543251      0.037735     42.613391     36.188582   \n",
       "min    ...      1.000000      0.000000      0.000000      0.000000   \n",
       "25%    ...      1.000000      0.000000     16.000000     34.000000   \n",
       "50%    ...      1.000000      0.000000     99.000000     98.000000   \n",
       "75%    ...      2.000000      0.000000     99.000000     99.000000   \n",
       "max    ...     99.000000      1.000000     99.000000     99.000000   \n",
       "\n",
       "           arr_hour       arr_min       hosp_hr       hosp_mn        fatals  \\\n",
       "count  35766.000000  35766.000000  35766.000000  35766.000000  35766.000000   \n",
       "mean      61.884387     68.736901     77.587765     80.760583      1.085500   \n",
       "std       42.549598     36.144762     32.738110     27.561744      0.342381   \n",
       "min        0.000000      0.000000      0.000000      0.000000      1.000000   \n",
       "25%       16.000000     34.000000     88.000000     88.000000      1.000000   \n",
       "50%       99.000000     98.000000     88.000000     88.000000      1.000000   \n",
       "75%       99.000000     99.000000     99.000000     99.000000      1.000000   \n",
       "max       99.000000     99.000000     99.000000     99.000000      8.000000   \n",
       "\n",
       "           drunk_dr  \n",
       "count  35766.000000  \n",
       "mean       0.266426  \n",
       "std        0.459707  \n",
       "min        0.000000  \n",
       "25%        0.000000  \n",
       "50%        0.000000  \n",
       "75%        1.000000  \n",
       "max        4.000000  \n",
       "\n",
       "[8 rows x 45 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_accident.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "704a9af8-ecf0-4f86-8b93-cb359847d0fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>fatals</th>\n",
       "      <th>day_week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>3</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>1</td>\n",
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       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
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       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>35761</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>35762</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35763</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>35764</th>\n",
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       "      <th>35765</th>\n",
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       "      <td>5</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>35766 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       fatals  day_week\n",
       "0           3         4\n",
       "1           1         5\n",
       "2           1         5\n",
       "3           1         6\n",
       "4           1         7\n",
       "...       ...       ...\n",
       "35761       1         7\n",
       "35762       2         6\n",
       "35763       1         6\n",
       "35764       1         3\n",
       "35765       1         5\n",
       "\n",
       "[35766 rows x 2 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_accident[['fatals', 'day_week']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b5208626-2838-4845-b74a-4c7b92c4d1a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fatals</th>\n",
       "      <th>day_week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fatals</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.004305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day_week</th>\n",
       "      <td>0.004305</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            fatals  day_week\n",
       "fatals    1.000000  0.004305\n",
       "day_week  0.004305  1.000000"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_accident[['fatals', 'day_week']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "86c7c658-2763-4322-b787-771c9f65a24c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,5))\n",
    "# Size\n",
    "\n",
    "# Plot it\n",
    "plt.hist(df_accident['day_week'], bins=20, color='teal');\n",
    "\n",
    "\n",
    "\n",
    "plt.title('Fatal Bar Chart');\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "c51a4bcb-457a-43dd-bf0f-6977e3687c18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9RsenVJwpzasVZ0oaHZ+KqCMAANqL0AMAgZsuFBuqAwAQGkIPAASuO5VsqA4AQGgIPUAMrLHG6nF19ZWXNVRHewz19yjZlZhXS3YlNNTfE1FHAAC0F6GnDoutBtaJq4QxSzzdP7JtQcDpxNXbjr7xhgUBh9XbojfQl9bIYK/SqaRMUjqV1MhgL6u3AQBWDXOP/yelM5mM53K5qNsAAAAAEFNmdsLdM7VOY08PAAAAgKARegAAAAAEjdADAAAAIGiEHgAAAABBI/QAAAAACBqhBwAAAEDQCD0AAAAAgkboAQAAABC0Na26YjO7VNInJD2lcjsfdPdbzWydpPdJ2ijplKRXufu3WtXHStm4+8iC2qkD2yLo5OIxSzyFMsvm/Uf14PnHL2xftfYSHd9zY4QdQZKyE3mNjk9pulBUdyqpof4eDfSlo24LAIC2aOWensck/bi7v0jSdZJuMrMtknZL+pi7Xy3pY5XtWKv1ZnSpepwxSzyFMkt14JGkB88/rs37j0bUEaRy4Bkem1S+UJRLyheKGh6bVHYiH3VrAAC0RctCj5d9u7LZVflxSa+UdEelfoekgVb1AKC9qgPPcnW0x+j4lIozpXm14kxJo+NTEXUEAEB7tfQzPWaWMLPPSnpI0lF3Py7pKnd/QJIqf165yGVfb2Y5M8udPXu2lW0CQNCmC8WG6gAAhKalocfdS+5+naRnS3qJmb2wgcve5u4Zd89cccUVLesRAELXnUo2VAcAIDRtWb3N3QuS7pJ0k6QHzexZklT586F29ACg9a5ae0lDdbTHUH+Pkl2JebVkV0JD/T0RdQQAQHu1LPSY2RVmlqr8npT0ckn3SfqQpFsqZ7tF0j+0qoeVstgKWp24shazxFMosxzfc+OCgMPqbdEb6EtrZLBX6VRSJimdSmpksJfV2wAAq4a5e2uu2OxalRcqSKgcrt7v7r9nZt8j6f2SNkg6Lenn3P3cUteVyWQ8l8u1pE8AAAAAnc/MTrh7ptZpLfueHne/V1Jfjfq/SXpZq24XAAAAAOZqy2d6AAAAACAqhB4AAAAAQSP0AAAAAAgaoQcAAABA0Ag9AAAAAIJG6AEAAAAQNEIPAAAAgKARegAAAAAErWVfThqajbuPLKidOrAtgk4uHrPEUyizhDKHJGUn8hodn9J0oajuVFJD/T0a6EtH3VZT9mYndfj4GZXclTDT9s3rtW+gN+q2EIiQXisAwsSenjrUehO3VD3OmCWeQpkllDmk8pu44bFJ5QtFuaR8oajhsUllJ/JRt9awvdlJHTx2WiV3SVLJXQePndbe7GTEnSEEIb1WAISL0AMANYyOT6k4U5pXK86UNDo+FVFHzTt8/ExDdaARIb1WAISL0AMANUwXig3V42x2D0+9daARIb1WAISL0AMANXSnkg3V4yxh1lAdaERIrxUA4SL0AEANQ/09SnYl5tWSXQkN9fdE1FHztm9e31AdaERIrxUA4SL01GGxlac6cUUqZomnUGYJZQ5JGuhLa2SwV+lUUiYpnUpqZLC3I1ek2jfQq51bNlzYs5Mw084tG1i9DSsipNcKgHCZd8Ax3ZlMxnO5XNRtAAAAAIgpMzvh7plap7GnBwAAAEDQCD0AAAAAgkboAQAAABA0Qg8AAACAoBF6AAAAAASN0AMAAAAgaIQeAAAAAEEj9AAAAAAI2pqoG+gUG3cfWVDrxG+Zl5glrq7Zc6ceLT35ZcGXJkz37b85wo6aE8ocodmbndTh42dUclfCTNs3r9e+gd6o2wIAoC3Y01OHWm+sl6rHGbPEU3VQkKRHS65r9twZUUfNCWWO0OzNTurgsdMqefmxKbnr4LHT2pudjLgzAADag9ADxEB1UFiuHlehzBGaw8fPNFQHACA0hB4ACNzsHp566wAAhIbQAwCBS5g1VAcAIDSEHiAGLk3UfvO5WD2uQpkjNNs3r2+oDgBAaAg9dVhsNbBOXCWMWeLpvv03LwgGnbjqWShzhGbfQK92btlwYc9Owkw7t2xg9TYAwKph3gHHdGcyGc/lclG3AQAAACCmzOyEu2dqncaeHgAAAABBI/QAAAAACBqhBwAAAEDQCD0AAAAAgkboAQAAABA0Qg8AAACAoBF6AAAAAASN0AMAAAAgaIQeAAAAAEFbE3UDnWLj7iMLaqcObIugk4vHLPEUyiyb9x/Vg+cfv7B91dpLdHzPjRF21LzsRF6j41OaLhTVnUpqqL9HA33pqNtqCrMAAFYz9vTUodab0aXqccYs8RTKLNWBR5IePP+4Nu8/GlFHzctO5DU8Nql8oSiXlC8UNTw2qexEPurWGsYsAIDVjtADYMVUB57l6nE2Oj6l4kxpXq04U9Lo+FREHTWPWQAAqx2hBwBqmC4UG6rHGbMAAFY7Qg8A1NCdSjZUjzNmAQCsdoQeACvmqrWXNFSPs6H+HiW7EvNqya6Ehvp7IuqoecwCAFjtCD11WGwFrU5cWYtZ4imUWY7vuXFBwOnU1dsG+tIaGexVOpWUSUqnkhoZ7O3IVcKYBQCw2pm7R93DsjKZjOdyuajbAAAAABBTZnbC3TO1TmNPDwAAAICgEXoAAAAABI3QAwAAACBohB4AAAAAQSP0AAAAAAgaoQcAAABA0Ag9AAAAAIJG6AEAAAAQtDVRN9ApNu4+sqB26sC2CDq5eMwST6HMsjc7qcPHz6jkroSZtm9er30DvVG31RRmiafsRF6j41OaLhTVnUpqqL9HA33pqNsCAMQYe3rqUOvN6FL1OGOWeApllr3ZSR08dlold0lSyV0Hj53W3uxkxJ01jlniKTuR1/DYpPKFolxSvlDU8NikshP5qFsDAMQYoQfAijl8/ExD9ThjlngaHZ9ScaY0r1acKWl0fCqijgAAnYDQA2DFzO5JqLceZ8wST9OFYkN1AAAkQg+AFZQwa6geZ8wST92pZEN1AAAkQg+AFbR98/qG6nHGLPE01N+jZFdiXi3ZldBQf09EHQEAOgGhpw6LraDViStrMUs8hTLLvoFe7dyy4cIehISZdm7Z0JGrhDFLPA30pTUy2Kt0KimTlE4lNTLYy+ptAIAlmXfAMd2ZTMZzuVzUbQAAAACIKTM74e6ZWqexpwcAAABA0Ag9AAAAAIJG6AEAAAAQNEIPAAAAgKARegAAAAAEjdADAAAAIGiEHgAAAABBI/QAAAAACBqhBwAAAEDQ1rTqis1svaT3SnqmpO9Ius3d32Fmb5G0S9LZylnf7O53tqqPlbJx95EFtVMHtkXQycVjlngKZZbN+4/qwfOPX9i+au0lOr7nxgg7at7e7KQOHz+jkrsSZtq+eb32DfRG3VZTdtx+j+4+ee7C9tZN63Ro1/URdgQAQPu0ck/PE5Le5O7fJ2mLpF81sxdUTvtjd7+u8tORgWepepwxSzyFMkt14JGkB88/rs37j0bUUfP2Zid18NhpldwlSSV3HTx2WnuzkxF31rjqwCNJd588px233xNRRwAAtFfLQo+7P+Dun6n8fl7SlySlW3V7AKJXHXiWq8fZ4eNnGqrHWXXgWa4OAEBo2vKZHjPbKKlP0vFK6dfM7F4ze5eZPWORy7zezHJmljt79mytswBAy8zu4am3DgAA4qvlocfMnirp7yS9wd0flvTnkjZJuk7SA5LeVuty7n6bu2fcPXPFFVe0uk0AmCdh1lAdAADEV0tDj5l1qRx4Drn7mCS5+4PuXnL370i6XdJLWtkDgPa5au0lDdXjbPvm9Q3V42zrpnUN1QEACE3LQo+ZmaR3SvqSu799Tv1Zc872M5I+36oeVspiK2h14spazBJPocxyfM+NCwJOp67etm+gVzu3bLiwZydhpp1bNnTk6m2Hdl2/IOCwehsAYDUxb9Hx6Wb2w5L+WdKkyktWS9KbJW1X+dA2l3RK0i+7+wNLXVcmk/FcLteSPgEAAAB0PjM74e6ZWqe17Ht63P2Tkmod/B77JaoBAAAAhKMtq7cBAAAAQFQIPQAAAACCRugBAAAAEDRCDwAAAICgEXoAAAAABI3QAwAAACBohB4AAAAAQWvZ9/SEZuPuIwtqpw5si6CTi8cs8RTKLM8fPqIn5nzn8RqT7h/pvDkkaW92UoePn1HJXQkzbd+8XvsGeqNuqykhzYL4yU7kNTo+pelCUd2ppIb6ezTQl466LQC4gD09daj1ZnSpepwxSzyFMkt14JGkJ7xc7zR7s5M6eOy0Sl4eqOSug8dOa292MuLOGhfSLIif7ERew2OTyheKckn5QlHDY5PKTuSjbg0ALiD0AFgx1YFnuXqcHT5+pqF6nIU0C+JndHxKxZnSvFpxpqTR8amIOgKAhQg9AFDD7F6ReutxFtIsiJ/pQrGhOgBEgdADADUkzBqqx1lIsyB+ulPJhuoAEAVCD4AVs2aR99CL1eNs++b1DdXjLKRZED9D/T1KdiXm1ZJdCQ3190TUEQAsROipw2IraHXiylrMEk+hzHL/yLYFAadTV2/bN9CrnVs2XNgbkjDTzi0bOnLFs5BmQfwM9KU1MtirdCopk5ROJTUy2MvqbQBixbwDjunOZDKey+WibgMAAABATJnZCXfP1DqNPT0AAAAAgkboAQAAABA0Qg8AAACAoBF6AAAAAASN0AMAAAAgaIQeAAAAAEEj9AAAAAAIGqEHAAAAQNAIPQAAAACCtibqBjrFxt1HFtROHdgWQScXj1niKZRZNu8/qgfPP35h+6q1l+j4nhsj7Kh5O26/R3efPHdhe+umdTq06/oIO4IkZSfyGh2f0nShqO5UUkP9PRroS0fdFgAgxtjTU4dab0aXqscZs8RTKLNUBx5JevD849q8/2hEHTWvOvBI0t0nz2nH7fdE1BGkcuAZHptUvlCUS8oXihoem1R2Ih91awCAGCP0AFgx1YFnuXqcVQee5epoj9HxKRVnSvNqxZmSRsenIuoIANAJCD0AgI4xXSg2VAcAQCL0AAA6SHcq2VAdAACJ0ANgBV219pKG6nG2ddO6hupoj6H+HiW7EvNqya6Ehvp7IuoIANAJCD11WGwFrU5cWYtZ4imUWY7vuXFBwOnU1dsO7bp+QcBh9bboDfSlNTLYq3QqKZOUTiU1MtjL6m0AgCWZu0fdw7IymYzncrmo2wAAAAAQU2Z2wt0ztU5jTw8AAACAoBF6AAAAAASN0AMAAAAgaIQeAAAAAEEj9AAAAAAIGqEHAAAAQNAIPQAAAACCRugBAAAAELQ1UTfQKTbuPrKgdurAtgg6uXjMEk+hzPL84SN6Ys53Hq8x6f6RzpsjNNmJvEbHpzRdKKo7ldRQf48G+tJRtwUAQFuwp6cOtd6MLlWPM2aJp1BmqQ48kvSEl+uITnYir+GxSeULRbmkfKGo4bFJZSfyUbcGAEBbEHoArJjqwLNcHe0xOj6l4kxpXq04U9Lo+FREHQEA0F6EHgAI3HSh2FAdAIDQEHoAIHDdqWRDdQAAQkPoAbBi1lhjdbTHUH+Pkl2JebVkV0JD/T0RdQQAQHsReuqw2ApanbiyFrPEUyiz3D+ybUHAYfW26A30pTUy2Kt0KimTlE4lNTLYy+ptAIBVw9zj/wnjTCbjuVwu6jYAAAAAxJSZnXD3TK3T2NMDAAAAIGiEHgAAAABBI/QAAAAACBqhBwAAAEDQCD0AAAAAgkboAQAAABA0Qg8AAACAoBF6AAAAAASN0AMAAAAgaGuibqBTbNx9ZEHt1IFtEXRy8ZglnkKZ5fnDR/SEP7m9xqT7RzpvDknKTuQ1Oj6l6UJR3amkhvp7NNCXjrqtpuy4/R7dffLche2tm9bp0K7rI+wIAID2YU9PHWq9GV2qHmfMEk+hzFIdeCTpCS/XO012Iq/hsUnlC0W5pHyhqOGxSWUn8lG31rDqwCNJd588px233xNRRwAAtBehB8CKqQ48y9XjbHR8SsWZ0rxacaak0fGpiDpqXnXgWa4OAEBoCD0AUMN0odhQHQAAxBehBwBq6E4lG6oDAID4IvQAWDFrrLF6nA319yjZlZhXS3YlNNTfE1FHzdu6aV1DdQAAQkPoqcNiK2h14spazBJPocxy/8i2BQGnU1dvG+hLa2SwV+lUUiYpnUpqZLC3I1dvO7Tr+gUBh9XbAACribnH/xPGmUzGc7lc1G0AAAAAiCkzO+HumVqnsacHAAAAQNAIPQAAAACCRugBAAAAEDRCDwAAAICgEXoAAAAABI3QAwAAACBohB4AAAAAQSP0AAAAAAjamlZdsZmtl/ReSc+U9B1Jt7n7O8xsnaT3Sdoo6ZSkV7n7t1rVx0rZuPvIgtqpA533LfMSs8RVKLNcs+dOPVp68kuPL02Y7tt/c4QdNS87kdfo+JSmC0V1p5Ia6u/RQF866raaEtIsiB+eXwDirpV7ep6Q9CZ3/z5JWyT9qpm9QNJuSR9z96slfayyHWu13owuVY8zZomnUGapDjyS9GjJdc2eOyPqqHnZibyGxyaVLxTlkvKFoobHJpWdyEfdWsNCmgXxw/MLQCdoWehx9wfc/TOV389L+pKktKRXSrqjcrY7JA20qgcA7VUdeJarx9no+JSKM6V5teJMSaPjUxF11LyQZkH88PwC0Ana8pkeM9soqU/ScUlXufsDUjkYSbpykcu83sxyZpY7e/ZsO9oEgAumC8WG6nEW0iyIH55fADpBy0OPmT1V0t9JeoO7P1zv5dz9NnfPuHvmiiuuaF2DAFBDdyrZUD3OQpoF8cPzC0AnaGnoMbMulQPPIXcfq5QfNLNnVU5/lqSHWtkDgPa5NGEN1eNsqL9Hya7EvFqyK6Gh/p6IOmpeSLMgfnh+AegELQs9ZmaS3inpS+7+9jknfUjSLZXfb5H0D63qYaUstoJWJ66sxSzxFMos9+2/eUHA6dTV2wb60hoZ7FU6lZRJSqeSGhns7cgVqUKaBfHD8wtAJzD31nzA2Mx+WNI/S5pUeclqSXqzyp/reb+kDZJOS/o5dz+31HVlMhnP5XIt6RMAAABA5zOzE+6eqXVay76nx90/KWmxY1pe1qrbBQAAAIC52rJ6GwAAAABEhdADAAAAIGiEHgAAAABBI/QAAAAACBqhBwAAAEDQCD0AAAAAgkboAQAAABA0Qg8AAACAoLXsy0lDs3H3kQW1Uwe2RdDJxWOWeApllmv23KlHS35h+9KE6b79N0fYUfN23H6P7j557sL21k3rdGjX9RF21LzsRF6j41OaLhTVnUpqqL9HA33pqNtqSkizAADagz09daj1ZnSpepwxSzyFMkt14JGkR0uua/bcGVFHzasOPJJ098lz2nH7PRF11LzsRF7DY5PKF4pySflCUcNjk8pO5KNurWEhzQIAaB9CD4AVUx14lqvHWXXgWa4eZ6PjUyrOlObVijMljY5PRdRR80KaBQDQPoQeAAjcdKHYUD3OQpoFANA+hB4ACFx3KtlQPc5CmgUA0D6EHgAr5tKENVSPs62b1jVUj7Oh/h4luxLzasmuhIb6eyLqqHkhzQIAaB9CTx0WW0GrE1fWYpZ4CmWW+/bfvCDgdOrqbYd2Xb8g4HTq6m0DfWmNDPYqnUrKJKVTSY0M9nbkimchzQIAaB9zj/8HjDOZjOdyuajbAAAAABBTZnbC3TO1TmNPDwAAAICgEXoAAAAABI3QAwAAACBohB4AAAAAQSP0AAAAAAgaoQcAAABA0BoKPWb2DDO7tlXNAAAAAMBKWzb0mNldZvY0M1sn6XOS3m1mb299awAAAABw8dbUcZ6nu/vDZvZLkt7t7rea2b2tbixuNu4+sqB26sC2CDq5eMwST6HMcs2eO/Vo6ckvPb40Ybpv/80RdtS87EReo+NTmi4U1Z1Kaqi/RwN96ajbakpIswAA0Kh6Dm9bY2bPkvQqSR9ucT+xVOvN6FL1OGOWeApllurAI0mPllzX7Lkzoo6al53Ia3hsUvlCUS4pXyhqeGxS2Yl81K01LKRZAABoRj2h5/ckjUu6390/bWbPk/Tl1rYFoBNVB57l6nE2Oj6l4kxpXq04U9Lo+FREHTUvpFkAAGjGsoe3ufsHJH1gzvZXJP2nVjYFAFGbLhQbqsdZSLMAANCMZUOPmV0haZekjXPP7+6va11bABCt7lRS+RqhoDuVjKCbixPSLAAANKOew9v+QdLTJf2jpCNzfgBgnksT1lA9zob6e5TsSsyrJbsSGurviaij5oU0CwAAzTD3pY+1N7PPuvt17Wmntkwm47lcLsoWgllZS2KWuAplFlZvi6eQZgEAoBYzO+HumZqn1RF69kn6lLtHtvxSHEIPAAAAgPhaKvTUc3jbb0r6sJk9ambnKz8Pr2yLAAAAANAa9azetrYdjQAAAABAKywbeiTJzF4h6Ucrm3e5+6r8klIAAAAAnWfZw9vM7IDKh7h9sfLzm5UaAAAAAMRePXt6bpZ0nbt/R5LM7A5JE5J2t7IxAAAAAFgJ9SxkIEmpOb8/vQV9AAAAAEBL1LOnZ0TShJn9kyRT+bM9wy3tCgAAAABWSD2rtx02s7sk/aDKoee33f0brW4MAAAAAFZCPQsZbJX0sLt/SNJaSf/dzJ7T8s4AAAAAYAXUc3jbn0t6kZm9SNKQpHdJeq+kl7aysbjZuPvIgtqpA9si6OTiMUs8hTJLKHNI0o7b79HdJ89d2N66aZ0O7bo+wo6al53Ia3R8StOForpTSQ3192igLx11W0Ds8FoBwlTPQgZPuLtLeqWkP3X3d6i8x2fVqPUmbql6nDFLPIUySyhzSAsDjyTdffKcdtx+T0QdNS87kdfw2KTyhaJcUr5Q1PDYpLIT+ahbA2KF1woQrnpCz3kzG5a0U9IRM0tI6mptWwAQrerAs1w9zkbHp1ScKc2rFWdKGh2fiqgjIJ54rQDhqif0vFrSY5J+sbKAQVrSaEu7AgCsmOlCsaE6sFrxWgHCteRneip7dQ66+8tna+5+WuXP9AAAOkB3Kql8jTdt3alkBN0A8cVrBQjXknt63L0k6T/MjC8kBbCqbN20rqF6nA319yjZlZhXS3YlNNTfE1FHQDzxWgHCVc/hbY9KmjSzd5rZn87+tLqxOFls5alOXJGKWeIplFlCmUOSDu26fkHA6dTV2wb60hoZ7FU6lZRJSqeSGhnsZUUqoAqvFSBcVl6YbYkzmN1Sq+7ud7SkoxoymYzncrl23RwAAACADmNmJ9w9U+u0Zb+nx93vMLOkpA3uzvIlAAAAADrKsoe3mdlPS/qspI9Utq8zsw+1uC8AAAAAWBH1fKbnLZJeIqkgSe7+WUnPbVlHAAAAALCC6gk9T7j7v1fVlv4gEAAAAADExLKf6ZH0eTP7z5ISZna1pN+Q9KnWtgUAAAAAK6OePT2/Lun7JT0m6bCkhyW9oYU9AQAAAMCKqWf1tv+QtMfM/rC86edb3xYAAAAArIx6Vm/7QTOblHSvyl9S+jkz+4HWtwYAAAAAF6+ez/S8U9J/dfd/liQz+2FJ75Z0bSsbi5uNu48sqHXit8xLzBJXocxyzZ479WjpybVOLk2Y7tt/c4QdNW/H7ffo7pPnLmxv3bROh3ZdH2FHzdubndTh42dUclfCTNs3r9e+gd6o22pKdiKv0fEpTReK6k4lNdTfo4G+dNRtAbHC6wSYr57P9JyfDTyS5O6flLSqDnGr9WZ0qXqcMUs8hTJLdeCRpEdLrmv23BlRR82rDjySdPfJc9px+z0RddS8vdlJHTx2WiUvPzYldx08dlp7s5MRd9a47ERew2OTyheKckn5QlHDY5PKTuSjbg2IDV4nwEL1hJ5/MbO/NLMbzOylZva/Jd1lZi82sxe3ukEAnaM68CxXj7PqwLNcPc4OHz/TUD3ORsenVJwpzasVZ0oaHZ+KqCMgfnidAAvVc3jbdZU/b62q/5DK39fz4yvZEABgZc3u4am3HmfThWJDdWA14nUCLFTP6m0/1o5GAACtkTCrGXASZhF0c3G6U0nla7xx604lI+gGiCdeJ8BC9RzeBgB1uTRR+030YvU427ppXUP1ONu+eX1D9Tgb6u9Rsisxr5bsSmiovyeijoD44XUCLEToqcNiK2h14spazBJPocxy3/6bFwScTl297dCu6xcEnE5dvW3fQK92btlwYc9Owkw7t2zoyNXbBvrSGhnsVTqVlElKp5IaGexlVSpgDl4nwELmixzTbWY/5+4fMLPnuvtX29zXPJlMxnO5XJQtAAAAAIgxMzvh7plapy21p2e48uffrXxLAAAAANAeSy1k8G9m9k+SnmtmH6o+0d1f0bq2AAAAAGBlLBV6tkl6saS/lvS29rQDAAAAACtr0dDj7o9LOmZmP+TuZ81sbbns325fewAAAABwcepZve0qM5uQ9HlJXzSzE2b2whb3BQAAAAArop7Qc5ukN7r7c9x9g6Q3VWoAAAAAEHv1hJ7L3P2fZjfc/S5Jl7WsIwAAAABYQUstZDDrK2b2OyovaCBJOyVF+r09AAAAAFCvekLP6yS9VdJYZfsTkn5huQuZ2bsk/ZSkh9z9hZXaWyTtknS2crY3u/udDfYciY27jyyonTqwLYJOLh6zxFMos1x760f08GOlC9tPe0pC9771pgg7giTtuP0e3X3y3IXtrZvW6dCu6yPsCACA9ln28DZ3/5a7/4a7v7jy8wZ3/1Yd1/0eSbXe6fyxu19X+enYwLNUPc6YJZ5CmaU68EjSw4+VdO2tH4moI0gLA48k3X3ynHbcfk9EHQEA0F71fKanKe7+CUnnlj0jgGBUB57l6miP6sCzXB0AgNC0LPQs4dfM7F4ze5eZPWOxM5nZ680sZ2a5s2fPLnY2AAAAAFhSu0PPn0vaJOk6SQ9IettiZ3T329w94+6ZK664ok3tAQAAAAjNsgsZmNlzJf26pI1zz+/ur2j0xtz9wTnXe7ukDzd6HQDi62lPSdQ8lO1pT0lE0A1mbd20ruahbFs3rYugGwAA2q+ePT1ZSack/ZnKe2ZmfxpmZs+as/kzkj7fzPW022IraHXiylrMEk+hzHLvW29aEHBYvS16h3ZdvyDgsHobAGA1MXdf+gxmx919c8NXbHZY0g2SLpf0oKRbK9vXSXKVg9Qvu/sDy11XJpPxXC7XaAsAAAAAVgkzO+HumVqn1fM9Pe8ws1slfVTSY7NFd//MUhdy9+01yu+s4/YAAAAAYMXUE3p6Jb1G0o9L+k6l5pVtAAAAAIi1ekLPz0h6nrs/3upmAAAAAGCl1bOQweckpVrcBwAAAAC0RD17eq6SdJ+ZfVrzP9PT8JLVAAAAANBu9YSeW1veBQAAAAC0yLKhx90/3o5GAAAAAKAVlg09ZnZe5dXaJOkSSV2SHnH3p7WyMQAAAABYCfXs6Vk7d9vMBiS9pFUNAQAAAMBKquczPfO4e9bMdreimTjbuPvIgtqpA9si6OTiMUs8hTLL3uykDh8/o5K7Embavnm99g30Rt1WU0KaJSTZibxGx6c0XSiqO5XUUH+PBvrSUbcFAKtKp/1dvOyS1WY2OOfnZ83sgJ483G1VqPVmdKl6nDFLPIUyy97spA4eO62Sl/+KKLnr4LHT2pudjLizxoU0S0iyE3kNj00qXyjKJeULRQ2PTSo7kY+6NQBYNTrx7+J6vqfnp+f89Es6L+mVrWwKQGc6fPxMQ/U4C2mWkIyOT6k4U5pXK86UNDo+FVFHALD6dOLfxfV8pucX2tEIgM43u1ek3nqchTRLSKYLxYbqAICV14l/Fy8aeszsd5e4nLv777egHwAdLGFWMxQkzCLo5uKENEtIulNJ5Wv8o9qdSkbQDQCsTp34d/FSh7c9UuNHkn5R0m+3uC8AHWj75vUN1eMspFlCMtTfo2RXYl4t2ZXQUH9PRB0BwOrTiX8XLxp63P1tsz+SbpOUlPQLkv5W0vPa1F8sLLaCVieurMUs8RTKLPsGerVzy4YLe0MSZtq5ZUNHrngW0iwhGehLa2SwV+lUUiYpnUpqZLA31isGAUBoOvHvYvMljk83s3WS3ihph6Q7JL3D3b/Vpt4uyGQynsvl2n2zAAAAADqEmZ1w90yt05b6TM+opEGV9/L0uvu3W9QfAAAAALTMUp/peZOkbkl7JU2b2cOVn/Nm9nB72gMAAACAi7Ponh53r+c7fAAAAAAg1gg2AAAAAIJG6AEAAAAQNEIPAAAAgKARegAAAAAEjdADAAAAIGiLrt6G+TbuPrKgdurAtgg6uXjMEk/P3X1Ec78q2CR9tQNn2bz/qB48//iF7avWXqLje26MsKPm7c1O6vDxMyq5K2Gm7ZvXa99Ab9RtNSU7kdfo+JSmC0V1p5Ia6u+J9TdnAwCwktjTU4dab6yXqscZs8RTdeCRJK/UO0l14JGkB88/rs37j0bUUfP2Zid18Nhplbz8yJTcdfDYae3NTkbcWeOyE3kNj00qXyjKJeULRQ2PTSo7kY+6NQAA2oLQA8RAdeBZrh5X1YFnuXqcHT5+pqF6nI2OT6k4U5pXK86UNDo+FVFHAAC0F6EHAGqY3cNTbz3OpgvFhuoAAISG0AMANSTMGqrHWXcq2VAdAIDQEHqAGFjsbXSnvb2+au0lDdXjbPvm9Q3V42yov0fJrsS8WrIroaH+nog6AgCgvQg9dVhsNbBOXCWMWeLpqwe2LQg4nbh62/E9Ny4IOJ26etu+gV7t3LLhwp6dhJl2btnQkau3DfSlNTLYq3QqKZOUTiU1MtjL6m0AgFXDvAOOT89kMp7L5aJuAwAAAEBMmdkJd8/UOo09PQAAAACCRugBAAAAEDRCDwAAAICgEXoAAAAABI3QAwAAACBohB4AAAAAQSP0AAAAAAgaoQcAAABA0Ag9AAAAAIK2JuoGOsXG3UcW1E4d2BZBJxePWeIplFlufPtd+vJDj1zYvvrKy3T0jTdE19BFyE7kNTo+pelCUd2ppIb6ezTQl466rVWPxwUA0Cj29NSh1pvRpepxxizxFMos1YFHkr780CO68e13RdPQRchO5DU8Nql8oSiXlC8UNTw2qexEPurWVjUeFwBAMwg9AFZMdeBZrh5no+NTKs6U5tWKMyWNjk9F1BEkHhcAQHMIPQBQw3Sh2FAd7cHjAgBoBqEHAGroTiUbqqM9eFwAAM0g9ABYMVdfeVlD9Tgb6u9Rsisxr5bsSmiovyeijiDxuAAAmkPoqcNiK2h14spazBJPocxy9I03LAg4nbp620BfWiODvUqnkjJJ6VRSI4O9rBIWMR4XAEAzzN2j7mFZmUzGc7lc1G0AAAAAiCkzO+HumVqnsacHAAAAQNAIPQAAAACCRugBAAAAEDRCDwAAAICgEXoAAAAABI3QAwAAACBohB4AAAAAQSP0AAAAAAjamqgb6BQbdx9ZUDt1YFsEnVw8ZomnUGbJTuQ1Oj6l6UJR3amkhvp7NNCXjrqtpuzNTurw8TMquSthpu2b12vfQG/UbTVlx+336O6T5y5sb920Tod2XR9hR80L6TkGAGgP9vTUodab0aXqccYs8RTKLNmJvIbHJpUvFOWS8oWihscmlZ3IR91aw/ZmJ3Xw2GmV3CVJJXcdPHZae7OTEXfWuOrAI0l3nzynHbffE1FHzQvpOQYAaB9CD4AVMzo+peJMaV6tOFPS6PhURB017/DxMw3V46w68CxXj7OQnmMAgPYh9ABYMdOFYkP1OJvdw1NvHe0R0nMMANA+hB4AK6Y7lWyoHmcJs4bqaI+QnmMAgPYh9ABYMUP9PUp2JebVkl0JDfX3RNRR87ZvXt9QPc62blrXUD3OQnqOAQDah9BTh8VW0OrElbWYJZ5CmWWgL62RwV6lU0mZpHQqqZHB3o5cWWvfQK92btlwYc9Owkw7t2zoyNXbDu26fkHA6dTV20J6jgEA2se8A45Pz2Qynsvlom4DAAAAQEyZ2Ql3z9Q6jT09AAAAAIJG6AEAAAAQNEIPAAAAgKARegAAAAAEjdADAAAAIGiEHgAAAABBI/QAAAAACBqhBwAAAEDQCD0AAAAAgramVVdsZu+S9FOSHnL3F1Zq6yS9T9JGSackvcrdv9WqHlbSxt1HFtROHdgWQScXj1niKZRZnrv7iHzOtkn6agfOIUk3vv0uffmhRy5sX33lZTr6xhuia+gi7M1O6vDxMyq5K2Gm7ZvXa99Ab9RtIRDZibxGx6c0XSiqO5XUUH+PBvrSUbcFABe0ck/PeyTdVFXbLelj7n61pI9VtmOv1pvRpepxxizxFMos1YFHkrxS7zTVgUeSvvzQI7rx7XdF09BF2Jud1MFjp1Xy8qNTctfBY6e1NzsZcWcIQXYir+GxSeULRbmkfKGo4bFJZSfyUbcGABe0LPS4+ycknasqv1LSHZXf75A00KrbB9B+1YFnuXqcVQee5epxdvj4mYbqQCNGx6dUnCnNqxVnShodn4qoIwBYqN2f6bnK3R+QpMqfVy52RjN7vZnlzCx39uzZtjUIAKGZ3cNTbx1oxHSh2FAdAKIQ24UM3P02d8+4e+aKK66Iuh0A6FgJs4bqQCO6U8mG6gAQhXaHngfN7FmSVPnzoTbfPoAWWuwtdCe+tb76yssaqsfZ9s3rG6oDjRjq71GyKzGvluxKaKi/J6KOAGChdoeeD0m6pfL7LZL+oc2335TFVtDqxJW1mCWeQpnlqwe2LQg4nbp629E33rAg4HTq6m37Bnq1c8uGC3t2EmbauWUDq7dhRQz0pTUy2Kt0KimTlE4lNTLYy+ptAGLFvEXHdJvZYUk3SLpc0oOSbpWUlfR+SRsknZb0c+5evdjBAplMxnO5XEv6BAAAAND5zOyEu2dqnday7+lx9+2LnPSyVt0mAAAAAFSL7UIGAAAAALASCD0AAAAAgkboAQAAABA0Qg8AAACAoBF6AAAAAASN0AMAAAAgaIQeAAAAAEFr2ff0hGbj7iMLaqc68FvmJWaJq1Bmef7wET0x5zuP15h0/0jnzSFJe7OTOnz8jEruSphp++b12jfQG3VbTQlpllBkJ/IaHZ/SdKGo7lRSQ/09GuhLR90WAASJPT11qPVmdKl6nDFLPIUyS3XgkaQnvFzvNHuzkzp47LRKXh6o5K6Dx05rb3Yy4s4aF9IsochO5DU8Nql8oSiXlC8UNTw2qexEPurWACBIhB4AK6Y68CxXj7PDx880VI+zkGYJxej4lIozpXm14kxJo+NTEXUEAGEj9ABADbN7Reqtx1lIs4RiulBsqA4AuDiEHgCoIWHWUD3OQpolFN2pZEN1AMDFIfQAWDFrFnkPvVg9zrZvXt9QPc5CmiUUQ/09SnYl5tWSXQkN9fdE1BEAhI3QU4fFVtDqxJW1mCWeQpnl/pFtCwJOp67etm+gVzu3bLiwNyRhpp1bNnTkimchzRKKgb60RgZ7lU4lZZLSqaRGBntZvQ0AWsS8A47pzmQynsvlom4DAAAAQEyZ2Ql3z9Q6jT09AAAAAIJG6AEAAAAQNEIPAAAAgKARegAAAAAEjdADAAAAIGiEHgAAAABBI/QAAAAACBqhBwAAAEDQCD0AAAAAgrYm6gY6xcbdRxbUTh3YFkEnF49Z4imUWW58+1368kOPXNi++srLdPSNN0TX0EXYcfs9uvvkuQvbWzet06Fd10fYEYBWy07kNTo+pelCUd2ppIb6ezTQl466LQAXiT09daj1ZnSpepwxSzyFMkt14JGkLz/0iG58+13RNHQRqgOPJN198px23H5PRB0BaLXsRF7DY5PKF4pySflCUcNjk8pO5KNuDcBFIvQAWDHVgWe5epxVB57l6gA63+j4lIozpXm14kxJo+NTEXUEYKUQegAAACRNF4oN1QF0DkIPAACApO5UsqE6gM5B6AGwYq6+8rKG6nG2ddO6huoAOt9Qf4+SXYl5tWRXQkP9PRF1BGClEHrqsNgKWp24shazxFMosxx94w0LAk6nrt52aNf1CwIOq7cBYRvoS2tksFfpVFImKZ1KamSwl9XbgACYu0fdw7IymYzncrmo2wAAAAAQU2Z2wt0ztU5jTw8AAACAoBF6AAAAAASN0AMAAAAgaIQeAAAAAEEj9AAAAAAIGqEHAAAAQNAIPQAAAACCRugBAAAAELQ1UTfQKTbuPrKgdurAtgg6uXjMEk/X7LlTj5ae/LLgSxOm+/bfHGFHzbn21o/o4cdKF7af9pSE7n3rTRF21LzsRF6j41OaLhTVnUpqqL+Hb2YHAKADsaenDrXeWC9VjzNmiafqwCNJj5Zc1+y5M6KOmlMdeCTp4cdKuvbWj0TUUfOyE3kNj00qXyjKJeULRQ2PTSo7kY+6NQAA0CBCDxAD1YFnuXpcVQee5epxNjo+peLM/L6LMyWNjk9F1BEAAGgWoQcAapguFBuqAwCA+CL0AEAN3alkQ3UAABBfhB4gBi5NWEP1uHraUxIN1eNsqL9Hya75fSe7Ehrq74moIwAA0CxCTx0WWw2sE1cJY5Z4um//zQsCTieu3nbvW29aEHA6dfW2gb60RgZ7lU4lZZLSqaRGBntZvQ0AgA5k7vH/oHQmk/FcLhd1GwAAAABiysxOuHum1mns6QEAAAAQNEIPAAAAgKARegAAAAAEjdADAAAAIGiEHgAAAABBI/QAAAAACBqhBwAAAEDQCD0AAAAAgkboAQAAABC0NVE30Ck27j6yoHbqwLYIOrl4zBJPIc0Sir3ZSR0+fkYldyXMtH3zeu0b6I26rVUvO5HX6PiUpgtFdaeSGurv0UBfOuq2AAAxxp6eOtR6M7pUPc6YJZ5CmiUUe7OTOnjstErukqSSuw4eO6292cmIO1vdshN5DY9NKl8oyiXlC0UNj00qO5GPujUAQIwRegCghsPHzzRUR3uMjk+pOFOaVyvOlDQ6PhVRRwCATkDoAYAaZvfw1FtHe0wXig3VAQCQCD0AUFPCrKE62qM7lWyoDgCAROgBgJq2b17fUB3tMdTfo2RXYl4t2ZXQUH9PRB0BADoBoacOi62g1YkrazFLPIU0Syj2DfRq55YNF/bsJMy0c8sGVm+L2EBfWiODvUqnkjJJ6VRSI4O9rN4GAFiSeQccn57JZDyXy0XdBgAAAICYMrMT7p6pdRp7egAAAAAEjdADAAAAIGiEHgAAAABBI/QAAAAACBqhBwAAAEDQCD0AAAAAgkboAQAAABA0Qg8AAACAoK2J4kbN7JSk85JKkp5Y7EuE4mTj7iMLaqcObIugk4vHLPEUyiyb9x/Vg+cfv7B91dpLdHzPjRF21LzsRF6j41OaLhTVnUpqqL9HA33pqNtqyt7spA4fP6OSuxJm2r55vfYN9EbdVlNCeVxCmQMAOkGUe3p+zN2v69TAs1Q9zpglnkKZpTrwSNKD5x/X5v1HI+qoedmJvIbHJpUvFOWS8oWihscmlZ3IR91aw/ZmJ3Xw2GmV3CVJJXcdPHZae7OTEXfWuFAel1DmAIBOweFtAFZMdeBZrh5no+NTKs6U5tWKMyWNjk9F1FHzDh8/01A9zkJ5XEKZAwA6RVShxyV91MxOmNnra53BzF5vZjkzy509e7bN7QFY7aYLxYbqcTa7h6feepyF8riEMgcAdIqoQs9Wd3+xpJ+U9Ktm9qPVZ3D329w94+6ZK664ov0dAljVulPJhupxljBrqB5noTwuocwBAJ0iktDj7tOVPx+S9PeSXhJFHwBW1lVrL2moHmdD/T1KdiXm1ZJdCQ3190TUUfO2b17fUD3OQnlcQpkDADpF20OPmV1mZmtnf5f0E5I+3+4+GrHYClqduLIWs8RTKLMc33PjgoDTqau3DfSlNTLYq3QqKZOUTiU1Mtjbkatr7Rvo1c4tGy7s2UmYaeeWDR25elsoj0socwBApzBv8zHdZvY8lffuSOUls//G3fcvdZlMJuO5XK7lvQEAAADoTGZ2YrGVodv+PT3u/hVJL2r37QIAAABYnViyGgAAAEDQCD0AAAAAgkboAQAAABA0Qg8AAACAoBF6AAAAAASN0AMAAAAgaIQeAAAAAEEj9AAAAAAIWtu/nLRTbdx9ZEHt1IFtEXRy8ZglnkKZ5dpbP6KHHytd2H7aUxK69603RdhR87ITeY2OT2m6UFR3Kqmh/h4N9KWjbqspe7OTOnz8jEruSphp++b12jfQG3VbAAC0BXt66lDrzehS9ThjlngKZZbqwCNJDz9W0rW3fiSijpqXnchreGxS+UJRLilfKGp4bFLZiXzUrTVsb3ZSB4+dVsldklRy18Fjp7U3OxlxZwAAtAehB8CKqQ48y9XjbHR8SsWZ+X0XZ0oaHZ+KqKPmHT5+pqE6AAChIfQAQA3ThWJD9Tib3cNTbx0AgNAQegCghu5UsqF6nCXMGqoDABAaQg+AFfO0pyQaqsfZUH+Pkl3z+052JTTU3xNRR83bvnl9Q3UAAEJD6KnDYitodeLKWswST6HMcu9bb1oQcDp19baBvrRGBnuVTiVlktKppEYGezty9bZ9A73auWXDhT07CTPt3LKB1dsAAKuGeQcc053JZDyXy0XdBgAAAICYMrMT7p6pdRp7egAAAAAEjdADAAAAIGiEHgAAAABBI/QAAAAACBqhBwAAAEDQCD0AAAAAgkboAQAAABA0Qg8AAACAoK2JuoFOsXH3kQW1Uwe2RdDJxWOWeApllr3ZSR0+fkYldyXMtH3zeu0b6I26rVUvO5HX6PiUpgtFdaeSGurv0UBfOuq2mhLSLACA9mBPTx1qvRldqh5nzBJPocyyNzupg8dOq+QuSSq56+Cx09qbnYy4s9UtO5HX8Nik8oWiXFK+UNTw2KSyE/moW2tYSLMAANqH0ANgxRw+fqahOtpjdHxKxZnSvFpxpqTR8amIOmpeSLMAANqH0ANgxczu4am3jvaYLhQbqsdZSLMAANqH0ANgxSTMGqqjPbpTyYbqcRbSLACA9iH0AFgx2zevb6iO9hjq71GyKzGvluxKaKi/J6KOmhfSLACA9iH01GGxFbQ6cWUtZomnUGbZN9CrnVs2XNizkzDTzi0bWL0tYgN9aY0M9iqdSsokpVNJjQz2duSKZyHNAgBoH/MOONY+k8l4LpeLug0AAAAAMWVmJ9w9U+s09vQAAAAACBqhBwAAAEDQCD0AAAAAgkboAQAAABA0Qg8AAACAoBF6AAAAAASN0AMAAAAgaIQeAAAAAEEj9AAAAAAI2pqoG+gUG3cfWVA7dWBbBJ1cPGaJp1Bm2XH7Pbr75LkL21s3rdOhXddH2FHzshN5jY5PabpQVHcqqaH+Hg30paNuqykhzQIAQKPY01OHWm9Gl6rHGbPEUyizVAceSbr75DntuP2eiDpqXnYir+GxSeULRbmkfKGo4bFJZSfyUbfWsJBmAQCgGYQeACumOvAsV4+z0fEpFWdK82rFmZJGx6ci6qh5Ic0CAEAzCD0AUMN0odhQPc5CmgUAgGYQegCghu5UsqF6nIU0CwAAzSD0AFgxWzeta6geZ0P9PUp2JebVkl0JDfX3RNRR80KaBQCAZhB66rDYClqduLIWs8RTKLMc2nX9goDTqau3DfSlNTLYq3QqKZOUTiU1MtjbkSuehTQLAADNMHePuodlZTIZz+VyUbcBAAAAIKbM7IS7Z2qdxp4eAAAAAEEj9AAAAAAIGqEHAAAAQNAIPQAAAACCRugBAAAAEDRCDwAAAICgEXoAAAAABI3QAwAAACBoa6JuoFNs3H1kQe3UgW0RdHLxmCWeQpkllDkkacft9+juk+cubG/dtE6Hdl0fYUfNC2kWAAAaxZ6eOtR6E7dUPc6YJZ5CmSWUOaSFIUGS7j55TjtuvyeijpoX0iwAADSD0AMANVSHhOXqcRbSLAAANIPQAwAAACBohB4AAAAAQSP0AEANWzeta6geZyHNAgBAMwg9dVhs5alOXJGKWeIplFlCmUOSDu26fkEo6NQVz0KaBQCAZpi7R93DsjKZjOdyuajbAAAAABBTZnbC3TO1TmNPDwAAAICgEXoAAAAABI3QAwAAACBohB4AAAAAQSP0AAAAAAgaoQcAAABA0Ag9AAAAAIJG6AEAAAAQNEIPAAAAgKBFEnrM7CYzmzKz+81sdxQ9AAAAAFgd2h56zCwh6X9J+klJL5C03cxe0O4+AAAAAKwOUezpeYmk+939K+7+uKS/lfTKCPoAAAAAsApEEXrSks7M2f56pTaPmb3ezHJmljt79mzbmgMAAAAQlihCj9Wo+YKC+23unnH3zBVXXNGGtgAAAACEKIrQ83VJ6+dsP1vSdAR9AAAAAFgFogg9n5Z0tZk918wukfTzkj4UQR8AAAAAVoE17b5Bd3/CzH5N0rikhKR3ufsX2t0HAAAAgNWh7aFHktz9Tkl3RnHbAAAAAFaXSL6cFAAAAADahdADAAAAIGjmvmC16Ngxs7OSvhZ1HwG6XNI3o24CC/C4xA+PSTzxuMQPj0n88JjEE49LazzH3Wt+101HhB60hpnl3D0TdR+Yj8clfnhM4onHJX54TOKHxySeeFzaj8PbAAAAAASN0AMAAAAgaISe1e22qBtATTwu8cNjEk88LvHDYxI/PCbxxOPSZnymBwAAAEDQ2NMDAAAAIGiEHgAAAABBI/SsQma23sz+ycy+ZGZfMLPfjLonlJlZwswmzOzDUfeCMjNLmdkHzey+ymvm+qh7Wu3M7L9V/u76vJkdNrNLo+5pNTKzd5nZQ2b2+Tm1dWZ21My+XPnzGVH2uNos8piMVv7+utfM/t7MUhG2uCrVelzmnPZbZuZmdnkUva0mhJ7V6QlJb3L375O0RdKvmtkLIu4JZb8p6UtRN4F53iHpI+5+jaQXiccnUmaWlvQbkjLu/kJJCUk/H21Xq9Z7JN1UVdst6WPufrWkj1W20T7v0cLH5KikF7r7tZL+VdJwu5tCzcdFZrZe0o2STre7odWI0LMKufsD7v6Zyu/nVX4Tl462K5jZsyVtk/RXUfeCMjN7mqQflfROSXL3x929EGlTkKQ1kpJmtkbSd0uajrifVcndPyHpXFX5lZLuqPx+h6SBdva02tV6TNz9o+7+RGXzmKRnt72xVW6R14ok/bGk/y6JVcXagNCzypnZRkl9ko5H3AqkP1H5L7/vRNwHnvQ8SWclvbty2OFfmdllUTe1mrl7XtL/UPl/Rh+Q9O/u/tFou8IcV7n7A1L5P9gkXRlxP5jvdZL+b9RNQDKzV0jKu/vnou5ltSD0rGJm9lRJfyfpDe7+cNT9rGZm9lOSHnL3E1H3gnnWSHqxpD939z5Jj4jDdSJV+YzIKyU9V1K3pMvMbGe0XQHxZ2Z7VD68/VDUvax2ZvbdkvZI+t2oe1lNCD2rlJl1qRx4Drn7WNT9QFslvcLMTkn6W0k/bmYHo20Jkr4u6evuPrsn9IMqhyBE5+WSvuruZ919RtKYpB+KuCc86UEze5YkVf58KOJ+IMnMbpH0U5J2OF/QGAebVP6Pm89V/t1/tqTPmNkzI+0qcISeVcjMTOXPKHzJ3d8edT+Q3H3Y3Z/t7htV/lD2/3N3/vc6Yu7+DUlnzKynUnqZpC9G2BLKh7VtMbPvrvxd9jKxuEScfEjSLZXfb5H0DxH2AklmdpOk35b0Cnf/j6j7geTuk+5+pbtvrPy7/3VJL678m4MWIfSsTlslvUblvQmfrfzcHHVTQEz9uqRDZnavpOsk/UG07axulb1uH5T0GUmTKv87dlukTa1SZnZY0j2Seszs62b2i5IOSLrRzL6s8qpUB6LscbVZ5DH5n5LWSjpa+ff+LyJtchVa5HFBmxl7OQEAAACEjD09AAAAAIJG6AEAAAAQNEIPAAAAgKARegAAAAAEjdADAAAAIGiEHgBATWbmZva2Odu/ZWZvaePtP8XM/rGyzO6rL/K67jKzTI16xsz+dJnLnjKzyy/m9gEA0VoTdQMAgNh6TNKgmY24+zcjuP0+SV3ufl2rbsDdc5Jyrbp+AEA8sKcHALCYJ1T+4s//Vn2Cmb3HzH52zva3K3/eYGYfN7P3m9m/mtkBM9thZv9iZpNmtqnGda0zs6yZ3Wtmx8zsWjO7UtJBSddV9vRsmnP+7zOzf5mzvbHy5bEysx+o3P4JMxs3s2fNuamfq/Txr2b2I3P6/XDl96ea2bsrfd5rZv+pRq87K9fxWTP7SzNLVH7eY2afr1x2wf0FAIgWoQcAsJT/JWmHmT29gcu8SNJvSuqV9BpJ3+vuL5H0V5J+vcb53yppwt2vlfRmSe9194ck/ZKkf3b369z95OyZ3f1Lki4xs+dVSq+W9H4z65L0Z5J+1t1/QNK7JO2fcztrKn28QdKtNfr4HUn/7u69lV7+39wTzez7Kre1tbL3qSRph6TrJKXd/YXu3ivp3XXcRwCANuLwNgDAotz9YTN7r6TfkFSs82KfdvcHJMnMTkr6aKU+KenHapz/hyX9p8rt/T8z+546Qtb7Jb1K0gGVg8irJfVIeqGko2YmSQlJD8y5zFjlzxOSNta4zpdL+vnZDXf/VtXpL5P0A5I+Xbn+pKSHJP0fSc8zsz+TdGTOvACAmCD0AACW8yeSPqP5ezCeUOVoASsngEvmnPbYnN+/M2f7O6r9747VqPkyPb1P0gfMbEySu/uXzaxX0hfc/fpFLjPbR2mJPpa6XZN0h7sPLzjB7EWS+iX9qsph7HXL9A8AaCMObwMALMndz6m8Z+UX55RPqbzXQ5JeKanrIm7iEyofJiYzu0HSN9394WV6OqlyePkdlQOQJE1JusLMrq9cV5eZfX8DfXxU0q/NbpjZM6pO/5ikn6183mj2s0jPqazs9l3u/neVfl7cwG0CANqA0AMAqMfbJM1dtvl2SS+tLCiwWdIjF3Hdb5GUqSxGcEDSLXVe7n2SdqocyOTuj0v6WUl/aGafk/RZST/UQB/7JD2jsiDB51R1KJ67f1HSXkkfrfR6VNKzJKUl3WVmn5X0HkkL9gQBAKJl7ssdQQAAAAAAnYs9PQAAAACCRugBAAAAEDRCDwAAAICgEXoAAAAABI3QAwAAACBohB4AAAAAQSP0AAAAAAja/wcDP112U6JAfgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,10))\n",
    "# Scatter plot\n",
    "plt.scatter(df_accident['ve_total'],df_accident['day'])\n",
    "\n",
    "# Create a descriptive title\n",
    "plt.title('Vehicle total tends to correlate with amount of people')\n",
    "# Add axis labels\n",
    "plt.xlabel('Num of vehicles')\n",
    "plt.ylabel('Num of persons');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77e2f23d-3977-455c-95a7-b9522da9e167",
   "metadata": {},
   "source": [
    "#### Vehicle Total and Persons analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "53a0c687-011c-4a07-9667-cb0ba48e30df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ve_total</th>\n",
       "      <th>persons</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ve_total  persons\n",
       "0         1        4\n",
       "1         4        6\n",
       "2         2        2\n",
       "3         1        5\n",
       "4         1        1"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_accident[['ve_total', 'persons']].head()\n",
    "#pretty strong correlation here but hard to extract value\n",
    "#lets plot this to see if there is any other information that can be extracted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "5c692979-f179-4852-9afd-19b1172b9cd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HrsmF526YUEUAAMConMhgBDMnK3D2NgAAWHmEnhFtOmO9kAMAACvQWA9vq6p1VfW2qvpoVX2kqp5SVcdV1bVV9bHh94ePswYAAGB1G/dnel6b5F2tta9P8oQkH0ny0iTXtdYel+S64TIAAMBYjC30VNWxSb4zyRuSpLV2T2ttb5Lzklw+vNnlSTaNqwYAAIBx7un5miR7kryxqrZX1e9W1dFJTmyt3Zokw+8nzHfnqnpxVW2rqm179uwZY5kAAEDPxhl6jkjypCS/1Vo7I8kXsoBD2Vprr2utbWytbTz++OPHVSMAANC5cYaeTyf5dGvthuHy2zIIQbdV1UlJMvx++xhrAAAAVrmxhZ7W2meS3FJVMx08z07yj0nekeSC4dgFSd4+rhoAAADG3afnJ5JcWVVHJflkkh/JIGhdXVUvTHJzkueMuQYAAGAVG2voaa19KMnGea46e5zPCwAAMGPcfXoAAAAmSugBAAC6JvQAAABdE3oAAICuCT0AAEDXhB4AAKBrQg8AANA1oQcAAOia0AMAAHRN6AEAALom9AAAAF0TegAAgK4JPQAAQNeEHgAAoGtCDwAA0DWhBwAA6JrQAwAAdE3oAQAAuib0AAAAXRN6AACArgk9AABA14QeAACga0IPAADQNaEHAADomtADAAB0TegBAAC6JvQAAABdE3oAAICuCT0AAEDXhB4AAKBrQg8AANA1oQcAAOia0AMAAHRN6AEAALom9AAAAF0TegAAgK4JPQAAQNeEHgAAoGtCDwAA0DWhBwAA6JrQAwAAdE3oAQAAuib0AAAAXRN6AACArgk9AABA14QeAACga0IPAADQNaEHAADomtADAAB0TegBAAC6JvQAAABdE3oAAICuCT0AAEDXhB4AAKBrQg8AANA1oQcAAOia0AMAAHRN6AEAALom9AAAAF0TegAAgK4JPQAAQNeEHgAAoGtCDwAA0DWhBwAA6JrQAwAAdE3oAQAAuib0AAAAXRN6AACArgk9AABA14QeAACga0IPAADQNaEHAADo2hHjfPCquinJXUn2J/lSa21jVR2X5C1JTktyU5LnttY+P846AACA1Ws59vR8d2vtia21jcPllya5rrX2uCTXDZcBAADGYhKHt52X5PLh5cuTbJpADQAAwCox7tDTkry7qj5QVS8ejp3YWrs1SYbfT5jvjlX14qraVlXb9uzZM+YyAQCAXo31Mz1Jzmqt7a6qE5JcW1UfHfWOrbXXJXldkmzcuLGNq0AAAKBvY93T01rbPfx+e5I/TvJtSW6rqpOSZPj99nHWAAAArG5jCz1VdXRVHTNzOcn3Jvn7JO9IcsHwZhckefu4agAAABjn4W0nJvnjqpp5nj9orb2rqt6f5OqqemGSm5M8Z4w1AAAAq9zYQk9r7ZNJnjDP+OeSnD2u5wUAAJhtEqesBgAAWDZCDwAA0DWhBwAA6JrQAwAAdE3oAQAAuib0AAAAXRN6AACArgk9AABA14QeAACga0IPAADQNaEHAADomtADAAB0TegBAAC6JvQAAABdE3oAAICuCT0AAEDXhB4AAKBrQg8AANA1oQcAAOia0AMAAHRN6AEAALom9AAAAF0TegAAgK4JPQAAQNeEHgAAoGtCDwAA0DWhBwAA6JrQAwAAdE3oAQAAuib0AAAAXRN6AACArgk9AABA14QeAACga0IPAADQNaEHAADomtADAAB0TegBAAC6JvQAAABdE3oAAICuCT0AAEDXhB4AAKBrQg8AANA1oQcAAOia0AMAAHRN6AEAALom9AAAAF0TegAAgK4JPQAAQNeEHgAAoGtCDwAA0DWhBwAA6JrQAwAAdE3oAQAAuib0AAAAXRN6AACArgk9AABA14QeAACga0IPAADQNaEHAADomtADAAB0TegBAAC6JvQAAABdE3oAAICuCT0AAEDXhB4AAKBrQg8AANA1oQcAAOia0AMAAHRN6AEAALom9AAAAF0TegAAgK4JPQAAQNfGHnqqak1Vba+qdw6Xj6uqa6vqY8PvDx93DQAAwOq1HHt6XpLkI7OWX5rkutba45JcN1wGAAAYi7GGnqp6VJJnJvndWcPnJbl8ePnyJJvGWQMAALC6jXtPz2uS/FySL88aO7G1dmuSDL+fMN8dq+rFVbWtqrbt2bNnzGUCAAC9GlvoqarvS3J7a+0Di7l/a+11rbWNrbWNxx9//BJXBwAArBZHjPGxz0ryrKp6RpKHJDm2qq5IcltVndRau7WqTkpy+xhrAAAAVrmx7elprV3UWntUa+20JD+Y5M9ba+cneUeSC4Y3uyDJ28dVAwAAwCT69LwqyTlV9bEk5wyXAQAAxmKch7fdp7V2fZLrh5c/l+Ts5XheAACASezpAQAAWDZCDwAA0DWhBwAA6JrQAwAAdE3oAQAAuib0AAAAXRN6AACArgk9AABA14QeAACga0IPAADQNaEHAADomtADAAB0TegBAAC6JvQAAABdE3oAAICuCT0AAEDXhB4AAKBrQg8AANA1oQcAAOia0AMAAHRN6AEAALom9AAAAF0TegAAgK4JPQAAQNeEHgAAoGtCDwAA0DWhBwAA6JrQAwAAdE3oAQAAuib0AAAAXRN6AACArgk9AABA14QeAACgawsKPVX18Kp6/LiKAQAAWGqHDT1VdX1VHVtVxyX5cJI3VtWrx18aAADAAzfKnp6HtdbuTPLsJG9srX1LkqeNtywAAIClMUroOaKqTkry3CTvHHM9AAAAS2qU0PPfk2xN8vHW2vur6muSfGy8ZQEAACyNIw53g9baW5O8ddbyJ5P8x3EWBQAAsFQOG3qq6vgkL0py2uzbt9Z+dHxlAQAALI3Dhp4kb0/y10n+LMn+8ZYDAACwtEYJPV/VWvv5sVcCAAAwBqOcyOCdVfWMsVcCAAAwBqOEnpdkEHy+WFV3Db/uHHdhAAAAS2GUs7cdsxyFAAAAjMMon+lJVT0ryXcOF69vrWlSCgAArAiHPbytql6VwSFu/zj8eslwDAAAYOqNsqfnGUme2Fr7cpJU1eVJtid56TgLAwAAWAqjnMggSdbNuvywMdQBAAAwFqPs6Xllku1V9RdJKoPP9lw01qoAAACWyChnb7uqqq5P8q0ZhJ6fb619ZtyFAQAALIVRTmRwVpI7W2vvSHJMkp+rqkePvTIAAIAlMMpnen4ryb9V1ROSXJjkU0nePNaqAAAAlsgooedLrbWW5Lwkv9Fae20Ge3wAAACm3ignMrirqi5Kcn6S76yqNUmOHG9ZAAAAS2OUPT3PS3J3khcOT2CwPsnmsVYFAACwRA65p2e4V+eK1trTZsZaazfHZ3oAAIAV4pB7elpr+zM4iYGGpAAAwIo0ymd6vphkR1Vdm+QLM4OttZ8cW1UAAABLZJTQ8yfDLwAAgBXnsKGntXZ5Va1Ncmprbecy1AQAALBkDnv2tqr6/iQfSvKu4fITq+odY64LAABgSYxyyuqXJ/m2JHuTpLX2oSSPGVtFAAAAS2iU0POl1tq/zBlr4ygGAABgqY1yIoO/r6r/lGRNVT0uyU8m+dvxlsU4bdm+K5u37szuvfty8rq1ufDcDdl0xvpJlwUAAGMxyp6en0jyTUnuTnJVkjuT/NQYa2KMtmzflYuu2ZFde/elJdm1d18uumZHtmzfNenSAABgLA4belpr/9ZauzjJ2Um+u7V2cWvti+MvjXHYvHVn9t27/35j++7dn81bnZgPAIA+jXL2tm+tqh1JbsygSemHq+pbxl8a47B7774FjQMAwEo3yuFtb0jy/7TWTmutnZbkx5O8caxVMTYnr1u7oHEAAFjpRgk9d7XW/npmobX2N0nuGl9JjNOF527I2iPX3G9s7ZFrcuG5GyZUEQAAjNcoZ2/7u6r6nQxOYtCSPC/J9VX1pCRprX1wjPWxxGbO0ubsbQAArBbV2qFb7lTVXxzi6tZa+56lLelAGzdubNu2bRv30wAAACtUVX2gtbZxvusOu6entfbdi3zShyT5qyQPHj7P21prL6uq45K8JclpSW5K8tzW2ucX8xwAAACHM8pnehbr7iTf01p7QpInJnl6VT05yUuTXNdae1yS64bLAAAAYzG20NMG/nW4eOTwqyU5L8nlw/HLk2waVw0AAAAHDT1V9Zzh98cs9sGrak1VfSjJ7Umuba3dkOTE1tqtSTL8fsJB7vviqtpWVdv27Nmz2BIAAIBV7lB7ei4afv+jxT54a21/a+2JSR6V5Nuq6psXcN/XtdY2ttY2Hn/88YstAQAAWOUOdSKDzw3P3PaYqnrH3Ctba88a9Ulaa3ur6vokT09yW1Wd1Fq7tapOymAvEAAAwFgcKvQ8M8mTkvx+kl9f6ANX1fFJ7h0GnrVJnpbkV5O8I8kFSV41/P72hT42AADAqA4aelpr9yR5X1X9u9banqo6ZjB838kJDuekJJdX1ZoMDqO7urX2zqp6b5Krq+qFSW5O8pwHOAcAAICDOmyfniQnVtW7kxyXpKpqT5ILWmt/f6g7tdZuTHLGPOOfS3L2YooFAABYqFFOWf26JD/dWnt0a+3UJD8zHAMAAJh6o4Seo1trfzGz0Fq7PsnRY6sIAABgCY1yeNsnq+oXMzihQZKcn+Sfx1cSAADA0hllT8+PJjk+yTXDr0ck+ZFxFgUAALBUDrunp7X2+SQ/uQy1AAAALLlR9vQAAACsWEIPAADQtVFOZEBntmzflc1bd2b33n05ed3aXHjuhmw6Y/2kywIAgLE4bOipqsck+Ykkp82+fWvtWeMri3HZsn1XLrpmR/bduz9Jsmvvvlx0zY4kEXwAAOjSKHt6tiR5Q5L/k+TLY62Gsdu8ded9gWfGvnv3Z/PWnUIPAABdGiX0fLG19htjr4RlsXvvvgWNAwDASjdK6HltVb0sybuT3D0z2Fr74NiqYmxOXrc2u+YJOCevWzuBagAAYPxGCT2nJ/mhJN+Trxze1obLrDAXnrvhfp/pSZK1R67JhedumGBVAAAwPqOEnv+Q5Gtaa/eMuxjGb+ZzO87eBgDAajFK6PlwknVJbh9vKSyXTWesF3IAAFg1Rgk9Jyb5aFW9P/f/TI9TVgMAAFNvlNDzsrFXAQAAMCaHDT2ttb9cjkIAAADG4bChp6ruyuBsbUlyVJIjk3yhtXbsOAsDAABYCqPs6Tlm9nJVbUrybeMqCAAAYCk9aKF3aK1tiR49AADACjHK4W3PnrX4oCQb85XD3QAAAKbaKGdv+/5Zl7+U5KYk542lGgAAgCU2ymd6fmQ5CgGA1WTL9l3ZvHVndu/dl5PXrc2F527QOBpgTA4aeqrqlw5xv9Za++Ux1AMA3duyfVcuumZH9t27P0mya+++XHTNjiQRfADG4FAnMvjCPF9J8sIkPz/mugCgW5u37rwv8MzYd+/+bN66c0IVAfTtoHt6Wmu/PnO5qo5J8pIkP5LkD5P8+sHuBwAc2u69+xY0DsADc8hTVlfVcVV1aZIbMwhIT2qt/Xxr7fZlqQ4AOnTyurULGgfggTlo6KmqzUnen+SuJKe31l7eWvv8slUGAJ268NwNWXvkmvuNrT1yTS48d8OEKgLo26HO3vYzSe5OckmSi6tqZrwyOJHBsWOuDQC6NHOyAmdvA1geh/pMzyEPfQMAFm/TGeuFHIBlItgAAABdE3oAAICuCT0AAEDXhB4AAKBrQg8AANA1oQcAAOia0AMAAHRN6AEAALom9AAAAF07YtIFAAAAK8uW7buyeevO7N67LyevW5sLz92QTWesn3RZByX0AAAAI9uyfVcuumZH9t27P0mya+++XHTNjiSZ2uDj8DYAAGBkm7fuvC/wzNh37/5s3rpzQhUdntADAACMbPfefQsanwZCDwAAMLKT161d0Pg0EHoAAICRXXjuhqw9cs39xtYeuSYXnrthQhUdnhMZAAAAI5s5WYGztwEAAN3adMb6qQ45czm8DQAA6JrQAwAAdE3oAQAAuib0AAAAXRN6AACArgk9AABA14QeAACga0IPAADQNc1JV6Et23etqA66AADwQAg9q8yW7bty0TU7su/e/UmSXXv35aJrdiSJ4AMAQJcc3rbKbN66877AM2PfvfuzeevOCVUEAADjJfSsMrv37lvQOAAArHRCzypz8rq1CxoHAICVTuhZZS48d0PWHrnmfmNrj1yTC8/dMKGKAABgvJzIYJWZOVmBs7cBALBaCD2r0KYz1gs5AACsGkLPiHrqbdPTXAAA4HCEnhH01Nump7kAAMAonMhgBD31tulpLgAAMAqhZwQ99bbpaS4AADAKoWcEPfW26WkuAAAwCqFnBD31tulpLgAAMAonMhhBT71tepoLAACMolprk67hsDZu3Ni2bds26TIAAIApVVUfaK1tnO+6se3pqapTkrw5ySOTfDnJ61prr62q45K8JclpSW5K8tzW2ufHVcdS6am3TU9zAQCAwxnnZ3q+lORnWmvfkOTJSX68qr4xyUuTXNdae1yS64bLU22mt82uvfvS8pXeNlu275p0aQvW01wAAGAUYws9rbVbW2sfHF6+K8lHkqxPcl6Sy4c3uzzJpnHVsFR66m3T01wAAGAUy3L2tqo6LckZSW5IcmJr7dZkEIySnHCQ+7y4qrZV1bY9e/YsR5kH1VNvm57mAgAAoxh76Kmqhyb5oyQ/1Vq7c9T7tdZe11rb2FrbePzxx4+vwBH01Nump7kAAMAoxhp6qurIDALPla21a4bDt1XVScPrT0py+zhrWAo99bbpaS4AADCKsYWeqqokb0jykdbaq2dd9Y4kFwwvX5Dk7eOqYalsOmN9Xvns07N+3dpUkvXr1uaVzz59RZ7xrKe5AADAKMbWp6eqvj3JXyfZkcEpq5PkFzL4XM/VSU5NcnOS57TW7jjUY+nTAwAAHMpE+vS01v4mSR3k6rPH9bzjorcNAMzP70hg2o0t9PRkprfNzKmeZ3rbJPFDHYBVze9IYCVYllNWr3R62wDA/PyOBFYCoWcEetsAwPz8jgRWAqFnBHrbAMD8/I4EVgKhZwR62wDA/PyOBFYCJzIYwcwHMZ2ZBgDuz+9IYCUYW5+epaRPDwAAcCiH6tPj8DYAAKBrQg8AANA1oQcAAOia0AMAAHRN6AEAALom9AAAAF0TegAAgK4JPQAAQNeOmHQBALAQW7bvyuatO7N7776cvG5tLjx3QzadsX7SZQEwxYQeAFaMLdt35aJrdmTfvfuTJLv27stF1+xIEsEHgINyeBsAK8bmrTvvCzwz9t27P5u37pxQRQCsBEIPACvG7r37FjQOAInQA8AKcvK6tQsaB4BE6AFgBbnw3A1Ze+Sa+42tPXJNLjx3w4QqAmAlcCIDAFaMmZMVOHsbAAsh9ACwomw6Y72QA8CCCD3AktJDBVYf73tg2gk9wJLRQwVWH+97YCVwIgNgyeihAquP9z2wEgg9wJLRQwVWH+97YCUQeoAlo4cKrD7e98BKIPQAS0YPFVh9vO+BlcCJDIAlo4cKrD7e98BKUK21SddwWBs3bmzbtm2bdBkAAMCUqqoPtNY2znedw9sAAICuObwNWFIveP17855P3HHf8lmPPS5XvugpE6xo8XpquHjJlh256oZbsr+1rKnK8888JZduOn3SZQHAsrCnB1gycwNPkrznE3fkBa9/74QqWryZhou79u5Ly1caLm7ZvmvSpS3YJVt25Ir33Zz9w8OZ97eWK953cy7ZsmPClQHA8hB6gCUzN/Acbnya9dRw8aobblnQOAD0RugBmEdPDRf3H+SENQcbB4DeCD0A8+ip4eKaqgWNA0BvhB5gyZz12OMWND7Nemq4+PwzT1nQOAD0RugBlsyVL3rKAQFnpZ69bdMZ6/PKZ5+e9evWppKsX7c2r3z26Svy7G2Xbjo95z/51Pv27KypyvlPPtXZ2wBYNTQnBQAAVrxDNSfVp2dE+nVMp57m0oue1klPc9E/CUZj+4I+ObxtBPp1TKee5tKLntZJT3PRPwlGY/uCfgk9I9CvYzr1NJde9LROepqL/kkwGtsX9EvoGYF+HdOpp7n0oqd10tNcetLTz2Omj+0L+iX0jEC/junU01x60dM66WkuPenp5zHTx/YF/RJ6RqBfx3TqaS696Gmd9DQX/ZNgNLYv6JfQMwL9OqZTT3PpRU/rpKe56J8Eo7F9Qb/06QEAAFY8fXq4n556j/TUT6GnPipMn3NefX0+dvsX7lt+3AlH59qffurkCqIrPf0sBvrk8LZVpqfeIz31U+ipjwrTZ27gSZKP3f6FnPPq6ydTEF3p6Wcx0C+hZ5XpqfdIT/0UeuqjwvSZG3gONw4L0dPPYqBfQs8q01PvEf0UACbPz2JgJRB6Vpmeeo/opwAweX4WAyuB0LPK9NR7pKd+Cj31UWH6PO6Eoxc0DgvR089ioF9CzyrTU++Rnvop9NRHhelz7U8/9YCA4+xtLJWefhYD/dKnBwAAWPH06VkCettMpzMvuza33XXPfcsnHnNUbrj4nAlWtHi9bGP6wUynXravpK+fYTAu3idwfw5vG4HeNtNpbuBJktvuuidnXnbthCpavF62Mf1gplMv21fS188wGBfvEziQ0DMCvW2m09zAc7jxadbLNqYfzHTqZftK+voZBuPifQIHEnpGoLcN49bTNsb06Wn78jMMDs/7BA4k9IxAbxvGradtjOnT0/blZxgcnvcJHEjoGYHeNtPpxGOOWtD4NOtlG9MPZjr1sn0lff0Mg3HxPoEDCT0j0NtmOt1w8TkHBJyVeva2XrYx/WCmUy/bV9LXzzAYF+8TOJA+PQAAwIqnTw/384LXvzfv+cQd9y2f9djjcuWLnjLBihbv8S97V+68+ytnqDn2wWty4yuePsGKFq+XnkM99enR22Y69TSXXlgnwLRzeNsqMzfwJMl7PnFHXvD6906oosWbG3iS5M679+fxL3vXhCpavF56DvXUp0dvm+nU01x6YZ0AK4HQs8rMDTyHG59mcwPP4canWS89h3rq06O3zXTqaS69sE6AlUDoAZiH3jbTqae59MI6AVYCoQdgHnrbTKee5tIL6wRYCYSeVeasxx63oPFpduyD1yxofJr10nOopz49ettMp57m0gvrBFgJhJ5V5soXPeWAgLNSz9524yuefkDAWalnb+ul51BPfXr0tplOPc2lF9YJsBLo0wMAAKx4E+nTU1W/l+T7ktzeWvvm4dhxSd6S5LQkNyV5bmvt8+OqYSn11Numpz4qp730Tw4Yu+lVz5xAJQ9cL3P5+ov/NF/c/5V/pjxkTeWjlz1jghUtXk99enp63+sJA8BCjfPwtjclmXuc0UuTXNdae1yS64bLU6+n3jY99VGZLyQcanya9TKXuYEnSb64v+XrL/7TCVW0eD316enpfa8nDACLMbbQ01r7qyRzm7+cl+Ty4eXLk2wa1/MvpZ562/TUR4XpMzfwHG58mvXUp6en972eMAAsxnKfyODE1tqtSTL8fsLBblhVL66qbVW1bc+ePctWIEDSV5+enugJA8BiTO3Z21prr2utbWytbTz++OMnXQ6wyvTUp6cnesIAsBjLHXpuq6qTkmT4/fZlfv5F6am3TU99VJg+D1kzfyA42Pg066lPT0/vez1hAFiM5Q4970hywfDyBUnevszPvyg99bbpqY/Kwc5sthLPeNbLXD562TMOCDgr9extPfXp6el9rycMAIsxtj49VXVVkqcmeUSS25K8LMmWJFcnOTXJzUme01o77NkA9OkBAAAOZSJ9elprzz/IVWeP6znH6fEve1fuvPsrZww69sFrcuMr5p6Re2XopR9MYi7T6DEv/ZPM/ldKJfnnFTiPpK/eNmdedm1uu+ue+5ZPPOao3HDxOROsaPF66p8EwPKY2hMZTJO5gSdJ7rx7fx7/sndNqKLF66UfTGIu02hu4EmSNhxfaXrqbTM38CTJbXfdkzMvu3ZCFS1eT/2TAFg+Qs8I5gaew43DanWwg2VX4kmee+ptMzfwHG58mvXUPwmA5SP0ALBi6J8EwGIIPQCsGPonAbAYQs8Ijn3wmgWNw2p1sD87V+Kfoz31tjnxmKMWND7NeuqfBMDyEXpGcOMrnn5AwFmpZ2/rpR9MYi7T6J9f9cwDAs5KPXtbT71tbrj4nAMCzko9e1tP/ZMAWD5j69OzlPTpAQAADmUifXp600sPlcRcplUvc+llHklfc+mp55A+PQAslMPbRtBLD5XEXKZVL3PpZR5JX3PpqeeQPj0ALIbQA9C5nnoO6dMDwGIIPQCsGPr0ALAYQg8AK4Y+PQAshtAD0Lmeeg7p0wPAYgg9I+ilh0piLtOql7n0Mo+kr7n01HNInx4AFkOfHgAAYMXTp2cJ9NSvw1ymUy9z6WUeSXLmZdfmtrvuuW/5xGOOyg0XnzPBihbv8S97V+68e/99y8c+eE1ufMXTJ1gRACwfh7eNoKd+HeYynXqZSy/zSA4MPEly21335MzLrp1QRYs3N/AkyZ1378/jX/auCVUEAMtL6AGYx9zAc7jxaTY38BxuHAB6I/QAAABdE3oAAICuCT0A8zjxmKMWND7Njn3wmgWNA0BvhJ4R9NSvw1ymUy9z6WUeSXLDxeccEHBW6tnbbnzF0w8IOM7eBsBqok8PAACw4unTswR66j1iLtOpl7n0Mo+kr7kAwGrm8LYR9NR7xFymUy9z6WUeSV9zAYDVTugBAAC6JvQAAABdE3oAAICuCT0AAEDXhJ4R9NR7xFymUy9z6WUeSV9zAYDVTp8eAABgxTtUnx57egAAgK5pTjqinpoUmst06mUuvcwjSS7ZsiNX3XBL9reWNVV5/pmn5NJNp0+6rEV5wevfm/d84o77ls967HG58kVPmWBFi7dl+65s3rozu/fuy8nr1ubCczdk0xnrJ10WAFPMnp4R9NSk0FymUy9z6WUeySDwXPG+m7N/eAjw/tZyxftuziVbdky4soWbG3iS5D2fuCMveP17J1TR4m3ZvisXXbMju/buS0uya+++XHTNjmzZvmvSpQEwxYQegHlcdcMtCxqfZnMDz+HGp9nmrTuz79799xvbd+/+bN66c0IVAbASCD0A89h/kJO8HGyc5bF7774FjQNAIvQAzGtN1YLGWR4nr1u7oHEASIQegHk9/8xTFjQ+zc567HELGp9mF567IWuPXHO/sbVHrsmF526YUEUArARCzwh6alJoLtOpl7n0Mo8kuXTT6Tn/yafet2dnTVXOf/KpK/LsbVe+6CkHBJyVeva2TWeszyuffXrWr1ubSrJ+3dq88tmnO3sbAIekOSkAALDiHao5qT49I+qp94i5TKde5tLLPJK++sGcedm1ue2ue+5bPvGYo3LDxedMsCJ62r4App3D20bQU+8Rc5lOvcyll3kkffWDmRt4kuS2u+7JmZddO6GK6Gn7AlgJhB6AefTUD2Zu4DncOOPX0/YFsBIIPQDz0A+GcbJ9ASwvoQdgHvrBME62L4DlJfQAzKOnfjAnHnPUgsYZv562L4CVQOgZQU+9R8xlOvUyl17mkfTVD+aGi885IOA4e9tk9bR9AawE+vQAAAArnj49S6Cn3iPmMp16mcs5r74+H7v9C/ctP+6Eo3PtTz91cgU9AJds2ZGrbrgl+1vLmqo8/8xTcumm0ydd1qL0NBcAWCiHt42gp94j5jKdepnL3MCTJB+7/Qs559XXT6agB+CSLTtyxftuzv7h3vD9reWK992cS7bsmHBlC9fTXABgMYQeYMnMDTyHG59mV91wy4LGp1lPcwGAxRB6AOax/yCfdzzY+DTraS4AsBhCD8A81lQtaHya9TQXAFgMoQdYMo874egFjU+z5595yoLGp1lPcwGAxRB6RtBT7xFzmU69zOXan37qAQFnpZ697dJNp+f8J596396QNVU5/8mnrsgznvU0FwBYDH16AACAFU+fniXQSw+VxFymVS9z6WUeSfKC17837/nEHfctn/XY43Lli54ywYoWb8v2Xdm8dWd2792Xk9etzYXnbsimM9ZPuqxF6WkuACwPh7eNoJceKom5TKte5tLLPJIDA0+SvOcTd+QFr3/vhCpavC3bd+Wia3Zk1959aUl27d2Xi67ZkS3bd026tAXraS4ALB+hB2AecwPP4can2eatO7Pv3v33G9t37/5s3rpzQhUtXk9zAWD5CD0Andu9d9+CxqdZT3MBYPkIPQCdO3nd2gWNT7Oe5gLA8hF6AOZx1mOPW9D4NLvw3A1Ze+Sa+42tPXJNLjx3w4QqWrye5gLA8hF6RtBLD5XEXKZVL3PpZR5JcuWLnnJAwFmpZ2/bdMb6vPLZp2f9urWpJOvXrc0rn336ijzjWU9zAWD56NMDAACsePr0LIGeeo+Yy3TqZS69zCPpq0/PJVt25Kobbsn+1rKmKs8/85Rcuun0SZcFU0cfKOiTw9tG0FPvEXOZTr3MpZd5JH316blky45c8b6bs3+4Z39/a7nifTfnki07JlwZTBd9oKBfQg/APHrq03PVDbcsaBxWK32goF9CD0Dn9h/ks5sHG4fVSh8o6JfQA9C5NVULGofVSh8o6JfQAzCPnvr0PP/MUxY0DquVPlDQL6FnBD31HjGX6dTLXHqZR9JXn55LN52e85986n17dtZU5fwnn+rsbTCHPlDQL316AACAFW/q+vRU1dOTvDbJmiS/21p71STqWIieeo/o1zGd9IYAABiPZT+8rarWJPnfSf59km9M8vyq+sblrmMheuo9ol/HdNIbAgBgfCbxmZ5vS/Lx1tonW2v3JPnDJOdNoI5VSb+O6aQ3BADA+Ewi9KxPMvsv7E8Px+6nql5cVduqatuePXuWrbje6dcxnfSGAAAYn0mEnvkaQxzwF3dr7XWttY2ttY3HH3/8MpS1OujXMZ30hgAAGJ9JhJ5PJ5ndHOJRSXZPoI5VSb+O6aQ3BADA+Ewi9Lw/yeOq6jFVdVSSH0zyjgnUMbKeeo/o1zGd9IYAABififTpqapnJHlNBqes/r3W2mWHur0+PQAAwKFMXZ+e1tqfJvnTSTw3AACwukzi8DYAAIBlI/QAAABdE3oAAICuCT0AAEDXhB4AAKBrQg8AANA1oQcAAOia0AMAAHRN6AEAALom9AAAAF0TegAAgK4JPQAAQNeEHgAAoGtCDwAA0DWhBwAA6JrQAwAAdE3oAQAAuib0AAAAXRN6AACArlVrbdI1HFZV7UnyqUnX0aFHJPnspIvgANbL9LFOppP1Mn2sk+ljnUwn62U8Ht1aO36+K1ZE6GE8qmpba23jpOvg/qyX6WOdTCfrZfpYJ9PHOplO1svyc3gbAADQNaEHAADomtCzur1u0gUwL+tl+lgn08l6mT7WyfSxTqaT9bLMfKYHAADomj09AABA14QeAACga0LPKlRVp1TVX1TVR6rqH6rqJZOuiYGqWlNV26vqnZOuhYGqWldVb6uqjw7fM0+ZdE2rXVX9t+HPrr+vqquq6iGTrmk1qqrfq6rbq+rvZ40dV1XXVtXHht8fPskaV5uDrJPNw59fN1bVH1fVugmWuCrNt15mXfezVdWq6hGTqG01EXpWpy8l+ZnW2jckeXKSH6+qb5xwTQy8JMlHJl0E9/PaJO9qrX19kifE+pmoqlqf5CeTbGytfXOSNUl+cLJVrVpvSvL0OWMvTXJda+1xSa4bLrN83pQD18m1Sb65tfb4JP+U5KLlLop510uq6pQk5yS5ebkLWo2EnlWotXZra+2Dw8t3ZfBH3PrJVkVVPSrJM5P87qRrYaCqjk3ynUnekCSttXtaa3snWhRJckSStVV1RJKvSrJ7wvWsSq21v0pyx5zh85JcPrx8eZJNy1nTajffOmmtvbu19qXh4vuSPGrZC1vlDvJeSZL/meTnkjir2DIQela5qjotyRlJbphwKSSvyeCH35cnXAdf8TVJ9iR54/Cww9+tqqMnXdRq1lrbleR/ZPCf0VuT/Etr7d2TrYpZTmyt3ZoM/sGW5IQJ18P9/WiS/zvpIkiq6llJdrXWPjzpWlYLoWcVq6qHJvmjJD/VWrtz0vWsZlX1fUlub619YNK1cD9HJHlSkt9qrZ2R5AtxuM5EDT8jcl6SxyQ5OcnRVXX+ZKuC6VdVF2dwePuVk65ltauqr0pycZJfmnQtq4nQs0pV1ZEZBJ4rW2vXTLoeclaSZ1XVTUn+MMn3VNUVky2JJJ9O8unW2sye0LdlEIKYnKcl+efW2p7W2r1Jrkny7yZcE19xW1WdlCTD77dPuB6SVNUFSb4vyQuaBo3T4LEZ/OPmw8Pf+49K8sGqeuREq+qc0LMKVVVl8BmFj7TWXj3pekhaaxe11h7VWjstgw9l/3lrzX+vJ6y19pkkt1TVhuHQ2Un+cYIlMTis7clV9VXDn2Vnx8klpsk7klwwvHxBkrdPsBaSVNXTk/x8kme11v5t0vWQtNZ2tNZOaK2dNvy9/+kkTxr+zmFMhJ7V6awkP5TB3oQPDb+eMemiYEr9RJIrq+rGJE9M8iuTLWd1G+51e1uSDybZkcHvsddNtKhVqqquSvLeJBuq6tNV9cIkr0pyTlV9LIOzUr1qkjWuNgdZJ/8ryTFJrh3+vv/tiRa5Ch1kvbDMyl5OAACgZ/b0AAAAXRN6AACArgk9AABA14QeAACga0IPAADQNaEHgHlVVauqX5+1/LNV9fJlfP4HV9WfDU+z+7wH+FjXV9XGecY3VtVvHOa+N1XVIx7I8wMwWUdMugAAptbdSZ5dVa9srX12As9/RpIjW2tPHNcTtNa2Jdk2rscHYDrY0wPAwXwpg8af/23uFVX1pqr6gVnL/zr8/tSq+suqurqq/qmqXlVVL6iqv6uqHVX12Hke67iq2lJVN1bV+6rq8VV1QpIrkjxxuKfnsbNu/w1V9Xezlk8bNo9NVX3L8Pk/UFVbq+qkWU/1nGEd/1RV3zGr3ncOLz+0qt44rPPGqvqP89R6/vAxPlRVv1NVa4Zfb6qqvx/e94DXC4DJEnoAOJT/neQFVfWwBdznCUlekuT0JD+U5Otaa9+W5HeT/MQ8t39Fku2ttccn+YUkb26t3Z7kvyT569baE1trn5i5cWvtI0mOqqqvGQ49L8nVVXVkkt9M8gOttW9J8ntJLpv1PEcM6/ipJC+bp45fTPIvrbXTh7X8+ewrq+obhs911nDv0/4kL0jyxCTrW2vf3Fo7PckbR3iNAFhGDm8D4KBaa3dW1ZuT/GSSfSPe7f2ttVuTpKo+keTdw/EdSb57ntt/e5L/OHy+P6+qrx4hZF2d5LlJXpVBEHlekg1JvjnJtVWVJGuS3DrrPtcMv38gyWnzPObTkvzgzEJr7fNzrj87ybckef/w8dcmuT3J/0nyNVX1m0n+ZNZ8AZgSQg8Ah/OaJB/M/fdgfCnDowVqkACOmnXd3bMuf3nW8pcz/++dmmesHaamtyR5a1Vdk6S11j5WVacn+YfW2lMOcp+ZOvYfoo5DPW8luby1dtEBV1Q9Icm5SX48gzD2o4epH4Bl5PA2AA6ptXZHBntWXjhr+KYM9nokyXlJjnwAT/FXGRwmlqp6apLPttbuPExNn8ggvPxiBgEoSXYmOb6qnjJ8rCOr6psWUMe7k/y/MwtV9fA511+X5AeGnzea+SzSo4dndntQa+2PhvU8aQHPCcAyEHoAGMWvJ5l92ubXJ/mu4QkFzkzyhQfw2C9PsnF4MoJXJblgxPu9Jcn5GQSytNbuSfIDSX61qj6c5ENJ/t0C6rg0ycOHJyT4cOYcitda+8cklyR597DWa5OclGR9kuur6kNJ3pTkgD1BAExWtXa4IwgAAABWLnt6AACArgk9AABA14QeAACga0IPAADQNaEHAADomtADAAB0TegBAAC69v8D2o9hDpwIVVkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# There looks to be a few outliers partcularly with the 60 person crash further analysis is needed with that as well as with the #of vehicles above 8\n",
    "plt.figure(figsize=(14,10))\n",
    "# Scatter plot\n",
    "plt.scatter(df_accident['ve_total'],df_accident['persons'])\n",
    "# Create a descriptive title\n",
    "plt.title('Vehicle total tends to correlate with amount of people')\n",
    "# Add axis labels\n",
    "plt.xlabel('Num of vehicles')\n",
    "plt.ylabel('Num of persons');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "c152048d-5c05-4cf4-ae98-95cc8940f1d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ve_total</th>\n",
       "      <th>persons</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ve_total</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.631536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>persons</th>\n",
       "      <td>0.631536</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "          ve_total   persons\n",
       "ve_total  1.000000  0.631536\n",
       "persons   0.631536  1.000000"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_accident[['ve_total', 'persons']].corr()\n",
    "#pretty strong correlation here but hard to extract value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "b8dcd1ca-63f1-46e3-8886-afaba08125a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>state</th>\n",
       "      <th>statename</th>\n",
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       "      <th>ve_total</th>\n",
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       "      <th>pvh_invl</th>\n",
       "      <th>peds</th>\n",
       "      <th>persons</th>\n",
       "      <th>permvit</th>\n",
       "      <th>pernotmvit</th>\n",
       "      <th>...</th>\n",
       "      <th>arr_hour</th>\n",
       "      <th>arr_hourname</th>\n",
       "      <th>arr_min</th>\n",
       "      <th>arr_minname</th>\n",
       "      <th>hosp_hr</th>\n",
       "      <th>hosp_hrname</th>\n",
       "      <th>hosp_mn</th>\n",
       "      <th>hosp_mnname</th>\n",
       "      <th>fatals</th>\n",
       "      <th>drunk_dr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26452</th>\n",
       "      <td>42</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>420105</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3:00am-3:59am</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>4</td>\n",
       "      <td>4:00am-4:59am</td>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       state     statename  st_case  ve_total  ve_forms  pvh_invl  peds  \\\n",
       "26452     42  Pennsylvania   420105         1         1         0     0   \n",
       "\n",
       "       persons  permvit  pernotmvit  ...  arr_hour   arr_hourname  arr_min  \\\n",
       "26452       61       61           0  ...         3  3:00am-3:59am       49   \n",
       "\n",
       "      arr_minname  hosp_hr    hosp_hrname  hosp_mn hosp_mnname  fatals  \\\n",
       "26452          49        4  4:00am-4:59am       41          41       1   \n",
       "\n",
       "       drunk_dr  \n",
       "26452         0  \n",
       "\n",
       "[1 rows x 81 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_accident[df_accident['persons'] > 40]\n",
    "# What does this mean?\n",
    "# Looks like there is a data error - interpretation of the charts is also unclear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "d30988b9-bc09-4a1a-9384-856c4051ab75",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>statename</th>\n",
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       "      <th>3684</th>\n",
       "      <td>6</td>\n",
       "      <td>California</td>\n",
       "      <td>61260</td>\n",
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       "      <td>99</td>\n",
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       "      <td>99</td>\n",
       "      <td>Unknown</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4898</th>\n",
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       "      <td>Unknown EMS Scene Arrival Hour</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Minutes</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Hospital Arrival Time</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5984</th>\n",
       "      <td>6</td>\n",
       "      <td>California</td>\n",
       "      <td>63594</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
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       "      <td>Unknown EMS Scene Arrival Hour</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Minutes</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Hospital Arrival Time</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6586</th>\n",
       "      <td>8</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>80575</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>16</td>\n",
       "      <td>4:00pm-4:59pm</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>16</td>\n",
       "      <td>4:00pm-4:59pm</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8366</th>\n",
       "      <td>12</td>\n",
       "      <td>Florida</td>\n",
       "      <td>121457</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Hour</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Minutes</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Hospital Arrival Time</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11131</th>\n",
       "      <td>13</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>131057</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>1:00pm-1:59pm</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>88</td>\n",
       "      <td>Not Applicable (Not Transported)</td>\n",
       "      <td>88</td>\n",
       "      <td>Not Applicable (Not Transported)</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13002</th>\n",
       "      <td>18</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>180021</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Hour</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Minutes</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Hospital Arrival Time</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20409</th>\n",
       "      <td>32</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>320051</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>15</td>\n",
       "      <td>3:00pm-3:59pm</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>16</td>\n",
       "      <td>4:00pm-4:59pm</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26970</th>\n",
       "      <td>42</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>420623</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>15</td>\n",
       "      <td>3:00pm-3:59pm</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Hospital Arrival Time</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33222</th>\n",
       "      <td>49</td>\n",
       "      <td>Utah</td>\n",
       "      <td>490018</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>8:00pm-8:59pm</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>20</td>\n",
       "      <td>8:00pm-8:59pm</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       state     statename  st_case  ve_total  ve_forms  pvh_invl  peds  \\\n",
       "3684       6    California    61260        10        10         0     0   \n",
       "4898       6    California    62494        11         1        10     0   \n",
       "5984       6    California    63594        11        11         0     0   \n",
       "6586       8      Colorado    80575        15        15         0     0   \n",
       "8366      12       Florida   121457        10        10         0     0   \n",
       "11131     13       Georgia   131057        10        10         0     0   \n",
       "13002     18       Indiana   180021         9         9         0     0   \n",
       "20409     32        Nevada   320051        10        10         0     0   \n",
       "26970     42  Pennsylvania   420623        12        12         0     0   \n",
       "33222     49          Utah   490018         9         3         6     0   \n",
       "\n",
       "       persons  permvit  pernotmvit  ...  arr_hour  \\\n",
       "3684        19       19           0  ...        99   \n",
       "4898         1        1           0  ...        99   \n",
       "5984        14       14           0  ...        99   \n",
       "6586        24       24           0  ...        16   \n",
       "8366        13       13           0  ...        99   \n",
       "11131       11       11           0  ...        13   \n",
       "13002       26       26           0  ...        99   \n",
       "20409       16       16           0  ...        15   \n",
       "26970       12       12           0  ...        15   \n",
       "33222        4        4           0  ...        20   \n",
       "\n",
       "                         arr_hourname  arr_min  \\\n",
       "3684   Unknown EMS Scene Arrival Hour       99   \n",
       "4898   Unknown EMS Scene Arrival Hour       99   \n",
       "5984   Unknown EMS Scene Arrival Hour       99   \n",
       "6586                    4:00pm-4:59pm       18   \n",
       "8366   Unknown EMS Scene Arrival Hour       99   \n",
       "11131                   1:00pm-1:59pm        1   \n",
       "13002  Unknown EMS Scene Arrival Hour       99   \n",
       "20409                   3:00pm-3:59pm       45   \n",
       "26970                   3:00pm-3:59pm       28   \n",
       "33222                   8:00pm-8:59pm       13   \n",
       "\n",
       "                             arr_minname  hosp_hr  \\\n",
       "3684   Unknown EMS Scene Arrival Minutes       99   \n",
       "4898   Unknown EMS Scene Arrival Minutes       99   \n",
       "5984   Unknown EMS Scene Arrival Minutes       99   \n",
       "6586                                  18       16   \n",
       "8366   Unknown EMS Scene Arrival Minutes       99   \n",
       "11131                                  1       88   \n",
       "13002  Unknown EMS Scene Arrival Minutes       99   \n",
       "20409                                 45       16   \n",
       "26970                                 28       99   \n",
       "33222                                 13       20   \n",
       "\n",
       "                            hosp_hrname  hosp_mn  \\\n",
       "3684                            Unknown       99   \n",
       "4898                            Unknown       99   \n",
       "5984                            Unknown       99   \n",
       "6586                      4:00pm-4:59pm       52   \n",
       "8366                            Unknown       99   \n",
       "11131  Not Applicable (Not Transported)       88   \n",
       "13002                           Unknown       99   \n",
       "20409                     4:00pm-4:59pm        4   \n",
       "26970                           Unknown       99   \n",
       "33222                     8:00pm-8:59pm       20   \n",
       "\n",
       "                             hosp_mnname  fatals  drunk_dr  \n",
       "3684   Unknown EMS Hospital Arrival Time       1         0  \n",
       "4898   Unknown EMS Hospital Arrival Time       1         1  \n",
       "5984   Unknown EMS Hospital Arrival Time       1         0  \n",
       "6586                                  52       1         0  \n",
       "8366   Unknown EMS Hospital Arrival Time       2         0  \n",
       "11131   Not Applicable (Not Transported)       1         0  \n",
       "13002  Unknown EMS Hospital Arrival Time       3         0  \n",
       "20409                                  4       1         1  \n",
       "26970  Unknown EMS Hospital Arrival Time       1         0  \n",
       "33222                                 20       2         0  \n",
       "\n",
       "[10 rows x 81 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_accident[df_accident['ve_total'] > 8]\n",
    "# Seems to be one outlier in the seond row where ve-total and ve form do not align"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cc27ec7-5986-4928-90c7-2a8296014357",
   "metadata": {},
   "source": [
    "#### Fatal Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "cdcfa47a-306e-4bd9-bbe5-150d52a7a20e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fatals\n",
       "1         33226\n",
       "2          2154\n",
       "3           289\n",
       "4            71\n",
       "5            20\n",
       "6             4\n",
       "7             1\n",
       "8             1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_accident[['fatals']].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "043469dc-e005-4ba2-bd3c-48bc8d2045a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### The majority of the fatals seem to be distributed toward the left of the diagram\n",
    "plt.figure(figsize=(12,5))\n",
    "# Size\n",
    "\n",
    "# Plot it\n",
    "plt.hist(df_accident['fatals'], bins=20, color='teal');\n",
    "\n",
    "\n",
    "\n",
    "plt.title('Fatal Bar Chart');\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e1e6359-7b5b-4e35-8559-5376955c66be",
   "metadata": {},
   "source": [
    "#### Fatals and State"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "6895924f-3051-4c63-89af-dcc1e9cd08eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fatals  statename     \n",
       "1       California        3319\n",
       "        Texas             3246\n",
       "        Florida           2906\n",
       "        Georgia           1405\n",
       "        North Carolina    1309\n",
       "                          ... \n",
       "6       Georgia              1\n",
       "        Kansas               1\n",
       "        Texas                1\n",
       "7       Texas                1\n",
       "8       Colorado             1\n",
       "Name: statename, Length: 188, dtype: int64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_accident.groupby('fatals')['statename'].value_counts()\n",
    "# It looks like the most dangerous state is california maybe we dig deeper into what constitutes 'danger'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "96a1a1b9-2b9d-4efb-967f-ee48eedcb0ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "California_df = df_accident[(df_accident['statename'] == 'California')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "da8be3c1-abca-44fc-82a3-131faa6a84d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>statename</th>\n",
       "      <th>st_case</th>\n",
       "      <th>ve_total</th>\n",
       "      <th>ve_forms</th>\n",
       "      <th>pvh_invl</th>\n",
       "      <th>peds</th>\n",
       "      <th>persons</th>\n",
       "      <th>permvit</th>\n",
       "      <th>pernotmvit</th>\n",
       "      <th>...</th>\n",
       "      <th>arr_hour</th>\n",
       "      <th>arr_hourname</th>\n",
       "      <th>arr_min</th>\n",
       "      <th>arr_minname</th>\n",
       "      <th>hosp_hr</th>\n",
       "      <th>hosp_hrname</th>\n",
       "      <th>hosp_mn</th>\n",
       "      <th>hosp_mnname</th>\n",
       "      <th>fatals</th>\n",
       "      <th>drunk_dr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3870</th>\n",
       "      <td>6</td>\n",
       "      <td>California</td>\n",
       "      <td>61447</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Hour</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Minutes</td>\n",
       "      <td>88</td>\n",
       "      <td>Not Applicable (Not Transported)</td>\n",
       "      <td>88</td>\n",
       "      <td>Not Applicable (Not Transported)</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5111</th>\n",
       "      <td>6</td>\n",
       "      <td>California</td>\n",
       "      <td>62708</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Hour</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Minutes</td>\n",
       "      <td>88</td>\n",
       "      <td>Not Applicable (Not Transported)</td>\n",
       "      <td>88</td>\n",
       "      <td>Not Applicable (Not Transported)</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3575</th>\n",
       "      <td>6</td>\n",
       "      <td>California</td>\n",
       "      <td>61151</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Hour</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Minutes</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Hospital Arrival Time</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      state   statename  st_case  ve_total  ve_forms  pvh_invl  peds  persons  \\\n",
       "3870      6  California    61447         1         1         0     0        4   \n",
       "5111      6  California    62708         1         1         0     0        4   \n",
       "3575      6  California    61151         1         1         0     4        1   \n",
       "\n",
       "      permvit  pernotmvit  ...  arr_hour                    arr_hourname  \\\n",
       "3870        4           0  ...        99  Unknown EMS Scene Arrival Hour   \n",
       "5111        4           0  ...        99  Unknown EMS Scene Arrival Hour   \n",
       "3575        1           4  ...        99  Unknown EMS Scene Arrival Hour   \n",
       "\n",
       "      arr_min                        arr_minname  hosp_hr  \\\n",
       "3870       99  Unknown EMS Scene Arrival Minutes       88   \n",
       "5111       99  Unknown EMS Scene Arrival Minutes       88   \n",
       "3575       99  Unknown EMS Scene Arrival Minutes       99   \n",
       "\n",
       "                           hosp_hrname  hosp_mn  \\\n",
       "3870  Not Applicable (Not Transported)       88   \n",
       "5111  Not Applicable (Not Transported)       88   \n",
       "3575                           Unknown       99   \n",
       "\n",
       "                            hosp_mnname  fatals  drunk_dr  \n",
       "3870   Not Applicable (Not Transported)       4         0  \n",
       "5111   Not Applicable (Not Transported)       4         0  \n",
       "3575  Unknown EMS Hospital Arrival Time       4         0  \n",
       "\n",
       "[3 rows x 81 columns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "California_df.sort_values('fatals', ascending=False).head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "9e69589e-8560-490e-b276-b4d838a20b8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>statename</th>\n",
       "      <th>st_case</th>\n",
       "      <th>ve_total</th>\n",
       "      <th>ve_forms</th>\n",
       "      <th>pvh_invl</th>\n",
       "      <th>peds</th>\n",
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       "      <th>permvit</th>\n",
       "      <th>pernotmvit</th>\n",
       "      <th>...</th>\n",
       "      <th>arr_hour</th>\n",
       "      <th>arr_hourname</th>\n",
       "      <th>arr_min</th>\n",
       "      <th>arr_minname</th>\n",
       "      <th>hosp_hr</th>\n",
       "      <th>hosp_hrname</th>\n",
       "      <th>hosp_mn</th>\n",
       "      <th>hosp_mnname</th>\n",
       "      <th>fatals</th>\n",
       "      <th>drunk_dr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4560</th>\n",
       "      <td>6</td>\n",
       "      <td>California</td>\n",
       "      <td>62154</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>88</td>\n",
       "      <td>Not Applicable (Not Transported)</td>\n",
       "      <td>88</td>\n",
       "      <td>Not Applicable (Not Transported)</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3773</th>\n",
       "      <td>6</td>\n",
       "      <td>California</td>\n",
       "      <td>61349</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0:00am-0:59am</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>88</td>\n",
       "      <td>Not Applicable (Not Transported)</td>\n",
       "      <td>88</td>\n",
       "      <td>Not Applicable (Not Transported)</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2994</th>\n",
       "      <td>6</td>\n",
       "      <td>California</td>\n",
       "      <td>60556</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0:00am-0:59am</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3633</th>\n",
       "      <td>6</td>\n",
       "      <td>California</td>\n",
       "      <td>61209</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0:00am-0:59am</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>88</td>\n",
       "      <td>Not Applicable (Not Transported)</td>\n",
       "      <td>88</td>\n",
       "      <td>Not Applicable (Not Transported)</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3989</th>\n",
       "      <td>6</td>\n",
       "      <td>California</td>\n",
       "      <td>61568</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0:00am-0:59am</td>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Hospital Arrival Time</td>\n",
       "      <td>1</td>\n",
       "      <td>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",
       "      <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>3694</th>\n",
       "      <td>6</td>\n",
       "      <td>California</td>\n",
       "      <td>61270</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Hour</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Minutes</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Hospital Arrival Time</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3695</th>\n",
       "      <td>6</td>\n",
       "      <td>California</td>\n",
       "      <td>61271</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Hour</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Minutes</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Hospital Arrival Time</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3697</th>\n",
       "      <td>6</td>\n",
       "      <td>California</td>\n",
       "      <td>61273</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Hour</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Minutes</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Hospital Arrival Time</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3664</th>\n",
       "      <td>6</td>\n",
       "      <td>California</td>\n",
       "      <td>61240</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Hour</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Minutes</td>\n",
       "      <td>88</td>\n",
       "      <td>Not Applicable (Not Transported)</td>\n",
       "      <td>88</td>\n",
       "      <td>Not Applicable (Not Transported)</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6014</th>\n",
       "      <td>6</td>\n",
       "      <td>California</td>\n",
       "      <td>63624</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Hour</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Minutes</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Hospital Arrival Time</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3558 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      state   statename  st_case  ve_total  ve_forms  pvh_invl  peds  persons  \\\n",
       "4560      6  California    62154         1         1         0     1        1   \n",
       "3773      6  California    61349         2         2         0     0        2   \n",
       "2994      6  California    60556         2         2         0     0        2   \n",
       "3633      6  California    61209         1         1         0     1        1   \n",
       "3989      6  California    61568         1         1         0     0        2   \n",
       "...     ...         ...      ...       ...       ...       ...   ...      ...   \n",
       "3694      6  California    61270         1         1         0     1        1   \n",
       "3695      6  California    61271         1         1         0     1        1   \n",
       "3697      6  California    61273         1         1         0     0        1   \n",
       "3664      6  California    61240         1         1         0     0        1   \n",
       "6014      6  California    63624         1         1         0     1        1   \n",
       "\n",
       "      permvit  pernotmvit  ...  arr_hour                    arr_hourname  \\\n",
       "4560        1           1  ...         0                   0:00am-0:59am   \n",
       "3773        2           0  ...         0                   0:00am-0:59am   \n",
       "2994        2           0  ...         0                   0:00am-0:59am   \n",
       "3633        1           1  ...         0                   0:00am-0:59am   \n",
       "3989        2           0  ...         0                   0:00am-0:59am   \n",
       "...       ...         ...  ...       ...                             ...   \n",
       "3694        1           1  ...        99  Unknown EMS Scene Arrival Hour   \n",
       "3695        1           1  ...        99  Unknown EMS Scene Arrival Hour   \n",
       "3697        1           0  ...        99  Unknown EMS Scene Arrival Hour   \n",
       "3664        1           0  ...        99  Unknown EMS Scene Arrival Hour   \n",
       "6014        1           1  ...        99  Unknown EMS Scene Arrival Hour   \n",
       "\n",
       "      arr_min                        arr_minname  hosp_hr  \\\n",
       "4560       49                                 49       88   \n",
       "3773       20                                 20       88   \n",
       "2994       49                                 49       99   \n",
       "3633       27                                 27       88   \n",
       "3989       34                                 34       99   \n",
       "...       ...                                ...      ...   \n",
       "3694       99  Unknown EMS Scene Arrival Minutes       99   \n",
       "3695       99  Unknown EMS Scene Arrival Minutes       99   \n",
       "3697       99  Unknown EMS Scene Arrival Minutes       99   \n",
       "3664       99  Unknown EMS Scene Arrival Minutes       88   \n",
       "6014       99  Unknown EMS Scene Arrival Minutes       99   \n",
       "\n",
       "                           hosp_hrname  hosp_mn  \\\n",
       "4560  Not Applicable (Not Transported)       88   \n",
       "3773  Not Applicable (Not Transported)       88   \n",
       "2994                           Unknown        0   \n",
       "3633  Not Applicable (Not Transported)       88   \n",
       "3989                           Unknown       99   \n",
       "...                                ...      ...   \n",
       "3694                           Unknown       99   \n",
       "3695                           Unknown       99   \n",
       "3697                           Unknown       99   \n",
       "3664  Not Applicable (Not Transported)       88   \n",
       "6014                           Unknown       99   \n",
       "\n",
       "                            hosp_mnname  fatals  drunk_dr  \n",
       "4560   Not Applicable (Not Transported)       1         0  \n",
       "3773   Not Applicable (Not Transported)       1         0  \n",
       "2994                                  0       2         0  \n",
       "3633   Not Applicable (Not Transported)       1         0  \n",
       "3989  Unknown EMS Hospital Arrival Time       1         0  \n",
       "...                                 ...     ...       ...  \n",
       "3694  Unknown EMS Hospital Arrival Time       1         0  \n",
       "3695  Unknown EMS Hospital Arrival Time       1         0  \n",
       "3697  Unknown EMS Hospital Arrival Time       1         0  \n",
       "3664   Not Applicable (Not Transported)       1         0  \n",
       "6014  Unknown EMS Hospital Arrival Time       1         0  \n",
       "\n",
       "[3558 rows x 81 columns]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "California_df.sort_values('arr_hour', ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "82ad3dd3-4a7e-4342-bb69-a3d6f289b673",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fatals  drunk_dr\n",
       "1       0           0.755047\n",
       "        1           0.240735\n",
       "        2           0.004218\n",
       "2       0           0.609137\n",
       "        1           0.375635\n",
       "        2           0.015228\n",
       "3       0           0.500000\n",
       "        1           0.441176\n",
       "        2           0.058824\n",
       "4       0           0.750000\n",
       "        1           0.250000\n",
       "Name: drunk_dr, dtype: float64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "California_df.groupby('fatals')['drunk_dr'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2780e2f-093b-40ba-aa8b-802a83c6924e",
   "metadata": {},
   "source": [
    "### Sort values by most fatals\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "05436ccd-8d97-48c8-9fe1-f33f474870f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "fatals = df_accident.sort_values(by='fatals', ascending=False)[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "7d18eb2a-70cd-46c7-8d94-bdd45f4743ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here we see the average total for fatals by state- most are coming in around 4\n",
    "plt.figure(figsize=(15,5))\n",
    "plt.bar(df_accident['statename'],\n",
    "       df_accident['fatals'])\n",
    "plt.xticks(rotation = 90)\n",
    "plt.ylabel('Max of Fatal')\n",
    "plt.xlabel('State')\n",
    "plt.title(\"State Fatals\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e12901ea-c939-4f69-9cd6-940201763c57",
   "metadata": {},
   "source": [
    "#### Let's Check out the road owner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "cd3c1048-649b-4e7c-a482-eb843614beb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  4,  1,  3, 96, 98, 99, 62, 64, 60, 11, 25, 66, 31, 12, 27, 63,\n",
       "       21, 26, 50, 80, 68, 32, 70], dtype=int64)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_accident['rd_owner'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "8e81097e-709a-450d-bac3-3455d3be8ef8",
   "metadata": {},
   "outputs": [],
   "source": [
    "california_roadowner = df_accident[(df_accident['statename'] == 'California') & (df_accident['rd_owner']) >= 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "b229a939-715f-43aa-88b4-83baa3d13afe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>state</th>\n",
       "      <th>statename</th>\n",
       "      <th>st_case</th>\n",
       "      <th>ve_total</th>\n",
       "      <th>ve_forms</th>\n",
       "      <th>pvh_invl</th>\n",
       "      <th>peds</th>\n",
       "      <th>persons</th>\n",
       "      <th>permvit</th>\n",
       "      <th>pernotmvit</th>\n",
       "      <th>...</th>\n",
       "      <th>arr_hour</th>\n",
       "      <th>arr_hourname</th>\n",
       "      <th>arr_min</th>\n",
       "      <th>arr_minname</th>\n",
       "      <th>hosp_hr</th>\n",
       "      <th>hosp_hrname</th>\n",
       "      <th>hosp_mn</th>\n",
       "      <th>hosp_mnname</th>\n",
       "      <th>fatals</th>\n",
       "      <th>drunk_dr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2458</th>\n",
       "      <td>6</td>\n",
       "      <td>California</td>\n",
       "      <td>60002</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Hour</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Minutes</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Hospital Arrival Time</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2459</th>\n",
       "      <td>6</td>\n",
       "      <td>California</td>\n",
       "      <td>60003</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Hour</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Minutes</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Hospital Arrival Time</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2460</th>\n",
       "      <td>6</td>\n",
       "      <td>California</td>\n",
       "      <td>60004</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Hour</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Minutes</td>\n",
       "      <td>88</td>\n",
       "      <td>Not Applicable (Not Transported)</td>\n",
       "      <td>88</td>\n",
       "      <td>Not Applicable (Not Transported)</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2461</th>\n",
       "      <td>6</td>\n",
       "      <td>California</td>\n",
       "      <td>60005</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Hour</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Minutes</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Hospital Arrival Time</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2464</th>\n",
       "      <td>6</td>\n",
       "      <td>California</td>\n",
       "      <td>60008</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Hour</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Minutes</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Hospital Arrival Time</td>\n",
       "      <td>1</td>\n",
       "      <td>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",
       "      <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>6009</th>\n",
       "      <td>6</td>\n",
       "      <td>California</td>\n",
       "      <td>63619</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Hour</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Minutes</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Hospital Arrival Time</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6010</th>\n",
       "      <td>6</td>\n",
       "      <td>California</td>\n",
       "      <td>63620</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Hour</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Minutes</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Hospital Arrival Time</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6011</th>\n",
       "      <td>6</td>\n",
       "      <td>California</td>\n",
       "      <td>63621</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Hour</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Minutes</td>\n",
       "      <td>88</td>\n",
       "      <td>Not Applicable (Not Transported)</td>\n",
       "      <td>88</td>\n",
       "      <td>Not Applicable (Not Transported)</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6012</th>\n",
       "      <td>6</td>\n",
       "      <td>California</td>\n",
       "      <td>63622</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>6:00pm-6:59pm</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>88</td>\n",
       "      <td>Not Applicable (Not Transported)</td>\n",
       "      <td>88</td>\n",
       "      <td>Not Applicable (Not Transported)</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6013</th>\n",
       "      <td>6</td>\n",
       "      <td>California</td>\n",
       "      <td>63623</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Hour</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Minutes</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Hospital Arrival Time</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1513 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      state   statename  st_case  ve_total  ve_forms  pvh_invl  peds  persons  \\\n",
       "2458      6  California    60002         2         2         0     0        3   \n",
       "2459      6  California    60003         2         2         0     0        2   \n",
       "2460      6  California    60004         2         2         0     0        2   \n",
       "2461      6  California    60005         2         2         0     0        5   \n",
       "2464      6  California    60008         3         3         0     0        5   \n",
       "...     ...         ...      ...       ...       ...       ...   ...      ...   \n",
       "6009      6  California    63619         2         2         0     0        2   \n",
       "6010      6  California    63620         2         2         0     0        3   \n",
       "6011      6  California    63621         1         1         0     1        2   \n",
       "6012      6  California    63622         1         1         0     0        1   \n",
       "6013      6  California    63623         1         1         0     0        1   \n",
       "\n",
       "      permvit  pernotmvit  ...  arr_hour                    arr_hourname  \\\n",
       "2458        3           0  ...        99  Unknown EMS Scene Arrival Hour   \n",
       "2459        2           0  ...        99  Unknown EMS Scene Arrival Hour   \n",
       "2460        2           0  ...        99  Unknown EMS Scene Arrival Hour   \n",
       "2461        5           0  ...        99  Unknown EMS Scene Arrival Hour   \n",
       "2464        5           0  ...        99  Unknown EMS Scene Arrival Hour   \n",
       "...       ...         ...  ...       ...                             ...   \n",
       "6009        2           0  ...        99  Unknown EMS Scene Arrival Hour   \n",
       "6010        3           0  ...        99  Unknown EMS Scene Arrival Hour   \n",
       "6011        2           1  ...        99  Unknown EMS Scene Arrival Hour   \n",
       "6012        1           0  ...        18                   6:00pm-6:59pm   \n",
       "6013        1           0  ...        99  Unknown EMS Scene Arrival Hour   \n",
       "\n",
       "      arr_min                        arr_minname  hosp_hr  \\\n",
       "2458       99  Unknown EMS Scene Arrival Minutes       99   \n",
       "2459       99  Unknown EMS Scene Arrival Minutes       99   \n",
       "2460       99  Unknown EMS Scene Arrival Minutes       88   \n",
       "2461       99  Unknown EMS Scene Arrival Minutes       99   \n",
       "2464       99  Unknown EMS Scene Arrival Minutes       99   \n",
       "...       ...                                ...      ...   \n",
       "6009       99  Unknown EMS Scene Arrival Minutes       99   \n",
       "6010       99  Unknown EMS Scene Arrival Minutes       99   \n",
       "6011       99  Unknown EMS Scene Arrival Minutes       88   \n",
       "6012       55                                 55       88   \n",
       "6013       99  Unknown EMS Scene Arrival Minutes       99   \n",
       "\n",
       "                           hosp_hrname  hosp_mn  \\\n",
       "2458                           Unknown       99   \n",
       "2459                           Unknown       99   \n",
       "2460  Not Applicable (Not Transported)       88   \n",
       "2461                           Unknown       99   \n",
       "2464                           Unknown       99   \n",
       "...                                ...      ...   \n",
       "6009                           Unknown       99   \n",
       "6010                           Unknown       99   \n",
       "6011  Not Applicable (Not Transported)       88   \n",
       "6012  Not Applicable (Not Transported)       88   \n",
       "6013                           Unknown       99   \n",
       "\n",
       "                            hosp_mnname  fatals  drunk_dr  \n",
       "2458  Unknown EMS Hospital Arrival Time       1         0  \n",
       "2459  Unknown EMS Hospital Arrival Time       1         0  \n",
       "2460   Not Applicable (Not Transported)       1         0  \n",
       "2461  Unknown EMS Hospital Arrival Time       1         0  \n",
       "2464  Unknown EMS Hospital Arrival Time       1         0  \n",
       "...                                 ...     ...       ...  \n",
       "6009  Unknown EMS Hospital Arrival Time       1         0  \n",
       "6010  Unknown EMS Hospital Arrival Time       1         1  \n",
       "6011   Not Applicable (Not Transported)       1         0  \n",
       "6012   Not Applicable (Not Transported)       1         0  \n",
       "6013  Unknown EMS Hospital Arrival Time       1         0  \n",
       "\n",
       "[1513 rows x 81 columns]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "california_roadowner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "037d065a-d1ae-4a1b-a37a-d8bc5e940481",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2458     1\n",
       "4504     1\n",
       "4501     1\n",
       "4500     1\n",
       "4499     1\n",
       "        ..\n",
       "3452     1\n",
       "3480     1\n",
       "5829     3\n",
       "4276    99\n",
       "3449    99\n",
       "Name: rd_owner, Length: 1513, dtype: int64"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "california_roadowner['rd_owner'].sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "3b412362-c011-4431-919f-4445c3792990",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fatals  rd_owner\n",
       "1       1           1376\n",
       "        99             2\n",
       "        3              1\n",
       "2       1            112\n",
       "3       1             18\n",
       "4       1              4\n",
       "Name: rd_owner, dtype: int64"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "california_roadowner.groupby('fatals')['rd_owner'].value_counts()\n",
    "#Here we see that for atleast in California - the most dangerous travel mode is by Highway"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42ebc2e2-b5f0-4ba9-9f32-c16d3e10931c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "6ab6df5e-2137-4533-b356-71f4271b67e7",
   "metadata": {},
   "source": [
    "#### Compare states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "5dfdf2f1-9a7e-415c-ba1f-f519057ba000",
   "metadata": {},
   "outputs": [],
   "source": [
    "# lets take a look at the dnagers of driving in different states\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "22e5e6de-9f94-4f46-9184-a2015306f295",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "statename  fatals\n",
       "Alabama    1         786\n",
       "           2          52\n",
       "           3          12\n",
       "           4           2\n",
       "Alaska     1          44\n",
       "                    ... \n",
       "Wisconsin  2          43\n",
       "           3           2\n",
       "           4           2\n",
       "Wyoming    1         101\n",
       "           2          13\n",
       "Name: fatals, Length: 188, dtype: int64"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_accident.groupby('statename')['fatals'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "b7d5c748-ca9e-4a94-89da-0164cd519585",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "190.24468085106383"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "3d2bced7-97c0-47e6-82de-a34770c6153c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>statename</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alabama</th>\n",
       "      <td>852</td>\n",
       "      <td>1.096244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>53</td>\n",
       "      <td>1.207547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <td>967</td>\n",
       "      <td>1.089969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arkansas</th>\n",
       "      <td>585</td>\n",
       "      <td>1.090598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>3558</td>\n",
       "      <td>1.081225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>574</td>\n",
       "      <td>1.083624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Connecticut</th>\n",
       "      <td>279</td>\n",
       "      <td>1.057348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delaware</th>\n",
       "      <td>104</td>\n",
       "      <td>1.115385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>District of Columbia</th>\n",
       "      <td>34</td>\n",
       "      <td>1.058824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>3098</td>\n",
       "      <td>1.075210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Georgia</th>\n",
       "      <td>1522</td>\n",
       "      <td>1.093298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hawaii</th>\n",
       "      <td>81</td>\n",
       "      <td>1.049383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Idaho</th>\n",
       "      <td>188</td>\n",
       "      <td>1.138298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>1087</td>\n",
       "      <td>1.098436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indiana</th>\n",
       "      <td>815</td>\n",
       "      <td>1.100613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iowa</th>\n",
       "      <td>304</td>\n",
       "      <td>1.108553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kansas</th>\n",
       "      <td>382</td>\n",
       "      <td>1.115183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kentucky</th>\n",
       "      <td>709</td>\n",
       "      <td>1.100141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Louisiana</th>\n",
       "      <td>762</td>\n",
       "      <td>1.086614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maine</th>\n",
       "      <td>151</td>\n",
       "      <td>1.086093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maryland</th>\n",
       "      <td>540</td>\n",
       "      <td>1.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Massachusetts</th>\n",
       "      <td>327</td>\n",
       "      <td>1.048930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Michigan</th>\n",
       "      <td>1011</td>\n",
       "      <td>1.072206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Minnesota</th>\n",
       "      <td>369</td>\n",
       "      <td>1.067751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mississippi</th>\n",
       "      <td>687</td>\n",
       "      <td>1.094614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Missouri</th>\n",
       "      <td>914</td>\n",
       "      <td>1.079869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Montana</th>\n",
       "      <td>190</td>\n",
       "      <td>1.121053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nebraska</th>\n",
       "      <td>217</td>\n",
       "      <td>1.073733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nevada</th>\n",
       "      <td>293</td>\n",
       "      <td>1.081911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Hampshire</th>\n",
       "      <td>98</td>\n",
       "      <td>1.061224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Jersey</th>\n",
       "      <td>547</td>\n",
       "      <td>1.067642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Mexico</th>\n",
       "      <td>365</td>\n",
       "      <td>1.090411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>963</td>\n",
       "      <td>1.086189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Carolina</th>\n",
       "      <td>1412</td>\n",
       "      <td>1.089235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Dakota</th>\n",
       "      <td>96</td>\n",
       "      <td>1.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>1154</td>\n",
       "      <td>1.065858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oklahoma</th>\n",
       "      <td>599</td>\n",
       "      <td>1.088481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>461</td>\n",
       "      <td>1.101952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pennsylvania</th>\n",
       "      <td>1060</td>\n",
       "      <td>1.065094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rhode Island</th>\n",
       "      <td>66</td>\n",
       "      <td>1.015152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Carolina</th>\n",
       "      <td>962</td>\n",
       "      <td>1.106029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Dakota</th>\n",
       "      <td>132</td>\n",
       "      <td>1.068182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tennessee</th>\n",
       "      <td>1119</td>\n",
       "      <td>1.087578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>3520</td>\n",
       "      <td>1.100568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>256</td>\n",
       "      <td>1.078125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vermont</th>\n",
       "      <td>58</td>\n",
       "      <td>1.068966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Virginia</th>\n",
       "      <td>796</td>\n",
       "      <td>1.067839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Washington</th>\n",
       "      <td>525</td>\n",
       "      <td>1.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Virginia</th>\n",
       "      <td>249</td>\n",
       "      <td>1.072289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wisconsin</th>\n",
       "      <td>561</td>\n",
       "      <td>1.094474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wyoming</th>\n",
       "      <td>114</td>\n",
       "      <td>1.114035</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      count      mean\n",
       "statename                            \n",
       "Alabama                 852  1.096244\n",
       "Alaska                   53  1.207547\n",
       "Arizona                 967  1.089969\n",
       "Arkansas                585  1.090598\n",
       "California             3558  1.081225\n",
       "Colorado                574  1.083624\n",
       "Connecticut             279  1.057348\n",
       "Delaware                104  1.115385\n",
       "District of Columbia     34  1.058824\n",
       "Florida                3098  1.075210\n",
       "Georgia                1522  1.093298\n",
       "Hawaii                   81  1.049383\n",
       "Idaho                   188  1.138298\n",
       "Illinois               1087  1.098436\n",
       "Indiana                 815  1.100613\n",
       "Iowa                    304  1.108553\n",
       "Kansas                  382  1.115183\n",
       "Kentucky                709  1.100141\n",
       "Louisiana               762  1.086614\n",
       "Maine                   151  1.086093\n",
       "Maryland                540  1.050000\n",
       "Massachusetts           327  1.048930\n",
       "Michigan               1011  1.072206\n",
       "Minnesota               369  1.067751\n",
       "Mississippi             687  1.094614\n",
       "Missouri                914  1.079869\n",
       "Montana                 190  1.121053\n",
       "Nebraska                217  1.073733\n",
       "Nevada                  293  1.081911\n",
       "New Hampshire            98  1.061224\n",
       "New Jersey              547  1.067642\n",
       "New Mexico              365  1.090411\n",
       "New York                963  1.086189\n",
       "North Carolina         1412  1.089235\n",
       "North Dakota             96  1.041667\n",
       "Ohio                   1154  1.065858\n",
       "Oklahoma                599  1.088481\n",
       "Oregon                  461  1.101952\n",
       "Pennsylvania           1060  1.065094\n",
       "Rhode Island             66  1.015152\n",
       "South Carolina          962  1.106029\n",
       "South Dakota            132  1.068182\n",
       "Tennessee              1119  1.087578\n",
       "Texas                  3520  1.100568\n",
       "Utah                    256  1.078125\n",
       "Vermont                  58  1.068966\n",
       "Virginia                796  1.067839\n",
       "Washington              525  1.066667\n",
       "West Virginia           249  1.072289\n",
       "Wisconsin               561  1.094474\n",
       "Wyoming                 114  1.114035"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_accident.groupby('statename')['fatals'].agg(['count', 'mean'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9257819-7f7d-49a6-a0af-8e4621bf9943",
   "metadata": {},
   "source": [
    "#### What makes driving in florida so dangerous?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a755926e-00df-4e9b-9e8e-b64ae07dac9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "### ares of interest are tway_id and route\n",
    "### lets see how many accidents occur on each and how many vehicles pile up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "d2453cb9-2b15-4df9-85c2-5c87ded4d4b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_wyoming = df_accident.loc[df_accident['statename'] == 'Wyoming', :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "d808b0c3-b72d-4211-9a51-d0ddf7024a2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tway_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35652</th>\n",
       "      <td>I-80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35653</th>\n",
       "      <td>I-80 US-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35654</th>\n",
       "      <td>US-237 S 3RD ST</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35655</th>\n",
       "      <td>US-30 BI-80 E GRAND AVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35656</th>\n",
       "      <td>SR-59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35761</th>\n",
       "      <td>CR-109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35762</th>\n",
       "      <td>US-89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35763</th>\n",
       "      <td>SR-137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35764</th>\n",
       "      <td>US-30 I-80 BUS E LINCOLNWAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35765</th>\n",
       "      <td>I-25 US-87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>114 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           tway_id\n",
       "35652                         I-80\n",
       "35653                   I-80 US-30\n",
       "35654              US-237 S 3RD ST\n",
       "35655      US-30 BI-80 E GRAND AVE\n",
       "35656                        SR-59\n",
       "...                            ...\n",
       "35761                       CR-109\n",
       "35762                        US-89\n",
       "35763                       SR-137\n",
       "35764  US-30 I-80 BUS E LINCOLNWAY\n",
       "35765                   I-25 US-87\n",
       "\n",
       "[114 rows x 1 columns]"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_wyoming[['tway_id']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "0d786bbe-bba6-41ec-adae-8e222eed7a57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['I-80', 'I-80 US-30', 'US-237 S 3RD ST', 'US-30 BI-80 E GRAND AVE',\n",
       "       'SR-59', 'US-310 SR-789', 'SR-414', 'US-20-26',\n",
       "       'SR-258 WYOMING BLVD', 'SR-319', 'SR-172', 'US-20/26/87',\n",
       "       'I-80 / US-30', 'US-26 / US-287', 'CR-12', 'SR-154', 'CR-93',\n",
       "       'SR-450', 'US-287  S 3RD ST', 'SR-351', 'US-30  E LINCOLNWAY',\n",
       "       'SR-223 TERRY RANCH DR', 'SR-345', 'CR-1 IRVINE RD', 'METZ DR',\n",
       "       'CR-134', 'US-16 US-20 SR-789', 'SR-530', 'US-16', 'US-85 BUS',\n",
       "       'US-89', 'CR-408', 'US-14 US-16 US-20', 'US-26', 'SR-22', 'SR-251',\n",
       "       'US-189 US-191', 'US-191', 'US-287 SR-789', 'EVENTS DR',\n",
       "       'US-20 US-26', 'US-30 US-287', 'US-212', 'I-25 US-85 US-87',\n",
       "       'I-90', 'US-20 US-26 US-87', 'I-25 US-87', 'SR-24',\n",
       "       'CR-409 BLUE SKY RD', 'I-25 RAMP 17', 'CR-109S S GUERNSEY RD',\n",
       "       'PINE ST', 'CR-120-1 TELEPHONE RD', 'US-26 WHALEN ST', 'SR-132',\n",
       "       'CR-28', 'US-287', 'I-80 US-30 US-287', 'FRONTIER MALL DR',\n",
       "       'FOREST SERVICE RD 876', 'SR-287-789', 'US-85', 'US-14A',\n",
       "       'US-14 GREYBULL HWY', 'US-14', 'US-14A SR-114 COULTER AVE',\n",
       "       'SR-135', 'US-30 SR-89', 'CR-8 BELL RD', 'CR-136-1', 'CR-409',\n",
       "       'FSR-123', 'I-25', 'US-26 US-287', 'I-25 US-20 US-26 US-87',\n",
       "       'CR-17 ROGER CANYON RD', 'CR-335', 'CR-43  N SECOND ST',\n",
       "       'US-14A WY 114', 'SR-338', 'CR-109', 'SR-137',\n",
       "       'US-30 I-80 BUS E LINCOLNWAY'], dtype=object)"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_wyoming['tway_id'].unique()\n",
    "#lets see if we can figure out which road is the most dangerous"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "2b4b1acc-1de8-4d70-8aa5-4d42f0188711",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fatals  tway_id         \n",
       "1       I-80                8\n",
       "        I-80 US-30          5\n",
       "        I-25                3\n",
       "        I-90                3\n",
       "        SR-132              3\n",
       "                           ..\n",
       "2       US-287              1\n",
       "        US-287  S 3RD ST    1\n",
       "        US-30 SR-89         1\n",
       "        US-30 US-287        1\n",
       "        US-89               1\n",
       "Name: tway_id, Length: 88, dtype: int64"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Itlooks like i-80 is the most dangerous route\n",
    "df_wyoming.groupby('fatals')['tway_id'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "d4328eb3-9d6e-4740-bd1b-8b29a756303d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>statename</th>\n",
       "      <th>st_case</th>\n",
       "      <th>ve_total</th>\n",
       "      <th>ve_forms</th>\n",
       "      <th>pvh_invl</th>\n",
       "      <th>peds</th>\n",
       "      <th>persons</th>\n",
       "      <th>permvit</th>\n",
       "      <th>pernotmvit</th>\n",
       "      <th>...</th>\n",
       "      <th>arr_hour</th>\n",
       "      <th>arr_hourname</th>\n",
       "      <th>arr_min</th>\n",
       "      <th>arr_minname</th>\n",
       "      <th>hosp_hr</th>\n",
       "      <th>hosp_hrname</th>\n",
       "      <th>hosp_mn</th>\n",
       "      <th>hosp_mnname</th>\n",
       "      <th>fatals</th>\n",
       "      <th>drunk_dr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35652</th>\n",
       "      <td>56</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>560001</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>7:00am-7:59am</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Hospital Arrival Time</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35662</th>\n",
       "      <td>56</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>560011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1:00am-1:59am</td>\n",
       "      <td>37</td>\n",
       "      <td>37</td>\n",
       "      <td>88</td>\n",
       "      <td>Not Applicable (Not Transported)</td>\n",
       "      <td>88</td>\n",
       "      <td>Not Applicable (Not Transported)</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35672</th>\n",
       "      <td>56</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>560021</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Hour</td>\n",
       "      <td>98</td>\n",
       "      <td>Unknown if Arrived</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>98</td>\n",
       "      <td>Unknown if Transported</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35674</th>\n",
       "      <td>56</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>560023</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>12:00pm-12:59pm</td>\n",
       "      <td>59</td>\n",
       "      <td>59</td>\n",
       "      <td>88</td>\n",
       "      <td>Not Applicable (Not Transported)</td>\n",
       "      <td>88</td>\n",
       "      <td>Not Applicable (Not Transported)</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35686</th>\n",
       "      <td>56</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>560035</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>11:00am-11:59am</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Hospital Arrival Time</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35716</th>\n",
       "      <td>56</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>560065</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Scene Arrival Hour</td>\n",
       "      <td>98</td>\n",
       "      <td>Unknown if Arrived</td>\n",
       "      <td>88</td>\n",
       "      <td>Not Applicable (Not Transported)</td>\n",
       "      <td>88</td>\n",
       "      <td>Not Applicable (Not Transported)</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35733</th>\n",
       "      <td>56</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>560083</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>8:00pm-8:59pm</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown EMS Hospital Arrival Time</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35740</th>\n",
       "      <td>56</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>560090</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>16</td>\n",
       "      <td>4:00pm-4:59pm</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>88</td>\n",
       "      <td>Not Applicable (Not Transported)</td>\n",
       "      <td>88</td>\n",
       "      <td>Not Applicable (Not Transported)</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       state statename  st_case  ve_total  ve_forms  pvh_invl  peds  persons  \\\n",
       "35652     56   Wyoming   560001         1         1         0     0        2   \n",
       "35662     56   Wyoming   560011         1         1         0     0        1   \n",
       "35672     56   Wyoming   560021         1         1         0     0        1   \n",
       "35674     56   Wyoming   560023         1         1         0     0        1   \n",
       "35686     56   Wyoming   560035         1         1         0     0        2   \n",
       "35716     56   Wyoming   560065         1         1         0     0        1   \n",
       "35733     56   Wyoming   560083         3         3         0     0        4   \n",
       "35740     56   Wyoming   560090         6         6         0     0        6   \n",
       "\n",
       "       permvit  pernotmvit  ...  arr_hour                    arr_hourname  \\\n",
       "35652        2           0  ...         7                   7:00am-7:59am   \n",
       "35662        1           0  ...         1                   1:00am-1:59am   \n",
       "35672        1           0  ...        99  Unknown EMS Scene Arrival Hour   \n",
       "35674        1           0  ...        12                 12:00pm-12:59pm   \n",
       "35686        2           0  ...        11                 11:00am-11:59am   \n",
       "35716        1           0  ...        99  Unknown EMS Scene Arrival Hour   \n",
       "35733        4           0  ...        20                   8:00pm-8:59pm   \n",
       "35740        6           0  ...        16                   4:00pm-4:59pm   \n",
       "\n",
       "       arr_min         arr_minname  hosp_hr                       hosp_hrname  \\\n",
       "35652       30                  30       99                           Unknown   \n",
       "35662       37                  37       88  Not Applicable (Not Transported)   \n",
       "35672       98  Unknown if Arrived       99                           Unknown   \n",
       "35674       59                  59       88  Not Applicable (Not Transported)   \n",
       "35686       18                  18       99                           Unknown   \n",
       "35716       98  Unknown if Arrived       88  Not Applicable (Not Transported)   \n",
       "35733       42                  42       99                           Unknown   \n",
       "35740       39                  39       88  Not Applicable (Not Transported)   \n",
       "\n",
       "       hosp_mn                        hosp_mnname  fatals  drunk_dr  \n",
       "35652       99  Unknown EMS Hospital Arrival Time       1         0  \n",
       "35662       88   Not Applicable (Not Transported)       1         0  \n",
       "35672       98             Unknown if Transported       1         1  \n",
       "35674       88   Not Applicable (Not Transported)       1         0  \n",
       "35686       99  Unknown EMS Hospital Arrival Time       1         0  \n",
       "35716       88   Not Applicable (Not Transported)       1         0  \n",
       "35733       99  Unknown EMS Hospital Arrival Time       1         0  \n",
       "35740       88   Not Applicable (Not Transported)       1         0  \n",
       "\n",
       "[8 rows x 80 columns]"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets take a closer look at i-80\n",
    "df_wyoming[(df_wyoming['tway_id'] == 'I-80')]"
   ]
  },
  {
   "cell_type": "raw",
   "id": "eb2a747a-adfd-42f1-893e-775987534dbd",
   "metadata": {},
   "source": [
    "There doesn't seem to be any particular pattern with regards to time the accident occured"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "13b3766e-ec1e-44d3-9ba7-d5743b0968ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>weather</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35652</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35653</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35654</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35655</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35656</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35761</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35762</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35763</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35764</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35765</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>114 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       weather\n",
       "35652        1\n",
       "35653        1\n",
       "35654        1\n",
       "35655        1\n",
       "35656       10\n",
       "...        ...\n",
       "35761        4\n",
       "35762        1\n",
       "35763        4\n",
       "35764        1\n",
       "35765        6\n",
       "\n",
       "[114 rows x 1 columns]"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lets see if weather plays a role in accident patterns\n",
    "df_wyoming[['weather']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "1b66db7a-7b6a-47cf-83d5-c849f3b9ff65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1, 10, 11,  4,  5,  2,  3,  6], dtype=int64)"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_wyoming['weather'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "8c9f2009-5a7b-4eae-b938-95a569fe4a64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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>count</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weather</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>91</td>\n",
       "      <td>1.120879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>1.090909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count      mean\n",
       "weather                 \n",
       "1           91  1.120879\n",
       "2            1  2.000000\n",
       "3            1  1.000000\n",
       "4           11  1.090909\n",
       "5            1  1.000000\n",
       "6            2  1.000000\n",
       "10           6  1.000000\n",
       "11           1  1.000000"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_wyoming.groupby('weather')['fatals'].agg(['count', 'mean'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "b382cf55-f3b0-4abb-b396-0300ac2bd20c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "weather  fatals\n",
       "1        1         80\n",
       "         2         11\n",
       "2        2          1\n",
       "3        1          1\n",
       "4        1         10\n",
       "         2          1\n",
       "5        1          1\n",
       "6        1          2\n",
       "10       1          6\n",
       "11       1          1\n",
       "Name: fatals, dtype: int64"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_wyoming.groupby('weather')['fatals'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "5be95fd2-586e-4978-a99c-64d7ae80f48c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 114 entries, 35652 to 35765\n",
      "Data columns (total 80 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   state         114 non-null    int64  \n",
      " 1   statename     114 non-null    object \n",
      " 2   st_case       114 non-null    int64  \n",
      " 3   ve_total      114 non-null    int64  \n",
      " 4   ve_forms      114 non-null    int64  \n",
      " 5   pvh_invl      114 non-null    int64  \n",
      " 6   peds          114 non-null    int64  \n",
      " 7   persons       114 non-null    int64  \n",
      " 8   permvit       114 non-null    int64  \n",
      " 9   pernotmvit    114 non-null    int64  \n",
      " 10  county        114 non-null    int64  \n",
      " 11  countyname    114 non-null    object \n",
      " 12  city          114 non-null    int64  \n",
      " 13  cityname      114 non-null    object \n",
      " 14  day           114 non-null    int64  \n",
      " 15  dayname       114 non-null    int64  \n",
      " 16  month         114 non-null    int64  \n",
      " 17  monthname     114 non-null    object \n",
      " 18  year          114 non-null    int64  \n",
      " 19  day_week      114 non-null    int64  \n",
      " 20  day_weekname  114 non-null    object \n",
      " 21  hour          114 non-null    int64  \n",
      " 22  hourname      114 non-null    object \n",
      " 23  minute        114 non-null    int64  \n",
      " 24  minutename    114 non-null    object \n",
      " 25  nhs           114 non-null    int64  \n",
      " 26  nhsname       114 non-null    object \n",
      " 27  route         114 non-null    int64  \n",
      " 28  routename     114 non-null    object \n",
      " 29  tway_id       114 non-null    object \n",
      " 30  rur_urb       114 non-null    int64  \n",
      " 31  rur_urbname   114 non-null    object \n",
      " 32  func_sys      114 non-null    int64  \n",
      " 33  func_sysname  114 non-null    object \n",
      " 34  rd_owner      114 non-null    int64  \n",
      " 35  rd_ownername  114 non-null    object \n",
      " 36  milept        114 non-null    int64  \n",
      " 37  mileptname    114 non-null    object \n",
      " 38  latitude      114 non-null    float64\n",
      " 39  latitudename  114 non-null    object \n",
      " 40  longitud      114 non-null    float64\n",
      " 41  longitudname  114 non-null    object \n",
      " 42  sp_jur        114 non-null    int64  \n",
      " 43  sp_jurname    114 non-null    object \n",
      " 44  harm_ev       114 non-null    int64  \n",
      " 45  harm_evname   114 non-null    object \n",
      " 46  man_coll      114 non-null    int64  \n",
      " 47  man_collname  114 non-null    object \n",
      " 48  reljct1       114 non-null    int64  \n",
      " 49  reljct1name   114 non-null    object \n",
      " 50  reljct2       114 non-null    int64  \n",
      " 51  reljct2name   114 non-null    object \n",
      " 52  typ_int       114 non-null    int64  \n",
      " 53  typ_intname   114 non-null    object \n",
      " 54  wrk_zone      114 non-null    int64  \n",
      " 55  wrk_zonename  114 non-null    object \n",
      " 56  rel_road      114 non-null    int64  \n",
      " 57  rel_roadname  114 non-null    object \n",
      " 58  lgt_cond      114 non-null    int64  \n",
      " 59  lgt_condname  114 non-null    object \n",
      " 60  weather       114 non-null    int64  \n",
      " 61  weathername   114 non-null    object \n",
      " 62  sch_bus       114 non-null    int64  \n",
      " 63  sch_busname   114 non-null    object \n",
      " 64  rail          114 non-null    object \n",
      " 65  railname      114 non-null    object \n",
      " 66  not_hour      114 non-null    int64  \n",
      " 67  not_hourname  114 non-null    object \n",
      " 68  not_min       114 non-null    int64  \n",
      " 69  not_minname   114 non-null    object \n",
      " 70  arr_hour      114 non-null    int64  \n",
      " 71  arr_hourname  114 non-null    object \n",
      " 72  arr_min       114 non-null    int64  \n",
      " 73  arr_minname   114 non-null    object \n",
      " 74  hosp_hr       114 non-null    int64  \n",
      " 75  hosp_hrname   114 non-null    object \n",
      " 76  hosp_mn       114 non-null    int64  \n",
      " 77  hosp_mnname   114 non-null    object \n",
      " 78  fatals        114 non-null    int64  \n",
      " 79  drunk_dr      114 non-null    int64  \n",
      "dtypes: float64(2), int64(43), object(35)\n",
      "memory usage: 72.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df_wyoming.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "9a44b9fa-b9f7-4fc8-ac19-b812889e09ab",
   "metadata": {},
   "outputs": [
    {
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       "<p>114 rows × 1 columns</p>\n",
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      "text/plain": [
       "       route\n",
       "35652      1\n",
       "35653      1\n",
       "35654      2\n",
       "35655      2\n",
       "35656      3\n",
       "...      ...\n",
       "35761      4\n",
       "35762      2\n",
       "35763      3\n",
       "35764      2\n",
       "35765      1\n",
       "\n",
       "[114 rows x 1 columns]"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_wyoming[['route']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1106be66-c93f-49da-b015-80782bd1e7c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_wyoming[['tway_id']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79f2195b-e8e8-401d-8559-8d7f01f7728f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20874db7-1f15-4a3f-b8e9-ace2189f9946",
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "986b578a-d61b-4692-9155-e1df9187b74e",
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e8c9ef16-9766-44d5-8b96-bc65999953c1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0608fce9-8630-4578-97f3-d67fb4974493",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d2700b9-cddf-4675-ae48-fd54f9d011f2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45e51306-c252-4187-9c47-ad6a09a35775",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e33d99c6-7c66-4b8f-ab3b-ca8cea295337",
   "metadata": {},
   "source": [
    "# Correlation Chart "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e9fe8d6b-b575-49d7-95db-5438734b28ea",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>st_case</th>\n",
       "      <th>ve_total</th>\n",
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       "      <th>pvh_invl</th>\n",
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       "    <tr>\n",
       "      <th>st_case</th>\n",
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       "      <td>-0.036379</td>\n",
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       "      <td>0.033472</td>\n",
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       "    <tr>\n",
       "      <th>ve_total</th>\n",
       "      <td>0.002132</td>\n",
       "      <td>0.002236</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.927414</td>\n",
       "      <td>0.338424</td>\n",
       "      <td>-0.204354</td>\n",
       "      <td>0.631536</td>\n",
       "      <td>0.622845</td>\n",
       "      <td>-0.161039</td>\n",
       "      <td>0.013819</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.022173</td>\n",
       "      <td>0.015563</td>\n",
       "      <td>-0.008226</td>\n",
       "      <td>-0.009710</td>\n",
       "      <td>-0.006649</td>\n",
       "      <td>-0.005580</td>\n",
       "      <td>-0.041900</td>\n",
       "      <td>-0.033069</td>\n",
       "      <td>0.130481</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>ve_forms</th>\n",
       "      <td>0.000546</td>\n",
       "      <td>0.000624</td>\n",
       "      <td>0.927414</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.038106</td>\n",
       "      <td>-0.238065</td>\n",
       "      <td>0.665099</td>\n",
       "      <td>0.668735</td>\n",
       "      <td>-0.232475</td>\n",
       "      <td>0.011161</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.022846</td>\n",
       "      <td>0.017711</td>\n",
       "      <td>-0.011250</td>\n",
       "      <td>-0.013661</td>\n",
       "      <td>-0.010240</td>\n",
       "      <td>-0.010137</td>\n",
       "      <td>-0.044531</td>\n",
       "      <td>-0.036741</td>\n",
       "      <td>0.132417</td>\n",
       "      <td>-0.030830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pvh_invl</th>\n",
       "      <td>0.004321</td>\n",
       "      <td>0.004405</td>\n",
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       "      <td>-0.038106</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>peds</th>\n",
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       "      <td>0.039447</td>\n",
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       "      <td>-0.194928</td>\n",
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       "    <tr>\n",
       "      <th>persons</th>\n",
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       "      <td>-0.003829</td>\n",
       "      <td>0.631536</td>\n",
       "      <td>0.665099</td>\n",
       "      <td>0.013966</td>\n",
       "      <td>-0.221193</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.996115</td>\n",
       "      <td>-0.189258</td>\n",
       "      <td>0.008456</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.030204</td>\n",
       "      <td>0.046587</td>\n",
       "      <td>-0.024323</td>\n",
       "      <td>-0.024829</td>\n",
       "      <td>-0.025549</td>\n",
       "      <td>-0.026043</td>\n",
       "      <td>-0.068591</td>\n",
       "      <td>-0.056525</td>\n",
       "      <td>0.284210</td>\n",
       "      <td>-0.012359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>permvit</th>\n",
       "      <td>-0.003721</td>\n",
       "      <td>-0.003659</td>\n",
       "      <td>0.622845</td>\n",
       "      <td>0.668735</td>\n",
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       "      <td>0.008672</td>\n",
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       "      <td>-0.026666</td>\n",
       "      <td>-0.069724</td>\n",
       "      <td>-0.057937</td>\n",
       "      <td>0.284496</td>\n",
       "      <td>-0.012794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pernotmvit</th>\n",
       "      <td>-0.052634</td>\n",
       "      <td>-0.052242</td>\n",
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       "      <td>-0.232475</td>\n",
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       "      <td>-0.189258</td>\n",
       "      <td>-0.216825</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.019719</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.027159</td>\n",
       "      <td>-0.000262</td>\n",
       "      <td>0.068391</td>\n",
       "      <td>0.062270</td>\n",
       "      <td>0.069508</td>\n",
       "      <td>0.064226</td>\n",
       "      <td>0.044931</td>\n",
       "      <td>0.041601</td>\n",
       "      <td>-0.057668</td>\n",
       "      <td>-0.184322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>county</th>\n",
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       "      <td>0.268991</td>\n",
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       "      <td>0.011161</td>\n",
       "      <td>0.008841</td>\n",
       "      <td>-0.020057</td>\n",
       "      <td>0.008456</td>\n",
       "      <td>0.008672</td>\n",
       "      <td>-0.019719</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.065921</td>\n",
       "      <td>-0.005752</td>\n",
       "      <td>0.126787</td>\n",
       "      <td>0.116379</td>\n",
       "      <td>0.122436</td>\n",
       "      <td>0.115700</td>\n",
       "      <td>0.048933</td>\n",
       "      <td>0.045879</td>\n",
       "      <td>0.010390</td>\n",
       "      <td>-0.008209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <td>-0.005058</td>\n",
       "      <td>-0.004792</td>\n",
       "      <td>0.019239</td>\n",
       "      <td>0.006434</td>\n",
       "      <td>0.035212</td>\n",
       "      <td>0.068019</td>\n",
       "      <td>0.000679</td>\n",
       "      <td>0.000110</td>\n",
       "      <td>0.066602</td>\n",
       "      <td>0.077573</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.023407</td>\n",
       "      <td>0.007142</td>\n",
       "      <td>-0.115219</td>\n",
       "      <td>-0.110755</td>\n",
       "      <td>-0.108284</td>\n",
       "      <td>-0.103042</td>\n",
       "      <td>-0.102837</td>\n",
       "      <td>-0.098481</td>\n",
       "      <td>-0.013386</td>\n",
       "      <td>-0.004789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
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       "      <td>0.005771</td>\n",
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       "      <td>...</td>\n",
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       "      <td>0.010480</td>\n",
       "      <td>0.011341</td>\n",
       "      <td>0.009952</td>\n",
       "      <td>0.010830</td>\n",
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       "      <td>0.001591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dayname</th>\n",
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       "      <td>0.005771</td>\n",
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       "      <td>0.006039</td>\n",
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       "      <td>0.009952</td>\n",
       "      <td>0.010830</td>\n",
       "      <td>0.002469</td>\n",
       "      <td>0.001591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <td>0.008881</td>\n",
       "      <td>0.011353</td>\n",
       "      <td>0.012440</td>\n",
       "      <td>0.012820</td>\n",
       "      <td>0.000984</td>\n",
       "      <td>-0.002370</td>\n",
       "      <td>0.010812</td>\n",
       "      <td>0.010856</td>\n",
       "      <td>-0.002304</td>\n",
       "      <td>0.001637</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003113</td>\n",
       "      <td>-0.007750</td>\n",
       "      <td>-0.017097</td>\n",
       "      <td>-0.012523</td>\n",
       "      <td>-0.014489</td>\n",
       "      <td>-0.015162</td>\n",
       "      <td>-0.004989</td>\n",
       "      <td>-0.005172</td>\n",
       "      <td>0.002160</td>\n",
       "      <td>-0.009315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day_week</th>\n",
       "      <td>0.004510</td>\n",
       "      <td>0.004419</td>\n",
       "      <td>0.005269</td>\n",
       "      <td>0.010232</td>\n",
       "      <td>-0.011663</td>\n",
       "      <td>-0.004775</td>\n",
       "      <td>0.006489</td>\n",
       "      <td>0.006953</td>\n",
       "      <td>-0.005981</td>\n",
       "      <td>-0.001423</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.007593</td>\n",
       "      <td>0.003275</td>\n",
       "      <td>-0.008942</td>\n",
       "      <td>-0.009520</td>\n",
       "      <td>-0.010242</td>\n",
       "      <td>-0.014078</td>\n",
       "      <td>-0.004172</td>\n",
       "      <td>-0.007865</td>\n",
       "      <td>0.004305</td>\n",
       "      <td>0.012765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hour</th>\n",
       "      <td>-0.017286</td>\n",
       "      <td>-0.017344</td>\n",
       "      <td>-0.023218</td>\n",
       "      <td>-0.015884</td>\n",
       "      <td>-0.022069</td>\n",
       "      <td>0.034681</td>\n",
       "      <td>-0.002114</td>\n",
       "      <td>-0.000770</td>\n",
       "      <td>0.028684</td>\n",
       "      <td>-0.020188</td>\n",
       "      <td>...</td>\n",
       "      <td>0.043733</td>\n",
       "      <td>-0.011840</td>\n",
       "      <td>0.028710</td>\n",
       "      <td>-0.007179</td>\n",
       "      <td>0.028603</td>\n",
       "      <td>-0.009889</td>\n",
       "      <td>0.012455</td>\n",
       "      <td>-0.004776</td>\n",
       "      <td>-0.015614</td>\n",
       "      <td>-0.005191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>minute</th>\n",
       "      <td>-0.005555</td>\n",
       "      <td>-0.005555</td>\n",
       "      <td>-0.009670</td>\n",
       "      <td>-0.007860</td>\n",
       "      <td>-0.006061</td>\n",
       "      <td>0.001896</td>\n",
       "      <td>-0.009426</td>\n",
       "      <td>-0.009294</td>\n",
       "      <td>0.001298</td>\n",
       "      <td>-0.005579</td>\n",
       "      <td>...</td>\n",
       "      <td>0.013393</td>\n",
       "      <td>0.006502</td>\n",
       "      <td>-0.001394</td>\n",
       "      <td>0.138023</td>\n",
       "      <td>-0.006184</td>\n",
       "      <td>0.030111</td>\n",
       "      <td>0.000353</td>\n",
       "      <td>-0.014384</td>\n",
       "      <td>-0.004226</td>\n",
       "      <td>0.003800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nhs</th>\n",
       "      <td>-0.161445</td>\n",
       "      <td>-0.161220</td>\n",
       "      <td>0.038031</td>\n",
       "      <td>0.042204</td>\n",
       "      <td>-0.004571</td>\n",
       "      <td>0.026836</td>\n",
       "      <td>0.027128</td>\n",
       "      <td>0.026174</td>\n",
       "      <td>0.028862</td>\n",
       "      <td>-0.032672</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.016361</td>\n",
       "      <td>-0.007501</td>\n",
       "      <td>0.003458</td>\n",
       "      <td>0.003719</td>\n",
       "      <td>0.002034</td>\n",
       "      <td>0.002071</td>\n",
       "      <td>0.044818</td>\n",
       "      <td>0.044656</td>\n",
       "      <td>-0.000576</td>\n",
       "      <td>-0.037539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>route</th>\n",
       "      <td>-0.134189</td>\n",
       "      <td>-0.133097</td>\n",
       "      <td>-0.101240</td>\n",
       "      <td>-0.122595</td>\n",
       "      <td>0.037949</td>\n",
       "      <td>0.083119</td>\n",
       "      <td>-0.101708</td>\n",
       "      <td>-0.098896</td>\n",
       "      <td>0.069204</td>\n",
       "      <td>0.002560</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.043050</td>\n",
       "      <td>0.004217</td>\n",
       "      <td>0.138460</td>\n",
       "      <td>0.133757</td>\n",
       "      <td>0.141296</td>\n",
       "      <td>0.135126</td>\n",
       "      <td>0.095884</td>\n",
       "      <td>0.094872</td>\n",
       "      <td>-0.053643</td>\n",
       "      <td>0.017690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rur_urb</th>\n",
       "      <td>-0.127636</td>\n",
       "      <td>-0.126878</td>\n",
       "      <td>0.020788</td>\n",
       "      <td>0.008673</td>\n",
       "      <td>0.033718</td>\n",
       "      <td>0.143456</td>\n",
       "      <td>-0.018685</td>\n",
       "      <td>-0.019300</td>\n",
       "      <td>0.138415</td>\n",
       "      <td>-0.027060</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001965</td>\n",
       "      <td>-0.010567</td>\n",
       "      <td>0.083362</td>\n",
       "      <td>0.080572</td>\n",
       "      <td>0.086302</td>\n",
       "      <td>0.084600</td>\n",
       "      <td>0.035148</td>\n",
       "      <td>0.037024</td>\n",
       "      <td>-0.049682</td>\n",
       "      <td>-0.042845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>func_sys</th>\n",
       "      <td>-0.137952</td>\n",
       "      <td>-0.137875</td>\n",
       "      <td>-0.043508</td>\n",
       "      <td>-0.044597</td>\n",
       "      <td>-0.004040</td>\n",
       "      <td>0.002034</td>\n",
       "      <td>-0.032649</td>\n",
       "      <td>-0.031427</td>\n",
       "      <td>-0.002282</td>\n",
       "      <td>-0.036841</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003675</td>\n",
       "      <td>-0.004667</td>\n",
       "      <td>-0.005461</td>\n",
       "      <td>-0.003757</td>\n",
       "      <td>-0.005790</td>\n",
       "      <td>-0.005275</td>\n",
       "      <td>0.043162</td>\n",
       "      <td>0.042963</td>\n",
       "      <td>-0.020080</td>\n",
       "      <td>-0.008393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rd_owner</th>\n",
       "      <td>0.003153</td>\n",
       "      <td>0.005861</td>\n",
       "      <td>-0.020989</td>\n",
       "      <td>-0.031039</td>\n",
       "      <td>0.022012</td>\n",
       "      <td>0.067076</td>\n",
       "      <td>-0.030812</td>\n",
       "      <td>-0.030225</td>\n",
       "      <td>0.061710</td>\n",
       "      <td>0.214157</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.088255</td>\n",
       "      <td>-0.007043</td>\n",
       "      <td>0.327520</td>\n",
       "      <td>0.313141</td>\n",
       "      <td>0.326321</td>\n",
       "      <td>0.312598</td>\n",
       "      <td>0.220912</td>\n",
       "      <td>0.212879</td>\n",
       "      <td>-0.013036</td>\n",
       "      <td>-0.014076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>milept</th>\n",
       "      <td>-0.145114</td>\n",
       "      <td>-0.144787</td>\n",
       "      <td>0.001555</td>\n",
       "      <td>0.005219</td>\n",
       "      <td>-0.008974</td>\n",
       "      <td>0.024776</td>\n",
       "      <td>-0.001527</td>\n",
       "      <td>-0.000945</td>\n",
       "      <td>0.021707</td>\n",
       "      <td>-0.119185</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.105821</td>\n",
       "      <td>0.001709</td>\n",
       "      <td>0.158323</td>\n",
       "      <td>0.151633</td>\n",
       "      <td>0.162311</td>\n",
       "      <td>0.159032</td>\n",
       "      <td>0.103482</td>\n",
       "      <td>0.101271</td>\n",
       "      <td>-0.002374</td>\n",
       "      <td>-0.041111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latitude</th>\n",
       "      <td>0.129910</td>\n",
       "      <td>0.128247</td>\n",
       "      <td>-0.023359</td>\n",
       "      <td>-0.026976</td>\n",
       "      <td>0.005458</td>\n",
       "      <td>-0.039074</td>\n",
       "      <td>-0.021188</td>\n",
       "      <td>-0.021364</td>\n",
       "      <td>-0.036829</td>\n",
       "      <td>-0.128655</td>\n",
       "      <td>...</td>\n",
       "      <td>0.152658</td>\n",
       "      <td>-0.000080</td>\n",
       "      <td>-0.198456</td>\n",
       "      <td>-0.182968</td>\n",
       "      <td>-0.186058</td>\n",
       "      <td>-0.176366</td>\n",
       "      <td>-0.083125</td>\n",
       "      <td>-0.080222</td>\n",
       "      <td>-0.005958</td>\n",
       "      <td>0.018220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>longitud</th>\n",
       "      <td>0.006878</td>\n",
       "      <td>0.006623</td>\n",
       "      <td>-0.021489</td>\n",
       "      <td>-0.019685</td>\n",
       "      <td>-0.007887</td>\n",
       "      <td>-0.000611</td>\n",
       "      <td>-0.016936</td>\n",
       "      <td>-0.017251</td>\n",
       "      <td>0.000282</td>\n",
       "      <td>0.002579</td>\n",
       "      <td>...</td>\n",
       "      <td>0.087085</td>\n",
       "      <td>-0.001913</td>\n",
       "      <td>0.002578</td>\n",
       "      <td>0.004182</td>\n",
       "      <td>0.002394</td>\n",
       "      <td>0.002375</td>\n",
       "      <td>0.010569</td>\n",
       "      <td>0.011277</td>\n",
       "      <td>-0.006209</td>\n",
       "      <td>-0.018916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sp_jur</th>\n",
       "      <td>-0.015314</td>\n",
       "      <td>-0.015448</td>\n",
       "      <td>-0.028141</td>\n",
       "      <td>-0.027780</td>\n",
       "      <td>-0.005293</td>\n",
       "      <td>-0.005110</td>\n",
       "      <td>-0.018898</td>\n",
       "      <td>-0.018480</td>\n",
       "      <td>-0.006378</td>\n",
       "      <td>-0.017160</td>\n",
       "      <td>...</td>\n",
       "      <td>0.051925</td>\n",
       "      <td>0.001361</td>\n",
       "      <td>-0.005014</td>\n",
       "      <td>-0.002237</td>\n",
       "      <td>-0.002720</td>\n",
       "      <td>-0.001389</td>\n",
       "      <td>0.004715</td>\n",
       "      <td>0.003836</td>\n",
       "      <td>-0.005906</td>\n",
       "      <td>0.003556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>harm_ev</th>\n",
       "      <td>0.017543</td>\n",
       "      <td>0.017305</td>\n",
       "      <td>-0.263083</td>\n",
       "      <td>-0.280766</td>\n",
       "      <td>0.003494</td>\n",
       "      <td>-0.295731</td>\n",
       "      <td>-0.174299</td>\n",
       "      <td>-0.173952</td>\n",
       "      <td>-0.284258</td>\n",
       "      <td>0.014871</td>\n",
       "      <td>...</td>\n",
       "      <td>0.045552</td>\n",
       "      <td>-0.017234</td>\n",
       "      <td>-0.013975</td>\n",
       "      <td>-0.009282</td>\n",
       "      <td>-0.018754</td>\n",
       "      <td>-0.014964</td>\n",
       "      <td>0.013903</td>\n",
       "      <td>0.013831</td>\n",
       "      <td>-0.000204</td>\n",
       "      <td>0.148828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>man_coll</th>\n",
       "      <td>-0.034638</td>\n",
       "      <td>-0.034682</td>\n",
       "      <td>0.231657</td>\n",
       "      <td>0.251308</td>\n",
       "      <td>-0.013342</td>\n",
       "      <td>-0.135867</td>\n",
       "      <td>0.169513</td>\n",
       "      <td>0.171208</td>\n",
       "      <td>-0.133320</td>\n",
       "      <td>-0.002078</td>\n",
       "      <td>...</td>\n",
       "      <td>0.082372</td>\n",
       "      <td>0.006966</td>\n",
       "      <td>0.011518</td>\n",
       "      <td>0.008891</td>\n",
       "      <td>0.011584</td>\n",
       "      <td>0.010268</td>\n",
       "      <td>-0.006629</td>\n",
       "      <td>-0.005170</td>\n",
       "      <td>0.040602</td>\n",
       "      <td>-0.027229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reljct1</th>\n",
       "      <td>-0.055950</td>\n",
       "      <td>-0.055943</td>\n",
       "      <td>-0.013383</td>\n",
       "      <td>-0.010683</td>\n",
       "      <td>-0.008878</td>\n",
       "      <td>0.004985</td>\n",
       "      <td>-0.017257</td>\n",
       "      <td>-0.017933</td>\n",
       "      <td>0.006765</td>\n",
       "      <td>-0.002273</td>\n",
       "      <td>...</td>\n",
       "      <td>0.124506</td>\n",
       "      <td>-0.004137</td>\n",
       "      <td>0.029740</td>\n",
       "      <td>0.028294</td>\n",
       "      <td>0.030514</td>\n",
       "      <td>0.027714</td>\n",
       "      <td>0.013397</td>\n",
       "      <td>0.013217</td>\n",
       "      <td>-0.004825</td>\n",
       "      <td>-0.020145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reljct2</th>\n",
       "      <td>-0.050519</td>\n",
       "      <td>-0.050475</td>\n",
       "      <td>0.001935</td>\n",
       "      <td>0.004977</td>\n",
       "      <td>-0.007351</td>\n",
       "      <td>0.021027</td>\n",
       "      <td>-0.007233</td>\n",
       "      <td>-0.008078</td>\n",
       "      <td>0.022672</td>\n",
       "      <td>-0.004267</td>\n",
       "      <td>...</td>\n",
       "      <td>0.127947</td>\n",
       "      <td>-0.002403</td>\n",
       "      <td>0.038882</td>\n",
       "      <td>0.036569</td>\n",
       "      <td>0.039537</td>\n",
       "      <td>0.036289</td>\n",
       "      <td>0.024182</td>\n",
       "      <td>0.023280</td>\n",
       "      <td>-0.010556</td>\n",
       "      <td>-0.030788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>typ_int</th>\n",
       "      <td>-0.063384</td>\n",
       "      <td>-0.063433</td>\n",
       "      <td>0.001920</td>\n",
       "      <td>-0.000749</td>\n",
       "      <td>0.007015</td>\n",
       "      <td>0.015919</td>\n",
       "      <td>-0.010332</td>\n",
       "      <td>-0.009709</td>\n",
       "      <td>0.013062</td>\n",
       "      <td>-0.011733</td>\n",
       "      <td>...</td>\n",
       "      <td>0.136901</td>\n",
       "      <td>-0.000701</td>\n",
       "      <td>0.026002</td>\n",
       "      <td>0.022888</td>\n",
       "      <td>0.025790</td>\n",
       "      <td>0.022623</td>\n",
       "      <td>0.012782</td>\n",
       "      <td>0.015278</td>\n",
       "      <td>-0.008434</td>\n",
       "      <td>-0.018645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wrk_zone</th>\n",
       "      <td>0.008925</td>\n",
       "      <td>0.009103</td>\n",
       "      <td>0.037186</td>\n",
       "      <td>0.026705</td>\n",
       "      <td>0.032164</td>\n",
       "      <td>0.007482</td>\n",
       "      <td>0.021601</td>\n",
       "      <td>0.018403</td>\n",
       "      <td>0.017599</td>\n",
       "      <td>0.024832</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.012491</td>\n",
       "      <td>-0.004703</td>\n",
       "      <td>0.014244</td>\n",
       "      <td>0.015204</td>\n",
       "      <td>0.015028</td>\n",
       "      <td>0.013569</td>\n",
       "      <td>0.010916</td>\n",
       "      <td>0.012446</td>\n",
       "      <td>0.005956</td>\n",
       "      <td>0.004881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rel_road</th>\n",
       "      <td>-0.030537</td>\n",
       "      <td>-0.030666</td>\n",
       "      <td>-0.140307</td>\n",
       "      <td>-0.154949</td>\n",
       "      <td>0.014973</td>\n",
       "      <td>-0.039420</td>\n",
       "      <td>-0.106496</td>\n",
       "      <td>-0.106736</td>\n",
       "      <td>-0.037710</td>\n",
       "      <td>-0.012589</td>\n",
       "      <td>...</td>\n",
       "      <td>0.147629</td>\n",
       "      <td>-0.009071</td>\n",
       "      <td>0.029396</td>\n",
       "      <td>0.028147</td>\n",
       "      <td>0.028489</td>\n",
       "      <td>0.026931</td>\n",
       "      <td>0.028420</td>\n",
       "      <td>0.026930</td>\n",
       "      <td>-0.016144</td>\n",
       "      <td>0.025815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lgt_cond</th>\n",
       "      <td>-0.040158</td>\n",
       "      <td>-0.039898</td>\n",
       "      <td>-0.103961</td>\n",
       "      <td>-0.114916</td>\n",
       "      <td>0.011363</td>\n",
       "      <td>0.145021</td>\n",
       "      <td>-0.085800</td>\n",
       "      <td>-0.085407</td>\n",
       "      <td>0.136065</td>\n",
       "      <td>-0.012170</td>\n",
       "      <td>...</td>\n",
       "      <td>0.126569</td>\n",
       "      <td>-0.013952</td>\n",
       "      <td>0.069703</td>\n",
       "      <td>0.063740</td>\n",
       "      <td>0.071457</td>\n",
       "      <td>0.069648</td>\n",
       "      <td>0.057673</td>\n",
       "      <td>0.054521</td>\n",
       "      <td>-0.014626</td>\n",
       "      <td>0.103913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weather</th>\n",
       "      <td>0.093840</td>\n",
       "      <td>0.093224</td>\n",
       "      <td>-0.022173</td>\n",
       "      <td>-0.022846</td>\n",
       "      <td>-0.001763</td>\n",
       "      <td>-0.027554</td>\n",
       "      <td>-0.030204</td>\n",
       "      <td>-0.030001</td>\n",
       "      <td>-0.027159</td>\n",
       "      <td>0.065921</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005104</td>\n",
       "      <td>0.014011</td>\n",
       "      <td>0.014057</td>\n",
       "      <td>0.016044</td>\n",
       "      <td>0.014599</td>\n",
       "      <td>-0.014999</td>\n",
       "      <td>-0.017345</td>\n",
       "      <td>-0.010106</td>\n",
       "      <td>0.016200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sch_bus</th>\n",
       "      <td>0.003259</td>\n",
       "      <td>0.003199</td>\n",
       "      <td>0.015563</td>\n",
       "      <td>0.017711</td>\n",
       "      <td>-0.002979</td>\n",
       "      <td>0.000547</td>\n",
       "      <td>0.046587</td>\n",
       "      <td>0.046959</td>\n",
       "      <td>-0.000262</td>\n",
       "      <td>-0.005752</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.005104</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.014155</td>\n",
       "      <td>-0.012455</td>\n",
       "      <td>-0.014525</td>\n",
       "      <td>-0.012660</td>\n",
       "      <td>-0.010094</td>\n",
       "      <td>-0.011823</td>\n",
       "      <td>-0.002944</td>\n",
       "      <td>-0.010618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>not_hour</th>\n",
       "      <td>0.061929</td>\n",
       "      <td>0.063868</td>\n",
       "      <td>-0.008226</td>\n",
       "      <td>-0.011250</td>\n",
       "      <td>0.006324</td>\n",
       "      <td>0.070433</td>\n",
       "      <td>-0.024323</td>\n",
       "      <td>-0.024802</td>\n",
       "      <td>0.068391</td>\n",
       "      <td>0.126787</td>\n",
       "      <td>...</td>\n",
       "      <td>0.014011</td>\n",
       "      <td>-0.014155</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.940338</td>\n",
       "      <td>0.967861</td>\n",
       "      <td>0.909640</td>\n",
       "      <td>0.587634</td>\n",
       "      <td>0.562595</td>\n",
       "      <td>-0.007405</td>\n",
       "      <td>-0.019327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>not_min</th>\n",
       "      <td>0.058871</td>\n",
       "      <td>0.060721</td>\n",
       "      <td>-0.009710</td>\n",
       "      <td>-0.013661</td>\n",
       "      <td>0.008426</td>\n",
       "      <td>0.063555</td>\n",
       "      <td>-0.024829</td>\n",
       "      <td>-0.025443</td>\n",
       "      <td>0.062270</td>\n",
       "      <td>0.116379</td>\n",
       "      <td>...</td>\n",
       "      <td>0.014057</td>\n",
       "      <td>-0.012455</td>\n",
       "      <td>0.940338</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.911795</td>\n",
       "      <td>0.895895</td>\n",
       "      <td>0.560993</td>\n",
       "      <td>0.533920</td>\n",
       "      <td>-0.005740</td>\n",
       "      <td>-0.017371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arr_hour</th>\n",
       "      <td>0.073113</td>\n",
       "      <td>0.075044</td>\n",
       "      <td>-0.006649</td>\n",
       "      <td>-0.010240</td>\n",
       "      <td>0.007998</td>\n",
       "      <td>0.071700</td>\n",
       "      <td>-0.025549</td>\n",
       "      <td>-0.026004</td>\n",
       "      <td>0.069508</td>\n",
       "      <td>0.122436</td>\n",
       "      <td>...</td>\n",
       "      <td>0.016044</td>\n",
       "      <td>-0.014525</td>\n",
       "      <td>0.967861</td>\n",
       "      <td>0.911795</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.942897</td>\n",
       "      <td>0.592537</td>\n",
       "      <td>0.567133</td>\n",
       "      <td>-0.009252</td>\n",
       "      <td>-0.018171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arr_min</th>\n",
       "      <td>0.070297</td>\n",
       "      <td>0.072138</td>\n",
       "      <td>-0.005580</td>\n",
       "      <td>-0.010137</td>\n",
       "      <td>0.010593</td>\n",
       "      <td>0.065588</td>\n",
       "      <td>-0.026043</td>\n",
       "      <td>-0.026666</td>\n",
       "      <td>0.064226</td>\n",
       "      <td>0.115700</td>\n",
       "      <td>...</td>\n",
       "      <td>0.014599</td>\n",
       "      <td>-0.012660</td>\n",
       "      <td>0.909640</td>\n",
       "      <td>0.895895</td>\n",
       "      <td>0.942897</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.566126</td>\n",
       "      <td>0.536585</td>\n",
       "      <td>-0.007798</td>\n",
       "      <td>-0.015478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hosp_hr</th>\n",
       "      <td>-0.040521</td>\n",
       "      <td>-0.039324</td>\n",
       "      <td>-0.041900</td>\n",
       "      <td>-0.044531</td>\n",
       "      <td>0.000092</td>\n",
       "      <td>0.043998</td>\n",
       "      <td>-0.068591</td>\n",
       "      <td>-0.069724</td>\n",
       "      <td>0.044931</td>\n",
       "      <td>0.048933</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.014999</td>\n",
       "      <td>-0.010094</td>\n",
       "      <td>0.587634</td>\n",
       "      <td>0.560993</td>\n",
       "      <td>0.592537</td>\n",
       "      <td>0.566126</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.950144</td>\n",
       "      <td>-0.000525</td>\n",
       "      <td>0.001221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hosp_mn</th>\n",
       "      <td>-0.037540</td>\n",
       "      <td>-0.036379</td>\n",
       "      <td>-0.033069</td>\n",
       "      <td>-0.036741</td>\n",
       "      <td>0.004086</td>\n",
       "      <td>0.039447</td>\n",
       "      <td>-0.056525</td>\n",
       "      <td>-0.057937</td>\n",
       "      <td>0.041601</td>\n",
       "      <td>0.045879</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.017345</td>\n",
       "      <td>-0.011823</td>\n",
       "      <td>0.562595</td>\n",
       "      <td>0.533920</td>\n",
       "      <td>0.567133</td>\n",
       "      <td>0.536585</td>\n",
       "      <td>0.950144</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001965</td>\n",
       "      <td>-0.000160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fatals</th>\n",
       "      <td>0.000584</td>\n",
       "      <td>0.000578</td>\n",
       "      <td>0.130481</td>\n",
       "      <td>0.132417</td>\n",
       "      <td>0.015459</td>\n",
       "      <td>-0.062247</td>\n",
       "      <td>0.284210</td>\n",
       "      <td>0.284496</td>\n",
       "      <td>-0.057668</td>\n",
       "      <td>0.010390</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.010106</td>\n",
       "      <td>-0.002944</td>\n",
       "      <td>-0.007405</td>\n",
       "      <td>-0.005740</td>\n",
       "      <td>-0.009252</td>\n",
       "      <td>-0.007798</td>\n",
       "      <td>-0.000525</td>\n",
       "      <td>0.001965</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.048722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drunk_dr</th>\n",
       "      <td>0.033658</td>\n",
       "      <td>0.033472</td>\n",
       "      <td>-0.023189</td>\n",
       "      <td>-0.030830</td>\n",
       "      <td>0.015610</td>\n",
       "      <td>-0.194928</td>\n",
       "      <td>-0.012359</td>\n",
       "      <td>-0.012794</td>\n",
       "      <td>-0.184322</td>\n",
       "      <td>-0.008209</td>\n",
       "      <td>...</td>\n",
       "      <td>0.016200</td>\n",
       "      <td>-0.010618</td>\n",
       "      <td>-0.019327</td>\n",
       "      <td>-0.017371</td>\n",
       "      <td>-0.018171</td>\n",
       "      <td>-0.015478</td>\n",
       "      <td>0.001221</td>\n",
       "      <td>-0.000160</td>\n",
       "      <td>0.048722</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>45 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               state   st_case  ve_total  ve_forms  pvh_invl      peds  \\\n",
       "state       1.000000  0.999986  0.002132  0.000546  0.004321 -0.054651   \n",
       "st_case     0.999986  1.000000  0.002236  0.000624  0.004405 -0.054255   \n",
       "ve_total    0.002132  0.002236  1.000000  0.927414  0.338424 -0.204354   \n",
       "ve_forms    0.000546  0.000624  0.927414  1.000000 -0.038106 -0.238065   \n",
       "pvh_invl    0.004321  0.004405  0.338424 -0.038106  1.000000  0.052968   \n",
       "peds       -0.054651 -0.054255 -0.204354 -0.238065  0.052968  1.000000   \n",
       "persons    -0.003895 -0.003829  0.631536  0.665099  0.013966 -0.221193   \n",
       "permvit    -0.003721 -0.003659  0.622845  0.668735 -0.018400 -0.223874   \n",
       "pernotmvit -0.052634 -0.052242 -0.161039 -0.232475  0.154626  0.958868   \n",
       "county      0.267884  0.268991  0.013819  0.011161  0.008841 -0.020057   \n",
       "city       -0.005058 -0.004792  0.019239  0.006434  0.035212  0.068019   \n",
       "day         0.000588  0.000828  0.005760  0.003823  0.005771 -0.014423   \n",
       "dayname     0.000588  0.000828  0.005760  0.003823  0.005771 -0.014423   \n",
       "month       0.008881  0.011353  0.012440  0.012820  0.000984 -0.002370   \n",
       "year             NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "day_week    0.004510  0.004419  0.005269  0.010232 -0.011663 -0.004775   \n",
       "hour       -0.017286 -0.017344 -0.023218 -0.015884 -0.022069  0.034681   \n",
       "minute     -0.005555 -0.005555 -0.009670 -0.007860 -0.006061  0.001896   \n",
       "nhs        -0.161445 -0.161220  0.038031  0.042204 -0.004571  0.026836   \n",
       "route      -0.134189 -0.133097 -0.101240 -0.122595  0.037949  0.083119   \n",
       "rur_urb    -0.127636 -0.126878  0.020788  0.008673  0.033718  0.143456   \n",
       "func_sys   -0.137952 -0.137875 -0.043508 -0.044597 -0.004040  0.002034   \n",
       "rd_owner    0.003153  0.005861 -0.020989 -0.031039  0.022012  0.067076   \n",
       "milept     -0.145114 -0.144787  0.001555  0.005219 -0.008974  0.024776   \n",
       "latitude    0.129910  0.128247 -0.023359 -0.026976  0.005458 -0.039074   \n",
       "longitud    0.006878  0.006623 -0.021489 -0.019685 -0.007887 -0.000611   \n",
       "sp_jur     -0.015314 -0.015448 -0.028141 -0.027780 -0.005293 -0.005110   \n",
       "harm_ev     0.017543  0.017305 -0.263083 -0.280766  0.003494 -0.295731   \n",
       "man_coll   -0.034638 -0.034682  0.231657  0.251308 -0.013342 -0.135867   \n",
       "reljct1    -0.055950 -0.055943 -0.013383 -0.010683 -0.008878  0.004985   \n",
       "reljct2    -0.050519 -0.050475  0.001935  0.004977 -0.007351  0.021027   \n",
       "typ_int    -0.063384 -0.063433  0.001920 -0.000749  0.007015  0.015919   \n",
       "wrk_zone    0.008925  0.009103  0.037186  0.026705  0.032164  0.007482   \n",
       "rel_road   -0.030537 -0.030666 -0.140307 -0.154949  0.014973 -0.039420   \n",
       "lgt_cond   -0.040158 -0.039898 -0.103961 -0.114916  0.011363  0.145021   \n",
       "weather     0.093840  0.093224 -0.022173 -0.022846 -0.001763 -0.027554   \n",
       "sch_bus     0.003259  0.003199  0.015563  0.017711 -0.002979  0.000547   \n",
       "not_hour    0.061929  0.063868 -0.008226 -0.011250  0.006324  0.070433   \n",
       "not_min     0.058871  0.060721 -0.009710 -0.013661  0.008426  0.063555   \n",
       "arr_hour    0.073113  0.075044 -0.006649 -0.010240  0.007998  0.071700   \n",
       "arr_min     0.070297  0.072138 -0.005580 -0.010137  0.010593  0.065588   \n",
       "hosp_hr    -0.040521 -0.039324 -0.041900 -0.044531  0.000092  0.043998   \n",
       "hosp_mn    -0.037540 -0.036379 -0.033069 -0.036741  0.004086  0.039447   \n",
       "fatals      0.000584  0.000578  0.130481  0.132417  0.015459 -0.062247   \n",
       "drunk_dr    0.033658  0.033472 -0.023189 -0.030830  0.015610 -0.194928   \n",
       "\n",
       "             persons   permvit  pernotmvit    county  ...   weather   sch_bus  \\\n",
       "state      -0.003895 -0.003721   -0.052634  0.267884  ...  0.093840  0.003259   \n",
       "st_case    -0.003829 -0.003659   -0.052242  0.268991  ...  0.093224  0.003199   \n",
       "ve_total    0.631536  0.622845   -0.161039  0.013819  ... -0.022173  0.015563   \n",
       "ve_forms    0.665099  0.668735   -0.232475  0.011161  ... -0.022846  0.017711   \n",
       "pvh_invl    0.013966 -0.018400    0.154626  0.008841  ... -0.001763 -0.002979   \n",
       "peds       -0.221193 -0.223874    0.958868 -0.020057  ... -0.027554  0.000547   \n",
       "persons     1.000000  0.996115   -0.189258  0.008456  ... -0.030204  0.046587   \n",
       "permvit     0.996115  1.000000   -0.216825  0.008672  ... -0.030001  0.046959   \n",
       "pernotmvit -0.189258 -0.216825    1.000000 -0.019719  ... -0.027159 -0.000262   \n",
       "county      0.008456  0.008672   -0.019719  1.000000  ...  0.065921 -0.005752   \n",
       "city        0.000679  0.000110    0.066602  0.077573  ... -0.023407  0.007142   \n",
       "day         0.006850  0.006039   -0.011067 -0.004832  ...  0.005217  0.001515   \n",
       "dayname     0.006850  0.006039   -0.011067 -0.004832  ...  0.005217  0.001515   \n",
       "month       0.010812  0.010856   -0.002304  0.001637  ...  0.003113 -0.007750   \n",
       "year             NaN       NaN         NaN       NaN  ...       NaN       NaN   \n",
       "day_week    0.006489  0.006953   -0.005981 -0.001423  ... -0.007593  0.003275   \n",
       "hour       -0.002114 -0.000770    0.028684 -0.020188  ...  0.043733 -0.011840   \n",
       "minute     -0.009426 -0.009294    0.001298 -0.005579  ...  0.013393  0.006502   \n",
       "nhs         0.027128  0.026174    0.028862 -0.032672  ... -0.016361 -0.007501   \n",
       "route      -0.101708 -0.098896    0.069204  0.002560  ... -0.043050  0.004217   \n",
       "rur_urb    -0.018685 -0.019300    0.138415 -0.027060  ...  0.001965 -0.010567   \n",
       "func_sys   -0.032649 -0.031427   -0.002282 -0.036841  ...  0.003675 -0.004667   \n",
       "rd_owner   -0.030812 -0.030225    0.061710  0.214157  ... -0.088255 -0.007043   \n",
       "milept     -0.001527 -0.000945    0.021707 -0.119185  ... -0.105821  0.001709   \n",
       "latitude   -0.021188 -0.021364   -0.036829 -0.128655  ...  0.152658 -0.000080   \n",
       "longitud   -0.016936 -0.017251    0.000282  0.002579  ...  0.087085 -0.001913   \n",
       "sp_jur     -0.018898 -0.018480   -0.006378 -0.017160  ...  0.051925  0.001361   \n",
       "harm_ev    -0.174299 -0.173952   -0.284258  0.014871  ...  0.045552 -0.017234   \n",
       "man_coll    0.169513  0.171208   -0.133320 -0.002078  ...  0.082372  0.006966   \n",
       "reljct1    -0.017257 -0.017933    0.006765 -0.002273  ...  0.124506 -0.004137   \n",
       "reljct2    -0.007233 -0.008078    0.022672 -0.004267  ...  0.127947 -0.002403   \n",
       "typ_int    -0.010332 -0.009709    0.013062 -0.011733  ...  0.136901 -0.000701   \n",
       "wrk_zone    0.021601  0.018403    0.017599  0.024832  ... -0.012491 -0.004703   \n",
       "rel_road   -0.106496 -0.106736   -0.037710 -0.012589  ...  0.147629 -0.009071   \n",
       "lgt_cond   -0.085800 -0.085407    0.136065 -0.012170  ...  0.126569 -0.013952   \n",
       "weather    -0.030204 -0.030001   -0.027159  0.065921  ...  1.000000 -0.005104   \n",
       "sch_bus     0.046587  0.046959   -0.000262 -0.005752  ... -0.005104  1.000000   \n",
       "not_hour   -0.024323 -0.024802    0.068391  0.126787  ...  0.014011 -0.014155   \n",
       "not_min    -0.024829 -0.025443    0.062270  0.116379  ...  0.014057 -0.012455   \n",
       "arr_hour   -0.025549 -0.026004    0.069508  0.122436  ...  0.016044 -0.014525   \n",
       "arr_min    -0.026043 -0.026666    0.064226  0.115700  ...  0.014599 -0.012660   \n",
       "hosp_hr    -0.068591 -0.069724    0.044931  0.048933  ... -0.014999 -0.010094   \n",
       "hosp_mn    -0.056525 -0.057937    0.041601  0.045879  ... -0.017345 -0.011823   \n",
       "fatals      0.284210  0.284496   -0.057668  0.010390  ... -0.010106 -0.002944   \n",
       "drunk_dr   -0.012359 -0.012794   -0.184322 -0.008209  ...  0.016200 -0.010618   \n",
       "\n",
       "            not_hour   not_min  arr_hour   arr_min   hosp_hr   hosp_mn  \\\n",
       "state       0.061929  0.058871  0.073113  0.070297 -0.040521 -0.037540   \n",
       "st_case     0.063868  0.060721  0.075044  0.072138 -0.039324 -0.036379   \n",
       "ve_total   -0.008226 -0.009710 -0.006649 -0.005580 -0.041900 -0.033069   \n",
       "ve_forms   -0.011250 -0.013661 -0.010240 -0.010137 -0.044531 -0.036741   \n",
       "pvh_invl    0.006324  0.008426  0.007998  0.010593  0.000092  0.004086   \n",
       "peds        0.070433  0.063555  0.071700  0.065588  0.043998  0.039447   \n",
       "persons    -0.024323 -0.024829 -0.025549 -0.026043 -0.068591 -0.056525   \n",
       "permvit    -0.024802 -0.025443 -0.026004 -0.026666 -0.069724 -0.057937   \n",
       "pernotmvit  0.068391  0.062270  0.069508  0.064226  0.044931  0.041601   \n",
       "county      0.126787  0.116379  0.122436  0.115700  0.048933  0.045879   \n",
       "city       -0.115219 -0.110755 -0.108284 -0.103042 -0.102837 -0.098481   \n",
       "day         0.008733  0.006366  0.010480  0.011341  0.009952  0.010830   \n",
       "dayname     0.008733  0.006366  0.010480  0.011341  0.009952  0.010830   \n",
       "month      -0.017097 -0.012523 -0.014489 -0.015162 -0.004989 -0.005172   \n",
       "year             NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "day_week   -0.008942 -0.009520 -0.010242 -0.014078 -0.004172 -0.007865   \n",
       "hour        0.028710 -0.007179  0.028603 -0.009889  0.012455 -0.004776   \n",
       "minute     -0.001394  0.138023 -0.006184  0.030111  0.000353 -0.014384   \n",
       "nhs         0.003458  0.003719  0.002034  0.002071  0.044818  0.044656   \n",
       "route       0.138460  0.133757  0.141296  0.135126  0.095884  0.094872   \n",
       "rur_urb     0.083362  0.080572  0.086302  0.084600  0.035148  0.037024   \n",
       "func_sys   -0.005461 -0.003757 -0.005790 -0.005275  0.043162  0.042963   \n",
       "rd_owner    0.327520  0.313141  0.326321  0.312598  0.220912  0.212879   \n",
       "milept      0.158323  0.151633  0.162311  0.159032  0.103482  0.101271   \n",
       "latitude   -0.198456 -0.182968 -0.186058 -0.176366 -0.083125 -0.080222   \n",
       "longitud    0.002578  0.004182  0.002394  0.002375  0.010569  0.011277   \n",
       "sp_jur     -0.005014 -0.002237 -0.002720 -0.001389  0.004715  0.003836   \n",
       "harm_ev    -0.013975 -0.009282 -0.018754 -0.014964  0.013903  0.013831   \n",
       "man_coll    0.011518  0.008891  0.011584  0.010268 -0.006629 -0.005170   \n",
       "reljct1     0.029740  0.028294  0.030514  0.027714  0.013397  0.013217   \n",
       "reljct2     0.038882  0.036569  0.039537  0.036289  0.024182  0.023280   \n",
       "typ_int     0.026002  0.022888  0.025790  0.022623  0.012782  0.015278   \n",
       "wrk_zone    0.014244  0.015204  0.015028  0.013569  0.010916  0.012446   \n",
       "rel_road    0.029396  0.028147  0.028489  0.026931  0.028420  0.026930   \n",
       "lgt_cond    0.069703  0.063740  0.071457  0.069648  0.057673  0.054521   \n",
       "weather     0.014011  0.014057  0.016044  0.014599 -0.014999 -0.017345   \n",
       "sch_bus    -0.014155 -0.012455 -0.014525 -0.012660 -0.010094 -0.011823   \n",
       "not_hour    1.000000  0.940338  0.967861  0.909640  0.587634  0.562595   \n",
       "not_min     0.940338  1.000000  0.911795  0.895895  0.560993  0.533920   \n",
       "arr_hour    0.967861  0.911795  1.000000  0.942897  0.592537  0.567133   \n",
       "arr_min     0.909640  0.895895  0.942897  1.000000  0.566126  0.536585   \n",
       "hosp_hr     0.587634  0.560993  0.592537  0.566126  1.000000  0.950144   \n",
       "hosp_mn     0.562595  0.533920  0.567133  0.536585  0.950144  1.000000   \n",
       "fatals     -0.007405 -0.005740 -0.009252 -0.007798 -0.000525  0.001965   \n",
       "drunk_dr   -0.019327 -0.017371 -0.018171 -0.015478  0.001221 -0.000160   \n",
       "\n",
       "              fatals  drunk_dr  \n",
       "state       0.000584  0.033658  \n",
       "st_case     0.000578  0.033472  \n",
       "ve_total    0.130481 -0.023189  \n",
       "ve_forms    0.132417 -0.030830  \n",
       "pvh_invl    0.015459  0.015610  \n",
       "peds       -0.062247 -0.194928  \n",
       "persons     0.284210 -0.012359  \n",
       "permvit     0.284496 -0.012794  \n",
       "pernotmvit -0.057668 -0.184322  \n",
       "county      0.010390 -0.008209  \n",
       "city       -0.013386 -0.004789  \n",
       "day         0.002469  0.001591  \n",
       "dayname     0.002469  0.001591  \n",
       "month       0.002160 -0.009315  \n",
       "year             NaN       NaN  \n",
       "day_week    0.004305  0.012765  \n",
       "hour       -0.015614 -0.005191  \n",
       "minute     -0.004226  0.003800  \n",
       "nhs        -0.000576 -0.037539  \n",
       "route      -0.053643  0.017690  \n",
       "rur_urb    -0.049682 -0.042845  \n",
       "func_sys   -0.020080 -0.008393  \n",
       "rd_owner   -0.013036 -0.014076  \n",
       "milept     -0.002374 -0.041111  \n",
       "latitude   -0.005958  0.018220  \n",
       "longitud   -0.006209 -0.018916  \n",
       "sp_jur     -0.005906  0.003556  \n",
       "harm_ev    -0.000204  0.148828  \n",
       "man_coll    0.040602 -0.027229  \n",
       "reljct1    -0.004825 -0.020145  \n",
       "reljct2    -0.010556 -0.030788  \n",
       "typ_int    -0.008434 -0.018645  \n",
       "wrk_zone    0.005956  0.004881  \n",
       "rel_road   -0.016144  0.025815  \n",
       "lgt_cond   -0.014626  0.103913  \n",
       "weather    -0.010106  0.016200  \n",
       "sch_bus    -0.002944 -0.010618  \n",
       "not_hour   -0.007405 -0.019327  \n",
       "not_min    -0.005740 -0.017371  \n",
       "arr_hour   -0.009252 -0.018171  \n",
       "arr_min    -0.007798 -0.015478  \n",
       "hosp_hr    -0.000525  0.001221  \n",
       "hosp_mn     0.001965 -0.000160  \n",
       "fatals      1.000000  0.048722  \n",
       "drunk_dr    0.048722  1.000000  \n",
       "\n",
       "[45 rows x 45 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_accident.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8da8fb92-0341-44ec-8314-357a048e1bc3",
   "metadata": {},
   "source": [
    "# Weather Data Frame\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "58dcf856-61ba-4dc3-a1c8-3fa4503e7879",
   "metadata": {},
   "outputs": [],
   "source": [
    "weather_df = pd.read_csv('./data/weather.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "169e04e1-1e92-46d6-a6ec-d57a3dec3aa3",
   "metadata": {},
   "outputs": [],
   "source": [
    "weather_df.columns = weather_df.columns.str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "3f7bda63-579c-46a5-9e28-f97f2d5ba5dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Alabama</td>\n",
       "      <td>10001</td>\n",
       "      <td>1</td>\n",
       "      <td>Clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>10002</td>\n",
       "      <td>2</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>10003</td>\n",
       "      <td>2</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>10004</td>\n",
       "      <td>10</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>10005</td>\n",
       "      <td>2</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   state statename  st_case  weather weathername\n",
       "0      1   Alabama    10001        1       Clear\n",
       "1      1   Alabama    10002        2        Rain\n",
       "2      1   Alabama    10003        2        Rain\n",
       "3      1   Alabama    10004       10      Cloudy\n",
       "4      1   Alabama    10005        2        Rain"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a841ef3-05c5-4c20-bcf4-0fd699bae54b",
   "metadata": {},
   "source": [
    "# Cevent CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2918910b-6cb5-4ee7-85c2-ea17b8504017",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cevent = pd.read_csv('./data/cevent.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "30ddb648-67d0-4474-b4cc-ecff0fd3b178",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cevent.columns = df_cevent.columns.str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "4c7ebeee-a6e0-49ae-b92b-e41fe88fc55c",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>statename</th>\n",
       "      <th>st_case</th>\n",
       "      <th>eventnum</th>\n",
       "      <th>vnumber1</th>\n",
       "      <th>aoi1</th>\n",
       "      <th>aoi1name</th>\n",
       "      <th>soe</th>\n",
       "      <th>soename</th>\n",
       "      <th>vnumber2</th>\n",
       "      <th>vnumber2name</th>\n",
       "      <th>aoi2</th>\n",
       "      <th>aoi2name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>10001</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "      <td>Non-Harmful Event</td>\n",
       "      <td>63</td>\n",
       "      <td>Ran Off Roadway - Right</td>\n",
       "      <td>5555</td>\n",
       "      <td>Non-Harmful Event</td>\n",
       "      <td>55</td>\n",
       "      <td>Non-Harmful Event</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>10001</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>12 Clock Point</td>\n",
       "      <td>42</td>\n",
       "      <td>Tree (Standing Only)</td>\n",
       "      <td>9999</td>\n",
       "      <td>Not a Motor Vehicle</td>\n",
       "      <td>77</td>\n",
       "      <td>Not a Motor Vehicle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>10001</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>12 Clock Point</td>\n",
       "      <td>42</td>\n",
       "      <td>Tree (Standing Only)</td>\n",
       "      <td>9999</td>\n",
       "      <td>Not a Motor Vehicle</td>\n",
       "      <td>77</td>\n",
       "      <td>Not a Motor Vehicle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>10001</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Non-Collision</td>\n",
       "      <td>1</td>\n",
       "      <td>Rollover/Overturn</td>\n",
       "      <td>9999</td>\n",
       "      <td>Not a Motor Vehicle</td>\n",
       "      <td>77</td>\n",
       "      <td>Not a Motor Vehicle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>10002</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>12 Clock Point</td>\n",
       "      <td>12</td>\n",
       "      <td>Motor Vehicle In-Transport</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2 Clock Point</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   state statename  st_case  eventnum  vnumber1  aoi1           aoi1name  soe  \\\n",
       "0      1   Alabama    10001         1         1    55  Non-Harmful Event   63   \n",
       "1      1   Alabama    10001         2         1    12     12 Clock Point   42   \n",
       "2      1   Alabama    10001         3         1    12     12 Clock Point   42   \n",
       "3      1   Alabama    10001         4         1     0      Non-Collision    1   \n",
       "4      1   Alabama    10002         1         2    12     12 Clock Point   12   \n",
       "\n",
       "                      soename  vnumber2         vnumber2name  aoi2  \\\n",
       "0     Ran Off Roadway - Right      5555    Non-Harmful Event    55   \n",
       "1        Tree (Standing Only)      9999  Not a Motor Vehicle    77   \n",
       "2        Tree (Standing Only)      9999  Not a Motor Vehicle    77   \n",
       "3           Rollover/Overturn      9999  Not a Motor Vehicle    77   \n",
       "4  Motor Vehicle In-Transport         1                    1     2   \n",
       "\n",
       "              aoi2name  \n",
       "0    Non-Harmful Event  \n",
       "1  Not a Motor Vehicle  \n",
       "2  Not a Motor Vehicle  \n",
       "3  Not a Motor Vehicle  \n",
       "4        2 Clock Point  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cevent.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82ea7d78-dc3b-4d57-83c2-ff43032314de",
   "metadata": {},
   "source": [
    "# CrashRF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "49c74a9b-98ee-4841-b76e-b49905196dab",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_crashrf = pd.read_csv('./data/crashrf.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8d042451-59c5-48ef-b619-ea9782feb3af",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_crashrf.columns = df_crashrf.columns.str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c20a9701-76ed-4bd2-9848-ac71d36675f3",
   "metadata": {},
   "outputs": [
    {
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       "    }\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>state</th>\n",
       "      <th>statename</th>\n",
       "      <th>st_case</th>\n",
       "      <th>crashrf</th>\n",
       "      <th>crashrfname</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>10001</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>10002</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>10003</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>10004</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>10005</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   state statename  st_case  crashrf crashrfname\n",
       "0      1   Alabama    10001        0        None\n",
       "1      1   Alabama    10002        0        None\n",
       "2      1   Alabama    10003        0        None\n",
       "3      1   Alabama    10004        0        None\n",
       "4      1   Alabama    10005        0        None"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_crashrf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5387c059-2d30-4a7c-82df-da41a095e1ff",
   "metadata": {},
   "source": [
    "# Column Names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de5980bc-afca-481b-8169-a40aa0c3e66c",
   "metadata": {},
   "outputs": [],
   "source": [
    "column_names_accident = ['state', 'peds', 've_total', 'persons', 'county', 'day', 'month', 'hour', 'minute', 'week', 'tway_id', 'route', 'rur_urb', 'rd_owner','nhs', 'sp_jur', 'milept', 'latitude', 'longitude', 'harm', 'man_coll', 'reljct1', 'reljct2', 'typ_int', 'rel_road', 'wrk_zone', 'lgt_cond', 'sch_bus', 'rail', 'not_hour', 'arr_hour', 'hosp_hr']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83550e1b-e832-4382-adf3-2c2f770082ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "col_name_cevent = ['eventnum', 'vnumber1', 'aoi1', 'soe', 'vnumber2', 'aoi2', ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc9ba3a8-54be-4339-a9be-ecd10c8ee46c",
   "metadata": {},
   "outputs": [],
   "source": [
    "col_name_weather = ['weather']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb5e0c17-107a-4f98-9f30-d3be9817d9de",
   "metadata": {},
   "outputs": [],
   "source": [
    "col_name_crashrf = ['crashrf']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb3d4252-ea3e-43d2-8264-b7f0830d080f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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