AircraftStrikes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "23a40c94",
   "metadata": {},
   "source": [
    "<div style=\"padding:30px; \n",
    "            color:white;\n",
    "            margin:10px;\n",
    "            font-size:180%;\n",
    "            text-align:center;\n",
    "            display:fill;\n",
    "            border-radius:20px;\n",
    "            border-width: 5px;\n",
    "            border-style: solid;\n",
    "            border-color:white;\n",
    "            background-color:black;\n",
    "            font-family:Luminari;\n",
    "            overflow:hidden;\n",
    "            font-weight:500\">Aircraft Wildlife Strikes 1990-2023</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d93596bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8d273a91",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\KPMal\\AppData\\Local\\Temp\\ipykernel_27604\\1840193475.py:1: DtypeWarning: Columns (18,21) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  df = pd.read_csv('STRIKE_REPORTS.csv')\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('STRIKE_REPORTS.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "372a6ac1",
   "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>INDEX_NR</th>\n",
       "      <th>INCIDENT_DATE</th>\n",
       "      <th>INCIDENT_MONTH</th>\n",
       "      <th>INCIDENT_YEAR</th>\n",
       "      <th>TIME</th>\n",
       "      <th>TIME_OF_DAY</th>\n",
       "      <th>AIRPORT_ID</th>\n",
       "      <th>AIRPORT</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>...</th>\n",
       "      <th>SIZE</th>\n",
       "      <th>NR_INJURIES</th>\n",
       "      <th>NR_FATALITIES</th>\n",
       "      <th>COMMENTS</th>\n",
       "      <th>REPORTED_NAME</th>\n",
       "      <th>REPORTED_TITLE</th>\n",
       "      <th>SOURCE</th>\n",
       "      <th>PERSON</th>\n",
       "      <th>LUPDATE</th>\n",
       "      <th>TRANSFER</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>608242</td>\n",
       "      <td>6/22/1996</td>\n",
       "      <td>6</td>\n",
       "      <td>1996</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>KSMF</td>\n",
       "      <td>SACRAMENTO INTL</td>\n",
       "      <td>38.69542</td>\n",
       "      <td>-121.59077</td>\n",
       "      <td>...</td>\n",
       "      <td>Medium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>/Legacy Record=100001/</td>\n",
       "      <td>REDACTED</td>\n",
       "      <td>REDACTED</td>\n",
       "      <td>Air Transport Report</td>\n",
       "      <td>Air Transport Operations</td>\n",
       "      <td>12/20/2007</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>608243</td>\n",
       "      <td>6/26/1996</td>\n",
       "      <td>6</td>\n",
       "      <td>1996</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>KDEN</td>\n",
       "      <td>DENVER INTL AIRPORT</td>\n",
       "      <td>39.85841</td>\n",
       "      <td>-104.66700</td>\n",
       "      <td>...</td>\n",
       "      <td>Medium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>/Legacy Record=100002/</td>\n",
       "      <td>REDACTED</td>\n",
       "      <td>REDACTED</td>\n",
       "      <td>Air Transport Report</td>\n",
       "      <td>Air Transport Operations</td>\n",
       "      <td>12/20/2007</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>608244</td>\n",
       "      <td>7/1/1996</td>\n",
       "      <td>7</td>\n",
       "      <td>1996</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>KOMA</td>\n",
       "      <td>EPPLEY AIRFIELD</td>\n",
       "      <td>41.30252</td>\n",
       "      <td>-95.89417</td>\n",
       "      <td>...</td>\n",
       "      <td>Medium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>/Legacy Record=100003/</td>\n",
       "      <td>REDACTED</td>\n",
       "      <td>REDACTED</td>\n",
       "      <td>Air Transport Report</td>\n",
       "      <td>Air Transport Operations</td>\n",
       "      <td>12/20/2007</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>608245</td>\n",
       "      <td>7/1/1996</td>\n",
       "      <td>7</td>\n",
       "      <td>1996</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>KIAD</td>\n",
       "      <td>WASHINGTON DULLES INTL ARPT</td>\n",
       "      <td>38.94453</td>\n",
       "      <td>-77.45581</td>\n",
       "      <td>...</td>\n",
       "      <td>Medium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>/Legacy Record=100004/</td>\n",
       "      <td>REDACTED</td>\n",
       "      <td>REDACTED</td>\n",
       "      <td>Air Transport Report</td>\n",
       "      <td>Air Transport Operations</td>\n",
       "      <td>12/20/2007</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>608246</td>\n",
       "      <td>7/1/1996</td>\n",
       "      <td>7</td>\n",
       "      <td>1996</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>KLGA</td>\n",
       "      <td>LA GUARDIA ARPT</td>\n",
       "      <td>40.77724</td>\n",
       "      <td>-73.87261</td>\n",
       "      <td>...</td>\n",
       "      <td>Medium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>/Legacy Record=100005/</td>\n",
       "      <td>REDACTED</td>\n",
       "      <td>REDACTED</td>\n",
       "      <td>Air Transport Report</td>\n",
       "      <td>Air Transport Operations</td>\n",
       "      <td>12/20/2007</td>\n",
       "      <td>False</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>288805</th>\n",
       "      <td>1472253</td>\n",
       "      <td>8/28/2023</td>\n",
       "      <td>8</td>\n",
       "      <td>2023</td>\n",
       "      <td>21:50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ZZZZ</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Small</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>REDACTED</td>\n",
       "      <td>REDACTED</td>\n",
       "      <td>FAA Form 5200-7-E</td>\n",
       "      <td>Airport Operations</td>\n",
       "      <td>11/17/2023</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288806</th>\n",
       "      <td>1472256</td>\n",
       "      <td>8/28/2023</td>\n",
       "      <td>8</td>\n",
       "      <td>2023</td>\n",
       "      <td>20:40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ZZZZ</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Small</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>REDACTED</td>\n",
       "      <td>REDACTED</td>\n",
       "      <td>FAA Form 5200-7-E</td>\n",
       "      <td>Airport Operations</td>\n",
       "      <td>11/17/2023</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288807</th>\n",
       "      <td>1472257</td>\n",
       "      <td>8/28/2023</td>\n",
       "      <td>8</td>\n",
       "      <td>2023</td>\n",
       "      <td>07:14</td>\n",
       "      <td>Dusk</td>\n",
       "      <td>KDTW</td>\n",
       "      <td>DETROIT METRO WAYNE COUNTY ARPT</td>\n",
       "      <td>42.21206</td>\n",
       "      <td>-83.34884</td>\n",
       "      <td>...</td>\n",
       "      <td>Small</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>REDACTED</td>\n",
       "      <td>REDACTED</td>\n",
       "      <td>FAA Form 5200-7-E</td>\n",
       "      <td>Airport Operations</td>\n",
       "      <td>11/17/2023</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288808</th>\n",
       "      <td>1472258</td>\n",
       "      <td>8/28/2023</td>\n",
       "      <td>8</td>\n",
       "      <td>2023</td>\n",
       "      <td>09:00</td>\n",
       "      <td>Day</td>\n",
       "      <td>KPIH</td>\n",
       "      <td>POCATELLO REGIONAL ARPT</td>\n",
       "      <td>42.91131</td>\n",
       "      <td>-112.59586</td>\n",
       "      <td>...</td>\n",
       "      <td>Medium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>REDACTED</td>\n",
       "      <td>REDACTED</td>\n",
       "      <td>FAA Form 5200-7-E</td>\n",
       "      <td>Airport Operations</td>\n",
       "      <td>11/17/2023</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288809</th>\n",
       "      <td>1472259</td>\n",
       "      <td>8/28/2023</td>\n",
       "      <td>8</td>\n",
       "      <td>2023</td>\n",
       "      <td>10:15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>KAMA</td>\n",
       "      <td>RICK HUSBAND AMARILLO INTL</td>\n",
       "      <td>35.21937</td>\n",
       "      <td>-101.70593</td>\n",
       "      <td>...</td>\n",
       "      <td>Small</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>REDACTED</td>\n",
       "      <td>REDACTED</td>\n",
       "      <td>FAA Form 5200-7-E</td>\n",
       "      <td>Carcass Found</td>\n",
       "      <td>11/17/2023</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>288810 rows × 100 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        INDEX_NR INCIDENT_DATE  INCIDENT_MONTH  INCIDENT_YEAR   TIME  \\\n",
       "0         608242     6/22/1996               6           1996    NaN   \n",
       "1         608243     6/26/1996               6           1996    NaN   \n",
       "2         608244      7/1/1996               7           1996    NaN   \n",
       "3         608245      7/1/1996               7           1996    NaN   \n",
       "4         608246      7/1/1996               7           1996    NaN   \n",
       "...          ...           ...             ...            ...    ...   \n",
       "288805   1472253     8/28/2023               8           2023  21:50   \n",
       "288806   1472256     8/28/2023               8           2023  20:40   \n",
       "288807   1472257     8/28/2023               8           2023  07:14   \n",
       "288808   1472258     8/28/2023               8           2023  09:00   \n",
       "288809   1472259     8/28/2023               8           2023  10:15   \n",
       "\n",
       "       TIME_OF_DAY AIRPORT_ID                          AIRPORT  LATITUDE  \\\n",
       "0              NaN       KSMF                  SACRAMENTO INTL  38.69542   \n",
       "1              NaN       KDEN              DENVER INTL AIRPORT  39.85841   \n",
       "2              NaN       KOMA                  EPPLEY AIRFIELD  41.30252   \n",
       "3              NaN       KIAD      WASHINGTON DULLES INTL ARPT  38.94453   \n",
       "4              NaN       KLGA                  LA GUARDIA ARPT  40.77724   \n",
       "...            ...        ...                              ...       ...   \n",
       "288805         NaN       ZZZZ                          UNKNOWN       NaN   \n",
       "288806         NaN       ZZZZ                          UNKNOWN       NaN   \n",
       "288807        Dusk       KDTW  DETROIT METRO WAYNE COUNTY ARPT  42.21206   \n",
       "288808         Day       KPIH          POCATELLO REGIONAL ARPT  42.91131   \n",
       "288809         NaN       KAMA       RICK HUSBAND AMARILLO INTL  35.21937   \n",
       "\n",
       "        LONGITUDE  ...    SIZE NR_INJURIES NR_FATALITIES  \\\n",
       "0      -121.59077  ...  Medium         NaN           NaN   \n",
       "1      -104.66700  ...  Medium         NaN           NaN   \n",
       "2       -95.89417  ...  Medium         NaN           NaN   \n",
       "3       -77.45581  ...  Medium         NaN           NaN   \n",
       "4       -73.87261  ...  Medium         NaN           NaN   \n",
       "...           ...  ...     ...         ...           ...   \n",
       "288805        NaN  ...   Small         NaN           NaN   \n",
       "288806        NaN  ...   Small         NaN           NaN   \n",
       "288807  -83.34884  ...   Small         NaN           NaN   \n",
       "288808 -112.59586  ...  Medium         NaN           NaN   \n",
       "288809 -101.70593  ...   Small         NaN           NaN   \n",
       "\n",
       "                      COMMENTS REPORTED_NAME REPORTED_TITLE  \\\n",
       "0       /Legacy Record=100001/      REDACTED       REDACTED   \n",
       "1       /Legacy Record=100002/      REDACTED       REDACTED   \n",
       "2       /Legacy Record=100003/      REDACTED       REDACTED   \n",
       "3       /Legacy Record=100004/      REDACTED       REDACTED   \n",
       "4       /Legacy Record=100005/      REDACTED       REDACTED   \n",
       "...                        ...           ...            ...   \n",
       "288805                     NaN      REDACTED       REDACTED   \n",
       "288806                     NaN      REDACTED       REDACTED   \n",
       "288807                     NaN      REDACTED       REDACTED   \n",
       "288808                     NaN      REDACTED       REDACTED   \n",
       "288809                     NaN      REDACTED       REDACTED   \n",
       "\n",
       "                      SOURCE                    PERSON     LUPDATE TRANSFER  \n",
       "0       Air Transport Report  Air Transport Operations  12/20/2007    False  \n",
       "1       Air Transport Report  Air Transport Operations  12/20/2007    False  \n",
       "2       Air Transport Report  Air Transport Operations  12/20/2007    False  \n",
       "3       Air Transport Report  Air Transport Operations  12/20/2007    False  \n",
       "4       Air Transport Report  Air Transport Operations  12/20/2007    False  \n",
       "...                      ...                       ...         ...      ...  \n",
       "288805     FAA Form 5200-7-E        Airport Operations  11/17/2023    False  \n",
       "288806     FAA Form 5200-7-E        Airport Operations  11/17/2023    False  \n",
       "288807     FAA Form 5200-7-E        Airport Operations  11/17/2023    False  \n",
       "288808     FAA Form 5200-7-E        Airport Operations  11/17/2023    False  \n",
       "288809     FAA Form 5200-7-E             Carcass Found  11/17/2023    False  \n",
       "\n",
       "[288810 rows x 100 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cc202008",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 288810 entries, 0 to 288809\n",
      "Data columns (total 100 columns):\n",
      " #   Column                 Non-Null Count   Dtype  \n",
      "---  ------                 --------------   -----  \n",
      " 0   INDEX_NR               288810 non-null  int64  \n",
      " 1   INCIDENT_DATE          288810 non-null  object \n",
      " 2   INCIDENT_MONTH         288810 non-null  int64  \n",
      " 3   INCIDENT_YEAR          288810 non-null  int64  \n",
      " 4   TIME                   163633 non-null  object \n",
      " 5   TIME_OF_DAY            166394 non-null  object \n",
      " 6   AIRPORT_ID             288804 non-null  object \n",
      " 7   AIRPORT                288810 non-null  object \n",
      " 8   LATITUDE               253309 non-null  float64\n",
      " 9   LONGITUDE              253308 non-null  float64\n",
      " 10  RUNWAY                 218852 non-null  object \n",
      " 11  STATE                  253309 non-null  object \n",
      " 12  FAAREGION              253309 non-null  object \n",
      " 13  LOCATION               33939 non-null   object \n",
      " 14  ENROUTE_STATE          5037 non-null    object \n",
      " 15  OPID                   288784 non-null  object \n",
      " 16  OPERATOR               288810 non-null  object \n",
      " 17  REG                    172543 non-null  object \n",
      " 18  FLT                    129249 non-null  object \n",
      " 19  AIRCRAFT               288810 non-null  object \n",
      " 20  AMA                    205475 non-null  object \n",
      " 21  AMO                    190159 non-null  object \n",
      " 22  EMA                    192151 non-null  float64\n",
      " 23  EMO                    186756 non-null  float64\n",
      " 24  AC_CLASS               206893 non-null  object \n",
      " 25  AC_MASS                206490 non-null  float64\n",
      " 26  TYPE_ENG               206044 non-null  object \n",
      " 27  NUM_ENGS               206259 non-null  float64\n",
      " 28  ENG_1_POS              205481 non-null  float64\n",
      " 29  ENG_2_POS              191612 non-null  float64\n",
      " 30  ENG_3_POS              13080 non-null   float64\n",
      " 31  ENG_4_POS              3290 non-null    float64\n",
      " 32  PHASE_OF_FLIGHT        177843 non-null  object \n",
      " 33  HEIGHT                 148807 non-null  float64\n",
      " 34  SPEED                  94237 non-null   float64\n",
      " 35  DISTANCE               188234 non-null  float64\n",
      " 36  SKY                    139382 non-null  object \n",
      " 37  PRECIPITATION          11175 non-null   object \n",
      " 38  AOS                    14196 non-null   float64\n",
      " 39  COST_REPAIRS           5040 non-null    object \n",
      " 40  COST_OTHER             4760 non-null    object \n",
      " 41  COST_REPAIRS_INFL_ADJ  5040 non-null    object \n",
      " 42  COST_OTHER_INFL_ADJ    4760 non-null    object \n",
      " 43  INGESTED_OTHER         288810 non-null  bool   \n",
      " 44  INDICATED_DAMAGE       288810 non-null  bool   \n",
      " 45  DAMAGE_LEVEL           187315 non-null  object \n",
      " 46  STR_RAD                288810 non-null  bool   \n",
      " 47  DAM_RAD                288810 non-null  bool   \n",
      " 48  STR_WINDSHLD           288810 non-null  bool   \n",
      " 49  DAM_WINDSHLD           288810 non-null  bool   \n",
      " 50  STR_NOSE               288810 non-null  bool   \n",
      " 51  DAM_NOSE               288810 non-null  bool   \n",
      " 52  STR_ENG1               288810 non-null  bool   \n",
      " 53  DAM_ENG1               288810 non-null  bool   \n",
      " 54  ING_ENG1               288810 non-null  bool   \n",
      " 55  STR_ENG2               288810 non-null  bool   \n",
      " 56  DAM_ENG2               288810 non-null  bool   \n",
      " 57  ING_ENG2               288810 non-null  bool   \n",
      " 58  STR_ENG3               288810 non-null  bool   \n",
      " 59  DAM_ENG3               288810 non-null  bool   \n",
      " 60  ING_ENG3               288810 non-null  bool   \n",
      " 61  STR_ENG4               288810 non-null  bool   \n",
      " 62  DAM_ENG4               288810 non-null  bool   \n",
      " 63  ING_ENG4               288810 non-null  bool   \n",
      " 64  STR_PROP               288810 non-null  bool   \n",
      " 65  DAM_PROP               288810 non-null  bool   \n",
      " 66  STR_WING_ROT           288810 non-null  bool   \n",
      " 67  DAM_WING_ROT           288810 non-null  bool   \n",
      " 68  STR_FUSE               288810 non-null  bool   \n",
      " 69  DAM_FUSE               288810 non-null  bool   \n",
      " 70  STR_LG                 288810 non-null  bool   \n",
      " 71  DAM_LG                 288810 non-null  bool   \n",
      " 72  STR_TAIL               288810 non-null  bool   \n",
      " 73  DAM_TAIL               288810 non-null  bool   \n",
      " 74  STR_LGHTS              288810 non-null  bool   \n",
      " 75  DAM_LGHTS              288810 non-null  bool   \n",
      " 76  STR_OTHER              288810 non-null  bool   \n",
      " 77  DAM_OTHER              288810 non-null  bool   \n",
      " 78  OTHER_SPECIFY          38462 non-null   object \n",
      " 79  EFFECT                 13718 non-null   object \n",
      " 80  EFFECT_OTHER           2221 non-null    object \n",
      " 81  BIRD_BAND_NUMBER       390 non-null     float64\n",
      " 82  SPECIES_ID             288806 non-null  object \n",
      " 83  SPECIES                288809 non-null  object \n",
      " 84  REMARKS                264305 non-null  object \n",
      " 85  REMAINS_COLLECTED      288810 non-null  bool   \n",
      " 86  REMAINS_SENT           288810 non-null  bool   \n",
      " 87  WARNED                 288810 non-null  object \n",
      " 88  NUM_SEEN               94773 non-null   object \n",
      " 89  NUM_STRUCK             288174 non-null  object \n",
      " 90  SIZE                   263538 non-null  object \n",
      " 91  NR_INJURIES            276 non-null     float64\n",
      " 92  NR_FATALITIES          24 non-null      float64\n",
      " 93  COMMENTS               218834 non-null  object \n",
      " 94  REPORTED_NAME          288810 non-null  object \n",
      " 95  REPORTED_TITLE         288810 non-null  object \n",
      " 96  SOURCE                 288810 non-null  object \n",
      " 97  PERSON                 267676 non-null  object \n",
      " 98  LUPDATE                288810 non-null  object \n",
      " 99  TRANSFER               288810 non-null  bool   \n",
      "dtypes: bool(37), float64(17), int64(3), object(43)\n",
      "memory usage: 149.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5a06e31d",
   "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>INDEX_NR</th>\n",
       "      <th>INCIDENT_MONTH</th>\n",
       "      <th>INCIDENT_YEAR</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>EMA</th>\n",
       "      <th>EMO</th>\n",
       "      <th>AC_MASS</th>\n",
       "      <th>NUM_ENGS</th>\n",
       "      <th>ENG_1_POS</th>\n",
       "      <th>ENG_2_POS</th>\n",
       "      <th>ENG_3_POS</th>\n",
       "      <th>ENG_4_POS</th>\n",
       "      <th>HEIGHT</th>\n",
       "      <th>SPEED</th>\n",
       "      <th>DISTANCE</th>\n",
       "      <th>AOS</th>\n",
       "      <th>BIRD_BAND_NUMBER</th>\n",
       "      <th>NR_INJURIES</th>\n",
       "      <th>NR_FATALITIES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.888100e+05</td>\n",
       "      <td>288810.000000</td>\n",
       "      <td>288810.000000</td>\n",
       "      <td>2.533090e+05</td>\n",
       "      <td>253308.000000</td>\n",
       "      <td>192151.000000</td>\n",
       "      <td>186756.000000</td>\n",
       "      <td>206490.000000</td>\n",
       "      <td>206259.000000</td>\n",
       "      <td>205481.000000</td>\n",
       "      <td>191612.000000</td>\n",
       "      <td>13080.000000</td>\n",
       "      <td>3290.000000</td>\n",
       "      <td>148807.000000</td>\n",
       "      <td>94237.000000</td>\n",
       "      <td>188234.000000</td>\n",
       "      <td>14196.000000</td>\n",
       "      <td>3.900000e+02</td>\n",
       "      <td>276.000000</td>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.736693e+05</td>\n",
       "      <td>7.175974</td>\n",
       "      <td>2011.697798</td>\n",
       "      <td>2.012754e+02</td>\n",
       "      <td>-90.880247</td>\n",
       "      <td>19.755812</td>\n",
       "      <td>8.522377</td>\n",
       "      <td>3.538312</td>\n",
       "      <td>2.013929</td>\n",
       "      <td>2.814956</td>\n",
       "      <td>2.667536</td>\n",
       "      <td>2.900306</td>\n",
       "      <td>2.043161</td>\n",
       "      <td>865.960358</td>\n",
       "      <td>142.585057</td>\n",
       "      <td>0.814466</td>\n",
       "      <td>91.454416</td>\n",
       "      <td>8.811022e+07</td>\n",
       "      <td>1.293478</td>\n",
       "      <td>2.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.526448e+05</td>\n",
       "      <td>2.765082</td>\n",
       "      <td>8.432311</td>\n",
       "      <td>8.177522e+04</td>\n",
       "      <td>327.243918</td>\n",
       "      <td>10.724278</td>\n",
       "      <td>12.866405</td>\n",
       "      <td>0.867312</td>\n",
       "      <td>0.420133</td>\n",
       "      <td>2.113806</td>\n",
       "      <td>1.981032</td>\n",
       "      <td>1.950649</td>\n",
       "      <td>1.435114</td>\n",
       "      <td>1843.098801</td>\n",
       "      <td>46.650050</td>\n",
       "      <td>3.547950</td>\n",
       "      <td>661.074472</td>\n",
       "      <td>9.110186e+07</td>\n",
       "      <td>0.756134</td>\n",
       "      <td>1.654484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.082420e+05</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1990.000000</td>\n",
       "      <td>-3.767333e+01</td>\n",
       "      <td>-177.381000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</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>0.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.893202e+05</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "      <td>3.298764e+01</td>\n",
       "      <td>-98.469780</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.646935e+05</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>3.880581e+01</td>\n",
       "      <td>-87.904460</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.897730e+07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.064714e+06</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2019.000000</td>\n",
       "      <td>4.085010e+01</td>\n",
       "      <td>-80.417940</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>900.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>1.893343e+08</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.472259e+06</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2023.000000</td>\n",
       "      <td>4.115443e+07</td>\n",
       "      <td>164140.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>2301.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>31300.000000</td>\n",
       "      <td>1250.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62848.000000</td>\n",
       "      <td>2.821218e+08</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           INDEX_NR  INCIDENT_MONTH  INCIDENT_YEAR      LATITUDE  \\\n",
       "count  2.888100e+05   288810.000000  288810.000000  2.533090e+05   \n",
       "mean   8.736693e+05        7.175974    2011.697798  2.012754e+02   \n",
       "std    2.526448e+05        2.765082       8.432311  8.177522e+04   \n",
       "min    6.082420e+05        1.000000    1990.000000 -3.767333e+01   \n",
       "25%    6.893202e+05        5.000000    2006.000000  3.298764e+01   \n",
       "50%    7.646935e+05        7.000000    2014.000000  3.880581e+01   \n",
       "75%    1.064714e+06        9.000000    2019.000000  4.085010e+01   \n",
       "max    1.472259e+06       12.000000    2023.000000  4.115443e+07   \n",
       "\n",
       "           LONGITUDE            EMA            EMO        AC_MASS  \\\n",
       "count  253308.000000  192151.000000  186756.000000  206490.000000   \n",
       "mean      -90.880247      19.755812       8.522377       3.538312   \n",
       "std       327.243918      10.724278      12.866405       0.867312   \n",
       "min      -177.381000       0.000000       0.000000       1.000000   \n",
       "25%       -98.469780      10.000000       1.000000       3.000000   \n",
       "50%       -87.904460      22.000000       4.000000       4.000000   \n",
       "75%       -80.417940      31.000000      10.000000       4.000000   \n",
       "max    164140.000000      92.000000    2301.000000       5.000000   \n",
       "\n",
       "            NUM_ENGS      ENG_1_POS      ENG_2_POS     ENG_3_POS    ENG_4_POS  \\\n",
       "count  206259.000000  205481.000000  191612.000000  13080.000000  3290.000000   \n",
       "mean        2.013929       2.814956       2.667536      2.900306     2.043161   \n",
       "std         0.420133       2.113806       1.981032      1.950649     1.435114   \n",
       "min         1.000000       1.000000       1.000000      1.000000     1.000000   \n",
       "25%         2.000000       1.000000       1.000000      1.000000     1.000000   \n",
       "50%         2.000000       1.000000       1.000000      1.000000     1.000000   \n",
       "75%         2.000000       5.000000       5.000000      5.000000     4.000000   \n",
       "max         4.000000       7.000000       7.000000      6.000000     5.000000   \n",
       "\n",
       "              HEIGHT         SPEED       DISTANCE           AOS  \\\n",
       "count  148807.000000  94237.000000  188234.000000  14196.000000   \n",
       "mean      865.960358    142.585057       0.814466     91.454416   \n",
       "std      1843.098801     46.650050       3.547950    661.074472   \n",
       "min         0.000000      0.000000       0.000000      0.000000   \n",
       "25%         0.000000    120.000000       0.000000      1.000000   \n",
       "50%        50.000000    140.000000       0.000000      3.000000   \n",
       "75%       900.000000    160.000000       0.000000     24.000000   \n",
       "max     31300.000000   1250.000000      99.000000  62848.000000   \n",
       "\n",
       "       BIRD_BAND_NUMBER  NR_INJURIES  NR_FATALITIES  \n",
       "count      3.900000e+02   276.000000      24.000000  \n",
       "mean       8.811022e+07     1.293478       2.041667  \n",
       "std        9.110186e+07     0.756134       1.654484  \n",
       "min        0.000000e+00     1.000000       1.000000  \n",
       "25%        0.000000e+00     1.000000       1.000000  \n",
       "50%        7.897730e+07     1.000000       1.500000  \n",
       "75%        1.893343e+08     1.000000       2.000000  \n",
       "max        2.821218e+08     7.000000       8.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b3b622de",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "INDEX_NR               0\n",
       "INCIDENT_DATE          0\n",
       "INCIDENT_MONTH         0\n",
       "INCIDENT_YEAR          0\n",
       "TIME              125177\n",
       "                   ...  \n",
       "REPORTED_TITLE         0\n",
       "SOURCE                 0\n",
       "PERSON             21134\n",
       "LUPDATE                0\n",
       "TRANSFER               0\n",
       "Length: 100, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2695a2e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping duplicate data\n",
    "df.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "81c6e534",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(288810, 100)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e36316ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Incidents by year\n",
    "groupedYr_df = df.groupby('INCIDENT_YEAR').size().reset_index(name='Count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cdafdf7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>INCIDENT_YEAR</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1990</td>\n",
       "      <td>2120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1991</td>\n",
       "      <td>2516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1992</td>\n",
       "      <td>2651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1993</td>\n",
       "      <td>2624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1994</td>\n",
       "      <td>2708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1995</td>\n",
       "      <td>2825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1996</td>\n",
       "      <td>3031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1997</td>\n",
       "      <td>3559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1998</td>\n",
       "      <td>3809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1999</td>\n",
       "      <td>5120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2000</td>\n",
       "      <td>6024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2001</td>\n",
       "      <td>5825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2002</td>\n",
       "      <td>6222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2003</td>\n",
       "      <td>5992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2004</td>\n",
       "      <td>6606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2005</td>\n",
       "      <td>7270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2006</td>\n",
       "      <td>7295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2007</td>\n",
       "      <td>7751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2008</td>\n",
       "      <td>7638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2009</td>\n",
       "      <td>9509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2010</td>\n",
       "      <td>9898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2011</td>\n",
       "      <td>10110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2012</td>\n",
       "      <td>10932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2013</td>\n",
       "      <td>11410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2014</td>\n",
       "      <td>13692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2015</td>\n",
       "      <td>13772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2016</td>\n",
       "      <td>13323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2017</td>\n",
       "      <td>14776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2018</td>\n",
       "      <td>16202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2019</td>\n",
       "      <td>17341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2020</td>\n",
       "      <td>11620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2021</td>\n",
       "      <td>15638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2022</td>\n",
       "      <td>17226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2023</td>\n",
       "      <td>11775</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    INCIDENT_YEAR  Count\n",
       "0            1990   2120\n",
       "1            1991   2516\n",
       "2            1992   2651\n",
       "3            1993   2624\n",
       "4            1994   2708\n",
       "5            1995   2825\n",
       "6            1996   3031\n",
       "7            1997   3559\n",
       "8            1998   3809\n",
       "9            1999   5120\n",
       "10           2000   6024\n",
       "11           2001   5825\n",
       "12           2002   6222\n",
       "13           2003   5992\n",
       "14           2004   6606\n",
       "15           2005   7270\n",
       "16           2006   7295\n",
       "17           2007   7751\n",
       "18           2008   7638\n",
       "19           2009   9509\n",
       "20           2010   9898\n",
       "21           2011  10110\n",
       "22           2012  10932\n",
       "23           2013  11410\n",
       "24           2014  13692\n",
       "25           2015  13772\n",
       "26           2016  13323\n",
       "27           2017  14776\n",
       "28           2018  16202\n",
       "29           2019  17341\n",
       "30           2020  11620\n",
       "31           2021  15638\n",
       "32           2022  17226\n",
       "33           2023  11775"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groupedYr_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17d40215",
   "metadata": {},
   "source": [
    "<div style=\"padding:30px; \n",
    "            color:white;\n",
    "            margin:10px;\n",
    "            font-size:180%;\n",
    "            text-align:center;\n",
    "            display:fill;\n",
    "            border-radius:20px;\n",
    "            border-width: 5px;\n",
    "            border-style: solid;\n",
    "            border-color:white;\n",
    "            background-color:black;\n",
    "            font-family:Luminari;\n",
    "            overflow:hidden;\n",
    "            font-weight:500\">Aircarft Wildlife Strikes by Year.</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "08a2698a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\KPMal\\anaconda3\\envs\\Py3-9\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\KPMal\\anaconda3\\envs\\Py3-9\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\KPMal\\anaconda3\\envs\\Py3-9\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='INCIDENT_YEAR', ylabel='Count'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(20,15)})\n",
    "#sns.set_style(\"ticks\")\n",
    "sns.set(rc={'axes.facecolor':'#000000', 'figure.facecolor':'#EBEBEB'})\n",
    "sns.barplot(groupedYr_df, x=\"INCIDENT_YEAR\", y=\"Count\")\n",
    "#plt.show(bar_plot)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "edc3cebd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\KPMal\\anaconda3\\envs\\Py3-9\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\KPMal\\anaconda3\\envs\\Py3-9\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\KPMal\\anaconda3\\envs\\Py3-9\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\KPMal\\anaconda3\\envs\\Py3-9\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='INCIDENT_YEAR', ylabel='Count'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(20,15)})\n",
    "#sns.set_style(\"ticks\")\n",
    "sns.set(rc={'axes.facecolor':'#000000', 'figure.facecolor':'#EBEBEB'})\n",
    "sns.lineplot(groupedYr_df, x=\"INCIDENT_YEAR\", y=\"Count\")\n",
    "#plt.show(bar_plot)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f0d1eca",
   "metadata": {},
   "source": [
    "<div style=\"padding:30px; \n",
    "            color:white;\n",
    "            margin:10px;\n",
    "            font-size:180%;\n",
    "            text-align:center;\n",
    "            display:fill;\n",
    "            border-radius:20px;\n",
    "            border-width: 5px;\n",
    "            border-style: solid;\n",
    "            border-color:white;\n",
    "            background-color:black;\n",
    "            font-family:Luminari;\n",
    "            overflow:hidden;\n",
    "            font-weight:500\">Aircarft Wildlife Strike Fatalities Reported in the U.S. by Month</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ccafe95d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['INCIDENT_MONTH'] = df['INCIDENT_MONTH'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e02fa7b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['INCIDENT_MONTH'] = df['INCIDENT_MONTH'].replace(['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12'], ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "aef85eb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "FatMo = df.groupby(['INCIDENT_MONTH'])['NR_FATALITIES'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5b1382ea",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#FatMo.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "17b666f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\KPMal\\anaconda3\\envs\\Py3-9\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\KPMal\\anaconda3\\envs\\Py3-9\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\KPMal\\anaconda3\\envs\\Py3-9\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\KPMal\\anaconda3\\envs\\Py3-9\\lib\\site-packages\\seaborn\\algorithms.py:98: RuntimeWarning: Mean of empty slice\n",
      "  boot_dist.append(f(*sample, **func_kwargs))\n",
      "C:\\Users\\KPMal\\anaconda3\\envs\\Py3-9\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:1384: RuntimeWarning: All-NaN slice encountered\n",
      "  return _nanquantile_unchecked(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Number of Reported Fatalities per month')]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=df, x='INCIDENT_MONTH', y='NR_FATALITIES').set(title='Number of Reported Fatalities per month')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18014a86",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4586c988",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan,  1.,  2.,  8.,  3.,  4.,  5.])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['NR_FATALITIES'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c7ed2c00",
   "metadata": {},
   "outputs": [],
   "source": [
    "FatST = df.groupby(['STATE'])['NR_FATALITIES'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a7a389c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    67.000000\n",
       "mean      0.179104\n",
       "std       0.716163\n",
       "min       0.000000\n",
       "25%       0.000000\n",
       "50%       0.000000\n",
       "75%       0.000000\n",
       "max       5.000000\n",
       "Name: NR_FATALITIES, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FatST.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1ed97812",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "STATE\n",
       "AB    0.0\n",
       "AK    0.0\n",
       "AL    0.0\n",
       "AR    0.0\n",
       "AS    0.0\n",
       "     ... \n",
       "VT    0.0\n",
       "WA    2.0\n",
       "WI    0.0\n",
       "WV    1.0\n",
       "WY    0.0\n",
       "Name: NR_FATALITIES, Length: 67, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FatST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9647a23b",
   "metadata": {},
   "outputs": [],
   "source": [
    "FatST = FatST.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0251b485",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATE</th>\n",
       "      <th>NR_FATALITIES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AB</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AK</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AL</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AR</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AS</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>VT</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>WA</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>WI</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>WV</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>WY</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>67 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   STATE  NR_FATALITIES\n",
       "0     AB            0.0\n",
       "1     AK            0.0\n",
       "2     AL            0.0\n",
       "3     AR            0.0\n",
       "4     AS            0.0\n",
       "..   ...            ...\n",
       "62    VT            0.0\n",
       "63    WA            2.0\n",
       "64    WI            0.0\n",
       "65    WV            1.0\n",
       "66    WY            0.0\n",
       "\n",
       "[67 rows x 2 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FatST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f2fcb8c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "deadlyST = FatST.nlargest(3, ['NR_FATALITIES'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "81734bce",
   "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>NR_FATALITIES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>OK</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>UT</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>WA</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   STATE  NR_FATALITIES\n",
       "46    OK            5.0\n",
       "59    UT            2.0\n",
       "63    WA            2.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deadlyST"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "611ec86c",
   "metadata": {},
   "source": [
    "<div style=\"padding:30px; \n",
    "            color:white;\n",
    "            margin:10px;\n",
    "            font-size:180%;\n",
    "            text-align:center;\n",
    "            display:fill;\n",
    "            border-radius:20px;\n",
    "            border-width: 5px;\n",
    "            border-style: solid;\n",
    "            border-color:white;\n",
    "            background-color:black;\n",
    "            font-family:Luminari;\n",
    "            overflow:hidden;\n",
    "            font-weight:500\">States with the most fatalities reported</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7052cbeb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\KPMal\\anaconda3\\envs\\Py3-9\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\KPMal\\anaconda3\\envs\\Py3-9\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\KPMal\\anaconda3\\envs\\Py3-9\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'States with the most fatalities reported')]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=deadlyST, x='STATE', y='NR_FATALITIES').set(title='States with the most fatalities reported')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "16f9ba67",
   "metadata": {},
   "outputs": [],
   "source": [
    "injST = df.groupby(['STATE'])['NR_INJURIES'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "80ccd2b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    67.000000\n",
       "mean      2.865672\n",
       "std       4.100612\n",
       "min       0.000000\n",
       "25%       0.000000\n",
       "50%       2.000000\n",
       "75%       3.500000\n",
       "max      25.000000\n",
       "Name: NR_INJURIES, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "injST.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "47f524e8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "STATE\n",
       "AB    0.0\n",
       "AK    0.0\n",
       "AL    2.0\n",
       "AR    1.0\n",
       "AS    0.0\n",
       "     ... \n",
       "VT    0.0\n",
       "WA    2.0\n",
       "WI    2.0\n",
       "WV    2.0\n",
       "WY    2.0\n",
       "Name: NR_INJURIES, Length: 67, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "injST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7a2292c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "injST = injST.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "db498fa2",
   "metadata": {},
   "outputs": [],
   "source": [
    "injuryST = injST.nlargest(10, ['NR_INJURIES'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0edac4c5",
   "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>NR_INJURIES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>FL</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>TX</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CA</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>NE</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>FN</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>IL</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>MD</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>ON</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>GA</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>TN</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   STATE  NR_INJURIES\n",
       "12    FL         25.0\n",
       "57    TX         13.0\n",
       "7     CA         12.0\n",
       "37    NE         11.0\n",
       "13    FN         10.0\n",
       "19    IL          7.0\n",
       "26    MD          7.0\n",
       "47    ON          7.0\n",
       "14    GA          6.0\n",
       "56    TN          6.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "injuryST"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cf48a7c",
   "metadata": {},
   "source": [
    "<div style=\"padding:30px; \n",
    "            color:white;\n",
    "            margin:10px;\n",
    "            font-size:180%;\n",
    "            text-align:center;\n",
    "            display:fill;\n",
    "            border-radius:20px;\n",
    "            border-width: 5px;\n",
    "            border-style: solid;\n",
    "            border-color:white;\n",
    "            background-color:black;\n",
    "            font-family:Luminari;\n",
    "            overflow:hidden;\n",
    "            font-weight:500\">States with the most injuries reported</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "345d9d6a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\KPMal\\anaconda3\\envs\\Py3-9\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\KPMal\\anaconda3\\envs\\Py3-9\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\KPMal\\anaconda3\\envs\\Py3-9\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'States with the most fatalities reported')]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=injuryST, x='STATE', y='NR_INJURIES').set(title='States with the most fatalities reported')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1783552a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'Day', 'Dawn', 'Night', 'Dusk'], dtype=object)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['TIME_OF_DAY'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "034c1b0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TIME_OF_DAY\n",
       "Day      102548\n",
       "Night     50446\n",
       "Dusk       7428\n",
       "Dawn       5972\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['TIME_OF_DAY'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "89c965cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          True\n",
       "1          True\n",
       "2          True\n",
       "3          True\n",
       "4          True\n",
       "          ...  \n",
       "288805     True\n",
       "288806     True\n",
       "288807    False\n",
       "288808    False\n",
       "288809     True\n",
       "Name: TIME_OF_DAY, Length: 288810, dtype: bool"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['TIME_OF_DAY'].isna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "424efb52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          True\n",
       "1          True\n",
       "2          True\n",
       "3          True\n",
       "4          True\n",
       "          ...  \n",
       "288805     True\n",
       "288806     True\n",
       "288807    False\n",
       "288808    False\n",
       "288809     True\n",
       "Name: TIME_OF_DAY, Length: 288810, dtype: bool"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['TIME_OF_DAY'].isnull()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f078a80c",
   "metadata": {},
   "source": [
    "<div style=\"padding:30px; \n",
    "            color:white;\n",
    "            margin:10px;\n",
    "            font-size:180%;\n",
    "            text-align:center;\n",
    "            display:fill;\n",
    "            border-radius:20px;\n",
    "            border-width: 5px;\n",
    "            border-style: solid;\n",
    "            border-color:white;\n",
    "            background-color:black;\n",
    "            font-family:Luminari;\n",
    "            overflow:hidden;\n",
    "            font-weight:500\">Injuries reported based on time of day</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f5f59af0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\KPMal\\anaconda3\\envs\\Py3-9\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\KPMal\\anaconda3\\envs\\Py3-9\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\KPMal\\anaconda3\\envs\\Py3-9\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\KPMal\\anaconda3\\envs\\Py3-9\\lib\\site-packages\\seaborn\\algorithms.py:98: RuntimeWarning: Mean of empty slice\n",
      "  boot_dist.append(f(*sample, **func_kwargs))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Injuries reported based on time of day')]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABmEAAATbCAYAAABhrRHgAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAAB+9UlEQVR4nOzde5zWdZ3//+fgMJxkCEEJEilRMBUE0UilzNZc0zJb4GuxmZmnLPHwzXWFzFPq4qH1BJGrcjAP2GFtXa11lzVdtWwVUaIvuCqleIJBUUROAvP7wx+zThwcYN5eDNzvt5u32zWf6319rtfnuoa5cB58rquqrq6uPgAAAAAAADSrVpUeAAAAAAAAYGskwgAAAAAAABQgwgAAAAAAABQgwgAAAAAAABQgwgAAAAAAABQgwgAAAAAAABQgwgAAAAAAABQgwgAAAAAAABQgwgAAAFuU+vr6So/AX/CcAADAphFhAAAgyc0335yDDjpoo27zyiuv5KCDDsq999672fd/77335qCDDsorr7yy2ftqqebPn5+/+7u/y6uvvrrZ+2rKc3PJJZdk6NChm31fJTXn99imWNdzMnTo0FxyySUVmaep5syZk+OPPz6f+cxn8rd/+7dNvt0TTzyRgw46KE888UTB6QAA2JaIMAAAsIm6dOmSG264IQceeOBm7+vAAw/MDTfckC5dujTDZC3TY489lt/+9reVHoP3WNdz8g//8A/5xje+UZmBmujmm2/OK6+8kssuuyznnXdepccBAGAbVl3pAQAAoKWqqanJ3nvv3Sz76ty5czp37tws+4KS+vTpU+kR3tebb76Z3r17N0sgBQCAzSHCAADAOtx77725/PLLM378+Fx77bV55pln0rlz5wwdOrTh7Y1eeeWVDBs2LKNHj86RRx6Ze++9N5dddll+/vOfp3v37g37Gjp0aAYOHNjwL/IPOuignHDCCXnkkUcyd+7cjBgxIjvuuONat33yySdz4403ZtasWampqclBBx2U0047rSHWrF69OjfffHPuu+++LFiwIF27ds2hhx6aE088MdXV6/6r/pr1hx9+eH72s59l++23z0033ZROnTrl7rvvzp133pmXXnopO+ywQ4488sgcd9xxDfu65JJL8uqrr+bwww/PhAkTsmjRonz84x/PyJEjG/1ifu7cufnxj3+cGTNmZMmSJfn4xz+ek08+Of3792/0uI0cOTJ33313Xn/99YwcOTKXXXZZkmTYsGH5/Oc/3/B4vd9cSfLAAw9k4sSJeeGFF/LRj340xx9/fJOf63/5l3/J5MmTs3DhwvTr1y+nnXZao+N58sknM3ny5MyaNStLly7NjjvumM9//vM5/vjj06rVu28uMHXq1PzkJz/JCy+8kPbt2+cTn/hEvvOd76Rr164N+yl5HP/93/+diRMn5rnnnst2222XwYMH59RTT023bt2SNO37+S+t+X5OGj8n7/1+XvNcXnLJJbnvvvvy2GOPpV27dhk2bFiGDx+e6667Lr/5zW9SU1OTI444IqeeemqqqqqSJMuXL89NN92UqVOnZuHChdlll13y9a9/PYceeugGj3XBggX5p3/6pzz22GN544030rt37xx33HH51Kc+lSSN3lbwoIMOavjzuS6//OUvM2XKlMybNy977rnnOte93/N/wgknpLq6OjfccEOj2/3f//t/s2LFiowdO3aDxwMAwNbN25EBAMB6rF69Ot///vdz6KGH5qqrrkr//v3zox/9KL///e83e9+TJk3KIYcckgsvvLDhl8fv9eSTT+aMM85I27Zt84Mf/CBnnHFGpk+fnpEjR2b58uVJkltvvTW/+MUvcvzxx+fqq6/Ol7/85dx+++2ZPHnyBu/71VdfzYMPPpiLLrooJ598cjp16pRbbrklV1xxRfbff/9cfvnlGTp0aG699dZceeWVjW77zDPP5IYbbsgJJ5yQ888/P4sWLcppp52Wurq6JMmf/vSnfPOb38zLL7+cs846KxdeeGGqqqoycuTITJ8+vdG+brjhhowYMSLnnntuBg0alOOOOy5JctlllzW83VVT5nr44Ydz3nnn5WMf+1j+4R/+IZ/97Gdz8cUXN+l5qKury80335yTTjopF110URYtWpSRI0fmjTfeaDje008/PZ06dcpFF12UK664Iv369cuECRMyderUJMmMGTNy8cUX5+CDD84Pf/jDnH766Zk2bVouvPDChvspeRz33XdfzjrrrOy444658MILc/rpp2fmzJk55ZRTsnDhwoZ1G/v9fOCBB67zOVmXMWPGZNddd80VV1yRQYMG5cYbb8yJJ56Ympqa/OAHP8iQIUNy22235Te/+U2SpL6+PqNHj84vf/nLHHPMMbn88suz995754ILLsivf/3r9d7P66+/nhNPPDHTp0/PySefnEsvvTTdu3fPqFGjct999yV59/uqT58+6dOnzwbfLvDnP/95rrzyygwePDhjxozJXnvtlSuuuKLRmqY8/1/4whcyc+bMvPjiiw23q6ury+OPP77e+AMAwLbDmTAAALAe9fX1Of744/PFL34xSdKvX788+OCDeeSRRzJ48ODN2vdee+2Vr33taw1fz549u9H1P/7xj7PLLrvkiiuuyHbbbdfoNvfcc0+GDh2aJ598Mn379m34Re/AgQPTpk2bbL/99hu871WrVuW0007L/vvvnyRZvHhxJk+enC996Us588wzkySDBw9ObW1txowZk2OOOSa77rprw9px48ZlwIABSZI999wzw4cPz5133pnTTjstEyZMSOvWrXP99dc3zHHAAQfk2GOPzY9+9KPceOONDXN85jOfyRe+8IWGrz/ykY8kefftrrp3797kuSZOnJi+ffs2RI9PfvKTqaqqyvjx49/3eVi1alUuu+yyhreV22uvvTJ8+PBMmTIl3/rWt/Lcc89l//33z/nnn99w1sv++++f3/72t3nyySdz2GGH5amnnkpNTU3+9m//Nm3atEmS1NbWZtasWamvr8/bb79d7DhWr16dcePGZb/99msUbPr165evfe1rueOOO/Ltb387ycZ/P3fu3Hmt52R9Bg8enJNPPjlJ8tGPfjRTp05N586d893vfjdJ8olPfCL3339/ZsyYkc9+9rN57LHH8uijj+aiiy5qOPNl8ODBWbZsWcaPH5/Pfe5z6zyba8qUKXnjjTdy++23p0ePHknejUVnnHFGxo0bl8997nPZe++906FDhyRZ79sF1tfXN4TQs846q+H+33777fzyl79sWNeU5/9zn/tcrr/++vzbv/1bTjzxxCTJv//7v6dNmzb5zGc+s97HDACAbYMzYQAAYAPe+0vcmpqadO7cOcuWLdvs/a6JGuuybNmy/PGPf8yBBx6Y+vr6rFy5MitXrkyPHj3Sq1evPPbYY0mSfffdN48//nhOPfXUTJkyJc8//3yGDRuWww8//H3vv3fv3g2XZ86cmWXLlmXIkCEN97Vy5coMGTIkSRruL0m6devWEGCSpGvXrunXr1+eeuqpJMn06dNz4IEHNgpB1dXVOfTQQzNr1qwsWbJknTOsS1PmWr58eZ5++um1zib67Gc/+76PwZrjee9z3KVLl+y99955/PHHkySHH354fvjDH+add97JnDlz8l//9V+5+eabs2rVqrzzzjtJ3o1fy5cvz9e//vXccMMNmTFjRj7xiU/km9/8ZqqqqooexwsvvJDXXnsthx12WKPtO++8c/bee+888cQTjbaX+n7u169fw+UuXbokeTdorVFVVZWOHTtm8eLFSZJp06alqqoqBx544FqPyWuvvZY5c+as836mT5+evffeuyHArHHYYYfltddey/PPP9+keV944YUsXLjwfR/vpjz/22+/fT7zmc80nImTJL/61a/y2c9+Nu3atWvSPAAAbL2cCQMAABvQtm3bRl9XVVVl9erVm73fHXbYYb3XvfXWW1m9enVuvfXW3HrrrWtdv+ZsixEjRqRdu3a59957M3bs2Fx//fXZddddc8YZZ2S//fZr8v2/+eabSZKzzz57nWsXLFjQcPm9n3GyRufOnTN//vwkyaJFixp+Cf+X97fmrJD33m5DmjLXokWLUl9fnw996EONrlvXDOuyrnWdO3fOvHnzkrz7uSX/+I//mPvuuy8rV65M9+7d069fv1RXV6e+vj7Ju2HjqquuypQpU3LHHXfklltuSZcuXfK1r30t/+f//J+ix7Fo0aL1rtthhx3yP//zP422lfp+bt++/Vrb/vK+3uvNN99MfX19Pve5z63z+gULFjT6XJ41Fi1alL59+661fc3xr4k872fN4/aXj/dffn835flP3n1Lsvvuu6/hrKg///nPOeecc5o0CwAAWzcRBgAAmsmaDxxftWpVo+1Lly7dqP20b98+VVVVOeaYY9b5IeVrfrndqlWrDB06NEOHDs3ChQvz29/+Nrfccku+973v5Z577knr1q2bdH8dO3ZMklxwwQXp2bPnWte/N9is+eX1e73++usNQaW2tjavvfbaWmvWbOvUqdM6r9/UuWpra9OqVau8/vrrja5b15zr8tZbb61z1jXHc8011+SBBx7IxRdfnP3337/hzIa//KyPwYMHN7yd1rRp0/LTn/401157bfbaa6+ix1FbW9sw87qOo1OnThu8faVsv/32adeuXa6//vp1Xr/zzjuvc3ttbe1aj1HS+PurKdas+8t9rQlmazT1+R84cGA+8pGP5P7770/r1q2z8847Z5999mnSLAAAbN28HRkAADSTNZ9DseaskCR5/vnn1/rFblP206dPnzz//PP5+Mc/3vDfrrvumgkTJjR8wP0pp5ySa665Jsm7Z28ceeSRGTp0aBYvXtzojJP3s9dee6V169apq6trdH+tW7fOj3/847zyyisNa1966aX86U9/avi6rq4uM2fObDjzZuDAgfntb3/b6IyEVatWZerUqfn4xz+empqa9c6x5rNvNmauNm3aZO+9986DDz7Y6MyEhx9+uEnH/uKLLzb6QPV58+Zl5syZ2XfffZMkM2bMyL777ptPf/rTDb+Anz17dt54442G+xs7dmxOPPHE1NfXp23btjnooINy2mmnJXn3e6Hkceyyyy7p0qVL/v3f/73R9pdeeikzZ85M//79m/Q4rM9fPifNZeDAgQ1x8r2PyZw5czJx4sS1QuYaAwYMyMyZM/Pyyy832n7fffelS5cu6403f6lnz57p1q1bfvOb3zTa/sgjjzT6uinPf/JugD3yyCPz0EMP5b/+679yxBFHNGkOAAC2fs6EAQCAZjJo0KC0bds2119/fU4++eQsWbIkEyZMaDhbYWOccsop+bu/+7tceOGFOeyww7J69erccccd+X//7//luOOOS/LuL6TvuOOOdO7cOf369UtdXV2mTJmSgQMHrvU2SxvSqVOnjBgxIjfddFPefvvt7Lvvvqmrq8uNN96Yqqqq7Lbbbg1r6+vrc+655+akk07KdtttlwkTJqRjx44ZPnx4kuT444/P7373u4wcOTLHHntsWrdunZ///Od5+eWX88Mf/nCDc6z5HJkHH3wwBxxwQHr16tWkuU455ZScfvrpGT16dL70pS9l7ty5mTRpUpOOvaamJn//93+fU045JatWrcpNN92U2trahuP5+Mc/nvvvvz933XVXPvrRj+bZZ5/NpEmTUlVV1RAR9ttvv0yZMiWXXHJJ/vqv/zorV67Mbbfdltra2gwaNCi1tbXFjqNVq1Y55ZRTctlll+X888/P5z//+bz55pu5+eabU1tbm6985StNehzWZ13PSXM44IADMmDAgPz93/99jj/++PTq1SuzZs3KTTfdlMGDB6/3+/crX/lK7rvvvpxxxhk54YQT0qlTp/z617/OtGnTMnr06LRq1bR/Z1hVVZVTTz01F154YcaMGZNDDjkkf/zjH3PXXXc1WteU53+NI444IjfffHPq6+ub9LlMAABsG0QYAABoJttvv30uvfTSjB8/PqNGjcqHP/zhfPOb38y//du/bfS+Bg8enH/8x3/MhAkTct5556V169bp27dvrrnmmoYPVz/ppJNSU1OTe++9N5MmTUqHDh0yZMiQnHrqqRt9fyeffHK6du2aX/ziF7n99tvTsWPH7LfffjnllFMafhGfvPtB9l/5yldy3XXXZdmyZdlvv/1yxRVXNISmXXfdNT/60Y9yww035LLLLktVVVX23HPPXH/99e/79kz77rtv9ttvv/z4xz/O448/nquuuqpJcw0YMCA//OEPc8MNN2TUqFHp0aNHRo8e3aTP5Ojdu3c+97nP5aqrrsrixYuz33775fTTT294O7LTTz89K1euzI033pgVK1akR48e+cY3vpE//elPefjhh7Nq1ap88pOfzAUXXJA77rgjo0ePTlVVVfr375+xY8c2PC4lj+PII49M+/bt85Of/CSjRo1Khw4dMnjw4JxyyilN/myc9VnXc9IcWrVqlauuuio33nhjbrnllixcuDA77rhjjjnmmBx//PHrvV2XLl3y4x//OOPHj88111yTd955J7vttlvGjBmTT33qUxs1w+c+97lUVVVl0qRJue+++7LrrrvmnHPOyQUXXNCwpinP/5qzhXbcccfstttu6dSpU7p167ZpDwwAAFudqrq6uvr3XwYAAPyl559/PiNGjMgPfvCDfPazn630OMVdcsklmT59en7xi19UehTY4tTV1WXo0KG56KKLcsghh1R6HAAAthDOhAEAgE3w+9//Pv/1X/+VJPnIRz5S4WmASvmf//mfPPzww3nggQfSo0ePfPrTn670SAAAbEFEGAAA2ATjx4/Pq6++muHDh6dv376VHgeokBUrVmTKlCnZcccdc/HFFze8PRkAACTejgwAAAAAAKCIVpUeAAAAAAAAYGskwgAAAAAAABQgwgAAAAAAABRQXekBWoIddtghVVVVWb16daVHAQAAAAAAKqxVq1apr6/P66+/vsF1IkwTVFVVZeXKlZk7d26lRwEAAAAAACqsZ8+eqa5+/8QiwjTB6tWrM3fu3PTu3bvSowAAAAAAABX23HPPpVevXu+7zmfCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFFBd6QEWLVqUH//4x/ntb3+bt99+O7vttlu+9a1vZZ999lnn+jfffDNXX311Hn300STJIYccktNPPz3t2rVrWHP//ffn5ptvzssvv5yePXvm29/+dj75yU9+IMcDAAAAAACQbAFnwpx//vn54x//mIsuuig33XRT+vTpk7POOivPP//8Otd/73vfy0svvZRrr702l156af77v/87V111VcP106ZNy0UXXZQvf/nLmTRpUj7xiU9k1KhR+fOf//wBHREAAAAAAECFI8yLL76Yxx57LGeffXb22Wef9OrVK2eeeWZ23HHH/Pu///ta62fOnJnp06fnvPPOS9++fTNo0KCcc845ue+++1JXV5ckufXWW3PwwQdn2LBh6dWrV0477bT06dMnP/3pTz/owwMAAAAAALZhFY0wnTp1ypVXXpm+ffs2bKuqqkp9fX0WLVq01vqnnnoqXbp0Sa9evRq27bvvvqmqqsqMGTOyevXq/OEPf8igQYMa3W7QoEF56qmnyh0IAMBWrn379tlnn32yzz77pH379pUeBwAAAFqEin4mTMeOHXPggQc22nb//ffnpZdeyuDBg9daP3/+/HTr1q3RttatW6e2tjbz5s3L4sWLs3Tp0uy0006N1nTt2jXz5s3brFmrq6szcODAzdoHAEBLtf3226d169ZJktra2ixevLjCEwEAAEDl1NTUNGldRSPMX5oxY0Yuu+yyfOpTn8qQIUPWun7ZsmUN//P/Xm3atMmKFSuybNmyJGsffE1NTVasWLFZs/Xs2TNPPPHEZu0DAKClGjFiRMM/atl5551z++23V3giAAAAqKxVq1a975otJsI89NBDufDCC7PXXnvloosuWueaNm3a5J133llr+/Lly9O2bdu0adMmSdYKLitWrEjbtm03a765c+fm6KOP3qx9AAC0VLW1tdluu+2SJC+//HL23XffCk8EAAAAlXP33Xene/fu77tui4gwP//5z3Pttdfm4IMPzvnnn7/e03i6deuWhx56qNG2d955J4sWLcpOO+2U2tratGvXLgsWLGi0ZsGCBdlxxx03a8aVK1dm+vTpm7UPAICW6oADDki7du2SvPsPXPy9CAAAgG1ZU999q1XhOd7XXXfdlauvvjpDhw7NxRdfvMH3Udtnn30yf/78vPjiiw3b1rxFWL9+/VJVVZX+/fuv9UuBadOmZZ999ilzAAAAAAAAAOtQ0Qjzwgsv5JprrsmnP/3pHHvssVm4cGFee+21vPbaa1m8eHFWrVqV1157LcuXL0+S7LXXXunfv3/OP//8zJo1K9OmTcuVV16Zww8/vOFMl2OOOSZTp07NlClT8vzzz2fcuHF55plncswxx1TyUAEAAAAAgG1MVV1dXX2l7nzy5Mn5p3/6p3Ve9/nPfz4nnHBChg0bltGjR+fII49MkixcuDA//OEP8+ijj6ZNmzY55JBDMnLkyIbPg0mSf/u3f8vEiRMzf/78fOxjH8t3vvOdDBo0aJPn7Ny5c55//vn07t17k/cBANCSvfftyJYuXZrf/e53FZ4IAAAAKue5555Lr169snDhwg2uq2iEaSlEGABgWyfCAAAAwP9qaoSp+GfCAAAAAAAAbI1EGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAJEGAAAAAAAgAKqKz3Ae02aNCmPP/54xo4du87rb7755kyYMGGd1x155JEZPXp0kmTYsGF55ZVXGl1/2GGH5YILLmjegQEAAAAAANZji4kwd955Z2666aYMGDBgvWu++tWv5uijj2607Z577snkyZMzfPjwJMnbb7+dV199NVdeeWX69u3bsK5NmzYlxgYAAAAAAFinikeYurq6jBkzJk899VR22WWXDa5t37592rdv3/D1iy++mFtuuSUjR47M7rvvniSZM2dO6uvr069fv3Ts2LHo7AAAAAAAAOtT8c+Eefrpp9OxY8dMnjw5e+6550bdduzYsdl1113zpS99qWHbs88+my5duggwAAAAAABARVX8TJghQ4ZkyJAhG327WbNm5aGHHsp1112XVq3+tyXNmTMn7dq1y+jRozNz5sx07tw5RxxxRIYPH95oHQAAAAAAQEkVjzCb6s4778yee+6ZQYMGNdo+Z86cLF68OIceemhOOOGEPPnkkxk/fnwWLVqUk046aZPvr7q6OgMHDtzcsQEAWqSamppGl/29CAAAgG3Ze/8/eUNaZIRZunRpHnzwwZx99tlrXXfttddm+fLl6dChQ5Kkd+/eWbJkSSZPnpwTTjhhk8+G6dmzZ5544onNmhsAoKUaMWJE5s2blyTp0aNHHnjggcoOBAAAABW2atWq913TIiPMo48+mvr6+nz6059e67rq6upUVzc+rN69e2fp0qV566230qlTp026z7lz5+boo4/epNsCALR0tbW12W677ZIkL7/8cvbdd98KTwQAAACVc/fdd6d79+7vu65FRpgZM2akT58+6dixY6Ptq1evzvDhw3PUUUfluOOOa9g+a9as7LDDDpscYJJk5cqVmT59+ibfHgCgJTvggAPSrl27JMmKFSv8vQgAAIBt2ooVK5q0bouOMKtWrcobb7yR7bffPm3atGnY/uyzz6Z3795rrW/VqlUOOeSQ3HbbbenZs2f69OmTadOm5bbbbsuZZ575AU4OAAAAAABs67boCDN//vwMGzYso0ePzpFHHtmw/fXXX8+ee+65ztt861vfSseOHTN+/PjU1dWle/fuOfPMM3PUUUd9UGMDAAAAAACkqq6urr7SQ2zpOnfunOeff36dZ98AAGwL3vt2ZEuXLs3vfve7Ck8EAAAAlfPcc8+lV69eWbhw4QbXtfqA5gEAAAAAANimiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFVFd6AABg8/Xs2TNdu3at9BhsxWpqahpdHjhwYAWnYVuwYMGCzJ07t9JjAAAAbBYRBgBauJ49e+bp2bPTrn37So/CVmzEiBGZN29ekqRHjx554IEHKjsQW72lS5ak7x57CDEAAECLJsIAQAvXtWvXtGvfPn+658Ysfe2VSo/DVuqdxW80uvz/Jl9cuWHY6rXr0j0f+8JJ6dq1qwgDAAC0aCIMAGwllr72SpbOe6HSY7CVql+1qtFl32sAAADw/lpVegAAAAAAAICtkQgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQgAgDAAAAAABQQHWlB3ivSZMm5fHHH8/YsWPXu+ZXv/pVLr300rW233nnndl5552TJPfff39uvvnmvPzyy+nZs2e+/e1v55Of/GSxuQEAAAAAAP7SFhNh7rzzztx0000ZMGDABtc999xzGThwYC666KJG2z/0oQ8lSaZNm5aLLrooI0eOzP77759//dd/zahRozJx4sR89KMfLTM8AAAAAADAX6h4hKmrq8uYMWPy1FNPZZdddnnf9c8991x22223dOnSZZ3X33rrrTn44IMzbNiwJMlpp52WP/zhD/npT3+ac845p1lnBwAAAAAAWJ+KfybM008/nY4dO2by5MnZc88933f9c889l4997GPrvG716tX5wx/+kEGDBjXaPmjQoDz11FPNMi8AAAAAAEBTVPxMmCFDhmTIkCFNWrtw4cK8/vrrefLJJ/Ozn/0sixYtyl577ZVTTz01u+yySxYvXpylS5dmp512anS7rl27Zt68eZs1Z3V1dQYOHLhZ+wCAEvbYY49KjwBQhJ9vAADAlqqmpqZJ6yoeYTbGnDlzkiStWrXK97///SxdujQTJ07Mqaeemp/85CdZuXJlkrUPvqamJitWrNis++7Zs2eeeOKJzdoHAADQdLfffnulRwAAAFivVatWve+aFhVhBg0alF//+tepra1t2DZmzJj8zd/8TX71q1/li1/8YpKsFVxWrFiRtm3bbtZ9z507N0cfffRm7QMASthjjz38ohLYKo0YMSKzZ8+u9BgAAABrufvuu9O9e/f3XdeiIkySRgEmSdq1a5cePXpk/vz5qa2tTbt27bJgwYJGaxYsWJAdd9xxs+535cqVmT59+mbtAwAAaLrZs2f7OzgAALBFauq7b7UqPEezuuuuu3LEEUdk+fLlDdvefvvtzJ07Nx/72MdSVVWV/v37r/U/atOmTcs+++zzQY8LAAAAAABsw7boCLNq1aq89tprDdHlwAMPzOrVq/ODH/wgc+bMyaxZszJ69Oh86EMfyhFHHJEkOeaYYzJ16tRMmTIlzz//fMaNG5dnnnkmxxxzTCUPBQAAAAAA2MZs0RFm/vz5OeqoozJ16tQkSbdu3XLdddfl7bffzqmnnpozzjgjHTt2zPXXX582bdokSQYPHpzRo0fnrrvuyje+8Y1MmzYtV155ZXr16lXJQwEAAAAAALYxW9Rnwpx33nmNvu7evXseeeSRRtv69OmTq6++eoP7Ofzww3P44Yc3+3wAAAAAAABNtUWfCQMAAAAAANBSiTAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFVFd6AGDL1r59++y+++5JkmeeeSZLliyp8EQAAAAAAC2DCANs0O67754uXbokSerr6zNjxowKTwQAAFSCf6AFALDxRBhgg9q3b99wuUOHDhWcBAAAqCT/QAsAYOP5TBgAAADgffkHWgAAG0+EAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKECEAQAAAAAAKKC60gOweXr27JmuXbtWegy2YjU1NY0uDxw4sILTsC1YsGBB5s6dW+kxAAAAAGCziTAtWM+ePTN79uy0b9++0qOwFRsxYkTmzZuXJOnRo0ceeOCByg7EVm/JkiXZY489hBgAAAAAWjwRpgXr2rVr2rdvn3F3PJKX5r9Z6XHYSi1ctLTR5dHX/qqC07C1+8hOnfKdrx6Url27ijAAAAAAtHgizFbgpflv5s8vLaz0GGylVq5a3eiy7zUAAAAAgKZpVekBAAAAAAAAtkYiDAAAAAAAQAEiDAAAAAAAQAEiDAAAAAAAQAEiDAAAAAAAQAEiDAAAAAAAQAEiDAAAAAAAQAEiDAAAAAAAQAHVlR4AAAAAAICWoX379tl9992TJM8880yWLFlS4YlgyybCAAAAAADQJLvvvnu6dOmSJKmvr8+MGTMqPBFs2bwdGQAAAAAATdK+ffuGyx06dKjgJNAyiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFiDAAAAAAAAAFVFd6gPeaNGlSHn/88YwdO3a9a+bMmZMf/ehH+eMf/5hWrVpl4MCBOe200/LhD3+4Yc2wYcPyyiuvNLrdYYcdlgsuuKDY7AAAAAAAAO+1xUSYO++8MzfddFMGDBiw3jVvvvlmzjzzzAwYMCDjxo3LihUrMnbs2Hz3u9/NhAkT0qZNm7z99tt59dVXc+WVV6Zv374Nt23Tps0HcBQAAAAAAADvqniEqaury5gxY/LUU09ll1122eDaBx98MMuWLcv3vve9hqhy/vnn58tf/nJmzpyZQYMGZc6cOamvr0+/fv3SsWPHD+IQAAAAAAAA1lLxCPP000+nY8eOmTx5ciZOnJhXX311vWv333///MM//MM6z2pZtGhRkuTZZ59Nly5dBBgAAGCb0rNnz3Tt2rXSY7AVq6mpaXR54MCBFZyGbcGCBQsyd+7cSo8BAJul4hFmyJAhGTJkSJPWdu/ePd27d2+07Sc/+Ulqamoa3sZszpw5adeuXUaPHp2ZM2emc+fOOeKIIzJ8+PC0atWquccHAACouJ49e2b27Nlp3759pUdhKzZixIjMmzcvSdKjR4888MADlR2Ird6SJUuyxx57CDEAtGgVjzCb42c/+1n++Z//Oaeffno6d+6c5N0Is3jx4hx66KE54YQT8uSTT2b8+PFZtGhRTjrppE2+r+rq6i3uX/nssccelR4BoAg/3zaOxwvYWvn51nR77LFH2rdvnxsevCUvvzmv0uOwlXpjyZuNLl9w95UVnIatXY9O3XLKwV/PkCFDMnv27EqPA7yHMyPhXe/9s7AhLTLC1NfX58Ybb8zkyZNz7LHH5phjjmm47tprr83y5cvToUOHJEnv3r2zZMmSTJ48OSeccMImnw3Ts2fPPPHEE80yPwAbdvvtt1d6BAC2AF4PNt7Lb87L86+9WOkx2EqtXL2q0WXfa3wQvBbAlseZkfC/Vq1a9b5rWlyEWblyZS699NL8x3/8R77zne9kxIgRja6vrq5OdXXjw+rdu3eWLl2at956K506ddqk+507d26OPvroTR27iD322MNfRoCt0ogRI/xrt43g9QDYWnk9aDqvBcDWymsBbHlqa2uz3XbbJUlefvnl7LvvvhWeCCrj7rvvXuvjU9alxUWYiy++OA8++GAuvPDCHHrooY2uW716dYYPH56jjjoqxx13XMP2WbNmZYcddtjkAJO8G3+mT5++ybcHoOlmz57tZy4AXg8A8FoAW6ADDjgg7dq1S5KsWLHCn1G2WStWrGjSui06wqxatSpvvPFGtt9++7Rp0yb33ntv/vM//zPf+c53MnDgwLz22msNa9esOeSQQ3LbbbelZ8+e6dOnT6ZNm5bbbrstZ555ZuUOBAAAAAAA2OZs0RFm/vz5GTZsWEaPHp0jjzwy//Ef/5EkGTduXMaNG9do7Zo13/rWt9KxY8eMHz8+dXV16d69e84888wcddRRlTgEAAAAAABgG7VFRZjzzjuv0dfdu3fPI4880vD1Nddc8777qK6uznHHHdfo7cgAAAAAAAA+aK0qPQAAAAAAAMDWSIQBAAAAAAAoQIQBAAAAAAAoQIQBAAAAAAAoQIQBAAAAAAAoQIQBAAAAAAAoQIQBAAAAAAAoQIQBAAAAAAAoQIQBAAAAAAAoQIQBAAAAAAAoQIQBAAAAAAAoQIQBNqi6baf3XP5Q5QYBAAAAAGhhRBhggzr2/GRqaj+SmtqPpGPPwZUeBwAAAACgxaiu9ADAlq26bad03v2vKz0GAAAAAECL40wYAAAAAACAAkQYAAAAAACAAkQYAAAAAACAAkQYAAAAAACAAkQYAAAAAACAAkQYAAAAAACAAkQYAAAAAACAAkQYAAAAAACAAkQYAAAAAACAAkQYAAAAAACAAkQYAAAAAACAAkQYAAAAAACAAkQYAAAAAACAAkQYAAAAAACAAkQYAAAAAACAAkQYAAAAAACAAkQYAAAAAACAAkQYAAAAAACAAkQYAAAAAACAAkQYAAAAAACAAkQYAAAAAACAAkQYAAAAAACAAkQYAAAAAACAAkQYAAAAAACAAkQYAAAAAACAAkQYAAAAAACAAkQYAAAAAACAAkQYAAAAAACAAkQYAAAAAACAAkQYAAAAAACAAkQYAAAAAACAAkQYAAAAAACAAkQYAAAAAACAAkQYAAAAAACAAkQYAAAAAACAAkQYAAAAAACAAkQYAAAAAACAAkQYAAAAAACAAkQYAAAAAACAAjYqwrz11lu54447Mn369IZtM2fOzIknnphDDz00J598cmbMmNHsQwIAAAAAALQ0TY4wCxcuzDe/+c386Ec/yjPPPJMkqaury1lnnZW5c+fmi1/8Yjp27Jgzzjgjc+bMKTYwAAAAAABAS1Dd1IWTJ0/OypUrM3HixOy2225JkilTpmTZsmW59NJL8+lPfzpJcu6552bixIn5wQ9+UGZiAAAAAACAFqDJZ8L87ne/y7HHHtsQYJLk4YcfTm1tbUOASZLDDz88Tz31VPNOCQAAAAAA0MI0OcLMnz8/H/vYxxq+XrBgQV566aXsu+++jdZ17tw5b731VvNNCAAAAAAA0AI1OcK0adMmS5cubfj6ySefTJK1IkxdXV06dOjQPNMBAAAAAAC0UE2OMH379s1vf/vbhq//8z//M61atcoBBxzQaN19992XPn36NN+EAAAAAAAALVB1UxcOHz485557bpYsWZLVq1fnoYceyiGHHJIPf/jDSZIXX3wxd955Zx599NFcfPHFxQYGAAAAAABoCZocYYYMGZLRo0dn0qRJef311/PZz342f//3f99w/SmnnJJFixbla1/7Wg455JAiwwIAAAAAALQUTY4wSXLEEUfkiCOOWOd1f/d3f5ddd901u+yyS7MMBgAAAAAA0JJtVITZkM985jPNtSsAAAAAAIAWr1VTF44bNy7z589vtK2uri6rVq1qtO25557L17/+9eaZDgAAAAAAoIVqcoSZMmVKFixY0PD1qlWr8jd/8zd59tlnG61btmxZ/vSnPzXfhAAAAAAAAC1QkyNMfX19k7YBAAAAAACwEREGAAAAAACAphNhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAACqjemMX33HNPHn300SRJfX19qqqq8i//8i/p2rVrw5q6urrmnRAAAAAAAKAF2qgIc/fddzdpW1VV1aZPBAAAAAAAsBVocoR5+OGHS84BAAAAAACwVfGZMAAAAAAAAAU0+UyYV199daN2/OEPf3ijhwEAAAAAANhaNDnCDBs2bKM+6+Whhx7apIEAAACALU/rTm2y6u13Gi4DAPD+mhxhRo0atVERBgAAANh6fGhQ97xR/0rDZQAA3l+TI8yRRx5Zcg4AAABgC9a6U5vs+FcfrfQYAAAtSqvm3uHbb7+d6667rrl3CwAAAAAA0KJsVIS5++67c9JJJ+Wkk07KXXfdtdb1v/71r/PVr341P/vZz5ptQAAAAAAAgJaoyW9H9rOf/SzXXnttdtppp7Rt2zb/+I//mFatWuVLX/pS5s6dm8suuywzZ87M9ttvn7POOqvkzAAAAAAAAFu8JkeYe++9N4MHD87ll1+e6urqjBs3LlOmTEnv3r3z3e9+N0uXLs2XvvSlnHzyyamtrS05MwAAAAAAwBavyW9H9tJLL+Xoo49OdfW73WbYsGGZO3duvv/976dbt275p3/6p5x99tkCDAAAAAAAQDbiTJhly5Zlhx12aPh6zeWPfOQj+eEPf5g2bdo0/3QAAAAAQJP17NkzXbt2rfQYbMVqamoaXR44cGAFp2FbsGDBgsydO7fSY2yyJkeY+vr6VFVVNXzdqtW7J9Ece+yxAgwAAAAAVFjPnj3z9OzZade+faVHYSs2YsSIzJs3L0nSo0ePPPDAA5UdiK3e0iVL0nePPVpsiGlyhFmfD33oQ80wBgAAAACwObp27Zp27dvnyfE3ZPHLr1R6HLZSy994o9Hlh79/YcVmYeu3fY/uGXDqKenateu2EWHeeybMhrYBAAAAAJWx+OVXsuj55ys9Blup1StXNrrsew02bKMizLnnntvoPf+S5Jxzzknr1q3XWvuzn/1s8yYDAAAAAABowZocYT7/+c+vtW3AgAHNOQsAAAAAAMBWo8kR5nvf+17JOQAAAAAAALYqrSo9AAAAAAAAwNaoyWfCjBw5cp3bq6qq0rZt23Tp0iWDBg3KX/3VX6WqqqrZBgQAAAAAAGiJmnwmTH19/Tr/W716dd588838/ve/z4UXXpiRI0fmnXfeKTkzAAAAAADAFq/JZ8KMHTv2fdf88Y9/zKhRo3L77bfnuOOO26zBAAAAAAAAWrJm/UyYvfbaKyNGjMjUqVObc7cAAAAAAAAtTrNGmCTp27dvXnnllebeLQAAAAAAQIvS7BFm9erV2W677Zp7twAAAAAAAC1Ks0eYp556Kj169Gju3QIAAAAAALQozRZhVq5cmd/85je5/fbbc+ihhzbXbgEAAAAAAFqk6qYuHDZsWKqqqtZ53YoVK7Jo0aKsXLkyBx10UL7yla8024AAAAAAAAAtUZMjzIABA9YZYaqqqtK2bdt06dIlAwcOTP/+/Zt1QAAAAAAAgJaoyRHmvPPOKzkHAAAAAADAVqXZPhMGAAAAAACA/9UsnwmTJDU1Ndlpp53yV3/1V/nCF77QLMMBAAAAAAC0VJv9mTBrrF69OvPmzcsVV1yRt956K1/96lebZUAAAAAAAICWqNk/E2bSpEn553/+ZxEGAAAAAADYpjX7Z8L069cvr7/+enPvFgAAAAAAoEVp1gizfPny3Hvvvdl5552bc7cAAAAAAAAtTpPfjuz9TJgwIT/5yU+yatWqXHjhhc21WwAAAAAAgBap2SJM//79c+yxx2bIkCHp06dPc+0WAAAAAACgRWq2CLPffvtlv/32a67dAQAAAAAAtGhNjjATJkxo8k6rqqpy/PHHb9JAAAAAAAAAW4NmjTBVVVUNl0UYAAAAAABgW9bkCPPwww9v8Pp77703119/fd55552cfPLJmz0YAAAAAABAS7bZnwlTV1eXyy+/PL///e+zzz775Nxzz83OO+/cHLMBAAAAAAC0WJsVYe65556MHTs2K1euzOmnn57hw4c311wAAAAAAAAt2iZFmLq6uowZMya///3vM2DAgIwaNSof+chHmns2AAAAAACAFmujI8y//uu/Zty4cVm5cmXOPPPMDBs2rMRcAAAAAAAALVqTI8z8+fMzZsyYPPbYYxk4cGBGjRqV7t27l5wNAAAAAACgxWpyhPna176WpUuXZvvtt0+3bt0yceLE9a6tqqrKqFGjmmVAAAAAAACAlqjJEaZjx46pra1NkkyfPn2Da6uqqjZvKgAAAAAAgBauyRHmF7/4Rck5AAAAAAAAtiqtKj0AAAAAAADA1qjJZ8L8+te/3qgdf/7zn9/oYQAAAAAAALYWTY4wl156aZN3WlVVJcIAAAAAAADbtCZHmJ///Ocl50iSTJo0KY8//njGjh273jVvvvlmrr766jz66KNJkkMOOSSnn3562rVr17Dm/vvvz80335yXX345PXv2zLe//e188pOfLD4/AAAAAADAGk3+TJgPf/jDG/VfktTX12fChAl57bXX3nf/d955Z2666ab3Xfe9730vL730Uq699tpceuml+e///u9cddVVDddPmzYtF110Ub785S9n0qRJ+cQnPpFRo0blz3/+c1MPFQAAAAAAYLM1OcJsitWrV2fixIlZsGDBetfU1dXlu9/9bm688cbssssuG9zfzJkzM3369Jx33nnp27dvBg0alHPOOSf33Xdf6urqkiS33nprDj744AwbNiy9evXKaaedlj59+uSnP/1psx4bAAAAAADAhhSNMMm7Z8NsyNNPP52OHTtm8uTJ2XPPPTe49qmnnkqXLl3Sq1evhm377rtvqqqqMmPGjKxevTp/+MMfMmjQoEa3GzRoUJ566qlNPwgAAAAAAICN1OTPhCllyJAhGTJkSJPWzp8/P926dWu0rXXr1qmtrc28efOyePHiLF26NDvttFOjNV27ds28efM2a87q6uoMHDhws/bR3PbYY49KjwBQhJ9vG8fjBWyt/HxrOo8VsLXy823jeLyArdWW+POtpqamSesqHmE2xrJly9K6deu1trdp0yYrVqzIsmXLkqx98DU1NVmxYsVm3XfPnj3zxBNPbNY+AGia22+/vdIjAH9hp+1bZ+GylQ2X4YPg9QAArwWw5dmhunUWrVqVJOlS7f8N+GBsqa8Hq/7/Pwsb0qIiTJs2bfLOO++stX358uVp27Zt2rRpkyRrBZcVK1akbdu2m3Xfc+fOzdFHH71Z+2hue+yxxxb7zQewOUaMGJHZs2dXeowWw+sBH4Qv7rFD6me93nAZPgheD5rOawGwtfJasHG8HvBBOKRT59z/5sIkyWc6da7wNGwrtsTXg7vvvjvdu3d/33UtKsJ069YtDz30UKNt77zzThYtWpSddtoptbW1adeuXRYsWNBozYIFC7Ljjjtu1n2vXLky06dP36x9ANA0s2fP9jMXtjA7dajJift9uNJjsI3xegCA1wLY8uzQunWGdd3p/RdCM9oSXw+a+u5brQrP0az22WefzJ8/Py+++GLDtjVvEdavX79UVVWlf//+az0Z06ZNyz777POBzgoAAAAAAGzbtugIs2rVqrz22mtZvnx5kmSvvfZK//79c/7552fWrFmZNm1arrzyyhx++OENZ7occ8wxmTp1aqZMmZLnn38+48aNyzPPPJNjjjmmkocCAAAAAABsY7boCDN//vwcddRRmTp1apKkqqoql112WXr06JGRI0fm/PPPzyc/+cmcffbZDbcZPHhwRo8enbvuuivf+MY3GkJNr169KnUYAAAAAADANqjZPxNm9erV+cUvfpHhw4dnu+22y/XXX59ddtmlSbc977zzGn3dvXv3PPLII422de7cOZdccskG93P44Yfn8MMP37jBAQAAAAAAmtFGRZjf//73uffee5O8GzoOPPDARtc/+eSTufrqqzNnzpwMHz48STJw4MBmGhUAAAAAAKDlaHKEmTp1ai688MK0bt06rVu3zm9+85tccsklOfjgg/Pmm2/mmmuuydSpU7PddtvlK1/5SsmZAQAAAAAAtnhNjjB33nln9txzz1x99dWpqanJmDFjMnHixHz0ox/NWWedlfnz52fw4ME544wzmvz2YwAAAAAAAFurJkeYF154Ieecc046dOiQJDn++OPz1a9+NaNHj87KlStz6aWX5uCDDy42KAAAAAAAQEvS5AizZMmSdOvWreHrnXbaKfX19amurs7kyZPTuXPnIgMCAAAAAAC0RK2aurC+vj6tWv3v8u222y5JcuKJJwowAAAAAAAAf6HJEWZ9dtppp+aYAwAAAAAAYKuyURGmqqqqSdsAAAAAAAC2dU3+TJgkufLKK9OhQ4ck7749WZJcfvnlad++faN1VVVVue6665ppRAAAAAAAgJanyRFmwIABSf43vqxv27q+BgAAAAAA2NY0OcKMHTu25BwAAAAAAABblSZHmMsuu6zJO62qqsqoUaM2aSAAAAAAAICtQZMjzLRp01JVVbXBNW+88UaWLVsmwgAAAAAAANu8JkeYX/ziF+u9buXKlZk0aVJ+8pOfZIcddsjZZ5/dLMMBAAAAAAC0VE2OMOvzP//zP7n00kszZ86cHHrooTnrrLNSW1vbHLMBAAAAAAC0WJscYVauXJmJEyfmtttuS21tbS677LJ86lOfas7ZAAAAAAAAWqxNijBrzn557rnn8td//dc588wz07Fjx+aeDQAAAAAAoMXaqAizcuXKTJgwIbfddls6d+6cyy+/PAcddFCp2QAAAAAAAFqsJkeYp59+Opdeemn+9Kc/5fDDD8+ZZ56ZDh06lJwNAAAAAACgxWpyhDnppJNSX1+fDh065NVXX82555673rVVVVW57rrrmmVAAAAAAACAlqjJEaZ///4Nl+vr6ze49v2uBwAAAAAA2No1OcKMHTu25BwAAAAAAABblVaVHgAAAAAAAGBrJMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUIMIAAAAAAAAUUF3pAVavXp0JEybkX//1X/PWW2+lf//+Ofvss7Pzzjuvtfbmm2/OhAkT1rmfI488MqNHj06SDBs2LK+88kqj6w877LBccMEFzX8AAAAAAAAA61DxCDNp0qT88pe/zOjRo7PjjjvmRz/6Ub773e/m1ltvTevWrRut/epXv5qjjz660bZ77rknkydPzvDhw5Mkb7/9dl599dVceeWV6du3b8O6Nm3aFD8WAAAAAACANSr6dmTvvPNO7rjjjpxwwgk58MADs/vuu+fiiy9OXV1dHnzwwbXWt2/fPl26dGn4b+nSpbnlllsycuTI7L777kmSOXPmpL6+Pv369Wu0dvvtt/+gDw8AAAAAANiGVTTCPPPMM1myZEkGDRrUsK1jx47p27dvnnzyyfe9/dixY7PrrrvmS1/6UsO2Z599Nl26dEnHjh1LjAwAAAAAANAkFX07svnz5ydJunXr1mh7165dM2/evA3edtasWXnooYdy3XXXpVWr/21Jc+bMSbt27TJ69OjMnDkznTt3zhFHHJHhw4c3WgcAAAAAAFBSRSPM8uXLk2Stz36pqanJokWLNnjbO++8M3vuuWejs2iSdyPM4sWLc+ihh+aEE07Ik08+mfHjx2fRokU56aSTNnnW6urqDBw4cJNvX8Iee+xR6REAivDzbeN4vICtlZ9vTeexArZWfr5tHI8XsLXaEn++1dTUNGldRSPMmiHfeeedtGnTpmH7ihUr0rZt2/XebunSpXnwwQdz9tlnr3Xdtddem+XLl6dDhw5Jkt69e2fJkiWZPHlyTjjhhE0+G6Znz5554oknNum2AGyc22+/vdIjALAF8HoAgNcCAJIt9/Vg1apV77umohFmzduQ1dXVZeedd27YvmDBguy2227rvd2jjz6a+vr6fPrTn17ruurq6lRXNz6s3r17Z+nSpXnrrbfSqVOnTZp17ty5OfroozfptqXsscceW+w3H8DmGDFiRGbPnl3pMVoMrwfA1srrQdN5LQC2Vl4LNo7XA2BrtSW+Htx9993p3r37+66raITZbbfd0qFDh0yfPr0hwrz11lt5+umnM3To0PXebsaMGenTp086duzYaPvq1aszfPjwHHXUUTnuuOMats+aNSs77LDDJgeYJFm5cmWmT5++ybcHoOlmz57tZy4AXg8A8FoAQJIt8/VgxYoVTVpX8bcjGzp0aMaPH58PfehD6d69e8aNG5eddtopBx98cFatWpU33ngj22+/faO3K3v22WfTu3fvtfbXqlWrHHLIIbntttvSs2fP9OnTJ9OmTcttt92WM8888wM8MgAAAAAAYFtX0QiTJCeeeGJWrVqVMWPGZPny5RkwYECuvvrqtG7dOq+88kqGDRuW0aNH58gjj2y4zeuvv54999xznfv71re+lY4dO2b8+PGpq6tL9+7dc+aZZ+aoo476oA4JAAAAAACg8hFmu+22y7e//e18+9vfXuu67t2755FHHllr+2233bbe/VVXV+e4445r9HZkAAAAAAAAH7RWlR4AAAAAAABgayTCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDCAAAAAAAAFCDC/H/t3XmQF/WB///XcA0joCiIjAsaIyKJiiBeERO84h0XFhR1YzQSU4pnBU0iJm7iqtFS1wOV6EbEeEVMlKh4RTQe2VIjh8YLUUvjCYMXiMgxw+8PfszXcUYB5e0QeDyqqGK6392fd3+0uuvDc7o/AAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABYgwAAAAAAAABbRq7gnU1dVlzJgxuf322zNnzpz07t07J598crp169bk+DvvvDNnnXVWo+U33XRT/Tb3339/rrrqqrz55pvp3r17hg8fnh133LHocQAAAAAAAHxSs98JM3bs2IwfPz4/+9nPcsUVV6SioiIjRozIwoULmxz/0ksvpW/fvrntttsa/Kmurk6STJo0Kb/+9a8zaNCgjB07Nttvv31OPfXUvPLKK1/hUQEAAAAAAGu6Zo0wCxcuzI033phhw4Zlp512ymabbZYzzjgjNTU1efDBB5vc5qWXXkqPHj3SqVOnBn9atmyZJLnuuusyYMCADBkyJBtvvHGOO+649OzZM+PGjfsqDw0AAAAAAFjDNWuEmT59ej766KP069evflmHDh2y+eabZ+rUqU1u89JLL2WTTTZpcl1dXV3+8Y9/NNhfkvTr1y9PPvnkSps3AAAAAADAsjTrd8LMnDkzSbLBBhs0WN65c+fMmDGj0fj33nsv7777bqZOnZqbb745s2fPzhZbbJFjjjkmG220UT788MPMmzcvXbp0Wa79rYhWrVqlb9++X2ofK1uvXr2aewoARTi/rRjvF7C6cn5bft4rYHXl/LZivF/A6mpVPL+1adNmucY1a4SZP39+kqR169YNlrdp0yazZ89uNP7ll19OkrRo0SK//OUvM2/evFx99dU55phjcu2112bRokX12396fwsWLPhSc+3evXsmT578pfYBwPK54YYbmnsKAKwCXA8AcC0AIFl1rwe1tbXLHNOsEWZpLFm4cGEqKyvrly9YsCBt27ZtNL5fv3656667svbaa9cvO+ecc/If//EfufPOO/O9732vfvtP+qz9rYjXXnstAwcO/FL7WNl69eq1yv7PB/BlHHrooXn++eebexr/MlwPgNWV68Hycy0AVleuBSvG9QBYXa2K14Pbbrst1dXVyxzXrBFm6WPIampq0q1bt/rls2bNSo8ePZrc5pMBJkmqqqqy4YYbZubMmVl77bVTVVWVWbNmNRgza9asrL/++l9qrosWLcqUKVO+1D4AWD7PP/+8cy4ArgcAuBYAkGTVvB4s79O3WhSex+fq0aNH2rVr1+DNmzNnTqZNm5att9660fhbb701++67b/1jzJJk7ty5ee2117LJJpukoqIivXv3bvQfY9KkSU3uDwAAAAAAoJRmjTBt2rTJ4MGDM3r06Dz88MN58cUXc/rpp6dLly4ZMGBAamtr884779RHl5122il1dXX57//+77z88st57rnnMnLkyHTs2DH77rtvkmTo0KG577778oc//CGvvvpqLrvsskyfPj1Dhw5tzkMFAAAAAADWMM0aYZLkRz/6Ufbff/+cc845Ofroo9OyZctceOGFad26dWbOnJkDDjgg9913X5Iljy+75JJLMnfu3BxzzDE58cQT06FDh4waNar+O2V22GGHjBw5MrfeemuOOOKITJo0Keedd1423njj5jxMAAAAAABgDdOs3wmTJC1btszw4cMzfPjwRuuqq6vzt7/9rcGynj175sILL/zcfe69997Ze++9V+o8AQAAAAAAVkSz3wkDAAAAAACwOhJhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAAChBhAAAAAAAACmjV3BOoq6vLmDFjcvvtt2fOnDnp3bt3Tj755HTr1q3J8S+//HIuv/zyPPPMM2nRokX69u2b4447Ll27dq0fM2TIkLz11lsNtttzzz3zX//1X0WPBQAAAAAAYKlmjzBjx47N+PHjM3LkyKy//vq5/PLLM2LEiFx33XVp3bp1g7EffPBBTjrppPTp0yeXXXZZFixYkEsvvTQjRozImDFjUllZmblz5+btt9/Oeeedl80337x+28rKyq/60AAAAAAAgDVYsz6ObOHChbnxxhszbNiw7LTTTtlss81yxhlnpKamJg8++GCj8Q8++GA+/vjjnHbaafn617+eXr165fTTT88rr7ySp59+OsmSO2UWL16crbbaKp06dar/0759+6/68AAAAAAAgDVYs0aY6dOn56OPPkq/fv3ql3Xo0CGbb755pk6d2mj8dtttl9/85jdN3tUye/bsJMmLL76YTp06pUOHDsXmDQAAAAAAsCzN+jiymTNnJkk22GCDBss7d+6cGTNmNBpfXV2d6urqBsuuvfbatGnTJn369Emy5E6YqqqqjBw5Mk8//XTWXXfd7LvvvjnwwAPTokWzNicAAAAAAGAN0qwRZv78+UnS6Ltf2rRpU39ny+e5+eabc8stt+SEE07Iuuuum2RJhPnwww+zxx57ZNiwYZk6dWpGjx6d2bNn56ijjvrCc23VqlX69u37hbcvoVevXs09BYAinN9WjPcLWF05vy0/7xWwunJ+WzHeL2B1tSqe39q0abNc45o1wiyd5MKFCxs8YmzBggVp27btZ263ePHi/O///m+uueaaHHbYYRk6dGj9uosvvjjz589Pu3btkiSbbrppPvroo1xzzTUZNmzYF74bpnv37pk8efIX2haAFXPDDTc09xQAWAW4HgDgWgBAsupeD2pra5c5plkjzNLHkNXU1KRbt271y2fNmpUePXo0uc2iRYty1lln5S9/+UuOPfbYHHrooQ3Wt2rVKq1aNTysTTfdNPPmzcucOXOyzjrrfKG5vvbaaxk4cOAX2raUXr16rbL/8wF8GYceemief/755p7GvwzXA2B15Xqw/FwLgNWVa8GKcT0AVler4vXgtttua/T1KU1p1gjTo0ePtGvXLlOmTKmPMHPmzMm0adMyePDgJrc544wz8uCDD+ZXv/pV9thjjwbr6urqcuCBB+aAAw7I4YcfXr/8ueeey3rrrfeFA0yyJP5MmTLlC28PwPJ7/vnnnXMBcD0AwLUAgCSr5vVgwYIFyzWu2R9HNnjw4IwePTodO3ZMdXV1LrvssnTp0iUDBgxIbW1t3n///bRv3z6VlZWZMGFCJk6cmGOPPTZ9+/bNO++8U7+vpWN23XXXXH/99enevXt69uyZSZMm5frrr89JJ53UfAcKAAAAAACscZo1wiTJj370o9TW1uacc87J/Pnz06dPn1x44YVp3bp13nrrrQwZMiQjR47Mfvvtl7/85S9JkssuuyyXXXZZg/0sHXP00UenQ4cOGT16dGpqalJdXZ2TTjopBxxwQHMcHgAAAAAAsIZq9gjTsmXLDB8+PMOHD2+0rrq6On/729/qf77ooouWub9WrVrl8MMPb/A4MgAAAAAAgK9ai+aeAAAAAAAAwOpIhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAAChAhAEAAAAAACigVXNPoK6uLmPGjMntt9+eOXPmpHfv3jn55JPTrVu3Jsd/8MEHufDCC/Poo48mSXbdddeccMIJqaqqqh9z//3356qrrsqbb76Z7t27Z/jw4dlxxx2/kuMBAAAAAABIVoE7YcaOHZvx48fnZz/7Wa644opUVFRkxIgRWbhwYZPjTzvttLzxxhu5+OKLc9ZZZ+Xxxx/P+eefX79+0qRJ+fWvf51BgwZl7Nix2X777XPqqafmlVde+YqOCAAAAAAAoJkjzMKFC3PjjTdm2LBh2WmnnbLZZpvljDPOSE1NTR588MFG459++ulMmTIlv/jFL7L55punX79++elPf5p77rknNTU1SZLrrrsuAwYMyJAhQ7LxxhvnuOOOS8+ePTNu3Liv+vAAAAAAAIA1WLNGmOnTp+ejjz5Kv3796pd16NAhm2++eaZOndpo/JNPPplOnTpl4403rl+2zTbbpKKiIk899VTq6uryj3/8o8H+kqRfv3558sknix0HAAAAAADApzXrd8LMnDkzSbLBBhs0WN65c+fMmDGjyfGfHtu6deusvfbamTFjRj788MPMmzcvXbp0Wa79La8WLVpko402ymuvvfaF91FCmzZtkiQ/G7Zbamvrmnk2AF9ey5ZLfjfg7rvvzoIFC5p5Nv86ll4PNhtyUhbX1TbzbAC+vIoWLZO4HqyIpdeCEd89OotcC4DVQCvXgi9k6fVgu1N+ksWLXA+Af30VrVbd60HXrl3TosWy73Np1ggzf/78JEtCyie1adMms2fPbjT+448/bjQ2SSorK7NgwYJ8/PHH9dt/en9f5j/Q4sWL07Jly1RXV3/hfZRSW1ub9lWN3xOAf1W1tbXp1KlTc0/jX05tbW1atG3X3NMAWGlcD1ZcbW1t2rVZq7mnAbDSuBZ8MbW1tWnVzmcDYPWxql4PWrRokcWLFy9zXLNGmKWxZOHChamsrKxfvmDBgrRt27bR+MrKyixcuLDR8vnz56dt27b1+/h0cPms/S2vd9999wtvCwAAAAAArJma9Tthlj5arKampsHyWbNmNXqk2NLxs2bNarBs4cKFmT17drp06ZK11147VVVVjcbMmjUr66+//kqePQAAAAAAwGdr1gjTo0ePtGvXLlOmTKlfNmfOnEybNi1bb711o/Fbb711Zs6cmddff71+2eTJk5MkW221VSoqKtK7d+8G+0uSSZMmNbk/AAAAAACAUpr9cWSDBw/O6NGj07Fjx1RXV+eyyy5Lly5dMmDAgNTW1ub9999P+/btU1lZmS222CK9e/fO6aefnlNOOSUfffRRzjvvvOy99971d7oMHTo0p5xySnr27JlvfetbueOOOzJ9+vSMHDmyOQ8VAAAAAABYw1TU1NQs+5tjCqqtrc0VV1yRCRMmZP78+enTp09GjBiR6urqvPXWWxkyZEhGjhyZ/fbbL0ny3nvv5YILLsijjz6aysrK7Lrrrjn++OMbfKfM3XffnauvvjozZ87MJptskmOPPTb9+vVrrkMEAAAAAADWQM0eYQAAAAAAAFZHzfqdMAAAAAAAAKsrEQYAAAAAAKAAEQYAAAAAAKAAEQYAAAAAAKAAEQYAAAAAAKAAEQYAAAAAAKAAEQYAAAAAAKCAVs09AaB5DB48OG+//Xb9z61bt856662X/v3750c/+lHWWWedZpwdACub8z4ASw0ePDiLFy/Otddem3bt2jVYd+aZZ+btt9/OpZdemv79+2fkyJHZb7/9lrnPt956K0OGDMmoUaOyzTbbNDlm0aJF+eMf/5iDDz54pRwHAF/OV/UZ4ZPXFlgTiTCwBjvkkENyyCGHJEnmz5+fl156KZdffnmmTp2a3/72t40+kAHwr815H4ClZsyYkVGjRuXnP//5Z4657bbb0r59+5X2mvfee29GjRolwgCsQnxGgPI8jgzWYFVVVenUqVM6deqUDTfcMN/+9rdz4YUX5q233soNN9zQ3NMDYCVz3gdgqQ033DC33357Hnvssc8c06lTp1RWVq6011y8ePFK2xcAK4fPCFCeCAM00LVr1wwYMCB/+ctfkiQvv/xyfv7zn2efffbJgAEDctBBB2XcuHFJkvfeey8DBgzIXXfd1WAfo0ePzg9/+MOvfO4ArLgVOe8//PDD2XnnnfP+++/Xb3/EEUfU/+Zcknz44YcZMGBAnnjiiVx11VU57rjjcv3112fgwIHZddddc/zxx+fVV1/9So8RgMb22muvbLvttjn33HMzd+7cJsf0798/EyZMqP953LhxGTJkSHbdddeceOKJGTNmTAYPHtxgm2effTZHHXVUdtlllxx44IH120+YMCFnn312/X4nT55c6MgA+LI+/Rnh09eDZMmjzK666qokSW1tbS6//PIMGjQou+yySw455JDceuutn7n/UaNGZa+99srTTz9d7iBgFSLCAI18/etfzxtvvJG5c+fmpJNOSlVVVUaPHp3rr78+u+22Wy6++OK88MILWXfddbPTTjvl7rvvrt+2rq4u9957b/bdd99mPAIAVsTynve33377tGnTJk888USS5P3338/LL7+cf/7zn6mpqUmSPP7446mqqkqfPn2SJE8//XSmTJmS8847LxdffHHefvvtXHDBBc11qAD8/yoqKnLqqadmzpw5ueSSS5Y5/k9/+lN++9vf5ogjjsg111yTbbbZJldffXWjcX/4wx9y+OGH57rrrssOO+yQc845J6+//nr22GOPnHjiiUmWPOZsq622WunHBMDKs/QzwkcffbTMsbfcckseeOCBnHHGGfnDH/6QwYMH5/zzz8+TTz7ZaOzo0aMzYcKEXHTRRdlyyy1LTB1WOSIM0EiHDh2SJHPmzMlBBx2Uk08+OV/72tfSrVu3DBs2LMmS35ROkv333z+TJ0+u/8e3J554Iu+991723HPP5pk8ACtsec/7lZWV2XbbbfP3v/89yZJzfo8ePbLBBhvU/0bz//3f/+Vb3/pWWrVa8tWDixYtyumnn57NNtssvXv3zoEHHpinnnqqGY4SgE/r2rVrhg8fnjvuuONzH0uWJDfccEMOPPDA7L///tloo41y+OGH59vf/najcUceeWR23nnndOvWLT/+8Y9TV1eXadOmpbKysv57BTp16pTWrVsXOSYAVo6lnxE+627JT3rjjTfStm3bbLjhhunatWuGDBmSiy66KBtttFGDcVdeeWVuu+22XHLJJfnGN75RZN6wKmrV3BMAVj0ffvhhkmTttdfOoEGDMnHixEyfPj2vv/56pk+fnmTJraZJsuOOO2bdddfNPffck+9///u56667svPOO2edddZptvkDsGJW5Ly/8847Z+zYsUmWRJh+/fpl1qxZmTx5cvbcc888+uij+clPflK/7/XWWy9rr712/c/t2rXLwoULv6IjA2BZBg4cmL/+9a8599xzc+211zY55oMPPsjbb7/d6DeWt95660ybNq3Bsk/+g9vS8//8+fNX8qwBKG3pZ4SlAf3zDB48OA899FAGDhyYXr16Zbvttsvuu++eddddt37MM888k6lTp6Zjx47p0qVLsXnDqsidMEAj06ZNS/fu3fPxxx/n8MMPz5///Od06tQpAwcOzJgxYxqMbdmyZfbZZ5/cc889mTdvXh566CGPIgP4F7Mi5/2ddtopM2fOzKuvvponnngi2267bbbddttMnjw5zz33XObOnZsddtihfrzfdAZYtS3PY8latmyZJFm8ePEy99eiReN/Zlie7QBYtSz9jLDWWms1uX7RokX1f+/evXvGjRuXCy64IH369MnDDz+cww8/PHfeeWf9mLZt2+a3v/1t2rZtm//5n/8pPn9YlYgwQAMzZ87Mww8/nD333DP33ntvPvjgg1xxxRU54ogjMmDAgMyZM6fRNvvtt19efvnl/PGPf0y7du0a/OMbAKu2FT3vd+7cOb169cr48eNTU1OT3r17Z9ttt82bb76ZP/3pT9lmm22W67flAFh1dO3aNccee2zuuOOOJp/f3759+3Tt2jXPPPNMg+XPPvvsCr1ORUXFl5onAF+NT35GSJJWrVrV3xmTLHlE2XvvvVf/880335y//vWv2X777XPsscfm2muvTb9+/TJx4sT6MZtuumm23HLLnHLKKZk4cWL++te/fmXHA83N48hgDTZv3ry88847SZY8IuCll17KFVdckerq6hx88MF59NFH8/HHH2fixInZeuut8+qrr9b/dtyCBQvq97PRRhuld+/eufrqq3PggQfW/6YcAKuWlXXe79+/f37/+9/nm9/8ZqqqqlJVVZXu3bvn3nvvzcknn9wsxwbAlzNw4MA88MADeeKJJ7LBBhs0Wv/9738/l156aTbeeOP07t07jzzySO6///4mx36WqqqqJMnzzz+fTTbZJJWVlStt/gB8Mcv6jJAkW221Vf785z+nT58+ad26da688sr674BMknfffTdXX311Kisrs9lmm+WVV17J9OnTc9BBBzV6vR122CF77bVXLrjggvTt29fj7FkjiDCwBrvxxhtz4403JlnygahLly4ZMGBADjnkkKy11lrZddddM23atFx66aWZO3duqqur873vfS8PP/xwnn322QwaNKh+X/vss0+eeuopjyIDWIWtrPP+zjvvnN/97nfp169f/b633XbbvP766+nfv3+zHBsAX96pp56aww47rMl1gwYNyuzZs3PllVfmgw8+SN++fbPvvvs2eefMZ+nXr1+++c1v5uijj87pp5+e3XbbbWVNHYAvaFmfEZLklFNOyfnnn5+jjz46HTt2zMEHH5x58+bV72PYsGGpra3NhRdemHfffTfrrbdeBg0a9JnXlBNPPDGHHnpoLrzwwvzqV78qfozQ3Cpqamo8nBX40saMGZO///3vGT16dHNPBQAAWMkeffTRbLLJJg3ufDn33HPzxhtvfOZ3yQAA4E4Y4Et68skn89prr2XcuHH56U9/2tzTAQAACrj77rvzyiuv5OSTT07nzp0zZcqU3HPPPRkxYkRzTw0AYJXmThjgS7n88svzpz/9Kfvtt19+8pOfNPd0AACAAmbPnp1LLrkkjz32WD788MNsuOGGOeigg/Lv//7vzT01AIBVmggDAAAAAABQQIvmngAAAAAAAMDqSIQBAAAAAAAoQIQBAAAAAAAoQIQBAAAAAAAooFVzTwAAAFh9nHnmmbnrrrs+d0zXrl1z2mmn5fjjj8+oUaOyzTbbJEn69++fJDnssMNy9NFHN9qurq4ugwYNyqxZszJy5Mjst99+Dbb7LIccckiOO+64FT6Wl156KTfccEMmTZqU999/P+utt1622mqrHHTQQdliiy0ajF3WcZ9++unZa6+9lut1J0+enOOPP77BstatW2edddZJ375988Mf/jAbb7xxk9u+8cYbGTp0aNq3b58///nPqaysTJL8/e9/z0knnZTvfve7+dWvftVou48//jiHHXZYkuSaa67JWmuttVxzBQAAPp8IAwAArDRHHHFEBg4cWP/z2LFj88ILL+Tss8+uX9amTZt8+OGHTW7fokWLPPDAA01GmKlTp2bWrFlNbrf//vvne9/7XpPrOnfuvAJHsMQ999yTs88+Oz179sxRRx2VDTfcMDNnzswdd9yRY445JsOHD8/BBx/cYJtOnTo1OM5P6tat2wrPYcSIEenZs2eSZP78+XnjjTdy/fXXZ9iwYbnkkkvyzW9+s9E2EyZMSPfu3fPmm29m4sSJ2XfffZMk2223XQ444IDcdttt2XPPPbPTTjs12O7KK6/M22+/nVGjRgkwAACwEokwAADAStOtW7cGwaFjx45p3bp1ttxyywbjJk+e3OT2W221VZ588slMmzYtm2++eYN19913XzbbbLNMnz690XZdunRp9Bpf1NJotOeee+bnP/95WrZsWb9ur732ykUXXZRLL700m266abbbbrv6dU0d55fxta99rcH++vXrl1122SVHHnlkzjzzzFx77bUN5lZXV5e77747e++9d1544YWMHz++PsIkyXHHHZfHHnss559/fq677rr62PLss8/m5ptvztChQ9OnT5+VNn8AAMB3wgAAAKuQvn37Zr311sv999/fYPmiRYvy4IMP5rvf/W7xOfz+979PVVVVfvKTnzSIHEsde+yx6dKlS66++uric/m0tddeO//5n/+ZV199NVOmTGmw7vHHH8+MGTPSv3//7LXXXnnmmWcaBKt27drlZz/7WWbMmJErr7wyyZL39Te/+U2+9rWv5cc//vFXeiwAALAmEGEAAIBVRosWLbLLLrs0ijCTJk3K/PnzGz1Ga6m6urosWrSoyT8roq6uLo8//ni23XbbVFVVNTmmdevW+c53vpOnnnoqH3zwQYN1Tb3+4sWLV2gOy7L99tsnSZ566qkGyydMmJCNNtooW2yxRb7zne+kQ4cOGT9+fIMxO+ywQ/bff//ccsstefHFF3PTTTfln//8Z375y1+mTZs2K3WeAACAx5EBAACrmN133z233HJLnn/++fTq1SvJkkeRffvb307btm2b3Gbs2LEZO3Zsk+tuvfXWdOnSZblee/bs2Zk7d26qq6s/d1y3bt2yePHizJgxI+uss06S5O23386AAQMajT3qqKNyxBFHLNfrL49OnTolSd55550G837kkUcybNiwJEllZWX22GOP3HPPPTn22GMbfM/LCSeckMcffzxnn312/vnPf+bII4+s/+4ZAABg5RJhAACAVcrWW2+d9ddfP/fff3969eqVhQsX5uGHH87pp5/+mdsccMABOeCAA5pct9566y33ay+9a6VVq8//qLT0MWWfvMulU6dOOffccxuNXX/99Zf79VdERUVF/d/vueeeLFq0KP3798+cOXOSJLvssktuvfXW3HvvvRk4cGD92KWPJRsxYkS22GKLfP/73y8yPwAAQIQBAABWMRUVFdl1113zwAMPZPjw4Xn00UdTUVGR7bffPjU1NU1u07lz53zjG9/40q/dsWPHVFVV5a233vrccW+++WaSZIMNNqhf1rp165Uyh2VZ+h58Mu5MmDAhdXV1TQaV8ePHN4gwSbLjjjsmWfJ4sqa+9wYAAFg5RBgAAGCVs/vuu2fcuHF57rnnMnHixOyyyy7LvDtlZaioqEj//v3z2GOPZd68eU1+L0xtbW0eeuih9O7dOx07diw+p0974oknkiR9+/ZNkrzwwguZPn16jjzyyPplSz3yyCO56aab8swzz2SLLbb4yucKAABruhbNPQEAAIBP23LLLdO1a9fce++9eeSRR7LHHnt8Za/9gx/8IB9//HHOP//81NXVNVp/xRVX5PXXX88PfvCDr2xOS82dOzc33HBDevToka222ipJcvvtt6dNmzYZOnRottlmmwZ/Dj300LRs2TLjx4//yucKAAC4EwYAAFhF7bbbbhk3blzWWWedRnd4fNrMmTPz9NNPN7mubdu26dGjx3K/7qabbprTTjstZ511Vl5//fUMGjQoXbt2zTvvvJM777wzjz/+eIYPH55vfetbK3Q8K+qVV15JmzZtkiQLFizIq6++mj/+8Y/54IMPcuaZZ6aioiILFizIfffdlx133DHt27dvtI/OnTtnu+22y8SJE3PCCSekQ4cORecMAAA0JMIAAACrpN133z033HBDdtttt7Ro8fk38d9xxx254447mly3ySab5Lrrrluh195jjz2y6aab5qabbsrvfve7vPPOO+nYsWO23nrrjB49OltuueUK7e+LuOCCC+r/XlVVlc6dO2f77bfP0KFD82//9m9JkoceeiizZ8/Onnvu+Zn72WefffLoo4/mrrvuykEHHVR83gAAwP9TUVNTs7i5JwEAAAAAALC6cScMAACw2qurq2vy+10+rWXLlqmoqCgyh0WLFi1zTIsWLZZ51w8AAPCvQ4QBAABWe1dffXXGjBmzzHGjRo3KNttss9Jf/6233sqQIUOWOW6fffbJL37xi5X++gAAQPPwODIAAGC1V1NTk1mzZi1z3EYbbZR27dqt9NdfuHBhXnzxxWWO69ixY6qrq1f66wMAAM1DhAEAAAAAACjAw4YBAAAAAAAKEGEAAAAAAAAKEGEAAAAAAAAKEGEAAAAAAAAKEGEAAAAAAAAKEGEAAAAAAAAKEGEAAAAAAAAKEGEAAAAAAAAK+P8A+lDLZPe4v1QAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 2000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=df, x='TIME_OF_DAY', y='NR_INJURIES').set(title='Injuries reported based on time of day')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7b67a38b",
   "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>INDEX_NR</th>\n",
       "      <th>INCIDENT_DATE</th>\n",
       "      <th>INCIDENT_MONTH</th>\n",
       "      <th>INCIDENT_YEAR</th>\n",
       "      <th>TIME</th>\n",
       "      <th>TIME_OF_DAY</th>\n",
       "      <th>AIRPORT_ID</th>\n",
       "      <th>AIRPORT</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>...</th>\n",
       "      <th>SIZE</th>\n",
       "      <th>NR_INJURIES</th>\n",
       "      <th>NR_FATALITIES</th>\n",
       "      <th>COMMENTS</th>\n",
       "      <th>REPORTED_NAME</th>\n",
       "      <th>REPORTED_TITLE</th>\n",
       "      <th>SOURCE</th>\n",
       "      <th>PERSON</th>\n",
       "      <th>LUPDATE</th>\n",
       "      <th>TRANSFER</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>608242</td>\n",
       "      <td>6/22/1996</td>\n",
       "      <td>June</td>\n",
       "      <td>1996</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>KSMF</td>\n",
       "      <td>SACRAMENTO INTL</td>\n",
       "      <td>38.69542</td>\n",
       "      <td>-121.59077</td>\n",
       "      <td>...</td>\n",
       "      <td>Medium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>/Legacy Record=100001/</td>\n",
       "      <td>REDACTED</td>\n",
       "      <td>REDACTED</td>\n",
       "      <td>Air Transport Report</td>\n",
       "      <td>Air Transport Operations</td>\n",
       "      <td>12/20/2007</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>608243</td>\n",
       "      <td>6/26/1996</td>\n",
       "      <td>June</td>\n",
       "      <td>1996</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>KDEN</td>\n",
       "      <td>DENVER INTL AIRPORT</td>\n",
       "      <td>39.85841</td>\n",
       "      <td>-104.66700</td>\n",
       "      <td>...</td>\n",
       "      <td>Medium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>/Legacy Record=100002/</td>\n",
       "      <td>REDACTED</td>\n",
       "      <td>REDACTED</td>\n",
       "      <td>Air Transport Report</td>\n",
       "      <td>Air Transport Operations</td>\n",
       "      <td>12/20/2007</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>608244</td>\n",
       "      <td>7/1/1996</td>\n",
       "      <td>July</td>\n",
       "      <td>1996</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>KOMA</td>\n",
       "      <td>EPPLEY AIRFIELD</td>\n",
       "      <td>41.30252</td>\n",
       "      <td>-95.89417</td>\n",
       "      <td>...</td>\n",
       "      <td>Medium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>/Legacy Record=100003/</td>\n",
       "      <td>REDACTED</td>\n",
       "      <td>REDACTED</td>\n",
       "      <td>Air Transport Report</td>\n",
       "      <td>Air Transport Operations</td>\n",
       "      <td>12/20/2007</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>608245</td>\n",
       "      <td>7/1/1996</td>\n",
       "      <td>July</td>\n",
       "      <td>1996</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>KIAD</td>\n",
       "      <td>WASHINGTON DULLES INTL ARPT</td>\n",
       "      <td>38.94453</td>\n",
       "      <td>-77.45581</td>\n",
       "      <td>...</td>\n",
       "      <td>Medium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>/Legacy Record=100004/</td>\n",
       "      <td>REDACTED</td>\n",
       "      <td>REDACTED</td>\n",
       "      <td>Air Transport Report</td>\n",
       "      <td>Air Transport Operations</td>\n",
       "      <td>12/20/2007</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>608246</td>\n",
       "      <td>7/1/1996</td>\n",
       "      <td>July</td>\n",
       "      <td>1996</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>KLGA</td>\n",
       "      <td>LA GUARDIA ARPT</td>\n",
       "      <td>40.77724</td>\n",
       "      <td>-73.87261</td>\n",
       "      <td>...</td>\n",
       "      <td>Medium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>/Legacy Record=100005/</td>\n",
       "      <td>REDACTED</td>\n",
       "      <td>REDACTED</td>\n",
       "      <td>Air Transport Report</td>\n",
       "      <td>Air Transport Operations</td>\n",
       "      <td>12/20/2007</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 100 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   INDEX_NR INCIDENT_DATE INCIDENT_MONTH  INCIDENT_YEAR TIME TIME_OF_DAY  \\\n",
       "0    608242     6/22/1996           June           1996  NaN         NaN   \n",
       "1    608243     6/26/1996           June           1996  NaN         NaN   \n",
       "2    608244      7/1/1996           July           1996  NaN         NaN   \n",
       "3    608245      7/1/1996           July           1996  NaN         NaN   \n",
       "4    608246      7/1/1996           July           1996  NaN         NaN   \n",
       "\n",
       "  AIRPORT_ID                      AIRPORT  LATITUDE  LONGITUDE  ...    SIZE  \\\n",
       "0       KSMF              SACRAMENTO INTL  38.69542 -121.59077  ...  Medium   \n",
       "1       KDEN          DENVER INTL AIRPORT  39.85841 -104.66700  ...  Medium   \n",
       "2       KOMA              EPPLEY AIRFIELD  41.30252  -95.89417  ...  Medium   \n",
       "3       KIAD  WASHINGTON DULLES INTL ARPT  38.94453  -77.45581  ...  Medium   \n",
       "4       KLGA              LA GUARDIA ARPT  40.77724  -73.87261  ...  Medium   \n",
       "\n",
       "  NR_INJURIES NR_FATALITIES                COMMENTS REPORTED_NAME  \\\n",
       "0         NaN           NaN  /Legacy Record=100001/      REDACTED   \n",
       "1         NaN           NaN  /Legacy Record=100002/      REDACTED   \n",
       "2         NaN           NaN  /Legacy Record=100003/      REDACTED   \n",
       "3         NaN           NaN  /Legacy Record=100004/      REDACTED   \n",
       "4         NaN           NaN  /Legacy Record=100005/      REDACTED   \n",
       "\n",
       "  REPORTED_TITLE                SOURCE                    PERSON     LUPDATE  \\\n",
       "0       REDACTED  Air Transport Report  Air Transport Operations  12/20/2007   \n",
       "1       REDACTED  Air Transport Report  Air Transport Operations  12/20/2007   \n",
       "2       REDACTED  Air Transport Report  Air Transport Operations  12/20/2007   \n",
       "3       REDACTED  Air Transport Report  Air Transport Operations  12/20/2007   \n",
       "4       REDACTED  Air Transport Report  Air Transport Operations  12/20/2007   \n",
       "\n",
       "  TRANSFER  \n",
       "0    False  \n",
       "1    False  \n",
       "2    False  \n",
       "3    False  \n",
       "4    False  \n",
       "\n",
       "[5 rows x 100 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "dc13016b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\KPMal\\anaconda3\\envs\\Py3-9\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\KPMal\\anaconda3\\envs\\Py3-9\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\KPMal\\anaconda3\\envs\\Py3-9\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Injuries reported based on size')]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=df, x='SIZE', y='NR_INJURIES').set(title='Injuries reported based on size')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2866715d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#sns.lineplot(x = \"INCIDENT_DATE\", y = \"Count\", data = df) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19659035",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.17"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}