wrangle_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2f4b353b-9447-4918-9730-7f6a07e6fcaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.model_selection import TimeSeriesSplit\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import wrangle as w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "a052acd4-79cc-4994-87cc-699850db7dfd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('strike_reports.csv', encoding='latin1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "adccae93-dcd1-4959-8aab-52524e50fd16",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>ENROUTE_STATE</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>119580</th>\n",
       "      <td>739071</td>\n",
       "      <td>11/1/2012 0:00:00</td>\n",
       "      <td>11</td>\n",
       "      <td>2012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>KILM</td>\n",
       "      <td>WILMINGTON INTL</td>\n",
       "      <td>34.27061</td>\n",
       "      <td>-77.90256</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-11-1-152553 /Legacy Record=330258/</td>\n",
       "      <td>REDACTED</td>\n",
       "      <td>REDACTED</td>\n",
       "      <td>FAA Form 5200-7-E</td>\n",
       "      <td>Carcass Found</td>\n",
       "      <td>1/31/2013 0:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264710</th>\n",
       "      <td>1347399</td>\n",
       "      <td>7/17/2022 0:00:00</td>\n",
       "      <td>7</td>\n",
       "      <td>2022</td>\n",
       "      <td>10:30</td>\n",
       "      <td>Day</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>*** Strike Report: 2022-07-22-161515-R1( Repor...</td>\n",
       "      <td>REDACTED</td>\n",
       "      <td>REDACTED</td>\n",
       "      <td>Multiple</td>\n",
       "      <td>Tower</td>\n",
       "      <td>12/5/2022 0:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 100 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        INDEX_NR      INCIDENT_DATE  INCIDENT_MONTH  INCIDENT_YEAR   TIME  \\\n",
       "119580    739071  11/1/2012 0:00:00              11           2012    NaN   \n",
       "264710   1347399  7/17/2022 0:00:00               7           2022  10:30   \n",
       "\n",
       "       TIME_OF_DAY AIRPORT_ID          AIRPORT  LATITUDE  LONGITUDE  ...  \\\n",
       "119580         NaN       KILM  WILMINGTON INTL  34.27061  -77.90256  ...   \n",
       "264710         Day       KOMA  EPPLEY AIRFIELD  41.30252  -95.89417  ...   \n",
       "\n",
       "       ENROUTE_STATE NR_INJURIES NR_FATALITIES  \\\n",
       "119580           NaN         NaN           NaN   \n",
       "264710           NaN         NaN           NaN   \n",
       "\n",
       "                                                 COMMENTS REPORTED_NAME  \\\n",
       "119580            2012-11-1-152553 /Legacy Record=330258/      REDACTED   \n",
       "264710  *** Strike Report: 2022-07-22-161515-R1( Repor...      REDACTED   \n",
       "\n",
       "       REPORTED_TITLE             SOURCE         PERSON            LUPDATE  \\\n",
       "119580       REDACTED  FAA Form 5200-7-E  Carcass Found  1/31/2013 0:00:00   \n",
       "264710       REDACTED           Multiple          Tower  12/5/2022 0:00:00   \n",
       "\n",
       "       TRANSFER  \n",
       "119580        0  \n",
       "264710        0  \n",
       "\n",
       "[2 rows x 100 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ce7cc22c-aafd-4a50-a46f-0df4a8f19500",
   "metadata": {},
   "outputs": [],
   "source": [
    "# make column names lowercase\n",
    "df.columns = df.columns.str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "539eac3c-de85-428b-9925-adaa5e31a663",
   "metadata": {},
   "outputs": [],
   "source": [
    "# make the date column a datetime and set it as index (Takes about 7 seconds)\n",
    "df = df.rename(columns={'incident_date' : 'date'})\n",
    "\n",
    "# tried to make datetime conversion faster, but I ran into problems and it really wasn't that slow\n",
    "# df.date = df.date.str.rstrip('0:00:00')\n",
    "df.date = df.date.astype('datetime64')\n",
    "df = df.set_index('date')\n",
    "df = df.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b1937053-be2d-4583-a3f0-b2702a27e22e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 279947 entries, 1990-01-02 to 2023-05-17\n",
      "Data columns (total 99 columns):\n",
      " #   Column                 Non-Null Count   Dtype  \n",
      "---  ------                 --------------   -----  \n",
      " 0   index_nr               279947 non-null  int64  \n",
      " 1   incident_month         279947 non-null  int64  \n",
      " 2   incident_year          279947 non-null  int64  \n",
      " 3   time                   156370 non-null  object \n",
      " 4   time_of_day            162273 non-null  object \n",
      " 5   airport_id             279942 non-null  object \n",
      " 6   airport                279947 non-null  object \n",
      " 7   latitude               245560 non-null  float64\n",
      " 8   longitude              245561 non-null  float64\n",
      " 9   runway                 212020 non-null  object \n",
      " 10  state                  245563 non-null  object \n",
      " 11  faaregion              245563 non-null  object \n",
      " 12  location               32868 non-null   object \n",
      " 13  opid                   279943 non-null  object \n",
      " 14  operator               279947 non-null  object \n",
      " 15  reg                    167025 non-null  object \n",
      " 16  flt                    124529 non-null  object \n",
      " 17  aircraft               279947 non-null  object \n",
      " 18  ama                    199865 non-null  object \n",
      " 19  amo                    185951 non-null  object \n",
      " 20  ema                    186771 non-null  float64\n",
      " 21  emo                    182131 non-null  object \n",
      " 22  ac_class               201215 non-null  object \n",
      " 23  ac_mass                200811 non-null  float64\n",
      " 24  type_eng               200377 non-null  object \n",
      " 25  num_engs               200587 non-null  float64\n",
      " 26  eng_1_pos              199828 non-null  float64\n",
      " 27  eng_2_pos              186401 non-null  float64\n",
      " 28  eng_3_pos              13002 non-null   float64\n",
      " 29  eng_4_pos              3260 non-null    float64\n",
      " 30  phase_of_flight        173131 non-null  object \n",
      " 31  height                 145365 non-null  float64\n",
      " 32  speed                  92638 non-null   float64\n",
      " 33  distance               181733 non-null  float64\n",
      " 34  sky                    136086 non-null  object \n",
      " 35  precipitation          130923 non-null  object \n",
      " 36  aos                    13809 non-null   float64\n",
      " 37  cost_repairs           5047 non-null    object \n",
      " 38  cost_other             4635 non-null    object \n",
      " 39  cost_repairs_infl_adj  5047 non-null    object \n",
      " 40  cost_other_infl_adj    4635 non-null    object \n",
      " 41  ingested_other         279947 non-null  int64  \n",
      " 42  indicated_damage       279947 non-null  int64  \n",
      " 43  damage_level           182535 non-null  object \n",
      " 44  str_rad                279947 non-null  int64  \n",
      " 45  dam_rad                279947 non-null  int64  \n",
      " 46  str_windshld           279947 non-null  int64  \n",
      " 47  dam_windshld           279947 non-null  int64  \n",
      " 48  str_nose               279947 non-null  int64  \n",
      " 49  dam_nose               279947 non-null  int64  \n",
      " 50  str_eng1               279947 non-null  int64  \n",
      " 51  dam_eng1               279947 non-null  int64  \n",
      " 52  ing_eng1               279947 non-null  int64  \n",
      " 53  str_eng2               279947 non-null  int64  \n",
      " 54  dam_eng2               279947 non-null  int64  \n",
      " 55  ing_eng2               279947 non-null  int64  \n",
      " 56  str_eng3               279947 non-null  int64  \n",
      " 57  dam_eng3               279947 non-null  int64  \n",
      " 58  ing_eng3               279947 non-null  int64  \n",
      " 59  str_eng4               279947 non-null  int64  \n",
      " 60  dam_eng4               279947 non-null  int64  \n",
      " 61  ing_eng4               279947 non-null  int64  \n",
      " 62  str_prop               279947 non-null  int64  \n",
      " 63  dam_prop               279947 non-null  int64  \n",
      " 64  str_wing_rot           279947 non-null  int64  \n",
      " 65  dam_wing_rot           279947 non-null  int64  \n",
      " 66  str_fuse               279947 non-null  int64  \n",
      " 67  dam_fuse               279947 non-null  int64  \n",
      " 68  str_lg                 279947 non-null  int64  \n",
      " 69  dam_lg                 279947 non-null  int64  \n",
      " 70  str_tail               279947 non-null  int64  \n",
      " 71  dam_tail               279947 non-null  int64  \n",
      " 72  str_lghts              279947 non-null  int64  \n",
      " 73  dam_lghts              279947 non-null  int64  \n",
      " 74  str_other              279947 non-null  int64  \n",
      " 75  dam_other              279947 non-null  int64  \n",
      " 76  other_specify          36726 non-null   object \n",
      " 77  effect                 140547 non-null  object \n",
      " 78  effect_other           2227 non-null    object \n",
      " 79  bird_band_number       181 non-null     float64\n",
      " 80  species_id             279943 non-null  object \n",
      " 81  species                279945 non-null  object \n",
      " 82  remarks                255724 non-null  object \n",
      " 83  remains_collected      279947 non-null  int64  \n",
      " 84  remains_sent           279947 non-null  int64  \n",
      " 85  warned                 279947 non-null  object \n",
      " 86  num_seen               92411 non-null   object \n",
      " 87  num_struck             279340 non-null  object \n",
      " 88  size                   256409 non-null  object \n",
      " 89  enroute_state          4921 non-null    object \n",
      " 90  nr_injuries            276 non-null     float64\n",
      " 91  nr_fatalities          24 non-null      float64\n",
      " 92  comments               216580 non-null  object \n",
      " 93  reported_name          279947 non-null  object \n",
      " 94  reported_title         279947 non-null  object \n",
      " 95  source                 279947 non-null  object \n",
      " 96  person                 258802 non-null  object \n",
      " 97  lupdate                279947 non-null  object \n",
      " 98  transfer               279947 non-null  int64  \n",
      "dtypes: float64(16), int64(40), object(43)\n",
      "memory usage: 213.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ab267c4b-fe25-49ff-be50-4b86b89b9486",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>time</th>\n",
       "      <th>time_of_day</th>\n",
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       "      <th>...</th>\n",
       "      <th>enroute_state</th>\n",
       "      <th>nr_injuries</th>\n",
       "      <th>nr_fatalities</th>\n",
       "      <th>comments</th>\n",
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       "      <th>reported_title</th>\n",
       "      <th>source</th>\n",
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       "      <th>lupdate</th>\n",
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       "    </tr>\n",
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       "      <th>date</th>\n",
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       "<p>0 rows × 99 columns</p>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [index_nr, incident_month, incident_year, time, time_of_day, airport_id, airport, latitude, longitude, runway, state, faaregion, location, opid, operator, reg, flt, aircraft, ama, amo, ema, emo, ac_class, ac_mass, type_eng, num_engs, eng_1_pos, eng_2_pos, eng_3_pos, eng_4_pos, phase_of_flight, height, speed, distance, sky, precipitation, aos, cost_repairs, cost_other, cost_repairs_infl_adj, cost_other_infl_adj, ingested_other, indicated_damage, damage_level, str_rad, dam_rad, str_windshld, dam_windshld, str_nose, dam_nose, str_eng1, dam_eng1, ing_eng1, str_eng2, dam_eng2, ing_eng2, str_eng3, dam_eng3, ing_eng3, str_eng4, dam_eng4, ing_eng4, str_prop, dam_prop, str_wing_rot, dam_wing_rot, str_fuse, dam_fuse, str_lg, dam_lg, str_tail, dam_tail, str_lghts, dam_lghts, str_other, dam_other, other_specify, effect, effect_other, bird_band_number, species_id, species, remarks, remains_collected, remains_sent, warned, num_seen, num_struck, size, enroute_state, nr_injuries, nr_fatalities, comments, reported_name, reported_title, source, person, lupdate, transfer]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 99 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# confirm the month and year columns are the same as the index datetime\n",
    "df[df.index.month != df.incident_month]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6c646781-9ddb-4ed0-9567-74a6a183245e",
   "metadata": {},
   "outputs": [
    {
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       "<p>0 rows × 99 columns</p>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [index_nr, incident_month, incident_year, time, time_of_day, airport_id, airport, latitude, longitude, runway, state, faaregion, location, opid, operator, reg, flt, aircraft, ama, amo, ema, emo, ac_class, ac_mass, type_eng, num_engs, eng_1_pos, eng_2_pos, eng_3_pos, eng_4_pos, phase_of_flight, height, speed, distance, sky, precipitation, aos, cost_repairs, cost_other, cost_repairs_infl_adj, cost_other_infl_adj, ingested_other, indicated_damage, damage_level, str_rad, dam_rad, str_windshld, dam_windshld, str_nose, dam_nose, str_eng1, dam_eng1, ing_eng1, str_eng2, dam_eng2, ing_eng2, str_eng3, dam_eng3, ing_eng3, str_eng4, dam_eng4, ing_eng4, str_prop, dam_prop, str_wing_rot, dam_wing_rot, str_fuse, dam_fuse, str_lg, dam_lg, str_tail, dam_tail, str_lghts, dam_lghts, str_other, dam_other, other_specify, effect, effect_other, bird_band_number, species_id, species, remarks, remains_collected, remains_sent, warned, num_seen, num_struck, size, enroute_state, nr_injuries, nr_fatalities, comments, reported_name, reported_title, source, person, lupdate, transfer]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 99 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.index.year != df.incident_year]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "def6f221-9025-4492-99e6-310b4c59ce47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(34384, 99)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# confirm all the nulls for state are the same as the nulls for faaregion\n",
    "df[(df.state.isnull()) & (df.faaregion.isnull())].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4c045d07-7a2c-4820-b167-5faa9005f46d",
   "metadata": {
    "tags": []
   },
   "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>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>runway</th>\n",
       "      <th>...</th>\n",
       "      <th>enroute_state</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",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-03-31</th>\n",
       "      <td>637715</td>\n",
       "      <td>3</td>\n",
       "      <td>1990</td>\n",
       "      <td>17:30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PVT</td>\n",
       "      <td>PRIVATE AIR STRIP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NTSB LAX90LA151 STRIKE WAS DELETED BY ERROR BT...</td>\n",
       "      <td>REDACTED</td>\n",
       "      <td>REDACTED</td>\n",
       "      <td>NTSB</td>\n",
       "      <td>Other</td>\n",
       "      <td>5/18/2015 0:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-08-10</th>\n",
       "      <td>1083895</td>\n",
       "      <td>8</td>\n",
       "      <td>1990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Day</td>\n",
       "      <td>H2O</td>\n",
       "      <td>REMOTE_WATER</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>REDACTED</td>\n",
       "      <td>REDACTED</td>\n",
       "      <td>FAA Form 5200-7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1/11/2021 0:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-12-21</th>\n",
       "      <td>612244</td>\n",
       "      <td>12</td>\n",
       "      <td>1991</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Day</td>\n",
       "      <td>RIGG</td>\n",
       "      <td>OIL RIG</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>/Legacy Record=104010/</td>\n",
       "      <td>REDACTED</td>\n",
       "      <td>REDACTED</td>\n",
       "      <td>FAA Form 5200-7</td>\n",
       "      <td>Pilot</td>\n",
       "      <td>4/1/1997 0:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-05-30</th>\n",
       "      <td>637035</td>\n",
       "      <td>5</td>\n",
       "      <td>1992</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PVT</td>\n",
       "      <td>PRIVATE AIR STRIP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(UPDATED LOCATION) /Legacy Record=128886/</td>\n",
       "      <td>REDACTED</td>\n",
       "      <td>REDACTED</td>\n",
       "      <td>NTSB</td>\n",
       "      <td>Other</td>\n",
       "      <td>5/18/2015 0:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-09-20</th>\n",
       "      <td>636076</td>\n",
       "      <td>9</td>\n",
       "      <td>1993</td>\n",
       "      <td>09:45</td>\n",
       "      <td>Day</td>\n",
       "      <td>PVT</td>\n",
       "      <td>PRIVATE AIR STRIP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SOURCE = 5200-7, NTSB SEA93LA203 &amp; AAIPN  OPER...</td>\n",
       "      <td>REDACTED</td>\n",
       "      <td>REDACTED</td>\n",
       "      <td>Multiple</td>\n",
       "      <td>Pilot</td>\n",
       "      <td>5/18/2015 0:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-10</th>\n",
       "      <td>1406874</td>\n",
       "      <td>1</td>\n",
       "      <td>2023</td>\n",
       "      <td>07:37</td>\n",
       "      <td>Dawn</td>\n",
       "      <td>NaN</td>\n",
       "      <td>JOHN GLENN COLUMBUS INTL ARPT - OH</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28L</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>*** Strike Report: 2023-01-10-095944-R1( Repor...</td>\n",
       "      <td>REDACTED</td>\n",
       "      <td>REDACTED</td>\n",
       "      <td>FAA Form 5200-7-E</td>\n",
       "      <td>Airport Operations</td>\n",
       "      <td>3/7/2023 0:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-06</th>\n",
       "      <td>1408363</td>\n",
       "      <td>2</td>\n",
       "      <td>2023</td>\n",
       "      <td>02:30</td>\n",
       "      <td>Night</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lanett, AL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>06</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</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>Air Transport Operations</td>\n",
       "      <td>3/16/2023 0:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-14</th>\n",
       "      <td>1408923</td>\n",
       "      <td>2</td>\n",
       "      <td>2023</td>\n",
       "      <td>21:02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UNKNOWN/REMOTE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>REDACTED</td>\n",
       "      <td>REDACTED</td>\n",
       "      <td>FAA Form 5200-7-E</td>\n",
       "      <td>Airport Operations</td>\n",
       "      <td>3/22/2023 0:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-06</th>\n",
       "      <td>1409498</td>\n",
       "      <td>3</td>\n",
       "      <td>2023</td>\n",
       "      <td>09:17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WICHITA DWIGHT D EISENHOWER NATL ARPT - KS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1L</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</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>3/30/2023 0:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-17</th>\n",
       "      <td>1411770</td>\n",
       "      <td>3</td>\n",
       "      <td>2023</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dusk</td>\n",
       "      <td>NaN</td>\n",
       "      <td>QUAD CITY ARPT - IL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>*** Strike Report: 2023-03-17-213840-R1( Repor...</td>\n",
       "      <td>REDACTED</td>\n",
       "      <td>REDACTED</td>\n",
       "      <td>FAA Form 5200-7-E</td>\n",
       "      <td>Air Transport Operations</td>\n",
       "      <td>4/7/2023 0:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>82 rows × 99 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            index_nr  incident_month  incident_year   time time_of_day  \\\n",
       "date                                                                     \n",
       "1990-03-31    637715               3           1990  17:30         NaN   \n",
       "1990-08-10   1083895               8           1990    NaN         Day   \n",
       "1991-12-21    612244              12           1991    NaN         Day   \n",
       "1992-05-30    637035               5           1992    NaN         NaN   \n",
       "1993-09-20    636076               9           1993  09:45         Day   \n",
       "...              ...             ...            ...    ...         ...   \n",
       "2023-01-10   1406874               1           2023  07:37        Dawn   \n",
       "2023-02-06   1408363               2           2023  02:30       Night   \n",
       "2023-02-14   1408923               2           2023  21:02         NaN   \n",
       "2023-03-06   1409498               3           2023  09:17         NaN   \n",
       "2023-03-17   1411770               3           2023    NaN        Dusk   \n",
       "\n",
       "           airport_id                                     airport  latitude  \\\n",
       "date                                                                          \n",
       "1990-03-31        PVT                           PRIVATE AIR STRIP       NaN   \n",
       "1990-08-10        H2O                                REMOTE_WATER       NaN   \n",
       "1991-12-21       RIGG                                     OIL RIG       NaN   \n",
       "1992-05-30        PVT                           PRIVATE AIR STRIP       NaN   \n",
       "1993-09-20        PVT                           PRIVATE AIR STRIP       NaN   \n",
       "...               ...                                         ...       ...   \n",
       "2023-01-10        NaN          JOHN GLENN COLUMBUS INTL ARPT - OH       NaN   \n",
       "2023-02-06        NaN                                  Lanett, AL       NaN   \n",
       "2023-02-14        NaN                              UNKNOWN/REMOTE       NaN   \n",
       "2023-03-06        NaN  WICHITA DWIGHT D EISENHOWER NATL ARPT - KS       NaN   \n",
       "2023-03-17        NaN                         QUAD CITY ARPT - IL       NaN   \n",
       "\n",
       "            longitude runway  ... enroute_state nr_injuries nr_fatalities  \\\n",
       "date                          ...                                           \n",
       "1990-03-31        NaN    NaN  ...           NaN         NaN           NaN   \n",
       "1990-08-10        NaN    NaN  ...           NaN         NaN           NaN   \n",
       "1991-12-21        NaN    NaN  ...           NaN         NaN           NaN   \n",
       "1992-05-30        NaN    NaN  ...           NaN         NaN           NaN   \n",
       "1993-09-20        NaN    NaN  ...           NaN         NaN           NaN   \n",
       "...               ...    ...  ...           ...         ...           ...   \n",
       "2023-01-10        NaN    28L  ...           NaN         NaN           NaN   \n",
       "2023-02-06        NaN     06  ...           NaN         NaN           NaN   \n",
       "2023-02-14        NaN    NaN  ...           NaN         NaN           NaN   \n",
       "2023-03-06        NaN     1L  ...           NaN         NaN           NaN   \n",
       "2023-03-17        NaN     27  ...           NaN         NaN           NaN   \n",
       "\n",
       "                                                     comments reported_name  \\\n",
       "date                                                                          \n",
       "1990-03-31  NTSB LAX90LA151 STRIKE WAS DELETED BY ERROR BT...      REDACTED   \n",
       "1990-08-10                                                NaN      REDACTED   \n",
       "1991-12-21                             /Legacy Record=104010/      REDACTED   \n",
       "1992-05-30          (UPDATED LOCATION) /Legacy Record=128886/      REDACTED   \n",
       "1993-09-20  SOURCE = 5200-7, NTSB SEA93LA203 & AAIPN  OPER...      REDACTED   \n",
       "...                                                       ...           ...   \n",
       "2023-01-10  *** Strike Report: 2023-01-10-095944-R1( Repor...      REDACTED   \n",
       "2023-02-06                                                NaN      REDACTED   \n",
       "2023-02-14                                                NaN      REDACTED   \n",
       "2023-03-06                                                NaN      REDACTED   \n",
       "2023-03-17  *** Strike Report: 2023-03-17-213840-R1( Repor...      REDACTED   \n",
       "\n",
       "           reported_title             source                    person  \\\n",
       "date                                                                     \n",
       "1990-03-31       REDACTED               NTSB                     Other   \n",
       "1990-08-10       REDACTED    FAA Form 5200-7                       NaN   \n",
       "1991-12-21       REDACTED    FAA Form 5200-7                     Pilot   \n",
       "1992-05-30       REDACTED               NTSB                     Other   \n",
       "1993-09-20       REDACTED           Multiple                     Pilot   \n",
       "...                   ...                ...                       ...   \n",
       "2023-01-10       REDACTED  FAA Form 5200-7-E        Airport Operations   \n",
       "2023-02-06       REDACTED  FAA Form 5200-7-E  Air Transport Operations   \n",
       "2023-02-14       REDACTED  FAA Form 5200-7-E        Airport Operations   \n",
       "2023-03-06       REDACTED  FAA Form 5200-7-E        Airport Operations   \n",
       "2023-03-17       REDACTED  FAA Form 5200-7-E  Air Transport Operations   \n",
       "\n",
       "                      lupdate transfer  \n",
       "date                                    \n",
       "1990-03-31  5/18/2015 0:00:00        0  \n",
       "1990-08-10  1/11/2021 0:00:00        0  \n",
       "1991-12-21   4/1/1997 0:00:00        0  \n",
       "1992-05-30  5/18/2015 0:00:00        0  \n",
       "1993-09-20  5/18/2015 0:00:00        0  \n",
       "...                       ...      ...  \n",
       "2023-01-10   3/7/2023 0:00:00        0  \n",
       "2023-02-06  3/16/2023 0:00:00        0  \n",
       "2023-02-14  3/22/2023 0:00:00        0  \n",
       "2023-03-06  3/30/2023 0:00:00        0  \n",
       "2023-03-17   4/7/2023 0:00:00        0  \n",
       "\n",
       "[82 rows x 99 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# state is null if airport_id is 'ZZZZ', \n",
    "# AND there are 82 additional rows where state is null and airport_id is NOT 'ZZZZ'\n",
    "df[(df.state.isnull()) & (df.airport_id != 'ZZZZ')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5051b491-fca7-469c-a4ac-f4ff8f43c5e5",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value Counts for index_nr\n",
      "1242675    1\n",
      "793126     1\n",
      "792678     1\n",
      "792677     1\n",
      "1091244    1\n",
      "          ..\n",
      "698690     1\n",
      "700415     1\n",
      "697820     1\n",
      "698691     1\n",
      "1427626    1\n",
      "Name: index_nr, Length: 279947, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279947\n",
      "\n",
      "Value Counts for incident_month\n",
      "8     39029\n",
      "9     37479\n",
      "7     35234\n",
      "10    35116\n",
      "5     26804\n",
      "6     22884\n",
      "4     20556\n",
      "11    19292\n",
      "3     13983\n",
      "12    11078\n",
      "1      9430\n",
      "2      9062\n",
      "Name: incident_month, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279947\n",
      "\n",
      "Value Counts for incident_year\n",
      "2019    17342\n",
      "2022    17203\n",
      "2018    16205\n",
      "2021    15643\n",
      "2017    14777\n",
      "2015    13773\n",
      "2014    13692\n",
      "2016    13327\n",
      "2020    11624\n",
      "2013    11410\n",
      "2012    10932\n",
      "2011    10111\n",
      "2010     9897\n",
      "2009     9504\n",
      "2007     7740\n",
      "2008     7627\n",
      "2006     7273\n",
      "2005     7243\n",
      "2004     6566\n",
      "2002     6217\n",
      "2000     6025\n",
      "2003     5989\n",
      "2001     5822\n",
      "1999     5120\n",
      "1998     3809\n",
      "1997     3559\n",
      "2023     3042\n",
      "1996     3031\n",
      "1995     2825\n",
      "1994     2708\n",
      "1992     2651\n",
      "1993     2624\n",
      "1991     2516\n",
      "1990     2120\n",
      "Name: incident_year, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279947\n",
      "\n",
      "Value Counts for time\n",
      "08:00     1637\n",
      "09:00     1586\n",
      "10:00     1428\n",
      "11:00     1265\n",
      "21:00     1202\n",
      "          ... \n",
      "14:14:       1\n",
      "19:19:       1\n",
      "22:22:       1\n",
      "18:18:       1\n",
      "24:20        1\n",
      "Name: time, Length: 1503, dtype: int64\n",
      "Num of nulls: 123577\n",
      "Num of NON-nulls: 156370\n",
      "\n",
      "Value Counts for time_of_day\n",
      "Day      99664\n",
      "Night    49431\n",
      "Dusk      7287\n",
      "Dawn      5891\n",
      "Name: time_of_day, dtype: int64\n",
      "Num of nulls: 117674\n",
      "Num of NON-nulls: 162273\n",
      "\n",
      "Value Counts for airport_id\n",
      "ZZZZ    34302\n",
      "KDEN     9260\n",
      "KDFW     7527\n",
      "KORD     6065\n",
      "KJFK     5882\n",
      "        ...  \n",
      "T40         1\n",
      "LIRP        1\n",
      "KTLR        1\n",
      "OI65        1\n",
      "PALP        1\n",
      "Name: airport_id, Length: 2591, dtype: int64\n",
      "Num of nulls: 5\n",
      "Num of NON-nulls: 279942\n",
      "\n",
      "Value Counts for airport\n",
      "UNKNOWN                              34302\n",
      "DENVER INTL AIRPORT                   9260\n",
      "DALLAS/FORT WORTH INTL ARPT           7527\n",
      "CHICAGO O'HARE INTL ARPT              6065\n",
      "JOHN F KENNEDY INTL                   5882\n",
      "                                     ...  \n",
      "COOPERSTOWN MUNICIPAL ARPT               1\n",
      "JESSE VIERTEL MEMORIAL ARPT              1\n",
      "GOOSE BAY                                1\n",
      "ERA HELICOPTERS FOURCHON HELIBASE        1\n",
      "ALPINE AIRSTRIP                          1\n",
      "Name: airport, Length: 2596, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279947\n",
      "\n",
      "Value Counts for latitude\n",
      "39.858410    9260\n",
      "32.895950    7527\n",
      "41.979600    6065\n",
      "40.639750    5882\n",
      "35.042420    4676\n",
      "             ... \n",
      "42.364250       1\n",
      "39.077222       1\n",
      "59.348259       1\n",
      "40.972775       1\n",
      "39.507431       1\n",
      "Name: latitude, Length: 2578, dtype: int64\n",
      "Num of nulls: 34387\n",
      "Num of NON-nulls: 245560\n",
      "\n",
      "Value Counts for longitude\n",
      "-104.667000    9260\n",
      "-97.037200     7527\n",
      "-87.904460     6065\n",
      "-73.778930     5882\n",
      "-89.976670     4676\n",
      "               ... \n",
      "-100.146569       1\n",
      "-77.476389        1\n",
      "-151.831539       1\n",
      "-75.409500        1\n",
      "-150.944750       1\n",
      "Name: longitude, Length: 2581, dtype: int64\n",
      "Num of nulls: 34386\n",
      "Num of NON-nulls: 245561\n",
      "\n",
      "Value Counts for runway\n",
      "27           3890\n",
      "17R          3852\n",
      "22L          3740\n",
      "22           3536\n",
      "23           3533\n",
      "             ... \n",
      "8L/35           1\n",
      "D5/D6           1\n",
      "HARBOR          1\n",
      "Term E          1\n",
      "Taxiway R       1\n",
      "Name: runway, Length: 1174, dtype: int64\n",
      "Num of nulls: 67927\n",
      "Num of NON-nulls: 212020\n",
      "\n",
      "Value Counts for state\n",
      "TX    23812\n",
      "FL    19975\n",
      "CA    19954\n",
      "NY    13949\n",
      "CO    11900\n",
      "      ...  \n",
      "AB       53\n",
      "MB       16\n",
      "NL        6\n",
      "SK        5\n",
      "NS        5\n",
      "Name: state, Length: 63, dtype: int64\n",
      "Num of nulls: 34384\n",
      "Num of NON-nulls: 245563\n",
      "\n",
      "Value Counts for faaregion\n",
      "ASO     52126\n",
      "AEA     39855\n",
      "AGL     38557\n",
      "ASW     32117\n",
      "AWP     30296\n",
      "ANM     25611\n",
      "ACE     11533\n",
      "ANE      9166\n",
      "FGN      4889\n",
      "AAL      1413\n",
      "Name: faaregion, dtype: int64\n",
      "Num of nulls: 34384\n",
      "Num of NON-nulls: 245563\n",
      "\n",
      "Value Counts for location\n",
      "FOUND KCLT             923\n",
      "FOUND KLGA             799\n",
      "FOUND AT KCLT          706\n",
      "FOUND AT KLGA          652\n",
      "FOUND KDFW             571\n",
      "                      ... \n",
      "SOUTH OF BEDFORD IN      1\n",
      "MANASSAS VA              1\n",
      "OVER DASH POINT          1\n",
      "14 ME SE PEORIA IL       1\n",
      "FORT WORTH, TX           1\n",
      "Name: location, Length: 8895, dtype: int64\n",
      "Num of nulls: 247079\n",
      "Num of NON-nulls: 32868\n",
      "\n",
      "Value Counts for opid\n",
      "UNK    78102\n",
      "BUS    23836\n",
      "SWA    23211\n",
      "AAL    19833\n",
      "UAL    17211\n",
      "       ...  \n",
      "RJA        1\n",
      "RKA        1\n",
      "SPG        1\n",
      "LIB        1\n",
      "GRO        1\n",
      "Name: opid, Length: 603, dtype: int64\n",
      "Num of nulls: 4\n",
      "Num of NON-nulls: 279943\n",
      "\n",
      "Value Counts for operator\n",
      "UNKNOWN                   78103\n",
      "BUSINESS                  23836\n",
      "SOUTHWEST AIRLINES        23211\n",
      "AMERICAN AIRLINES         19834\n",
      "UNITED AIRLINES           17211\n",
      "                          ...  \n",
      "ABELAG AVIATION               1\n",
      "ROYAL JORDANIAN               1\n",
      "AIR AFRIQUE                   1\n",
      "SPRINGDALE AIR SERVICE        1\n",
      "BALAIR                        1\n",
      "Name: operator, Length: 598, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279947\n",
      "\n",
      "Value Counts for reg\n",
      "N477HA    56\n",
      "N475HA    53\n",
      "N484HA    53\n",
      "N479HA    51\n",
      "N487HA    50\n",
      "          ..\n",
      "N2950H     1\n",
      "N425NS     1\n",
      "N777MR     1\n",
      "N904LX     1\n",
      "N794CP     1\n",
      "Name: reg, Length: 41556, dtype: int64\n",
      "Num of nulls: 112922\n",
      "Num of NON-nulls: 167025\n",
      "\n",
      "Value Counts for flt\n",
      "1          131\n",
      "301        128\n",
      "3          107\n",
      "2          103\n",
      "503         90\n",
      "          ... \n",
      "9208         1\n",
      "7102         1\n",
      "7287         1\n",
      "1593;        1\n",
      "AAL2790      1\n",
      "Name: flt, Length: 11468, dtype: int64\n",
      "Num of nulls: 155418\n",
      "Num of NON-nulls: 124529\n",
      "\n",
      "Value Counts for aircraft\n",
      "UNKNOWN              78370\n",
      "B-737-700            13936\n",
      "A-320                13442\n",
      "B-737-800            10442\n",
      "CRJ100/200            9405\n",
      "                     ...  \n",
      "BELL 230                 1\n",
      "HELIO COURIER 800        1\n",
      "URBAN AIR LAMBADA        1\n",
      "CAP AVION MUDRY          1\n",
      "A220-300                 1\n",
      "Name: aircraft, Length: 594, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279947\n",
      "\n",
      "Value Counts for ama\n",
      "148    70787\n",
      "04A    32673\n",
      "188    18526\n",
      "332    17828\n",
      "583    17659\n",
      "       ...  \n",
      "Q          1\n",
      "228        1\n",
      "34         1\n",
      "167        1\n",
      "04a        1\n",
      "Name: ama, Length: 99, dtype: int64\n",
      "Num of nulls: 80082\n",
      "Num of NON-nulls: 199865\n",
      "\n",
      "Value Counts for amo\n",
      "3.0     14428\n",
      "42.0    14029\n",
      "10.0    12894\n",
      "43.0    10468\n",
      "24.0     9991\n",
      "        ...  \n",
      "451         1\n",
      "28.0        1\n",
      "93.0        1\n",
      "N33         1\n",
      "N16         1\n",
      "Name: amo, Length: 119, dtype: int64\n",
      "Num of nulls: 93996\n",
      "Num of NON-nulls: 185951\n",
      "\n",
      "Value Counts for ema\n",
      "10.0     51633\n",
      "22.0     39458\n",
      "34.0     30553\n",
      "31.0     15179\n",
      "23.0     12188\n",
      "1.0       9536\n",
      "7.0       7798\n",
      "37.0      5708\n",
      "19.0      4678\n",
      "13.0      4145\n",
      "9.0       3092\n",
      "43.0      1237\n",
      "2.0        700\n",
      "46.0       654\n",
      "11.0       140\n",
      "25.0        10\n",
      "44.0         9\n",
      "33.0         6\n",
      "29.0         5\n",
      "36.0         4\n",
      "16.0         4\n",
      "3.0          4\n",
      "0.0          3\n",
      "35.0         3\n",
      "32.0         3\n",
      "42.0         2\n",
      "47.0         2\n",
      "41.0         2\n",
      "12.0         2\n",
      "20.0         2\n",
      "18.0         2\n",
      "24.0         1\n",
      "14.0         1\n",
      "45.0         1\n",
      "30.0         1\n",
      "27.0         1\n",
      "101.0        1\n",
      "222.0        1\n",
      "92.0         1\n",
      "110.0        1\n",
      "Name: ema, dtype: int64\n",
      "Num of nulls: 93176\n",
      "Num of NON-nulls: 186771\n",
      "\n",
      "Value Counts for emo\n",
      "1.0     46431\n",
      "01      24620\n",
      "4.0     22615\n",
      "10.0    19477\n",
      "10      15413\n",
      "        ...  \n",
      "97          1\n",
      "05          1\n",
      "??          1\n",
      "32.0        1\n",
      "90.0        1\n",
      "Name: emo, Length: 102, dtype: int64\n",
      "Num of nulls: 97816\n",
      "Num of NON-nulls: 182131\n",
      "\n",
      "Value Counts for ac_class\n",
      "A      196904\n",
      "B        4297\n",
      "C           6\n",
      "J           5\n",
      "Y           3\n",
      "Name: ac_class, dtype: int64\n",
      "Num of nulls: 78732\n",
      "Num of NON-nulls: 201215\n",
      "\n",
      "Value Counts for ac_mass\n",
      "4.0    139645\n",
      "3.0     33287\n",
      "1.0     12882\n",
      "2.0     12821\n",
      "5.0      2176\n",
      "Name: ac_mass, dtype: int64\n",
      "Num of nulls: 79136\n",
      "Num of NON-nulls: 200811\n",
      "\n",
      "Value Counts for type_eng\n",
      "D    168262\n",
      "C     14663\n",
      "A     13159\n",
      "F      3986\n",
      "B       300\n",
      "E         4\n",
      "Y         3\n",
      "Name: type_eng, dtype: int64\n",
      "Num of nulls: 79570\n",
      "Num of NON-nulls: 200377\n",
      "\n",
      "Value Counts for num_engs\n",
      "2.0    174510\n",
      "1.0     13075\n",
      "3.0      9742\n",
      "4.0      3260\n",
      "Name: num_engs, dtype: int64\n",
      "Num of nulls: 79360\n",
      "Num of NON-nulls: 200587\n",
      "\n",
      "Value Counts for eng_1_pos\n",
      "1.0    110378\n",
      "5.0     58313\n",
      "4.0     16516\n",
      "7.0     11008\n",
      "6.0      3405\n",
      "3.0       171\n",
      "2.0        37\n",
      "Name: eng_1_pos, dtype: int64\n",
      "Num of nulls: 80119\n",
      "Num of NON-nulls: 199828\n",
      "\n",
      "Value Counts for eng_2_pos\n",
      "1.0    106213\n",
      "5.0     52052\n",
      "4.0     16517\n",
      "6.0     11416\n",
      "3.0       171\n",
      "2.0        31\n",
      "7.0         1\n",
      "Name: eng_2_pos, dtype: int64\n",
      "Num of nulls: 93546\n",
      "Num of NON-nulls: 186401\n",
      "\n",
      "Value Counts for eng_3_pos\n",
      "1.0    6616\n",
      "5.0    5593\n",
      "4.0     789\n",
      "3.0       3\n",
      "6.0       1\n",
      "Name: eng_3_pos, dtype: int64\n",
      "Num of nulls: 266945\n",
      "Num of NON-nulls: 13002\n",
      "\n",
      "Value Counts for eng_4_pos\n",
      "1.0    2136\n",
      "4.0    1104\n",
      "5.0      17\n",
      "3.0       3\n",
      "Name: eng_4_pos, dtype: int64\n",
      "Num of nulls: 276687\n",
      "Num of NON-nulls: 3260\n",
      "\n",
      "Value Counts for phase_of_flight\n",
      "Approach        73361\n",
      "Landing Roll    31204\n",
      "Take-off Run    29422\n",
      "Climb           26412\n",
      "En Route         5230\n",
      "Departure        2682\n",
      "Descent          2266\n",
      "Local            1064\n",
      "Arrival           727\n",
      "Taxi              650\n",
      "Parked            113\n",
      "Name: phase_of_flight, dtype: int64\n",
      "Num of nulls: 106816\n",
      "Num of NON-nulls: 173131\n",
      "\n",
      "Value Counts for height\n",
      "0.0       61502\n",
      "100.0      6386\n",
      "50.0       5696\n",
      "500.0      5013\n",
      "200.0      4943\n",
      "          ...  \n",
      "1075.0        1\n",
      "634.0         1\n",
      "5450.0        1\n",
      "1560.0        1\n",
      "4640.0        1\n",
      "Name: height, Length: 695, dtype: int64\n",
      "Num of nulls: 134582\n",
      "Num of NON-nulls: 145365\n",
      "\n",
      "Value Counts for speed\n",
      "140.0    10055\n",
      "130.0     7179\n",
      "120.0     6944\n",
      "150.0     6186\n",
      "100.0     5775\n",
      "         ...  \n",
      "277.0        1\n",
      "197.0        1\n",
      "354.0        1\n",
      "244.0        1\n",
      "282.0        1\n",
      "Name: speed, Length: 285, dtype: int64\n",
      "Num of nulls: 187309\n",
      "Num of NON-nulls: 92638\n",
      "\n",
      "Value Counts for distance\n",
      "0.00     160506\n",
      "1.00       3036\n",
      "5.00       2458\n",
      "3.00       2045\n",
      "2.00       2013\n",
      "          ...  \n",
      "32.00         1\n",
      "16.50         1\n",
      "41.00         1\n",
      "14.10         1\n",
      "4.34          1\n",
      "Name: distance, Length: 201, dtype: int64\n",
      "Num of nulls: 98214\n",
      "Num of NON-nulls: 181733\n",
      "\n",
      "Value Counts for sky\n",
      "No Cloud      65779\n",
      "Some Cloud    46957\n",
      "Overcast      23350\n",
      "Name: sky, dtype: int64\n",
      "Num of nulls: 143861\n",
      "Num of NON-nulls: 136086\n",
      "\n",
      "Value Counts for precipitation\n",
      "None                119918\n",
      "Rain                  7652\n",
      "Fog                   2472\n",
      "Snow                   479\n",
      "Fog, Rain              310\n",
      "Rain, Snow              27\n",
      "None, Snow              26\n",
      "Fog, Snow               15\n",
      "None, Rain              10\n",
      "Fog, None                7\n",
      "Fog, Rain, Snow          6\n",
      "None, Rain, Snow         1\n",
      "Name: precipitation, dtype: int64\n",
      "Num of nulls: 149024\n",
      "Num of NON-nulls: 130923\n",
      "\n",
      "Value Counts for aos\n",
      "1.0        4318\n",
      "2.0        1550\n",
      "24.0        685\n",
      "3.0         576\n",
      "4.0         527\n",
      "           ... \n",
      "1117.0        1\n",
      "1056.0        1\n",
      "71.0          1\n",
      "498.0         1\n",
      "62848.0       1\n",
      "Name: aos, Length: 458, dtype: int64\n",
      "Num of nulls: 266138\n",
      "Num of NON-nulls: 13809\n",
      "\n",
      "Value Counts for cost_repairs\n",
      "$10000.00       190\n",
      "$5000.00        180\n",
      "$1000.00        165\n",
      "$500.00         142\n",
      "$2000.00        141\n",
      "               ... \n",
      "$14359.00         1\n",
      "$24605.00         1\n",
      "$37468.00         1\n",
      "$28850.00         1\n",
      "$14500000.00      1\n",
      "Name: cost_repairs, Length: 1301, dtype: int64\n",
      "Num of nulls: 274900\n",
      "Num of NON-nulls: 5047\n",
      "\n",
      "Value Counts for cost_other\n",
      "$500.00       392\n",
      "$100.00       339\n",
      "$200.00       324\n",
      "$1000.00      264\n",
      "$300.00       245\n",
      "             ... \n",
      "$230000.00      1\n",
      "$195931.00      1\n",
      "$19069.00       1\n",
      "$962.00         1\n",
      "$5513.00        1\n",
      "Name: cost_other, Length: 646, dtype: int64\n",
      "Num of nulls: 275312\n",
      "Num of NON-nulls: 4635\n",
      "\n",
      "Value Counts for cost_repairs_infl_adj\n",
      "$22880.00    13\n",
      "$12340.00    13\n",
      "$6500.00     13\n",
      "$11930.00    13\n",
      "$13000.00    12\n",
      "             ..\n",
      "$9276.00      1\n",
      "$2067.00      1\n",
      "$1855.00      1\n",
      "$38347.00     1\n",
      "$3500.00      1\n",
      "Name: cost_repairs_infl_adj, Length: 2934, dtype: int64\n",
      "Num of nulls: 274900\n",
      "Num of NON-nulls: 5047\n",
      "\n",
      "Value Counts for cost_other_infl_adj\n",
      "$247.00       64\n",
      "$583.00       50\n",
      "$637.00       36\n",
      "$260.00       35\n",
      "$618.00       35\n",
      "              ..\n",
      "$17713.00      1\n",
      "$56909.00      1\n",
      "$812500.00     1\n",
      "$68250.00      1\n",
      "$5513.00       1\n",
      "Name: cost_other_infl_adj, Length: 1627, dtype: int64\n",
      "Num of nulls: 275312\n",
      "Num of NON-nulls: 4635\n",
      "\n",
      "Value Counts for ingested_other\n",
      "0    268567\n",
      "1     11380\n",
      "Name: ingested_other, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279947\n",
      "\n",
      "Value Counts for indicated_damage\n",
      "0    260421\n",
      "1     19526\n",
      "Name: indicated_damage, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279947\n",
      "\n",
      "Value Counts for damage_level\n",
      "N     163007\n",
      "M       8550\n",
      "M?      6660\n",
      "S       4236\n",
      "D         82\n",
      "Name: damage_level, dtype: int64\n",
      "Num of nulls: 97412\n",
      "Num of NON-nulls: 182535\n",
      "\n",
      "Value Counts for str_rad\n",
      "0    254113\n",
      "1     25834\n",
      "Name: str_rad, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279947\n",
      "\n",
      "Value Counts for dam_rad\n",
      "0    277945\n",
      "1      2002\n",
      "Name: dam_rad, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279947\n",
      "\n",
      "Value Counts for str_windshld\n",
      "0    245454\n",
      "1     34493\n",
      "Name: str_windshld, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279947\n",
      "\n",
      "Value Counts for dam_windshld\n",
      "0    278567\n",
      "1      1380\n",
      "Name: dam_windshld, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279947\n",
      "\n",
      "Value Counts for str_nose\n",
      "0    247569\n",
      "1     32378\n",
      "Name: str_nose, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279947\n",
      "\n",
      "Value Counts for dam_nose\n",
      "0    278330\n",
      "1      1617\n",
      "Name: dam_nose, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279947\n",
      "\n",
      "Value Counts for str_eng1\n",
      "0    266850\n",
      "1     13097\n",
      "Name: str_eng1, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279947\n",
      "\n",
      "Value Counts for dam_eng1\n",
      "0    277015\n",
      "1      2932\n",
      "Name: dam_eng1, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279947\n",
      "\n",
      "Value Counts for ing_eng1\n",
      "0    279364\n",
      "1       583\n",
      "Name: ing_eng1, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279947\n",
      "\n",
      "Value Counts for str_eng2\n",
      "0    268907\n",
      "1     11040\n",
      "Name: str_eng2, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279947\n",
      "\n",
      "Value Counts for dam_eng2\n",
      "0    277502\n",
      "1      2445\n",
      "Name: dam_eng2, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279947\n",
      "\n",
      "Value Counts for ing_eng2\n",
      "0    279304\n",
      "1       643\n",
      "Name: ing_eng2, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279947\n",
      "\n",
      "Value Counts for str_eng3\n",
      "0    279469\n",
      "1       478\n",
      "Name: str_eng3, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279947\n",
      "\n",
      "Value Counts for dam_eng3\n",
      "0    279780\n",
      "1       167\n",
      "Name: dam_eng3, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279947\n",
      "\n",
      "Value Counts for ing_eng3\n",
      "0    279939\n",
      "1         8\n",
      "Name: ing_eng3, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279947\n",
      "\n",
      "Value Counts for str_eng4\n",
      "0    279715\n",
      "1       232\n",
      "Name: str_eng4, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279947\n",
      "\n",
      "Value Counts for dam_eng4\n",
      "0    279880\n",
      "1        67\n",
      "Name: dam_eng4, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279947\n",
      "\n",
      "Value Counts for ing_eng4\n",
      "0    279946\n",
      "1         1\n",
      "Name: ing_eng4, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279947\n",
      "\n",
      "Value Counts for str_prop\n",
      "0    275165\n",
      "1      4782\n",
      "Name: str_prop, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279947\n",
      "\n",
      "Value Counts for dam_prop\n",
      "0    279275\n",
      "1       672\n",
      "Name: dam_prop, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279947\n",
      "\n",
      "Value Counts for str_wing_rot\n",
      "0    247057\n",
      "1     32890\n",
      "Name: str_wing_rot, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279947\n",
      "\n",
      "Value Counts for dam_wing_rot\n",
      "0    274124\n",
      "1      5823\n",
      "Name: dam_wing_rot, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279947\n",
      "\n",
      "Value Counts for str_fuse\n",
      "0    254806\n",
      "1     25141\n",
      "Name: str_fuse, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279947\n",
      "\n",
      "Value Counts for dam_fuse\n",
      "0    278801\n",
      "1      1146\n",
      "Name: dam_fuse, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279947\n",
      "\n",
      "Value Counts for str_lg\n",
      "0    268093\n",
      "1     11854\n",
      "Name: str_lg, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279947\n",
      "\n",
      "Value Counts for dam_lg\n",
      "0    278691\n",
      "1      1256\n",
      "Name: dam_lg, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279947\n",
      "\n",
      "Value Counts for str_tail\n",
      "0    277081\n",
      "1      2866\n",
      "Name: str_tail, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279947\n",
      "\n",
      "Value Counts for dam_tail\n",
      "0    278963\n",
      "1       984\n",
      "Name: dam_tail, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279947\n",
      "\n",
      "Value Counts for str_lghts\n",
      "0    278538\n",
      "1      1409\n",
      "Name: str_lghts, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279947\n",
      "\n",
      "Value Counts for dam_lghts\n",
      "0    278979\n",
      "1       968\n",
      "Name: dam_lghts, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279947\n",
      "\n",
      "Value Counts for str_other\n",
      "0    243356\n",
      "1     36591\n",
      "Name: str_other, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279947\n",
      "\n",
      "Value Counts for dam_other\n",
      "0    277629\n",
      "1      2318\n",
      "Name: dam_other, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279947\n",
      "\n",
      "Value Counts for other_specify\n",
      "PART NOT REPORTED     14588\n",
      "PART NOT REPTD        13817\n",
      "part not reported       844\n",
      "parts not reported      601\n",
      "PITOT TUBE              308\n",
      "                      ...  \n",
      "#4 ENG COWLING            1\n",
      "RH PITOT TUBE             1\n",
      "ENG #2 PYLON              1\n",
      "Brake hose                1\n",
      "right flap                1\n",
      "Name: other_specify, Length: 2829, dtype: int64\n",
      "Num of nulls: 243221\n",
      "Num of NON-nulls: 36726\n",
      "\n",
      "Value Counts for effect\n",
      "None                                             127100\n",
      "Precautionary Landing                              7749\n",
      "Aborted Take-off                                   2979\n",
      "Other                                              1860\n",
      "Engine Shutdown                                     430\n",
      "Other, Precautionary Landing                        320\n",
      "Engine Shutdown, Precautionary Landing               44\n",
      "Engine Shutdown, Other, Precautionary Landing        42\n",
      "Aborted Take-off, Engine Shutdown                    17\n",
      "Aborted Take-off, Other                               5\n",
      "Engine Shutdown, Other                                1\n",
      "Name: effect, dtype: int64\n",
      "Num of nulls: 139400\n",
      "Num of NON-nulls: 140547\n",
      "\n",
      "Value Counts for effect_other\n",
      "GO-AROUND                       229\n",
      "EVASIVE MANEUVER                227\n",
      "AIRCRAFT CONTROLS EFFECTED      190\n",
      "OVERWEIGHT LANDING              131\n",
      "FUEL BURN                       118\n",
      "                               ... \n",
      "LOSS OF CONTROL                   1\n",
      "CONCERNS WITH STEERING            1\n",
      "EVASIVE MANEUVER, NOSED OVER      1\n",
      "EMERGENCY STOP                    1\n",
      "RETURN TO BASE                    1\n",
      "Name: effect_other, Length: 661, dtype: int64\n",
      "Num of nulls: 277720\n",
      "Num of NON-nulls: 2227\n",
      "\n",
      "Value Counts for bird_band_number\n",
      "195707683.0    2\n",
      "168702515.0    2\n",
      "78478661.0     1\n",
      "120791572.0    1\n",
      "180746861.0    1\n",
      "              ..\n",
      "196713266.0    1\n",
      "183337175.0    1\n",
      "168731226.0    1\n",
      "189314535.0    1\n",
      "189343000.0    1\n",
      "Name: bird_band_number, Length: 179, dtype: int64\n",
      "Num of nulls: 279766\n",
      "Num of NON-nulls: 181\n",
      "\n",
      "Value Counts for species_id\n",
      "UNKBS    47902\n",
      "UNKBM    38117\n",
      "UNKB     23114\n",
      "O2205    13975\n",
      "N5111     9214\n",
      "         ...  \n",
      "Q2102        1\n",
      "X6110        1\n",
      "2D13         1\n",
      "K3202        1\n",
      "2            1\n",
      "Name: species_id, Length: 892, dtype: int64\n",
      "Num of nulls: 4\n",
      "Num of NON-nulls: 279943\n",
      "\n",
      "Value Counts for species\n",
      "Unknown bird - small     47902\n",
      "Unknown bird - medium    38117\n",
      "Unknown bird             23114\n",
      "Mourning dove            13975\n",
      "Killdeer                  9214\n",
      "                         ...  \n",
      "Common ringed plover         1\n",
      "Hooded crow                  1\n",
      "Common kingsnake             1\n",
      "Kites                        1\n",
      "Reptiles                     1\n",
      "Name: species, Length: 900, dtype: int64\n",
      "Num of nulls: 2\n",
      "Num of NON-nulls: 279945\n",
      "\n",
      "Value Counts for remarks\n",
      "NO DMG.                                                                                                                 2530\n",
      "NO DMG                                                                                                                  1066\n",
      "NO DAMAGE                                                                                                               1018\n",
      "NO DAMAGE.                                                                                                               844\n",
      "NO DMG NOTED.                                                                                                            838\n",
      "                                                                                                                        ... \n",
      "PILOT REPTD A BIRDSTRIKE ON APRCH RWY 33. OPS RESPONDED AND COLLECTED THE REMAINS OF SIX RING-BILLED GULLS FROM RWY.       1\n",
      "ON FINAL APRCH, SAW 1 BIRD, HEARD 2 STRIKES. POSTFLT REVEALED NO DMG.                                                      1\n",
      "BIRD WAS NOT FOUND. NO A/C DMG.                                                                                            1\n",
      "TIRE ROLL. TIRE STRUCK OPOSSUM WHILE CROSSING RWY.                                                                         1\n",
      "Collected on RWY 17R/35L between B4 and B5. Found during morning inspection                                                1\n",
      "Name: remarks, Length: 231916, dtype: int64\n",
      "Num of nulls: 24223\n",
      "Num of NON-nulls: 255724\n",
      "\n",
      "Value Counts for remains_collected\n",
      "1    143358\n",
      "0    136589\n",
      "Name: remains_collected, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279947\n",
      "\n",
      "Value Counts for remains_sent\n",
      "0    235845\n",
      "1     44102\n",
      "Name: remains_sent, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279947\n",
      "\n",
      "Value Counts for warned\n",
      "Unknown    169257\n",
      "No          60963\n",
      "Yes         49727\n",
      "Name: warned, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279947\n",
      "\n",
      "Value Counts for num_seen\n",
      "1                55189\n",
      "2-10             29088\n",
      "11-100            7155\n",
      "More than 100      979\n",
      "Name: num_seen, dtype: int64\n",
      "Num of nulls: 187536\n",
      "Num of NON-nulls: 92411\n",
      "\n",
      "Value Counts for num_struck\n",
      "1                247341\n",
      "2-10              30450\n",
      "11-100             1491\n",
      "More than 100        58\n",
      "Name: num_struck, dtype: int64\n",
      "Num of nulls: 607\n",
      "Num of NON-nulls: 279340\n",
      "\n",
      "Value Counts for size\n",
      "Small     172535\n",
      "Medium     65927\n",
      "Large      17947\n",
      "Name: size, dtype: int64\n",
      "Num of nulls: 23538\n",
      "Num of NON-nulls: 256409\n",
      "\n",
      "Value Counts for enroute_state\n",
      "FL    504\n",
      "TX    493\n",
      "CA    382\n",
      "LA    231\n",
      "AZ    218\n",
      "PA    166\n",
      "OH    162\n",
      "KY    144\n",
      "NY    144\n",
      "IL    138\n",
      "VA    134\n",
      "MD    130\n",
      "NC    124\n",
      "MO    118\n",
      "TN    100\n",
      "MN    100\n",
      "IN     90\n",
      "IA     86\n",
      "NJ     84\n",
      "WI     80\n",
      "HI     77\n",
      "CO     72\n",
      "GA     70\n",
      "MI     68\n",
      "OR     60\n",
      "AK     59\n",
      "AL     58\n",
      "WA     57\n",
      "SC     56\n",
      "OK     55\n",
      "KS     54\n",
      "UT     54\n",
      "NE     52\n",
      "MS     50\n",
      "ND     49\n",
      "MA     48\n",
      "WV     44\n",
      "AR     41\n",
      "NM     40\n",
      "SD     28\n",
      "CT     26\n",
      "ID     26\n",
      "NV     25\n",
      "MT     18\n",
      "NH     16\n",
      "DC     15\n",
      "PR     14\n",
      "ME     14\n",
      "DE     14\n",
      "FN     12\n",
      "WY      7\n",
      "RI      4\n",
      "VT      3\n",
      "VI      2\n",
      "PI      1\n",
      "AB      1\n",
      "NL      1\n",
      "BC      1\n",
      "ON      1\n",
      "Name: enroute_state, dtype: int64\n",
      "Num of nulls: 275026\n",
      "Num of NON-nulls: 4921\n",
      "\n",
      "Value Counts for nr_injuries\n",
      "1.0    217\n",
      "2.0     51\n",
      "7.0      2\n",
      "5.0      2\n",
      "4.0      2\n",
      "3.0      2\n",
      "Name: nr_injuries, dtype: int64\n",
      "Num of nulls: 279671\n",
      "Num of NON-nulls: 276\n",
      "\n",
      "Value Counts for nr_fatalities\n",
      "1.0    12\n",
      "2.0     7\n",
      "3.0     2\n",
      "5.0     1\n",
      "8.0     1\n",
      "4.0     1\n",
      "Name: nr_fatalities, dtype: int64\n",
      "Num of nulls: 279923\n",
      "Num of NON-nulls: 24\n",
      "\n",
      "Value Counts for comments\n",
      "SPECIES SIZE UPDATED DUE TO MISMATCH WITH MANAGED TABLE                                                                                                                                                         3086\n",
      "SI ID                                                                                                                                                                                                           2381\n",
      "REVISED A/C MODEL                                                                                                                                                                                                204\n",
      "0 DISTANCE                                                                                                                                                                                                       157\n",
      "si id                                                                                                                                                                                                            140\n",
      "                                                                                                                                                                                                                ... \n",
      "2007-5-30-12472 /Legacy Record=244012/                                                                                                                                                                             1\n",
      "2007-8-17-122242 /Legacy Record=245000/                                                                                                                                                                            1\n",
      "TWO AIRLINE REPTS /Legacy Record=248579/                                                                                                                                                                           1\n",
      "THREE 5200-7 (2007-5-17-191437 /Legacy Record=247867/                                                                                                                                                              1\n",
      "*** Strike Report: 2023-05-15-132614-R0( Report ID: 1427109  [ Comment: ] )  Merged From 2023-05-15-132631-R1( Report ID: 1427115  [ Comment: ] ) on May 15 2023  1:52PM By phyllis.r.miller@usda.gov ***          1\n",
      "Name: comments, Length: 207148, dtype: int64\n",
      "Num of nulls: 63367\n",
      "Num of NON-nulls: 216580\n",
      "\n",
      "Value Counts for reported_name\n",
      "REDACTED    279947\n",
      "Name: reported_name, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279947\n",
      "\n",
      "Value Counts for reported_title\n",
      "REDACTED    279947\n",
      "Name: reported_title, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279947\n",
      "\n",
      "Value Counts for source\n",
      "FAA Form 5200-7-E       166366\n",
      "FAA Form 5200-7          41733\n",
      "Multiple                 29095\n",
      "Air Transport Report     16915\n",
      "MOR                      11222\n",
      "Airport Report            7339\n",
      "Other                     2248\n",
      "Daily Report              2183\n",
      "PACIR                      879\n",
      "Engine Manufacturer        811\n",
      "AC Incident Rept           715\n",
      "ASRS                       195\n",
      "NTSB                       138\n",
      "AAIPN-1                     67\n",
      "Transport Canada            37\n",
      "BASH                         4\n",
      "Name: source, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279947\n",
      "\n",
      "Value Counts for person\n",
      "Carcass Found               76489\n",
      "Airport Operations          62743\n",
      "Pilot                       50699\n",
      "Air Transport Operations    33213\n",
      "Tower                       29217\n",
      "Other                        6441\n",
      "Name: person, dtype: int64\n",
      "Num of nulls: 21145\n",
      "Num of NON-nulls: 258802\n",
      "\n",
      "Value Counts for lupdate\n",
      "6/30/2018 0:00:00     6765\n",
      "7/27/2019 0:00:00     5910\n",
      "1/11/2021 0:00:00     3397\n",
      "5/8/2018 0:00:00      3039\n",
      "1/9/2019 0:00:00      2676\n",
      "                      ... \n",
      "4/11/2013 0:00:00        1\n",
      "10/30/2013 0:00:00       1\n",
      "4/7/2014 0:00:00         1\n",
      "6/7/2013 0:00:00         1\n",
      "4/28/2023 0:00:00        1\n",
      "Name: lupdate, Length: 4052, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279947\n",
      "\n",
      "Value Counts for transfer\n",
      "0    279947\n",
      "Name: transfer, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279947\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for col in df.columns:\n",
    "    print(f'Value Counts for {col}')\n",
    "    print(df[col].value_counts())\n",
    "    print(f'Num of nulls: {df[col].isnull().sum()}')\n",
    "    print(f'Num of NON-nulls: {df[col].notnull().sum()}')\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0966df62-a0a7-4d62-ac5e-c2a02685cbce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# decide which columns to keep\n",
    "keep_cols = ['time_of_day', 'airport_id', 'airport', 'runway', 'state', 'opid', 'operator'\n",
    "             , 'aircraft', 'ac_class', 'ac_mass', 'type_eng', 'num_engs', 'phase_of_flight'\n",
    "             , 'precipitation', 'damage_level', 'species_id', 'species', 'size', 'nr_injuries', 'nr_fatalities']\n",
    "df = df[keep_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "efcbf80d-99c7-4275-9d28-5a63e466c360",
   "metadata": {},
   "outputs": [],
   "source": [
    "# changing nulls to 'Unknown' for time of day (Dusk, Dawn, ..., Unknown)\n",
    "df.time_of_day = df.time_of_day.fillna('Unknown')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "971194c4-9634-479c-adb4-5a48852b988f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unknown    117674\n",
       "Day         99664\n",
       "Night       49431\n",
       "Dusk         7287\n",
       "Dawn         5891\n",
       "Name: time_of_day, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.time_of_day.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "36c216ed-eb33-4807-aeb9-eaae56493a20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 20)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.airport_id.isnull()].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6793bb2d-9164-457a-bdea-17b144bbe374",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(279947, 20)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "adf69071-09a0-4cc2-8e63-f7285309d7a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(279942, 20)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# there are only 5 nulls for airport_id, so I'm just going to remove them. It's easier than fixing them one at a time\n",
    "df = df[df.airport_id.notnull()]\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1fcd9e2f-145b-40d4-b591-2b0b1362545a",
   "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>time_of_day</th>\n",
       "      <th>airport_id</th>\n",
       "      <th>airport</th>\n",
       "      <th>runway</th>\n",
       "      <th>state</th>\n",
       "      <th>opid</th>\n",
       "      <th>operator</th>\n",
       "      <th>aircraft</th>\n",
       "      <th>ac_class</th>\n",
       "      <th>ac_mass</th>\n",
       "      <th>type_eng</th>\n",
       "      <th>num_engs</th>\n",
       "      <th>phase_of_flight</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>damage_level</th>\n",
       "      <th>species_id</th>\n",
       "      <th>species</th>\n",
       "      <th>size</th>\n",
       "      <th>nr_injuries</th>\n",
       "      <th>nr_fatalities</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-05-07</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>ZZZZ</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>JBU</td>\n",
       "      <td>JETBLUE AIRWAYS</td>\n",
       "      <td>EMB-190</td>\n",
       "      <td>A</td>\n",
       "      <td>4.0</td>\n",
       "      <td>D</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>Z5301</td>\n",
       "      <td>Gray catbird</td>\n",
       "      <td>Small</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-11-15</th>\n",
       "      <td>Night</td>\n",
       "      <td>ZZZZ</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PVT</td>\n",
       "      <td>PRIVATELY OWNED</td>\n",
       "      <td>C-172</td>\n",
       "      <td>A</td>\n",
       "      <td>1.0</td>\n",
       "      <td>A</td>\n",
       "      <td>1.0</td>\n",
       "      <td>En Route</td>\n",
       "      <td>None</td>\n",
       "      <td>N</td>\n",
       "      <td>NE1</td>\n",
       "      <td>Gulls</td>\n",
       "      <td>Medium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-08</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>ZZZZ</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AAL</td>\n",
       "      <td>AMERICAN AIRLINES</td>\n",
       "      <td>A-319</td>\n",
       "      <td>A</td>\n",
       "      <td>4.0</td>\n",
       "      <td>D</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>X6102</td>\n",
       "      <td>Yellow-bellied sapsucker</td>\n",
       "      <td>Small</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           time_of_day airport_id  airport runway state opid  \\\n",
       "date                                                           \n",
       "2021-05-07     Unknown       ZZZZ  UNKNOWN    NaN   NaN  JBU   \n",
       "1991-11-15       Night       ZZZZ  UNKNOWN    NaN   NaN  PVT   \n",
       "2015-10-08     Unknown       ZZZZ  UNKNOWN    NaN   NaN  AAL   \n",
       "\n",
       "                     operator aircraft ac_class  ac_mass type_eng  num_engs  \\\n",
       "date                                                                          \n",
       "2021-05-07    JETBLUE AIRWAYS  EMB-190      A        4.0        D       2.0   \n",
       "1991-11-15    PRIVATELY OWNED    C-172      A        1.0        A       1.0   \n",
       "2015-10-08  AMERICAN AIRLINES    A-319      A        4.0        D       2.0   \n",
       "\n",
       "           phase_of_flight precipitation damage_level species_id  \\\n",
       "date                                                               \n",
       "2021-05-07             NaN           NaN            N      Z5301   \n",
       "1991-11-15        En Route          None            N        NE1   \n",
       "2015-10-08             NaN           NaN            N      X6102   \n",
       "\n",
       "                             species    size  nr_injuries  nr_fatalities  \n",
       "date                                                                      \n",
       "2021-05-07              Gray catbird   Small          NaN            NaN  \n",
       "1991-11-15                     Gulls  Medium          NaN            NaN  \n",
       "2015-10-08  Yellow-bellied sapsucker   Small          NaN            NaN  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.airport_id == 'ZZZZ'].sample(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ad8929fc-6f75-47d0-8ef7-79d8e6544028",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(34302, 20)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# there are enough airport_id's with 'ZZZZ', i.e. the location of the birdstrike is unknown, that I want to save them\n",
    "df[df.airport_id == 'ZZZZ'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "587b1499-e483-4b33-84c7-39baccaf3391",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "67926"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# handling other nulls - runway\n",
    "df.runway.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "111f3656-769f-4b6c-8fbe-b1a44defba7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fill runway nulls with 99 (only valid runways are 01-36\n",
    "# Also note this is a text field with inconsistent values (e.g. 18L, 18, 18/36)\n",
    "# , so it may not be useful\n",
    "df.runway = np.where(df.runway.isnull(), '99', df.runway)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "08f62d07-85cb-4f4d-a0a7-8ddb30181832",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34379"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.state.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "887d41c1-fbbc-4c73-b120-e0b549a301d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fill state nulls with 'ZZ'; this column also may be of marginal utility\n",
    "df.state = np.where(df.state.isnull(), 'ZZ', df.state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "fe9860db-e9db-4ae2-8260-3c57742f2ee3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "106815"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.phase_of_flight.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "954f7c2e-3e99-441b-94a9-44c20ca100a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fill phase_of_flight nulls with Unknown\n",
    "df.phase_of_flight = np.where(df.phase_of_flight.isnull(), 'Unknown', df.phase_of_flight)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7f9ab858-248d-467d-9716-278502ec60b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "149021"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.precipitation.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2eadd5bb-8150-4d33-af45-ec14479071d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fill precipitation nulls with 'Unknown'\n",
    "df.precipitation = np.where(df.precipitation.isnull(), 'Unknown', df.precipitation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "06fad411-3d09-4052-b6bb-5512dfc19980",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time_of_day</th>\n",
       "      <th>airport_id</th>\n",
       "      <th>airport</th>\n",
       "      <th>runway</th>\n",
       "      <th>state</th>\n",
       "      <th>opid</th>\n",
       "      <th>operator</th>\n",
       "      <th>aircraft</th>\n",
       "      <th>ac_class</th>\n",
       "      <th>ac_mass</th>\n",
       "      <th>type_eng</th>\n",
       "      <th>num_engs</th>\n",
       "      <th>phase_of_flight</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>damage_level</th>\n",
       "      <th>species_id</th>\n",
       "      <th>species</th>\n",
       "      <th>size</th>\n",
       "      <th>nr_injuries</th>\n",
       "      <th>nr_fatalities</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-01-02</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>KJFK</td>\n",
       "      <td>JOHN F KENNEDY INTL</td>\n",
       "      <td>31L</td>\n",
       "      <td>NY</td>\n",
       "      <td>FDX</td>\n",
       "      <td>FEDEX EXPRESS</td>\n",
       "      <td>DC-10</td>\n",
       "      <td>A</td>\n",
       "      <td>4.0</td>\n",
       "      <td>D</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Departure</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>N</td>\n",
       "      <td>UNKB</td>\n",
       "      <td>Unknown bird</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-02</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>PHLI</td>\n",
       "      <td>LIHUE ARPT</td>\n",
       "      <td>99</td>\n",
       "      <td>HI</td>\n",
       "      <td>UNK</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R1101</td>\n",
       "      <td>Barn owl</td>\n",
       "      <td>Small</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-04</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>PHLI</td>\n",
       "      <td>LIHUE ARPT</td>\n",
       "      <td>99</td>\n",
       "      <td>HI</td>\n",
       "      <td>HAL</td>\n",
       "      <td>HAWAIIAN AIR</td>\n",
       "      <td>DC-9</td>\n",
       "      <td>A</td>\n",
       "      <td>4.0</td>\n",
       "      <td>D</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Landing Roll</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ZY1</td>\n",
       "      <td>Munias</td>\n",
       "      <td>Small</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-05</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>PHLI</td>\n",
       "      <td>LIHUE ARPT</td>\n",
       "      <td>99</td>\n",
       "      <td>HI</td>\n",
       "      <td>UNK</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N5122</td>\n",
       "      <td>Pacific golden-plover</td>\n",
       "      <td>Small</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-05</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>PHLI</td>\n",
       "      <td>LIHUE ARPT</td>\n",
       "      <td>99</td>\n",
       "      <td>HI</td>\n",
       "      <td>UNK</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ZY1</td>\n",
       "      <td>Munias</td>\n",
       "      <td>Small</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           time_of_day airport_id              airport runway state opid  \\\n",
       "date                                                                       \n",
       "1990-01-02     Unknown       KJFK  JOHN F KENNEDY INTL    31L    NY  FDX   \n",
       "1990-01-02     Unknown       PHLI           LIHUE ARPT     99    HI  UNK   \n",
       "1990-01-04     Unknown       PHLI           LIHUE ARPT     99    HI  HAL   \n",
       "1990-01-05     Unknown       PHLI           LIHUE ARPT     99    HI  UNK   \n",
       "1990-01-05     Unknown       PHLI           LIHUE ARPT     99    HI  UNK   \n",
       "\n",
       "                 operator aircraft ac_class  ac_mass type_eng  num_engs  \\\n",
       "date                                                                      \n",
       "1990-01-02  FEDEX EXPRESS    DC-10      A        4.0        D       3.0   \n",
       "1990-01-02        UNKNOWN  UNKNOWN      NaN      NaN      NaN       NaN   \n",
       "1990-01-04   HAWAIIAN AIR     DC-9      A        4.0        D       2.0   \n",
       "1990-01-05        UNKNOWN  UNKNOWN      NaN      NaN      NaN       NaN   \n",
       "1990-01-05        UNKNOWN  UNKNOWN      NaN      NaN      NaN       NaN   \n",
       "\n",
       "           phase_of_flight precipitation damage_level species_id  \\\n",
       "date                                                               \n",
       "1990-01-02       Departure       Unknown            N       UNKB   \n",
       "1990-01-02         Unknown       Unknown          NaN      R1101   \n",
       "1990-01-04    Landing Roll       Unknown          NaN        ZY1   \n",
       "1990-01-05         Unknown       Unknown          NaN      N5122   \n",
       "1990-01-05         Unknown       Unknown          NaN        ZY1   \n",
       "\n",
       "                          species   size  nr_injuries  nr_fatalities  \n",
       "date                                                                  \n",
       "1990-01-02           Unknown bird    NaN          NaN            NaN  \n",
       "1990-01-02               Barn owl  Small          NaN            NaN  \n",
       "1990-01-04                 Munias  Small          NaN            NaN  \n",
       "1990-01-05  Pacific golden-plover  Small          NaN            NaN  \n",
       "1990-01-05                 Munias  Small          NaN            NaN  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5fb579e8-b6d7-4f21-b08c-7de4804f1566",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78732"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ac_class.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "38c1bb25-0c2b-4e6c-9449-1f100c6d07bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fill ac_class nulls with 'Z' which is the database code for Unknown\n",
    "df.ac_class = np.where(df.ac_class.isnull(), 'ZZ', df.ac_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "46e1f42f-2890-4d38-9308-341b790bd47f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "79136"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ac_mass.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "dc29b928-e0eb-4ac3-aea3-a549f289d672",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fill ac_mass nulls with 0.0 (unknown)\n",
    "df.ac_mass = np.where(df.ac_mass.isnull(), 0, df.ac_mass)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "373c97f3-5dd8-4d71-9b29-bedabd2d3118",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "79570"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.type_eng.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4c273702-994d-4d8d-b6d3-0fb6a4dd9fa8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fill type_eng nulls with 'Z' - Unknown\n",
    "df.type_eng = np.where(df.type_eng.isnull(), 'Z', df.type_eng)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a4d6004b-2460-46fd-bde4-f808141ff1fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0    174506\n",
       "1.0     13074\n",
       "3.0      9742\n",
       "4.0      3260\n",
       "Name: num_engs, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.num_engs.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "992acbe9-735a-4b88-ac01-91c02fa651ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# if type_eng = 'E' that means it's a glider; for those rows, change num_engs to 0\n",
    "df.num_engs = np.where(df.type_eng == 'E', 0, df.num_engs)\n",
    "# fill num_engs remaining nulls with 99 (unknown)\n",
    "df.num_engs = np.where(df.num_engs.isnull(), 99, df.num_engs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d8f3aa6e-3406-4e01-80ca-054e27822095",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "97412"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.damage_level.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "cc758eba-b2dd-483e-8593-4cd7e028d0be",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fill nulls in damage_level with 'U' (unknown)\n",
    "df.damage_level = np.where(df.damage_level.isnull(), 'U', df.damage_level)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "aa03eb4e-f356-4b41-ba4a-b7547b48cc89",
   "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>time_of_day</th>\n",
       "      <th>airport_id</th>\n",
       "      <th>airport</th>\n",
       "      <th>runway</th>\n",
       "      <th>state</th>\n",
       "      <th>opid</th>\n",
       "      <th>operator</th>\n",
       "      <th>aircraft</th>\n",
       "      <th>ac_class</th>\n",
       "      <th>ac_mass</th>\n",
       "      <th>type_eng</th>\n",
       "      <th>num_engs</th>\n",
       "      <th>phase_of_flight</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>damage_level</th>\n",
       "      <th>species_id</th>\n",
       "      <th>species</th>\n",
       "      <th>size</th>\n",
       "      <th>nr_injuries</th>\n",
       "      <th>nr_fatalities</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-04-04</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>KLIT</td>\n",
       "      <td>BILL AND  HILLARY CLINTON NATL ARPT/ADAMS FIELD</td>\n",
       "      <td>22L</td>\n",
       "      <td>AR</td>\n",
       "      <td>UNK</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>ZZ</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Z</td>\n",
       "      <td>99.0</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>U</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Small</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-19</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>ZZZZ</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>99</td>\n",
       "      <td>ZZ</td>\n",
       "      <td>UNK</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>ZZ</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Z</td>\n",
       "      <td>99.0</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>U</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           time_of_day airport_id  \\\n",
       "date                                \n",
       "2023-04-04     Unknown       KLIT   \n",
       "2023-04-19     Unknown       ZZZZ   \n",
       "\n",
       "                                                    airport runway state opid  \\\n",
       "date                                                                            \n",
       "2023-04-04  BILL AND  HILLARY CLINTON NATL ARPT/ADAMS FIELD    22L    AR  UNK   \n",
       "2023-04-19                                          UNKNOWN     99    ZZ  UNK   \n",
       "\n",
       "           operator aircraft ac_class  ac_mass type_eng  num_engs  \\\n",
       "date                                                                \n",
       "2023-04-04  UNKNOWN  UNKNOWN       ZZ      0.0        Z      99.0   \n",
       "2023-04-19  UNKNOWN  UNKNOWN       ZZ      0.0        Z      99.0   \n",
       "\n",
       "           phase_of_flight precipitation damage_level species_id species  \\\n",
       "date                                                                       \n",
       "2023-04-04         Unknown       Unknown            U        NaN     NaN   \n",
       "2023-04-19         Unknown       Unknown            U        NaN     NaN   \n",
       "\n",
       "             size  nr_injuries  nr_fatalities  \n",
       "date                                           \n",
       "2023-04-04  Small          NaN            NaN  \n",
       "2023-04-19    NaN          NaN            NaN  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.species.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "2c7c8575-6b04-4d0e-b6e2-d2e4fffff15b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "UNKBS    47901\n",
       "UNKBM    38117\n",
       "UNKB     23112\n",
       "O2205    13975\n",
       "N5111     9214\n",
       "         ...  \n",
       "Q2102        1\n",
       "X6110        1\n",
       "2D13         1\n",
       "K3202        1\n",
       "2            1\n",
       "Name: species_id, Length: 892, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.species_id.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "7cdb3994-10d4-4382-b6b4-76e3661dc040",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unknown bird - small     47901\n",
       "Unknown bird - medium    38117\n",
       "Unknown bird             23112\n",
       "Mourning dove            13975\n",
       "Killdeer                  9214\n",
       "                         ...  \n",
       "Common ringed plover         1\n",
       "Hooded crow                  1\n",
       "Common kingsnake             1\n",
       "Kites                        1\n",
       "Reptiles                     1\n",
       "Name: species, Length: 900, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.species.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "78dd6831-8f4d-4e7f-b711-d87c00dbe8ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Small     172533\n",
       "Medium     65926\n",
       "Large      17947\n",
       "Name: size, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['size'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f0e2f87f-f717-46fe-a4d3-0f60e6a19a06",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fill size nulls with 'Unknown'\n",
    "# first change size to size_of_species since size is a reserved word\n",
    "df = df.rename (columns = {'size': 'size_of_species'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "afd5ced2-290d-4364-a488-ef8205d8d3fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23536"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.size_of_species.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "169faaa2-1fb7-4a88-a479-fc7d3803253e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.size_of_species = np.where(df.size_of_species.isnull(), 'Unknown', df.size_of_species)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "4827d571-aaa2-416c-8023-1a9725717453",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fill nulls for species and species_id\n",
    "df.species = np.where(df.species.isnull(), 'Unknown bird', df.species)\n",
    "df.species_id = np.where(df.species_id.isnull(), 'UNKB', df.species_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "aaac7bfe-197a-408d-b977-49f8b0a8e75d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time_of_day</th>\n",
       "      <th>airport_id</th>\n",
       "      <th>airport</th>\n",
       "      <th>runway</th>\n",
       "      <th>state</th>\n",
       "      <th>opid</th>\n",
       "      <th>operator</th>\n",
       "      <th>aircraft</th>\n",
       "      <th>ac_class</th>\n",
       "      <th>ac_mass</th>\n",
       "      <th>type_eng</th>\n",
       "      <th>num_engs</th>\n",
       "      <th>phase_of_flight</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>damage_level</th>\n",
       "      <th>species_id</th>\n",
       "      <th>species</th>\n",
       "      <th>size_of_species</th>\n",
       "      <th>nr_injuries</th>\n",
       "      <th>nr_fatalities</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-01-22</th>\n",
       "      <td>Day</td>\n",
       "      <td>KDEN</td>\n",
       "      <td>DENVER INTL AIRPORT</td>\n",
       "      <td>17L/35R</td>\n",
       "      <td>CO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UAL - UNITED AIRLINES</td>\n",
       "      <td>B-777-200</td>\n",
       "      <td>A</td>\n",
       "      <td>4.0</td>\n",
       "      <td>D</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Take-off Run</td>\n",
       "      <td>None</td>\n",
       "      <td>N</td>\n",
       "      <td>YH004</td>\n",
       "      <td>Horned lark</td>\n",
       "      <td>Small</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-16</th>\n",
       "      <td>Day</td>\n",
       "      <td>KRSW</td>\n",
       "      <td>SOUTHWEST FLORIDA INTL ARPT</td>\n",
       "      <td>6</td>\n",
       "      <td>FL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DELTA AIR LINES</td>\n",
       "      <td>B-737-900</td>\n",
       "      <td>A</td>\n",
       "      <td>4.0</td>\n",
       "      <td>D</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Approach</td>\n",
       "      <td>None</td>\n",
       "      <td>N</td>\n",
       "      <td>K2001</td>\n",
       "      <td>Osprey</td>\n",
       "      <td>Large</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-01</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>KCLT</td>\n",
       "      <td>CHARLOTTE/DOUGLAS INTL ARPT</td>\n",
       "      <td>Twy M</td>\n",
       "      <td>NC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>ZZ</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Z</td>\n",
       "      <td>99.0</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>U</td>\n",
       "      <td>UNKBS</td>\n",
       "      <td>Unknown bird - small</td>\n",
       "      <td>Small</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           time_of_day airport_id                      airport   runway state  \\\n",
       "date                                                                            \n",
       "2023-01-22         Day       KDEN          DENVER INTL AIRPORT  17L/35R    CO   \n",
       "2023-02-16         Day       KRSW  SOUTHWEST FLORIDA INTL ARPT        6    FL   \n",
       "2023-05-01     Unknown       KCLT  CHARLOTTE/DOUGLAS INTL ARPT    Twy M    NC   \n",
       "\n",
       "           opid               operator   aircraft ac_class  ac_mass type_eng  \\\n",
       "date                                                                           \n",
       "2023-01-22  NaN  UAL - UNITED AIRLINES  B-777-200      A        4.0        D   \n",
       "2023-02-16  NaN        DELTA AIR LINES  B-737-900      A        4.0        D   \n",
       "2023-05-01  NaN                UNKNOWN    UNKNOWN       ZZ      0.0        Z   \n",
       "\n",
       "            num_engs phase_of_flight precipitation damage_level species_id  \\\n",
       "date                                                                         \n",
       "2023-01-22       2.0    Take-off Run          None            N      YH004   \n",
       "2023-02-16       2.0        Approach          None            N      K2001   \n",
       "2023-05-01      99.0         Unknown       Unknown            U      UNKBS   \n",
       "\n",
       "                         species size_of_species  nr_injuries  nr_fatalities  \n",
       "date                                                                          \n",
       "2023-01-22           Horned lark           Small          NaN            NaN  \n",
       "2023-02-16                Osprey           Large          NaN            NaN  \n",
       "2023-05-01  Unknown bird - small           Small          NaN            NaN  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.opid.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "e9ac4a83-a7df-4613-ae2e-b56c0729a0c1",
   "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>time_of_day</th>\n",
       "      <th>airport_id</th>\n",
       "      <th>airport</th>\n",
       "      <th>runway</th>\n",
       "      <th>state</th>\n",
       "      <th>opid</th>\n",
       "      <th>operator</th>\n",
       "      <th>aircraft</th>\n",
       "      <th>ac_class</th>\n",
       "      <th>ac_mass</th>\n",
       "      <th>type_eng</th>\n",
       "      <th>num_engs</th>\n",
       "      <th>phase_of_flight</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>damage_level</th>\n",
       "      <th>species_id</th>\n",
       "      <th>species</th>\n",
       "      <th>size_of_species</th>\n",
       "      <th>nr_injuries</th>\n",
       "      <th>nr_fatalities</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-01-22</th>\n",
       "      <td>Day</td>\n",
       "      <td>KDEN</td>\n",
       "      <td>DENVER INTL AIRPORT</td>\n",
       "      <td>17L/35R</td>\n",
       "      <td>CO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UAL - UNITED AIRLINES</td>\n",
       "      <td>B-777-200</td>\n",
       "      <td>A</td>\n",
       "      <td>4.0</td>\n",
       "      <td>D</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Take-off Run</td>\n",
       "      <td>None</td>\n",
       "      <td>N</td>\n",
       "      <td>YH004</td>\n",
       "      <td>Horned lark</td>\n",
       "      <td>Small</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           time_of_day airport_id              airport   runway state opid  \\\n",
       "date                                                                         \n",
       "2023-01-22         Day       KDEN  DENVER INTL AIRPORT  17L/35R    CO  NaN   \n",
       "\n",
       "                         operator   aircraft ac_class  ac_mass type_eng  \\\n",
       "date                                                                      \n",
       "2023-01-22  UAL - UNITED AIRLINES  B-777-200      A        4.0        D   \n",
       "\n",
       "            num_engs phase_of_flight precipitation damage_level species_id  \\\n",
       "date                                                                         \n",
       "2023-01-22       2.0    Take-off Run          None            N      YH004   \n",
       "\n",
       "                species size_of_species  nr_injuries  nr_fatalities  \n",
       "date                                                                 \n",
       "2023-01-22  Horned lark           Small          NaN            NaN  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.opid.isnull() & (df.operator.str.contains('UNITED AIRLINES'))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "073f2546-07e4-4433-84ec-e87850206115",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# couple of hanging chads where opid is null\n",
    "df.opid = np.where(df.opid.isnull() & (df.operator.str.contains('UNITED AIRLINES')), 'UAL', df.opid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "a4fe2074-c57a-4e00-8877-90a1c9a7ff58",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.opid = np.where(df.opid.isnull() & (df.operator.str.contains('DELTA')), 'DAL', df.opid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "981f0401-89d7-4b01-9309-4529ef34dcf5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.opid = np.where(df.opid.isnull() & (df.operator == 'UNKNOWN'), 'UNK', df.opid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "e7718379-8c2d-4217-96d4-e10b29bc6e7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.nr_injuries = df.nr_injuries.fillna(0)\n",
    "df.nr_fatalities = df.nr_fatalities.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "5edd18c9-24f4-4fa8-9680-6e43a6ad5f48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.opid.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "608fece8-da3f-4752-84f4-76ae8b92f1aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df = wrangle_wildlife_strike_df()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "ac9ebaee-32f7-4c72-9887-e6304aee8ca3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.ac_mass = df.ac_mass.astype(int)\n",
    "df.num_engs = df.num_engs.astype(int)\n",
    "df.nr_injuries = df.nr_injuries.astype(int)\n",
    "df.nr_fatalities = df.nr_fatalities.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "550b968a-bbd7-4985-88e9-72dc15e49dc8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 279942 entries, 1990-01-02 to 2023-05-17\n",
      "Data columns (total 20 columns):\n",
      " #   Column           Non-Null Count   Dtype \n",
      "---  ------           --------------   ----- \n",
      " 0   time_of_day      279942 non-null  object\n",
      " 1   airport_id       279942 non-null  object\n",
      " 2   airport          279942 non-null  object\n",
      " 3   runway           279942 non-null  object\n",
      " 4   state            279942 non-null  object\n",
      " 5   opid             279942 non-null  object\n",
      " 6   operator         279942 non-null  object\n",
      " 7   aircraft         279942 non-null  object\n",
      " 8   ac_class         279942 non-null  object\n",
      " 9   ac_mass          279942 non-null  int64 \n",
      " 10  type_eng         279942 non-null  object\n",
      " 11  num_engs         279942 non-null  int64 \n",
      " 12  phase_of_flight  279942 non-null  object\n",
      " 13  precipitation    279942 non-null  object\n",
      " 14  damage_level     279942 non-null  object\n",
      " 15  species_id       279942 non-null  object\n",
      " 16  species          279942 non-null  object\n",
      " 17  size_of_species  279942 non-null  object\n",
      " 18  nr_injuries      279942 non-null  int64 \n",
      " 19  nr_fatalities    279942 non-null  int64 \n",
      "dtypes: int64(4), object(16)\n",
      "memory usage: 44.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "6def0f70-2187-41f9-a8f4-5a99a99acc2e",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value Counts for time_of_day\n",
      "Unknown    117672\n",
      "Day         99664\n",
      "Night       49430\n",
      "Dusk         7286\n",
      "Dawn         5890\n",
      "Name: time_of_day, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279942\n",
      "\n",
      "Value Counts for airport_id\n",
      "ZZZZ    34302\n",
      "KDEN     9260\n",
      "KDFW     7527\n",
      "KORD     6065\n",
      "KJFK     5882\n",
      "        ...  \n",
      "T40         1\n",
      "LIRP        1\n",
      "KTLR        1\n",
      "OI65        1\n",
      "PALP        1\n",
      "Name: airport_id, Length: 2591, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279942\n",
      "\n",
      "Value Counts for airport\n",
      "UNKNOWN                        34302\n",
      "DENVER INTL AIRPORT             9260\n",
      "DALLAS/FORT WORTH INTL ARPT     7527\n",
      "CHICAGO O'HARE INTL ARPT        6065\n",
      "JOHN F KENNEDY INTL             5882\n",
      "                               ...  \n",
      "HOUSTON GULF ARPT                  1\n",
      "GALILEO GALILEI ARPT               1\n",
      "MCGEHEE CATFISH REST               1\n",
      "MEFFORD FIELD ARPT                 1\n",
      "ALPINE AIRSTRIP                    1\n",
      "Name: airport, Length: 2591, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279942\n",
      "\n",
      "Value Counts for runway\n",
      "99           67927\n",
      "27            3889\n",
      "17R           3852\n",
      "22L           3740\n",
      "22            3536\n",
      "             ...  \n",
      "8L/35            1\n",
      "D5/D6            1\n",
      "8.               1\n",
      "Term E           1\n",
      "Taxiway R        1\n",
      "Name: runway, Length: 1174, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279942\n",
      "\n",
      "Value Counts for state\n",
      "ZZ    34379\n",
      "TX    23812\n",
      "FL    19975\n",
      "CA    19954\n",
      "NY    13949\n",
      "      ...  \n",
      "AB       53\n",
      "MB       16\n",
      "NL        6\n",
      "SK        5\n",
      "NS        5\n",
      "Name: state, Length: 64, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279942\n",
      "\n",
      "Value Counts for opid\n",
      "UNK    78103\n",
      "BUS    23836\n",
      "SWA    23211\n",
      "AAL    19833\n",
      "UAL    17212\n",
      "       ...  \n",
      "RJA        1\n",
      "RKA        1\n",
      "SPG        1\n",
      "LIB        1\n",
      "GRO        1\n",
      "Name: opid, Length: 603, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279942\n",
      "\n",
      "Value Counts for operator\n",
      "UNKNOWN                   78103\n",
      "BUSINESS                  23836\n",
      "SOUTHWEST AIRLINES        23211\n",
      "AMERICAN AIRLINES         19833\n",
      "UNITED AIRLINES           17211\n",
      "                          ...  \n",
      "ABELAG AVIATION               1\n",
      "ROYAL JORDANIAN               1\n",
      "AIR AFRIQUE                   1\n",
      "SPRINGDALE AIR SERVICE        1\n",
      "BALAIR                        1\n",
      "Name: operator, Length: 598, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279942\n",
      "\n",
      "Value Counts for aircraft\n",
      "UNKNOWN              78370\n",
      "B-737-700            13936\n",
      "A-320                13441\n",
      "B-737-800            10442\n",
      "CRJ100/200            9405\n",
      "                     ...  \n",
      "BELL 230                 1\n",
      "HELIO COURIER 800        1\n",
      "URBAN AIR LAMBADA        1\n",
      "CAP AVION MUDRY          1\n",
      "A220-300                 1\n",
      "Name: aircraft, Length: 594, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279942\n",
      "\n",
      "Value Counts for ac_class\n",
      "A      196899\n",
      "ZZ      78732\n",
      "B        4297\n",
      "C           6\n",
      "J           5\n",
      "Y           3\n",
      "Name: ac_class, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279942\n",
      "\n",
      "Value Counts for ac_mass\n",
      "4    139641\n",
      "0     79136\n",
      "3     33287\n",
      "1     12881\n",
      "2     12821\n",
      "5      2176\n",
      "Name: ac_mass, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279942\n",
      "\n",
      "Value Counts for type_eng\n",
      "D    168258\n",
      "Z     79570\n",
      "C     14663\n",
      "A     13158\n",
      "F      3986\n",
      "B       300\n",
      "E         4\n",
      "Y         3\n",
      "Name: type_eng, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279942\n",
      "\n",
      "Value Counts for num_engs\n",
      "2     174506\n",
      "99     79356\n",
      "1      13074\n",
      "3       9742\n",
      "4       3260\n",
      "0          4\n",
      "Name: num_engs, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279942\n",
      "\n",
      "Value Counts for phase_of_flight\n",
      "Unknown         106815\n",
      "Approach         73360\n",
      "Landing Roll     31204\n",
      "Take-off Run     29421\n",
      "Climb            26410\n",
      "En Route          5230\n",
      "Departure         2682\n",
      "Descent           2266\n",
      "Local             1064\n",
      "Arrival            727\n",
      "Taxi               650\n",
      "Parked             113\n",
      "Name: phase_of_flight, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279942\n",
      "\n",
      "Value Counts for precipitation\n",
      "Unknown             149021\n",
      "None                119916\n",
      "Rain                  7652\n",
      "Fog                   2472\n",
      "Snow                   479\n",
      "Fog, Rain              310\n",
      "Rain, Snow              27\n",
      "None, Snow              26\n",
      "Fog, Snow               15\n",
      "None, Rain              10\n",
      "Fog, None                7\n",
      "Fog, Rain, Snow          6\n",
      "None, Rain, Snow         1\n",
      "Name: precipitation, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279942\n",
      "\n",
      "Value Counts for damage_level\n",
      "N     163004\n",
      "U      97412\n",
      "M       8549\n",
      "M?      6659\n",
      "S       4236\n",
      "D         82\n",
      "Name: damage_level, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279942\n",
      "\n",
      "Value Counts for species_id\n",
      "UNKBS    47901\n",
      "UNKBM    38117\n",
      "UNKB     23116\n",
      "O2205    13975\n",
      "N5111     9214\n",
      "         ...  \n",
      "Q2102        1\n",
      "X6110        1\n",
      "2D13         1\n",
      "K3202        1\n",
      "2            1\n",
      "Name: species_id, Length: 892, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279942\n",
      "\n",
      "Value Counts for species\n",
      "Unknown bird - small     47901\n",
      "Unknown bird - medium    38117\n",
      "Unknown bird             23114\n",
      "Mourning dove            13975\n",
      "Killdeer                  9214\n",
      "                         ...  \n",
      "Common ringed plover         1\n",
      "Hooded crow                  1\n",
      "Common kingsnake             1\n",
      "Kites                        1\n",
      "Reptiles                     1\n",
      "Name: species, Length: 900, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279942\n",
      "\n",
      "Value Counts for size_of_species\n",
      "Small      172533\n",
      "Medium      65926\n",
      "Unknown     23536\n",
      "Large       17947\n",
      "Name: size_of_species, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279942\n",
      "\n",
      "Value Counts for nr_injuries\n",
      "0    279666\n",
      "1       217\n",
      "2        51\n",
      "7         2\n",
      "5         2\n",
      "4         2\n",
      "3         2\n",
      "Name: nr_injuries, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279942\n",
      "\n",
      "Value Counts for nr_fatalities\n",
      "0    279918\n",
      "1        12\n",
      "2         7\n",
      "3         2\n",
      "5         1\n",
      "8         1\n",
      "4         1\n",
      "Name: nr_fatalities, dtype: int64\n",
      "Num of nulls: 0\n",
      "Num of NON-nulls: 279942\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for col in df.columns:\n",
    "    print(f'Value Counts for {col}')\n",
    "    print(df[col].value_counts())\n",
    "    print(f'Num of nulls: {df[col].isnull().sum()}')\n",
    "    print(f'Num of NON-nulls: {df[col].notnull().sum()}')\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "c06bb2d5-1e19-44a5-9637-eb009f47d3ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# DataFrame is now free from nulls\n",
    "# Kept 20 columns\n",
    "# Conduct initial univariate analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "a14ef52d-2c24-47cf-b036-c87f7b5306b8",
   "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>time_of_day</th>\n",
       "      <th>airport_id</th>\n",
       "      <th>airport</th>\n",
       "      <th>runway</th>\n",
       "      <th>state</th>\n",
       "      <th>opid</th>\n",
       "      <th>operator</th>\n",
       "      <th>aircraft</th>\n",
       "      <th>ac_class</th>\n",
       "      <th>ac_mass</th>\n",
       "      <th>type_eng</th>\n",
       "      <th>num_engs</th>\n",
       "      <th>phase_of_flight</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>damage_level</th>\n",
       "      <th>species_id</th>\n",
       "      <th>species</th>\n",
       "      <th>size_of_species</th>\n",
       "      <th>nr_injuries</th>\n",
       "      <th>nr_fatalities</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-01-02</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>KJFK</td>\n",
       "      <td>JOHN F KENNEDY INTL</td>\n",
       "      <td>31L</td>\n",
       "      <td>NY</td>\n",
       "      <td>FDX</td>\n",
       "      <td>FEDEX EXPRESS</td>\n",
       "      <td>DC-10</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>D</td>\n",
       "      <td>3</td>\n",
       "      <td>Departure</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>N</td>\n",
       "      <td>UNKB</td>\n",
       "      <td>Unknown bird</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-02</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>PHLI</td>\n",
       "      <td>LIHUE ARPT</td>\n",
       "      <td>99</td>\n",
       "      <td>HI</td>\n",
       "      <td>UNK</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>ZZ</td>\n",
       "      <td>0</td>\n",
       "      <td>Z</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>U</td>\n",
       "      <td>R1101</td>\n",
       "      <td>Barn owl</td>\n",
       "      <td>Small</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-04</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>PHLI</td>\n",
       "      <td>LIHUE ARPT</td>\n",
       "      <td>99</td>\n",
       "      <td>HI</td>\n",
       "      <td>HAL</td>\n",
       "      <td>HAWAIIAN AIR</td>\n",
       "      <td>DC-9</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>D</td>\n",
       "      <td>2</td>\n",
       "      <td>Landing Roll</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>U</td>\n",
       "      <td>ZY1</td>\n",
       "      <td>Munias</td>\n",
       "      <td>Small</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-05</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>PHLI</td>\n",
       "      <td>LIHUE ARPT</td>\n",
       "      <td>99</td>\n",
       "      <td>HI</td>\n",
       "      <td>UNK</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>ZZ</td>\n",
       "      <td>0</td>\n",
       "      <td>Z</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>U</td>\n",
       "      <td>N5122</td>\n",
       "      <td>Pacific golden-plover</td>\n",
       "      <td>Small</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-05</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>PHLI</td>\n",
       "      <td>LIHUE ARPT</td>\n",
       "      <td>99</td>\n",
       "      <td>HI</td>\n",
       "      <td>UNK</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>ZZ</td>\n",
       "      <td>0</td>\n",
       "      <td>Z</td>\n",
       "      <td>99</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>U</td>\n",
       "      <td>ZY1</td>\n",
       "      <td>Munias</td>\n",
       "      <td>Small</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           time_of_day airport_id              airport runway state opid  \\\n",
       "date                                                                       \n",
       "1990-01-02     Unknown       KJFK  JOHN F KENNEDY INTL    31L    NY  FDX   \n",
       "1990-01-02     Unknown       PHLI           LIHUE ARPT     99    HI  UNK   \n",
       "1990-01-04     Unknown       PHLI           LIHUE ARPT     99    HI  HAL   \n",
       "1990-01-05     Unknown       PHLI           LIHUE ARPT     99    HI  UNK   \n",
       "1990-01-05     Unknown       PHLI           LIHUE ARPT     99    HI  UNK   \n",
       "\n",
       "                 operator aircraft ac_class  ac_mass type_eng  num_engs  \\\n",
       "date                                                                      \n",
       "1990-01-02  FEDEX EXPRESS    DC-10      A          4        D         3   \n",
       "1990-01-02        UNKNOWN  UNKNOWN       ZZ        0        Z        99   \n",
       "1990-01-04   HAWAIIAN AIR     DC-9      A          4        D         2   \n",
       "1990-01-05        UNKNOWN  UNKNOWN       ZZ        0        Z        99   \n",
       "1990-01-05        UNKNOWN  UNKNOWN       ZZ        0        Z        99   \n",
       "\n",
       "           phase_of_flight precipitation damage_level species_id  \\\n",
       "date                                                               \n",
       "1990-01-02       Departure       Unknown            N       UNKB   \n",
       "1990-01-02         Unknown       Unknown            U      R1101   \n",
       "1990-01-04    Landing Roll       Unknown            U        ZY1   \n",
       "1990-01-05         Unknown       Unknown            U      N5122   \n",
       "1990-01-05         Unknown       Unknown            U        ZY1   \n",
       "\n",
       "                          species size_of_species  nr_injuries  nr_fatalities  \n",
       "date                                                                           \n",
       "1990-01-02           Unknown bird         Unknown            0              0  \n",
       "1990-01-02               Barn owl           Small            0              0  \n",
       "1990-01-04                 Munias           Small            0              0  \n",
       "1990-01-05  Pacific golden-plover           Small            0              0  \n",
       "1990-01-05                 Munias           Small            0              0  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "cc9b5f48-cfb1-4bff-816b-f345aed97518",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,7))\n",
    "# df.resample('D').aircraft.count().plot(label='daily')\n",
    "df.resample('M').aircraft.count().plot(label='monthly')\n",
    "df.resample('Y').aircraft.count().plot(label='yearly')\n",
    "plt.legend()\n",
    "plt.title('Number of birdstrikes over time')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "8ae00635-3da8-48bd-86a1-4d41afe8de79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "N     163004\n",
       "U      97412\n",
       "M       8549\n",
       "M?      6659\n",
       "S       4236\n",
       "D         82\n",
       "Name: damage_level, dtype: int64"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.damage_level.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "c3d64589-ef97-4999-9290-eac9603f3e8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='damage_level', ylabel='Count'>"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tWze99NJLcrlcpp4TAADguzwarBoaGnTXXXcpNze33dhXX32lffv26fnnn9e+ffv01ltv6dChQ0pOTnary8zM1Lp165Sfn6/i4mLV19crKSlJra2tRk1KSopKS0tVUFCggoIClZaWyuFwGOOtra0aN26cGhoaVFxcrPz8fK1du1bZ2dlGTV1dnUaPHi273a49e/ZoyZIlWrBggXJycq7BmQEAAL7I35NfPnbsWI0dO/aCYzabTUVFRW7rlixZonvuuUcVFRWKjo6W0+nUsmXLtHLlSo0aNUqStGrVKkVFRWnTpk1KTExUeXm5CgoKtHPnTg0aNEiStHTpUsXHx+vgwYOKiYlRYWGhDhw4oMrKStntdknSwoULlZqaqtmzZyskJESrV6/W2bNnlZeXJ6vVqtjYWB06dEg5OTnKysqSxWK5hmcKAAD4Ap+6x8rpdMpisejmm2+WJJWUlKilpUUJCQlGjd1uV2xsrLZv3y5J2rFjh2w2mxGqJGnw4MGy2WxuNbGxsUaokqTExEQ1NTWppKTEqBk2bJisVqtbzbFjx3TkyJGL9tzU1KS6ujq3BQAA3Jh8JlidPXtWv/71r5WSkqKQkBBJUnV1tQIDAxUaGupWGxERoerqaqMmPDy83f7Cw8PdaiIiItzGQ0NDFRgYeMmac5/P1VzI3LlzjXu7bDaboqKiruSwAQCAD/GJYNXS0qJf/OIXamtr0x/+8IdvrXe5XG6X5i50mc6MmnM3rl/qMuCMGTPkdDqNpbKy8lv7BwAAvsnrg1VLS4smTpyow4cPq6ioyJitkqTIyEg1NzertrbWbZuamhpjNikyMlLHjx9vt98TJ0641Zw/61RbW6uWlpZL1tTU1EhSu5msf2a1WhUSEuK2AACAG5NXB6tzoervf/+7Nm3apK5du7qNx8XFKSAgwO0m96qqKpWVlWnIkCGSpPj4eDmdTu3evduo2bVrl5xOp1tNWVmZqqqqjJrCwkJZrVbFxcUZNdu2bXN7BUNhYaHsdru6d+9u+rEDAADf49FgVV9fr9LSUpWWlkqSDh8+rNLSUlVUVOjrr7/Wv/3bv2nv3r1avXq1WltbVV1drerqaiPc2Gw2TZ48WdnZ2dq8ebM++OADPfTQQ+rbt6/xlGDv3r01ZswYpaWlaefOndq5c6fS0tKUlJSkmJgYSVJCQoL69Okjh8OhDz74QJs3b9b06dOVlpZmzDClpKTIarUqNTVVZWVlWrdunebMmcMTgQAAwODR1y3s3btXI0aMMD5nZWVJkh555BHNmjVLGzZskCT169fPbbv33ntPw4cPlyQtWrRI/v7+mjhxohobGzVy5Ejl5eXJz8/PqF+9erUyMjKMpweTk5Pd3p3l5+enjRs3Kj09XUOHDlVQUJBSUlK0YMECo+bc6x+mTp2qgQMHKjQ0VFlZWUbPAAAAFhevDr+u6urqZLPZ5HQ6Tb3fat++fYqLi9PomcvVJTrGtP3eiE5VHFTR7EdVUlKiAQMGeLodAIAPuNzf3159jxUAAIAvIVgBAACYhGAFAABgEoIVAACASQhWAAAAJiFYAQAAmIRgBQAAYBKCFQAAgEkIVgAAACYhWAEAAJiEYAUAAGASghUAAIBJCFYAAAAmIVgBAACYhGAFAABgEoIVAACASQhWAAAAJiFYAQAAmIRgBQAAYBKCFQAAgEkIVgAAACYhWAEAAJiEYAUAAGASghUAAIBJCFYAAAAmIVgBAACYhGAFAABgEoIVAACASQhWAAAAJiFYAQAAmIRgBQAAYBKCFQAAgEk8Gqy2bdum8ePHy263y2KxaP369W7jLpdLs2bNkt1uV1BQkIYPH66PP/7YraapqUnTpk1TWFiYgoODlZycrKNHj7rV1NbWyuFwyGazyWazyeFw6PTp0241FRUVGj9+vIKDgxUWFqaMjAw1Nze71ezfv1/Dhg1TUFCQunXrppdeekkul8u08wEAAHybR4NVQ0OD7rrrLuXm5l5wfN68ecrJyVFubq727NmjyMhIjR49WmfOnDFqMjMztW7dOuXn56u4uFj19fVKSkpSa2urUZOSkqLS0lIVFBSooKBApaWlcjgcxnhra6vGjRunhoYGFRcXKz8/X2vXrlV2drZRU1dXp9GjR8tut2vPnj1asmSJFixYoJycnGtwZgAAgC/y9+SXjx07VmPHjr3gmMvl0uLFizVz5kw98MADkqQVK1YoIiJCa9as0ZQpU+R0OrVs2TKtXLlSo0aNkiStWrVKUVFR2rRpkxITE1VeXq6CggLt3LlTgwYNkiQtXbpU8fHxOnjwoGJiYlRYWKgDBw6osrJSdrtdkrRw4UKlpqZq9uzZCgkJ0erVq3X27Fnl5eXJarUqNjZWhw4dUk5OjrKysmSxWK7DGQMAAN7Ma++xOnz4sKqrq5WQkGCss1qtGjZsmLZv3y5JKikpUUtLi1uN3W5XbGysUbNjxw7ZbDYjVEnS4MGDZbPZ3GpiY2ONUCVJiYmJampqUklJiVEzbNgwWa1Wt5pjx47pyJEj5p8AAADgc7w2WFVXV0uSIiIi3NZHREQYY9XV1QoMDFRoaOgla8LDw9vtPzw83K3m/O8JDQ1VYGDgJWvOfT5XcyFNTU2qq6tzWwAAwI3Ja4PVOedfYnO5XN962e38mgvVm1Fz7sb1S/Uzd+5c46Z5m82mqKioS/YOAAB8l9cGq8jISEntZ4NqamqMmaLIyEg1Nzertrb2kjXHjx9vt/8TJ0641Zz/PbW1tWppablkTU1NjaT2s2r/bMaMGXI6ncZSWVl56QMHAAA+y2uDVY8ePRQZGamioiJjXXNzs7Zu3aohQ4ZIkuLi4hQQEOBWU1VVpbKyMqMmPj5eTqdTu3fvNmp27dolp9PpVlNWVqaqqiqjprCwUFarVXFxcUbNtm3b3F7BUFhYKLvdru7du1/0OKxWq0JCQtwWAABwY/JosKqvr1dpaalKS0slfXPDemlpqSoqKmSxWJSZmak5c+Zo3bp1KisrU2pqqjp27KiUlBRJks1m0+TJk5Wdna3Nmzfrgw8+0EMPPaS+ffsaTwn27t1bY8aMUVpamnbu3KmdO3cqLS1NSUlJiomJkSQlJCSoT58+cjgc+uCDD7R582ZNnz5daWlpRhBKSUmR1WpVamqqysrKtG7dOs2ZM4cnAgEAgMGjr1vYu3evRowYYXzOysqSJD3yyCPKy8vTM888o8bGRqWnp6u2tlaDBg1SYWGhOnfubGyzaNEi+fv7a+LEiWpsbNTIkSOVl5cnPz8/o2b16tXKyMgwnh5MTk52e3eWn5+fNm7cqPT0dA0dOlRBQUFKSUnRggULjBqbzaaioiJNnTpVAwcOVGhoqLKysoye4XvKy8s93YJPCAsLU3R0tKfbAACfYHHx6vDrqq6uTjabTU6n09TLgvv27VNcXJxGz1yuLtExpu33RvTF/u0qzn1aEv/TvxxBQR31ySflhCsA32uX+/vbozNWgCe0fHVGkkv9Up7VD3rc7ul2vFpd1RHteuNFnTx5kmAFAJeBYIXvrU7h0czuAQBM5bVPBQIAAPgaghUAAIBJCFYAAAAmIVgBAACY5KqCVc+ePfXll1+2W3/69Gn17NnzOzcFAADgi64qWB05ckStra3t1jc1NemLL774zk0BAAD4oit63cKGDRuMf7/zzjuy2WzG59bWVm3evPmSfzcPAADgRnZFweq+++6TJFksFj3yyCNuYwEBAerevbsWLlxoWnMAAAC+5IqCVVtbmySpR48e2rNnj8LCwq5JUwAAAL7oqt68fvjwYbP7AAAA8HlX/SdtNm/erM2bN6umpsaYyTrnjTfe+M6NAQAA+JqrClYvvviiXnrpJQ0cOFC33HKLLBaL2X0BAAD4nKsKVn/84x+Vl5cnh8Nhdj8AAAA+66reY9Xc3KwhQ4aY3QsAAIBPu6pg9atf/Upr1qwxuxcAAACfdlWXAs+ePavXX39dmzZt0p133qmAgAC38ZycHFOaAwAA8CVXFaw++ugj9evXT5JUVlbmNsaN7AAA4PvqqoLVe++9Z3YfAAAAPu+q7rECAABAe1c1YzVixIhLXvJ79913r7ohAAAAX3VVwerc/VXntLS0qLS0VGVlZe3+ODMAAMD3xVUFq0WLFl1w/axZs1RfX/+dGgIAAPBVpt5j9dBDD/F3AgEAwPeWqcFqx44d6tChg5m7BAAA8BlXdSnwgQcecPvscrlUVVWlvXv36vnnnzelMQAAAF9zVcHKZrO5fb7pppsUExOjl156SQkJCaY0BgAA4GuuKlgtX77c7D4AAAB83lUFq3NKSkpUXl4ui8WiPn36qH///mb1BQAA4HOuKljV1NToF7/4hbZs2aKbb75ZLpdLTqdTI0aMUH5+vn7wgx+Y3ScAAIDXu6qnAqdNm6a6ujp9/PHHOnXqlGpra1VWVqa6ujplZGSY3SMAAIBPuKpgVVBQoFdffVW9e/c21vXp00e///3v9fbbb5vW3Ndff63/+I//UI8ePRQUFKSePXvqpZdeUltbm1Hjcrk0a9Ys2e12BQUFafjw4fr444/d9tPU1KRp06YpLCxMwcHBSk5O1tGjR91qamtr5XA4ZLPZZLPZ5HA4dPr0abeaiooKjR8/XsHBwQoLC1NGRoaam5tNO14AAODbripYtbW1KSAgoN36gIAAt9DzXb3yyiv64x//qNzcXJWXl2vevHmaP3++lixZYtTMmzdPOTk5ys3N1Z49exQZGanRo0frzJkzRk1mZqbWrVun/Px8FRcXq76+XklJSWptbTVqUlJSVFpaqoKCAhUUFKi0tFQOh8MYb21t1bhx49TQ0KDi4mLl5+dr7dq1ys7ONu14AQCAb7uqe6x+8pOf6Mknn9Sbb74pu90uSfriiy/01FNPaeTIkaY1t2PHDk2YMEHjxo2TJHXv3l1vvvmm9u7dK+mb2arFixdr5syZxru1VqxYoYiICK1Zs0ZTpkyR0+nUsmXLtHLlSo0aNUqStGrVKkVFRWnTpk1KTExUeXm5CgoKtHPnTg0aNEiStHTpUsXHx+vgwYOKiYlRYWGhDhw4oMrKSuOYFy5cqNTUVM2ePVshISGmHTcAAPBNVzVjlZubqzNnzqh79+76l3/5F/3oRz9Sjx49dObMGbfZpO/q3nvv1ebNm3Xo0CFJ0ocffqji4mL99Kc/lSQdPnxY1dXVbu/OslqtGjZsmLZv3y7pmycXW1pa3GrsdrtiY2ONmh07dshmsxmhSpIGDx4sm83mVhMbG2uEKklKTExUU1OTSkpKTDtmAADgu65qxioqKkr79u1TUVGRPvnkE7lcLvXp08eYETLLs88+K6fTqdtvv11+fn5qbW3V7Nmz9ctf/lKSVF1dLUmKiIhw2y4iIkKff/65URMYGKjQ0NB2Nee2r66uVnh4eLvvDw8Pd6s5/3tCQ0MVGBho1FxIU1OTmpqajM91dXWXdewAAMD3XNGM1bvvvqs+ffoY4WD06NGaNm2aMjIydPfdd+uOO+7Q+++/b1pzf/rTn7Rq1SqtWbNG+/bt04oVK7RgwQKtWLHCrc5isbh9drlc7dad7/yaC9VfTc355s6da9wQb7PZFBUVdcm+AACA77qiYLV48WKlpaVd8H4im82mKVOmKCcnx7Tmnn76af3617/WL37xC/Xt21cOh0NPPfWU5s6dK0mKjIyUpHYzRjU1NcbsUmRkpJqbm1VbW3vJmuPHj7f7/hMnTrjVnP89tbW1amlpaTeT9c9mzJghp9NpLJWVlVdyCgAAgA+5omD14YcfasyYMRcdT0hIMPV+o6+++ko33eTeop+fn/HkYY8ePRQZGamioiJjvLm5WVu3btWQIUMkSXFxcQoICHCrqaqqUllZmVETHx8vp9Op3bt3GzW7du2S0+l0qykrK1NVVZVRU1hYKKvVqri4uIseg9VqVUhIiNsCAABuTFd0j9Xx48cv+JoFY2f+/jpx4sR3buqc8ePHa/bs2YqOjtYdd9yhDz74QDk5OXrsscckfXNpLjMzU3PmzFGvXr3Uq1cvzZkzRx07dlRKSoqkb2bSJk+erOzsbHXt2lVdunTR9OnT1bdvX+OesN69e2vMmDFKS0vTa6+9Jkl6/PHHlZSUpJiYGEnfhMY+ffrI4XBo/vz5OnXqlKZPn37RGTwAAPD9c0XBqlu3btq/f79+9KMfXXD8o48+0i233GJKY5K0ZMkSPf/880pPT1dNTY3sdrumTJmiF154wah55pln1NjYqPT0dNXW1mrQoEEqLCxU586djZpFixbJ399fEydOVGNjo0aOHKm8vDz5+fkZNatXr1ZGRobx9GBycrJyc3ONcT8/P23cuFHp6ekaOnSogoKClJKSogULFph2vAAAwLdZXC6X63KLp02bpi1btmjPnj3q0KGD21hjY6PuuecejRgxQr/73e9Mb/RGUVdXJ5vNJqfTaepM1759+xQXF6fRM5erS3SMafu9ER3Z9Y52vfGi7s38vbr15g+HX8qpioMqmv2oSkpKNGDAAE+3AwAec7m/v69oxuo//uM/9NZbb+m2227TE088oZiYGFksFpWXl+v3v/+9WltbNXPmzO/cPAAAgC+6omAVERGh7du36//8n/+jGTNm6Nxkl8ViUWJiov7whz9c8gk5AACAG9kVvyD01ltv1V//+lfV1tbq008/lcvlUq9evdq9gBMAAOD75qrevC5989bxu+++28xeAAAAfNpV/a1AAAAAtEewAgAAMAnBCgAAwCQEKwAAAJMQrAAAAExCsAIAADAJwQoAAMAkBCsAAACTEKwAAABMQrACAAAwCcEKAADAJAQrAAAAkxCsAAAATEKwAgAAMAnBCgAAwCQEKwAAAJMQrAAAAExCsAIAADAJwQoAAMAkBCsAAACTEKwAAABMQrACAAAwCcEKAADAJAQrAAAAkxCsAAAATEKwAgAAMAnBCgAAwCQEKwAAAJMQrAAAAEzi9cHqiy++0EMPPaSuXbuqY8eO6tevn0pKSoxxl8ulWbNmyW63KygoSMOHD9fHH3/sto+mpiZNmzZNYWFhCg4OVnJyso4ePepWU1tbK4fDIZvNJpvNJofDodOnT7vVVFRUaPz48QoODlZYWJgyMjLU3Nx8zY4dAAD4Fq8OVrW1tRo6dKgCAgL09ttv68CBA1q4cKFuvvlmo2bevHnKyclRbm6u9uzZo8jISI0ePVpnzpwxajIzM7Vu3Trl5+eruLhY9fX1SkpKUmtrq1GTkpKi0tJSFRQUqKCgQKWlpXI4HMZ4a2urxo0bp4aGBhUXFys/P19r165Vdnb2dTkXAADA+/l7uoFLeeWVVxQVFaXly5cb67p372782+VyafHixZo5c6YeeOABSdKKFSsUERGhNWvWaMqUKXI6nVq2bJlWrlypUaNGSZJWrVqlqKgobdq0SYmJiSovL1dBQYF27typQYMGSZKWLl2q+Ph4HTx4UDExMSosLNSBAwdUWVkpu90uSVq4cKFSU1M1e/ZshYSEXKezAgAAvJVXz1ht2LBBAwcO1IMPPqjw8HD1799fS5cuNcYPHz6s6upqJSQkGOusVquGDRum7du3S5JKSkrU0tLiVmO32xUbG2vU7NixQzabzQhVkjR48GDZbDa3mtjYWCNUSVJiYqKamprcLk2er6mpSXV1dW4LAAC4MXl1sPrHP/6hV199Vb169dI777yjf//3f1dGRob+67/+S5JUXV0tSYqIiHDbLiIiwhirrq5WYGCgQkNDL1kTHh7e7vvDw8Pdas7/ntDQUAUGBho1FzJ37lzjvi2bzaaoqKgrOQUAAMCHeHWwamtr04ABAzRnzhz1799fU6ZMUVpaml599VW3OovF4vbZ5XK1W3e+82suVH81NeebMWOGnE6nsVRWVl6yLwAA4Lu8Oljdcsst6tOnj9u63r17q6KiQpIUGRkpSe1mjGpqaozZpcjISDU3N6u2tvaSNcePH2/3/SdOnHCrOf97amtr1dLS0m4m659ZrVaFhIS4LQAA4Mbk1cFq6NChOnjwoNu6Q4cO6dZbb5Uk9ejRQ5GRkSoqKjLGm5ubtXXrVg0ZMkSSFBcXp4CAALeaqqoqlZWVGTXx8fFyOp3avXu3UbNr1y45nU63mrKyMlVVVRk1hYWFslqtiouLM/nIAQCAL/LqpwKfeuopDRkyRHPmzNHEiRO1e/duvf7663r99dclfXNpLjMzU3PmzFGvXr3Uq1cvzZkzRx07dlRKSookyWazafLkycrOzlbXrl3VpUsXTZ8+XX379jWeEuzdu7fGjBmjtLQ0vfbaa5Kkxx9/XElJSYqJiZEkJSQkqE+fPnI4HJo/f75OnTql6dOnKy0tjVkoAAAgycuD1d13361169ZpxowZeumll9SjRw8tXrxYkyZNMmqeeeYZNTY2Kj09XbW1tRo0aJAKCwvVuXNno2bRokXy9/fXxIkT1djYqJEjRyovL09+fn5GzerVq5WRkWE8PZicnKzc3Fxj3M/PTxs3blR6erqGDh2qoKAgpaSkaMGCBdfhTAAAAF9gcblcLk838X1SV1cnm80mp9Np6kzXvn37FBcXp9Ezl6tLdIxp+70RHdn1jna98aLuzfy9uvXu7+l2vNqpioMqmv2oSkpKNGDAAE+3AwAec7m/v736HisAAABfQrACAAAwCcEKAADAJAQrAAAAkxCsAAAATEKwAgAAMAnBCgAAwCQEKwAAAJMQrAAAAExCsAIAADAJwQoAAMAkBCsAAACTEKwAAABMQrACAAAwCcEKAADAJAQrAAAAkxCsAAAATEKwAgAAMAnBCgAAwCQEKwAAAJMQrAAAAExCsAIAADAJwQoAAMAkBCsAAACTEKwAAABMQrACAAAwCcEKAADAJAQrAAAAkxCsAAAATEKwAgAAMAnBCgAAwCQEKwAAAJP4VLCaO3euLBaLMjMzjXUul0uzZs2S3W5XUFCQhg8fro8//thtu6amJk2bNk1hYWEKDg5WcnKyjh496lZTW1srh8Mhm80mm80mh8Oh06dPu9VUVFRo/PjxCg4OVlhYmDIyMtTc3HytDhcAAPgYnwlWe/bs0euvv64777zTbf28efOUk5Oj3Nxc7dmzR5GRkRo9erTOnDlj1GRmZmrdunXKz89XcXGx6uvrlZSUpNbWVqMmJSVFpaWlKigoUEFBgUpLS+VwOIzx1tZWjRs3Tg0NDSouLlZ+fr7Wrl2r7Ozsa3/wAADAJ/hEsKqvr9ekSZO0dOlShYaGGutdLpcWL16smTNn6oEHHlBsbKxWrFihr776SmvWrJEkOZ1OLVu2TAsXLtSoUaPUv39/rVq1Svv379emTZskSeXl5SooKNB//ud/Kj4+XvHx8Vq6dKn+8pe/6ODBg5KkwsJCHThwQKtWrVL//v01atQoLVy4UEuXLlVdXd31PykAAMDr+ESwmjp1qsaNG6dRo0a5rT98+LCqq6uVkJBgrLNarRo2bJi2b98uSSopKVFLS4tbjd1uV2xsrFGzY8cO2Ww2DRo0yKgZPHiwbDabW01sbKzsdrtRk5iYqKamJpWUlFy096amJtXV1bktAADgxuTv6Qa+TX5+vvbt26c9e/a0G6uurpYkRUREuK2PiIjQ559/btQEBga6zXSdqzm3fXV1tcLDw9vtPzw83K3m/O8JDQ1VYGCgUXMhc+fO1YsvvvhthwkAAG4AXj1jVVlZqSeffFKrVq1Shw4dLlpnsVjcPrtcrnbrznd+zYXqr6bmfDNmzJDT6TSWysrKS/YFAAB8l1cHq5KSEtXU1CguLk7+/v7y9/fX1q1b9bvf/U7+/v7GDNL5M0Y1NTXGWGRkpJqbm1VbW3vJmuPHj7f7/hMnTrjVnP89tbW1amlpaTeT9c+sVqtCQkLcFgAAcGPy6mA1cuRI7d+/X6WlpcYycOBATZo0SaWlperZs6ciIyNVVFRkbNPc3KytW7dqyJAhkqS4uDgFBAS41VRVVamsrMyoiY+Pl9Pp1O7du42aXbt2yel0utWUlZWpqqrKqCksLJTValVcXNw1PQ8AAMA3ePU9Vp07d1ZsbKzbuuDgYHXt2tVYn5mZqTlz5qhXr17q1auX5syZo44dOyolJUWSZLPZNHnyZGVnZ6tr167q0qWLpk+frr59+xo3w/fu3VtjxoxRWlqaXnvtNUnS448/rqSkJMXExEiSEhIS1KdPHzkcDs2fP1+nTp3S9OnTlZaWxiwUAACQ5OXB6nI888wzamxsVHp6umprazVo0CAVFhaqc+fORs2iRYvk7++viRMnqrGxUSNHjlReXp78/PyMmtWrVysjI8N4ejA5OVm5ubnGuJ+fnzZu3Kj09HQNHTpUQUFBSklJ0YIFC67fwQIAAK/mc8Fqy5Ytbp8tFotmzZqlWbNmXXSbDh06aMmSJVqyZMlFa7p06aJVq1Zd8rujo6P1l7/85UraBQAA3yNefY8VAACALyFYAQAAmIRgBQAAYBKCFQAAgEkIVgAAACYhWAEAAJiEYAUAAGASghUAAIBJCFYAAAAmIVgBAACYhGAFAABgEoIVAACASQhWAAAAJiFYAQAAmIRgBQAAYBKCFQAAgEkIVgAAACYhWAEAAJiEYAUAAGASghUAAIBJCFYAAAAmIVgBAACYhGAFAABgEoIVAACASQhWAAAAJiFYAQAAmIRgBQAAYBKCFQAAgEkIVgAAACYhWAEAAJiEYAUAAGASghUAAIBJvDpYzZ07V3fffbc6d+6s8PBw3XfffTp48KBbjcvl0qxZs2S32xUUFKThw4fr448/dqtpamrStGnTFBYWpuDgYCUnJ+vo0aNuNbW1tXI4HLLZbLLZbHI4HDp9+rRbTUVFhcaPH6/g4GCFhYUpIyNDzc3N1+TYAQCA7/HqYLV161ZNnTpVO3fuVFFRkb7++mslJCSooaHBqJk3b55ycnKUm5urPXv2KDIyUqNHj9aZM2eMmszMTK1bt075+fkqLi5WfX29kpKS1NraatSkpKSotLRUBQUFKigoUGlpqRwOhzHe2tqqcePGqaGhQcXFxcrPz9fatWuVnZ19fU4GAADwev6ebuBSCgoK3D4vX75c4eHhKikp0Y9//GO5XC4tXrxYM2fO1AMPPCBJWrFihSIiIrRmzRpNmTJFTqdTy5Yt08qVKzVq1ChJ0qpVqxQVFaVNmzYpMTFR5eXlKigo0M6dOzVo0CBJ0tKlSxUfH6+DBw8qJiZGhYWFOnDggCorK2W32yVJCxcuVGpqqmbPnq2QkJDreGYAAIA38uoZq/M5nU5JUpcuXSRJhw8fVnV1tRISEowaq9WqYcOGafv27ZKkkpIStbS0uNXY7XbFxsYaNTt27JDNZjNClSQNHjxYNpvNrSY2NtYIVZKUmJiopqYmlZSUXLTnpqYm1dXVuS0AAODG5DPByuVyKSsrS/fee69iY2MlSdXV1ZKkiIgIt9qIiAhjrLq6WoGBgQoNDb1kTXh4eLvvDA8Pd6s5/3tCQ0MVGBho1FzI3Llzjfu2bDaboqKiruSwAQCAD/GZYPXEE0/oo48+0ptvvtluzGKxuH12uVzt1p3v/JoL1V9NzflmzJghp9NpLJWVlZfsCwAA+C6fCFbTpk3Thg0b9N577+mHP/yhsT4yMlKS2s0Y1dTUGLNLkZGRam5uVm1t7SVrjh8/3u57T5w44VZz/vfU1taqpaWl3UzWP7NarQoJCXFbAADAjcmrg5XL5dITTzyht956S++++6569OjhNt6jRw9FRkaqqKjIWNfc3KytW7dqyJAhkqS4uDgFBAS41VRVVamsrMyoiY+Pl9Pp1O7du42aXbt2yel0utWUlZWpqqrKqCksLJTValVcXJz5Bw8AAHyOVz8VOHXqVK1Zs0b/+7//q86dOxszRjabTUFBQbJYLMrMzNScOXPUq1cv9erVS3PmzFHHjh2VkpJi1E6ePFnZ2dnq2rWrunTpounTp6tv377GU4K9e/fWmDFjlJaWptdee02S9PjjjyspKUkxMTGSpISEBPXp00cOh0Pz58/XqVOnNH36dKWlpTELBQAAJHl5sHr11VclScOHD3dbv3z5cqWmpkqSnnnmGTU2Nio9PV21tbUaNGiQCgsL1blzZ6N+0aJF8vf318SJE9XY2KiRI0cqLy9Pfn5+Rs3q1auVkZFhPD2YnJys3NxcY9zPz08bN25Uenq6hg4dqqCgIKWkpGjBggXX6OgBAICv8epg5XK5vrXGYrFo1qxZmjVr1kVrOnTooCVLlmjJkiUXrenSpYtWrVp1ye+Kjo7WX/7yl2/tCQAAfD959T1WAAAAvoRgBQAAYBKCFQAAgEkIVgAAACYhWAEAAJiEYAUAAGASghUAAIBJCFYAAAAmIVgBAACYhGAFAABgEoIVAACASQhWAAAAJiFYAQAAmIRgBQAAYBKCFQAAgEkIVgAAACYhWAEAAJjE39MNAPB+5eXlnm7BZ4SFhSk6OtrTbQDwEIIVgItqdH4pyaKHHnrI0634jKCgjvrkk3LCFfA9RbACcFEtX52R5FK/lGf1gx63e7odr1dXdUS73nhRJ0+eJFgB31MEKwDfqlN4tLpEx3i6DQDwegQrAAC8XEVFhU6ePOnpNnyCp+9zJFgBAODFKioqdPvtvdXY+JWnW/EJnr7PkWAFAIAXO3nypBobv9Kgx36jkFu6e7odr+YN9zkSrAAA8AEht3TnXkcfwAtCAQAATMKMFQCYjBeqXh5P32QMXAsEKwAwCS9UvTKevskYuBYIVgBgEl6oevm84SZj4FogWAGAyXihKvD9xc3rAAAAJiFYAQAAmIRgdRX+8Ic/qEePHurQoYPi4uL0/vvve7olAADgBQhWV+hPf/qTMjMzNXPmTH3wwQf613/9V40dO1YVFRWebg0AAHgYweoK5eTkaPLkyfrVr36l3r17a/HixYqKitKrr77q6dYAAICH8VTgFWhublZJSYl+/etfu61PSEjQ9u3bPdQVAPguXqb67ThHvoVgdQVOnjyp1tZWRUREuK2PiIhQdXX1BbdpampSU1OT8dnpdEqS6urqTO2tvr5eknTq84P6uqnR1H3faOqqPpckOb/4uwL8LR7uxrtxrq4M5+vynfxsvyTxMtUrcOLTMv77/i3qqr+5Lae+vt7037Pn9udyuS5d6MJl++KLL1ySXNu3b3db/3//7/91xcTEXHCb3/zmNy5JLCwsLCwsLDfAUllZecmswIzVFQgLC5Ofn1+72amampp2s1jnzJgxQ1lZWcbntrY2nTp1Sl27dpXFcuP/f7R1dXWKiopSZWWlQkJCPN2OV+NcXT7O1eXjXF0+ztXl+z6eK5fLpTNnzshut1+yjmB1BQIDAxUXF6eioiLdf//9xvqioiJNmDDhgttYrVZZrVa3dTfffPO1bNMrhYSEfG9++L4rztXl41xdPs7V5eNcXb7v27my2WzfWkOwukJZWVlyOBwaOHCg4uPj9frrr6uiokL//u//7unWAACAhxGsrtDPf/5zffnll3rppZdUVVWl2NhY/fWvf9Wtt97q6dYAAICHEayuQnp6utLT0z3dhk+wWq36zW9+0+5yKNrjXF0+ztXl41xdPs7V5eNcXZzF5fq25wYBAABwOXjzOgAAgEkIVgAAACYhWAEAAJiEYAXTpaamymKx6OWXX3Zbv379+u/FS1GvxPDhw5WZmdluPefK3bn/TV3otSbp6emyWCxKTU29/o15uZqaGk2ZMkXR0dGyWq2KjIxUYmKiduzY4enWvMa5/21ZLBYFBAQoIiJCo0eP1htvvKG2tjZPt+dRV/Jz9/LLL+uOO+5Qx44dddttt2nNmjXXuVvvQbDCNdGhQwe98sorqq2t9XQruEFERUUpPz9fjY3//99KO3v2rN58801FR0d7sDPv9bOf/UwffvihVqxYoUOHDmnDhg0aPny4Tp065enWvMqYMWNUVVWlI0eO6O2339aIESP05JNPKikpSV9//bWn2/Ooy/25e//997Vo0SKVlZXpoYce0sMPP6x//OMfnmjZ4whWuCZGjRqlyMhIzZ0719Ot4AYxYMAARUdH66233jLWvfXWW4qKilL//v092Jl3On36tIqLi/XKK69oxIgRuvXWW3XPPfdoxowZGjdunKfb8yrnZvO6deumAQMG6LnnntP//u//6u2331ZeXp6n2/Ooy/2527hxoxISEtSzZ0898cQTam1t1bFjxzzRsscRrHBN+Pn5ac6cOVqyZImOHj3q6XZwg3j00Ue1fPly4/Mbb7yhxx57zIMdea9OnTqpU6dOWr9+vZqamjzdjs/5yU9+orvuusstUHxfXcnPncvlUnZ2tmJjY3XPPfdcrxa9CsEK18z999+vfv366Te/+Y2nW8ENwuFwqLi4WEeOHNHnn3+uv/3tb3rooYc83ZZX8vf3V15enlasWKGbb75ZQ4cO1XPPPaePPvrI0635jNtvv11HjhzxdBsedyU/d7/61a+0fft2FRQUKDAw8Dp36h0IVrimXnnlFa1YsUIHDhzwdCu4AYSFhWncuHFasWKFli9frnHjxiksLMzTbXmtn/3sZzp27Jg2bNigxMREbdmyRQMGDPjeX966XC6Xi4dIdPk/dx999JHeeOMNbdiwQd26dfNAp96BYIVr6sc//rESExP13HPPeboVrxQSEiKn09lu/enTp79XfzH+Sjz22GPGTAyXAb9dhw4dNHr0aL3wwgvavn27UlNTmUW+TOXl5erRo4en2/AKl/Nzd/jwYUlSTEzM9WzN6xCscM29/PLL+vOf/6zt27d7uhWvc/vtt2vv3r3t1u/Zs+d7/x+nixkzZoyam5vV3NysxMRET7fjc/r06aOGhgZPt+H13n33Xe3fv18/+9nPPN2KV7icn7thw4Zpz54917kz78MfYcY117dvX02aNElLlizxdCteJz09Xbm5uZo6daoef/xxBQUFqaioSMuWLdPKlSs93Z5X8vPzU3l5ufFvXNiXX36pBx98UI899pjuvPNOde7cWXv37tW8efM0YcIET7fnVZqamlRdXa3W1lYdP35cBQUFmjt3rpKSkvTwww97uj2vcDk/d++9955mzJihTz755Hq25nUIVrgufvvb3+q///u/Pd2G1+nevbvef/99zZw5UwkJCTp79qxuu+025eXl6cEHH/R0e16Ly6TfrlOnTho0aJAWLVqkzz77TC0tLYqKilJaWhqX5s9TUFCgW265Rf7+/goNDdVdd92l3/3ud3rkkUd0001c2Dnn237unE6nDh48eJ268V4Wl8vl8nQTAAAANwKiOAAAgEkIVgAAACYhWAEAAJiEYAUAAGASghUAAIBJCFYAAAAmIVgBAACYhGAFAABgEoIVAJ8wfPhwZWZmerqNa84Tx5mamqr77rvvun4ncKMiWAEAAJiEYAUAAGASghUAr9PQ0KCHH35YnTp10i233KKFCxe6ja9atUoDBw5U586dFRkZqZSUFNXU1BjjW7ZskcVi0TvvvKP+/fsrKChIP/nJT1RTU6O3335bvXv3VkhIiH75y1/qq6++MrYrKCjQvffeq5tvvlldu3ZVUlKSPvvsM7fv3r59u/r166cOHTpo4MCBWr9+vSwWi0pLS42aAwcO6Kc//ak6deqkiIgIORwOnTx58qrORXNzs5555hl169ZNwcHBGjRokLZs2SLpmz96GxQUpIKCArdt3nrrLQUHB6u+vl6S9MUXX+jnP/+5QkND1bVrV02YMEFHjhy5qn4AXBrBCoDXefrpp/Xee+9p3bp1Kiws1JYtW1RSUmKMNzc367e//a0+/PBDrV+/XocPH1Zqamq7/cyaNUu5ubnavn27KisrNXHiRC1evFhr1qzRxo0bVVRUpCVLlhj1DQ0NysrK0p49e7R582bddNNNuv/++9XW1iZJOnPmjMaPH6++fftq3759+u1vf6tnn33W7Turqqo0bNgw9evXT3v37lVBQYGOHz+uiRMnXtW5ePTRR/W3v/1N+fn5+uijj/Tggw9qzJgx+vvf/y6bzaZx48Zp9erVbtusWbNGEyZMUKdOnfTVV19pxIgR6tSpk7Zt26bi4mJ16tRJY8aMUXNz81X1BOASXADgRc6cOeMKDAx05efnG+u+/PJLV1BQkOvJJ5+84Da7d+92SXKdOXPG5XK5XO+9955LkmvTpk1Gzdy5c12SXJ999pmxbsqUKa7ExMSL9lJTU+OS5Nq/f7/L5XK5Xn31VVfXrl1djY2NRs3SpUtdklwffPCBy+VyuZ5//nlXQkKC234qKytdklwHDx781uMfNmyYcZyffvqpy2KxuL744gu3mpEjR7pmzJjhcrlcrrfeesvVqVMnV0NDg8vlcrmcTqerQ4cOro0bN7pcLpdr2bJlrpiYGFdbW5uxfVNTkysoKMj1zjvvuFwul+uRRx5xTZgw4Vt7A/DtmLEC4FU+++wzNTc3Kz4+3ljXpUsXxcTEGJ8/+OADTZgwQbfeeqs6d+6s4cOHS5IqKirc9nXnnXca/46IiFDHjh3Vs2dPt3X/fAnxs88+U0pKinr27KmQkBD16NHDbb8HDx7UnXfeqQ4dOhjb3HPPPW7fWVJSovfee0+dOnUylttvv93Y/5XYt2+fXC6XbrvtNrf9bd261djXuHHj5O/vrw0bNkiS1q5dq86dOyshIcHo59NPP1Xnzp2N7bt06aKzZ89ecT8Avp2/pxsAgH/mcrkuOd7Q0KCEhAQlJCRo1apV+sEPfqCKigolJia2u7QVEBBg/Ntisbh9Prfu3GU+SRo/fryioqK0dOlS2e12tbW1KTY21tivy+WSxWK5ZL9tbW0aP368XnnllXa933LLLZc8tvO1tbXJz89PJSUl8vPzcxvr1KmTJCkwMFD/9m//pjVr1ugXv/iF1qxZo5///Ofy9/c39hEXF9fucqEk/eAHP7iifgB8O4IVAK/yox/9SAEBAdq5c6eio6MlSbW1tTp06JCGDRumTz75RCdPntTLL7+sqKgoSdLevXu/8/d++eWXKi8v12uvvaZ//dd/lSQVFxe71dx+++1avXq1mpqaZLVaL/jdAwYM0Nq1a9W9e3cj3Fyt/v37q7W1VTU1NUZPFzJp0iQlJCTo448/1nvvvaff/va3bv386U9/Unh4uEJCQr5TPwC+HZcCAXiVTp06afLkyXr66ae1efNmlZWVKTU1VTfd9M1/rqKjoxUYGKglS5boH//4hzZs2OAWJK7WuSfmXn/9dX366ad69913lZWV5VaTkpKitrY2Pf744yovL9c777yjBQsWSJIxkzV16lSdOnVKv/zlL7V792794x//UGFhoR577DG1trZeUU+33XabJk2apIcfflhvvfWWDh8+rD179uiVV17RX//6V6Nu2LBhioiI0KRJk9S9e3cNHjzYGJs0aZLCwsI0YcIEvf/++zp8+LC2bt2qJ598UkePHr3a0wXgIghWALzO/Pnz9eMf/1jJyckaNWqU7r33XsXFxUn65vJVXl6e/ud//kd9+vTRyy+/bISb7+Kmm25Sfn6+SkpKFBsbq6eeekrz5893qwkJCdGf//xnlZaWql+/fpo5c6ZeeOEFSTLuu7Lb7frb3/6m1tZWJSYmKjY2Vk8++aRsNpsRDq/E8uXL9fDDDys7O1sxMTFKTk7Wrl27jNk66ZtQ98tf/lIffvihJk2a5LZ9x44dtW3bNkVHR+uBBx5Q79699dhjj6mxsZEZLOAasLi+7YYGAMBFrV69Wo8++qjxTikA32/cYwUAV+C//uu/1LNnT3Xr1k0ffvihnn32WU2cOJFQBUASwQoArkh1dbVeeOEFVVdX65ZbbtGDDz6o2bNnX9a2FRUV6tOnz0XHDxw4YNywD8A3cSkQAK6Tr7/++pJ/SsaMJwkBeBbBCgAAwCQ8FQgAAGASghUAAIBJCFYAAAAmIVgBAACYhGAFAABgEoIVAACASQhWAAAAJiFYAQAAmOT/A8zxMbnBQEnGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=df.damage_level)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "601c8f9b-b265-4cb0-8589-35b2c0d78c7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PpmlERkYydepUnnzySUC1loSFhfHyyy/zwAMPkJmZSePGjfniiy+49dZbgbKckl9//ZVhw4Zd8bxZWVno9XoyMzOlBUUIIYRwEDX5/K7TMOPMzEwAgoODAUhISCA1NdVs1VRPT08GDBjApk2bANi+fTtFRUVmZSIjI+nQoYOpjBBCCCEatlonyWqaxrRp07j66qvp0KEDAKmpqQCEhYWZlQ0LC+PEiROmMh4eHgQFBVUoY3z+pQoKCigoKDD9nJWVVdtqCyGEsAMGg4HCwkJbV0NYgYeHxxWHEFdHrQOURx55hN27d7Nx48YKj12a/KJp2hUTYi5XZu7cucyePbu2VRVCCGFHCgsLSUhIwGAw2LoqwgpcXFxo1qxZnVdrr1WAMmXKFH7++Wc2bNhgNvImPDwcUK0kERERpv1paWmmVpXw8HAKCwtJT083a0VJS0ujb9++lZ5vxowZTJs2zfSzcZiSEEIIx6JpGikpKbi6uhIdHW2Rb9rCfhgnUk1JSSEmJqZOk6nWKEDRNI0pU6awePFi1q1bR7Nmzcweb9asGeHh4axcuZIuXboAKlJev349L7/8MgDdunXD3d2dlStXcssttwCQkpLC3r17eeWVVyo9r6enJ56enjV+cUIIIexLcXExFy9eJDIyEh8fH1tXR1hB48aNSU5Opri4GHd391ofp0YByuTJk1m4cCE//fQT/v7+ppwRvV6Pt7c3Op2OqVOnMmfOHFq1akWrVq2YM2cOPj4+jB8/3lT23nvv5bHHHqNRo0YEBwczffp0OnbsyODBg2v9QoQQQtg/43QSdW3+F/bL+LstKSmpvwDl3XffBWDgwIFm+xcsWMDEiRMBeOKJJ8jLy+Phhx82TdT2+++/m80a98Ybb+Dm5sYtt9ximqjt008/xdXVtdYvRAghhOOQddScl6V+t3WaB8VWZB4UIYRwTPn5+SQkJNCsWTO8vLxsXR1hBZf7HdfbPChCCCFEQzZw4ECmTp1q62rUG51Ox5IlS+rlXBKgCCGEEDa2bt06dDodGRkZtq6K3ZAARQghREUlRVAsE6nZm4Y0uZ0EKEIIIcwVF8D7A2B+N8iXmbuNcnNzmTBhAn5+fkRERDBv3jyzxwsLC3niiSeIiorC19eXXr16sW7dOtPjJ06cYPTo0QQFBeHr60v79u359ddfSUxMZNCgQQAEBQWh0+lMA08GDhzII488wrRp0wgJCWHIkCGAWry3Z8+eeHp6EhERwVNPPUVxcTEAn3/+OY0aNTKbgR3gpptuYsKECaafly5dSrdu3fDy8qJ58+bMnj3bdAyAI0eO0L9/f7y8vGjXrh0rV6602LWsjlrPJCsagOJCOLwcgmIhtD24yttFiAZh+2eQtk9txy+E3g9a7VSappFXZJuV7L3dXWs04uTxxx9n7dq1LF68mPDwcP7zn/+wfft2OnfuDMA///lPEhMTWbRoEZGRkSxevJjhw4ezZ88eWrVqxeTJkyksLGTDhg34+vqyf/9+/Pz8iI6O5ocffuCmm27i0KFDBAQE4O3tbTrvZ599xkMPPcSff/6JpmmcPn2a66+/nokTJ/L5559z8OBBJk2ahJeXF7NmzeLmm2/m0Ucf5eeff+bmm28G4Ny5cyxbtozly5cDsGLFCu68807++9//cs0113Ds2DHuv/9+AGbOnInBYGDcuHGEhISwZcsWsrKy6j3XRj5xRNXWzYGNb6htDz9o0h2ie6lbkx7gJSOohHA6RXnwR7mWgb/fh573g5VmfM0rKqHdcyuscuwr2f/8MHw8qvcxmJOTw8cff8znn39uasX47LPPTLOpHzt2jK+//ppTp04RGRkJwPTp01m+fDkLFixgzpw5JCUlcdNNN9GxY0cAmjdvbjq+cdHd0NBQAgMDzc7dsmVLs4lMn376aaKjo5k/fz46nY6rrrqK5ORknnzySZ577jm8vb0ZP348CxYsMAUoX331FU2aNDFNE/Liiy/y1FNPcffdd5vq8sILL/DEE08wc+ZMVq1axYEDB0hMTDS9xjlz5jBixIhqX9+6kgBFVK4wF7Z9orbdfaAwB46vUzcAnYtqVYnpBdG91b0+GmRuAyEc29aPISdV/T0XZMGF43Dkd2gz3NY1s6ljx45RWFhInz59TPuCg4Np06YNADt27EDTNFq3bm32vIKCAho1agTAo48+ykMPPcTvv//O4MGDuemmm+jUqdMVz929e3eznw8cOECfPn3MWn/69etHTk4Op06dIiYmhkmTJtGjRw9Onz5NVFSUab4y43O2b9/O1q1befHFF03HKCkpIT8/n4sXL3LgwAFiYmLMlrMp/9rrgwQoonK7vob8TAhuDpO3wrlDkLQFTv6l7jNOwJk96rb1I/Uc/0jzgCWso3QLCeFICnPLWk0HPAHnDsOmt+Gvd60WoHi7u7L/+WFWOXZ1zl1dV5oyzGAw4Orqyvbt2ytMOurn5wfAfffdx7Bhw/jll1/4/fffmTt3LvPmzWPKlCmXPbavr2+FulS2KC+UTZLWpUsX4uLi+Pzzzxk2bBh79uxh6dKlZvWdPXs248aNq3A+Ly+vSl9vfU+uJ58eoiKDAf56X233fEAFGWHt1a3HvWp/VooKVowBS+puyE6GfYvVDcDDH8a9D1eNtM3rEELUzN8fwMVzENQU4m6H7BTY/D/VcnpmP4S1s/gpdTpdtbtZbKlly5a4u7uzZcsWYmJiAEhPT+fw4cMMGDCALl26UFJSQlpaGtdcc02Vx4mOjubBBx/kwQcfZMaMGXz44YdMmTLFbHr4K2nXrh0//PCDWaCyadMm/P39iYqKMpW77777eOONNzh9+jSDBw82W2S3a9euHDp0iJYtW1Z5jqSkJJKTk01dVps3b75i3SxJRvGIio6vUd+cPPyh8/jKywREQPuxMHwu3L8WnkqCu5fBtc9AyyHgqYfCbNj5Zb1WXQhRS/lZ8OdbanvAU+DqDoExZV8w/nrPdnWzA35+ftx77708/vjjrF69mr179zJx4kTTasytW7fmjjvuYMKECfz4448kJCSwdetWXn75ZX799VcApk6dyooVK0hISGDHjh2sWbOGtm3bAhAbG4tOp2PZsmWcPXuWnJycKuvy8MMPc/LkSaZMmcLBgwf56aefmDlzJtOmTTNbHfqOO+7g9OnTfPjhh9xzzz1mx3juuef4/PPPmTVrFvv27ePAgQN88803PPPMMwAMHjyYNm3aMGHCBHbt2sUff/zB008/bdFreiUSoIiKtpT+I+pyZ/UTYT18odk10P9xuPN7GL9I7U/ZZZ06CiEs66/3IS8dGrWCTreU7e/9sLrf/Q1cvGCbutmJV199lf79+zNmzBgGDx7M1VdfTbdu3UyPL1iwgAkTJvDYY4/Rpk0bxowZw19//WVquSgpKWHy5Mm0bduW4cOH06ZNG9555x0AoqKimD17Nk899RRhYWE88sgjVdYjKiqKX3/9lb///pu4uDgefPBB7r33XlNwYRQQEMBNN92En58fY8eONXts2LBhLFu2jJUrV9KjRw969+7N66+/TmxsLAAuLi4sXryYgoICevbsyX333WeWr1IfZC0eYe7sYfhfD0AHj+5QOSi1kZ8FL5U2Jz5+HHwbWayKQggLy8uAtzqpvLObPoaO/yh7TNPg/f6qG/e6mXDNtDqdStbiqV9Dhgyhbdu2/Pe//623c8paPMI6/i7NPWk9vPbBCaiWl+AWajtVWlGEsGub/6eCk8Ztof0lSZM6HfR+SG1v/UjNMCvs3oULF1i0aBFr1qxh8uTJtq5OrUiAIsrkZUD812rbEhMzRZQOn5NuHiHs18ULsOVdtT1oRuXznXS4CXwbQ9ZpOLC04uPC7nTt2pUHHniAl19+2TQU2tFIgCLK7PwCinIhtB00G1D344UbA5TddT+WEMI6Nv1XJbSHd4SrRldexs0TupcmWTbwZFlHkZiYSGZmJtOnT7d1VWpNAhShGErUEEOAXg9YZsK1iDh1Ly0oQtinnLNlUwoM/M/lZ4vtfi+4uKupBU7vqJ/6iQZNAhShHPoVMpLAOwg63nLl8tVhDFAuHIOCbMscUwhhOX++CUUXIbIrtLnCFOb+YdChND9FWlFEPZAARSjGocXd/gkePpY5pm+Iml0WIHWvZY4phLCM7NSyWaAHPV29VtNepblpe39UzxfCiiRAESpH5MRG0LlCj/sse2zp5hHCPv3xOhTnQ5Oe0PK66j0nqqtaLNRQVLZWlxBWIgGKKOuDbncD6KMuX7amjCN5UiVRVgi7kXkKti9Q29dWs/XEyNiKsu0TKMq3fN2EKCUBSkOXew72fKe2jXMdWJKpBUUCFCHsxh/zoKQQYq+u+Yi9tqMhIApyz8LeH6xTPyGQAEVsWwAlBSpJrkkPyx/fONT47AEoLrD88YUQNZN+AnZ8obYH/afmI/Zc3cu6gv96V80028BNnDixwlTydZWYmIhOpyM+Pt60788//6Rjx464u7szduxY1q1bh06nIyMjw6LnthcSoDRkxYWw9UO13fshywwtvpS+iRoZZCiGtP2WP74QomY2vKJySJoPhKb9aneMbhPBzRtS98CJTZasnUN66623+PTTTy16zOjoaFJSUujQoYNp37Rp0+jcuTMJCQl8+umn9O3bl5SUFPR6vUXPfTmffvopgYGB9XIuCVAasv1LIOcM+IVDu7HWOYdOJ4myQtiL88fKZose9Mzly16OTzDE3aq2/3q37vVycHq93uIf2q6uroSHh+Pm5mbad+zYMa699lqaNGlCYGAgHh4ehIeHo7PGl0s7IAFKQ6VpZdNb97gX3Dysdy6ZUVYI+7D+FdBKoNVQiK5jl64xWfbgL6rbqAH4/vvv6dixI97e3jRq1IjBgweTm5tboYsnOzubO+64A19fXyIiInjjjTcYOHAgU6dONZVp2rQpc+bM4Z577sHf35+YmBg++OAD0+Plu3iM2+fPn+eee+5Bp9Px6aefVtrF8+effzJgwAB8fHwICgpi2LBhpKenA7B8+XKuvvpqAgMDadSoEaNGjeLYsWMVzvnjjz8yaNAgfHx8iIuLY/PmzQCsW7eOf/7zn2RmZqLT6dDpdMyaNcsq1xokQGm4Tm2F5B3g6qHmPrEmYwuKjOQRwnbOHoY936rtgTPqfrzQtqqbSDOUdRXXhqZBYa5tbjXIn0lJSeH222/nnnvu4cCBA6xbt45x48ahVXKMadOm8eeff/Lzzz+zcuVK/vjjD3bsqDj77rx58+jevTs7d+7k4Ycf5qGHHuLgwYMVyhm7ewICAnjzzTdJSUnh1ltvrVAuPj6e6667jvbt27N582Y2btzI6NGjKSkpASA3N5dp06axdetWVq9ejYuLCzfeeCMGg8HsOE8//TTTp08nPj6e1q1bc/vtt1NcXEzfvn158803CQgIICUlhZSUFKtOpe925SLCKRlbTzreDH6NrXsuU4CyV02p7+Jq3fMJISpaN1cFE21GqvlMLKHXQ3B8Hez4XAU9Hr41P0bRRZgTaZn61NR/kqtd55SUFIqLixk3bhyxsbEAdOzYsUK57OxsPvvsMxYuXMh116n5ZRYsWEBkZMXXeP311/Pwww8D8OSTT/LGG2+wbt06rrrqKrNyxu4enU6HXq8nPDy80jq+8sordO/enXfeece0r3379qbtm266yaz8xx9/TGhoKPv37zfLdZk+fTojR44EYPbs2bRv356jR49y1VVXodfr0el0VdbBkqQFpSHKPA37f1LbvSywavGVBLcAd18ozoNzR6x7rvPH4MPrYOGtaiKqE5tkrgYhzuyDfYvV9qD/WO64rYZCcHPIz4RdX1vuuHYoLi6O6667jo4dO3LzzTfz4YcfmrpOyjt+/DhFRUX07NnTtE+v11e6onCnTp1M28YP/bS0tFrX0diCUpVjx44xfvx4mjdvTkBAAM2aNQMgKSmpynpFREQA1KletSUtKA3R1o9UP3Rsv7KJ1KzJxQXCO6hFxlJ3Q+hVV35ObW1fAKe3qe3Dy0vP7w6RndUMmDG9Ibq39VuNhLAn6+YCmkqGD+9wpdLV5+ICPR+A5U+qCR+73XP5BQcr4+6jWjJswb36y3q4urqycuVKNm3axO+//87bb7/N008/zV9//WVWztjlc2niamVdQe7u7mY/63S6Ct0tNeHt7X3Zx0ePHk10dDQffvghkZGRGAwGOnToQGFhYZX1Mr6OutSrtqQFpaEpyoPtn6pta0zMVpX6GsmTsEHdd7oV2o4BvzA1pPLUVtg8H765E15rCf/tAosfUtci7SDY4I9PiHqRsgsOLAV0MPApyx+/83jw8Idzh+H4mpo/X6dT3Sy2uNVw9ItOp6Nfv37Mnj2bnTt34uHhweLFi83KtGjRAnd3d/7++2/TvqysLI4csXLrMarlY/Xq1ZU+dv78eQ4cOMAzzzzDddddR9u2bSttAboSDw8PU06LtUkLSkOz+1vIuwCBMdDm+vo7r2kkjxUDlLz0spFCg2dDQIRKgktPVK03SVvUfdoBuHBc3XYtVOW9AktbWHpBlwnSwiKcx9q56r7jP1Riq6V5BUCXO9Vw4y3vQcvBlj+HHfjrr79YvXo1Q4cOJTQ0lL/++ouzZ8/Stm1bdu8uGwDg7+/P3XffzeOPP05wcDChoaHMnDkTFxcXqw8HnjFjBh07duThhx/mwQcfxMPDg7Vr13LzzTcTHBxMo0aN+OCDD4iIiCApKYmnnqp5wNq0aVNycnJYvXo1cXFx+Pj44ONjoQVmLyEtKA2JppUtk97z/vpNVi0/ksdaM0+e2ARo0KiVCk5AfUMKbgZxt8HoN+HhzfBkItzxPVwzHZpeoyacys+AIytg9fPw62PWqZ8Q9e3Udjj8G+hcYIAVWk+Met0P6ODoSuvnmdlIQEAAGzZs4Prrr6d169Y888wzzJs3jxEjRlQo+/rrr9OnTx9GjRrF4MGD6devH23btsXLy8uqdWzdujW///47u3btomfPnvTp04effvoJNzc3XFxcWLRoEdu3b6dDhw78+9//5tVXX63xOfr27cuDDz7IrbfeSuPGjXnllVes8EoUnVZZx5idy8rKQq/Xk5mZSUBAgK2r4ziOr4fPx6iE1Wn7wTuw/s5dXKgy9Q1F8K9dENTU8uf47UkVgHW/B0a9Uf3nlRSpwOnwClj/smpNeSKh5n3pQtibL2+Co6sgbjzcaOUJ1RbepoKhHvfByHlVFsvPzychIYFmzZpZ/QPbXuTm5hIVFcW8efO49957bV0dq7vc77gmn981/g+8YcMGRo8eTWRkJDqdjiVLlpg9bpy85dJb+Uht4MCBFR6/7bbbaloVUVPGocWdb6/f4ATURHDG5mVrTdiW8Ie6b3pNzZ7n6g5R3aD/E+Dhp1pT0vZZvHpC1KvsVBWcoIMBT1j/fL1LRwTGfw15GdY/nx3buXMnX3/9NceOHWPHjh3ccccdANxwww02rpljqXGAkpubS1xcHPPnz6/0cePkLcbbJ598gk6nqzD+etKkSWbl3n///dq9AlE9F46XjWqpj6HFlbFmomzuubKgoqYBipGrmxrlA5C40TL1EsJWjGvkhHdQ3ZzW1mwAhLaDolzY+YX1z2fnXnvtNeLi4kyzzf7xxx+EhITYuloOpcZJsiNGjKi0z83o0slbfvrpJwYNGkTz5s3N9vv4+NTLRC+i1F8fAJpKYAtpZZs6RMSpf1zWmFE2sbT1JLRd3RJcY/upb52JG+t3lJMQlpakpicntpYLAtaUTqe+/Cx9FP7+AHo/3GAnZezSpQvbt2+3dTUcnlU72c+cOcMvv/xSaZ/bV199RUhICO3bt2f69OlkZ2dXeZyCggKysrLMbqIG8rNg55dqu5cNP3StuSZPbbt3LmV8/ok/ZeixcGzGFpSYPvV3zk63gHcwZCTBoV/r77zCKVk1QPnss8/w9/dn3LhxZvvvuOMOvv76a9atW8ezzz7LDz/8UKFMeXPnzkWv15tu0dHR1qy284lfCIXZanRLi2ttV4/wDoAOclIh+4xlj21sQWnWv27Hieyskojz0uHsgTpXSwibuHhBzR4LENu3/s7r7g3dJqrtLe9dtqgDjs8Q1WSp361VA5RPPvmEO+64o0IW76RJkxg8eDAdOnTgtttu4/vvv2fVqlWVLqYEamx3Zmam6Xby5ElrVtu5GAzwd2l+T68HbDsyxcO3rHvJkt08WSlqkih00LSOzdmu7mouFJA8FOG4Tv6Faci9X2j9nrvHfaBzhRMb1RxEl3B1Vd0+l85eKpyH8Xdr/F3XltUmavvjjz84dOgQ33zzzRXLdu3aFXd3d44cOULXrhUXsfL09MTT09Ma1XR+R35XCbKeeoi73da1UXko5w6rRNlWQyxzTGMgEd4RvIPqfrzYfnBsjTpurwfqfjwh6puxeye2Hrt3jPRR0KQHnNyiZna+ZEoBNzc3fHx8OHv2LO7u7rjIcH6nYjAYOHv2LD4+Pri51S3EsFqA8vHHH9OtWzfi4uKuWHbfvn0UFRWZFiUSFhT/lbrvehd4+tm2LqDyUPZ8Z9mRPIml09vXtXvHqHweiqbVeDpsIWzOFKDUU4LspZr1Lw1Q/oCuE8we0ul0REREkJCQwIkTJ2xTP2FVLi4uxMTE1Hnm3BoHKDk5ORw9etT0c0JCAvHx8QQHBxMTEwOoiVi+++475s2rOFnPsWPH+Oqrr7j++usJCQlh//79PPbYY3Tp0oV+/Wz0x+SsCnJUCwpAx5ttWxcj4+KEluziSbBwgBLZRc0ue/E8nD1onenBhbCWwlxIiVfb9Zl/Ul6za2DDK+pvs5Ig38PDg1atWkk3j5Py8PCwSMtYjQOUbdu2MWjQINPP06ZNA+Duu+/m008/BWDRokVomsbtt1fsUvDw8GD16tW89dZb5OTkEB0dzciRI5k5c2ad+6vEJY6sgOJ8CGpWNgeJrRlH8qQnqsmc6jphXMZJdSydq+VGK7h5qDyU4+tUN48EKMKRnNoKhmIIaKLW3LKFJj3B1VMlxJ8/WunUBi4uLg1mJllROzUOUAYOHHjFDN3777+f+++/v9LHoqOjWb9+fU1PK2pj3xJ1336s/XRT+ASDPgYykyB1j/qmVRfG0TuRXdSiZZYSe3VZgNJzkuWOK4S1mbp3bNR6AuDuBdE91d9nwgbbzb0kHJpkJzmr8t077W+0bV0uZcluHlP3Th0DnUs1vVrdG/NQhHAU9hCgQFmXq/FvVIgakgDFWZXv3jF2q9gLS015r2mWm6DtUlFdwc0Lcs+WDmEWwgEUF6ouHrBdgqyRMUBJ3ChBvqgVCVCc1b7F6r79jfbTvWNkqRll0xMg6xS4uJetoWMpbp6qiRpkPhThOJJ3qi8mPiG271aJ7AruPnDxHKTJpIei5iRAcUYFOXBkpdpuP9amVamUsQXl3CEovFj74xibjpt0V5PAWVpsaTePBCiVKym2dQ3EpU78qe5j+9j+i4mbR9kXB+nmEbUgAYozOrxcfYsKbm5/3TsA/uHg2xg0A6Ttr/1xEiw0vX1VjLPSSh5KReeOwrzW8KNMZGdXbD3/yaVM3Tx/2LYewiFJgOKM9i9R9+3G2v5bVGV0unLdPLXMQ9G0sn96ls4/MYrqXjpU8owaKinKrJqp5onZ+4Oad0PYnqGkdIp76neBwMtpWi4PxVBi27oIhyMBirOx9+4dI2M3T21H8pw7rAIHV081rbY1uHuVHVu6ecokbYGDy9S2oUj9LGzvzF4oyAIPf7Xsgz2IiFP1yc9Q0woIUQMSoDgbe+/eMYqoYwuKsU87ppcKJKylqeShmNE0WPmc2taVTqwozff2wdi9E9MbXOxk0ktXt7KuUnmfiBqSAMXZ2Hv3jpExeDqzH0qKav58U/eOlfJPjCQPxdyhX1U3gps3XPu02pcgHzx2wV7mP7mUsQtW3ieihiRAcSZm3Tt2NjnbpYKagWcAlBTA2UM1e67BUC5B1kr5J0ZNeoCrB2SnqFWhG7KSYlg1S233eRg63qK2k3dCfpbNqiVQwbO9BijGv9ETm2Tkl6gRCVCciVn3jp30QVfFxaWsjjXNQ0nbD3kXwN1XzbVgTe7eKlkWpJsn/kuV++MdDP3+BYHRKtDUSiBps61r17CdO6LmG3HzUss+2JOwjuAVCIXZZYsYClENEqA4E3uenK0ytZ2wzdi9E9NbzbVgbZKHokbqrJ2rtgc8AV56tW38dizzXNhWUmnrSZMeapJBe+LiUvY3JO8TUQMSoDiLgmw4ukpttxtr06pUW22nvLfW+jtVkTwU2PKOWpk2MBa631O2v6mst2IXTAmydjK8+FKyLo+oBQlQnMXh0rV3glvYf/eOkWnRwD0qr6Q6DCWQWDpbprUmaLtUk55qOv2s02p6/YYm9xxsfEttX/ec+Td0Y5CYugcuXqj/ugnFXvNPjIx/q0lb1HpBQlSDBCjOwtS9M9YxuncAQtqoPvPC7Op/8KfsgoJMlWAbHmfd+hl5+EBUN7VtDI4akg2vqt9RRBy0H2f+mH84hLQGyiVpivqVkQSZJ8HFrWz9KHvT+Co1e3RxHpzeZuvaCAchAYozKN+9Y++jd8pzdYPQdmq7ut08xvyT2L7q+fWloeahXEiArR+r7cGzVT7BpZpKHopNnShNUI6Is86aVJag05XLQ5HhxqJ6JEBxBuW7d8I62Lo2NWPq5qlmoqy119+pSvk8lIZkzf+p2WJbXAstBlVeRtZbsS3TAoF22r1jJO8TUUMSoDgDRxu9U54pUbYaAUpJUVk3grXW36lKdC/VhJ55EtJP1O+5bSV5J+z9HtCp1pOqGH8Xafsh52y9VE2UY28LBFbFmFB98i8oyrNtXYRDkADF0RVkO8baO1UJLzeS50ojZJJ3QlEueAfVf0uRR7k5VxpCN4+mwcqZarvTLWUtXZXxbQSh7dW2fDuuXzln4fwRQKeG3duzRi3APxJKCuHk37aujXAAEqA4usMr1Gysjti9AxDWTq3pcvGcmq31cow5Dk2vrjwXwtoaUjfPsdWQsF7Nojvo6SuXl+Z72zDOfxLaTgXu9kynKxv1Je8TUQ0SoDg6R+7eATVTa+M2avtKibKmAKWe80+MTImyTv7P1WCAlbPUdo9JEBR75ec0k/VWbMLehxdfShKqRQ1IgOLIHL17x6g6M8oWF6i+a6j/BFmj6F6qtScjSd2c1Z5v4cwe8NRD/+nVe05sX0CnuhuyrtASJizH0QIU49/u6e1q7TAhLkMCFEd2aLnq3mnU0jG7d4yMibKXG8lzapsaqeQbWtbiUt88/cvWOXHW+VCK8tXIHYCrp4JPcPWe5x1U9nt09hYme5GfqSbIA8cJUIJiITAGDMVwcoutayPsnAQojmz/EnXfbqxjdu8YGRMwL9fFUz7/xJav1ZSH4qSJsls/VCOV/COh90M1e66sy1O/kv4CNLU4qH+4rWtTfbI8gqgmCVAclVn3jgNNzlYZ49T8mSerni490Ubzn1zK2IfujCN58jJgw2tqe9B/VH5QTcgHT9UuJEDmacse01HmP7mUaV0eaWkTlycBiqMy695pb+va1I2XHoKaqe3KunkKL8KprWrb1gFKdC/QuUB6ImSesm1dLG3jG5CfAY3bQufxNX9+bJ/SHJ0Tzp2jU1M5afDe1epmyXlikkpnkLX3+U8uZWxpS4lX3VRCVEECFEdl7N5x1NE7l7pcN8/Jv9TcCf6RqjnblrwCIKKz2namPJTMU/DXe2p78Exwca35MTz9Iap0rhj5dlxm9zdQmAN5F2Dt/1nmmIUX4fQOtW2vKxhXJSBSTYugGWT9JnFZEqA4ovyssu6ddmNtWhWLudxInvLdO/YQjDljHsrauSoJOaYvtB5e++M0k24eM5oGO78q+3n7Z9WbNflKTm9TSxD4R0JQ07ofr75JN4+oBglQHJFxcrZGrRy/e8fI2CpRWQuKaf2dep7evirOlodyZj/sWqi2hzxftyCwabmJuK40M3BDkLwTzh5Qq3a3Hg5osPypul8b4wKBsX3tI2ivKdOEbRLIiqpJgOKITJOzjXXMf06VMXbxnD9qPj9CQbaaMwHqf/2dqsT0VnkoF45DVrKta1N3q2ap5va2YyC6R92OFd0LXNwh67S6Pg1dfGnrSdvRcP1r4OatkluNXbS1ZUqQdbDuHSPj33LqnqoT40WDJwGKo8nPgqOr1LazdO8A+IWCfwSgwZl9ZfuTtoBWAoGx1ZvRtD546cu6pBw9DyVxIxxZoZJbr5tZ9+N5+EB0T7Xd0Lt5ivJhz3dqu/N4CIxWc8sA/P5s7RfMKykqSxp3tARZI79QlYwNztMSKSxOAhRH44zdO0bhlSTKGj/k7KV7x8g47b0j56FoGqx8Tm13mwghLS1z3Kay3goAh35Vo1QCmkCzAWpf30fVz5knYdPbtTtuyi4ougjewRBio0kLLUHW5RFXUOMAZcOGDYwePZrIyEh0Oh1Lliwxe3zixInodDqzW+/e5qtsFhQUMGXKFEJCQvD19WXMmDGcOuVkQzatxRm7d4yM3TyplQUoA+q/PpdjWpfHQQMUTYPVz6vuM3dfGPCk5Y5dfl2ehpyHYuzeibutbFSUhw8MfV5t//F67YaqG7t3YvrYZtFMS5F1ecQV1PjdnZubS1xcHPPnz6+yzPDhw0lJSTHdfv31V7PHp06dyuLFi1m0aBEbN24kJyeHUaNGUVJSUvNX0JCU795x9MnZKmOcKt04yiEvo2xeFHvJPzGK6YNae+YoZKfaujY1t+FV2Pi62h4+B/zDLHfsJj1UUmhuGpw9ZLnjOpKsZDi2Rm1fOqdM+3Hq/VOcBytr0a3maOvvVKXp1YAOzh5Uc8UIcYkaBygjRozg//7v/xg3blyVZTw9PQkPDzfdgoPL1vPIzMzk448/Zt68eQwePJguXbrw5ZdfsmfPHlatWlW7V9FQHF5e1r0T2s7WtbE8YxdP2gEoLlT/iDWDmowuIMK2dbuUdyCEl65/5GitKH/+F9a+qLaHvqi6dyzJzVMly0LDbb7ftUi9d2P6QqMW5o/pdDD8JUAHe78vG5FTHQZDuQnaHDxA8Qku9zfUQN8n4rKs0j64bt06QkNDad26NZMmTSItrSw63r59O0VFRQwdOtS0LzIykg4dOrBpU+WT9hQUFJCVlWV2a5D2LVH3zjI526UCY8ArUM3vcPZAue4dG88eWxVjq84JB0qU/ftDWPms2r72Gej7iHXO05DX5dG0su6dqmbkjewMXe9S28ufVIFHdaTtV3ktHn5lAb0jk+URxGVYPEAZMWIEX331FWvWrGHevHls3bqVa6+9loKCAgBSU1Px8PAgKCjI7HlhYWGkplbeVD537lz0er3pFh0dbelq2z+z7p2xNq2K1eh05WaU3V32rcreuneMjCMoHKUFZcfn8Ot0tX3NY9D/ceudy5gzlPhH9T98ncWprarrz93n8n+r1z4HngEq6TX+q6rLlWfs3onuCa5uda6qzcmEbeIyLB6g3HrrrYwcOZIOHTowevRofvvtNw4fPswvv/xy2edpmoauilaBGTNmkJmZabqdPHnS0tW2f8bunZDWztm9Y2T8VnhsDZzZq7btNkDpC+jg3GH770Pf/S38/Kja7j0Zrn3WuueL7KKSb/PSIW3flcs7k51fqvt2N6jp/6vi17gsOXn1bPUl5EocdYHAqsT2KZ1T6JjlF1MUDs/qKeARERHExsZy5MgRAMLDwyksLCQ9Pd2sXFpaGmFhlSfqeXp6EhAQYHZrcIyjd9qNdc7uHSNjouz+n9R9aDv1j9we+QSXDfW2526e/T/B4gcBDbrfC8NetP57yNW9bBKxhvTtuPBi2d9q5zuuXL7n/SrHKvesSly+HE1z3AUCq+KlL7e2VQN6n4hqsXqAcv78eU6ePElEhEpy7NatG+7u7qxcudJUJiUlhb1799K3r5N8K7C0/Cw4ulptO+PonfKMAYpWOqLLXltPjOx9uPGh5fD9Pep6dr5DzWZaXwFuQ1yX5+AyKMhSEwtWJ4hw84Bhc9X2lnfh/LGqy144DjlnwNUDIrtapr72QLp5RBVqHKDk5OQQHx9PfHw8AAkJCcTHx5OUlEROTg7Tp09n8+bNJCYmsm7dOkaPHk1ISAg33qg+WPV6Pffeey+PPfYYq1evZufOndx555107NiRwYMHW/TFOQ2z7p22tq6NdTVqqfrujextgrZLmfJQ7LAF5dga+PYuMBRDh5tgzNv1O2+GKYl4ExgayBQCxu6dzuOrf61bD4WWQ1Ry+Iqnqy5nbKWL6g7uXnWrpz2RdXlEFWr832rbtm106dKFLl26ADBt2jS6dOnCc889h6urK3v27OGGG26gdevW3H333bRu3ZrNmzfj71/WF/vGG28wduxYbrnlFvr164ePjw9Lly7F1bUWS7w3BMb5FNqOdu7uHVATWoWVDj1EZ/9N2cb6nT0AuedsW5fyEv+Er8dDSSFcNQpufL9ssrD6EhEHnnooyKx8EUhnk5FU1loUd3vNnjtsDri4weHfypLhL+Us859cKqaPeu0ZSZCeaOvaCDtS4zTwgQMHol1mdsgVK1Zc8RheXl68/fbbvP12Lad6bmiM/9yb1HEhN0cR0QlO/Q3hHVWehz3zbaTyZNL2q2+47W6wdY3g5FZYeIuaCKzlEPjHJyonpL65uELTfmrK94QNEOVE3RKV2bUI0FTLUU3XjWrcGno+AFv+B8v/Aw8NqPg7c9YAxcNXtQqd3KK6eYKa2rpGwk448DzJDURRXtlsnM4w70F1tB+nFq/rOsHWNakee8pDSY6HL2+CwhzVt3/rF2riNFtpKOvylJ/7pMudtTvGgCfApxGcOwRbPzJ/LPMUZJxQI16MizE6k4Y8b46okgQo9i5tv0pw9AmBgEhb16Z+NO0Hz52HHvfZuibVYy95KGf2wxc3qi6V6N5w+yJw97ZtnYwfPCc2q1V4ndWJTap7wsNfdcXWhndg2fDvtXPNuwyNs81GxF1+6LKjMibKJjbw9ZuEGQlQ7J2xeyeik/Pnn5Sn0znO6zUGKGn7IPe8bepw7gh8PgbyLqgRHnd8p5rObS20vVp1tygXTu+wdW2sx9h60n5s3a571wmqa7Mgs2w5AoAkY/eOnedk1VaTnuDqCdkplx/JJBoUCVDsnXHhPOPwW2F//BpD46vUdlLlyzVY1YUE+GyMmksjrCPc+QN42clcQS4u5brAnLT5viCnbBmK2nbvGLm4wvCX1fb2TyF1j9o25p/E9Knb8e2Vu1dZ11XCetvWRdgNCVDsnXE134aSf+KojB/Cf70PJ/+un2bq7DPqfJ+OguxkFSRNWGJ/icXOPs/F/p9UC1Fwi7JFEuuiaT8135FmgOUzVKvc2YPqMWcNUMC8m0cIajGKR9SjkmI4UzpNuLSg2LeWQ1RiY+If8PEQ0MdAh3HQ8R9q2LSluqvy0uHAUtjzfWl/fek6N8HNYcJP4BtimfNYkvGD5+RfUFxg26Rda4hfqO47j7fc73nI83DoN/U7XjFD7WvcVo0ac1bGhOqE0jwUR+niFVYjAYo9O3cYivNV4l1QM1vXRlxO62Gqa2X3t3DwF8hMgj/fVLeQ1tDhH2qytJCWNT92QY76sNr7g5ojw1Au2TSquzpu59vBO6jqY9hSSGvwC1OzoJ7aWtba5AwuJMCJjYCu5nOfXE5gDPT7F6x/GXZ/o/bFOnHrCUBUNzVJ48VzkHYAwpx4zTFRLRKg2DNT907H+p0BVNScTgctB6tb4UU4skIFFId/V4HmujnqFhGnAor24yDwMqtyFxfAkZWw93s1XX1xXtljoe1V60yHmyDYAQJXnU59O977vfp27EwBirH1pMUg0EdZ9tj9/qVmps0qXUTPWRNkjdw8IKa3mpgy8Q8JUIQEKHat/Age4Tg8fFQOQfsbIT8TDv6qgpVja9TvNGUXrHxODQXu+A81uZtfqOrSS1ivyh5YpkZyGAU1U2U73OSYyx00MwYoG2DQDFvXxjIMBtj1tdquzsKANeXhq7p6frhX/ezM+SdGTa9RfycJG6DXA7aujbAxCVDsmYzgcXxeetX90vl2lex44CfY84OadfbkFnX77QkVrJw7rJq3jfwjy1pKIrs4dp+8Mb/g1FbVwuThc/nyjiBxA2SeVNP5XzXKOufocJOaC8nD1/ItNPbIlCi7UQWA0nLcoEmAYq8MBhnB42x8G0H3e9QtKxn2LVatJae3lw1P9mkE7caq1pLo3s7zDzq4OQQ0gaxTKlm2xSBb16jujN07HW+y3uJ9Oh1c95x1jm2PIjqDuy/kZ8D5I9C4ja1rJGxIAhR7lZGolm139ZQ/UmcUEAl9JqvbheOqWTuoKTQbCK5O+Gep06lunl1fq+Z7Rw9Q8jNh/89qu3Md5z4RZVzdoFFzNf9LeqL872vgnPA/oZMwdu+EtbPNQm+i/gQ3Vzdn17Q0QHGGeS72LVaJyyFtnH8RxPoW1LQ0QDlh65oIG3OS9mMnZEyQle4d4SyM6/Kc3gEF2batS10Zu3e63OHYuUH2KLB0Jej0RJtWQ9hewwtQ8tLVsE1Dia1rcnmpkiArnExgjPp2rJWULX7niM4dUXk0OlfodKuta+N8gpqqewlQGryGF6D88hh8fSv8/oyta1I1TSs3xFgCFOFEjKN5HHldHmPrScvB4B9u27o4I+OklBnSxdPQNawApSBHzfIJsOUdNRGWPcpOVQu/6VwgVCYrEk6k2QB176jr8hhKYNcitd3FCnOfCAgq18VTH2taCbvVsAKUw8vV1PFGix9UC67ZG2P3Tkgb55gvQggjYx5Kyi7V3epojq9VCzN6B0Hr4baujXPSRwM6KMyBi+dtXRthQw0rQNm/RN33eUQt4HbxHCx5UM05Yk9ME7RJgqxwMv7h0KgVoMGJTbauTc3t/Erdd7zF+RY9tBfuXmoYPshIngau4QQoBTllXTqdboWbPgY3bzX/xJb/2bZul0qJV/cygkc4I+NsoY7WzZOXXtZFLN071mUayZNg23oIm2o4AYqxeye4hVp8L/QqGD5XPbZqNiTvtG39ypMRPMKZGbt5Di9X6w85ir0/QEmBan2VLw/WJSN5BA0pQDF277QfWzZvQbeJ0Ha0Wr7++3tVK4utXbwAGUlqO7yjbesihDW0uFblcKQnwPYFtq5N9Rm7dzrL3CdWZwxQZCRPg9YwApTy3Tvtxpbt1+lg9H8hIAouHFOLttla6h51HxgL3oE2rYoQVuGlh0FPq+21L6qg3N6lJ0LyDnBxg44327o2zi9IJmsTDSVAubR7pzyfYBj3oRrSG/8V7PneNnU0ku4d0RB0+6caQp+XDuvm2ro2V5a0Rd1HdgG/xratS0MgXTyChhKg7Fus7st375TXtB9cM11tL/u3bf8oTBO0SR+3cGKubmU5YFs/hjP7bVufKzn5l7qP7mXbejQUxgAl8zSUFNm0KsJ2nD9AKciGo6vUdvsbqy434En1z6cgC36YZLvkPdMQ4862Ob8Q9aX5QLhqlJr6fvlT9j0p18m/1b0EKPXDLwzcvNR7I/OUrWsjbMT5A5TDK8q6d8I6VF3O1U119Xjq4dTfsP6l+qujUWEunD+itmWUgGgIhv4fuHpAwno49Kuta1O5/Ew4s09tS4BSP3Q6WTRQNIAAxdS9c+OVM++DYmH0G2p7w2v1P0/DmX2gGdS3B/+w+j23ELYQ3ExNnAiw4j9QXGDb+lTm1DZAU90O8ndZf2QkT4Pn3AFKQXbZ6J32Y6v3nA43Qec7AQ1+vL9+RxjIAoGiIbrmMfALV9+Ut7xj69pUJPkntiEjeRo85w5QDq9QEys1ann57p1LjXhZPSc7GX6eUn9948YARbp3REPi6QdDZqvtDa+pxTLtiQQotiEjeRo85w5QjN077cbWbGIlTz81Fb6LOxxcBts+sUr1KkiVNXhEA9XxFojqrhaIWzXb1rUpU1Jc2sWDBCj1zRSgSBdPQ+W8AYpZ985lRu9UJbIzDJ6ltlf8B9IOWKpmlSsuLDuHdPGIhsbFRbVcAuxaCKe227Y+Rmn7VdDkGQChbW1dm4ZFkmQbvBoHKBs2bGD06NFERkai0+lYsmSJ6bGioiKefPJJOnbsiK+vL5GRkUyYMIHk5GSzYwwcOBCdTmd2u+222+r8YsyYde+0r90xej8MLa5To4C+vxeK8i1bx/LOHoSSQjXLpvEPU4iGpEl3iLtdbf/2hH2sMm7s3mnSHVxcbVuXhsaYg5J3AfKzbFsXYRM1DlByc3OJi4tj/vz5FR67ePEiO3bs4Nlnn2XHjh38+OOPHD58mDFjxlQoO2nSJFJSUky3999/v3avoCq17d4pz8UFbnwPfBtD2j5Y+azFqleBsXsnvJOs8yEarutmgrsvnN4Ge76zdW3K5Z/0tm09GiJPf/AJUdsykqdBcqvpE0aMGMGIESMqfUyv17Ny5UqzfW+//TY9e/YkKSmJmJgY034fHx/Cw8NrevrqqWv3Tnl+oTD2PfjqJvj7A7XQWZvKX3+dpMgU90IQEAH9H4PVz8OqmXDVSJUTZitJxgClp+3q0JAFxcLFc6qbRxZPbXCsnoOSmZmJTqcjMDDQbP9XX31FSEgI7du3Z/r06WRnZ1d5jIKCArKyssxul3Voed27d8prNRh6T1bbSx6GrJS6H/NSMoJHCKX3ZNXNmZ0CG9+wXT2ykiEzSa3T1aS77erRkMlIngbNqgFKfn4+Tz31FOPHjycgIMC0/4477uDrr79m3bp1PPvss/zwww+MGzeuyuPMnTsXvV5vukVHR1/+xPuXqPvqTM5WXYNnquAh7wJsrti9VScGA5zZq7alBUU0dO5eMOxFtb3pbdt9OBmntw9rr7obRP0zJcpKF09DZLUApaioiNtuuw2DwcA775hPvjRp0iQGDx5Mhw4duO222/j+++9ZtWoVO3bsqPRYM2bMIDMz03Q7efJk1SfOzyrr3mk31kKvBnDzhIEz1Pbubyy7gNWF42qkgJs3hLSy3HGFcFRXjYJmA1RL6O9WzP26HMk/sT1pQWnQrBKgFBUVccstt5CQkMDKlSvNWk8q07VrV9zd3Tly5Eilj3t6ehIQEGB2q5Jp9E4ry3TvlNdqiEqYzT1bFgRZQkq8ug9rLyMFhADV8jn8JdW9cuBnSNhQ/3WQCdpsT6a7b9AsHqAYg5MjR46watUqGjVqdMXn7Nu3j6KiIiIiIupeAdPaO2MtPxrG1R063aq247+y3HFTJUFWiArC2kH3e9X2b0/V7wrjhRfL8sJiJECxmaByXTz2MOxc1KsaByg5OTnEx8cTHx8PQEJCAvHx8SQlJVFcXMw//vEPtm3bxldffUVJSQmpqamkpqZSWFgIwLFjx3j++efZtm0biYmJ/Prrr9x888106dKFfv361e3V5GfB0VVqu66jd6rSeby6P7wccs9Z5pimNXgkQVYIM4P+A16Bapj/jk/r77zJO8FQDP4RoL9CzpuwnoAmoHNVreI5drYEgrC6Ggco27Zto0uXLnTp0gWAadOm0aVLF5577jlOnTrFzz//zKlTp+jcuTMRERGm26ZNmwDw8PBg9erVDBs2jDZt2vDoo48ydOhQVq1ahatrHbs3ynfvhLar27GqEtYeIjqrf16WmKdB08qGGMsIHiHM+QTDoKfV9poX62/xzpNb1H10T5mXyJZc3SCwNECURNkGp8bzoAwcOBDtMovnXe4xgOjoaNavX1/T01aPqXvHgqN3KtPlTpU3svMr6P1Q3Y6VdVqNDHJxs15QJYQj636PWg/r7AFY/3LZlPjWZBzBIwmythcYq5Jk0xMhto+tayPqkfOsxWPWvTPWuufqcBO4esCZPWXdM7VlfH7jq9TwSiGEOVc3GD5Xbf/9IaQdtO75DAZJkLUnMpKnwXKeAOXwcut37xj5BEOb69V2/MK6HUu6d4S4shaDoM1I0Epg+VOqa9Razh+FvHQ17F/ywmxPRvI0WM4ToOxbou6t3b1j1OVOdb/7W7UScW3JCB4hqmfY/6mWy+Nr4dBv1juPsfUkqqsauSdsK0hWNW6onCNAqc/uHaPmg8AvXOWPHF5e++PICB4hqie4OfQpXXJi1UzrtaKYEmSle8cuSBdPg+UcAYqxeyekdf0lmrq6Qdxtaru2c6LknlNJsiALYQlRHVdPA3cfOHcYTm2zzjlMCbISoNiFoGbqPjsFivJtWxdRr5wjQDGO3mk3tn6HBHa+Q90fWQnZZ2r+fGPrSXALWetDiOrwClDT4INacsLSLl5QwQ/ICsb2wjsIPEr/P2Yk2bYuol45foBSH5OzVaVxa2jSQyXu1eafpSn/RLp3hKg242zOe3+oW/5XZYytJyGtVTK8sD2dTrp5GijHD1AO/QYlhaXdO23r//zGVpT4r2reJ54iCbJC1FjzgeAbqvK/jq227LEl/8Q+GRNlZSRPg+L4Acr+Jeq+vkbvXKrDOHDzgrMHIbny1ZirZOzikSHGQlSfqxt0vFlt71pk2WNL/ol9khaUBsmxA5Ty3TvtxtqmDl56aDtabe+sQbJsQTZcOKa2pQVFiJrpdIu6P/Qb5Gda5pjFhXB6u9qOkRlk7YoEKA2SYwcoR1aWdu+0sU33jpGxm2fv99XPMk/dq+4DosA3xDr1EsJZRcSp2ZdLCmD/T5Y5ZuoeKM4H72Bo1NIyxxSWYQpQpIunIXHsAOXgL+q+/VjbLujVrL9adTM/Ew79Ur3nSPeOELWn05W1ouyy0Gie8vknskCgfQksN1mbNWcRFnbFsQOU42vVva26d4xcXKHz7Wq7ut08MoJHiLrpWBqgnNgIGSfrfjzT+jsyvNjuBMao+8Ls+lvRWticYwcohiLbd+8YxZUGKMfXQlbylcvLCB4h6iYwGmKvVtt7vq3bsTRNEmTtmbsX+Eeq7YxEm1ZF1B/HDlDA9t07Ro1aQExf0Ayw6+vLly0uUEvHg3TxCFEXcaVzouz6pm5N/xlJaqZSFze1Bo+wP7ImT4PjBAFKPU/OdjldjHOiLLz8P8u0/WAoVsl4+ib1UzchnFG7G8DVE84dKsvrqg1j60lEHLh7W6ZuwrJkJE+D49gBSnAr++jeMWp3g1on5PzRsn94lSm/QKA9tP4I4ai89NBmhNreXYduHlOCrAwvtlsykqfBcewApe0oW9fAnKd/WcJu/JdVlzPmn0j3jhB1Z1y0c893UFJcu2NIgqz9C5QunoZGAhRL6zxe3e9dDIUXKy+TKgmyQlhMi+tUd2luGiSsq/nzC7LhzD61LQmy9ku6eBocxw5QGrexdQ0qiu2nIv3CbDiwtOLjhpKySdokQBGi7tw8oMNNars2c6Kc2qaS2wNjICDCsnUTlmNMks08VfuWMuFQHDtAsUcuLuYLCF7q3BEozgN3XwhuUb91E8JZGVc4PrgMCnJq9lxT947kn9g1v3CVEK2VQNYpW9dG1AMJUKzB2CeesEENXyzP2L0T3lEFM0KIumvSXQX8RRdVkFITkn/iGFxcZKhxAyOfkNYQFKumv0eD+EvmRCk/gkcIYRk6XVkrSk1WODaUwMmtalsWCLR/pkRZGcnTEEiAYi2d71T38V+BwVC2X9bgEcI6Ot2s7hPWQ1ZK9Z6TdkDli3n4Q2g769VNWIYkyjYoEqBYS9vR6p9exglI2qT2aZqM4BHCWoKbq1E4mkGtLF4dxu6dJt3VmlrCvkmA0qBIgGItHj7QoXSW2/iF6j7jhFrx2MVdLRUvhLAs4wrHu6s5mseUfyLDix2CMQclQ7p4GgIJUKzJOJpn3xI1ssA4QVtYOzU0UghhWe3HqS8AqXvgzP4rlzcGKDESoDgEaUFpUCRAsaboXtCoJRTlwv4lkn8ihLX5BEOroWr7Sq0o2WdKP+h0ENXd2jUTlmBMkr14Xk2wJ5yaBCjWpNOVzSwbv1DyT4SoD8YVjvd8Z56gfilj60lYe/AKsH69RN15BahZg0FG8jQAEqBYW6fbQOcCJ/6EE6XJshKgCGE9rYapRQSzTkPiH1WXk/wTxyTdPA2GBCjWpo+C5oPUdmEOoFPf2IQQ1uHuVbZo5+VWOJYAxTFJgNJgSIBSH7rcUbYd0go8fG1XFyEaAuNszvt/qnzRzqI8SI5X25Ig61hkJE+DUeMAZcOGDYwePZrIyEh0Oh1Lliwxe1zTNGbNmkVkZCTe3t4MHDiQffv2mZUpKChgypQphISE4Ovry5gxYzh1yonXVmgzUjU5g3TvCFEfonuDPkZNwnb4t4qPJ8eDoQj8wsoSL4VjkBaUBqPGAUpubi5xcXHMnz+/0sdfeeUVXn/9debPn8/WrVsJDw9nyJAhZGeXZVxPnTqVxYsXs2jRIjZu3EhOTg6jRo2ipKSk9q/Enrl7QdcJarv5QJtWRYgGwcWlbE6UylY4Lt+9o9PVX71E3UmA0mC41fQJI0aMYMSIEZU+pmkab775Jk8//TTjxo0D4LPPPiMsLIyFCxfywAMPkJmZyccff8wXX3zB4MGDAfjyyy+Jjo5m1apVDBs2rA4vx45dN0vN0RDZxdY1EaJh6HQr/PEaHF0FuefAN6TsMck/cVzGFq+MJDVKSxZddVoW/c0mJCSQmprK0KFDTfs8PT0ZMGAAmzapESzbt2+nqKjIrExkZCQdOnQwlXFKrm4Q1VW+rQlRXxq3Vl8ItBLY+0PZfk0rN0GbLBDocPRNQOcKxfmQc8bWtRFWZNEAJTU1FYCwsDCz/WFhYabHUlNT8fDwICgoqMoylyooKCArK8vsJoQQV9SpNFm2/ArH54+pib7cvGTSREfk6q6CFJBuHidnlbYx3SWtBJqmVdh3qcuVmTt3Lnq93nSLjo62WF2FEE6sw03q23byDjh3RO0ztp5EdpUlJxyVjORpECwaoISHhwNUaAlJS0sztaqEh4dTWFhIenp6lWUuNWPGDDIzM023kydPWrLaQghn5dcYWl6nto1T35/cou6je9qmTqLuJFG2QbBogNKsWTPCw8NZuXKlaV9hYSHr16+nb9++AHTr1g13d3ezMikpKezdu9dU5lKenp4EBASY3YQQolo6lU59v/ub0vyTv9XPkn/iuCRAaRBqPIonJyeHo0ePmn5OSEggPj6e4OBgYmJimDp1KnPmzKFVq1a0atWKOXPm4OPjw/jxak0avV7Pvffey2OPPUajRo0IDg5m+vTpdOzY0TSqRwghLKbN9eDhp0Z9HF4OZw+q/U2kBcVhGUfyyHo8Tq3GAcq2bdsYNGiQ6edp06YBcPfdd/Ppp5/yxBNPkJeXx8MPP0x6ejq9evXi999/x9/f3/ScN954Azc3N2655Rby8vK47rrr+PTTT3F1dbXASxJCiHI8fKDtGNi1EJbPUPsatQLfRratl6i9oGbqXlpQnJpO0zTN1pWoqaysLPR6PZmZmdLdI4S4suPr4PMbyn7ufCeM/Z/NqiPqKPc8vNpcbT99Rk2GKRxCTT6/ZYYbIYTza3oN+EeW/SwJso7NJ1h12wFkyqAJZyUBihDC+bm4Qsd/lP0sCbKOTaeTRNkGQAIUIUTD0Hk86FxUS0qjVraujagrU6Jsok2rIaynxkmyQgjhkELbwj9/A+9gWb/FGUgLitOTAEUI0XBI147zkADF6cnXCCGEEI4nSOZCcXYSoAghhHA8xhaUjBNqhmDhdCRAEUII4XgCY9R9QRbkpV++rHBIEqAIIYRwPO7e4KcWqCU9wbZ1EVYhAYoQQgjHZEqUlTwUZyQBihBCCMckI3mcmgQoQgghHFOQTNbmzCRAEUII4ZjKj+QRTkcCFCGEEI5JunicmgQoQgghHJNxPZ7MU1BSbNu6CIuTAEUIIYRj8o8AVw8wFEPWaVvXRliYBChCCCEck4uLrGrsxCRAEUII4biMI3kkUdbpSIAihBDCcUmirNOSAEUIIYTjkgDFaUmAIoQQwnGZclCki8fZSIAihBDCcUkLitOSAEUIIYTjMibJXjwHBTm2rYuwKAlQhBBCOC4vPXgHqW0ZyeNUJEARQgjh2KSbxylJgCKEEMKxSYDilCRAEUII4dhkJI9TkgBFCCGEY5MWFKckAYoQQgjHJgGKU5IARQghhGMrvx6Pptm2LsJiJEARQgjh2PTRoHOB4nzIOWPr2ggLkQBFCCGEY3N1h4Amalu6eZyGBChCCCEcX5CM5HE2Fg9QmjZtik6nq3CbPHkyABMnTqzwWO/evS1dDSGEEA2JJMo6HTdLH3Dr1q2UlJSYft67dy9Dhgzh5ptvNu0bPnw4CxYsMP3s4eFh6WoIIYRoSEwtKIk2rYawHIsHKI0bNzb7+aWXXqJFixYMGDDAtM/T05Pw8HBLn1oIIURDFdRM3ct6PE7DqjkohYWFfPnll9xzzz3odDrT/nXr1hEaGkrr1q2ZNGkSaWlplz1OQUEBWVlZZjchhBDCRLp4nI5VA5QlS5aQkZHBxIkTTftGjBjBV199xZo1a5g3bx5bt27l2muvpaCgoMrjzJ07F71eb7pFR0dbs9pCCCEcjXG6+6xkKK7680Q4Dp2mWW9Wm2HDhuHh4cHSpUurLJOSkkJsbCyLFi1i3LhxlZYpKCgwC2CysrKIjo4mMzOTgIAAi9dbCCGEg9E0mBMFRbnw8BYIbWvrGolKZGVlodfrq/X5bfEcFKMTJ06watUqfvzxx8uWi4iIIDY2liNHjlRZxtPTE09PT0tXUQghhLPQ6aBJd0hYD8fWSIDiBKzWxbNgwQJCQ0MZOXLkZcudP3+ekydPEhERYa2qCCGEaAjajFD3h36zbT2ERVglQDEYDCxYsIC7774bN7eyRpqcnBymT5/O5s2bSUxMZN26dYwePZqQkBBuvPFGa1RFCCFEQ9F6mLpP2gx5GTatiqg7qwQoq1atIikpiXvuucdsv6urK3v27OGGG26gdevW3H333bRu3ZrNmzfj7+9vjaoIIYRoKIKbQ0gbMBTDsdW2ro2oI6vkoAwdOpTKcm+9vb1ZsWKFNU4phBBCQJvhcO4QHFoOHW6ydW1EHchaPEIIIZxH6+Hq/sjvUFJs27qIOpEARQghhPNo0hO8gyA/A079bevaiDqQAEUIIYTzcHWDVkPVtozmcWgSoAghhHAuxtE8hyXn0ZFJgCKEEMK5tLgOXNxUsuyF47aujaglCVCEEEI4F+9AiOmjtg8tt2lVRO1JgCKEEML5GGeVPSwBiqOSAEUIIYTzMQ43PvEn5Gfati6iViRAEUII4XwatYBGrUpnlV1j69qIWpAARQghhHNqU9qKInkoDkkCFCGEEM6pdWkeypHfwVBi27qIGpMARQghhHOK7gVegZB3AU5ttXVtRA1JgCKEEMI5ubpBqyFqW2aVdTgSoAghhHBextE8MtzY4UiAIoQQwnm1vA50rnD2IFxIsHVtRA1IgCKEEMJ5eQdBbF+1LWvzOBQJUIQQQjg30+KB0s3jSCRAEUII4dyMw40TN0J+lm3rIqpNAhQhhBDOLaQlBLcAQ5HMKutAJEARQgjh/EyLB0oeiqOQAEUIIYTzMw43PrJCZpV1EBKgCCGEcH4xvcFTDxfPw6lttq6NqAYJUIQQQjg/V3doNVhty2gehyABihBCiIZBZpV1KBKgCCGEaBhaDlazyqbth/QTtq6NuAIJUIQQQjQMPsEqFwVkNI8DkABFCCFEw2GaVVZWN7Z3EqAIIYRoOMrPKluQbdu6iMuSAEUIIUTDEdIKgptDSSEcW2vr2ojLkABFCCFEw6HTyWgeByEBihBCiIbFFKCsAIPBtnURVZIARQghRMMS0wc8A+DiOTi93da1EVWweIAya9YsdDqd2S08PNz0uKZpzJo1i8jISLy9vRk4cCD79u2zdDWEEEKIyrl5QMvr1LZ089gtq7SgtG/fnpSUFNNtz549psdeeeUVXn/9debPn8/WrVsJDw9nyJAhZGdLNrUQQoh6YhzNY4kAJXUvbP0YDv4CKbsg9zxoWt2P28C5WeWgbm5mrSZGmqbx5ptv8vTTTzNu3DgAPvvsM8LCwli4cCEPPPCANaojhBBCmGs1BHQucGYvZCRBYEzNj1FSBOtfgT/mgXbJCslu3qCPAn0TCGii7vVREBAF+mi17eFrmdfipKwSoBw5coTIyEg8PT3p1asXc+bMoXnz5iQkJJCamsrQoUNNZT09PRkwYACbNm2qMkApKCigoKDA9HNWVpY1qi2EEKKh8AmG6F6QtFkly/acVLPnnz0EP94PKfHq55g+UFwAmacgNw2K8+D8UXWrineQCl6ie8CQ58HTv9YvxxlZPEDp1asXn3/+Oa1bt+bMmTP83//9H3379mXfvn2kpqYCEBYWZvacsLAwTpyoel2EuXPnMnv2bEtXVQghREPWelhpgLK8+gGKwQB/vw+rZkFxPngFwqjXocNNZWWKCyDrNGSeVgFL1il1b/w58xQUZkNeurqd2QOaAUa/ZY1X6bB0mmbdjrLc3FxatGjBE088Qe/evenXrx/JyclERESYykyaNImTJ0+yfHnlfYGVtaBER0eTmZlJQECANasvhBDCWaUdhHd6gasHPJEAnn6XL595CpY8DAnr1c8troMb/gcBEZd/XmXyM1XAcnob/DxF7btrMbS4tubHciBZWVno9fpqfX5bfZixr68vHTt25MiRI6a8FGNLilFaWlqFVpXyPD09CQgIMLsJIYQQddK4DQQ1VbPKHl9XdTlNg13fwDt9VXDi7gMj58GdP9QuOAHw0kNYO+g6AXqUtt78NAXyJYXByOoBSkFBAQcOHCAiIoJmzZoRHh7OypUrTY8XFhayfv16+vbta+2qCCGEEGXMZpWtYvHAixfgu4mw+H4oyISo7vDgRuhxn3q+JQyeBYGxqito5bOWOaYTsHiAMn36dNavX09CQgJ//fUX//jHP8jKyuLuu+9Gp9MxdepU5syZw+LFi9m7dy8TJ07Ex8eH8ePHW7oqQgghxOWZApTfK84qe2QlvNMb9i8BFzcY9AzcswIatbBsHTz9VFcRwPZP4dgayx7fQVk8SfbUqVPcfvvtnDt3jsaNG9O7d2+2bNlCbGwsAE888QR5eXk8/PDDpKen06tXL37//Xf8/SV7WQghRD2L7Qce/mrkTfJOaNINCnLg92dg+wJVJqQNjHsfIrtYrx7NroGe98PfH6iunoc3g1fDTmewepKsNdQkyUYIIYS4rG8nwP6foP/j0GqoGj6cnqAe6/0wXPccuHtbvx6FufBuX0hPhK53w5j/Wv+c9cyukmSFEEIIu2acVfbvD+GTYSo4CWgCE36G4XPrJzgBNXGbsatnx2dwdHX9nNdOSYAihBCiYWs1BNBBfoaaj6TTbfDQn9B8QP3XpenV0LN00tKfH1XDkRsoCVCEEEI0bL4hEHcb+IXDzZ+pfBPvQNvVZ/BMNfw565TKhWmgJAdFCCGEADXfiaWGDtdV4kb4dKTavvMHaDnYtvWxEMlBEUIIIWrKXoITkK4eJEARQggh7NPgmRDUTK3r0wC7eiRAEUIIIeyR2aiez+HoKtvWp55JgCKEEELYq6b9oNeDaruBdfVIgCKEEELYs+ueK+vqWfG0rWtTbyRAEUIIIeyZhy+MfQfQwc4v4EjD6OqRAEUIIYSwd7F9y7p6ljaMrh4JUIQQQghHcN1zENy8tKvnP7aujdVJgCKEEEI4Ag+f0lE9Otj5JRxZaesaWZUEKEIIIYSjKN/V8/OjkJdh0+pYkwQoQgghhCMxdvVkJ8PvzjuqRwIUIYQQwpF4+MANxlE9X8LauWAw2LpWFicBihBCCOFoYvvAwKfU9vqX4Nu7oCC7fs597ijs+R4MJVY9jQQoQgghhCMa+JRKmnX1gIPL4KPBcP6Y9c5nMMCm+fBuX/jhXtj4hvXOhQQoQgghhOPqcif88zfwj4CzB+GDQdaZyC0jCT4fo3JeSgrUvj9eh6xky5+rlAQoQgghhCNr0h3uXwdNekJBJnz1D9W6oWl1P7amQfxCeLcfJP4B7j4w6g2I7gVFubBqdt3PUQUJUIQQQghH5x8OE5dB1wmABqtmwff3QGFu7Y+Zew6+uROWPAQFWSoAenAjdL8Hhr+kyuxeBCe3WuIVVCABihBCCOEM3Dxh9H9h5DxwcYN9P8LHwyD9RM2PdWg5vNNH5ba4uMG1z6qupEYt1ONRXaHznWr7tyesMopIAhQhhBDCWeh00OM+uHsp+DaGM3vgg4FwfH31nl+QDT9Pga9vhdw0aHwVTFoD/aeDq5t52eueAw9/SN6hWlIsTAIUIYQQwtnE9lV5KRGdIe8CfHEjbHn38nkpSVvgvathx+eADvo8Avevh4i4ysv7h8GAx9X2qlkWH+YsAYoQQgjhjPRN4J7l0OlW0Epg+VOw5GEoyjcvV1yoAowFIyA9EfTRqgVm2Ivg7nX5c/R6UM1qm3MG/phn0epLgCKEEEI4K3dvuPF9GDYHdC6wa6EKRDJPq8fP7IMPry0d9WOAuPHw0J/Q7JrqHd/NUx0bYPP/4MJxi1VdAhQhhBDCmel00Gcy3PkjeAepnJEPBsLK59T9mT3g0whu/RJufBe89DU7fuvh0OJaKCmE35+1WLUlQBFCCCEaghaDYNJaCG2vEmD/fEsFFa1HwMNboO3o2h1Xp4Nhc0Hnqkb9HFtrkepKgCKEEEI0FMHN4L6VKi/Ft7Ealnz71+AXWrfjhl4FPSep7eUzoKS4zlV1u3IRIYQQQjgND18Y94Ea0aPTWe64A5+C3d/C2QOwfUFZwFJL0oIihBBCNESWDE5A5bdc+4zaXvN/cPFCnQ4nAYoQQgghLKPbRAjrAPkZsHZOnQ5l8QBl7ty59OjRA39/f0JDQxk7diyHDh0yKzNx4kR0Op3ZrXfv3pauihBCCCHqk4srDJ+rtrd9rIYx1/ZQFqqSyfr165k8eTJbtmxh5cqVFBcXM3ToUHJzzRcsGj58OCkpKabbr7/+aumqCCGEEKK+NesPbceoeVWWP1XrVZUtniS7fPlys58XLFhAaGgo27dvp3///qb9np6ehIeHW/r0QgghhLC1oS/A4RWQsAEO/gJtR9X4EFbPQcnMzAQgODjYbP+6desIDQ2ldevWTJo0ibS0NGtXRQghhBD1Iagp9J2itn9/uuL0+tWg07Ratr1Ug6Zp3HDDDaSnp/PHH3+Y9n/zzTf4+fkRGxtLQkICzz77LMXFxWzfvh1PT88KxykoKKCgoMD0c1ZWFtHR0WRmZhIQEGCt6gshhBCitgpyYH53yE6B62bCNdPIyspCr9dX6/PbqgHK5MmT+eWXX9i4cSNNmjSpslxKSgqxsbEsWrSIcePGVXh81qxZzJ49u8J+CVCEEEIIO7brG1h8P7j7wpTtZOFb7QDFal08U6ZM4eeff2bt2rWXDU4AIiIiiI2N5ciRI5U+PmPGDDIzM023kydPWqPKQgghhLCkTrdAk55QlAurn6/RUy0eoGiaxiOPPMKPP/7ImjVraNas2RWfc/78eU6ePElERESlj3t6ehIQEGB2E0IIIYSd0+lgxEtqe9dCOL2z2k+1eIAyefJkvvzySxYuXIi/vz+pqamkpqaSl5cHQE5ODtOnT2fz5s0kJiaybt06Ro8eTUhICDfeeKOlqyOEEEIIW4rqBp3vUNsrn6v20yyeg6KrYurcBQsWMHHiRPLy8hg7diw7d+4kIyODiIgIBg0axAsvvEB0dHS1zlGTJBshhBBC2Fh2KrzdjazsbPQvZVfr89vi86BcKd7x9vZmxYoVlj6tEEIIIeyVfzj0nw6/zKz2U2QtHiGEEEJYX++HITC22sUlQBFCCCGE9bl5wnXVz0GRAEUIIYQQ9aP1sGoXlQBFCCGEEHZHAhQhhBBC2B0JUIQQQghhdyRAEUIIIYTdkQBFCCGEEHZHAhQhhBBC2B0JUIQQQghhdyRAEUIIIYTdkQBFCCGEEHZHAhQhhBBC2B0JUIQQQghhdyRAEUIIIYTdkQBFCCGEEHZHAhQhhBBC2B0JUIQQQghhdyRAEUIIIYTdkQBFCCGEEHZHAhQhhBBC2B0JUIQQQghhdyRAEUIIIYTdkQBFCCGEEHZHAhQhhBBC2B0JUOycwaDxd8IFPthwjDNZ+bauTr1Iy8rnoz+Ok3Au19ZVsXvncgr4cssJft+XSkFxia2rI4QQFqPTNE2zdSVqKisrC71eT2ZmJgEBAbaujsVpmsae05ks3ZXMst0ppGSqwCTQx53/G9uBUZ0ibVxD68gvKuHjjQm8s/YouYUleLm78J/r23JX71h0Op2tq2dXTpzP5cM/jvPdtlMUFBsA8PdyY1j7cMbERdK3RSPcXOX7hxDCvtTk81sCFDty+Ew2S3cls3RXMonnL5r2+3u6EeLvaWpRuKFzJM+P6YDex91WVbUoTdNYse8ML/66n5MX8gAI8fPgXE4hANe0CuHVf8QRrveyZTXtwq6TGXyw4Ti/7U3BUPqX2y4igAu5haSWa2Fr5OvB9R0jGB0XSffYIFxcHD/Ay8ov4vd9Z1i+N4XzuYW1Pk6E3ouuMUF0bxpM+8gA3CWQMykoLmHv6Uy2JaazIykdHw83JvSJpUtMkK2rJpyEBChWVGLQcLXgP/uk8xdZulsFJQdTs037vdxdGNw2jNFxkQxo3RgXnY631xzhf2uPYtAgPMCL126O4+pWIRariy0cSMni+aX72Xz8PABhAZ48NeIqxsRF8cXmROb+dpCCYgN6b3deGNuBMXHO2Xp0OZqmsf7wWd5ff9x0nQAGtWnMAwNa0KtZMJoGWxMvsHR3Mr/uSeVCuQ/wCL0XozpFMCYuig5RARZrjSoqMeDmorNq61ZeYQmrD55h6a5k1h46S2Fpa5GleLm7ENckkO5Ng+geG0zXmCCnCfyr40JuIdtPpLPtxAW2J6az+3Rmpde4Z7NgHhzQnIGtQ50i2BW2IwGKhWmaxoYj53h//TE2HTuP3tudCL0XUYHeRAR6ERnoTaTem8hAbyL0XoTrvS77rexMVj7Ldqfw865kdp3MMO13d9UxoHVjRsdFMrhtGL6ebhWeuyMpnWnfxJtaWCb2bcqTw6/C28PV4q/bms7nFPD6ysN8/XcSBg083Vy4v39zHhzQwux1H03L4d/fxLPndCYAo+MieeGG9gT6eNiq6vWmqMTAst3JvL/+uCl4dXPRMaZzJPf3b85V4ZW/94tKDGw6dp6f45P5fV8q2QXFpseahfgyupNqWWkV5l/luQ0GjbM5BSRn5JGckU9KZh6nM/JIMW3ncy6ngEa+HnSLDaJbbBDdmwbRIUqPp1vd3ouFxQY2HD7L0t3JrNx/houFZbk1LUP9GBMXyVXh/rUKjAyaxtG0HLafSGf7iXQy84rMHtfpoFWoH91ig+le+ppign2cootR0zSOn8tle6IKSLadSOf42Yp5XuV/p0fTclgSf5qiEvUx0TrMj/v7t2BMXCQebtLyJGpOAhQLKSox8MvuFN5bf8ysdeNKdDoI9fcsF7h4EaH3xkUHy/el8lfCBYxX3UUHfVuEMDouguHtI6r17e1iYTFzfj3Al1uSAGje2Jc3bulMXHRgbV5mvSoqMfD55hO8ueow2fnqg3NkxwieGnEV0cE+VT5n/pqjzF97lBKDRliAJ6/8I44BrRvXZ9XrTW5BMYu2nuTjP46TXJp/5OPhyu09Y7jn6mZEBXpX+1j5RSWsO6Q+7FcfOEN+Udm346vC/RkdF0mAtzspGXkqGMnMJzkjjzNZ+aYPpZrwcHOhU5SebqUtEt1igwj2vXIwWWLQ2HL8PEt3JfPb3lSzwKFJkDdj4iIZXYfApDIGg8axszlsO5HOtsR0tp+4YNa1atTY35NuMSpY6RYbRPtIfb19OOcXqS4XY3dnzWkknr9o6rK5UEnXWMtQP7qbgsxgmjYyD8hSM/NZ8GcCX/2VRE5psBse4MW9Vzfjtp7R+HvZR4tTcYmBAynZXLhYSITeiwi9l93UTZSRAKWOcguK+WbrST7emMDpDJUT4ePhym09YrijdwwlBq3CN8vkjDxSMvNJycinsOTKzdDdYoMYExfJiI7hhPrXLrdi3aE0nvh+N2nZBbi66JhybUsmD2ppt33qaw+l8cKy/aZvbe0iApg5uh29mjeq1vPjT2Yw7Zt4jpfm4kzoE8uMEW0drvWoKmezC/hsUyKfb04kqzR4C/Hz4J/9mnFnr9g6dz3kFhSz6sAZfo5PZsORs1cMQFx06oMoItC7NNj2MrUSRgZ6ExbgRdKFXLYlprOttEWisg/A5o19VWtEbDDdmgbRPMQXnU6HpmnsSEpn6a4Ulu1O4VxOgek5of6ejOwUwZi4SDpHB9ZbC8bZ7ILS1hXVwrD3dGaF6+Tl7kKnJoGmFpZuMcEW6xY6l1Ngat3ZlniBvaezqvX/pLo83FyIa6I3tRB1iw0iqBoBJKgcoIV/JfHJxgTSstXvyt/LjTt7x/LPvk0JDajfHLGs/CJ2nEhnxwn1/os/mWHW2masn+lLYqC3avXWl7V6h+u9pCWonjWYAGXhHwfo3yG2Rt8oL+dcjvED4oTpG1yInwcT+zblzt6x1epWMBg0zucWlgYsqik8JSOP5Mw8svOL6dcyhFGdImgSVHlrQU2l5xbyzE97+WV3CgBxTfS8fmtnWjT2q/UxNU0j4Vwu206kcyg1m0Bvd/UHHaj+0MP1XjVqxj+alsP//bKfdYfOAqoJ+fFhbbi5e3SN83nyCkt46bcDfLb5BADNQ3x5/dbOdHaA1iOjEoNGWnY+yRn5pvfJodQclu5ONvX/NwvxZdI1zRnXNQovd8sHYBkXC1m+N5WV+8+g0+mICqwYiIT6e9ZoJFD5942xG+FYJV0Iwb4exDXRc/hMjukLAKhRaiM6RDA6LoJezRpZNNertvKLSth9KtOUo7E9KZ2Mi0UVyrUK9SttYVEf/LGNrtwtZDBoHD+XYxbgVTa0PsTPk5hg71oHaSF+xi6bYDpEBdS5C66guISfdibz/oZjpt+vh6sL47pGMal/8zr976mKpmmcSs9TXVOJ6lodOpPNpZ9exoAkNSu/QvddZXQ6dX0vDcBV9736fxfi62nRvJucgmJSMkq7TEtbLCt7T9WXW3tE0yFKX2/nc5gA5Z133uHVV18lJSWF9u3b8+abb3LNNddc8XnGFxg99VtcPH2I0Hup5snSJsqrwv1r9I818Zwasvn99rIhm00b+XB//xZW+4CwJE3T+HlXMs8u2UtWfnGNh+eWz9zfVvqN5EqjJEL8PNWHmr4scCn/xx3i50l2QTFvrTrC55sTKTZouLvq+Ge/ZjxybUsC6tj0uuHwWZ74fjepWfm4uuiYPKglU661feuRpmlk5hWZ8jWSM/PMApHkjHxSs/IpMVT+ZxcXHchDA5ozpF24XXxA11V6biE7ktJNQUv8qQyzJExfD1eGlg6N7tcyxO6/zdYkqOgWG2hqNeoQqcegadUKdlqHOUYOjMGgsfpgGu+tP8b2E+mA+sAf0jaMBwa0oFts7Uf+FJUY2J+cVXqNL7D9RDpnsgoqlIsJ9lEtQaVdiq1C/UzBRG5BsfmXxHJdmCmZ+ZzOyKtW0rWHqwvhpV1GleYdBnqZ/p8VFhs4k5Vfeq7yf/ul+zLyTK2j9uKdO7pyfceIejufQwQo33zzDXfddRfvvPMO/fr14/333+ejjz5i//79xMTEXPa5xhc4/JXlHE4vqfDP3sfDlS4xgaY/8i4xgZX2Re46mcH7G47x295UUyTuyB8QKZl5PP7dbjYePQdUPTy3Opn7xqbgDlF6cvKLy/7AMvPM8hiq4u6qw83Fhbwi1eQ6uG0oT49sR7MQXwu9Wsi8WMSzP+3l513JAHRqouf1WzrTMvTy3+DyCktIziwNIEz/SNQ/kdTMfIqrCB6upMSgcTa7wPSaL8fVRUd4gJcpsIsM9GZg68b0bBZslx9GllJYbGBvcia7TmYQHuDFoKtC7f4LwJVc2i2zp5JuIU83FwyaVml3kTOMItqWeIH31h9n1YEzpn1Rgd61Cjg1TeNMVsW/IzcXHe2j9KXdhap7qi7dSpqmcSG3UAURpf8DjEFMSmkXflp2PtX5d+Dv6YaXhyvncgoqtOpUJsDLzfR3H6H3opGvh83+7kd1irhswrylOUSA0qtXL7p27cq7775r2te2bVvGjh3L3LlzL/vc8i/QzcuH+JMZpU3KKhEs+5II1UUHbcIDTN9IvN1d+eTPBLYcv2Aqc+1VoTzQv7nDf0AYDBqflxueG+DlxjMj24GOyza7GzP3jc3UVTUFa5pG+sWisj/mct9GUsolWBr/qFuF+vHsqHb0t2JC69JdyTyzZC+ZeUV4urnw5PCr6NREX/ZtKaP0W1TpP6H0emhObeTrYZavUdbCpPrBG/t7OlwALKonv6iEPaUtksZv/8b3XGN/T7OE1HYRAXbfalQTR9Oy+WDDcRbvPF2rJOvyArzcTNepW2wQcU0C6z3frKhEtYiUtYDkmwUyyRl5FbqSPFxdVCuLXrWuRJX+3ZdvZW7Iybt2H6AUFhbi4+PDd999x4033mja/69//Yv4+HjWr19vVr6goICCgrLmvaysLKKjoyt9gQaDxuG0bFM/5bYTF0yTf13KzUXHDZ2juL9/c9qE118EWR+OpuXw2Lfx7DqVWenjV8rcr4viEgNnsgvIvFhE6zC/epnRNDUznyd+2M2Gw2erVd7Xw7VcXk1Z/7PKr6l9fRv5eRKh93L4VgFhOcbcHDcXF6LrkEviSM7lFJBYh6Uq9N7utGjs5xBzrhi7kvIKDYSXtoY4Qr1txe4DlOTkZKKiovjzzz/p27evaf+cOXP47LPPOHTokFn5WbNmMXv27ArHqe4onrSsfLOhhGeyChgdF8E/+zUj0kIJtvaoqMTA/9Ye5bttp4gM9KpV5r4j0TSNL7ec4J11x3Bz1ZlaKyprxQjwcmsQHxRCCGFPHCZA2bRpE3369DHtf/HFF/niiy84ePCgWfmatKAIIYQQwj7VJECpOFVpPQgJCcHV1ZXU1FSz/WlpaYSFhVUo7+npiaenZ31VTwghhBA2ZpPsLA8PD7p168bKlSvN9q9cudKsy0cIIYQQDZNNWlAApk2bxl133UX37t3p06cPH3zwAUlJSTz44IO2qpIQQggh7ITNApRbb72V8+fP8/zzz5OSkkKHDh349ddfiY2NtVWVhBBCCGEnHHqqe0mSFUIIIRxHTT6/nWeGICGEEEI4DQlQhBBCCGF3JEARQgghhN2RAEUIIYQQdkcCFCGEEELYHQlQhBBCCGF3JEARQgghhN2RAEUIIYQQdkcCFCGEEELYHZtNdV8Xxslvs7KybFwTIYQQQlSX8XO7OpPYO2SAcv78eQCio6NtXBMhhBBC1FR2djZ6vf6yZRwyQAkODgYgKSnpii+wMj169GDr1q21OrejPTcrK4vo6GhOnjxZ63WLHO011+W5db1ejvZ66/JcuVbVJ9eq+uRaVZ8jXitN0+jWrRuRkZFXLOuQAYqLi0qd0ev1tfqluLq61vrD2hGfCxAQEOBw9XbE6+WIr1euVf08F+Ra1YRcq+pztGvl4eFh+hy/nAaZJDt58uQG9dy6csTXbKvr5YivV65V/Ty3Lhzx9cq1qp/n1oW9v16dVp1MFTtTk+WaGzq5VjUj16v65FpVn1yr6pNrVX3Ofq0csgXF09OTmTNn4unpaeuq2D25VjUj16v65FpVn1yr6pNrVX3Ofq0csgVFCCGEEM7NIVtQhBBCCOHcJEARQgghhN2RAEUIIYQQdkcCFCGEEELYHZsFKBs2bGD06NFERkai0+lYsmSJ2eNnzpxh4sSJREZG4uPjw/Dhwzly5IhZmWPHjnHjjTfSuHFjAgICuOWWWzhz5oxZmfT0dO666y70ej16vZ677rqLjIwMK786y6qva9W0aVN0Op3Z7amnnrL2y7OouXPn0qNHD/z9/QkNDWXs2LEcOnTIrIymacyaNYvIyEi8vb0ZOHAg+/btMytTUFDAlClTCAkJwdfXlzFjxnDq1CmzMo7+3qrPa+Xo7y1LXasPPviAgQMHEhAQgE6nq/T9Iu8rpTrXytHfV2CZ63XhwgWmTJlCmzZt8PHxISYmhkcffZTMzEyz4zjae8tmAUpubi5xcXHMnz+/wmOapjF27FiOHz/OTz/9xM6dO4mNjWXw4MHk5uaanj906FB0Oh1r1qzhzz//pLCwkNGjR2MwGEzHGj9+PPHx8Sxfvpzly5cTHx/PXXfdVW+v0xLq61oBPP/886SkpJhuzzzzTL28RktZv349kydPZsuWLaxcuZLi4mKGDh1quhYAr7zyCq+//jrz589n69athIeHM2TIELKzs01lpk6dyuLFi1m0aBEbN24kJyeHUaNGUVJSYirj6O+t+rxW4NjvLUtdq4sXLzJ8+HD+85//VHkueV8p1blW4NjvK7DM9UpOTiY5OZnXXnuNPXv28Omnn7J8+XLuvfdes3M53HtLswOAtnjxYtPPhw4d0gBt7969pn3FxcVacHCw9uGHH2qapmkrVqzQXFxctMzMTFOZCxcuaIC2cuVKTdM0bf/+/RqgbdmyxVRm8+bNGqAdPHjQyq/KOqx1rTRN02JjY7U33njD6q+hPqWlpWmAtn79ek3TNM1gMGjh4eHaSy+9ZCqTn5+v6fV67b333tM0TdMyMjI0d3d3bdGiRaYyp0+f1lxcXLTly5drmuac7y1rXStNc773Vm2uVXlr167VAC09Pd1sv7yvqn+tNM353leaVvfrZfTtt99qHh4eWlFRkaZpjvnesssclIKCAgC8vLxM+1xdXfHw8GDjxo2mMjqdzmyCGi8vL1xcXExlNm/ejF6vp1evXqYyvXv3Rq/Xs2nTpvp4KVZnqWtl9PLLL9OoUSM6d+7Miy++SGFhYT28CusxNnEaF5hMSEggNTWVoUOHmsp4enoyYMAA03ti+/btFBUVmZWJjIykQ4cOpjLO+N6y1rUycqb3Vm2uVXXI+6rmr9GZ3ldguetlnF3WzU0tueeI7y27DFCuuuoqYmNjmTFjBunp6RQWFvLSSy+RmppKSkoKoC6sr68vTz75JBcvXiQ3N5fHH38cg8FgKpOamkpoaGiF44eGhpKamlqvr8laLHWtAP71r3+xaNEi1q5dyyOPPMKbb77Jww8/bKuXVmeapjFt2jSuvvpqOnToAGD6vYeFhZmVDQsLMz2WmpqKh4cHQUFBly3jTO8ta14rcK73Vm2vVXXI+6pmr9GZ3ldguet1/vx5XnjhBR544AHTPkd8b9nlasbu7u788MMP3HvvvQQHB+Pq6srgwYMZMWKEqUzjxo357rvveOihh/jvf/+Li4sLt99+O127dsXV1dVUTqfTVTi+pmmV7ndElrxW//73v03bnTp1IigoiH/84x+mbyiO5pFHHmH37t0VWomg4vuiOu+JS8s403vL2tfKmd5blr5WVzpGbY9jD6x9rZzpfQWWuV5ZWVmMHDmSdu3aMXPmzMse43LHsQd22YIC0K1bN+Lj48nIyCAlJYXly5dz/vx5mjVrZiozdOhQjh07RlpaGufOneOLL77g9OnTpjLh4eEVRqoAnD17tkI06sgsca0q07t3bwCOHj1q9ddgaVOmTOHnn39m7dq1NGnSxLQ/PDwcoMI3hrS0NNN7Ijw8nMLCQtLT0y9bxlneW9a+VpVx1PdWXa5Vdcj7qm6v0VHfV2CZ65Wdnc3w4cPx8/Nj8eLFuLu7mx3H4d5b9Z/2UhGXJH5W5vDhw5qLi4u2YsWKKsusXr1a0+l0poQfY1LQX3/9ZSqzZcsWu04KuhJrXavKLF26VAO0EydO1La69c5gMGiTJ0/WIiMjtcOHD1f6eHh4uPbyyy+b9hUUFFSa+PnNN9+YyiQnJ1eaJOvI7636ulaVcbT3liWuVXlXSpJt6O+r8i6XJHspR3tfaZrlrldmZqbWu3dvbcCAAVpubm6F4zjie8tmAUp2dra2c+dObefOnRqgvf7669rOnTtNb6xvv/1WW7t2rXbs2DFtyZIlWmxsrDZu3DizY3zyySfa5s2btaNHj2pffPGFFhwcrE2bNs2szPDhw7VOnTppmzdv1jZv3qx17NhRGzVqVL29Tkuoj2u1adMm03GPHz+uffPNN1pkZKQ2ZsyYen2tdfXQQw9per1eW7dunZaSkmK6Xbx40VTmpZde0vR6vfbjjz9qe/bs0W6//XYtIiJCy8rKMpV58MEHtSZNmmirVq3SduzYoV177bVaXFycVlxcbCrj6O+t+rpWzvDestS1SklJ0Xbu3Kl9+OGHGqBt2LBB27lzp3b+/HlTGXlfKVe6Vs7wvtI0y1yvrKwsrVevXlrHjh21o0ePmh3Hkf9n2SxAMUbFl97uvvtuTdM07a233tKaNGmiubu7azExMdozzzyjFRQUmB3jySef1MLCwjR3d3etVatW2rx58zSDwWBW5vz589odd9yh+fv7a/7+/todd9xRrUjcntTHtdq+fbvWq1cvTa/Xa15eXlqbNm20mTNnVhqJ27PKrhOgLViwwFTGYDBoM2fO1MLDwzVPT0+tf//+2p49e8yOk5eXpz3yyCNacHCw5u3trY0aNUpLSkoyK+Po7636ulbO8N6y1LWaOXPmFY8j7yvlStfKGd5XmmaZ61XVZwSgJSQkmMo52ntLp2maVsveISGEEEIIq7DbJFkhhBBCNFwSoAghhBDC7kiAIoQQQgi7IwGKEEIIIeyOBChCCCGEsDsSoAghhBDC7kiAIoQQQgi7IwGKEKJeDRw4kKlTp9q6GkIIOycBihDCbq1btw6dTkdGRoatqyKEqGcSoAghhBDC7kiAIoSwmtzcXCZMmICfnx8RERHMmzfP7PEvv/yS7t274+/vT3h4OOPHjyctLQ2AxMREBg0aBEBQUBA6nY6JEycCoGkar7zyCs2bN8fb25u4uDi+//77en1tQgjrkgBFCGE1jz/+OGvXrmXx4sX8/vvvrFu3ju3bt5seLyws5IUXXmDXrl0sWbKEhIQEUxASHR3NDz/8AMChQ4dISUnhrbfeAuCZZ55hwYIFvPvuu+zbt49///vf3Hnnnaxfv77eX6MQwjpksUAhhFXk5OTQqFEjPv/8c2699VYALly4QJMmTbj//vt58803Kzxn69at9OzZk+zsbPz8/Fi3bh2DBg0iPT2dwMBAQLXKhISEsGbNGvr06WN67n333cfFixdZuHBhfbw8IYSVudm6AkII53Ts2DEKCwvNgojg4GDatGlj+nnnzp3MmjWL+Ph4Lly4gMFgACApKYl27dpVetz9+/eTn5/PkCFDzPYXFhbSpUsXK7wSIYQtSIAihLCKKzXO5ubmMnToUIYOHcqXX35J48aNSUpKYtiwYRQWFlb5PGMQ88svvxAVFWX2mKenZ90rLoSwCxKgCCGsomXLlri7u7NlyxZiYmIASE9P5/DhwwwYMICDBw9y7tw5XnrpJaKjowHYtm2b2TE8PDwAKCkpMe1r164dnp6eJCUlMWDAgHp6NUKI+iYBihDCKvz8/Lj33nt5/PHHadSoEWFhYTz99NO4uKjc/JiYGDw8PHj77bd58MEH2bt3Ly+88ILZMWJjY9HpdCxbtozrr78eb29v/P39mT59Ov/+978xGAxcffXVZGVlsWnTJvz8/Lj77rtt8XKFEBYmo3iEEFbz6quv0r9/f8aMGcPgwYO5+uqr6datGwCNGzfm008/5bvvvqNdu3a89NJLvPbaa2bPj4qKYvbs2Tz11FOEhYXxyCOPAPDCCy/w3HPPMXfuXNq2bcuwYcNYunQpzZo1q/fXKISwDhnFI4QQQgi7Iy0oQgghhLA7EqAIIYQQwu5IgCKEEEIIuyMBihBCCCHsjgQoQgghhLA7EqAIIYQQwu5IgCKEEEIIuyMBihBCCCHsjgQoQgghhLA7EqAIIYQQwu5IgCKEEEIIuyMBihBCCCHszv8DI6Era6UwNTwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df.damage_level.str.contains('D')].resample('Y').aircraft.count().plot(label='destroyed')\n",
    "df[df.damage_level.str.contains('S')].resample('Y').aircraft.count().plot(label='significant')\n",
    "plt.legend()\n",
    "plt.title('High Damage numbers')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "2325a39a-8d48-49bd-a5fe-31d6efa58ffb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.resample('Y').nr_fatalities.sum().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "c7ef6e8d-00a9-44ec-9ec4-95c37e947049",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.resample('Y').nr_injuries.sum().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "33030ae1-fce1-4d78-8a25-55baa49c00e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='size_of_species', ylabel='Count'>"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=df.size_of_species)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "1393f0a6-0b6d-43d2-8def-49fee3ffab4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='time_of_day', ylabel='Count'>"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=df.time_of_day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "f6d60e4c-636f-4820-a77f-d36710967455",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ZZZZ    34302\n",
       "KDEN     9260\n",
       "KDFW     7527\n",
       "KORD     6065\n",
       "KJFK     5882\n",
       "        ...  \n",
       "T40         1\n",
       "LIRP        1\n",
       "KTLR        1\n",
       "OI65        1\n",
       "PALP        1\n",
       "Name: airport_id, Length: 2591, dtype: int64"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.airport_id.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "7ede2a27-9e4c-4f26-a0d4-e308c142e219",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = w.wrangle_wildlife_strike_df()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "764a76b8-c36f-45f7-a77e-0a48c208babd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time_of_day</th>\n",
       "      <th>airport_id</th>\n",
       "      <th>airport</th>\n",
       "      <th>runway</th>\n",
       "      <th>state</th>\n",
       "      <th>opid</th>\n",
       "      <th>operator</th>\n",
       "      <th>aircraft</th>\n",
       "      <th>ac_class</th>\n",
       "      <th>ac_mass</th>\n",
       "      <th>type_eng</th>\n",
       "      <th>num_engs</th>\n",
       "      <th>phase_of_flight</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>damage_level</th>\n",
       "      <th>species_id</th>\n",
       "      <th>species</th>\n",
       "      <th>size_of_species</th>\n",
       "      <th>nr_injuries</th>\n",
       "      <th>nr_fatalities</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-01-02</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>KJFK</td>\n",
       "      <td>JOHN F KENNEDY INTL</td>\n",
       "      <td>31L</td>\n",
       "      <td>NY</td>\n",
       "      <td>FDX</td>\n",
       "      <td>FEDEX EXPRESS</td>\n",
       "      <td>DC-10</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>D</td>\n",
       "      <td>3</td>\n",
       "      <td>Departure</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>N</td>\n",
       "      <td>UNKB</td>\n",
       "      <td>Unknown bird</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-02</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>PHLI</td>\n",
       "      <td>LIHUE ARPT</td>\n",
       "      <td>99</td>\n",
       "      <td>HI</td>\n",
       "      <td>UNK</td>\n",
       "      <td>UNKNOWN</td>\n",
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       "      <td>Unknown</td>\n",
       "      <td>N</td>\n",
       "      <td>R1101</td>\n",
       "      <td>Barn owl</td>\n",
       "      <td>Small</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-04</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>PHLI</td>\n",
       "      <td>LIHUE ARPT</td>\n",
       "      <td>99</td>\n",
       "      <td>HI</td>\n",
       "      <td>HAL</td>\n",
       "      <td>HAWAIIAN AIR</td>\n",
       "      <td>DC-9</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>D</td>\n",
       "      <td>2</td>\n",
       "      <td>Landing Roll</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>N</td>\n",
       "      <td>ZY1</td>\n",
       "      <td>Munias</td>\n",
       "      <td>Small</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-05</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>PHLI</td>\n",
       "      <td>LIHUE ARPT</td>\n",
       "      <td>99</td>\n",
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       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>N</td>\n",
       "      <td>N5122</td>\n",
       "      <td>Pacific golden-plover</td>\n",
       "      <td>Small</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>Unknown</td>\n",
       "      <td>PHLI</td>\n",
       "      <td>LIHUE ARPT</td>\n",
       "      <td>99</td>\n",
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       "      <td>ZY1</td>\n",
       "      <td>Munias</td>\n",
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       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "           time_of_day airport_id              airport runway state opid  \\\n",
       "date                                                                       \n",
       "1990-01-02     Unknown       KJFK  JOHN F KENNEDY INTL    31L    NY  FDX   \n",
       "1990-01-02     Unknown       PHLI           LIHUE ARPT     99    HI  UNK   \n",
       "1990-01-04     Unknown       PHLI           LIHUE ARPT     99    HI  HAL   \n",
       "1990-01-05     Unknown       PHLI           LIHUE ARPT     99    HI  UNK   \n",
       "1990-01-05     Unknown       PHLI           LIHUE ARPT     99    HI  UNK   \n",
       "\n",
       "                 operator aircraft ac_class  ac_mass type_eng  num_engs  \\\n",
       "date                                                                      \n",
       "1990-01-02  FEDEX EXPRESS    DC-10      A          4        D         3   \n",
       "1990-01-02        UNKNOWN  UNKNOWN        Z        0        Z        -1   \n",
       "1990-01-04   HAWAIIAN AIR     DC-9      A          4        D         2   \n",
       "1990-01-05        UNKNOWN  UNKNOWN        Z        0        Z        -1   \n",
       "1990-01-05        UNKNOWN  UNKNOWN        Z        0        Z        -1   \n",
       "\n",
       "           phase_of_flight precipitation damage_level species_id  \\\n",
       "date                                                               \n",
       "1990-01-02       Departure       Unknown            N       UNKB   \n",
       "1990-01-02         Unknown       Unknown            N      R1101   \n",
       "1990-01-04    Landing Roll       Unknown            N        ZY1   \n",
       "1990-01-05         Unknown       Unknown            N      N5122   \n",
       "1990-01-05         Unknown       Unknown            N        ZY1   \n",
       "\n",
       "                          species size_of_species  nr_injuries  nr_fatalities  \n",
       "date                                                                           \n",
       "1990-01-02           Unknown bird         Unknown            0              0  \n",
       "1990-01-02               Barn owl           Small            0              0  \n",
       "1990-01-04                 Munias           Small            0              0  \n",
       "1990-01-05  Pacific golden-plover           Small            0              0  \n",
       "1990-01-05                 Munias           Small            0              0  "
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "27f0ddfd-ed0d-4e8f-82f3-fc414e7b1936",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 279942 entries, 1990-01-02 to 2023-05-17\n",
      "Data columns (total 20 columns):\n",
      " #   Column           Non-Null Count   Dtype \n",
      "---  ------           --------------   ----- \n",
      " 0   time_of_day      279942 non-null  object\n",
      " 1   airport_id       279942 non-null  object\n",
      " 2   airport          279942 non-null  object\n",
      " 3   runway           279942 non-null  object\n",
      " 4   state            279942 non-null  object\n",
      " 5   opid             279942 non-null  object\n",
      " 6   operator         279942 non-null  object\n",
      " 7   aircraft         279942 non-null  object\n",
      " 8   ac_class         279942 non-null  object\n",
      " 9   ac_mass          279942 non-null  int64 \n",
      " 10  type_eng         279942 non-null  object\n",
      " 11  num_engs         279942 non-null  int64 \n",
      " 12  phase_of_flight  279942 non-null  object\n",
      " 13  precipitation    279942 non-null  object\n",
      " 14  damage_level     279942 non-null  object\n",
      " 15  species_id       279942 non-null  object\n",
      " 16  species          279942 non-null  object\n",
      " 17  size_of_species  279942 non-null  object\n",
      " 18  nr_injuries      279942 non-null  int64 \n",
      " 19  nr_fatalities    279942 non-null  int64 \n",
      "dtypes: int64(4), object(16)\n",
      "memory usage: 44.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7151e87-0763-4bc4-89d8-857be53a7c4a",
   "metadata": {},
   "source": [
    "# BOTTOM LINE ON BOTTOM:\n",
    "- My target will be damage level. This will be a classification project\n",
    "    - (may be difficult since I'll essentially be trying to predict outliers)\n",
    "- categories will be \n",
    "    - D - Aircraft Destroyed\n",
    "    - S - Substantial Damage\n",
    "    - M - Minor Damage\n",
    "    - N - Negligible consisting of these values from the original database\n",
    "        - N - None\n",
    "        - U - Unknown (nulls)\n",
    "        - M? - Undetermined level (per database)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d7f2487-e150-4565-bdd3-f5afe0a93099",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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