BiggerDataSet.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The City of Chicago has made it's police database available from it's city data hub.\n",
    "\n",
    "I'm curious about what it shows about accidents involving bicycles and, specifically, what happened during covid.\n",
    "\n",
    "Allegedly, more people started biking in 2020 because public transit was either limited or perceived to be unsafe; many bike shops reported they couldn't keep up with demand (with supply chain problems doubtless contributing to the problem).\n",
    "\n",
    "But drivers -- taking advantage of fewer cars on the road -- allegedly began to drive faster and more recklessly.\n",
    "\n",
    "Questions:\n",
    "    1) Was there an increase in the amount of bicycling being done during 2020? This question is better answered by analyzing DIVVY data. This cannot show how private bike use changed, but it can show whether there was an uptick in the amount of useage with the DIVVY system.\n",
    "    2) Was there an increase in the number of bike accidents in 2020? This might suggest either/both more bikes on the road and/or more reckless driving being done.\n",
    "\n",
    "To access the Chicago databases, one needs an API app token. One needs to create an account on their website (https://data.cityofchicago.org/login) and then go to the developer settings page (https://data.cityofchicago.org/profile/edit/developer_settings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "from sodapy import Socrata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get API tokens\n",
    "with open('.env.development') as f:\n",
    "    data = f.read()\n",
    "\n",
    "for line in data.split('\\n'):\n",
    "    head,sep,tail = line.partition(' = ')\n",
    "    \n",
    "    if head == 'crashAPIkey':\n",
    "        crashAPIkey = tail\n",
    "    elif head == 'weatherAPIkey':\n",
    "        weatherAPIkey = tail"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One can query the city's database with a URL like this one:\n",
    "    url= \"https://data.cityofchicago.org/resource/85ca-t3if.json?$$app_token=AppTok$where=crash_date%20between$20'2021-01-01T00:00:00'%20and%20'2022-01-01T00:00:00'\"\n",
    "\n",
    "but it is clearer to use the Socrata client and sodapy. The syntax for it is a little confusing and the next few cells show my trial- and error-efforts to get the syntax correct.\n",
    "\n",
    "One problem is that the URL method returns a JSON object, which Pandas does a better job of turning into a DF: numbers are recognized as numbers, mostly.\n",
    "\n",
    "The Socrata client returns a list/dictionary and Pandas turns all the fields into string objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "client=Socrata('data.cityofchicago.org',crashAPIkey)\n",
    "# chicago data, App_token\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#results=client.get('85ca-t3if',limit=2000)\n",
    "# this works - pulls 2K crash records\n",
    "#len(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#results2 = client.get('85ca-t3if',where=\"first_crash_type== 'PEDALCYCLIST'\",limit=2000)\n",
    "#len(results2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#results3 = client.get('85ca-t3if',where= \"crash_date>'21-01-01T00:00:00'\",limit=2000)\n",
    "#len(results3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above cells are my experiments to get the syntax of the query correct. The documentation isn't terribly helpful about how to format the \"where\" statement. What I found was:\n",
    "\n",
    "* field names don't need to be in quotes but the criteria do:\n",
    "    * `first_crash_type` (field name) `== 'PEDALCYCLIST'` (criteria)\n",
    "* Socrata supports multiple criterial; each needs to follow the above format:\n",
    "    * `where= \"crash_date>'19-01-01T00:00:00'\" and \"first_crash_type== 'PEDALCYCLIST'\"`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = client.get('85ca-t3if', \n",
    "    where= \"crash_date>'22-01-01T00:00:00'\" and \"first_crash_type== 'PEDALCYCLIST'\",\n",
    "    limit = 50000)\n",
    "# returns a JSON object: list of DICT entries, one per crash record "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10063"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crashes = pd.DataFrame.from_dict(results)\n",
    "len(crashes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "crash_record_id                  object\n",
       "crash_date                       object\n",
       "posted_speed_limit               object\n",
       "traffic_control_device           object\n",
       "device_condition                 object\n",
       "weather_condition                object\n",
       "lighting_condition               object\n",
       "first_crash_type                 object\n",
       "trafficway_type                  object\n",
       "alignment                        object\n",
       "roadway_surface_cond             object\n",
       "road_defect                      object\n",
       "report_type                      object\n",
       "crash_type                       object\n",
       "damage                           object\n",
       "date_police_notified             object\n",
       "prim_contributory_cause          object\n",
       "sec_contributory_cause           object\n",
       "street_no                        object\n",
       "street_direction                 object\n",
       "street_name                      object\n",
       "beat_of_occurrence               object\n",
       "dooring_i                        object\n",
       "num_units                        object\n",
       "most_severe_injury               object\n",
       "injuries_total                   object\n",
       "injuries_fatal                   object\n",
       "injuries_incapacitating          object\n",
       "injuries_non_incapacitating      object\n",
       "injuries_reported_not_evident    object\n",
       "injuries_no_indication           object\n",
       "injuries_unknown                 object\n",
       "crash_hour                       object\n",
       "crash_day_of_week                object\n",
       "crash_month                      object\n",
       "latitude                         object\n",
       "longitude                        object\n",
       "location                         object\n",
       "intersection_related_i           object\n",
       "hit_and_run_i                    object\n",
       "statements_taken_i               object\n",
       "rd_no                            object\n",
       "crash_date_est_i                 object\n",
       "private_property_i               object\n",
       "photos_taken_i                   object\n",
       "work_zone_i                      object\n",
       "work_zone_type                   object\n",
       "workers_present_i                object\n",
       "lane_cnt                         object\n",
       "dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crashes.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since Jan 2019 to July 2022, there are only 9253 reports of crashes involving bicyclists? This seems low. Let's chart it by year to see if there are major differences. . .\n",
    "\n",
    "First a bit of data cleaning, including changing the date field to datetime format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "crashes.crash_date = pd.to_datetime(crashes.crash_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "crashes['crash_year'] = crashes['crash_date'].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "crashes['posted_speed_limit'] = pd.to_numeric(crashes['posted_speed_limit'])\n",
    "crashes['crash_month'] = pd.to_numeric(crashes['crash_month'])\n",
    "crashes['beat_of_occurrence'] = pd.to_numeric( crashes['beat_of_occurrence'] )\n",
    "crashes['injuries_total'] = pd.to_numeric(crashes['injuries_total'])\n",
    "crashes['crash_hour'] = pd.to_numeric( crashes['crash_hour'] )\n",
    "crashes['crash_year'] = pd.to_numeric( crashes['crash_year'] )\n",
    "crashes['crash_day_of_week'] = pd.to_numeric( crashes['crash_day_of_week'] )\n",
    "crashes['latitude'] = pd.to_numeric( crashes['latitude'] )\n",
    "crashes['longitude'] = pd.to_numeric( crashes['longitude'] )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "crash_record_id                          object\n",
       "crash_date                       datetime64[ns]\n",
       "posted_speed_limit                        int64\n",
       "traffic_control_device                   object\n",
       "device_condition                         object\n",
       "weather_condition                        object\n",
       "lighting_condition                       object\n",
       "first_crash_type                         object\n",
       "trafficway_type                          object\n",
       "alignment                                object\n",
       "roadway_surface_cond                     object\n",
       "road_defect                              object\n",
       "report_type                              object\n",
       "crash_type                               object\n",
       "damage                                   object\n",
       "date_police_notified                     object\n",
       "prim_contributory_cause                  object\n",
       "sec_contributory_cause                   object\n",
       "street_no                                object\n",
       "street_direction                         object\n",
       "street_name                              object\n",
       "beat_of_occurrence                        int64\n",
       "dooring_i                                object\n",
       "num_units                                object\n",
       "most_severe_injury                       object\n",
       "injuries_total                            int64\n",
       "injuries_fatal                           object\n",
       "injuries_incapacitating                  object\n",
       "injuries_non_incapacitating              object\n",
       "injuries_reported_not_evident            object\n",
       "injuries_no_indication                   object\n",
       "injuries_unknown                         object\n",
       "crash_hour                                int64\n",
       "crash_day_of_week                         int64\n",
       "crash_month                               int64\n",
       "latitude                                float64\n",
       "longitude                               float64\n",
       "location                                 object\n",
       "intersection_related_i                   object\n",
       "hit_and_run_i                            object\n",
       "statements_taken_i                       object\n",
       "rd_no                                    object\n",
       "crash_date_est_i                         object\n",
       "private_property_i                       object\n",
       "photos_taken_i                           object\n",
       "work_zone_i                              object\n",
       "work_zone_type                           object\n",
       "workers_present_i                        object\n",
       "lane_cnt                                 object\n",
       "crash_year                                int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crashes.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: title={'center': 'Annual N of Reported Crashes'}>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline \n",
    "crashes['crash_year'].value_counts().sort_index().plot.bar(title='Annual N of Reported Crashes')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hard to know how to interpret this. The dataset was created in October 2017 (https://data.cityofchicago.org/Transportation/Traffic-Crashes-Crashes/85ca-t3if), so it's likely that the data from before January 2018 is not recorded in the same fashion. Let's dump the pre-2018 data from our analysis. . . "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1755"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(crashes[crashes['crash_year']<2018])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we're going to dump roughly 1700 records."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8308"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crashes = crashes[crashes['crash_year']>2017]\n",
    "len(crashes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: title={'center': 'Annual N of Reported Crashes'}>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "crashes['crash_year'].value_counts().sort_index().plot.bar(title='Annual N of Reported Crashes')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This graph shows that there were significantly fewer crashes reported in 2020 than in the two years prior. 2021 saw an increase, but not to the prior levels.\n",
    "\n",
    "Let's break it out by month, so we can compare what's going on annually . . "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: title={'center': 'Bicycle crashes by Month'}>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "crashes['crash_month'].value_counts().sort_index().plot.bar(title='Bicycle crashes by Month')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To do this, we can use a pivot table to summarize the data, annually and by month.\n",
    "\n",
    "In a lot of records (>2000!), the \"injuries_total\" is zero, so it's better to count them then to sum them!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "crashes_pivoted = crashes.pivot_table(index = 'crash_month',\n",
    "    columns = 'crash_year', \n",
    "    values = 'injuries_total',\n",
    "    aggfunc = 'count'\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>crash_year</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "      <th>2022</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>crash_month</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>1</th>\n",
       "      <td>57.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>76.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>103.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>216.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>182.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>235.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>234.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>289.0</td>\n",
       "      <td>294.0</td>\n",
       "      <td>227.0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>233.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>304.0</td>\n",
       "      <td>291.0</td>\n",
       "      <td>243.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>229.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>253.0</td>\n",
       "      <td>257.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>233.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>173.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>160.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>97.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>68.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "crash_year    2018   2019   2020   2021   2022\n",
       "crash_month                                   \n",
       "1             57.0   44.0   46.0   38.0   28.0\n",
       "2             45.0   45.0   51.0   14.0   32.0\n",
       "3             76.0   76.0   55.0   70.0   76.0\n",
       "4            103.0  126.0   36.0  104.0  100.0\n",
       "5            216.0  158.0   90.0  144.0  182.0\n",
       "6            235.0  219.0  161.0  198.0  234.0\n",
       "7            289.0  294.0  227.0  229.0  233.0\n",
       "8            304.0  291.0  243.0  213.0  229.0\n",
       "9            253.0  257.0  191.0  203.0  233.0\n",
       "10           173.0  195.0  130.0  172.0  160.0\n",
       "11            97.0  107.0   86.0   86.0   97.0\n",
       "12            68.0   72.0   51.0   66.0    NaN"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crashes_pivoted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Month of Year')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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f+EYbp+TNjj766Dz55JNJkh//+Mf5P//n/2SPPfbIN77xjey555457rjj/KFyAd1222058MAD853vfCfbb799Dj744Nx8882pr69v62hswPnnn5+vf/3refnllzNu3LhcdNFFGTduXI444oiMGTMmP/7xj/PNb36zrWPyJieffHLGjRuXBx54IH/4wx8yZcqUPP7445k2bVr+9re/5eWXX86ZZ57Z1jF5kyeeeCKDBw/OmDFj8uCDD6a+vj719fV58MEHc+SRR+Z973tfnnjiibaOSTPV1tbm3HPPbesYvMkzzzyTD37wgxkwYEB69+6dI488Mi+99FLp/AsvvJCPfexjbZiwbViJuxn5zW9+s9Hzf/vb3/LVr37Vqs4C6du3b37/+99n6NChSV77gqX/+Z//yW9/+9vMnj07PXv2tBK3gCoqKjJv3rzstNNOqa+vT9euXfPHP/4xu+++e5JkwYIFGTFiROlP5ymOP/3pT/nCF76QT33qU5k4cWI6d+6czp075y9/+cs625lQDB06dEhtbW0qKyvz7ne/O1dddVU+/vGPl87fe++9Oeyww/zhV8H06NEjCxcuzIABA/KBD3wgJ554Yo477rjS+alTp+Zb3/pWHnnkkTZMyRu98Z+1NWvWZMaMGfnJT36S3//+9+nbt2+OOuqoHH300dlpp53aOipvsNNOO+Xiiy/OoYcemr/85S8ZNmxYrrnmmhxxxBFJkhkzZuTUU0/NX//61zZOyhv16NEjCxYsyI477pgkqa+vT7du3fL000+nb9++uf3223PUUUfln//8Zxsn5Y0+/vGPp2fPnvnpT3+a8vLyRueWL1+eI488Mq+88kpuvfXWNkrI+jz00EMbPf/YY4/l8MMP99/cBTNmzJgsWrQol19+eV588cWcfvrpKSsry2233ZZ3vOMdWbZsWfr167fF/UGzv1ezGTnkkENSVla20W9aLysr24SJ+E9eeeWVRn+9raysLFdeeWXGjh2bj370o5k6dWobpmNjXv9nqUOHDunWrVsqKipK53r16pW6urq2isZG7Lnnnpk3b15qamqyxx575Oc//7n/v9gOvP6MVq5cWdpu5nXvete78txzz7VFLDaiR48e+d///d8MGDAg//znP/PBD36w0fm99torixcvbqN0/CedO3fOZz/72Xz2s5/NkiVL8pOf/CRTpkzJhRde6D9yC2bp0qXZY489kiS77rprOnTokN122610/gMf+ECWLl3aRunYkMrKyjzzzDOlEnfZsmVZu3ZtqRjceeed88ILL7RlRNbjnnvuyR//+Md1CtwkKS8vzze/+c3stddebZCMjdltt9022JO8ftx/DxTP73//+8yYMaP077h77rknn/nMZzJ8+PDMmjUryZbZb9lOYTPSr1+/TJ8+vfTXOt78+vOf/9zWEXmTQYMG5YEHHljn+OWXX56DDz44//Vf/9UGqfhPdthhh0YrWubOnZvtt9++9H7JkiXrFE0Ux1ZbbZVrrrkmZ5xxRkaMGKGQaAf233//fOADH8jy5cuzaNGiRuf+/ve/+2KzAjrggANy5ZVXJkk++tGP5sYbb2x0/vrrr7eis53YfvvtM2HChCxevDgzZ85s6zi8SVVVVR599NEkyV//+te8+uqrpfdJ8sgjj6SysrKt4rEBhxxySE444YTMnDkzs2fPzhFHHJGPfvSj6d69e5Jk0aJFede73tXGKXmz3r1756mnntrg+aeeemqd716g7fXp0yc/+tGPsnjx4nVef/vb33LzzTe3dUTWo66uLu94xztK77t27Zrp06dnhx12yMc+9rE8++yzbZiu7ViJuxkZNmxY5s2bl4MPPni95//TKl02vf/+7//OL37xi3zxi19c59zll1+e+vr6XHXVVW2QjI058cQTGxV/u+yyS6Pzv/vd73ypWTtw2GGH5cMf/nDmzZuXAQMGtHUcNuCcc85p9P7NX2pw00035SMf+cimjEQTXHTRRdlnn33y0Y9+NHvssUcuueSS3HnnnRk8eHAWLVqU++67LzNmzGjrmLzBgAED0rFjxw2eLysra7SVCcVwxBFH5Mgjj8zBBx+cWbNm5dRTT83Xvva1PP/88ykrK8u3vvWtfPrTn27rmLzJ+eefn2eeeSaf+tSn8uqrr6a6ujrXXntt6XxZWVkmTpzYhglZny996Us58sgjc9ZZZ2X//fdP3759k7y2knrWrFk5//zzc9JJJ7VxSt5s2LBhWbp06Qb/9/6LL76oJymgHXfcMQ899FB23nnn0rFOnTrlhhtuyGc+85l88pOfbMN0bceeuJuRP/zhD1mxYkU+8YlPrPf8ihUr8sADD+SjH/3oJk4GAGxpXnzxxVx44YW56aab8re//S319fXp169f9tlnn4wbN6701+OAt66+vj4XXnhh5s6dmw996EM5/fTTc9111+XUU0/Nyy+/nE996lO5/PLL07Nnz7aOynqsXLkya9euXecPKCmuiy66KN/73vdSW1tb+qvcDQ0Nqaqqysknn5xTTz21jRPyZjNmzMiKFSvyhS98Yb3n//Wvf+U3v/lNxowZs4mTsTGnnXZa5s+fv949pteuXZvRo0fnpptu2uL2xFXiAgAAADTR4sWLS19iXFVVlYEDB7ZxIti8rF27Ni+//PJ696B+/fw///nPLe5vVNoTFwAAAKCJBg4cmOrq6lRXV5cK3KeffjrHHHNMGyejuTy3YurUqdMGC9wkeeaZZ3LuueduwkTFYCUuAAAA/397dxoS5drHcfx3q6lpirbRNhY17bbZZsuLlkNaWtEORWZWtJcQSlFEQ3tQURFkRY7QRqWYRFG0CCURLahlJa0UJJgthNoy6jwv4szTPE5WJ8nb53w/IOh9bf+Z6438uLhuAL8hLy9PERERvDS3nmHf6qd/677xYjMAAAAAAIAaZGVl1dj+9OnTP1QJfgX7Vj+xb55xEhcAAAAAAKAGXl5eMgxDNUUohmH8604Gmh37Vj+xb55xJy4AAAAAAEANWrZsqYyMDFVVVXn8uXPnTl2XCA/Yt/qJffOMEBcAAAAAAKAGffv21e3bt7/b/qNTg6gb7Fv9xL55xp24AAAAAAAANUhKSlJZWdl3261Wq65cufIHK8LPYN/qJ/bNM+7EBQAAAAAAAAAT4zoFAAAAAAAAADAxQlwAAAAAAAAAMDFCXAAAAAAAAAAwMUJcAAAAAAAAADAxQlwAAADgfxiGoczMzFqfNzMzU1arVd7e3kpMTKz1+QEAAPD/iRAXAAAAphAfHy/DMLRgwYJqbYsXL5ZhGIqPj6/VNdetW6fevXvX6pw1mT9/viZPnqyXL19q/fr1bm0lJSVq0aKFNm3aVG3c1KlTFRkZqcrKyj9VKgAAAEyEEBcAAACmYbFYdPz4cX38+NH17NOnTzp69KjCwsLqsLLfV1paquLiYkVFRalVq1YKCgpya2/atKn2798vm82mu3fvup6fPHlSZ86cUVpamry9vWu1psrKSlVVVdXqnAAAAKh9hLgAAAAwjYiICFksFmVkZLieZWRkKCwsTH369HHr+/nzZy1btkzNmzeXv7+/hg4dqps3b7ras7OzZRiGLl26pH79+ikgIECDBw9WYWGhJMlut8tmsykvL0+GYcgwDNntdtf4kpISTZgwQQEBAerYsaOysrJqrP3du3eKi4tTaGioAgICNHr0aD169MhVy9+h7YgRI2QYhrKzs6vNMW7cOE2fPl2zZs2Sw+HQ69evtXjxYm3ZskWdO3fW6dOnFRERIX9/f7Vv3142m00VFRWu8Tt27FCPHj0UGBgoi8WiRYsWqbS01NVut9sVEhKirKwsdevWTX5+fnrx4sUPdgUAAAB1jRAXAAAAppKQkKDU1FTX34cOHdLs2bOr9UtOTlZ6errS0tJ0584dWa1WRUVF6e3bt279Vq9ere3bt+vWrVvy8fFRQkKCJGnatGlasWKFunfvrqKiIhUVFWnatGmucTabTVOnTlV+fr7GjBmjGTNmVJv7W/Hx8bp165aysrJ0/fp1OZ1OjRkzRg6Hwy08Tk9PV1FRkQYPHuxxnl27dunNmzdav369Fi1apPDwcC1dulRXr15VXFycli9frvv37yslJUV2u10bN250jfXy8tLu3btVUFCgtLQ0Xb58WcnJyW7zl5eXa+vWrTp48KAKCgrUvHnz734mAAAAmIPhdDqddV0EAAAAEB8fr/fv3+vAgQOyWCyu0LNLly56+fKl5s6dq5CQENntdpWVlSk0NFR2u13Tp0+XJDkcDrVr106JiYlKSkpSdna2hg8frosXL2rkyJGSpLNnzyomJkYfP36Uv7+/1q1bp8zMTOXm5rrVYhiG1qxZ47q3tqysTI0aNdK5c+cUHR1drfZHjx6pU6dOysnJcYWzb968kcViUVpamqZMmaL3798rNDRUV65c0bBhw2r8Li5fvqxRo0YpMDBQ+fn5atu2rf766y+NHDlSq1atcvU7fPiwkpOT9erVK4/znDp1SgsWLFBJSYmkrydxZ8+erdzcXPXq1esHOwIAAACz8KnrAgAAAIBvNWvWTDExMbLb7XI6nYqJiVHTpk3d+jx58kQOh0NDhgxxPWvQoIEGDBigBw8euPXt2bOn6/eWLVtKkoqLi394x+634wIDAxUcHKzi4mKPfR88eCAfHx8NHDjQ9axJkybq3LlztXp+xogRIxQZGanevXurbdu2kqS8vDzl5OS4nbytrKzUp0+fVF5eroCAAF28eFGbN2/Ww4cP9eHDB1VUVLi1S5Kvr6/bZwMAAID5EeICAADAdBISErRkyRJJ0t69e39rrgYNGrh+NwxDkn7qZV7fjvt77J98CZiPj498fP7773ppaalsNpsmTpxYra+/v7+eP3+u2NhYLVy4UBs3blTjxo117do1zZkzR1++fHGFuA0bNnR9DwAAAKgfCHEBAABgOtHR0fry5YsMw1BUVFS19g4dOsjX11c5OTmuk6oOh0M3b95UYmLiT6/j6+urysr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      "text/plain": [
       "<Figure size 1700x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "crashes_pivoted.plot(kind ='bar',figsize=(17,10))\n",
    "plt.ylabel('Number of injuries')\n",
    "plt.xlabel('Month of Year')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This graph shows the effects of Covid on the accidents reporting starkly.\n",
    "\n",
    "Recall that in Jan and Feb, Covid was mostly seen as a foreign problem, first in China, then in Europe. On March 13, President Trump declared a nationwide emergency and states began to shutdown schools and businesses. Some states began to roll these restrictions back as early as late April, but Chicago continued to have strong restrictions throughout 2020. (https://www.cdc.gov/museum/timeline/covid19.html)\n",
    "\n",
    "This timeline is reflected in the graph above. In Jan and Feb, the N reported in 2020 was very similar to the figures from 2018 and 2019. In March, the number dips and in April and May plummets. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bike accident reports are not evenly distributed across the city. Rather, they tend to be clustered in certain police beats. Let's examine this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: title={'center': 'Pct of reports by police beat'}>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Tb7/9NkaPHo3JkydDLpcjOzsbSqUScXFxsLe3x8aNGxEWFobf/e53+POf/wxnZ2eUlpbiq6++wpNPPomPPvqoxWvdu3cP48ePx7Rp01BQUICPP/4Yo0aNQnBwMAC0+jr29vYYM2YM3nvvPdTW1qJXr144ePCgwTuYGnh7ewMA3nzzTfz5z3+GpaUlJk6c2KpJ1kSiDn02iuhX1PA4aXZ2dot1r1+/LsybN0/o1auXYGVlJfTu3VuIiIgQrl27JtZJTU0VBg8eLFhYWLTqceu8vDwhICBAsLOzE2xtbQVfX1/h+PHjBnUe5BHr3bt3N1l+8uRJYfLkyYKTk5Mgl8sFNzc3Ydq0acJ//vMfsU7DY71nzpwRpkyZInTt2lXo1q2bMG/ePOHOnTsG7dXW1gorVqwQ3N3dBUtLS0GlUgkxMTHC3bt3Deq5ubkJEyZMaDKmH374QRgzZoxgY2MjABAiIiIEvV4vREdHC15eXkLXrl2FLl26CF5eXsLHH3/c4s/AmPgbNDw+/O6777bY/i/7dODAAeHRRx8V5HK5MGjQoCZ/9ufPnxemTJkiODg4CNbW1sLw4cOFvXv3GtRpzSPWDbZs2SI8/vjjglwuF7p16yaMHTtWOHTokEGd9PR0ISAgQFAoFIK1tbXg4eEhzJgxQ8jJyblvvxr+mzh69KgQGRkpdOvWTbCzsxOmT58uXL9+vVH91lzn0qVLwp/+9CfBwcFBUCgUwtSpU4Xy8nIBgBAbG2vQ3ttvvy306tVLMDMz4+PW9EBkgvCLcT8i+s1Yvnw5VqxYgR9//FF84ZipW79+PRYtWoSSkhKo1epWndOnTx8MHToUe/fubefoiMgYnBNDRL8ZgiDg73//O8aOHdvqBIaIOi/OiSEik6fT6ZCWlob09HScOnUKqampHR0SEbUBJjFEZPJ+/PFHPPfcc3BwcMAbb7whTlglImnjnBgiIiKSJM6JISIiIkliEkNERESSZLJzYurr61FeXo6uXbvy9dZEREQSIQgCbt26BaVS2eILIk02iSkvL2/T9WiIiIjo11NWVobevXvft47JJjFdu3YF8NMPoS3XpSEiIqL2U11dDZVKJX6P34/JJjENt5Ds7e2ZxBAREUlMa6aCcGIvERERSZLJjsQ0GBp7AGZy244Og0hSSlZN6OgQiIhaZPRITGZmJiZOnAilUgmZTIaUlBSDcplM1uS2evVqsU5wcDDUajWsra3h6uqKsLAwlJeXi+UFBQXw9fVFz549YW1tjb59+2LJkiWora198J4SERGRSTE6idHpdPDy8kJCQkKT5RUVFQbbli1bIJPJ8Mwzz4h1fH19sWvXLhQUFGDPnj04f/48pkyZIpZbWloiPDwcBw8eREFBAdatW4dPP/0UsbGxD9BFIiIiMkVG304KCgpCUFBQs+UuLi4G+6mpqfD19UXfvn3FY4sWLRL/3c3NDa+//jpCQkJQW1sLS0tL9O3b16C+m5sbMjIycOzYMWPDJSIiIhPVrnNirly5gq+++grbtm1rtk5VVRV27NgBHx8fWFpaNlmnqKgI+/fvx+TJk5ttR6/XQ6/Xi/vV1dUPHjgRERF1eu36dNK2bdvQtWvXJpOPxYsXo0uXLnByckJpaSlSU1Mb1fHx8YG1tTX69++P0aNH46233mr2WnFxcVAoFOLGF90RERGZtnZNYrZs2YLp06fD2tq6UVl0dDROnjyJgwcPwtzcHOHh4fjlgto7d+5EXl4e/vGPf+Crr77CmjVrmr1WTEwMtFqtuJWVlbV5f4iIiKjzaLfbSceOHUNBQQF27tzZZHn37t3RvXt3DBgwAJ6enlCpVMjKysLIkSPFOg2jKYMHD0ZdXR0iIyPx2muvwdzcvFF7crkccrm8fTpDREREnU67jcT8/e9/h7e3N7y8vFqsW19fDwAGc1qaqlNbWyvWJSIiot82o0diampqUFRUJO4XFxdDo9HA0dERarUawE+Tanfv3o3333+/0fknTpxAdnY2Ro0ahW7duuH8+fNYunQpPDw8xFGYHTt2wNLSEo888gjkcjlycnIQExODZ599ttnJv0RERPTbYnQSk5OTA19fX3E/KioKABAREYHExEQAwJdffglBEBAaGtrofFtbWyQlJSE2NhY6nQ6urq4IDAzEkiVLxNtBFhYWiI+Px7lz5yAIAtzc3DBv3jyDR7NbK39FANdOIiIiMkEy4ZezaU1EdXU1FAoFtFotkxgiIiKJMOb7mwtAEhERkSQxiSEiIiJJYhJDREREksQkhoiIiCSJSQwRERFJEpMYIiIikiQmMURERCRJ7bZ2UmcxNPYAzOS2HR0GkWSVrJrQ0SEQETXJ6JGYzMxMTJw4EUqlEjKZDCkpKQblV65cwYwZM6BUKmFra4vAwEAUFhaK5VVVVXj11VcxcOBA2NjYQK1WY/78+dBqtWKd69evIzAwEEqlEnK5HCqVCvPmzUN1dfWD95SIiIhMitFJjE6ng5eXFxISEhqVCYKAkJAQXLhwAampqTh58iTc3Nzg5+cHnU4HACgvL0d5eTnWrFmD/Px8JCYmYv/+/Zg1a9b/gjIzw6RJk5CWloZz584hMTERhw8fxpw5cx6iq0RERGRKHmrZAZlMhuTkZISEhAAAzp07h4EDByI/Px9DhgwB8NPq0y4uLnj33Xcxe/bsJtvZvXs3nn/+eeh0OlhYNH2H68MPP8Tq1atRVlbWZLlerzdYBbu6uhoqlQqqhbt4O4noIfB2EhH9mjps2YGGJMLa2vp/FzAzg1wux9dff93seQ2BNpfAlJeXIykpCWPHjm22jbi4OCgUCnFTqVQP2AsiIiKSgjZNYgYNGgS1Wo2YmBjcuHED9+7dQ3x8PC5duoSKioomz7l27RrefvttREZGNioLDQ2Fra0tevXqBXt7e/ztb39r9toxMTHQarXi1tyIDREREZmGNk1iLC0tkZSUhHPnzsHR0RG2trZIT09HUFAQzMwaX6q6uhoTJkzA4MGDsXz58kbla9euRV5eHlJTU3H+/HlERUU1e225XA57e3uDjYiIiExXmz9i7e3tDY1GA61Wi3v37sHZ2RkjRozAsGHDDOrdunULgYGB6Nq1K5KTk2FpadmoLRcXF7i4uGDQoEFwdHTE6NGjsXTpUri6urZ12ERERCQx7fayO4VCAWdnZxQWFiInJweTJk0Sy6qrq+Hv7w8rKyukpaUZzKFpTn19PQAYTN4lIiKi3y6jR2JqampQVFQk7hcXF0Oj0cDR0RFqtRq7d++Gs7Mz1Go1Tp06hQULFiAkJAT+/v4A/pfA3L59G9u3b0d1dbX4/hdnZ2eYm5vj3//+N65cuYInnngCdnZ2OH36NKKjo/Hkk0+iT58+RsWbvyKAt5aIiIhMkNFJTE5ODnx9fcX9hnkqERERSExMREVFBaKionDlyhW4uroiPDwcS5cuFevn5eXhxIkTAIB+/foZtF1cXIw+ffrAxsYGn376KRYtWgS9Xg+VSoXJkyfj9ddff6BOEhERkel5qPfEdGbGPGdOREREnUOHvSeGiIiI6NfCJIaIiIgkiUkMERERSRKTGCIiIpIkJjFEREQkSUxiiIiISJLafNmBzmZo7AGYyW07OgwiySpZNaGjQyAiapLRIzGZmZmYOHEilEolZDIZUlJSDMpramowb9489O7dGzY2Nhg8eDA++eQTgzovvfQSPDw8YGNjA2dnZ0yaNAk//PCDWP7dd98hNDQUKpUKNjY28PT0xPr16x+sh0RERGSSjE5idDodvLy8kJCQ0GR5VFQU9u/fj+3bt+Ps2bNYuHAh5s2bh7S0NLGOt7c3tm7dirNnz+LAgQMQBAH+/v6oq6sDAOTm5qJHjx7Yvn07Tp8+jTfffBMxMTH46KOPHrCbREREZGoe6o29MpkMycnJCAkJEY8NHToUzz77rMFSA97e3ggKCsI777zTZDvff/89vLy8UFRUBA8PjybrzJ07F2fPnsWRI0daFVvDG/9UC3fxdhLRQ+DtJCL6NXXoG3t9fHyQlpaGy5cvQxAEpKen49y5c+ICkL+k0+mwdetWuLu7Q6VSNduuVquFo6Njs+V6vV5cTPLni0oSERGRaWrzJGbDhg0YPHgwevfuDSsrKwQGBiIhIQFjxowxqPfxxx/Dzs4OdnZ22LdvHw4dOgQrK6sm2zx+/Dh27tyJyMjIZq8bFxcHhUIhbvdLiIiIiEj62iWJycrKQlpaGnJzc/H+++9j7ty5OHz4sEG96dOn4+TJkzh69CgGDBiAadOm4e7du43ay8/Px6RJkxAbG9vsaA4AxMTEQKvViltZWVlbd42IiIg6kTZ9xPrOnTt44403kJycjAkTfrqP/uijj0Kj0WDNmjXw8/MT6zaMmPTv3x+///3v0a1bNyQnJyM0NFSsc+bMGYwfPx6RkZFYsmTJfa8tl8shl8vbsjtERETUibXpSExtbS1qa2thZmbYrLm5Oerr65s9TxAECIIAvV4vHjt9+jR8fX0RERGBlStXtmWYREREZAKMHompqalBUVGRuF9cXAyNRgNHR0eo1WqMHTsW0dHRsLGxgZubG44ePYrPPvsMH3zwAQDgwoUL2LlzJ/z9/eHs7IxLly5h1apVsLGxwdNPPw3gp1tITz31FAICAhAVFYXKykoAPyVDzs7ORsWbvyKgxdnNREREJD1GP2KdkZEBX1/fRscjIiKQmJiIyspKxMTE4ODBg6iqqoKbmxsiIyOxaNEiyGQylJeXY/bs2cjNzcWNGzfQs2dPjBkzBsuWLcPAgQMBAMuXL8eKFSsaXcPNzQ0lJSWtitOYR7SIiIioczDm+/uh3hPTmTGJISIikp4OfU8MERER0a+BSQwRERFJEpMYIiIikiQmMURERCRJTGKIiIhIkpjEEBERkSQxiSEiIiJJatO1kzqjobEHYCa37egwiExCyaoJHR0CEZHI6JGYzMxMTJw4EUqlEjKZDCkpKQblMpmsyW316tVinZUrV8LHxwe2trZwcHBodI3vvvsOoaGhUKlUsLGxgaenJ9avX29054iIiMh0GZ3E6HQ6eHl5ISEhocnyiooKg23Lli2QyWR45plnxDr37t3D1KlT8fLLLzfZRm5uLnr06IHt27fj9OnTePPNNxETE4OPPvrI2HCJiIjIRBl9OykoKAhBQUHNlru4uBjsp6amwtfXF3379hWPNayLlJiY2GQbL7zwgsF+37598c033yApKQnz5s1r8hy9Xm+wCnZ1dfV9+0FERETS1q4Te69cuYKvvvoKs2bNeui2tFotHB0dmy2Pi4uDQqEQN5VK9dDXJCIios6rXZOYbdu2oWvXrpg8efJDtXP8+HHs3LkTkZGRzdaJiYmBVqsVt7Kysoe6JhEREXVu7fp00pYtWzB9+nRYW1s/cBv5+fmYNGkSYmNj4e/v32w9uVwOuVz+wNchIiIiaWm3JObYsWMoKCjAzp07H7iNM2fOYPz48YiMjMSSJUvaMDoiIiKSuna7nfT3v/8d3t7e8PLyeqDzT58+DV9fX0RERGDlypVtHB0RERFJndEjMTU1NSgqKhL3i4uLodFo4OjoCLVaDeCnJ4N2796N999/v8k2SktLUVVVhdLSUtTV1UGj0QAA+vXrBzs7O+Tn5+Opp55CQEAAoqKiUFlZCQAwNzeHs7OzUfHmrwiAvb29sd0kIiKiTk4mCIJgzAkZGRnw9fVtdDwiIkJ8ZHrz5s1YuHAhKioqoFAoGtWdMWMGtm3b1uh4eno6xo0bh+XLl4uPYf+cm5sbSkpKWhVndXU1FAoFtFotkxgiIiKJMOb72+gkRiqYxBAREUmPMd/fXACSiIiIJIlJDBEREUkSkxgiIiKSJCYxREREJElMYoiIiEiSmMQQERGRJLXr2kmdwdDYAzCT23Z0GEQmpWTVhI4OgYjI+JGYzMxMTJw4EUqlEjKZDCkpKWJZbW0tFi9ejEceeQRdunSBUqlEeHg4ysvLDdoIDg6GWq2GtbU1XF1dERYWZlCnpKQEMpms0ZaVlfXgPSUiIiKTYnQSo9Pp4OXlhYSEhEZlt2/fRl5eHpYuXYq8vDwkJSWhoKAAwcHBBvV8fX2xa9cuFBQUYM+ePTh//jymTJnSqL3Dhw+joqJC3Ly9vY0Nl4iIiEyU0beTgoKCEBQU1GSZQqHAoUOHDI599NFHGD58OEpLS8W1lRYtWiSWu7m54fXXX0dISAhqa2thaWkpljk5OcHFxcXYEImIiOg3oN0n9mq1WshkMjg4ODRZXlVVhR07dsDHx8cggQF+uu3Uo0cPjBo1Cmlpafe9jl6vR3V1tcFGREREpqtdk5i7d+9i8eLFCA0NbbT+weLFi9GlSxc4OTmhtLQUqampYpmdnR3ef/997N69G1999RVGjRqFkJCQ+yYycXFxUCgU4qZSqdqtX0RERNTxHmoBSJlMhuTkZISEhDQqq62txTPPPINLly4hIyOjURJz7do1VFVV4eLFi1ixYgUUCgX27t0LmUzW5LXCw8NRXFyMY8eONVmu1+uh1+vF/erqaqhUKqgW7uLTSURtjE8nEVF7MWYByHZ5xLq2thbTpk3DxYsXceTIkSaD6N69O7p3744BAwbA09MTKpUKWVlZGDlyZJNtjhgxotF8m5+Ty+WQy+Vt1gciIiLq3No8iWlIYAoLC5Geng4nJ6cWz6mvrwcAg5GUX9JoNHB1dW2zOImIiEjajE5iampqUFRUJO4XFxdDo9HA0dERrq6umDJlCvLy8rB3717U1dWhsrISAODo6AgrKyucOHEC2dnZGDVqFLp164bz589j6dKl8PDwEEdhtm3bBisrKzz++OMAgKSkJGzZsgV/+9vfjO5g/oqAFoejiIiISHqMTmJycnLg6+sr7kdFRQEAIiIisHz5cnHy7WOPPWZwXnp6OsaNGwdbW1skJSUhNjYWOp0Orq6uCAwMxJIlSwxuB7399tu4ePEiLCwsMGjQIOzcubPJd8kQERHRb9NDTeztzIyZGERERESdgzHf31wAkoiIiCSJSQwRERFJEpMYIiIikiQmMURERCRJTGKIiIhIkpjEEBERkSQxiSEiIiJJMvpld5mZmVi9ejVyc3NRUVHR7AKQADBnzhxs2rQJa9euxcKFC8XjwcHB0Gg0uHr1Krp16wY/Pz/Ex8dDqVQCAAoKCjBnzhycOXMGWq0WSqUSzz33HGJjY2FpaWlUvENjD3ABSKI2xgUgiagzMHokRqfTwcvLCwkJCfetl5ycjKysLDEx+TlfX1/s2rULBQUF2LNnD86fP2/wNl5LS0uEh4fj4MGDKCgowLp16/Dpp58iNjbW2HCJiIjIRBk9EhMUFISgoKD71rl8+TJeffVVHDhwABMmNP4/tkWLFon/7ubmhtdffx0hISGora2FpaUl+vbti759+xrUycjIwLFjx4wNl4iIiExUm69iXV9fj7CwMERHR2PIkCEt1q+qqsKOHTvg4+PT7K2ioqIi7N+/H5MnT262Hb1eb7AKdnV1tfHBExERkWS0+cTe+Ph4WFhYYP78+fett3jxYnTp0gVOTk4oLS1Fampqozo+Pj6wtrZG//79MXr0aLz11lvNthcXFweFQiFuKpXqoftCREREnVebJjG5ublYv349EhMTIZPJ7ls3OjoaJ0+exMGDB2Fubo7w8HD8ci3KnTt3Ii8vD//4xz/w1VdfYc2aNc22FxMTA61WK25lZWVt0iciIiLqnNr0dtKxY8dw9epVqNVq8VhdXR1ee+01rFu3DiUlJeLx7t27o3v37hgwYAA8PT2hUqmQlZWFkSNHinUaRlMGDx6Muro6REZG4rXXXoO5uXmja8vlcsjl8rbsDhEREXVibZrEhIWFwc/Pz+BYQEAAwsLCMHPmzGbPq6+vBwCDOS1N1amtrUV9fX2TSQwRERH9thidxNTU1KCoqEjcLy4uhkajgaOjI9RqNZycnAzqW1pawsXFBQMHDgQAnDhxAtnZ2Rg1ahS6deuG8+fPY+nSpfDw8BBHYXbs2AFLS0s88sgjkMvlyMnJQUxMDJ599lmj3xNDREREpsnoJCYnJwe+vr7iflRUFAAgIiICiYmJLZ5va2uLpKQkxMbGQqfTwdXVFYGBgViyZIl4O8jCwgLx8fE4d+4cBEGAm5sb5s2bZ/BodmvlrwiAvb290ecRERFR5yYTfjmb1kRUV1dDoVBAq9UyiSEiIpIIY76/uXYSERERSRKTGCIiIpIkJjFEREQkSUxiiIiISJKYxBAREZEkMYkhIiIiSWISQ0RERJLUpssOdEZDYw/ATG7b0WEQmZSSVRM6OgQiIuNHYjIzMzFx4kQolUrIZDKkpKQ0W3fOnDmQyWRYt26dwfGVK1fCx8cHtra2cHBwuO/1rl+/jt69e0Mmk+HmzZvGhktEREQmyugkRqfTwcvLCwkJCfetl5ycjKysLCiVykZl9+7dw9SpU/Hyyy+3eL1Zs2bh0UcfNTZMIiIiMnFG304KCgpCUFDQfetcvnwZr776Kg4cOIAJExoPO69YsQIAWlxraePGjbh58yaWLVuGffv2GRsqERERmbA2nxNTX1+PsLAwREdHY8iQIQ/czpkzZ/DWW2/hxIkTuHDhQov19Xo99Hq9uF9dXf3A1yYiIqLOr82fToqPj4eFhQXmz5//wG3o9XqEhoZi9erVUKvVrTonLi4OCoVC3FQq1QNfn4iIiDq/Nk1icnNzsX79eiQmJkImkz1wOzExMfD09MTzzz9v1DlarVbcysrKHvj6RERE1Pm1aRJz7NgxXL16FWq1GhYWFrCwsMDFixfx2muvoU+fPq1u58iRI9i9e7fYxvjx4wEA3bt3R2xsbJPnyOVy2NvbG2xERERkutp0TkxYWBj8/PwMjgUEBCAsLAwzZ85sdTt79uzBnTt3xP3s7Gy88MILOHbsGDw8PNosXiIiIpIuo5OYmpoaFBUVifvFxcXQaDRwdHSEWq2Gk5OTQX1LS0u4uLhg4MCB4rHS0lJUVVWhtLQUdXV10Gg0AIB+/frBzs6uUaJy7do1AICnp2eL75X5pfwVARyVISIiMkFGJzE5OTnw9fUV96OiogAAERERLT4y3WDZsmXYtm2buP/4448DANLT0zFu3DhjQyIiIqLfIJkgCEJHB9EeqquroVAooNVqORJDREQkEcZ8f3MBSCIiIpIkJjFEREQkSUxiiIiISJKYxBAREZEkMYkhIiIiSWISQ0RERJLEJIaIiIgkyeiX3WVmZmL16tXIzc1FRUUFkpOTERISIpbX1NTg9ddfR0pKCq5fvw53d3fMnz8fc+bMEets3rwZ//jHP5CXl4dbt27hxo0bzb6JV6/XY8SIEfjuu+9w8uRJPPbYY0bFOzT2AMzktsZ2k4juo2TVhI4OgYjI+JEYnU4HLy8vJCQkNFkeFRWF/fv3Y/v27Th79iwWLlyIefPmIS0tTaxz+/ZtBAYG4o033mjxen/961+hVCqNDZOIiIhMnNEjMUFBQQgKCmq2/Pjx44iIiBCXD4iMjMSmTZvw7bffIjg4GACwcOFCAEBGRsZ9r7Vv3z4cPHgQe/bswb59+4wNlYiIiExYm8+J8fHxQVpaGi5fvgxBEJCeno5z587B39/fqHauXLmCF198EZ9//jlsbVu+HaTX61FdXW2wERERkelq8yRmw4YNGDx4MHr37g0rKysEBgYiISEBY8aMaXUbgiBgxowZmDNnDoYNG9aqc+Li4qBQKMRNpVI9aBeIiIhIAtolicnKykJaWhpyc3Px/vvvY+7cuTh8+LBRbdy6dQsxMTGtPicmJgZarVbcysrKHiR8IiIikgij58Tcz507d/DGG28gOTkZEyb89PTCo48+Co1GgzVr1sDPz69V7Rw5cgTffPMN5HK5wfFhw4Zh+vTp2LZtW6Nz5HJ5o/pERERkuto0iamtrUVtbS3MzAwHeMzNzVFfX9/qdj788EO888474n55eTkCAgKwc+dOjBgxos3iJSIiIukyOompqalBUVGRuF9cXAyNRgNHR0eo1WqMHTsW0dHRsLGxgZubG44ePYrPPvsMH3zwgXhOZWUlKisrxXZOnTqFrl27Qq1Wi+38nJ2dHQDAw8MDvXv3fqCOEhERkWmRCYIgGHNCRkYGfH19Gx2PiIhAYmIiKisrERMTg4MHD6Kqqgpubm6IjIzEokWLIJPJAADLly/HihUrGrWxdetWzJgxo9HxkpISuLu7G/Wyu+rqaigUCmi1Wtjb2xvTRSIiIuogxnx/G53ESAWTGCIiIukx5vubaycRERGRJDGJISIiIkliEkNERESSxCSGiIiIJIlJDBEREUkSkxgiIiKSJCYxREREJEltuuxAZzQ09gDM5LYdHQYR/ULJqgkdHQIRSZzRIzGZmZmYOHEilEolZDIZUlJSDMqvXLmCGTNmQKlUwtbWFoGBgSgsLDSos3nzZowbNw729vaQyWS4efNmo+usXLkSPj4+sLW1hYODg7FhEhERkYkzOonR6XTw8vJCQkJCozJBEBASEoILFy4gNTUVJ0+ehJubG/z8/KDT6cR6t2/fRmBgIN54441mr3Pv3j1MnToVL7/8srEhEhER0W+A0beTgoKCEBQU1GRZYWEhsrKykJ+fjyFDhgAANm7cCBcXF3zxxReYPXs2AGDhwoUAflqHqTkNayslJiYaGyIRERH9BrTpxF69Xg8AsLa2/t8FzMwgl8vx9ddft+Wlmrx2dXW1wUZERESmq02TmEGDBkGtViMmJgY3btzAvXv3EB8fj0uXLqGioqItL9VIXFwcFAqFuKlUqna9HhEREXWsNk1iLC0tkZSUhHPnzsHR0RG2trZIT09HUFAQzMza92numJgYaLVacSsrK2vX6xEREVHHavNHrL29vaHRaKDVanHv3j04OztjxIgRGDZsWFtfyoBcLodcLm/XaxAREVHn0W7DIwqFAs7OzigsLEROTg4mTZrUXpciIiKi3yCjR2JqampQVFQk7hcXF0Oj0cDR0RFqtRq7d++Gs7Mz1Go1Tp06hQULFiAkJAT+/v7iOZWVlaisrBTbOXXqFLp27Qq1Wg1HR0cAQGlpKaqqqlBaWoq6ujpoNBoAQL9+/WBnZ9fqePNXBMDe3t7YbhIREVFnJxgpPT1dANBoi4iIEARBENavXy/07t1bsLS0FNRqtbBkyRJBr9cbtBEbG9tkG1u3bhXrRERENFknPT29VXFqtVoBgKDVao3tIhEREXUQY76/ZYIgCL965vQrqK6uhkKhgFar5UgMERGRRBjz/c0FIImIiEiSmMQQERGRJDGJISIiIkliEkNERESSxCSGiIiIJIlJDBEREUkSkxgiIiKSpDZfOwkAMjMzsXr1auTm5qKiogLJyckICQkBANTW1mLJkiX497//jQsXLkChUMDPzw+rVq2CUqkEAJSUlODtt9/GkSNHUFlZCaVSieeffx5vvvkmrKysjIplaOwBmMlt27qLRNROSlZN6OgQiEgi2mUkRqfTwcvLCwkJCY3Kbt++jby8PCxduhR5eXlISkpCQUEBgoODxTo//PAD6uvrsWnTJpw+fRpr167FJ598gjfeeKM9wiUiIiIJavc39spkMoORmKZkZ2dj+PDhuHjxItRqdZN1Vq9ejY0bN+LChQutum7DG/9UC3dxJIZIQjgSQ/TbZswbe9vldpKxtFotZDIZHBwc7lunYXHIpuj1euj1enG/urq6LUMkIiKiTqbDJ/bevXsXixcvRmhoaLMZV1FRETZs2ICXXnqp2Xbi4uKgUCjETaVStVfIRERE1Al0aBJTW1uLadOmQRAEbNy4sck6ly9fRmBgIKZOnYoXX3yx2bZiYmKg1WrFraysrL3CJiIiok6gw24nNSQwFy9exJEjR5ochSkvL4evry98fHywefPm+7Ynl8shl8vbK1wiIiLqZDokiWlIYAoLC5Geng4nJ6dGdS5fvgxfX194e3tj69atMDPr8DtfRERE1Im0SxJTU1ODoqIicb+4uBgajQaOjo5wdXXFlClTkJeXh71796Kurg6VlZUAAEdHR1hZWeHy5csYN24c3NzcsGbNGvz4449iWy4uLu0RMhEREUlMuzxinZGRAV9f30bHIyIisHz5cri7uzd5Xnp6OsaNG4fExETMnDmzyTqtDdeYR7SIiIioczDm+7vd3xPTUZjEEBERSY8x39+caEJERESSxCSGiIiIJIlJDBEREUkSkxgiIiKSJCYxREREJElMYoiIiEiSmMQQERGRJHXY2km/lqGxB2Amt+3oMIioEylZNaGjQyCiNmD0SExmZiYmTpwIpVIJmUyGlJQUg/IrV65gxowZUCqVsLW1RWBgIAoLC8XyqqoqvPrqqxg4cCBsbGygVqsxf/58aLVasc53332H0NBQqFQq2NjYwNPTE+vXr3/wXhIREZHJMTqJ0el08PLyQkJCQqMyQRAQEhKCCxcuIDU1FSdPnoSbmxv8/Pyg0+kA/LQydXl5OdasWYP8/HwkJiZi//79mDVrlthObm4uevToge3bt+P06dN48803ERMTg48++ughukpERESm5KGWHZDJZEhOTkZISAgA4Ny5cxg4cCDy8/MxZMgQAEB9fT1cXFzw7rvvYvbs2U22s3v3bjz//PPQ6XSwsGj6DtfcuXNx9uxZHDlypFWxNby2WLVwF28nEZEB3k4i6rw6bNkBvV4PALC2tv7fBczMIJfL8fXXXzd7XkOgzSUwDXUcHR3ve+3q6mqDjYiIiExXmyYxgwYNglqtRkxMDG7cuIF79+4hPj4ely5dQkVFRZPnXLt2DW+//TYiIyObbff48ePYuXPnfevExcVBoVCIm0qleuj+EBERUefVpkmMpaUlkpKScO7cOTg6OsLW1hbp6ekICgqCmVnjS1VXV2PChAkYPHgwli9f3mSb+fn5mDRpEmJjY+Hv79/stWNiYqDVasWtrKysrbpFREREnVCbP2Lt7e0NjUYDrVaLe/fuwdnZGSNGjMCwYcMM6t26dQuBgYHo2rUrkpOTYWlp2aitM2fOYPz48YiMjMSSJUvue125XA65XN6mfSEiIqLOq91edqdQKODs7IzCwkLk5ORg0qRJYll1dTX8/f1hZWWFtLQ0gzk0DU6fPg1fX19ERERg5cqV7RUmERERSZTRIzE1NTUoKioS94uLi6HRaODo6Ai1Wo3du3fD2dkZarUap06dwoIFCxASEiLeCmpIYG7fvo3t27cbTMJ1dnaGubk58vPz8dRTTyEgIABRUVGorKwEAJibm8PZ2dmoePNXBLQ4u5mIiIikx+gkJicnB76+vuJ+VFQUACAiIgKJiYmoqKhAVFQUrly5AldXV4SHh2Pp0qVi/by8PJw4cQIA0K9fP4O2i4uL0adPH/zzn//Ejz/+iO3bt2P79u1iuZubG0pKSowNmYiIiEzQQ70npjMz5jlzIiIi6hw67D0xRERERL8WJjFEREQkSUxiiIiISJKYxBAREZEkMYkhIiIiSWISQ0RERJLEJIaIiIgkyeiX3WVmZmL16tXIzc1FRUUFkpOTERIS0mTdOXPmYNOmTVi7di0WLlwoHg8ODoZGo8HVq1fRrVs3+Pn5IT4+HkqlEgCQkZGBtWvX4ttvv0V1dTX69++P6OhoTJ8+3egODo09ADO5rdHnEZHpKlk1oaNDIKI2YPRIjE6ng5eXFxISEu5bLzk5GVlZWWJi8nO+vr7YtWsXCgoKsGfPHpw/fx5TpkwRy48fP45HH30Ue/bswffff4+ZM2ciPDwce/fuNTZcIiIiMlEP9cZemUzW5EjM5cuXMWLECBw4cAATJkzAwoULDUZifiktLQ0hISHQ6/VNrmYNABMmTEDPnj2xZcuWVsXW8MY/1cJdHIkhIgMciSHqvIx5Y6/Rt5NaUl9fj7CwMERHR2PIkCEt1q+qqsKOHTvg4+PTbAIDAFqtFp6ens2W6/V66PV6cb9hUUkiIiIyTW0+sTc+Ph4WFhaYP3/+festXrwYXbp0gZOTE0pLS5Gamtps3V27diE7OxszZ85stk5cXBwUCoW4qVSqB+4DERERdX5tmsTk5uZi/fr1SExMhEwmu2/d6OhonDx5EgcPHoS5uTnCw8PR1J2t9PR0zJw5E59++ul9R3ZiYmKg1WrFrays7KH7Q0RERJ1Xm95OOnbsGK5evQq1Wi0eq6urw2uvvYZ169ahpKREPN69e3d0794dAwYMgKenJ1QqFbKysjBy5EixztGjRzFx4kSsXbsW4eHh9722XC6HXC5vy+4QERFRJ9amSUxYWBj8/PwMjgUEBCAsLOy+t4Lq6+sBwGBOS0ZGBv74xz8iPj4ekZGRbRkmERERmQCjk5iamhoUFRWJ+8XFxdBoNHB0dIRarYaTk5NBfUtLS7i4uGDgwIEAgBMnTiA7OxujRo1Ct27dcP78eSxduhQeHh7iKEx6ejr++Mc/YsGCBXjmmWdQWVkJALCysoKjo+MDd5aIiIhMh9FJTE5ODnx9fcX9qKgoAEBERAQSExNbPN/W1hZJSUmIjY2FTqeDq6srAgMDsWTJEvF20LZt23D79m3ExcUhLi5OPHfs2LHIyMgwKt78FQEtPqJFRERE0vNQ74npzIx5zpyIiIg6B2O+v7l2EhEREUkSkxgiIiKSJCYxREREJElMYoiIiEiSmMQQERGRJDGJISIiIkliEkNERESS1KbLDnRGQ2MPwExu29FhEFEnVbJqQkeHQEQPyOiRmMzMTEycOBFKpRIymQwpKSkG5TU1NZg3bx569+4NGxsbDB48GJ988olBnZdeegkeHh6wsbGBs7MzJk2ahB9++KHJ612/fh29e/eGTCbDzZs3jQ2XiIiITJTRSYxOp4OXlxcSEhKaLI+KisL+/fuxfft2nD17FgsXLsS8efOQlpYm1vH29sbWrVtx9uxZHDhwAIIgwN/fH3V1dY3amzVrFh599FFjwyQiIiITZ/TtpKCgIAQFBTVbfvz4cURERGDcuHEAgMjISGzatAnffvstgoODxWMN+vTpg3feeQdeXl4oKSmBh4eHWLZx40bcvHkTy5Ytw759+4wNlYiIiExYm0/s9fHxQVpaGi5fvgxBEJCeno5z587B39+/yfo6nQ5bt26Fu7s7VCqVePzMmTN466238Nlnn8HMrOUw9Xo9qqurDTYiIiIyXW2exGzYsAGDBw9G7969YWVlhcDAQCQkJGDMmDEG9T7++GPY2dnBzs4O+/btw6FDh2BlZQXgp4QkNDQUq1evhlqtbtV14+LioFAoxO3nCRERERGZnnZJYrKyspCWlobc3Fy8//77mDt3Lg4fPmxQb/r06Th58iSOHj2KAQMGYNq0abh79y4AICYmBp6ennj++edbfd2YmBhotVpxKysra9N+ERERUeciEwRBeOCTZTIkJycjJCQEAHDnzh0oFAokJydjwoT/PbY4e/ZsXLp0Cfv372+ynXv37qFbt27429/+htDQUDz22GM4deoUZDIZAEAQBNTX18Pc3BxvvvkmVqxY0WJsDUt5qxbu4iPWRNQsPmJN1Lk0fH9rtVrY29vft26bviemtrYWtbW1jeawmJubo76+vtnzBEGAIAjQ6/UAgD179uDOnTtieXZ2Nl544QUcO3bMYOIvERER/XYZncTU1NSgqKhI3C8uLoZGo4GjoyPUajXGjh2L6Oho2NjYwM3NDUePHsVnn32GDz74AABw4cIF7Ny5E/7+/nB2dsalS5ewatUq2NjY4OmnnwaARonKtWvXAACenp5wcHAwKt78FQEtZnJEREQkPUYnMTk5OfD19RX3o6KiAAARERFITEzEl19+iZiYGEyfPh1VVVVwc3PDypUrMWfOHACAtbU1jh07hnXr1uHGjRvo2bMnxowZg+PHj6NHjx5t1C0iIiIydQ81J6YzM+aeGhEREXUOxnx/cwFIIiIikiQmMURERCRJTGKIiIhIkpjEEBERkSQxiSEiIiJJYhJDREREksQkhoiIiCTJ6JfdZWZmYvXq1cjNzUVFRYXB2km1tbVYsmQJ/v3vf+PChQtQKBTw8/PDqlWroFQqxTZWrlyJr776ChqNBlZWVrh586bBNa5fv47p06fj+++/x/Xr19GjRw9MmjQJ7777rtHvfBkae4BrJxFRp8G1mojajtEjMTqdDl5eXkhISGhUdvv2beTl5WHp0qXIy8tDUlISCgoKEBwcbFDv3r17mDp1Kl5++eWmgzIzw6RJk5CWloZz584hMTERhw8fFt/6S0RERGT0SExQUBCCgoKaLFMoFDh06JDBsY8++gjDhw9HaWkp1Go1AIirUCcmJjbZTrdu3QwSHDc3N7zyyitYvXq1seESERGRiWrTVaybotVqIZPJjF648efKy8uRlJSEsWPHNltHr9eLq2ADP722mIiIiExXu07svXv3LhYvXozQ0NAHWr8oNDQUtra26NWrF+zt7fG3v/2t2bpxcXFQKBTiplKpHiZ0IiIi6uTaLYmpra3FtGnTIAgCNm7c+EBtrF27Fnl5eUhNTcX58+fFFbObEhMTA61WK25lZWUPGjoRERFJQLvcTmpIYC5evIgjR4488CrSLi4ucHFxwaBBg+Do6IjRo0dj6dKlcHV1bVRXLpdDLpc/bOhEREQkEW2exDQkMIWFhUhPT4eTk1ObtFtfXw8ABvNeiIiI6LfL6CSmpqYGRUVF4n5xcTE0Gg0cHR3h6uqKKVOmIC8vD3v37kVdXR0qKysBAI6OjrCysgIAlJaWoqqqCqWlpairq4NGowEA9OvXD3Z2dvj3v/+NK1eu4IknnoCdnR1Onz6N6OhoPPnkk+jTp8/D95qIiIgkTyYIgmDMCRkZGfD19W10PCIiAsuXL4e7u3uT56Wnp2PcuHEAgBkzZmDbtm3N1klPT8ebb76JM2fOQK/XQ6VSYfLkyXj99ddb/ZRTdXU1FAoFtFrtA9/OIiIiol+XMd/fRicxUsEkhoiISHqM+f7m2klEREQkSUxiiIiISJKYxBAREZEkMYkhIiIiSWISQ0RERJLEJIaIiIgkiUkMERERSVK7rJ3UmQyNPQAzuW1Hh0FE9JtQsmpCR4dAvyFGj8RkZmZi4sSJUCqVkMlkSElJEctqa2uxePFiPPLII+jSpQuUSiXCw8NRXl5u0EZwcDDUajWsra3h6uqKsLAwgzoZGRmYNGkSXF1d0aVLFzz22GPYsWPHg/eSiIiITI7RSYxOp4OXlxcSEhIald2+fRt5eXlYunQp8vLykJSUhIKCAgQHBxvU8/X1xa5du1BQUIA9e/bg/PnzmDJlilh+/PhxPProo9izZw++//57zJw5E+Hh4di7d+8DdJGIiIhM0UMtOyCTyZCcnIyQkJBm62RnZ2P48OG4ePEi1Gp1k3XS0tIQEhICvV4PS0vLJutMmDABPXv2xJYtW1oVW8Nri1ULd/F2EhHRr4S3k+hhGbPsQLvPidFqtZDJZM0u3FhVVYUdO3bAx8en2QSmoR1PT89my/V6PfR6vbhfXV39wDETERFR59euTyfdvXsXixcvRmhoaKNsavHixejSpQucnJxQWlqK1NTUZtvZtWsXsrOzMXPmzGbrxMXFQaFQiJtKpWqzfhAREVHn025JTG1tLaZNmwZBELBx48ZG5dHR0Th58iQOHjwIc3NzhIeHo6k7W+np6Zg5cyY+/fRTDBkypNnrxcTEQKvViltZWVmb9oeIiIg6l3a5ndSQwFy8eBFHjhxp8p5W9+7d0b17dwwYMACenp5QqVTIysrCyJEjxTpHjx7FxIkTsXbtWoSHh9/3mnK5HHK5vM37QkRERJ1TmycxDQlMYWEh0tPT4eTk1OI59fX1AGAwpyUjIwN//OMfER8fj8jIyLYOk4iIiCTO6CSmpqYGRUVF4n5xcTE0Gg0cHR3h6uqKKVOmIC8vD3v37kVdXR0qKysBAI6OjrCyssKJEyeQnZ2NUaNGoVu3bjh//jyWLl0KDw8PcRQmPT0df/zjH7FgwQI888wzYhtWVlZwdHQ0Kt78FQEtzm4mIiIi6TH6EeuMjAz4+vo2Oh4REYHly5fD3d29yfPS09Mxbtw4nDp1CgsWLMB3330HnU4HV1dXBAYGYsmSJejVqxcAYMaMGdi2bVujNsaOHYuMjIxWxWnMI1pERETUORjz/f1Q74npzJjEEBERSY8x399cAJKIiIgkiUkMERERSRKTGCIiIpIkJjFEREQkSUxiiIiISJKYxBAREZEktfsq1h1taOwBmMltOzoMIiIyUsmqCR0dAnVyRo/EZGZmYuLEiVAqlZDJZEhJSWm27pw5cyCTybBu3TrxWElJCWbNmgV3d3fY2NjAw8MDsbGxuHfvXpNtFBUVoWvXrnBwcDA2VCIiIjJhRicxOp0OXl5eSEhIuG+95ORkZGVlQalUGhz/4YcfUF9fj02bNuH06dNYu3YtPvnkE7zxxhuN2qitrUVoaChGjx5tbJhERERk4oy+nRQUFISgoKD71rl8+TJeffVVHDhwABMmGA4HBgYGIjAwUNzv27cvCgoKsHHjRqxZs8ag7pIlSzBo0CCMHz8ex48fNzZUIiIiMmFtPiemvr4eYWFhiI6OxpAhQ1p1jlarbbSw45EjR7B7925oNBokJSW12IZerzdYBbu6utq4wImIiEhS2vzppPj4eFhYWGD+/Pmtql9UVIQNGzbgpZdeEo9dv34dM2bMQGJiYqvXPYqLi4NCoRA3lUr1QPETERGRNLRpEpObm4v169cjMTERMpmsxfqXL19GYGAgpk6dihdffFE8/uKLL+K5557DmDFjWn3tmJgYaLVacSsrK3ugPhAREZE0tGkSc+zYMVy9ehVqtRoWFhawsLDAxYsX8dprr6FPnz4GdcvLy+Hr6wsfHx9s3rzZoOzIkSNYs2aN2MasWbOg1WphYWGBLVu2NHltuVwOe3t7g42IiIhMV5vOiQkLC4Ofn5/BsYCAAISFhWHmzJniscuXL8PX1xfe3t7YunUrzMwMc6lvvvkGdXV14n5qairi4+Nx/Phx9OrVqy1DJiIiIokyOompqalBUVGRuF9cXAyNRgNHR0eo1Wo4OTkZ1Le0tISLiwsGDhwI4KcEZty4cXBzc8OaNWvw448/inVdXFwAAJ6engZt5OTkwMzMDEOHDjU2XCIiIjJRRicxOTk58PX1FfejoqIAABEREUhMTGzx/EOHDqGoqAhFRUXo3bu3QZkgCMaG06L8FQG8tURERGSCZEJ7ZA6dQHV1NRQKBbRaLZMYIiIiiTDm+5sLQBIREZEkMYkhIiIiSWISQ0RERJLEJIaIiIgkiUkMERERSRKTGCIiIpIkJjFEREQkSW267EBnNDT2AMzkth0dBhERGalk1YSODoE6OaNHYjIzMzFx4kQolUrIZDKkpKQYlF+5cgUzZsyAUqmEra0tAgMDUVhYaFDnpZdegoeHB2xsbODs7IxJkybhhx9+MKgjk8kabV9++aXxPSQiIiKTZHQSo9Pp4OXlhYSEhEZlgiAgJCQEFy5cQGpqKk6ePAk3Nzf4+flBp9OJ9RoWfjx79iwOHDgAQRDg7+9vsOgjAGzduhUVFRXiFhISYnwPiYiIyCQZfTspKCgIQUFBTZYVFhYiKysL+fn5GDJkCABg48aNcHFxwRdffIHZs2cDACIjI8Vz+vTpg3feeQdeXl4oKSmBh4eHWObg4CAuCklERET0c206sVev1wMArK2t/3cBMzPI5XJ8/fXXTZ6j0+mwdetWuLu7Q6VSGZTNnTsX3bt3x/Dhw7Fly5b7LhCp1+tRXV1tsBEREZHpatMkZtCgQVCr1YiJicGNGzdw7949xMfH49KlS6ioqDCo+/HHH8POzg52dnbYt28fDh06BCsrK7H8rbfewq5du3Do0CE888wzeOWVV7Bhw4Zmrx0XFweFQiFuv0yIiIiIyLQ81CrWMpkMycnJBnNVcnNzMWvWLHz33XcwNzeHn58fzMzMIAgC9u3bJ9bTarW4evUqKioqsGbNGly+fBn/93//ZzCK83PLli3D1q1bUVZW1mS5Xq8XR4KAn1bBVKlUUC3cxaeTiIgkiE8n/TZ16CrW3t7e0Gg0uHnzJioqKrB//35cv34dffv2NainUCjQv39/jBkzBv/85z/xww8/IDk5udl2R4wYgUuXLhkkKj8nl8thb29vsBEREZHpareX3SkUCjg7O6OwsBA5OTmYNGlSs3UFQYAgCM0mKACg0WjQrVs3yOXy9giXiIiIJMbop5NqampQVFQk7hcXF0Oj0cDR0RFqtRq7d++Gs7Mz1Go1Tp06hQULFiAkJAT+/v4AgAsXLmDnzp3w9/eHs7MzLl26hFWrVsHGxgZPP/00AOBf//oXrly5gt///vewtrbGoUOH8O677+Ivf/mL0R3MXxHAURkiIiITZHQSk5OTA19fX3E/KioKABAREYHExERUVFQgKioKV65cgaurK8LDw7F06VKxvrW1NY4dO4Z169bhxo0b6NmzJ8aMGYPjx4+jR48eAABLS0skJCRg0aJFEAQB/fr1wwcffIAXX3zxYftLREREJuKhJvZ2ZsZMDCIiIqLOoUMn9hIRERH9GpjEEBERkSQxiSEiIiJJYhJDREREksQkhoiIiCSJSQwRERFJktHviZGaobEHuHYSEZGJ4zpLv01Gj8RkZmZi4sSJUCqVkMlkSElJabbunDlzIJPJsG7dOoPjwcHBUKvVsLa2hqurK8LCwlBeXt5kG0VFRejatSscHByMDZWIiIhMmNFJjE6ng5eXFxISEu5bLzk5GVlZWVAqlY3KfH19sWvXLhQUFGDPnj04f/48pkyZ0qhebW0tQkNDMXr0aGPDJCIiIhNn9O2koKAgBAUF3bfO5cuX8eqrr+LAgQOYMKHxEN+iRYvEf3dzc8Prr7+OkJAQ1NbWwtLSUixbsmQJBg0ahPHjx+P48ePGhkpEREQmrM3nxNTX1yMsLAzR0dEYMmRIi/WrqqqwY8cO+Pj4GCQwR44cwe7du6HRaJCUlNRiO3q93mAV7Orq6gfrABEREUlCmz+dFB8fDwsLC8yfP/++9RYvXowuXbrAyckJpaWlSE1NFcuuX7+OGTNmIDExsdXrHsXFxUGhUIibSqV6qH4QERFR59amSUxubi7Wr1+PxMREyGSy+9aNjo7GyZMncfDgQZibmyM8PBwNa1G++OKLeO655zBmzJhWXzsmJgZarVbcysrKHqovRERE1Lk91CrWMpkMycnJCAkJAQCsW7cOUVFRMDP7X25UV1cHMzMzqFQqlJSUNNnOpUuXoFKpcPz4cYwcORIODg6oqakRywVBQH19PczNzbF582a88MILLcbWsAqmauEuPmJNRGTi+Ii16TBmFes2nRMTFhYGPz8/g2MBAQEICwvDzJkzmz2vvr4eAMQ5Ld988w3q6urE8tTUVMTHx+P48ePo1atXW4ZMREREEmV0ElNTU4OioiJxv7i4GBqNBo6OjlCr1XBycjKob2lpCRcXFwwcOBAAcOLECWRnZ2PUqFHo1q0bzp8/j6VLl8LDwwMjR44EAHh6ehq0kZOTAzMzMwwdOtToDhIREZFpMjqJycnJga+vr7gfFRUFAIiIiEBiYmKL59va2iIpKQmxsbHQ6XRwdXVFYGAglixZArlcbmw4LcpfEdDqycFEREQkHQ81J6YzM+aeGhEREXUOxnx/cwFIIiIikiQmMURERCRJTGKIiIhIkpjEEBERkSQxiSEiIiJJYhJDREREksQkhoiIiCSpTZcd6IyGxh7g2klERPSr43pO7c/okZjMzExMnDgRSqUSMpkMKSkpYlltbS0WL16MRx55BF26dIFSqUR4eDjKy8sN2li5ciV8fHxga2sLBweH+17v+vXr6N27N2QyGW7evGlsuERERGSijE5idDodvLy8kJCQ0Kjs9u3byMvLw9KlS5GXl4ekpCQUFBQgODjYoN69e/cwdepUvPzyyy1eb9asWXj00UeNDZOIiIhMnNG3k4KCghAUFNRkmUKhwKFDhwyOffTRRxg+fDhKS0uhVqsBACtWrACAFtda2rhxI27evIlly5Zh3759xoZKREREJqzd58RotVrIZLIWbxv90pkzZ/DWW2/hxIkTuHDhQov19Xo99Hq9uF9dXW1sqERERCQh7fp00t27d7F48WKEhoYatQijXq9HaGgoVq9eLY7etCQuLg4KhULcVCrVg4ZNREREEtBuSUxtbS2mTZsGQRCwceNGo86NiYmBp6cnnn/+eaPO0Wq14lZWVmZsyERERCQh7ZLENCQwFy9exKFDh4wahQGAI0eOYPfu3bCwsICFhQXGjx8PAOjevTtiY2ObPEcul8Pe3t5gIyIiItPV5nNiGhKYwsJCpKenw8nJyeg29uzZgzt37oj72dnZeOGFF3Ds2DF4eHi0ZbhEREQkUUYnMTU1NSgqKhL3i4uLodFo4OjoCFdXV0yZMgV5eXnYu3cv6urqUFlZCQBwdHSElZUVAKC0tBRVVVUoLS1FXV0dNBoNAKBfv36ws7NrlKhcu3YNAODp6Wn0BOH8FQEclSEiIjJBRicxOTk58PX1FfejoqIAABEREVi+fDnS0tIAAI899pjBeenp6Rg3bhwAYNmyZdi2bZtY9vjjjzeqQ0RERHQ/MkEQhI4Ooj1UV1dDoVBAq9VyJIaIiEgijPn+5gKQREREJElMYoiIiEiSmMQQERGRJDGJISIiIkliEkNERESSxCSGiIiIJKndV7HuaENjD8BMbtvRYRAREZmUklUTOjoE40diMjMzMXHiRCiVSshkMqSkpBiU19TUYN68eejduzdsbGwwePBgfPLJJwZ1XnrpJXh4eMDGxgbOzs6YNGkSfvjhB4M68+fPh7e3N+RyeaMX5xEREREZncTodDp4eXkhISGhyfKoqCjs378f27dvx9mzZ7Fw4ULMmzdPfJMvAHh7e2Pr1q04e/YsDhw4AEEQ4O/vj7q6OoO2XnjhBTz77LPGhkhERES/AUbfTgoKCkJQUFCz5cePH0dERIS4fEBkZCQ2bdqEb7/9FsHBweKxBn369ME777wDLy8vlJSUiOsmffjhhwCAH3/8Ed9//72xYRIREZGJa/OJvT4+PkhLS8Ply5chCALS09Nx7tw5+Pv7N1lfp9Nh69atcHd3h0qleuDr6vV6VFdXG2xERERkuto8idmwYQMGDx6M3r17w8rKCoGBgUhISMCYMWMM6n388cews7ODnZ0d9u3bh0OHDomrXD+IuLg4KBQKcXuYhIiIiIg6v3ZJYrKyspCWlobc3Fy8//77mDt3Lg4fPmxQb/r06Th58iSOHj2KAQMGYNq0abh79+4DXzcmJgZarVbcysrKHrYrRERE1Im16SPWd+7cwRtvvIHk5GRMmPDTo1ePPvooNBoN1qxZAz8/P7Fuw4hJ//798fvf/x7dunVDcnIyQkNDH+jacrkccrm8TfpBREREnV+bjsTU1taitrYWZmaGzZqbm6O+vr7Z8wRBgCAI0Ov1bRkOERERmTCjR2JqampQVFQk7hcXF0Oj0cDR0RFqtRpjx45FdHQ0bGxs4ObmhqNHj+Kzzz7DBx98AAC4cOECdu7cCX9/fzg7O+PSpUtYtWoVbGxs8PTTT4vtFhUVoaamBpWVlbhz5w40Gg0AYPDgwQ81d4aIiIhMg0wQBMGYEzIyMuDr69voeEREBBITE1FZWYmYmBgcPHgQVVVVcHNzQ2RkJBYtWgSZTIby8nLMnj0bubm5uHHjBnr27IkxY8Zg2bJlGDhwoNjeuHHjcPTo0UbXKS4uRp8+fVqMs7q6GgqFAlqtFvb29sZ0kYiIiDqIMd/fRicxUsEkhoiISHqM+f7mApBEREQkSSa7AGTDABNfekdERCQdDd/brblRZLJJzPXr1wGAL70jIiKSoFu3bkGhUNy3jskmMY6OjgCA0tLSFn8IUlRdXQ2VSoWysjKTnPPD/kkb+ydt7J90mULfBEHArVu3oFQqW6xrsklMw7tqFAqFZH+RrWFvb8/+SRj7J23sn7SZcv+k3rfWDj5wYi8RERFJEpMYIiIikiSTTWLkcjliY2NNdj0l9k/a2D9pY/+kzZT7Z8p9a4rJvuyOiIiITJvJjsQQERGRaWMSQ0RERJLEJIaIiIgkiUkMERERSRKTGCIiIpIkk01iEhIS0KdPH1hbW2PEiBH49ttvOzoko8XFxeGJJ55A165d0aNHD4SEhKCgoMCgzt27dzF37lw4OTnBzs4OzzzzDK5cudJBET+cVatWQSaTYeHCheIxqffv8uXLeP755+Hk5AQbGxs88sgjyMnJEcsFQcCyZcvg6uoKGxsb+Pn5obCwsAMjbr26ujosXboU7u7usLGxgYeHB95++22DRduk1L/MzExMnDgRSqUSMpkMKSkpBuWt6UtVVRWmT58Oe3t7ODg4YNasWaipqfkVe9G8+/WvtrYWixcvxiOPPIIuXbpAqVQiPDwc5eXlBm1ItX+/NGfOHMhkMqxbt87guNT7d/bsWQQHB0OhUKBLly544oknUFpaKpZL/fO0KSaZxOzcuRNRUVGIjY1FXl4evLy8EBAQgKtXr3Z0aEY5evQo5s6di6ysLBw6dAi1tbXw9/eHTqcT6yxatAj/+te/sHv3bhw9ehTl5eWYPHlyB0b9YLKzs7Fp0yY8+uijBsel3L8bN27gySefhKWlJfbt24czZ87g/fffR7du3cQ67733Hj788EN88sknOHHiBLp06YKAgADcvXu3AyNvnfj4eGzcuBEfffQRzp49i/j4eLz33nvYsGGDWEdK/dPpdPDy8kJCQkKT5a3py/Tp03H69GkcOnQIe/fuRWZmJiIjI3+tLtzX/fp3+/Zt5OXlYenSpcjLy0NSUhIKCgoQHBxsUE+q/fu55ORkZGVlNbkuj5T7d/78eYwaNQqDBg1CRkYGvv/+eyxduhTW1tZiHSl/njZLMEHDhw8X5s6dK+7X1dUJSqVSiIuL68CoHt7Vq1cFAMLRo0cFQRCEmzdvCpaWlsLu3bvFOmfPnhUACN98801HhWm0W7duCf379xcOHTokjB07VliwYIEgCNLv3+LFi4VRo0Y1W15fXy+4uLgIq1evFo/dvHlTkMvlwhdffPFrhPhQJkyYILzwwgsGxyZPnixMnz5dEARp9w+AkJycLO63pi9nzpwRAAjZ2dlinX379gkymUy4fPnyrxZ7a/yyf0359ttvBQDCxYsXBUEwjf5dunRJ6NWrl5Cfny+4ubkJa9euFcuk3r9nn31WeP7555s9R+qfp80xuZGYe/fuITc3F35+fuIxMzMz+Pn54ZtvvunAyB6eVqsF8L8VunNzc1FbW2vQ10GDBkGtVkuqr3PnzsWECRMM+gFIv39paWkYNmwYpk6dih49euDxxx/Hp59+KpYXFxejsrLSoH8KhQIjRoyQRP98fHzwn//8B+fOnQMAfPfdd/j6668RFBQEQPr9+7nW9OWbb76Bg4MDhg0bJtbx8/ODmZkZTpw48avH/LC0Wi1kMhkcHBwASL9/9fX1CAsLQ3R0NIYMGdKoXMr9q6+vx1dffYUBAwYgICAAPXr0wIgRIwxuOUn987Q5JpfEXLt2DXV1dejZs6fB8Z49e6KysrKDonp49fX1WLhwIZ588kkMHToUAFBZWQkrKyvxQ6aBlPr65ZdfIi8vD3FxcY3KpN6/CxcuYOPGjejfvz8OHDiAl19+GfPnz8e2bdsAQOyDVP9WX3/9dfz5z3/GoEGDYGlpiccffxwLFy7E9OnTAUi/fz/Xmr5UVlaiR48eBuUWFhZwdHSUXH/v3r2LxYsXIzQ0VFwJWer9i4+Ph4WFBebPn99kuZT7d/XqVdTU1GDVqlUIDAzEwYMH8ac//QmTJ0/G0aNHAUj/87Q5Fh0dALXO3LlzkZ+fj6+//rqjQ2kzZWVlWLBgAQ4dOmRw39ZU1NfXY9iwYXj33XcBAI8//jjy8/PxySefICIiooOje3i7du3Cjh078I9//ANDhgyBRqPBwoULoVQqTaJ/v1W1tbWYNm0aBEHAxo0bOzqcNpGbm4v169cjLy8PMpmso8Npc/X19QCASZMmYdGiRQCAxx57DMePH8cnn3yCsWPHdmR47crkRmK6d+8Oc3PzRjOur1y5AhcXlw6K6uHMmzcPe/fuRXp6Onr37i0ed3Fxwb1793Dz5k2D+lLpa25uLq5evYrf/e53sLCwgIWFBY4ePYoPP/wQFhYW6Nmzp6T75+rqisGDBxsc8/T0FJ8WaOiDVP9Wo6OjxdGYRx55BGFhYVi0aJE4qib1/v1ca/ri4uLS6OGB//73v6iqqpJMfxsSmIsXL+LQoUPiKAwg7f4dO3YMV69ehVqtFj9rLl68iNdeew19+vQBIO3+de/eHRYWFi1+3kj587Q5JpfEWFlZwdvbG//5z3/EY/X19fjPf/6DkSNHdmBkxhMEAfPmzUNycjKOHDkCd3d3g3Jvb29YWloa9LWgoAClpaWS6Ov48eNx6tQpaDQacRs2bBimT58u/ruU+/fkk082eiT+3LlzcHNzAwC4u7vDxcXFoH/V1dU4ceKEJPp3+/ZtmJkZfoSYm5uL/1co9f79XGv6MnLkSNy8eRO5ublinSNHjqC+vh4jRoz41WM2VkMCU1hYiMOHD8PJycmgXMr9CwsLw/fff2/wWaNUKhEdHY0DBw4AkHb/rKys8MQTT9z380bq3xfN6uiZxe3hyy+/FORyuZCYmCicOXNGiIyMFBwcHITKysqODs0oL7/8sqBQKISMjAyhoqJC3G7fvi3WmTNnjqBWq4UjR44IOTk5wsiRI4WRI0d2YNQP5+dPJwmCtPv37bffChYWFsLKlSuFwsJCYceOHYKtra2wfft2sc6qVasEBwcHITU1Vfj++++FSZMmCe7u7sKdO3c6MPLWiYiIEHr16iXs3btXKC4uFpKSkoTu3bsLf/3rX8U6UurfrVu3hJMnTwonT54UAAgffPCBcPLkSfHpnNb0JTAwUHj88ceFEydOCF9//bXQv39/ITQ0tKO6ZOB+/bt3754QHBws9O7dW9BoNAafN3q9XmxDqv1ryi+fThIEafcvKSlJsLS0FDZv3iwUFhYKGzZsEMzNzYVjx46JbUj587Q5JpnECIIgbNiwQVCr1YKVlZUwfPhwISsrq6NDMhqAJretW7eKde7cuSO88sorQrdu3QRbW1vhT3/6k1BRUdFxQT+kXyYxUu/fv/71L2Ho0KGCXC4XBg0aJGzevNmgvL6+Xli6dKnQs2dPQS6XC+PHjxcKCgo6KFrjVFdXCwsWLBDUarVgbW0t9O3bV3jzzTcNvvSk1L/09PQm/3uLiIgQBKF1fbl+/boQGhoq2NnZCfb29sLMmTOFW7dudUBvGrtf/4qLi5v9vElPTxfbkGr/mtJUEiP1/v39738X+vXrJ1hbWwteXl5CSkqKQRtS/zxtikwQfvZ6TSIiIiKJMLk5MURERPTbwCSGiIiIJIlJDBEREUkSkxgiIiKSJCYxREREJElMYoiIiEiSmMQQERGRJDGJISIiIkliEkNERESSxCSGiIiIJIlJDBEREUnS/wORkUj5c4lRCgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "crashes['beat_of_occurrence'].value_counts().head(20).plot.barh(title='Pct of reports by police beat')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Its clear that 1831 and 1213 are far ahead of some of the other beats. Does this vary by year? Again, we can look at this with a pivot table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "beats_pivoted = crashes.pivot_table(index = 'beat_of_occurrence',\n",
    "    columns = 'crash_year', \n",
    "    values = 'injuries_total',\n",
    "    aggfunc = 'count',\n",
    "    margins = True, margins_name='Total'\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>crash_year</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "      <th>2022</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beat_of_occurrence</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>111</th>\n",
       "      <td>16.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>13.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>16.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>59</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2532</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2533</th>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2534</th>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2535</th>\n",
       "      <td>16.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>1916.0</td>\n",
       "      <td>1884.0</td>\n",
       "      <td>1367.0</td>\n",
       "      <td>1537.0</td>\n",
       "      <td>1604.0</td>\n",
       "      <td>8308</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>270 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "crash_year            2018    2019    2020    2021    2022  Total\n",
       "beat_of_occurrence                                               \n",
       "111                   16.0    10.0     1.0    10.0    13.0     50\n",
       "112                    8.0    10.0     1.0    11.0     7.0     37\n",
       "113                    3.0     4.0     5.0     5.0     4.0     21\n",
       "114                   13.0    16.0    12.0    15.0    10.0     66\n",
       "121                   16.0    19.0     4.0    13.0     7.0     59\n",
       "...                    ...     ...     ...     ...     ...    ...\n",
       "2532                   1.0     2.0     2.0     NaN     2.0      7\n",
       "2533                   7.0     7.0     4.0    11.0     7.0     36\n",
       "2534                   6.0     9.0     4.0     8.0     8.0     35\n",
       "2535                  16.0     7.0     4.0     6.0     8.0     41\n",
       "Total               1916.0  1884.0  1367.0  1537.0  1604.0   8308\n",
       "\n",
       "[270 rows x 6 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beats_pivoted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "bp_sorted = beats_pivoted.sort_values(by=('Total'),ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: ylabel='beat_of_occurrence'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bp_sorted.iloc[1:21,0:5].plot(kind ='barh',figsize=(10,17))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> How does the time of day / weather affect crashes?<br> Are the police accurate in reporting weather conditions?\n",
    "\n",
    "Let's look at 2021 to examine these questions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "crash21 = crashes[crashes['crash_year'] == 2021]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>crash_record_id</th>\n",
       "      <th>crash_date</th>\n",
       "      <th>posted_speed_limit</th>\n",
       "      <th>traffic_control_device</th>\n",
       "      <th>device_condition</th>\n",
       "      <th>weather_condition</th>\n",
       "      <th>lighting_condition</th>\n",
       "      <th>first_crash_type</th>\n",
       "      <th>trafficway_type</th>\n",
       "      <th>alignment</th>\n",
       "      <th>...</th>\n",
       "      <th>statements_taken_i</th>\n",
       "      <th>rd_no</th>\n",
       "      <th>crash_date_est_i</th>\n",
       "      <th>private_property_i</th>\n",
       "      <th>photos_taken_i</th>\n",
       "      <th>work_zone_i</th>\n",
       "      <th>work_zone_type</th>\n",
       "      <th>workers_present_i</th>\n",
       "      <th>lane_cnt</th>\n",
       "      <th>crash_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1604</th>\n",
       "      <td>4603435fbb4ef5d45c0d805c3e9aa5558a311a140a737e...</td>\n",
       "      <td>2021-12-31 13:46:00</td>\n",
       "      <td>30</td>\n",
       "      <td>NO CONTROLS</td>\n",
       "      <td>NO CONTROLS</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>DAYLIGHT</td>\n",
       "      <td>PEDALCYCLIST</td>\n",
       "      <td>DIVIDED - W/MEDIAN BARRIER</td>\n",
       "      <td>STRAIGHT AND LEVEL</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>JE494049</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1605</th>\n",
       "      <td>38459767b9b1a9bc1faed4a942998a04b6e29aa369541f...</td>\n",
       "      <td>2021-12-30 16:30:00</td>\n",
       "      <td>30</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>CLEAR</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>PEDALCYCLIST</td>\n",
       "      <td>NOT DIVIDED</td>\n",
       "      <td>STRAIGHT AND LEVEL</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>JE493188</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1606</th>\n",
       "      <td>39504218c0295e46e36c845c09dea92af78a1a096c59ca...</td>\n",
       "      <td>2021-12-29 14:28:00</td>\n",
       "      <td>30</td>\n",
       "      <td>TRAFFIC SIGNAL</td>\n",
       "      <td>FUNCTIONING PROPERLY</td>\n",
       "      <td>CLEAR</td>\n",
       "      <td>DAYLIGHT</td>\n",
       "      <td>PEDALCYCLIST</td>\n",
       "      <td>FOUR WAY</td>\n",
       "      <td>STRAIGHT AND LEVEL</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>JE492056</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        crash_record_id          crash_date  \\\n",
       "1604  4603435fbb4ef5d45c0d805c3e9aa5558a311a140a737e... 2021-12-31 13:46:00   \n",
       "1605  38459767b9b1a9bc1faed4a942998a04b6e29aa369541f... 2021-12-30 16:30:00   \n",
       "1606  39504218c0295e46e36c845c09dea92af78a1a096c59ca... 2021-12-29 14:28:00   \n",
       "\n",
       "      posted_speed_limit traffic_control_device      device_condition  \\\n",
       "1604                  30            NO CONTROLS           NO CONTROLS   \n",
       "1605                  30                UNKNOWN               UNKNOWN   \n",
       "1606                  30         TRAFFIC SIGNAL  FUNCTIONING PROPERLY   \n",
       "\n",
       "     weather_condition lighting_condition first_crash_type  \\\n",
       "1604              RAIN           DAYLIGHT     PEDALCYCLIST   \n",
       "1605             CLEAR            UNKNOWN     PEDALCYCLIST   \n",
       "1606             CLEAR           DAYLIGHT     PEDALCYCLIST   \n",
       "\n",
       "                 trafficway_type           alignment  ... statements_taken_i  \\\n",
       "1604  DIVIDED - W/MEDIAN BARRIER  STRAIGHT AND LEVEL  ...                NaN   \n",
       "1605                 NOT DIVIDED  STRAIGHT AND LEVEL  ...                NaN   \n",
       "1606                    FOUR WAY  STRAIGHT AND LEVEL  ...                NaN   \n",
       "\n",
       "         rd_no crash_date_est_i private_property_i photos_taken_i work_zone_i  \\\n",
       "1604  JE494049              NaN                NaN            NaN         NaN   \n",
       "1605  JE493188              NaN                NaN            NaN         NaN   \n",
       "1606  JE492056              NaN                NaN            NaN         NaN   \n",
       "\n",
       "     work_zone_type workers_present_i lane_cnt crash_year  \n",
       "1604            NaN               NaN      NaN       2021  \n",
       "1605            NaN               NaN      NaN       2021  \n",
       "1606            NaN               NaN      NaN       2021  \n",
       "\n",
       "[3 rows x 50 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crash21.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Y    42\n",
       "N     4\n",
       "Name: statements_taken_i, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crash21['statements_taken_i'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.max_rows = 600\n",
    "pd.options.display.max_colwidth = 400"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>crash_record_id</th>\n",
       "      <th>crash_date</th>\n",
       "      <th>posted_speed_limit</th>\n",
       "      <th>traffic_control_device</th>\n",
       "      <th>device_condition</th>\n",
       "      <th>weather_condition</th>\n",
       "      <th>lighting_condition</th>\n",
       "      <th>first_crash_type</th>\n",
       "      <th>trafficway_type</th>\n",
       "      <th>alignment</th>\n",
       "      <th>...</th>\n",
       "      <th>statements_taken_i</th>\n",
       "      <th>rd_no</th>\n",
       "      <th>crash_date_est_i</th>\n",
       "      <th>private_property_i</th>\n",
       "      <th>photos_taken_i</th>\n",
       "      <th>work_zone_i</th>\n",
       "      <th>work_zone_type</th>\n",
       "      <th>workers_present_i</th>\n",
       "      <th>lane_cnt</th>\n",
       "      <th>crash_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1640</th>\n",
       "      <td>53531e4027c00300354e3cb66d57bea2e36c7ef66b40643f4c197fd3ed1d4657f9434b15b8465ebc963ef4d1bd883c4faedb13fbf8527d54199e969d0da01af9</td>\n",
       "      <td>2021-12-10 10:20:00</td>\n",
       "      <td>35</td>\n",
       "      <td>NO CONTROLS</td>\n",
       "      <td>NO CONTROLS</td>\n",
       "      <td>CLEAR</td>\n",
       "      <td>DAYLIGHT</td>\n",
       "      <td>PEDALCYCLIST</td>\n",
       "      <td>DIVIDED - W/MEDIAN (NOT RAISED)</td>\n",
       "      <td>STRAIGHT ON GRADE</td>\n",
       "      <td>...</td>\n",
       "      <td>Y</td>\n",
       "      <td>JE472385</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1647</th>\n",
       "      <td>ecd4180e2569076913cf4d1eddd7d3030b76474e5c38c581dcda4c56e3d09f86805d58674761fce621bc4dbbeeda3e395b8691ce7cf0d350f1ea4e73239534f3</td>\n",
       "      <td>2021-12-07 16:49:00</td>\n",
       "      <td>30</td>\n",
       "      <td>TRAFFIC SIGNAL</td>\n",
       "      <td>FUNCTIONING PROPERLY</td>\n",
       "      <td>CLEAR</td>\n",
       "      <td>DARKNESS, LIGHTED ROAD</td>\n",
       "      <td>PEDALCYCLIST</td>\n",
       "      <td>T-INTERSECTION</td>\n",
       "      <td>STRAIGHT AND LEVEL</td>\n",
       "      <td>...</td>\n",
       "      <td>Y</td>\n",
       "      <td>JE469591</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1670</th>\n",
       "      <td>905a01840212a61de466903eadc80b72113a6f418e3265a624a6191f0cd5233fd320405054486761a5ecbfaaf0f97bffd58269555d5be642243d8ef2ceba69e0</td>\n",
       "      <td>2021-11-30 19:09:00</td>\n",
       "      <td>25</td>\n",
       "      <td>NO CONTROLS</td>\n",
       "      <td>NO CONTROLS</td>\n",
       "      <td>CLEAR</td>\n",
       "      <td>DARKNESS, LIGHTED ROAD</td>\n",
       "      <td>PEDALCYCLIST</td>\n",
       "      <td>NOT DIVIDED</td>\n",
       "      <td>STRAIGHT AND LEVEL</td>\n",
       "      <td>...</td>\n",
       "      <td>Y</td>\n",
       "      <td>JE461767</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1762</th>\n",
       "      <td>7dbaa670b350388c5b2a67c8c84b1e203ae8f7ff0ce55e7da89ea18e36188cabee2a6cfad8f6492fb302e9ea5bc47f866a33dd8ad793abf72fe73bed731e372a</td>\n",
       "      <td>2021-10-31 11:00:00</td>\n",
       "      <td>35</td>\n",
       "      <td>TRAFFIC SIGNAL</td>\n",
       "      <td>FUNCTIONING PROPERLY</td>\n",
       "      <td>CLEAR</td>\n",
       "      <td>DAYLIGHT</td>\n",
       "      <td>PEDALCYCLIST</td>\n",
       "      <td>NOT DIVIDED</td>\n",
       "      <td>STRAIGHT AND LEVEL</td>\n",
       "      <td>...</td>\n",
       "      <td>Y</td>\n",
       "      <td>JE428872</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1763</th>\n",
       "      <td>aeae6291505dbac1a9ffde241d7cedb174268286719e7af6ebc99d4c944ae993e3252710207f315d9d72299059811986fa2f40b8b73fdbfc1e4af4fa4330d2b6</td>\n",
       "      <td>2021-10-31 10:45:00</td>\n",
       "      <td>20</td>\n",
       "      <td>STOP SIGN/FLASHER</td>\n",
       "      <td>FUNCTIONING PROPERLY</td>\n",
       "      <td>CLEAR</td>\n",
       "      <td>DAYLIGHT</td>\n",
       "      <td>PEDALCYCLIST</td>\n",
       "      <td>DIVIDED - W/MEDIAN (NOT RAISED)</td>\n",
       "      <td>STRAIGHT AND LEVEL</td>\n",
       "      <td>...</td>\n",
       "      <td>Y</td>\n",
       "      <td>JE428868</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                       crash_record_id  \\\n",
       "1640  53531e4027c00300354e3cb66d57bea2e36c7ef66b40643f4c197fd3ed1d4657f9434b15b8465ebc963ef4d1bd883c4faedb13fbf8527d54199e969d0da01af9   \n",
       "1647  ecd4180e2569076913cf4d1eddd7d3030b76474e5c38c581dcda4c56e3d09f86805d58674761fce621bc4dbbeeda3e395b8691ce7cf0d350f1ea4e73239534f3   \n",
       "1670  905a01840212a61de466903eadc80b72113a6f418e3265a624a6191f0cd5233fd320405054486761a5ecbfaaf0f97bffd58269555d5be642243d8ef2ceba69e0   \n",
       "1762  7dbaa670b350388c5b2a67c8c84b1e203ae8f7ff0ce55e7da89ea18e36188cabee2a6cfad8f6492fb302e9ea5bc47f866a33dd8ad793abf72fe73bed731e372a   \n",
       "1763  aeae6291505dbac1a9ffde241d7cedb174268286719e7af6ebc99d4c944ae993e3252710207f315d9d72299059811986fa2f40b8b73fdbfc1e4af4fa4330d2b6   \n",
       "\n",
       "              crash_date  posted_speed_limit traffic_control_device  \\\n",
       "1640 2021-12-10 10:20:00                  35            NO CONTROLS   \n",
       "1647 2021-12-07 16:49:00                  30         TRAFFIC SIGNAL   \n",
       "1670 2021-11-30 19:09:00                  25            NO CONTROLS   \n",
       "1762 2021-10-31 11:00:00                  35         TRAFFIC SIGNAL   \n",
       "1763 2021-10-31 10:45:00                  20      STOP SIGN/FLASHER   \n",
       "\n",
       "          device_condition weather_condition      lighting_condition  \\\n",
       "1640           NO CONTROLS             CLEAR                DAYLIGHT   \n",
       "1647  FUNCTIONING PROPERLY             CLEAR  DARKNESS, LIGHTED ROAD   \n",
       "1670           NO CONTROLS             CLEAR  DARKNESS, LIGHTED ROAD   \n",
       "1762  FUNCTIONING PROPERLY             CLEAR                DAYLIGHT   \n",
       "1763  FUNCTIONING PROPERLY             CLEAR                DAYLIGHT   \n",
       "\n",
       "     first_crash_type                  trafficway_type           alignment  \\\n",
       "1640     PEDALCYCLIST  DIVIDED - W/MEDIAN (NOT RAISED)   STRAIGHT ON GRADE   \n",
       "1647     PEDALCYCLIST                   T-INTERSECTION  STRAIGHT AND LEVEL   \n",
       "1670     PEDALCYCLIST                      NOT DIVIDED  STRAIGHT AND LEVEL   \n",
       "1762     PEDALCYCLIST                      NOT DIVIDED  STRAIGHT AND LEVEL   \n",
       "1763     PEDALCYCLIST  DIVIDED - W/MEDIAN (NOT RAISED)  STRAIGHT AND LEVEL   \n",
       "\n",
       "      ... statements_taken_i     rd_no crash_date_est_i private_property_i  \\\n",
       "1640  ...                  Y  JE472385              NaN                NaN   \n",
       "1647  ...                  Y  JE469591              NaN                NaN   \n",
       "1670  ...                  Y  JE461767              NaN                NaN   \n",
       "1762  ...                  Y  JE428872              NaN                NaN   \n",
       "1763  ...                  Y  JE428868              NaN                NaN   \n",
       "\n",
       "     photos_taken_i work_zone_i work_zone_type workers_present_i lane_cnt  \\\n",
       "1640            NaN         NaN            NaN               NaN      NaN   \n",
       "1647            NaN         NaN            NaN               NaN      NaN   \n",
       "1670            NaN         NaN            NaN               NaN      NaN   \n",
       "1762            NaN         NaN            NaN               NaN      NaN   \n",
       "1763            NaN         NaN            NaN               NaN      NaN   \n",
       "\n",
       "     crash_year  \n",
       "1640       2021  \n",
       "1647       2021  \n",
       "1670       2021  \n",
       "1762       2021  \n",
       "1763       2021  \n",
       "\n",
       "[5 rows x 50 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crash21[crash21['statements_taken_i'] == \"Y\"].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DAYLIGHT                  1113\n",
      "DARKNESS, LIGHTED ROAD     291\n",
      "DUSK                        48\n",
      "DARKNESS                    38\n",
      "UNKNOWN                     24\n",
      "DAWN                        23\n",
      "Name: lighting_condition, dtype: int64\n",
      "CLEAR                    1344\n",
      "RAIN                      101\n",
      "UNKNOWN                    44\n",
      "CLOUDY/OVERCAST            39\n",
      "SNOW                        5\n",
      "FREEZING RAIN/DRIZZLE       3\n",
      "OTHER                       1\n",
      "Name: weather_condition, dtype: int64\n",
      "30    1220\n",
      "25     114\n",
      "35      87\n",
      "20      56\n",
      "15      38\n",
      "10      12\n",
      "0        4\n",
      "45       2\n",
      "24       1\n",
      "40       1\n",
      "5        1\n",
      "2        1\n",
      "Name: posted_speed_limit, dtype: int64\n",
      "STRAIGHT AND LEVEL       1504\n",
      "STRAIGHT ON GRADE          25\n",
      "STRAIGHT ON HILLCREST       5\n",
      "CURVE, LEVEL                3\n",
      "Name: alignment, dtype: int64\n",
      "JE494049    1\n",
      "JE317804    1\n",
      "JE272503    1\n",
      "JE273231    1\n",
      "JE273440    1\n",
      "           ..\n",
      "JE360408    1\n",
      "JE360558    1\n",
      "JE360641    1\n",
      "JE360823    1\n",
      "JE100653    1\n",
      "Name: rd_no, Length: 1537, dtype: int64\n",
      "Y    12\n",
      "N     5\n",
      "Name: photos_taken_i, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "headings = ['lighting_condition','weather_condition','posted_speed_limit','alignment','rd_no','photos_taken_i']\n",
    "for head in headings:\n",
    "    print(crash21[head].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>crash_record_id</th>\n",
       "      <th>crash_date</th>\n",
       "      <th>posted_speed_limit</th>\n",
       "      <th>traffic_control_device</th>\n",
       "      <th>device_condition</th>\n",
       "      <th>weather_condition</th>\n",
       "      <th>lighting_condition</th>\n",
       "      <th>first_crash_type</th>\n",
       "      <th>trafficway_type</th>\n",
       "      <th>alignment</th>\n",
       "      <th>...</th>\n",
       "      <th>statements_taken_i</th>\n",
       "      <th>rd_no</th>\n",
       "      <th>crash_date_est_i</th>\n",
       "      <th>private_property_i</th>\n",
       "      <th>photos_taken_i</th>\n",
       "      <th>work_zone_i</th>\n",
       "      <th>work_zone_type</th>\n",
       "      <th>workers_present_i</th>\n",
       "      <th>lane_cnt</th>\n",
       "      <th>crash_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1604</th>\n",
       "      <td>4603435fbb4ef5d45c0d805c3e9aa5558a311a140a737e37aa56d4f70ea0161f8f2e187270d33f36070c6a1b0f0c26401d0f0ad8b3f9dae797f8041f754d6297</td>\n",
       "      <td>2021-12-31 13:46:00</td>\n",
       "      <td>30</td>\n",
       "      <td>NO CONTROLS</td>\n",
       "      <td>NO CONTROLS</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>DAYLIGHT</td>\n",
       "      <td>PEDALCYCLIST</td>\n",
       "      <td>DIVIDED - W/MEDIAN BARRIER</td>\n",
       "      <td>STRAIGHT AND LEVEL</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>JE494049</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1608</th>\n",
       "      <td>69d7b94e1fedc18c90e4f82830cbdc66229d9d924add0eabab78552736c745b177313a182a8757262c0c6dc2447e69aae11136c5ac276163eafc0b844ffa7559</td>\n",
       "      <td>2021-12-27 09:10:00</td>\n",
       "      <td>30</td>\n",
       "      <td>TRAFFIC SIGNAL</td>\n",
       "      <td>FUNCTIONING PROPERLY</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>DAYLIGHT</td>\n",
       "      <td>PEDALCYCLIST</td>\n",
       "      <td>FOUR WAY</td>\n",
       "      <td>STRAIGHT AND LEVEL</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>JE489861</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1625</th>\n",
       "      <td>0ee18d13dfcd5d09e35be73fb5e070ba623d33355224fbb23fef590805a57653372bb79ac7be359e273d69cf66af591941e70ad2751e35d106759eba9a5f92bf</td>\n",
       "      <td>2021-12-14 21:00:00</td>\n",
       "      <td>20</td>\n",
       "      <td>STOP SIGN/FLASHER</td>\n",
       "      <td>FUNCTIONING PROPERLY</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>DARKNESS, LIGHTED ROAD</td>\n",
       "      <td>PEDALCYCLIST</td>\n",
       "      <td>NOT DIVIDED</td>\n",
       "      <td>STRAIGHT AND LEVEL</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>JE477739</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1638</th>\n",
       "      <td>8d272e0b17c3c0f52e34ef91eb64d94a6847f2375e35ad506c0f152366b171fda437a4e9a669666db0cc3824b1ea08dd0ea67e416c094964ffa3a869fb5bb135</td>\n",
       "      <td>2021-12-10 19:26:00</td>\n",
       "      <td>35</td>\n",
       "      <td>TRAFFIC SIGNAL</td>\n",
       "      <td>FUNCTIONING PROPERLY</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>DARKNESS, LIGHTED ROAD</td>\n",
       "      <td>PEDALCYCLIST</td>\n",
       "      <td>NOT DIVIDED</td>\n",
       "      <td>STRAIGHT AND LEVEL</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>JE473100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1639</th>\n",
       "      <td>b518490c791bf6b75a8958b503a2999fd824ba8fabdba5305d7e95e51e59fa43e08ce608f87b4395dbebd1165662a310f12da2040f7a50ee2fd240cb52b9e1bf</td>\n",
       "      <td>2021-12-10 18:30:00</td>\n",
       "      <td>35</td>\n",
       "      <td>TRAFFIC SIGNAL</td>\n",
       "      <td>NO CONTROLS</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>DARKNESS</td>\n",
       "      <td>PEDALCYCLIST</td>\n",
       "      <td>ONE-WAY</td>\n",
       "      <td>STRAIGHT AND LEVEL</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>JE476355</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                       crash_record_id  \\\n",
       "1604  4603435fbb4ef5d45c0d805c3e9aa5558a311a140a737e37aa56d4f70ea0161f8f2e187270d33f36070c6a1b0f0c26401d0f0ad8b3f9dae797f8041f754d6297   \n",
       "1608  69d7b94e1fedc18c90e4f82830cbdc66229d9d924add0eabab78552736c745b177313a182a8757262c0c6dc2447e69aae11136c5ac276163eafc0b844ffa7559   \n",
       "1625  0ee18d13dfcd5d09e35be73fb5e070ba623d33355224fbb23fef590805a57653372bb79ac7be359e273d69cf66af591941e70ad2751e35d106759eba9a5f92bf   \n",
       "1638  8d272e0b17c3c0f52e34ef91eb64d94a6847f2375e35ad506c0f152366b171fda437a4e9a669666db0cc3824b1ea08dd0ea67e416c094964ffa3a869fb5bb135   \n",
       "1639  b518490c791bf6b75a8958b503a2999fd824ba8fabdba5305d7e95e51e59fa43e08ce608f87b4395dbebd1165662a310f12da2040f7a50ee2fd240cb52b9e1bf   \n",
       "\n",
       "              crash_date  posted_speed_limit traffic_control_device  \\\n",
       "1604 2021-12-31 13:46:00                  30            NO CONTROLS   \n",
       "1608 2021-12-27 09:10:00                  30         TRAFFIC SIGNAL   \n",
       "1625 2021-12-14 21:00:00                  20      STOP SIGN/FLASHER   \n",
       "1638 2021-12-10 19:26:00                  35         TRAFFIC SIGNAL   \n",
       "1639 2021-12-10 18:30:00                  35         TRAFFIC SIGNAL   \n",
       "\n",
       "          device_condition weather_condition      lighting_condition  \\\n",
       "1604           NO CONTROLS              RAIN                DAYLIGHT   \n",
       "1608  FUNCTIONING PROPERLY              RAIN                DAYLIGHT   \n",
       "1625  FUNCTIONING PROPERLY              RAIN  DARKNESS, LIGHTED ROAD   \n",
       "1638  FUNCTIONING PROPERLY              RAIN  DARKNESS, LIGHTED ROAD   \n",
       "1639           NO CONTROLS              RAIN                DARKNESS   \n",
       "\n",
       "     first_crash_type             trafficway_type           alignment  ...  \\\n",
       "1604     PEDALCYCLIST  DIVIDED - W/MEDIAN BARRIER  STRAIGHT AND LEVEL  ...   \n",
       "1608     PEDALCYCLIST                    FOUR WAY  STRAIGHT AND LEVEL  ...   \n",
       "1625     PEDALCYCLIST                 NOT DIVIDED  STRAIGHT AND LEVEL  ...   \n",
       "1638     PEDALCYCLIST                 NOT DIVIDED  STRAIGHT AND LEVEL  ...   \n",
       "1639     PEDALCYCLIST                     ONE-WAY  STRAIGHT AND LEVEL  ...   \n",
       "\n",
       "     statements_taken_i     rd_no crash_date_est_i private_property_i  \\\n",
       "1604                NaN  JE494049              NaN                NaN   \n",
       "1608                NaN  JE489861              NaN                NaN   \n",
       "1625                NaN  JE477739              NaN                NaN   \n",
       "1638                NaN  JE473100              NaN                NaN   \n",
       "1639                NaN  JE476355              NaN                NaN   \n",
       "\n",
       "     photos_taken_i work_zone_i work_zone_type workers_present_i lane_cnt  \\\n",
       "1604            NaN         NaN            NaN               NaN      NaN   \n",
       "1608            NaN         NaN            NaN               NaN      NaN   \n",
       "1625            NaN         NaN            NaN               NaN      NaN   \n",
       "1638            NaN         NaN            NaN               NaN      NaN   \n",
       "1639            NaN         NaN            NaN               NaN      NaN   \n",
       "\n",
       "     crash_year  \n",
       "1604       2021  \n",
       "1608       2021  \n",
       "1625       2021  \n",
       "1638       2021  \n",
       "1639       2021  \n",
       "\n",
       "[5 rows x 50 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rainy = crash21[crash21['weather_condition']=='RAIN']\n",
    "rainy.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1604    {'type': 'Point', 'coordinates': [-87.578269718478, 41.766335621716]}\n",
       "1608    {'type': 'Point', 'coordinates': [-87.649787129108, 41.779581012899]}\n",
       "1625    {'type': 'Point', 'coordinates': [-87.766869741854, 41.918941942487]}\n",
       "Name: location, dtype: object"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rainy['location'].head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1604    41.766336\n",
       "Name: latitude, dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rainy['latitude'].head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "def weatherLookup(lat,lon,dateTime,APIkey=weatherAPIkey):\n",
    "    import urllib\n",
    "    import json\n",
    "    latS, lonS  = str(lat), str(lon)\n",
    "    dateTimeS = dateTime.strftime('%Y-%m-%dT%H:%M:%S')\n",
    "    ApiQuery = 'https://weather.visualcrossing.com/VisualCrossingWebServices/rest/services/timeline/'+\\\n",
    "            latS+'%2C'+lonS+'/'+dateTimeS+'?unitGroup=us&key='+APIkey+'&include=current&contentType=json'\n",
    "    try: \n",
    "        with urllib.request.urlopen(ApiQuery) as response:\n",
    "            html = response.read()\n",
    "    except urllib.error.HTTPError  as e:\n",
    "        ErrorInfo= e.read().decode() \n",
    "        print('Error code: ', e.code, ErrorInfo)\n",
    "        sys.exit()\n",
    "    except  urllib.error.URLError as e:\n",
    "        ErrorInfo= e.read().decode() \n",
    "        print('Error code: ', e.code,ErrorInfo)\n",
    "        sys.exit()\n",
    "\n",
    "    weather = json.loads(html.decode('utf-8'))\n",
    "    return weather['currentConditions']['temp'], weather['currentConditions']['conditions'], weather['currentConditions']['preciptype']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(-8.4, 'Overcast', None)\n"
     ]
    }
   ],
   "source": [
    "print(weatherLookup(str(-87.578269718478),str(-87.578269718478),'2021-12-31','13:46:00',weatherAPIkey))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1604    2021-12-31T13:46:00\n",
       "Name: crash_date, dtype: object"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#crashes['crash_year'] = crashes['crash_date'].dt.year\n",
    "rainy['crash_date'].dt.strftime('%Y-%m-%dT%H:%M:%S').head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['wCondit'] = test.apply(lambda test: weatherLookup(test.latitude, test.longitude, test.crash_date,weatherAPIkey),axis = 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test[['wTemp','wCond','wPrecip']] = \n",
    "    [weatherLookup(*a) for a in tuple(zip(test['latitude'],test['longitude'],test['crash_date']))]\n",
    "\n",
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['crash_record_id', 'crash_date', 'posted_speed_limit',\n",
       "       'traffic_control_device', 'device_condition', 'weather_condition',\n",
       "       'lighting_condition', 'first_crash_type', 'trafficway_type',\n",
       "       'alignment', 'roadway_surface_cond', 'road_defect', 'report_type',\n",
       "       'crash_type', 'damage', 'date_police_notified',\n",
       "       'prim_contributory_cause', 'sec_contributory_cause', 'street_no',\n",
       "       'street_direction', 'street_name', 'beat_of_occurrence', 'dooring_i',\n",
       "       'num_units', 'most_severe_injury', 'injuries_total', 'injuries_fatal',\n",
       "       'injuries_incapacitating', 'injuries_non_incapacitating',\n",
       "       'injuries_reported_not_evident', 'injuries_no_indication',\n",
       "       'injuries_unknown', 'crash_hour', 'crash_day_of_week', 'crash_month',\n",
       "       'latitude', 'longitude', 'location', 'intersection_related_i',\n",
       "       'hit_and_run_i', 'statements_taken_i', 'rd_no', 'crash_date_est_i',\n",
       "       'private_property_i', 'photos_taken_i', 'work_zone_i', 'work_zone_type',\n",
       "       'workers_present_i', 'lane_cnt', 'crash_year'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crash21.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FATAL                         7\n",
       "INCAPACITATING INJURY       146\n",
       "NO INDICATION OF INJURY     403\n",
       "NONINCAPACITATING INJURY    810\n",
       "REPORTED, NOT EVIDENT       171\n",
       "Name: most_severe_injury, dtype: int64"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crash21['most_severe_injury'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "badInjury = crash21[crash21['most_severe_injury']=='INCAPACITATING INJURY']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CLEAR              126\n",
       "RAIN                11\n",
       "UNKNOWN              4\n",
       "CLOUDY/OVERCAST      3\n",
       "SNOW                 2\n",
       "Name: weather_condition, dtype: int64"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "badInjury['weather_condition'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_635984/681675443.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  badInjury[['wTemp','wCond','wPrecip']] = \\\n",
      "/tmp/ipykernel_635984/681675443.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  badInjury[['wTemp','wCond','wPrecip']] = \\\n",
      "/tmp/ipykernel_635984/681675443.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  badInjury[['wTemp','wCond','wPrecip']] = \\\n"
     ]
    }
   ],
   "source": [
    "badInjury[['wTemp','wCond','wPrecip']] = \\\n",
    "    [weatherLookup(*a) for a in tuple(zip(badInjury['latitude'],badInjury['longitude'],badInjury['crash_date']))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "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>crash_record_id</th>\n",
       "      <th>crash_date</th>\n",
       "      <th>posted_speed_limit</th>\n",
       "      <th>traffic_control_device</th>\n",
       "      <th>device_condition</th>\n",
       "      <th>weather_condition</th>\n",
       "      <th>lighting_condition</th>\n",
       "      <th>first_crash_type</th>\n",
       "      <th>trafficway_type</th>\n",
       "      <th>alignment</th>\n",
       "      <th>...</th>\n",
       "      <th>private_property_i</th>\n",
       "      <th>photos_taken_i</th>\n",
       "      <th>work_zone_i</th>\n",
       "      <th>work_zone_type</th>\n",
       "      <th>workers_present_i</th>\n",
       "      <th>lane_cnt</th>\n",
       "      <th>crash_year</th>\n",
       "      <th>wTemp</th>\n",
       "      <th>wCond</th>\n",
       "      <th>wPrecip</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1911</th>\n",
       "      <td>c19ce8fe678b1693e27a4f2cf1b5f2efffa16f594ce21227c3aed84d5a145a7b27379e084f143b3ad36709b00c964b529d4b1baf125a80713c66465a7dfcc503</td>\n",
       "      <td>2021-10-04 19:44:00</td>\n",
       "      <td>30</td>\n",
       "      <td>TRAFFIC SIGNAL</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>CLEAR</td>\n",
       "      <td>DARKNESS, LIGHTED ROAD</td>\n",
       "      <td>PEDALCYCLIST</td>\n",
       "      <td>FOUR WAY</td>\n",
       "      <td>STRAIGHT AND LEVEL</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
       "      <td>67.3</td>\n",
       "      <td>Overcast</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3085</th>\n",
       "      <td>967783f483ead199e53dccee44b2dd5b1ef42a6251b129d49b9caa66ddb05b484114809526f7e3e4c3ed6aa69151ffa5dddf935e4e056cb88d1e1010f7e70d60</td>\n",
       "      <td>2021-03-02 15:55:00</td>\n",
       "      <td>30</td>\n",
       "      <td>TRAFFIC SIGNAL</td>\n",
       "      <td>FUNCTIONING PROPERLY</td>\n",
       "      <td>CLEAR</td>\n",
       "      <td>DAYLIGHT</td>\n",
       "      <td>PEDALCYCLIST</td>\n",
       "      <td>FOUR WAY</td>\n",
       "      <td>STRAIGHT AND LEVEL</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
       "      <td>42.0</td>\n",
       "      <td>Clear</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1848</th>\n",
       "      <td>f6198c266f65265e6868d066cd4f29b1ff76f2e18f4da95f3bf3053f3af9521b94e8e09aa336b118a0cc1efcc91ec8d3362498fe882ef9ddd03c0124bcb86025</td>\n",
       "      <td>2021-10-16 15:00:00</td>\n",
       "      <td>30</td>\n",
       "      <td>TRAFFIC SIGNAL</td>\n",
       "      <td>FUNCTIONING PROPERLY</td>\n",
       "      <td>CLEAR</td>\n",
       "      <td>DAYLIGHT</td>\n",
       "      <td>PEDALCYCLIST</td>\n",
       "      <td>NOT DIVIDED</td>\n",
       "      <td>STRAIGHT AND LEVEL</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
       "      <td>59.3</td>\n",
       "      <td>Partially cloudy</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1657</th>\n",
       "      <td>070c71bfc229a6b429868790eb90a890b25941b5ab8396a4b4ffb0992980949dacce76860d9d1f0d8dc41068e471934abcdbc6dc3ea9b312368a8284508fdee5</td>\n",
       "      <td>2021-12-02 17:23:00</td>\n",
       "      <td>30</td>\n",
       "      <td>TRAFFIC SIGNAL</td>\n",
       "      <td>FUNCTIONING PROPERLY</td>\n",
       "      <td>CLEAR</td>\n",
       "      <td>DUSK</td>\n",
       "      <td>PEDALCYCLIST</td>\n",
       "      <td>FIVE POINT, OR MORE</td>\n",
       "      <td>STRAIGHT AND LEVEL</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Partially cloudy</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1814</th>\n",
       "      <td>f5556a1237866e0c5c8e38f3bafd287f7c7c70f64a424a6fb11bb2a4516fcf75188c554bf19b17fb8b15ba5197f2ea55f885da658960897b55308a8ff60d3d90</td>\n",
       "      <td>2021-10-21 14:35:00</td>\n",
       "      <td>30</td>\n",
       "      <td>NO CONTROLS</td>\n",
       "      <td>NO CONTROLS</td>\n",
       "      <td>CLOUDY/OVERCAST</td>\n",
       "      <td>DAYLIGHT</td>\n",
       "      <td>PEDALCYCLIST</td>\n",
       "      <td>DIVIDED - W/MEDIAN (NOT RAISED)</td>\n",
       "      <td>STRAIGHT AND LEVEL</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
       "      <td>51.0</td>\n",
       "      <td>Overcast</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2147</th>\n",
       "      <td>76551113b651c6743b16a596e469859699682d4d209356356c3560c9048672557171d8cd74e7955b92d05f751720e133f8247b978383b0ca9f5d884d7a04ff40</td>\n",
       "      <td>2021-08-29 15:15:00</td>\n",
       "      <td>30</td>\n",
       "      <td>NO CONTROLS</td>\n",
       "      <td>NO CONTROLS</td>\n",
       "      <td>CLEAR</td>\n",
       "      <td>DAYLIGHT</td>\n",
       "      <td>PEDALCYCLIST</td>\n",
       "      <td>NOT DIVIDED</td>\n",
       "      <td>STRAIGHT AND LEVEL</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
       "      <td>86.9</td>\n",
       "      <td>Partially cloudy</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1809</th>\n",
       "      <td>f270be9716c4e236805651c1dcef7caeb0fe5f700b9b568cfa40dbf2b1eb829c0f326dee1eb6655f294c72c0edd60e6a69be118844ee3772b18e68e481c8394a</td>\n",
       "      <td>2021-10-22 18:46:00</td>\n",
       "      <td>30</td>\n",
       "      <td>TRAFFIC SIGNAL</td>\n",
       "      <td>FUNCTIONING PROPERLY</td>\n",
       "      <td>CLEAR</td>\n",
       "      <td>DARKNESS, LIGHTED ROAD</td>\n",
       "      <td>PEDALCYCLIST</td>\n",
       "      <td>DIVIDED - W/MEDIAN (NOT RAISED)</td>\n",
       "      <td>STRAIGHT AND LEVEL</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
       "      <td>52.4</td>\n",
       "      <td>Overcast</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1874</th>\n",
       "      <td>d9625dcdc791d0091982d7395c457c81d220c3215dd9cb93bf0e5e9edb3ac79c8d768b697d53237f87aa317fecb565b637c6618a51a6bcfb56060cd7384f7cb7</td>\n",
       "      <td>2021-10-12 19:30:00</td>\n",
       "      <td>25</td>\n",
       "      <td>NO CONTROLS</td>\n",
       "      <td>NO CONTROLS</td>\n",
       "      <td>CLEAR</td>\n",
       "      <td>DARKNESS</td>\n",
       "      <td>PEDALCYCLIST</td>\n",
       "      <td>NOT DIVIDED</td>\n",
       "      <td>STRAIGHT AND LEVEL</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
       "      <td>63.4</td>\n",
       "      <td>Partially cloudy</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1769</th>\n",
       "      <td>28319bf0472112ff33380b34d2a762f8d7bf0914edb5cc0807979ce447063adc491c401fdd8a753ed81672517927ad17eb522e8389dd8e745a28a18d5b4471ff</td>\n",
       "      <td>2021-10-30 09:15:00</td>\n",
       "      <td>30</td>\n",
       "      <td>TRAFFIC SIGNAL</td>\n",
       "      <td>FUNCTIONING PROPERLY</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>DAYLIGHT</td>\n",
       "      <td>PEDALCYCLIST</td>\n",
       "      <td>NOT DIVIDED</td>\n",
       "      <td>STRAIGHT AND LEVEL</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
       "      <td>53.2</td>\n",
       "      <td>Overcast</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2621</th>\n",
       "      <td>094ca7525dfa7644d88cfe39dd1d6fe01423e6d530a7ce991ff62c528b9343015143fd43c5c66ad6f5cbff3341c4c5fbf43ed7da8a0446c89cdf0a7689270d9f</td>\n",
       "      <td>2021-06-23 09:20:00</td>\n",
       "      <td>30</td>\n",
       "      <td>NO CONTROLS</td>\n",
       "      <td>NO CONTROLS</td>\n",
       "      <td>CLEAR</td>\n",
       "      <td>DAYLIGHT</td>\n",
       "      <td>PEDALCYCLIST</td>\n",
       "      <td>DIVIDED - W/MEDIAN (NOT RAISED)</td>\n",
       "      <td>STRAIGHT AND LEVEL</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
       "      <td>65.8</td>\n",
       "      <td>Overcast</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                       crash_record_id  \\\n",
       "1911  c19ce8fe678b1693e27a4f2cf1b5f2efffa16f594ce21227c3aed84d5a145a7b27379e084f143b3ad36709b00c964b529d4b1baf125a80713c66465a7dfcc503   \n",
       "3085  967783f483ead199e53dccee44b2dd5b1ef42a6251b129d49b9caa66ddb05b484114809526f7e3e4c3ed6aa69151ffa5dddf935e4e056cb88d1e1010f7e70d60   \n",
       "1848  f6198c266f65265e6868d066cd4f29b1ff76f2e18f4da95f3bf3053f3af9521b94e8e09aa336b118a0cc1efcc91ec8d3362498fe882ef9ddd03c0124bcb86025   \n",
       "1657  070c71bfc229a6b429868790eb90a890b25941b5ab8396a4b4ffb0992980949dacce76860d9d1f0d8dc41068e471934abcdbc6dc3ea9b312368a8284508fdee5   \n",
       "1814  f5556a1237866e0c5c8e38f3bafd287f7c7c70f64a424a6fb11bb2a4516fcf75188c554bf19b17fb8b15ba5197f2ea55f885da658960897b55308a8ff60d3d90   \n",
       "2147  76551113b651c6743b16a596e469859699682d4d209356356c3560c9048672557171d8cd74e7955b92d05f751720e133f8247b978383b0ca9f5d884d7a04ff40   \n",
       "1809  f270be9716c4e236805651c1dcef7caeb0fe5f700b9b568cfa40dbf2b1eb829c0f326dee1eb6655f294c72c0edd60e6a69be118844ee3772b18e68e481c8394a   \n",
       "1874  d9625dcdc791d0091982d7395c457c81d220c3215dd9cb93bf0e5e9edb3ac79c8d768b697d53237f87aa317fecb565b637c6618a51a6bcfb56060cd7384f7cb7   \n",
       "1769  28319bf0472112ff33380b34d2a762f8d7bf0914edb5cc0807979ce447063adc491c401fdd8a753ed81672517927ad17eb522e8389dd8e745a28a18d5b4471ff   \n",
       "2621  094ca7525dfa7644d88cfe39dd1d6fe01423e6d530a7ce991ff62c528b9343015143fd43c5c66ad6f5cbff3341c4c5fbf43ed7da8a0446c89cdf0a7689270d9f   \n",
       "\n",
       "              crash_date  posted_speed_limit traffic_control_device  \\\n",
       "1911 2021-10-04 19:44:00                  30         TRAFFIC SIGNAL   \n",
       "3085 2021-03-02 15:55:00                  30         TRAFFIC SIGNAL   \n",
       "1848 2021-10-16 15:00:00                  30         TRAFFIC SIGNAL   \n",
       "1657 2021-12-02 17:23:00                  30         TRAFFIC SIGNAL   \n",
       "1814 2021-10-21 14:35:00                  30            NO CONTROLS   \n",
       "2147 2021-08-29 15:15:00                  30            NO CONTROLS   \n",
       "1809 2021-10-22 18:46:00                  30         TRAFFIC SIGNAL   \n",
       "1874 2021-10-12 19:30:00                  25            NO CONTROLS   \n",
       "1769 2021-10-30 09:15:00                  30         TRAFFIC SIGNAL   \n",
       "2621 2021-06-23 09:20:00                  30            NO CONTROLS   \n",
       "\n",
       "          device_condition weather_condition      lighting_condition  \\\n",
       "1911               UNKNOWN             CLEAR  DARKNESS, LIGHTED ROAD   \n",
       "3085  FUNCTIONING PROPERLY             CLEAR                DAYLIGHT   \n",
       "1848  FUNCTIONING PROPERLY             CLEAR                DAYLIGHT   \n",
       "1657  FUNCTIONING PROPERLY             CLEAR                    DUSK   \n",
       "1814           NO CONTROLS   CLOUDY/OVERCAST                DAYLIGHT   \n",
       "2147           NO CONTROLS             CLEAR                DAYLIGHT   \n",
       "1809  FUNCTIONING PROPERLY             CLEAR  DARKNESS, LIGHTED ROAD   \n",
       "1874           NO CONTROLS             CLEAR                DARKNESS   \n",
       "1769  FUNCTIONING PROPERLY              RAIN                DAYLIGHT   \n",
       "2621           NO CONTROLS             CLEAR                DAYLIGHT   \n",
       "\n",
       "     first_crash_type                  trafficway_type           alignment  \\\n",
       "1911     PEDALCYCLIST                         FOUR WAY  STRAIGHT AND LEVEL   \n",
       "3085     PEDALCYCLIST                         FOUR WAY  STRAIGHT AND LEVEL   \n",
       "1848     PEDALCYCLIST                      NOT DIVIDED  STRAIGHT AND LEVEL   \n",
       "1657     PEDALCYCLIST              FIVE POINT, OR MORE  STRAIGHT AND LEVEL   \n",
       "1814     PEDALCYCLIST  DIVIDED - W/MEDIAN (NOT RAISED)  STRAIGHT AND LEVEL   \n",
       "2147     PEDALCYCLIST                      NOT DIVIDED  STRAIGHT AND LEVEL   \n",
       "1809     PEDALCYCLIST  DIVIDED - W/MEDIAN (NOT RAISED)  STRAIGHT AND LEVEL   \n",
       "1874     PEDALCYCLIST                      NOT DIVIDED  STRAIGHT AND LEVEL   \n",
       "1769     PEDALCYCLIST                      NOT DIVIDED  STRAIGHT AND LEVEL   \n",
       "2621     PEDALCYCLIST  DIVIDED - W/MEDIAN (NOT RAISED)  STRAIGHT AND LEVEL   \n",
       "\n",
       "      ... private_property_i photos_taken_i work_zone_i work_zone_type  \\\n",
       "1911  ...                NaN            NaN         NaN            NaN   \n",
       "3085  ...                NaN            NaN         NaN            NaN   \n",
       "1848  ...                NaN            NaN         NaN            NaN   \n",
       "1657  ...                NaN            NaN         NaN            NaN   \n",
       "1814  ...                NaN            NaN         NaN            NaN   \n",
       "2147  ...                NaN            NaN         NaN            NaN   \n",
       "1809  ...                NaN            NaN         NaN            NaN   \n",
       "1874  ...                NaN            NaN         NaN            NaN   \n",
       "1769  ...                NaN            NaN         NaN            NaN   \n",
       "2621  ...                NaN            NaN         NaN            NaN   \n",
       "\n",
       "     workers_present_i lane_cnt crash_year wTemp             wCond wPrecip  \n",
       "1911               NaN      NaN       2021  67.3          Overcast    None  \n",
       "3085               NaN      NaN       2021  42.0             Clear    None  \n",
       "1848               NaN      NaN       2021  59.3  Partially cloudy    None  \n",
       "1657               NaN      NaN       2021  50.0  Partially cloudy    None  \n",
       "1814               NaN      NaN       2021  51.0          Overcast    None  \n",
       "2147               NaN      NaN       2021  86.9  Partially cloudy    None  \n",
       "1809               NaN      NaN       2021  52.4          Overcast    None  \n",
       "1874               NaN      NaN       2021  63.4  Partially cloudy    None  \n",
       "1769               NaN      NaN       2021  53.2          Overcast    None  \n",
       "2621               NaN      NaN       2021  65.8          Overcast    None  \n",
       "\n",
       "[10 rows x 53 columns]"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "badInjury.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CLEAR                    0.874431\n",
       "RAIN                     0.065712\n",
       "UNKNOWN                  0.028627\n",
       "CLOUDY/OVERCAST          0.025374\n",
       "SNOW                     0.003253\n",
       "FREEZING RAIN/DRIZZLE    0.001952\n",
       "OTHER                    0.000651\n",
       "Name: weather_condition, dtype: float64"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crash21['weather_condition'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1537 entries, 1604 to 3140\n",
      "Data columns (total 50 columns):\n",
      " #   Column                         Non-Null Count  Dtype         \n",
      "---  ------                         --------------  -----         \n",
      " 0   crash_record_id                1537 non-null   object        \n",
      " 1   crash_date                     1537 non-null   datetime64[ns]\n",
      " 2   posted_speed_limit             1537 non-null   int64         \n",
      " 3   traffic_control_device         1537 non-null   object        \n",
      " 4   device_condition               1537 non-null   object        \n",
      " 5   weather_condition              1537 non-null   object        \n",
      " 6   lighting_condition             1537 non-null   object        \n",
      " 7   first_crash_type               1537 non-null   object        \n",
      " 8   trafficway_type                1537 non-null   object        \n",
      " 9   alignment                      1537 non-null   object        \n",
      " 10  roadway_surface_cond           1537 non-null   object        \n",
      " 11  road_defect                    1537 non-null   object        \n",
      " 12  report_type                    1463 non-null   object        \n",
      " 13  crash_type                     1537 non-null   object        \n",
      " 14  damage                         1537 non-null   object        \n",
      " 15  date_police_notified           1537 non-null   object        \n",
      " 16  prim_contributory_cause        1537 non-null   object        \n",
      " 17  sec_contributory_cause         1537 non-null   object        \n",
      " 18  street_no                      1537 non-null   object        \n",
      " 19  street_direction               1537 non-null   object        \n",
      " 20  street_name                    1537 non-null   object        \n",
      " 21  beat_of_occurrence             1537 non-null   int64         \n",
      " 22  dooring_i                      241 non-null    object        \n",
      " 23  num_units                      1537 non-null   object        \n",
      " 24  most_severe_injury             1537 non-null   object        \n",
      " 25  injuries_total                 1537 non-null   int64         \n",
      " 26  injuries_fatal                 1537 non-null   object        \n",
      " 27  injuries_incapacitating        1537 non-null   object        \n",
      " 28  injuries_non_incapacitating    1537 non-null   object        \n",
      " 29  injuries_reported_not_evident  1537 non-null   object        \n",
      " 30  injuries_no_indication         1537 non-null   object        \n",
      " 31  injuries_unknown               1537 non-null   object        \n",
      " 32  crash_hour                     1537 non-null   int64         \n",
      " 33  crash_day_of_week              1537 non-null   int64         \n",
      " 34  crash_month                    1537 non-null   int64         \n",
      " 35  latitude                       1530 non-null   float64       \n",
      " 36  longitude                      1530 non-null   float64       \n",
      " 37  location                       1530 non-null   object        \n",
      " 38  intersection_related_i         579 non-null    object        \n",
      " 39  hit_and_run_i                  483 non-null    object        \n",
      " 40  statements_taken_i             46 non-null     object        \n",
      " 41  rd_no                          1537 non-null   object        \n",
      " 42  crash_date_est_i               84 non-null     object        \n",
      " 43  private_property_i             13 non-null     object        \n",
      " 44  photos_taken_i                 17 non-null     object        \n",
      " 45  work_zone_i                    4 non-null      object        \n",
      " 46  work_zone_type                 3 non-null      object        \n",
      " 47  workers_present_i              1 non-null      object        \n",
      " 48  lane_cnt                       0 non-null      object        \n",
      " 49  crash_year                     1537 non-null   int64         \n",
      "dtypes: datetime64[ns](1), float64(2), int64(7), object(40)\n",
      "memory usage: 612.4+ KB\n"
     ]
    }
   ],
   "source": [
    "crash21.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41.89529666462941"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crash21['latitude'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-87.67682552741503"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crash21['longitude'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "166.66666666666666"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1000 / 6\n",
    "\n"
   ]
  }
 ],
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