SLOPoliceTidy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SLO Police Logs: Tidy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this exercise, you will parse police log data from the [City of San Luis Obispo](http://www.slocity.org) in California. Each day, the police department posts a text log file of all the incidents for that day:\n",
    "\n",
    "http://www.slocity.org/government/department-directory/police-department/police-log\n",
    "\n",
    "A dataset of these log files is available in the data directory. You will parse these files into a tidy `DataFrame`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parse a single incident entry"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, you will write a series of functions that parse a single incident entry. You may find the following `MARKER` text to be helpful:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "403bf5e5a51a57cfebca208b2692169a",
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     "grade_id": "cell-e4fa707a404b16ac",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "MARKER = '====='"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A single text file consists of a series of incident entries, where each entry looks something like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "db1bc921183ed8f466b0770c2ad2278c",
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     "locked": true,
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   },
   "outputs": [],
   "source": [
    "entry = \"\"\"===============================================================================\n",
    "160728012 07/28/16 Received:07:11 Dispatched:07:13 Arrived:      Cleared:07:15\n",
    "===============================================================================\n",
    "Type: Alarm Audible                                           Location:PN13\n",
    "As Observed:\n",
    "\n",
    "\n",
    "Addr: 12318 LOS OSOS VALLEY; ENTERPRISE RENT A   Clearance Code:Call Cancelled\n",
    "\n",
    "Responsible Officer: Benson, G\n",
    "Units: 4245  ,4231\n",
    " Des: incid#=160728012 Completed call disp:CAN clr:CAN call=18l\n",
    "CALL COMMENTS: FRONT DOOR, REAR MOTION\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is important to note that the exact format of the entries varies widely. To get a better sense of what you are up against, you should `cat` some of the data files before starting this exercise.\n",
    "\n",
    "First, write a function that takes the header line of an entry (the line between the two `MARKER` lines), and returns a Python `dict` for the values in the header. See the tests below for the exact fields you should extract. You will likely have to deal with the possibility of missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "checksum": "c18789dbaed840022ded017f097cf7f0",
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     "schema_version": 1,
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    }
   },
   "outputs": [],
   "source": [
    "def parse_header(text):\n",
    "    \"\"\"Parse the incident header line as text and return a python dict.\"\"\"\n",
    "    # YOUR CODE HERE\n",
    "    text = text.lower().strip()\n",
    "    text = text.split()\n",
    "    text = [x.split(\":\",1) for x in text]\n",
    "    header = {\"arrived\": \"\", \"cleared\": \"\", \"date\": \"\", \"dispatched\": \"\", \"id\": \"\", \"received\": \"\"}\n",
    "    header[\"id\"] = text[0][0]\n",
    "    header[\"date\"] = text[1][0]\n",
    "    categories = [\"received\", \"dispatched\", \"arrived\", \"cleared\"]\n",
    "    for i in range(2, len(text)):\n",
    "        if text[i][0] in categories and text[i][1] != \"\":\n",
    "            header[text[i][0]] = text[i][1]\n",
    "    return header"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "26d8235496fc380fbb01306a4f21f022",
     "grade": true,
     "grade_id": "cell-3b0dbf73ca0c63be",
     "locked": true,
     "points": 2,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "h = parse_header(entry.splitlines()[1])\n",
    "assert h == \\\n",
    "    {'arrived': '',\n",
    "     'cleared': '07:15',\n",
    "     'date': '07/28/16',\n",
    "     'dispatched': '07:13',\n",
    "     'id': '160728012',\n",
    "     'received': '07:11'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not write a function that takes takes the line of text containing the `Type` and `Location` and return a `dict` of those fields. Again, you will have to deal with the possibility of missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "checksum": "498f8a513d919ec544d783726d6849ef",
     "grade": false,
     "grade_id": "cell-237e145caebab328",
     "locked": false,
     "schema_version": 1,
     "solution": true
    }
   },
   "outputs": [],
   "source": [
    "def parse_type_location(text):\n",
    "    \"\"\"Parse the type/location line of text into a dict.\"\"\"\n",
    "    # YOUR CODE HERE\n",
    "    text = text.strip().lower()\n",
    "    text = text.split()\n",
    "    type_location = {\"location\" : \"\", \"type\" : \"\"}\n",
    "    for i in range(len(text)):\n",
    "        if text[i] == \"type:\":\n",
    "            next_word_index = i + 1\n",
    "            insert_string = \"\"\n",
    "            while \"location\" not in text[next_word_index]:\n",
    "                if len(insert_string) > 1:\n",
    "                    insert_string += \" \"\n",
    "                insert_string += text[next_word_index]\n",
    "                next_word_index += 1\n",
    "            type_location[\"type\"] = insert_string\n",
    "        \n",
    "        elif \"location\" in text[i]:\n",
    "            info = text[i].split(\":\")\n",
    "            if len(info) == 2:\n",
    "                type_location[\"location\"] = info[1]\n",
    "    return type_location "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "803bd6533b9d10f2a657cbc0105a8332",
     "grade": true,
     "grade_id": "cell-0d2aa771338fd182",
     "locked": true,
     "points": 4,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "type_location = parse_type_location(entry.splitlines()[3])\n",
    "assert type_location=={'location': 'pn13', 'type': 'alarm audible'}\n",
    "type_location = parse_type_location('Type: this that Location:')\n",
    "assert type_location == {'location': '', 'type': 'this that'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write a function to parse the line with `Units` into a `dict`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "checksum": "6220f1e6c04294b5f842d17a013564bc",
     "grade": false,
     "grade_id": "cell-10a6cdb06fa3ab4c",
     "locked": false,
     "schema_version": 1,
     "solution": true
    }
   },
   "outputs": [],
   "source": [
    "def parse_units(text):\n",
    "    \"\"\"Parse the line containing `Units:` into a dict.\"\"\"\n",
    "    text = text.strip().lower()\n",
    "    text = text.split()\n",
    "    units = {\"units\": []}\n",
    "    if len(text) == 1:\n",
    "        return units\n",
    "    for i in range(1, len(text)):\n",
    "        num = text[i].split(\",\")\n",
    "        if len(num) == 1:\n",
    "            units[\"units\"].append(num[0])\n",
    "        else:\n",
    "            for entry in num:\n",
    "                if entry != \"\":\n",
    "                    units[\"units\"].append(entry)\n",
    "    return units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "68ef4de4e74f44bd612d0e862a043e77",
     "grade": true,
     "grade_id": "cell-73b24c8dc28352af",
     "locked": true,
     "points": 2,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "units = parse_units(entry.splitlines()[-3])\n",
    "assert units=={'units': ['4245', '4231']}\n",
    "units = parse_units('Units:')\n",
    "assert units=={'units': []}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write a function that parses an entire incident entry. Handle the following lines of the entry:\n",
    "\n",
    "* The header line\n",
    "* The type/location line\n",
    "* The units line\n",
    "\n",
    "Your function should call the above functions that parse each of these lines; other lines should be ignored."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "checksum": "ea9b345158ae49dcaf74d8866990b75d",
     "grade": false,
     "grade_id": "cell-d019e6c0cdb37061",
     "locked": false,
     "schema_version": 1,
     "solution": true
    }
   },
   "outputs": [],
   "source": [
    "def parse_incident(text):\n",
    "    \"\"\"Parse an indicent entry into a python dict.\"\"\"\n",
    "    # making sure the line actually contains the thing we want\n",
    "    lines = text.splitlines()\n",
    "    dictionary = {}\n",
    "    for line in lines: \n",
    "        if \"Arrived\" in line:\n",
    "            dictionary.update(parse_header(line))\n",
    "        if \"Type\" in line:\n",
    "            dictionary.update(parse_type_location(line))\n",
    "        if \"Unit\" in line:\n",
    "            dictionary.update(parse_units(line))\n",
    "    return dictionary\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "367f2147539820c21f102ff1a7d3ef87",
     "grade": true,
     "grade_id": "cell-9d00e8a1a0ebeec3",
     "locked": true,
     "points": 2,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "incident = parse_incident(entry)\n",
    "assert incident == \\\n",
    "    {'arrived': '',\n",
    "     'cleared': '07:15',\n",
    "     'date': '07/28/16',\n",
    "     'dispatched': '07:13',\n",
    "     'id': '160728012',\n",
    "     'location': 'pn13',\n",
    "     'received': '07:11',\n",
    "     'type': 'alarm audible',\n",
    "     'units': ['4245', '4231']}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read all files and parse the entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "e11cafc31ecd7b8c69da96427002d24f",
     "grade": false,
     "grade_id": "cell-302554d3d3ae336a",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/data/slo_police_logs_2017-02/SLOPolice_TDGAcq2016-08-05-1901.txt',\n",
       " '/data/slo_police_logs_2017-02/SLOPolice_TDGAcq2016-08-03-1700.txt',\n",
       " '/data/slo_police_logs_2017-02/SLOPolice_TDGAcq2016-08-22-1700.txt',\n",
       " '/data/slo_police_logs_2017-02/SLOPolice_TDGAcq2016-08-15-1700.txt',\n",
       " '/data/slo_police_logs_2017-02/SLOPolice_TDGAcq2016-08-29-1700.txt',\n",
       " '/data/slo_police_logs_2017-02/SLOPolice_TDGAcq2016-08-19-1700.txt',\n",
       " '/data/slo_police_logs_2017-02/SLOPolice_TDGAcq2016-08-18-1700.txt',\n",
       " '/data/slo_police_logs_2017-02/SLOPolice_TDGAcq2016-08-10-2052.txt',\n",
       " '/data/slo_police_logs_2017-02/SLOPolice_TDGAcq2016-08-02-1200.txt',\n",
       " '/data/slo_police_logs_2017-02/SLOPolice_TDGAcq2016-08-31-1700.txt',\n",
       " '/data/slo_police_logs_2017-02/SLOPolice_TDGAcq2016-08-10-2050.txt',\n",
       " '/data/slo_police_logs_2017-02/SLOPolice_TDGAcq2016-08-23-1700.txt',\n",
       " '/data/slo_police_logs_2017-02/SLOPolice_TDGAcq2016-08-24-1700.txt',\n",
       " '/data/slo_police_logs_2017-02/SLOPolice_TDGAcq2016-08-11-1920.txt',\n",
       " '/data/slo_police_logs_2017-02/SLOPolice_TDGAcq2016-08-25-1700.txt',\n",
       " '/data/slo_police_logs_2017-02/SLOPolice_TDGAcq2016-08-17-1700.txt',\n",
       " '/data/slo_police_logs_2017-02/SLOPolice_TDGAcq2016-08-03-1200.txt',\n",
       " '/data/slo_police_logs_2017-02/SLOPolice_TDGAcq2016-08-01-1200.txt',\n",
       " '/data/slo_police_logs_2017-02/SLOPolice_TDGAcq2016-08-16-1700.txt',\n",
       " '/data/slo_police_logs_2017-02/SLOPolice_TDGAcq2016-08-12-1700.txt',\n",
       " '/data/slo_police_logs_2017-02/SLOPolice_TDGAcq2016-08-26-1700.txt',\n",
       " '/data/slo_police_logs_2017-02/SLOPolice_TDGAcq2016-08-04-1700.txt']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import glob\n",
    "files = glob.glob('/data/slo_police_logs_2017-02/*2016-08*.txt')\n",
    "files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write a generator function that takes a list of filenames and yields the lines of the files:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "checksum": "65e623a167861f4955a8ec3855fd4ffa",
     "grade": false,
     "grade_id": "cell-ce47cada736c170d",
     "locked": false,
     "schema_version": 1,
     "solution": true
    }
   },
   "outputs": [],
   "source": [
    "def files_to_lines(files):\n",
    "    \"\"\"Take a list of files and yield their lines.\"\"\"\n",
    "    for file in files:\n",
    "        with open(file) as f:\n",
    "            lines = f.readlines()\n",
    "            for line in lines:\n",
    "                yield line.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "1bd557b7f936e89fda8b1b27609e10e9",
     "grade": true,
     "grade_id": "cell-5f34b3cb797d2a9a",
     "locked": true,
     "points": 2,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "import hashlib, types\n",
    "lines_hash = hashlib.md5()\n",
    "for line in files_to_lines(files):\n",
    "    lines_hash.update(line.encode())\n",
    "assert lines_hash.hexdigest()=='5aec19b16d5457832d5ccc4ba02930a2'\n",
    "assert type(files_to_lines(files)) == types.GeneratorType"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write a generator function that consumes an iterator of lines from the incident files, and yields a sequence of text based incident entries. Each entry that is yielded should be a single string, with embedded newlines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "checksum": "1ae8c1a715767be079e7f7cd692841b6",
     "grade": false,
     "grade_id": "cell-4e5e6943f11474f1",
     "locked": false,
     "schema_version": 1,
     "solution": true
    }
   },
   "outputs": [],
   "source": [
    "def lines_to_indicents(lines):\n",
    "    \"\"\"Consume an iterator of lines, yield text blocks of for each incident.\"\"\"\n",
    "    return_string = \"\"\n",
    "    marker_count = 0\n",
    "    end = \"--------------------------------------------------------------------------------\\n\"\n",
    "    for line in lines:\n",
    "        if MARKER in line:\n",
    "            marker_count += 1\n",
    "        if marker_count >= 3:\n",
    "            marker_count = 1\n",
    "            yield return_string \n",
    "            return_string = \"\"\n",
    "        if line == end:\n",
    "            yield return_string \n",
    "            break\n",
    "        if marker_count != 0:\n",
    "            return_string += line + \"\\n\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the functions you have defined above, iterate through the filenames in the variable `files`, parse all the incident entries into Python `dict` objects and return a list of those objects named `incidents`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "checksum": "d21f175d2795a40d0b183d08f75c5ec8",
     "grade": false,
     "grade_id": "cell-c8d761b7652523ae",
     "locked": false,
     "schema_version": 1,
     "solution": true
    }
   },
   "outputs": [],
   "source": [
    "incidents = []\n",
    "\n",
    "lines = files_to_lines(files)\n",
    "incidents_dicts = lines_to_indicents(lines)\n",
    "for incident in incidents_dicts:\n",
    "    incident = incident.strip()\n",
    "    incidents.append(parse_incident(incident))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "0f8836b5f1de6cef146bebfbb3903f6d",
     "grade": false,
     "grade_id": "cell-b079d0d17ceb5cf1",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'arrived': '11:01',\n",
       " 'cleared': '11:25',\n",
       " 'date': '08/19/16',\n",
       " 'dispatched': '10:58',\n",
       " 'id': '160819036',\n",
       " 'location': 'pn5',\n",
       " 'received': '10:57',\n",
       " 'type': 'disorderly',\n",
       " 'units': ['4268', '4231']}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "incidents[200]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'arrived': '06:33',\n",
       " 'cleared': '06:34',\n",
       " 'date': '08/05/16',\n",
       " 'dispatched': '06:18',\n",
       " 'id': '160805013',\n",
       " 'location': 'pn11',\n",
       " 'received': '06:16',\n",
       " 'type': 'alarm audible',\n",
       " 'units': ['4266', '4266', '4265']}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "incidents[100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "20c84f2c7d775766760321d0ec48c187",
     "grade": true,
     "grade_id": "cell-cabbf9527e1af387",
     "locked": true,
     "points": 4,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "assert len(incidents) == 2616\n",
    "assert type(incidents) == list\n",
    "assert incidents[100] == \\\n",
    "    {'arrived': '06:33',\n",
    "     'cleared': '06:34',\n",
    "     'date': '08/05/16',\n",
    "     'dispatched': '06:18',\n",
    "     'id': '160805013',\n",
    "     'location': 'pn11',\n",
    "     'received': '06:16',\n",
    "     'type': 'alarm audible',\n",
    "     'units': ['4266', '4266', '4265']}\n",
    "assert incidents[200] == \\\n",
    "    {'arrived': '11:01',\n",
    "     'cleared': '11:25',\n",
    "     'date': '08/19/16',\n",
    "     'dispatched': '10:58',\n",
    "     'id': '160819036',\n",
    "     'location': 'pn5',\n",
    "     'received': '10:57',\n",
    "     'type': 'disorderly',\n",
    "     'units': ['4268', '4231']}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create the incident DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert the `incidents` list-of-dicts to a Pandas `DataFrame` named `incidents_df` with a format given by the tests below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "checksum": "f7d2e74a51e121c102ede1fe5fe1d137",
     "grade": false,
     "grade_id": "cell-5c58f74eb9b71d4b",
     "locked": false,
     "schema_version": 1,
     "solution": true
    }
   },
   "outputs": [],
   "source": [
    "# YOUR CODE HERE\n",
    "incidents_df = pd.DataFrame(incidents)\n",
    "incidents_df = incidents_df[[\"arrived\", \"cleared\", \"date\", \"dispatched\", \"id\", \"location\", \"received\", \"type\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "f634fcd33e1f0726fbf438c84d766595",
     "grade": false,
     "grade_id": "cell-31e97820b3aa9d0c",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "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>arrived</th>\n",
       "      <th>cleared</th>\n",
       "      <th>date</th>\n",
       "      <th>dispatched</th>\n",
       "      <th>id</th>\n",
       "      <th>location</th>\n",
       "      <th>received</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>07:37</td>\n",
       "      <td>07:48</td>\n",
       "      <td>08/04/16</td>\n",
       "      <td>07:26</td>\n",
       "      <td>160804007</td>\n",
       "      <td>pn7</td>\n",
       "      <td>07:23</td>\n",
       "      <td>traffic hazard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>07:52</td>\n",
       "      <td>08:01</td>\n",
       "      <td>08/04/16</td>\n",
       "      <td>07:42</td>\n",
       "      <td>160804008</td>\n",
       "      <td>pn5</td>\n",
       "      <td>07:40</td>\n",
       "      <td>loitering</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>08/04/16</td>\n",
       "      <td></td>\n",
       "      <td>160804009</td>\n",
       "      <td>pn8</td>\n",
       "      <td>08:02</td>\n",
       "      <td>muni code viol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td></td>\n",
       "      <td>08:07</td>\n",
       "      <td>08/04/16</td>\n",
       "      <td>08:07</td>\n",
       "      <td>160804010</td>\n",
       "      <td>pn1</td>\n",
       "      <td>08:03</td>\n",
       "      <td>aband vehicle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>08:22</td>\n",
       "      <td>08:28</td>\n",
       "      <td>08/04/16</td>\n",
       "      <td></td>\n",
       "      <td>160804011</td>\n",
       "      <td>pn5</td>\n",
       "      <td>08:14</td>\n",
       "      <td>mc-panhandling</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  arrived cleared      date dispatched         id location received  \\\n",
       "0   07:37   07:48  08/04/16      07:26  160804007      pn7    07:23   \n",
       "1   07:52   08:01  08/04/16      07:42  160804008      pn5    07:40   \n",
       "2                  08/04/16             160804009      pn8    08:02   \n",
       "3           08:07  08/04/16      08:07  160804010      pn1    08:03   \n",
       "4   08:22   08:28  08/04/16             160804011      pn5    08:14   \n",
       "\n",
       "             type  \n",
       "0  traffic hazard  \n",
       "1       loitering  \n",
       "2  muni code viol  \n",
       "3   aband vehicle  \n",
       "4  mc-panhandling  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "incidents_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "bfac1b171132b4e8d68834a7e823c9a3",
     "grade": true,
     "grade_id": "cell-1c829a6806eb322b",
     "locked": true,
     "points": 0,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "assert list(incidents_df.location.unique()) == \\\n",
    "    ['pn7', 'pn5', 'pn8', 'pn1', 'pn13', 'pn10', 'pn2', 'pn9', 'pn12',\n",
    "    'pn11', 'pn6', 'pn3', 'lz0', 'pn4', '']\n",
    "assert len(incidents_df['type'].unique()) == 81\n",
    "assert list(incidents_df.columns) == \\\n",
    "    ['arrived', 'cleared', 'date', 'dispatched', 'id', 'location',\n",
    "     'received', 'type']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a second `DataFrame`, named `units_df` with two columns:\n",
    "\n",
    "* The `id` of the incident\n",
    "* The `unit` that handled the call.\n",
    "\n",
    "If there are multiple units for an id, insert multiple rows for that id (one for each unit). This is needed to make this `DataFrame` tidy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "checksum": "c0f9f9f71f4a709cebc67b5baa7862ed",
     "grade": false,
     "grade_id": "cell-a519559ee8d92add",
     "locked": false,
     "schema_version": 1,
     "solution": true
    }
   },
   "outputs": [],
   "source": [
    "# YOUR CODE HERE\n",
    "incidents_df_new = pd.DataFrame(incidents)\n",
    "new_dataframe = []\n",
    "for idx, row in incidents_df_new.iterrows():\n",
    "    if len(row.loc[\"units\"]) == 1:\n",
    "        if row.loc[\"units\"][0] == \"\":\n",
    "            continue\n",
    "        temp_dict = {\"id\": row.loc[\"id\"], \"unit\": row.loc[\"units\"][0]}\n",
    "        new_dataframe.append(temp_dict)\n",
    "    else:\n",
    "        for i in range(len(row.loc[\"units\"])):\n",
    "            if row.loc[\"units\"][i] == \"\":\n",
    "                continue\n",
    "            temp_dict = {\"id\": row.loc[\"id\"], \"unit\": row.loc[\"units\"][i]}\n",
    "            new_dataframe.append(temp_dict)\n",
    "units_df = pd.DataFrame(new_dataframe)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "2efa484185e192df0fdc05bcffd22f72",
     "grade": false,
     "grade_id": "cell-f7b9171349ac28e8",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "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>id</th>\n",
       "      <th>unit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>160804007</td>\n",
       "      <td>42k4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>160804007</td>\n",
       "      <td>4202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>160804008</td>\n",
       "      <td>4226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>160804008</td>\n",
       "      <td>4218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>160804009</td>\n",
       "      <td>set</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  unit\n",
       "0  160804007  42k4\n",
       "1  160804007  4202\n",
       "2  160804008  4226\n",
       "3  160804008  4218\n",
       "4  160804009   set"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "units_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#units_df.groupby(by = \"unit\").agg(\"total\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "996af2472a5405d95f3b12bf492e5507",
     "grade": true,
     "grade_id": "cell-b5d3234b36530109",
     "locked": true,
     "points": 2,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "assert len(units_df) == 4617\n",
    "assert list(units_df.columns) == ['id', 'unit']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "ab35bc57d6b3161d5486df8ff1a83ab3",
     "grade": false,
     "grade_id": "cell-c8f76ae3348da3c6",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "import altair as alt\n",
    "alt.enable_mime_rendering()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a single visualization using Altair that explores this dataset visually and begins to provide a high level overview of the information in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "checksum": "86dfb9acdbee2fb4d99f69b36a630521",
     "grade": true,
     "grade_id": "cell-fef4805a7e513fc6",
     "locked": false,
     "points": 2,
     "schema_version": 1,
     "solution": true
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.vegalite.v1+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v1.2.1.json",
       "data": {
        "values": [
         {
          "arrived": "07:37",
          "cleared": "07:48",
          "date": "08/04/16",
          "dispatched": "07:26",
          "id": "160804007",
          "location": "pn7",
          "received": "07:23",
          "type": "traffic hazard"
         },
         {
          "arrived": "07:52",
          "cleared": "08:01",
          "date": "08/04/16",
          "dispatched": "07:42",
          "id": "160804008",
          "location": "pn5",
          "received": "07:40",
          "type": "loitering"
         },
         {
          "arrived": "",
          "cleared": "",
          "date": "08/04/16",
          "dispatched": "",
          "id": "160804009",
          "location": "pn8",
          "received": "08:02",
          "type": "muni code viol"
         },
         {
          "arrived": "",
          "cleared": "08:07",
          "date": "08/04/16",
          "dispatched": "08:07",
          "id": "160804010",
          "location": "pn1",
          "received": "08:03",
          "type": "aband vehicle"
         },
         {
          "arrived": "08:22",
          "cleared": "08:28",
          "date": "08/04/16",
          "dispatched": "",
          "id": "160804011",
          "location": "pn5",
          "received": "08:14",
          "type": "mc-panhandling"
         },
         {
          "arrived": "08:28",
          "cleared": "08:56",
          "date": "08/04/16",
          "dispatched": "08:17",
          "id": "160804012",
          "location": "pn8",
          "received": "08:14",
          "type": "trespassing"
         },
         {
          "arrived": "",
          "cleared": "09:08",
          "date": "08/04/16",
          "dispatched": "08:26",
          "id": "160804013",
          "location": "pn5",
          "received": "08:23",
          "type": "attempt-locate"
         },
         {
          "arrived": "08:50",
          "cleared": "10:42",
          "date": "08/04/16",
          "dispatched": "",
          "id": "160804014",
          "location": "pn13",
          "received": "08:49",
          "type": "mc-camping"
         },
         {
          "arrived": "08:59",
          "cleared": "09:15",
          "date": "08/04/16",
          "dispatched": "08:56",
          "id": "160804015",
          "location": "pn5",
          "received": "08:50",
          "type": "suspicious"
         },
         {
          "arrived": "",
          "cleared": "09:15",
          "date": "08/04/16",
          "dispatched": "09:05",
          "id": "160804016",
          "location": "pn7",
          "received": "09:02",
          "type": "aband vehicle"
         },
         {
          "arrived": "09:13",
          "cleared": "09:30",
          "date": "08/04/16",
          "dispatched": "09:07",
          "id": "160804017",
          "location": "pn10",
          "received": "09:06",
          "type": "posting vehs"
         },
         {
          "arrived": "",
          "cleared": "09:25",
          "date": "08/04/16",
          "dispatched": "09:25",
          "id": "160804018",
          "location": "pn8",
          "received": "09:22",
          "type": "aband vehicle"
         },
         {
          "arrived": "",
          "cleared": "09:41",
          "date": "08/04/16",
          "dispatched": "09:41",
          "id": "160804019",
          "location": "pn7",
          "received": "09:40",
          "type": "fire structure"
         },
         {
          "arrived": "",
          "cleared": "09:55",
          "date": "08/04/16",
          "dispatched": "09:54",
          "id": "160804020",
          "location": "pn2",
          "received": "09:54",
          "type": "9-1-1 abandon"
         },
         {
          "arrived": "10:11",
          "cleared": "10:13",
          "date": "08/04/16",
          "dispatched": "09:59",
          "id": "160804021",
          "location": "pn5",
          "received": "09:58",
          "type": "posting vehs"
         },
         {
          "arrived": "10:11",
          "cleared": "10:51",
          "date": "08/04/16",
          "dispatched": "10:05",
          "id": "160804022",
          "location": "pn9",
          "received": "10:04",
          "type": "suicide attempt"
         },
         {
          "arrived": "10:12",
          "cleared": "10:14",
          "date": "08/04/16",
          "dispatched": "",
          "id": "160804023",
          "location": "pn5",
          "received": "10:12",
          "type": "towed vehicle"
         },
         {
          "arrived": "10:16",
          "cleared": "10:18",
          "date": "08/04/16",
          "dispatched": "",
          "id": "160804024",
          "location": "pn5",
          "received": "10:15",
          "type": "alcohol offense"
         },
         {
          "arrived": "10:49",
          "cleared": "10:51",
          "date": "08/04/16",
          "dispatched": "",
          "id": "160804025",
          "location": "pn1",
          "received": "10:49",
          "type": "aband vehicle"
         },
         {
          "arrived": "10:50",
          "cleared": "10:52",
          "date": "08/04/16",
          "dispatched": "",
          "id": "160804026",
          "location": "pn2",
          "received": "10:50",
          "type": "suspicious"
         },
         {
          "arrived": "10:51",
          "cleared": "11:07",
          "date": "08/04/16",
          "dispatched": "",
          "id": "160804027",
          "location": "pn10",
          "received": "10:51",
          "type": "aband vehicle"
         },
         {
          "arrived": "10:57",
          "cleared": "11:08",
          "date": "08/04/16",
          "dispatched": "",
          "id": "160804028",
          "location": "pn2",
          "received": "10:57",
          "type": "controlled narc"
         },
         {
          "arrived": "11:06",
          "cleared": "11:12",
          "date": "08/04/16",
          "dispatched": "11:00",
          "id": "160804029",
          "location": "pn12",
          "received": "10:57",
          "type": "suspicious"
         },
         {
          "arrived": "",
          "cleared": "11:18",
          "date": "08/04/16",
          "dispatched": "11:16",
          "id": "160804030",
          "location": "pn7",
          "received": "11:15",
          "type": "assist req"
         },
         {
          "arrived": "11:25",
          "cleared": "11:32",
          "date": "08/04/16",
          "dispatched": "",
          "id": "160804031",
          "location": "pn11",
          "received": "11:18",
          "type": "coll non inj"
         },
         {
          "arrived": "11:53",
          "cleared": "12:37",
          "date": "08/04/16",
          "dispatched": "11:27",
          "id": "160804032",
          "location": "pn11",
          "received": "11:23",
          "type": "fraud"
         },
         {
          "arrived": "11:25",
          "cleared": "11:33",
          "date": "08/04/16",
          "dispatched": "",
          "id": "160804033",
          "location": "pn11",
          "received": "11:24",
          "type": "suspicious"
         },
         {
          "arrived": "11:41",
          "cleared": "11:53",
          "date": "08/04/16",
          "dispatched": "11:29",
          "id": "160804034",
          "location": "pn12",
          "received": "11:27",
          "type": "fraud"
         },
         {
          "arrived": "12:06",
          "cleared": "12:21",
          "date": "08/04/16",
          "dispatched": "11:59",
          "id": "160804035",
          "location": "pn11",
          "received": "11:57",
          "type": "welfare check"
         },
         {
          "arrived": "12:09",
          "cleared": "12:09",
          "date": "08/04/16",
          "dispatched": "",
          "id": "160804036",
          "location": "pn6",
          "received": "12:08",
          "type": "alcohol offense"
         },
         {
          "arrived": "12:31",
          "cleared": "12:41",
          "date": "08/04/16",
          "dispatched": "",
          "id": "160804037",
          "location": "pn5",
          "received": "12:25",
          "type": "welfare check"
         },
         {
          "arrived": "12:47",
          "cleared": "13:28",
          "date": "08/04/16",
          "dispatched": "12:36",
          "id": "160804038",
          "location": "pn8",
          "received": "12:33",
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         },
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          "type": "welfare check"
         },
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          "date": "08/04/16",
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          "id": "160804040",
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         },
         {
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          "date": "08/04/16",
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          "id": "160804041",
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         },
         {
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          "date": "08/04/16",
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          "id": "160804042",
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          "type": "coll hit and ru"
         },
         {
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          "date": "08/04/16",
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          "id": "160804043",
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         },
         {
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         },
         {
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         },
         {
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         },
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          "date": "08/04/16",
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         },
         {
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         },
         {
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         },
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          "type": "robbery"
         },
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         {
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         {
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          "type": "citizen dispute"
         },
         {
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         },
         {
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          "date": "08/04/16",
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         {
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         {
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         {
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         {
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         {
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         {
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         {
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         {
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         {
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         },
         {
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         {
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         {
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         {
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         {
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          "id": "160827036",
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          "type": "burg com"
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         {
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         },
         {
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         {
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         {
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         {
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         },
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          "date": "08/29/16",
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          "id": "160829007",
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          "type": "mc-alcohol"
         },
         {
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          "date": "08/29/16",
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         {
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          "date": "08/29/16",
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         {
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         {
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          "date": "08/29/16",
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          "type": "muni code viol"
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          "date": "08/29/16",
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          "date": "08/29/16",
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          "date": "08/18/16",
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         {
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         {
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         {
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         {
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         {
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         {
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         {
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         {
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         {
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         },
         {
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         {
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         {
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         {
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         {
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         {
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         {
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         {
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         {
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         {
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         {
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         {
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         {
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          "type": "muni code viol"
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          "type": "assault"
         },
         {
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          "date": "07/30/16",
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          "received": "02:18",
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         },
         {
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          "date": "07/30/16",
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          "id": "160730013",
          "location": "pn2",
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          "type": "prowler"
         },
         {
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          "date": "07/30/16",
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          "id": "160730014",
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          "type": "suspicious"
         },
         {
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          "date": "07/30/16",
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          "id": "160730015",
          "location": "pn5",
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         },
         {
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          "date": "07/30/16",
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          "id": "160730016",
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          "type": "noise party"
         },
         {
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          "date": "07/30/16",
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         },
         {
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          "date": "07/30/16",
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          "type": "found property"
         },
         {
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          "date": "07/30/16",
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          "id": "160730019",
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          "type": "alarm audible"
         },
         {
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          "date": "07/30/16",
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          "id": "160730020",
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          "type": "aband vehicle"
         },
         {
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          "date": "07/30/16",
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          "id": "160730021",
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          "type": "assist req"
         },
         {
          "arrived": "",
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          "date": "07/30/16",
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          "id": "160730022",
          "location": "pn2",
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          "type": "traffic offense"
         },
         {
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          "date": "07/30/16",
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          "id": "160730023",
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          "received": "10:28",
          "type": "9-1-1 abandon"
         },
         {
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          "date": "07/30/16",
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          "id": "160730024",
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          "type": "lost property"
         },
         {
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          "date": "07/30/16",
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          "id": "160730025",
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          "type": "theft"
         },
         {
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          "date": "07/30/16",
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          "id": "160730026",
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          "type": "posting vehs"
         },
         {
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          "date": "07/30/16",
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          "type": "suspicious"
         },
         {
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          "date": "07/30/16",
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          "id": "160730028",
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          "type": "suspicious"
         },
         {
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          "date": "07/30/16",
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          "id": "160730029",
          "location": "pn5",
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          "type": "towed vehicle"
         },
         {
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          "date": "07/30/16",
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          "id": "160730030",
          "location": "pn5",
          "received": "11:57",
          "type": "muni code viol"
         },
         {
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          "date": "07/30/16",
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          "id": "160730031",
          "location": "pn8",
          "received": "12:09",
          "type": "public works"
         },
         {
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          "date": "07/30/16",
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          "id": "160730032",
          "location": "pn6",
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          "type": "traffic hazard"
         },
         {
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          "date": "07/30/16",
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          "id": "160730033",
          "location": "pn10",
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          "type": "trespassing"
         },
         {
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          "date": "07/30/16",
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          "id": "160730034",
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          "type": "warrant"
         },
         {
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          "date": "07/30/16",
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          "id": "160730035",
          "location": "pn6",
          "received": "12:33",
          "type": "embezzlement"
         },
         {
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          "date": "07/30/16",
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          "id": "160730036",
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         },
         {
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         {
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          "date": "07/30/16",
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         },
         {
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          "date": "07/30/16",
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         },
         {
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          "date": "07/30/16",
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          "id": "160730040",
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          "received": "13:29",
          "type": "9-1-1 abandon"
         },
         {
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          "date": "07/30/16",
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          "id": "160730041",
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          "type": "assist req"
         },
         {
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          "date": "07/30/16",
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          "id": "160730042",
          "location": "pn5",
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          "type": "lost property"
         },
         {
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          "date": "07/30/16",
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          "id": "160730043",
          "location": "pn6",
          "received": "14:07",
          "type": "welfare check"
         },
         {
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          "date": "07/30/16",
          "dispatched": "14:17",
          "id": "160730044",
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         },
         {
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          "date": "07/30/16",
          "dispatched": "",
          "id": "160730045",
          "location": "pn1",
          "received": "14:16",
          "type": "theft"
         },
         {
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          "date": "07/30/16",
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         },
         {
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          "date": "07/30/16",
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         },
         {
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          "date": "07/30/16",
          "dispatched": "",
          "id": "160730048",
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          "type": "traffic hazard"
         },
         {
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          "date": "07/30/16",
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          "id": "160730049",
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         },
         {
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          "date": "07/30/16",
          "dispatched": "",
          "id": "160730050",
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          "type": "assist o police"
         },
         {
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          "date": "07/30/16",
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          "id": "160730051",
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         },
         {
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          "date": "07/30/16",
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         {
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         },
         {
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         },
         {
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          "date": "07/30/16",
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          "id": "160730055",
          "location": "pn13",
          "received": "16:11",
          "type": "animal problem"
         },
         {
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          "date": "07/30/16",
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          "id": "160730056",
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         },
         {
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         },
         {
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          "date": "07/30/16",
          "dispatched": "17:59",
          "id": "160730058",
          "location": "pn10",
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         },
         {
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         },
         {
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          "received": "17:24",
          "type": "alarm audible"
         },
         {
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          "date": "07/30/16",
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          "id": "160730061",
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          "type": "welfare check"
         },
         {
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          "date": "07/30/16",
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         {
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          "date": "07/30/16",
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          "type": "assist o police"
         },
         {
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          "date": "07/30/16",
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          "id": "160730064",
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         },
         {
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          "date": "07/30/16",
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          "id": "160730065",
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          "type": "mc-camping"
         },
         {
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          "date": "07/30/16",
          "dispatched": "19:09",
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          "type": "theft"
         },
         {
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          "date": "07/30/16",
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          "id": "160730067",
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          "type": "muni code viol"
         },
         {
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         {
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          "id": "160730069",
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         },
         {
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         {
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         {
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         {
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         {
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         {
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         },
         {
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          "type": "vandalism"
         },
         {
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          "date": "07/30/16",
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         },
         {
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          "date": "07/30/16",
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          "id": "160730078",
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         {
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          "type": "noise party"
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         {
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         {
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         {
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         {
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          "date": "07/30/16",
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         {
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          "id": "160730084",
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         {
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         {
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         {
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         {
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         {
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         {
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         {
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         {
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         {
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         {
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         {
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         {
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         {
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",
      "text/plain": [
       "<altair.VegaLite object>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alt.Chart(incidents_df).mark_bar().encode(\n",
    "    alt.Y(\"type:O\", sort = alt.SortField(field = \"type\", op = \"count\", order = \"descending\")),\n",
    "    alt.X(\"count(*)\"),\n",
    "    alt.Color(\"location:N\")\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "checksum": "afff675582cbd27f67aac60d9e6569c9",
     "grade": true,
     "grade_id": "cell-87193db06b518fe9",
     "locked": false,
     "points": 2,
     "schema_version": 1,
     "solution": true
    }
   },
   "source": [
    "Looking at the occurances of each reason for police call, we see that the top 3 is \"suspicious activity\", \"assistance required\", and \"abandoned vehicle\". The suspicious activity is mostly occurs in locations pn5, pn6, pn8, and pn13. While assistance required occurs mostly in locations lz0, pn1, and pn13."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}