exercises/exercise-3-EVALUATED.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "298d705d-8105-44cb-ba13-6d58e34e1c6a",
   "metadata": {},
   "source": [
    "# Exercise 3: Computing path lengths of taxi trips"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac4b9211-e5f8-4100-b64f-d8cc3734166c",
   "metadata": {},
   "source": [
    "This notebook uses the packages and versions described in [requirements.txt](https://github.com/jpivarski/scipy2022-loopy/blob/main/requirements.txt).\n",
    "\n",
    "Note that `awkward._v2` is a preview of Awkward Array 2.0, which will be released in December 2022."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "325e6b23-1d5d-40b6-a56d-41302ad73e21",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import awkward._v2 as ak"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ba51b98-b1b5-4259-9292-ce5a7b425571",
   "metadata": {},
   "source": [
    "<br><br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0df2027-ca23-428c-9cad-dc88aa9a14fa",
   "metadata": {},
   "source": [
    "## Chicago taxis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fc1dfff-92f6-4763-b3b6-8aa624a06b44",
   "metadata": {},
   "source": [
    "The City of Chicago has a [Data Portal](https://data.cityofchicago.org/) with lots of interesting datasets.\n",
    "\n",
    "This exercise uses a dataset of [Chicago taxi trips](https://data.cityofchicago.org/Transportation/Taxi-Trips/wrvz-psew) taken from 2019 through 2021 (3 years).\n",
    "\n",
    "Here's a map of Chicago, for reference:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1973fd5d-3067-4dc3-aa27-56721c1184f7",
   "metadata": {},
   "source": [
    "<img src=\"https://upload.wikimedia.org/wikipedia/commons/3/3f/USA_Chicago_location_map.svg\" width=\"500\">"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5db7a48-cf7c-44ac-aae9-2f971885d565",
   "metadata": {},
   "source": [
    "Taxis rarely drive into the lake (blue on the right), but they do often go to the airport (yellow ball on the top-left).\n",
    "\n",
    "The dataset that the Data Portal provides has trip start and stop points as longitude, latitude pairs, as well as start and end times (date-stamps), payment details, and the name of each taxi company.\n",
    "\n",
    "For this exercise, I also added an estimated route of each taxi trip using [Open Source Routing Machine (OSRM)](http://project-osrm.org/).\n",
    "\n",
    "The dataset has three levels of nesting:\n",
    "\n",
    "   1. taxis\n",
    "   2. trips taken by each taxi\n",
    "   3. points along each trip taking by each taxi\n",
    "\n",
    "and those levels have variable lengths: each taxi takes a different number of trips and each trip has a different number of points.\n",
    "\n",
    "Our dataset is formatted as a 611 MB [Apache Parquet](https://parquet.apache.org/) file, provided here: [https://pivarski-princeton.s3.amazonaws.com/chicago-taxi.parquet](https://pivarski-princeton.s3.amazonaws.com/chicago-taxi.parquet)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1422d98c-67f6-4419-88fe-dcd4b71b77d8",
   "metadata": {},
   "source": [
    "<br><br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3c55b49-a6e7-41c5-a4d4-92369fecff4e",
   "metadata": {},
   "source": [
    "## Reading the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d1765a4-de0b-4f02-970a-6d13f3bcdf52",
   "metadata": {},
   "source": [
    "We can use Awkward Array's `ak.from_parquet` function to read the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f47b4a9d-da6d-448e-9767-79280d79ffda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Array [[{trip: {...}, ...}, ..., {...}], ...] type='353 * var * {trip: {se...'>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi = ak.from_parquet(\n",
    "    \"https://pivarski-princeton.s3.amazonaws.com/chicago-taxi.parquet\",\n",
    "    row_groups=[0],\n",
    ")\n",
    "taxi"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f7d41bf-58ba-421f-9fe9-a1e1c61d0a8a",
   "metadata": {},
   "source": [
    "### Specifying row groups"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "649c7127-0efe-4dba-8405-f9ec6d51d786",
   "metadata": {},
   "source": [
    "We use `row_groups=[0]` to read (that is, download) only a representative part of the dataset. Row groups are contiguous rows of data in the file; the smallest granularity that can be read.\n",
    "\n",
    "This row group has 353 entries (taxis), which is about 4% of the dataset because the file has 25 row groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7122a238-ceb4-4053-8acc-e4eae0161ec5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ak.metadata_from_parquet(\n",
    "    \"https://pivarski-princeton.s3.amazonaws.com/chicago-taxi.parquet\"\n",
    ").metadata.num_row_groups"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57728212-c0a1-43d6-8362-ed07bbef78a5",
   "metadata": {},
   "source": [
    "<img src=\"../img/columns-and-row-groups.svg\" width=\"1200\">"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21450f14-b892-45b5-a764-3b223a65dd36",
   "metadata": {},
   "source": [
    "Despite the terms \"row\" and \"column\", the data have a complex type:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4002312f-5e04-4aec-a9ed-40379fe99834",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "353 * var * {\n",
      "    trip: {\n",
      "        sec: ?float32,\n",
      "        km: ?float32,\n",
      "        begin: {\n",
      "            lon: ?float64,\n",
      "            lat: ?float64,\n",
      "            time: ?datetime64[ms]\n",
      "        },\n",
      "        end: {\n",
      "            lon: ?float64,\n",
      "            lat: ?float64,\n",
      "            time: ?datetime64[ms]\n",
      "        },\n",
      "        path: var * {\n",
      "            londiff: float32,\n",
      "            latdiff: float32\n",
      "        }\n",
      "    },\n",
      "    payment: {\n",
      "        fare: ?float32,\n",
      "        tips: ?float32,\n",
      "        total: ?float32,\n",
      "        type: string\n",
      "    },\n",
      "    company: string\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "taxi.type.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b4b5313-3671-4ec1-ad56-ad90ba50425d",
   "metadata": {},
   "source": [
    "According to the above, this is an array of `353` elements, and each element is a variable length list (`var`) of records. Each list represents one taxi and each record in each list is a taxi trip.\n",
    "\n",
    "The `trip` field contains a record with\n",
    "\n",
    "   * `sec`: duration of taxi ride in seconds\n",
    "   * `km`: distance traveled in kilometers\n",
    "   * `begin.lon`, `begin.lat`: beginning longitude and latitude\n",
    "   * `begin.time`: beginning time of trip\n",
    "   * `end.*`: ending longitude, latitude, and time of trip\n",
    "   * `path.londiff`, `path.latdiff`: reconstructed path relative to `begin.lon`, `begin.lat`\n",
    "\n",
    "The `payment` field contains a record with\n",
    "\n",
    "   * `fare`: nominal taxi fare in U.S. dollars\n",
    "   * `tips`: optional tips\n",
    "   * `total`: total paid (including any taxes or other fees)\n",
    "   * `type`: method of payment\n",
    "\n",
    "And the `company` field contains the name of the taxi company."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27f56583-fcb8-4ed0-8b01-05035268dbfa",
   "metadata": {},
   "source": [
    "### Specifying columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37a9196c-8375-4dee-ac74-d3423f81d666",
   "metadata": {},
   "source": [
    "This exercise only uses a few of the columns:\n",
    "\n",
    "   * `trip.km`\n",
    "   * `trip.begin.l*` (begin longitude and latitude, but not time)\n",
    "   * `trip.path.*` (full paths)\n",
    "\n",
    "We can avoid reading (that is, downloading) the data we won't use by specifying `columns`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "46ce58e8-d293-4cc1-8b54-2e7ff87bb7d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Array [[{trip: {...}}, ..., {...}], ...] type='353 * var * ?{trip: {km: ?f...'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi_columns = ak.from_parquet(\n",
    "    \"https://pivarski-princeton.s3.amazonaws.com/chicago-taxi.parquet\",\n",
    "    columns=[\"trip.km\", \"trip.begin.l*\", \"trip.path.*\"],\n",
    "    row_groups=[0],\n",
    ")\n",
    "taxi_columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0ef4e4d-916b-419d-a831-95f0d3d214df",
   "metadata": {},
   "source": [
    "That produces a projection of the data type—records with only the fields we want."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9dddd0e9-cb94-49cf-be50-8bb0c64dd04f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "353 * var * ?{\n",
      "    trip: {\n",
      "        km: ?float32,\n",
      "        begin: {\n",
      "            lon: ?float64,\n",
      "            lat: ?float64\n",
      "        },\n",
      "        path: var * {\n",
      "            londiff: float32,\n",
      "            latdiff: float32\n",
      "        }\n",
      "    }\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "taxi_columns.type.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e23090b6-ea03-4851-9dcc-2150f6e6aeed",
   "metadata": {},
   "source": [
    "It's possible to get the list of columns without reading the whole dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "943644f7-d852-428e-800b-6fce02c41efd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['trip.sec',\n",
       " 'trip.km',\n",
       " 'trip.begin.lon',\n",
       " 'trip.begin.lat',\n",
       " 'trip.begin.time',\n",
       " 'trip.end.lon',\n",
       " 'trip.end.lat',\n",
       " 'trip.end.time',\n",
       " 'trip.path.londiff',\n",
       " 'trip.path.latdiff',\n",
       " 'payment.fare',\n",
       " 'payment.tips',\n",
       " 'payment.total',\n",
       " 'payment.type',\n",
       " 'company']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ak.metadata_from_parquet(\n",
    "    \"https://pivarski-princeton.s3.amazonaws.com/chicago-taxi.parquet\"\n",
    ").form.columns()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f55b5aec-b851-4a2a-b718-5c2ce8f8fd59",
   "metadata": {},
   "source": [
    "Between row group-selection and column-selection, you can read just the data you want for the type of analysis you'll be doing."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "231264ea-f228-47b7-8a40-3a9c04f6a7fe",
   "metadata": {},
   "source": [
    "<br><br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e0c0a2d-d03f-4a51-90c8-750dd5d5dc2f",
   "metadata": {},
   "source": [
    "## Exploring the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01e5f611-a4c5-47fc-98c3-368c69bab263",
   "metadata": {},
   "source": [
    "Returning to the all-column dataset, here's one taxi in detail. It took 11 trips (most of which couldn't be routed)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "900eca61-075c-407a-bfbe-4818c55053f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'trip': {'sec': 0.0,\n",
       "   'km': 0.0,\n",
       "   'begin': {'lon': -87.66351755,\n",
       "    'lat': 41.874005383,\n",
       "    'time': datetime.datetime(2020, 7, 22, 13, 30)},\n",
       "   'end': {'lon': -87.66351755,\n",
       "    'lat': 41.874005383,\n",
       "    'time': datetime.datetime(2020, 7, 22, 13, 30)},\n",
       "   'path': [{'londiff': -4.500000159168849e-07,\n",
       "     'latdiff': 8.617000275989994e-06},\n",
       "    {'londiff': -4.500000159168849e-07, 'latdiff': 8.617000275989994e-06}]},\n",
       "  'payment': {'fare': 3.25, 'tips': 0.0, 'total': 3.25, 'type': 'Cash'},\n",
       "  'company': 'Taxi Affiliation Services'},\n",
       " {'trip': {'sec': 0.0,\n",
       "   'km': 0.0,\n",
       "   'begin': {'lon': None,\n",
       "    'lat': None,\n",
       "    'time': datetime.datetime(2020, 7, 22, 13, 30)},\n",
       "   'end': {'lon': None,\n",
       "    'lat': None,\n",
       "    'time': datetime.datetime(2020, 7, 22, 13, 30)},\n",
       "   'path': []},\n",
       "  'payment': {'fare': 3.25, 'tips': 0.0, 'total': 3.25, 'type': 'Cash'},\n",
       "  'company': 'Taxi Affiliation Services'},\n",
       " {'trip': {'sec': 60.0,\n",
       "   'km': 0.0,\n",
       "   'begin': {'lon': None,\n",
       "    'lat': None,\n",
       "    'time': datetime.datetime(2020, 7, 22, 13, 30)},\n",
       "   'end': {'lon': None,\n",
       "    'lat': None,\n",
       "    'time': datetime.datetime(2020, 7, 22, 13, 30)},\n",
       "   'path': []},\n",
       "  'payment': {'fare': 3.25, 'tips': 0.0, 'total': 3.25, 'type': 'Cash'},\n",
       "  'company': 'Taxi Affiliation Services'},\n",
       " {'trip': {'sec': 0.0,\n",
       "   'km': 0.0,\n",
       "   'begin': {'lon': None,\n",
       "    'lat': None,\n",
       "    'time': datetime.datetime(2020, 7, 23, 15, 0)},\n",
       "   'end': {'lon': None,\n",
       "    'lat': None,\n",
       "    'time': datetime.datetime(2020, 7, 23, 15, 0)},\n",
       "   'path': []},\n",
       "  'payment': {'fare': 3.25, 'tips': 0.0, 'total': 3.25, 'type': 'Cash'},\n",
       "  'company': 'Taxi Affiliation Services'},\n",
       " {'trip': {'sec': 0.0,\n",
       "   'km': 0.0,\n",
       "   'begin': {'lon': None,\n",
       "    'lat': None,\n",
       "    'time': datetime.datetime(2020, 7, 23, 15, 15)},\n",
       "   'end': {'lon': None,\n",
       "    'lat': None,\n",
       "    'time': datetime.datetime(2020, 7, 23, 15, 15)},\n",
       "   'path': []},\n",
       "  'payment': {'fare': 3.25, 'tips': 0.0, 'total': 4.75, 'type': 'Cash'},\n",
       "  'company': 'Taxi Affiliation Services'},\n",
       " {'trip': {'sec': 60.0,\n",
       "   'km': 0.0,\n",
       "   'begin': {'lon': None,\n",
       "    'lat': None,\n",
       "    'time': datetime.datetime(2020, 7, 23, 15, 15)},\n",
       "   'end': {'lon': None,\n",
       "    'lat': None,\n",
       "    'time': datetime.datetime(2020, 7, 23, 15, 15)},\n",
       "   'path': []},\n",
       "  'payment': {'fare': 3.25, 'tips': 0.0, 'total': 4.75, 'type': 'Cash'},\n",
       "  'company': 'Taxi Affiliation Services'},\n",
       " {'trip': {'sec': 0.0,\n",
       "   'km': 0.0,\n",
       "   'begin': {'lon': None,\n",
       "    'lat': None,\n",
       "    'time': datetime.datetime(2020, 7, 27, 15, 15)},\n",
       "   'end': {'lon': None,\n",
       "    'lat': None,\n",
       "    'time': datetime.datetime(2020, 7, 27, 15, 15)},\n",
       "   'path': []},\n",
       "  'payment': {'fare': 3.25, 'tips': 0.0, 'total': 3.25, 'type': 'Cash'},\n",
       "  'company': 'Taxi Affiliation Services'},\n",
       " {'trip': {'sec': 0.0,\n",
       "   'km': 0.0,\n",
       "   'begin': {'lon': None,\n",
       "    'lat': None,\n",
       "    'time': datetime.datetime(2020, 7, 27, 15, 30)},\n",
       "   'end': {'lon': None,\n",
       "    'lat': None,\n",
       "    'time': datetime.datetime(2020, 7, 27, 15, 30)},\n",
       "   'path': []},\n",
       "  'payment': {'fare': 3.25, 'tips': 0.0, 'total': 3.25, 'type': 'Cash'},\n",
       "  'company': 'Taxi Affiliation Services'},\n",
       " {'trip': {'sec': 0.0,\n",
       "   'km': 0.0,\n",
       "   'begin': {'lon': None,\n",
       "    'lat': None,\n",
       "    'time': datetime.datetime(2020, 7, 27, 15, 30)},\n",
       "   'end': {'lon': None,\n",
       "    'lat': None,\n",
       "    'time': datetime.datetime(2020, 7, 27, 15, 30)},\n",
       "   'path': []},\n",
       "  'payment': {'fare': 3.25, 'tips': 0.0, 'total': 4.25, 'type': 'Cash'},\n",
       "  'company': 'Taxi Affiliation Services'},\n",
       " {'trip': {'sec': 1500.0,\n",
       "   'km': 0.0,\n",
       "   'begin': {'lon': -87.66351755,\n",
       "    'lat': 41.874005383,\n",
       "    'time': datetime.datetime(2020, 7, 27, 15, 30)},\n",
       "   'end': {'lon': -87.618677767,\n",
       "    'lat': 41.835117986,\n",
       "    'time': datetime.datetime(2020, 7, 27, 16, 0)},\n",
       "   'path': [{'londiff': 7.550000191258732e-06,\n",
       "     'latdiff': 8.617000275989994e-06},\n",
       "    {'londiff': 7.550000191258732e-06, 'latdiff': 0.00012061699817422777},\n",
       "    {'londiff': 0.011706549674272537, 'latdiff': 0.00031161701190285385},\n",
       "    {'londiff': 0.016331549733877182, 'latdiff': 0.00028461701003834605},\n",
       "    {'londiff': 0.01660054922103882, 'latdiff': -0.006792382802814245},\n",
       "    {'londiff': 0.018389549106359482, 'latdiff': -0.006867383141070604},\n",
       "    {'londiff': 0.018419550731778145, 'latdiff': -0.007153382990509272},\n",
       "    {'londiff': 0.018912550061941147, 'latdiff': -0.010070382617413998},\n",
       "    {'londiff': 0.025673549622297287, 'latdiff': -0.027933383360505104},\n",
       "    {'londiff': 0.03200655058026314, 'latdiff': -0.032208383083343506},\n",
       "    {'londiff': 0.0317845493555069, 'latdiff': -0.03355138376355171},\n",
       "    {'londiff': 0.031837549060583115, 'latdiff': -0.03576338291168213},\n",
       "    {'londiff': 0.03675854951143265, 'latdiff': -0.03571038320660591},\n",
       "    {'londiff': 0.04403354972600937, 'latdiff': -0.035612381994724274},\n",
       "    {'londiff': 0.0452425517141819, 'latdiff': -0.03559638187289238},\n",
       "    {'londiff': 0.045320551842451096, 'latdiff': -0.03888138383626938}]},\n",
       "  'payment': {'fare': 17.5, 'tips': 0.0, 'total': 17.5, 'type': 'Cash'},\n",
       "  'company': 'Taxi Affiliation Services'},\n",
       " {'trip': {'sec': 0.0,\n",
       "   'km': 10.138867378234863,\n",
       "   'begin': {'lon': -87.618677767,\n",
       "    'lat': 41.835117986,\n",
       "    'time': datetime.datetime(2020, 7, 27, 16, 0)},\n",
       "   'end': {'lon': -87.618677767,\n",
       "    'lat': 41.835117986,\n",
       "    'time': datetime.datetime(2020, 7, 27, 16, 0)},\n",
       "   'path': [{'londiff': 0.00048076699022203684,\n",
       "     'latdiff': 6.014000064169522e-06},\n",
       "    {'londiff': 0.00048076699022203684, 'latdiff': 6.014000064169522e-06}]},\n",
       "  'payment': {'fare': 3.25, 'tips': 0.0, 'total': 3.25, 'type': 'Cash'},\n",
       "  'company': 'Taxi Affiliation Services'}]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi[75].to_list()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80e0d73b-aada-4567-a718-7b665e000cf3",
   "metadata": {},
   "source": [
    "The second-to-last of these has an interesting route."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6065681b-6de4-4837-9f91-22acd8391daa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'trip': {'sec': 1500.0,\n",
       "  'km': 0.0,\n",
       "  'begin': {'lon': -87.66351755,\n",
       "   'lat': 41.874005383,\n",
       "   'time': datetime.datetime(2020, 7, 27, 15, 30)},\n",
       "  'end': {'lon': -87.618677767,\n",
       "   'lat': 41.835117986,\n",
       "   'time': datetime.datetime(2020, 7, 27, 16, 0)},\n",
       "  'path': [{'londiff': 7.550000191258732e-06,\n",
       "    'latdiff': 8.617000275989994e-06},\n",
       "   {'londiff': 7.550000191258732e-06, 'latdiff': 0.00012061699817422777},\n",
       "   {'londiff': 0.011706549674272537, 'latdiff': 0.00031161701190285385},\n",
       "   {'londiff': 0.016331549733877182, 'latdiff': 0.00028461701003834605},\n",
       "   {'londiff': 0.01660054922103882, 'latdiff': -0.006792382802814245},\n",
       "   {'londiff': 0.018389549106359482, 'latdiff': -0.006867383141070604},\n",
       "   {'londiff': 0.018419550731778145, 'latdiff': -0.007153382990509272},\n",
       "   {'londiff': 0.018912550061941147, 'latdiff': -0.010070382617413998},\n",
       "   {'londiff': 0.025673549622297287, 'latdiff': -0.027933383360505104},\n",
       "   {'londiff': 0.03200655058026314, 'latdiff': -0.032208383083343506},\n",
       "   {'londiff': 0.0317845493555069, 'latdiff': -0.03355138376355171},\n",
       "   {'londiff': 0.031837549060583115, 'latdiff': -0.03576338291168213},\n",
       "   {'londiff': 0.03675854951143265, 'latdiff': -0.03571038320660591},\n",
       "   {'londiff': 0.04403354972600937, 'latdiff': -0.035612381994724274},\n",
       "   {'londiff': 0.0452425517141819, 'latdiff': -0.03559638187289238},\n",
       "   {'londiff': 0.045320551842451096, 'latdiff': -0.03888138383626938}]},\n",
       " 'payment': {'fare': 17.5, 'tips': 0.0, 'total': 17.5, 'type': 'Cash'},\n",
       " 'company': 'Taxi Affiliation Services'}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi[75, -2].to_list()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa8cee96-bb32-4cd7-8d95-97194e242365",
   "metadata": {},
   "source": [
    "Although individual rows can be presented in a JSON-like way, the dataset was made to be investigated columns at a time.\n",
    "\n",
    "We can extract numerical data by unpacking field names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "15381827-920c-4a2a-81c5-10d22eb82027",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Array [7.55e-06, 7.55e-06, 0.0117, ..., 0.0452, 0.0453] type='16 * float32'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi[75, -2].trip.path.londiff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "50dca5bd-375c-420d-b955-bfc78cdc584f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Array [8.62e-06, 0.000121, ..., -0.0356, -0.0389] type='16 * float32'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi[75, -2].trip.path.latdiff"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34acbcfa-ed59-490c-8046-094b49c472c4",
   "metadata": {},
   "source": [
    "Let's plot it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2a47edcf-5a86-4240-9292-4728c3459002",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(10, 10))\n",
    "\n",
    "ax.plot(taxi[75, -2].trip.path.londiff, taxi[75, -2].trip.path.latdiff);\n",
    "\n",
    "ax.set_xlim(-0.05, 0.05);\n",
    "ax.set_ylim(-0.05, 0.05);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95193a33-4383-4592-9c73-5582a98ffa16",
   "metadata": {},
   "source": [
    "Note that this path starts at `(0, 0)`. All of the paths do because these are `londiff` (longitude differences) and `latdiff` (latitude differences).\n",
    "\n",
    "`taxi[1]` went on a lot of trips."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "82278812-693c-4ad2-bd16-cd49d902d478",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(10, 10))\n",
    "\n",
    "for x in taxi[1]:\n",
    "    ax.plot(x.trip.path.londiff, x.trip.path.latdiff);\n",
    "\n",
    "ax.set_xlim(-0.3, 0.3);\n",
    "ax.set_ylim(-0.3, 0.3);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33eedf40-1f2f-42fd-b361-70c53f8c4bfa",
   "metadata": {},
   "source": [
    "Notice that some of these appear to take the same roads, but they're offset in starting point.\n",
    "\n",
    "We can fix this by adding the `begin.lon` and `begin.lat` to each of these paths.\n",
    "\n",
    "Each trip has one `begin.lon`, `begin.lat` but a sequence of points along the `path`, so adding the `begin` position involves broadcasting:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6f4d055a-fe0b-40a4-b219-599ba2cdc975",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Array [10.1, 10.2, 10.3, 10.4, 10.5] type='5 * float64'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "begin_position = 10\n",
    "diffs = ak.Array([0.1, 0.2, 0.3, 0.4, 0.5])\n",
    "\n",
    "begin_position + diffs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2991d1b7-0631-4b73-a9fc-e5db8b9c3022",
   "metadata": {},
   "source": [
    "Here's what that looks like for a single trip:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8021c6de-c97e-409b-817f-2d71dbf1a30a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-87.66351755,\n",
       " <Array [7.55e-06, 7.55e-06, 0.0117, ..., 0.0452, 0.0453] type='16 * float32'>)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "begin_position = taxi[75, -2].trip.begin.lon\n",
    "diffs = taxi[75, -2].trip.path.londiff\n",
    "\n",
    "(\n",
    "    begin_position,\n",
    "    diffs,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "802b7a4a-27ec-431d-aacf-c08dc8177126",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Array [-87.7, -87.7, -87.7, ..., -87.6, -87.6, -87.6] type='16 * float32'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "broadcasted = begin_position + diffs\n",
    "broadcasted"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e2d5fca-1d1b-433d-8626-1688089096ec",
   "metadata": {},
   "source": [
    "And here's what that looks like for a lot of trips:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "04309246-44ea-4d6e-8fd5-dd24265ffb11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Array [-87.7, -87.7, -87.7, ..., -87.6, -87.9, -87.9] type='499 * ?float64'>,\n",
       " <Array [[-2.41e-05, -2.41e-05], [...], ..., []] type='499 * var * float32'>)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "many_begin_positions = taxi[1].trip.begin.lon\n",
    "many_diffs = taxi[1].trip.path.londiff\n",
    "\n",
    "(\n",
    "    many_begin_positions,\n",
    "    many_diffs,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8d19bd17-4ef7-406a-ac0b-9c996cc060da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Array [[-87.7, -87.7], [-87.7, ...], ..., []] type='499 * option[var * flo...'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "broadcasted = many_begin_positions + many_diffs\n",
    "broadcasted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2fb93cf1-8cdb-43df-b7c9-39bfb9d425ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-87.7, -87.7],\n",
      " [-87.7, -87.7],\n",
      " [-87.7, -87.7],\n",
      " [-87.7, -87.7],\n",
      " [-87.7, -87.7],\n",
      " [-87.7, -87.7],\n",
      " [-87.7, -87.7],\n",
      " [-87.6, -87.6, -87.6, -87.6, -87.6, -87.6, -87.6, -87.6, -87.6],\n",
      " None,\n",
      " None,\n",
      " ...,\n",
      " [-87.9, -87.9, -87.9, -87.9, -87.9, ..., -87.7, -87.7, -87.7, -87.7, -87.7],\n",
      " [],\n",
      " [-87.9, -87.9, -87.9, -87.9, -87.9, ..., -87.7, -87.7, -87.7, -87.7, -87.7],\n",
      " [-87.7, -87.7],\n",
      " [-87.6, -87.6, -87.6, -87.6, -87.6, ..., -87.6, -87.6, -87.6, -87.6, -87.6],\n",
      " [-87.6, -87.6, -87.6, -87.6, -87.6, -87.6, -87.6],\n",
      " [-87.6, -87.6, -87.6, -87.6, -87.6, -87.6, -87.6, -87.6, -87.6, -87.6],\n",
      " [-87.9, -87.9, -87.9, -87.9, -87.9, ..., -87.7, -87.7, -87.7, -87.7, -87.7],\n",
      " []]\n"
     ]
    }
   ],
   "source": [
    "broadcasted.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "da7217cc-76ff-4bc0-8e41-a77447716dbe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "499 * option[var * float64]\n"
     ]
    }
   ],
   "source": [
    "broadcasted.type.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bcc93c0-0d03-4c37-b5ed-427d352ffb30",
   "metadata": {},
   "source": [
    "Take a close look at the types: `many_begin_positions` is 499 floating point numbers and `many_diffs` is 499 lists of floating point numbers.\n",
    "\n",
    "The broadcasted sum is 499 lists of floating point numbers, with `option` type to allow for missing values (`None`).\n",
    "\n",
    "The missing values can be identified and removed with `ak.is_none`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "be36cf29-061f-4cf9-93f5-afcb2f4ed31d",
   "metadata": {},
   "outputs": [],
   "source": [
    "longitudes = taxi[1].trip.begin.lon + taxi[1].trip.path.londiff\n",
    "latitudes = taxi[1].trip.begin.lat + taxi[1].trip.path.latdiff\n",
    "\n",
    "longitudes = longitudes[~ak.is_none(longitudes)]\n",
    "latitudes = latitudes[~ak.is_none(latitudes)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2ff6933f-f43e-4c71-9a7a-410db27ad119",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-87.7, -87.7],\n",
      " [-87.7, -87.7],\n",
      " [-87.7, -87.7],\n",
      " [-87.7, -87.7],\n",
      " [-87.7, -87.7],\n",
      " [-87.7, -87.7],\n",
      " [-87.7, -87.7],\n",
      " [-87.6, -87.6, -87.6, -87.6, -87.6, -87.6, -87.6, -87.6, -87.6],\n",
      " [-87.7, -87.7],\n",
      " [-87.6, -87.6, -87.6, -87.6, -87.6, -87.6, -87.6, -87.6, -87.6],\n",
      " ...,\n",
      " [-87.9, -87.9, -87.9, -87.9, -87.9, ..., -87.7, -87.7, -87.7, -87.7, -87.7],\n",
      " [],\n",
      " [-87.9, -87.9, -87.9, -87.9, -87.9, ..., -87.7, -87.7, -87.7, -87.7, -87.7],\n",
      " [-87.7, -87.7],\n",
      " [-87.6, -87.6, -87.6, -87.6, -87.6, ..., -87.6, -87.6, -87.6, -87.6, -87.6],\n",
      " [-87.6, -87.6, -87.6, -87.6, -87.6, -87.6, -87.6],\n",
      " [-87.6, -87.6, -87.6, -87.6, -87.6, -87.6, -87.6, -87.6, -87.6, -87.6],\n",
      " [-87.9, -87.9, -87.9, -87.9, -87.9, ..., -87.7, -87.7, -87.7, -87.7, -87.7],\n",
      " []]\n"
     ]
    }
   ],
   "source": [
    "longitudes.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb60a517-146b-4c02-83ee-ec48f922bd89",
   "metadata": {},
   "source": [
    "Now the trips to the airport and along the lake front are beginning to line up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4ce30871-a513-4315-974f-d58ac36a9166",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(10, 10))\n",
    "\n",
    "for x, y in zip(longitudes, latitudes):\n",
    "    ax.plot(x, y);\n",
    "\n",
    "ax.set_xlim(-87.95, -87.57);\n",
    "ax.set_ylim(41.77, 42.02);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f3fc5ee-0691-4bd0-9479-c9103a6f406b",
   "metadata": {},
   "source": [
    "Broadcasting `begin.lon`, `begin.lat` offsets into lists of lists, we use the same syntax as for lists.\n",
    "\n",
    "Just replace `taxi[1]` → `taxi` to apply it to all taxis at once."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "08510dad-92be-420a-bc47-3084cc3ee9bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[-87.7, -87.7], [-87.7, -87.7, ..., -87.7, -87.7], ..., [-87.7, -87.7]],\n",
      " [[-87.7, -87.7], [-87.7, -87.7], ..., [-87.9, -87.9, ..., -87.7, -87.7], []],\n",
      " [[-87.6, -87.6, -87.6, -87.6, -87.6, -87.6, -87.6], [-87.6, ...], ..., None],\n",
      " [[-87.6, -87.6, -87.6, -87.6, -87.6, -87.6], ..., [-87.6, -87.6, ..., -87.7]],\n",
      " [[-87.7, -87.7], ..., [-87.6, -87.6, -87.6, -87.6, -87.6, -87.6, -87.6]],\n",
      " [[-87.9, -87.9, -87.9, -87.9, -87.9, ..., -87.6, -87.6, -87.6, -87.6], ...],\n",
      " [[-87.9, -87.9, -87.9, -87.9, -87.9, ..., -87.6, -87.6, -87.6, -87.6], ...],\n",
      " [],\n",
      " [[-87.6, -87.6, -87.6, -87.6, -87.6, ..., -87.7, -87.7, -87.7, -87.7], ...],\n",
      " [[-87.7, -87.7], ..., [-87.6, -87.6, -87.6, -87.6, ..., -87.7, -87.7, -87.7]],\n",
      " ...,\n",
      " [[-87.6, -87.6, -87.6, -87.6, -87.6, -87.6], ..., [-87.6, -87.6, ..., -87.6]],\n",
      " [None, None, None, None, None, None, None],\n",
      " [None, None, None, None, None, None, ..., None, None, None, None, None, None],\n",
      " [None, None, None, None, None, None, ..., None, None, None, None, None, None],\n",
      " [None, None, None, None, None, None, ..., None, None, None, None, None, None],\n",
      " [[-87.9, -87.9], [-87.9, -87.9, ..., -87.8, -87.8], ..., [-87.6, -87.6]],\n",
      " [None, [-87.7, -87.7], [-87.7, -87.7], ..., [...], [-87.9, -87.9], None],\n",
      " [[-87.6, -87.6, -87.6, -87.6, -87.6, -87.6], ..., [-87.7, -87.7, ..., -87.7]],\n",
      " [[-87.7, -87.7], [-87.7, -87.7], ..., [-87.6, ..., -87.7], [-87.6, -87.6]]]\n"
     ]
    }
   ],
   "source": [
    "all_longitudes = taxi.trip.begin.lon + taxi.trip.path.londiff\n",
    "all_latitudes = taxi.trip.begin.lat + taxi.trip.path.latdiff\n",
    "\n",
    "all_longitudes.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "01b54b5d-620b-48c4-86d9-e5956444b213",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "353 * var * option[var * float64]\n"
     ]
    }
   ],
   "source": [
    "all_longitudes.type.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57ffe1f3-a973-4733-adfc-b6df1dfb5a3d",
   "metadata": {},
   "source": [
    "Since we're not plotting, we'll leave the missing values in for now."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6a54191-cb09-415d-9edb-2b7a882da345",
   "metadata": {},
   "source": [
    "<br><br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f51dd70-41c0-4255-8c58-424b2f395871",
   "metadata": {},
   "source": [
    "## Longitude, latitude → kilometers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4446571-549c-4568-ae5b-8bc06c77018a",
   "metadata": {},
   "source": [
    "Longitudes and latitudes are not uniform distances. One degree of longitude covers 1/360 of the Earth at the equator, but zero length at the north and south poles!\n",
    "\n",
    "To do meaningful distance calculations, we need to convert them into distance units, such as kilometers.\n",
    "\n",
    "At Chicago's latitude, one degree of longitude is about 83 km and one degree of longitude is about 111 km.\n",
    "\n",
    "Let's apply this correction to all path points, across all lists of lists, using broadcasting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3e3553b8-26f8-498a-a9f5-7ec2b576cedb",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "LONGITUDE, LATITUDE = -87.66178320769112, 41.896777262111726   # mean position (center of town)\n",
    "LON_TO_KM, LAT_TO_KM = 82.98452409203695, 111.07127961503745   # lon, lat → km conversion factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "dde31eea-f892-4131-b123-496fc2e513bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Array [[[-4.63, -4.63], ..., [...]], ...] type='353 * var * option[var * f...'>,\n",
       " <Array [[[5.12, 5.12], ..., [10, ...]], ...] type='353 * var * option[var *...'>)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km_east, km_north = (\n",
    "    (all_longitudes - LONGITUDE) * LON_TO_KM,\n",
    "    (all_latitudes - LATITUDE) * LAT_TO_KM,\n",
    ")\n",
    "km_east, km_north"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8860bb3-9837-4df2-8261-ba4842606a72",
   "metadata": {},
   "source": [
    "In real distance units, `taxi[1]`'s trips look like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "054ccf9b-980b-472a-bdc5-8446da2ba01d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(10, 10))\n",
    "\n",
    "for x, y in zip(km_east[1], km_north[1]):\n",
    "    if x is not None and y is not None:\n",
    "        ax.plot(x, y);\n",
    "\n",
    "ax.set_xlim(-23, -23 + 30);\n",
    "ax.set_ylim(-15, -15 + 30);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f07d9c4d-d720-46cf-9d10-366714b98c74",
   "metadata": {},
   "source": [
    "<br><br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2581caf0-57ab-4959-b1b7-7c514d7584e7",
   "metadata": {},
   "source": [
    "## THE EXERCISE: calculating lengths of paths"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f570a4d1-cbd2-426d-98c0-632442ebddb4",
   "metadata": {},
   "source": [
    "The goal of this exercise is to calculate the length of each `path`.\n",
    "\n",
    "The length of a given `path` is the sum of segment lengths, $\\displaystyle \\sum_i^{n - 1} \\Delta r_i$, where\n",
    "\n",
    "   * the segment length $\\Delta r_i = \\sqrt{\\Delta x_i^2 + \\Delta y_i^2}$\n",
    "   * $\\Delta x_i = x_{i + 1} - x_i$, and $x_i$ is a `km_east` point in the `path`\n",
    "   * $\\Delta y_i = y_{i + 1} - y_i$, and $y_i$ is a `km_north` point in the `path`\n",
    "   * $n$ is the (variable) number of points in the `path`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "656577c9-280a-433b-83f4-90431ae26779",
   "metadata": {},
   "source": [
    "<img src=\"../img/length-by-segment.svg\" width=\"500\">"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60c05551-bb4d-413e-a242-fe0cbe69b13a",
   "metadata": {},
   "source": [
    "Here's an example of the calculation for one taxi ride, using an imperative `for` loop:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "34fab6e0-fcd1-4b91-aef4-4ab4e11b400e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Array [-0.143, -0.143, 0.828, 1.21, ..., 3.51, 3.61, 3.62] type='16 * float64'>,\n",
       " <Array [-2.53, -2.52, -2.49, ..., -6.48, -6.48, -6.85] type='16 * float64'>)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example_east, example_north = (\n",
    "    km_east[75, -2],\n",
    "    km_north[75, -2],\n",
    ")\n",
    "example_east, example_north"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a6fa7c81-8a4b-4fe8-8f41-96e7a8b45bea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.30448400458981"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "length = 0\n",
    "for i in range(len(example_east) - 1):\n",
    "    Δx = example_east[i + 1] - example_east[i]\n",
    "    Δy = example_north[i + 1] - example_north[i]\n",
    "    Δr = np.sqrt(Δx**2 + Δy**2)\n",
    "    length += Δr\n",
    "\n",
    "length"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "376f5d2e-46c1-4e56-b30b-8128dba23352",
   "metadata": {},
   "source": [
    "**Your solution must** (a) apply to all trips of all taxis in the dataset, so output type is `353 * var * ?float64`, and (b) use array-at-a-time syntax.\n",
    "\n",
    "It would probably fit on one line.\n",
    "\n",
    "```python\n",
    "distances = ???\n",
    "```\n",
    "\n",
    "If you call `distances.show()`, it looks like this:\n",
    "\n",
    "```\n",
    "[[0, 9.63, 6.2, 2.07, 10.1, 0, 1.21, ..., 1.34, 12.2, 6.27, 2.58, 7.27, 0, 0],\n",
    " [0, 0, 0, 0, 0, 0, 0, 3.56, None, ..., 0, 24.5, 0, 6.22, 2.26, 7.24, 22.1, 0],\n",
    " [1.75, 4.82, 3.71, 2.86, 6.41, 6.89, ..., 2.72, 3.65, 28.3, 0, 24.8, None],\n",
    " [1.17, 2.39, 1.07, 4.85, 2.95, 2.78, ..., 0.899, 2.61, 2.22, 1.43, 11.9, 3.65],\n",
    " [0, 0, 0, 0, 0, 0, 0, 0, 0, ..., 22.9, 23.4, 0, 0, 20.5, 22.9, 23.2, 3.09],\n",
    " [29, 2.25, 2.91, 0, 27.9, 16.7, 21.1, ..., 2.49, 0, 14.5, 0, 0, 23.6, 3.44],\n",
    " [27.5, 1.59, 1.36, 1.48, 2.46, 1.24, ..., 4.43, 6.22, 26.9, None, 0, 1.24],\n",
    " [],\n",
    " [4.67, 6.23, 1.35, 4.36, 0, 20.9, 0, ..., 26.9, 23.1, None, 20, 0, 16.6, 21.4],\n",
    " [0, 0, 0, 0, 0, 0, 0, 2.88, ..., 14.9, 19.9, 17.8, 9.15, 10.6, 12.2, 16.5, 14],\n",
    " ...,\n",
    " [1.52, 1.93, 5.76, 5.76, 2.95, 0, ..., 4.18, 1.45, 26.9, 3.55, 4.69, 2.92],\n",
    " [None, None, None, None, None, None, None],\n",
    " [None, None, None, None, None, None, ..., None, None, None, None, None, None],\n",
    " [None, None, None, None, None, None, ..., None, None, None, None, None, None],\n",
    " [None, None, None, None, None, None, ..., None, None, None, None, None, None],\n",
    " [0, 9.56, 0, 0, 0, 0, 0, 30.2, 1.41, 0, 0, ..., 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
    " [None, 0, 0, 0, 4.89, 0, 0, None, ..., 4.11, 1.6, 0, 9.89, 21.1, 0, None],\n",
    " [1.42, 27.6, 1.62, 3.36, 1.62, 1.62, ..., 1.41, 1.62, 2.22, 1.62, 5.11, 7.12],\n",
    " [0, 0, 13.9, 27.8, 3.36, 1.41, 2.26, ..., 5.12, 2.94, 5.53, 0, 1.28, 4.67, 0]]\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19138671-1c01-4ced-a109-a4e4062cf539",
   "metadata": {},
   "source": [
    "<br><br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b26a582-5a15-40bd-92d2-fcd5edb715c4",
   "metadata": {},
   "source": [
    "## Hints (only use if you're stuck)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1a87a4f-09da-46b5-a8f6-4643b307dcf6",
   "metadata": {},
   "source": [
    "<details>\n",
    "    <summary>Differences between neighboring elements in an array?</summary>\n",
    "\n",
    "<br>\n",
    "\n",
    "Suppose you have some fenceposts and you want the differences between the fenceposts.\n",
    "\n",
    "```python\n",
    "fenceposts = np.array([1.1, 2.2, 3.3, 4.4, 5.5, 10, 20, 30])\n",
    "```\n",
    "\n",
    "<br>\n",
    "\n",
    "Note that `fenceposts[1:]` is `array([ 2.2,  3.3,  4.4,  5.5, 10. , 20. , 30. ])` and `fenceposts[:-1]` is `array([ 1.1,  2.2,  3.3,  4.4,  5.5, 10. , 20. ])`. Just subtract them!\n",
    "\n",
    "```python\n",
    "fenceposts[1:] - fenceposts[:-1] → array([ 1.1,  1.1,  1.1,  1.1,  4.5, 10. , 10. ])\n",
    "```\n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "614130bc-e1a5-46fd-9b0b-21832be3240c",
   "metadata": {},
   "source": [
    "<details>\n",
    "    <summary>Differences between neighboring elements in deeply nested lists of lists?</summary>\n",
    "\n",
    "<br>\n",
    "\n",
    "Suppose you have lists of fenceposts and you want the differences between the fenceposts within each list.\n",
    "\n",
    "```python\n",
    "fenceposts = ak.Array([[1.1, 2.2, 3.3, 4.4, 5.5], [], [10, 20, 30]])\n",
    "```\n",
    "\n",
    "<br>\n",
    "\n",
    "Note that `fenceposts[:, 1:]` is `<Array [[2.2, 3.3, 4.4, 5.5], [], [20, 30]] type='3 * var * float64'>` and `fenceposts[:, :-1]` is `<Array [[1.1, 2.2, 3.3, 4.4], [], [10, 20]] type='3 * var * float64'>`. Be sure to get the right number of colons (`:`) to pass over the levels of nesting that you don't want to disturb, or use ellipsis (`...`) to descend to the deepest level.\n",
    "\n",
    "```python\n",
    "fenceposts[:, 1:] - fenceposts[:, :-1] → <Array [[1.1, 1.1, 1.1, 1.1], [], [10, 10]] type='3 * var * float64'>\n",
    "```\n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "223e56db-617e-4556-b3a2-b11df727f538",
   "metadata": {},
   "source": [
    "<details>\n",
    "    <summary>Mathematical operations on all elements of an array of lists of lists?</summary>\n",
    "\n",
    "<br>\n",
    "\n",
    "When a [NumPy ufunc](https://numpy.org/doc/stable/reference/ufuncs.html) is applied to an Awkward Array, the output is an Awkward Array with the same structure and that ufunc applied to each element.\n",
    "\n",
    "<br>\n",
    "\n",
    "Some ufuncs are in the NumPy namespace, such as `np.sqrt`, and some are binary operators, such as `-` and `**`.\n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "716c62bc-0d67-4ab4-9352-bbfc01ffcb62",
   "metadata": {},
   "source": [
    "<details>\n",
    "    <summary>Summing over the deepest levels of lists without affecting outer ones?</summary>\n",
    "\n",
    "<br>\n",
    "\n",
    "Remember that `ak.sum` takes an `axis` argument. It works like NumPy's `axis`, assuming that all irregular-length lists are left-aligned and missing values are skipped over.\n",
    "\n",
    "<img src=\"../img/example-reduction-sum.svg\" width=\"500\">\n",
    "\n",
    "The innermost lists correspond to the largest allowed `axis` value. As usual in Python, you can start counting from the other end; an alias for the largest allowed `axis` is `axis=-1`.\n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5998c447-3c30-428f-bbb5-78ccbffd5e64",
   "metadata": {},
   "source": [
    "<br><br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de839ad7-303a-4ccd-82a1-03352f2e0230",
   "metadata": {},
   "source": [
    "### Bonus exercise: as the crow flies"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3acef67f-a5cb-4336-bf59-e98fe63d4fcd",
   "metadata": {},
   "source": [
    "If the main exercise was easy and you need something else to do, try calculating the distance between each beginning (`path[0]`) and each endpoint (`path[-1]`) \"as the crow flies.\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a606461-c59a-4e02-9aa2-aebd42817763",
   "metadata": {},
   "source": [
    "<img src=\"../img/as-the-crow-flies.svg\" width=\"500\">"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2cf6f02-5fea-4a6e-9431-4c3f7011af54",
   "metadata": {},
   "source": [
    "(Don't use the `begin.lon`, `begin.lat` and `end.lon`, `end.lat`; use the `path[0]` and `path[-1]`.)\n",
    "\n",
    "You will encounter an issue when you try to select the first and last item from empty lists—it's an `IndexError`!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2099a3a1-b62c-4f15-a6f7-c9c306b56208",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IndexError: while attempting to slice (from /tmp/ipykernel_144072/3702568404.py, line 4)\n",
      "\n",
      "    <Array [[1.1, 2.2, 3.3], [], [4.4, 5.5]] type='3 * var * float64'>\n",
      "\n",
      "with\n",
      "\n",
      "    (:, 0)\n",
      "\n",
      "at inner ListArray of length 3, using sub-slice 0.\n",
      "\n",
      "Error details: index out of range while attempting to get index 0 (in compiled code: https://github.com/scikit-hep/awkward-1.0/blob/1.9.0rc4/src/cpu-kernels/awkward_NumpyArray_getitem_next_at.cpp#L21).\n"
     ]
    }
   ],
   "source": [
    "contains_empty_lists = ak.Array([[1.1, 2.2, 3.3], [], [4.4, 5.5]])\n",
    "\n",
    "try:\n",
    "    contains_empty_lists[:, 0]\n",
    "except Exception as err:\n",
    "    print(f\"{type(err).__name__}: {str(err)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3625b657-9f6d-4e84-932a-bf01df2df859",
   "metadata": {},
   "source": [
    "### First way to fix it: padding the lists"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfc00bc6-eee5-4c37-9507-75f643f3a380",
   "metadata": {},
   "source": [
    "One way to solve this is with `ak.pad_none` and `ak.fill_none`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7360528a-78a5-4b28-abc0-55feaa30d795",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.1, 2.2, 3.3],\n",
      " [],\n",
      " [4.4, 5.5]]\n"
     ]
    }
   ],
   "source": [
    "contains_empty_lists.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c89fca4d-e5d3-48ef-becf-f4e30b3fb460",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.1, 2.2, 3.3],\n",
      " [None, None],\n",
      " [4.4, 5.5]]\n"
     ]
    }
   ],
   "source": [
    "ak.pad_none(contains_empty_lists, 2).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a581ada9-f145-436f-b966-e7bbb03e914b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.1, 2.2, 3.3],\n",
      " [0, 0],\n",
      " [4.4, 5.5]]\n"
     ]
    }
   ],
   "source": [
    "ak.fill_none(ak.pad_none(contains_empty_lists, 2), 0).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d39dbbed-a886-47f1-95c5-cb466b19f54b",
   "metadata": {},
   "source": [
    "Now you can select element `0` and element `-1`: they're zero!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "dd6952a5-d924-463f-b156-715ba9799ac0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Array [1.1, 0, 4.4] type='3 * float64'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doesnt_contain_empty_lists = ak.fill_none(ak.pad_none(contains_empty_lists, 2), 0)\n",
    "\n",
    "doesnt_contain_empty_lists[:, 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d51fc27-e816-4269-aaa8-e44ddc9784bb",
   "metadata": {},
   "source": [
    "How would you apply this technique to _deeply_ nested lists?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d93334bf-1b4d-4544-9c07-f4d3708f80f5",
   "metadata": {},
   "source": [
    "### Second way to fix it: turning empty lists into None"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "361581f0-f96c-49f3-a1df-72d1c1d26e86",
   "metadata": {},
   "source": [
    "The `ak.num` function tells you how many items are in each list. (Note that it takes an `axis` argument; useful if you need to know the lengths of deeply nested lists.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0697bd89-4ef7-4627-b249-83bb846d6d07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Array [3, 0, 2] type='3 * int64'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ak.num(contains_empty_lists)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14507d40-cff2-4814-b925-87b7cad5b721",
   "metadata": {},
   "source": [
    "You could use this to remove the empty lists from your dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "7c2a0a67-f25e-44f2-b723-6850a0d55cd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Array [[1.1, 2.2, 3.3], [4.4, 5.5]] type='2 * var * float64'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contains_empty_lists[ak.num(contains_empty_lists) != 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94b948c0-a4b6-4c8c-ba8b-22bf5b513745",
   "metadata": {},
   "source": [
    "But that changes the indexing: the sliced array has length `2` instead of length `3`, which would be a problem if you want to align this result with something else derived from the original array.\n",
    "\n",
    "Awkward Array also has a \"mask-slice,\" which is like slicing with a boolean array except that elements are replaced with `None`, rather than removed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "0162462e-1f92-41bf-a20b-5bd39a2fe79f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Array [[1.1, 2.2, 3.3], None, [4.4, 5.5]] type='3 * option[var * float64]'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doesnt_contain_empty_lists = contains_empty_lists.mask[ak.num(contains_empty_lists) != 0]\n",
    "doesnt_contain_empty_lists"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82cbf5ff-7e40-44ce-8fa6-3b8d66cc5bf1",
   "metadata": {},
   "source": [
    "The `doesnt_contain_empty_lists` array has length `3`, and you can select item `0` from each list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c4b3fde9-3728-45cf-b7c5-d7e7151c23d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Array [1.1, None, 4.4] type='3 * ?float64'>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doesnt_contain_empty_lists[:, 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfaffc8a-ffda-4f99-aac9-c6324affa634",
   "metadata": {},
   "source": [
    "because item `0` of `None` is `None`. Any calculation you do on `None` is `None`.\n",
    "\n",
    "How would you apply this technique to _deeply_ nested lists?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f9f7983-1173-485a-950d-99dee3ce0248",
   "metadata": {},
   "source": [
    "<br><br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b057f1a1-a3ad-44c7-8a1b-3b95caa244dc",
   "metadata": {},
   "source": [
    "## Bonus exercise: compare computed distances with the `trip.km` field"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "957310a0-dc3b-4329-9114-2a5e5c33a607",
   "metadata": {},
   "source": [
    "The distances you computed derive from OSRM routing between `begin.lon`, `begin.lat` and `end.lon`, `end.lat` on a map.\n",
    "\n",
    "The distances in the `trip.km` field were provided by the City of Chicago Data Portal.\n",
    "\n",
    "Taxis might not have taken the shortest route. They might have misrepresented their distance traveled. The matching to OSRM or Open Street Map might be in error.\n",
    "\n",
    "How well do your distances and the `trip.km` distances compare? How about the \"as the crow flies\" distances? Which do you _expect_ to be more accurate?\n",
    "\n",
    "```python\n",
    "fig, ax = plt.subplots(1, 1, figsize=(10, 10))\n",
    "\n",
    "ax.hist2d(\n",
    "    ak.flatten(ak.fill_none(distances, 0)),    # None → 0 on the plot\n",
    "    ak.flatten(ak.fill_none(taxi.trip.km, 0)), # None → 0 on the plot\n",
    "    bins=(200, 200),\n",
    "    range=((0, 50), (0, 50)),\n",
    "    norm=matplotlib.colors.LogNorm(),\n",
    ");\n",
    "ax.plot([0, 50], [0, 50], c=\"orange\", lw=3);\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81c733ba-afae-4082-bdaa-bfd9b74e6052",
   "metadata": {},
   "source": [
    "<br><br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70eb456b-9ee4-480e-a8dc-40b830a3a6d1",
   "metadata": {},
   "source": [
    "## More exercises: if you're bored"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2d8ef90-84ba-4776-a8b7-981e4487a472",
   "metadata": {},
   "source": [
    "   * How fast are those taxis driving? On short trips versus long trips?\n",
    "   * How does the `fare` compare with distance? Is it proportional? Does it depend on taxi `company`?\n",
    "   * How were taxi rides affected by the pandemic?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02487a1b-480c-4381-8e65-7746203d787e",
   "metadata": {},
   "source": [
    "<br><br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46785027-073c-4634-b0d5-650151e8933d",
   "metadata": {},
   "source": [
    "## How does this compare with \"tidy\" data?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd702ca1-a02c-4dbc-ae3c-5d38bddfbf9b",
   "metadata": {},
   "source": [
    "Array-oriented computing on nested data structures is an _alternative_ to queries about data in rectangular tables, related by keys.\n",
    "\n",
    "Here's how the Awkward structure can be converted into a [Tidy](https://www.jstatsoft.org/article/view/v059i10) one (3rd normal form). First, make a surrogate key for each taxi."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "cd146dbb-b4b1-4267-8bab-fe694ee5b9d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0,\n",
      " 1,\n",
      " 2,\n",
      " 3,\n",
      " 4,\n",
      " 5,\n",
      " 6,\n",
      " 7,\n",
      " 8,\n",
      " 9,\n",
      " ...,\n",
      " 344,\n",
      " 345,\n",
      " 346,\n",
      " 347,\n",
      " 348,\n",
      " 349,\n",
      " 350,\n",
      " 351,\n",
      " 352]\n"
     ]
    }
   ],
   "source": [
    "taxi_key = ak.Array(np.arange(len(taxi)))\n",
    "taxi_key.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3054b197-2e56-4195-95d3-b3fe6729cd28",
   "metadata": {},
   "source": [
    "Next, duplicate those keys for each trip by broadcasting the `taxi_key` array onto a field from the trips."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f8f469b4-3167-4f46-bf6c-87d744e7329d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ..., 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
      " [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ..., 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
      " [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, ..., 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n",
      " [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, ..., 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n",
      " [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, ..., 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n",
      " [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, ..., 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n",
      " [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, ..., 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6],\n",
      " [],\n",
      " [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, ..., 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n",
      " [9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, ..., 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9],\n",
      " ...,\n",
      " [344, 344, 344, 344, 344, 344, 344, ..., 344, 344, 344, 344, 344, 344, 344],\n",
      " [345, 345, 345, 345, 345, 345, 345],\n",
      " [346, 346, 346, 346, 346, 346, 346, ..., 346, 346, 346, 346, 346, 346, 346],\n",
      " [347, 347, 347, 347, 347, 347, 347, ..., 347, 347, 347, 347, 347, 347, 347],\n",
      " [348, 348, 348, 348, 348, 348, 348, ..., 348, 348, 348, 348, 348, 348, 348],\n",
      " [349, 349, 349, 349, 349, 349, 349, ..., 349, 349, 349, 349, 349, 349, 349],\n",
      " [350, 350, 350, 350, 350, 350, 350, ..., 350, 350, 350, 350, 350, 350, 350],\n",
      " [351, 351, 351, 351, 351, 351, 351, ..., 351, 351, 351, 351, 351, 351, 351],\n",
      " [352, 352, 352, 352, 352, 352, 352, ..., 352, 352, 352, 352, 352, 352, 352]]\n"
     ]
    }
   ],
   "source": [
    "taxi_key_for_trips, _ = ak.broadcast_arrays(taxi_key, taxi.trip.km)\n",
    "taxi_key_for_trips.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "120da18a-6228-432e-af7e-3c496d51f4f0",
   "metadata": {},
   "source": [
    "The trip table is a flattened version of these keys and the trip fields."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "fe550bba-ca34-4ef4-9556-762fb19597a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "flat_taxi_key_for_trips = ak.flatten(taxi_key_for_trips)\n",
    "flat_trips = ak.flatten(taxi.trip)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "075f211e-7bf3-4987-9cfa-277f9582f12e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>taxi_key</th>\n",
       "      <th>sec</th>\n",
       "      <th>km</th>\n",
       "      <th>begin_lon</th>\n",
       "      <th>begin_lat</th>\n",
       "      <th>begin_time</th>\n",
       "      <th>end_lon</th>\n",
       "      <th>end_lat</th>\n",
       "      <th>end_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-87.717504</td>\n",
       "      <td>41.942859</td>\n",
       "      <td>2019-01-02 13:00:00</td>\n",
       "      <td>-87.717504</td>\n",
       "      <td>41.942859</td>\n",
       "      <td>2019-01-02 13:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1380.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-87.655998</td>\n",
       "      <td>41.944227</td>\n",
       "      <td>2019-01-02 13:30:00</td>\n",
       "      <td>-87.663518</td>\n",
       "      <td>41.874005</td>\n",
       "      <td>2019-01-02 13:45:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-87.625192</td>\n",
       "      <td>41.878866</td>\n",
       "      <td>2019-01-02 14:00:00</td>\n",
       "      <td>-87.676356</td>\n",
       "      <td>41.901207</td>\n",
       "      <td>2019-01-02 14:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-87.626211</td>\n",
       "      <td>41.899156</td>\n",
       "      <td>2019-01-02 15:30:00</td>\n",
       "      <td>-87.620993</td>\n",
       "      <td>41.884987</td>\n",
       "      <td>2019-01-02 15:45:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>960.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-87.625192</td>\n",
       "      <td>41.878866</td>\n",
       "      <td>2019-01-02 16:15:00</td>\n",
       "      <td>-87.717220</td>\n",
       "      <td>41.860190</td>\n",
       "      <td>2019-01-02 16:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003512</th>\n",
       "      <td>352.0</td>\n",
       "      <td>1980.0</td>\n",
       "      <td>12.713818</td>\n",
       "      <td>-87.620335</td>\n",
       "      <td>41.857184</td>\n",
       "      <td>2021-12-31 17:15:00</td>\n",
       "      <td>-87.663518</td>\n",
       "      <td>41.874005</td>\n",
       "      <td>2021-12-31 18:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003513</th>\n",
       "      <td>352.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>2.253082</td>\n",
       "      <td>-87.633308</td>\n",
       "      <td>41.899602</td>\n",
       "      <td>2021-12-31 20:15:00</td>\n",
       "      <td>-87.633308</td>\n",
       "      <td>41.899602</td>\n",
       "      <td>2021-12-31 20:15:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003514</th>\n",
       "      <td>352.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>1.931213</td>\n",
       "      <td>-87.612945</td>\n",
       "      <td>41.891972</td>\n",
       "      <td>2021-12-31 21:00:00</td>\n",
       "      <td>-87.626215</td>\n",
       "      <td>41.892508</td>\n",
       "      <td>2021-12-31 21:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003515</th>\n",
       "      <td>352.0</td>\n",
       "      <td>840.0</td>\n",
       "      <td>5.471770</td>\n",
       "      <td>-87.633308</td>\n",
       "      <td>41.899602</td>\n",
       "      <td>2021-12-31 22:45:00</td>\n",
       "      <td>-87.676356</td>\n",
       "      <td>41.901207</td>\n",
       "      <td>2021-12-31 23:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003516</th>\n",
       "      <td>352.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>0.965606</td>\n",
       "      <td>-87.633308</td>\n",
       "      <td>41.899602</td>\n",
       "      <td>2021-12-31 23:30:00</td>\n",
       "      <td>-87.633308</td>\n",
       "      <td>41.899602</td>\n",
       "      <td>2021-12-31 23:45:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1003517 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         taxi_key     sec         km  begin_lon  begin_lat  \\\n",
       "0             0.0    60.0   0.000000 -87.717504  41.942859   \n",
       "1             0.0  1380.0   0.000000 -87.655998  41.944227   \n",
       "2             0.0   360.0   0.000000 -87.625192  41.878866   \n",
       "3             0.0   420.0   0.000000 -87.626211  41.899156   \n",
       "4             0.0   960.0   0.000000 -87.625192  41.878866   \n",
       "...           ...     ...        ...        ...        ...   \n",
       "1003512     352.0  1980.0  12.713818 -87.620335  41.857184   \n",
       "1003513     352.0   600.0   2.253082 -87.633308  41.899602   \n",
       "1003514     352.0   480.0   1.931213 -87.612945  41.891972   \n",
       "1003515     352.0   840.0   5.471770 -87.633308  41.899602   \n",
       "1003516     352.0   240.0   0.965606 -87.633308  41.899602   \n",
       "\n",
       "                 begin_time    end_lon    end_lat            end_time  \n",
       "0       2019-01-02 13:00:00 -87.717504  41.942859 2019-01-02 13:00:00  \n",
       "1       2019-01-02 13:30:00 -87.663518  41.874005 2019-01-02 13:45:00  \n",
       "2       2019-01-02 14:00:00 -87.676356  41.901207 2019-01-02 14:00:00  \n",
       "3       2019-01-02 15:30:00 -87.620993  41.884987 2019-01-02 15:45:00  \n",
       "4       2019-01-02 16:15:00 -87.717220  41.860190 2019-01-02 16:30:00  \n",
       "...                     ...        ...        ...                 ...  \n",
       "1003512 2021-12-31 17:15:00 -87.663518  41.874005 2021-12-31 18:00:00  \n",
       "1003513 2021-12-31 20:15:00 -87.633308  41.899602 2021-12-31 20:15:00  \n",
       "1003514 2021-12-31 21:00:00 -87.626215  41.892508 2021-12-31 21:00:00  \n",
       "1003515 2021-12-31 22:45:00 -87.676356  41.901207 2021-12-31 23:00:00  \n",
       "1003516 2021-12-31 23:30:00 -87.633308  41.899602 2021-12-31 23:45:00  \n",
       "\n",
       "[1003517 rows x 9 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trip_table = pd.DataFrame({\n",
    "    \"taxi_key\": ak.to_numpy(flat_taxi_key_for_trips),\n",
    "    \"sec\": ak.to_numpy(flat_trips.sec),\n",
    "    \"km\": ak.to_numpy(flat_trips.km),\n",
    "    \"begin_lon\": ak.to_numpy(flat_trips.begin.lon),\n",
    "    \"begin_lat\": ak.to_numpy(flat_trips.begin.lat),\n",
    "    \"begin_time\": ak.to_numpy(flat_trips.begin.time),\n",
    "    \"end_lon\": ak.to_numpy(flat_trips.end.lon),\n",
    "    \"end_lat\": ak.to_numpy(flat_trips.end.lat),\n",
    "    \"end_time\": ak.to_numpy(flat_trips.end.time),\n",
    "})\n",
    "trip_table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4db4ddf2-9c30-4fa9-a5a0-777bc48929f1",
   "metadata": {},
   "source": [
    "We can do exactly the same thing for the paths within each trip."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "5bdd1e06-e26f-4e20-b190-a75598f58d0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0,\n",
      " 1,\n",
      " 2,\n",
      " 3,\n",
      " 4,\n",
      " 5,\n",
      " 6,\n",
      " 7,\n",
      " 8,\n",
      " 9,\n",
      " ...,\n",
      " 1003508,\n",
      " 1003509,\n",
      " 1003510,\n",
      " 1003511,\n",
      " 1003512,\n",
      " 1003513,\n",
      " 1003514,\n",
      " 1003515,\n",
      " 1003516]\n"
     ]
    }
   ],
   "source": [
    "trip_key = ak.Array(np.arange(len(flat_trips)))\n",
    "trip_key.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "a074739f-9857-497f-ab2f-bf77173c0729",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0, 0],\n",
      " [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
      " [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n",
      " [3, 3, 3, 3, 3],\n",
      " [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n",
      " [5, 5],\n",
      " [6, 6, 6, 6, 6, 6, 6, 6],\n",
      " [7, 7, 7, 7, 7, 7, 7],\n",
      " [8, 8, 8, 8, 8, 8, 8, 8, 8],\n",
      " [9, 9, 9, 9, 9, 9],\n",
      " ...,\n",
      " [1003508, 1003508, 1003508, 1003508, ..., 1003508, 1003508, 1003508, 1003508],\n",
      " [1003509, 1003509, 1003509, 1003509, ..., 1003509, 1003509, 1003509, 1003509],\n",
      " [1003510, 1003510, 1003510, 1003510, ..., 1003510, 1003510, 1003510, 1003510],\n",
      " [1003511, 1003511, 1003511, 1003511, 1003511, 1003511, 1003511],\n",
      " [1003512, 1003512, 1003512, 1003512, ..., 1003512, 1003512, 1003512, 1003512],\n",
      " [1003513, 1003513],\n",
      " [1003514, 1003514, 1003514, 1003514, 1003514],\n",
      " [1003515, 1003515, 1003515, 1003515, ..., 1003515, 1003515, 1003515, 1003515],\n",
      " [1003516, 1003516]]\n"
     ]
    }
   ],
   "source": [
    "trip_key_for_paths, _ = ak.broadcast_arrays(trip_key, flat_trips.path.londiff)\n",
    "trip_key_for_paths.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "f3e3704d-402f-42ac-9b63-a31768f44272",
   "metadata": {},
   "outputs": [],
   "source": [
    "flat_trip_key_for_paths = ak.flatten(trip_key_for_paths)\n",
    "flat_paths = ak.flatten(flat_trips.path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "5399aa5b-6ff6-4682-844f-5cc41cf94fab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trip_key</th>\n",
       "      <th>londiff</th>\n",
       "      <th>latdiff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.000024</td>\n",
       "      <td>-3.030000e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.000024</td>\n",
       "      <td>-3.030000e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>-0.000060</td>\n",
       "      <td>-6.010000e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>-0.000066</td>\n",
       "      <td>2.283990e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.001725</td>\n",
       "      <td>2.323990e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10551419</th>\n",
       "      <td>1003515</td>\n",
       "      <td>-0.043903</td>\n",
       "      <td>1.770889e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10551420</th>\n",
       "      <td>1003515</td>\n",
       "      <td>-0.043284</td>\n",
       "      <td>1.778889e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10551421</th>\n",
       "      <td>1003515</td>\n",
       "      <td>-0.043279</td>\n",
       "      <td>1.600889e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10551422</th>\n",
       "      <td>1003516</td>\n",
       "      <td>-0.000040</td>\n",
       "      <td>1.858890e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10551423</th>\n",
       "      <td>1003516</td>\n",
       "      <td>-0.000040</td>\n",
       "      <td>1.858890e-04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10551424 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          trip_key   londiff       latdiff\n",
       "0                0 -0.000024 -3.030000e-07\n",
       "1                0 -0.000024 -3.030000e-07\n",
       "2                1 -0.000060 -6.010000e-07\n",
       "3                1 -0.000066  2.283990e-04\n",
       "4                1  0.001725  2.323990e-04\n",
       "...            ...       ...           ...\n",
       "10551419   1003515 -0.043903  1.770889e-03\n",
       "10551420   1003515 -0.043284  1.778889e-03\n",
       "10551421   1003515 -0.043279  1.600889e-03\n",
       "10551422   1003516 -0.000040  1.858890e-04\n",
       "10551423   1003516 -0.000040  1.858890e-04\n",
       "\n",
       "[10551424 rows x 3 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path_table = pd.DataFrame({\n",
    "    \"trip_key\": ak.to_numpy(flat_trip_key_for_paths),\n",
    "    \"londiff\": ak.to_numpy(flat_paths.londiff),\n",
    "    \"latdiff\": ak.to_numpy(flat_paths.latdiff),\n",
    "})\n",
    "path_table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "261e8e19-9802-44ab-ac6e-47277d60ae8e",
   "metadata": {},
   "source": [
    "(Note: making a Pandas MultiIndex out of the `taxi_key` and `trip_key` would duplicate attribute values.)\n",
    "\n",
    "**Question:** how would you compute distances using `group_by` on these tables?"
   ]
  }
 ],
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