python/pydeck-example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Install"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pydeck\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/c2/d6/f7b5ed0cde59833077a9c65ce2c08dca06557edd42eda70046441761a8ce/pydeck-0.1.dev5-py2.py3-none-any.whl (66kB)\n",
      "\u001b[K     |████████████████████████████████| 71kB 233kB/s eta 0:00:01\n",
      "\u001b[?25hCollecting jinja2>=2.10.1 (from pydeck)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/65/e0/eb35e762802015cab1ccee04e8a277b03f1d8e53da3ec3106882ec42558b/Jinja2-2.10.3-py2.py3-none-any.whl (125kB)\n",
      "\u001b[K     |████████████████████████████████| 133kB 595kB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: traitlets>=4.3.2 in /usr/local/lib/python3.7/site-packages (from pydeck) (4.3.2)\n",
      "Requirement already satisfied: ipywidgets<8,>=7.0.0 in /usr/local/lib/python3.7/site-packages (from pydeck) (7.5.0)\n",
      "Collecting ipykernel>=5.1.2; python_version >= \"3.4\" (from pydeck)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/e1/92/8fec943b5b81078399f969f00557804d884c96fcd0bc296e81a2ed4fd270/ipykernel-5.1.3-py3-none-any.whl (116kB)\n",
      "\u001b[K     |████████████████████████████████| 122kB 112kB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/site-packages (from jinja2>=2.10.1->pydeck) (1.1.1)\n",
      "Requirement already satisfied: decorator in /usr/local/lib/python3.7/site-packages (from traitlets>=4.3.2->pydeck) (4.4.0)\n",
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      "Requirement already satisfied: nbformat>=4.2.0 in /usr/local/lib/python3.7/site-packages (from ipywidgets<8,>=7.0.0->pydeck) (4.4.0)\n",
      "Requirement already satisfied: ipython>=4.0.0; python_version >= \"3.3\" in /usr/local/lib/python3.7/site-packages (from ipywidgets<8,>=7.0.0->pydeck) (7.5.0)\n",
      "Requirement already satisfied: widgetsnbextension~=3.5.0 in /usr/local/lib/python3.7/site-packages (from ipywidgets<8,>=7.0.0->pydeck) (3.5.0)\n",
      "Requirement already satisfied: appnope; platform_system == \"Darwin\" in /usr/local/lib/python3.7/site-packages (from ipykernel>=5.1.2; python_version >= \"3.4\"->pydeck) (0.1.0)\n",
      "Requirement already satisfied: jupyter-client in /usr/local/lib/python3.7/site-packages (from ipykernel>=5.1.2; python_version >= \"3.4\"->pydeck) (5.2.4)\n",
      "Requirement already satisfied: tornado>=4.2 in /usr/local/lib/python3.7/site-packages (from ipykernel>=5.1.2; python_version >= \"3.4\"->pydeck) (6.0.2)\n",
      "Requirement already satisfied: jupyter-core in /usr/local/lib/python3.7/site-packages (from nbformat>=4.2.0->ipywidgets<8,>=7.0.0->pydeck) (4.4.0)\n",
      "Requirement already satisfied: jsonschema!=2.5.0,>=2.4 in /usr/local/lib/python3.7/site-packages (from nbformat>=4.2.0->ipywidgets<8,>=7.0.0->pydeck) (3.0.1)\n",
      "Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.7/site-packages (from ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets<8,>=7.0.0->pydeck) (41.0.1)\n",
      "Requirement already satisfied: pexpect; sys_platform != \"win32\" in /usr/local/lib/python3.7/site-packages (from ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets<8,>=7.0.0->pydeck) (4.7.0)\n",
      "Requirement already satisfied: jedi>=0.10 in /usr/local/lib/python3.7/site-packages (from ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets<8,>=7.0.0->pydeck) (0.13.3)\n",
      "Requirement already satisfied: pickleshare in /usr/local/lib/python3.7/site-packages (from ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets<8,>=7.0.0->pydeck) (0.7.5)\n",
      "Requirement already satisfied: prompt-toolkit<2.1.0,>=2.0.0 in /usr/local/lib/python3.7/site-packages (from ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets<8,>=7.0.0->pydeck) (2.0.9)\n",
      "Requirement already satisfied: backcall in /usr/local/lib/python3.7/site-packages (from ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets<8,>=7.0.0->pydeck) (0.1.0)\n",
      "Requirement already satisfied: pygments in /usr/local/lib/python3.7/site-packages (from ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets<8,>=7.0.0->pydeck) (2.4.2)\n",
      "Requirement already satisfied: notebook>=4.4.1 in /usr/local/lib/python3.7/site-packages (from widgetsnbextension~=3.5.0->ipywidgets<8,>=7.0.0->pydeck) (5.7.8)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/site-packages/python_dateutil-2.7.3-py3.7.egg (from jupyter-client->ipykernel>=5.1.2; python_version >= \"3.4\"->pydeck) (2.7.3)\n",
      "Requirement already satisfied: pyzmq>=13 in /usr/local/lib/python3.7/site-packages (from jupyter-client->ipykernel>=5.1.2; python_version >= \"3.4\"->pydeck) (18.0.1)\n",
      "Requirement already satisfied: attrs>=17.4.0 in /usr/local/lib/python3.7/site-packages (from jsonschema!=2.5.0,>=2.4->nbformat>=4.2.0->ipywidgets<8,>=7.0.0->pydeck) (19.1.0)\n",
      "Requirement already satisfied: pyrsistent>=0.14.0 in /usr/local/lib/python3.7/site-packages (from jsonschema!=2.5.0,>=2.4->nbformat>=4.2.0->ipywidgets<8,>=7.0.0->pydeck) (0.15.2)\n",
      "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.7/site-packages (from pexpect; sys_platform != \"win32\"->ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets<8,>=7.0.0->pydeck) (0.6.0)\n",
      "Requirement already satisfied: parso>=0.3.0 in /usr/local/lib/python3.7/site-packages (from jedi>=0.10->ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets<8,>=7.0.0->pydeck) (0.4.0)\n",
      "Requirement already satisfied: wcwidth in /usr/local/lib/python3.7/site-packages (from prompt-toolkit<2.1.0,>=2.0.0->ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets<8,>=7.0.0->pydeck) (0.1.7)\n",
      "Requirement already satisfied: Send2Trash in /usr/local/lib/python3.7/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets<8,>=7.0.0->pydeck) (1.5.0)\n",
      "Requirement already satisfied: prometheus-client in /usr/local/lib/python3.7/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets<8,>=7.0.0->pydeck) (0.7.1)\n",
      "Requirement already satisfied: nbconvert in /usr/local/lib/python3.7/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets<8,>=7.0.0->pydeck) (5.5.0)\n",
      "Requirement already satisfied: terminado>=0.8.1 in /usr/local/lib/python3.7/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets<8,>=7.0.0->pydeck) (0.8.2)\n",
      "Requirement already satisfied: bleach in /usr/local/lib/python3.7/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets<8,>=7.0.0->pydeck) (3.1.0)\n",
      "Requirement already satisfied: defusedxml in /usr/local/lib/python3.7/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets<8,>=7.0.0->pydeck) (0.6.0)\n",
      "Requirement already satisfied: testpath in /usr/local/lib/python3.7/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets<8,>=7.0.0->pydeck) (0.4.2)\n",
      "Requirement already satisfied: entrypoints>=0.2.2 in /usr/local/lib/python3.7/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets<8,>=7.0.0->pydeck) (0.3)\n",
      "Requirement already satisfied: pandocfilters>=1.4.1 in /usr/local/lib/python3.7/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets<8,>=7.0.0->pydeck) (1.4.2)\n",
      "Requirement already satisfied: mistune>=0.8.1 in /usr/local/lib/python3.7/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets<8,>=7.0.0->pydeck) (0.8.4)\n",
      "Requirement already satisfied: webencodings in /usr/local/lib/python3.7/site-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets<8,>=7.0.0->pydeck) (0.5.1)\n",
      "\u001b[31mERROR: numpydoc 0.9.1 requires sphinx>=1.6.5, which is not installed.\u001b[0m\n",
      "\u001b[31mERROR: flask 1.1.0 has requirement Werkzeug>=0.15, but you'll have werkzeug 0.14.1 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: dash 1.0.0 has requirement dash-core-components==1.0.0, but you'll have dash-core-components 0.14.0 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: dash 1.0.0 has requirement dash-html-components==1.0.0, but you'll have dash-html-components 0.8.0 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: dash 1.0.0 has requirement dash-renderer==1.0.0, but you'll have dash-renderer 0.11.1 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: apache-airflow 1.10.3rc2 has requirement jinja2<=2.10.0,>=2.7.3, but you'll have jinja2 2.10.3 which is incompatible.\u001b[0m\n",
      "Installing collected packages: jinja2, ipykernel, pydeck\n",
      "  Found existing installation: Jinja2 2.10\n",
      "    Uninstalling Jinja2-2.10:\n",
      "      Successfully uninstalled Jinja2-2.10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Found existing installation: ipykernel 5.1.1\n",
      "    Uninstalling ipykernel-5.1.1:\n",
      "      Successfully uninstalled ipykernel-5.1.1\n",
      "Successfully installed ipykernel-5.1.3 jinja2-2.10.3 pydeck-0.1.dev5\n"
     ]
    }
   ],
   "source": [
    "!pip3 install pydeck"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Installing /usr/local/lib/python3.7/site-packages/pydeck/nbextension/static -> pydeck\n",
      "Symlinking: /usr/local/Cellar/python/3.7.4/Frameworks/Python.framework/Versions/3.7/share/jupyter/nbextensions/pydeck -> /usr/local/lib/python3.7/site-packages/pydeck/nbextension/static\n",
      "- Validating: \u001b[32mOK\u001b[0m\n",
      "\n",
      "    To initialize this nbextension in the browser every time the notebook (or other app) loads:\n",
      "    \n",
      "          jupyter nbextension enable pydeck --py --sys-prefix\n",
      "    \n",
      "Enabling notebook extension pydeck/extensionRequires...\n",
      "      - Validating: \u001b[32mOK\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!jupyter nbextension install --sys-prefix --symlink --overwrite --py pydeck\n",
    "!jupyter nbextension enable --sys-prefix --py pydeck"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# export MAPBOX_API_KEY=\"your key\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 공식 홈페이지 예시"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c870e0e52df24ecbb91f114dfb1ae8b3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "DeckGLWidget(json_input='{\"initialViewState\": {\"bearing\": -27.36, \"latitude\": 52.2323, \"longitude\": -1.415, \"m…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pydeck\n",
    "\n",
    "# 2014 locations of car accidents in the UK\n",
    "UK_ACCIDENTS_DATA = ('https://raw.githubusercontent.com/uber-common/'\n",
    "                     'deck.gl-data/master/examples/3d-heatmap/heatmap-data.csv')\n",
    "\n",
    "# Define a layer to display on a map\n",
    "layer = pydeck.Layer(\n",
    "    'HexagonLayer',\n",
    "    UK_ACCIDENTS_DATA,\n",
    "    get_position='[lng, lat]',\n",
    "    auto_highlight=True,\n",
    "    elevation_scale=50,\n",
    "    pickable=True,\n",
    "    elevation_range=[0, 3000],\n",
    "    extruded=True,                 \n",
    "    coverage=1)\n",
    "\n",
    "# Set the viewport location\n",
    "view_state = pydeck.ViewState(\n",
    "    longitude=-1.415,\n",
    "    latitude=52.2323,\n",
    "    zoom=6,\n",
    "    min_zoom=5,\n",
    "    max_zoom=15,\n",
    "    pitch=40.5,\n",
    "    bearing=-27.36)\n",
    "\n",
    "# Render\n",
    "r = pydeck.Deck(layers=[layer], initial_view_state=view_state)\n",
    "# r.to_html('demo.html')\n",
    "r.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conway의 생명 게임\n",
    "- [Example](https://github.com/uber/deck.gl/blob/master/bindings/python/pydeck/examples/06%20-%20Conway's%20Game%20of%20Life.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "def new_board(x, y, num_live_cells=2, num_dead_cells=3):\n",
    "    \"\"\"Initializes a board for Conway's Game of Life\"\"\"\n",
    "    board = []\n",
    "    for i in range(0, y):\n",
    "        # Defaults to a 3:2 dead cell:live cell ratio\n",
    "        board.append([random.choice([0] * num_dead_cells + [1] * num_live_cells) for _ in range(0, x)])\n",
    "    return board\n",
    "\n",
    "        \n",
    "def get(board, x, y):\n",
    "    \"\"\"Return the value at location (x, y) on a board, wrapping around if out-of-bounds\"\"\"\n",
    "    return board[y % len(board)][x % len(board[0])]\n",
    "\n",
    "\n",
    "def assign(board, x, y, value):\n",
    "    \"\"\"Assigns a value at location (x, y) on a board, wrapping around if out-of-bounds\"\"\"\n",
    "    board[y % len(board)][x % len(board[0])] = value\n",
    "\n",
    "\n",
    "def count_neighbors(board, x, y):\n",
    "    \"\"\"Counts the number of living neighbors a cell at (x, y) on a board has\"\"\"\n",
    "    return sum([\n",
    "        get(board, x - 1, y),\n",
    "        get(board, x + 1, y),\n",
    "        get(board, x, y - 1),\n",
    "        get(board, x, y + 1),\n",
    "        get(board, x + 1, y + 1),\n",
    "        get(board, x + 1, y - 1),\n",
    "        get(board, x - 1, y + 1),\n",
    "        get(board, x - 1, y - 1)])\n",
    "\n",
    "\n",
    "def process_life(board):\n",
    "    \"\"\"Creates the next iteration from a passed state of Conway's Game of Life\"\"\"\n",
    "    next_board = new_board(len(board[0]), len(board))\n",
    "    for y in range(0, len(board)):\n",
    "        for x in range(0, len(board[y])):\n",
    "            num_neighbors = count_neighbors(board, x, y)\n",
    "            is_alive = get(board, x, y) == 1\n",
    "            if num_neighbors < 2 and is_alive:\n",
    "                assign(next_board, x, y, 0)\n",
    "            elif 2 <= num_neighbors <= 3 and is_alive:\n",
    "                assign(next_board, x, y, 1)\n",
    "            elif num_neighbors > 3 and is_alive:\n",
    "                assign(next_board, x, y, 0)\n",
    "            elif num_neighbors == 3 and not is_alive:\n",
    "                assign(next_board, x, y, 1)\n",
    "            else:\n",
    "                assign(next_board, x, y, 0)\n",
    "    return next_board\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 100\n",
      "                                        \n",
      "                                        \n",
      "                                        \n",
      "                                        \n",
      "      *                                 \n",
      "      *                                 \n",
      "      *                                 \n",
      "                                        \n",
      "                                        \n",
      "                                        \n",
      "                                        \n",
      "                                        \n",
      "                                        \n",
      "                                        \n",
      "                                        \n",
      "                                        \n",
      "                                        \n",
      "                                        \n",
      "                                        \n",
      "                                        \n",
      "\n"
     ]
    }
   ],
   "source": [
    "from IPython.display import clear_output\n",
    "import time\n",
    "\n",
    "def draw_board(board):\n",
    "    res = ''\n",
    "    for row in board:\n",
    "        for col in row:\n",
    "            if col == 1:\n",
    "                res += '* '\n",
    "            else:\n",
    "                res += '  '\n",
    "        res += '\\n'\n",
    "    return res\n",
    "\n",
    "board = new_board(20, 20)\n",
    "\n",
    "NUM_ITERATIONS = 100\n",
    "\n",
    "for i in range(0, NUM_ITERATIONS):\n",
    "    print('Iteration ' + str(i + 1))\n",
    "    board = process_life(board)\n",
    "    res = draw_board(board)\n",
    "    print(res)\n",
    "    time.sleep(0.1)\n",
    "    clear_output(wait=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5ee1b3f477324368854d37828a0ec822",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "DeckGLWidget(json_input='{\"initialViewState\": {\"bearing\": 44, \"latitude\": 0.0, \"longitude\": 0.0, \"maxZoom\": 20…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pydeck as deck\n",
    "\n",
    "PINK = [155, 155, 255, 245]\n",
    "PURPLE = [255, 155, 255, 245]\n",
    "\n",
    "SCALING_FACTOR = 1000.0\n",
    "\n",
    "def convert_board_to_df(board):\n",
    "    \"\"\"Makes the board matrix into a list for easier processing\"\"\"\n",
    "    rows = []\n",
    "    for x in range(0, len(board[0])):\n",
    "        for y in range(0, len(board)):\n",
    "            rows.append([[x / SCALING_FACTOR, y / SCALING_FACTOR], PURPLE if board[y][x] else PINK])\n",
    "    return pd.DataFrame(rows, columns=['position', 'color'])\n",
    "\n",
    "board = new_board(30, 30)\n",
    "records = convert_board_to_df(board)\n",
    "layer = deck.Layer(\n",
    "    'PointCloudLayer',\n",
    "    records,\n",
    "    get_position='position',\n",
    "    get_color='color',\n",
    "    get_radius=40)\n",
    "view_state = deck.ViewState(latitude=0.00, longitude=0.00, zoom=13, bearing=44, pitch=45)\n",
    "r = deck.Deck(layers=[layer], initial_view_state=view_state, map_style='')\n",
    "r.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5ee1b3f477324368854d37828a0ec822",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "DeckGLWidget(json_input='{\"initialViewState\": {\"bearing\": 44, \"latitude\": 0.0, \"longitude\": 0.0, \"maxZoom\": 20…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "NUM_ITERATIONS = 100\n",
    "display(r.show())\n",
    "for i in range(0, NUM_ITERATIONS):\n",
    "    board = process_life(board)\n",
    "    records = convert_board_to_df(board)\n",
    "    layer.data = records\n",
    "    r.update()\n",
    "    time.sleep(0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lng</th>\n",
       "      <th>lat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.198465</td>\n",
       "      <td>51.505538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.178838</td>\n",
       "      <td>51.491836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.205590</td>\n",
       "      <td>51.514910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.208327</td>\n",
       "      <td>51.514952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.206022</td>\n",
       "      <td>51.496572</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        lng        lat\n",
       "0 -0.198465  51.505538\n",
       "1 -0.178838  51.491836\n",
       "2 -0.205590  51.514910\n",
       "3 -0.208327  51.514952\n",
       "4 -0.206022  51.496572"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "UK_ACCIDENTS_DATA = 'https://raw.githubusercontent.com/uber-common/deck.gl-data/master/examples/3d-heatmap/heatmap-data.csv'\n",
    "\n",
    "pd.read_csv(UK_ACCIDENTS_DATA).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'https://raw.githubusercontent.com/uber-common/deck.gl-data/master/examples/3d-heatmap/heatmap-data.csv'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "UK_ACCIDENTS_DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cec9dfb752d74fa7b7df1f34321e1960",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "DeckGLWidget(json_input='{\"initialViewState\": {\"bearing\": -27.36, \"latitude\": 52.2323, \"longitude\": -1.415, \"m…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "layer = pydeck.Layer(\n",
    "    'HexagonLayer',\n",
    "    UK_ACCIDENTS_DATA,\n",
    "    get_position='[lng,lat]',\n",
    "    auto_highlight=True,\n",
    "    elevation_scale=50,\n",
    "    pickable=True,\n",
    "    elevation_range=[0, 3000],\n",
    "    extruded=True,                 \n",
    "    coverage=1)\n",
    "\n",
    "# Set the viewport location\n",
    "view_state = pydeck.ViewState(\n",
    "    longitude=-1.415,\n",
    "    latitude=52.2323,\n",
    "    zoom=6,\n",
    "    min_zoom=5,\n",
    "    max_zoom=15,\n",
    "    pitch=40.5,\n",
    "    bearing=-27.36)\n",
    "\n",
    "# Combined all of it and render a viewport\n",
    "r = pydeck.Deck(layers=[layer], initial_view_state=view_state)\n",
    "r.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "layer.elevation_range = [0, 10000]\n",
    "\n",
    "r.update()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pydeck as pdk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "pdk.Deck?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cec9dfb752d74fa7b7df1f34321e1960",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "DeckGLWidget(json_input='{\"initialViewState\": {\"bearing\": -27.36, \"latitude\": 52.2323, \"longitude\": -1.415, \"m…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import time\n",
    "r.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(0, 10000, 1000):\n",
    "    layer.elevation_range = [0, i]\n",
    "    r.update()\n",
    "    time.sleep(0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scatter Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>osm_id</th>\n",
       "      <th>station_name</th>\n",
       "      <th>chinese_name</th>\n",
       "      <th>opening_date</th>\n",
       "      <th>color</th>\n",
       "      <th>line_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39.940249</td>\n",
       "      <td>116.456359</td>\n",
       "      <td>1351272524</td>\n",
       "      <td>Agricultural Exhibition Center</td>\n",
       "      <td>农业展览馆</td>\n",
       "      <td>2008-07-19</td>\n",
       "      <td>[0, 146, 188, 255]</td>\n",
       "      <td>Line 10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39.955570</td>\n",
       "      <td>116.388507</td>\n",
       "      <td>5057476994</td>\n",
       "      <td>Andelibeijie</td>\n",
       "      <td>安德里北街</td>\n",
       "      <td>2015-12-26</td>\n",
       "      <td>[0, 155, 119, 255]</td>\n",
       "      <td>Line 8 (North section)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>39.947729</td>\n",
       "      <td>116.402067</td>\n",
       "      <td>339088654</td>\n",
       "      <td>Andingmen</td>\n",
       "      <td>安定门</td>\n",
       "      <td>1984-09-20</td>\n",
       "      <td>[0, 75, 135, 255]</td>\n",
       "      <td>Line 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40.011026</td>\n",
       "      <td>116.263981</td>\n",
       "      <td>1362259113</td>\n",
       "      <td>Anheqiao North</td>\n",
       "      <td>安河桥北</td>\n",
       "      <td>2009-09-28</td>\n",
       "      <td>[0, 140, 149, 255]</td>\n",
       "      <td>Line 4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>39.967112</td>\n",
       "      <td>116.388398</td>\n",
       "      <td>5305505996</td>\n",
       "      <td>Anhuaqiao</td>\n",
       "      <td>安华桥</td>\n",
       "      <td>2012-12-30</td>\n",
       "      <td>[0, 155, 119, 255]</td>\n",
       "      <td>Line 8 (North section)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         lat         lng      osm_id                    station_name  \\\n",
       "0  39.940249  116.456359  1351272524  Agricultural Exhibition Center   \n",
       "1  39.955570  116.388507  5057476994                    Andelibeijie   \n",
       "2  39.947729  116.402067   339088654                       Andingmen   \n",
       "3  40.011026  116.263981  1362259113                  Anheqiao North   \n",
       "4  39.967112  116.388398  5305505996                       Anhuaqiao   \n",
       "\n",
       "  chinese_name opening_date               color               line_name  \n",
       "0        农业展览馆   2008-07-19  [0, 146, 188, 255]                 Line 10  \n",
       "1        安德里北街   2015-12-26  [0, 155, 119, 255]  Line 8 (North section)  \n",
       "2          安定门   1984-09-20   [0, 75, 135, 255]                  Line 2  \n",
       "3         安河桥北   2009-09-28  [0, 140, 149, 255]                  Line 4  \n",
       "4          安华桥   2012-12-30  [0, 155, 119, 255]  Line 8 (North section)  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from pydeck import (\n",
    "    data_utils,\n",
    "    Deck,\n",
    "    Layer\n",
    ")\n",
    "\n",
    "# First, let's use Pandas to download our data\n",
    "URL = 'https://raw.githubusercontent.com/ajduberstein/data_sets/master/beijing_subway_station.csv'\n",
    "df = pd.read_csv(URL)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ast import literal_eval\n",
    "# We have to re-code position to be one field in a list, so we'll do that here:\n",
    "# The CSV encodes the [R, G, B, A] color values listed in it as a string\n",
    "df['color'] = df.apply(lambda x: literal_eval(x['color']), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>osm_id</th>\n",
       "      <th>station_name</th>\n",
       "      <th>chinese_name</th>\n",
       "      <th>opening_date</th>\n",
       "      <th>color</th>\n",
       "      <th>line_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39.940249</td>\n",
       "      <td>116.456359</td>\n",
       "      <td>1351272524</td>\n",
       "      <td>Agricultural Exhibition Center</td>\n",
       "      <td>农业展览馆</td>\n",
       "      <td>2008-07-19</td>\n",
       "      <td>[0, 146, 188, 255]</td>\n",
       "      <td>Line 10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39.955570</td>\n",
       "      <td>116.388507</td>\n",
       "      <td>5057476994</td>\n",
       "      <td>Andelibeijie</td>\n",
       "      <td>安德里北街</td>\n",
       "      <td>2015-12-26</td>\n",
       "      <td>[0, 155, 119, 255]</td>\n",
       "      <td>Line 8 (North section)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>39.947729</td>\n",
       "      <td>116.402067</td>\n",
       "      <td>339088654</td>\n",
       "      <td>Andingmen</td>\n",
       "      <td>安定门</td>\n",
       "      <td>1984-09-20</td>\n",
       "      <td>[0, 75, 135, 255]</td>\n",
       "      <td>Line 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40.011026</td>\n",
       "      <td>116.263981</td>\n",
       "      <td>1362259113</td>\n",
       "      <td>Anheqiao North</td>\n",
       "      <td>安河桥北</td>\n",
       "      <td>2009-09-28</td>\n",
       "      <td>[0, 140, 149, 255]</td>\n",
       "      <td>Line 4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>39.967112</td>\n",
       "      <td>116.388398</td>\n",
       "      <td>5305505996</td>\n",
       "      <td>Anhuaqiao</td>\n",
       "      <td>安华桥</td>\n",
       "      <td>2012-12-30</td>\n",
       "      <td>[0, 155, 119, 255]</td>\n",
       "      <td>Line 8 (North section)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         lat         lng      osm_id                    station_name  \\\n",
       "0  39.940249  116.456359  1351272524  Agricultural Exhibition Center   \n",
       "1  39.955570  116.388507  5057476994                    Andelibeijie   \n",
       "2  39.947729  116.402067   339088654                       Andingmen   \n",
       "3  40.011026  116.263981  1362259113                  Anheqiao North   \n",
       "4  39.967112  116.388398  5305505996                       Anhuaqiao   \n",
       "\n",
       "  chinese_name opening_date               color               line_name  \n",
       "0        农业展览馆   2008-07-19  [0, 146, 188, 255]                 Line 10  \n",
       "1        安德里北街   2015-12-26  [0, 155, 119, 255]  Line 8 (North section)  \n",
       "2          安定门   1984-09-20   [0, 75, 135, 255]                  Line 2  \n",
       "3         安河桥北   2009-09-28  [0, 140, 149, 255]                  Line 4  \n",
       "4          安华桥   2012-12-30  [0, 155, 119, 255]  Line 8 (North section)  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "851b880d9c7840c6af088c2ad711e95b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "DeckGLWidget(json_input='{\"initialViewState\": {\"bearing\": 0, \"latitude\": 39.92295563963415, \"longitude\": 116.3…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use pydeck's data_utils module to fit a viewport to the central 90% of the data\n",
    "viewport = data_utils.compute_view(points=df[['lng', 'lat']], view_proportion=0.9)\n",
    "auto_zoom_map = Deck(layers=None, initial_view_state=viewport)\n",
    "auto_zoom_map.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fdf0f0f83c6e47e8bd77db60f17a38a7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HTML(value='<h1>2019</h1>')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8436ed25497a47e184341be162f81957",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "DeckGLWidget(json_input='{\"initialViewState\": {\"bearing\": 0, \"latitude\": 39.92295563963415, \"longitude\": 116.3…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.core.display import display\n",
    "import ipywidgets\n",
    "\n",
    "year = 2019\n",
    "\n",
    "scatterplot = Layer(\n",
    "    'ScatterplotLayer',\n",
    "    df,\n",
    "    id='scatterplot-layer',\n",
    "    get_radius=500,\n",
    "    get_fill_color='color',\n",
    "    get_position='[lng, lat]')\n",
    "r = Deck(layers=[scatterplot], initial_view_state=viewport)\n",
    "\n",
    "# Create an HTML header to display the year\n",
    "display_el = ipywidgets.HTML('<h1>{}</h1>'.format(year))\n",
    "display(display_el)\n",
    "# Show the current visualization\n",
    "r.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "for y in range(1971, 2020):\n",
    "    scatterplot.data = df[df['opening_date'] <= str(y)]\n",
    "    year = y\n",
    "    # Reset the header to display the year\n",
    "    display_el.value = '<h1>{}</h1>'.format(year)\n",
    "    r.update()\n",
    "    time.sleep(0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using pydeck to manipulate data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pydeck as pdk\n",
    "\n",
    "DATA_URL = 'https://api.data.gov.sg/v1/transport/taxi-availability'\n",
    "COLOR_RANGE = [\n",
    "  [255, 255, 178, 25],\n",
    "  [254, 217, 118, 85],\n",
    "  [254, 178, 76, 127],\n",
    "  [253, 141, 60, 170],\n",
    "  [240, 59, 32, 212],\n",
    "  [189, 0, 38, 255]\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import requests\n",
    "\n",
    "json = requests.get(DATA_URL).json()\n",
    "df = pd.DataFrame(json[\"features\"][0][\"geometry\"][\"coordinates\"])\n",
    "df.columns = ['lng', 'lat']\n",
    "\n",
    "viewport = pdk.data_utils.compute_view(df[['lng', 'lat']])\n",
    "layer = pdk.Layer(\n",
    "    'ScreenGridLayer',\n",
    "    df,\n",
    "    cell_size_pixels=20,\n",
    "    color_range=COLOR_RANGE,\n",
    "    get_position='[lng, lat]',\n",
    "    pickable=True,\n",
    "    auto_highlight=True)\n",
    "r = pdk.Deck(layers=[layer], initial_view_state=viewport)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a28aa2a009264ca7b000c781e09e165e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "DeckGLWidget(json_input='{\"initialViewState\": {\"bearing\": 0, \"latitude\": 1.3426320730635841, \"longitude\": 103.…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "r.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cellCount</th>\n",
       "      <th>cellWeight</th>\n",
       "      <th>maxCellWieght</th>\n",
       "      <th>totalCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59</td>\n",
       "      <td>59</td>\n",
       "      <td>232</td>\n",
       "      <td>2595</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cellCount  cellWeight  maxCellWieght  totalCount\n",
       "0         59          59            232        2595"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame([r.deck_widget.selected_data])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting massive data sets.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/site-packages/pandas/core/indexing.py:543: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "all_lidar = pd.concat([\n",
    "    pd.read_csv('https://raw.githubusercontent.com/ajduberstein/kitti_subset/master/kitti_1.csv'),\n",
    "    pd.read_csv('https://raw.githubusercontent.com/ajduberstein/kitti_subset/master/kitti_2.csv'),\n",
    "    pd.read_csv('https://raw.githubusercontent.com/ajduberstein/kitti_subset/master/kitti_3.csv'),\n",
    "    pd.read_csv('https://raw.githubusercontent.com/ajduberstein/kitti_subset/master/kitti_4.csv'),\n",
    "])\n",
    "\n",
    "# Filter to one frame of data\n",
    "lidar = all_lidar[all_lidar['source'] == 136]\n",
    "lidar.loc[: , ['x', 'y']] = lidar[['x', 'y']] / 10000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4c104563fcc24120a1ee22774cbd1e94",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "DeckGLWidget(json_input='{\"initialViewState\": {\"bearing\": 120, \"latitude\": -3.0079270065674496e-05, \"longitude…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pydeck as pdk\n",
    "\n",
    "\n",
    "point_cloud = pdk.Layer(\n",
    "    'PointCloudLayer',\n",
    "    lidar[['x', 'y', 'z']],\n",
    "    get_position='[x, y, z * 10]',\n",
    "    get_normal=[0, 0, 1],\n",
    "    get_color=[255, 0, 100, 200],\n",
    "    pickable=True,  \n",
    "    auto_highlight=True,\n",
    "    point_size=1)\n",
    "\n",
    "\n",
    "view_state = pdk.data_utils.compute_view(lidar[['x', 'y']], 0.9)\n",
    "view_state.max_pitch = 360\n",
    "view_state.pitch = 80\n",
    "view_state.bearing = 120\n",
    "\n",
    "r = pdk.Deck(\n",
    "    point_cloud,\n",
    "    initial_view_state=view_state,\n",
    "    map_style='')\n",
    "r.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Press the stop icon to exit\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-49-03782f910cca>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0mframe_buffer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrotate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m     \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "import time\n",
    "from collections import deque\n",
    "\n",
    "# Choose a handful of frames to loop through\n",
    "frame_buffer = deque([42, 56, 81, 95])\n",
    "print('Press the stop icon to exit')\n",
    "while True:\n",
    "    current_frame = frame_buffer[0]\n",
    "    lidar = all_lidar[all_lidar['source'] == current_frame]\n",
    "    r.layers[0].get_position = '[x / 10000, y / 10000, z * 10]'\n",
    "    r.layers[0].data = lidar.to_dict(orient='records')\n",
    "    frame_buffer.rotate()\n",
    "    r.update()\n",
    "    time.sleep(0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interacting with other Jupyter widgets.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>lng</th>\n",
       "      <th>lat</th>\n",
       "      <th>brightness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1993</td>\n",
       "      <td>104.575</td>\n",
       "      <td>31.808</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1993</td>\n",
       "      <td>104.583</td>\n",
       "      <td>31.808</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1993</td>\n",
       "      <td>104.592</td>\n",
       "      <td>31.808</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1993</td>\n",
       "      <td>104.600</td>\n",
       "      <td>31.808</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1993</td>\n",
       "      <td>104.675</td>\n",
       "      <td>31.808</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year      lng     lat  brightness\n",
       "0  1993  104.575  31.808           4\n",
       "1  1993  104.583  31.808           4\n",
       "2  1993  104.592  31.808           4\n",
       "3  1993  104.600  31.808           4\n",
       "4  1993  104.675  31.808           4"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LIGHTS_URL = 'https://raw.githubusercontent.com/ajduberstein/lights_at_night/master/chengdu_lights_at_night.csv'\n",
    "df = pd.read_csv(LIGHTS_URL)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['color'] = df['brightness'].apply(lambda val: [255, val * 4,  255, 255])\n",
    "df.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e1e26ab1e8d24f408050711bf4fca0e7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "DeckGLWidget(json_input='{\"initialViewState\": {\"bearing\": 0, \"latitude\": 31.0, \"longitude\": 104.5, \"maxZoom\": …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plottable = df[df['year'] == 1993].to_dict(orient='records')\n",
    "\n",
    "view_state = pdk.ViewState(\n",
    "    latitude=31.0,\n",
    "    longitude=104.5,\n",
    "    zoom=8,\n",
    "    max_zoom=8,\n",
    "    min_zoom=8)\n",
    "scatterplot = pdk.Layer(\n",
    "    'HeatmapLayer',\n",
    "    data=plottable,\n",
    "    get_position='[lng, lat]',\n",
    "    get_weight='brightness',\n",
    "    opacity=0.5,\n",
    "    pickable=False,\n",
    "    get_radius=800)\n",
    "r = pdk.Deck(\n",
    "    layers=[scatterplot],\n",
    "    initial_view_state=view_state,\n",
    "    views=[pdk.View(type='MapView', controller=None)])\n",
    "r.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "26cccc78b06543f09cab4322e467bd02",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "IntSlider(value=1993, max=2013, min=1993, step=2)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import ipywidgets as widgets\n",
    "from IPython.display import display\n",
    "slider = widgets.IntSlider(1992, min=1993, max=2013, step=2)\n",
    "def on_change(v):\n",
    "    results = df[df['year'] == slider.value].to_dict(orient='records')\n",
    "    scatterplot.data = results\n",
    "    r.update()\n",
    "    \n",
    "slider.observe(on_change, names='value')\n",
    "display(slider)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tooltip = {\n",
    "   \"html\": \"<b>Elevation Value:</b> {elevationValue}\",\n",
    "   \"style\": {\n",
    "        \"backgroundColor\": \"steelblue\",\n",
    "        \"color\": \"white\"\n",
    "   }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tooltip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "86b24465303d4bf087445c1d6071d96d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "DeckGLWidget(json_input='{\"initialViewState\": {\"bearing\": -27.36, \"latitude\": 52.2323, \"longitude\": -1.415, \"m…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pydeck as pdk\n",
    "\n",
    "layer = pdk.Layer(\n",
    "    'HexagonLayer',\n",
    "    UK_ACCIDENTS_DATA,\n",
    "    get_position='[lng, lat]',\n",
    "    auto_highlight=True,\n",
    "    elevation_scale=50,\n",
    "    pickable=True,\n",
    "    elevation_range=[0, 3000],\n",
    "    extruded=True,\n",
    "    coverage=1)\n",
    "\n",
    "# Set the viewport location\n",
    "view_state = pdk.ViewState(\n",
    "    longitude=-1.415,\n",
    "    latitude=52.2323,\n",
    "    zoom=6,\n",
    "    min_zoom=5,\n",
    "    max_zoom=15,\n",
    "    pitch=40.5,\n",
    "    bearing=-27.36)\n",
    "\n",
    "# Combined all of it and render a viewport\n",
    "r = pdk.Deck(\n",
    "    layers=[layer],\n",
    "    initial_view_state=view_state,\n",
    "    tooltip={\n",
    "        'html': '<b>Elevation Value:</b> {elevationValue}',\n",
    "        'style': {\n",
    "            'color': 'white'\n",
    "        }\n",
    "    }\n",
    ")\n",
    "r.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 그냥 텍스트로 하기\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7cb21c045ebb49b3b2cd86dcadd929ae",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "DeckGLWidget(json_input='{\"initialViewState\": {\"bearing\": -27.36, \"latitude\": 52.2323, \"longitude\": -1.415, \"m…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pydeck as pdk\n",
    "\n",
    "layer = pdk.Layer(\n",
    "    'HexagonLayer',\n",
    "    UK_ACCIDENTS_DATA,\n",
    "    get_position='[lng, lat]',\n",
    "    auto_highlight=True,\n",
    "    elevation_scale=50,\n",
    "    pickable=True,\n",
    "    elevation_range=[0, 3000],\n",
    "    extruded=True,\n",
    "    coverage=1)\n",
    "\n",
    "# Set the viewport location\n",
    "view_state = pdk.ViewState(\n",
    "    longitude=-1.415,\n",
    "    latitude=52.2323,\n",
    "    zoom=6,\n",
    "    min_zoom=5,\n",
    "    max_zoom=15,\n",
    "    pitch=40.5,\n",
    "    bearing=-27.36)\n",
    "\n",
    "# Combined all of it and render a viewport\n",
    "r = pdk.Deck(\n",
    "    layers=[layer],\n",
    "    initial_view_state=view_state,\n",
    "    tooltip = {\n",
    "    \"text\": \"Elevation: {elevationValue}\"\n",
    "    }\n",
    ")\n",
    "r.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Tooltip을 그냥 True값만 주기\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0c3ea9240e814cefbfa393482288e114",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "DeckGLWidget(json_input='{\"initialViewState\": {\"bearing\": -27.36, \"latitude\": 52.2323, \"longitude\": -1.415, \"m…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pydeck as pdk\n",
    "\n",
    "layer = pdk.Layer(\n",
    "    'HexagonLayer',\n",
    "    UK_ACCIDENTS_DATA,\n",
    "    get_position='[lng, lat]',\n",
    "    auto_highlight=True,\n",
    "    elevation_scale=50,\n",
    "    pickable=True,\n",
    "    elevation_range=[0, 3000],\n",
    "    extruded=True,\n",
    "    coverage=1)\n",
    "\n",
    "# Set the viewport location\n",
    "view_state = pdk.ViewState(\n",
    "    longitude=-1.415,\n",
    "    latitude=52.2323,\n",
    "    zoom=6,\n",
    "    min_zoom=5,\n",
    "    max_zoom=15,\n",
    "    pitch=40.5,\n",
    "    bearing=-27.36)\n",
    "\n",
    "# Combined all of it and render a viewport\n",
    "r = pdk.Deck(\n",
    "    layers=[layer],\n",
    "    initial_view_state=view_state,\n",
    "    tooltip=True\n",
    ")\n",
    "r.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "UK_ACCIDENTS_DATA = 'https://raw.githubusercontent.com/uber-common/deck.gl-data/master/examples/3d-heatmap/heatmap-data.csv'\n",
    "\n",
    "uk_data = pd.read_csv(UK_ACCIDENTS_DATA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lng</th>\n",
       "      <th>lat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.198465</td>\n",
       "      <td>51.505538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.178838</td>\n",
       "      <td>51.491836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.205590</td>\n",
       "      <td>51.514910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.208327</td>\n",
       "      <td>51.514952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.206022</td>\n",
       "      <td>51.496572</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        lng        lat\n",
       "0 -0.198465  51.505538\n",
       "1 -0.178838  51.491836\n",
       "2 -0.205590  51.514910\n",
       "3 -0.208327  51.514952\n",
       "4 -0.206022  51.496572"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uk_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 미국 택시 데이터 시각화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "query = \"\"\"\n",
    "SELECT \n",
    "    *\n",
    "FROM `bigquery-public-data.new_york_taxi_trips.tlc_yellow_trips_2015` \n",
    "WHERE EXTRACT(MONTH from pickup_datetime) = 1\n",
    "LIMIT 100000\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 8.49 s, sys: 536 ms, total: 9.03 s\n",
      "Wall time: 1min\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "taxi_df = pd.read_gbq(query=query, dialect='standard', project_id='geultto')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GridLayer\n",
    "- 10만개 데이터"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4ae7dd6865664406ae47899ad9f7f84c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "DeckGLWidget(json_input='{\"initialViewState\": {\"bearing\": 0, \"latitude\": 40.7648, \"longitude\": -73.9808, \"maxZ…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "arc_layer = pdk.Layer(\n",
    "    'GridLayer',\n",
    "    taxi_df,\n",
    "    get_position='[pickup_longitude, pickup_latitude]',\n",
    "    pickable=True, \n",
    "    auto_highlight=True,\n",
    "    tooltip=True\n",
    ")\n",
    "\n",
    "nyc_center = [-73.9808, 40.7648] \n",
    "view_state = pdk.ViewState(longitude=nyc_center[0], latitude=nyc_center[1], zoom=9)\n",
    "\n",
    "r = pdk.Deck(layers=[arc_layer], initial_view_state=view_state)\n",
    "r.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Arc Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "zip_code_query = \"\"\"\n",
    "WITH base_data AS \n",
    "(\n",
    "  SELECT \n",
    "    nyc_taxi.*, \n",
    "    pickup.zip_code as pickup_zip_code,\n",
    "    pickup.internal_point_lat as pickup_zip_code_lat,\n",
    "    pickup.internal_point_lon as pickup_zip_code_lon,\n",
    "    dropoff.zip_code as dropoff_zip_code,\n",
    "    dropoff.internal_point_lat as dropoff_zip_code_lat,\n",
    "    dropoff.internal_point_lon as dropoff_zip_code_lon\n",
    "  FROM (\n",
    "    SELECT *\n",
    "    FROM `bigquery-public-data.new_york_taxi_trips.tlc_yellow_trips_2015`\n",
    "    WHERE \n",
    "        EXTRACT(MONTH from pickup_datetime) = 1\n",
    "        and pickup_latitude <= 90 and pickup_latitude >= -90\n",
    "        and dropoff_latitude <= 90 and dropoff_latitude >= -90\n",
    "    ) AS nyc_taxi\n",
    "  JOIN (\n",
    "    SELECT zip_code, state_code, state_name, city, county, zip_code_geom, internal_point_lat, internal_point_lon \n",
    "    FROM `bigquery-public-data.geo_us_boundaries.zip_codes`\n",
    "    WHERE state_code='NY'\n",
    "    ) AS pickup \n",
    "  ON ST_CONTAINS(pickup.zip_code_geom, st_geogpoint(pickup_longitude, pickup_latitude))\n",
    "  JOIN (\n",
    "    SELECT zip_code, state_code, state_name, city, county, zip_code_geom, internal_point_lat, internal_point_lon \n",
    "    FROM `bigquery-public-data.geo_us_boundaries.zip_codes`\n",
    "    WHERE state_code='NY' \n",
    "    ) AS dropoff\n",
    "  ON ST_CONTAINS(dropoff.zip_code_geom, st_geogpoint(dropoff_longitude, dropoff_latitude))\n",
    "  \n",
    ")\n",
    "\n",
    "SELECT \n",
    "  *\n",
    "FROM base_data \n",
    "limit 10000\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.15 s, sys: 56 ms, total: 1.2 s\n",
      "Wall time: 42.3 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "taxi_df_by_zipcode = pd.read_gbq(query=zip_code_query, dialect='standard', project_id='geultto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vendor_id</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>rate_code</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>...</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>tolls_amount</th>\n",
       "      <th>imp_surcharge</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>pickup_zip_code</th>\n",
       "      <th>pickup_zip_code_lat</th>\n",
       "      <th>pickup_zip_code_lon</th>\n",
       "      <th>dropoff_zip_code</th>\n",
       "      <th>dropoff_zip_code_lat</th>\n",
       "      <th>dropoff_zip_code_lon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-08 23:10:48</td>\n",
       "      <td>2015-01-08 23:24:56</td>\n",
       "      <td>1</td>\n",
       "      <td>6.00</td>\n",
       "      <td>-73.964203</td>\n",
       "      <td>40.807423</td>\n",
       "      <td>None</td>\n",
       "      <td>N</td>\n",
       "      <td>-73.915398</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.44</td>\n",
       "      <td>0.3</td>\n",
       "      <td>22.74</td>\n",
       "      <td>10027</td>\n",
       "      <td>40.811408</td>\n",
       "      <td>-73.953060</td>\n",
       "      <td>10463</td>\n",
       "      <td>40.880678</td>\n",
       "      <td>-73.906540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2015-01-10 23:41:12</td>\n",
       "      <td>2015-01-11 00:04:56</td>\n",
       "      <td>1</td>\n",
       "      <td>9.12</td>\n",
       "      <td>-73.950119</td>\n",
       "      <td>40.771908</td>\n",
       "      <td>None</td>\n",
       "      <td>N</td>\n",
       "      <td>-73.845802</td>\n",
       "      <td>...</td>\n",
       "      <td>5.7</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>35.00</td>\n",
       "      <td>10075</td>\n",
       "      <td>40.773363</td>\n",
       "      <td>-73.956222</td>\n",
       "      <td>10461</td>\n",
       "      <td>40.847394</td>\n",
       "      <td>-73.840583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2015-01-01 01:19:22</td>\n",
       "      <td>2015-01-01 01:33:47</td>\n",
       "      <td>3</td>\n",
       "      <td>2.30</td>\n",
       "      <td>-73.936562</td>\n",
       "      <td>40.802635</td>\n",
       "      <td>None</td>\n",
       "      <td>N</td>\n",
       "      <td>-73.907959</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>13.30</td>\n",
       "      <td>10035</td>\n",
       "      <td>40.795458</td>\n",
       "      <td>-73.929570</td>\n",
       "      <td>10455</td>\n",
       "      <td>40.814713</td>\n",
       "      <td>-73.908590</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  vendor_id     pickup_datetime    dropoff_datetime  passenger_count  \\\n",
       "0         1 2015-01-08 23:10:48 2015-01-08 23:24:56                1   \n",
       "1         2 2015-01-10 23:41:12 2015-01-11 00:04:56                1   \n",
       "2         2 2015-01-01 01:19:22 2015-01-01 01:33:47                3   \n",
       "\n",
       "   trip_distance  pickup_longitude  pickup_latitude rate_code  \\\n",
       "0           6.00        -73.964203        40.807423      None   \n",
       "1           9.12        -73.950119        40.771908      None   \n",
       "2           2.30        -73.936562        40.802635      None   \n",
       "\n",
       "  store_and_fwd_flag  dropoff_longitude  ...  tip_amount tolls_amount  \\\n",
       "0                  N         -73.915398  ...         0.0         2.44   \n",
       "1                  N         -73.845802  ...         5.7         0.00   \n",
       "2                  N         -73.907959  ...         0.0         0.00   \n",
       "\n",
       "   imp_surcharge  total_amount  pickup_zip_code  pickup_zip_code_lat  \\\n",
       "0            0.3         22.74            10027            40.811408   \n",
       "1            0.3         35.00            10075            40.773363   \n",
       "2            0.3         13.30            10035            40.795458   \n",
       "\n",
       "   pickup_zip_code_lon  dropoff_zip_code  dropoff_zip_code_lat  \\\n",
       "0           -73.953060             10463             40.880678   \n",
       "1           -73.956222             10461             40.847394   \n",
       "2           -73.929570             10455             40.814713   \n",
       "\n",
       "  dropoff_zip_code_lon  \n",
       "0           -73.906540  \n",
       "1           -73.840583  \n",
       "2           -73.908590  \n",
       "\n",
       "[3 rows x 25 columns]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi_df_by_zipcode.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c9972547a6c2405ca8e931a945076d36",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "DeckGLWidget(json_input='{\"initialViewState\": {\"bearing\": 0, \"latitude\": 40.7648, \"longitude\": -73.9808, \"maxZ…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "arc_layer = pdk.Layer(\n",
    "    'ArcLayer',\n",
    "    taxi_df_by_zipcode,\n",
    "    get_source_position='[pickup_zip_code_lon, pickup_zip_code_lat]',\n",
    "    get_target_position='[dropoff_zip_code_lon, dropoff_zip_code_lat]',\n",
    "    get_source_color='[255, 255, 120]', \n",
    "    get_target_color='[255, 0, 0]',\n",
    "    get_widht='elevationValue',\n",
    "    pickable=True, \n",
    "    auto_highlight=True,\n",
    ")\n",
    "\n",
    "nyc_center = [-73.9808, 40.7648] \n",
    "view_state = pdk.ViewState(longitude=nyc_center[0], latitude=nyc_center[1], zoom=9)\n",
    "\n",
    "r = pdk.Deck(layers=[arc_layer], initial_view_state=view_state)\n",
    "r.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aggregate "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "agg_query = \"\"\"\n",
    "WITH base_data AS \n",
    "(\n",
    "  SELECT \n",
    "    nyc_taxi.*, \n",
    "    pickup.zip_code as pickup_zip_code,\n",
    "    pickup.internal_point_lat as pickup_zip_code_lat,\n",
    "    pickup.internal_point_lon as pickup_zip_code_lon,\n",
    "    dropoff.zip_code as dropoff_zip_code,\n",
    "    dropoff.internal_point_lat as dropoff_zip_code_lat,\n",
    "    dropoff.internal_point_lon as dropoff_zip_code_lon\n",
    "  FROM (\n",
    "    SELECT *\n",
    "    FROM `bigquery-public-data.new_york_taxi_trips.tlc_yellow_trips_2015`\n",
    "    WHERE \n",
    "        EXTRACT(MONTH from pickup_datetime) = 1\n",
    "        and pickup_latitude <= 90 and pickup_latitude >= -90\n",
    "        and dropoff_latitude <= 90 and dropoff_latitude >= -90\n",
    "    ) AS nyc_taxi\n",
    "  JOIN (\n",
    "    SELECT zip_code, state_code, state_name, city, county, zip_code_geom, internal_point_lat, internal_point_lon \n",
    "    FROM `bigquery-public-data.geo_us_boundaries.zip_codes`\n",
    "    WHERE state_code='NY'\n",
    "    ) AS pickup \n",
    "  ON ST_CONTAINS(pickup.zip_code_geom, st_geogpoint(pickup_longitude, pickup_latitude))\n",
    "  JOIN (\n",
    "    SELECT zip_code, state_code, state_name, city, county, zip_code_geom, internal_point_lat, internal_point_lon \n",
    "    FROM `bigquery-public-data.geo_us_boundaries.zip_codes`\n",
    "    WHERE state_code='NY' \n",
    "    ) AS dropoff\n",
    "  ON ST_CONTAINS(dropoff.zip_code_geom, st_geogpoint(dropoff_longitude, dropoff_latitude))\n",
    "  \n",
    ")\n",
    "\n",
    "SELECT \n",
    "  pickup_zip_code,\n",
    "  pickup_zip_code_lat,\n",
    "  pickup_zip_code_lon,\n",
    "  dropoff_zip_code,\n",
    "  dropoff_zip_code_lat,\n",
    "  dropoff_zip_code_lon,\n",
    "  COUNT(*) AS cnt\n",
    "FROM base_data \n",
    "GROUP BY 1,2,3,4,5,6\n",
    "limit 10000\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 366 ms, sys: 63.2 ms, total: 429 ms\n",
      "Wall time: 33.7 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "agg_df = pd.read_gbq(query=agg_query, dialect='standard', project_id='geultto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pickup_zip_code</th>\n",
       "      <th>pickup_zip_code_lat</th>\n",
       "      <th>pickup_zip_code_lon</th>\n",
       "      <th>dropoff_zip_code</th>\n",
       "      <th>dropoff_zip_code_lat</th>\n",
       "      <th>dropoff_zip_code_lon</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11358</td>\n",
       "      <td>40.760473</td>\n",
       "      <td>-73.796373</td>\n",
       "      <td>11358</td>\n",
       "      <td>40.760473</td>\n",
       "      <td>-73.796373</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10039</td>\n",
       "      <td>40.830869</td>\n",
       "      <td>-73.936216</td>\n",
       "      <td>10456</td>\n",
       "      <td>40.829886</td>\n",
       "      <td>-73.908121</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11420</td>\n",
       "      <td>40.673563</td>\n",
       "      <td>-73.817829</td>\n",
       "      <td>11201</td>\n",
       "      <td>40.693700</td>\n",
       "      <td>-73.989859</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11416</td>\n",
       "      <td>40.684625</td>\n",
       "      <td>-73.849582</td>\n",
       "      <td>11419</td>\n",
       "      <td>40.688638</td>\n",
       "      <td>-73.822942</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11230</td>\n",
       "      <td>40.622164</td>\n",
       "      <td>-73.965110</td>\n",
       "      <td>11218</td>\n",
       "      <td>40.643477</td>\n",
       "      <td>-73.976042</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  pickup_zip_code  pickup_zip_code_lat  pickup_zip_code_lon dropoff_zip_code  \\\n",
       "0           11358            40.760473           -73.796373            11358   \n",
       "1           10039            40.830869           -73.936216            10456   \n",
       "2           11420            40.673563           -73.817829            11201   \n",
       "3           11416            40.684625           -73.849582            11419   \n",
       "4           11230            40.622164           -73.965110            11218   \n",
       "\n",
       "   dropoff_zip_code_lat  dropoff_zip_code_lon  cnt  \n",
       "0             40.760473            -73.796373   35  \n",
       "1             40.829886            -73.908121   20  \n",
       "2             40.693700            -73.989859    2  \n",
       "3             40.688638            -73.822942    5  \n",
       "4             40.643477            -73.976042   66  "
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "agg_df = agg_df.sort_values('cnt', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "agg_df = agg_df[:100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ed53f5202676425fb8ab222730748eff",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "DeckGLWidget(json_input='{\"initialViewState\": {\"bearing\": 0, \"latitude\": 40.7648, \"longitude\": -73.9808, \"maxZ…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "arc_layer = pdk.Layer(\n",
    "    'ArcLayer',\n",
    "    agg_df,\n",
    "    get_source_position='[pickup_zip_code_lon, pickup_zip_code_lat]',\n",
    "    get_target_position='[dropoff_zip_code_lon, dropoff_zip_code_lat]',\n",
    "    get_source_color='[255, 255, 120]', \n",
    "    get_target_color='[255, 0, 0]',\n",
    "    width_units='meters',\n",
    "    get_width=\"1+10*cnt/500\",\n",
    "    pickable=True, \n",
    "    auto_highlight=True,\n",
    ")\n",
    "\n",
    "nyc_center = [-73.9808, 40.7648] \n",
    "view_state = pdk.ViewState(longitude=nyc_center[0], latitude=nyc_center[1], zoom=9)\n",
    "\n",
    "r = pdk.Deck(layers=[arc_layer], initial_view_state=view_state,\n",
    "             tooltip={\n",
    "                 'html': '<b>count:</b> {cnt}',\n",
    "                 'style': {\n",
    "                     'color': 'white'\n",
    "                 }\n",
    "             }\n",
    "            )\n",
    "r.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 요일별 위젯"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [],
   "source": [
    "agg_query2 = \"\"\"\n",
    "WITH base_data AS \n",
    "(\n",
    "  SELECT \n",
    "    nyc_taxi.*, \n",
    "    pickup.zip_code as pickup_zip_code,\n",
    "    pickup.internal_point_lat as pickup_zip_code_lat,\n",
    "    pickup.internal_point_lon as pickup_zip_code_lon,\n",
    "    dropoff.zip_code as dropoff_zip_code,\n",
    "    dropoff.internal_point_lat as dropoff_zip_code_lat,\n",
    "    dropoff.internal_point_lon as dropoff_zip_code_lon\n",
    "  FROM (\n",
    "    SELECT *\n",
    "    FROM `bigquery-public-data.new_york_taxi_trips.tlc_yellow_trips_2015`\n",
    "    WHERE \n",
    "        EXTRACT(MONTH from pickup_datetime) = 1\n",
    "        and pickup_latitude <= 90 and pickup_latitude >= -90\n",
    "        and dropoff_latitude <= 90 and dropoff_latitude >= -90\n",
    "    LIMIT 100000\n",
    "    ) AS nyc_taxi\n",
    "  JOIN (\n",
    "    SELECT zip_code, state_code, state_name, city, county, zip_code_geom, internal_point_lat, internal_point_lon \n",
    "    FROM `bigquery-public-data.geo_us_boundaries.zip_codes`\n",
    "    WHERE state_code='NY'\n",
    "    ) AS pickup \n",
    "  ON ST_CONTAINS(pickup.zip_code_geom, st_geogpoint(pickup_longitude, pickup_latitude))\n",
    "  JOIN (\n",
    "    SELECT zip_code, state_code, state_name, city, county, zip_code_geom, internal_point_lat, internal_point_lon \n",
    "    FROM `bigquery-public-data.geo_us_boundaries.zip_codes`\n",
    "    WHERE state_code='NY' \n",
    "    ) AS dropoff\n",
    "  ON ST_CONTAINS(dropoff.zip_code_geom, st_geogpoint(dropoff_longitude, dropoff_latitude))\n",
    "  \n",
    ")\n",
    "\n",
    "SELECT \n",
    "  CAST(format_datetime('%u', pickup_datetime) AS INT64) -1 AS weekday,\n",
    "  pickup_zip_code,\n",
    "  pickup_zip_code_lat,\n",
    "  pickup_zip_code_lon,\n",
    "  dropoff_zip_code,\n",
    "  dropoff_zip_code_lat,\n",
    "  dropoff_zip_code_lon,\n",
    "  COUNT(*) AS cnt\n",
    "FROM base_data \n",
    "GROUP BY 1,2,3,4,5,6,7\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 586 ms, sys: 250 ms, total: 836 ms\n",
      "Wall time: 5.95 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "agg_df2 = pd.read_gbq(query=agg_query2, dialect='standard', project_id='geultto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>weekday</th>\n",
       "      <th>pickup_zip_code</th>\n",
       "      <th>pickup_zip_code_lat</th>\n",
       "      <th>pickup_zip_code_lon</th>\n",
       "      <th>dropoff_zip_code</th>\n",
       "      <th>dropoff_zip_code_lat</th>\n",
       "      <th>dropoff_zip_code_lon</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>10103</td>\n",
       "      <td>40.760780</td>\n",
       "      <td>-73.977670</td>\n",
       "      <td>10022</td>\n",
       "      <td>40.758630</td>\n",
       "      <td>-73.967949</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>10110</td>\n",
       "      <td>40.754499</td>\n",
       "      <td>-73.982256</td>\n",
       "      <td>10020</td>\n",
       "      <td>40.758236</td>\n",
       "      <td>-73.978833</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>11221</td>\n",
       "      <td>40.691340</td>\n",
       "      <td>-73.927879</td>\n",
       "      <td>11221</td>\n",
       "      <td>40.691340</td>\n",
       "      <td>-73.927879</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>11206</td>\n",
       "      <td>40.701954</td>\n",
       "      <td>-73.942358</td>\n",
       "      <td>11221</td>\n",
       "      <td>40.691340</td>\n",
       "      <td>-73.927879</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>10110</td>\n",
       "      <td>40.754499</td>\n",
       "      <td>-73.982256</td>\n",
       "      <td>10017</td>\n",
       "      <td>40.752359</td>\n",
       "      <td>-73.972489</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   weekday pickup_zip_code  pickup_zip_code_lat  pickup_zip_code_lon  \\\n",
       "0        6           10103            40.760780           -73.977670   \n",
       "1        4           10110            40.754499           -73.982256   \n",
       "2        3           11221            40.691340           -73.927879   \n",
       "3        3           11206            40.701954           -73.942358   \n",
       "4        6           10110            40.754499           -73.982256   \n",
       "\n",
       "  dropoff_zip_code  dropoff_zip_code_lat  dropoff_zip_code_lon  cnt  \n",
       "0            10022             40.758630            -73.967949    3  \n",
       "1            10020             40.758236            -73.978833    1  \n",
       "2            11221             40.691340            -73.927879    2  \n",
       "3            11221             40.691340            -73.927879    3  \n",
       "4            10017             40.752359            -73.972489    1  "
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [],
   "source": [
    "default_data = agg_df2[agg_df2['weekday'] == 0].to_dict(orient='records')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "81bfc2bf4c8944e7b685c642338541ce",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "DeckGLWidget(json_input='{\"initialViewState\": {\"bearing\": 0, \"latitude\": 40.7648, \"longitude\": -73.9808, \"maxZ…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "arc_layer = pdk.Layer(\n",
    "    'ArcLayer',\n",
    "    default_data,\n",
    "    get_source_position='[pickup_zip_code_lon, pickup_zip_code_lat]',\n",
    "    get_target_position='[dropoff_zip_code_lon, dropoff_zip_code_lat]',\n",
    "    get_source_color='[255, 255, 120]', \n",
    "    get_target_color='[255, 0, 0]',\n",
    "    width_units='meters',\n",
    "    get_width=\"1+10*cnt/500\",\n",
    "    pickable=True, \n",
    "    auto_highlight=True,\n",
    ")\n",
    "\n",
    "nyc_center = [-73.9808, 40.7648] \n",
    "view_state = pdk.ViewState(longitude=nyc_center[0], latitude=nyc_center[1], zoom=9)\n",
    "\n",
    "r = pdk.Deck(layers=[arc_layer], initial_view_state=view_state,\n",
    "             tooltip={\n",
    "                 'html': '<b>count:</b> {cnt}',\n",
    "                 'style': {\n",
    "                     'color': 'white'\n",
    "                 }\n",
    "             }\n",
    "            )\n",
    "r.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "aa7b54f8420f4360ab6069b7ab9324de",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "IntSlider(value=0, max=6)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Widget 슬라이더 생성\n",
    "import ipywidgets as widgets\n",
    "from IPython.display import display\n",
    "slider = widgets.IntSlider(0, min=0, max=6, step=1)\n",
    "\n",
    "# Widget에서 사용할 함수 정의 \n",
    "def on_change(v):\n",
    "    results = agg_df2[agg_df2['weekday'] == slider.value].to_dict(orient='records')\n",
    "    arc_layer.data = results\n",
    "    r.update()\n",
    "\n",
    "# Deck과 슬라이더 연결\n",
    "slider.observe(on_change, names='value')\n",
    "display(slider)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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