{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-11-05T13:43:47.748445Z",
     "iopub.status.busy": "2025-11-05T13:43:47.748330Z",
     "iopub.status.idle": "2025-11-05T13:43:47.751840Z",
     "shell.execute_reply": "2025-11-05T13:43:47.751574Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "from lets_plot import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-11-05T13:43:47.752802Z",
     "iopub.status.busy": "2025-11-05T13:43:47.752729Z",
     "iopub.status.idle": "2025-11-05T13:43:47.754666Z",
     "shell.execute_reply": "2025-11-05T13:43:47.754434Z"
    }
   },
   "outputs": [
    {
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       "                };\n",
       "                (function() {\n",
       "                    var script = document.createElement(\"script\");\n",
       "                    script.type = \"text/javascript\";\n",
       "                    script.src = \"https://cdn.jsdelivr.net/gh/JetBrains/lets-plot@v4.8.0/js-package/distr/lets-plot.min.js\";\n",
       "                    script.onload = function() {\n",
       "                        window.letsPlotCall = function(f) {f();};\n",
       "                        window.letsPlotCallQueue.forEach(function(f) {f();});\n",
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       "                        \n",
       "                    };\n",
       "                    script.onerror = function(event) {\n",
       "                        window.letsPlotCall = function(f) {};    // noop\n",
       "                        window.letsPlotCallQueue = [];\n",
       "                        var div = document.createElement(\"div\");\n",
       "                        div.style.color = 'darkred';\n",
       "                        div.textContent = 'Error loading Lets-Plot JS';\n",
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       "                    };\n",
       "                    var e = document.getElementById(\"wqlVF4\");\n",
       "                    e.appendChild(script);\n",
       "                })()\n",
       "            </script>\n",
       "            "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "LetsPlot.setup_html()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-11-05T13:43:47.767176Z",
     "iopub.status.busy": "2025-11-05T13:43:47.767098Z",
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     "shell.execute_reply": "2025-11-05T13:43:47.861540Z"
    }
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   "outputs": [
    {
     "data": {
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       "        text-align: right;\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>Unnamed: 0</th>\n",
       "      <th>manufacturer</th>\n",
       "      <th>model</th>\n",
       "      <th>displ</th>\n",
       "      <th>year</th>\n",
       "      <th>cyl</th>\n",
       "      <th>trans</th>\n",
       "      <th>drv</th>\n",
       "      <th>cty</th>\n",
       "      <th>hwy</th>\n",
       "      <th>fl</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>audi</td>\n",
       "      <td>a4</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1999</td>\n",
       "      <td>4</td>\n",
       "      <td>auto(l5)</td>\n",
       "      <td>f</td>\n",
       "      <td>18</td>\n",
       "      <td>29</td>\n",
       "      <td>p</td>\n",
       "      <td>compact</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>audi</td>\n",
       "      <td>a4</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1999</td>\n",
       "      <td>4</td>\n",
       "      <td>manual(m5)</td>\n",
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       "      <td>21</td>\n",
       "      <td>29</td>\n",
       "      <td>p</td>\n",
       "      <td>compact</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>audi</td>\n",
       "      <td>a4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>4</td>\n",
       "      <td>manual(m6)</td>\n",
       "      <td>f</td>\n",
       "      <td>20</td>\n",
       "      <td>31</td>\n",
       "      <td>p</td>\n",
       "      <td>compact</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>audi</td>\n",
       "      <td>a4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>4</td>\n",
       "      <td>auto(av)</td>\n",
       "      <td>f</td>\n",
       "      <td>21</td>\n",
       "      <td>30</td>\n",
       "      <td>p</td>\n",
       "      <td>compact</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>audi</td>\n",
       "      <td>a4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>1999</td>\n",
       "      <td>6</td>\n",
       "      <td>auto(l5)</td>\n",
       "      <td>f</td>\n",
       "      <td>16</td>\n",
       "      <td>26</td>\n",
       "      <td>p</td>\n",
       "      <td>compact</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 manufacturer model  displ  year  cyl       trans drv  cty  hwy  \\\n",
       "0           1         audi    a4    1.8  1999    4    auto(l5)   f   18   29   \n",
       "1           2         audi    a4    1.8  1999    4  manual(m5)   f   21   29   \n",
       "2           3         audi    a4    2.0  2008    4  manual(m6)   f   20   31   \n",
       "3           4         audi    a4    2.0  2008    4    auto(av)   f   21   30   \n",
       "4           5         audi    a4    2.8  1999    6    auto(l5)   f   16   26   \n",
       "\n",
       "  fl    class  \n",
       "0  p  compact  \n",
       "1  p  compact  \n",
       "2  p  compact  \n",
       "3  p  compact  \n",
       "4  p  compact  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg_df = pd.read_csv (\"https://raw.githubusercontent.com/JetBrains/lets-plot-docs/master/data/mpg.csv\")\n",
    "mpg_df.head()"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-11-05T13:43:47.863142Z",
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     "iopub.status.idle": "2025-11-05T13:43:47.896604Z",
     "shell.execute_reply": "2025-11-05T13:43:47.896167Z"
    }
   },
   "outputs": [
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
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    "tooltip_content = (layer_tooltips()\n",
    "                       .title('@{class}')\n",
    "                       .line('count|@{..count..} (@{..prop..})')\n",
    "                       .line('total|@{..sum..}')\n",
    "                       .format('..prop..', '.0%'))\n",
    "\n",
    "ggplot(mpg_df) + \\\n",
    "    geom_bar(aes(as_discrete('manufacturer', order=1), fill='class'), tooltips=tooltip_content) + \\\n",
    "    scale_fill_brewer(palette='Dark2') + \\\n",
    "    ggsize(800, 450)"
   ]
  },
  {
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       "\"..count..\":[15.0,3.0,5.0,9.0,5.0,7.0,11.0,19.0,9.0,7.0,9.0,9.0,7.0,7.0,8.0,4.0,3.0,4.0,2.0,7.0,4.0,5.0,4.0,6.0,4.0,12.0,7.0,8.0,7.0,14.0,7.0,6.0],\n",
       "\"class\":[\"compact\",\"midsize\",\"midsize\",\"suv\",\"2seater\",\"suv\",\"minivan\",\"pickup\",\"suv\",\"pickup\",\"subcompact\",\"subcompact\",\"midsize\",\"subcompact\",\"suv\",\"suv\",\"suv\",\"suv\",\"compact\",\"midsize\",\"suv\",\"midsize\",\"compact\",\"suv\",\"subcompact\",\"compact\",\"midsize\",\"suv\",\"pickup\",\"compact\",\"midsize\",\"subcompact\"],\n",
       "\"manufacturer\":[\"audi\",\"audi\",\"chevrolet\",\"chevrolet\",\"chevrolet\",\"dodge\",\"dodge\",\"dodge\",\"ford\",\"ford\",\"ford\",\"honda\",\"hyundai\",\"hyundai\",\"jeep\",\"land rover\",\"lincoln\",\"mercury\",\"nissan\",\"nissan\",\"nissan\",\"pontiac\",\"subaru\",\"subaru\",\"subaru\",\"toyota\",\"toyota\",\"toyota\",\"toyota\",\"volkswagen\",\"volkswagen\",\"volkswagen\"],\n",
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       "           window.letsPlotCall(function() { fig = LetsPlot.buildPlotFromProcessedSpecs(plotSpec, containerDiv, sizing); });\n",
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       "           fig.updateView({});\n",
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       "       forceImmediateRender || (\n",
       "           sizing.width_mode === 'FIXED' && \n",
       "           (sizing.height_mode === 'FIXED' || sizing.height_mode === 'SCALED')\n",
       "       );\n",
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       "   if (renderImmediately) {\n",
       "       renderPlot();\n",
       "   }\n",
       "   \n",
       "   if (!renderImmediately || responsive) {\n",
       "       // Set up observer for initial sizing or continuous monitoring\n",
       "       var observer = new ResizeObserver(function(entries) {\n",
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       "                   if (!responsive && observer) {\n",
       "                       observer.disconnect();\n",
       "                       observer = null;\n",
       "                   }\n",
       "                   renderPlot();\n",
       "                   if (!responsive) {\n",
       "                       break;\n",
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       "       \n",
       "       observer.observe(containerDiv);\n",
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       "   \n",
       "   // ----------\n",
       "   })();\n",
       "   \n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.core.PlotSpec at 0x756c6b418700>"
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     },
     "execution_count": 5,
     "metadata": {},
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    }
   ],
   "source": [
    "ggplot(mpg_df) + \\\n",
    "    geom_pie(aes(fill='class', size = '..sum..'),\n",
    "             tooltips=tooltip_content) + \\\n",
    "    scale_fill_brewer(palette='Dark2') + \\\n",
    "    facet_wrap(facets='manufacturer', ncol=5)  + \\\n",
    "    scale_size(range=[4, 10]) + \\\n",
    "    guides(size='none') + \\\n",
    "    theme_void() + theme(strip_background='blank')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
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     "iopub.execute_input": "2025-11-05T13:43:47.906388Z",
     "iopub.status.busy": "2025-11-05T13:43:47.906319Z",
     "iopub.status.idle": "2025-11-05T13:43:47.912371Z",
     "shell.execute_reply": "2025-11-05T13:43:47.912137Z"
    }
   },
   "outputs": [
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     },
     "execution_count": 6,
     "metadata": {},
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    }
   ],
   "source": [
    "ggplot(mpg_df) + \\\n",
    "    geom_pie(aes(fill=as_discrete('class', order_by='..count..'), size = '..sum..'), \n",
    "             size=24, hole=0.2, stroke=1.0,\n",
    "             tooltips=layer_tooltips()\n",
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    "                       .line('Percent|@{..proppct..}')\n",
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    "    scale_fill_brewer(palette='Dark2') + \\\n",
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