{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8abf1a99-2b6f-499b-9680-da1a9df9c21b",
   "metadata": {},
   "source": [
    "# Customize Legend Appearance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6a81b6d2-6a4b-4448-8927-635122e0492a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-27T17:04:14.262281Z",
     "iopub.status.busy": "2026-01-27T17:04:14.262200Z",
     "iopub.status.idle": "2026-01-27T17:04:14.280926Z",
     "shell.execute_reply": "2026-01-27T17:04:14.280554Z"
    }
   },
   "outputs": [],
   "source": [
    "import io\n",
    "import requests\n",
    "\n",
    "import pandas as pd\n",
    "from PIL import Image\n",
    "\n",
    "from lets_plot import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4fa2ab2d-c35e-4957-838f-a72969bd1ca5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-27T17:04:14.281945Z",
     "iopub.status.busy": "2026-01-27T17:04:14.281870Z",
     "iopub.status.idle": "2026-01-27T17:04:14.283437Z",
     "shell.execute_reply": "2026-01-27T17:04:14.283237Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <div id=\"zUnzRg\"></div>\n",
       "        <script type=\"text/javascript\" data-lets-plot-script=\"library\">\n",
       "            if(!window.letsPlotCallQueue) {\n",
       "                window.letsPlotCallQueue = [];\n",
       "            };\n",
       "            window.letsPlotCall = function(f) {\n",
       "                window.letsPlotCallQueue.push(f);\n",
       "            };\n",
       "            (function() {\n",
       "                var script = document.createElement(\"script\");\n",
       "                script.type = \"text/javascript\";\n",
       "                script.src = \"https://cdn.jsdelivr.net/gh/JetBrains/lets-plot@v4.8.2/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",
       "                    window.letsPlotCallQueue = [];\n",
       "                    \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",
       "                    document.getElementById(\"zUnzRg\").appendChild(div);\n",
       "                };\n",
       "                var e = document.getElementById(\"zUnzRg\");\n",
       "                e.appendChild(script);\n",
       "            })()\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "LetsPlot.setup_html()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e9eb4d18-e02d-41f9-afe6-f5530aae3d7b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-27T17:04:14.284377Z",
     "iopub.status.busy": "2026-01-27T17:04:14.284307Z",
     "iopub.status.idle": "2026-01-27T17:04:14.697992Z",
     "shell.execute_reply": "2026-01-27T17:04:14.697650Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=2000x1600>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response = requests.get(\"https://raw.githubusercontent.com/JetBrains/lets-plot-docs/master/source/examples/cookbook/images/theme_legend_scheme.png\", timeout=5*60)\n",
    "Image.open(io.BytesIO(response.content))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b1fe0b5c-22b0-4999-8bce-a230f9273ca3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-27T17:04:14.699192Z",
     "iopub.status.busy": "2026-01-27T17:04:14.699114Z",
     "iopub.status.idle": "2026-01-27T17:04:14.790960Z",
     "shell.execute_reply": "2026-01-27T17:04:14.790620Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(234, 12)\n"
     ]
    },
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       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        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",
       "      <td>f</td>\n",
       "      <td>21</td>\n",
       "      <td>29</td>\n",
       "      <td>p</td>\n",
       "      <td>compact</td>\n",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "    <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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"https://raw.githubusercontent.com/JetBrains/lets-plot-docs/master/data/mpg.csv\")\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c46e1902-159d-43df-908a-486ad76c38e0",
   "metadata": {},
   "source": [
    "#### Legend Position\n",
    "\n",
    "Parameters to customize the legend position and justification:\n",
    "\n",
    "- `legend_position` - the position of legends;\n",
    "\n",
    "- `legend_justification` - anchor point for positioning legend;\n",
    "\n",
    "- `legend_direction` - layout of items in legends."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "114d10a7-70c4-4617-8b9b-80b084a1cff7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-27T17:04:14.791993Z",
     "iopub.status.busy": "2026-01-27T17:04:14.791873Z",
     "iopub.status.idle": "2026-01-27T17:04:14.850565Z",
     "shell.execute_reply": "2026-01-27T17:04:14.850290Z"
    }
   },
   "outputs": [
    {
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       "},\n",
       "\"mapping\":{\n",
       "\"x\":\"displ\",\n",
       "\"y\":\"hwy\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "\"series_annotations\":[{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"Unnamed: 0\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"manufacturer\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"model\"\n",
       "},{\n",
       "\"type\":\"float\",\n",
       "\"column\":\"displ\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"year\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"cyl\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"trans\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"drv\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"cty\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"hwy\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"fl\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"class\"\n",
       "}]\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[],\n",
       "\"layers\":[{\n",
       "\"geom\":\"point\",\n",
       "\"mapping\":{\n",
       "\"color\":\"cty\",\n",
       "\"shape\":\"drv\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"size\":5.0,\n",
       "\"data\":{\n",
       "}\n",
       "}],\n",
       "\"metainfo_list\":[],\n",
       "\"spec_id\":\"1\"\n",
       "};\n",
       "           window.letsPlotCall(function() { fig = LetsPlot.buildPlotFromProcessedSpecs(plotSpec, containerDiv, sizing); });\n",
       "       } else {\n",
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       "   const renderImmediately = \n",
       "       forceImmediateRender || (\n",
       "           sizing.width_mode === 'FIXED' && \n",
       "           (sizing.height_mode === 'FIXED' || sizing.height_mode === 'SCALED')\n",
       "       );\n",
       "   \n",
       "   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",
       "           for (let entry of entries) {\n",
       "               if (entry.contentBoxSize && \n",
       "                   entry.contentBoxSize[0].inlineSize > 0) {\n",
       "                   if (!responsive && observer) {\n",
       "                       observer.disconnect();\n",
       "                       observer = null;\n",
       "                   }\n",
       "                   renderPlot();\n",
       "                   if (!responsive) {\n",
       "                       break;\n",
       "                   }\n",
       "               }\n",
       "           }\n",
       "       });\n",
       "       \n",
       "       observer.observe(containerDiv);\n",
       "   }\n",
       "   \n",
       "   // ----------\n",
       "   })();\n",
       "   \n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.core.PlotSpec at 0x71c43138e710>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p1 = ggplot(df, aes(\"displ\", \"hwy\")) + geom_point(aes(color=\"cty\", shape=\"drv\"), size=5)\n",
    "p1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c751cc74-daa2-4b4d-a541-5fa69a163347",
   "metadata": {
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     "iopub.execute_input": "2026-01-27T17:04:14.851691Z",
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     "shell.execute_reply": "2026-01-27T17:04:14.856067Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"uNqgE8\" ></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
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       "\"column\":\"Unnamed: 0\"\n",
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       "\"type\":\"int\",\n",
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       "\"type\":\"str\",\n",
       "\"column\":\"fl\"\n",
       "},{\n",
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       "\"shape\":\"drv\"\n",
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       "\"data_meta\":{\n",
       "},\n",
       "\"size\":5.0,\n",
       "\"data\":{\n",
       "}\n",
       "}],\n",
       "\"metainfo_list\":[],\n",
       "\"spec_id\":\"2\"\n",
       "};\n",
       "           window.letsPlotCall(function() { fig = LetsPlot.buildPlotFromProcessedSpecs(plotSpec, containerDiv, sizing); });\n",
       "       } else {\n",
       "           fig.updateView({});\n",
       "       }\n",
       "   }\n",
       "   \n",
       "   const renderImmediately = \n",
       "       forceImmediateRender || (\n",
       "           sizing.width_mode === 'FIXED' && \n",
       "           (sizing.height_mode === 'FIXED' || sizing.height_mode === 'SCALED')\n",
       "       );\n",
       "   \n",
       "   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",
       "           for (let entry of entries) {\n",
       "               if (entry.contentBoxSize && \n",
       "                   entry.contentBoxSize[0].inlineSize > 0) {\n",
       "                   if (!responsive && observer) {\n",
       "                       observer.disconnect();\n",
       "                       observer = null;\n",
       "                   }\n",
       "                   renderPlot();\n",
       "                   if (!responsive) {\n",
       "                       break;\n",
       "                   }\n",
       "               }\n",
       "           }\n",
       "       });\n",
       "       \n",
       "       observer.observe(containerDiv);\n",
       "   }\n",
       "   \n",
       "   // ----------\n",
       "   })();\n",
       "   \n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.core.PlotSpec at 0x71c43138e7d0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Draw legend below the plot area\n",
    "p1 + theme(legend_position='bottom')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "786274d7-5762-48e5-9c79-18304f4e6ddd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-27T17:04:14.857121Z",
     "iopub.status.busy": "2026-01-27T17:04:14.857046Z",
     "iopub.status.idle": "2026-01-27T17:04:14.861576Z",
     "shell.execute_reply": "2026-01-27T17:04:14.861366Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
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       "   \n",
       "   const forceImmediateRender = false;\n",
       "   const responsive = false;\n",
       "   \n",
       "   let sizing = {\n",
       "       width_mode: \"MIN\",\n",
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       "       width: null, \n",
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       "   \n",
       "   const preferredWidth = document.body.dataset.letsPlotPreferredWidth;\n",
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       "   const containerDiv = document.getElementById(\"CffVrr\");\n",
       "   let fig = null;\n",
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       "   function renderPlot() {\n",
       "       if (fig === null) {\n",
       "           const plotSpec = {\n",
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       "},\n",
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       "},\n",
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       "\"type\":\"int\",\n",
       "\"column\":\"Unnamed: 0\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"manufacturer\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"model\"\n",
       "},{\n",
       "\"type\":\"float\",\n",
       "\"column\":\"displ\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"year\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"cyl\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"trans\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"drv\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"cty\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"hwy\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"fl\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"class\"\n",
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       "},\n",
       "\"theme\":{\n",
       "\"legend_position\":[1.0,1.0],\n",
       "\"legend_justification\":[1.0,1.0],\n",
       "\"legend_direction\":\"horizontal\"\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[],\n",
       "\"layers\":[{\n",
       "\"geom\":\"point\",\n",
       "\"mapping\":{\n",
       "\"color\":\"cty\",\n",
       "\"shape\":\"drv\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"size\":5.0,\n",
       "\"data\":{\n",
       "}\n",
       "}],\n",
       "\"metainfo_list\":[],\n",
       "\"spec_id\":\"3\"\n",
       "};\n",
       "           window.letsPlotCall(function() { fig = LetsPlot.buildPlotFromProcessedSpecs(plotSpec, containerDiv, sizing); });\n",
       "       } else {\n",
       "           fig.updateView({});\n",
       "       }\n",
       "   }\n",
       "   \n",
       "   const renderImmediately = \n",
       "       forceImmediateRender || (\n",
       "           sizing.width_mode === 'FIXED' && \n",
       "           (sizing.height_mode === 'FIXED' || sizing.height_mode === 'SCALED')\n",
       "       );\n",
       "   \n",
       "   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",
       "           for (let entry of entries) {\n",
       "               if (entry.contentBoxSize && \n",
       "                   entry.contentBoxSize[0].inlineSize > 0) {\n",
       "                   if (!responsive && observer) {\n",
       "                       observer.disconnect();\n",
       "                       observer = null;\n",
       "                   }\n",
       "                   renderPlot();\n",
       "                   if (!responsive) {\n",
       "                       break;\n",
       "                   }\n",
       "               }\n",
       "           }\n",
       "       });\n",
       "       \n",
       "       observer.observe(containerDiv);\n",
       "   }\n",
       "   \n",
       "   // ----------\n",
       "   })();\n",
       "   \n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.core.PlotSpec at 0x71c42a0a4460>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Specify position, justification and orientation\n",
    "p1 + theme(legend_position=[1, 1], legend_justification=[1, 1], legend_direction='horizontal')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c5b978f-ac88-4efe-adad-8b939c226328",
   "metadata": {},
   "source": [
    "#### Legend Spacing and Margins\n",
    "\n",
    "Parameters to customize the legend spacing and margins:\n",
    "\n",
    "- `legend_box` - arrangement of multiple legends (\"horizontal\" or \"vertical\");\n",
    "\n",
    "- `legend_box_just` - justification of each legend within the overall bounding box (\"top\", \"bottom\", \"left\", \"right\", \"center\");\n",
    "\n",
    "- `legend_box_spacing` - spacing between plotting area and legend box;\n",
    "\n",
    "- `legend_margin` - margin around each legend;\n",
    "\n",
    "- `legend_spacing` - spacing between legends, `legend_spacing_x`/`legend_spacing_y` - in the horizontal/vertical direction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9eb5fb7a-fe09-44ed-bfff-f332d419d85a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-27T17:04:14.862428Z",
     "iopub.status.busy": "2026-01-27T17:04:14.862357Z",
     "iopub.status.idle": "2026-01-27T17:04:14.867866Z",
     "shell.execute_reply": "2026-01-27T17:04:14.867667Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "   \n",
       "   (function() {\n",
       "   // ----------\n",
       "   \n",
       "   const forceImmediateRender = false;\n",
       "   const responsive = false;\n",
       "   \n",
       "   let sizing = {\n",
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       "   const preferredWidth = document.body.dataset.letsPlotPreferredWidth;\n",
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       "       sizing = {\n",
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       "   const containerDiv = document.getElementById(\"xdpxlf\");\n",
       "   let fig = null;\n",
       "   \n",
       "   function renderPlot() {\n",
       "       if (fig === null) {\n",
       "           const plotSpec = {\n",
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       "},\n",
       "\"mapping\":{\n",
       "\"x\":\"displ\",\n",
       "\"y\":\"cty\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "\"series_annotations\":[{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"Unnamed: 0\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"manufacturer\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"model\"\n",
       "},{\n",
       "\"type\":\"float\",\n",
       "\"column\":\"displ\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"year\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"cyl\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"trans\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"drv\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"cty\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"hwy\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"fl\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"class\"\n",
       "}]\n",
       "},\n",
       "\"theme\":{\n",
       "\"legend_background\":{\n",
       "\"size\":1.0,\n",
       "\"blank\":false\n",
       "},\n",
       "\"legend_position\":\"bottom\"\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[{\n",
       "\"aesthetic\":\"size\",\n",
       "\"breaks\":[15.0,30.0,40.0],\n",
       "\"range\":[1.0,10.0]\n",
       "}],\n",
       "\"layers\":[{\n",
       "\"geom\":\"point\",\n",
       "\"mapping\":{\n",
       "\"fill\":\"drv\",\n",
       "\"size\":\"hwy\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"shape\":21.0,\n",
       "\"data\":{\n",
       "}\n",
       "}],\n",
       "\"metainfo_list\":[],\n",
       "\"spec_id\":\"4\"\n",
       "};\n",
       "           window.letsPlotCall(function() { fig = LetsPlot.buildPlotFromProcessedSpecs(plotSpec, containerDiv, sizing); });\n",
       "       } else {\n",
       "           fig.updateView({});\n",
       "       }\n",
       "   }\n",
       "   \n",
       "   const renderImmediately = \n",
       "       forceImmediateRender || (\n",
       "           sizing.width_mode === 'FIXED' && \n",
       "           (sizing.height_mode === 'FIXED' || sizing.height_mode === 'SCALED')\n",
       "       );\n",
       "   \n",
       "   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",
       "           for (let entry of entries) {\n",
       "               if (entry.contentBoxSize && \n",
       "                   entry.contentBoxSize[0].inlineSize > 0) {\n",
       "                   if (!responsive && observer) {\n",
       "                       observer.disconnect();\n",
       "                       observer = null;\n",
       "                   }\n",
       "                   renderPlot();\n",
       "                   if (!responsive) {\n",
       "                       break;\n",
       "                   }\n",
       "               }\n",
       "           }\n",
       "       });\n",
       "       \n",
       "       observer.observe(containerDiv);\n",
       "   }\n",
       "   \n",
       "   // ----------\n",
       "   })();\n",
       "   \n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.core.PlotSpec at 0x71c42a0b1060>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p2 = ggplot(df, aes('displ', 'cty')) + \\\n",
    "    geom_point(aes(fill='drv', size='hwy'), shape=21) + \\\n",
    "    scale_size(range=[1, 10], breaks=[15, 30, 40]) + \\\n",
    "    theme(legend_position='bottom', legend_background=element_rect(size=1))\n",
    "p2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "67abb1ea-5a3b-4f29-aab7-8186060d7d88",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-27T17:04:14.868658Z",
     "iopub.status.busy": "2026-01-27T17:04:14.868586Z",
     "iopub.status.idle": "2026-01-27T17:04:14.873179Z",
     "shell.execute_reply": "2026-01-27T17:04:14.872950Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "   const forceImmediateRender = false;\n",
       "   const responsive = false;\n",
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       "   let sizing = {\n",
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       "   function renderPlot() {\n",
       "       if (fig === null) {\n",
       "           const plotSpec = {\n",
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       "},\n",
       "\"mapping\":{\n",
       "\"x\":\"displ\",\n",
       "\"y\":\"cty\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "\"series_annotations\":[{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"Unnamed: 0\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"manufacturer\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"model\"\n",
       "},{\n",
       "\"type\":\"float\",\n",
       "\"column\":\"displ\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"year\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"cyl\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"trans\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"drv\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"cty\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"hwy\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"fl\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"class\"\n",
       "}]\n",
       "},\n",
       "\"theme\":{\n",
       "\"legend_background\":{\n",
       "\"size\":1.0,\n",
       "\"blank\":false\n",
       "},\n",
       "\"legend_position\":\"bottom\",\n",
       "\"legend_margin\":[10.0,20.0]\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[{\n",
       "\"aesthetic\":\"size\",\n",
       "\"breaks\":[15.0,30.0,40.0],\n",
       "\"range\":[1.0,10.0]\n",
       "}],\n",
       "\"layers\":[{\n",
       "\"geom\":\"point\",\n",
       "\"mapping\":{\n",
       "\"fill\":\"drv\",\n",
       "\"size\":\"hwy\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"shape\":21.0,\n",
       "\"data\":{\n",
       "}\n",
       "}],\n",
       "\"metainfo_list\":[],\n",
       "\"spec_id\":\"5\"\n",
       "};\n",
       "           window.letsPlotCall(function() { fig = LetsPlot.buildPlotFromProcessedSpecs(plotSpec, containerDiv, sizing); });\n",
       "       } else {\n",
       "           fig.updateView({});\n",
       "       }\n",
       "   }\n",
       "   \n",
       "   const renderImmediately = \n",
       "       forceImmediateRender || (\n",
       "           sizing.width_mode === 'FIXED' && \n",
       "           (sizing.height_mode === 'FIXED' || sizing.height_mode === 'SCALED')\n",
       "       );\n",
       "   \n",
       "   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",
       "           for (let entry of entries) {\n",
       "               if (entry.contentBoxSize && \n",
       "                   entry.contentBoxSize[0].inlineSize > 0) {\n",
       "                   if (!responsive && observer) {\n",
       "                       observer.disconnect();\n",
       "                       observer = null;\n",
       "                   }\n",
       "                   renderPlot();\n",
       "                   if (!responsive) {\n",
       "                       break;\n",
       "                   }\n",
       "               }\n",
       "           }\n",
       "       });\n",
       "       \n",
       "       observer.observe(containerDiv);\n",
       "   }\n",
       "   \n",
       "   // ----------\n",
       "   })();\n",
       "   \n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.core.PlotSpec at 0x71c433d17580>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# legend_margin\n",
    "p2 + theme(legend_margin=[10, 20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8a0092c2-a7be-47c6-86c3-24869e2c17af",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-27T17:04:14.873894Z",
     "iopub.status.busy": "2026-01-27T17:04:14.873823Z",
     "iopub.status.idle": "2026-01-27T17:04:14.878501Z",
     "shell.execute_reply": "2026-01-27T17:04:14.878300Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"O2wJmJ\" ></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
       "   \n",
       "   (function() {\n",
       "   // ----------\n",
       "   \n",
       "   const forceImmediateRender = false;\n",
       "   const responsive = false;\n",
       "   \n",
       "   let sizing = {\n",
       "       width_mode: \"MIN\",\n",
       "       height_mode: \"SCALED\",\n",
       "       width: null, \n",
       "       height: null \n",
       "   };\n",
       "   \n",
       "   const preferredWidth = document.body.dataset.letsPlotPreferredWidth;\n",
       "   if (preferredWidth !== undefined) {\n",
       "       sizing = {\n",
       "           width_mode: 'FIXED',\n",
       "           height_mode: 'SCALED',\n",
       "           width: parseFloat(preferredWidth)\n",
       "       };\n",
       "   }\n",
       "   \n",
       "   const containerDiv = document.getElementById(\"O2wJmJ\");\n",
       "   let fig = null;\n",
       "   \n",
       "   function renderPlot() {\n",
       "       if (fig === null) {\n",
       "           const plotSpec = {\n",
       "\"data\":{\n",
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       "\"hwy\":[29.0,29.0,31.0,30.0,26.0,26.0,27.0,26.0,25.0,28.0,27.0,25.0,25.0,25.0,25.0,24.0,25.0,23.0,20.0,15.0,20.0,17.0,17.0,26.0,23.0,26.0,25.0,24.0,19.0,14.0,15.0,17.0,27.0,30.0,26.0,29.0,26.0,24.0,24.0,22.0,22.0,24.0,24.0,17.0,22.0,21.0,23.0,23.0,19.0,18.0,17.0,17.0,19.0,19.0,12.0,17.0,15.0,17.0,17.0,12.0,17.0,16.0,18.0,15.0,16.0,12.0,17.0,17.0,16.0,12.0,15.0,16.0,17.0,15.0,17.0,17.0,18.0,17.0,19.0,17.0,19.0,19.0,17.0,17.0,17.0,16.0,16.0,17.0,15.0,17.0,26.0,25.0,26.0,24.0,21.0,22.0,23.0,22.0,20.0,33.0,32.0,32.0,29.0,32.0,34.0,36.0,36.0,29.0,26.0,27.0,30.0,31.0,26.0,26.0,28.0,26.0,29.0,28.0,27.0,24.0,24.0,24.0,22.0,19.0,20.0,17.0,12.0,19.0,18.0,14.0,15.0,18.0,18.0,15.0,17.0,16.0,18.0,17.0,19.0,19.0,17.0,29.0,27.0,31.0,32.0,27.0,26.0,26.0,25.0,25.0,17.0,17.0,20.0,18.0,26.0,26.0,27.0,28.0,25.0,25.0,24.0,27.0,25.0,26.0,23.0,26.0,26.0,26.0,26.0,25.0,27.0,25.0,27.0,20.0,20.0,19.0,17.0,20.0,17.0,29.0,27.0,31.0,31.0,26.0,26.0,28.0,27.0,29.0,31.0,31.0,26.0,26.0,27.0,30.0,33.0,35.0,37.0,35.0,15.0,18.0,20.0,20.0,22.0,17.0,19.0,18.0,20.0,29.0,26.0,29.0,29.0,24.0,44.0,29.0,26.0,29.0,29.0,29.0,29.0,23.0,24.0,44.0,41.0,29.0,26.0,28.0,29.0,29.0,29.0,28.0,29.0,26.0,26.0,26.0]\n",
       "},\n",
       "\"mapping\":{\n",
       "\"x\":\"displ\",\n",
       "\"y\":\"cty\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "\"series_annotations\":[{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"Unnamed: 0\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"manufacturer\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"model\"\n",
       "},{\n",
       "\"type\":\"float\",\n",
       "\"column\":\"displ\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"year\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"cyl\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"trans\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"drv\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"cty\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"hwy\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"fl\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"class\"\n",
       "}]\n",
       "},\n",
       "\"theme\":{\n",
       "\"legend_background\":{\n",
       "\"size\":1.0,\n",
       "\"blank\":false\n",
       "},\n",
       "\"legend_position\":\"bottom\",\n",
       "\"legend_box\":\"horizontal\"\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[{\n",
       "\"aesthetic\":\"size\",\n",
       "\"breaks\":[15.0,30.0,40.0],\n",
       "\"range\":[1.0,10.0]\n",
       "}],\n",
       "\"layers\":[{\n",
       "\"geom\":\"point\",\n",
       "\"mapping\":{\n",
       "\"fill\":\"drv\",\n",
       "\"size\":\"hwy\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"shape\":21.0,\n",
       "\"data\":{\n",
       "}\n",
       "}],\n",
       "\"metainfo_list\":[],\n",
       "\"spec_id\":\"6\"\n",
       "};\n",
       "           window.letsPlotCall(function() { fig = LetsPlot.buildPlotFromProcessedSpecs(plotSpec, containerDiv, sizing); });\n",
       "       } else {\n",
       "           fig.updateView({});\n",
       "       }\n",
       "   }\n",
       "   \n",
       "   const renderImmediately = \n",
       "       forceImmediateRender || (\n",
       "           sizing.width_mode === 'FIXED' && \n",
       "           (sizing.height_mode === 'FIXED' || sizing.height_mode === 'SCALED')\n",
       "       );\n",
       "   \n",
       "   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",
       "           for (let entry of entries) {\n",
       "               if (entry.contentBoxSize && \n",
       "                   entry.contentBoxSize[0].inlineSize > 0) {\n",
       "                   if (!responsive && observer) {\n",
       "                       observer.disconnect();\n",
       "                       observer = null;\n",
       "                   }\n",
       "                   renderPlot();\n",
       "                   if (!responsive) {\n",
       "                       break;\n",
       "                   }\n",
       "               }\n",
       "           }\n",
       "       });\n",
       "       \n",
       "       observer.observe(containerDiv);\n",
       "   }\n",
       "   \n",
       "   // ----------\n",
       "   })();\n",
       "   \n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.core.PlotSpec at 0x71c42a0cf2b0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# legend_box\n",
    "p2 + theme(legend_box='horizontal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "36f75815-f356-49df-9743-8db0fc502120",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-27T17:04:14.879283Z",
     "iopub.status.busy": "2026-01-27T17:04:14.879213Z",
     "iopub.status.idle": "2026-01-27T17:04:14.883752Z",
     "shell.execute_reply": "2026-01-27T17:04:14.883556Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"sbRya0\" ></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
       "   \n",
       "   (function() {\n",
       "   // ----------\n",
       "   \n",
       "   const forceImmediateRender = false;\n",
       "   const responsive = false;\n",
       "   \n",
       "   let sizing = {\n",
       "       width_mode: \"MIN\",\n",
       "       height_mode: \"SCALED\",\n",
       "       width: null, \n",
       "       height: null \n",
       "   };\n",
       "   \n",
       "   const preferredWidth = document.body.dataset.letsPlotPreferredWidth;\n",
       "   if (preferredWidth !== undefined) {\n",
       "       sizing = {\n",
       "           width_mode: 'FIXED',\n",
       "           height_mode: 'SCALED',\n",
       "           width: parseFloat(preferredWidth)\n",
       "       };\n",
       "   }\n",
       "   \n",
       "   const containerDiv = document.getElementById(\"sbRya0\");\n",
       "   let fig = null;\n",
       "   \n",
       "   function renderPlot() {\n",
       "       if (fig === null) {\n",
       "           const plotSpec = {\n",
       "\"data\":{\n",
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       "},\n",
       "\"mapping\":{\n",
       "\"x\":\"displ\",\n",
       "\"y\":\"cty\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "\"series_annotations\":[{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"Unnamed: 0\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"manufacturer\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"model\"\n",
       "},{\n",
       "\"type\":\"float\",\n",
       "\"column\":\"displ\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"year\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"cyl\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"trans\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"drv\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"cty\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"hwy\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"fl\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"class\"\n",
       "}]\n",
       "},\n",
       "\"theme\":{\n",
       "\"legend_background\":{\n",
       "\"size\":1.0,\n",
       "\"blank\":false\n",
       "},\n",
       "\"legend_position\":\"bottom\",\n",
       "\"legend_box_just\":\"center\"\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[{\n",
       "\"aesthetic\":\"size\",\n",
       "\"breaks\":[15.0,30.0,40.0],\n",
       "\"range\":[1.0,10.0]\n",
       "}],\n",
       "\"layers\":[{\n",
       "\"geom\":\"point\",\n",
       "\"mapping\":{\n",
       "\"fill\":\"drv\",\n",
       "\"size\":\"hwy\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"shape\":21.0,\n",
       "\"data\":{\n",
       "}\n",
       "}],\n",
       "\"metainfo_list\":[],\n",
       "\"spec_id\":\"7\"\n",
       "};\n",
       "           window.letsPlotCall(function() { fig = LetsPlot.buildPlotFromProcessedSpecs(plotSpec, containerDiv, sizing); });\n",
       "       } else {\n",
       "           fig.updateView({});\n",
       "       }\n",
       "   }\n",
       "   \n",
       "   const renderImmediately = \n",
       "       forceImmediateRender || (\n",
       "           sizing.width_mode === 'FIXED' && \n",
       "           (sizing.height_mode === 'FIXED' || sizing.height_mode === 'SCALED')\n",
       "       );\n",
       "   \n",
       "   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",
       "           for (let entry of entries) {\n",
       "               if (entry.contentBoxSize && \n",
       "                   entry.contentBoxSize[0].inlineSize > 0) {\n",
       "                   if (!responsive && observer) {\n",
       "                       observer.disconnect();\n",
       "                       observer = null;\n",
       "                   }\n",
       "                   renderPlot();\n",
       "                   if (!responsive) {\n",
       "                       break;\n",
       "                   }\n",
       "               }\n",
       "           }\n",
       "       });\n",
       "       \n",
       "       observer.observe(containerDiv);\n",
       "   }\n",
       "   \n",
       "   // ----------\n",
       "   })();\n",
       "   \n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.core.PlotSpec at 0x71c42a0b01c0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# legend_box_just\n",
    "p2 + theme(legend_box_just='center')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0eebd0e5-d542-452b-ad25-e117d0c68127",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-27T17:04:14.884431Z",
     "iopub.status.busy": "2026-01-27T17:04:14.884361Z",
     "iopub.status.idle": "2026-01-27T17:04:14.889197Z",
     "shell.execute_reply": "2026-01-27T17:04:14.888958Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"MR16nb\" ></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
       "   \n",
       "   (function() {\n",
       "   // ----------\n",
       "   \n",
       "   const forceImmediateRender = false;\n",
       "   const responsive = false;\n",
       "   \n",
       "   let sizing = {\n",
       "       width_mode: \"MIN\",\n",
       "       height_mode: \"SCALED\",\n",
       "       width: null, \n",
       "       height: null \n",
       "   };\n",
       "   \n",
       "   const preferredWidth = document.body.dataset.letsPlotPreferredWidth;\n",
       "   if (preferredWidth !== undefined) {\n",
       "       sizing = {\n",
       "           width_mode: 'FIXED',\n",
       "           height_mode: 'SCALED',\n",
       "           width: parseFloat(preferredWidth)\n",
       "       };\n",
       "   }\n",
       "   \n",
       "   const containerDiv = document.getElementById(\"MR16nb\");\n",
       "   let fig = null;\n",
       "   \n",
       "   function renderPlot() {\n",
       "       if (fig === null) {\n",
       "           const plotSpec = {\n",
       "\"data\":{\n",
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       "\"drv\":[\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"r\",\"r\",\"r\",\"r\",\"r\",\"r\",\"r\",\"r\",\"r\",\"r\",\"4\",\"4\",\"4\",\"4\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"r\",\"r\",\"r\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"r\",\"r\",\"r\",\"r\",\"r\",\"r\",\"r\",\"r\",\"r\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"r\",\"r\",\"r\",\"4\",\"4\",\"4\",\"4\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"4\",\"4\",\"4\",\"4\",\"f\",\"f\",\"f\",\"f\",\"f\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"4\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\",\"f\"],\n",
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       "},\n",
       "\"mapping\":{\n",
       "\"x\":\"displ\",\n",
       "\"y\":\"cty\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "\"series_annotations\":[{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"Unnamed: 0\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"manufacturer\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"model\"\n",
       "},{\n",
       "\"type\":\"float\",\n",
       "\"column\":\"displ\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"year\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"cyl\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"trans\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"drv\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"cty\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"hwy\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"fl\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"class\"\n",
       "}]\n",
       "},\n",
       "\"theme\":{\n",
       "\"legend_background\":{\n",
       "\"size\":1.0,\n",
       "\"blank\":false\n",
       "},\n",
       "\"legend_position\":\"bottom\",\n",
       "\"legend_box\":\"horizontal\",\n",
       "\"legend_box_spacing\":50.0\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[{\n",
       "\"aesthetic\":\"size\",\n",
       "\"breaks\":[15.0,30.0,40.0],\n",
       "\"range\":[1.0,10.0]\n",
       "}],\n",
       "\"layers\":[{\n",
       "\"geom\":\"point\",\n",
       "\"mapping\":{\n",
       "\"fill\":\"drv\",\n",
       "\"size\":\"hwy\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"shape\":21.0,\n",
       "\"data\":{\n",
       "}\n",
       "}],\n",
       "\"metainfo_list\":[],\n",
       "\"spec_id\":\"8\"\n",
       "};\n",
       "           window.letsPlotCall(function() { fig = LetsPlot.buildPlotFromProcessedSpecs(plotSpec, containerDiv, sizing); });\n",
       "       } else {\n",
       "           fig.updateView({});\n",
       "       }\n",
       "   }\n",
       "   \n",
       "   const renderImmediately = \n",
       "       forceImmediateRender || (\n",
       "           sizing.width_mode === 'FIXED' && \n",
       "           (sizing.height_mode === 'FIXED' || sizing.height_mode === 'SCALED')\n",
       "       );\n",
       "   \n",
       "   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",
       "           for (let entry of entries) {\n",
       "               if (entry.contentBoxSize && \n",
       "                   entry.contentBoxSize[0].inlineSize > 0) {\n",
       "                   if (!responsive && observer) {\n",
       "                       observer.disconnect();\n",
       "                       observer = null;\n",
       "                   }\n",
       "                   renderPlot();\n",
       "                   if (!responsive) {\n",
       "                       break;\n",
       "                   }\n",
       "               }\n",
       "           }\n",
       "       });\n",
       "       \n",
       "       observer.observe(containerDiv);\n",
       "   }\n",
       "   \n",
       "   // ----------\n",
       "   })();\n",
       "   \n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.core.PlotSpec at 0x71c42a0b0250>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# legend_box_spacing\n",
    "p2 + theme(legend_box='horizontal',  legend_box_spacing=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a4d86f96-9160-4aea-9f39-f66593294fbb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-27T17:04:14.889970Z",
     "iopub.status.busy": "2026-01-27T17:04:14.889884Z",
     "iopub.status.idle": "2026-01-27T17:04:14.894273Z",
     "shell.execute_reply": "2026-01-27T17:04:14.894082Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"MZR92h\" ></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
       "   \n",
       "   (function() {\n",
       "   // ----------\n",
       "   \n",
       "   const forceImmediateRender = false;\n",
       "   const responsive = false;\n",
       "   \n",
       "   let sizing = {\n",
       "       width_mode: \"MIN\",\n",
       "       height_mode: \"SCALED\",\n",
       "       width: null, \n",
       "       height: null \n",
       "   };\n",
       "   \n",
       "   const preferredWidth = document.body.dataset.letsPlotPreferredWidth;\n",
       "   if (preferredWidth !== undefined) {\n",
       "       sizing = {\n",
       "           width_mode: 'FIXED',\n",
       "           height_mode: 'SCALED',\n",
       "           width: parseFloat(preferredWidth)\n",
       "       };\n",
       "   }\n",
       "   \n",
       "   const containerDiv = document.getElementById(\"MZR92h\");\n",
       "   let fig = null;\n",
       "   \n",
       "   function renderPlot() {\n",
       "       if (fig === null) {\n",
       "           const plotSpec = {\n",
       "\"data\":{\n",
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       "\"y\":\"cty\"\n",
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       "\"type\":\"int\",\n",
       "\"column\":\"Unnamed: 0\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"manufacturer\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"model\"\n",
       "},{\n",
       "\"type\":\"float\",\n",
       "\"column\":\"displ\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"year\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"cyl\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"trans\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"drv\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"cty\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"hwy\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"fl\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"class\"\n",
       "}]\n",
       "},\n",
       "\"theme\":{\n",
       "\"legend_background\":{\n",
       "\"size\":1.0,\n",
       "\"blank\":false\n",
       "},\n",
       "\"legend_position\":\"bottom\",\n",
       "\"legend_spacing_x\":50.0,\n",
       "\"legend_box\":\"horizontal\"\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[{\n",
       "\"aesthetic\":\"size\",\n",
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       "},\n",
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       "},\n",
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       "\"data\":{\n",
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       "}],\n",
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       "\"spec_id\":\"9\"\n",
       "};\n",
       "           window.letsPlotCall(function() { fig = LetsPlot.buildPlotFromProcessedSpecs(plotSpec, containerDiv, sizing); });\n",
       "       } else {\n",
       "           fig.updateView({});\n",
       "       }\n",
       "   }\n",
       "   \n",
       "   const renderImmediately = \n",
       "       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",
       "           for (let entry of entries) {\n",
       "               if (entry.contentBoxSize && \n",
       "                   entry.contentBoxSize[0].inlineSize > 0) {\n",
       "                   if (!responsive && observer) {\n",
       "                       observer.disconnect();\n",
       "                       observer = null;\n",
       "                   }\n",
       "                   renderPlot();\n",
       "                   if (!responsive) {\n",
       "                       break;\n",
       "                   }\n",
       "               }\n",
       "           }\n",
       "       });\n",
       "       \n",
       "       observer.observe(containerDiv);\n",
       "   }\n",
       "   \n",
       "   // ----------\n",
       "   })();\n",
       "   \n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.core.PlotSpec at 0x71c42a0b00a0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# legend_spacing\n",
    "p2 + theme(legend_box='horizontal', legend_spacing_x=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26d9d1a1-9651-4432-8b76-09b4300c626a",
   "metadata": {},
   "source": [
    "#### Legend Key Parameters\n",
    "\n",
    "Parameters to customize the legend key:\n",
    "\n",
    "- `legend_key` - background underneath legend keys, set with `element_rect()`;\n",
    "\n",
    "- `legend_key_size` - size of legend keys;\n",
    "\n",
    "- `legend_key_width` - key background width;\n",
    "\n",
    "- `legend_key_height` - key background height;\n",
    "\n",
    "- `legend_key_spacing` - spacing between legend keys;\n",
    "\n",
    "- `legend_key_spacing_x` - spacing in the horizontal direction;\n",
    "\n",
    "- `legend_key_spacing_y` - spacing in the vertical direction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e61aba29-a6c7-4751-b32e-ef0bc96df51d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-27T17:04:14.894994Z",
     "iopub.status.busy": "2026-01-27T17:04:14.894919Z",
     "iopub.status.idle": "2026-01-27T17:04:14.900784Z",
     "shell.execute_reply": "2026-01-27T17:04:14.900571Z"
    }
   },
   "outputs": [
    {
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       "       if (fig === null) {\n",
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       "\"data_meta\":{\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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       "           (sizing.height_mode === 'FIXED' || sizing.height_mode === 'SCALED')\n",
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       "               if (entry.contentBoxSize && \n",
       "                   entry.contentBoxSize[0].inlineSize > 0) {\n",
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       "<lets_plot.plot.core.PlotSpec at 0x71c42a105d50>"
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   "source": [
    "p3 = ggplot(df, aes(x='hwy')) + \\\n",
    "    geom_dotplot(aes(fill='class'), color='pen') + \\\n",
    "    theme(legend_background=element_rect(size=0.5))\n",
    "p3"
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  {
   "cell_type": "markdown",
   "id": "f7f677e7-ed4c-4843-a012-5745ba8ca908",
   "metadata": {},
   "source": [
    "Add background underneath legend keys:"
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  },
  {
   "cell_type": "code",
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     "shell.execute_reply": "2026-01-27T17:04:14.905819Z"
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   "outputs": [
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   "source": [
    "Change size of legend keys:"
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       "           window.letsPlotCall(function() { fig = LetsPlot.buildPlotFromProcessedSpecs(plotSpec, containerDiv, sizing); });\n",
       "       } else {\n",
       "           fig.updateView({});\n",
       "       }\n",
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       "   \n",
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       "       forceImmediateRender || (\n",
       "           sizing.width_mode === 'FIXED' && \n",
       "           (sizing.height_mode === 'FIXED' || sizing.height_mode === 'SCALED')\n",
       "       );\n",
       "   \n",
       "   if (renderImmediately) {\n",
       "       renderPlot();\n",
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       "   \n",
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       "       var observer = new ResizeObserver(function(entries) {\n",
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       "                   entry.contentBoxSize[0].inlineSize > 0) {\n",
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       "                       observer.disconnect();\n",
       "                       observer = null;\n",
       "                   }\n",
       "                   renderPlot();\n",
       "                   if (!responsive) {\n",
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       "                   }\n",
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       "           }\n",
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       "       \n",
       "       observer.observe(containerDiv);\n",
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       "   \n",
       "   // ----------\n",
       "   })();\n",
       "   \n",
       "   </script>"
      ],
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       "<lets_plot.plot.core.PlotSpec at 0x71c42a12ca60>"
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     },
     "execution_count": 17,
     "metadata": {},
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    }
   ],
   "source": [
    "p5 = p4 + theme(legend_key_width=80, legend_key_height=20)\n",
    "p5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6e711d1-c3b0-4715-88e7-7e6b603b9a2a",
   "metadata": {},
   "source": [
    "Add spacing between legend keys:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f39d210c-1bb4-41ee-926b-29b734d33dca",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-27T17:04:14.917177Z",
     "iopub.status.busy": "2026-01-27T17:04:14.917106Z",
     "iopub.status.idle": "2026-01-27T17:04:14.922324Z",
     "shell.execute_reply": "2026-01-27T17:04:14.922123Z"
    }
   },
   "outputs": [
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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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       "<lets_plot.plot.core.PlotSpec at 0x71c42a0b01f0>"
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     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p5 + theme(legend_key_spacing_y=10)"
   ]
  }
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