{
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    {
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       "                    var script = document.createElement(\"script\");\n",
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       "                        window.letsPlotCall = function(f) {f();};\n",
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       "                })()\n",
       "            </script>\n",
       "            "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from lets_plot import *\n",
    "LetsPlot.setup_html()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2025-11-05T13:44:23.180639Z"
    }
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   "outputs": [
    {
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       "        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>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19.352466</td>\n",
       "      <td>0.481977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>47.585001</td>\n",
       "      <td>-0.444944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36.867703</td>\n",
       "      <td>-0.738881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30.334266</td>\n",
       "      <td>-0.882739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.644913</td>\n",
       "      <td>0.703183</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           x         y\n",
       "0  19.352466  0.481977\n",
       "1  47.585001 -0.444944\n",
       "2  36.867703 -0.738881\n",
       "3  30.334266 -0.882739\n",
       "4   8.644913  0.703183"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "\n",
    "x = np.random.uniform(1, 50, size=40)\n",
    "\n",
    "data = pd.DataFrame({'x': x, 'y': np.sin(x)})\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**geom_line()** connects points in order of the variable on the x-axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2025-11-05T13:44:23.210780Z"
    }
   },
   "outputs": [
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       "<lets_plot.plot.core.PlotSpec at 0x7d0518419f90>"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(data) + geom_point(aes(x='x', y='y', color='x'), alpha=0.7, size=4) + \\\n",
    "    scale_color_discrete() + \\\n",
    "    theme(legend_position='none') + \\\n",
    "    geom_line(aes(x='x', y='y'), linetype=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**geom_path()** connects observations in the order how they appear in data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2025-11-05T13:44:23.215275Z"
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   "outputs": [
    {
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      ],
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       "<lets_plot.plot.core.PlotSpec at 0x7d0510e13a30>"
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     },
     "execution_count": 4,
     "metadata": {},
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    }
   ],
   "source": [
    "ggplot(data) + geom_point(aes(x='x', y='y', color='x'), alpha=0.7, size=4) + \\\n",
    "    scale_color_discrete() + \\\n",
    "    theme(legend_position='none') + \\\n",
    "    geom_path(aes(x='x', y='y'), size=0.7, linetype='dotted')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another example to demonstrate the difference between geom_path() and geom_line()."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-11-05T13:44:23.216337Z",
     "iopub.status.busy": "2025-11-05T13:44:23.216265Z",
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     "shell.execute_reply": "2025-11-05T13:44:23.217693Z"
    }
   },
   "outputs": [],
   "source": [
    "a = [5, 1, 1, 5, 5, 2, 2, 4, 4, 3]\n",
    "b = [5, 5, 1, 1, 4, 4, 2, 2, 3, 3]\n",
    "\n",
    "snail = pd.DataFrame({'x': a, 'y': b})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2025-11-05T13:44:23.221062Z"
    }
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   "outputs": [
    {
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       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
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       "   }\n",
       "   \n",
       "   const containerDiv = document.getElementById(\"HtGIbv\");\n",
       "   let fig = null;\n",
       "   \n",
       "   function renderPlot() {\n",
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       "           const plotSpec = {\n",
       "\"mapping\":{\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
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       "\"scales\":[],\n",
       "\"layers\":[{\n",
       "\"geom\":\"path\",\n",
       "\"data\":{\n",
       "\"x\":[5.0,1.0,1.0,5.0,5.0,2.0,2.0,4.0,4.0,3.0],\n",
       "\"y\":[5.0,5.0,1.0,1.0,4.0,4.0,2.0,2.0,3.0,3.0]\n",
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       "\"alpha\":0.7\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 0x7d0510e13760>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot() + geom_path(data=snail, mapping=aes(x='x', y='y'), size=2, alpha=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-11-05T13:44:23.221981Z",
     "iopub.status.busy": "2025-11-05T13:44:23.221909Z",
     "iopub.status.idle": "2025-11-05T13:44:23.224551Z",
     "shell.execute_reply": "2025-11-05T13:44:23.224386Z"
    }
   },
   "outputs": [
    {
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       "   <div id=\"WJfJQi\"></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
       "   \n",
       "   (function() {\n",
       "   // ----------\n",
       "   \n",
       "   const forceImmediateRender = false;\n",
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       "   \n",
       "   let sizing = {\n",
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       "       height_mode: \"SCALED\",\n",
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       "   \n",
       "   const containerDiv = document.getElementById(\"WJfJQi\");\n",
       "   let fig = null;\n",
       "   \n",
       "   function renderPlot() {\n",
       "       if (fig === null) {\n",
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       "\"mapping\":{\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
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       "\"scales\":[],\n",
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       "\"data\":{\n",
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       "\"column\":\"y\"\n",
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       "\"size\":2.0,\n",
       "\"alpha\":0.7\n",
       "}],\n",
       "\"metainfo_list\":[],\n",
       "\"spec_id\":\"4\"\n",
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       "           window.letsPlotCall(function() { fig = LetsPlot.buildPlotFromProcessedSpecs(plotSpec, containerDiv, sizing); });\n",
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       "   \n",
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       "   \n",
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       "       var observer = new ResizeObserver(function(entries) {\n",
       "           for (let entry of entries) {\n",
       "               if (entry.contentBoxSize && \n",
       "                   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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       "       observer.observe(containerDiv);\n",
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       "   // ----------\n",
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   ],
   "source": [
    "ggplot() + geom_line(data=snail, mapping=aes(x='x', y='y'), size=2, alpha=0.7)"
   ]
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