{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Advanced Plotting\n",
    "\n",
    "Previous notebooks [[006_statistics_quickstart](https://github.com/tlint101/py50/blob/main/tutorials/006_statistics_quickstart.ipynb), [007_parametric](https://github.com/tlint101/py50/blob/main/tutorials/007_parametric_example.ipynb), [008_non-parametric](https://github.com/tlint101/py50/blob/main/tutorials/008_non-parametric_example.ipynb)] show some examples of plot modifications. \n",
    "\n",
    "This notebook will give further examples for modifying plots. Hopefully users will find a style that they like for that matches their projects. \n",
    "\n",
    "py50 wraps [Statannotations](https://github.com/trevismd/statannotations/tree/master), which is built on top of Seaborn and Matplotlib. Many customizations that work for Seaborn and Matplotlib should work for py50 plots. Of course there are tons of customizations and I have not tested them all. If there are issues, please let me know. "
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "54f004fbb182e2e4"
  },
  {
   "cell_type": "code",
   "source": [
    "from py50 import Stats, Plots\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2026-05-21T04:13:21.893603Z",
     "start_time": "2026-05-21T04:13:20.980579Z"
    }
   },
   "id": "213702e86ffbb96f",
   "outputs": [],
   "execution_count": 1
  },
  {
   "cell_type": "markdown",
   "source": [
    "Load dataset. The Seaborn tips dataset will be used for plotting and generating examples. "
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "e9129c41fa6252a0"
  },
  {
   "cell_type": "code",
   "source": [
    "data = sns.load_dataset(\"tips\")\n",
    "\n",
    "# Initialize Class\n",
    "tips_stats = Stats(data)\n",
    "\n",
    "tips_stats.show(2)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2026-05-21T04:13:21.915549Z",
     "start_time": "2026-05-21T04:13:21.902683Z"
    }
   },
   "id": "8cac7a7aaf2e608f",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size\n",
       "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
       "1       10.34  1.66    Male     No  Sun  Dinner     3"
      ],
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
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     },
     "execution_count": 2,
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   ],
   "execution_count": 2
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "The Plots() class was written to recalculate the statistics. Essentially, the same statistic result from the Plots() should match with Stats(). As this tutorial will work with Mann-Whitney U tests, the statistics results will be printed here and can be used to check the plot results. ",
   "id": "195582d6d3f6e5db"
  },
  {
   "metadata": {
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     "end_time": "2026-05-21T04:13:21.967729Z",
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   },
   "cell_type": "code",
   "source": [
    "mannu = tips_stats.get_mannu(value_col='tip', group_col='day')\n",
    "mannu"
   ],
   "id": "b8eae45d05b4c7fe",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      A     B   U-val     p-val significance       RBC      CLES\n",
       "0   Sun   Sat  3960.0  0.029497            *  0.197822  0.598911\n",
       "1   Sun  Thur  2956.5  0.010006            *  0.254881  0.627441\n",
       "2   Sun   Fri   910.5  0.079741         n.s.  0.261080  0.630540\n",
       "3   Sat  Thur  2907.5  0.417663         n.s.  0.078050  0.539025\n",
       "4   Sat   Fri   845.0  0.881937         n.s.  0.022384  0.511192\n",
       "5  Thur   Fri   561.0  0.758381         n.s. -0.047538  0.476231"
      ],
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       "      <th>U-val</th>\n",
       "      <th>p-val</th>\n",
       "      <th>significance</th>\n",
       "      <th>RBC</th>\n",
       "      <th>CLES</th>\n",
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       "      <th>0</th>\n",
       "      <td>Sun</td>\n",
       "      <td>Sat</td>\n",
       "      <td>3960.0</td>\n",
       "      <td>0.029497</td>\n",
       "      <td>*</td>\n",
       "      <td>0.197822</td>\n",
       "      <td>0.598911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sun</td>\n",
       "      <td>Thur</td>\n",
       "      <td>2956.5</td>\n",
       "      <td>0.010006</td>\n",
       "      <td>*</td>\n",
       "      <td>0.254881</td>\n",
       "      <td>0.627441</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sun</td>\n",
       "      <td>Fri</td>\n",
       "      <td>910.5</td>\n",
       "      <td>0.079741</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>0.261080</td>\n",
       "      <td>0.630540</td>\n",
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       "      <th>3</th>\n",
       "      <td>Sat</td>\n",
       "      <td>Thur</td>\n",
       "      <td>2907.5</td>\n",
       "      <td>0.417663</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>0.078050</td>\n",
       "      <td>0.539025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sat</td>\n",
       "      <td>Fri</td>\n",
       "      <td>845.0</td>\n",
       "      <td>0.881937</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>0.022384</td>\n",
       "      <td>0.511192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Thur</td>\n",
       "      <td>Fri</td>\n",
       "      <td>561.0</td>\n",
       "      <td>0.758381</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>-0.047538</td>\n",
       "      <td>0.476231</td>\n",
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       "  </tbody>\n",
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     "execution_count": 3,
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     "output_type": "execute_result"
    }
   ],
   "execution_count": 3
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Plot Test Types\n",
    "\n",
    "py50 includes plotting for 4 types of plots -> boxplot, barplot, violinplot, and swarmplot\n",
    "\n",
    "All plots essentially take in the same parameters. At least four parameters are needed to generate the plot. The plots are essentially \"re-running\" the statistics, so each plot has to be notified for a specific test. \n",
    "\n",
    "**Reminders for tests** can be called using the following function:  "
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "e879c32dca529cab"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-05-21T04:13:22.458951Z",
     "start_time": "2026-05-21T04:13:22.432667Z"
    }
   },
   "cell_type": "code",
   "source": "Plots.list_test()",
   "id": "587fce1219c1024f",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "List of tests available for plotting: 'tukey', 'gameshowell', 'pairwise-parametric', 'pairwise-rm', 'pairwise-mixed', 'pairwise-nonparametric', 'wilcoxon', 'mannu', 'kruskal''kruskal'\n"
     ]
    }
   ],
   "execution_count": 4
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## Basic Plot\n",
    "\n",
    "Because all the plots use the same parameters, the plot types will be periodically switched in this notebook. Only the barplot contains specific parameters, which will be noted below.  \n",
    "\n",
    "A basic plot will be generated using the tips dataset. The Mann-Whitney U Test will be used as the example, which will be passed as **test='mannu'**. Optionally, if users want the calculated results, they can pass the **return_df=True** parameter."
   ],
   "id": "d7944445254fe92e"
  },
  {
   "cell_type": "code",
   "source": [
    "# Initialize class\n",
    "# tips_plot = Plots(mannu)\n",
    "tips_plot = Plots(data)\n",
    "\n",
    "tips_plot.show(2)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2026-05-21T04:13:22.474610Z",
     "start_time": "2026-05-21T04:13:22.466391Z"
    }
   },
   "id": "c591b0004f58995c",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size\n",
       "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
       "1       10.34  1.66    Male     No  Sun  Dinner     3"
      ],
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       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
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       "      <td>1.01</td>\n",
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       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
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       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
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   "cell_type": "code",
   "source": "tips_plot.boxplot(test='mannu', value_col='tip', group_col='day')",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "<statannotations.Annotator.Annotator at 0x10e293310>"
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     },
     "execution_count": 6,
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     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 6
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## Modifying Annotations\n",
    "\n",
    "The annotations for the plot can be modified. For readability, the non-significant groups will not be annotated. This will be done by passing in the **pairs** parameters. Based on the order of the pairs, this could also change the order of the group. As I prefer to have the typical Sun-Sat week order, I have made the group order accordingly.  "
   ],
   "id": "b738191ab7744142"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-05-21T04:13:23.035290Z",
     "start_time": "2026-05-21T04:13:22.871044Z"
    }
   },
   "cell_type": "code",
   "source": [
    "pairs = [('Thur', 'Sun'), ('Sat', 'Sun')]\n",
    "group_order = ['Sun', 'Thur', 'Fri', 'Sat']\n",
    "\n",
    "tips_plot.boxplot(test='mannu', value_col='tip', group_col='day', group_order=group_order, pairs=pairs)"
   ],
   "id": "8f903ee49f944c91",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<statannotations.Annotator.Annotator at 0x10e56cdc0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 7
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "The annotations can also be modified. The order of the annotation must match the order of the statistics table. This can be checked by passing the **return_df** parameter. ",
   "id": "a3ca3425bdf2cc12"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-05-21T04:13:23.246107Z",
     "start_time": "2026-05-21T04:13:23.042741Z"
    }
   },
   "cell_type": "code",
   "source": [
    "pvalue_label = ['This is between Sun-Thur', 'This is for the weekend. Yay!']\n",
    "\n",
    "tips_plot.boxplot(test='mannu', value_col='tip', group_col='day', group_order=group_order, pairs=pairs,\n",
    "                  pvalue_label=pvalue_label, return_df=True)"
   ],
   "id": "eb39020d7a2dade1",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(     A     B   U-val     p-val significance       RBC      CLES\n",
       " 1  Sun  Thur  2956.5  0.010006            *  0.254881  0.627441\n",
       " 0  Sun   Sat  3960.0  0.029497            *  0.197822  0.598911,\n",
       " <statannotations.Annotator.Annotator at 0x10e718040>)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 8
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Adding Title and Changing Fonts \n",
    "\n",
    "Keeping visual information consistent and color coordinated is important for a project. The basic Seaborn font style rarely matches the style of a project (at least in my experience). This can be easily fixed using the Matplotlib customization features. These customizations should be called **after** generating the annotated plots."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "fe4eb92c892883b1"
  },
  {
   "cell_type": "code",
   "source": [
    "title = 'Hello Title!'\n",
    "tips_plot.swarmplot(test='mannu', value_col='tip', group_col='day', pairs=pairs)\n",
    "\n",
    "# Set Ticks\n",
    "plt.xticks(fontname=\"Arial\", fontsize=8, fontweight='bold', rotation=45)\n",
    "plt.yticks(fontname=\"Arial\", fontsize=18, fontweight='bold', rotation=180)\n",
    "\n",
    "# Set Title and Labels\n",
    "plt.xlabel(\"X Label\", fontname='Times New Roman', fontstyle='italic', fontsize=14)\n",
    "plt.ylabel(\"Y Label\", fontname='Times New Roman', fontstyle='italic', fontsize=14)\n",
    "plt.title(title, fontname='Times New Roman', fontweight='bold', fontsize=18)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2026-05-21T04:13:23.503209Z",
     "start_time": "2026-05-21T04:13:23.269149Z"
    }
   },
   "id": "f5a17afdf747c6b9",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 9
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "That figure may look garish, but a lot of customizations were demoed! Many customizations were passed using matplotlib and additional args or kwargs can be found [here](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.xticks.html).",
   "id": "ae0c843bd9a25ef8"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## Error Bars\n",
    "\n",
    "As of this writing, Statannotations does not support Seaborn ≥0.12. As a result, the error bars between plots are inconsistent.  \n",
    "\n",
    "The boxplot can be modified by passing in the **whis** parameter. This will modify the bars of the box plot. Points outside the bars are outliers. By default, this is set to 1.5."
   ],
   "id": "2cf1bbe8bf5c98e9"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-05-21T04:13:23.630307Z",
     "start_time": "2026-05-21T04:13:23.515541Z"
    }
   },
   "cell_type": "code",
   "source": "tips_plot.boxplot(test='mannu', value_col='tip', group_col='day', pairs=pairs, whis=0.5)",
   "id": "bf353709ec6aa285",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<statannotations.Annotator.Annotator at 0x10f07cb80>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 10
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "In contrast, the barplot contains a few more options for modifying its error bars. By default, the confidence interval, or the **ci** parameter, is set to **\"sd\"** or standard deviation. However, this can be changed by passing in an integer. The **capsize** parameter will determine the size of the ends. Additionally, Seaborn contains **kwargs that can further modify the errorbars. We will use **errcolor** as an example, which is part of the **kwarg for the py50 barplot. \n",
    "\n",
    "**Testing on my machine has not shown any issues with error bars on bar plot**"
   ],
   "id": "501d39cb33cd561"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-05-21T04:13:23.742533Z",
     "start_time": "2026-05-21T04:13:23.649611Z"
    }
   },
   "cell_type": "code",
   "source": [
    "tips_plot.barplot(test='mannu', value_col='tip', group_col='day', pairs=pairs, errorbar=\"sd\", capsize=0.2,\n",
    "                  errcolor='purple')"
   ],
   "id": "b2d28ee58a726c07",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<statannotations.Annotator.Annotator at 0x10e11d3f0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 11
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Color Palette\n",
    "\n",
    "Color coding groups or data is important in communicating results. We can use that conditions into the **palette** parameter. This can be a string or a list of colors. The colors can be a name or a list of hex codes. "
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "bc649848285fa6ee"
  },
  {
   "cell_type": "code",
   "source": [
    "# Palette as a string\n",
    "palette = 'Oranges'\n",
    "tips_plot.boxplot(test='mannu', value_col='tip', group_col='day', pairs=pairs, palette=palette)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2026-05-21T04:13:23.886155Z",
     "start_time": "2026-05-21T04:13:23.752662Z"
    }
   },
   "id": "dae9874c15ddc006",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<statannotations.Annotator.Annotator at 0x10f0c0520>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 12
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-05-21T04:13:23.990061Z",
     "start_time": "2026-05-21T04:13:23.887519Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Palette as a list\n",
    "palette = [\"#E69F00\", \"#56B4E9\", \"#009E73\", \"#F0E442\"]  # Colorblind palette\n",
    "\n",
    "tips_plot.boxplot(test='mannu', value_col='tip', group_col='day', pairs=pairs, palette=palette)"
   ],
   "id": "da5b307e4eec8710",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<statannotations.Annotator.Annotator at 0x10f25c6a0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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AgEwIdgAAmRDsAAAyIdgBAGRCsAMAyIRgBwCQCcEOACATrctdAACavs2bN8eCBQti1apV0aVLl+jfv3+0atWq3MWCZmengt3q1aujZcuW8bnPfa7hSkTZuUADn8XcuXPj6quvjhUrVtRu69atW4wfPz6GDh3qYEIlB7vq6uq45ppr4s4774x333232LbXXnvFaaedFmeeeWZjlJEScoEGPus1Y8KECTFo0KC44oorolevXrFkyZKYMWNGsX3y5MnCHVRyH7tJkybFrFmzijux++67r/h5zJgxccstt8S0adMap5SU9ALdu3fvuPnmm2PevHnFMq2n7el9gK1r91NNXQp1U6dOjb59+0bHjh2LZVpP26dMmVLsB1RosEth7qqrrorhw4fH/vvvH1/+8pfj5JNPjp/+9Kfx61//unFKSaNzgQY+q9SnLjW/jho1quiWs7W0PnLkyFi+fHmxH1Chwa59+/bRpk2bj23fddddo0WLFg1VLkrMBRr4rNJAiSQ1v9anZnvNfkAFBrt/+7d/i4kTJ8bjjz8ea9asiXXr1sXvfve7+NGPfhQjRowo7t5qXjQdLtDAZ5VGvyapT119arbX7AdU4OCJ1Ncq+d73vldbQ7dly5Zi+dJLLxX9KtJ6ei+t0/Qu0Kl/zLZcoIFtpSlN0ujXNFAiDZJYuHBh7XQnBx98cMycOTO6d+9e7AdUaLDTgT7/C3QK51v3l0kjoV2ggW2leerSQLr0GjhwYGzYsKFOt520ngZXmM8OKjjYpbsv8r1ApxrZsWPHFp2ea6YtSKHuySefLO7IXaCBbaUWmpqWm223A6XVYkt9/xu30adPn3jqqadijz32KEbBftJ/1rT9xRdfjEqQ+v4deuihMX/+/OjUqVO5i9Ok57FLYX7cuHHmogI+Npr+61//ejElUn1NselGMd0czp49200hlCjTbFeN3U033VT7dInUXHfBBRdEVVVVnX3SQIo0gIKmLc0SP2TIEI8GArZ7NH2amDjNljBgwIA676ea/zSoLu237XtUhmXLlsXatWsjN1VVVdGjR49ojrYr2KX/sA888EDx88qVK4vXtifCa6+9Fh9++GHjlJKSSs2tLsLAX2I0fdOWHgs6bNiwoh91jt9jc+bMid122y2am+0Kdh06dIif/exnRR+K9LrhhhvqdK5PTbBptvGaEbMA5M9o+qYthZ7UTF6KGrulS5cWU6Vdfvnlse+++zb670s1ds0x1G13sEv96mpGw55++unFo8NqmmYBaJ6Mpm/6St1cmUJd6rdPBU1QnJ4JK9QBUDOaPo2aT6PpFy1aFOvXry+WaT1tTwOvjKaHCp7uBAC2HnCVRsSm0fRpoMTWo+nT9vQ+UDqCHQA7xWh6qByCHQA7zWh6aKJ97AAAqEyCHQBAJgQ7AIBMCHYAAJmo6GC3adOm+PGPf1w83urII4+MKVOmFE++AACgiY2K/clPfhLPPPNMzJgxo5j0Mk142a1btzj11FPLXTQAgIpTsTV2a9asibvvvjsuvfTS6Nu3bxxxxBExcuTIYkZzAACaUI3d/Pnzo1OnTnHYYYfVbjvzzDPLWiYAgEpWsTV2b775ZvFImnvvvTeOO+64Ymbza6+9Nqqrq8tdNAC2sXnz5njuuefi4YcfLpZpHSi9iq2xe//99+P111+P22+/PSZNmhTvvPNOXHjhhdGhQ4eiSba+gRbpVWPdunUlLjFA8zR37tziWbErVqyo3Zb6Q48fP96zYqHEKjbYtW7dughn6WKRau6SdNG47bbb6g1206dPj2nTppWhpPlJd9oLFiyIVatWRZcuXaJ///7F44IA6gt1EyZMiEGDBsUVV1wRvXr1iiVLlhSD3tL2yZMnC3dQQhUb7Pbcc89o165dbahL9t1331i5cmW9+48ePTrOOOOM2vUUCgcPHlySsubEnTfwWW4C0813CnVTp06Nli3/X++eNOAtraeZDNI0VUOGDHFzCM29j12/fv1i48aNsXTp0tptr732Wp2gt7W2bdsWgy22frFjd969e/eOm2++OebNm1cs03rant4HqJFq9lNLyqhRo2pDXY20nlpXli9fXuwHNPNg96Uvfam4yzv//PPj5Zdfjt/85jfxi1/8Ir75zW+Wu2jN4s473XF37Nix9s47bU933jpEAzVSd40kNb/Wp2Z7zX5AMw52Seqb8cUvfrEIc+eee26cdtppcfrpp5e7WFly5w18VqkPbpL61NWnZnvNfkAz7mOXVFVVxZVXXlnuYjQL7ryBzyoNrEqjX9NAia372CVpaqqZM2cW3WfSfkBpVHSNHaXjzhv4rNJo+TSlyZNPPlkMlEhPBkqPf0zLtJ62jxs3zsAJKCHBjo/deW87CbQ7b+CTpMnjU7eZxYsXx4gRI+Jv//Zvi2VqhjXVCZReRTfFUvo77zT6Nd1pp9FsNfNRpeaUdOedLtLmswPqC3dpsJv5L6H8BDs+duedRsemO+4aqY+MO2/g06SbvgEDBjhIUGaCHXW48waApkuw42PceQNA02TwBABAJgQ7AIBMCHYAAJkQ7AAAMiHYAQBkQrADAMiEYAcAkAnBDgAgE4IdAEAmBDsAgEwIdgAAmRDsAAAyIdgBAGRCsAMAyETrcheAz2bZsmWxdu3a7A5bVVVV9OjRo9zFAIAmTbBrQlavXh3Dhg2L6urqyE2rVq1izpw5sdtuu5W7KJAdN4TQfAh2TUgKPbNnzy5Jjd3SpUtj4sSJcfnll8e+++5bkho7oQ4anhtCaF4Euyam1M2VKdT16dOnpL8TaDhuCKF5EewAMueGEJoPo2IBADIh2AEAZEKwAwDIhGAHAJAJwQ4AIBOCHQBAJgQ7AIBMCHYAAJkQ7AAAMiHYAQBkwiPFIBPLli2LtWvXRm6qqqpK/kgsgKZKsIMMrF69OoYNGxbV1dWRm1atWsWcOXOKh9kD8OkEO8hACj2zZ88uSY3d0qVLY+LEiXH55ZfHvvvuW5IaO6EOYPsIdpCJUjdXplDXp0+fkv5OAD6dwRMAAJkQ7AAAMiHYAQBkQrADAMiEYAcAkAnBDgAgE4IdAEAmBDsAgEwIdgAAmRDsAAAyIdgBAGRCsAMAyIRgBwCQCcEOACATgh0AQCYEOwCATDSZYHfmmWfGeeedV+5iAABUrCYR7B588MF44oknyl0MAICKVvHBbs2aNXHllVfGQQcdVO6iAABUtNZR4X7605/GiSeeGG+//Xa5iwIAUNEqOtj99re/jd/97ndx//33x8UXX/yp+27atKl41Vi3bl0JSggADWvlypVFa1VOli5dWmeZk86dO0fXrl2jUlRssNu4cWNcdNFFceGFF0b79u3/4v7Tp0+PadOmlaRsANBYoe6kk74RGzZszPIAT5w4MXLTvn27mDXr3ooJdxUb7FJIO/DAA2PgwIHbtf/o0aPjjDPOqFNjN3jw4EYsIQA0rFRTl0LdOd/tFt27tXV4K9zyFZvimutXFJ+bYLcdI2FXrVoVhxxySLFe08z66KOPxu9///uP7d+2bdviBQBNXQp1X9qnQ7mLQRNUsTV2t9xyS3z00Ue165MnTy6WEyZMKGOpAAAqV8UGu+7du9dZ32WXXYplz549y1QiAIDKVvHz2AEA0MRr7LZ1xRVXlLsIAAAVTY0dAEAmBDsAgEwIdgAAmRDsAAAyIdgBAGRCsAMAyIRgBwCQCcEOACATgh0AQCYEOwCATAh2AACZEOwAADIh2AEAZKJ1uQvQ1K1cuTLWrFkTuVm6dGmdZU46d+4cXbt2LcnvyvH8cG40DOdG01LK6wbsDMFuJy/MJ33jxNiwcVPkauLEiZGb9u3axqx772v0i3Q6P75x0kmxccOGyFGO50a79u3j3lmzSnJunPiNb8SmjRsjRzmeG23btYv77r1XuKPiCXY7IdXEpFA36qvrYu/OmxvuU6HR/GlNq5jxeKfis2vsL+/0O1Ko6zV8THTo0r1Rfxc774NVy2PJPdNKdm6kUPfukb3jo891bNTfxc5r/d77sfu8xSU5N2BnCXYNIIW6nl0EO+qXQl2nbvs6PHxMCnUf7t7JkQEajMETAACZEOwAADIh2AEAZEIfOwCoMMtX5DliOjfLK/BzEuwAoMJcc/3KcheBJkqwA4AKc853u0b3bu3KXQy2o8au0kK4YAcAFSaFui/t06HcxaAJMngCACATauygBE80oPL5nIAcCHbQyNJjqgCgFAQ7aGSeFdu0nhUL0JQJdtDIPCsWgFIxeAIAIBOCHQBAJgQ7AIBMCHYAAJkQ7AAAMmFULEC5LsDvve/YNwE+J5oSwQ6gTHaft9ixBxqUYAdQJu8e2Ts++lxHx78J1NgJ4TQVgh1AmaRQ9+HunRx/oMEYPAEAkAnBDgAgE4IdAEAmBDsAgEwIdgAAmRDsAAAyIdgBAGRCsAMAyIRgBwCQCcEOACATHinWAFaukY+bCp8VADkT7BrAzMerGuKvAQDYKYJdAxj51bXRtXN1Q/xVlKDGThAHIFeCXQNIoa5nl80N8VcBAOwwncMAADIh2AEAZKKig91bb70V55xzThx22GExcODAmDRpUmzcuLHcxQIAqEgV28duy5YtRajbdddd41e/+lW89957MXHixGjZsmWce+655S4eAEDFqdgau9deey0WLlxY1NL17t07/uZv/qYIeg888EC5iwYAUJEqtsZuzz33jBtuuCG6dOlSZ/u6devKViYAKIXlKzY50E3A8gr8nCo22KUm2NSvrkZ1dXXceuut8ZWvfKWs5QKAxtK5c+do375dXHP9Cge5iWjfvl3xuVWKig1227rqqqvixRdfjLvuuqve9zdt2lS8ylGz96c1rUr2u9g5PisqSev33i93Eaiwz6lr164xa9a9sWbNmsjJ0qVLi37yl19+eey7776Rk86dOxefW6Vo3VRC3U033RRTp06N/fffv959pk+fHtOmTSv9nVW7tjHj8U4l/b3snPSZlfLu6oNVy0v2u2gan1M6/9q2axe7z1tcst/JzkmfV6muGykkVFJQaEgp1PXp06fcxchaxQe7Sy+9NG677bYi3B177LGfuN/o0aPjjDPOqFNjN3jw4Ma/s7r3vuzurBJ3VzsvfQm0a98+ltxT2hsOdlz6vErx5Z2uHffdq1amKam0WhloksEu1cDdfvvtMWXKlDjuuOM+dd+2bdsWr1LL+c4qcXe149J5ce+sWdkFf6G/YeR87XDdgPKp2GD36quvxs9//vM488wz49BDD4133nmnzohZaAp8eQNQShUb7ObOnRubN2+O6667rnht7ZVXXilbuQAAKlXFBrtUU5deAAA08SdPAADw2Qh2AACZEOwAADIh2AEAZEKwAwDIhGAHAJAJwQ4AIBOCHQBAJgQ7AIBMCHYAAJkQ7AAAMiHYAQBkQrADAMiEYAcAkAnBDgAgE4IdAEAmBDsAgEy0LncB+GyWLVsWa9eubfTDtnTp0jrLxlZVVRU9evQoye8CgFwJdk3I6tWrY9iwYVFdXV2y3zlx4sSS/J5WrVrFnDlzYrfddivJ7wOAHAl2TUgKPbNnzy5JjV2ppRo7oQ4Ado5g18RorgQAPonBEwAAmRDsAAAyIdgBAGRCsAMAyITBEwDQTJkbNT+CHWTCBRrnBp+FuVHz1GLLli1bIkPr1q2LQw89NObPnx+dOnUqd3Gg0S/QQ4cOLenk1aVi8uqd49ygEm4IS60qs6cZfZZMo8YOMmDyapwb7Iicwg//j2AHmXCBxrkBGBULAJAJwQ4AIBOCHQBAJgQ7AIBMCHYAAJkQ7AAAMiHYAQBkQrADAMiEYAcAkAnBDgAgE4IdAEAmsn1W7JYtW4rlunXryl0UAIAdVpNlarJNswx269evL5aDBw8ud1EAABok21RVVX3qPi22bE/8a4Kqq6vj7bffjl122SVatGhR7uI0ybuDFIqfeOKJ6NSpU7mLQwVxbuDcwHWjtFJUS6Fur732ipYtWzbPGrv0D997773LXYwmL4U6wQ7nBq4b+E4pr79UU1fD4AkAgEwIdgAAmRDsqFfbtm1jzJgxxRKcG2wP1w2cG+WX7eAJAIDmRo0dAEAmBDsAgExkO90J/7/zzjsvZs2a9YmHpHv37nHYYYfFFVdc4bBR66ijjorly5d/7Ij0798/brvttjrbli1bFkOHDo25c+dGjx49HMVMffjhh3H99dfHvffeG2+99VZ06dIljj322Dj77LO3a1qkl156KT744IPiHCIPO3tO0PD0sWsG1q5dGxs2bCh+fuihh2LmzJlx11131b6fAl2bNm0EOz4W7EaMGBHHH398ne3pXOncuXOdbZs3b4533303dt9992jVqpUjmalJkybFvHnzYuLEifGFL3wh3nzzzbjsssuKMJ++3LfnnEqDsoYPH16S8lL55wQNT41dM5nUsGZiw7RMX7x77rlnnS9q+KRzZ+tz5ZNse06Rp1Tzf/nll8cRRxxRrKcv74svvjhOO+204kk/aVZ8mhfnROXRx47ax0SNHTs2+vXrF0OGDIn777+/zl32PffcU7v+zDPPxF/91V/VNsGln6+99toYMGBAXHLJJY5oM3D66afHpZdeWjS/pvPllVdeKc6DdD6Qr/R4xqeffrp4ZGONQw45JB588MHYbbfdiqa4c845p7gWHHjggXHSSSfF/Pnza8+Z1LR//vnnF91DaB7nxPZ8fzz22GNx9NFHx0EHHRSjR4+ONWvWlOXfkgvBjsJ//dd/xV//9V/HAw88EF/72teKavXUhLu9FixYEHfffXd8+9vfdkSbiXSxvuqqq2LatGnFM5nJX/r/fcsttxRf1hdddFE8+uijRTePXr16FTX/EyZMKJrlb7/99qLP1ec///miRi/52c9+VjzmMV1bLrjggnL/UyjRObE9UpPtlClT4tZbb43//d//jRtvvNHnsxMEO2rvsL7zne8UfSTOOuus2LRpU7z22mvbfXRSX6wvfvGLsc8++ziiGUkX6nRubP16//33i/dSTV3qBJ9qZmgevv/97xdhPgW0//zP/yxq5wYOHFjc1KUpUVOty49+9KPYb7/9ii/21ES7ZMmS4s+mfpmpyX7rriHkfU5sr/Rn+vbtW7QYff3rXy/CHTtOHzsKKdDVqLnobty4cbuPThpZS37SBfeYY46ps61Dhw7F0mfePA0bNqx4rV69Op566qmiliXVwKUmtW9+85vFAK1Ug7906dJ44YUX6jTR0fzOie3Rs2fP2p/TSNo00pYdp8aOQn0jGT/poSSpqWVb7dq1cyQztMceexQX3a1fqU9N4jNvXl5++eU6I+dT/6lUu5Ka4VJtTRoZOXLkyGLUfbdu3WLUqFFx5ZVXlrXMlPecSH3vtuf7wwC+hiXY8Rel/3Tr16+vXU/D2YHmJX0hp75PL7744seeD9u+ffvo2LFjPPfcc/HLX/4yvvvd7xZN9WmkbOLJlc3znEjTH/n+KD3Bjr8ojVRK89793//9XzGiKd2RA81LGlyVwlrqg5tGzacRjQsXLiz6YaY+uWmEdMuWLYvRkGn06yOPPFIMmEjS+0kKf6nvrlGPzeOcSN04fH+UnmDHX/SDH/wgdt1112JS0TTx5L/+6786atAM/fu//3uceOKJxUjoNHo+TU2RpkpKfaq6du1ajID9j//4jzjhhBPiF7/4Rfzwhz+M1q1b19bopD54v/rVr4rt5H9OpP5yvj9Kz5MnAAAyocYOACATgh0AQCYEOwCATAh2AACZEOwAADIh2AEAZEKwAwDIhGAHAJAJwQ5gO6VHZJ1++umOF1CxBDsAgEwIdgAAmRDsAD7BkiVLigfX9+vXL7797W/H6tWra9+7884747jjjosDDzwwDj/88Pjxj38cmzdvjpUrV8aXv/zl+MMf/lC775///Oc44IAD4vXXX3esgUYl2AHUY9OmTXHmmWfGF77whbjnnnvi2GOPjTvuuKN479lnn42f/OQnMW7cuHjkkUeKUHfXXXfF3Llzo2vXrnHooYfGo48+Wvt3pZ/79OkTPXv2dKyBRiXYAdRj3rx5sWbNmrj44otjv/32i9NOOy2OPvro4r2OHTvGZZddFsccc0z06NGjqLlLNXKLFy8u3v/7v//7IvDVePjhh4ttAI1NsAP4hGbYffbZpwhxNQ466KBimZpfU3PrNddcE+ecc05Rm7do0aKorq4u3k9Bb/ny5fHSSy/FqlWrYsGCBXH88cc7zkCjE+wAPsGWLVvqrLdp06ZY/uY3v4nhw4cXoW3gwIFFwOvfv3/tfrvvvnscccQRRRPsY489VvTR23vvvR1noNG1bvxfAdD09O7dO/74xz/G2rVro6qqqtiWauBqBk78wz/8Q1x00UXF+kcffRRvvPFGfOUrX6n98yeccELceOONRaDTDAuUiho7gHoceeSRxUCICy64IF599dViAMVDDz1UvNe5c+f4/e9/H6+88krRr+68886Ld955pxhwUSP1x0vBMA20SE2zAKUg2AHUIzW7Tp8+Pd5777046aST4rbbbisGUCRjxoyJPfbYI0455ZQ444wzol27dsW0KDU1ekmnTp1i0KBBcfDBBxf7ApRCiy3bdiIBoEGceuqpcfLJJxfNtgCloI8dQAN7+umni5GwqQlXMyxQSoIdQAO77777ismKL7nkkthll10cX6BkNMUCAGTC4AkAgEwIdgAAmRDsAAAyIdgBAGRCsAMAyIRgBwCQCcEOACATgh0AQCYEOwCAyMP/B5lq56QYvLFEAAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 13
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Reorder group\n",
    "The groups can also be reordered. Keep in mind that if this is done, the annotations will also be moved modified accordingly. Let us order the group by the days of the week. "
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "4e9a3073e75ae8b0"
  },
  {
   "cell_type": "code",
   "source": [
    "group_order = ['Sun', 'Thur', 'Fri', 'Sat']\n",
    "\n",
    "tips_plot.boxplot(test='mannu', value_col='tip', group_col='day', group_order=group_order)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2026-05-21T04:13:24.138482Z",
     "start_time": "2026-05-21T04:13:24.018373Z"
    }
   },
   "id": "989be5a3da137934",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<statannotations.Annotator.Annotator at 0x10f31b2e0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 14
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## Moving Annotations Out of the Plot\n",
    "\n",
    "Depending on the dataset and the type of plot used, the annotations may interfere with the plot. The annotations can be moved \"away\" from the plot by passing the **loc** parameter. However, using this parameter will itnerfere with the plot title. "
   ],
   "id": "afd91b3317c90b49"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-05-21T04:13:24.294318Z",
     "start_time": "2026-05-21T04:13:24.161220Z"
    }
   },
   "cell_type": "code",
   "source": [
    "tips_plot.violinplot(test='mannu', value_col='tip', group_col='day', group_order=group_order, palette=palette,\n",
    "                     loc=\"outside\")"
   ],
   "id": "34f6c92648ce4212",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<statannotations.Annotator.Annotator at 0x10f409480>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 15
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "This can also be useful if the figure is rotated. Rotation of the figure is done using the **orient** parameter. ",
   "id": "79087e889e9dbf1a"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-05-21T04:13:25.755846Z",
     "start_time": "2026-05-21T04:13:24.298125Z"
    }
   },
   "cell_type": "code",
   "source": [
    "tips_plot.violinplot(test='mannu', value_col='tip', group_col='day', group_order=group_order, palette=palette,\n",
    "                     loc=\"outside\", orient='h')"
   ],
   "id": "3e0d710c8b327c99",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<statannotations.Annotator.Annotator at 0x10f4bbc10>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 16
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Annotations with subgroups\n",
    "When including subgroups will modify the hues of box. This can be more complicated as there are more elements on the plot. It will also reduce the readability of the plot. Luckily if the group_col and subgroup_col is set to 'day' and 'smoker', respectively, only three groups show significance. Only those groups will be annotated. \n",
    "\n",
    "Passing a color palette will change the hues of the groups. In this case, the plot will be color coded to the smoker group.  "
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "80bf4c8aa37348d5"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-05-21T04:13:26.766977Z",
     "start_time": "2026-05-21T04:13:25.804435Z"
    }
   },
   "cell_type": "code",
   "source": [
    "title = 'Basic Plot with Subgroup Hues!'\n",
    "palette = [\"#E69F00\", \"#56B4E9\"]\n",
    "\n",
    "tips_plot.boxplot(test='mannu', value_col='total_bill', group_col='day', subgroup_col='smoker', palette=palette)\n",
    "\n",
    "plt.title(title)"
   ],
   "id": "3857b71ef27c9a61",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Basic Plot with Subgroup Hues!')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 17
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "That looks... interesting. For the given dataset, there are a lot of Non-Significant groups. Their presence has cluttered the graph and reduced its readability. The n.s. labels can be quickly removed using the \"hide_ns\" boolean parameter. ",
   "id": "b58569e3f889e3d6"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-05-21T04:13:27.002520Z",
     "start_time": "2026-05-21T04:13:26.781921Z"
    }
   },
   "cell_type": "code",
   "source": [
    "title = 'Plot with no n.s. groups!'\n",
    "\n",
    "tips_plot.boxplot(test='mannu', value_col='total_bill', group_col='day', subgroup_col='smoker',\n",
    "                                palette=palette, hide_ns=True)\n",
    "\n",
    "plt.title(title)"
   ],
   "id": "32f638a44a90a5d6",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Plot with no n.s. groups!')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 18
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Pairs to annotate on teh plot can also be selected manually. Here only the groups showing significant differences are annotated. ",
   "id": "f6e0ab36f3952531"
  },
  {
   "cell_type": "code",
   "source": [
    "# Pairs to annotate\n",
    "pairs = [\n",
    "    (('Sun', 'No'), ('Thur', 'No')),\n",
    "    (('Sat', 'Yes'), ('Thur', 'No')),\n",
    "    (('Thur', 'No'), ('Sun', 'Yes')),\n",
    "    (('Sun', 'Yes'), ('Fri', 'Yes'))\n",
    "]\n",
    "\n",
    "title = 'Plot with Subgroup Changes the Hues!'\n",
    "\n",
    "tips_plot.boxplot(test='mannu', value_col='total_bill', group_col='day', subgroup_col='smoker', pairs=pairs,\n",
    "                  palette=palette, return_df=True)\n",
    "\n",
    "plt.title(title)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2026-05-21T04:13:27.239103Z",
     "start_time": "2026-05-21T04:13:27.014697Z"
    }
   },
   "id": "e77efcf159edda08",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Plot with Subgroup Changes the Hues!')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 19
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Finally, the groups on the X-axis can be organized in a specific manner. Here the data will be organized into the traditional Sun-Sat order. ",
   "id": "f1ef2327e9b6c374"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-05-21T04:13:27.479548Z",
     "start_time": "2026-05-21T04:13:27.249640Z"
    }
   },
   "cell_type": "code",
   "source": [
    "title = 'Plot with Subgroup Changes the Hues!'\n",
    "\n",
    "# Group order\n",
    "order = ['Sun', 'Thur', 'Fri', 'Sat']\n",
    "\n",
    "tips_plot.boxplot(test='mannu', value_col='total_bill', group_col='day', subgroup_col='smoker', pairs=pairs,\n",
    "                  palette=palette, group_order=order)\n",
    "\n",
    "plt.title(title)"
   ],
   "id": "33e04988084af3c4",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Plot with Subgroup Changes the Hues!')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 20
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Figure Legend Placement\n",
    "With the use of the subgroup, the plot will handle this input as the hue. As a result, a figure legend will be generated. Depending on the size of the data or parameter input, the legend may be located in a position obscuring the plot. This can be modified using plt.legend() as follows: "
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "20acc0ae6ce8779b"
  },
  {
   "cell_type": "code",
   "source": [
    "tips_plot.boxplot(test='mannu', value_col='total_bill', group_col='day', subgroup_col='smoker', pairs=pairs,\n",
    "                  palette=palette, group_order=order)\n",
    "\n",
    "plt.legend(loc='upper right', bbox_to_anchor=(1.22, 1))\n",
    "plt.title(title)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2026-05-21T04:13:27.812895Z",
     "start_time": "2026-05-21T04:13:27.493659Z"
    }
   },
   "id": "c654fc95d5835",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Plot with Subgroup Changes the Hues!')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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Bmm5bBy2a/jEc4bQnEsJ5Xai8R5fpda7Xhg7kdIAS+n+u/08dVOu14g0q1bcxev68g1cd+OngRoOFi6qnB4DCyuSVZsJZAIgwlVEoxOnARKHUo7Cmml/18IcbTI+HesHVUxqkMOWXfQsAKDl61gH4gspI1COuUhP13KvMQLXZWkBJZTmESfYtACQjetYB+IZKTjRVoXp6VeKhGn3V/qqEo7gSi0gLYs+6X/YtAKDkCOsAAACATzF1IwAAAOBThHUAAADApwjrAAAAgE/FbTYYbxBXYZqXdtWqVbZy5Uq38IZmg9CCJ1puWyvXhUur+2k1PS0f7y2HDgAAkGg0y7YWnNP6EZFaqRqJI24DTLXAyt69ewvMA3zddde5BWG0nHWXLl3cojFabEKLi/z97393y0sfa2EUj1aIO9YCMgAAAInigw8+sDp16sS7GUiWnnWteqcfj1bj03GDVtR75ZVX3LLSWrFOveJanvzDDz90KylqafdwqEfde2FrqW0AAIBEpJWx1QHpZRskF18sirR79263GIpW0tMy58uWLXNLNHvlKzpt06aNLV26NOyw7v2tgjphHQAAJDrKepOTL8K6ylxUh9WtWzd3fseOHa5OPVSNGjVszZo1xd5GTk6O+wk9CgUAAAASWdzDukpfnnvuObvhhhvyt+3fv9/1sIfS+dAwXpjKaKZMmRLVtgIAAABJFdb//e9/2/bt2+3SSy/N36Z69cLBXOdDa9wL05LZGRkZR9R3AQAAAIkq7mH9o48+snPOOcfS09Pzt9WuXdt27txZ4Ho6r1KZ4qjnvXBvPAAAAOLr8OHDdujQIZ6GEMqs4U7DGfewvnz5cjd4NFTr1q3dgFOVyGgwhU6XLFligwYNils7AQAAED7lN02lrYlEUJCCepMmTcLqaI57WNeg0SuuuKLANg00ffDBB23ChAnWu3dvmzdvnqtj7969e9zaCQAAgPB5QV2VEVonh9ls/rtw55YtW2zr1q3WsGHDY+6XuId1lbeceOKJBbZpqkUNGNUKps8++6ydeuqpNn369LAXRAIAAEB8S1+8oK4Z/VBQzZo1XWDXoqDlypUz35fBFKVVq1b24osvxrw9AAAAOD5ejTodrUXzyl90UHOssB5eZTsAAABQQpS+HP9+IawDAAAAPhX3MhgAAAAkh6ysLNuzZ0/M7i89Pf2oU38XpnGSl112mZvoJNSCBQvc4pvvvvuuxRphHQAQMV27drXZs2e79TIAoHBQ798vww7mxG7O9bTUcjZzVmaJAvtrr71mvXr1sg4dOpgfENYBAMftlVdesVWrVrmBUgsXLrTNmzfbyJEj2bMA8qlHXUG9V5stVqvKwajvmay9afb8knrufksS1uvXr2/33HOPvfzyy75YcJOwDgA4bprBa8OGDW7tjEWLFlnfvn3ZqwCKpKBer2r0w3ppDR8+3MaOHWszZ860G2+8sdg55CdOnGifffaZGyx6+eWX22233RaVcM8AUwDAcWvUqJGtWLHChg4d6k51HgASUe3atW3YsGE2depU27Rp0xGX5+Tk2HXXXecW7JwzZ449/PDD9v7779v9998flfYQ1gEAx009Sz179rTBgwfbXXfdZVWqVGGvAkhYffv2dZ0OEyZMOOKyjz76yLZv324PPPCAG5Cq2va7777b5s6daz/++GPE20JYBwBERLdu3Vxo79Spk1WoUIG9CiBhlS1b1pXCqMdc43BCrVu3zho3buxmmvG0adPGrUa6cePGiLeFsA4AAAAUogB+1VVXud51lbx40tLSCl/VrUQaehpJhHUAAACgCLfeeqvt27fPDTb1NGnSxA2o3717d/62pUuX2gknnGANGza0SGM2GAAAAMSMplRMlPupVq2aC+wai6MpHeWCCy6wBg0auNlfbrnlFtu1a5eNHz/eLaZ04oknWqQR1gEAABB1qvHWIkWa+zxW0lLLFagtLw0tkPTCCy+4RZ28evbHH3/cBfTf/va3VqlSJTd144gRIywaCOsAAACIOi1MpNVEtUhRrKSnp5doQaTVq1cfsU0D5+fNm1dgm3rWp0+fbrFAWAcAAEBMKDiXJDyDsA4EQl5enh04cCDezQAAXyhfvrzrDQWCgJ51IABBvX///rZ8+fJ4NwUAfKF169Y2Y8YMAjsCgakbgQSnHnWCOgD817Jly/i2EYFBzzoQIG+99RYrRwJIWlq4pkuXLvFuBhBRhHUgQLTEO8u8AwAQHJTBAAAAAD5FWAcAAAB8irAOAAAA+BQ160AA5hPWNGXe7wCQrHg/9L+srCzfrmA6YsQIW7x4sb355ptHjP/KyMhwA5jnzp0b8ylBCetAgtObhuYT9n4HgGTF+6H/g3q//v0t5+DBmN1nalqazZo5M6zAPnLkSOvevbtNnTrVbr755gIzrX3xxRe2YMGCuHzOEtaBACCkAwDvh36nHnUF9Vqd+1pqtTpRv7+cXdssa+Ecd7/hhPXatWvb0KFD7c9//rP16tXLGjRo4ObrnzRpkutZP+WUUyweCOsAAACIGQX1tJoNfLnH+/bt63rQH3jgAXv00UfdN9cpKSk2ePBg27p1q40bN84+++wzq1GjhvXs2dNuvPFGK1u2rB06dMhd9vbbb1tOTo61b9/endcBwPFigCkAAABgZieccILdfffdrvRl4cKFNnPmTBszZoylpaXZkCFDXEh/8cUXbeLEifbqq6+6khl55plnXKnMrFmz7Pnnn7cff/zR7rvvvojsU3rWAQAAgP9z7rnn2uWXX2433XSTde3a1S666CLXm75lyxZ77rnnXE/7z372M1fjfscdd7he982bN7tAX79+fatataorndm9e7dFAmEdAAAACDFo0CB75ZVXXBCXdevWufDdtm3b/Ovk5ua6mvZdu3bZ1Vdfba+//rpdeOGF1q5dO+vcubMrk4kEwjoAAAAQQr3koac//fST601//PHHrbAqVapYtWrV7N1337X333/f/Tz00EP22muvufKY450EgrAOAAAAHEWTJk1cGUz16tVdOJdPPvnEDUa9//777aWXXrLU1FTr0aOHm/5x6dKlrrf9P//5j5100kl2PAjrAAAAiBlNqZho93PhhRe6evQ//vGPbg72vXv32ujRo+388893s8HovAabqof95JNPdoNP69Sp484fL8I6AAAAYrKaqBYp0tznsZKalubu93gpkD/xxBM2fvx4++1vf2sVK1a0bt26uUGmcs0119i2bdtcmNe87i1btnTX198drzJ5eXl5FkDZ2dluEICWja1cuXK8mwOglDQSf/bs2RGZqxYAElEiZhoNvFy/fr0rHylfvnyBVUwVZmMlPT09rAWR/LJ/ikLPOgBf0ij8VatWWbly5dxct5oWy+vBAAAkJgVnP4ZnP2NRJAC+1KpVK9fbsGbNGlu0aJF16tQp3k0CACDmCOsAfKlRo0a2YsUKGzp0qDvVeQAAkg1lMAB8SfPSakEJ1ay3aNEif6osAACSCWEdgG9ppL1QAgMAiSmg85jEdL9QBgMAAICI0uQAsm/fPvZsEXJyctxpOFM70rMOAACAiFIIrVq1qpuqUTQvucobYZabm2s7duxw++SEE44dxQnrAAAAiDit4CleYMd/paSkWMOGDcM6gCGsAwAAIOIUROvWrevmVT906BB7OERqaqoL7OEgrAMAACCqJTHh1GajaAwwBQAAAHzqhHiPhJ04caK99tprbtRwr1697Oabb3Zfm6xcudLGjBlj33zzjTVr1szGjRtnLVu2jGdzEcXpiw4cOMD+BVAiWuGWAWsAgi6uYf3ee+91y4jPnDnTfvzxRxfU69WrZ1dccYUNGDDALr/8cps0aZLNnTvXBg4caG+//bYbOYtgBfX+/fvb8uXL490UAAmmdevWNmPGDAI7gECLW1jfvXu3vfDCC5aZmWmtWrVy2/r162fLli1z09ikpaXZbbfd5t6ER40aZR9++KG9+eabbkVDBId61AnqAEpDnxd6D6lQoQI7EEBgxS2sL1682CpXrmzt2rXL36bedBk9erS1bds2v7dEp23atLGlS5cS1gPsrbfe4kMXwDHt37/funTpwp4CkBTiFtY3bdpk9evXt5deesmmTp3qpvRRr/mNN97oJopXnXqoGjVq2Jo1a45a/+6tBiXZ2dlRbT8iT71j9JABAAD4IKxr+dnvvvvO5s2b5waZKqDffffdLqyp10TzT4bS+dAwXti0adNsypQpMWg5AAAAEPCwrrp09X4/+OCDroddtmzZ4gaTNmrU6IhgrvMa+V8cDUDNyMjIP6/b7tixYxQfAQAAABDQsF6zZk03iNQL6tKkSRPbunWrq2PfuXNngevrvFbAKo563gv3xgMAAACJLCWeU24dPHjQ1q9fn7/t22+/deFdl3355ZduWj/R6ZIlS9x2BIu+LdHzqp+jfXMCALxvAEhGZfK8RBwHKl3Zs2ePjR071tWsa6pGDTDVQNNLLrnELr30Uuvdu7era9e0jZotJNx51lUGoxllvFln4F/eS5DFTQDwvgEciUyT3OLWsy5/+tOfrGHDhtanTx8bOXKkXXPNNda3b18XrjVgVEFbwV1z6U6fPp0FkQJKIZ2gDoD3DQDwWc96NHEUCgAAgoBMk9zi2rMOAAAAoHiEdQAAAMCnCOsAAACATxHWAQAAAJ8irAMAAAA+RVgHAAAAfIqwDgAAAPgUYR0AAADwKcI6ElbXrl1t+/bt8W4GAABA1JwQvZsGouOVV16xVatWWbly5WzhwoW2efNmGzlyJLsbAAAEDj3rSDitWrWy8uXL25o1a2zRokXWqVOneDcJAAAgKgjrSDiNGjWyFStW2NChQ92pzgMAAAQRZTBIOGXKlLGePXu6mvUWLVpYlSpV4t0kAACAqCCsIyF169bNnVICAwAAgowyGAAAAMCnCOsAAACATxHWAQAAAJ8irAMAAAA+RVgHAAAAfIqwDgAAAPgUUzcmkLy8PDtw4EC8mwGf0qqumoMeAAAEB2E9gYJ6//79bfny5fFuCnyqdevWNmPGDAI7AAABQhlMglCPOkEdR7Ns2TK+eQEAIGDoWU9Ab731llWoUCHezYBP7N+/37p06RLvZgAAgCggrCcgBXXCOgAAQPBRBgMAAAD4FGEdAAAA8CnCOgAAAOBT1Kwn0BzamprP+x3gtQEAQPAR1hOEFrvRHNre7wCvDQAAgo+wnkAI6eC1AQBAcqFmHQAAAPApwjoAAADgU4R1AAAAwKcI6wAAAIBPEdYBAAAAnyKsAwAAAD5FWAcAAAB8irAOAAAA+BRhHShG165dbfv27fnnp02bZlOnTmV/AQCAmGEFU6CQV155xVatWmXlypWzhQsX2nfffWebN2+26tWrW0pKig0bNszGjRtn1apVY98BAICoomcdKKRVq1ZWvnx5W7NmjS1atMguueQS6969u3366af24YcfWufOnS01NZX9BgA+c/jwYVu2bJm999577lTngURHzzpQSKNGjWzFihU2dOhQW7BggTu/Y8cOd5qXl2eHDh2ySpUqsd8AwEc+/vhjV64YWr5Yu3ZtGzhwoF144YVxbRtwPAjrQCFlypSxnj17upr1Fi1aWJUqVaxZs2Z21113uTKY/fv3s88AwGdBffz48da+fXu78847rXHjxrZhwwabO3eu2z569GgCOxJWmTx1FQZQdna2tW3b1hYvXmyVK1eOd3MAAEAUqNTl+uuvtyZNmtjYsWNdp4onNzfXbVNwz8zMtLJlyybkc0CmSW7UrAMAgISlskWVvvTp06dAUBed7927t23bts1dD0hEcQ3rb7/9tp166qkFfjTThqxcudJ+85vfWOvWre2qq67inwwAABzhhx9+cKcqfSmKt927HpBo4hrW165da7/4xS9crZn3c++999q+fftswIABds4557gBfmeffbYbIKLtAAAAHk2rKyp1KYq33bsekGjiGtbXrVtnp5xyitWsWTP/58QTT7Q33njD0tLS7LbbbrOmTZvaqFGj3Owbb775ZjybCwAAfKZly5Zu1hcNJlWNeiidnzdvntWpU8ddD0hEcQ/rRX1tpblRNThUs3KITtu0aWNLly6NQysBAIBfadCovn3XuhgaTKoyWn0Tr1Od13Z9W5+og0uBuE3dqElo1q9fnz8vqkZzd+vWzdWsa05rTZUXqkaNGm6RmuLk5OS4n9CR0wAAIPg0j7qmZ1SeGD58eP529agzbSMSXdzC+pYtW9x81VoJ8uGHH3bLuate/cCBA/nbQ+l8aBgvTP+gU6ZMiUHLEY0DNz3vOD5addX7NgoAkjGwd+jQwU1IocGkqlFX6Qs96kh0cQvr9evXd19Npaenu4Bx+umnu9qyP/7xj9auXbsjgrnOK4wUR1+BZWRkFOhZ79ixY1QfAyIT1Pv372/Lly9ndx4nzZw0Y8YMAjuApKVgrvdCIEjiuoJp1apVC5zXYNKDBw+6gaY7d+4scJnO16pVq9jbUs974d54+J961AnqkaGxHtqfFSpUiNAtAgCApA3rH330kd166632/vvv54eLr7/+2gV4DS598sknXa+ret11umTJEhs0aFC8mosYeOuttwiapaCysS5dukT+CQEAAMkb1jV3uqZnvOuuu2zw4MG2adMmu//+++2GG25wA00ffPBBmzBhglt5TNMuKZB07949Xs1FDOigjV5hAIg+TepAbTeQGOIW1itXrmwzZ860++67z61QqnnUFcwV1tWbrgGjY8aMsWeffdatbDp9+nSrWLGiJSveWAEAkeDNwrZ9+/b8bZqnXGO/NEgTgL/EtWa9efPmlpmZWeRlrVq1shdffDHmbfIj3lgBAJH6PLnnnnuOGOO1a9cut/3uu+8msAM+E9dFkRDeG+v48eOtSZMm9sgjj9jLL7/sTnVe23U5AADhfEP76KOP5peihn6m6Lzocl0PgH8Q1n1Mb5j6qrJ9+/ZuFTZNb6mabp3qvLarPCiR31g1Haem2dLP0abmBPsQwPHRzFu7d++2M844w8aNG1fgM0XntV2XM0MX4C+EdR/T4B/VFPbp08dSUgo+VTqvGv9t27a56yUqjU/Q3ODMD84+BBD96V3luuuuK/IzpW/fvgWuB8Af4lqzjqPTCmzSuHHjIi/3tnvXS1Ssusk+BAAARaNn3ce0VLJs2LChyMu97d71AAAojrey5+zZs92K4aF0fs6cOQWuB8AfCOs+1rJlSzed1ty5c4t8Y9X883Xq1HHXAwDgaDTLWnp6uiud1NTIK1eutH379rlTnf/qq6/cwoS6HgD/oAzGx8qWLevmvdWsLxpQqhp1lb6oR11BfdGiRTZ69Gh3PQAAjvWZctNNN7kpGpcuXeo+QzxapFCGDRvGZwrgM2Xy8vLyLICys7Otbdu2tnjxYrcAU9DmWVeP+oABA5gPFwBQ4s+UqVOnWlZWVv42FkXytyBlGpQcPesJQCvKdejQgaWhAQB8pgBJhrCeQF9fMugHAMBnCpBcGGAKAAAA+BRhHQAAAPApwjoAAADgU4R1AAAAwKcI6wAAAIBPEdYBAAAAnyKsAwAAAD5FWAcAAAB8ikWREFFbt251yyLHgpZcrlu3bkzuCwAAIB4I64iYPXv2WEZGhuXm5sZkr6akpNj8+fMtPT09JvcHAAAQa4R1RIxCc2ZmZol71jdu3GiTJ0+2kSNHWsOGDUvUs05QBwAAQUZYR0QdT1mKgnrz5s0j2h4AAIBExgBTAAAAwKcI6wAAAIBPUQYDAAB8i1nGkOwI6wAAwJeYZQwgrAMAgADNMlbaGcaEWcbgR/SsA/Clw4cP24oVK+yHH36w6tWrW8uWLa1s2bLxbhaABJlljBnGEBSEdQC+8/HHH9u0adNs+/bt+dtq165tAwcOtAsvvDCubQMAIJaYDQaA74L6+PHjrUmTJvbII4/Yyy+/7E51Xtt1OQAAyYKwDsBXpS/qUW/fvr2NHTvWTj/9dKtQoYI71Xltnz59urseAADJgLAOwDdUo67Slz59+lhKSsG3J53v3bu3bdu2zV0PAIBkQFgH4BsaTCqNGzcu8nJvu3c9AACCjrAOwDc064ts2LChyMu97d71AAAIOsI6AN/Q9Iya9WXu3LmWm5tb4DKdnzdvntWpU8ddDwCAZEBYB+Abmkdd0zMuWrTIDShduXKl7du3z53qvLYPGDCA+dYBAEmDedYB+IrmUR89erSbFWb48OH529Wjru3Msw4ASCaEdQC+o0DeoUMHVjAFACQ9wjqQ4DTnuKYy1AwpGnipem6VkyQ6PYbWrVvHuxkAAMQVYR1IYFrNU+UimpvcowGaqvumXAQAgMTHAFMggYP6+PHjrUmTJvbII4/Yyy+/7E51Xtt1OQAASGyEdSBBS1/Uo96+fXs3S8rpp59uFSpUcKc6r+3Tp0931wMAAImLsA4kINWoq/SlT58+lpJS8N9Y53v37m3btm1z1wMAAImLsA4kIA0mlcaNGxd5ubfdux4AAEhMhHUgAWnWF9mwYUORl3vbvesBQaLyrmXLltl7773nTin3AhBkvpkNRqsSKlhMmjTJndeKhWPGjLFvvvnGmjVrZuPGjWOJceD/aHpGzfoyd+5cV6MeWgqTm5tr8+bNc4sI6XpAkDADEoBk44ue9ddff90++OCD/PNaXlzh/ZxzzrEFCxbY2Wef7aai03YA/38Ocv1PLFq0yIV1Hdzq/0OnOq/t+h8KwnzrgIcZkAAko7iH9d27d9v9999vZ555Zv62N954w9LS0uy2226zpk2b2qhRo6xSpUr25ptvxrWtgJ9oHvXRo0fb+vXrbfjw4farX/3KnaoERtuZZx1BwgxIAJJV3MtgJk+ebFdeeaVlZWXlb1MNYtu2ba1MmTLuvE7btGljS5cutZ49e8axtYC/KJB36NAhkCuYAkXNgHTnnXcWOwOSDlZ1PVa+BRAkcQ3rn332mf3rX/+yV1991X1179mxY4erUw9Vo0YNW7NmTbG3lZOT43482dnZUWo14C8K5oQTBB0zIEX+mwoO8oHEELewfvDgQTeA9O6777by5csXuGz//v2WmppaYJvOh4bxwrRAzJQpU6LWXgCAP2ZA0uJfhTEDUvgYpAsklrjVrCtY6+v6iy666IjLVK9eOJjrfOFQH0qD7RYvXpz/EzpgFQAQnBmQNONRKGZACh+DdIGA9qx/8cUXYd/gueeeG/YMMDt37nQzvYgXzv/xj3/YZZdd5i4LpfO1atUq9vbU8164Nx4AEKwZkMaPH+/KJlWjrsW/1KOuqUo1A5IGVjNeI/xBul7tv76p0Hn9TJ8+3Y2DYT8CCRbW+/btG9aNaSDo119/HdZ158yZYz/99FP++T/96U/u9NZbb3UHB08++aTl5eW529TpkiVLbNCgQWHdNgAguDMgKXBqMKlHawowA9KxMUgXCHBYX7VqVcTvuH79+gXOa2pGadSokRtM+uCDD9qECRNc74l6TVTH3r1794i3AwCQOJgBqfQYpAsEOKxv2bIl7BusV6+eHa/KlSu7nhMNQH322Wft1FNPdV/NVaxY8bhvGwCQ2JgBqXQYpAsEOKx36tQpvxylKN5lJSmDKWzSpEkFzrdq1cpefPHFUt0WAAAofpBuaM26MEgXSPCw/s4770S/JQAAIGoYpAsEOKwXri8HAACJh0G6QEDDuqZ10tysGvh52mmnuXKX4pS2DAYAAEQfg3SBAIb1v/71r5aenu5+nz17drTbBAAAoohBukDAwnq7du2O+H3fvn22ceNGK1eunJ188slu1VGEb+vWrZadnR2TXabZderWrRuT+wIAAECMw3qovXv32n333Wevvvpq/qJGWjlU86FrQSNWET22PXv2WEZGxhFLZkeLRvzPnz8//9sRAAAABDSsa5W49evXW2ZmprVo0cJN2bhs2TK3gJEWLtJS0Dg6hWbtv5L2rOubjMmTJ9vIkSOtYcOGJepZJ6gDAAAkQVh///337W9/+5sL6p4LLrjA9bb369ePsB6m4ylLUVBv3rx5qf8eAAAAieG/KyKESQsqeEsWh1KvetWqVSPVLgAAACDphdWz/sUXX+T/fvnll7syjCFDhtiZZ57pRpSvXr3aHn74Ybv++uuTfocCAAAAMQ3rffv2PWLbuHHjjtimemoCOwAAABDDsL5q1aoS3/DixYtdzzuzwwAAAAAxqlkP1+9//3vbvn17tG4eAACU0uHDh91Mbu+995471XkAAZkNJlya0hEAAPjLxx9/bNOmTSvQoabJIwYOHGgXXnhhXNsGIIY96wAAwH9BXeuhNGnSxB555BF7+eWX3anOa7suB+AvhHUAAJKASl3Uo96+fXsbO3asnX766VahQgV3qvPaPn36dEpiAJ8hrAMAkARWrFjhSl/69OljKSkFP/51vnfv3rZt2zZ3PQD+QVgHACAJeAsaNm7cuMjLve1FLXwIIH4I60CCY1YHAOGoXr26O92wYUORl3vbvesBCPhsMBqsUq5cuWjdPABmdQBQAi1btnSzvsydO9fVqIeWwuTm5tq8efOsTp067noAEiysv/TSS2Hf4K9+9St3umDBgtK3CkDYszpoUNidd97pvsJWz5g+iLV99OjRTMMGIF/ZsmXd9Ix6f1BYV426976hoL5o0SL3vqHrAUiwsP7oo4+GdWNlypTJD+sAYjOrgz5cV65caZ9//rn7+lrn9WGsWR06dOjABy+AfJpHXe8Rev8YPnx4/nb1qHOADyRwWH/33Xej3xIAJZ7V4dJLL7V+/fodsbhJjx49XHjX9Vq3bs2eBVAgsOtAXu8PGkyqg3yVvtCjDgSoZl3/3OvXr3c1bt5qpTk5Oa53b8CAAZFuI4Ai/gclMzOzyDKYp556qsD1ACCUgjkH8kBAw/qzzz5r99xzj/3000+u7EVBXfR7q1atCOtADFStWtWdnnHGGQUGinmLm9xyyy321Vdf5V8PAAAkydSNU6dOtUGDBtny5cutRo0a9t5779lrr73mQsIll1wSnVYCKJJ3sAwAAIKpxGE9KyvLDSJNTU11vXpLly61Zs2aua/hn3vuuei0EkABu3fvdqfqPVdPukrQ9u3b5051XttDrwcAAJIkrGsgilcH+7Of/cy+/vrr/EFtoYPcAESPt2iJBpdq/IhmddBBtE5Vt56RkVHgegAAIElq1rt3724jR460CRMm2EUXXWS33Xab62FXOUzDhg2j00oARS5uop70WbNmuVNvVocWLVq4qRtZ3AQAgCTsWb/11lvddHG7du2y888/36666iobM2aMK4cZN25cdFoJoMjFTbSIiYK5VgvWrDA61Xlt18xMTMUGAECS9awrlCskKBTIzTff7H40deOHH34YjTYCKAKLmwAAEHwlDuvXXnutffLJJ0fUwq5du9ZGjBjhZokBEBtBXtxEq7QG8XEBABDxsP63v/3Nza3uzat+wQUXFHk9lcUAiK0gLm7y8ccfu+XQC6/Mqm/1dIACAECyCCus/+53v7PmzZu7FUuvu+46e/TRRy09PT3/coX4ChUq2CmnnBLNtgJIAgrqqrsvamVWbR89ejSBHQCQNMIugzn33HPd6TvvvGP16tVzAT07O9t9VR0a3AGgtPR+oh51BfWiVmbVz/Tp013pDyUxAIBkUOLZYOrXr2+zZ8920zYqwJ933nmuLGbKlCnRaSGApKEadZW+9OnTJz+oe3S+d+/etm3bNnc9AACSQYkHmD722GP29NNP20033WRnn322K41ZsmSJC+ta1VTTxSEYtFrtnj17on4/GzduLHAabfomqFatWjG5L5SMt+CaSl+K4m33rgcAQNCVOKw/++yzbkGkTp065W/TV9Qa/KXthPXgBPV+/ftbzsGDMbvPyZMnx+R+UtPSbNbMmQR2H/JmmVKNut5XCtP20OsBABB0JQ7rqlMvqterSZMm9HYFiHrUFdRrde5rqdXqWFDk7NpmWQvnuMdH77p/V2bVYNLQmnXRt3jz5s1jZVYAQFIpcVg/66yz3PLmmsrR+yDVoLCZM2famWeeGY02Io4U1NNqNuA5QExXZtWsLwrrqlH3ZoNRUNfKrJoNhsGlwPFhHQMgwGFdU6ldc8019umnn9oZZ5zhtn311VduBdMZM2ZEo40AkggrswLRxToGQMDDugL5ggUL3BSO69ats7S0NDcbzMUXX2yTJk1yc7ADwPEI8sqsQDyxjgEQ0LD+5Zdf2nfffed+f+mll1yPepUqVVxJjEdfUetNAAAiIYgrswLxxDoGQIDDulYn/ctf/mJ5eXnuR73roQO/tEBSxYoV7dZbb41mWwEAwHGuY6By1uLWMRg+fLi7HgfKQIKF9dNOO82VvUjfvn3dnOqsWgoAQOJgHQMgSVYwnTNnDkEdAIAEXsegKKxjAAQkrEeS6uD79+/vVkL9+c9/XmA2mU2bNtn111/v6uJ79OhBPTwAABFax0DrFoRiHQPAv+IW1vXGoNVOq1WrZi+++KKNGzfOnnjiCXv11VddXfzgwYPtpJNOshdeeMGuvPJKGzJkiG3ZsiVezQUAIBDrGGi9Aq1jsHLlStu3b5871Xlt1+cysy4BCT51Y6Ts3LnTLSeuN4jKlSu7hU80VdvixYtdSFfPumaY0cDVpk2b2meffeaC+9ChQ+PVZAAAEhrrGACJJ25hXUu9P/zww+539aQvWbLEvvjiCxszZowtW7bMWrRo4YK6p23btrZ06dJ4NRcAgEBgHQMgscQtrIfq1KmTK3H5xS9+YV27drX77rvPhflQNWrUsG3bthV7G1pBVT+e7OzsqLYZAIBExToGQOLwRVjXqqcqi1FJzMSJE23//v2Wmppa4Do6HxrGC5s2bZqbUhJIdFu3bo3ZwaZK0OrWrRuT+wIAAAka1s8880x3evDgQbew0lVXXeUCeygF9fLlyxd7Gxo0k5GRkX9eYadjx45RbDUQeXv27HGv48IzNUSLFkKZP38+07ECAOBTcR1gqhr0zp07529r1qyZHTp0yGrWrGnffvvtEdcvXBpTuOe9cG88kGi02FhmZmaJe9Y3btxokydPtpEjR1rDhg1L1LPOAmcAAPhX3ML65s2b3XSMH3zwgZv3VbTEsRZt0GDSWbNm2YEDB/J70zVLjLYDQXc8ZSkK6s2bN49oewAAQBLOs67SlzPOOMPuvPNOW7t2rQvtDzzwgA0aNMjatWvnAssdd9xha9assenTp9vy5cutV69e8WouAAAAkDw96xqJ/vjjj9v48ePt6quvtgoVKljfvn3t2muvtTJlyrjLRo0aZT179rRGjRrZY489ZvXq1YtXcwEAUcTAavYhAB8OMFX5S3EzuCigP/300zFvEwAgthhYzT4E4PPZYAAAyas0A6tLO6g6qAOr2YdAcBHWAcQEZQ6IxsBqBlWzD4GgI6wDiDrKHAAAKB3COoCoY/54AABKh7AOICaYPx4AgASaZx0AAADA0RHWAQAAAJ8irAMAAAA+RVgHAAAAfIqwDgAAAPgUYR0AAADwKaZuBAAAUZeVleUWSIu2jRs3FjiNxToStWrVisl9ITkR1gEAQNSDer/+/S3n4MGY7enJkyfH5H5S09Js1syZBHZEDWEdAABElXrUFdRrde5rqdXqBGZv5+zaZlkL57jHR+86ooWwDgAAYkJBPa1mA/Y2UAIMMAUAAAB8irAOAAAA+BRhHQAAAPApwjoAAADgU4R1AAAAwKcI6wAAAIBPMXVjBARxVbZYrfwGAACA4hHWIxDU+/fLsIM5hyxoq7IBAAAgvgjrx0k96grqvdpssVpVYreMcrSt3l7Z3llVM97NAAAASGqE9QhRUK9XNThhfcfe1Hg3AQAAIOkxwBQAAADwKcI6AAAA4FOEdQAAAMCnCOsAAACATxHWAQAAAJ8irAMAAAA+RVgHAAAAfIp51nFUObu2B2oPBe3xAACAYCOs46iyFs5mDwEAAMQJYR1HVavztZZarXagetY5AEGi2rp1q2VnZ8fs/ipXrmx169aN2f0BAI5EWMdRKain1WzAXgIiLCsry/bs2RP29RXSb7/9dsvLy4vZc5GSkmITJ050oT1c6enpVqtWrai2CwCSCWEdAOIQ1Pv3y7CDOYd8ve9zc3Nt5MiRJfqbtNRyNnNWJoEdACKEsA4AMaYedQX1Xm22WK0qBwOz/7P2ptnzS+q5x0fvOgBEBmEdAOJEQb1e1eCEdQBA5DHPOgAAAOBThHUAAADApwjrAAAAgE8R1gEAAACfIqwDAAAAPkVYBwAAAHyKsA4AAAD4VFznWd++fbtNmDDBPv/8c0tLS7MePXrYiBEj3O+bNm2y0aNH29KlS61evXp255132oUXXhjP5gIAEJMVbrWwVLRt3LixwGks7gtAAoX1vLw8GzZsmJ144on2zDPPuDcmBfKUlBS77bbbbPDgwXbKKafYCy+8YAsXLrQhQ4bYG2+84YI7AABBDer9+2W4FW5jZfLkyTG7LwAJFNa//fZb12v+ySef2EknneS2KbzrTePiiy92Pevz5s2zihUrWtOmTe2zzz5zwX3o0KHxajJQYkHsIZP09HSWkweiQO8XCuq92mxxK9wGxertle2dVTUtZ9d2C5KgPR74U9zCes2aNW3GjBn5Qd2TnZ1ty5YtsxYtWrig7mnbtq0L90CiCHIPWVpqOZs5K5PADkSJgnq9qsEJ6zv2prrTrIWz490UIOHELayr/OWiiy7KP5+bm2tPP/20nXfeebZjx44jQkCNGjVs27Ztxd5eTk6O+wkN/UA8BbWHLGtvmj2/pJ57fIX/TwHgaGp1vtZSq9UOVM86ByAI9ADTUA888ICtXLnSnn/+eXvqqacsNfX/H4V7dD40jBc2bdo0mzJlSgxaCiR3D5knloPSKO8BgkFBPa1mg3g3A0goJ/glqP/1r3+1P//5z25QqWaD2b17d4HrKKiXL1++2NsYOHCgZWRkFOhZ79ixY1TbDSSjvQfKmpUpE9NBabG6r9S0NJs1cybfGAAAfCPuYX38+PE2d+5cF9i7du3qttWuXdvWrl1b4Ho7d+486geoet4L98YDiLwDh8pqOier1bmvpVarE5hdnLNrm2UtnEN5DwDAV+Ia1lW2ohlfHnroIevWrVv+9tatW9v06dPtwIED+b3pixcvdoNMAfiDgjpfZ0dm0F1QBO3xAEBSh/V169bZ448/bgMGDHAhXINKPe3atbO6devaHXfcYX/4wx/svffes+XLl9vEiRPj1VwAiLjnltRnrwIA/BnW33nnHTt8+LA98cQT7ifU6tWrXZAfNWqU9ezZ0xo1amSPPfYYCyIBCJTftPnealYpfuB8IvascwACAAEJ6+pR109xFNA1lSMABJWCehBnCgIABGiAKQAgWJjWEwAih7AeIUEbWLVrX7l4NwFAgmFaTwCIPMJ6hFCnCSDZMa0nAEQeYT1CgjZQ7JvtlWzhKpaSB1ByTOsJAJFDWI+QoA0UC1pZDwAAQCIirAMA4DNB6zBhHBRQeoR1AAB8hnFQADyEdQAAfIZxUAA8hHUcVc6ubYHaQ0F7PACCiXFQADyEdRSpfLnDZmXKWNbCOYHbQ6lpaZaenh7vZgAAABwTYR1FqlL+sFleno0cOdIaNmwY9dUOJ0+eHJP7EgX1WrWYlhIAAPgfYR1HpfDcvHnzwN0XAABAIiCsAyiVnF3bA7XngvZ4AADBQFgHUCpZC2ez5wAAiDLCOoBSqdX5WkutVjtQPescgAAA/IawDkRZUFciVFBPq9kg3s0BACDQCOtAlLESIQAAKC3COhBlrEQIAABKi7AORBkrEQIAgNJKKfVfAgAAAIgqetYBIE6y9qYFat//sC9Yg6kBwA8I6wAQY+np6ZaWWs6eX1KPfQ8AOCrCeoQErYcsaI8H8JNatWrZzFmZtmfPnqjf18aNG23y5Mk2cuRIa9iwYUzuC8cvaO/BfOsClB5h/TgFuYdMj0uPD0B0Art+YkVBvXnz5jG7P5ROkD9TAJQOYf04BbWHzPvQiGWYAIBkF9TPFL51AUqPsB4B9JABACKFzxQAoZi6EQAAAPApwjoAAADgU4R1AAAAwKcI6wAAAIBPMcAUQKnk7NoWqD0XtMcTTzm7tluQBO3xxFPQ/s+C9njgT4R1ACVSvtxhszJlLGvhnMDtudS0NNYWiICshbMjcTMIEE0FrP8v3jeAkiOsAyiRKuUPm+XlxXRuZtYWSCy1Ol9rqdVqW5B61jkAOf7pKGfNnBm4+eOFNUkQbYR1AL5fEZPVNxOLgnpazQbxbgZ8hvnjgdJhgCkAAADgU4R1AAAAwKcogwGABLF161bLzs4ucf1u6GlJVK5c2erWrVvivwMARA5hHQASgAbmZWRkWG5ubqn+XgPuSiolJcXmz5/PDDkAEEeEdQBIAJpxIjMzs8Q968dDPeu6XwBA/BDWASBBUJICAMmHsA5EWdbetEDt46A9HkRe0FZ1DNrjAZBYCOtAlKh8IC21nD2/pF7g9rEeF+URKIxVKgEg8gjrQBQXAJk5K5MV+5A0WKUSACKPsA5EESv2IdnwmgeAyGJRJAAAAMCnCOsAAACAT/kirOfk5Nhll11mixYtyt+2adMmu/766+2ss86yHj162McffxzXNgIAAABJF9YPHjxoI0aMsDVr1uRvy8vLs8GDB9tJJ51kL7zwgl155ZU2ZMgQ27JlS1zbCgAAACTNANO1a9faLbfc4sJ5qM8//9z1rM+bN88qVqxoTZs2tc8++8wF96FDh8atvQBKb+vWrSVefXPjxo0FTkuy8iYLCAEAgiCuYf2f//yntW/f3m6++WZX7uJZtmyZtWjRwgV1T9u2bW3p0qVxaimA47Fnzx7LyMiw3NzcUv395MmTS3T9lJQUmz9/PnPBAwASXlzD+u9+97sit+/YscNN/xWqRo0atm3btqPWvevHU9IePADRXSwnMzMzZv+X6lln0SYAQBD4cp71/fv3W2pqaoFtOh8axgubNm2aTZkyJQatA1AalKUAABCQsJ6Wlma7d+8usE1BvXz58sX+zcCBA93X7B714HXs2DGq7QQAAACSLqzXrl3bDT4NtXPnziNKYwr3vBfujQcAAAASWdynbixK69at7auvvrIDBw7kb1u8eLHbDgAAACQLX4b1du3aufrWO+64w82/Pn36dFu+fLn16tUr3k0DAAAAkjusly1b1h5//HE3K0zPnj3tlVdesccee8zq1asX76YBAAAAyVezvnr16gLnGzVqZE8//bQFFQvEAAAAIGHCejJhgRgAAACEg7AeBywQAwAAgHAQ1uOEBWIAAACQkANMAQAAABDWAQAAAN+iZx0AAADwKWrWAZ9hWk8AAOAhrAM+wrSeAAAgFGEdEUWv8PFhWk8AsXr/3bhxY4HTkqhcuTKzmgExQlhHxNArHBlM6wkglu+/kydPLvHfpKSk2Pz5810HA4DoIqwjYugVBoD44P0XCC7COiKKXmEAiA/ef4FgYupGAAAAwKcI6wAAAIBPEdYBAAAAnyKsAwAAAD7FAFMAQNwxRzgAFI2wDgCIK+YIB4DiEdYBAHHFHOEAUDzCOgAg7pgjHACKxgBTAAAAwKcI6wAAAIBPEdYBAAAAnyKsAwAAAD5FWAcAAAB8irAOAAAA+BRhHQAAAPApwjoAAADgU4R1AAAAwKcI6wAAAIBPEdYBAAAAnyKsAwAAAD51QrwbAAAAUJytW7dadnZ22Dto48aNBU5LonLlyla3bl2eDPgKYR0AAPjSnj17LCMjw3Jzc0v8t5MnTy7x36SkpNj8+fMtPT29xH8LRAthHQAA+JJCc2ZmZol61o+HetYJ6vAbwjoAAPAtylKQ7BhgCgAAAPgUYR0AAADwKcI6AAAA4FOEdQAAAMCnCOsAAACATxHWAQAAAJ8irAMAAAA+RVgHAAAAfIqwDgAAAPgUYR0AAADwqRMsoPLy8txpdnZ2vJsCAABQal6W8bINkktgw/qPP/7oTjt27BjvpgAAAEQk21SpUoU9mWTK5AX0MC03N9eysrKsUqVKVqZMmXg3JyGP4nWg88EHH1jlypXj3Rz4CK8N8NoA7xuxpaimoF6rVi1LSaGCOdkEtmddL+Y6derEuxkJT0GdsA5eG+B9A3ymxBc96smLwzMAAADApwjrAAAAgE8R1lGk1NRUGzJkiDsFeG0gHLxvgNcGEHmBHWAKAAAAJDp61gEAAACfIqwDAAAAPkVYBwAAAHwqsPOso2iHDh2yqVOn2ksvvWTbt2+3k046ybp27WpDhw5lPvUkdPvtt9uLL75Y7OX169e3du3a2aRJk2LaLvhbp06d7Pvvvz9ie5s2bWzu3LkFtm3evNl++ctf2jvvvGMnn3xyDFuJRPps+frrr23//v3uNQSgIAaYJpmJEyfap59+anfeeac1aNDANm3aZBMmTHAfonqjRXLZu3evHThwwP3+xhtv2KxZs+z555/Pv1whvVy5coR1HBHWr7vuOuvRo0eB7XqtVK1atcC2w4cP2w8//GDVq1e3smXLsicD6ng/W/Sa0gxkPXv2jEl7gURCz3qSUS/qfffdZx06dHDn9UY6duxYu+aaaywrK8stZYzkWhHPWxVPpwpTNWvWLBC+gOJeO6GvleIUfk0hmPhsAaKHmvUkU6ZMGfv8888tNzc3f9vZZ59tr7/+ulWrVs31bixYsCD/skWLFtmpp56a/3W2fn/rrbesc+fOduaZZ9rAgQNt9+7dcXksiI3s7Gy7+eabrXXr1vbzn//cXn311fzLwnm9PPbYY3buuefaPffcw1OWBPr27Wvjx493pS96vaxevdq9DvR6QPJ+tqg0ZtiwYe69oGXLlvbrX//aFi9enP+aUVnVHXfc4UrzABREWE8y1157rc2ZM8eFrDFjxtg//vEPVwbRrFmzsHtR9ZXmQw89ZE8//bT9+9//tszMzKi3G/Hz9ttv2xlnnGGvvfaade/e3X3NrfKZcC1ZssReeOEF99pDctAB3AMPPGBTpkyxSpUqxbs58MFny6233upKoubNm+fq2mvXru2+1ZW//OUvVqdOHffeMmrUKJ4voBDCepIZPHiw+xDVG+Ozzz7rejouuugiF6bCpb9p1aqV62m9/PLLXWBHcKl37IYbbnB1qH/4wx8sJyfHvv3227D/XrXNDRs2tMaNG0e1nYgtBTK9NkJ/9u3b5y5Tj7oGCqoHFcnhaJ8tWntR38aOHj3amjZt6gK8Si/Xrl3r/lbjHFQuFVqWB+C/qFlPQldccYX72bVrl3388ceuh1y9GV75wrE0atQo/3eN8tcsAAguhXSP90F68ODBsP9eM8ogeBTGunTpUmBbhQoV3CnPeXI62mdLnz593CB2fdO2fv16W7FiRYGSGQDFo2c9iaxatarArB6qI1TPuL66VG+I6g0L09eWhTHoMLkUNYOHesqKUtTrJS0tLSrtQnzVqFHDHbiH/qhuWXjOk8uxPls0S0y/fv3cbFP16tWz/v372/333x/XNgOJhLCeRBSkVF++cuXKAttTU1OtfPnybmo1BfEff/wx/zJNvwUUh9cLgGN9tlSsWNG++OILe+qpp2zQoEGuTEqzjx3twB/AfxHWk4gGCepNUnXHmtFDszMsXbrU1Z6qDllfaWuGF82z/c0337iZPdQTAhSH1wuAY322aGaglJQUNzOMZn1588033aBS0eWiQK+xMMwuBhyJsJ5kHn74YbvyyivdLA2a2UNTL2pqPtUWqv58+PDhduKJJ7qFKbSgxU033RTvJsPHeL0AONZnS926dd3ML08++aRddtllNn36dLvrrrvshBNOyO+NV037M88847YDKIgVTAEAAACfomcdAAAA8CnCOgAAAOBThHUAAADApwjrAAAAgE8R1gEAAACfIqwDAAAAPkVYBwAAAHyKsA4AAAD4FGEdAMKkJdL79u3L/gIAxAxhHQAAAPApwjoAAADgU4R1ACjG2rVrrU+fPta6dWu79tprbdeuXfmXPffcc9atWzdr2bKltW/f3saNG2eHDx+2rVu32mmnnWZfffVV/nX/85//WIsWLey7775jXwMASoSwDgBFyMnJsQEDBliDBg1swYIF1rVrV5s/f7677J///Kfde++9NmLECHvzzTddUH/++eftnXfesbp161rbtm3tH//4R/5t6ffTTz/dGjVqxL4GAJQIYR0AivDpp5/a7t27bezYsda0aVO75pprrHPnzu6yihUr2oQJE6xLly528sknux529ZyvWbPGXX7ppZe6EO/5+9//7rYBAFBShHUAKKYEpnHjxi6Ye84880x3qtIXlbo8+uijNmzYMNfrvmzZMsvNzXWXK7x///339vXXX9vOnTttyZIl1qNHD/YzAKDECOsAUIy8vLwC58uVK+dOP/roI+vZs6cL4hdddJEL7W3atMm/XvXq1a1Dhw6u/OWtt95yNe916tRhPwMASuyEkv8JAARf8+bNbcOGDbZ3716rUqWK26aecm9w6VVXXWVjxoxx53/66SfbuHGjnXfeefl/f9lll1lmZqYL6ZTAAABKi551ACjC+eef7waLjho1ytatW+cGmb7xxhvusqpVq9qXX35pq1evdnXqt99+u+3YscMNSvWovl1hX4NRVRYDAEBpENYBoAgqeZk2bZrt2bPHfv3rX9vcuXPdIFMZMmSI1ahRw66++mrLyMiwtLQ0N8Wj1/MulStXtosvvtjOOussd10AAEqjTF7hokwAQET07t3bfvOb37iSGQAASoOadQCIsM8//9zNAKPyGUpgAADHg7AOABH28ssvuwWS7rnnHqtUqRL7FwBQapTBAAAAAD7FAFMAAADApwjrAAAAgE8R1gEAAACfIqwDAAAAPkVYBwAAAHyKsA4AAAD4FGEdAAAA8CnCOgAAAGD+9P8AbBV+nUfWvUQAAAAASUVORK5CYII="
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 21
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## Plotting a Matrix of P Values\n",
    "\n",
    "py50 also comes wrapped with [scikit-posthocs](https://github.com/maximtrp/scikit-posthocs). This requires a matrix of pvalues calculated from the py50.Stats module. The resulting heatmap will be generated based on the calculate matrix from the Stats module. "
   ],
   "id": "d7c398fa70d66322"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-05-21T04:13:27.843231Z",
     "start_time": "2026-05-21T04:13:27.823232Z"
    }
   },
   "cell_type": "code",
   "source": [
    "example1 = tips_stats.get_mannu(value_col='tip', group_col='day')\n",
    "example1"
   ],
   "id": "27779b9283dbbb42",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      A     B   U-val     p-val significance       RBC      CLES\n",
       "0   Sun   Sat  3960.0  0.029497            *  0.197822  0.598911\n",
       "1   Sun  Thur  2956.5  0.010006            *  0.254881  0.627441\n",
       "2   Sun   Fri   910.5  0.079741         n.s.  0.261080  0.630540\n",
       "3   Sat  Thur  2907.5  0.417663         n.s.  0.078050  0.539025\n",
       "4   Sat   Fri   845.0  0.881937         n.s.  0.022384  0.511192\n",
       "5  Thur   Fri   561.0  0.758381         n.s. -0.047538  0.476231"
      ],
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       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>U-val</th>\n",
       "      <th>p-val</th>\n",
       "      <th>significance</th>\n",
       "      <th>RBC</th>\n",
       "      <th>CLES</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sun</td>\n",
       "      <td>Sat</td>\n",
       "      <td>3960.0</td>\n",
       "      <td>0.029497</td>\n",
       "      <td>*</td>\n",
       "      <td>0.197822</td>\n",
       "      <td>0.598911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sun</td>\n",
       "      <td>Thur</td>\n",
       "      <td>2956.5</td>\n",
       "      <td>0.010006</td>\n",
       "      <td>*</td>\n",
       "      <td>0.254881</td>\n",
       "      <td>0.627441</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sun</td>\n",
       "      <td>Fri</td>\n",
       "      <td>910.5</td>\n",
       "      <td>0.079741</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>0.261080</td>\n",
       "      <td>0.630540</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sat</td>\n",
       "      <td>Thur</td>\n",
       "      <td>2907.5</td>\n",
       "      <td>0.417663</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>0.078050</td>\n",
       "      <td>0.539025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sat</td>\n",
       "      <td>Fri</td>\n",
       "      <td>845.0</td>\n",
       "      <td>0.881937</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>0.022384</td>\n",
       "      <td>0.511192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Thur</td>\n",
       "      <td>Fri</td>\n",
       "      <td>561.0</td>\n",
       "      <td>0.758381</td>\n",
       "      <td>n.s.</td>\n",
       "      <td>-0.047538</td>\n",
       "      <td>0.476231</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 22
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-05-21T04:13:27.919588Z",
     "start_time": "2026-05-21T04:13:27.883816Z"
    }
   },
   "cell_type": "code",
   "source": [
    "matrix1 = Stats.get_p_matrix(data=example1, test='mannu')\n",
    "matrix1"
   ],
   "id": "fcce1e93c7073ff6",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "           Fri       Sat       Sun      Thur\n",
       "Fri   1.000000  0.881937  0.079741  0.758381\n",
       "Sat   0.881937  1.000000  0.029497  0.417663\n",
       "Sun   0.079741  0.029497  1.000000  0.010006\n",
       "Thur  0.758381  0.417663  0.010006  1.000000"
      ],
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       "      <th>Fri</th>\n",
       "      <th>Sat</th>\n",
       "      <th>Sun</th>\n",
       "      <th>Thur</th>\n",
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       "  <tbody>\n",
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       "      <th>Fri</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.881937</td>\n",
       "      <td>0.079741</td>\n",
       "      <td>0.758381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <td>0.881937</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.029497</td>\n",
       "      <td>0.417663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <td>0.079741</td>\n",
       "      <td>0.029497</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.010006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thur</th>\n",
       "      <td>0.758381</td>\n",
       "      <td>0.417663</td>\n",
       "      <td>0.010006</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 23
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-05-21T04:13:28.479809Z",
     "start_time": "2026-05-21T04:13:28.413951Z"
    }
   },
   "cell_type": "code",
   "source": [
    "title = 'A Matrix of P Values!'\n",
    "plot_matrix_1 = Plots(matrix1)\n",
    "plot_matrix_1.p_matrix(title=title)"
   ],
   "id": "3e123927e3b2bcfd",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Axes: title={'center': 'A Matrix of P Values!'}>,\n",
       " <matplotlib.colorbar.Colorbar at 0x10f66a7d0>)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 24
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "That looks good. However, the figure can be modified further. We will reorder the groups to follow the typical Sun-Sat week format and use a blind color palette.\n",
    "\n",
    "First, the matrix from the Stats module must be reordered. This can be done by passing in a list of the groups. If the user prefers the groups to be in alphabetical order, then the **alpha** can be passed instead of a list. \n",
    "\n",
    "The **palette** parameter is modified by passing in a list of colors. This can be a list of color names or a list of hexcodes. "
   ],
   "id": "6c4afb7bbd6fe814"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-05-21T04:13:28.561742Z",
     "start_time": "2026-05-21T04:13:28.497147Z"
    }
   },
   "cell_type": "code",
   "source": [
    "order = ['Sun', 'Thur', 'Fri', 'Sat']\n",
    "palette = [\"1\", \"#56B4E9\", \"#009E73\", \"#F0E442\", \"#E69F00\"]\n",
    "\n",
    "matrix2 = Stats.get_p_matrix(example1, test='mannu', order=order)\n",
    "\n",
    "plot_matrix_2 = Plots(matrix2)\n",
    "plot_matrix_2.p_matrix(title=title, cmap=palette)"
   ],
   "id": "50ae4d8d66fe10eb",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Axes: title={'center': 'A Matrix of P Values!'}>,\n",
       " <matplotlib.colorbar.Colorbar at 0x10fdb8af0>)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ],
      "image/png": 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gwcxrIQAAAHJfoIyNjc38lgAAAMB/AmX58uUzvyUAAADwn0BZpUoVj67u5O7X5Z2YmGhuAAAA8NNAmXzh8nv37smJEydk6dKlMmTIkPu+fsGCBRITE+OxrUuXLhlpCgAAAHJioGzQoEGKbY0aNZLKlSvLpEmTpFWrVmm+Pjw8XN544w2PbdOnT89IUwAAAJATA2VqihcvLkePHr3vfkFBQeYGAAAAPw2UGzduTLEtPj5e1q5dK7Vq1cqMdgEAACA3B8rZs2d7PNYJOrq4+XPPPZeuMZQAAADw40B56dIl2bp1q+TL98tL//GPf8iuXbukRIkS0rJlSylUqJAv2gkAAICHVLovyK1d2v369ZPQ0FA5fvy42bZhwwb5zW9+I6tWrTIzt8PCwuTcuXO+bC8AAAByaqCcM2eOnD59WlauXClPPvmkJCQkyPjx46VGjRry2WefyZYtW6Rx48Yybdo037YYAAAAOTNQajf3yJEjpW7dumbM5I4dO0zVsnv37mb8pOrQoYPZDgAAAP+R7kB58eJFqVixouvxzp07JW/evKYq6VSyZEm5efNm5rcSAAAAOT9QBgcHy8mTJ819h8MhX375pdSsWVOKFi3q2uf777+XsmXL+qalAAAAyNmBsl27djJhwgSJjY2ViRMnytmzZz0ul3jo0CGZMWPGfa+SAwAAAD9dNqh///4SFxcnI0aMMGMoIyMjpW3btua5yZMnm+t4N23a1OwHAAAA/5HuQKnrTkZHR5tbcu3btzdLBlWtWjWz2wcAAAB/uJZ3SEhIZrwNAAAAcvMYSgAAAMAbAiUAAACsECgBAABghUAJAAAAKwRKAAAAWCFQAgAAwAqBEgAAAFYIlAAAALBCoAQAAIAVAiUAAACsECgBAABghUAJAAAAKwRKAAAAWCFQAgAAwAqBEgAAAFYIlAAAALBCoAQAAIAVAiUAAACsECgBAABghUAJAAAAKwRKAAAAWCFQAgAAwAqBEgAAAFYIlAAAALBCoAQAAIAVAiUAAACsECgBAABghUAJAAAAKwRKAAAAWCFQAgAAwEqAw+FwyENg3Lhx2d0EAADwgMaMGcMxAxVKAAAA2MknD5G+lcZmdxOQBRYe/+U881etf3D2Phyo1z+7m4IsUHX3PPOV8+1f5xtgDCUAAACsECgBAABghUAJAAAAKwRKAAAAWCFQAgAAwAqBEgAAAFYIlAAAALBCoAQAAIAVAiUAAACsECgBAABghUAJAAAAKwRKAAAAWCFQAgAAwAqBEgAAAFYIlAAAALCSz+7lAAAA2ef69euSkJDg088oVKiQFC1a1KefkdMRKAEAQI4NkzNmx0iepLs+/ZzAwECJiIggVKaBQAkAAHIkrUxqmDz1RAtJLFDMJ58RdOuqVDgWaz6LKmXqCJQAACBH0zB5q3Cp7G6GX2NSDgAAAKwQKAEAAGCFQAkAAAArBEoAAABYIVACAADACoESAAAAVgiUAAAAsEKgBAAAgBUCJQAAAKwQKAEAAGCFQAkAAJABISEhMmzYsBTb169fL82bN3c9vnPnjsyZM0datGgh1atXl6ZNm8qkSZMkLi4u1xx3AiUAAEAGbd68Wb7++us095k2bZps3bpVxo8fL59++qkJk1999ZVERUXlmuNOoAQAAMig8uXLy9tvvy2JiYmp7rNhwwYZPHiwNGrUSCpUqGC+jh07Vr744gu5cOGCfwZKTdcnTpzwTWsAAABykCFDhsj58+dl8eLFqe4TEBAgu3btkqSkJNe22rVry8cffyzFihUTvwyUH330kTkwAAAA/i44OFgiIyNl/vz5cvLkSa/79OjRQ1asWGHGVY4ZM0Y+++wzuXXrljz99NMSGBgofhkoe/bsaUq72vd/7NgxOXPmjMcNAADAn3Tv3l0qVaokEyZM8Pp8RESETJ06VcqUKSOrV682ATQ0NFTWrVsnuUW+B33B7Nmzzde//e1v5quzWulwOMz9gwcPZnYbAQAAHlp58+Y1YyK7dOkin3/+udd9Xn31VXO7evWq7NixQ1auXCkjR440M8V15rffBcrY2FjftAQAACCHqlOnjnTs2NFUKfv06ePafujQIdm4caO89dZb5rGOmQwLC5OXX35ZWrZsacZW+mWg1NlMAAAA8BQVFSWtWrXymKBz7949Wbp0qalOVq1a1bU9KChIChQoIMWLF88Vh/GBA2WVKlXSnJRDlzcAAPBHxYoVM6Fy1KhRrgJctWrVzELmAwYMMIug6+zuS5cumaWEdKkhrVL6ZaBcvny5x2NN3rqMkKZvnToPAADgrzp16mQm27ivL/nuu++aWeAxMTFmAnOhQoWkcePGZhxlkSJFxC8DZYMGDVJs0wU6K1eubFZ+11IvAABAbnf48OEU2wICAuTDDz/02FawYEEZOnSoueVWmXalHB0DcPTo0cx6OwAAAOTWCqXOVEouPj5e1q5dK7Vq1cqsdgEAACC3r0PpXtrVVd6fe+65dI+h1EGoaV3zEgAAALk4UG7fvt36QxcsWGAGprrTxUABAADgB4FSxcXFmfGSWmXUK+S4q1+//n1fHx4eLm+88YbHtunTp2ekKQAAAMhpgXLz5s0yYsQIr13W6b30oi7mqTcAAAD4YaDUSmK3bt3MAp25Ze0kAAAAZOGyQXpRcx3vSJgEAABAhgJl8+bNZdu2bRw9AAAApL/LOzo62nX/zp07MmXKFNm6datUrFhR8uTxzKR6tRwAAAD4j3RVKL/55hvXJBzt6m7fvr251GLyMAkAAAD/k64K5dmzZ2XNmjVSokQJ37cIAADgAURd/bWUTDjrk2N26XZZWS/hnI/7SFeJMflakwAAAIBTuvusdY1JAAAAIMPrUHbs2DFdYyZjY2PT+5YAAADwp0Cpl0p85JFHfNsaAAAA5M5Aqd3dbdq0YVIOAAAAUmBSDgAAAHwfKF977TXJnz+/3ScBAADAf7u8ufoNAAAAUsOlbgAAAGCFQAkAAAArBEoAAABYIVACAADACoESAADgIbRz505p27at1KxZU3r06CEnT55MdV+HwyHTpk2Thg0bSoMGDWTKlCmSlJTken7ZsmUSEhLicZs8eXLWXykHAAAAWePMmTMSEREhgwYNktDQUJk7d64MGDBAPvroI3PBmeSWLl0qmzdvlpiYGLl79668+eab5oI0vXv3Ns8fOXJEunTpYt7DqWDBgpnWXiqUAAAAWSwxMVHWrVsnw4YN8/r8mjVrpHr16tKrVy955plnzBKOp0+flm+//dbr/suXL5fIyEipV6+eqVJGRUXJqlWrXM///PPPUqVKFSlVqpTrVqRIkUz7fgiUAAAAD+jUqVOm23jTpk2mgqhBbvz48aY6mJa4uDhZtGiRtGjRQmbOnCm1atXyut/evXvNe7pXE6tVqyY//PBDin3Pnz8vZ8+elfr167u21a1b1wTQCxcumMdHjx6VypUr++w80+UNAACQQdrFPHPmTBMkhw8fLoULF5ahQ4d6DX06jnH16tVSrlw5GTJkiISFhUlQUJDX97148aKULl3aY5t2YZ87d87rvsp9/5IlS5qvun+ePHnk2rVrsmHDBomOjjZXP+zUqZOpfnrrPs8IAiUAAEAG6VjFev+uJA4ePNhMjNGwmDyoaRf04cOHzfPNmjW77/vevHkzRdjUx9pVntytW7dcz7vvq3R/rU46A+m8efPk4MGDppqaN29e6dmzp2QGurwBAAAyqE6dOq77OubxypUrcvXq1RT7de7cWYoVKyajR4+WBQsWyPXr19N8X60iJg+P+tjbRBr38Oi+r9L9ddb3rl27TPjVbvr27dubyTkffPCBZBYCJQAAQAYFBga67juX6fHWjdy6dWvZsmWLjBo1SrZt2yZNmzY1VcLUlgIKDg6WS5cueWzTxzqZxtu+7l3f7ved+2uYdffUU0+ZbvjMQqAEAADIIO0+dtq/f78Zx5g8vDnpWMZWrVrJ2rVr5b333pNjx46ZZYG80bUn9+zZ49EFfuDAAbPdW6DUcZnu++t93abt0RnjL7/8slmr0r3dTz75pGQWAiUAAEAGTZgwQfbt22cWIZ81a5Z07do1Xa9r1KiRLF682Ixp9KZjx47y3XffycKFC+Wnn34yk2kqVKggzz//vHk+Pj7edK+7d6nr+MxvvvnG3KZPn24WQ1cvvPCCqVjqQubHjx+Xjz/+2Mw079OnT6addyblAAAAZJB2ZYeHh5vubg11ffv2faDXly1b1ut2DY9z5syRiRMnmkXNa9eubb46u9OXLFliZm1v377dPNYFzC9fviwDBw40k210Frdzwk358uVNMJ06daoZN6mTc3SSkLY9sxAoAQAAMqhNmzYmUPpCkyZNzM0b7Sp37y7XEKlVTL15ozPR//KXv4iv0OUNAAAAKwRKAAAAWKHLGwAA4AHpGEddqBy/oEIJAAAAKwRKAAAAWCFQAgAAwAqBEgAAAFYIlAAAALBCoAQAAIAVAiUAAACsECgBAABghYXNAQBAjlagYX0pVDLON+99qYjIRp+8da7yUAXKhcfHZncTkIXGjRvH8fYjVXfPy+4mIAtxvgH/Qpc3AAAAck+F8kC9/tndBGRh5YLz7V/ne8yYMdndFGRhzwP/vv0DlWg4UaEEAACAFQIlAAAArBAoAQAAYIVACQAAACsESgAAAFghUAIAAMAKgRIAAABWCJQAAACwQqAEAACAFQIlAADAQ2jnzp3Stm1bqVmzpvTo0UNOnjyZ6r4Oh0OmTZsmDRs2lAYNGsiUKVMkKSkpxX7Xrl2TF154QU6dOpWpbSVQAgAAPGTOnDkjERER0qFDB1m7dq0UL15cBgwYYIKjN0uXLpXNmzdLTEyMzJ49WzZt2mS2ubt+/br069dPLl++nOntJVACAABkscTERFm3bp0MGzbM6/Nr1qyR6tWrS69eveSZZ56RSZMmyenTp+Xbb7/1uv/y5cslMjJS6tWrZ6qUUVFRsmrVKtfzu3fvNuE0ISHBJ98PgRIAAOABaZdxSEiIqQSGhoaaIDd+/Hi5e/dumq+Li4uTRYsWSYsWLWTmzJlSq1Ytr/vt3bvXvKdTwYIFpVq1avLDDz+k2Pf8+fNy9uxZqV+/vmtb3bp1TQC9cOGCebxjxw7p2LGjzJkzxyfnOp9P3hUAAMAPaBfzzJkzTZAcPny4FC5cWIYOHeo19C1btkxWr14t5cqVkyFDhkhYWJgEBQV5fd+LFy9K6dKlPbaVKFFCzp0753Vf5b5/yZIlzVfdX7fr56nMHjvpRKAEAADIoDfffNNVSRw8eLCZGKPhLSAgwGM/7YI+fPiweb5Zs2b3fd+bN2+mCJv6WLvKk7t165brefd9lbf9fYEubwAAgAyqU6eO676Oebxy5YpcvXo1xX6dO3eWYsWKyejRo2XBggVmgkxa8ufPnyIM6mPt+k7OW3h03ve2vy8QKAEAADIoMDDQdd+5TE/y6qRq3bq1bNmyRUaNGiXbtm2Tpk2bmjGXqS0FFBwcLJcuXfLYpo9LlSrldV/3rm/3+9729wUCJQAAQAYdPHjQdX///v1mvKJWIr2Grjx5pFWrVmYZoPfee0+OHTsmgwYN8rqvrj25Z88ejy7wAwcOmO3eAqWOy3TfX+/rtuTjMH2FMZQAAAAZNGHCBFNpvHHjhsyaNUu6deuWrtc1atTI3HR2tjc6I3vx4sWycOFCM+Zy7ty5UqFCBXn++efN8/Hx8XL79m2zPqWzS13HZ5YpU8Y8nj59ullyKKsQKAEAADJIu7LDw8NNd7eGur59+z7Q68uWLet1u4ZHXeJn4sSJJkzWrl3bfHV2py9ZskQ2bNgg27dvN4979+5tFiwfOHCg5M2bVzp16iQ9e/bMsvNKoAQAAMigNm3amEDpC02aNDE3b7Sr3L27XENkdHS0uaVFg6rONs9sjKEEAACAFQIlAAAArNDlDQAA8IB81XWcU1GhBAAAgBUCJQAAAKwQKAEAAGCFQAkAAAArBEoAAABYIVACAADACoESAAAAVgiUAAAAsMLC5gAAIEdrsitEzgYl+eS9yybmEd9cqTt3oUIJAAAAKwRKAAAAWCFQAgAAwAqBEgAAAFYIlAAAALBCoAQAAIAVAiUAAACyZh3KM2fOSNmyZSUgIMDcT0u5cuXsWgUAAIDcFyibN28uX331lZQoUcLc12CZnMPhMNsPHjyY2e0EAABATg+UsbGxUrx4cXP/o48+ksKFC/uyXQAAAMhtgbJ8+fKu+/369ZOYmBipWrWqr9oFAACA3Hwt7zx58sidO3cy/KGJiYnmBgAAAD8NlE2bNpU33nhDmjVrZiqXQUFBHs8PHDgwzdcvWLDAVDjddenSJSNNAQAAyJV27twpEydOlJMnT0rNmjVlwoQJ8vjjj3vdV+exTJ8+XdauXStJSUnSqVMniYqKMkVAdfXqVfnDH/4gO3bskGLFisngwYOlXbt2Kd5n9+7d8vvf/94MdfR5oDx8+LBUq1ZNLly4YG7uvE3WSS48PNwEUnd6EAAAACBmRZ2IiAgZNGiQhIaGyty5c2XAgAFmHou3rLV06VLZvHmzKdjdvXtX3nzzTTORunfv3ub56OhouXXrlvzlL3+RvXv3yqhRo+SJJ56QGjVqeOQ7DZr58+d/4FOQ7kDZtWtXmTdvnjz66KOyYsUKs00bVqBAgQf+UK1oJq9qAgAA+IvExETZtGmTqUJ6K6qtWbNGqlevLr169TKPJ02aJC+++KJ8++238vzzz6fYf/ny5RIZGSn16tUzj7U6OWvWLBMoT5w4IV988YWpOlaoUEGeffZZ+eGHH+TPf/6zK1B++OGHMnnyZFMBjYuL893C5nv27EkxbvKFF14wZVgAAAB/curUKQkJCTGhMDQ01AS58ePHm+pgWjSsLVq0SFq0aCEzZ86UWrVqed1Pq4jOcKgKFixoeoc1CCZ3/vx5OXv2rNSvX9+1rW7dunL69GnTk6zvpWuJa5h0f/777793Pf7rX/9qAmXPnj0lIzLU5e3eXw8AAOCvtIt55syZJkgOHz7cLKs4dOhQr6Fv2bJlsnr1anMBmCFDhkhYWFiqPbYXL16U0qVLe2zTLuxz58553Ve571+yZEnzVfdP7b20TU7vvfee+bp+/XrJ8kAJAADgz3SsYr1/VxJ1/OG0adNMWEw+zlG7oHWMoj6vk5rv5+bNmynCpj72tkqODkF0Pu++r9L9H+S9MopreQMAAGRQnTp1XPd1zOOVK1fMjOrkOnfubGZXjx492qx2c/369TTfVyfGJA98+li7vpNzD4/u+yrdP7X3ysg8mEypUG7ZskWKFCnieqzT0rdt2+a6go5T+/btM62BAAAAD6vAwECPXKS8zcJu3bq1tGrVSrZu3Srvv/++zJ8/Xzp27Civv/6616WAgoOD5dKlSx7b9PGvfvUrr/sq7dp2jpN0doOXKlUq1ffS57I8UGp//5IlS1L0v69cudJjmx5EAiUAAPAHBw8elAYNGpj7+/fvN2MVtRLpja4JqaFSb19//bUJlros0MaNG1Psq+tO6oRoJ+22PnDggNe1vjUwak7T/Z2BUu/rNm2PTvzRCTo6nrJMmTKu51ObEOTTQLl9+/ZM+1AAAIDcQBcbHz9+vNy4ccMs09OtW7d0va5Ro0bmprOzvdHq5eLFi2XhwoVmzKWuQ6lh0blkUHx8vNy+fdvVS6xd6jo+0xkYdSki55JDWgFt3LixGe85cuRI2bdvn1mzMnlR0AaTcgAAADJIu7LDw8NNd7eGur59+z7Q63U5H280PM6ZM8dcKUfDZO3atc1XZ3e69hpv2LDBVfDT9SYvX75sKph58+Y1V8pxXwJoypQpJkz+53/+p+nq1vd1X9TcFoESAAAgg9q0aWMCpS80adLE3LzRrnK9OWmI1Kvh6M0bHaao4zbvp0OHDub2oJjlDQAAACsESgAAAFihyxsAAOAB6RhHXagcv6BCCQAAACsESgAAAFghUAIAAMAKgRIAAABWCJQAAACwQqAEAACAFQIlAAAArLAOJQAAyNFK3smTI987NyFQAgCAHKlQoUISGBgoHS/79nP0M/SzkDoCJQAAyJGKFi0qERERkpCQ4NPP0TCpn4XUESgBAECOpUGPsJf9GBgAAAAAKwRKAAAAWCFQAgAAwAqBEgAAAFYIlAAAALBCoAQAAIAVAiUAAACsECgBAABghUAJAAAAKwRKAAAAWCFQAgAAwAqBEgAAAFYIlAAAALBCoAQAAIAVAiUAAACsECgBAABghUAJAAAAKwRKAAAAWCFQAgAAwEqAw+FwyENg3Lhx2d0EAADwgMaMGcMxAxVKAAAA2MknD5G+lcZmdxOQBRYe/+U8H6jXn+PtB6runme+cr7963xTtfIP9C7CiTGUAAAAsEKgBAAAgBUCJQAAAKwQKAEAAGCFQAkAAAArBEoAAABYIVACAADACoESAAAAVgiUAAAAsEKgBAAAgBUCJQAAAKwQKAEAAGCFQAkAAAArBEoAAABYIVACAADACoESAAAAVgiUAAAAsEKgBAAAgBUCJQAAAKwQKAEAAGCFQAkAAAArBEoAAABYIVACAADACoESAAAABEoAAABkHyqUAAAAsEKgBAAAgBUCJQAAAKwQKAEAAGCFQAkAAAArBEoAAABYIVACAADACoESAAAAVgiUAAAAsEKgBAAAgBUCJQAAALI+UEZHR0tcXFyK7devX5fIyEi7FgEAACBHyZfeHb///ns5fvy4ub9x40apVq2aFClSxGOfo0ePyo4dOzK/lQAAAMj5gbJgwYIyZ84ccTgc5vb+++9Lnjz/v8AZEBAghQoVkqioKF+1FQAAADk5UFapUkViY2PN/e7du0tMTIwULVrUl20DAABAbh1DuWLFCq9hMjExUfbu3ZsZ7QIAAEBuq1AmH085duxYOXLkiCQlJXk8lzdvXtm/f39mtQ8AAAC5sUL5zjvvSPny5WX+/PmusZWjRo2Sxx57TKZMmZL5rQQAAEDuqlD+9NNPMnXqVHnqqafMbO/AwEDp2rWrlChRQhYtWiStW7dO8/XaNa43AAAA+GmFUquS2rWtnnzySTl8+LC5X6NGDTl27Nh9X79gwQKpW7euxw0AAAB+FCgbNmwo06dPl/Pnz0vt2rXlk08+kWvXrsn27dvl0Ucfve/rw8PDZc+ePR43AAAA+FGgHDlypLkqztatW6VNmzbyyCOPSKNGjWTSpEkSERFx39cHBQWZRdHdbwAAAPCTMZSXLl2SkiVLyvLly83jAwcOSJMmTaRVq1by0ksvSXBwsC/aCQAAgJxeoYyPj5d+/fpJaGio/POf/zTbNmzYIJ06dZJVq1bJypUrpUuXLnLu3DlfthcAAAA5NVDq0kCnT582wVEn4iQkJMj48ePNRBzt+t6yZYs0btxYpk2b5tsWAwAAIGcGSg2NOnZSZ2Trdbt37NhhqpZ6GUZdNkh16NDBbAcAAID/SHegvHjxolSsWNH1eOfOnWbpIK1KOunYyps3b2Z+KwEAAJDzA6VOtjl58qS573A45Msvv5SaNWt6XNNbL8lYtmxZ37QUAAAAOTtQtmvXTiZMmCCxsbEyceJEOXv2rJmE43To0CGZMWOGme0NAAAA/5HuZYP69+8vcXFxMmLECDOGMjIyUtq2bWuemzx5sixdulSaNm1q9gMAAID/SHegzJcvn0RHR5tbcu3bt5ewsDCpWrVqZrcPAAAAuW1hc29CQkIy420AAADgL5deBAAAAJwIlAAAALBCoAQAAIAVAiUAAACsECgBAABghUAJAAAAKwRKAAAAWCFQAgAAwAqBEgAAAFYIlAAAALBCoAQAAIAVAiUAAACsECgBAABghUAJAAAAKwRKAAAAWCFQAgAAwAqBEgAAAFYIlAAAALBCoAQAAIAVAiUAAACsECgBAABghUAJAAAAKwRKAAAAWCFQAgAAwAqBEgAAAFYIlAAAALBCoAQAAIAVAiUAAACsECgBAABAoAQAAED2oUIJAAAAKwRKAAAAWAlwOBwOeQiMGzcuu5sAAAAe0JgxYzhmeHgCpT9KTEyUBQsWSHh4uAQFBWV3c+BjnG//wvn2L5xv+Du6vLP5F1BMTIz5ityP8+1fON/+hfMNf0egBAAAgBUCJQAAAKwQKAEAAGCFQJmNdCLOwIEDmZDjJzjf/oXz7V843/B3zPIGAACAFSqUAAAAsEKgBAAAgBUCJQAAAKwQKAEAAGAln93L4c2dO3dk/vz5snHjRjl//ryULFlSXn75ZRk0aJAUKVKEg5YLvPXWW7Jhw4ZUny9fvrw0aNBA/vjHP2Zpu5D1mjdvLqdPn06xvU6dOvLBBx94bDt16pS0aNFCYmNjpUKFClnYSmT37/WDBw/KzZs3zc8FkBsxy9sHJk2aJDt37pQRI0bI448/LidPnpQJEyaY/0D0FxJyvhs3bsitW7fM/U8++USWLFkia9eudT2vQTIwMJBA6SeB8vXXX5fWrVt7bNfz/9hjj3lsu3fvnly5ckWKFy8uefPmzeKWIjt/r+vPiS4T16FDB04EciUqlD6glauJEydKo0aNzGP9hTN27Fjp2rWrXLhwQUqXLu2Lj0UWeuSRR8zNeV/DQalSpTzCBPyH/gy4n//UJP85Qc7B73UgbYyh9IGAgADZtWuXJCUlubbVrl1bPv74YylWrJj5S3X9+vWu57755hsJCQlxdYnp/a1bt8pLL70kzz33nISHh8u1a9d80VT4UFxcnAwdOlRq1qwpTZs2lU2bNrmeS8/PwNy5c6V+/fry9ttvc55yqO7du8s777xjurn1Z+Dw4cPm3Oo5Ru76va7d4JGRkebfbPXq1eW1116TPXv2uH4OdFhEdHS0GS4D5EYESh/o0aOHrFixwoSGMWPGyGeffWa6R59++ul0V660C2XGjBmycuVK2bdvnyxdutQXTYUPbdu2TapVqyabN2+WV155xXSVaVd5en333Xeybt068/OEnEv/cJg6darExMRI4cKFs7s58NHv9aioKDOk4cMPPzTjLIODg03PlJozZ46UKVPG/A4YOXIk5wC5EoHSByIiIsx/IPoLZPXq1eav1tDQUBMO0ktfU6NGDVPdCgsLM6ESOYtWL/r06WPGWw0YMEASExPl6NGj6X69jsurWLGiVK5c2afthD0NGHq+3W8JCQnmOa1M6kQMrVohd/5edzgcpkdp9OjR8tRTT5mQqUOcjhw5Yl6rY2l1uIP7UBkgt2EMpY+8+uqr5nb16lXZsWOHqTTqX6bObs37qVSpkuu+ziDUGYbIWTRIOjn/E7l9+3a6X68zxZEzaLho2bKlx7aCBQuar5xH//i93rlzZzNBT3sWjh07Jvv37/foHgdyOyqUmezQoUMeM3t1bI1WGLWrRP+y1TE4yWk3SXJM6sj5vM3i1UqGN95+BvLnz++TdiHzlShRwvwR6H7TMXeK85j7f6/r7O9evXqZ1R7KlSsnvXv3lilTpmRrm4GsRqDMZBoMdLzjgQMHPLYHBQVJgQIFzHIhGhbj4+Ndz+nyE/Av/AwAuef3eqFCheTvf/+7LFu2TPr162eGOeiKHmn9EQnkNgTKTKaTMPSXiY6Z01m9Opvzhx9+MGOsdAyddovpzG1ds/DHH380s3v1r1r4F34GgNzze11n8efJk8fM+NbZ3J9++qmZiKP0eaWhU8dQs2IHcisCpQ+8++670q5dOzOrU2f36rI/uoSMjrfR8ZBDhgyRRx991CxwqwvjDh482BfNwEOMnwEg9/xeL1u2rJnRvWjRImnbtq0sXLhQRo0aJfny5XNVNXWM5apVq8x2IDfiSjkAAACwQoUSAAAAVgiUAAAAsEKgBAAAgBUCJQAAAKwQKAEAAGCFQAkAAAArBEoAAABYIVACAADACoESAAAAVgiUAAAAsEKgBAAAgNj4f0thi7QEAxMaAAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 25
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## A Matrix From a Cluttered Plot\n",
    "\n",
    "Previously, we generated a plot with subgroup annotations. This modifies the hue. Adding all the annotations on the plot vastly reduces its readability."
   ],
   "id": "d80d9ac09a083146"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-05-21T04:13:29.146969Z",
     "start_time": "2026-05-21T04:13:28.565853Z"
    }
   },
   "cell_type": "code",
   "source": [
    "tips_plot.barplot(test='mannu', value_col='total_bill', group_col='day', subgroup_col='smoker')\n",
    "plt.legend(loc='upper right', bbox_to_anchor=(1.22, 1))"
   ],
   "id": "e7fc05bc8bba11a0",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x10fe473d0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 26
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "Fortunately, the pvalues can be drawn as a matrix.\n",
    "\n",
    "Like before, the statistics must be calculated first using the **get_X** where **X** is the specific statistic test. The output table is then converted into a matrix using the **get_p_matrix**. Finally, it can be plotted using the **Plot.p_matrix()** function. Steps can be seen below. "
   ],
   "id": "eb5d8dc1c38cddb5"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-05-21T04:13:29.228422Z",
     "start_time": "2026-05-21T04:13:29.163294Z"
    }
   },
   "cell_type": "code",
   "source": [
    "example2 = tips_stats.get_mannu(value_col='total_bill', group_col='day', subgroup_col='smoker')\n",
    "matrix3 = Stats.get_p_matrix(example2, test='mannu')\n",
    "matrix3"
   ],
   "id": "2d97936886158de3",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "            Sun-No    Sat-No   Sat-Yes   Fri-Yes   Thur-No  Thur-Yes  \\\n",
       "Sun-No    1.000000  0.428422  0.823521  0.051393  0.018167  0.392813   \n",
       "Sat-No    0.428422  1.000000  0.412393  0.112359  0.098890  0.635937   \n",
       "Sat-Yes   0.823521  0.412393  1.000000  0.116979  0.037041  0.563656   \n",
       "Fri-Yes   0.051393  0.112359  0.116979  1.000000  0.707217  0.151217   \n",
       "Thur-No   0.018167  0.098890  0.037041  0.707217  1.000000  0.262565   \n",
       "Thur-Yes  0.392813  0.635937  0.563656  0.151217  0.262565  1.000000   \n",
       "Sun-Yes   0.191027  0.084238  0.262149  0.018338  0.009704  0.075961   \n",
       "Fri-No    0.683619  0.874077  0.720998  0.395118  0.510998  0.964270   \n",
       "\n",
       "           Sun-Yes    Fri-No  \n",
       "Sun-No    0.191027  0.683619  \n",
       "Sat-No    0.084238  0.874077  \n",
       "Sat-Yes   0.262149  0.720998  \n",
       "Fri-Yes   0.018338  0.395118  \n",
       "Thur-No   0.009704  0.510998  \n",
       "Thur-Yes  0.075961  0.964270  \n",
       "Sun-Yes   1.000000  0.286166  \n",
       "Fri-No    0.286166  1.000000  "
      ],
      "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sun-No</th>\n",
       "      <th>Sat-No</th>\n",
       "      <th>Sat-Yes</th>\n",
       "      <th>Fri-Yes</th>\n",
       "      <th>Thur-No</th>\n",
       "      <th>Thur-Yes</th>\n",
       "      <th>Sun-Yes</th>\n",
       "      <th>Fri-No</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Sun-No</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.428422</td>\n",
       "      <td>0.823521</td>\n",
       "      <td>0.051393</td>\n",
       "      <td>0.018167</td>\n",
       "      <td>0.392813</td>\n",
       "      <td>0.191027</td>\n",
       "      <td>0.683619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat-No</th>\n",
       "      <td>0.428422</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.412393</td>\n",
       "      <td>0.112359</td>\n",
       "      <td>0.098890</td>\n",
       "      <td>0.635937</td>\n",
       "      <td>0.084238</td>\n",
       "      <td>0.874077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat-Yes</th>\n",
       "      <td>0.823521</td>\n",
       "      <td>0.412393</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.116979</td>\n",
       "      <td>0.037041</td>\n",
       "      <td>0.563656</td>\n",
       "      <td>0.262149</td>\n",
       "      <td>0.720998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fri-Yes</th>\n",
       "      <td>0.051393</td>\n",
       "      <td>0.112359</td>\n",
       "      <td>0.116979</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.707217</td>\n",
       "      <td>0.151217</td>\n",
       "      <td>0.018338</td>\n",
       "      <td>0.395118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thur-No</th>\n",
       "      <td>0.018167</td>\n",
       "      <td>0.098890</td>\n",
       "      <td>0.037041</td>\n",
       "      <td>0.707217</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.262565</td>\n",
       "      <td>0.009704</td>\n",
       "      <td>0.510998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thur-Yes</th>\n",
       "      <td>0.392813</td>\n",
       "      <td>0.635937</td>\n",
       "      <td>0.563656</td>\n",
       "      <td>0.151217</td>\n",
       "      <td>0.262565</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.075961</td>\n",
       "      <td>0.964270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun-Yes</th>\n",
       "      <td>0.191027</td>\n",
       "      <td>0.084238</td>\n",
       "      <td>0.262149</td>\n",
       "      <td>0.018338</td>\n",
       "      <td>0.009704</td>\n",
       "      <td>0.075961</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.286166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fri-No</th>\n",
       "      <td>0.683619</td>\n",
       "      <td>0.874077</td>\n",
       "      <td>0.720998</td>\n",
       "      <td>0.395118</td>\n",
       "      <td>0.510998</td>\n",
       "      <td>0.964270</td>\n",
       "      <td>0.286166</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 27
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-05-21T04:13:29.362769Z",
     "start_time": "2026-05-21T04:13:29.268217Z"
    }
   },
   "cell_type": "code",
   "source": [
    "title = 'A Matrix of P Values with Subgroup Changes!'\n",
    "plot_matrix_3 = Plots(matrix3)\n",
    "plot_matrix_3.p_matrix(title=title)"
   ],
   "id": "d47bb787eaee5625",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Axes: title={'center': 'A Matrix of P Values with Subgroup Changes!'}>,\n",
       " <matplotlib.colorbar.Colorbar at 0x10fff99f0>)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 28
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Looks good. But as seen above, I prefer the data to be organized better. This can be done by passing in a list of the groups in a specified order. Because this dataset has more groups, it will be alphabetized instead. This will be done by passing in the **\"alpha** into the order parameter.  ",
   "id": "c394999ef1f51a50"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-05-21T04:13:29.764794Z",
     "start_time": "2026-05-21T04:13:29.642626Z"
    }
   },
   "cell_type": "code",
   "source": [
    "title = 'A Matrix of P Values with Groups in Alphabetical Order'\n",
    "\n",
    "matrix4 = Stats.get_p_matrix(example2, test='mannu', order='alpha')\n",
    "plot_matrix_4 = Plots(matrix4)\n",
    "plot_matrix_4.p_matrix(title=title)"
   ],
   "id": "83c1c3f987a62bb3",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Axes: title={'center': 'A Matrix of P Values with Groups in Alphabetical Order'}>,\n",
       " <matplotlib.colorbar.Colorbar at 0x10ff92c50>)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 29
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}