diff --git a/example/example.ipynb b/example/example.ipynb
index cea07fc..1f9db5e 100644
--- a/example/example.ipynb
+++ b/example/example.ipynb
@@ -2,9 +2,18 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/vector/anaconda3/lib/python3.7/site-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
+      "  import pandas.util.testing as tm\n"
+     ]
+    }
+   ],
    "source": [
     "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
@@ -40,7 +49,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -49,8 +58,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": false
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -70,12 +81,14 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -90,6 +103,59 @@
     "plt.savefig('example_non-hue_outside.png', dpi=300, bbox_inches='tight')"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Horizontal plot\n",
+    "Plots can be horizontal or vertical"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "p-value annotation legend:\n",
+      "ns: 5.00e-02 < p <= 1.00e+00\n",
+      "*: 1.00e-02 < p <= 5.00e-02\n",
+      "**: 1.00e-03 < p <= 1.00e-02\n",
+      "***: 1.00e-04 < p <= 1.00e-03\n",
+      "****: p <= 1.00e-04\n",
+      "\n",
+      "Thur v.s. Fri: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=1.000e+00 U_stat=6.305e+02\n",
+      "Thur v.s. Sat: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=1.407e-01 U_stat=2.180e+03\n",
+      "Sun v.s. Fri: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=8.041e-02 U_stat=9.605e+02\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAf0AAAEMCAYAAADOAm80AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAbWklEQVR4nO3df3Rcd3nn8bdk1UkgPpFEUkIgYNSGp9tkwSB3F1J+2I7dPS1QWH4UkU1Dobgbw2Hbs+GARaGFnrL2sslyzrZhgeyhbEJAS80S2AUCcmwD2QWKlZjgmDwpmSQGAmkSSVkFEjsjaf+YUaIE/9DIM3NHuu/XOT6ee+fe7/e5c2f0me+dO3e6ZmdnkSRJy1930QVIkqT2MPQlSSoJQ1+SpJIw9CVJKglDX5KkkugpuoB2GRsbOwn4LeCnwHTB5UjSUrECeBrw3cHBwUNFF6MTU5rQpxb43yy6CElaol4C3FB0EToxZQr9nwI85znPYeXKlUXX0nL79+/nvPPOK7qMwrj9bn9Zt7/Z23748GFuu+02qP8N1dJWptCfBli5ciUnnXRS0bW0RVm282jcfre/rFq07X4sugx4Ip8kSSVRppG+2uTKK6+kUqm0vd+JiQkA+vr6mJqaYseOHW2v4WgGBgbYvHlz0WVIKjlDX01XqVTYfyBZcXJvW/udfngSgJ9NHK7NuPcXbe3/aObqkqSiGfpqiRUn9/KkZ13Q1j5/cdf1AG3v93jm6pKkovmZviRJJWHoS5JUEoa+JEklYehLklQShr4kSSVh6EuSVBKGviRJJWHoS5JUEoZ+h9m1axe7du0qugxpWfD1JD2eV+TrMKOjowBs2LCh4Eqkpc/Xk/R4jvQlSSoJQ1+SpJIw9CVJKglDX5KkkjD0JUkqCUNfkqSSKCz0I+L1EXFTROyLiFsj4tNF1SJJzTY+Ps7WrVuZmJjoiHYkKCj0I+JpwEeA38/MNcA/Az5URC2S1AojIyMcOHCAkZGRjmhHguJG+mcCjwD3A2TmbGbui4jVEXHf3ELzp+duR8QH60cIMiJeXEz5knR04+PjXH/99czOzrJz585Fj9Kb1Y40p6gr8n0P+AfgYETsAW4Arl7Aek8BvpWZfx4R/wb4j8Bvt6zKAkxOTjI+Ps7w8PAJtTM1NcWOHTuaVFVjKpUKM9UVhfTdiWaqD1OpVE54nzaiyP3fCea2v1Kp0N/f3/b+R0ZGmJmZAWBmZoaRkRG2bNlSWDvSnEJG+pk5k5mvBtYBu4GXAzcDx3t1PpiZ/7t++9vAr7WsSElapD179lCtVgGoVqvs3r270HakOYVeez8z9wP7gSsi4gBwHo9/I3LyE1Y5NO/2NMvwtwN6e3vp7e1l27ZtJ9TO2NgYg4ODTaqqMcPDw/ygck8hfXei7p6TGRh46gnv00YUuf87wdz2t/Poynzr1q1jdHSUarVKT08P69evL7QdFWft2rUXA285xiKf3rt378fbVU9RJ/I9PSJeNG/6GcAZwA+AX4mIX6/fdWER9UnSiRgaGqK7u/bntbu7m6GhoULbUcdaQ5tzrqiRcg/wgYh4FvAQtTcf783M70bEnwKjEXEXtUP/krSk9Pf3c8EFF3DdddexceNG+vr6Cm1Hxdm7d+9VwFVHum/t2rV72ltNQaGfmXcBv3OU+z4BfGLerA/U598JnD5vucdNS1InGRoa4uDBgyc8Om9WOxIsw8/EJakT9Pf3s3379o5pRwIvwytJUmkY+pIklYShL0lSSRj6kiSVhKEvSVJJePZ+h9m0aVPRJUjLhq8n6fEM/Q6zYcOGokuQlg1fT9LjeXhfkqSSMPQlSSoJQ1+SpJIw9CVJKglDX5KkkjD0JUkqCUNfkqSSMPQlSSoJL86jlph+eJJf3HV92/sE2t7v8dTqemrRZUiSoa/mGxgYKKTfiYmVAPT19TE1NcWqVasKqeOXPbWwx0SS5jP01XSbN28uugTGxsYYHBwsugxJ6ih+pi9JUkkY+pIklYShL0lSSRj6kiSVhKEvSVJJGPqSJJWEoS9JUkkY+pIklYQX59EvufLKK6lUKi1rf2JiAqhdOa9VpqamuPHGGzviQkGS1CkMff2SSqXCP/7gFs48tTVPj/serALQ8+A/taR9gJ89WO2gy/BKUmcw9HVEZ57aw5uf29+Stv/u5nGAlrU/vw9J0mP8TF+SpJIw9CVJKglDX5KkkjD0JUkqCUNfkqSSMPQlSSoJQ1+SpJIw9CVJKgkvztOBdu3aBcCGDRsKrkRLzdxz57TTTiu4EkmdyJF+BxodHWV0dLToMrQE+dyRdCyGviRJJWHoS5JUEoa+JEklYehLklQShr4kSSVh6Ev6JePj42zdupWJiYmGllvoekVptL5O3x6pUS0P/Yj4TkTsi4gDEVGt394XEXdExN5W9y+pcSMjIxw4cICRkZGGllvoekVptL5O3x6pUS0P/cz8l5m5Bvg9YDIz19Sn39yM9iNiRTPakVQzPj7O9ddfz+zsLDt37jzqKPeJy1UqlQWtV5SFbtdil5eWgqKvyNcTER8DXgTMAkOZ+YOI+CPgFZn5OoD50/XbQ8C9wG8CfwzsK6D2lpmcnGR8fJzh4eFFtzE1NcWOHTsWtW6lUuGU2ZlF990JHjw8w72Vygk9hktRpVKhv7//hNoYGRlhZqa2/2dmZhgZGWHLli3HXe7yyy9f0HpFWeh2LXZ5qZUiYjXwFeAG4HzgJ8CrgM3AJUAVOJCZQ8dqp+jP9M8FPpqZzwU+C7x3geu9GHh/Zg5m5rIKfKloe/bsoVqtAlCtVtm9e/eCljt48OCC1ivKQrdrsctLbXAOcEVmngtMAq8FtgLPr+foJcdroOiRfmbmTfXb3wZeucD1bsjM21tUU+F6e3vp7e1l27Zti25jbGyMwcHBRa07PDzM1I9y0X13glNXdvO0swdO6DFcippxZGPdunWMjo5SrVbp6elh/fr1C1rurLPO4u677z7uekVZ6HYtdvmyW7t27cXAW45y96f37t378Q6ppe31NNEd8wa6Y8Bq4Gbgmoi4Frj2eA0UPdJ/eN7taR57E1Ll8bWd/IT1HmxlUVKZDQ0N0d1de/l1d3czNHTko4VPXO7SSy9d0HpFWeh2LXZ5HdUa4MKii5in0+ppxKF5t+cy8+XAFcAgMBYRxxzMFz3SP5rbgedGxEnUPut/HbVDGZJarL+/nwsuuIDrrruOjRs30tfXt6DlBgYGFrReURa6XYtdvuz27t17FXDVE+evXbt2T6fUAsXU00LdwNmZuTsibqD2ZuZUjpGXHRn6mfmtiNgJ7AfuAH4APK3YqqTyGBoa4uDBgwsaDc9fbqHrFaXR+jp9e1R6K4BPRcRpQBfw4cw85gC5baGfmXcCp8+b3gOsPcb0EU9IyMxPAp9sSZGSgNood/v27Q0vt9D1itJofZ2+PSqPeoaeN2/6ssW0U/Rn+pIkqU0MfUmSSsLQlySpJAx9SZJKwtCXJKkkOvIre2W3adOmokvQEuVzR9KxGPodaMOGDUWXoCVq7rkzNjZWcCWSOpGH9yVJKglDX5KkkjD0JUkqCUNfkqSSMPQlSSoJQ1+SpJJY8Ff2IuJ/Uvt94i9l5iOtK0mSJLVCIyP9/wP8BfCziPivEXF+i2qSJEktsOCRfmZeDlweEecCFwGfiYhHqI3+r8nM21tUowrwswer/N3N4y1rG2hZ+3N9rGpZ65K0NDV8Rb7MvAUYjogvA38L/CVwaUR8F7g0M7/X5BrVZgMDAy1tvzoxAcCqvr7WdTI11fLtkKSlpqHQj4igNsq/EDgMXA28ArgXeBtwLfDsJteoNtu8eXPRJZywsbExBgcHiy5DkjpKIyfy7QVWA/8DuDAzv/OERf5zRLyjibVJkqQmamSkvx34YmYePtoCmekoX5KkDtXIiXw75m5HRBfQNe++mSbXJUkqoYhYDXwFuAE4H/gJ8CpgM3AJUAUOZOZQUTUuZQv+yl5EnBURn4+I+6k96I/M+ydJUrOcA1yRmecCk8Brga3A8zPzudTCX4vQyPf0P0bt5L0LgAeBFwBfxAdfktRcd2TmvvrtMWrnk90MXBMRF1EbeGoRGgn984G31HfEbP2reX8MXNqSyiRJZXVo3u1pah9Fvxy4AhgExiKi4a+cq7HQn+axd1eTEXEG8HPg6U2vSpKkx3QDZ2fmbuBdQC9warElLU2NvFP6DvB7wOeBr1L76t5DwN4W1CVJ0pwVwKci4jRqJ5F/ODMnC65pSWok9P+Qx87Y/zNqh/VXAR9udlEqxpVXXkmlUmlqmxP1q+/1tfLqe0cwNTXFjh07jr8gtSsQLocLEknLQWbeCZw3b/qy4qpZfo4Z+hHxV0eYN3/yELUr8f1Fc8tSESqVCrfkAVactrJpbVYnax/N3XPo/qa1uWA/v+e4i0w/cNTLTkjSsnO8kf7Z826fTO1rE98F7gKeCfwL4HOtKU1FWHHaSk576VlNa++Bb9wN0NQ2m2muPkkqg2OGfma+ee52RIwAb8zMz82b9xrg9a0rT5IkNUsjZ+//LrUf1JnvC9RO7pMkSR2ukdD/IfD2J8x7G3B788qRJEmt0sjZ+28FPh8R76J2LeSnU/ve/mtaUZgkSWquRn5w56aIOAd4IXAW8FPgW5nptfclSVoCGrqMYT3gv9miWiRJUgs18pm+JElawgx9SZJKwtBvk127drFr166iy9Ay4fNJ0mL404RtMjo6CsCGDRsKrkTLgc8nSYvhSF+SpJIw9CVJKglDX5KkkjD0JUkqCUNfkqSSMPQlSSqJjgv9iLgzIm6NiH31fx8+ynL7IuKUdtcnqfNNTU2xdetWJiYmii5F6iid+j3912Xm/iPdERE9mVnNzDXtLkrS0vD1r3+dAwcOMDIywpYtW4oupxTWrl17MfCWI9y1BtjX5nI6xjEeFyjgsenU0H+ciPgkMAWcA5wBDEbELLAqMx8ssjZJnWV8fJx9+/YxOzvLzp07GRoaoq+vr+iyymwf8Omii1iMiFgNfAW4ATif2s/KvwrYDFxC7eflD2Tm0CK7aPtj06mhvyMiHq7ffnf9/xcBL8vMnxdU0wmZnJxkfHyc4eHhtvQ3NTXFjh07GlqnUqkw0z3dooo608zD01Qqlbbtl2apVCr09/cXXUZHGhkZYXZ2FoCZmRlH+22yd+/eq4Criq6jBc4B3piZmyPis8Brga3AszPzUET0HmvlTntcOu4z/brXZeaa+r+v1uftWKqBL6l99uzZw/R07c1rtVpl9+7dBVekJe6OzJw7BD8GrAZuBq6JiIuojfaXjE4d6R/Jkj6M39vbS29vL9u2bWtLf2NjYwwODja0zvDwMLf+7IctqqgzdZ+8goEzB9q2X5plqR2ZaKd169bxta99jenpaXp6eli/fn3RJWlpOzTv9jRwCvBy4KXA7wPvi4hzM3NJhH+njvQlaVGGhobo6uoCoLu7m6GhxX7cKh1RN3B2Zu4G3gX0AqcWW9LCGfqSlpX+/n7WrFlDV1cXGzdu9CQ+NdsK4FMR8X3gJuDDmTlZcE0L1nGH9zNz9RHm/dER5nW1ox5JS8/LXvYyHnroIUf5OiGZeSdw3rzpy4qrpjk6LvQl6UStWrWK7du3F12G1HE8vC9JUkkY+pIklYShL0lSSRj6kiSVhKEvSVJJePZ+m2zatKnoErSM+HyStBiGfpts2LCh6BK0jPh8krQYHt6XJKkkDH1JkkrC0JckqSQMfUmSSsLQlySpJAx9SZJKwtCXJKkkDH1JkkrCi/PocaYfOMwD37i7ae1VJw8BNLXNZpp+4DCcWXQVktQehr4eNTAw0PQ2J06aAKCvr6/pbR/L1NQUq1atOv6CZ7ZmuyWpExn6etTmzZuLLqFpxsbGGBwcLLoMSeoofqYvSVJJGPqSJJWEoS9JUkkY+pIklYShL0lSSRj6kiSVhKEvSVJJ+D19LcqVV15JpVJpW38TE41d5GdqaoodO3a0sqQjGhgYWFbXO5C0vBj6WpRKpcJtt9zC6StWtKW/+6pVAGbvuWfB64y3qpijuG96us09SlJjDH0t2ukrVvCqVb1t6esLU5MAbetvMeZqlKRO5Wf6kiSVhKEvSVJJGPqSJJWEoS9JUkkY+pIklYShL0lSSRj6kiSVhKEvSVJJGPoLsGvXLnbt2lV0GZKOwNentHBekW8BRkdHAdiwYUPBlUh6Il+f0sI50pckqSQMfUmSSsLQlySpJAx9SZJKwtCXJKkkDH1JkkqiI0I/Il4fETdFxL6IuDUiPn2c5VdHxJ+0qz5JOpLx8XG2bt3KxMRES/u49NJLeec737noftpRp5aGwr+nHxFPAz4CvCAzfxQRXcDzjrPaauBPgI+3uDxJOqqRkREOHDjAyMgIW7ZsaVkft91226O3F9NPO+rsJGvXrr0YeMtR7l4D7GtjOR2lE0b6ZwKPAPcDZOZsZu4DiIhrImJvRHw/Ij4fEX31da4AfrN+ZGBHMWVLKrPx8XGuv/56Zmdn2blzZ0tG0ePj4+zcufPR6dHR0Yb7aUedS8w+4JhHk5ezwkf6wPeAfwAORsQe4Abg6sy8H/jTzLwPICL+Gng3sBV4O3BZZq5tR4GTk5OMj48zPDzcju6aYmpqih07Wvd+qFKpsHJmpmXtL0W/mJlhslLpiOdJq/d/J6lUKvT397e935GREWbqr4GZmZmWjKJHRkaoVquPTler1Yb7aUednWbv3r1XAVcVXUcnKnykn5kzmflqYB2wG3g5cHNE9AMXR8RYRHwfuJDaYRlJKtyePXseDeRqtcru3btb0sfs7Oyj07Ozsw330446tXR0wkgfgMzcD+wHroiIA8A7gIuA8zPz3oi4kNrn+G3X29tLb28v27ZtK6L7RRkbG2NwcLBl7Q8PDzN+660ta38pelJ3N88YGOiI50mr938nKerIyrp16xgdHaVardLT08P69etb0sd11133aPB3dXU13E876tTSUfhIPyKeHhEvmjf9DOAMYAZ4ALg/Ik7i8Sdl/D/gtLYWKknzDA0N0d1d+xPa3d3N0NBQS/ro6XlsbNbT09NwP+2oU0tH4aFP7WjDByIiI2If8GXgvcA24HbgVuArwI3z1rkZyIjY74l8korQ39/PBRdcQFdXFxs3bqSvr+/4Ky2ij40bNz46vWnTpob7aUedWjoKP7yfmXcBv3OUu99wlHWqwCtaVpQkLcDQ0BAHDx5s6eh5aGiI22+/na6urkX30446tTQUHvqStFT19/ezffv2lvdx+eWXn3Abra5TS0MnHN6XJEltYOhLklQShr4kSSVh6EuSVBKGviRJJeHZ+wuwadOmokuQdBS+PqWFM/QXYMOGDUWXIOkofH1KC+fhfUmSSsLQlySpJAx9SZJKwtCXJKkkDH1JkkrC0JckqSQMfUmSSsLQlySpJLw4jxbtvulpvjA12Z6+qlWAtvW3GPdNT9NfdBGSdAyGvhZlYGCgrf11TUwA0NfXt6Dlp6amWLVqVStL+iX9tP9xkaRGGPpalM2bNxddwjGNjY0xODhYdBmS1FH8TF+SpJIw9CVJKokyHd5fAXD48OGi62ibQ4cOFV1Codx+t7+smrnt8/5mrmhaoypM1+zsbNE1tMXY2NiLgW8WXYckLVEvGRwcvKHoInRiyjTS/y7wEuCnwHTBtUjSUrECeBq1v6Fa4koz0pckqew8kU+SpJIw9CVJKglDX5KkkjD0JUkqCUNfkqSSMPQlSSoJQ1+SpJIo08V5lq2IuAx4LbAa+OeZub8+/znAfweeAtwPXJyZ/1hUna0QEU8BrgZ+DTgE/BD4t5l5b0S8EPgYcApwJ3BRZv5TUbW2SkRcCzwbmAEeBN6RmfvKsP/nRMRfAu+n/vwv0b6/E3i4/g/g3Zn51bJsvxrnSH95uBZ4KXDXE+Z/FLgiM58DXEHtj8ByMwt8KDMjM58L3A5sj4gu4FPA2+vb/w1ge4F1ttKbMvN5mfl84DLgE/X5Zdj/RMQLgBcCB+vTZdr3AK/LzDX1f18t4farAYb+MpCZN2Tmj+bPi4hfBV4AfKY+6zPACyLijHbX10qZOZ6Ze+bN+jbwLGAt8HBmzl0r/KPAH7S5vLbIzAfmTZ4GzJRl/0fESdTe0LyN2htAKNG+P4qyb7+OwdBfvs4GfpKZ0wD1/++uz1+WIqIb2AJ8EXgm8458ZOZ9QHdE9BdUXktFxH+LiIPAB4E3UZ79/1fApzLzjnnzSrXvgWsi4uaI+EhE9FK+7VcDDH0tJ39D7TPtvy26kHbLzLdm5jOB9wD/qeh62iEiXgT8FvCRomsp0Esy83nUHocuSvjcV2MM/eXrR8DTI2IFQP3/s+rzl536yYznAG/IzBlqn+8+a979pwOzmTleUIltkZlXA+uBH7P89//LgN8A7qif0PYM4KvAr1OSfT/3sV5mHqL25ue3KelzXwtj6C9T9TN19wFvrM96I3BTZt5bXFWtEREfBAaBV9f/+AGMAadExIvr05cAny2ivlaKiFMj4ux5068ExoFlv/8zc3tmnpWZqzNzNbU3Ov+K2pGOMuz7J0fEafXbXcAQtX1eiue+Fsef1l0GIuK/AK8BzgTuA+7PzHMj4jeofWWrD5ig9pWtLK7S5ouIc4H9wG3AQ/XZd2Tmv46I86mdsX4yj31t6Z5CCm2RiHgq8AXgycA0tcB/Z2beWIb9P199tP+K+lf2yrDvB4DPUfu9+xXAAeDfZeZPy7D9WhxDX5KkkvDwviRJJWHoS5JUEoa+JEklYehLklQShr4kSSVh6EuSVBKGvlSwiPhkRPz1cZZZFxE/Psb9H42I9x1p2Yi4MyI2Nq9iSUtVT9EFSEtB/cIvb83Mnc1ctlky85J29SVp6XKkL0lSSTjSl44jIq6m9nOl/ysipqn9nOutwDbg6dSud74lM39wpGUz80MR8ffAS4BTgO/Vl79lEbW8B/j31H5N8M8z85r6/E8CP87M957Y1kpazhzpS8eRmX9I7ZfLXpmZpwLXAp8B/gw4A/gytZBf+cRlM/ND9Wa+Qu1XAH8VuBG4ZhGlnAmcTu2NxpuAj0dELH7LJJWNoS817g3AlzJzNDMfAS6jNoI//2grZOYnMnOq/iuA7weeN/cLaQ16X2YeysyvA18C/mARbUgqKQ/vS407C7hrbiIzZyLiR9RG4L+k/lv2HwReT+3IwEz9rtOBBxrodyIzfz5v+q56LZK0II70pYWZ/3OUdwPPmpuo/5b52cBPjrAswIXAq4CNwGnA6vr8rgZr6IuIJ8+bfma9FklaEENfWph7gIH67c8CL4+ICyLiV4BLgUPA/z3CsgCr6vffDzwJ+A8nUMcHImJlRLwEeAXw9yfQlqSSMfSlhdkGvDciJoFXAhcBfwPcV59+ZWYefuKyEfFO4Cpqh+J/AhwAvr3IGn4GTFAb3V8DXJKZty6yLUkl1DU7+8QjkZIkaTlypC9JUkl49r7UIeoX3nnPEe76Zmb+brvrkbT8eHhfkqSS8PC+JEklYehLklQShr4kSSVh6EuSVBKGviRJJfH/ARDNzIqvuGPMAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = \"total_bill\"\n",
+    "y = \"day\"\n",
+    "order = ['Sun', 'Thur', 'Fri', 'Sat']\n",
+    "ax = sns.boxplot(data=df, x=x, y=y, order=order)\n",
+    "ax, test_results = add_stat_annotation(ax, data=df, x=x, y=y, order=order,\n",
+    "                                   box_pairs=[(\"Thur\", \"Fri\"), (\"Thur\", \"Sat\"), (\"Fri\", \"Sun\")],\n",
+    "                                   test='Mann-Whitney', text_format='star', loc='outside', verbose=2)\n",
+    "plt.savefig('example_non-hue_outside_horizontal.png', dpi=300, bbox_inches='tight')"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -106,7 +172,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -137,7 +203,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -158,12 +224,14 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAFNCAYAAADvmHORAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAbjElEQVR4nO3de5ScdZ3n8XcnbUKQLHQLCgiBZFe+uxIwkMwKjmAIMMsZx6MzXmgy6KozKKwHPHOGWYO6zuysDiyyjmdWlMt6G7lEJ7urHmcnnkCSRbzskizRjcgXsQMJIpfQSTYZTGJf9o+qfuhAku5OVddTT+X9OofTVU91PfXNQ1d96vf8Lk/XyMgIkiQBTCu7AElS+zAUJEkFQ0GSVDAUJEkFQ0GSVDAUJEkFQ0GVERFd9Z9/Mfa+pObpcp6CqiIiLgdOBHqBAeDJzLyj3KqkzmIoqFIi4jLgTmBpZi4vux6p0xgKqoyIWAqcxAsthScy865yq5I6i30KqpK7M/NGYHf9591lFyR1GlsKkqSCLQVJUsFQkCQVDAVJUqG77AIOB4sWLfoAsLTsOqQDuGvdunW3lV2E2oMthdZYCiwouwhpPxbgFxaNYUuhdTasW7ducdlFSGMtWrRobdk1qL3YUpAkFQwFSVLBUJAkFexTaI0vlV2AdAD+bWofLnMhSSp4+kiSVDAUJEkFQ0GSVDAUJEkFQ0GSVDAUpMNURHTVf/7F2Ps6vDkkVTpMRcTlwIm8cM3rJzPzjnKrUtkMBekwFhGXAXcCSzNzedn1qHyGgnSYioilwEm80FJ4IjPvKrcqlc0+BenwdXdm3gjsrv+8u+yCVD5bCpKkgi0FSVLBUJAkFQwFSVLB6ymoUhYtWvQBvNC82ttd69atu63sIg6VLQVVzVJgQdlFSAewgIp/abGloCrasG7dusVlFyG92KJFi9aWXUOjbClIkgqGgiSp4OkjVY0Xmlc7q/zfpzOaJUkFTx9JkgqGgiSpYChIkgqGgiSpYChIkgqGgiSpYChIkgqGgiSp4IxmqQki4lTgH4D7gTcAvwTeClwBXAkMAg9lZl9ZNUoTYUtBap7XADdn5unAduDtwDLgrMw8k1o4SG3NUJCaZ1NmbqjfXg+cCvwEuDMiLqfWWpDamqEgNc+eMbeHqJ2efTNwM7AQWB8RnrJVWzMUpKkzDTg5M9cA/xY4Bjiq3JKkg/NbizR1pgN3RMTRQBfw15m5veSapINy6WxJUsGWgipl0aJFH6DiF0ZvQ3etW7futrKLUHuwT0FVsxRYUHYRHWQBhqzGsKWgKtqwbt26xWUX0QkWLVq0tuwa1F5sKUiSCoaCJKng6SNVzZfKLqDDeDy1D4ekSpIKnj6SJBUMBUlSwVCQJBUMBUlSodKjj9avXz8T+C3gV9SWKpYkjW86cALwwMKFC8cu+V7tUKAWCN8ruwhJqqjzqF1CtlD1UPgVwGmnncaMGTPKrkWSKmHv3r088sgjUP8MHavqoTAEMGPGDGbOnFl2LZJUNS857W5HsySpYChIkgqGgiSpYChIUpMMDAywbNkytm3bVnYph8xQkKQmWb58OQ899BDLly8vu5RDZihIUhMMDAxw7733MjIywj333FPZ1oKhIElNsHz5coaHhwEYHh6ubGuhZfMUIuIxYHf9P4CPZOZ3I+Ic4FZgFvAYcHlmPtOquiSpGdauXcvg4CAAg4ODrFmzhquuuqrkqiav1S2Fd2Tmgvp/342ILuAO4EOZeRpwH3BDi2uSpIYtXryY7u7a9+zu7m4uuOCCkis6NGWfPloE7M7M0bU3bgHeVWI9knRI+vr6mDat9pE6bdo0+vr6Sq7o0LR6mYs7662D+4GPAnOAx0cfzMytETEtInozc2CiO924cWPzK5WkSTrzzDNZv349Z555Jv39/WWXc0haGQrnZeaWiJgJfBb4HPDfm7Hj+fPnu/aRpNLNnTuXG2+8kWuuuYaenp6yyzmgPXv2HPDLdMtOH2XmlvrPPcDngd8GNgOnjP5ORBwLjEymlSBJ7aK3t5cbbrihrQNhPC0JhYh4eUQcXb/dBfQBG4D1wKyIeGP9V68EvtGKmiSp2fr7+7n00kvZtGlT2aUcsla1FF4FrI2InwAbgdOAf5OZw8C7gS9ExM+BNwHLWlRTy3TC1HdJ47vpppt4/vnnuemmm8ou5ZC1pE8hM/uBsw7w2A+AM1pRR1nGTn2v4rhlSePr7+9ny5YtAGzevJlNmzYxd+7ckquavLKHpHa8Tpn6LungXtw6qGprwVCYYp0y9V3SwY22EkZt3ry5pEoaYyhMsf1NfZfUeU4++eR97s+ZM6ekShpjKEyxTpn6Lungrr322oPerwpDYYp1ytR3SQc3b968orUwZ86cSnYyg6Ew5Xp7e7nwwgvp6urioosuqvSkFkkHd+2113LkkUdWtpUArV/76LDU19fH5s2bbSVIHW7evHl8/etfL7uMhhgKLTA69V2S2p2njyRJBUOhBVzmQlJVGAotMHaZC0lqZ4bCFHOZC0lVYihMMZe5kFQlhsIUc5kLSVViKEwxl7mQDh+dMKjEUJhiLnMhHT46YVCJoTDFXOZCOjx0yqASQ6EF+vr6eO1rX2srQepgnTKoxFBogdFlLmwlSJ2rUwaVGAqqlE7oyFNn6pRBJYaCKqUTOvLUmTplUImhoMrolI48daZOGVRiKKgyOqUjT52rEwaVGAqqjE7pyFPn6oRBJYaCKqNTOvKkdmYoqDI6pSNPameGgiqjUzrypHZmKKhSLrnkEmbNmsUll1xSdilSRzIUVCkrV67k17/+NStXriy7FKkjGQqqDOcpSFPPUFBlOE9BmnqGgirDeQrN51pSerHuVr9gRPw58BfAGZm5MSLOAW4FZgGPAZdn5jOtrutAVq9ezapVqxrax/bt2wE45phjDnkfF198MUuWLGmojqpbvHgxq1atYnBw0HkKTTJ2Lamrrrqq7HLUBlraUoiIs4FzgM31+13AHcCHMvM04D7ghlbW1AoDAwMMDAyUXUblOU+hueyj0f60rKUQETOBm4GlwGi7fxGwOzPvr9+/hVpr4f2tqms8S5Ysafgb+nXXXQfA9ddf34ySDluj8xRWrlzpPIUm2F8fja0FtbKl8JfAHZm5acy2OcDjo3cycyswLSJ6W1iXKqQTFhxrF/bRaH9a0lKIiHOB3wKWTcX+N27cOBW7bZqdO3cCsH79+pIr6QzvfOc76e/vL7uMyjv99NN58MEHGRoaYvr06Zx++un+japlp4/eBPxzYFNEAJwEfBf4G+CU0V+KiGOBkcyc1An4+fPnM3PmzOZV22QrVqwAYOHChSVXIr1g7ty5XHHFFUUoXHPNNYf1KbnDaVDJnj17DvhluiWnjzLzhsw8MTNPzcxTgSeAfwV8GpgVEW+s/+qVwDdaUZN0uHMtqebrhEElLR+SOlZmDkfEu4FbI+II6kNSy6xJOpz09fWxefNm+2hwUMmoUkKh3loYvf0D4Iwy6pAOd6MXhZFGOaNZklQwFCRJBUNBklQwFCRJBUNBklQwFCRJBUNBklQwFCRJBUNBklQwFCRJBUNBklQwFCRJBUNBlTIwMMCyZcu8nrA0RQwFVcry5ct56KGHWL58edmlSB3JUFBlDAwMcM899zAyMsKqVatsLUhTwFBQZSxfvnyfC83bWpCaz1BQZaxZs4aRkREARkZGWL16dckVSZ3HUFBlHHfccfvcf+UrX1lSJVLnMhRUGc8+++xB70tqnKGgyrjgggvo6uoCoKuriwsuuKDkiqTOYyioMvr6+uju7gagu7ubvr6+kiuSOo+hoMro7e3lvPPOA+D888+np6en5IqkzmMoqFJGRx9JmhqGgipjYGCA73//+wB873vfc/KaNAUMBVXG8uXLGR4eBmB4eNjJa9IUMBRUGWvXrt1nRvOaNWtKrkjqPN0HezAilkxkJ5np1FJNucWLF7Nq1SoGBwfp7u52SKo0BQ4aCsAXJ7CPEWBeE2qRDqqvr497770XgGnTpjkkVZoCBw2FzJzbqkKk8fT29nLhhReycuVKLrroIoekSlNgvJaC1Fb6+vrYvHmzrQRpiozXp7CF2umhg8rMOU2rSDqI3t5ebrjhhrLLkDrWeC2Fy1tShSSpLYzXp/A/W1WIJKl8450++lhmfqp++y8P9HuZ+YnxXigivgnMBYaBXcDVmbkhIk4Dvgq8AngOeE9m/nzi/wRJUrOMd/ropDG3T27wtf51Zu4AiIi3Al8CzgZuAW7OzDsi4nLgVmBC8yMkSc013umjq8bcfl8jLzQaCHVHA8MR8UpqwXBxffvdwOci4rjM9AoqktRikxqSGhGvAd4FnAg8CXxjMqd6IuK/AL8DdAGXUGt9/DIzhwAycyginqxvn3AobNy4ccL/hjLs3LkTgPXr15dcSfk2bNjAgw8+eMjP37VrFwBHHXVUQ3WcddZZLFiwoKF9SC/WCe/1CYdCRCwFbgP+HngcOANYFhEfzMy7JrKPzPzj+r7eDXwa+HeTrng/5s+fz8yZM5uxqymxYsUKABYuXFhyJeXbsWMHjz766CE/f/QSnCeccEJDdcydO9f/H2q6qrzX9+zZc8Av05NpKXwS+N3MvG90Q0ScB3wNmFAojMrMr0XEbcATwKsjYnq9lTCdWitky2T2p+pYsmQJS5YcepfRddddB8D111/frJIkjTGZUJgN/PBF234EvHy8J0bEUUBPZm6p338LMAA8A2wALgPuqP980P4EaXyrV69m1apVDe1j+/btABxzzDEN7efiiy9uKOzVPiazdPZngL+KiCMAImIW8Kn69vG8HPi7iPi/EbEB+BPgLZk5AlwJXB0RjwBX1+9LaoGBgQEGBgbKLkNtZDLLXHQBxwMfjohtQE9926+Ag7blM/Np4JwDPPYw8PrJlS2p0VNx4Ok4vZTLXEiSCk1d5iIi/j4z39xYSZKksjT7cpznNXl/kqQW8hrNkqSCoSBJKhgKkqRCs0Ohq8n7kyS1ULND4a+avD9JUguNN3ntgBfWGWv0IjuZ6QwYSaqw8SavNXphHUlShYw3ea2hC+tIkqplUhfZAYiI2cCxjOlUzsz+ZhbVLLfffjv9/eWXNlrD6DozZZk3bx5XXHFFqTVIam+TucjOa4E7gddRWySvixcWy5ve/NIa19/fz8aHkulHNLYscKOGB2uH52f9T5dWw9Du7aW9tqTqmExL4fPAGuACYBNwKrXVUX/Q/LKaZ/oRx3DkKReWXUbpnn/83rJLkFQBkxmS+jrgI5m5HejKzB3AnwH/YUoqkyS13GRaCruBlwG/AbZGxBxgG/CKqShMkibK/sN9NdJ/OJlQ+B7wLuArwArgH4A9wOpDemVJapL+/n4e+elPOXZ6ud2bM4aHARh4+OHSatg6NNTQ8yccCpn5rjF3PwpspHbd5q82VIEkNcGx06fz1tnlDippB9/a2digkgn3KUTEtaO3M3M4M+/IzC/gNZUlqWNMpqP5EwfY/vFmFCJJKt+4p48iYvTK4NMj4gL2XQl1HrBzKgqTJLXeRPoUvlj/eQTwpTHbR4CngKubXZQkqRzjhkJmzgWIiL/NzPdMfUmSpLJMZvTReyKiG3gD8GrgCeCHmTk4VcVJklprMmsfBfAdYBawhdqy2rsj4i2Z+bMpqk+S1EKTGX30BeA24OTMPDczTwJuobYmkiSpA0wmFBYAn8nMkTHbPlvfLknqAJNZ5uJJ4E3su6zFefXtkibBtXr25bU+2sdkQuE64NsR8R3gceAU4M3A5VNRmNTJ+vv7+Wk+xPSjZ5Rax/C02jo5Dz/1aGk1DO3YW9pr66UmEwoBnAVcCpxIbe2jTwC/NwV1SR1v+tEzOPr8E8suo3Q77vNkQzuZTCh8IjNvAj45dmNEfBz4TFOrkiSVwmUuJEmFRpe5eJoJLHMREa8Avgb8U2rXYHgU+GBmPhsR5wC3Upv/8BhweWY+M9F/gCSpeVq1zMUIcGNmrq3v69PADRHxx8AdwHsz8/76qagbgPcf4utIkhowqWUuDvVFMnMAWDtm04+Aq4BFwO7MvL++/RZqrQVDoQ21wzDKdhlCCQ6jVGeaTEdzU0TENGqB8G1gDrXhrQBk5taImBYRvfUgmZCNGzfud/vOnXZ3jLVz507Wr19/yM//8Y9/zFNPbOb4o1r+Z1OYNVK73OHOLVlaDQBP7Rps6Hj6t7mvRv82PZ77auR4lvHu/s/ALuBzwO83Y4fz589n5syZL9l+++23M7R7O88/fm8zXqbShnZvZ3BwBgsXLjzkfaxYsQKO6uZ9Z/Y2sbJq+vJPBpg9e/YhH88VK1bAPz7d5Kqqq5FjCbXjOeFvkYeB8Y7nnj17DvhlejLLXDQsIm4CXgNcmpnDwGZqk+BGHz8WGJlMK0GS1DwtaylExKeAhcCbM3NPffN6YFZEvLHer3Al8I1mvWZPTw9PbdvLkadc2KxdVtbzj99LT09P2WVIanMtCYWIOB34KPAI8IPaKtxsyszfj4h3A7dGxBHUh6S2oiZJ0ku1JBQy86fsO+lt7GM/AM5oRR2SpINraZ+CJKm9lTe2UJKaZNu2bWwdHORbO7eXXUrptg4O0rVt2yE/35aCJKlgS0FS5fX09DDy9NO8dfYxZZdSum/t3N7QSENbCpKkgqEgSSoYCpKkgqEgSSoYCpKkgqOPNGHbtm1j665BvvwT1yt8atcggw2MBZfalS0FSVLBloImrKenh+5dz3g9BerXU3DVWXUgQ0EqwbZt2xjcvocd9z1ZdimlG9y+h20zPRXXLjx9JEkqdHxLoR0uxzk8uBuAad1HlFbD0O7twKtKe33tq6enh6f3PMfR559Ydiml23Hfk14Aqo10dCjMmzev7BIA6O/vB2DevDI/lF/VNsdDUvvq6FC44ooryi4BgOuuuw6A66+/vuRKJOng7FOQJBUMBUlSoaNPH0k6fGwdGir9ymvPDw8DcOS08r5vbx0aopGZRIaCpMprl0EU2+uDSk4qsZ5eGjsehoKkynNQSfPYpyBJKhgKkqSCoSBJKtinoEl5quTrKezaWxvdcdSMcr/PPLVrkNmlViBNDUNBE9YOIzyerY/uOOHkcmuZTePHY2jH3tJXSR3ePQTAtCOml1bD0I69cHxpL68XMRQ0Ye0wwqMTRndAewQsjFmX6/gS6zm+fY6HDAWpFO0QsNA5IavmsaNZklQwFCRJBUNBklQwFCRJhZZ0NEfETcDbgVOBMzJzY337acBXgVcAzwHvycyft6ImSdJLtaql8E3gfODxF22/Bbg5M08DbgZubVE9kqT9aEkoZOb9mbll7LaIeCVwNnB3fdPdwNkRcVwrapIkvVSZ8xROBn6ZmUMAmTkUEU/Wtz87mR1t3LhxCsprnp07dwKwfv36kiupPo9lc3k8m6sTjmdHTF6bP38+M2fOLLuMA1qxYgUACxcuLLmS6vNYNpfHs7mqcjz37NlzwC/TZY4+2gK8OiKmA9R/nljfLkkqQWmhkJnPABuAy+qbLgMezMxJnTqSJDVPS0IhIv4mIp4ATgLuiYif1h+6Erg6Ih4Brq7flySVpCV9Cpl5DXDNfrY/DLy+FTVIksbnjGZJUsFQkCQVDAVJUqEj5imoOlavXs2qVasO+fmjVwobvTjMobr44otZsmRJQ/uQOpGhMI5GP8SgOR9kfojV9Pb2ll2C1NEMhRbwg+wFS5YsMdykNmYojMMPMUmHEzuaJUkFQ0GSVDAUJEkF+xSkimqXkXHg6LhOYihIhzFHxunFDAWpohwZp6lgn4IkqWAoSJIKhoIkqWAoSJIKhoIkqWAoSJIKhoIkqWAoSJIKTl6TJNpn2ZCylwwxFCSpSTph2RBDQZJw2ZBR9ilIkgqGgiSpYChIkgqGgiSpYChIkgqGgiSpYChIkgpVn6cwHWDv3r1l1yFJlTHmM3P6ix+reiicAPDII4+UXYckVdEJwC/Gbqh6KDwAnAf8ChgquRZJqorp1ALhgRc/0DUyMtL6ciRJbcmOZklSwVCQJBUMBUlSwVCQJBUMBUlSwVCQJBUMBUlSwVCQJBWqPqO5LUTEO4GPAl3AEcD/ycyl5VZVHRHxv4CZwAzgNGBj/aGjgecyc1FZtXWSiHgM2F3/D2BNZv7Jfn5vA3BuZv66ddVVx2Tf7xFxKvA7mXlbaypsjKHQoIg4Afg8cHZmbomILuB1JZdVKZn5eijePOsyc0H9/mLgpkb3HxHTM9NlUGrekZkb9/dARHRn5uDo8ddLHeL7/VTgA4ChcJg4HvgN8BxAZo4AG8Z8wB0L+3zgHTt6G7gV+F3gSOCPMvP+llff/roj4lbgXGAE6MvMn0XEe4Hfy8x3AIy9X7/dBzwLvBb4I2BDCbW3vYj4CrATeA1wHLAwIkaA2Zm5q8za2tR+3+8AEXEnENRavY8C78/MbcDNwNx6C+zR0b/ZdmUoNO7HwP8GNkfEWuB+4GsTeN4rgB9m5sci4g+B/wj89pRVWV2nA+/LzA9GxMeAjwN/OIHnvRF4XWb+YtzfPLysiIjR00cfqf88F3hTZv5jSTVVyX7f75n5HPDhzNwKEBGfpHZ8lwEfAm6qymlQQ6FBmTkMvC0i5gNvAt4G/BnwlnGeuiszv1O//SPgP01dlZWWmflg/faPGP+4jrrfQNivfU4fRcRlwAoDYWIO9H6PiDOA99S/4M0AXg5Uck1/Q6FJ6m+0jcDNEfEQMJ99R3cd8aKn7Blzewj/XxzI7jG3xx6nQQ5+fD31MXEeq0naz/v9auBy4A2Z+WxELKXWj1A5DkltUES8OiLOHXP/JGrnZn8GvCwi/ln9IUcjNdcvgDMjYmZEzADa+jytOsNB3u/DwA7guYiYCbx/zNP+H7WRdJXgt9PGdQP/PiJOAX5NLWg/npkPRMSHgVUR8TiwpswiO01m/jAi7qH2bW0TtRA+odyqdBjY7/sd+CK1swMPA09QG0jyL+vP+QmQEbEReLjdO5q9yI4kqeDpI0lSwVCQJBUMBUlSwVCQJBUMBUlSwVCQmiAivlJf2kCqNENBklQwFCRJBWc0S4cgIs6iNov1NcD/oLasNxHRQ22V3NdTe399H7gyM5+oX5xlWWYuHLOfPwXOy8y3tfifIO2XLQVpkuprLX2T2od/L/B3wNvrD08DvgycAsyhthTC5+qPfZvauvr/YszuLmdiS61LLWEoSJN3DvAy4LOZ+ZvMXAE8AJCZz2Xmf83M5zNzJ/Apakssk5l7gK9TCwIi4nRqV+X6zktfQiqHp4+kyTsR+GX9qlujHgeIiCOBvwYuAXrqj80ec0nQrwJ3R8THgXcD36iHhdQWbClIk/cr4NX16/OOmlP/+afULsn4+sz8J8D59e1dAJn5I2AvcB615dQ9daS2YihIk/dDahf5uSYiuiPiD3hhmeTZ1PoRtkdEL/Dn+3n+31LrZxj0utxqN4aCNEmZuRf4A+C9wDbgUuC/1R/+LDAL2Ert8qEr97OLr1Fbe99WgtqO11OQWiwiZgHPAGdn5s/Lrkcay5aC1HpXAQ8YCGpHjj6SWigiHqPW6exkNbUlTx9JkgqePpIkFQwFSVLBUJAkFQwFSVLBUJAkFf4/rb0TeZsc5kUAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -194,7 +262,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -215,12 +283,14 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -252,7 +322,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -367,7 +437,7 @@
        "4   0.31     Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -385,7 +455,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 9,
    "metadata": {
     "scrolled": false
    },
@@ -414,12 +484,14 @@
     },
     {
      "data": {
-      "image/png": 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eeADl5eV48cUX0bFjR8ycOdPmcYkJk38bueo8Y1e9LrJcSkoKKisrkZKSgvfff1/ocMgKWVlZ0Ol0AGp7t1999ZUok7+Qbt26hb179+LYsWN44IEHAAA+Pj5Ys2YNfvzxRxQXF2PVqlW4ceMGZDIZli5disjISFRVVWH16tW4dOkSJBIJnnvuOUyePBk1NTVYtWoVzp07h6CgINGPtWDyb6NJkyYJHYJduOp1kWXy8vJQWFgIALh69Sry8/PZ+3ciI0eOxMGDB6HT6SCTyTBq1CihQxKdnJwc9OrVC506dTLb36tXL/Tq1Qsvv/wyhg8fjrlz56KwsBDTp09Heno6Pv30U/j6+iIzMxNqtRpPPfUU+vTpg6+//hoAsH//fhQUFIi+A8XR/kTUQP21Mlx17QxXFR8fb3bbPz4+XuCIxOn+5/oqlQpxcXGIjY3F1KlT8e9//xtPPvkkACAkJAT9+/fH2bNnzfYrFAqMGTMG2dnZyM7ORkxMDADgwQcfxK9//WvHX5AVmPyJqIG6Xn+dq1evChQJkX1ERETgp59+Qnl5OYDauiYZGRnYtm0bSkpKUL/4rdFohF6vb3J//eV6ZTJx31hn8ieiBkJCQsy2u3fvLlAk1BqpqammXq1EIkFqaqrAEYlPYGAgHn/8cbz22mv4+eefAdSOj8jKyoJUKsXw4cOxe/duALVfhr/99lsMGDDAbH/drIqhQ4dixIgR+OKLL2AwGHD9+nV8++23gl2bJcT91YSIBLFs2TK8/PLLZtvkPLKysqDX6wEAer2eA/6asG7dOnz22WeYPXs29Ho9KioqMGzYMHz88cdo37491qxZg7S0NADAm2++CaVSiRdffBHr1q1DbGws9Ho9Fi5ciPDwcPTu3RuXL19GTEwMgoKC0Lt3b4GvrnlM/kTUQGhoKEJCQlBYWIju3btzsJ+TcZYBf119u1g8Lc/a81pCKpXiueeew3PPPdfozz/88MMG+3x8fBodAyOXy/Hmm29aF6iAmPyJqFHz58/HunXrWO3RCcXHx+Pw4cMAahOcWAf8WVKIh+yDz/wdRKPR4O7du0hISEB1dTU0Gg0+/vhjHD58GBqNBjqdDjqdDtOnT4fRaERNTQ00Gg1ycnKQlJQEjUYj9CW4NXdsv2+++QZGoxEnTpwQOhSbaU07OqO6UegSiQRjx44VZXU/EhZ7/g6iUqmQmZmJ/Px8vPDCC9BqtSgrK0P79u2RnZ2NJ554Ahs3bkRRURHmz5+PuXPn4sKFCzh+/DjUajXy8/OxZcsWeHp6Cn0pbsnd2s9ZysNaqzXt+OijjwoddqvEx8fj6tWrou31k7DY83eQSZMmwdPTE+Hh4YiLi8OaNWvQpUsXSKVSPPvss+jTpw+GDx+OiIgIBAYG4tFHH8WMGTMglUoREhKCuXPnOk3icEXu1n6NlYd1Ba1pR2elUCiQnJzsEl/ayPYkxvqTFl1cdXU1zp07h4iICHh5eTns9xqNRuTk5CAsLAz5+flQKpWorKxEZWUlgoKC4OPjg7Nnz6Jfv3747rvvMHDgQNTU1CA3NxcBAQGoqKjgdCsBuVv7TZs2DVVVVabtdu3aYdeuXQJGZButaUeqZYvPzosXL6Jv3742joya0tz7zeRPRA1s3boVKpUKRqMREokEMTExnCrm5pj8nU9z7zdv+xNRA9HR0aZqZUajEdHR0QJHRES2xAF/RNSASqUylSuVSCRQqVTs+ZPNrVuxAqXFxTY/b+euXbEuKanZY6ZPn47f/va3mDhxomlfZWUlRo0ahf3790OhUNgsHrVajbfffhvZ2dmQyWTw9vZGQkICxowZY5Pzh4WF4dKlS1a9hsmfiBrIysoy6/mzQhzZQ2lxMcZX62x+3i8t+EIxdepUfPHFF2bJ/8svv8SwYcNsmvhramowZ84cTJgwASqVCh4eHsjLy8Nzzz2HoKAg9OnTx2a/yxpM/kTUgLNUiCNqrZiYGGzcuBGlpaXo3LkzAGDv3r2YM2cOAJjV6PD19cX69esREhKCWbNmoVOnTrh8+TKefvppnD9/Hm+//TYA4L333oOXl5dZYawDBw7Ay8sLCQkJpn2hoaFYt26dqQTz999/jw0bNqC6uhq+vr54/fXX0aNHD+Tn52PNmjUoLS1F+/btsWrVKvTr1w/Xrl1DYmIiKisr0b9//1ZdP5N/G6WlpUGlUgkdhpno6GhMmTJF6DCcAtuvcc5SIU5s7SeGtiPLdOjQAWPGjIFKpUJ8fDyKioqQn5+Pxx57DDU1NVi9ejU++OADBAYG4vjx4/j973+PP//5zwBqb7Nv2bIFFRUVGDduHMrLy+Hj44PMzEz85S9/Mfs9Z8+exZAhQxr8/qioKAC1dwZeeeUVbN68Gf369cP+/fvxyiuv4PPPP0diYiKef/55jB8/Ht9//z1efvllHDhwAG+88QamTJmCp556Cunp6di5c6fV188Bf22kUqmQm5srdBgmubm5ovowFDu2X+OcpUKcmNpPLG1HlpsyZQoyMzMBAF988QUef/xxeHh4oKCgAIWFhVi0aBHi4uKQkpJitsx1v379ANR+gYiKisLBgwdx+vRphISEoFu3bs3+zpSUFMTFxWHChAl48803UVBQgAceeMB0zpiYGFy9ehVlZWW4evUqxo8fDwAYMGAAOnXqhLy8PGRnZyMmJgYA8Pjjj0Mul1t97ez520Dv3r3x0UcfCR0GALAOeyuw/RrnLBXixNJ+Ymo7ssyQIUNw584d3Lx5E3v37sWWLVsA1Ba2Cg4ORkZGBoDalRGL7xtH4O3tbfrvqVOnYtu2bQgODm70rk9ERIRZkaxly5Zh2bJlSEtLQ3Z2tqmY1v2MRiPKysoa3V/3qKBuTI5EIoFUan0/nj1/ImoUK8SRO5g8eTK2bduGTp06mQpxhYaG4t69ezh9+jQA4PPPP29yWevBgwfj1q1bOHnyJMaOHdvg57/5zW9QVVWFbdu2QavVAgDKyspw8uRJSKVShIaGorS0FDk5OQCAffv2ITAwEIGBgQgODsaXX34JoHZcQHFxMR566CE88sgj2Lt3L4DaQYrV1dVWXzd7/kRELigvLw8rVqxAcnKyaJdk7ty1q0Uj81tzXktNmTIFo0ePxoYNG0z7PD098c4775gG4fn4+OAPf/hDk+cYN24cSktLGy3h7enpib/85S/YvHkzJk+eDKD2TsKECRMwb948eHp6YtOmTXjjjTdQVVWFTp06YdOmTQCAP/7xj1i3bh3ee+89yOVyvPfee/D09MSaNWuQmJiInTt3IiIiAh06dLD4euuwwl8b1T0vmjRpUpvPZQtii0fsxPZ+iSketVqNjRs34rXXXhNt719M75eYYgGAF154AYWFhejevTvef//9Np+PFf4aMhqN0Gq1mDt3LlauXInw8HChQzLT3PvNnn8bieUPvY7Y4hF7AhHb+yWmeFJTU3HhwgWkpqaKdo6/mN4vMcWSl5dnGqB29epV5Ofni7b378zu3LmDiRMn4qmnnhJd4m8Jn/mTXW3fvh3nz5/H9u3bhQ6FrFB/Sd+SkhKhQyIrpKSkNLtNtqFUKnHq1Cm8+uqrQodiNSZ/shu1Wo2srCwAwFdffcUE4kRcdUlfd3H/tDSgtvdPdD8mf7Kb7du3myUQ9v6dR1ZWFnS62rKrOp0OX331lcARkTVCQkLMtp1pOWlyDCZ/sptjx46ZbR89elSgSMhaI0eOhEQiAVA7j5jlfZ1L/WlpTU1TI/fF5E92U38iiZtNLHFqXNLXuYWGhpp6/927d+dgP2rA7qP9y8vLER8fjw8++ADBwcFYsWIFzpw5g3bt2gEAEhISMG7cOJw4cQJJSUmorq5GTEwMli5dCqB2qsKqVatQUVGBwYMHY/369ZDJZLhx4wYSExNx9+5d9OzZEykpKa2a6+hqFi5ciOvXryM4OBjbtm0TNJZu3brhxo0bpm1/f38BoyFr1C9TyyV9W7Z9+3bs3r0b06ZNw6xZs4QOB8uWLcOKFStE3etfvWot1HdtPxZI0cUXb25Y3+Jx165dQ3R0NHr16mW2/4MPPkBAQECD4//xj38AqF0O2NnZNfmfPXsWq1evRkFBgWnfuXPn8Ne//hVKpdK0T6PRYOXKldixYwcCAgKwYMECHD16FFFRUUhMTMSbb76JAQMGYOXKldi1axdmzJiB9evXY8aMGZg4cSLef/99bN26FYmJifa8HKdw/fp1ALX/qIWmVqvNtu/evStQJGSt+s/4uaRvy3bv3g0A2LVrlyiSf2hoaKsWfHEk9d0SBHeJtPl5r9091vJB/6VUKk1lfFviCkm/jl1v++/atQtr1641JfqqqircuHEDK1euRGxsLN59910YDAbk5OSgR48eCAkJgUwmQ2xsLFQqFa5fvw6NRoMBAwYAqK3EpFKpoNVqcerUKUyYMMFsv7tbuHCh2bbQH9aPPPKI2fajjz4qUCRkLT8/v2a3yVz9waw7duwQKJJfqNVqLF++nLNsWiE3NxezZs3C1KlTMWrUKFOP/7333sN7770HABg+fDjmzZuHuLg4U9leZ2LXnv/95RIBoLi4GMOHD8fatWvRsWNHLFiwALt370b79u3NPlyUSiWKiopw+/Zts/1+fn4oKipCSUkJfHx8IJPJzPa7u7pefx2he/98xt+8I0eO4ODBg2b7SktLAcC0vjhQWzp09OjRDo3tzp07Ztu3b9926O93NnW9/jpi6P3fX2NjyZIlgsYiZrdv30ZcXJxpOzY2FkVFRXjhhRcwYsQIFBYW4vHHH2/Q6y8pKcH8+fMxbNgwR4dsEw6t8BcSEmJWZnLWrFlIT0/HhAkTTCOLgdqkIZFIYDAYGt1f9//3q7/dknPnzrXyKpzLmTNnBPvdJ06cMNv+17/+hf/93/8VKBrxyc/Pb7ByV13S9fDwMDvO0e0YERFhWtSkblvIf0vOSMj3q6yszPTo5siRIxgwYAA6duwoWDxi1thtf71ej+PHj+PDDz9Ebm4uKisrG31t//79HRGiXTg0+V+6dAkFBQWm2/VGoxEymQz+/v5mPY07d+5AqVQ22F9cXAylUgmFQoGysjLo9Xp4eHiYjreGrWr7i92gQYME+92jR4/Gl19+aWqnMWPGCBqP2DT2XqxYsQIAkJSU5OhwzPTs2RPz5s2DVquFXC7HSy+9JMryzGIm5L/1TZs2mc3W+P7779vc+6+r7e8OlixZggceeACjRo3Cb37zG9O6DfXdv7Svs3HoVD+j0Yi33noL9+7dg1arxc6dOzFu3Dj0798f+fn5uHLlCvR6PTIzMxEZGYmgoCB4eXmZvkFnZGQgMjIScrkcgwcPxr59+wAA6enpiIy0/aARapv4+HhTD9bDw0P068LTLxQKBcaOHQuJRIJx48Yx8bfgySefNNueNm2aQJHUYo2Ntvn666/x0ksvYezYsab3Uq/XCxyVbTm059+nTx88//zzmD59OnQ6HcaPH29aDCM5ORmLFy9GdXU1oqKiTPOKU1JSsHr1apSXlyM8PByzZ88GAKxduxbLly/Htm3bEBAQgD/96U+OvBSygEKhwJgxY6BSqTB27FgmECcTHx+Pq1ev8kubBWJjY82e+wu9yA9rbLTN4sWLMWPGDHh5eaFPnz4ICgoSfAyVrTkk+R85csT03zNnzsTMmTMbHDNixAjs3bu3wf4+ffo0GEwDAEFBQaIYUSsmUqnUVE63bltoTCDOS6FQIDk5WegwnEL9tQ+EXgnRWWpsKLr4WjUtz5rzWiI4ONgsP9WZO3cu5s6d22D/4sWLTf996dKl1gcoAlzS14VERUWZzc8WQ0lWJhByB2Kri+AsNTYsKcRD9iF815BsZvLkyWbb909fISL7EVtdhPpf/B09VZTEj8nfhTRWkpWotVgkxnJiq4sQHx9vqoMik8n42I0aYPJ3IfVvPTb2LIvIUqmpqbhw4UKD59nU0KhRo8xWQRS6p61QKExTmb29vUU12JaDDx2jpfeZyd+F3F8YBoDpmz+RtdRqNQ4fPgyj0YhDhw6x99+C+j1roXvaeXl5qKioAFC7uFp+fr6g8dTx9vbG3bt3+QXAzoxGI+7evdtsHQJmBxdS98dep7y8XKBIyNmlpqaaZo4YDAbBR687g6aqjwohJSX/aDdOAAAdYElEQVSlwfb91VWFEhwcjGvXrjV4TEK25+3tjeDg4CZ/zuTvQjp06GD2BcDHx0fAaMiZZWVlQafTAQB0Op3go9fFLjU11ey2v9BflgoLC822r169KlAk5uRyOXr27Cl0GAQmf5dS92FdxxlXmiJxGDlyJA4ePAidTgeZTCaKaaNic//CTBcuXDDdKdHr9VCpVKaEK8TCTIGBgWbz/IOCghz6+0n8+MzfhQwePNhse8iQIQJFQs4uPj7eVCRKKpUK/gxb7O5fhbGxbUd78MEHzbbZ26b62PN3IfUH9YhlkA85H5Zmbtno0aNNPXq1Wo1nnnkGRqMRnp6e2Lx5s6Dv2XfffWe2zRUZqT72/F3I/bf5AOD69esCRfILzhV3XtHR0WjXrp1pnQ1qmkKhMCV7MXxZGjlypNmdGz62ofqY/F1IYGCg2bYYnvNt374d58+fx/bt24UOhaykUqlQVVXFYlEW8vPzQ/v27UXxiERsUw9JfJj8XUj9ZC908ler1cjKygJQW4CIvX/noVarcejQIRiNRhw8eJBtZwG5XI7Q0FDBe/1ElmDydyHffvut2bbQz/m2b99uNlecvX/nkZqaajbVj1X+nEtjUw+J7sfk70L0en2z24527Jj5Up1Hjx4VKBKy1ldffWWqwmY0GhuUjiZxy8rKMv396/V6th81wORPdlO/hCdLejoPsa1SR9YZOXKk2cI+HPBH9TH5u5C60b116tf6d7SuXbuabTOBOA+xrVJH1mGdBmqJxck/JycHqampqKmpaTCHlMSh7vl6HaFv+xcXF5tts5638+B68M6trk6DRCIRxdRDEh+Lkn9aWhpWrFiB//u//0NZWRleeOEF7Nq1y96xEZFA6s/t51x/58M6DdQci5L/jh07sHPnTvj4+KBLly5IS0vjyG0R6tatm9m2v7+/QJHUioyMNNseOXKkMIGQ1VQqldlocc71dz6s00DNsSj5S6VSsxXiAgICBH+eTA2VlpY2u+1ozzzzjNlzxzlz5ggaD1kuKyuLo/2dmFqtxuHDh2E0GnHo0CHWaaAGLEr+nTt3xsWLF009gb1796JTp052DYys16VLl2a3HU2hUCAqKgpA7TNkPnd0Hhwt7txSU1PNamxwnj/VZ1HyX7lyJRITE/HTTz/hsccewzvvvIPVq1fbOzay0q1bt8y2b968KVAkv5g8eTLat2+PuLg4oUMhK3C0uHPLysoyK9LEOzdUn0XJv1evXtizZw/27NmDTz/9FAcOHEBYWJi9YyMriXFePZ87OieOFnduvHNDLbEo+f/73//G1KlT0atXL0gkEkRFRXG6H7WIzx2dG0eLOy/euaGWWJT8N27ciKSkJADAQw89hI8++si0TeJRNyajTv2iP47G547OjXdtnBfv3FBLLMoOWq0W4eHhpu3w8HDU1NTYLShqHbEV+eFzR+fFuzbOLz4+Hg8//DB7/dQoi5J/u3btzBZp+eabb9C+fXu7BUWuYcSIEWbbjzzyiECRkLV418b5KRQKJCcns9dPjZJZctCqVavw4osvmgaQSKVSvPfee3YNjJxfWVlZs9skXo3dtVm0aJHAUVFzjhw5goMHD5q26+p8dO7c2bRv3LhxLNVMACxM/v3790dWVhZyc3Ph4eGBnj17wtPT096xkZM7ffq02fapU6cEioSsNXLkSBw8eBA6nY6jxZ2UWq0GYJ78ieo0m/wzMjIQFxeHzz77zGz/iRMnAABz5861X2RkNYlEYja9r/4AQEcT49RDskx8fDwOHz4MgKPFncXo0aPNevUrVqwAAA7OpkY1m/yvXLkCAMjNzXVIMNQ23t7eqKqqMtsWUmBgIG7cuGHaDgoKEjAaakn928ZyuRw1NTXw8fHBxo0bTft565jI+TWb/F966SUAteuy/+53v3NIQNR69yf+xrYd7bXXXsPLL79stk3Ow2AwQCqVws/PT+hQiMjGLHrmn5WVxeRPVgsNDTX1/oOCgtCzZ0+hQ6Jm8LYxkfuwKPkHBwfj2WefxcCBA9GhQwfTfj7zp5a89tprWLFiBXv9REQiYlHyrxstev78eXh4eKBjx452DYpcR+fOndGzZ0+OOCYiEhGLivzMmzcPubm5OHr0KI4cOYLCwkIsXry4xdeVl5dj0qRJuHbtGoDaWQKxsbEYP348Nm3aZDru4sWLmDJlCiZMmIBVq1aZ5hffuHEDM2fORHR0NBYtWoSKigoAwM8//4znn38eMTExmDlzJu7cuWP1hbui+kV0HnvsMYEi+UVqaiouXLjAIjFERCJi8ZK+06ZNw9mzZ/H999+bknRzzp49i+nTp6OgoAAAoNFosHLlSmzduhX79u3DuXPncPToUQBAYmIi1qxZgwMHDsBoNGLXrl0AgPXr12PGjBlQqVSIiIjA1q1bAQCbN2/G4MGDsX//fjz11FPYsGFDa6/fpTz99NNm29OmTRMoklosEUtEJE4WJf+qqio8/fTTkMvl8PT0xKxZs1BcXNzsa3bt2oW1a9dCqVQCAHJyctCjRw+EhIRAJpMhNjYWKpUK169fh0ajwYABAwAAU6ZMgUqlglarxalTpzBhwgSz/UDtAMTY2FgAwKRJk3Ds2DFotdrWvQM2oNFocPfuXSQkJKC6uhoajQYff/wxDh8+DI1GA51OB51Oh+nTp8NoNKKmpgYajQY5OTlISkqCRqOxSRz1F2ARekEWZykRK5b2o9Zh+xFZz6Jn/qGhofj2228xcOBAALXz/oODg5t9Tf3e+O3bt82mDCmVShQVFTXY7+fnh6KiIpSUlMDHx8dUUrhuf/1zyWQy+Pj4QK1Wo1u3bpZcjs2pVCpkZmYiPz8fL7zwArRaLcrKytC+fXtkZ2fjiSeewMaNG1FUVIT58+dj7ty5uHDhAo4fPw61Wo38/Hxs2bKlzVUTs7KyzLaFLsnqLCVixdJ+1DpsPyLrWZT8b9y4gVmzZiEsLAwymQwXLlyAn5+fqff9xRdftHgOg8FgVnHOaDRCIpE0ub/u/+/XVMU6o9Fo9fK1586ds+r45gQGBqKyshKBgYHo168fHnzwQfztb39DRUUFhgwZgoqKCoSEhECr1cLT0xPe3t54+OGHsX//fjzwwAN47LHH8J///KfNcYSHh+O7776DXq+Hh4cHwsPDcebMGRtcoWvE0xSxtB/wy/oHYnifxBRLc8TSfmJ7v8QWD4mLRcl/2bJlbf5F/v7+ZgPz7ty5A6VS2WB/cXExlEolFAoFysrKTImj7nig9q5BcXEx/P39odPpUFFRYfVo8oiICHh5ebX5uoDaLx+vvfYawsLCkJ+fD6VSiYEDB6KyshJBQUGmOxj9+vXDd999h4EDB6Kmpgavv/46AgICUFFRge7du7fqd99flU2r1ZqW8TUYDCgrK8Pu3bsFq8jWs2dPzJ8/39SGL730kihXGBOy/erbvXs3AGDQoEE2OV9biCmW5oil/cT2ftk6nurqapt2mkhYFiX/oUOHtvkX9e/fH/n5+bhy5QqCg4ORmZmJqVOnIigoCF5eXjhz5gwGDRqEjIwMREZGQi6XY/Dgwdi3bx9iY2ORnp6OyMhIAEBUVBTS09OxcOFC7Nu3D4MHD4ZcLm9zjK0lkUjQv39/AEDfvn0BAF26dDE7pu7ndY9OPD09ERER0eixrSWXy+Hh4QG9Xg9fX19B3xOgdknRMWPGQKVSYezYsaJM/IB42o9ah+1HZD2Lkr8teHl5ITk5GYsXL0Z1dTWioqIQHR0NAEhJScHq1atRXl6O8PBwzJ49GwCwdu1aLF++HNu2bUNAQAD+9Kc/AQBefvllLF++HBMnTkTHjh2RkpLiqMsQnfpV2ZYtW4bCwkJs3rxZFMk2Pj4eV69e5cIwREQiYvfkf+TIEdN/jxgxAnv37m1wTJ8+fUy3qO4XFBSEHTt2NNjfuXNnfPDBB7YN1EXI5XKEhoaKIvEDtb3/5ORkocMgIqL7WDdKjoiIiJwekz/ZlVqtxvLly1ngh4hIRBz2zN9VpaWl2ayYTnR0NKZMmWKTc4nF/eV9xTjHn+3n3GzVfmw7cjfs+beRSqVCbm5um8+Tm5sreEU+W3OG8r5sP+dmi/Zj25E7Ys/fBnr37o2PPvqoTed4/vnnbRSNcO6vOQDUFoeqqakBANTU1GDJkiUIDAwUrO5AU9h+zq2t7ce2I3fEnj/ZTWlpabPbREQkDPb8yWbq1xzYunUr9u/fD6B2DYbx48eL8rk/EZG7YfJvo8cff1xU5xGT+Ph4qFQq09oLYiz0w/ZzbrZ439l25I6Y/Nto0qRJojqPmCgUCvj6+kKtVou2vC/bz7nZ4n1n25E7YvInu/Lz84NGoxFlr5+IyF1xwB/ZldjKDRMREZM/ERGR22HyJyIicjN85k/kIB9//DHy8vKaPabu5ytWrGjymNDQUMyfP9+msRGRe2HyJ3KQvLw8nL90AR6dPJs8xiDVAwB+uPVjoz/X36uxS2xE5F6Y/IkcyKOTJzpFBrb69feO3bBhNETkrvjMn4iIyM2w5y+Q+ovg1NW979y5s9lxYlsExx3Vf1ZfUlJi0QqFvr6+ZlMc8/LygPZ2CZGaYav241gLciVM/iKhVqsBNEz+JLy8vDxcvnge/j61fy6aGgP0NYYWX6dRV6Os/LZpu6pSC4/23naLkxpni/a7Va6za4xEjsbkL5D6i+DUje5OSkoSKiRqhr+PDHP7Kdp0jqQTt6G3UTxknba232c5ahtGQyQ8PvMnIiJyM0z+REREbobJn4iIyM0w+RMREbkZJn8iIiI3w+RPRETkZjjVj4jIAjdv3mx2wSVLFmUCWCyIxIHJn4jIAlVVVc0uzNTSokwAF2Yi8WDyJyKyEBdmIlfBZ/5ERERuhsmfiIjIzfC2P5Gbqr/aXX0cwCZeLbUdwPaj5jH5E7mpvLw8XLxwCR3aNb7gjUFX+/FwNf9Ok+eoqOKCN0Joqe0Ath81j8mfyI11aKdAeK/oVr/+/E8qG0ZD1mhr2wFsP3fGZ/5ERERuhsmfiIjIzTD5ExERuRlBnvnPmjULarUaMlntr3/99ddx9epVbNu2DTqdDnPmzMHMmTMBACdOnEBSUhKqq6sRExODpUuXAgAuXryIVatWoaKiAoMHD8b69etN5yMiIqKmObznbzQaUVBQgIyMDNP//P39sWnTJvz9739Heno6du7ciR9//BEajQYrV67E1q1bsW/fPpw7dw5Hjx4FACQmJmLNmjU4cOAAjEYjdu3a5ehLISIickoO7yrXzT199tlnUVpaimnTpqFDhw4YPnw4OnfuDACYMGECVCoVhg4dih49eiAkJAQAEBsbC5VKhV/96lfQaDQYMGAAAGDKlCl49913MWPGDLvHf+TIERw8eNC0XVpaCgCm2OuMGzcOo0ePtns81LTG5kKXlJSgpKSk2df5+vrC19fXtJ2Xlwc/uV1CpGbUbz9L2g5o2H4FBQXQVunwWU7rp7XdKtfB6CEBn5SSq3B48v/5558xYsQI/P73v4dWq8Xs2bMRExMDPz8/0zFKpRI5OTm4fft2g/1FRUUN9vv5+aGoqMiqOM6dO9eq+PPz81FWVmbavnOndg6th4dHg+POnDlj8XnrzmnNa+x5HlsRMp6zZ8/iSuENeHj/8sXMoNPAqNM2+7qqOz/jVskvC7DoK6uATuJ5pFRWVmaT9/P+f8dtPY892rd++1nSdoB5++k1pZBJjfBo4TWWMBgMsEXyt8X7Zau2qzuXWD4vyHEc/on261//Gr/+9a9N208++SSSkpKwaNEi0z6j0QiJRAKDwQCJRGLxfmtERETAy8vL6vgHDRpktl1XPSspKcnqc91v9+7djZ5fqPPYipDx7N69Gx7endG+x5g2nafs0uc2isg2OnbsaJP3c/fu3Sgp1ogmnvps0X6VVw7DExXwk2sxt1/TBXFa8lmOGne0ctTA0Opz1LHF+2WrtrMmnurq6lZ3mkh8HH4P6/Tp0/jmm29M20ajEUFBQaYeNFDbm1YqlfD397dof3FxMZRKpWMugIiIyMk5vOdfVlaGd999F6mpqdBqtdizZw/++Mc/IjExEWq1Gu3atcOXX36JN954A2FhYcjPz8eVK1cQHByMzMxMTJ06FUFBQfDy8sKZM2cwaNAgZGRkIDIy0tGXYhXWUXdiRgNulRva9MwYAGr0Rkg0ehsFRUTUeg5P/qNGjcLZs2cxefJkGAwGzJgxA4MGDcLSpUsxe/ZsaLVaPPnkk+jXrx8AIDk5GYsXL0Z1dTWioqIQHV1bzjIlJQWrV69GeXk5wsPDMXv2bEdfilXy8vJw+eJ5+Ps0/pa3M9beTiwrvNTkOa79rEVeXp5ovkRwcREiIuckyCimJUuWYMmSJWb7YmNjERsb2+DYESNGYO/evQ329+nTx/Q82Vn4+8ja9Nwx6cRtVGk1+OHWj00eY5DW9iybO0Z/r6bJn1nDbRYXkUjh7yNtU9sBte2n97bF0DMiorYRzxBmsohHJ090igxs0znuHbtho2i4uAgRkTPipFUiIiI3w54/EYlSSUkJ9JpSVF453Opz6DWl0MkkAIs0EZlhz5+IiMjNsOdPRKLk6+uLWyU1bS7yI0MFgJYrAxK5EyZ/IiIL6HQ66Eq1bRowqyutRolXy+sTENkbkz+Rg+gNRuhKq5k8iEhwTP5ERBaQyWQwtJe2aartvWM3zFYcJBIKkz+Rg3hIJUBnLyYPIhIckz+RmyopKUFFlbpNRZYqqtQoKeHHiKPZou0Atp8741Q/IiIiN8OvfERuytfXF2WlujaVZz7/k4qPIQRgi7YD2H7ujMm/BbZaivfmzZvwsWlkracvr10d8P6YS0pKUFLS/ChyX19fsw+K3Nxc6HVG3np0sIKCAjz99NOmbZ1OB51O1+LrZDIZZLJf3mepVAopOtglRmqc7l41LqgvtLn9NBoNPGVi+UQhZ8RP3Bbk5eXh3IVL8PDu3OjPDbraVdou5hU1eQ69phTtveXwEUmJUaPOAJ2+CuoffjDtqzQYoDMYmn1dhUYDY9Ev11mj08FDKpKLciM1NTXQ1tSYKtbqARgteJ2+pgaoqV3RUQtA7ukJbzmTv0PpjTAajNBVVv6yC9a3X/N/qUQtY/K3gId35zZXGQMqbBeQDXT18EBcx8a/0Fjqk5JieNtoVT/eerScTCZDV4OhTe2XUVaKUhn//B1OKkGA1DZ/e57ydjYKitwR//rJZdliYRgAgEGH8hqOjSUi18FPNCIiIjfDnr8TsUV5WACAzohKqes/NbTFwjAAUHbpc/h42igoIiIRYPIncmPNFYqp0VYBQLPPliuq1AD87BEataClIj9sP2oOk78TsUV5WAC4uzcf7fnEx+21a9cOoaGhTf68bhpr957NJQe/Zs8hFrfKdfgsR93oz8prau+C+Xg2/Tdxq1wHDy/xzGyRSySQecubbRtXaj+yPSZ/IjcVEBCApKSkJn9eVweiuWOcQbt27RDQTIK7898kGRDS9DEdUVuro0Yks3Y6eXhAERrqFu1H9sHk7yA6nQ63qpvufViiRm+ERKNvezAGI4oNOmSUlbbpNFoA0v/eWiQSK1t9yVmxYgXu3frRprERCYXJn4jIQvp7NU0OuDX894u51NujmRMYUQx9s1+8K/9bbKu9tOnHEMV6PRQWxEvUFCZ/B5HJZPBrZ8Tcfq3/k006cRv65j5YLCWVoCsLjTi1Yn3bEgiTh/VaejZe94w91L/p424abwIAFAEBTR5T+t/zBDfz+xQWxEPUHCZ/Igs0N2AMsGzQWI3eCDTTcwRa7j3q79Wg3QO+zT7DBlpOIEwe1ps/f36zP7fVM3Y+qydHYPKnNnGH6UYSqQwennJ0bGZAmCWDxhSy2l5fgH/Tvb4We4/+tUnbUYmIiFwTk38LbFEiVq8phU4mAcQzU8gm3GW6kdTTB6Gh3RwysppJm4gcgcmfWo3TjYiInBOTfwtsUSK28sphyFCB2slxREREwmLyJyLRaumRm0GnAQBIZd5Nvh7oZo/QiJwak78DtbXEqC1GiwMA9EbABjMGiezJknEgpgGSoU0l+G6iHk9CJBQmfws01/toqedR9/p2vg+0ucSoTUaLA8irzANqapr8uStpa8+x7hzsPTpeSzMaAI4pIWotJv8WWFzYo8meB1DX+2juw8yRo8VXrFiB3PPnXb7KmG16joC79B6PHDmCgwcPmrbr3pu6f1N1xo0bh9GjRzs0NiKyLSb/FrjifGpLEpkrVBljz7FtFAoxf7UjorZg8ndDTIrUmNGjR7NHT+QmmPyJiFyAJY9t+MiG6kiMRqNR6CBa64svvsC2bdug0+kwZ84czJw5s8XXVFdX49y5c4iIiICXl5fVv7OpP7D6t79b+iNz5Hks+YO3xXnqn6Mt8diLmK5TbOcRO3u9X87wN9ya85SW1o7p6dz5lwW82vJvoK2fnSQuTtvzLyoqwqZNm5CWlgZPT0/Ex8dj2LBh+NWvfuXQOGz1XJTnEYbYrlNs5xE7W1yn2N7z1p6Hj23IGk7b89+zZw9OnTqFt956CwDw/vvvw2g0IiEhodnX8dsrEZH1+NnpWpqexyVyt2/fhp/fL4vFKJVKFBUVCRgRERGRc3Da2/4GgwESicS0bTQazbZbcu7cOXuERUREJHpOm/z9/f1x+vRp0/adO3egVCotfj1vXRERWa7utj+5Bqe97f/II4/gm2++gVqtRlVVFb788ktERkYKHRYREZHoOW3Pv1u3bli6dClmz54NrVaLJ598Ev369RM6LCIiItFz2uQPALGxsYiNjbXqNXWTG2rcZGEbIiJbqPvMdNIJYlSPUyf/1tBqtQCA3NxcgSMhInI+Wq0W3t5Nr4JJzsFp5/m3lsFgQEVFBeRyuVWzA4iI3JnRaIRWq0WHDh0gbWa1T3IObpf8iYiI3B2/vhEREbkZJn8iIiI3w+RPRETkZpj8iYiI3AyTPxERkZth8iciInIzTP5ERERuxu0q/Lmqa9euITo6Gr169TLbP23aNMycOVOgqMhaYWFhuHTpktBhUBukpaUhOzsbycnJQodC1CQmfxeiVCqRkZEhdBhERCRyvO1PRETkZtjzdyG3b99GXFyc2b6NGzciLCxMoIiIiEiMmPxdCG/7ExGRJXjbn4iojU6fPo2ioiIAtavfeXh4CBwRUfOY/ImI2ujzzz/HoUOHAACXLl1CSEiIwBERNY9L+rqIpqb6DRkyBKtXrxYoKrIWp/o5p/z8fLz66qsoLy+Hv78/Nm/ejE6dOgkdFlGTmPyJiIjcDG/7ExERuRkmfyIiIjfD5E9ERORmmPyJiIjcDJM/ERGRm2HyJxKR5cuX45NPPhE6DCJycUz+REREboa1/YnsbPfu3fjss88glUrh6+uLP/zhDzh27Bh27NgBqVSKrl274ve//z169uxp9rrTp09j48aNqKqqglwux5IlSxAZGYm0tDTs3r0bVVVV8PHxwY4dOwS6MiJyVkz+RHb0ww8/ICUlBXv27EFAQAD+/Oc/45lnnoHBYMDOnTuhUCiQlpaGF198Ef/85z9NryspKcFLL72Ebdu2oX///rh8+TJ++9vfYvfu3QCAH3/8EUeOHIGPj49Ql0ZETozJn8iOvvnmGzz22GMICAgAADzzzDO4ffs25HI5FAoFAGDKlCnYsGEDrl27ZnpdTk4Ounfvjv79+wMAHnroIQwcOBDZ2dmQSCQICwtj4ieiVuMzfyI78vDwgEQiMW1rNBoUFhY2OM5oNEKn05m29Xq92evqH9O+fXs7RUxE7oDJn8iOhg0bhm+++Qa3b98GAKSmpuLo0aPYt28f1Go1gNoV4Tp37owePXqYXjdgwADk5eUhJycHAHD58mWcOnUKQ4cOdfxFEJHL4W1/IjsKCwtDYmIi5s2bBwDw8/PDwYMHcejQIcyZMwcGgwEKhQIffvghpNJfvosrFAq88847eOONN6DRaCCRSJCUlISePXviu+++E+pyiMhFcFU/IiIiN8Pb/kRERG6GyZ+IiMjNMPkTERG5GSZ/IiIiN8PkT0RE5GaY/ImIiNwMkz8REZGbYfInIiJyM/8POPv91nKKyncAAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -455,7 +527,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -558,7 +630,7 @@
        "4       24.59  3.61  Female     No  Sun  Dinner     4  (0.991, 4.0]"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -571,7 +643,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 11,
    "metadata": {
     "scrolled": true
    },
@@ -583,7 +655,7 @@
        "Categories (3, interval[float64]): [(0.991, 4.0] < (4.0, 7.0] < (7.0, 10.0]]"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -596,7 +668,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 12,
    "metadata": {
     "scrolled": false
    },
@@ -617,12 +689,14 @@
     },
     {
      "data": {
-      "image/png": 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f3iqB5+fn49133wXQ1Gb0+eefh16vF73MKruRETkAjUZj6pLk5OQEjUYjc0TKxvGUzy/biRr99a9/xSOPPIJevXq12qesrKzFLcs+Pj64fPlyh++zatUqTJkypdVxmhcj8/HxabMtqbXajDKBEzkAtVqNyZMnQ6VSYcqUKYqfAcuN4ymf5u1Eja5evYrPP/8c9913X5v7GNcrNGdJ209L25JK1WaUCZzIQWg0Gtx+++2cLYqE4ymP5u1EjQ4dOoSJEye2W6XT399flCJivzxORUVFm8cxthkFIGmbUSZwIgehVquRlJTE2aJIOJ7yaN5O1Ojrr7/G6NGj291n4sSJ+Pjjj3Hjxg1otVocOXLEVCW0K/r37w93d3dTzf/09HRMnDix1XbGNqMAJG0zygRORESK0bydqNH58+fh5+fXYrtvv/3WVG9/xIgRmDVrFubMmWNqhGLcfvbs2W1ex27P5s2bkZiYiPDwcNTW1mLBggUAmlbA7927F0BTx7Kvv/4aM2fOxD//+U+sX7++W5+5PVyFTkR2jSVPxXezd1+zb/nq6nE701Y70V27drXa7ne/+12L5x9++GE8/PDDrbYLCgqCp6dnu+/XvEUp0PQFoq2mPXK0GWUCJyK71rzkqSPdqy0lc4qtSEmsdqKCICAwMLDNletik6LNKE+hE5Hd+mXJ08pKZVeAoybGdqLd7QWuUqkwa9YskaLqmLHNqFhV2AAmcCKbo9VqkZCQwGQjgrZKnhLZCyZwIhvDLlfiaavkKXVdW/dRk/QEQejwdSZwIhvCU77iCg0NNd0zzJKnlvH09MTFixdRX1/faUIh8QiCgCtXrsDDo/2a81zERmRDHL3Lldg0Gg1yc3MBsOSppQYMGICKigqcO3fOdDaDrMPDwwMDBgxo93UmcCIb4uhdrsRmLHmalZXFkqcWcnJygq+vr0WVy0haTOBENsTWulw1XK03qx94Y10DAMDJw9msY8K/081Eo9FoUFJSwtk32R0mcCIbYkunfAMCAszetrCwsGkffzP28e/asbvLWPKUyN4wgRPZEFs65WssQ2mOp59+GgCQmJgoVThE9AtM4BbKy8tDdnZ2q+erqqoAoFXnmalTpyIsLMwqsZGy8ZQvEZmDCVxkxqbtUrSOI8fAU75EZA4mcAuFhYW1OaPmqUQiIrIGFnIhIiJSICZwIiIiBWICJyIiUiAmcCKya+zuRvaKCZyI7Bq7u5G9YgInIrvF7m5kzyRP4C+++CISEhIAAKdOnUJ0dDTuuecerFmzhp1tiEhSbXV3I7IXkibw//znP/jggw9Mj1euXIn169fj3//+NwRBQEpKipRvT0QOrq3ubkT2QrJCLlVVVdiyZQuWLFmC06dP4+LFi6irq8PIkSMBANHR0di+fTseeOABqUIgBWFpWpKCrXV3IxKTZAl8/fr1WLFiBS5dugQAKCsrg4+Pj+l1Hx8flJaWdvm4BQUFosUohZqaGgDAsWPHZI5EWYqKikxj11x5eTkAwNnZudX2HGPbYau/97fffnuLL4bDhw+3uRiJLCVJAt+3bx/69euH4OBgpKWlAWi6/qRSqUzbCILQ4rG5AgMD4e7uLlqsYktNTQUAjBo1SuZIlKW98XLE0rRarRabNm1CfHy8rN3I2tLemRLjFy3j77+RLZwpOXnyJLKysjBt2jSEhobKGoscdDqdzU98yDKSJPAPP/wQ5eXlmD17Nq5evYra2lqoVCrTP3IAqKiogK+vrxRvT6RozW97Wrp0qdzhmEWtVssdQrvY3Y3slSQJ/M033zT9nJaWhqNHjyIxMRERERE4duwYRo0ahfT0dEycOFGKtydSrF/e9qTRaGxqFt5eEx9bxu5uZK+seh/45s2bkZiYiPDwcNTW1mLBggXWfHsim8fbnojIXJK3E42OjkZ0dDQAYNiwYa2ukRHRz9q67Ukpp9GJyLrYD5zIhgQHByMvL8/0ePz48TJGoyy8FZEcDRM4kQ0RBEHuEOyOVqsF0DqBEykdEziRDTly5EiLx4cPH8aTTz4pUzTK0t4CO0e8FZEcA5uZENmQ0NBQU9EaZ2dnVg4jonYxgRPZEI1G0yKB895lImoPEziRDVGr1Zg8eTJUKhWmTJliU/eAE5Ft4TVwIhvDymFEZA4mcCIbw8phRGQOnkInIiJSIM7AiexQW0VN2itoArCoCZESMYETOQgWNCGyL0zgRHaoraImLGhiua6c0eDZDLKWDhP4nXfeCZVK1ep5QRCgUqlw/PhxyQKzJbt27UJhYaFZ2xq3M/6x7ExAQABiY2Mtjo2I5MEzGiS3DhN4ZmamteKwaYWFhfj+1Hfw79X5CYseQlMryJrzZzrd9vI1Q7djIyLp8YwG2aIOM9J3333X4c79+/cXNRhb5t/LBQtHqEU95psntKIej4iIHEeHCXzPnj3tvqZSqTBt2jTRAyL7x0sSRETdZ3ECJ7IUL0kQEXVfh39BN27ciDVr1mDJkiVtvr5jxw5JgiL7x0sS7dNqtdi0aRPi4+NZC52I2tVhAg8ODgYA3HPPPVYJhoiA5ORknDx5EsnJyVi6dKnc4RCRjeowgRtXXd57772orKzE119/DRcXF9xxxx246aabrBIgkSPRarXIzc2FIAjIycmBRqPhLJyI2mRWIZf8/HzEx8dj6NChaGhowPnz57FlyxaMGTNG6vhsQmVlJSquGUQ/RXv5mgGGykpRj0nKlpycjMbGpuv+jY2NnIUTUbvMSuDbtm3DO++8g6FDhwJour1s3bp1SEtLkzQ4IkeTn58Pg6FpMZ7BYMDBgweZwImoTWYlcJVKZUreAPDb3/4WgiBIFpSt8fb2hsu1MkkWXXnx9Cg1ExoaiuzsbBgMBri4uGDSpElyh0RENqrDBG6s9RsYGIjdu3dDo9HAyckJaWlpGDdunFUCJHIkGo0Gubm5AAAnJydoNBqZI3IsrFFAStJhAh83bhxUKpVptv3SSy+ZHqtUKsTHx1slSCJHoVarMXnyZGRlZWHKlClcwGZlrFFAStLhb+np06c7PUBmZiYiIiJEC4jI0Wk0GpSUlHD2LRPWKCClcOruAXbv3i1GHET0P2q1GklJSZx9E1GHup3AHWkxGxERka3odgJvq1+40bZt2zBjxgzMnDkTb775JgDg8OHDiIyMxLRp07Bly5buvj0REZFDMus2MkscPXoUR44cQUZGBgwGA2bMmIHg4GCsXr0ae/bsQb9+/bB48WIcOnQIISEhUoVBRERklyRL4GPHjsXbb78NFxcXlJaWoqGhAdXV1Rg0aBAGDhwIAIiMjERWVhYTODmsvLw8ZGdnt3jOePtmnz59Wjw/depUU3ljIqJuJ/COroG7urpi+/bteOONNxAeHo6ysjL4+PiYXvf19UVpaWmX3q+goMDiWC1VU1Mj6bGPHTsm2fFtEcfzZ0VFRa3Go7y8HADg7OzcatvufDbj+yhpfMTQlc996dIlXJOobPK1S5ccbuxJWt1O4JGRkR2+/vjjjyM2NhZLlixBcXFxi2vmxvvJuyIwMBDu7u4WxWqp1NRU1FRJc2wvLy+MGjVKmoPbKI7nz9qK1VgYJDExsdP9u1J4xPjFIDU11azt7aXwiPHzmvN74eHhgWsSxeHh4SHL76ZOp5Nl4kPS6zCBd5acDxw4gEceeaTN13788UfU19dj+PDh6NGjB6ZNm4asrKwWs4ry8nL4+vpaEHb3secy2QMWHhEXyyaTknT4r37dunUWH/jChQvYvn079u7dCwDIzc2FRqPBpk2bcO7cOQwYMACZmZmIiYmx+D26gz2XLdOVa7YAr9taAwuPEDmmDhP42LFjTT9XVVXhxo0bEAQBDQ0NKCkp6fDAISEhOHHiBKKiouDs7Ixp06Zh5syZUKvViIuLg06nQ0hICMLDw8X5JF3Ansvi0mqb/ti3lcCJiEgaZrcT3blzJ4CmhTV6vR5DhgzBgQMHOtwvLi4OcXFxLZ4LDg5GRkaGheGKgz2XLRcWFtZqRt2Va7ZERCQOsxJ4eno6Dh48iKSkJKxatQpHjhzBoUOHpI5NMuy5LK/KykpUSLTS11BZKeoxiYhslVmV2NRqNXx9fREQEIDTp08jKioKZ8+elTo2yYSGhsLFpem7C3suExGREpk1A3dxcUFJSQkCAgLw5ZdfYsKECdDpdFLHJhn2XJYXV/oSEXWfWQl88eLFWLduHV5//XVs27YN+/fvR2hoqMShiaOtVdNAU5GZ+vp69OrVC5s2bTI9z1XTRESkBGYl8Ntvvx1vvfUWAGD//v04d+4cnJy63QdFVo2NjXBycmpRGa4jl828ZnutvmlxXC+3zsfn8jUDvMx6dyIiopY6TODG+3tjY2OxZ88eU9nUm2++GQ899BCysrKkj7Cb2lo1DXRt5XRAQIDZ71f+v6pY/QZ2vo9XF49NRERk1GECf+qpp/D5558DAIKCgn7eycUF99xzj7SR2ZCulJPkLVVERGQNHSbw3bt3A2hKSkxIREREtsOsa+CJiYn45ptv8Omnn0Kv12PChAkYM2aM1LERERFRO8xK4Pv378eWLVswbdo0CIKAP/7xj4iLi8PcuXOljo+IOsCiOESOy6wE/o9//AP79u0zdQ6LjY3FI488wgROREQkE7MSeGNjY4u2n35+foq/jYzIHrAoDpHjMisL9+nTBzk5OabHOTk56N27t2RBERERUcfMmoHHxcVh9erV2LBhA4CmKmavvfaapIERERFR+8wq5LJhwwbs27cPP/zwA1QqFfr3748//OEPiijkQkREZI/MLuQSHBwMABAEweEKuRCRbdm1axcK/1f1sDPG7YxFljrb1se1W6ERWQ0LuRCR4hQWFqLg5Bk4e/TpdNtGgzMA4FRhaafbNtTeAHqbdWWRSHZmF3IhIrIlzh590HPQZFGPWXPmfVGPRyQlftUkImqGnQdJKZjAiYj+R+XkAmc3V3iZ0U2QnQdJbkzgRET/4+TWCwEBfmZdNmTnQZIby6kREREpEGfgJAteZyQi6h4mcLK6rlwL5HVGIqK2MYGT1cXGxpq9La8zEhG1jQmcSOF4OYLIMTGBU5ukKlUJNJ3m7sosnNrHyxFEjkvSBP7qq6/io48+AgCEhIRg1apVOHz4MBITE6HT6TB9+nSsWLFCyhDIQpKVqqyr6nZs9DNejiByXJIl8MOHD+Ozzz7DBx98AJVKhUWLFiEzMxObN2/Gnj170K9fPyxevBiHDh1CSEiIVGFQN0hRqrL2XK6ox1MKntFQtry8PGRnZ7d4rr3/T1OnTkVYWJjVYiPHJVkC9/HxQUJCAtzc3AAAt956K4qLizFo0CAMHDgQABAZGYmsrCwmcLJ7PKNhf9RqtdwhkIOTLIEPHTrU9HNxcTE++ugjPPTQQ/Dx8TE97+vri9LSzv9IEdkDntFQrrCwMM6qyeZIvojt+++/x+LFi7Fq1So4OzujuLjY9JogCFCpVF06XkFBgShx1dTUAACOHTtm0f5ff/01vvrqq1bPX758GQCwfPnyFs/feeedGDlypEXvJQfj+Eh1bHPHvbv/n2yFLYynvYwlYBvjSSQ3SRP4sWPH8Pjjj2P16tWYOXMmjh49ivLyctPr5eXl8PX17dIxAwMD4e7u3u3YUlNTAQCjRo2yaP+rV6/ihx9+aPV8Q0MDAMDLq+VNOIMHD7b4veSQmpoKlNdKcmwvLy+zx6K7/59shS2Mp72MJWAb46kUOp1OtIkP2RbJEvilS5ewbNkybNmyBcHBwQCAO+64A0VFRTh37hwGDBiAzMxMxMTESBWCpHhKjYiI5CRZAt+9ezd0Oh2SkpJMz2k0GiQlJSEuLg46nQ4hISEIDw+XKgQiIiK7JVkCX7t2LdauXdvmaxkZGVK9LRERkUOwq0psvNeWiIgchV0lcN5rS0REjsKuEjjAe22JgK5VDgNYPYxIiewugRNR2+ypclhlZSUa6qpE/3LdUFeFyko3UY9JJBUmcCI7xNsciewfEziRFXDGKC5vb29crqyX5HKZt7e3qMckkoqT3AEQERFR13EGTmQFnDESkdiYwMkmtLVqGmDPZSKi9jCBk02zp5XTRERiYgInm8BV00REXcNFbERERArEBE5ERKRATOBEREQKZFfXwFksg8hxmPvLzWwfAAAKKklEQVRvvdFQBwBwcvEw65iAX3dDI7IKu0rgROQYAgICzN7WeCtiQIA5idmvS8cmkpNdJXAWyyByDLGxsWZva6whkJiYKFU4RLLgNXAiIiIFsqsZOImH6wmIiGwbEziRlXDRFRGJiQmc2sT1BOLioisiEhsTOJEVcNEVEYmNi9iIiIgUiDNwahev2RIR2S67S+BMOuLgNVsiIttmVwmcSUc8vGZLRGTb7CqBM+kQEZGj4CI2IiIiBZI8gV+7dg0RERG4cOECAODw4cOIjIzEtGnTsGXLFqnfnoiIyC5JmsC/+eYbzJs3D8XFxQCAuro6rF69Gn/961/x4YcfoqCgAIcOHZIyBCIiIrskaQJPSUnBM888A19fXwDAiRMnMGjQIAwcOBAuLi6IjIxEVlaWlCEQERHZJUkXsW3cuLHF47KyMvj4+Jge+/r6orS0VMoQiIiI7JJVV6E3NjZCpVKZHguC0OKxOQoKCkSJpaamBgBw7NgxUY7nyDiW4uJ4iovjSfbKqgnc398f5eXlpsfl5eWm0+vmCgwMhLu7e7djSU1NBQCMGjWq28dydBxLcXE8xeXo46nT6USb+JBtseptZHfccQeKiopw7tw5NDQ0IDMzExMnTrRmCERERHbBqjNwd3d3JCUlIS4uDjqdDiEhIQgPD7dmCERkp/Ly8pCdnd3qeWPVRWPxJqOpU6ciLCzMKrERScEqCTwvL8/0c3BwMDIyMqzxtkREUKvVcodAJAm7KqVKRI4rLCyMM2pyKCylSkRkAa1Wi4SEBFRWVsodCjkoJnAiIgskJyfj5MmTSE5OljsUclB2fwqdC1uISGxarRa5ubkQBAE5OTnQaDTw9vaWOyxyMA47A1er1VzcQkQWSU5ORmNjI4CmAlWchZMc7H4GzoUtRCS2/Px8GAwGAIDBYMDBgwexdOlSmaMiR+OwM3AiIkuFhobCxaVp/uPi4oJJkybJHBE5IiZwIqIu0mg0cHJq+vPp5OQEjUYjc0TkiJjAiYi6SK1WY/LkyVCpVJgyZQoXsJEs7P4aOBGRFDQaDUpKSjj7JtkwgVOXtHVbXnu35AG8LY/sl1qtRlJSktxhkANjAqdu4+14RETWxwROXcLb8oiIbAMXsRERESkQEzgREZECMYETEREpEBM4ERGRAjGBExERKRATOBERkQLxNjIiGXWlMA6L4hBRc0zgRDaGhXGIyBxM4EQyYmEcIrIUr4ETEREpEBM4ERGRAjGBExERKRATOBERkQIxgRMRESkQEzgREZECKeY2MkEQAAD19fUyR0JEpBzGv5nGv6FkPxSTwPV6PQDg7NmzMkdCRKQ8er0eHh4ecodBIlIJCvla1tjYiOvXr8PV1RUqlUrucIiIFEEQBOj1enh6esLJiVdN7YliEjgRERH9jF/HiIiIFIgJnIiISIGYwImIiBSICZyIiEiBmMCJiIgUiAmciIhIgZjAiYiIFEgxldjk9txzz+H48ePQ6/UoKSnBrbfeCgCorq5GdHQ04uLiZI5Q+S5cuIDw8HDT2Brt2LED/fr1Mz2OjY3FCy+8AD8/P2uHqAhZWVnYuXMnDAYDBEHA7NmzsWjRona3T0lJQc+ePREREWHFKJWhq2NJZE1M4GZ65plnADQlmQULFiA9PR0A8Morr8gZlt3x9fU1jW17du3aZaVolKe0tBQvvvgi0tLS4O3tjevXr2P+/PkYPHgwJk+e3OY+x48fx9ixY60cqe2zZCyJrImn0EVw4sQJaDQaTJo0yZTQ09LSkJCQYNpm/vz5+OKLL/DFF19gzpw5iI6ORnx8vFwhK0pCQgKWLFmC6dOnIy8vD2FhYbhw4YLcYdmkyspK6PV61NXVAQA8PT2RlJSEIUOG4KOPPsLcuXMxa9YshIeH4/jx4zh8+DDy8vKwfft2fPrppzJHb1s6Gsvmv4NffPEF5s+fD6Dp3/mmTZtw//33Y+rUqTh06JBs8ZP94wxcBFeuXEFycjKuXbuGsLAwLFy4sMPti4uLcfDgQXh5eVkpQuUoKyvD7NmzTY8jIyMBAH369MGOHTsAAC+88IIssSnBsGHDMHnyZEyZMgXDhw9HUFAQIiMjMXDgQKxfvx47duyAWq1Gamoqdu7ciR07diAsLAxjx47F3XffLXf4NqW9sRw0aFCH++n1erz33nvIy8vDtm3bEBISYqWIydEwgYvg7rvvhpubG9RqNby9vXH16tUOtx88eDCTdzvaOoWekJCAESNGyBSR8jz33HN47LHH8Nlnn+Gzzz7D3LlzsXnzZrz22mvIy8tDUVERjh49ysYWZmhvLDti/CI0dOhQVFVVWSNMclBM4CJwcfl5GFUqFQRBMP3XyNgOFQBb+lmAY2ae/Px81NbWYsaMGYiJiUFMTAxSUlLw7rvv4uWXX8asWbMwZswY3HbbbXj33XflDtemtTeWqampAH7ur20wGFrs5+7uDgDsmkiS41dwiXh7e+PHH3+EIAg4f/48zpw5I3dI5AA8PDzwl7/8xXR9VhAEnDp1Cm5ublCpVFiyZAmCgoKQnZ2NhoYGAICzs7PpZ/pZe2M5fPhweHt744cffgAA5ObmyhkmOTDOwCUyfvx4vP/++wgPD8fgwYMxatQouUMiBzBu3DgsX74cS5YsMZ31ufvuu/Haa68hISEB06dPh0qlwoQJE3Ds2DEATb+rL7/8Mry8vBAeHi5n+DalvbFctmwZ7rrrLmzYsAGvvvoqJkyYIHOk5KjYD5yIiEiBeAqdiIhIgZjAiYiIFIgJnIiISIGYwImIiBSICZyIiEiBmMCJuigrK8tU+5qISC5M4ERERArEBE5khm3btmHKlCmYM2cOsrOzAQBFRUVYuHAh5s6di0mTJmHp0qXQ6XTIyMiARqMx7fvTTz9hwoQJqK+vlyt8IrJDTOBEncjJycHHH3+M/fv3m7rOAUBKSgqioqKQkpKCjz/+GBcuXEB+fj7Cw8NRUlKC77//HgCwb98+3HvvvXBzc5PzYxCRnWECJ+rEf/7zH0ydOhW9evWCi4sLYmJiAAArV66EWq3Grl278Oyzz6KsrAy1tbVwc3PDfffdh3379qGhoQEffPAB5s6dK/OnICJ7w1roRGZoXnHY2dkZAPDHP/4RDQ0NmD59OkJDQ3Hp0iXTdhqNBnPmzMHYsWMxdOhQDBw4UJa4ich+cQZO1ImJEyciKysL1dXVaGxsNPUr/+yzz7Bs2TLMmDEDAPDNN9+Yunr169cPI0eOxJ///GfMmzdPttiJyH5xBk7UiZCQEJw5cwYxMTG46aabMGzYMFRWVmLFihVYtmwZevbsiV69emHMmDEoKSkx7RcdHY0NGzYgJCRExuiJyF6xGxmRBBobG/H888/jlltuwaOPPip3OERkh3gKnUhk165dQ1BQEC5duoQFCxbIHQ4R2SnOwImIiBSIM3AiIiIFYgInIiJSICZwIiIiBWICJyIiUiAmcCIiIgViAiciIlKg/wd8FwKYubCyHAAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -655,7 +729,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -682,12 +756,14 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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TjyeMWbNmnZBUWraBlPtyrSVxzZ49u8Ptk+0TEcmVTBLeQ8DFwDPAA8C/kOhpubCzA5NJclxXApT2lFRERDKXScK7z92bAdx9pZn9Hujl7q/lJDIREZEsSivhmVkMqDOz0929HsDd38ppZCIiIlmUVsJz9yYz2wb0B3blNiQpdFo4V0TCKJMmzSeAajNbDOwkMbUYAO6e7vRikiVhTiotC+eOGDGiw3Iti+sq4YlIEDJJeDclH+9qsz8OfDwr0Ujawp5UtHBuYevq8JJU+9Rh6iMnu64SjLQTnrsPy2UgkrmwJhUtnFv4nn/+efbs2UN9fT0PPfTQ8dl8Wm8Dae0LatKDQpDquur6BCgej4f636ZNm4Zu2rQpfvTo0bh8ZM2aNfE1a9ZkrZxIW2vWrImbWby6ujrldib75CNBXZ+jR4/GN23aFN+0adPQeAg+y8PwL5OZVvqQaM4cT/vJoz+Wi2QMmmlFJB+6OptPqn1BzPBTKFJd11xdH8200l4m9/CWkljw9XskJpGeAcwlMRBdRIpId2bzyccMP4Ui1XWV4GRSw9sDjHT3fWb2obufbmbnAGvcPWcLuaqGJyKSOdXw2stktYRSoGUR1zozOx3YDQzPelQiIiJZlkmT5hYS9+9+B6wHHgTqgG05iEtERCSrMqnhzeKjlcdvBo4AfYFrsx2UiIhItmVSw/sWsAp4w93fB240s/8EfD35nIhkoHVHkLAP7g7zzD4i6cok4U0DbmuzrwZ4DiU8kYwV0uDusM/sE1b6ohAumSS8OO2bQGMp9olIGqZMmUJ1dTULFy5k4cKFTJgwod02kPa+XAvrzD5hpi8K4ZLJsIRnSNzDm+fuzWZWCtwDfMLdv5SrADUsQYpVIQ3urq6uBhLjyLJRLipavgCk+0Whs3KZ0LCE9jKp4c0BqoHdZvYm8DESwxI0jYJIFxTS4O50E5gS3Yk0r2y4pF3DA0jW6j4NnAu8DfypZRX0XFENT0Qkc6rhtZdJDY9kctuY/CciIlIw1OFEREQiQQlPREQiQQlPREQiIaN7eCKSOxqkLJJbquGJhMTatWupra3ttFxtbW3aiVFEPqIankhITJ48mcmTJ6c9uFtEMqOEJxISGtwtkltq0hQRkUhQwhMRkUgIpEnTzPoDjwHnAfXAX4CvJ9fVExERybmganhx4F53N3e/APgriZUWREREAhFIwnP3D9z99612bQSGBHFuERGAjRs3cuutt7Jxo6YCjqrAe2kmV1y4CXg+6HOLSHStWLGC119/ncOHDzNu3Lh8hyN5kI9hCUuAOuAnmRyUXOZCRKRL9u/ff/yxpqYmz9FIPgSa8MzsfuATwKRM19HTengi0h0tnx89evSgqqoqz9HkXqv18CQpsIRnZguAKmCCu9cHdV4REREIbljCKOA7wDbgj2YGsN3dvxTE+UVERAJJeO7+ClASxLlERERS0UwrIiISCUp4IiISCUp4IiISCUp4IiISCUp4IiISCUp4IlKUGhqPZbWcFD6teC4iRamirJyZj8w5vv3ewfePP7bev+K6xYHHJvmhGp6IiESCEp6IiESCEp6IiESCEp6IFDQt7CrpUqcVESloWthV0qUanogUtMOHD5/wKHIySngiIhIJSnhZoHsIIuFXUl56wqNEj+7hZYHuIYiEX98LBnLotb2cNvLMfIcieaKvOh1It+amewgi4XdK5WkM+PwwTqk8Ld+hSJ6ohteBk9XcGhqPUVFW3unx6ZYTkfQ0Nx6jVH9T0kVKeEmp/pBOVnPTHH0i+VFaVk7NvTeesK9+/3vHH1s/VzVveaCxSfgp4SV15w9JN8NFRMJPn9Ad6FFWesLjyfS9YCA9Bvai7wUDgwhLRES6QDW8DlwxvB8vbj/A+GF9Oyx3SuVpuhEuIhJySngdGHnWqYw869R8hyEiHUi3JUZEvyEiUtCuGN6Pj/fryRXD++U7FAk51fCK3MaNG3nyySf58pe/rEHxUpTUEiPpUg2vyK1YsYItW7awYsWKfIcikjZN1ye5oIRX5DQLTNfoAze/9EVNckFNmkVGs8Bkh+ZHzS99UZNcUMIrMm1ngdlzZN/xR80Cc3JtvwCc7AM3DF8UdF9WpGuU8ApUuh96miH+RCe7boU0XVwx1j4bjjVRUR7LdxhS5AJJeGZ2P/D3wFDgfHffGsR5i1m6H3phHhSfj5pKutctzNPFFWNzX0V5jOnznji+vXfvIQDe3XvohP2/uPergccmxSOov+bngEuBNwM6X1FpbjzWbl8xfOjlumNCd66bposTKT6B1PDcfQOAmQVxuqKTamJrDr1//LFQZ4jPddLuznULc81YRLomfO01kpZCnF2iIUWNqzvlukLXrTCUxMpPeBTJhoLptLJ1a25v+1VVVeXkdWtqarr9Gqliy8bsEtmILRNVVVVpdwzRdftI0NetO1577TVefPFFxo8fz8iRI9M+ru171XvwWP727sv0Ovv8bIeYUr6vmwSjYBLe6NGj6dGjR7dfJ+iOErlKpNmQq9jSvcYddQyJ4nXLhnzH9tBDD/HGG28Qi8WYMWNGl1+nR99z6dH33CxG1rF8X7dcqK+vz3lFodAUTMLLlmLs0h026V5jDZnoGvUgFemaoIYl/BiYCpwN/NbM9rn7qCDOrfE9udXceIzSNgOx0/3QU8eQrtEXBZGuCaqX5s3AzUGcq62243vq40Mp732I9+NDNb4nC1L1hKzf/97xx0LtQRpm+qJQfDR7TjAi16QZ9L2BKNKCnCKZ0a2WYOgTSbKuELv+S9dEcchELujeZzAiV8OT3NOCnNFRSHOQiqiGJyIikaCEFyJadDTa9P6L5JaaNENEN66jrRje/zCPERTRb2UeNRxryncIEiLF0HFBq0ykR5198kM1vDzSGMHoSjVgvxhojGB61NknP5TwQkRjBKNDA/ZFgqcmTSl4xdLZQwP2RXJLf1mSljAllbb3PnO9cnpQwjxgP0zvfyHRdQsXNWlKSm0n3Q5TD8K29z73vfMBANvf+aCg732GecB+mN7/fMtk3ktdt3BRwpOUCimpBL1YaLHrzgoYUZBJEkv3umk4RzCU8CQtYU4qYe7sU4iz4KfqUMOh948/Rr1DzcmSWHd63mrJp2Ao4UlawpxUwqxYmrSuGN6PF7cfYPywvvkOJXDprqnZnZ63Gs4RDCU8kSxq++EY5qbATGqfYb6/mGttm/f37j0EwLt7D3XavK+et+GihCeSRe3ufe4/CsB7+4+G7t5nsdQ+g1YSKz/hsSNRrhmHkRKeSA6F6d5nuk1z0rFM3tMo14zDSAlPJIfCdO9TU9llR5jeU8mMEp5IROmDW6JGd1JFRCQSlPBERCQSlPBERCQSlPBERCQSlPBERCQSlPBERCQSlPBERCQSlPBERCQSlPBERCQSlPBERCQSAptazMxGAI8C/YF9wD+4++tBnV9ERKItyBrePwIPuvsI4EHgZwGeW0REIi6QGp6ZDQDGAp9P7von4Cdmdpa7v9/J4TGAhoaGLp+/z6mdr1tVX18PPdNbcbi+vp7Tynul95qKTbEpNsWWw9hOptVnptaESiqJx+M5P4mZVQEr3X1Uq32vAjPcvbajY2tqai4G1uc4RBGRYnVJVVXVhnwHEQaFsDzQn4FLgN1AU55jEREpFDFgEInPUCG4Gt4AYBvQ392bzCxGouPKJ9Jo0hQREem2QDqtuPse4CVgWnLXNGCzkp2IiAQlkBoegJl9ksSwhH7AfhLDEjyQk4uISOQFlvBERETySTOtiIhIJCjhiYhIJCjhiYhIJCjhiYhIJBTCwPOcM7P/BnwHKAF6ArXuPt3MdgAT3X2rmT0IfA6oB+qAOe6+KSSxzQDmAX8HfMvdf5KjWNYC/+zuP2u1rwR4A5gJ/A/gfCBOYpKAW939BTO7kMT8qZ8Cfu3uV4cotlnAbBLXNw7c6+6P5yiW5uQ5yt29qdVz1wHXuvtnO3i/+wOPAeeR+B38C/D1TIf25CK2ZLn7gb8HhgLnu/vWTOLKVMvvP3AhAfzuZ6JVbLcB/wXYm3zqkLtfkqewBNXwMLNBwFJgsrtfCIwE7k1R9Dck/pDHAD8Efhmi2F4CvgL8IschPQxc12bfZUAj8GVgJ4lrdD6JLwd/SZbZA9wK3BLC2F4Hxif3XwX8yMyG5iiWvwCv8dGcsi1mAg938n63JGNz9wuAvwL3hCQ2gOeAS4E3uxBTdwT1u99V97j7hcl/SnZ5FvmEB5wNHCMx8wvuHnf3l9oWcvdqdz+W3PxXoNLMcn390o1tq7u/SuJbei49Bww3s79rte864BGgEtjl7vFkTPvc/a3kz7vc/d9I1EzCFtvv3X1/8uedJKawq8xhLI/QKuGY2XnABcAzdPB+u/sH7v77Vq+3ERgShtiS2xvc/e0uxNMtAf7uSxFQwoMtwJ+At8zsaTP7VrL5qCPfBH7l7rn+I+tKbDnj7g0kvknPBDCz04AvkphQ4MfA/zazP5nZj8zss4UWm5ldBpwO1OQwlpXAVWbWL1l8JvBLdz9Cmu938ovWTcDzYYtN2rnDzF5K/vtf+Q4m6iKf8Ny92d2/SKJZ51+ACcC/m9kZqcqb2VeA6SQ+cEIVW0B+DlxrZmXANcAGd3/H3X8HfAy4G2gAnjKzOwoltmSNZyUwLfkBn6tY9gC/A6YnE9c/kGhmzOT9XkLiPnJX71flMjY5UesmzQX5DibqIp/wWiSbRh50988DB0j8YZ/AzL4ELAD+q7u/F6bYAoxlC4lmvytJNH893Oq5Q+7+vLvPA75B4otB6GMzs08AvybRCSQry6h0FAsf3Uf7HImODH9qc+xJ3+9k55BPANd0tYUhV7GJhF3kE56ZnWNmn2m1XQmcBWxvU24isIhEstsRptjy4GHgLmAEyWY1M/u8mfVJ/lxCokdmPuLMKDYz+ziwDrjZ3X+T61iSfg2cA/yAVsmms/fbzBYAVcAX3b2790OzGptIIdCwhMQ1+K6ZDQGOkPgScKe7bzaz1uUeIdEc9nSr/Z9z9335js3MpgH3kZiYe0qyue6K5M38XHgieb6fJe8JQaJzw6JkQoFE78dvJuMbCmwATgV6mtlOYL67/zzfsQH/B+gPfM/Mvpfcd7u7r8tRLLh7o5mtJNFr9apW5Tt6v0eRGBKwDfhj8v3f7u5fyndsAGb2Y2Aqic4tvzWzfd5qwedcycPvvhQwTR4tIiKREPkmTRERiQYlPBERiQQlPBERiQQlPBERiQQlPBERiQQlPJFWzGymmXV58LmZfcfMlmczJhHJDo3DE8kid/9By8/J8YfbSSy505i3oEQEUA1PJGuSc1OKSEjpD1Qiy8zOBRYDl5D48vdPwKY2ZRaTmEGkL4kZWr7l7uuTz90FjAaOApOBW5NTbg139xnAH5Iv82FyZpQJwD+TWH/v5eRrDCCxhtzHPMPFXEUkM6rhSSSZWQyoJpFshpKYP3JViqJ/JrGq9hkkltV5ysx6tnp+CvA0iWWFnmhz7KXJx9Pdvbe7v5g8x4xWZaYBv1WyE8k91fAkqj4NDAbmtrq/tsHMhrcu5O6Pt9pcaGZ3AkZifTiAf3X355I/H2kz/2oqj5KYj/XbydUOriX1KvYikmVKeBJV5wJvdtaZxMz+J3AjieQYB/oAZ7YqktEq3+7+b2b2N2C8me0GhtOFhVxFJHNq0pSoehv4WEcdTczsEuB24MtAP3c/ncQacCWtinU0+/rJnnuURLPmtcDT7n40k8BFpGtUw5Oo+hOJRVDvMbP5QBOJteZaOw1oBN4HypJLz/TJ4BzvA83Ax0ks69PiMeDfgUMkkp6IBEA1PIkkd28CJpFoUnwL2Alc06bYOuA3JJLVmyR6Y6bdhOnuh4EFwP8zsw/NbFxy/06glkQNcH33/iciki6thyeSB2b2MLDL3e/MdywiUaEmTZGAJWdgmQp8Ks+hiESKmjRFAmRm3we2Ave5+/Z8xyMSJWrSFBGRSFANT0REIkEJT0REIkEJT0REIkEJT0REIkEJT0REIkEJT0REIuH/A2jc+/9wO7v5AAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -727,7 +803,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -741,12 +817,14 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -778,7 +856,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 15,
    "metadata": {
     "scrolled": true
    },
@@ -817,7 +895,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 16,
    "metadata": {
     "scrolled": false
    },
@@ -841,24 +919,26 @@
     {
      "data": {
       "text/plain": [
-       "(<matplotlib.axes._subplots.AxesSubplot at 0x7f1e20d22f98>,\n",
-       " [<statannot.StatResult.StatResult at 0x7f1e20ab9240>,\n",
-       "  <statannot.StatResult.StatResult at 0x7f1e20ab9e10>,\n",
-       "  <statannot.StatResult.StatResult at 0x7f1e20ab99b0>])"
+       "(<matplotlib.axes._subplots.AxesSubplot at 0x7f8687998438>,\n",
+       " [<statannot.StatResult.StatResult at 0x7f868792dda0>,\n",
+       "  <statannot.StatResult.StatResult at 0x7f868793a4a8>,\n",
+       "  <statannot.StatResult.StatResult at 0x7f868793aac8>])"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -880,19 +960,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 17,
    "metadata": {
     "scrolled": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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Zwssvv8zBgwcpKSnh8uXL7Nixg0GDBrkrgoiI24SFhZGSkuLVxdAYbhs5DB06lCNHjhAXF4fdbmfKlClEREQwd+5cpk6dSlVVFZMmTaJPnz7uiiAi4jZ5eXkkJyeTkpJCly5dzI7jchaHw+EwO0RdXB8aeftupZKSEpYtW8b8+fP99hOFiMCTTz7J6dOnufPOO1mzZo3ZcW6qoe+dOkPaxfz9lHoRuTZqOH36NACFhYWcOnXK5ESup3JwoaZwSr2IwPLly2t97A9UDi5U0yn1IuJ/ro8arissLDQpifuoHFyoplPqRcT/dOrU6YbHd955p0lJ3Efl4EJN4ZR6EYFnnnmm1sf+QOXgQk3hlHoRga5duzpHD3feeadfTmVVObhQWFgYw4YNw2Kx8NBDD2kqq4gfe+aZZ2jRooVfjhrAzfdzaIqsViuFhYUaNYj4ua5du7Jx40azY7iNysHFrp9SLyLiy7RbSUREDFQOLlZSUkJSUpJOgBMRn6ZycDFdPkNE/IHKwYV0+QwR8RcqBxfS5TNExF+oHFxIl88QEX+hcnAhXT5DpOnw98knKgcX0uUzRJoOf598onJwIV0+Q6RpaAqTT1QOLma1Wrn33ns1ahDxY01h8onKwcWuXz5DowYR/9UUJp+oHMSr+ftBP/FNTWHyicpBvJq/H/QT39QUJp+oHMRrNYWDfuKbmsLkE5WDeK2mcNBPfJe/Tz5ROYjXagoH/cR3+fvkE5WDeK2mcNBPxFupHMRrNYWDfiLeSuUgXqspHPQT8VYqB/FqMTEx3HLLLcTExJgdRaRJUTmIV8vKyuLy5ctkZWWZHUWkSVE5iNfSeQ4i5lE5iNfSeQ4i5lE5iNfSeQ6upetUSX0EufsFXnrpJUpLS0lJSeEvf/kLv/rVr6ioqOCBBx7ghRdecM5j9wY5OTlkZ2c3ah1lZWUAtG3btlHrGT58ONHR0Y1ah68bMmQI2dnZ2Gw2nefgAj++TtUTTzxhdhzxcm4dORw8eJAtW7Y4H8+bN4/nn3+eDz74AIfDwaZNm9z58qYoKSmhpKTE7Bh+Qec5uI6O30h9ue1je1lZGStWrGDmzJkcP36cb775hitXrnD//fcDEB8fz6pVq5gyZYq7ItRbdHR0oz+tJycnA7B06VJXRGrSrp/nkJWVpfMcGqmm4zcaPUht3DZyeP7555k7dy5t2rQB4Ny5c7Rv3975/fbt21NUVOSulxc/4e8XN/MUHb+R+nLLyGHz5s106NCByMhI0tPTgWufViwWi/M5Dofjhsd1dfToUZfldIeLFy8CcPjwYZOT+I9f/OIX5OXlmR3Dp/Xq1Ys///nPVFdXExgYSK9evfQ7KrVySzls376d4uJiYmNjOX/+PJcuXcJisVBcXOx8zvfff094eHi91927d29CQkJcGdel0tLSAIiIiDA5ichfdenShcTERGc5zJkzp0nvpmtKk08qKysb9KHaLeXw+9//3vl1eno6hw4dYunSpYwdO5bDhw8TERFBRkYGgwYNcsfLi8j/o+M3rnd94kljy8FbeXQe6fLly1m4cCHl5eX06tWLqVOnevLlRZo0q9VKYWGhjt+gySd14fZyiI+PJz4+HoCePXs6d7uIiGddvzmNSF3oDGkRETFQOYiIiIHKQUREDFQOIiJioHIQEREDlYOIiBioHERExEDlICIiBioHERExUDmIiIiBykFERAxUDiIiYqByEK9WUlJCUlKS7nks4mEqB/FqqampfPHFF6SmppodRaRJUTmI1yopKWHnzp04HA6ys7M1ehDxIJWDeK3U1FRsNhsANptNowcRD1I5iNfavXs3DocDAIfDQU5OjsmJRJoOlYN4rfbt29/wODw83KQkIk2PykG8VnFxca2PRcR9VA7itYYOHYrFYgHAYrEwdOhQkxOJNB0qB/FaVquVoKAgAIKCgrBarSYnEmk6VA7itcLCwhg4cCAAgwYNIjQ01OREIk2HykG82vXZSiLiWSoH8VolJSXs378fgA8//FAnwYl4kMpBvFZqaip2ux0Au92uk+BEPEjlIF5rz549N5whvXv3bpMTiTQdKgfxWkOGDLlhtpKmsop4jspBvJbVaiUg4NqvaEBAgKayiniQykG8VlhYGMOGDcNisfDQQw9pKquIBwWZHUCkNlarlcLCQo0aRDys1nLo27ev8/IFP+ZwOLBYLHzyySduCyYC10YPKSkpZscQaXJqLYfMzExP5RARES9SazkcO3as1oU7duxY6/dXrlzJBx98gMViYdKkSUyfPp0DBw6wdOlSKisrGTVqFHPnzq1/ahERcatay+HNN9+86fcsFgsjRoy46fcPHTrERx99xLZt27DZbIwePZrIyEgWLFjAm2++SYcOHXj88cfZu3cvgwcPbvhPICIiLtfgcvgp/fv357/+678ICgqiqKiI6upqLly4QOfOnenUqRMA48aNIysrS+UgIuJlai2H3/zmN/zqV79i5syZNX5/3bp1ta68WbNmrFq1itdff52YmBjOnTt3w929wsPDKSoqakBsERFxp1rLITIyEoCRI0c2+AXmzJlDYmIiM2fOJD8//4bZT9dnPdXH0aNHG5zFEy5evAjA4cOHTU5ivk8//ZQ///nPjVpHeXk5AK1atWrwOvr27cv999/fqBwi/5+//63XWg7R0dEATJgwgdLSUj799FOCgoK47777aNOmTa0r/vrrr7l69Sr33HMPt9xyCyNGjCArK4vAwEDnc4qLi+t9X+DevXsTEhJSr2U8KS0tDYCIiAiTk5jv/PnznDx5slHruH5r0A4dOjR4HV26dNH/D3E5X/lbr6ysbNCH6jqdBLdnzx7mz59Pt27dqK6u5vTp06xYsYJ+/frddJkzZ86watUq/vjHPwKwa9curFYry5Yto6CggL/9278lMzOTiRMn1ju0+Ibo6GjnB4yGSk5OBmDp0qWuiCQidVSncli5ciVvvfUW3bp1A65NcX3uuedIT0+/6TKDBw/ms88+Iy4ujsDAQEaMGMGYMWMICwtj9uzZVFZWMnjwYGJiYlzzk4j4uZycHLKzsxu8fFlZGQBt27ZtVI7hw4c3uvTF+9WpHCwWi7MYAHr16lWnO3TNnj2b2bNn3/BvkZGRbNu2rZ4xRaSxSkpKgMaXgzQNtZbD9U8avXv35rXXXnNeJTM9PZ0HH3zQIwFF5JrG7qbTLjqpj1rL4cEHH8RisThHCS+//LLzscViYf78+R4JKSIinlVrORw/fvwnV5CZmcnYsWNdFkhERMzX6Ps5vPbaa67IISIiXqTR5VCXA9MiIuJbGl0O9T3DWUREvJ9uEyoiIgYqBxERMdAxBxERMWh0OYwbN84VOURExIvUep7DT73xv/vuuzz22GMuDSQiIuartRyee+45T+UQEREvUms59O/f3/l1WVkZly9fxuFwUF1dTWFhodvD1ceGDRvIy8szO4Yzw/Xr2Jila9euJCYmmppBRHxXnS/ZvX79egACAwOpqqri7rvv5t1333VruPrIy8vj6BdfEtjc3CtO2m3Xbmb0lzzzbn9afaXMtNcWEf9Qp3LIyMhg9+7dpKSk8Oyzz/LRRx+xd+9ed2ert8DmbWnReZjZMUx3qWCX2RFExMfVabZSWFgY4eHhdO3alePHjxMXF8eJEyfcnU1ERExSp5FDUFAQhYWFdO3alY8//pioqCgqKyvdnU1EpEbecIzRW44vgnuOMdapHB5//HGee+451q5dy8qVK9m6dStDhgxxaRARkbrKy8vjxLFjtAsMNC1DsN0OQEkdbm3gTt9XV7tlvXUqh3vvvZc33ngDgK1bt1JQUEBAgK68ISLmaRcYSGxr3fI046J7JqDU+g5fVlZGWVkZiYmJnD9/nrKyMiorK2nXrh1z5sxxSyARETFfrSOHf/u3f2P//v0ADBgw4K8LBQUxcuRI9yYTERHT1FoO1+/ylpycrJuSi4g0IXU65rB06VKOHDnChx9+SFVVFVFRUfTr18/d2URExCR1Oqq8detW5syZw/nz56moqODpp59m06ZN7s4mIiImqdPI4Q9/+AObN28mPDwcgMTERB577DEmT57s1nAiImKOOo0c7Ha7sxgAbrvtNk1lFRHxY3V6h2/bti07d+50Pt65cye33nqr20KJiIi56rRbafbs2SxYsIAlS5YA0KxZM9asWePWYCIiYp5ay6Gs7NqZd0uWLGHz5s2cPHkSi8VCx44d+eUvf0lWVpZHQor4Ol0L6Ea634j3q/NJcJGRkQA4HA6dBCdST3l5eRz78gsCbw02LYM94No1eI5/d9K0DADV56+a+vpSNzoJTsRDAm8N5tZBd5gdw3Tn//tbsyNIHdTpgLSKQUSkaXHrfNRXX32VMWPGMGbMGJYtWwbAgQMHGDduHCNGjGDFihXufHkREWkgt5XDgQMH2LdvH1u2bGHr1q0cO3aMzMxMFixYwO9+9zu2b9/O0aNHvfJ2oyIiTZ3byqF9+/YkJSURHBxMs2bNuOuuu8jPz6dz58506tSJoKAgxo0bpxlPIiJeqE7nOTREt27dnF/n5+fz/vvv88gjj9C+fXvnv4eHh1NUVOSuCNII3jD1Erxn+qWmXkpT47ZyuO6rr77i8ccf59lnnyUwMJD8/Hzn9xwOBxaLpV7rO3r0aI3/fvHixcbE9DsXL17k8OHDDV7+yJEjfHemkNtbuf1XpFa3OK7divHi6S9Ny/Bdua3R21O/nzfS9nStxm7Pmrj1L//w4cPMmTOHBQsWMGbMGA4dOkRxcbHz+8XFxTdcs6kuevfuTUhIiOHfN2zYQPWVMi4V7Gp0bl9XfaUMmy2YiIiIBq8jLS0NWgUxvU+YC5P5pt9/VkLr1q0bvz0rNEq+zhXbs8SFeXxdbduzsrLyph+qa+O2Yw5nz55l1qxZLF++nDFjxgBw3333cerUKQoKCqiuriYzM5NBgwa5K4KIiDSQ20YOr732GpWVlaSkpDj/zWq1kpKSwuzZs6msrGTw4MHExMS45PVCQ0P5rvQqLToPc8n6fNmlgl2EhoaaHUNEfJjbymHhwoUsXLiwxu9t27bNXS8rIiIuoJsyiIiIgcpBREQMzJ2nKCLSAKWlpXxvs5FxsczsKKb73mbDUlrq8vVq5CAiIgYaOYiIzwkNDcVRVERs67ZmRzFdxsUyt8xO1MhBREQMVA4iImKgchAREQOVg4iIGKgcRETEQLOVpEalpaV8X27j95/p2pfflduwuWEeuYg308hBREQMNHKQGoWGhhJUfk73c+D/7uegq9xKE6NyEPGA0tJSbGWVnP/vb82OYjpbWSWlIdpN5+20W0lERAz8auTgDbcJtduuABAQ1Ny0DNVXyoDbTHt9MQoNDaWo8gduHXSH2VFMd/6/v9XNqHyA35RD165dzY4AQF5eHgBdu5r55nyb12wPEfFNflMOiYmJZkcAIDk5GYClS5eanEREpOF0zEFERAxUDiIiYuA3u5VEpGn5vrra1DvBXbLbAWgRYO5n7O+rq3HH2UgqBxHxOd4w4aLs/yaf/K3JWcJwz/ZQOYiIz/GGCSj+PvlExxxERMRA5SAiIgYqBxERMdAxB7mp77zgfg7lV6/NCGkVbN7nmO/KbbQ27dVFzKFykBp5w2wQgOL/mxHSoZN5eVrjmu1Rff6qqVdltV+pBiCgeaBpGeDaduB2UyNIHagcpEbeMBsE/GdGiDeUrfO6X7ebnOV279geUjuVg4gHeEPZ+kvRimfogLSIiBioHERExEDlICIiBm4vh/LycsaOHcuZM2cAOHDgAOPGjWPEiBGsWLHC3S8vIiIN4NZyOHLkCA8//DD5+fkAXLlyhQULFvC73/2O7du3c/ToUfbu3evOCCIi0gBuLYdNmzaxaNEiwsPDAfjss8/o3LkznTp1IigoiHHjxpGVleXOCCIi0gBuncr6m9/85obH586do3379s7H4eHhFBUVuTOCiIg0gEfPc7Db7VgsFudjh8Nxw+O6OHr0qKtjudTFixcBOHz4sMlJ/IO2p+toW7qWv29Pj5bD7bffTnFxsfNxcXGxc5dTXfXu3ZuQkBBXR3OZtLQ0ACIiIkxO4h+0PV1H29K1fGV7VlZWNuhDtUenst53332cOnWKgoICqquryczMZNAVsa5bAAAHhUlEQVSgQZ6MICIideDRkUNISAgpKSnMnj2byspKBg8eTExMjCcjiIhIHXikHHJycpxfR0ZGsm3bNk+8rIiINJDOkBYREQOVg4iIGKgcRETEQOUgIiIGutmPuE1OTg7Z2dmNWsf1u5ddv1FNQwwfPpzo6OhG5RBpalQOP+Itb2agN7TrwsLCzI4g0iSpHFxMb2Z/FR0drYIT8VEqhx/Rm5mIyDU6IC0iIgYqBxERMVA5iIiIgY45iPiIxs6m00w6qQ+Vg0gToZl0Uh8qBxEfodl04kk65iAiIgYqBxERMVA5iIiIgcpBREQMVA4iImKgchAREQOVg4iIGKgcRETEQCfBiUiToxt7/TSVg4hIA/j75UhUDiLS5OhSJD9NxxxERMRA5SAiIgYqBxERMVA5iIiIgcpBREQMVA4iImKgchAREQOfOc/B4XAAcPXqVZOTiIj4juvvmdffQ+vKZ8qhqqoKgBMnTpicRETE91RVVdG8efM6P9/iqG+dmMRut1NRUUGzZs2wWCxmxxER8QkOh4OqqipatmxJQEDdjyT4TDmIiIjn6IC0iIgYqBxERMRA5SAiIgYqBxERMVA5iIiIgcpBREQMVA4iImLgM2dIe5OsrCzWr1+PzWbD4XAQGxvLP//zP5sdy6e88MILfPLJJ1RVVVFYWMhdd90FwIULF4iPj2f27NkmJ/R9Z86cISYmxrltr1u3bh0dOnRwPk5MTOTFF1/ktttu83REn1Dfv/dNmzbRokULxo4d68GUrqdyqKeioiJeeukl0tPTCQ0NpaKigoSEBLp06cKwYcPMjuczFi1aBFx7A5s6dSoZGRkArF692sxYfic8PNy5bW9mw4YNHkrjexry9/7JJ5/Qv39/Dyd1Pe1WqqfS0lKqqqq4cuUKAC1btiQlJYW7776b6Ohozpw5A0Bubi4JCQkAJCQksGzZMv7xH/+R4cOHs3fvXtPy+4LPPvsMq9XK0KFDnWWRnp5OUlKS8zkJCQnk5uaSm5vLpEmTiI+PZ/78+WZF9ilJSUnMnDmTUaNGkZOTc8Pvrdyotr/3999/n8mTJzN+/HhiYmL45JNPOHDgADk5OaxatYoPP/zQ5PSNo5FDPfXs2ZNhw4bx0EMPcc899zBgwADGjRtH586da12uqqqKjRs3kpOTw8qVKxk8eLCHEvueH374gdTUVMrLy4mOjmb69Om1Pj8/P5/du3fTunVrDyX0HefOnSM2Ntb5eNy4cQC0bduWdevWAfDiiy+aks0X3OzvvVOnTjz//POsW7eOsLAw0tLSWL9+PevWrSM6Opr+/fszcOBAs+M3isqhAV544QWefPJJ9u3bx759+5g8eTLLly+vdZnrvyjdunWjrKzMEzF91sCBAwkODiYsLIzQ0FDOnz9f6/O7dOmiYriJmnYrJSUl0adPH5MS+Z6b/b2vWbOGnJwcTp06xaFDh+p1UTtfoHKopz179nDp0iVGjx7NxIkTmThxIps2bSItLQ346zXTbTbbDcuFhIQA6IqydRAU9NdfS4vFgsPhcP73uuuXcAfqdRliuUbbrG5u9vf+9ttv89vf/pbx48fTr18/evTowdtvv212XJfyr6rzgObNm/Mf//Efzn20DoeDv/zlL9xzzz2EhoZy8uRJAHbt2mVmTL8TGhrK119/jcPh4PTp03z55ZdmR5Im4GZ/78HBwVgsFmbOnMmAAQPIzs6muroagMDAQOfXvkwjh3p68MEH+Zd/+Rdmzpzp/PQ6cOBAZs2axT/8wz+wZMkSXn31VaKiokxO6l9+9rOf8ac//YmYmBi6dOlCRESE2ZGkCbjZ3/uaNWtISkpi1KhRWCwWoqKiOHz4MHDtd/W3v/0trVu3JiYmxsz4jaL7OYiIiIF2K4mIiIHKQUREDFQOIiJioHIQEREDlYOIiBioHETqKSsry3ndLBF/pXIQEREDlYNIHaxcuZKHHnqISZMmkZ2dDcCpU6eYPn06kydPZujQoTzxxBNUVlaybds2rFarc9lvv/2WqKgorl69alZ8kXpTOYj8hJ07d7Jjxw62bt3qvFosXLupS1xcHJs2bWLHjh2cOXOGPXv2EBMTQ2FhIV999RUAmzdvZsKECQQHB5v5Y4jUi8pB5CccPHiQ4cOH06pVK4KCgpg4cSIA8+bNIywsjA0bNvDrX/+ac+fOcenSJYKDg/nFL37B5s2bqa6uZsuWLUyePNnkn0KkfnRtJZE6+PFVZgIDAwF4+umnqa6uZtSoUQwZMoSzZ886n2e1Wpk0aRL9+/enW7dudOrUyZTcIg2lkYPITxg0aBBZWVlcuHABu93uvD/Cvn37mDVrFqNHjwbgyJEjzqtxdujQgfvvv59///d/5+GHHzYtu0hDaeQg8hMGDx7Ml19+ycSJE2nTpg09e/aktLSUuXPnMmvWLFq0aEGrVq3o168fhYWFzuXi4+NZsmSJ7vonPklXZRVxA7vdzuLFi7njjjuYMWOG2XFE6k27lURcrLy8nAEDBnD27FmmTp1qdhyRBtHIQUREDDRyEBERA5WDiIgYqBxERMRA5SAiIgYqBxERMVA5iIiIwf8C/TLn3M0lmSsAAAAASUVORK5CYII=\n",
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       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
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    "pygments_lexer": "ipython3",
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diff --git a/example/example_barplot_hue.png b/example/example_barplot_hue.png
index 53af174..675a548 100644
Binary files a/example/example_barplot_hue.png and b/example/example_barplot_hue.png differ
diff --git a/example/example_bucketed.png b/example/example_bucketed.png
index 749f1fe..c9e58fb 100644
Binary files a/example/example_bucketed.png and b/example/example_bucketed.png differ
diff --git a/example/example_custom_text_annot.png b/example/example_custom_text_annot.png
index cddee74..755f041 100644
Binary files a/example/example_custom_text_annot.png and b/example/example_custom_text_annot.png differ
diff --git a/example/example_hue_layout.png b/example/example_hue_layout.png
index 03b1852..1ec2991 100644
Binary files a/example/example_hue_layout.png and b/example/example_hue_layout.png differ
diff --git a/example/example_non-hue_inside.png b/example/example_non-hue_inside.png
index 8be4f5e..42c22dd 100644
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diff --git a/example/example_non-hue_outside.png b/example/example_non-hue_outside.png
index 4142e23..8f56539 100644
Binary files a/example/example_non-hue_outside.png and b/example/example_non-hue_outside.png differ
diff --git a/example/example_non-hue_outside_horizontal.png b/example/example_non-hue_outside_horizontal.png
new file mode 100644
index 0000000..fc026df
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diff --git a/example/example_tuning_y_offsets.png b/example/example_tuning_y_offsets.png
index 8181cad..dc879c5 100644
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diff --git a/example/example_tuning_y_offsets.svg b/example/example_tuning_y_offsets.svg
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diff --git a/readme.md b/readme.md
index e26b0ca..a5ed49f 100644
--- a/readme.md
+++ b/readme.md
@@ -16,6 +16,7 @@ Python package to optionnally compute statistical test and add statistical annot
 - Annotations can be located inside or outside the plot.
 - Format of the statistical test annotation can be customized: star annotation, simplified p-value, or explicit p-value.
 - Optionally, custom p-values can be given as input. In this case, no statistical test is performed.
+- Plots can be verticle or horizontal.
 
 ## Installation
 
@@ -61,7 +62,9 @@ More examples are available in the jupyter notebook `example/example.ipynb`.
 
 ![Example 1](/example/example_non-hue_outside.png)
 
-![Example 2](/example/example_hue_layout.png)
+![Example 2](/example/example_non-hue_outside_horizontal.png)
+
+![Example 3](/example/example_hue_layout.png)
 
 ## Requirements
 
diff --git a/statannot/statannot.py b/statannot/statannot.py
index 05e3009..8b89f3b 100644
--- a/statannot/statannot.py
+++ b/statannot/statannot.py
@@ -266,7 +266,7 @@ def simple_text(pval, pvalue_format, pvalue_thresholds, test_short_name=None):
 
 def add_stat_annotation(ax, plot='boxplot',
                         data=None, x=None, y=None, hue=None, units=None, order=None,
-                        hue_order=None, box_pairs=None, width=0.8,
+                        hue_order=None, box_pairs=None, width=0.8, orient = None,
                         perform_stat_test=True,
                         pvalues=None, test_short_name=None,
                         test=None, text_format='star', pvalue_format_string=DEFAULT,
@@ -407,8 +407,12 @@ def get_box_data(box_plotter, boxName):
         print()
 
     ylim = ax.get_ylim()
-    yrange = ylim[1] - ylim[0]
+    yrange = ylim[1] - ylim[0]  
+    xrange =  ax.get_xlim()[1] -  ax.get_xlim()[0]
+    xmax = ax.get_xlim()[1]
+    
 
+    
     if line_offset is None:
         if loc == 'inside':
             line_offset = 0.05
@@ -424,21 +428,21 @@ def get_box_data(box_plotter, boxName):
             if line_offset_to_box is None:
                 line_offset_to_box = 0.06
         elif loc == 'outside':
-            line_offset_to_box = line_offset
+            line_offset_to_box = line_offset  
     y_offset = line_offset*yrange
     y_offset_to_box = line_offset_to_box*yrange
 
     if plot == 'boxplot':
         # Create the same plotter object as seaborn's boxplot
         box_plotter = sns.categorical._BoxPlotter(
-            x, y, hue, data, order, hue_order, orient=None, width=width, color=None,
+            x, y, hue, data, order, hue_order, orient=orient, width=width, color=None, 
             palette=None, saturation=.75, dodge=True, fliersize=5, linewidth=None)
     elif plot == 'barplot':
         # Create the same plotter object as seaborn's barplot
         box_plotter = sns.categorical._BarPlotter(
             x, y, hue, data, order, hue_order,
             estimator=np.mean, ci=95, n_boot=1000, units=None,
-            orient=None, color=None, palette=None, saturation=.75,
+            orient=orient, color=None, palette=None, saturation=.75, 
             errcolor=".26", errwidth=None, capsize=None, dodge=True)
 
     # Build the list of box data structures with the x and ymax positions
@@ -497,6 +501,7 @@ def get_box_data(box_plotter, boxName):
     test_result_list = []
     ymaxs = []
     y_stack = []
+    
 
     for box_struct1, box_struct2 in box_struct_pairs:
 
@@ -557,21 +562,41 @@ def get_box_data(box_plotter, boxName):
             elif text_format is 'simple':
                 test_short_name = show_test_name and test_short_name or ""
                 text = simple_text(result.pval, simple_format_string, pvalue_thresholds, test_short_name)
-
-        yref = ymax_in_range_x1_x2
-        yref2 = yref
-
+                
         # Choose the best offset depending on wether there is an annotation below
         # at the x position in the range [x1, x2] where the stack is the highest
-        if y_stack_arr[2, i_ymax_in_range_x1_x2] == 0:
-            # there is only a box below
-            offset = y_offset_to_box
-        else:
-            # there is an annotation below
-            offset = y_offset
-        y = yref2 + offset
-        h = line_height*yrange
-        line_x, line_y = [x1, x1, x2, x2], [y, y + h, y + h, y]
+        if box_plotter.orient == 'v':
+            # if plot is verticle
+            if y_stack_arr[2, i_ymax_in_range_x1_x2] == 0:
+                # there is only a box below
+                offset = y_offset_to_box
+            else:
+                # there is an annotation below
+                offset = y_offset 
+                
+        elif box_plotter.orient == 'h':
+            # if plot is horizontal
+            if loc == 'outside':
+                if y_stack_arr[2, i_ymax_in_range_x1_x2] == 0:
+                    offset = line_offset_to_box*np.diff(ax.get_xlim())[0] 
+                else:
+                    offset = np.abs(y_stack_arr[1, i_ymax_in_range_x1_x2] -xmax) + line_height * xrange 
+            else:
+                offset = np.max(np.append(y_stack_arr[1,  i_ymax_in_range_x1_x2],[box_struct1['ymax'],box_struct2['ymax']])) - xmax + line_height * xrange                                              
+
+        # define locations of bracket, and text
+        if box_plotter.orient == 'v':
+            # if verticle
+            y = ymax_in_range_x1_x2 + offset 
+            h = line_height*yrange 
+            line_x, line_y = [x1, x1, x2, x2], [y, y + h, y + h, y]
+        else: 
+            # if horizontal
+            y = xmax + offset
+            h = line_height * xrange       
+            line_x, line_y = [y, y + h, y + h, y], [x1, x1, x2, x2]
+
+        # bracket drawing
         if loc == 'inside':
             ax.plot(line_x, line_y, lw=linewidth, c=color)
         elif loc == 'outside':
@@ -581,13 +606,24 @@ def get_box_data(box_plotter, boxName):
 
         # why should we change here the ylim if at the very end we set it to the correct range????
         # ax.set_ylim((ylim[0], 1.1*(y + h)))
-
+            
+        # annotate text
         if text is not None:
-            ann = ax.annotate(
-                text, xy=(np.mean([x1, x2]), y + h),
-                xytext=(0, text_offset), textcoords='offset points',
-                xycoords='data', ha='center', va='bottom',
-                fontsize=fontsize, clip_on=False, annotation_clip=False)
+            # annotate in different locations depending on vert or horiz plot
+            if box_plotter.orient == 'v':
+                ann = ax.annotate(
+                    text, xy=(np.mean([x1, x2]), y + h),
+                    xytext=(0, text_offset), textcoords='offset points',
+                    xycoords='data', ha='center', va='bottom',
+                    fontsize=fontsize, clip_on=False, annotation_clip=False) 
+            else:
+                ann = ax.annotate(
+                    text, xy=(y + h, np.mean([x1, x2])),
+                    xytext=(text_offset, 0),
+                    rotation=-90,
+                    textcoords='offset points',
+                    xycoords='data', ha='left', va='center',
+                    fontsize=fontsize, clip_on=False, annotation_clip=False)
             ann_list.append(ann)
 
             plt.draw()
@@ -597,7 +633,11 @@ def get_box_data(box_plotter, boxName):
                 try:
                     bbox = ann.get_window_extent()
                     bbox_data = bbox.transformed(ax.transData.inverted())
-                    y_top_annot = bbox_data.ymax
+                    if box_plotter.orient == 'v':
+                        y_top_annot = bbox_data.ymax
+                    else:
+                        y_top_annot = bbox_data.xmax 
+                        
                 except RuntimeError:
                     got_mpl_error = True
 
@@ -614,20 +654,27 @@ def get_box_data(box_plotter, boxName):
                 y_top_display = offset_trans.transform((0, y + h))
                 y_top_annot = ax.transData.inverted().transform(y_top_display)[1]
         else:
-            y_top_annot = y + h
+            if box_plotter.orient == 'h':
+                y_top_annot = y + h 
 
         y_stack.append(y_top_annot)    # remark: y_stack is not really necessary if we have the stack_array
         ymaxs.append(max(y_stack))
+
         # Fill the highest y position of the annotation into the y_stack array
         # for all positions in the range x1 to x2
-        y_stack_arr[1, (x1 <= y_stack_arr[0, :]) & (y_stack_arr[0, :] <= x2)] = y_top_annot
+        y_stack_arr[1, (x1 <= y_stack_arr[0, :]) & (y_stack_arr[0, :] <= x2)] = y_top_annot 
         # Increment the counter of annotations in the y_stack array
-        y_stack_arr[2, xi1:xi2 + 1] = y_stack_arr[2, xi1:xi2 + 1] + 1
+        y_stack_arr[2, xi1:xi2 + 1] = y_stack_arr[2, xi1:xi2 + 1] + 1          
 
     y_stack_max = max(ymaxs)
     if loc == 'inside':
-        ax.set_ylim((ylim[0], max(1.03*y_stack_max, ylim[1])))
+        if box_plotter.orient == 'v':
+            ax.set_ylim((ylim[0], max(1.03*y_stack_max, ylim[1]))) 
+        else:     
+            # Gets the left most annotation by assuming the last annotation is the leftmost, then extrating it's coordinates
+            text_dim = ax.transData.inverted().transform(ax.texts[-1].get_window_extent())
+            ax.set_xlim((ax.get_xlim()[0], max(1.06*np.max(text_dim[:,0]),xmax)))        
+
     elif loc == 'outside':
         ax.set_ylim((ylim[0], ylim[1]))
-
     return ax, test_result_list