{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Customizing Seaborn Plots\n",
    "> In this final chapter, you will learn how to add informative plot titles and axis labels, which are one of the most important parts of any data visualization! You will also learn how to customize the style of your visualizations in order to more quickly orient your audience to the key takeaways. Then, you will put everything you have learned together for the final exercises of the course! This is the Summary of lecture \"Introduction to Data Visualization with Seaborn\", via datacamp.\n",
    "\n",
    "- toc: true \n",
    "- badges: true\n",
    "- comments: true\n",
    "- author: Chanseok Kang\n",
    "- categories: [Python, Datacamp, Visualization]\n",
    "- image: images/parents_advice.png"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (10, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Changing plot style and color\n",
    "- Why customize?\n",
    "    - Reasons to change style:\n",
    "        - Personal preference\n",
    "        - Improve readability\n",
    "        - Guide interpretation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Changing style and palette\n",
    "et's return to our dataset containing the results of a survey given to young people about their habits and preferences. We've provided the code to create a count plot of their responses to the question \"How often do you listen to your parents' advice?\". Now let's change the style and palette to make this plot easier to interpret."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Music</th>\n",
       "      <th>Techno</th>\n",
       "      <th>Movies</th>\n",
       "      <th>History</th>\n",
       "      <th>Mathematics</th>\n",
       "      <th>Pets</th>\n",
       "      <th>Spiders</th>\n",
       "      <th>Loneliness</th>\n",
       "      <th>Parents' advice</th>\n",
       "      <th>Internet usage</th>\n",
       "      <th>Finances</th>\n",
       "      <th>Age</th>\n",
       "      <th>Siblings</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Village - town</th>\n",
       "      <th>Age Category</th>\n",
       "      <th>Interested in Math</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>few hours a day</td>\n",
       "      <td>3.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>female</td>\n",
       "      <td>village</td>\n",
       "      <td>Less than 21</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>few hours a day</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>female</td>\n",
       "      <td>city</td>\n",
       "      <td>Less than 21</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>few hours a day</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>female</td>\n",
       "      <td>city</td>\n",
       "      <td>Less than 21</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>most of the day</td>\n",
       "      <td>2.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>female</td>\n",
       "      <td>city</td>\n",
       "      <td>21+</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>few hours a day</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>female</td>\n",
       "      <td>village</td>\n",
       "      <td>Less than 21</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Music  Techno  Movies  History  Mathematics  Pets  Spiders  Loneliness  \\\n",
       "0    5.0     1.0     5.0      1.0          3.0   4.0      1.0         3.0   \n",
       "1    4.0     1.0     5.0      1.0          5.0   5.0      1.0         2.0   \n",
       "2    5.0     1.0     5.0      1.0          5.0   5.0      1.0         5.0   \n",
       "3    5.0     2.0     5.0      4.0          4.0   1.0      5.0         5.0   \n",
       "4    5.0     2.0     5.0      3.0          2.0   1.0      1.0         3.0   \n",
       "\n",
       "   Parents' advice   Internet usage  Finances   Age  Siblings  Gender  \\\n",
       "0              4.0  few hours a day       3.0  20.0       1.0  female   \n",
       "1              2.0  few hours a day       3.0  19.0       2.0  female   \n",
       "2              3.0  few hours a day       2.0  20.0       2.0  female   \n",
       "3              2.0  most of the day       2.0  22.0       1.0  female   \n",
       "4              3.0  few hours a day       4.0  20.0       1.0  female   \n",
       "\n",
       "  Village - town  Age Category  Interested in Math  \n",
       "0        village  Less than 21               False  \n",
       "1           city  Less than 21                True  \n",
       "2           city  Less than 21                True  \n",
       "3           city           21+                True  \n",
       "4        village  Less than 21               False  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey_data = pd.read_csv('./dataset/young-people-survey-responses.csv', index_col=0)\n",
    "survey_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "survey_data['Parents\\' advice'] = survey_data['Parents\\' advice'].map({1:'Never', \n",
    "                                                                       2:'Rarely', \n",
    "                                                                       3:'Sometimes', \n",
    "                                                                       4:'Often', \n",
    "                                                                       5:'Always'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the style to \"whitegrid\"\n",
    "sns.set_style('whitegrid')\n",
    "\n",
    "# Create a count plot of survey responses\n",
    "category_order = [\"Never\", \"Rarely\", \"Sometimes\", \n",
    "                  \"Often\", \"Always\"]\n",
    "\n",
    "sns.catplot(x='Parents\\' advice', \n",
    "            data=survey_data, \n",
    "            kind=\"count\", \n",
    "            order=category_order);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the color palette to \"Purples\"\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.set_palette('Purples')\n",
    "\n",
    "sns.catplot(x='Parents\\' advice', \n",
    "            data=survey_data, \n",
    "            kind=\"count\", \n",
    "            order=category_order);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Change the color palette to \"RdBu\"\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.set_palette(\"RdBu\")\n",
    "\n",
    "sns.catplot(x='Parents\\' advice', \n",
    "            data=survey_data, \n",
    "            kind=\"count\", \n",
    "            order=category_order);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This style and diverging color palette best highlights the difference between the number of young people who usually listen to their parents' advice versus those who don't."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Changing the scale\n",
    "In this exercise, we'll continue to look at the dataset containing responses from a survey of young people. Does the percentage of people reporting that they feel lonely vary depending on how many siblings they have? Let's find out using a bar plot, while also exploring Seaborn's four different plot scales (\"contexts\")."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the context to \"paper\"\n",
    "sns.set_context('paper')\n",
    "\n",
    "# Create bar plot\n",
    "sns.catplot(x=\"Siblings\", y=\"Loneliness\",\n",
    "            data=survey_data, kind=\"bar\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Change the context to \"notebook\"\n",
    "sns.set_context(\"notebook\")\n",
    "\n",
    "# Create bar plot\n",
    "sns.catplot(x=\"Siblings\", y=\"Loneliness\",\n",
    "            data=survey_data, kind=\"bar\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PmJiYushBVqqtqw1Gj+PmZkNTVlqOWwVVj+9WR6OpOp7njd/LYOxA8s2aq2FnX/+eYw37OhNUP9iogGb2wK2KMxDgbF8xjerMrQItjn5506h5tdqqZyAc++oW1Grj6sX/IWe0bH0Plme3bt0QFxenu5Z6165dDe668ZgnmZNKpYJdYAeUfVkxyLbdkA48fECKM1ieoaGhcHd31/uZT1yyNBuPZmj0tI/SMYh0DJbnkiVL9H7mMU8iIonzPImIyIgtz2HDhpm8UpVKhfR0476xQURUHxksz/bt21siBxFRvWKwPBMTEy2Rg4ioXpE+z1Oj0eCnn37C5cuX4e/vj8aNG0Or1cLZ2dmc+YiIrJLUB0apqakIDAzE+PHjMXfuXPzyyy/IzMzEkCFDkJCQYO6MRERWx+Ty/OabbzB37lx07NgRkZGREKJi0N4OHTrAy8sLb7/9NlJSUswelIjImpi827527Vr06NEDW7Zswc2bN3XnfXp6emLr1q2YNGkSNm/ejLFjx5o9LNG9rqhUgws3bxs1b3XfKT91/Q/Y2toZtXxHZwc0sec3tGWZ/MidPn0ac+bMgU01Y5PZ2tpi9OjReOutt8wSjqihuXDzNl4+9ItR84pqvlP+2te/QmXkd8pfHdwFPq2ampSP/p/Ju+12dnbVXoqgUkFBAezsjHvnIyKqr0wuT39/f2zfvh0lJSVVbrt27Rq2bt2Kvn37miUcEZG1Mnm3/YUXXkBYWBhCQkIwePBgqFQqHDhwAF999RV27dqF0tJSzJ49uy6yEhFZDZO3PD09PZGcnIzWrVsjMTERQggkJSVh8+bNcHd3x6ZNm9CtW7e6yEpEZDWkPmrz9vZGYmIiCgoKkJOTg/Lycri6uqJVq1bmzkdEZJXu6jyF5s2bo3nz5ubKQkRUb0iV5wcffIA9e/bg+vXr0FZzHWuOqkRE9zqTy3PNmjVYs2YNnJ2d0alTJ56WREQNksnluWPHDvj7+yMhIQH29vZ1kYmIyOqZ/Gl7Xl4exowZw+KkhsnGBqpm//+tHFWzpkA137aje5/Jf/UuXbogOzu7LrIQWT2VSgW7QQFQNXWCqqlTxf95QcQGyeTd9ueffx5z5szB3/72NwwZMqQuMhFZNbV7B6gnPql0DFKYyeW5efNmODo6YsaMGWjcuDFatGhR5Z2Xn7YT0b3O5PIsKSmBh4cHPDw86iIPEVG9YHJ58ppGRER38Q2jgoICZGRk4OLFi7Czs0P79u3Rv39/ODk5Gb2O8vJyfPTRR9i6dSv++9//omXLlhg2bBhmzZpl0nqIiCxNqjy3bt2Kt956C3fu3NFdhgMAGjVqhAULFmDChAlGrSchIQErVqzA1KlTERAQgOzsbKxatQq//vor3n//fZloREQWYXJ5pqen47XXXkP37t0xbdo0dO7cGUIInD9/Hhs3bkR0dDTat2+Phx56qNb1CCGQkJCAsLAwzJ07FwDQv39/tGjRAnPmzMHp06c5OhMRWS2Ty/O9995D9+7d8eGHH+qdKN+tWzcEBwcjLCwMCQkJBsuzqKgIISEhGDlypN70zp07AwBycnJYnkRktUw+Sf7MmTMYO3Zstd8wsrOzw9ixY3H69GmD63FycsKiRYuqjDpfeYrT/fffb2o0IiKLMXnL097eHrdv13x1v6KiIqjVaqkwP/zwA+Lj4zF8+HB4enqavLxWq0VWVhbc3Nzubqw9CcXFxcjNza0y3c3NzcJJrCeLteRgFtOyVOSw7IA/tT0mSr6Wvb29a5zP5C1PPz8/JCcn49q1a1Vuu3r1qvQ1jDIzMzFt2jR06NAB0dHRJi9PRMqwsVHDuWkb3c/Nm7WBjY3cBlR9IvX1zLCwMIwcORKhoaHo2LEjAOD8+fP45JNPoNVq8dxzz5m0zn379iEqKgodO3ZEQkICWrRoYWosAIBarda9U5Tm/yG1DlmOjo41vkvdKrBolFqzFNyyjhwotlwOg1mKrOe5Yi1ZbhdWvSZ8TVQqFYYPnILPv04AAAwbMMXk7/vX9piU5ln2yVLr3+dPTC5PLy8vbN68GdHR0UhOTta7rUePHli0aJFJH/Rs3LgRsbGx8Pf3x9q1a9G0Ka8jTVTfdHLrjenj1ygdw6KkDic88MAD+Pjjj5GXl4eLFy9CCAFXV1fcd999+Pbbb7FlyxZMmjTJ4Hq2bduGmJgYjBo1CrGxsRzmjojqjbs6FtuyZUu0bNlSb1pqaio+/vhjg+WZl5eHN954A66urpgwYQJOnTqld7u7uztcXFzuJh4RUZ2x9AdZOl9//TVu376NixcvVvuNpLi4OIwdO1aBZEREhilWnqGhoQgNDVXq7omI7gqvH0BEJIHlSUQkweBu+6VLl0xaYVFRkXQYIqL6wmB5Dh061KQTXoUQvCAWEd3zDJZnaGgoy5CI6C8MlmdMTIwlchAR1Sv8wIiISALLk4hIAsuTiEgCy5OISALLk6i+slHDptn/D55j06wl0AAGIbYWLE+iekqlUsFx8GOwadoCNk1bwHHwozyt0IIUGxiEiO6enbs3nP+xUOkYDRK3PImIJLA8iYgksDyJiCSwPImIJLA8iYgksDyJiCSwPImIJLA8iYgksDyJiCSwPImIJLA8iYgksDyJiCSwPImIJLA8iYgksDyJiCSwPImIJLA8iYgksDyJiCSwPImIJLA8iYgksDyJiCSwPImIJLA8iYgksDyJiCSwPImIJLA8iYgkWE15nj59Gj4+Prhy5YrSUYiIDLKK8jx//jz++c9/QqPRKB2FiMgoipanRqNBcnIynnjiCZSUlCgZhYjIJIqWZ2ZmJpYuXYopU6Zg3rx5SkYhIjKJrZJ37unpifT0dLRs2RI7d+5UMgoRkUkULc/77rvPrOvTarXIysqCm5ubxX+x4uJi5ObmVpnu5uZm4STWk8VacjCLaVkqctgpnqMyi5KvZW9v7xrns4oPjIiI6htFtzzNTa1W694pSvP/sOh9Ozo61vgudavAolFqzVJwyzpyoNhyOQxmKbKe54q1ZLldWGYVOQCgNM+yT5Za/z5/wi1PIiIJLE8iIgksTyIiCSxPIiIJVlOejz32GLKystC2bVuloxARGWQ15UlEVJ+wPImIJLA8iYgksDyJiCSwPImIJLA8iYgksDyJiCSwPImIJLA8iYgksDyJiCSwPImIJLA8iYgksDyJiCSwPImIJLA8iYgksDyJiCSwPImIJLA8iYgksDyJiCSwPImIJLA8iYgksDyJiCSwPImIJLA8iYgksDyJiCSwPImIJLA8iYgksDyJiCSwPImIJLA8iYgksDyJiCSwPImIJLA8iYgksDyJiCSwPImIJLA8iYgksDyJiCSwPImIJLA8iYgkKF6ee/fuxSOPPIIHHngAI0eOxO7du5WORERkkKLlmZqainnz5mHAgAFYu3Yt/P39ERkZic8++0zJWEREBtkqeefLli3DyJEjsXDhQgDAoEGDcPPmTaxcuRIPP/ywktGIiGql2JZnbm4ucnJyEBwcrDd9xIgROH/+PHJzcxVKRkRkmEoIIZS444MHD2L69OlISUlB165dddNPnTqFRx99FO+99x4GDx5s9PoyMzN1/7exsYEKgKV+NZVKBQGgvLy8ym02Nv97fxJVb6ubMBX3V1sWYYEsKiNylFvoMbExKotlnis2KpURWSwSBTYVUapkqcihsujrBxA1PiYVr2WLRIFKBb3Xsr29PXr27FntvIrttv/xxx8AACcnJ73pTZo0AQAUFhZKrVetVuv+r/rfE9USVH+576oz1HJbHagti8qCWWrLobaix0RtwecKYCiLBYOg5iyWfP0AKgPPWUsmMfBa/h/FyrPyXe2vf6DK6botNiP17dvXPMGIiIyg2DHPpk2bAqi6hVlUVKR3OxGRNVKsPDt16gQAyMnJ0Zv+22+/6d1ORGSNFCtPDw8PdOjQoco5nfv370fHjh3Rvn17hZIRERmm6Hmezz77LF588UU4OzsjMDAQX3zxBVJTU7F8+XIlYxERGaTYqUqVPvzwQ2zYsAGXL1+Gm5sbpk+fjtDQUCUjEREZpHh5EhHVR4oPDEJEVB+xPImIJLA8iYgksDyJiCSwPImIJDS48jR15PqioiK8+uqrGDBgAHx9fREREYELFy6YLc/p06fh4+ODK1euKJKjvLwcH3zwAcaMGQNfX18MHz4cS5YsqXVgFo1GgxUrVmDIkCHo1asXxo8fjx9//PGuswghsGnTJowYMQIPPPAAQkJCsGfPnlqXqeu/DwDMnDkTQUFBiuTQaDR44IEH4O3trffP19fX4lkA4NixYwgPD0evXr0wcOBAvP7667qvVFsiS02vl2+++QaPP/44evXqhaFDh2LDhg0G13XXz2PRgOzbt094e3uLN954Qxw6dEgsXrxYeHl5idTU1BqXiYiIEA8++KDYuXOnSEtLE2PGjBGDBg0St27duus8586dE4MGDRJeXl7i8uXLtc5bVznWr18vunXrJpYuXSoOHz4skpKShL+/v5gyZUqNy7zyyiuiV69eIjExURw4cEBMnDhR+Pr6ipycnLvKsm7dOtGtWzfxzjvviIyMDBETEyO8vLzEp59+WuMydfn3EUKI3bt3Cy8vLzF8+PBa56urHGfPnhVeXl5i165d4sSJE7p/P/zwg8WznDhxQvj4+IhZs2aJw4cPi61bt4p+/fqJ559/3iJZanq9ZGZmCh8fHzFv3jxx8OBBsWzZMuHt7S0SEhJqXd/dPo8bVHkOHz68yh/6ueeeEw8//HC18x87dkx4eXmJgwcP6qbl5eWJ3r17i/Xr10vnKCsrE0lJScLX11f4+/sbLM+6ylFeXi78/PzEK6+8ojf9008/FV5eXuLUqVNVlsnNzRXdunUTW7du1U0rKSkRgYGBYvHixdJZSktLhZ+fn3jttdf0pk+cOFGEh4dXu0xdPS6Vrly5Ivz8/MTgwYNrLc+6zPHJJ5+Irl27iuLiYqPmr8ssEyZMEBMmTBDl5eW6aUlJSWLYsGHV5jNXFkOvl6eeekqMGzdOb5m4uDjRr18/UVJSUu06zfE8bjC77TIj1x8+fBhNmjTBgAEDdNNcXFzg5+eHQ4cOSWfJzMzE0qVLMWXKFMybN8/g/HWVo6ioCCEhIRg9erTe9M6dOwOoOmgLAHz77bfQarUYMWKEbpq9vT0CAwPvKotarUZiYiKmT5+uN93Ozg4lJSXVLlNXj0ulRYsWYcCAAQgICKh1vrrMcfr0abi7u8PBwcGo+esqy40bN3D8+HGEh4frDSM5YcIEpKenV5vPXFlqe72UlJTg+PHj1b6ub926he+++67adZrjedxgyvP8+fMAqo7W5OHhAQDIzs6udhkPD48qA6O6u7tXO7+xPD09kZ6ejpkzZxo16Gpd5XBycsKiRYuqjIWanp4OALj//vurzeLs7AwXFxe96R4eHrh06RLu3LkjlcXGxgbe3t5o06YNhBC4fv064uPjkZGRgbCwsGqXqavHBQC2bduGn3/+Gf/+978NzluXObKysmBvb4+pU6fC19cXfn5+WLx4cY3HpOsqy9mzZyGEgLOzM55//nn07t0bffv2xcsvv1zj39xcWWp7veTm5qKsrMyk13Vltrt9His6MIglyYxcX1hYWGX+ymVkR7oHgPvuu8+k+esqR3V++OEHxMfHY/jw4fD09DQpC1CxNdu4ceO7yrB//37Mnj0bABAYGIiQkJBq56urx+XixYtYsmQJlixZUuXFZckcAHDmzBkUFhZi3LhxmDFjBk6ePInVq1cjOzsbW7ZsqTKYeF1luXHjBgAgKioKQUFBWLduHbKysrBixQqUlJQgJiamyjLmylLb60X2ihTmeB43mPIUEiPXi1q+9m/qSPd3w1I5MjMzMWPGDHTo0AHR0dEmZanp8ZXRvXt3JCUlISsrCytXrsT06dOxZcsWo7MA8o+LEAILFy7EkCFD9HbpDC1j7hyVli9fDmdnZ3h7ewMA/Pz80LJlS8yfPx8ZGRl6u8R1maWsrAwA0KdPH7z88ssAgICAAAghEBsbi2effRZubm4WyVLdfdT0vKvpfszxPG4wu+0yI9c7OTlVexpGUVFRte9adcUSOfbt24fJkyejXbt22LRpE1q0aGFylsrb75Fxc7YAAAqDSURBVJabmxv8/PwwceJEvPTSSzhy5AhOnDhhUhbZHMnJycjKysLChQuh0Wig0Wh0L6g//7+uc1Ty9/fXFWelwMBAABVbpZbKUrlF9teLMg4cOBBCCGRlZVksy5/V9Lqu/LmmK1KY43ncYMpTZuT6Tp06ITc3t8oL5rfffrPoSPd1nWPjxo144YUX0Lt3byQnJ6N169Y1ztu5c2cUFBTg5s2bVbJ06NAB9vb2UhkKCgqwe/duXL16VW969+7dAaDKdKBuHpe0tDTk5+dj4MCB8PHxgY+PD3bv3o2cnBz4+Phg165dFskBAHl5edi2bVuVDzMrj8dV9wZXV1k6duwIACgtLdWbXrlFWt2WmiVeP+7u7lCr1VVe15U/13Q/5ngeN5jylBm5fuDAgbh16xYyMjJ00yo/dezfv3+dZ7ZEjm3btiEmJgYjR45EQkKCwWtHVd5fWlqablppaSkOHjx4V1nKy8sRFRWFjz76SG/64cOHAQBeXl5VlqmLx+XVV1/F9u3b9f499NBDaNu2re7/lsgBVBTS4sWLkZSUpDd93759UKvV1V70sK6yeHp6wtXVFfv27dOb/uWXX8LW1rbak/Yt8fpp1KgR+vXrh/379+uVdFpaGpo2bYoePXpUu5w5nsfqV1555RX56PVL06ZNsW7dOuTn50OlUmHjxo3YtWsXXn75ZXTp0gU3btxAVlYWnJycYG9vD1dXVxw9ehRbt25F8+bNcenSJSxcuBBCCLz55pt3/cEIUHEqyoEDBzB58mTdroKlcuTl5WHatGlo06YN5s6di7y8PFy5ckX3z97eHlqtFqdOnYK9vT0cHBzQrFkzXLx4Ee+//z4cHByQn5+P1157Dbm5uYiLi0Pz5s2lsjg4OODGjRvYsmULbG1tUVpaipSUFKxZswaPPfYYHn/8cYs8Li1atECbNm30/n3zzTe4du0a5s2bp8tpib+Pg4MDCgoKkJycjPLycpSXlyMlJQWrVq3C+PHjMWbMGItlUalUaNWqFTZu3IgLFy7AyckJqampWLt2Lf7xj38gKCjIIlmqe720bdsW7777Ls6dOwcHBwfs3r0b7733HmbNmoW//e1vACp2483+PDbqbNB7yAcffCCCgoJEjx49xMiRI8WuXbt0t+3YsUN4eXmJb7/9VjetoKBAREVFiX79+ok+ffqIiIgIce7cObPlqbzPP5/0a6kcu3btEl5eXjX+2717t/j222+Fl5eX2LFjh265kpIS8cYbb4iAgADRq1cvMX78ePH999/fVRYhKk6Uj4+PF8HBwaJHjx5i+PDhYv369UKr1QohlPn7CCFEZGSk3knylsxR+ZiMGDFC9OjRQwwbNkzRx+Tzzz8XoaGhokePHmLIkCHinXfesWiW6l4vQgixf/9+MXr0aOHj4yOGDh0q3n//fb3b6+J5zJHkiYgkNJhjnkRE5sTyJCKSwPIkIpLA8iQiksDyJCKSwPIkIpLA8iTFlZaWIj4+HiEhIejduzf69OmDxx57DPHx8XpjeUZFRel9z/uvP9fkr/OtXr0a3t7e+O9//2veX4QalAYzqhJZJ41Gg6lTp+L7779HaGgowsLCoNVqcfz4cSxbtgxffPEFtmzZAnt7e4SFhRkcmNgYQUFBcHd3N2q4OaKasDxJUampqTh69ChWr16tNxr4pEmTkJCQgLfeegvbt2/H+PHj4evrW+uFz4zVtWtXdO3a9a7XQw0bd9tJUZVDzf11XEqg4hIPdnZ2+P777y0di8gglicpqnKcyL+OpgRUDIzx3XffIS4uDkDNxzhPnDiBxx9/HD179kRwcDA2bdpU633+9Zjn6tWr0bNnT1y4cAH//Oc/dZe7iIyMRH5+vt6yV69exfz58/Hggw+ib9++mD9/PtLT0+Ht7Y0jR47o5ktLS8Pjjz8OX19f9O3bF5MnT0ZmZqZJjw1ZN5YnKSokJAR2dnaIjY3F6NGjsWLFChw5ckQ3bqQx4ypOmTIFnTt3RlRUFFxdXbFkyRKsXr3apBzl5eWYNGkSmjRpgsjISAQHB2P37t3486BjhYWFmDhxIj7//HOEhYVh1qxZOHXqFF566SW9dR09ehRz5sxBq1atEBkZiZkzZyInJweTJ0+u9kKDVD/xmCcpqkuXLlizZg0WLlyIX375Bb/88gvWrVsHR0dHDB06FDNnzjQ4cG54eDgWLFig+//kyZMRHx+PiRMn1jgi/l9pNBqMGjUKUVFRAIC///3vuHr1KtLT03H79m04ODhg8+bNyMnJwcaNG3VjPj7xxBMYM2YMCgoKdOvat28fGjdujHXr1ukGCe7fvz9mz56Nn3/+ucrlKqh+4pYnKS4wMBBffvklli9fjrFjx6JVq1YoLi7G3r17MXbsWBw9erTW5adNm6b7v42NDSZOnIjS0lK9QXiNMXLkSL2fu3XrBo1GoyvG9PR0eHl56Q2W6+TkhPDwcL3l2rZti6KiIkRHR+PcuXMAAG9vb6SlpeHhhx82KRNZL5YnWYVGjRph1KhRiIuLwzfffIOdO3di9OjRKCkp0V1wrDrNmzevcspR5ZbdxYsXTcrw1/VUHjLQarUAgAsXLuguR/Fnlde5rzRx4kT4+fkhKSkJo0aNwrBhwxAdHV3tNYeo/mJ5kmKKi4uxfPly7N+/v8ptPj4+ePvttzF48GCcP3++ygc3laq7do6o5YqotTF0xUSNRlPtMdhGjRrp/ezk5ISkpCR89NFHiIiIQJMmTZCYmIhHH30Ue/bsMSkTWS+WJymmUaNGeP/995GYmFjjPPfffz9UKlWNl2y4efNmlSsnXrhwAUDFxcHMyc3NDdnZ2VWmV15EsFJ2djZ+/PFH9O7dG/PmzcMnn3yCTz/9FM2aNcPGjRvNmomUw/IkxajVaowaNQpHjx5FSkpKldsLCgqQlpaG/v37w8HBodp1lJeXY/v27bqfNRoNNm/eDEdHR7N8G+nPgoKCcOrUKb3zTktLS/XuHwCio6Pxr3/9S+/Stp07d0azZs3Mdr1yUh4/bSdFRUVF4ccff8SCBQvwySefYNCgQXByckJOTg527tyJsrIyLF68uMblHRwcsGrVKly+fBnu7u7Yt28fTpw4gZdfftnglUBNNWXKFKSkpGDy5MmYNGkSXFxckJKSotsardztnzx5MiIiIjBhwgSEhoaiUaNGSE9PR05ODmJjY82aiZTD8iRFubi4YOfOndi0aRMOHDiAtWvX4vbt22jdujWCg4MxY8aMWq8j36xZM8TGxuLNN99EcnIyPDw88NZbbyEkJMTsWZ2dnZGUlISYmBgkJiZCpVIhODgYo0ePRmxsrO546MCBA7Fu3TqsX78e77zzDkpKStClSxcsW7YMjzzyiNlzkTJ4ATgiI924cQPOzs5Qq9V60zds2IDY2Fikp6fzHM4GhAdgiIwUGxuLgIAA3LlzRzdNq9Xis88+g4uLC1xdXRVMR5bG3XYiI4WEhCAlJQWTJk1CSEgIVCoV0tLS8MMPPyA6OpofBjUw3G0nMsGhQ4fw3nvvISsrC2VlZfD29saUKVP0htOjhoHlSUQkgfsZREQSWJ5ERBJYnkREElieREQSWJ5ERBJYnkREEv4PdEVbnv4+ccYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Change the context to \"talk\"\n",
    "sns.set_context(\"talk\")\n",
    "\n",
    "# Create bar plot\n",
    "sns.catplot(x=\"Siblings\", y=\"Loneliness\",\n",
    "            data=survey_data, kind=\"bar\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fhOivSFbPBbC3B/7vkA8AwN4esvocx7Q2stij+uvWrQs3NzeGINUKMrkcioF9n4UhANjbQzGwL5+QXUuZtUWYlZWF5ORk5OXl6e4zNmT48OEVXq9Go8HOnTuxb98+XL9+HRqNBp6enhg8eDAmTpwI5Z9HHCeqAjYe7rB/eyyEvDzInJwYgrWYqCAUBAFLlizBjh07TAagVkWDUKPRYMqUKTh69CgcHBzQsWNH2Nra4vz58wgPD8exY8ewdetWjoFCViGTyyH70y2kVPuICsKdO3ciKioKwLNBmZo2bWqxrbRvv/0WR48ehY+PDzZu3Ag3NzcAQHZ2NqZMmYKkpCSsWbMGs2bNskh7RESignD37t2QyWQIDQ3F9OnTYWdnZ7GC9u3bBwD4+OOPdSEIPLtVb8GCBRg2bBgOHTrEICQiixEVhNevX0fDhg0xe/Zsix83qV+/Plq1alXmsV5aLVu2BAA8ePDAom0SkbSJCkKlUomGDRtWycFjY7ftXbhwAQDg7u5u8XaJSLpEXT7Tvn173LhxA2q12tL1GCQIAsLDwwEAAwYMsFq7RFT7ib7XeMKECVi6dCnCwsIsXZNeX375JRITE9GwYUNMnDjRIuu09gBTZ8+eLfNzQEAA27Ziu1JtWwq/6+fbFtOmqCB0dHREUFAQtm/fjnPnzqFnz55wc3MzetIkKChITFMAgFWrVmHDhg1QKBRYuXIlXF1dRa+LiAyztdH/HTY0vbYQFYQjR46ETCaDIAj4/fffK/QEajFBWFJSgkWLFiEmJgZKpRKrV69G586dxZSsl6Ojo8XWVRHW/OvItqXZZ3Pbdq3XFHXsnfC04IluWh17JzSoX7Hxzf+q/RYVhJYMI0PUajX+67/+C8ePH4ezszPWrFljlXaJpEwul+O1/jPxnx+/xNOCJ6hj74TX+s+ETGaxu3FrJFFBuG3bNkvXUUZOTg5CQkJw6dIlNGnSBBs2bIBKparSNonoGc8mvnh3/Abk5mXCxalRrQ9BwMx7jatCUVERJk2ahEuXLsHb2xubN2/m5TJEViaXy1HP2c30grWE2UF4/PhxHD16VHc5jYODA5o3b46ePXvilVdeqfS1huHh4Th37hyaNGmCbdu28cQIEVU50UGYlZWF6dOn48yZMwCeXeenderUKcTExMDf3x8rVqwoc6ucMY8fP9btdru6uuK///u/DS67fPlysaUTEZUhKggLCwsRGhqKlJQU1KlTBwMGDICvry8cHByQl5eHixcv4pdffsFvv/2GyZMnIyYmBgqFwuR6k5OTdWOfXLp0CZcuXTK4LIOQiCxFVBBGR0cjJSUFbdq0wfr16/Vu8d27dw+TJk3C1atX8c0332D8+PEm19u7d2+kpKSIKYmISDRRp4MOHToEuVyOVatWGdztdXd3x6pVqwAAsbGx4iskIqpiooIwPT0d3t7eaNGihdHlvLy84O3tjfT0dFHFERFZg6ggFAQBNhUczcvGxgbFxcVimiEisgpRQdi8eXP8/vvvJp8LeP/+fVy7dg3NmjUTVRwRkTWICsL+/fujpKQEc+bMMfgEl7y8PMyZMwcajQb9+/c3q0gioqok6qzxhAkTsHfvXiQmJuLVV1/F0KFD4evri7p16yIvLw9XrlxBbGwsHj58CA8PD4SEhFi6biIiixEVhE5OTvj6668xefJk3LhxA1u2bCm3jCAI8PLyQmRkJMc6JqoEma3+r6Wh6WQ+0e9sy5YtcfDgQRw6dAjHjh0rc4udl5cXAgMDMXjwYIsO7EQkBfJ6jSGzrwuh4P+fAC+zrwt5/cbVWFXtZtafGFtbWwwbNgzDhg3TO7+oqAgREREAgPfee8+cpogkQyaXo+6rb0EdFwWhQA2ZfV3UffUtSTwFprpU6bZ2YWEhIiIiIJPJGIRElWDn0QouE/6F0iePIHeuzxCsYjzoQFRDyeRy2Lg0qO4yJIF/ZohI8hiERCR5DEIikjwGIRFJHoOQiCSPQUhEkmfy8pm2bdtaow4iompjMgifH5SJiKg2MhmEUVFR1qiDiKjamAzCLl26WKMOIqJqw5MlRCR5DEIikjwGIRFJHoOQiCSPQUhEkscgJCLJYxASkeQxCIlI8hiERCR5DEIikjwGIRFJHoOQiCSPQUhEkscgJCLJYxASkeQxCIlI8hiERCR5DEIikjwGIRFJHoOQiCSPQUhEkscgJCLJYxASkeQxCIlI8hiERCR5DEIikjwGIRFJHoOQiCSPQUhEkscgJCLJYxASkeQxCIlI8hiERCR5DEIikjwGIRFJHoOQiCSPQUhEkscgJCLJYxASkeQxCIlI8hiERCR5DEIikjwGIRFJHoOQiCSPQUhEkscgJCLJYxASkeQxCIlI8hiERCR5DEIikjwGIRFJHoOQiCSPQUhEkscgJCLJYxASkeQxCIlI8mpsEJ48eRJvvfUWunbtik6dOiE4OBjHjx+v7rKIqBaqkUG4d+9ehISEICkpCX5+fvD390dSUhImTpyImJiY6i6PiGoZ2+ou4M8ePHiA+fPnw8nJCTt27IBKpQIAJCcnIyQkBP/+97/xt7/9DW5ubtVcKRHVFjVuizA6OhpFRUWYMGGCLgQBwM/PDxMnTkRhYSG3ConIompcEGqPA/br16/cvP79+wMA4uPjrVoTEdVuNSoIBUHAtWvXIJfL0apVq3LzW7ZsCblcjmvXrkEQhGqokIhqI5lQgxLl8ePH6Nq1K1xdXXHq1Cm9y/To0QNZWVk4e/YsHB0dRbWTnJyM4uJiABC9DjHy8vLK/My2rduuVNuWwu/6+bYdHR3h4OAAT0/PCr+2Rp0sefr0KQCgTp06Bpext7cHAKjVatFvskaj0f1f3wfWWtg2267N7VZX22LarFFBKJeb3lO3xAasUqlEYWEhbGxsoFQqzV4fEdUsDg4OlVq+RgWhtvjCwkKDy2jnGdtqNMXX11f0a4mo9qlRJ0u0+/aPHj1CSUlJufklJSV49OgRlEolnJ2dq6FCIqqNalQQymQyeHt7Q6PR4MaNG+Xmp6eno7S0tMz1hURE5qpRQQgAvXr1AgD89NNP5eZppwUGBlq1JiKq3WpcEI4YMQJKpRIbN27ExYsXddMvXLiATZs2wd7eHuPGjavGComotqlR1xFqbd++HYsWLYKdnR26desGQRCQkJCAkpISLFu2DMOGDavuEomoFqmRQQgAR44cwaZNm3D58mUoFAr4+Pjg3XffRffu3au7NCKqZWpsEBIRWUuNO0ZIRGRtDEIikjwGIRFJHoOQiCSPQUhEkscgJCLJYxD+H0sMH5qeno6ZM2ciMDAQHTt2xNChQxEdHY3S0tIKvX7v3r3w8fHBmTNnKtXu/fv38a9//QuvvPIK/Pz8MHDgQERGRqKoqMjo6zQaDaKjozFy5Ej4+/vDz88PQ4YMQWRkpNEnAP2ZmH5rNBpERUXhtddeQ4cOHdClSxeEhobi6NGjFW4XEN93rcePH6Nnz57w8fGxSrv79++Hj4+PwX8rVqyoUPti3vM7d+7g448/Ru/evdG+fXv06tULn376KTIzM6uk36Y+z+Z+X7Ryc3Px+eefY+DAgfDz80Pfvn2xdOnSSj2XkNcR4tkv7KOPPoJCoUC3bt1QWlqKhIQEFBcXY9GiRRgzZozJdVy9ehVBQUHIy8tDp06d0KBBAyQkJCA3NxdDhw7F8uXLjb4+KSkJoaGhyM/Px/bt2/HSSy9VqPZ79+5hzJgxuHfvHnx9feHp6YnffvsNmZmZ6NKlC7766ivY2dmVe51Go8GUKVNw9OhRODg4oGPHjrC1tcX58+eRm5uLjh07YuvWrSYfdya233PnzsWBAwfg6OiIgIAAFBcX4/Tp0yguLsa0adMwderUKuv782bMmIHDhw8DAFJSUky2aW67S5YswZYtW/Dyyy/D1dW13Px+/frh1VdfNdq+mPf8woULCAkJwZMnT6BSqdC8eXNcvHgR9+7dQ/PmzbF79264uLhYrN+mPs/mfl+08vLyMG7cOKSkpMDLywsqlQqXLl3C7du34e3tjV27dsHJycn0igSJu3//vtC+fXshICBASElJ0U0/f/680KlTJ6FDhw7CvXv3jK6jtLRUGDp0qKBSqYT9+/frpmdlZemmx8XFGXz9999/L/j7+wsqlUpQqVTC6dOnK1z/O++8I6hUKiEyMlI3Ta1WCxMmTBBUKpWwefNmva/buXOnoFKphKFDh5bpX1ZWljBmzBhBpVIJy5cvN9q22H4fOnRIUKlUwsCBA4XMzEzd9NTUVCEgIEBo06aNkJ6eXmV914qNjdW95yqVymR7lmh3/PjxgkqlMvmZMkTMe15YWCgMGDBAUKlUQlRUlG56QUGB8P777wsqlUoICwsz2XZF+23q82zu9+V5YWFhgkqlEubNmydoNBpBEAShuLhYmDNnjqBSqYRFixZVaD2SD8IvvvhCUKlUwurVq8vNW7NmjaBSqYRVq1YZXcfx48cFlUoljB8/vty8M2fOCCqVSggKCio37+7du7pfWMeOHYUePXpUKgjT0tIEHx8foV+/froPgdadO3eEtm3bCn369NH72jfeeENQqVTCqVOnys27cuWKoFKpDL5WS2y/tV+oH3/8sdy8BQsWCCqVSoiOjjbatjl9FwRBuHfvntClSxdhzJgxQtu2bSschOa2+9JLLwk9evSoUFv6iHnP9+3bJ6hUKmHWrFnlXpORkSH06NFDCA0NNdpuRfrdu3fvCn2exX5u/iwnJ0fw8/MTOnXqJDx58qTMvLy8PKFz586Cn5+foFarTa5L8scILTF8qLF1BAQEoEGDBjh79my5YxYrV67EgQMH0L59e8TExOgduc+YX3/9FYIgoE+fPuWGOfDw8ICvry/u3LmDa9eulXtt/fr10apVK/j5+ZWb17JlSwDAgwcPjLYvtt/h4eGIjY1F7969y71OrVYDAGxsbIy2bU7fAeCTTz5BYWEhli1bZrQdS7Z769Yt5Obmol27dpVq83li3vMffvgBABASElLuNU2aNMGJEyewefNmo+1WpN/37t2r0OdZ7Ofmz06fPo2CggJ069at3PhFdevWRffu3VFQUIDTp08bXQ8g8ZMlgoWGD9V+6A09MNbLywulpaVIS0srM71Vq1ZYtmwZvv3220ofrH++3datW+udr+1TampquXnr1q3Dd999p3dshwsXLgAA3N3dK9R+ZfutUCigUqmgUCjKTD9y5Aji4uLg4OCg90uir20xfd+xYweOHz+O2bNno0WLFkbbsWS7V65cAQA0aNAAYWFh6N+/Pzp06KA74VCRE1Ri3vPLly/Dzs4Obdq0wd27d7FhwwbMmzcPy5cvR3Jyssk2n2/XVL/HjRtn8vMs9nMjtqaKHPutUWOWWFtOTg6Kiorg6upa7ksJALa2tqhfvz6ysrKMjpqn3XJq1KiR3vna6Q8fPiwzfdKkSeaUr2u3cePGlWrXGEEQEB4eDgAYMGBAhdqvbL+fV1BQgLlz5+LatWtIS0uDh4cHPvvsMzRs2LBCbVe27zdv3sTnn3+Obt26ISgoyGgblmwXeBZIwLOTcy4uLggICICbmxsuXryI8PBwHD9+HFu2bNGN1Gis/Yq+50VFRbh79y7c3d0RFxeHTz75RDdaJABs3LgR//jHPzB37lyL9NvLy8vkIGyW+NwA0J3tNrWerKwso+sBJL5FWNnhQ02tx9AHWDs9Pz9fVJ3WbPfLL79EYmIiGjZsiIkTJ1Z5+xkZGfj+++/L/PWvyF9wMW1rNBrMnTsXMpkMS5YsgUwmM9mOJdrV0m4RDho0CEePHsXatWsRHR2NgwcPok2bNkhKSsLKlSst2r529zInJwcffPAB+vXrh7i4OJw+fRorVqxAvXr1sHnzZsTExFRZv6tqXdr5hr6/lalJ0kFoqeFDtesx9MXSrqMi66oMS7e7atUqbNiwAQqFAitXrtR7eYel23d3d8f//M//IDExEStXrkRxcTHCwsKwYcMGi7e9adMmJCUl4aOPPoKHh4fR9VuyXa3w8HAcOnQIn332WZlDEs2aNcPSpUshk8kQExOD4uJii7Wv3d1++vQpunbtiuXLl8PLywvOzs4YPHiw7hhpZGSk0d+TJT9rllqXRWsyuUQtZqnhQ7XrKRfVV9wAAA7HSURBVCgoMLqOyo61akpF2zV1LWBJSQn+9a9/Yc2aNVAqlYiIiEDnzp0t1r6xfjs4OKB+/fpwcXHBoEGDEBERAZlMhvXr1xv9vVS271evXsXq1asRGBiI0aNHm+iZYea850qlEt7e3noPw7Rt2xbu7u7Iz8/XO3BZZdvXLvd8HW+++Wa55f/2t7/Bzc0N9+/fxx9//GF2uxUZZtdS3xdL1iTpILTU8KHa4yaGjmmYOpYhVkXbNXRcB3i2yz958mTExMTA2dkZmzdvrvDgWFXR7xdffBHNmzdHXl4ebt26ZXbb2uVWrFiB4uJilJSUYPbs2WX+ae9k0P6cnZ1tsXYrQ3tc9PljeGLb177nTk5OuoucmzVrpvc12q3jR48emd1uRfptqc+NJT9/kg5CSw0fqj1rpe+SCUEQcP36ddjY2OCFF16wSN0VaReA7ribofpzcnJ0txI2adIE27dvr9CWYEXaN9RvQRDw2WefYcaMGXr/+ADQbTEZmm+qbaB837XHiU6cOIHY2Ngy/7S7TtqfjR1TEvue5+Xl4dNPP8W0adMM9uv27dsAADc3N1Ht63vPn////fv39a5TGyTGDoWY+1kzpw/m1lSRKzIkHYSAZYYP1a7j559/Ljfvt99+Q3Z2NgICAgyedRZL2+4vv/xS7v7MjIwMXLlyBU2bNoW3t3e51xYVFWHSpEm4dOmS7lakyo4XLabfMpkMP//8Mw4fPowTJ06Ue92tW7eQnp4OBwcHeHl5mWy7on3ftm0bUlJS9P7TXrOo/dnQlpOYdrXq1q2LH3/8Ed9//73e69ri4+Px6NEjqFQqo0Eo5j3XXq8ZFxdX7jXXr1/HnTt30LhxY3h6eppsV8xnzRJ90Kdz586wt7fHqVOnyv3xUqvVOHXqFBwcHBAQEGCyJskHYWWHD7158ybS0tLw5MkT3bQuXbqgdevWOHHiBL755hvd9OzsbCxcuBCA/otZKyMjIwNpaWlldts8PT3Rq1cvpKenY9WqVbrp+fn5mDdvHjQajcF2w8PDce7cOTRp0gTbtm0zec2gJfv9xhtvAAAWL16Me/fu6abfv38fM2fORElJCcaNGwelUlklfa8oS7Yrk8l0/Q4LCyuzdXbz5k3d+/Xuu++WmW6J93zs2LFwcHDA/v37ERsbq5uek5ODefPmobS0FEFBQbqTD1X9fovpw4MHD5CWllbmIn8HBwcMHz4cOTk5WLhwoW5Lu6SkBIsWLUJubi7GjBlToQ0QPnQBlRs+tG/fvrhz5w6WLFmCESNG6KYnJyfj7bffRn5+Pjp27IjGjRsjMTEROTk5eOONNxAWFmayjuDgYCQmJuq9SV0777333sP777+vm37r1i28+eabyMzMhEqlgpeXl+5G+N69e2Pt2rWwtS17uejjx48RGBiIgoICtGvXzugdLdqb3y3Z7+LiYkydOhXHjh2Dg4MDOnXqBI1Gg/PnzyM/Px+BgYGIiIjQ7SJbsu/6+Pr6QqPRlLtsx9LtFhQUIDQ0FGfPni2zpZKQkICioiKEhITgww8/1C1vyff88OHDmDNnDkpKStCuXTs0btwY586dw6NHj9CtWzds2rRJdyzRUv029nmubB8+/PBD7Nu3D6+//jqWLl2qm/748WOMHTsW6enp8PT0hK+vLy5fvoxbt27B19cX0dHRqFu3rp7felmSvqBaKygoCB4eHti0aRPOnj0LhUKBTp06VWr4UD8/P3z77bcIDw9HQkICfv/9d7Ro0QIzZ8406yylKZ6enrp24+Pj8ccff8DT0xNvvfUW3n77bb1fyOTkZN2ZtkuXLuHSpUsG12/qKSBi+m1nZ4e1a9dix44d2Lt3L06fPg25XA6VSoURI0bgjTfeqNClTWL6bgli27W3t8eWLVuwZcsWxMbGIiEhAQqFAi+++CKCg4NNXsCuJeY9Hzx4MLy8vLB27VokJibi2rVr8PT0RGhoKEJCQkw+pcecfluqD/rUq1cPu3btQkREBH766SccOXIETZo0wcSJEzF58uQKhSDALUIiIh4jJCJiEBKR5DEIiUjyGIREJHkMQiKSPAYhEUkeg5CIJI9BSBZ17NgxzJw5E3379oWfnx9eeuklDB48GAsXLixzC6OWduzb5++cAKAb51ffI+8NCQ4Oho+PD6Kjo3XTEhIS4OPjg65du4rvFNV6vLOELKKkpARz5szRjRHs7u4OHx8f5Obm4vbt20hLS8POnTsREhKCDz74oJqrJSqLQUgWsXLlShw+fBheXl748ssv4evrq5tXUFCAqKgorFixAl999RU8PDwQHBwM4NlIgR07djQ6Toc5/Pz8cPjw4Sq73Y5qB+4ak9ny8/Oxfft2AM8C8fkQBJ7dYztp0iTdk1XWr1+ve5STk5MTXnjhBTRt2rRKaqtTpw5eeOGFSo9WR9LCICSz3bhxA/n5+VAoFGjTpo3B5bSPocrMzMTdu3etVR6RSQxCMpt2t7OoqAinTp0yuJy7uzv279+PX375Rff8Q0MnS7RKS0uxdetWDBo0CB06dEBgYCDmz59f5jmGxhg6WdK3b1/4+PggOzsbP/zwA8aNG4dOnTrB398fY8eOxaFDhwyuMy4uDuPHj0fXrl3RqVMnhIaG4syZM7q+PP8oLeDZ06nDw8MxcuRIdO7cGS+++CIGDRqEf//73wafGk3WxQMnZLZWrVrpBgCaOnUq3n77bQwdOlTvcw7btm1bqXUvWrQIZ8+ehaurK1q3bo20tDTs2rULcXFx2Lp1q9Et0IpYu3YtoqKi4ODggJYtWyIjIwNJSUlISkpCZmYmJkyYUGb5xYsXY9u2bQCejQHi7OyMhIQEnDp1Su+TzAsKChAcHIzLly9DqVSiRYsWkMvlSE9PR1RUFA4ePIhvvvnG6NOhqepxi5DMZmtri08//RQymQxqtRpr1qzBoEGD0KdPH3zwwQfYu3dvmScLV8bZs2cxdepU/Prrr9i7dy+OHTuGnj174vHjx5g1axY0Go1ZtUdFRWHy5MlISEjAvn37cPz4cQwZMgQAEBERUWZozbi4OGzbtg329vaIjIzEzz//jH379uHHH39Eu3btcOTIkXLr37NnDy5fvoyAgADEx8cjNjYWBw4cwJEjR/Diiy8iOzsba9euNasPZD4GIVlE//79sXHjxjInPTIyMrB//3589NFHCAwMREhIiNGHwOozcOBATJs2TTeuSL169bBy5UrUq1cP165dw7Fjx8yqu1evXpgxY4buadgKhQJz584FADx58qTMwPNr1qwBAMyaNQv9+vXTTffw8MCaNWv0Dj+pfer1gAEDUK9ePd30Bg0a4MMPP0RgYGCVnSiiimMQksX06tULP/zwAzZu3IixY8eiefPmunmlpaU4efIkRo0ahV27dlV4nfrG4nVyckL//v0BAMePHzerZn27s+7u7rqxcPPy8gA8C/WUlBTY2tpi5MiR5V7TuHFjXU3P074HmzZtwuHDh6FWq3Xz/P39sWHDBkydOtWsPpD5eIyQLMrW1ha9e/fWjZx29+5dnDx5EnFxcYiPj0dpaSkWLlwIf3//Cg2zaOiYona0tPT0dLPqNTRinFKpxNOnT3WX+Wi3DJs1a2bw8e++vr44cOBAmWmjR49GTEwMbt68iRkzZsDOzg7+/v7o1asX+vbtW6FR36jqcYuQqlSTJk0wcuRIbNy4EVu2bIGDgwNKS0uxe/fuCr1e3+4mAF0YacdeEcvUWB3akSweP34MALotRWM1Pc/FxQW7d+9GaGgo3NzcUFxcjMTERHzxxRcYMmQIxo4da3BcXrIeBiGZbdasWXjllVfw3XffGV2ue/fuGDVqFADgjz/+qNC6nz59qne6dhfTycmpEpWKpw3A53dtDdX0Zy4uLvjggw8QHx+PAwcO4KOPPkLPnj1ha2uLpKQkhISEGOwnWQeDkMymVqtx+/ZtxMfHm1y2YcOGAFDmxIExN27c0Dv9ypUrAGC1XUttO3fu3NEdN/wzfQ+IyMzMREJCgm4A8jZt2mDChAnYvHkz9u/fD3t7ezx48AAJCQlVVzyZxCAksw0aNAgAcPDgQSQnJxtcTqPR4McffwQAvPzyyxVa9/79+8tNe/jwoW49ffr0qWy5orRs2RLe3t7QaDTljgMCzwZL/+mnn8pNDw4OxltvvaX30prWrVvr/jCYexkQmYdBSGYbMmQI/P39UVRUhNDQUGzbtg1Pnjwps0xaWhqmTJmCCxcuwNfXF4MHD67Qunfu3Ildu3bpjtU9ePAA7733HtRqNbp06YIuXbpYvD+GTJkyBcCzsZ6PHj2qm/7w4UO8//77yMnJKfeav//97wCAJUuW4MKFC7rp2jtmbt++XWawd6oePGtMZrO1tcW6deswY8YMnDx5EosXL8ayZcvg6ekJR0fHMvcWd+jQAZGRkRUaUBwA+vXrh/nz52Pt2rVo0KABUlNTUVxcDC8vL5ODz1vakCFDkJCQgJiYGLzzzjvw9PSEs7OzbpdYpVIhNTVVd80jAEyaNAm//vorkpKSMGrUKDRr1gwuLi64e/cusrOzIZfLsXDhwgofKqCqwS1Csoh69erh66+/xvr16zFixAg0a9YMWVlZuHLlCkpLSxEYGIhly5YhJibG4CUr+nzxxReYNm0abG1tkZqaCjc3N/zzn//E7t27K7UeS1m0aBGWLl2Kjh07IisrCzdu3EC3bt2wc+dO+Pv7A0CZR4opFAps3rwZ06ZNg6+vL7Kzs5GamgqFQoGhQ4di9+7deO2116zeDypLJmj3OYjILNOnT8d3332H6dOn6x45Rn8N3CIkqqBBgwZh7NixuHPnTrl5hYWFSExMBIByz2Okmo9BSFRBzZs3R1JSEpYvX17mEppHjx5h7ty5yMrKQosWLdC9e/dqrJLE4K4xUQWlpKRg/PjxyM3NRZ06ddCiRQtoNBrcvHkThYWFaNiwIdavX4/27dtXd6lUSQxCokrIzMzE1q1bER8fj4yMDGg0Gnh4eKBPnz5466230Lhx4+oukURgEBKR5PEYIRFJHoOQiCSPQUhEkscgJCLJYxASkeQxCIlI8v4XttZejm7OQRkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Change the context to \"poster\"\n",
    "sns.set_context(\"poster\")\n",
    "\n",
    "# Create bar plot\n",
    "sns.catplot(x=\"Siblings\", y=\"Loneliness\",\n",
    "            data=survey_data, kind=\"bar\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each context name gives Seaborn's suggestion on when to use a given plot scale (in a paper, in an iPython notebook, in a talk/presentation, or in a poster session)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using a custom palette\n",
    "So far, we've looked at several things in the dataset of survey responses from young people, including their internet usage, how often they listen to their parents, and how many of them report feeling lonely. However, one thing we haven't done is a basic summary of the type of people answering this survey, including their age and gender. Providing these basic summaries is always a good practice when dealing with an unfamiliar dataset.\n",
    "\n",
    "The code provided will create a box plot showing the distribution of ages for male versus female respondents. Let's adjust the code to customize the appearance, this time using a custom color palette."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Change the context to \"notebook\"\n",
    "sns.set_context(\"notebook\")\n",
    "\n",
    "# Set the style to \"darkgrid\"\n",
    "sns.set_style('darkgrid')\n",
    "\n",
    "# Set a custom color palette\n",
    "sns.set_palette(['#39A7D0', '#36ADA4'])\n",
    "\n",
    "# Create the box plot of age distribution by gender\n",
    "sns.catplot(x=\"Gender\", y=\"Age\", \n",
    "            data=survey_data, kind=\"box\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " It looks like the median age is the same for males and females, but distribution of females skews younger than the males."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding titles and labels: Part 1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### FacetGrids vs. AxesSubplots\n",
    "In the recent lesson, we learned that Seaborn plot functions create two different types of objects: `FacetGrid` objects and `AxesSubplot` objects. The method for adding a title to your plot will differ depending on the type of object it is.\n",
    "\n",
    "In the code provided, we've used `relplot()` with the miles per gallon dataset to create a scatter plot showing the relationship between a car's weight and its horsepower. This scatter plot is assigned to the variable name `g`. Let's identify which type of object it is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model_year</th>\n",
       "      <th>origin</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3504</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>usa</td>\n",
       "      <td>chevrolet chevelle malibu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>3693</td>\n",
       "      <td>11.5</td>\n",
       "      <td>70</td>\n",
       "      <td>usa</td>\n",
       "      <td>buick skylark 320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3436</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "      <td>usa</td>\n",
       "      <td>plymouth satellite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.0</td>\n",
       "      <td>8</td>\n",
       "      <td>304.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3433</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>usa</td>\n",
       "      <td>amc rebel sst</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.0</td>\n",
       "      <td>8</td>\n",
       "      <td>302.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>3449</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70</td>\n",
       "      <td>usa</td>\n",
       "      <td>ford torino</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
       "0  18.0          8         307.0       130.0    3504          12.0   \n",
       "1  15.0          8         350.0       165.0    3693          11.5   \n",
       "2  18.0          8         318.0       150.0    3436          11.0   \n",
       "3  16.0          8         304.0       150.0    3433          12.0   \n",
       "4  17.0          8         302.0       140.0    3449          10.5   \n",
       "\n",
       "   model_year origin                       name  \n",
       "0          70    usa  chevrolet chevelle malibu  \n",
       "1          70    usa          buick skylark 320  \n",
       "2          70    usa         plymouth satellite  \n",
       "3          70    usa              amc rebel sst  \n",
       "4          70    usa                ford torino  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg = pd.read_csv('./dataset/mpg.csv')\n",
    "mpg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'seaborn.axisgrid.FacetGrid'>\n"
     ]
    },
    {
     "data": {
      "image/png": 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U9omEtdw/QXQiRFEC5xZg9wjg3IJl/qijYTW7RVECWgXYXF7Y3V6wbq9qUyy2Wm1BUAsa4RKEBonUf1q5eq9eVVur2huJSItsVnG55HAJIoRIU9NEtpVqJ6Zl23MTBsErypa0Nxp6+RystMhGIQWCCCGRU1MrT3O1bKtps8+K9kbDaguCWtAIlyBCSKT+08rTXC3bImliU21vNKy2IKgFjXAJIoRE6j+trCXVsi2SJrYzYLUFwVDI4RJECImcmlp5mqtlW/82+6xob1eAQgoEEUIip6ZWnubq2QbAkvZ2BcjhEoQG8aTn05N/mZXqLxHo2ZaoZyeCIYdLEAnAyvIvs0mkjK6rQ2+EIBKAleVfZtOdnz1WaIRLEAnAyvKvRKEbMumCaRTNghwuQSSAzrDLqSNECht09WdPJBRSIIgEYGX5VyKIFDbo6s+eSGiESxAJwMryr0QQKWzQ1Z89kRga4T777LNm20EQnR6r73LqCNF2zHXlZ08khka4u3fvxgMPPGC2LQRBWBS7IOK5CYNQ0yIg3cbC5ZPQP5P3j2Q1rlcW2GpbvICDpxFvG4Yc7oABAzB//nyMGjUKmZmZ6vF58+aZZhhBENbCK8p45ovTQYtmWnRnTXI0DDnciy66CABQXV1tqjEEQVgTL8+pDhRoXzTbPHEwuBAnGsu13Q1DDnfdunUAgAsXLqBnz56mGkQQROJRNbQAOIYBAxmybHxxKxadcXfQJMeLoUWzyspKTJ06FcXFxairq8ONN96IiooKs20jCCIBKFP8RbtPYsb7J7DokwqcahGw4csaXOBYSA571Cq3saSZtHJKylRjyOGuXr0aK1asQHZ2Nvr164e5c+fikUceMds2giASgJaGdu3BU5h5aQ7OuH1YuLsCM3aewKLdJ9EIRtPpxqK1JV2uPoZCCufOncPYsWPx9NNPAwDmzJmD7du3m2oYQRCJQW+Kn5PO4/5PjcVaY9HaBl4LlgUkiVQKbRjeaebxeMAw/jlBQ0MDJMnYy2tubsa0adNw+vRpAMDy5csxefJkTJ8+HdOnT8eHH34IADhw4ABKSkowefJkbNiwIdbnIIgOw3EsRAcPr4OHmJ4GKcMOX7odyNCfakeCt3MQ0+3wOuwQ0+3g7VzwuQz/OV96GpgMu2Yfqk1tU367w6bbZuh9ksN/HceyGJ+fFXR+fH4WWIbRjrWCgeQItyea1jboedNsYJn4YgihzxzPu4/Ubm2LN6HtxoKhEe7s2bNx5513wul04tlnn8X777+Pu+66K+p9hw8fxsqVK1FVVaUeO3r0KF599VXk5uaqx9xuN8rKyvDKK6/g4osvxoIFC/Dpp5/i2muvjf2JCCIOtKRMZaML8Ma/GvDfl+agj8OGWJaLeTuHOhFYvqdCbW/d2EL0a3OQoedWji5Q+1AcWahNtw7Jxg8G9kHZfu02w5+l/bq1bRKuvdVNGJ+fhXnD8/BNk0czB8K/z7vx/KHqmKRcgc+b7eCx8PI8PHHwVMyyMLMkZVaRqhly8TNmzMCSJUtQUlICn8+H1atX49Zbb4163/bt27Fq1SrVubpcLtTU1KCsrAwlJSX45S9/CUmScOTIEQwcOBAFBQWw2WwoKSnBrl27OvZkBBEDenHOqYOy8cTBU6hpEWJKN+jmOCwPaW/5/iq4OU7znFYfoTYVD+6LMp02oz1L2f4q3H15f2y6rgh3X94fZfursLW8DmWjC4JirWWjC/DqsfqgXAlGRpyBzzR3WK7qbJX+jaZrNCvVo1VSSBoa4f7sZz/D5MmTcc899yA9Pd1w42vWrAn63NjYiKuuugqrVq1CVlYWFixYgB07diAjIwM5OTnqdbm5uairqzPcDwBkZ/cIO5aTk6VxZeqxol1WtAlInl21LV7N6XVPO4fatt1VYFnDNlU3eSKmLNQ6p/ShtB9qE8dEToOod59y3TmPD4s/rsCm64pQ2yKgtkXAliO1WDIyHz3tHLIdNqz+/BTKna3qPRzHwinKeChgZPjk+EEo6p0RFDIIfF7lnYX2H/hsoUiyjLNuH7w+CUtG5uPVY/VBdkS61wh676Sj7caKIYc7ceJE7Nq1C6tXr8aoUaMwefJkTJo0CT16hDu5SBQUFGDTpk3q59tuuw1//OMfMWXKFDU+DACyLAd9NoLT2QxJak/AmZOThYaGppjaSAZWtMuKNgFJtsvBa06vL3hF5GXycPkkoG3dwohNXLo9YspCrXNKH2r7ITaJcuQ0iHr3BT4LAPWZalsElDtbsXxfFfIyeSy7coDq5JR7REnGQ3srg0aGD+2t9C+suQP+MQh43sD2A9sKerbAd6VM99tGoMpIe8uRWpQ7WyPeaxidd9LhdjWI5MANhRRuuukm/OIXv8Du3bsxefJkPPfcc7jmmmtiNuTEiRP485//rH6WZRk2mw15eXloaGhQjzc0NATFeAnCbLSkTGWjC7Cz0omVowvUvAFGcYgi1oW0t25sIRyiqHlOq49Qm94/2Yi1Om1Ge5a1Ywuxs9IJANhZ6dS0rXcaFyblEmXZUHLxwGd69Vg9VoaEKiLJwvTCOXOH5SZMUmYVqRojy3LUvOwHDx7E/v37ceDAAdTX1+Oqq67CuHHjUFJSYqiT6667Dr///e/R3NyMBQsW4N1330VGRgbuvvtu/OhHP8L111+PyZMn4/e//z0GDBiABQsW4JZbbsGNN95o+EFohBs/VrQJSL5dym4sCQDLsGAYGZIM2BgZnMcva4rFJt7uj9cqMiqHKEJoG2Xydg5uGwdRAliGAc/IYD2+sAWc0CoL6ZDhYtiwNkPt4jgWAm+DDwDLAGmsDEFk4JNl/32SBDcb0I4kQbCx8MmM/5kB8IIPXp7DooBtuoDfWYWOcEOf184CsgxIDBNVFuZN4zFj54mw428UDwUHOWGSMuVdmi1VizTCNRRSuP3225GTk4OFCxdi5syZsNniS6M7dOhQ/PSnP8Xs2bPh8/kwefJkTJs2DQCwfv163HvvvfB4PLj22mtxww03xNUHQcRLaAVboH0KGM84SPCK4CCq7Qmh57zt52SdPkJt8sK/PVarzdD7WNELe4D9LKB+FoBw27xikEMQ265fP7YwaLqvjAxD7Q19XgZAXts/BJHen27FCMh++VmEe2NBeZc5BmwyC0Mj3Pr6euzduxf79u3DkSNHcOmll2LcuHGYM2dOMmw0BI1w48eKNgGpsytSye9k2hSt9HjgeQfPgXF5AUA3Z4J6LsYk4bHYEc/70orhrh9biL6QTRuBmvkdRhrhGnK4CmfOnMHu3bvxm9/8Bg0NDfjb3/6WEAMTATnc+LGiTUBq7Ir2x58sm6LZoXX+uQmD4BXlsMWnN/7VgIWXXxx2LhFOLVHvK5pTTySpdLiGFs2ef/553HLLLbjppptw6NAhPPTQQzhw4EDCDCQIq2AVvWY0O7TO17Rdo6Ul1jpnJX1rd6kYYSgY29LSguXLl+PKK6+MWa5FEJ0Jq6QWjGaH1vl0G6urJVb+W689s+wkgjHkcB966CH89re/xfPPPw+fz4exY8fi7rvvjnvxjCCsilVKfkezQ+u8yydhfH4Wpg7KRk87hwteETsrnbjgFcGzjCnPZZX31VkwFFLYsGEDPvvsM/zkJz/BvHnzcOjQITz11FNm20YQSccqes1odmidH5hlx/zheXj+UDUWf1yB5w9V447hefhHQxP6t92f6OeyyvvqLBhaNLvpppvw5ptvguf9L9Xr9eKmm26yVL4DWjSLHyvaBJBKIVaVgs8nYWFbwhqFvEwemycVwe71AYhPpdARO63420rlopmhmIAsy6qzBQC73R70mSC6Eope097mSFw2DhzPJWTUFstqfKgGN7T3wPPZPR2ovuA2tCvMiD2JtDMSyVQnWAFDDnfo0KFYu3Yt5s6dC4Zh8Oqrr+LSSy812zaCSBl66fwuMq6iNNxmolIE6sVTv27yoK/DBjvH4GcBfasyshB7cu0c6kWYnsrQKikTk4mhGO6qVatw/vx5zJo1CzNmzIDT6cTDDz9stm0EETOJSl6tJ3c66/bF3Z/ZkjO9fBBby+tQur8KNW0ZwpS+9aRibi450jirSPCSiaERbo8ePfDkk0/i3LlzsNlsMWcJI4hkkMgRk57cSZBkdeoca3+JklBpTcOB9tI2myYVobbVn7VLybgF+GVjgejJyJIl9eqOkjJD//xXVFTglltuwdixYzFmzBjMnTsXNTU1ZttGEDGRyBGTXuVZnm3XO8XaXyKq2QZV4A0o/CjJcntyFgCrP/8Gy/dVqc5WTf8YgMsn6dqTjKq73bG6ryGHW1ZWhhkzZuDLL7/EoUOHMGXKFKxYscJs2wgiJiKNmGJFT+7U29E+KYy1v0RIqCKFOhRH/Mhfv9FMj9g/kw86picVc4jJkXp1R0mZoZCCy+XCrFmz1M+33XYbVe0lLEciRfh6VWoDqxzE2l8slW917dJx8m5RUh1xbYuAF47UYtmVAzAwK01NcQhRDusbQNgxwdtxOw09SwLeR2fD0Ah38ODB+L//+z/187/+9S8MGDDANKMIIh4SPWKKtr8/nv4itWlkAU5/Gh5cgbfc2YpleyoBWVb70epbz55k5TboLjkUFAyNcGtqanDbbbdhyJAhsNls+Oc//4mcnBw1Afl7771nqpEEYYRkj5gS2Z/RBTjFyYdm50rjzNm6SyQWQw532bJlZttBEAmhIyL8VPbn5TnV2QLtsdnNEweDC3C4ek6+V5rNcKJwInUYcrijR4/GkSNH8M9//hM333wzysvLMXLkSLNtI4ikk6qdT7FIpLScPMsw6IvwGG1Xn6J3NgzFcN966y0sX74cv/nNb9DU1IRFixbRohnR5dCTXMW7eSKmvhMgkepu8dDOiKFf0iuvvILXX38dPXr0QHZ2Nt566y38z//8j9m2EV2YRO0ISySp3PnUHSVS3RFDIQWWZYN2l1188cXguK66F4QwG6vuoU/lzqfuKJHqjhgaVlx00UU4duyYWu3h3XffRa9evUw1jOi6WHUPfap3PlFIoOtjaIRbVlaGJUuW4JtvvsG4ceOQlpaGzZs3m20b0UWx6h56PcmVstLPcSycLgHeNN60Eai6aIfwqrvxys3MqNRLxIchh1tUVIR33nkHVVVVEEURgwYNony4RNyYUZYlEQ4i0rReDYP871dxh0GMJBQPDbUoVXfvHJ4Xc8gl3tCNVUM+XQFDIYXGxkbs3r0bRUVFePfdd3HXXXfh+PHjZttGdFESvUCUSHWB3rS+o2EQIzZq9aFU3Y0n5BKvzVYN+XQFDP0iS0tLcerUKfz1r3/Fnj17MH36dDzxxBNm20Z0UURRUjWjb0wdgs0TB6Mv5LhHT8lwEB1NjGPERr0+etq5uJLwxGtzIpMAEcEYcrjnzp3DHXfcgT179mDatGm4+eab4XK5zLaN6MIkcoEoGQ6iowtqRmzU6+OCV4wr5BKvzalePOzKGHK4giBAEATs3bsX11xzDVwuF1pbW822jSAMkQwH0dEwiBEb9So27Kx0xhVyiddm0gSbh6FFs+9///u4+uqrMWzYMFx22WWYNm0apk2bZrZtBGGIaOqCRKAsqL30g0vgFsSYF+aM2Bi4aCfBv12XgYylI/onfBHQjPuI6Bgqk15eXo7s7Gz069cPDMPg+PHjGDp0qKEOmpubMWvWLLz44osYMGAAXn/9dbzyyitgGAaXXXYZHnvsMdjtdmzcuBFvvvkmevbsCQCYOXMm5syZY/hBqEx6/FjRJiC6XYGr/jaGAcvK8IrmOgg9m4yoJMyUaHXW7zAVWL5M+rJly/DBBx+on40628OHD2PlypWoqqoCAFRWVuLll1/GW2+9hczMTJSWluIPf/gD7rjjDhw9ehTPPfccJcUhDKErXWpbfEvm5NeojCqezGIk0epaGIrhDhkyBO+99x5qampw7tw59X/R2L59O1atWoXc3FwAgN1ux6pVq9CjRw8wDINLL71UrY129OhRbNmyBSUlJXj88cfh8Xg68FhEV8dK0iUzbbHScxIdx9AI96OPPsKuXbuCjjEMg2PHjkW8b82aNUGf8/PzkZ+fDwA4c+YMtm3bhnXr1qGlpQXDhg3Dgw8+iIEDB6K0tBSbN2/G0qVLDT9IdnZ4JY8RB9AAACAASURBVOFIQ/tUYkW7rGgToG9XbYtXc9UfLGv6s4S2b6YtsbTd2b7DVJIqmww53H/84x8J7bSurg533XUXbrnlFowZMwYA8NJLL6nn58+fj7KyspgcLsVw48eKNgFR7HLwmrvVIEmmx+fC2jfTFoNtd8rvMEWkMoZrKKQgSRJeeukl3HbbbZg9ezY2btwIn88XlzEVFRWYNWsWfvSjH2Hx4sUA/CV8duzYoV4jyzJsNkP/FhAWxez0i1aSLplpi5Wek+g4hrzas88+i+PHj+MnP/kJJEnC66+/jieffDLmUunNzc248847cf/99+OHP/yhetzhcODpp5/GmDFjMGDAAGzbtg3XX399bE9CWIZkLPRYSbpkpi1Wek6i4xhyuHv37sWbb76pJqyZOHEibrrpppg727FjBxobG7F161Zs3boVAHDddddhyZIlePzxx7Fw4UIIgoBRo0Zh3rx5MbdPWAOj9bk6SrLrl6XKFis9J9ExDDlcWZaDsoPZ7faYsoV9/PHHAIA77rgDd9xxh+Y1U6ZMwZQpUwy3SViXWNIvWi0NYCrtMdq31nVE58CQwx06dCjWrl2LuXPngmEYvPLKK7j00kvNto3opBhNv2g1jWkq7THat951F0Xfv0RYAEMrGatWrcKFCxcwe/ZszJw5E2fPnsUjjzxitm1EJ8XoQo/VNKaptMdo33rXnXXHt4hNJBdDI9wTJ06gtrYWffr0AQAcP34cc+fOxXvvvWeqcUTnxOhCj9UqP6S0ppnBvvWuEyQ5pdUyCGMYcrgPP/wwZs6ciWHDhql1zQgiEkYWesyo/NARUmmP4TCMznU8y4B0C9bHUEjBbrfjjjvuwJgxYzB69Gj1fwTREaymMU2lPUb71ruut4N0650BQ9/S4MGD8Y9//AP/+Z//abY9RDdAWWX3ykBvDnhhYhF8spxylUIqNa+GwzA617E08+wURHS4JSUlAICWlhbMnj0bBQUFQTvAKIbbddGTKCnHGQaQwUCU/bHDQOcQSd6UzCxf8Ui8Uql5Ndo36XI7LxEd7sMPP5wsOwgLoecUc+0c6kXg5S9rMOPSHKw9eCpMwgQgorwpWZsirCY5IwggisOlOG33RNcpTipC6Z4KLBmZrzrboPMTBwNARIeaLCVAshw7QcRCYjOKEKZidkIYhUhOMbCKrNb5aMUSk1WgkCrPElaEHG4nQZkiL9p9EjN2nsCi3SfRCMYUpxvJKQZWkdU6H82hJksJQJVnCStCDreTkMxdUHpO0SH6j++sdKJsdIGm0wy9d3x+Fn41sQgiAMlhh5jGoZedxeZJRdhRPAQvTCxC7zQWLhuX0FG71SRnBAEYlIURqSeZu6D0pEeC13986Yj+YBhg86QiSLIMFsEKAOVehgHOCxLu3V2hLlytHF2AF47UwukW/A6QY/Cz3ZUJX9iitIaEFaERbich2VNkUZTAuQXYPQI4t6A6KuU46xLAubzg3cHnA6+RZaB0X/Co/ImDpzB3WK46Qq9pEUwbtes9A0GkChrhdhKUKbISVgicIps1SQ7VsTokCW6WDRbccwzcXMA1ogjB67dIb1Te086p/51uYzE8OwNzh+Wip53DBa8Ihm2PTcejpY3lHt7OBdmfLktwgdG9V5JliA5e9zxv5+C2cRAlgGUY8IwM1uMzpFFOxPPE2weRHMjhdhKSPUXW0rGuG1uI3x6pwd7qJuRl8vjVxMFoFiQs31MRdE0/OwfBK+ru+7/Q5pDzMnmwABZcnhek6V1zTSHSbSx6SlLMWtpY9Le8nUOdCNX+8flZmDc8D2X7tUMcHMei4rwbD+3VPh/anhJC6eOwoWdbn2Y+TzzXE8mFQgqdiGROkbUW6Zbvr8LUQdnqZ0EGlmtc4+b8I1ithauVowvw6rF61RHkZPBhmt4VB/yhBqfLF/NCYSyLi26OC7J/6qBslEW418tzqrPVOh/anhJCqWkR/FuZ41j4jPUeq6W8JIKhES6hSbRwAOD/1zrSQl7oqNzGMGBZGY+OKVBH6G6dftJtLHySHPNCYSyLi6HXRtIXcwba1jufbmNV/a+ZzxPP9URyoRFuNyR0A4WkUS1Ab5FOCQcAgIToC3mBo3LW7QVa20foACDrtOHySbCxTMwLhbEsLoZeG0lfbKRtvfMun2RIo9zR54nneiK5kMPtZmhtoKg47w7Tv2qFA9a1aXCVzzwDrNO4xiEaW8bz8hx++WV1mKZ3zTWF6J/JIzvdFrOWNhb9rUMUg+zfWenE2gj32gURT44fpHs+tD0lhNI/k9fUKCf6eeK5nkgujCx3jWJITmczJKn9UXJystDQ0JRCi7RJtV2ig8eigBwDgP+PcvPEweqoU6GjKoVoeNN4zNh5IkylMLS3A7zHhz59MnHmTIulVArZfXugvtljOZVC4O/KSiqFVP/etTDbppycLN1zFMPtZsQU4wxJAygA4CAGpQUURf8xe9sfeQvDgnOwms459I9emf6WO1uxfF8VgHbnH6T71UhFGMmpGE1fyHEs3CwLBjI4xp9q0gUGDkmCh+XgA+C182AYGZAAXvCBZRjYBVHt28tzsCt9trUnim25fb2+oJSTyZCExfL8RPIhh9vNMKOMTKgUaXx+FuYPz8PyPZGlSfFqixMhfVLa0Eo16Ze/Vavyt7LRBXjjXw24c3ierlRNSV2pZ1M8NpPEq+tBMdxuhlaM78nxgzoU4wuVIk0dlB0mj9KSJomihL6QsXniYLwxdQg2TxysJiKPpb94pE9KG1MHZYfJ0kLlb2sPnsLUQdko3V+lK1Vzc5FtSoYkjLA+NMLtZmhtoMjtkQZnY3P8bRqVV4GBPWR0Fs/0NxHSp2ipJgPlb4HXRZKqdURSZtZzEtaCRrjdkNANFB2th2VUXvV1kychKSUD+xuenYF14wrx4veLwLHG246WajJQ/hZ4XSSpWkckZdGe0+g9hLUhh0t0mNAwxc5KZ5g8qmx0AbaW1yVkSqz0Nz4/Cwsuz8Pzh6px90cVWPRJhWGHrrShlWoyVP5WNroAOyudWD+2UFeqpqSujCQpM1sSRlgfkoUlGSvalQibtCRkLo5DbauAC14Rrx6rR7mzFQDwxtQhsHuEKC1GtovjWHjtNiz6pMKQxC2SzUpBTCXVZKBKgWOYIJVCJKlaNEWBmSoFK/6uAGva1WVlYc3NzZg1axZefPFFDBgwAAcOHMC6devg8Xhw4403YunSpQCAY8eOYcWKFWhpacF//dd/4bHHHguqDkx0jI7oMjmOhcDb4APAMoCNkcF5tMt3K7FYjmPhbhvFrv78mzCHaGMYIIOHT2YgyQDPAAwjwyvGJn0SI8RT7SHPnA4ZLobzVxlmGDg4GW4JECV/PDRQPyxxLGRW9m+DgwzeKwbJu/TizqIowQ7/YpcIwGu3gYEMWQ5wyiH3RftuSOLVtTAtpHD48GHMnj0bVVVVAAC3242ysjJs3rwZO3fuxNGjR/Hpp58CAB588EE88sgj+POf/wxZlrF9+3azzOp2dKQ0j3Lvwt0VmPH+cSz+pAKnmgVc4Fjd+wP7e+Sv32ClRmUIH2Scahaw+BN/u3d/UoGaVh8e/fxUbPbpxDhtDBP2zN8KMjYcOo0Z7x/HhkOnUesSsehjf/+LPqlAnejftJCI97Vo90nMeP8EFn1SgVMtAjZ8WaPZRjLLJhHWwLRvdvv27Vi1ahVyc3MBAEeOHMHAgQNRUFAAm82GkpIS7Nq1C9XV1XC73RgxYgQA4Oabb8auXbvMMqvb0RFpkda9gdmvot1T7mzFC0dqsezKAXijeCg2TxyM3mksvm7y4okQKdZjn30TlJjciH16MU6WlcPsLguQemllBVOynCX6fQVKykLbINlX98O0efuaNWuCPtfX1yMnJ0f9nJubi7q6urDjOTk5qKuri7m/7OweYccixVJSSTLtqm3xak67wbJBdmjZpHdvuo0Nu1/vnnJnK5btqcRbJcOQl5mG2hYv0m1s0DXK1t5BPdOwblwhXj1WD7Csrl2BXCTLeOkHl0CQZPAsg94OG+pbBU27FalXxKxgLBvxfUmyDLaHI6g/ReWh977U/kLemdHvxij0ezdOqmxKWqBUkiQwAfIjWZbBMIzu8VihRTMdHLzmzjJIkmqHrk0697p8UtD9MfXn8N+vXDM8OyMsAfnK0QXg4I9jGn1XHPzZy5zN+jYoUi9F4qW5206SdO0/c6YFTpYNT0CubNaI0G/oOzf0rmKAfu/GSeWiWdKCRXl5eWhoaFA/NzQ0IDc3N+x4Y2OjGoYgoqOmWnTwENPtkNL5oOq3DknC+nEaMiZJUu+ra/FCSueBDB6Sw66mbXRIkmYCcSX7lRaB/Q3PzsAzEwbh+YlFYNpik3ZBRP+2dvIyecwdlhu20+uJg6fgkxk4XUJc8UytUMPaAKmXVlYwJctZJClWtATkWvcGSspC3xnJvrofSRvhXnHFFaisrMTXX3+NAQMG4E9/+hNuueUW5OfnIy0tDV988QWuvPJKvPPOO5gwYUKyzOrUaO21f3jMd/DaiXrcOTxP3d//cnktlozMR+80DtkOHj0goU6QURqQ62D1NQPhk2Q89tk3wTkCOOCFiUWqSiGNlSGIDFwsAy5kFR6A2t+K0QXI5Dk1Vho4GuwpSsjswWPTpCJIOrupalsFrP78m7hyB2jtpkuHjKUjB+C+Ee0qhc3XFflVCiFZzvRKGYm2KAnKA/qV4M8WxkDG0hH9NZUXVFm4+5G0EW5aWhrWr1+Pe++9F1OnTsXgwYNxww03AACeeeYZrFu3DjfccANaW1tx++23J8usTo3A28IWXVZ//g1uuSQnaH//3uomLN9Xhbs/qsC9uyvgYtiw+857RNXZKsdK91fBzbJg3V7Y3V6keX1odEttqoXwVXgxrb2/ZkHSLVcjihLQKsDm8oKDrLvTqyOLSKG76bxuHziXB3a3F5zLA6HZC67V2/bZG5RSUq+UkZGdX8q9vNtf1Zh1RS6HRJWFuxemj3A//vhj9b+vvvpqvPvuu2HXDB06FDt27DDblE5PoGbTzgE+SXt0mJvBR93frxxXFqwG9LBjych87K8+j7H5vdor6AY4Ey/PqaNppa21B0/h2QmD8eKRGtw/Ml89p7cwJQGqVlfZdLB+XKFaTl2Zhm85Uhtkb6DLDX0PksTAJ8tqCZ9QPW+i8sMqCchDY7gdqZxspdy1hPnQ7oJOQmD4INvBY+HlecjJ0F50YQGMz88CEyEVY14mj2wHH14xd2whfldeq6YmXD+ufUqv58CbvD7MuDQHbEB/egtTMhjwIakMx+dn4VcTiwAAXzd5sOVIrborLXQEqfUenghZcHvhSC2cbsFQ2sRYEEUJRb0zEhYCoPSL3Q9SWHcSAjWbc4fl4omDp1DfKoRtLFg5ugCtPhHzhvtzDITmCQjc9z9veL/wirkhqQlL97VP6fWm1Gc9ItYePAVZZtRFoFeP1YfZVja6AL/8sjosleHe6ibcu7sCdlZGX4cNzrZtuVqLSFrvIXTBLVDPGy1tYqywDJOwEADpcLsfNMLtJASOLpXp+otHanH/qP5YduUApNtYuHwSejts6GHjsODjfyPbwYMB8ItrB0MGkMaxsHsECF7/Yk1mVlpEvaryWZnSayUMV6b/tS0CJLk9v60oAzzHoPR7BeBZBhe8ojpyvW+E9kjZKyLofgfPgXF5g7e6arwHPfuNpE1MJVa2jTAHcridBE5jug4Abp+EAT3sbQ6Vgd3jg4tl1XDBmrYR4Pj8LNw7Mh8iz4HjOXCCqC5YaelGldhubroNHMv65WYykCtJ2DypCPUuAWc97U5UCWUE7v0XHTzW/619BKrIxADgmQmDsLW8Lix0EHh/dk8HGprdUd+Dnt42MHyiqblNMVa2jTAH7tFHH3001UYkApfLi8C8Z5mZaWht9abOIB3itYuXgWu+0xuf1Tbh3+fcWDm6ACNyMrHm4Cn87p/12F9zAeP790IGAMbGYmAvB578+2l1c8Ht/9EPZfursLW8Hp/WXMA13+mNXoyMqwdchM9q/aoCRY/615rz+OF3++KZL07jD8cb8Wn1eVyR2wMvl9dhSN8e6AUZHjBYc/AUKs971Kl/liQhMPlcoM0Dezqw8PI8rP/7aWwtr8eRxhY8MCofXzd5wLHQvF/rXYW+hwdG5eP/GppV+1eOLsBvy+vUNnsh/Bm1+jL7+9Mi8Fk6altX+72bidk2ZWam6Z6j9IxJpiN2Ba1ocwwWfaydmtAuiGjhbfjv948DANaNK8Tzh6p1r5XT7XALon8RiAMu+ICffRpe2XfJyHw8f6havc/I6rpis8wwWKyRSnHTpCIwshy1Eq3ee0imSiGSTfGSKNu64u/dLLpsekYiNkJLdoeWHFem2w47B5dsw8NjvhOUa1aJ/4miBDvfPl2NlDtAFCXkpPNoaHaD41h4GBtYaKc9VNoRwcBjt4FlAE5mwDASvHabP/UhtCvoetN4zTYlSUa6r81527igcuy1LV7AwYc5Tm9bjDPdJ0L0SGAB2APazbRzcLMcXLwNnB2wy9EdmOr40JYDNyStollQ+sXuBTlci8DbOdSJwPI9FeqC1Lqxhehn54Kcbvt1/w5buHK6BTX+xws+dYFLL9ZpCxDZqhKlTyqwZGR+xJwAoizjl23Vbt/4V0NY1VstaZNevNLOAY2+YGmUv2puTbs0LQZ5V+h7HJ+fhXnD81C2v1L3Pi15VmClXpJpEYmCZGEWwc1xYZVulZSB0a5be/AU5g3vFyShEkUJvdNYLLtyAPpl2LDmmvCcCCzbHoIJlCi9eqw+TE6m5ARYc00hNh2uUavdalW9Ld1fBYEP/rdcL2+AJDFh0qjQqrmxyLtC349WKsbQ+2JNq0gQ8UIjXAvAcSy8IRIhRSUgygiaVutJiQZmpSFTEiF420dikgwIkgyXT4YgSVgxugAswyDbYcPqz0/hiasLIDt41LZ4ITMMsh3+EWi5sxVbjtSi9HsFyM+0g2UAOwssHZkPQZYxdVA2+mXYIoYrvAAyA0aGoigh185h86SitpAJAxsjwaOzWy5Qmpbt8G+Y0AyhgIGYxqvxz9D3GKmCsPJeQ+8JtIFkWkQiIYebYpTpbGOTJ2LKQmUarDc1/7rJg74Omzr95TgWZwVJXSxTRqmvnajH1EHZ6OPgcF6QgrbUKru0FKmWjQHu3V2huaNrzTX+Io564YpTTR4MzrKDC5i214tAaUDIpGx0ATiGiSjtGp6dgYWX52HxJ9ohlK+bPFi2p1J9Rz1D2tOzr7rFi/V/O+VXMkSwgWRaRCKhkEKKUaazW8vr1Gm8VspCZWrrEEVDFXG9PKc6U6WNtQdPYfEV/bGz0on7RuSHnX+iLTQBAPOG91MdrNaOrhUHqtS2QsMPa64pxNbyOv/oPOQ5Q+2RZDlsR9qagFSKgXYE3jdveD+sbHvuwHdkZ6Wg96OVinHl6AJwTPs9LCvHlFaRIOKFRrgpRgkR1LYI2HLEn0ZxUE/tHWAiGLg4DhfZgE2TvovaVm/QDi6lPQ76u5g4hsHSkfkRQxNvTB0CMEzUHV0cw2DRFf0hiBKenTAYLYIIp9sHh83vKMEwEKNM21mGwebD3+IX1w6G0+3DBa+It79qxNRB2Vh8RX/d9I0Ds9Lw6GffqM+tHHf5gH4c1NAFyzDY2paeUknI88KRWiy64mL1nsAdbkbSKhJEvJDDTTGBIYJyZyuW76vCMxMGaU5xRVnGvW0r6c9MGIRnvjitu0vJpjNNBoBFEZQIHGRwHgFiQDUCvWm5V5LxwJ5gve74/Czc8R95qm3KVL83px0KueAV4XQLON3sxbI9leq5v9c3Y8nIfPU6red0hpRCV44LHhEcRHW329/rm/GnyrNh/QbeEyrPUqCxLZFIaKdZkgm1S2u30Y2DemNCfi98Ud++g2rNNYV46ei3+KfTBQCobvaqu6wG9nTgoe8NwH8PyYENDGyyv/b4iJweQbuw1lxTiC3/8LdR2+LF0lH5OBRwXtnlxLIMZBuH6wv74KqLs7D39HnMH94vqK2y0QV4599O3HlZHv4vwM5HrxqIBwOqIjQLEj6rbcK0wj4YN6BX0HOWjS7AOxWN+PmVA9A33YYxF/fErCF98d9DcjC9qA962jmkcSxKBvdBXasX3zR51QxmF7HA1fnRd5Bpvd/Q3WiRdnZxHAtfmg0+jgN4Dhl2Dq5O8LuyCla0i3aaJYDOvNNMEd1LYPzTWUYGC0BuUxmwLIPzXh/m/+XfQfcNz87A+rHfgUsEzrj9uQ12Vjqx8PKL4eBYnPOK6GnnwECGWwQACXM++Eq99//9Zx5y03lk2FhIkCFKMmxg4JIk/GxPu2517dhC5DhsEGTAI0qoaxXw0j/8YYzx+VlYOiofouSvCCHJwIy2HW6BvF48FBu/rMbUQdnITbfhojQeHCNBkvwbHeol4OWjtbra3nXjCsHIMr5t9WFnpdNf0YID3CxreLebKEN3N5oWqj43IFnPk+MHIVuSLBdm6Ey/91RDO826OXrTWQb+HVSig8cZd/i0vo+Dg9MjBikNHh7zHbh8wQ5TEfHf8R95GJ+fhTNuUVVB6OWUVSRitS3+EuPLrhygqgFWji4A4J+O3zk8D3aPr13+pVMY8VSTB3urm7C3ukk9tnniYHBuAW4Hj9I9J7FkZD7WHjyl/n+QNndfFZaMzMfyfVUAgK/OudX7o+3S0nq/ys60SCEDrYTrD+2t9PdrMYdLdA5IpZBC1AKQacGFH0NxSBIG9UzDLycW4fXiIfjVpMEYn5+lqTRY/fk3OOsRNUX8Kw5U4b4R+UF5cCPllFWobWkrjR5wfvXV38HmiYPbK9a2obXBYd3YQlVNENimomJQFvCUxbloaRdD7zeLSOkTCSIeaISbIoxm+9fSr64cXYCFl18MBtoOQXGOgccUJ8ZAxsCAPLhGnFvgIpNyXpCBTGVXm4NXp/WcIAbltOUYgGP1F7iA9oVDZXEuWtrF0PvNgtInEomGRrgpwmi2f63rnjh4CjUtglrSJpC8TB4unxR2THFiLBBUuDEwt65WG4qDf/VYfdD5U00eiGkcGsFg0e6TmLHzBBbtPolG+L1RYFUEzhO5HLgyKlY0vVra3nUB2tzQ+81Ca7T+5PhBpMsl4oYWzZKMYpc3jceMnSfCzr8xdQjsnvYRld51v/7Bd5Hj4HDeKwUt6qwfVwg7y2jGcO8cnoe+8L8jZTFIK4a7fmwh+qRx8LSVEHeJEh4IaW/LkVo8fvV3NFMuKrHVQKKlIQwsKimD8S/AgYEoyf4y57IEF5ikF1sMtTu3Rxqcjc2m9xsrVv+9WwlaNOsixJLbVHe6yjKQ0nnIDANR8uccGJ+fpS42Kdf1tHNgGYRN3+2CCIjaIn5/2kO/fb054IWJRfDJMjLsnPrfqt2tXnVhqUcGr5bxESQZDIB7R1ysuylBK/dAtDSEynnlPYaqA9aPLURftKsDoo0xE5VnNtRuNssRcxsEoUAhhQShOInQ6bXeQpje4tKOrxpQ0+rDoo8rMOP941j0yb8xb7hfXaBcVza6AJsO10CSGL9DCClqqBzj3QI4lxesS4BdEFEvQrVvwccncV4Qke4T0dvBg3V7dQsjch4RfR02vHaiHiwDrDl4Cnd/VIGv2/I/BJKIGGdHiyvG+l0QRLKgX2CCiNVJiGJ7wcU3pg7B5klF+G15Lf4zJwurP/8mqJ2y/VW4+/L+2HRdEZaMzMdfqs5i6qBs+ICI6oaO2BdKL57DfSPy4RVlZDv8TnZreV1YHoRExFY7qg6gariEVaGQQoLQdRJgYNdJYB04XfWm8dhb3YRZQ3KD2pk2qDdmD80FzzK4KM2GL+ubMLmwd3AmsXGF6JXOR6xQEKsTC4ypnhUklO6uCIvhljtb8cKRWmyaVARJkhMWW+2oOiCZ1XATWb6H6PrQCDdBcDqKga+bPIams6HSKMDvbH90SV88sOckZr5/HA/sOYmr+/fCZzXng0dv+6pw/Jwn4tRZzz4tJxY4JT9+zqOZdUzR6Trdgr8mmU44Ih70kpUbHTnH8qwdgUIXRKzQLyNBaDkJrbSJ0e7fWenEw2O+g7xMHrOH5mJFyNS4bH8Vigf3Dbo3UGer11csTixwSh5Jp2uWPEsJt7z0g0v84RaNDRaR6KjDjoayYcXF29Do9qkhFgpdENGgkEKCEEUJfTkWmyYVobZV0E2bqBA6FVU2DCwb1R+izOBXE4sA6KVYDO47cFNAJJVAX44NVzQAcLoEeHWqJuhtQsjLaK/6a8YUOrC4JRBZlaD3LkOfNRF2SrKsWf9M+a7NCl0QXYOkO9w33ngDr776qvr59OnTmD59OlwuF7744gukp6cDAO655x5cf/31yTavQ4iiBEaWgxa9gPDprN4us1wOaPS062r10jTyLKseD/yD1+or1L6gnAKKHf/7VZAdgRUQlPpmodUn0rz+/Amp3gKgu2MPsqE8C7Fy1u3TTKSu5HmgnWhEJFK68eGrr77C4sWL8dprr+EnP/kJXn75ZeTm5ka/UQOrbHzQ15D6p8Q5OVmobXJj0e7wPLJLRw3Aoo//rR5XysscqDmP4sF9wTEAz7LIsMlw+Rh/WXKGwZ7TZ/GLL2uRl8njuQmDkM6ywZpanZGd6OBVO5TFOY4B7BwLt09UN08oeRsY+LOYJWthyMh3GPgMCnqbLxKBmJGGm987FnZ803VFWP35N0HfdTKx4gYDwJp2dduND48++iiWLl2K9PR01NTUoKysDHV1dbj++utxzz33gGU7X4g5dOpu5wBJYuCSZXA8B0mWw1bRh2dnYNaQXNS3eoOOlztbUd3kxvUD+6iJvpV0if/79Rn84YRT1e++W9ILMoCzHgk/C8i7oJWfQbW1zY7AxTk1HeLYQvz6uiJ4RLntGQCfDMBio7dkKhIAgGe1E7ubHWIhugYp82gHDhyA2+3GjTfeiMbGRlx11VVYu3Yttm/fWmHNWwAAGRVJREFUjr///e/YsWNHqkzrMMrGg3SfiLMeCQt3V6ir2BXn3bBzwavoc4flqlm+Ao8Pz87Af+X1DCuLHrhwVtviLyvuk/0702LRnyqr+VqLc/42ofkMVlqJT5YiQaG3w6a5IJfm9SVMpUF0XVIWUrjvvvswefJkTJs2Lezchx9+iD/+8Y/YtGlTCixLHE6XgP/XFh9VyMvk8dIPLsEZjw8PtVVGePH7Rbj7owoMz87AA1fmoywghjughx2zNHIpvDZ1SNDxN4qHgmMZzenuWyXDkJdpDzsuyTIqzruRxjKafbxRPBQOG6v7DNnpfNg9yUZ5BuVdKglmino5wDLmeF1JlnHW7YMgyeBZBr0dNtP6IroWKQkpeL1e/O1vf8P69esBACdOnEBVVRWmTJkCAJBlGTZbbKZZJYYbiDeN15zuugUR2ZLUvoreNk0td7aiRRDVvAW902wQZe1NAKLsHwHPHZaL3mkcOIYBI8ma10IGqps84BjAIYoQAtIcZnMsvLx20nCOYeD2ibrPoCgIzMLod5hj59SikcozdiTBTKTNDDk5WWrbHAAJgNMCuWys8HvXwop2pTKGm5J54YkTJ1BYWIiMjAwAfge7du1anD9/HoIg4PXXX7esQiFa0vDA84ojDSQvk4eNCc6BYPf61Gnqi0dqkcmzWP35N/i6yYP3TzZiTcgUdu3YQvzt2/NYcHkenj9Ujbs/qsCiT/4NlySFTXfXji3EhkOn2/IyVKBOBPiAXLeiKKFvuk2z9PqGQ6cBaD+DVVbilXzBiz6pwKOffYOTFzxoYjlIDntcYQ/azECYSUpCCjt37sSHH36IDRs2qMe2bduGbdu2wefzYfLkyVi2bFlMbSZjhBtNgRB6fnx+FuYPz1NjsEpu2T4OG3qKwXWxOI6FwNvgA2Bn/akJARnnvSL+UnUmSKXAMRIkmcWiT/4dNirdct1giFKb7pdlseHQ6bBMY5snFYFztRfRy8nJwrnzrXBzNtS7vDjrEfHqsXq1Ztmdl+UFlfFJ1kp8LCqFbAevlg3qiJ3RVA9WHLEB1hxJAta0K5UjXMqHGwPR/hi1zo/Pz8LCK/rjrNuHC16/I3O6BWyeONifLpFrn7pmMDJaZSXnKwMbI0GG3/m6RX9axGZB9FefzeBRecGjOkaFwHy6Xodds6DjG8VDYXcHO9xIOXrfnjYEsoyk5wsw8h0qNq8bV4jnD1V3WB4WLU+xFR0IYE3HBljTrm4rC0sFHUk2Ek2CpHXen5DGh8UfVwQdl8CgTgSWt0m4bh2SjR8M7KMumAXKv34wsA+2ltfijFvEwsvzsP5v7aO4wF1OYRssYkwCo3e9LMOUTQSJQLFZbwtyrPIwKqtDmEm3Ckx1ND4XTYKkd16r5A3LMEFyr5KivqqzBdrlX8rxqYOyNQs+KolktPIFOEQxLDa7bmwhHKK2yzQ7B4EZKDa7fFJCYs2d8R0QnYduNcLVKntdur/KcNlr5Y8xNIZrF0SIbefXjSvE8n3tMdx7R+QDAF4vHoImrwiPKCPbYQMYqKXIAX8IQTtvAqMmi1GOhV7z3V4OTdG94BXRT2MFP1ClEIhevgUra0sVm3v3smPd2MKgeHngdxNre53pHRCdh27lcDu6Kynwj1GpvSXKMrwA7G3n+2SkYdmVA9DHwaEHz8Eny0hj/df2tNvQ4BKw+vNTcLoFrLmmEAwjo67VB5vOVFY5riSn0ZzutuUN0HIsglcEB1F9vmjRzGilcMygozllRVECWiXk6CTnCawqbKTtVLwDonvQvUIKCdiVJIoS7IKI814Jiz6pwIz3g0MTdklEH4etbYFLwguHa1DdIuCeTyow8/3jWP35N1hweR6yHTxWHKhCXasPzx+qhkuUwqb/K0cXwCVKeHbCIOysdOLVY/WmVFhIJYmUYYWWGwJAEi/CUnQrlUI0WVckgkZhLINFEarV8j14uEW/bGvJyHzN1XMlu9Sm64qw+OOKNknXd/HVeTfSbWyQomHjpCL4JBnnPD70y+DByICQwAoLqVxJjqT8yMtydMguMxLbWHHVHSC7YoFUCkkinvgcx7EQ0zg0eCQsb/vjffH7RRFDEy0CIDOyoQTeF6XZMDw7A+XOVnglGcv2VIbZIEgy7CyDXIcNvMefFlHZqKs3tu0spV86Wr8s3rYpXy2RCrrd3Eqryq0eyoj4q/PeIEVBaJIZIFytoFUyJ/Bal09C2egCvHikBgsu91flFWRZ81qeZWJKjtKZdkuZmXwm2YltCCIa1vsLtBBKqZl0Gxs0UlKScudl8hienYFnJgzC8xOLwChxXEEEy8hY11Yypywk7rp2bCHSOAZbjtRib3UT1h48hcVX9McfAtpVrl0/rhAZYmyj085UtdZMGRZJvAir0a1CCrGiTElDy8yUO1vxxr8a8MuJg9EsSEGbFZSYMO8Rke2w4e7L+8PBARsnFeGCV0SGjcXqz08F7Q5T+qi84IEkA89NGAyWARwcC5tHgOCVYgoRdKaptJkyLJJ4EVaDHG4ElCmpVpmZGZfmoKZFUHd9ASG6XrcAUYKa1FvhmQmD4AxZsMnL5CFKcngugHGF6IsIZWR0Eot3tt1SZsqwSOJFWAkKKURAmZI63QK2HKnFsisHYHvxUPxqYhE+qzmP3HTt9IvKgo/WTq/eaRwevWpg0LHVVw9ETgavOlulndJ9/jBArCECmkoThDWhEW4EtKekPrAcg8mFfVDT4o04kgzd6WVjGfAscN7bnvPW5ZMABpARebU+lhABTaUJwpqQw42C1pTUy/Mo3eNPCahV0TZwO6ngFWHnZFX/++yEwXigrTijgj9l4ncjOu9YQwQ0lSYI60EONwKBC1U2hgHLyvDvsPXnN6ht8YcalozM9+tqM3i1fHjg/V4waGzyINvBo0XQrqDAMlJ4noZxhWAYgGUQMYcDQRCdA3K4OmgtVK0cXYAXjtRi3vB+6oiz3NmK5fuq1B1Mgc429P6y0QVobctqFTpalSSgL2RsnjgYEvy5F375ZTX2VjchL9Nf/vyFiUWGyp8TBGFNaNFMB62FqifaUiFuLa8Lz2kwLnhRSuv+tQdPgWMQrrVVRqttmzJYAPfurlArNdS2CPjZnkrIkA1t2CAIwprQCFcHPS1rTzuHcmcrXjhSi42TinDG7YPT7UMvnoXoEqLezzIMXjtRj19NLAIDGSzCR6udSUdLEIRxaISrg43RLp4otCXIcboFVF3wwC1K2FnpRGgKIL1tpRdn2rF0RH+kCz7wbu3RKm1JJYiuCTlcHVhWDgsbrBxdoDrDstEF2Fpeh7UHT+G+EflhGlc9LWxuBh81JEA6WoLomlBIQQevCLwQoEC44BXxwpFaPDymAEtG5qt1xACA0UjvqKeFZZnow1TS0RJE14Qcrg4c4w8bLN9XpR7Ly+RxutkbdkxvmtARLSzpaAmi60EhBR30pvX9M3ma6hMEERc0wtVBb1oPUU75VL+zJBcnCCIYcrgRCJzW83YObrtNdXKZkgjBk/ydXrFmDiMIwjpQSMEAvJ1DnYi2opHHseiTCtSJ/uPJpjMlFycIIphu7XA5joXo4OFN4yE6eN0SNG6OCyqxU9siYPn+Kri56E4utA+fJOn2acQeM2uAEQRhLt02pBDL1DzenV9afawbW4jflteqORKUPgEYsqezJRcnCKKdlIxwb7vtNhQXF2P69OmYPn06Dh8+jPfeew9Tp07F5MmTsW3bNtNtiGVqHu/OL60+lu+vwtRB2WF9GrWHNkUQROcl6SNcWZZRVVWFTz75BDabv/u6ujosXboUb731Fux2O2bNmoUxY8bgu9/9rml2xDJqVSo3LA9Ij7hubCEcoggB+kTKxxDap/Lf0eyhTREE0XlJusM9efIkAGD+/Pk4d+4cZs6ciczMTFx11VW46KKLAABTpkzBrl27cM8995hmRyxT89DKDRzjd8KSKEN08LqOT6+PC14x6HOsScZpUwRBdE6SHlK4cOECrr76amzatAm/+93v8Nprr6GmpgY5OTnqNbm5uairqzPVjlin5oJXBOfywu72gnN5IYn+Kg6Ldp/EjJ0nsGj3STS2lUmP1IdSOj20TwoVEETXh5Hl0DxXyeV3v/sd1q1bh4ULF+L+++8HAGzfvh1Hjx7F448/bmrfkizjrNsHQZLBswx6O2yGch0AgNMl4P/971dhI9KXfnAJstPb470+SYLT5YNPkmFjGfRxcLjgldQ+e6VxOO8RIUgy0jgGkoy47Ono8xAEYT5JDyn8/e9/hyAIuPrqqwH4Y7r5+floaGhQr2loaEBubm5M7TqdzZCk9n87cnKy0NDQZOheDoAEwNlsvD9vmnbFXrcgoqHZ7W9XUSkExH6fHD8I2ZI/JMBwLE66mLDSOX3bkuHEYo9WX4FtRSKWd5VMrGiXFW0CyK5YMNumnJws3XNJDyk0NTXhqaeegsfjQXNzM95++208/fTT+Otf/4ozZ87A5XLhL3/5CyZMmJBs02LCiHJBS3nw0N5KVXmQyE0MtCGCIKxP0ke4kyZNwuHDh/HDH/4QkiTh1ltvxZVXXomlS5fi9ttvhyAI+PGPf4zLL7882aYZhuNYgEWYciGwsCPHsfC2FZsMJFB5kMjKDlQlgiCsT0o2Ptx///1qvFahpKQEJSUlqTAnJtSp+8f+MunLrhyAgqw02AHwgr9ir3JNY5MnovIgkZsYaEMEQVifbr21Nx4Cp+7lzlYs21OJJbsrIAfESpVrtpbXhRWMfHL8IFV5kEhlAqkcCML6dNutvfFiZOquXFPbImBLQNWIvAwe/TLtcDb6V8MSuYmBNkQQhPWhEW6MGFksC7ym3NmK5fuqsPrzb8DIcphMSymNnojy54lsiyCIxEMON0aMTN1pek8QhBYUUogRI1N3mt4TBKEFOdw4MJLLgPIdEAQRCoUUCIIgkgQ5XIIgiCRBDpcgCCJJkMMlCIJIEuRwCYIgkgQ5XIIgiCRBDpcgCCJJdBkdLsuGp8XSOmYFrGiXFW0CrGmXFW0CyK5YSJVNKS+xQxAE0V2gkAJBEESSIIdLEASRJMjhEgRBJAlyuARBEEmCHC5BEESSIIdLEASRJMjhEgRBJAlyuARBEEmCHC5BEESS6HQOt7m5GdOmTcPp06cBAMuXL8fkyZMxffp0TJ8+HR9++CEA4MCBAygpKcHkyZOxYcMG9f5jx47h5ptvxpQpU7BixQr4fL4O27Rx40YUFxejuLgYTz31VFz919TUYM6cObjhhhuwcOFCtLS0JNwmK7yr559/HlOnTkVxcTG2bt0aV/+Jfld6dlnhfQHAk08+idLS0oh96L2TCxcu4Kc//SluvPFGzJkzBw0NDQmxKdSujRs3YtKkSeq72rZtW1z2doTbbrsNxcXFqg2HDx/G/2/v/mOqqv84jj9vXCrSitm4yRjVotactXRRiT9gMO5FuFwc5oTNcu1uatlG001CKp06aAPTdqdt6prmpJURiDC6Mt1AJ2SiWLq5phKlojdJlAvivUfu+/uHeb6Y4LxyuVfo89j4g3PPPZ/XfX/ufXM5997Pra6uJiMjA4vFomeC4M+hTkaQY8eOSWZmpkycOFHOnj0rIiKZmZnicrlu26+3t1eSkpLkzz//FE3TxG63S319vYiIWK1WaWlpERGR5cuXS1lZ2ZAyHTx4UHJycsTj8YjX65X58+dLdXW13+MvXLhQampqRERkw4YNUlJSEtBMdXV1Ia/VoUOHJDc3VzRNk97eXklOTpaTJ0+GtFaD5Tpz5kzI6yUi0tjYKG+++aZ89NFHdx1jsJqsWrVKNm3aJCIilZWV8uGHHw4500C5Fi1aJEePHr1jv2DNoc/nk+nTp4umafq2ixcvSnJysnR2dkpPT4/YbDY5depU0OewvxH1DHfnzp2sXLkSk8kEQG9vL+3t7RQWFmKz2XA4HPh8Pn799VeeffZZYmNjMRqN2Gw2nE4n58+f5/r160yaNAmA2bNn43Q6h5QpKiqKgoICHn74YcLDw4mLi6Otrc2v8TVN4/Dhw6SlpQUk10CZ2tvbQ16rN954g+3bt2M0Gvn777/p6+ujq6srpLUaLNejjz4a8npduXKF9evX89577wHcV03q6+ux2WwAZGZmsn//fjRNC2gugBMnTrBp0yZsNhurV6/G4/EEdQ5bW1sBsNvtZGVlsWPHDhobG5kyZQqRkZE89thjpKWl4XQ6gzqH/zaiGm5RURHx8fH67x0dHUyZMoXi4mJ27txJc3Mz5eXl/PXXX0RFRen7mUwmXC7XHdujoqJwuVxDyvTiiy/qE9TW1saPP/6IwWDwa/zOzk7Gjh2L0WgMSK6BMs2YMSPktQIIDw/H4XBgtVpJSEjwe/xA12qwXDdu3Ah5vVasWMGSJUt44oknAO6rJv2vYzQaGTt2LJcvXw5orp6eHiZMmMCyZcuorKykq6uLL7/8Mqhz2NXVRUJCAhs3bmTbtm18++23tLe339NcDfd9vr8R1XD/LTY2lo0bN2IymYiIiOCdd96hoaEBn8+HwfD/5ddEBIPBMOj2QDh16hR2u538/HxiY2P9Gn+gHIHI1T/T888//8DUKi8vj6amJi5cuEBbW9sDUat/52pqagppvb7//nuio6NJSEjQtwWiJiLCQw/d/8N+oFxjxoxhy5YtxMXFYTQasdvtd63VcMzh5MmTKSkp4fHHH2fcuHHMmTMHh8Ph11wN533+lhG9Hu5vv/1GW1ub/q+JiGA0Ghk/fvxtLw5cunQJk8l0x/aOjg799MRQHDlyhLy8PAoLC7Farfz8889+jT9u3Djcbjd9fX2EhYXp+wcy04NQqzNnzuD1epkwYQIRERFYLBacTidhYWH3PP5w1GqgXLW1tURGRoasXrW1tVy6dIlZs2Zx9epVrl27hsFg8LsmJpOJjo4Oxo8fz40bN+jp6SEyMjKguZYvX85rr73GnDlzgMFrNZxz2NzcjKZp+h8CESEmJuae5mq4+0N/I/oZrohQXFzM1atX0TSN7777DrPZzKuvvsrvv//OH3/8QV9fHzU1NSQmJhITE8MjjzzCkSNHAKiqqiIxMXFIGS5cuMAHH3zA2rVrsVqtAH6PHx4eTnx8PLW1tQDs2rVrSLkGyvQg1OrcuXN88skneL1evF4v+/btIzc3N6S1GizX66+/HtJ6bd26lZqaGqqqqsjLyyMlJYXPPvvM75okJSWxa9cu4GazjI+PJzw8PKC5li1bRmlpKWfPnkVEKCsrw2w2B3UO3W43JSUleDweuru7qayspLS0lKamJi5fvkxvby91dXUkJiYG9T5/h4C+BBckycnJ+rsUduzYIenp6WI2m6W0tFTfp7GxUWw2m1gsFikqKhKfzyciIidPnpS33npL0tLSZOnSpeLxeIaUZc2aNTJp0iTJysrSf7755hu/xz937py8/fbbkp6eLna7Xa5cuRLwTKGulYiIw+GQ9PR0yczMFIfDcV/jB7JWd8v1INRLROSHH37Q3w3gb006Oztl0aJFkpGRITk5OfrjJtC5nE6nWK1WsVgsUlBQoOcK5hyuX79eZs6cKRaLRbZt2yYiIrt379Zzbd68Wd832HN4i/rGB0VRlCAZ0acUFEVRRhLVcBVFUYJENVxFUZQgUQ1XURQlSFTDVRRFCRLVcJX/vAULFnD69Om77lNQUMBXX3014GUbNmxg7969wxFNGWVG9CfNFCUQtmzZMqTrHzp0iBdeeCFAaZTRTD3DVUaFWbNm0dTUBEBNTQ2vvPIK169fB+Djjz/m66+/pri4mOzsbLKysigoKKC7uxuAlJQUjh8/DsDmzZuxWCxkZ2dTVFRESkqKPkZLSwu5ubmkpqby/vvvc+3aNcrKyjhx4gQlJSX6ermKMhjVcJVRwWw2s3//fgAOHDjAk08+SXNzMyJCQ0MDbrebsLAwKioq2L17NyaTibVr1952jAMHDlBRUUF5eTkVFRV3LIrtcrnYunUre/bsweVyUVdXx7x583j55ZfJz8/HbDYH7fYqI5M6paCMCmazmaVLl5Kfn09zczPvvvsuBw8eZMyYMTzzzDPU19fjdrtpbGwEQNM0nnrqqduO0dDQwMyZM/VlB+fNm8dPP/2kX56amkpERARwcwnMoS5zqPz3qIarjAovvfQSmqaxb98+nnvuOZKTk1myZAlGo5G0tDSqqqooLCwkKSkJuLmGq8fjue0YRqOR/p9077+K2a3Lb7m1zKCi+EOdUlBGjdTUVD7//HOmTZtGXFwc3d3dVFdXY7FYmD59OmVlZXi9Xnw+H59++inr1q277fpJSUnU1dXhdrsBKC8vv6dxw8LCAv/dV8qopBquMmqYzWZaW1uZOnUqAFOnTiUqKoro6GgWL15MTEwM2dnZZGRkICL6FyDekpCQwNy5c8nJyWH27Nm43W79FMLdpKSksG7dOiorK4fldimjh1otTFH+cfz4cVpaWpg/fz5wc+3XX375hS+++CLEyZTRQjVcRflHd3c3hYWFtLa2YjAYiI6OZs2aNTz99NOhjqaMEqrhKoqiBIk6h6soihIkquEqiqIEiWq4iqIoQaIarqIoSpCohqsoihIkquEqiqIEyf8A9cEM2U9hQ9EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create scatter plot\n",
    "g = sns.relplot(x=\"weight\", \n",
    "                y=\"horsepower\", \n",
    "                data=mpg,\n",
    "                kind=\"scatter\");\n",
    "\n",
    "# Identify plot type\n",
    "type_of_g = type(g)\n",
    "\n",
    "# Print type\n",
    "print(type_of_g)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding a title to a FacetGrid object\n",
    "In the previous exercise, we used `relplot()` with the miles per gallon dataset to create a scatter plot showing the relationship between a car's weight and its horsepower. This created a `FacetGrid` object. Now that we know what type of object it is, let's add a title to this plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create scatter plot\n",
    "g = sns.relplot(x=\"weight\", \n",
    "                y=\"horsepower\", \n",
    "                data=mpg,\n",
    "                kind=\"scatter\");\n",
    "\n",
    "# Add a title \"Car Weight vs. Horsepower\"\n",
    "g.fig.suptitle('Car Weight vs. Horsepower');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " It looks like a car's weight is positively correlated with its horsepower."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding titles and labels: Part 2\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding a title and axis labels\n",
    "Let's continue to look at the miles per gallon dataset. This time we'll create a line plot to answer the question: How does the average miles per gallon achieved by cars change over time for each of the three places of origin? To improve the readability of this plot, we'll add a title and more informative axis labels.\n",
    "\n",
    "In the code provided, we create the line plot using the `lineplot()` function. Note that `lineplot()` does not support the creation of subplots, so it returns an `AxesSubplot` object instead of an `FacetGrid` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "mpg_mean = pd.read_csv('./dataset/mpg_mean.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create line plot\n",
    "g = sns.lineplot(x=\"model_year\", y=\"mpg_mean\", \n",
    "                 data=mpg_mean,\n",
    "                 hue=\"origin\");\n",
    "\n",
    "# Add a title \"Average MPG Over Time\"\n",
    "g.set_title(\"Average MPG Over Time\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create line plot\n",
    "g = sns.lineplot(x=\"model_year\", y=\"mpg_mean\", \n",
    "                 data=mpg_mean,\n",
    "                 hue=\"origin\");\n",
    "\n",
    "# Add a title \"Average MPG Over Time\"\n",
    "g.set_title(\"Average MPG Over Time\");\n",
    "\n",
    "# Add x-axis and y-axis labels\n",
    "g.set(xlabel=\"Car Model Year\",\n",
    "      ylabel=\"Average MPG\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The average miles per gallon achieved is increasing over time for all three places of origin, but the USA is always lower than Europe and Japan."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rotating x-tick labels\n",
    "In this exercise, we'll continue looking at the miles per gallon dataset. In the code provided, we create a point plot that displays the average acceleration for cars in each of the three places of origin. Note that the \"acceleration\" variable is the time to accelerate from 0 to 60 miles per hour, in seconds. Higher values indicate slower acceleration.\n",
    "\n",
    "Let's use this plot to practice rotating the x-tick labels. Recall that the function to rotate x-tick labels is a standalone Matplotlib function and not a function applied to the plot object itself."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create point plot\n",
    "sns.catplot(x=\"origin\", \n",
    "            y=\"acceleration\", \n",
    "            data=mpg, \n",
    "            kind=\"point\", \n",
    "            join=False, \n",
    "            capsize=0.1);\n",
    "\n",
    "# Rotate x-tick labels\n",
    "plt.xticks(rotation=90);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since higher values indicate slower acceleration, it looks like cars from Japan and Europe have significantly slower acceleration compares to the USA."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Putting it all together\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Box plot with subgroups\n",
    "In this exercise, we'll look at the dataset containing responses from a survey given to young people. One of the questions asked of the young people was: \"Are you interested in having pets?\" Let's explore whether the distribution of ages of those answering \"yes\" tends to be higher or lower than those answering \"no\", controlling for gender."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "survey_data['Interested in Pets'] = survey_data['Pets'].apply(lambda x: 'Yes' if x >= 4.0 else 'No')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'Age of Those Interested in Pets vs. Not')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 628.175x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set palette to \"Blues\"\n",
    "sns.set_palette(\"Blues\")\n",
    "\n",
    "# Adjust to add subgroups based on \"Interested in Pets\"\n",
    "g = sns.catplot(x=\"Gender\",\n",
    "                y=\"Age\", data=survey_data, \n",
    "                kind=\"box\", hue='Interested in Pets', aspect=1.5)\n",
    "\n",
    "# Set title to \"Age of Those Interested in Pets vs. Not\"\n",
    "g.fig.suptitle(\"Age of Those Interested in Pets vs. Not\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After controlling for gender, it looks like the age distributions of people who are interested in pets are similar than those who aren't."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bar plot with subgroups and subplots\n",
    "In this exercise, we'll return to our young people survey dataset and investigate whether the proportion of people who like techno music (\"Likes Techno\") varies by their gender (\"Gender\") or where they live (\"Village - town\"). This exercise will give us an opportunity to practice the many things we've learned throughout this course!\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "survey_data['Likes Techno'] = survey_data['Techno'].apply(lambda x: True if x >= 4.0 else False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the figure style to \"dark\"\n",
    "plt.style.use('seaborn')\n",
    "\n",
    "# Adjust to add subplots per gender\n",
    "g = sns.catplot(x=\"Village - town\", y=\"Likes Techno\", \n",
    "                data=survey_data, kind=\"bar\",\n",
    "                col='Gender');\n",
    "\n",
    "# Add title and axis labels\n",
    "g.fig.suptitle(\"Percentage of Young People Who Like Techno\", y=1.02);\n",
    "g.set(xlabel=\"Location of Residence\", \n",
    "       ylabel=\"% Who Like Techno\");"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}