{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "afeddd85-7c0a-4dd6-9fb0-401f9e8a2211",
   "metadata": {},
   "source": [
    "# Appendix E — Seaborn tutorial\n",
    "\n",
    "\n",
    "See outline here:  \n",
    "https://docs.google.com/document/d/1fwep23-95U-w1QMPU31nOvUnUXE2X3s_Dbk5JuLlKAY/edit#bookmark=id.3i7cktuf1u3i\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dbba95c-fcfc-4e49-9bdf-dc73821b3ef1",
   "metadata": {},
   "source": [
    "In this tutorial,\n",
    "we'll learn  about Seaborn data visualizations.\n",
    "We'll discuss Seaborn plot functions\n",
    "We'll also describe the various options for customize plots' the appearance,\n",
    "add annotations, and export plots as publication-quality images.\n",
    "\n",
    "If you want to pursue a career in a data-related field,\n",
    "I highly recommend you get to know Seaborn by reading this tutorial\n",
    "and the other resources in the links section."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "114bbade-03a8-4dd4-96ac-663dd2bc07d2",
   "metadata": {},
   "source": [
    "## Seaborn overview\n",
    "\n",
    "The Seaborn library is a powerful toolbox for generating statistical data visualizations.\n",
    "Seaborn makes it very easy to visualize data stored in Pandas data frames.\n",
    "You can generate standard statistical plots like `barplot`s, `stripplot`s, `scatterplot`s,\n",
    "using a single line of code.\n",
    "We'll look at a few examples of the Seaborn functions\n",
    "for generating statistical visualizations of data stored in Pandas data frames.\n",
    "The combination of the JupyterLab computational environment\n",
    "and the Python libraries Pandas and Seaborn\n",
    "is a best-in-class toolset for doing statistics in Python.\n",
    "\n",
    "Seaborn includes numerous plot functions like `stripplot`, `scatterplot`,\n",
    "`histplot`, `boxplot`, `barplot`, and `countplot`.\n",
    "In this subsection,\n",
    "we'll show some examples of these function.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b559419b-7fe5-4e07-8f96-ff56fde511cd",
   "metadata": {},
   "source": [
    "## Basic plots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "761835cd-fa8d-4cb2-af9d-6b5cb0cb2162",
   "metadata": {},
   "source": [
    "### Line plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "46bc082b-6817-429b-b932-0b3ae5b59fe1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "adf3ff99-2d96-4321-b212-4a90d4c7c0b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "days = [1, 2, 3, 4]\n",
    "cakes = [2, 5, 3, 4]\n",
    "sns.lineplot(x=days, y=cakes);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c4097f04-654a-440b-8fa0-3f32eb895cd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # (optional) use Matplotlib axis methods to add labels\n",
    "# ax = sns.lineplot(x=days, y=cakes)\n",
    "# ax.set_xlabel(\"days\")\n",
    "# ax.set_ylabel(\"cakes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "37218c72-f6d0-4d68-a2b9-5b98d0d1511e",
   "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>days</th>\n",
       "      <th>cakes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   days  cakes\n",
       "0     1      2\n",
       "1     2      5\n",
       "2     3      3\n",
       "3     4      4"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\"days\":days, \"cakes\":cakes})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1a81e964-c18e-4737-80fc-8a608fbf6abc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['days', 'cakes'], dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c9d55c8f-7760-4e6c-b6b2-427654fc7345",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tWrXCgAED5MqnyMhIBAQEKBonaYuYRCsM7egNV3sraN0/HmsFLydrubfWp9tYniIi5ZnoxRi7hoiESKyaEpOLOf+GqkrML+k1Z5ccqdjxWg80c7NXOiSDsOdcGp5bckCefz+hG4KbuiodEhFp+PXboEduiAzN0vBYmdj09ndjYnOLsOYN8ExpiW7a2ijk5LM8RUTKYXJDVEnpOQVYc7iklcA4lW+1UB3vDGiFhvVscCn9JmZuPaV0OESkYUxuiCpp1YEE5BXq0NrLEcFNWHa5k72VOT59qmR/uBX7EhBx/qrSIRGRRjG5IaqE/KJiLI8sbdoX1lhzTfsqK7RZfTzXzU+ev7n2OLLyCpUOiYg0iMkNUSVsikpCWla+bFo3MFCbTfsq6+3+/vBxscHlGzfx7y2nlQ6HiDSIyQ3RfYgFhYv3XJTnY0IawdKc/2zuxU6Up4a2leffH0jAn2fTlA6JiDSGf6WJ7iPi/DWcTs6CraUZnumi7aZ9lSWWgj8f0kiev/XTcbmDOhFRXWFyQ3Qfi0pHbYZ38pE7YlPlvPloS/i52iIpIw//2szVU0RUd5jcEN3D2ZQs7D6bBlMTYGwol39Xha2lOWY/1RZi7vUPhxLxx5lUpUMiIo1gckN0D0v2lGy10K+1B3xdbZUOx+h0aexSnhS+/dNxZOSyPEVEtY/JDVEFxOqo9cculy//pup5o29LNK5vh5TMfHy8+aTS4RCRBjC5IarAd/viUVCkQ3vfeujo56J0OEbLxtIMnw0LkuWpn45cws5TKUqHREQqx+SG6C7yCouxYl+8PB/fvYnS4Rg9kRxOCCu5jtPXReNGboHSIRGRijG5IbqLdUcu43pOAbydbdCvtbvS4ajCa4+0QNMGdkjNysdHm1ieIqLaw+SG6A46nR6Lw0uWf78Q2hjmZvxnUhOsLUR5qq1cebb+6GX8eiJZ6ZCISKX4V5voDrvOpuJiWg4crMwxorOP0uGoSntfZ/ztoaby/B/rY+RO60RENY3JDdEdFv1Zsvx7ZFdfudM11aypfZqjuZs9rmbn44OfTygdDhGpEJMbolvEXM7A3ovXYGZqUr59ANVOeUpc45+jrmBrdJLSIRGRyjC5IbrFkvCSUZuBgZ7wqmejdDiq1danHl7qUbJ66t0NMbiWna90SESkIkxuiEolZ+RhU9QVec6mfbVvcu/maOnugGs5BXh/I8tTRFRzmNwQlVoeGYcinV5uGRDkXU/pcFTPytwMc4aXlKd+iU7C5uMliSUR0YNickMEICe/CKv2lzTtK2s2R7WvTUMnTHy4mTx/b0OM3PKCiOhBMbkhArDmUCIy84rkHki9/d2UDkdTJj3cDK08HZGeW4h3N0RDr9crHRIRGTkmN6R5xTo9lkbEyfOx3RvDVHSZozpjaW4q954yNzXBrydS5AoqIqIHweSGNG/7yWQkXM9FPVsLPNXBW+lwNKm1lxNe7dVcnoveN6lZeUqHRERGjMkNad7iPSXLv5/t6id3sCZlvPJwU7T2csSN3ELZvZjlKSKqLiY3pGlHE9JxKD4dlmamGB3sp3Q4mmZhZipXT1mYmWD7yRRsOHZZ6ZCIyEgxuSFNW1zatO/xdl5wc7RWOhzN8/dwxJTepeWpjSeQksnyFBFVHZMb0qzE67nlrf/HdWfTPkPxUo+mCGzoJFevTV/H1VNEVHVMbkjTTft0eiCseX25FJkMg3lpeUqUCn8/nYqfjrA8RURVw+SGNCkzrxA/HEyU5xy1MTwt3B3w90dayPOPNp1AUsZNpUMiIiPC5IY06YcDicjOL0JzN3v0aNFA6XDoLiaENZYbbGblFeHtn1ieIqLKY3JDmlNYrMOyiNjyDTJNTNi0z2DLU8OCZJO/3WfT8OOhkpE2IqL7YXJDmrM1JhlXMvJQ394Sg9s1VDocuodmbg54o29JeWrG5lO4fIPlKSK6PyY3pCmitLF4z0V5/ly3RrC2YNM+QzeuexN08K0ny4hv/3Sc5Skiui8mN6QpB+PScfxSBqzMTfFsN1+lw6FKMDM1wWfD2sr/Z3vOXcX3B1ieIqJ7Y3JDmrKodNTmyQ7ecLW3UjocqqQmDewxrV9Lef6vX07KHkVERBVhckOaEXs1BztOpchzLv82Pi+ENkbnRs7IKSjGWz8dh040KSIiugsmN6QZS8NjIaZr9PZ3QzM3e6XDoWqUp2Y/1RbWFqaIvHANKw8kKB0SERkoJjekCTdyC7DmcGnTvjCO2hirRvXt8Paj/vJ85pZTSLjG8hQR/RWTG9KElfsTkFeoQ2svRwQ3cVU6HHoAo4MboWtjF+QWFGPa2iiWp4joL5jckOrlFxXLfaQENu0zfqal5SlbSzPsj72O/9tb8v+WiKgMkxtSvU1RSUjLyoeHozUGBnopHQ7VAF9XW0zvX1Ke+mTbGcRdzVE6JCIyIExuSDNN+8aENJKt/EkdRnX1Q0hTV9wsZHmKiG7Hv/SkahHnr+F0cpYsYTzThU371Fae+mRoEOwszWRzxmWlpUciIoNJbmbNmiXnQkydOvWej1uzZg38/f1hbW2NwMBAbNmypc5iJONt2je8kw+cbC2UDodqmI+LLd4Z2Eqef7rtNC6mZSsdEhEZAINIbg4ePIiFCxciKCjono+LjIzEyJEjMW7cOBw9ehRDhgyRR0xMTJ3FSsbjbEqW3E1azB8eG8rl32olRuS6N6uP/CId3lgThWKWp4g0T/HkJjs7G6NGjcKiRYvg7Ox8z8fOnTsXjz76KKZNm4ZWrVphxowZ6NChA+bPn19n8ZLxWLInVn7sF+AhJ6CSOokR30+eCoK9lTmOJNyQzRqJSDl7zqUhr7BY28nNxIkTMXDgQPTp0+e+j927d+9fHtevXz95f0Xy8/ORmZl520HqJ1ZHrT92WZ5PeIijNmrXsJ4N3nuspDw1+7czOJ/K8hRRXSso0mHG5pN4bskB+VGzyc3q1atx5MgRzJw5s1KPT05Ohru7+233idvi/oqI7+3k5FR++Pj4PHDcZPi+2xcv/6G186mHDr73HhEkdRDzqnq0aCD/v7++JgpFxTqlQyLSjMTruRj2dSSWlI6cWpmbKbqCUbHkJjExEVOmTMHKlSvl5ODaMn36dGRkZJQf4ueSuonh0BX74uX5hLAmbNqnEeL/86yhgXCwNkdU4g0sKi1LElHt2haThAHz9iDqUgacbCywaHQnvD8oQK5o1Fxyc/jwYaSmpso5M+bm5vLYvXs35s2bJ8+Li/9ar/Pw8EBKSsmuzmXEbXF/RaysrODo6HjbQeq27shlXM8pkKWKfq1vH+kjdfN0ssH7jwXI8y+2n8W5lCylQyJS9RvJDzbG4KUVR5CVV4T2vvXwy+TueCRA+b+7iiU3vXv3RnR0NI4dO1Z+dOrUSU4uFudmZmZ/+Zrg4GDs3Lnztvu2b98u7ycSxDDokvCS5d9juzeGuZni08qojj3V0Ru9/N1QUMzyFFFtEV3Bhy6IxLd7S0bJX+zRBD++GAxvZ8NYvGGu1A92cHBAmzZtbrvPzs4Orq6u5fePHj0aDRs2LJ+TI8pYPXr0wJw5c+QkZDFn59ChQ/jmm28U+R3I8Ow6m4oLaTlwsDLH8E7eSodDCpWnZj4ZiEc+343jlzKw8M+LmPhwM6XDIlKNTVFXMH1dNLLzi+Bsa4HPh7fDw/5uMCQG/bY2ISEBSUlJ5bdDQkKwatUqmcy0bdsWa9euxYYNG/6SJJF2LS6dZzGyqy8crNm0T6vcHa3x4eOt5fl/dpzF6WSukiSqiTLUO+uj8er3R2Vi07mRM7ZMCTO4xEYw0YvNdzRELAUXq6bE5GLOv1GXE1cyMHBeOMxMTfDnmw/LOTekXeJP24T/O4wdp1LQ2ssRGyaGwoJlSqJquZCWjYkrj8jtbMQajVd6NsXf+7So09J/VV6/+S+dVNe0b2CgJxMbkuWpfz/ZBvVsLXDiSiYW7LqgdEhERmn90UsY9GW4TGxc7Szx7QtdMK2fv0HPaTTcyIiqIDkjDz9HXZHn48PYtI9KuDlY46PS8tS8nefk6B4RVc7NgmK8uTYKf/8hCrkFxQhu4oqtU8LwUIsGMHRMbkgVvt0bhyKdHl0auyDIu57S4ZABebytl2wJIJ4fb6w5Lpv8EdG9iTYKj88Px4+HLsky1JTezbFifFe4OdZeX7qaxOSGjF5OfhFWljbtG9+dozb01/LUP4cEylUdp5IyMf+P80qHRGTQc9V+PJSIQfPDcS41Gw0crLByXFf8/ZEWcj6jsWByQ0Zv7eFLyMwrQiNXW/RppXzzKDI84g/0jCElqyr/+8d5xFxmeYrobm8UX/8xCm+uPY68Qh3CmtfHlslhCGlWH8aGyQ0ZtWLZtK9kIvG47o0VbfdNhu2xIC852bykPBWF/CJldy0mMiSnkjLlaM26o5ch/oy+0beFnDgs3hgYIyY3ZNS2n0xBwvVcuSJmaEc27aN7+3hwa7naQ6z6+HIny1NEer0eq/YnYMhXEbiYlgN3Ryt8P6EbJvVqbtRvFpnckFFbvKdkq4VRXX1ha6lYw20yEq72VvhnaXlqwe4LcoNNIq3KyivE5NXHZGO+/CIderZsIMtQXZu4wtgxuSGjdTQhHYfi02FhZoIxwY2UDoeMRP9ATwxq6yVLmqI8JbquEmlNzOUM2btGbKUgJgq/3d8fS8d0lm8A1IDJDRmtxaVzbR5v29BolieSYfj48daob28lV4PM3XlO6XCI6rQM9X974/DkfyMRdy0XXk7W+PHFbnipR1OjLkPdickNGaXE67nYGl2y7xib9lFVOdtZ4l9PlJSnFu6+IEcBidQu42YhJq46gvc3nkBBsU6uLhV7Q3X0c4HaMLkho7Q8Mg46PeRSxVae3COMqq5faw8Maecln0csT5HaRSXewGNf7sGW6GRZyn93YCssGt0R9WwtoUZMbsjoZOYV4oeDieXLv4mqS+wcLpa6XkjLwefbzyodDlGtlKGWhMfiqa8jkXj9JrydbbDmpRCMD2siG1yqFZMbMjo/HEhEdn4RmrvZo4cR7HFChku8a535RKA8X7TnIg7HX1c6JKIacyO3AH/77jBmbD6JwmI9Hm3tgV8mh6Gdj/q3qGFyQ0alsFiHZRGx5XNt1PzOg+pGnwB3DO3gDb0sTx2XmwUSGbsjCekYOC9c9gKzNDOVG8gueLYDnGwsoAVMbsiobI1JxpWMPNS3t8Tgdg2VDodU4v1BAbJ5WezVHHz22xmlwyGqNp1OLyfJD/96Ly7fuAk/V1useyUEY0IaaerNIJMbMqracVnTvue6NYK1hZnSIZFKiHezs54MkudLI2JxIJblKTI+13MKMO7bg5i59bTcZuSxIE9sfrU72jR0gtYwuSGjcTAuHccvZcDK3BTPdvNVOhxSmYf93TC8U0l56s21UcgtKFI6JKJKEwn5gLl78MeZNFiam+LfTwTiy5Ht4WCtjTLUnZjckNEQEz6FJzt4q6aLJhmWdx8LgKeTtWxu9uk2lqfIOMpQX/1xHiMX7UNyZh6a1LfDxomheKarr6bKUHdickNGQcyF2HEqRZ5z+TfVFkdrC8waGlTeS2nfxWtKh0RUoavZ+Riz7ABm/3pGbifyRPuG2PRqd/b+YnJDxmJpeKwsF/Tyd0MzN3ulwyEVE+0FRnbxkefT1kYhJ5/lKTI8ey9ck2WoPeeuwtrCFJ8ODcLnw9vCzoobCAtMbsgoejWsOVzStI9bLVBdeGdAKzSsZyObns3aelrpcIjKiRGa/+w4i1GL9yE1K1/2+/p5UncM7+yj6TLUnZjckMFbuT8BeYU6BHg6IriJq9LhkAaISZiflJanvtsXj8jzV5UOiQipWXl4bsl+/GfHObltyLCO3tg4KRQt3B2UDs3gMLkhg5ZfVCznPggTHmLTPqo73ZvXx6iuJavypq09LrtiEykl/NxVWYaKvHANtpZmsgQ1e1hb2FqyDHU3TG7IoG2KSkJaVr5ssDYw0EvpcEhjpg9oJffiEc3Q/r3llNLhkAYVFevw2a9n8NzS/biaXQB/DwdZhhKrRqliTG7IKJr2PR/SWPZuIKpL9lbm+PSpkvLUqv0J2HMuTemQSEOSM/LwzKL9mP/HebmgYmQXX2yYGMpFFZXAVwsyWBHnr+F0cpYcgn2mC5v2kTJCmtbHmGA/ef7W2uPIyitUOiTSgD/OpGLAvD04EHcddpZmmDeyPWY+GcjO7JXE5IYM1uLwklGb4Z184GSrzS6bZBje6u8PXxdbua/Zv35heYpqd3PgmVtP4YVlB+V2Cq29HLF5chgeb8uyfFUwuSGDdC4lC7vOpEHMH34htJHS4ZDGiUmbs0vLU6sPJmLXmVSlQyIVEnO7nv5mHxbuLnljNzrYDz+9HILG9e2UDs3oMLkhg7QkPFZ+7BfgAT9X/sMm5XVt4lqeaL/9UzQybrI8RTVnx8kUuRrqcHw6HKzM8d9RHfDx4DYsQ1UTkxsyOGJ11Lqjl+U5m/aRIXmznz8audrKPXxmbD6pdDikAgVFOvxz80mM/79DMmEO8nbCL5PDMCDQU+nQjBqTGzI4K/bFy3/w7XzqoaOfs9LhEJWzsTTDZ8PaynLp2sOX8Pvpkv3OiKoj8Xouhi3ci8WlI9VjQxtj7Ush8HW1VTo0o8fkhgxKXmGx7AhbNmrDpn1kaDo1csG40Mb/K0/lsjxFVbctJkmuhopKvAFHa3N881xHvD8ogC0vaki1ruK2bdsQHh5efvurr75Cu3bt8MwzzyA9Pb2mYiMNWn/0slwhIPb1ebS1h9LhEN3VG/1aokl9O7m3z0ebTigdDhlZ1/UPNsbgpRVHkJVXhPa+9bBlShj68u+d8snNtGnTkJmZKc+jo6Px+uuvY8CAAYiNjcVrr71WsxGSZuh0/2vaJyZumpvxHQwZJjHJ87PhbWFqAjk/bPtJlqfo/uKu5mDogkh8u7dkdPrFh5rgxxeD4e3MMlRNq9amFCKJCQgIkOc//fQTHnvsMfz73//GkSNHZJJDVB27z6bhQlqOXCkworOP0uEQ3VMHX2dMeKiJXLb7zvpodPJzhrOdpdJhkYHafPyKLGOKPcqcbS0wZ3hb9PJ3Vzos1arWW2NLS0vk5ubK8x07dqBv377y3MXFpXxEh6iqFpWO2jzdxUfuykxk6P7ep4VshS9W+H3I8hRVMI9QJL+TVh2ViU3nRs6yDMXExgCTm+7du8vy04wZM3DgwAEMHDhQ3n/27Fl4e3MzL6q6E1cy5G63ZqYmeL50siaRUZSnhpWUpzYeuyIniRKVuZCWjSFfRch9ycTaiIkPN8X3E7rB08lG6dBUr1rJzfz582Fubo61a9diwYIFaNiwobx/69atePTRR2s6RtKAJXtKlkKK3g5iMjGRsRAtC17q0VSev7shRk6IJ9pw9DIGfRku98dztbPEty90wbR+/pxLWEdM9GLrZQ0RZTMnJydkZGTA0dFR6XCodOfb7p/8jiKdHj9PCkWQdz2lQyKq8goY8UJ2NiUbjwV5Yv4zHZQOiRRys6AYH/58Aj8cSpS3uzVxwdyn28Pd0Vrp0DT1+l3tFPLChQt49913MXLkSKSmppaP3Jw4wbozVc23e+NkYtOlkQsTGzJKVuZmmDOsnSyrbj6ehF+Oszyl1T3xBn8VLhMbUYaa0rs5Vo7vxsRGAdVKbnbv3o3AwEDs378f69atQ3Z2trw/KioKH3zwQU3HSCqWk1+Elbc07SMyVoHeTnilZ0l56r2NMbiana90SFSH1hxKxOPzI+ToXQMHK6wc1xV/f6SFTHjJSJKbt99+G//85z+xfft2uXKqTK9evbBv376ajI9UTrSwz8wrkvv19GnF1QNk3F7t1Rz+Hg5y3s17G2Kgsaq/Zt+gvfbjMUxbexw3C4vRvVl9bJkchpBm9ZUOTdOqldyIxn1PPPHEX+53c3PD1atXayIu0oBinb589+9x3RvDlO9wyMiJ1vli9ZS5qQm2xiRjE8tTqnY6OROPzw/HuiOX5Yq5N/q2wLdju8iRGzLC5KZevXpISvrrP9qjR4+Wr5wiuh/R1TXhei7q2VpgaEe2ECB1aNPQCZN6NZPn72+MQWpWntIhUQ0TI3LfH0jA4PkRsvGou6OVXOI9qVdzlqGMObl5+umn8dZbbyE5OVlubKjT6RAREYE33ngDo0ePrvT3EcvIg4KC5KxncQQHB8tJyRVZvny5/Hm3HtbWnKhlrMq2WhjV1Re2ltVqlk1kkCY+3AwBno64kVuIf6xneUpNsvIKMXn1MUxfF438Ih16tGggy1Bdm7gqHRo9aHIjtlrw9/eHj4+PnEwstmJ46KGHEBISIldQVZZo+Ddr1iwcPnwYhw4dknN2Bg8efM8VVyIJEqNGZUd8fMlkVDIuRxPScSg+HRZmJhgT3EjpcIhqlIVZSXlKPL/FCKVo8EfGL+Zyhlzyvynqihyhebu/P5Y93xmu9ixDGZpqvV0Wk4gXLVqE999/X86/EQlO+/bt0bx5c9y8eRM2NpVrwjZo0KDbbv/rX/+SozliUnLr1q3v+jVitMbDg7unGrvFpXNtHm/bEG5cJkkqFODliMm9mmPO9rP44OcTCGnqyue6kRIjbyv2xWPG5lMoKNbBy8kaXz7THh39XJQOjWpy5Gby5Mnyoxi5ERtlDh8+XCY2OTk51d44s7i4GKtXr5bfQ5SnKiISKT8/P/mz7zfKI+Tn58vGP7cepKzE67nYGl0yZ4vLv0nNXurZFIENnZBxs1DuL8TylPHJzCvExFVH8N7GEzKx6dPKDb9MDmNio8bk5pdffvlLPxuRlIitF4qKiqr0vcTIj729PaysrPDSSy9h/fr15TuO36lly5ZYunQpNm7ciBUrVsi5PqIUdunSpQq//8yZM2VHw7JDJEWkrOWRcdDpIZdMtvJkl2hSf3nK0swUO06lylU1ZDyOX7qBgfP2YEt0slwB9+7AVlg0uhN3f1fr9guiO3FYWBjefPNNTJ06FVlZWejXr5/cb0pMCLazs6v09yooKEBCQoJspyz2qlq8eLFsElhRgnOrwsJCtGrVSnZJFpt4VjRyI44yYuRGJDjcfkG5d0EhM3+Xu+Muf6EzerZ0Uzokolr31R/nMfvXM3CwNsf2v/eAhxPLU4ZMvCwui4jDzK2nUFish7ezjdxSQ+wjRsax/UK15tw0bdoU27Ztw8MPPwxTU1N8//33cuRFjOhUJbEpm7/TrFnJssmOHTvi4MGDmDt3LhYuXHjfr7WwsJBzfc6fP1/hY0Rc4iDD8MOBRJnYNHezl6sMiLTgxYea4LcTyYi6lIG31x2Xk1DF/EEyPBm5hZi2Ngq/nUyRtx9t7YFPngqCk42F0qFRFVR7bymxhHvz5s145513YGtrW+URm4qIUtOtIy33m6cjylqenp4P/HOp9hUV67AsIrZ8rg3/uJNWmJeVp8xNsetMGtYcqriUTso5kpCOAfP2yMRGlBI/erw1FjzbgYmNEar0yI0YIbnbi5EYFbly5QpCQ0PL7zty5Eilvuf06dPRv39/+Pr6ytLWqlWrsGvXLvz666/y86JnjmgKKObNCB9//DG6desmR3pu3LiB2bNny6Xg48ePr+yvQQraEpOMKxl5qG9vicHt2OyRtKW5uwNef6QFZm49jRmbT6J78/rwqle5laVUu3Q6PRaHX8Sn287ITXz9XG0xf2QHuV8YqTy5GTJkSI3/cLGbuEhgRL8aUUcTo0EisXnkkUfk58VcHFH2KpOeno4JEybI5oHOzs6yjBUZGVmp+TmkfA27rGnfc90awdrCTOmQiOrc+LAm2HYiGUcTbuCtn47j/8Z24QimwsQ+YG+sicLvp1Pl7YFBnpj1ZCAcrDlao7kJxVqZkEQ150DsdQxfuBdW5qaIfLsXm16RZl1Iy8aAuXtkd9uZTwZiZBdfpUPSrINx1zH5+6NIysiTJcMPBgXgmS6+TDhV8Ppd7Tk3RFVRNmrzZAdvJjakaU0b2GNav5by/J+bT+JSeq7SIWmyDCVWsD39zT6Z2DSpb4cNr4RiVFc/JjYqUa3kRkzk/eyzz9ClSxfZLdjFxeW2g+hWsVdzsP1USvnu30Ra90JoY3Tyc0ZOQbEsT2lsAF1RV7PzMWbZAbk0v1inx5B2Xvj51e6yozRpPLn56KOP8Pnnn2PEiBFyeOi1117Dk08+KefHfPjhhzUfJRk1sUJK/O3u5e+GZm72SodDpDixL9HsYW1hbWGKiPPXsHJ/gtIhacLeC9dkSXDPuavy2n86NAhfjGgHeytu3Ks21UpuVq5cKfeWev3112XjPtFETzTfE3tNiX2hiMrcyC0oX/Y6nqM2ROUa17fDm/385fm/t5yS25JQ7RAjNHN3nMOoxfuQmpUv32RtnNgdwzv7sAylUtVKbsRqpcDAQHkutk4QozfCY489Jhv5EZUR70hvFhYjwNMRwU1dlQ6HyKA8H9IIXRq5ILegWDaOE3NBqGalZuXhuSX78cWOs3Lbl2EdvfHzpFC09HBQOjQytOTG29tbLt8u61b822+/yXPRXZjdgKlMQZEO30bGyXM27SP6K1NZngqCjYUZ9l28ju/2xSsdkqqEn7uKAXPDEXnhmrzGnw9vK8uBtpYsQ6ldtZKbJ554Ajt37pTnr776Kt577z25K7joWTN27NiajpGM1KaoK3II2N3RCo8FeSkdDpFB8nO1w/QBJeWpWVtPI/5ajtIhqaIb+pzfzuC5pfvlBGJ/DwdserW7XK1J2lAjfW7EPBvRTE8kOIMGDYIhY5+buiGeVv3n7sHp5Cy8+WhLvNKzZP8wIvorUY56ZvE+OXojylSr/9ZNjupQ1SVn5GHy6qOyt5YwsosPPhjUmo1DVaDW+9yI7RCWLl1afltsiSBWTKWlpeGTTz6pzrcklRHDwCKxEUPBo7r4KR0OkeGXp54S5RIzHIi7juWl5Vyqml1nUuXeUCKxsbM0w9yn22Hmk0FMbDSoWsmN2LHb379kGPVWrVu3xtdff10TcZGRW1TatG94J2842bKNOdH9+LjY4p0BreT5p7+elv2hqHIKi3WypPf8soNyOwWxgGHz5DDuYadh1V4tdbeduBs0aFA+0Zi061xKltz5WMwfHsvl30SVNqqrL7o3q4+8Qh2mrYmSS5jp3q7cuCk7DX+9+4K8/Vw3P6x7JUQutSftqlZy4+Pjg4iIiL/cL+7z8uLEUa1bEh4rP/YNcJeTJYmocsSKwllDA2VTuUPx6bIBJlVsx8kUWYY6HJ8OBytz/HdUB8wY0oZlKKr8ruC3EjtzT506FYWFhejVq5e8T6yeevPNN2VjP9IusTJh3dHL8nxCWBOlwyEyOt7OtvjHwFaYvi5abhHQsyU7e9+tzcSn205jcekbqSBvJ8wf2QG+rrZKh0bGnNxMmzYN165dwyuvvIKCggJ5n7W1Nd566y1Mnz69pmMkI/Ld3nj5h6etTz109HNWOhwio/R0Zx9siU6S2wSI5n5rXwqRWzYQZCfnSd8fRVTiDXl7bGhjvNW/JazMOVpDNbQUPDs7G6dOnYKNjY1cBm4MDfy4FLz25BUWI2TW73JC3/xn2rO3DdEDziXp98WfyMovwvT+/nixR1No3baYZLy5NgqZeUVwtDbHZ8Paom9rD6XDIrUsBS8jtl7o3Lkz2rRpYxSJDdWu9Ucvy8SmYT0bPMo/OEQPxKueDd57LECez9l+Vk7U16r8omJ8+PMJvLTisExs2vvWw5YpYUxsqHaSG6Jbm5AtLl3+/UJoI5ib8alF9KCGdfJGz5YNZKn3jTVRsvOu1oiOzU8t2Fve++dvDzXBjy8Gy7lJRBXhKxDViN1n03AhLUeuWBjR2UfpcIjUs3rqySA4WJsj6lIGFv5Z8gZCKzYfv4KB88IRfTkDzrYWWPp8J9kLyIJvnug++AyhGm3a93QXHzhYs2kfUU3xcLLGh4Nay/P/7DiLM8lZmpi/94/10Zi06iiy84vQyc9ZlqF6+bsrHRoZCSY39MBOXMmQ2y2I1RzPh7JpH1FNe7JDQ/Rp5YbCYr0sT4mOvGp1MS0bT/w3Eiv3J8jbr/RsKvfa8nSyUTo0MiJMbuiBLdlT0mtiQKCnnExMRDVfnvr3E4FwsrGQJZqvd5V041WbDUcv47Evw3EqKROudpb4dmwXvPmoP+fwUZXxGUMPvAPvz1FX5PmEMI7aENUWN0drfPR4SXlq3u/ncPJKJtTiZkEx3lp7HFN/OIbcgmJ0a+Iiy1A9WjRQOjQyUkxu6IF8uzcORTo9ujRyQZB3PaXDIVK1we285LYmaipPnU/NwpCvIvDDoUS5H93k3s2xcnw3uDtaKx0aGTEmN1RtOflFWLkvXp6P56gNUZ2Up/71RKBcOXQyKRNf/XEexmzt4UsY9GUEzqRkob69FVaO64rXHmnBbsz0wJjc0AP9YRINtRq52qJ3K65iIKoLDRys8NHgNvJ8/u/nEXM5A8Ymt6AIr/14TI4+3Swsljuhb50ShpBm9ZUOjVSCyQ1VS7FOj6WlOxaP696Y77SI6tCgIE/0b+MhS8IiQRBN/ozF6eRMDPoyHOuOXIb4s/H6Iy3kxGGRtBHVFCY3VC3bT6Yg/lquXL0xtKO30uEQaa48NWNIG7jYWeJ0cha+/P0cDJ3YxnD1gQQMnh8hG366O1ph1YRueLV3c745ohrH5IaqZUl4SdO+Z7v5wtayWpvLE9EDEHNUZpSWp/676wKOXyrZJdsQiUZ8U1Yfw9vropFfpJOroLZMDkO3Jq5Kh0YqxeSGquxY4g0cjEuHhZkJRgc3UjocIs0aGOSJx4I8ZZlYlKfEBpOG2ORTlKFEywgxQvPWo/5Y9nxnuNqzDEW1h8kNVVnZBpmPt23I5ZpECvt4cBvUt7fE2ZRszN1xzqDKUN/ti5fdhmOv5sDLyRo/vtgNL/dsClOWoaiWMbmhKrmUnoutMcnlE4mJSFli3s0/hwTK8693X5Ajq0rLzCuU+0K9tyFGTnYWW0f8MjkMHf1clA6NNILJDVXJ8og4OQQulm4GeDkqHQ4RAXi0jYds8KfTA6//eExuPKkUMffnsXnh+CU6CeamJnh3YCssGt0JznaWisVE2sPkhqr0bmz1wUR5Po5N+4gMitg5XCynFiuRvthxVpEy1LKIWAxdEImE67lyn7k1LwVjfFgTubqLqC4xuaFK+/Fgolz10NzNHj255wuRQREjI2JzTWHRnxdxOD69zn52Rm4hXvzuMD7adFJuDdGvtbtcDdXe17nOYiC6FZMbqpSiYh2WRcSVz7XhOzEiw/NIgDuebN9QlqemrYmqk/LU0YR0DJi3B7+dTIGlmSk+HBSAr5/tCCdbi1r/2UQVYXJDlSImEV++cROudpYY0r6h0uEQUQU+GNQabg5WuHg1B5/9eqZWy1BihGjY13vl3wZfF1v89HIIng/lmx9SHpMbqtQfsbLl388F+8HawkzpkIioAmLEZNbQkvLUkohYHIy7XuM/Iz2nAOO/PYR/bTklt4AQ/XY2T+6OQG+nGv9ZRNXB5Ibu61B8OqIuZcDS3BTPdfNTOhwiuo9e/u54qqM39KXlqZsFNVeeOhR3XZahdp5OlX8T/jmkDeaPbA9Ha5ahyHAwuaH7EkPPwtAODdlVlMhIvPdYADwcrRF3LRef/nr6gb+fTqfHf3edx4hv9iEpIw9N6tthwyuheLabH8tQZHCY3NA9xV3NwfZTKfKcTfuIjIfY1LasPCUWA+y7eK3a3+tqdj6eX34Qn247I/tcDWnnhZ9f7c5eV2SwmNzQPS2NiJVD2w+3bIBmbg5Kh0NEVdCzpRue7uwjz99cexy5BUVV/h4iKRowdw/+PJsGawtTfDI0EF+MaAd7K26YS4aLyQ1V6EZuAdYcuiTPJ4Q1UTocIqqGfwxsJfd1Eo31Ptla+fKUGKGZt/Mcnlm0D6lZ+WjmZo+NE7tjRGdflqHI4DG5oQqt3J+Am4XFaOXpiOCmrkqHQ0TV4GBtgU+eCpLn3+6NR+SFq/f9mtSsPIxeuh+fbz8re+aIyck/TwpFSw+O3pJxYHJDdyU2u/s2sqRp34Qw9q0gMmZhzRvgma6+5eUp0Wm8IhHnr2LA3HBEnL8GGwszzBnWFp8NawtbS5ahyHgwuaG72hR1RQ5Fuzta4bEgL6XDIaIH9M6AVnK/p0vpNzFzy6m7lqE+/+0Mnl2yX04gbunugE2vhmJoR29F4iUy2uRmwYIFCAoKgqOjozyCg4OxdevWe37NmjVr4O/vD2trawQGBmLLli11Fq9WyM6jpU37xoQ0kr0siMi4iQnAs0vLU6LkHH7uf+WplMw8Obdm3u/n5QKCkV18sHFSKBcRkNFS9FXL29sbs2bNwuHDh3Ho0CH06tULgwcPxokTJ+76+MjISIwcORLjxo3D0aNHMWTIEHnExMTUeexqFnnhGk4nZ8kh6VFd2LSPSC1CmtUvb8T51k/HkZVXiF1nUtF/7h7sj70OO0szzH26HWY+GcRO5GTUTPTibboBcXFxwezZs2UCc6cRI0YgJycHmzdvLr+vW7duaNeuHb7++utKff/MzEw4OTkhIyNDjhbRXz2/7AB2nUnDmGA/fDS4jdLhEFENyskvwqNz/0Ti9Zvw93CQb2SEAE9HzH+mPZo0sFc6RKIHfv02mHpDcXExVq9eLZMXUZ66m71796JPnz633devXz95f0Xy8/PlBbn1oIqdS8mSiY2YPzyWTfuIVMdOlqfayvOyxEaM5qx7JYSJDamG4tPfo6OjZTKTl5cHe3t7rF+/HgEBAXd9bHJyMtzd3W+7T9wW91dk5syZ+Oijj2o8brVaEh4rP/YNcIefq53S4RBRLejWxBXT+rXE2sOX8EbflnLjSyI1UXzkpmXLljh27Bj279+Pl19+GWPGjMHJkydr7PtPnz5dDmGVHYmJiTX2vdVGrJBYd/SyPGfTPiJ1m/hwM/zxRk8mNqRKio/cWFpaolmzZvK8Y8eOOHjwIObOnYuFCxf+5bEeHh5ISSnZ56iMuC3ur4iVlZU86P6+2xsv+9u09amHjn7OSodDRERknCM3d9LpdHKezN2I8tXOnTtvu2/79u0VztGhyssrLMaKffHynE37iIjImCk6ciNKRv3794evry+ysrKwatUq7Nq1C7/++qv8/OjRo9GwYUM5b0aYMmUKevTogTlz5mDgwIFyArJYQv7NN98o+Wuowvqjl3Etp0A2+Xq0dcUjYURERIZO0eQmNTVVJjBJSUlyeZdo6CcSm0ceeUR+PiEhAaam/xtcCgkJkQnQu+++i3feeQfNmzfHhg0b0KYNlys/CJ1OXz6R+IXQRjA3M7gBPSIiIuPtc1Pb2Ofmr/44nYoXlh+Eg5U5Iqf3khvtERERGRKj7HNDylkcXrLVwtNdfJjYEBGR0WNyo3EnrmTI3X/NTE3wfCib9hERkfFjcqNxZXNtBgR6ysnERERExo7JjYaJnYA3RV2R5+O51QIREakEkxsN+zYyDoXFenRp5CIb9xEREakBkxuNyi0owsr9CfJ8XBhHbYiISD2Y3GiU2DAv42YhGrnaok+r2zcjJSIiMmZMbjSo+JamfWO7N5YrpYiIiNSCyY0G7TiVgvhruXCyscBTHb2VDoeIiKhGMbnRoMV7Spr2jerqC1tLxTeGJyIiqlFMbjTmWOINHIxLh4WZCcaENFI6HCIiohrH5EajozaD2nrB3dFa6XCIiIhqHJMbDbmUnoutMcnyfHz3JkqHQ0REVCuY3GjI8og4uVIqtJkrAry4IzoREakTkxuNyMorxOqDifJ8fBhHbYiISL2Y3GjEDwcTkZ1fhGZu9ujRvIHS4RAREdUaJjcaUFSsw7KIuPINMk3ZtI+IiFSMyY0GiEnEl2/chKudJYa0b6h0OERERLWKyY3K6fX68uXfzwX7wdrCTOmQiIiIahWTG5U7FJ+OqEsZsDQ3xXPd/JQOh4iIqNYxuVG5RX+WjNoM7dAQrvZWSodDRERU65jcqFjc1RxsP5Uiz8d1b6x0OERERHWCyY2KLY2IhV4PPNyyAZq5OSgdDhERUZ1gcqNSN3ILsObQJXk+gU37iIhIQ5jcqNTK/Qm4WViMVp6OCG7qqnQ4REREdYbJjQoVFOnwbWRJ074JYY1hYsKmfUREpB1MblRoU9QVpGblw93RCo8FeSkdDhERUZ1icqPGpn3hsfJ8TEgj2d+GiIhIS/jKpzKRF67hVFImbCzM8EwXX6XDISIiqnNMblSmbKuF4Z28Uc/WUulwiIiI6hyTGxU5n5qFP86kQcwffiGUTfuIiEibmNyoyJLSuTZ9A9zRqL6d0uEQEREpgsmNSlzNzsdPRy7L8/Fs2kdERBrG5EYlVuyLl/1t2vrUQyc/Z6XDISIiUgyTGxXIKyzGd3vj5fn47mzaR0RE2sbkRgU2HL2MazkFaFjPBv3beCgdDhERkaKY3Bg5ne5/TfteCG0EczP+LyUiIm3jK6GR230uDedTs2FvZY4RnX2UDoeIiEhxTG5U0rTv6c4+cLC2UDocIiIixTG5MWInr2Qi4vw1mJma4PnQRkqHQ0REZBCY3BixxeElozZiErG3s63S4RARERkEJjdGKiUzD5uirshzNu0jIiL6HyY3RurbyDgUFuvRuZEz2vnUUzocIiIig8HkxgjlFhRh5f4Eec5RGyIiotsxuTFCaw9fQsbNQvi52qJPK3elwyEiIjIoiiY3M2fOROfOneHg4AA3NzcMGTIEZ86cuefXLF++XG4vcOthbW0NrSjW6ct3/x7XvbFcKUVEREQGktzs3r0bEydOxL59+7B9+3YUFhaib9++yMnJuefXOTo6IikpqfyIjy/ZV0kLdpxKQfy1XDjZWOCpjt5Kh0NERGRwzJX84du2bfvLqIwYwTl8+DAeeuihCr9OjNZ4eHhoumnfqK6+sLVU9H8fERGRQTKoOTcZGRnyo4uLyz0fl52dDT8/P/j4+GDw4ME4ceJEhY/Nz89HZmbmbYexOpZ4Awfj0mFhZoIxIWzaR0REZNDJjU6nw9SpUxEaGoo2bdpU+LiWLVti6dKl2LhxI1asWCG/LiQkBJcuXapwXo+Tk1P5IRIiYx+1GdTWC+6O2plnREREVBUmer1eDwPw8ssvY+vWrQgPD4e3d+Xnkoh5Oq1atcLIkSMxY8aMu47ciKOMGLkRCY4YJRJzd4zFpfRc9Ji9S04o3jI5DAFexhM7ERHRgxKv32KQojKv3wYxaWPSpEnYvHkz/vzzzyolNoKFhQXat2+P8+fP3/XzVlZW8jB2yyPiZGIT2syViQ0REZGhlqXEoJFIbNavX4/ff/8djRs3rvL3KC4uRnR0NDw9PaFWWXmFWH0wUZ6zaR8REREMd+RGLANftWqVnD8jet0kJyfL+8Wwk42NjTwfPXo0GjZsKOfOCB9//DG6deuGZs2a4caNG5g9e7ZcCj5+/Hio1Q8HE5GdX4Rmbvbo0byB0uEQEREZNEWTmwULFsiPPXv2vO3+ZcuW4fnnn5fnCQkJMDX93wBTeno6JkyYIBMhZ2dndOzYEZGRkQgICIAaFRXrsCwiTp6P794YpmzaR0REZBwTig1xQpIhEDt/v/r9UbjaWSLi7V6wtjBTOiQiIiKDfv02mKXg9Fci7yxb/v1csB8TGyIiokpgcmPADsWnI+pSBizNTfFsNz+lwyEiIjIKTG4MWNmozdAODVHf3viXsxMREdUFJjcGKu5qDn47mVK++zcRERFVDpMbA7UsIhZiqvfDLRugmZuD0uEQEREZDSY3BuhGbgF+PFSyVxab9hEREVUNkxsDtOpAAm4WFqOVpyNCmroqHQ4REZFRYXJjYAqKdPg28n9N+0xM2LSPiIioKpjcGJjNx68gJTMfbg5WGNTWS+lwiIiIjA6TGwNr2rdoT6w8HxPSSPa3ISIioqrhq6cB2XvhGk4lZcLGwgyjuvoqHQ4REZFRYnJjQBaVNu0b1skb9WwtlQ6HiIjIKDG5MRDnU7Pwx5k0iPnDY0PZtI+IiKi6mNwYiCXhJXNtHmnljkb17ZQOh4iIyGgxuTEA17Lz8dORy/J8wkNs2kdERPQgmNwYgO/2xcv+Nm29ndDJz1npcIiIiIwakxuF5RUW47u98eVbLbBpHxER0YNhcqOwDUcv41pOARrWs0H/Nh5Kh0NERGT0mNwoSKfTY3HpROIXQhvB3Iz/O4iIiB4UX00VtPtcGs6nZsPeyhzDO/soHQ4REZEqMLlR0OLSpn1Pd/aBo7WF0uEQERGpApMbhZy8komI89dgZmqC50MbKR0OERGRajC5Ucji8JJRGzGJ2NvZVulwiIiIVIPJjQJSMvOwKepK+fJvIiIiqjlMbhTwbWQcCov16NzIGe186ikdDhERkaowualjuQVFWLk/QZ5z1IaIiKjmMbmpY2sPX0LGzUL4udqiTyt3pcMhIiJSHSY3dahYp8fS0qZ947o3liuliIiIqGYxualDO06lIO5aLpxsLPBUR2+lwyEiIlIlJjd1aMmeklGbUV19YWtprnQ4REREqsTkpo5EJd7AgbjrsDAzwZgQNu0jIiKqLUxu6kjZBpmD2nrB3dFa6XCIiIhUi8lNHbh84ya2RCfJ8/HdufybiIioNjG5qQPLI2LlSqnQZq4I8HJUOhwiIiJVY3JTy7LyCrH6QKI856gNERFR7WNyU8t+OJiIrPwiNHOzR48WDZQOh4iISPWY3NSiomIdlkXElTftM2XTPiIiolrH5KYWbTuRLCcTu9pZ4on2DZUOh4iISBOY3NQSvV6PRaVN+57t5gdrCzOlQyIiItIEJje15HB8umzcZ2luiueC/ZQOh4iISDOY3NSSRXsuyo9Ptm+I+vZWSodDRESkGUxuakH8tRz8djKlfCIxERER1R0mN7VgaXgs9HqgZ8sGaO7uoHQ4REREmsLkpoZl5Bbix0OX5PmEMDbtIyIiqmtMbmrYygPxuFlYDH8PB4Q0dVU6HCIiIs1RNLmZOXMmOnfuDAcHB7i5uWHIkCE4c+bMfb9uzZo18Pf3h7W1NQIDA7FlyxYYgoIiHb6NjCsftTExYdM+IiIiTSU3u3fvxsSJE7Fv3z5s374dhYWF6Nu3L3Jycir8msjISIwcORLjxo3D0aNHZUIkjpiYGCht8/ErSMnMh5uDFQa19VI6HCIiIk0y0YtucwYiLS1NjuCIpOehhx6662NGjBghk5/NmzeX39etWze0a9cOX3/99X1/RmZmJpycnJCRkQFHx5rboVtcxgHzwnEqKRPT+rXExIeb1dj3JiIi0rqqvH4b1JwbEbDg4uJS4WP27t2LPn363HZfv3795P13k5+fLy/IrUdt2HvhmkxsbCzMMKqrb638DCIiIro/g0ludDodpk6ditDQULRp06bCxyUnJ8Pd3f22+8RtcX9F83pEpld2+Pj41HjsMq7MPDham2NYJ2/Us7WslZ9BRERE92cOAyHm3oh5M+Hh4TX6fadPn47XXnut/LYYuamNBOfJDt7o19pDTiomIiIijSc3kyZNknNo/vzzT3h7e9/zsR4eHkhJKen+W0bcFvffjZWVlTzqgp2VOey40wIREZF2y1JiEq5IbNavX4/ff/8djRvff6uC4OBg7Ny587b7xEorcT8RERGRudKlqFWrVmHjxo2y103ZvBkxN8bGxkaejx49Gg0bNpRzZ4QpU6agR48emDNnDgYOHIjVq1fj0KFD+Oabb5T8VYiIiMhAKDpys2DBArlCqmfPnvD09Cw/fvjhh/LHJCQkICkpqfx2SEiITIhEMtO2bVusXbsWGzZsuOckZCIiItIOg+pzUxdqq88NERER1R6j7XNDRERE9KCY3BAREZGqMLkhIiIiVWFyQ0RERKrC5IaIiIhUhckNERERqQqTGyIiIlIVJjdERESkKkxuiIiISFUMYlfwulTWkFl0OiQiIiLjUPa6XZmNFTSX3GRlZcmPPj4+SodCRERE1XgdF9sw3Ivm9pbS6XS4cuWK3IXcxMSkxrNKkTQlJiZy36r74LWqPF6ryuO1qjxeq8rjtTKM6yXSFZHYeHl5wdT03rNqNDdyIy6It7d3rf4M8T+T/wAqh9eq8nitKo/XqvJ4rSqP10r563W/EZsynFBMREREqsLkhoiIiFSFyU0NsrKywgcffCA/0r3xWlUer1Xl8VpVHq9V5fFaGd/10tyEYiIiIlI3jtwQERGRqjC5ISIiIlVhckNERESqwuSGiIiIVIXJTSX9+eefGDRokOyMKDobb9iw4b5fs2vXLnTo0EHOGG/WrBmWL18OLajqtRLXSTzuziM5ORlqN3PmTHTu3Fl2zHZzc8OQIUNw5syZ+37dmjVr4O/vD2trawQGBmLLli1Qu+pcK/Fv7s7nlbhmardgwQIEBQWVN1ELDg7G1q1b7/k1WnxOVfd6afV5dadZs2bJ333q1KkwtOcWk5tKysnJQdu2bfHVV19V6vGxsbEYOHAgHn74YRw7dkz+zx8/fjx+/fVXqF1Vr1UZ8UKVlJRUfogXMLXbvXs3Jk6ciH379mH79u0oLCxE37595TWsSGRkJEaOHIlx48bh6NGj8kVeHDExMVCz6lwrQbxY3fq8io+Ph9qJLuzihefw4cM4dOgQevXqhcGDB+PEiRN3fbxWn1PVvV5afV7d6uDBg1i4cKFMCu9FseeWWApOVSMu2/r16+/5mDfffFPfunXr2+4bMWKEvl+/fnotqcy1+uOPP+Tj0tPT9VqXmpoqr8Xu3bsrfMzw4cP1AwcOvO2+rl276l988UW9llTmWi1btkzv5ORUp3EZKmdnZ/3ixYvv+jk+p6p2vbT+vMrKytI3b95cv337dn2PHj30U6ZMqfCxSj23OHJTS/bu3Ys+ffrcdl+/fv3k/XR37dq1g6enJx555BFERERAizIyMuRHFxeXCh/D51blr5WQnZ0NPz8/uZHf/d6Nq1FxcTFWr14tR7hEueVu+Jyq2vXS+vNq4sSJsjJx53PGkJ5bmts4s66I+SLu7u633Sdui91Sb968CRsbG8ViMzQiofn666/RqVMn5OfnY/HixejZsyf2798v5yxpacd6Ub4MDQ1FmzZtqvzc0sIcpapeq5YtW2Lp0qVy6FwkQ5999hlCQkLkC1Ftb6CrtOjoaPninJeXB3t7e6xfvx4BAQF3fSyfU1W7Xlp+Xq1evRpHjhyRZanKUOq5xeSGFCf+UIijjPgjceHCBXzxxRf47rvvoKV3Q6IOHR4ernQoqrlW4sXq1nff4rnVqlUrOVdgxowZUDPxb0rM9xMvvmvXrsWYMWPkvKWKXrC1rirXS6vPq8TEREyZMkXOeTP0CdRMbmqJh4cHUlJSbrtP3BaT0Dhqc39dunTR1Iv8pEmTsHnzZrnS7H7v/Cp6bon7taAq1+pOFhYWaN++Pc6fPw+1s7S0lKs0hY4dO8p32nPnzpUvwHfS+nOqqtdLq8+rw4cPIzU19bYRdVHGE/8W58+fL0fezczMDOK5xTk3tURk9Tt37rztPpHt3quGS/8j3kGJcpXaiTnX4sVaDIH//vvvaNy48X2/RqvPrepcqzuJP8Si/KCF59bdSnnixedutPqcqu710urzqnfv3vL3FH+fyw4xnWDUqFHy/M7ERtHnVq1OV1bZ7PCjR4/KQ1y2zz//XJ7Hx8fLz7/99tv65557rvzxFy9e1Nva2uqnTZumP3XqlP6rr77Sm5mZ6bdt26ZXu6peqy+++EK/YcMG/blz5/TR0dFy5r2pqal+x44derV7+eWX5aqLXbt26ZOSksqP3Nzc8seIayWuWZmIiAi9ubm5/rPPPpPPrQ8++EBvYWEhr52aVedaffTRR/pff/1Vf+HCBf3hw4f1Tz/9tN7a2lp/4sQJvZqJayBWkcXGxuqPHz8ub5uYmOh/++03+Xk+px7semn1eXU3d66WMpTnFpObSipbrnznMWbMGPl58VH8T77za9q1a6e3tLTUN2nSRC4f1IKqXqtPPvlE37RpU/nHwcXFRd+zZ0/977//rteCu10ncdz6XBHXquzalfnxxx/1LVq0kM8t0XLgl19+0atdda7V1KlT9b6+vvI6ubu76wcMGKA/cuSIXu3Gjh2r9/Pzk793gwYN9L179y5/oRb4nHqw66XV51VlkhtDeW6ZiP/U7tgQERERUd3hnBsiIiJSFSY3REREpCpMboiIiEhVmNwQERGRqjC5ISIiIlVhckNERESqwuSGiIiIVIXJDREREakKkxsiMgo9e/bE1KlTlQ6DiIwAkxsiIiJSFSY3REREpCpMbojI4OTk5GD06NGwt7eHp6cn5syZc9vnv/vuO3Tq1AkODg7w8PDAM888g9TUVPk5sV1es2bN8Nlnn932NceOHYOJiQnOnz8vH/Phhx/C19cXVlZW8PLywuTJk+v0dySi2sPkhogMzrRp07B7925s3LgRv/32G3bt2oUjR46Uf76wsBAzZsxAVFQUNmzYgLi4ODz//PPycyKBGTt2LJYtW3bb9xS3H3roIZn4/PTTT/jiiy+wcOFCnDt3Tn6PwMDAOv89iah2cFdwIjIo2dnZcHV1xYoVKzBs2DB53/Xr1+Ht7Y2//e1v+M9//vOXrzl06BA6d+6MrKwsOdpz5coVOSoTGRmJLl26yGRIjM6I0ZwxY8bg888/l4lNTEwMLCwsFPgtiag2ceSGiAzKhQsXUFBQgK5du5bf5+LigpYtW5bfPnz4MAYNGiQTGFGa6tGjh7w/ISFBfhSJzMCBA7F06VJ5e9OmTcjPzy9PlsTHmzdvokmTJpgwYQLWr1+PoqKiOv5Niai2MLkhIqObj9OvXz84Ojpi5cqVOHjwoExOBJEUlRk/fjxWr14tkxhRkhoxYgRsbW3l53x8fHDmzBn897//hY2NDV555RVZshIjPERk/JjcEJFBadq0qSwV7d+/v/y+9PR0nD17Vp6fPn0a165dw6xZsxAWFgZ/f//yycS3GjBgAOzs7LBgwQJs27ZNzsO5lUhqxOjPvHnz5JyevXv3Ijo6ug5+QyKqbea1/hOIiKpAzJkZN26cnFQs5t64ubnhH//4B0xNS96LiVKUpaUlvvzyS7z00kty3oyYXHwnMzMzOcl4+vTpaN68OYKDg8s/t3z5chQXF8vSlxjNEfN7RLLj5+dXp78rEdUOjtwQkcGZPXu2HJURIyt9+vRB9+7d0bFjR/m5Bg0ayORkzZo1CAgIkCM4dy77LiOSJFGqeuGFF267v169eli0aBFCQ0MRFBSEHTt2yHk5IpkiIuPH1VJEpFp79uxB7969kZiYCHd3d6XDIaI6wuSGiFRHrIxKS0uTy75Fkz8x8ZiItINlKSJSne+//17On7lx4wY+/fRTpcMhojrGkRsiIiJSFY7cEBERkaowuSEiIiJVYXJDREREqsLkhoiIiFSFyQ0RERGpCpMbIiIiUhUmN0RERKQqTG6IiIgIavL/6YJ8k09vutYAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=\"days\", y=\"cakes\", data=df);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b02c81bd-e214-4424-88fc-d3ee2c91f8db",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # ALT. hybrid approach\n",
    "# sns.lineplot(x=df[\"days\"], y=df[\"cakes\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2629efb-0156-4feb-ac54-fd6d38ea2882",
   "metadata": {},
   "source": [
    "### Plotting function graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7119e715-37ca-408b-95fc-b19676ce016b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def g(x):\n",
    "    return 0.5 * x**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "29f4e8aa-02bc-4cbd-b9e9-f6ad49799e53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "xs = np.linspace(0, 10, 1000)\n",
    "gxs = g(xs)\n",
    "sns.lineplot(x=xs, y=gxs, label=\"Graph of g(x)\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e6040ce0-6f75-45c7-bb7c-bc3ee26877a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # FIGURES ONLY\n",
    "# from ministats.utils import savefigure\n",
    "# ax = sns.lineplot(x=xs, y=gxs, label=\"Graph of g(x)\");\n",
    "# filename = \"figures/tutorials/seaborn/graph_of_function_g_eq_halfx2.pdf\"\n",
    "# savefigure(ax, filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "842e0f1c-5c3d-4bb0-9203-4f344742cfe0",
   "metadata": {},
   "source": [
    "## Distribution plots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23531e40-0e42-4adb-85cd-f5e0651b67a3",
   "metadata": {},
   "source": [
    "### Strip plots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74511ab8-5ff7-4ace-a0e2-3dbf32a8dbbc",
   "metadata": {},
   "source": [
    "### Scatter plots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e783c2fa-37af-4af8-8c8e-1a53bd81a683",
   "metadata": {},
   "source": [
    "### Density plots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7616378-6fcf-41ae-ae78-882c5e2224a4",
   "metadata": {},
   "source": [
    "### Histograms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13c72cdc-abc7-4264-b1f6-a88f872e21c4",
   "metadata": {},
   "source": [
    "### Box plots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93f5db57-7339-4398-bd67-b580cd640981",
   "metadata": {},
   "source": [
    "### Violin plots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b607d304-cbc3-485d-a865-62bc1bfbae40",
   "metadata": {},
   "source": [
    "## Categorical plots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c2dd6d8-5e58-451b-87ce-f2806a3804c9",
   "metadata": {},
   "source": [
    "### Bar plots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d624244-f95a-4121-b003-accb38a540ba",
   "metadata": {},
   "source": [
    "## Linear model plots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77befe42-d69d-44d3-964b-8505fa515c89",
   "metadata": {},
   "source": [
    "### Linear model plots using `seaborn`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e348984b-ffe4-4343-8cde-1b41b8fe840c",
   "metadata": {},
   "source": [
    "### Linear model plots from scratch"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "130f4657-5de9-4de7-a4eb-94cbe08343fc",
   "metadata": {},
   "source": [
    "### Linear model plots using `statsmodels`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fa751fb-93fc-4383-a9c5-b29b8bb0550c",
   "metadata": {},
   "source": [
    "## Other plots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e8eb947-1ca5-4b6f-9d9c-c3406bead8c8",
   "metadata": {},
   "source": [
    "### Stem plot for discrete random variables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7877f98-9835-442d-8576-61a2184018e2",
   "metadata": {},
   "source": [
    "## Customizing plots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0beddb30-e0fa-41db-9e2c-f2c64cb6ebe8",
   "metadata": {},
   "source": [
    "## Bonus topics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d6af596-6116-45d1-a22a-0a56d0e9f7fe",
   "metadata": {},
   "source": [
    "## Data visualization tips"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0a3ec94-4538-4339-9829-53e132307b43",
   "metadata": {},
   "source": [
    "# Links\n",
    "\n",
    "Here are some links to learning resources for Seaborn and data visualization techniques.\n",
    "\n",
    "\n",
    "## Official docs\n",
    "\n",
    "- An introduction to seaborn  \n",
    "  http://seaborn.pydata.org/introduction.html\n",
    "\n",
    "- Seaborn tutorials featuring lots of useful plot examples  \n",
    "  https://seaborn.pydata.org/tutorial.html\n",
    "\n",
    "- Gallery of data visualizations produced using Seaborn  \n",
    "  https://seaborn.pydata.org/examples/index.html\n",
    "\n",
    "\n",
    "\n",
    "## Tutorials\n",
    "\n",
    "- Python Seaborn Tutorial For Beginners  \n",
    "  https://www.datacamp.com/community/tutorials/seaborn-python-tutorial\n",
    "\n",
    "- The Ultimate Python Seaborn Tutorial  \n",
    "  https://elitedatascience.com/python-seaborn-tutorial\n",
    "  \n",
    "- Python Seaborn Tutorial  \n",
    "  https://www.geeksforgeeks.org/python-seaborn-tutorial/\n",
    "\n",
    "\n",
    "\n",
    "## Video tutorials\n",
    "\n",
    "- *Intro to Seaborn* by Kimberly Fessel  (excellent!)  \n",
    "  https://www.youtube.com/playlist?list=PLtPIclEQf-3cG31dxSMZ8KTcDG7zYng1j  \n",
    "  see also [notebooks](https://github.com/kimfetti/Videos/tree/master/Seaborn) from the videos.\n",
    "\n",
    "- *Seaborn Tutorial 2021* by Derek Banas  \n",
    "  https://www.youtube.com/watch?v=6GUZXDef2U0\n",
    "  \n",
    "- *Data Visualisation with Seaborn Crash Course* by Valentine Mwangi  \n",
    "  https://www.youtube.com/watch?v=zafPvR4MmBA\n",
    "  See also the [colab notebook](https://colab.research.google.com/drive/1M0PT25aKwetQbADNxEfDQYpeJ0KKpr70) for the course.\n",
    "\n",
    "<!-- SEE ALSO\n",
    "\n",
    "%DATA VIZ LINKS: (imported from \\tt{dataviz\\_dictionary.tex})\n",
    "\n",
    "%\t\\url{https://www.data-to-viz.com/} \\\\\n",
    "%\t\\url{http://vita.had.co.nz/papers/layered-grammar.pdf} \\\\\n",
    "%\t\\url{https://www.tandfonline.com/doi/abs/10.1198/jcgs.2009.07098} \\\\\n",
    "%\tPlease see the online version of this article for supplementary materials. \\\\\n",
    "%\t\\url{https://www.tandfonline.com/doi/suppl/10.1198/jcgs.2009.07098/suppl_file/ucgs_a_10712021_sm0001.zip}\n",
    "\n",
    "-->"
   ]
  }
 ],
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   "language": "python",
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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