mod-1/week-2/day-2/matplotlib_seaborn_murat.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![matplotlib](https://matplotlib.org/_static/logo2.png)\n",
    "\n",
    "# Data Visualization with Matplotlib\n",
    "\n",
    "\n",
    "## Why is visualization important?\n",
    "\n",
    "\n",
    "There are a lot of visualizations out there these days.\n",
    "\n",
    "Ultimately they are about quickly revealing relationships and anomolies.\n",
    "\n",
    "While we will review what makes a **GOOD** visualization later, we will cover a few basic types today.\n",
    "\n",
    "Let's look at some [examples from python graphs](https://python-graph-gallery.com/)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# %matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Matplotlib - 101\n",
    "\n",
    "- First release is in 2003\n",
    "\n",
    "- Compatible with different operating systems and graphic backends: [Backends?](https://askubuntu.com/questions/417160/what-is-a-backend-in-computer-graphics)\n",
    "\n",
    "- Everything to everyone approach\n",
    "\n",
    "- Didn't age well :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using matplotlib backend: MacOSX\n"
     ]
    }
   ],
   "source": [
    "%matplotlib\n",
    "\n",
    "## you might see TkAgg, nbAgg for example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting Styles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['seaborn-dark',\n",
       " 'seaborn-darkgrid',\n",
       " 'seaborn-ticks',\n",
       " 'fivethirtyeight',\n",
       " 'seaborn-whitegrid']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.style.available[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "## for no reason :)\n",
    "plt.style.use('fivethirtyeight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x = [1,4,6,8], y = [1,8,2,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<img src=\"https://python-graph-gallery.com/wp-content/uploads/Logo_PGG_full-3.jpg\" width=600>\n",
    "\n",
    "The [Python Graph Gallery](https://python-graph-gallery.com/matplotlib/) has some great resources to guide you through the code needed to create each type of matplotlib plot. \n",
    "\n",
    "## Plot Anatomy\n",
    "Let's spend some time on the terminology of a plot. There are a lot of options to set in matplotlib plots, so let's identify some common terminology. \n",
    "\n",
    "The sequence of events with matplotlib are:\n",
    "\n",
    "| Step | Code Example |\n",
    "| :---- | :------------ |\n",
    "| 1. Create figure | `fig = plt.figure(figsize=(2, 4))` |\n",
    "| 2. Create axis object | `ax = plt.subplot()` |\n",
    "| 3. Make adjustments | `plt.title`, `ax.set_xticks`, etc |\n",
    "| 4. Show final plot | `plt.show()` . |\n",
    "\n",
    "\n",
    "The [Pyplot tutorial](https://matplotlib.org/tutorials/introductory/pyplot.html) shows a list of options you can set within a plot.\n",
    "\n",
    "<img src=\"https://matplotlib.org/_images/sphx_glr_anatomy_001.png\" width=550>\n",
    "\n",
    "\n",
    "Let's look [how they created this plot.](https://matplotlib.org/gallery/showcase/anatomy.html)\n",
    "\n",
    "[What is ax and figure?](https://stackoverflow.com/questions/34162443/why-do-many-examples-use-fig-ax-plt-subplots-in-matplotlib-pyplot-python)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.linspace(0, 10, 1000)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = plt.axes()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x119a4fc50>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "ax.plot(x, np.sin(x))\n",
    "ax.plot(x, np.cos(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11964e2e8>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, np.sin(x), color = 'c')\n",
    "plt.plot(x, np.cos(x), linestyle= 'dashed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x119704780>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4+/DVcrijq339zRbzX4Aj+kGrrr6TpL9uZ0BeA9v304AVfx9adIH6zfceMZrMtXUNrP4EemfnSqRJV9DIfQAsfNmmefbMunr6EApBz1FujdfyXVDQ0HciU1t7mnys+NfY4B+6JjKNYsef/ti/vC+9Rrt5zpdP9Z3E1MWCF6FNH2h9jO8kmeOoE92sp2E79vQpsOIvIh1F5G0RWSgi80XkJ7Htt4rIGhGZHbucHVSGtHbUELfS0cJXfCcxtbV9A6z6yE701sbW1fDxP206DI+C/OitBG5W1ZmxpRxniEh8Idu7VfXOAF87/YXzoftIWGwjHjPWktcAdWvVmsPz+TR47efuIOiIvr7T5KTAjvxVdZ2qzoxd3w4sBNoH9XoZqddot7Tjyvd8JzG1sWMjtOkNbXr5TpJ5ep0LE+Za4fcoJW3+ItIZGAB8HNt0g4jMEZEHRaTFAX5nnIiUiEhJaWlpKmKmXtdhkN/Qmn4y1bd+Dtd9kNuLtNdWvcbQvJPvFDkt8OIvIo2BZ4EJqroNuA/oChQB64A/Vfd7qjpRVYtVtbiwMEsnRMtvAMec7kY8Wre3zBIfoRqyPhO1VroEJl3ufpqUC/R0u4jk4wr/Y6r6HICqbki4/34gtw97h/0a8upZEck0L1wHu7+GK571nSRzFTSERa+4hY4Kb/KdJucE2dtHgP8DFqrqXQnb2yU87HxgXlAZMkJhD2jR2XcKc7g6DnInK03tNesARw5w33xNygV55D8EuBKYKyKzY9t+BYwVkSJAgZXADwLMkBk+n+amCBj1J2s/zhQDr/WdIDv0HAX//j1sWwdN2x368SZpguzt876qiqr2V9Wi2GWyql6pqv1i289V1XVBZcgYXy1zRz87NvpOYmpi7Ww3Lbepu56x9Q8WT/abIwdZQ3M6KLoCbloITdr6TmIOJRqFJ8bCCz/0nSQ7FPaElkdb048HVvzTQV6BO+Frox3T37pZsH0t9MjNgelJJ+Kafj6f5sa8mJSx4p8uVrwDd/WGr1f6TmIOZtGrIGG3KIlJjp7nQLQClk459GNN0ljxTxfNj3JHlDbgK70tehWOOgkatvSdJHt0KIZGbazpJ8Ws+KeLll2gbV97A6SzTcugdNHek5QmOUJhOHkCdDnFd5KcYnOqppOeo2DaHbCjFBpn6ajmTLY49sHc09r7k+7E8b4T5Bw78k8nPUe7BS6WvO47ianOosluuUabkyYYu7+G1SW+U+QMK/7p5Ih+0KyTG/Ju0suOjfDlx9bkE6RXbnLdaG2eq5Sw4p9O4t3elr8NZTt8pzGJVk0H1P3/mGCcfCOMnWSj3FPEin+66TUaImW2vGO66X0u3LTInZQ3wWjXHzocb8U/Raz4p5uOg6FBS+vymY6atrPCFLQ1M2Hq72zAYwpY8U834Tw3wdug63wnMXGLJsPjl7heWCZYa2fCe3e6LrUmUFb801HfC9zXX5MeynfA9vXQoNpF50wyxafNsPEugbPin65Wvg/znvOdwgD0vxh+8K77VmaC1fRIt7iLFf/AWfFPV9Pvc/OcW9unX99sg2jEd4rc0nOUa/7ZttZ3kqxmxT9djbwdrnvfTjD69u/fwT1F9gGQSjbHf0p4K/4iMkJEFovIMhH5pa8caatZe7fGqfFH1TU/tOvv5p8xqdG6O7Tsak0/AfPSiCkiYeBe4AxgNfCpiLykqgt85EkUiSrllVHKK6OUVUYoq4xSHolSVuF+Jt5XnnBfWSRKRWWUSFSpjCpRVSojSkSVaJVtUdW9j0u4TxOaeESE47ZOod/293mkw39D7AuAxK7InttOXlgIh4S8UIi8kBAOC/mhUGybkBeObQ8J+WEhHHtcXljID4coyHOXerFLQThMvfwQBeHQnp/u/jD5YUFy4RvJutmwbQ0M+7XvJCmhqm5/rkzcz/ff58si1d8X36cjVS96gG2RvfdFE/d94BxO4NTlT/Ofj7/HN+EmB93/wyGpdj8/2D7vtofID4vb3w+xzxfseV+ECIWyY9/3dQZrILBMVVcAiMgkYAyQ1OK/dXcFtzw3Z0/hLttnh45UW9Qro8lvYw+HhLDInp00JJAXDhES2bNzhkIQiu3R8ffBURXrKap4hz+umMuXofb7Nf/HPywU9rzxKiN7P4Dib8Qg7PtBEaJefjjhzbLvz4K8cMIbzL2x6lV5Y+3z+Gqeb+9z7vv4vFCAH0SLXgUJQfcRwTx/THUHHGWJxbZKId5zUHKAx+x7f5XiXW3hju45kEm2kOwtzonvgXAoRDgEeaHQnn1fcPsyQHllEcOZRMHKfzMtPBTY//SXqqKw52CqIhI/qIpSGdFA3stA7AMjXM0+G/uZ5z5E6sX213p5sX0+fr2a+/fs5/nhPft7/HGN6+XRtmn9pP8dvop/e+DLhNurgUGJDxCRccA4gE6daj+R1pINO/b5D2pSP4/WVf7B4/cdqBjV2+dxVf7TqzxHXjhEWFwxzwuFCAm1L05bjoE/P8ATQzfByVfW6ik04VtGZdQdbVVGo3tuV0aiVOz3wZhYECJ7PzwTPkSrL0yRfYrKjrLKhN+NJPyuuz8ZRNjzAVQQ+1YS2qfISOz/w33QhkJCOLEghWTPh3BIZE/xUVV+u/Zpdhb05X8nLdtnu/sZu42iyp5vdZVVik/VYlQZif3bxwpVRTSalHP6if8O8Q/Oqvt4w4I8mlcpVNXt8/sXtOr3+XoJ74eCvFDs6HrfQl/rfT96CvzrRW4t7s2t/YfV6ilUlahCReIB0T7Xde+BX6T6D9MDtQC4n9V/8JZXRtm50+37ic8Xv78icnj/4cd2bM6L44fU6t/gYHwV/+r2iH3+RVR1IjARoLi4uFZvj2YN8nnrpm/V5lfTQ/OO0K7IHYGefGOtnkIk9jU3zZqsVd2RWtXms8Q3YfVHr9V/a4u/uSKxD7ZoVIkosWa1KJForEDH74s1PVRURPc0O0SiiohrWjgiso6OFSt5oNG1bNlVDiJ7dlr3mL0f6gKEQuIKbKypId68EA6FyK/SDBFvdkhslqh6BBgvynuOAvOrb5aIF/qsa4oLheF7dZvdViT+QZ9eO3/8m96eb3kVid/4IrHbe7c1rZ8fSA5fxX810DHhdgfA+nVVp+doePv3bpBRkyN8p0kaEaEgzxXMtPTh3+BNuOaaG7imRWffaXJXpALKtmfVymnhkNCgIEyDAr8fSr7eeZ8C3USki4gUAJcCL3nKkt7is0hat7fUWvSqm8TNCr8/0ajrZjv1t76TZCUvxV9VK4EbgDeAhcBTqjrfR5a016YXtDzaJnpLpZ1fwZfT9041YPwIhdzyjr3P9Z0kK3kbr66qkwE7nD2U+Bz/0/8B32yF+s18J8p+DVvCuHegYSvfSczAa30nyFpp2uBq9tHzHIhWwNIpvpPkBhFodyw06+A7iQHYMB9WvOs7Rdax4p8JOhRD8fes/TkVynfByxNg/TzfSUzc67fA5J/6TpF1rPhnglAYRt/tPgRMsEoXwdxnYKfN3Z82eo6GTUugdInvJFnFin+mUIWNi+Cr5b6TZLf2x8HPl0Pnob6TmLiesRPvi22un2Sy4p8pIuXwwHD44B7fSbJXfKhtXj2buz+dNOsQG+xo/UOSyYp/psirBxc/Aqfe4jtJ9lr1Edw7CDZ4n1/QVNVzNKz+1A12NElhxT+THDPcLSJugrHoVdi8wnr5pKOeowCFxa/5TpI1rPhnmtmPw6zHfKfIPvG5+7ucAvWb+k5jqmrTC1p0sTn+k8iKf6aZ+wy8d6ct75hsGxfC15/vnU7DpJf4YMfP33Vz/Zg6s+KfaXqOck0TpYt8J8ku8Z4k3Uf6zWEOrOcoyG9oXT6TxIp/ponPN7PI5vpJqkWvQvtiO6eSzjoOhp8tgw7H+06SFaz4Z5qm7aDDCdb2mUxb18DaWXv7k5v0FApBON81eVqzZ51Z8c9EPUe5YrV1te8k2SE+XXbP0X5zmEMrXQx/GQAr3vadJONZ8c9E8SJlg16SY/FkaNkVWnf3ncQcSvNOUNgT8pK/pm2usWGMmah1N2jdw7X7DxrnO03mO3E8lO90PUpMestvAJdN8p0iK1jxz1Q9R7mpHnZ/DQ1a+E6T2Y453XcCc7i2b3Af1o3b+E6SsQJp9hGRO0RkkYjMEZHnRaR5bHtnEdktIrNjl38E8fo5odc5bkDSzq98J8lsc5+x6ZszTdl2uLsPfPxP30kyWlBt/lOAvqraH1gCJE5Is1xVi2KX6wJ6/ezX/ji46gVofYzvJJkrUgGv3ASfPuA7iTkc9ZpAp8HW462OAin+qvpmbJ1egOmATZYSlJ2boLLMd4rMFM6HH8+CU37mO4k5XD1HQ+lCm+K8DlLR2+d7QOJsTF1EZJaIvCsiB5w0XUTGiUiJiJSUltrCGtVaPQPu7AbLpvpOkrkatYJm7X2nMIcrPibDjv5rrdbFX0TeEpF51VzGJDzmP4BKID4T2Tqgk6oOAG4CHheRamfRUtWJqlqsqsWFhYW1jZnd2vV3Uzy36eU7SeapLIfHL4UV7/hOYmqjeSc4op8V/zqodW8fVT1oFwkRuRoYDQxXdcPxVLUMKItdnyEiy4HuQEltc+S0cD586+e+U2Smle/BktfguKt8JzG11XM0vHMb7CiFxnaAeLiC6u0zAvgFcK6q7krYXigi4dj1o4FuwIogMuSMynJY/Lob+WhqbuFLUNAYug7zncTUVnyO/yU2x39tBNXm/zegCTClSpfOU4A5IvIZ8AxwnapuDihDboiUw1NXwYx/+U6SOaIR11zQ7UzIt5GiGattX9f8s/Bl30kyUiCDvFS12v6Hqvos8GwQr5mz6jWGrqfBwlfgrP+xUao1seoj2FnqxkqYzCUCvc51Bz4Vu93oX1NjNrdPNug5CraugvVzfSfJDAtfdnPDdDvTdxJTV0MmwI3zrPDXghX/bNDjbJCQ9XyoiWjUFf+uw923JpPZGhfa9Ca1ZMU/GzRq7Ra6sAVeDm3tTNi2xpp8ssnKD+ChUfDNVt9JMooV/2zRcxRsmAebP/edJL0teBFCedBjhO8kJlnC+fDNFrcoj6kxK/7ZIr7w+GKb4/+gOg6EoTdbU0E26TgQrv8A2vb2nSSjWPHPFi27uK5v1u3t4HqdA6f9yncKE4SK3TbP1WGw4p9Nep3rprut+MZ3kvS06mPYts53ChOETcvg9q6w4CXfSTKGFf9sMvRm9/XXBi7tTxWeHwcvjvedxASh5dFQvykseMF3koxhK3llk3Dsv7OyDPLq+c2SbkTgsqehcrfvJCYIodDeAV9l292c/+ag7Mg/2yya7L7+fv2F7yTpp7A7tDvWdwoTlD7nQaQMlrzhO0lGsOKfbdr2hr4XAOo7SfqIRuGVG12bv8leHQdD4yOs6aeGrPhnmxad4dy/uJ/GWf0plDwIX9sYiKwWCrneXEunuKYfc1BW/LORKqyZYT1b4uY/B+F6bhoMk936fRsqv7GpTmrAin822r4e7h8Gsx71ncS/aATmvwDdznC9QUx26zAQmnWCuU/7TpL2rPhno6btoNOJ7og31636CHasj50HMVkvFIJ+F7rlOXd/7TtNWgus+IvIrSKyJraYy2wROTvhvltEZJmILBaRs4LKkNP6XggbF8DGhb6T+DXvOchvCN1tLp+cMfAHcEOJTeFxCEEf+d+tqkWxy2QAEekNXAr0AUYAf48v7WiSqPcYN83zvBw++o9UuJ4f3UdAQSPfaUyqNG3npjsxB+Wj2WcMMElVy1T1c2AZMNBDjuzWuA10HuraPjVHu30uewt2fQX9L/GdxKRa6WKYdDl8vdJ3krQVdPG/QUTmiMiDIhL/DtYe+DLhMatj2/YhIuNEpERESkpLSwOOmaX6X+K6N67+1HcSPz6bBA1bwTHDfScxqZZXH1aXwOYVvpOkrToVfxF5S0TmVXMZA9wHdAWKgHXAn+K/Vs1T7XdoqqoTVbVYVYsLCwvrEjN39T4X8hrAZ0/4TuJHp8Fumb9wvu8kJtVaHAU3LYSuw3wnSVt1mttHVU+vyeNE5H4gvszUaqBjwt0dgLV1yWEOoF4T6DXatfuPuC335vsZfL3vBManUMiN7q7cbed8qhFkb592CTfPB+bFrr8EXCoi9USkC9AN+CSoHOW6gbsAAA4vSURBVDmv/6UQLoCvlvlOklrLpkL5Lt8pjE+V5fCXY+Hd230nSUtBtvnfLiJzRWQOcBpwI4CqzgeeAhYArwPjVTUSYI7c1vU09/W3bR/fSVJny5fw6AUw/V7fSYxPeQXQpjfMecoN9jP7CGxKZ1W98iD3/QH4Q1CvbRKEYr1oo1GIVro3RLZr2h6+8yq07Oo7ifGt6DJY8josfxu61aiVOmfYCN9csHMT/LkvzHzYd5LUCIWg88muv7fJbd1HQoOWMNumOqnKin8uaNQaeoyEVsf4ThK8dZ/Ba7+AHRt9JzHpIK8A+l/sJnrbtdl3mrRixT9XjPqTa//PdiUPwYyH3UluYwCKLodIOcx71neStGLFP5dsXePaPrNV+U73Bu9zHjRo7juNSRft+kPbfjD7Md9J0ooV/1zy+i/g2WtcF7hstOBFKNsGAw7Y18DkqgGXw9pZsGGB7yRpw4p/Ljnuati1CRZn6UIXMx9xPXyOOsl3EpNu+l0MoXxY9MqhH5sjrPjnkq7DoGkHVySzzaalbu7+464CqW4GEZPTGrWC6z+AU37mO0nasOKfS0JhGHCFa/f/+gvfaZJr5iMQyoNjx/pOYtJVYQ87MEhgxT/XDLjC/Zz1//zmSKZIhZu8rvsIaNLWdxqTzt67C56wAwSw4p97mnd0UxzPehQilb7TJMeiV2BnqWvyMeZg8hu4Sd6ytdPDYbDin4uO/w5sX+eGvWeDI/rD0JvhGBu+bw5h8PVw4QO5Mc3JIVjxz0XdR7oTv5/803eS5GjVFYb/1955jIw5lA3zoWyH7xReWfHPReE8dwTUuK1rL89kMx+BVdN9pzCZZP08uO8kt8RpDrPin6tOusF9/c3kVa4iFfDObe78hTE11bYPtO0LJf+Xu+tbY8XfrJ8Hu7f4TlE74Xy4oQSG/8Z3EpNJROCEa2D9XPjiQ99pvLHin8u+Wg7/GJKZ3T6jUXcpaAiNbY1nc5j6XwINW8GHf/WdxJtAir+IPCkis2OXlSIyO7a9s4jsTrjvH0G8vqmhVl3h/Ilu1sNMs+gVuPeE7BusZlKjoKE7+l/ymhsdnoMCKf6qeomqFqlqEfAs8FzC3cvj96nqdUG8vjkMx14CDVv6TnF4VOGDP7ul+Zp18J3GZKoTroVwPfjob76TeBFos4+ICHAx8ESQr2PqaOlbbrbPTDn59cWHsGYGnPQj695paq9xIRSNhdlPwI5S32lSLug2/6HABlVN/F7VRURmici7IjL0QL8oIuNEpERESkpLc+8/JqV2bnTd3pZO8Z2kZj64Bxq2duuzGlMXg8dDpAw+vd93kpSrdfEXkbdEZF41lzEJDxvLvkf964BOqjoAuAl4XESaVvf8qjpRVYtVtbiw0E7oBarfRW7Q1/t3+05yaBsWwNI3YNAP3FB9Y+qisDv0/XZOrvyWV9tfVNWDjqUXkTzgAuD4hN8pA8pi12eIyHKgO1BS2xwmCcL5rgnl9V+4AVOdBvtOdGDT7oCCxu5knTHJcOEDOTnbZ5DNPqcDi1R1dXyDiBSKSDh2/WigG7AiwAympo67ynV9m3aH7yQHtmE+zH/eHfVn2klqk75E3PmupW9B2XbfaVImyOJ/Kfuf6D0FmCMinwHPANep6uYAM5iaKmgIQ34Cy95K34Ev79zmjvpPvMF3EpNtNi6Axy6EmRk45qWWRDOgh0dxcbGWlFjLUODKd8FfBkDLLvDd19Lrq3DZDvjnUHd+4rRf+U5jstGSN+HoU7Nqxk8RmaGqxdXdZyN8zV4FDeGUn7rlEJdN9Z1mX/Uaw/hP4OQbfScx2ar7ma7wRyO+k6SEFX+zr+OuhuadoORB30n22rwCyne6E9PWw8cEacW78NfjYOsa30kCZ8Xf7CuvAC5/Fi56yHcSRxWe/g48MiZzBqGZzNWiM2xbC//+ve8kTuniwPZ7K/5mf4XdIa+eO9r2veCFCIy8A069Jb3OQZjs1OIoGHSdWxN63Wd+s2xaCvcNgel/D+Tprfib6pVth3sHwbt/9JchfsTTaZBbd9iYVBh6MzRoAa/9ws0c64Oqe/38htDv4kBewoq/qV69JlD8Xehxtr8Mr/0cJv/MmntMajVoDmf81nV8mPWInwxrZ8LyqXDaLYFNWW7F3xzY0JvhqBP9vPaqj+GT+0HC1txjUm/AFXDUyTDlv2D7htS/fvvj4TuT3cyjAbHibw4uUgGv3gwfpnDa28oyePnHbrrmYb9O3esaEycC5/wZKnbDayn+9rlllfvZeYhbbzsgVvzNwYXyYr0ffpe6RS/euhVKF8Hou13/fmN8aN0NTv0lLHgRZj+emtdc8qYbaLnincBfyoq/OTgRV4TzG8JTV7tRwEFaOsX1bhj4A+h2RrCvZcyhDJngmoDa9k7N63Ua5CZZ7BR8c6sVf3NoTY6AC+53859M/mlwr7NlFTx/HbTp7U64GeNbKAxj7oUjB7jbkcpgXidSARXfQP1mcPqtrqt1wKz4m5rpdjp86+cw+zH49IHkP3/ZDnjiMvcmuOhhyK+f/NcwprZUXc+zZ7+f/O6fqvDyBHjwzOC/WSew4m9q7lu/gG5nuTfBwleS+9zTboeN8+GiB90gM2PSiQg06+imPkm29+6E2Y9C9xFufq0UsVk9zeEp3wkPnwsb5sHYSdD1tOQ87+4t7iRXn/OS83zGBGnnV9CoVd2f56N74Y1fuYFcF0xMerdmm9XTJE9BI7jsKWjZ1c1/UpevwOW73HNU7HYDa6zwm0yw5Uv4+2B4679rv/+rwrQ7XeHvPQbO+3vKx7ME14nUZK9GreC7r7qTX6GQa6/Pb+BOjh2OVR/Be3dBhxOg+1nBZDUm2ZoeCT1GwPt3uYnXzv8H1K92KfLqle90bfxzn3LrU5z3j0D78x9InY78ReQiEZkvIlERKa5y3y0iskxEFovIWQnbR8S2LRORX9bl9Y1HDVq4Yeeq8Mz34LGLajYQJlKxtw/zMcPhRzOs8JvMEgrDOX+BEX+EJa+5bwGLXq3Z/r9sKvz9RJj7tBvAeMH9Xgo/1P3Ifx5ukfZ/Jm4Ukd64ZRz7AEcCb4lI/CzevcAZwGrgUxF5SVUX1DGH8anP+VCx031tjUZcX/2jTtp7NKTq5uRf+BLM+Jfr0nlDCbTq6lYNMybTiMDg69w0DC/9CCZdBu2K3HxY3UdCk7Z7H7uj1B0shfPceyOcD9951Y3g9ahOxV9VFwLI/m1VY4BJqloGfC4iy4CBsfuWqeqK2O9Nij3Win+mEoGisXtvL34NnrwcEGjcBvLqw+6voWybu7/jYBh5O7Q82ktcY5Kq4wnwg2kwZxJ8cA+8/BPgJ3DmH+CkG9x62A+NhCufh67D3ERtp9+aFl2Zg/q+0R6YnnB7dWwbwJdVtg+q7glEZBwwDqBTpwC6V5lgdDsTrn7F7fRbV7lmnvrNoG1f6HyyO9o3JpvkFcBxV8GAK2H9XFj5HnQe6u5r0wvO/L1bJAbceyFNHLL4i8hbwBHV3PUfqvrigX6tmm1K9ecYqm0oU9WJwERwXT0PldOkibwC6DLUXYzJJSLQrr+7xDVo4aZrSEOHLP6qenotnnc10DHhdgdgbez6gbYbY4xJkaD6+b8EXCoi9USkC9AN+AT4FOgmIl1EpAB3UvilgDIYY4w5gDq1+YvI+cBfgULgVRGZrapnqep8EXkKdyK3EhivqpHY79wAvAGEgQdVdX6d/gJjjDGHzaZ3MMaYLGXTOxhjjNmHFX9jjMlBVvyNMSYHWfE3xpgclBEnfEWkFPiiDk/RGtiUpDiZItf+5lz7e8H+5lxRl7/5KFUtrO6OjCj+dSUiJQc6452tcu1vzrW/F+xvzhVB/c3W7GOMMTnIir8xxuSgXCn+E30H8CDX/uZc+3vB/uZcEcjfnBNt/sYYY/aVK0f+xhhjEljxN8aYHJTVxT/XFosXkY4i8raILBSR+SLyE9+ZUkVEwiIyS0Re8Z0lFUSkuYg8IyKLYv/fJ/rOFDQRuTG2X88TkSdExP9aiEkmIg+KyEYRmZewraWITBGRpbGfLZLxWllb/EUkjFssfiTQGxgbW1g+m1UCN6tqL2AwMD4H/ua4nwALfYdIoXuA11W1J3AsWf63i0h74MdAsar2xU0Jf6nfVIH4FzCiyrZfAlNVtRswNXa7zrK2+OMWjF+mqitUtRyILxaftVR1narOjF3fjisI7Q/+W5lPRDoAo4AHfGdJBRFpCpwC/B+Aqpar6ha/qVIiD2ggInlAQ7JwFUBVnQZsrrJ5DPBw7PrDwHnJeK1sLv7t2X+x+KwvhHEi0hkYAHzsN0lK/Bn4ORD1HSRFjgZKgYdiTV0PiEgj36GCpKprgDuBVcA6YKuqvuk3Vcq0VdV14A7wgDbJeNJsLv4HWkQ+64lIY+BZYIKqbvOdJ0giMhrYqKozfGdJoTzgOOA+VR0A7CRJTQHpKtbOPQboAhwJNBKRK/ymymzZXPwPtoh81hKRfFzhf0xVn/OdJwWGAOeKyEpc094wEXnUb6TArQZWq2r8W90zuA+DbHY68LmqlqpqBfAccJLnTKmyQUTaAcR+bkzGk2Zz8c+5xeJFRHDtwAtV9S7feVJBVW9R1Q6q2hn3f/xvVc3qI0JVXQ98KSI9YpuG49bLzmargMEi0jC2nw8ny09yJ3gJuDp2/WrgxWQ8aZ0WcE9nqlqZg4vFDwGuBOaKyOzYtl+p6mSPmUwwfgQ8FjuwWQF813OeQKnqxyLyDDAT16ttFlk41YOIPAGcCrQWkdXAb4DbgKdE5Pu4D8GLkvJaNr2DMcbknmxu9jHGGHMAVvyNMSYHWfE3xpgcZMXfGGNykBV/Y4zJQVb8jTEmB1nxN8aYHPT/ASw2hSENeZ71AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## limits of the axes\n",
    "\n",
    "plt.plot(x, np.sin(x))\n",
    "plt.plot(x, 100*np.cos(x), linestyle= '-.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-4, 4)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## limits of the axes\n",
    "\n",
    "plt.plot(x, np.sin(x))\n",
    "plt.plot(x, 4*np.cos(x), linestyle= '-.')\n",
    "plt.xlim(-5*np.pi, 5*np.pi)\n",
    "plt.ylim(-4,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 10, -5, 5]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## limits of the axes\n",
    "\n",
    "plt.plot(x, np.sin(x))\n",
    "plt.plot(x, 4*np.cos(x), linestyle= '-.')\n",
    "## [xmin, xmax, ymin, ymax]\n",
    "plt.axis([-0,10, -5, 5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 10.5, -4.3999949515635155, 6.6000050484364845)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, np.sin(x))\n",
    "plt.plot(x, 4*np.cos(x), linestyle= '-.')\n",
    "## we can use 'equal', 'tight', 'scaled' etc.\n",
    "plt.axis('square')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Labeling Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x11d70e390>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ue098/JJ+5zuLc1Bx9rCoLx/LFTNZNVHI6ejK6sqGdKpiYqg3BwAoRhz6QJ1N9BcykUaCj+e6kv/gFo9VZwt8H1XLlmbg+9eUfSdlXyBFRTGjvx1Ijco+ijXPbsMbD5yPhb35yLEnHzKItZv3TKtYp4od42WYNmsINFg64BiVV1sInY0CdwXN73YO6UV9uY64vwCgv5DFXkXXjQiMMYyXapjXlcGAu984YjcHP9j78hlX24i3Rp2pFBrWUwE/UBf25lA1bdQU/y0WKya6swa6MroSiwAcVjTYnQMRUI7hzprfnY10ZwHOZ1ng/j1EGaFi1ULONUBRBm6qYiKjO14H2Wf1GxzZOP9nSY1KCile2DGBDTsnce5Ri5XGv+GAAZRqVluS8NoNnnOxbKCZqagmuyUBTzRb0OMcDgv78okTAoOwbCeEeNA9ePq6MokirTjKNRumzdCbNzC/OwMAiUKUuVjekzMcQ5fU/TXA3V/qRmWiYiJnaJjnMi1l1lG10J3TUcjqSiwCcIxdT94xRCr3maqYMDRCb16dqfCHkSgjVKpa6C84f2cy9sEYQ8W00Zd3xlas8HUrpg3D1R9l9/ezKNXaY+1AalT2Qfzy2e0gAs46Qu764uBhrXEK5k0XeIa2n6n05jPoyxsdLS/BmcqAe8jN786iYtrKB5cME+UaGHMYCgD0d2WUReaw9QDne5nX5ayZxEhxn323G5gQh2kA9UinRX0OC4jl/io7Okd3VgegXnJlymUqeUUDwef05Fx2o2gkClkdhYyuxFTKNcv7d6Mi1POxsoOdl4nhWpBsbNW0PUMl11Qsz/hEZem3E6lR2Qfx6+d34pjl/VjYF+36AoAl/V1YPC+PtbPQqHCmwv30HEv6u9oa4hvE7qmqF1UEAAPuL2mShMAguCjND4j+QqYl9xfPN+jLO8YAiCeSc3Aj0ps30J1TP6Q5uIDcnTPQnTUakvBU7t2TM1CIuf9i1UIhZ6CQVTvw+b26c44hUjMqfLwWqZEADlOY1+X8e1FhKnysLFmUH/o9KkbFstGbj75/2bTR1xXNktqN1KjsYxidrOCZ4b049dCFseYds7wfzw6rF9ibLgzvKWJRXw45Q2+4PtSbw0gHo79Gp6qeXxyos4pWBXWg7ibiv/j9LQr1dabiGAOgHqIbB5NlE7pGyBkaClkjdkQQN0KFjI7unB6bqfQkZirxhHrHgLkuMxX3V9VCl3sPFSNU8R3WUeuXaha6cwayuibVNSrufflDg3SsaXvjZNpUpWahT8FItRupUdnH8OCLu8AYcOph8aorH764D5tHi7FdHp3GjokK9pvX1XR9qCfX0YJ4o5MVzy8O1AX7djIV/gs9r5DFeLmWOO+mznwy6M46a8ZhCRz8gCYidMc4pDlKNQtZXYOha+iO6T6bcN1YhWx8ptKdM9CVMZQMBGMM5ZqNrqyhbCSKPhdb1D1My4ZlszpTiTisi1UTXVkdOUOLZB9A/UFENrZi2t7DhWxcuWbVmYpEo2k3UqOyj+H+53diQXdWmvAowuFL+gAAf5xldcBGJioY6sk1XR/qdYxKpyoB7J5yQnQ5uPsrSahuEJxZ8F/o/q6MUkmP8PXqbitNcwxCkryDyYrlHVqFnBG7gGbJPSABxNZkSq5x4Iehyr0ZY5iqcqaiKRkIfjjnDE1dqHc1lS4FTYWvP0+VqVQtFDI6soYmd3+Z3KgYDfcJG8uZStUK//2oWaw+LmUqKUSwbIbfvDCCNx865GWfq4IblfWz0aj0io1KxbS9HiLtRifdX0FNhR9AcTLQG9drdKd154yGoomqmKzUvEPdMUwxNZWqha4Mnx/PfVaqWchntDpTUTjsSzULjMHVVNSMIHcb5QwNXYo6TLnmuL/yCkal4mou+YyGrKE15IKIUHTXzhmaN1e2714FI1A1LeQMHVldk7q/qpbtfd+pUUkhxDPDe7GnWMObY7q+AGC/vjwGCplZVbHYshl2T4UbFaAzPSEYY9gzVW1wf/V3tVOobzYCQLy8jsb1Gt1pPXkDkwnK9E9VLO/JtZA1UKpZsdooF2vOEz2A2O6vcs1C3tDrTEVhLmcZXRl+4EdXBeYHdy6jK+e2VE0bWV3zosVk7LhutJz1ZYaCr50zNOQyulQnqTMVNVE/a2jI6BQRUWajx/2+0zyVFEL87iWnc96bVsY3KkSEw5f0tb0ZVSsYnarAZhAblZ7OGZXxkgnTZg1GxdA19OaNlvJJvPUDTKXu8knm154o10AET0/pycUX2YF6VJR/T3FCqEuuoM3nx5lbrtnIZXTvyVnFIPGD0GE4zn2jmAFnGpypqHznFdPZW1dWh82iXU98/XxGizRaVdMxAFldrqlUgu6vCAOU1R2mJGMqpsW8v+80pDiFEI9sHMVhi3obDsM4WLVfH/64fSLW02knsdPNYBdpKoMdZCo8Oou7vDj68pnIXhYqmCjX0JXRkdGdX69WmcqU65fnLs/ubFL3l9nAVAA1xsBRqtaZSt5QD/EFnEikfEbz3GcqT84Vz0Do3n2jjESD+0sx74Qf0nl3b7KwYs4gcu5nkRk522YwbYaMriGXkWsq/D0eUhwV/ZXLaMhEGKqqZaeaSopw1Cynz/UJB82PHhyClYt6UDHtjpY/iQMeMjztTIUL6e4vMEdv3mhLY6OJcr2gIQBfbkkyo1KsWujK+tbLx09cBJqZBhCvJLqjDzj7UM3p4CibFvIZHRmdoFHdYMhQaWAFuvcZ5HMaDZEqU8m6zAOQ537wPXEjJNtPzXbHGpqjqSi4v1RCirkRzOhaKANhjKFmOa43Q5O7ydqNWWdUiOgsInqeiDYQ0ScE73+ZiJ5y/7xARHt971m+9+6c3p13Fs++OoZi1cIJKxYkXuNgtzrtS7tmRxl8bjAWCoxKfyGDjE7Y2RH3V2N0Fkf7mEqjUelu0aiUqqb3pA7Ej7ziKNfqQns9tDcOUzFRcOeriNocls1QsxjyhhPOnDP0yFBcwGdUfO6vKJebf05UxBWHI3zXWZTMUNSZio5cRHJl1WeAcoYe6dIC4JVpiXR/uYYqbJxlMzAGZFw32XQaFSN6yPSBiHQANwJ4C4BhAI8T0Z2Msef4GMbY3/rGXwXgWN8SJcbYMdO13+nEoxudzsj+PvRx4RmVnZM47bB4yZOdADcqgwL3FxFhoJBN3JBKhjpTaTQqvXkDW9uQxT9ernmCKwD0tJBbAtTLiHDETTzkKNUao7f42nH2wZkOF55tm0VGInLj05XV3LmaGlPxub9MN3Q26nD0hHpDQ1Z3NBLTsmHo4c/PFS6mG9GuOT97iso94aVX+MEuawCmqqlYrkvNEerDNRXv3oZrVOawpvJGABsYYxsZY1UA3wNwgWT8xQBum5adzTAe3TSKQxb2CF1FqhjozmJ+dxYvtbEXeysYmaigN2d4B1UQA4VsW/JGghgvudFUXZ1xf42XzQYWVIgR8SRCqWY1fEfdbuRWHDhJgZbnRuJ7isdU6vvgriIVbYQbFX7vKFcQh1+oz3lajJr7K+/mhgBykZox1hBNBURkqZt+oxUR0msG3V/RrKYnIk+lHiigSxkIn5/Ro4ME2o3ZZlSWAtjiez3sXmsCER0AYAWAX/su54loLRE9QkQXht2EiC53x60dGZmdfUb8MC0bazfvwQktsBSOgwa729qLvRWMTIrDiTn6C633IRFhvCx2f/XmM4kTFP2YKNca3F/8STVJGDDQzFRUw2v9qFo2bAbPKHR5onSM6K+aVXd/GerzuauLz8lHhNdy+MN3s7qaEQsyCUDObkzXTcSjqaLuwZkQN0I1afJh/WBXzaj3NJUQvcpvqGT35/fO6jTt7q/ZZlREPDrsb+09AO5gjPn/Ve/PGFsN4BIANxDRwaKJjLGvMcZWM8ZWDw3FD8+dbjy3bRyTFRMnHJRcT+E4eKgHG2eLUZmo9xwRYaCQTVTiPQrjJSdEtyfbzFQmK2bLWfwTZbMpCCBpGDDAkw7r6+VjRFBxlH1uIf8aUSG6HIwxx6hwo6QY4uvcux4xxfegYozqorvmzVU3Kj6moqBl8LBfIIqp1F1yUSG9Ff/aEeyMu/oKWQO6RqFlVXhJ/GyEW4vvy3CNZWUOu7+GASz3vV4GYGvI2Pcg4PpijG11/78RwP1o1Fv2WXA95cQ2MJWDF3Zj12S1LfkYrWJXSDY9x0B3/J4fKhgvO6G1QS2gN5+BZbPE+SQck+V66C6Ho4MkWzco1He5B2y8PBGua9RdUM51tcOmYtpgDJ4bqh4plcT9pchUas1MJVpTqRsiJeYROPij7uFnQrLoK6CRLUR9Zr5OVE6L5/7S5SHFNdOn58xx99fjAFYS0QoiysIxHE1RXER0GIABAA/7rg0QUc79eRDAyQCeC87dF/HoplGsGOxWLnUvw0GDjli/cdfM6yrBUilBDLjur3bX/xov15pEeqAukrbiArNt/kQfMCrZZBFbQLP7q0sxEsoP72D3uaAAtdBeoLGmln8dtTIoXBvxayoxQoozaq4soO5qa5ijlMxYzytS0Ulymfiaigpj4sYwyqjwNcPuX9dUKPLe7casMiqMMRPAlQB+BWA9gB8wxtYR0fVEdL5v6MUAvscaT5tVANYS0dMA7gPwRX/U2L4Ky2Z4bNPutugpAHDAAqcZFm+ONVOwbIbxcq0pAdGPgUIWps3aXv9rvGQ26SlA3agkKSvPUfLcGI3BBz0JCjh6a1brAjsA5ZwN0b6CQrtKaC/QeJj596BaWwsA8r65UeVNgID7SyEyC2iMGFNhN9WkTEXXI5MP/ZpKVHhzxbRBBBhua4Kwz9nElELGmTxHRuHe7casCikGAMbYGgBrAteuC7z+jGDe7wAc2dHNzQDWbxvHeNlsKenRD95hcaaNyljJ6Y7IqwOLwLvb7Z0SM4ukcJhK8z99fo9WclW8niMB91chZ0hDSmXwaxlAMpG97CuECMA7pFXX8OdcAHV9JJn7S1Mqrln2u78Mu2EfYagEnvij5lR9GgVnKjJm48+ozxhyoZ7flxuVmsVCQ7B5QiORXFhvFOolIcVmYzhznAeQVjGrmEqKZjy6ydFTWkl69KMrq2OoN4dXZtio8FDhgQim4h/bLoyXalKm0or7i7ORQibIVJLlllRNpz99q+4vfqjwg13XCBmdlMV+/wHpXyeR+ysjr9jLwQ9w7upx9qFepkUlpJjvLaurudgqvvFZXUdNUSfxXGt2OAPh+5UJ65yBRCU1eu4v997TWZopNSqzHI9uHMXy+V1Y0t/cyCop9p9fmHmjwnvEyzSV7vb1OPEjmPHOwRMWW8lV4UyFl0HhiFsqnoMbA3+ZFpXM7yDKZqNRAeLV7/IfkHwuENP95WNJqiHFGZ2ga6QkujvvW8ga7hO/ivuLa0UZ38EfESZsaARNI2QMUtJrnJIqDjsxJSHAfL9ZXQs1VnxvhkZuoIA8pDijEwxNzqjajdSozGLYNsNjm3e3jaVwLB/owpbdM1v/a4+bfyJzfw20sceJH+Olzgn1nKl0BYX6hKVVijWX+WQFmkoc9xdnKr62zTk330UFQU0lTkixP8wXcIyLklBfs705Kq6s+pz6E3/UHH80VVaBDZk2g+EaiCihnh/kWUODoTlrhxkV06qvq2sEM4RZ8PmGLhfq65Fnzr3NEIbUCaRGZRbjhZ0T2Fus4cQ25Kf4sf/8AraNlaY1IiSIOO6vdvQ44bBthslqcx4J4DcqyY0YDxvuFgr18l4dIngajcD9FUtTMRuFesCNwlJcoxIwKnFCimsBlpMz1IxZxa3JBThP5hpFM5Wq1WxUVAo5NmbUy5lKxjUQGV3zysCEjeXjvLVDDnfTZp7hMXQt3Ki487n7MjSk2HdvXQ83Up1AalRmMXh+SrsivziWzy/AZsDWvTPHVnj+Sb+EqXDdI6nALULR7SbYIzAq3VkDRK0ylebDG3DKolg2i90syXN/ZZqF+ljur4BQz3+Oq6nk9Pjur2ajoh5SzFmZJ2BHJPGZlu25sVRCioV5KhE9SjijiHKX+Q0Wrz0WylRsu76uRqGGyvTqiZE0+bLqE+qd9VKjkgJOfsrS/i4sn19o67p8vS17Zk5X2VOswdCoKUnQD10j9OYMr6xKO8DrbwXdUwCgaYSCYl/z0Azfx/4AACAASURBVPVd91d3YP1CAkPgrFfPtOZIVGJFYJziVBoOaiqqGe7+MfxpnRejjGJtvNAjh0oSX83yu6ecz6qWH6J7DERe1qVenJJ/ntA6XQFdw9lfuLHgYww93AhwxqG7moppM2G5Hr+momupUJ8CTlmMduan+LG/a1RmUqzfM1XFQHcWRPIKt31dGa8AZDvgCekhRSwLLeST+NcP5qlwoxC3EGRdoxFFf6mzHqFQH9Fkyo+gpqKa4Q74DjitziAYk7uZACfnJOszKo4xiui06GMqcUOKNe5SitBJMu7hzw1eOFtozBWRja359p3RNYmbzO9SC2dWQdebTPtpN1KjMkvx0sgkdk1W25af4seivjwyOs2sUSlWpSI9R7sqB3NM8ZBfAVMBHGOTtJwK4AspDjCwfIIwYKDOMAoBLSTuWlyo9z/5O5pKMqHe0DVX41Bzf2V08vIzvBIxEXNrlt1gVLJ6tLvO9EdRKQjvwc+VkURe8T0ZvsOfXwsby9f2hPoQxmDZDDpnKhJ3FWcchmsA/ddE984YGgydUqaSAnh4Y3vzU/zQNcKygcKMJkDuKcqz6Tn6ujLtdX+FhPxyFLLtYSpdgTyVJDpI2HpEpNwql6Ns2shntAZmGIup8Cd6X18S1eq3VbP+FA5AKR8EcFmBf14mpvtLpUyLT6Pgc6I0lUxQUzHlmkpG17w9hRogm3nGylDok2JoGnRJRFnN97kMTR6l1m6kRmWW4tGNo1jUl/PKqrQbywa6ZrSt8F5FptKXb6/7i+eKBN1THKotaMNQrDq92PVA1nQSHQRwAgv4vhrWy8pb2QZRqlpNhi5OS+DgEz2AyG6GHEHjEOUK8u7pMhxvngJT8buRVFx0ZsA1Fx0m7HNTeUZL/PdQs+qlV6LyVEzL9txqGUm0luW6vwzdt6bAVdYYUpwylTkPxhge3eTkp0RpDkmxeF6+LV0Ok2JPsSYNJ+bo62qvUF8SCN9+FHJGrL7tQRSrpnBt3vUwrvurHBJNlje02AUl8wGjkouV/FjPueBQ7ShYDbixMop6jP8Ad/arwlT82gRF3seLpvK5v2SGqzFPha8vPrArbvgxEfncX5KQYp27vzRJmHLd/cUfXEQGKBhSXEuNytzGpl1TGJmodERP4Vg8rwu7JiszkqvCGHOYiiSbnsNhKu3UVLhQH66pJO3QCADFiiVkQfxAj8uCKgKBHXA0mlhlWmpiphI/pLi+hgpz4HP9bjOVasD8/UajEi3U13zuKR6GLOslwo2i4dN7ojPqGzUbWUQX30vd/RXOVLw8FUkGvKep6JrHrsRGxRclJjFSnUBqVGYh2l3vS4Ql/XkwBuwYn362MlkxUbOYmvurK4OJihmry6EMdSFdpqm05v4SGZWk7q+KaUOj+qHnX68cM08l1wpTEbq/1DSVWtCNpVhypWaypnlR9zODhigiDLme91FnKtKMepGmEmpU1EX9BqYiEdb5fN3PVARr+j+XrhFshrb9DkUhNSqzEI9uHMVgTw4HD3V37B6L5zm1xGYiAZKXXVES6vMGGEPiVrxB8MiuME2lO6d7EWKJ1g91fyUT6ss1CzlDb3KDdmXiMZVyzfKae3GolqAHxEZFVagXRXE51+WHXJCpRLXvBRw3HX/iBxy3lowRmbaje/ADOhvBVEy7ziiiqhrX7GYDFF77K5hRH258nPVIyn4s3+eqay+pUZmT8PSUg+Z3TE8BHKYCANtmQFdRKdHCwbPq2+UCK1VNEDXWwPKjkDVQbCGkuBTBVOJqKhXT9hING9aL6f4SayqOJqLyBFu1rIanYyCGphKI/lKt41W1mt1mKi6zrFHfY0aSSMjvETRc8h4p6hn1lt9QRCQ/WrbtjckouL90ra7TiFiNU/aF1xILH9cJzDqjQkRnEdHzRLSBiD4heP8yIhohoqfcP3/le+9SInrR/XPp9O68Pdiyu4RtY+W2tA6WwWMqY9PPVFSKSXLwGl3tigCbqlooZHRhTwvA0VSqlp1Ya5qqNnd9BJIVgQScAokiA5jPxIv+Cman+/eURBcB1DLcAYc9ZNqgqUSxDqDZ/WVoCiHCQUMZkVGvGl1Ws23PCHtMRVIosu7+kgn19Wg1WZiy6ct7iao71m7MqiZdRKQDuBHAW+D0q3+ciO4UdHD8PmPsysDc+QA+DWA1AAbgCXfunmnYetvwyMZRAMAJbS4iGUR3zkBf3sC2vdPPVHjdL1WhHkDbIsCKVbMpMdEP/l6p2pjNrYpS1WyK1AIcVkCEWDoI4BZVFDCVOCVWANcoNBkVXhTSEu45an5W0qHQj1pgrkr+COCK7j7W4YT7RrnMAu6vCKbi1z2c8RomzfAHGH85Fb63eEJ9uAHihseQRGtZNgORU1KI70PIVAQsyZqm+l+zjam8EcAGxthGxlgVwPcAXKA4920A7maM7XYNyd0AzurQPjuGRzaOYkF3FisX9nT8Xkv6u7BtBpgK11TmCRplBdFu99dUxQot0QLUy7ck1VVKNaupQRdQT1iMHVJca2YYgBNSHIdNORV/m4V6QK18fTAs2JmvylSCbqzoUF+gmak4UVFx3V8ReSd2YzCALJs9uKdo8d0n1EeUvrf8xioipDjj0174fZrW8xWo1CMahLUbs82oLAWwxfd62L0WxDuI6BkiuoOIlsecO2vBGMMjG0dx4kGdy0/xY/G8PLbOAFPhBkKlRXA7Wvz6UaxawmKSHPyJPWkEWLlmN1QCblg7gVERGQPA7Z4Yw6jImIpaF0aB+yth9FdUzSz/vGxM95c/5Bfg2ekSIxHQe2TZ7M76Pk1Fk2sqwSKRQHieSi0Q/RUWrWUFqhnz+zTd22YNGo0zd24yFdFJGvwmfgbgQMbYUQDuAfCtGHOdgUSXE9FaIlo7MjKSeLPtxiu7i9g6VsaJHcxP8WPxDDGV8XINXRldyb3U18U1lfa5v+RMxfDGJUFJIIhzODpIvKfFiik2UqrdEzlETMNrCazCVASaTNbQlYX6ZMmPgUx8RfdX4xySNqjyh/I6e5P3HvFrNrpXeys6TLgeKRadpyJrPVyz6lqJLPnRFI2bo+6vYQDLfa+XAdjqH8AYG2WMVdyXXwfwBtW5vjW+xhhbzRhbPTQ01JaNtwNcT2l3U64wLJmXx55iLXaYa6sYL5mesYgCL43fLk1lqmpFaCqu+ytBBJhtM1R9PUCC6MrG00EALrCLNRqVYo7eOgI3WpxGW0JNRVmoF0d/yRiBZTNYdlDgV3N/+XWYaOZRb7oFyLPZgUCYsBfRFZ5TUjcU4TklzvXGzo+AmFn4vxNDIv77m35FBQm0G7PNqDwOYCURrSCiLID3ALjTP4CIFvteng9gvfvzrwC8lYgGiGgAwFvda/sMHtm4G4M9WRwyDXoKUI8Am262Ml6uKekpgPOL05Mz2hb9Vax0jqlw5hBmVAoxw4ABnqciZio1iym7NCoCpqLSb4RDxHSUhXpBaHDUfetVdtUNBJ/XaCTkuS1mgNkYEbkwDZnvXu6JilAv11T87ipDYqxMX0SZoYUbqgZNRTKuE5hV0V+MMZOIroRjDHQAtzDG1hHR9QDWMsbuBPA3RHQ+ABPAbgCXuXN3E9Fn4RgmALieMbZ72j9EQjDG8PBLozhhmvQUAFjs5qpsHyvjoKHpMWSA08lRRU/h6Mu3r/6Xo6lIjApnKgnYGzcYwSRDjrhhwEB4ngq/VjXtyMgtxhwGlRNoInyNKIhCih2hXs11JnR/RQjiAAR5KgyMMeHviGUz2AxNrGhSUnbHKWUfjDCThyAbgYguWevfpjwVSZ+UYPZ9WKZ8pkmnETOVppDiaXJ/zSqjAgCMsTUA1gSuXef7+RoA14TMvQXALR3dYIfw8mgR28fLOGmaXF+A01cFAHZOVCJGthfj5RoW9uaVxzuNutqpqUjcX5ypJKj/xV1boe6vjB67NXKoUO+VOlEIB3YPp2CZlnpob3zDwOeraCpNJewVjFm9dHujPgI4B6Zf+K/PqVfw5YiM5vKVnOdzZW4if+hvpFBvM+Qz0Rn1jLGG5l+RxsJXeDJsTX+QwJxPfpyrmG49Bagblemu/zVeMr2kRhX05Azp02YcOJpKtPurJaYScsjH7YECuMmPIUI9oJ64CECYvOh/X7qG0H2mKbUFDkZYqSQ/+qvsqs4LZzfy0vf+5Meo3iPCiC6ZTuITy4nEY/lZHww/Fu3D796TRZT5NRUvR2aOhhTPWTy8cRRDvZ2t9xVET85AIatjx/j0M5U+RU0FAHryhtcHpRXU3Ex5GVPxQopbYCoiZsHXTlSmRcZUFEV2AEKmwe+hsoYopJixaAE4qOfwci8qveMbckgimmLViyjGyFMRlGkJYzaMMTeiq9GlFfb5/V0iAcdYiFx+/iKRgN9YiYX6Zk0lKvQ4ZSpzDtOdn+LHor48dk5MH1NhjGE8pqbSkzMw0QajEtY/3o+soSGra4mYSt391T5NJUyoz/rcX1HgRiO4jmoHRj5GlPwYNd9x7die64ojKpLL34qXw+tfEsFUjKCRkLmzrMaQYqVijlq9tL6uhYcsB910jsESswr+vn//4uZbjR0iw8b5NRXdE/5TpjJnsHm0iB3jlWnLT/FjqDeHndPIVKaqFmymlk3P0Zs3MNmG5MeoBl0chZyOUoLoLx6aG+xbwtEV06gwxoQ1uwC/ptI6U1HRRSohmop/fREsm4EFxHPADUeOSDIExO6vsEO8KnB/Gbq857y/lhdQL+YoculxRtCgwUT0k9cDiZjinBLXGPIQYEn0l7/wpIwpNZSTkfSy7wRSozIL8LuXdgGYXj2FY7qZCheqVfNUAEfnaIemwkuvhPWn5yhk9GSaSlUu1OczmnJPeCBcYPdfU2EqVcFTPxBfU2lOfow2SqH3jsjGl2oqUe6vQJViWddD/+EL1A2G6ADmmkSzey3c/eXXa8LYGTcKzSHAIfknuor7q66pzPXkxzmJ3764C0vm5XHQ4PTpKRyLenPYMV6JFFvbhTglWjh68k7jrFaftIoVVaZiJMpT4QYjTKjPZ2LmloS4rfzXlEqs1Pg6IdFfLVQpjprPDYCIqcjcMVWr+QA3VN1fWqMhirpPxvCzifCnf854Go2QxP0VdK1pWmikln/fMkZm+mt/STo/mn5NZY4nP845mJaNhzbswptWDk27ngIAC/tyKNWstkVXRcEzKnGE+hyPyGptj3y+TFMBnKKSkwky6rn7K6xXSy6GDuKs5wr/IqYSx/3lhgwncV95a0jcX7I9eMYhMDcTxVQEEWv15l7hxRaBRgMWdpBzBEvfyyKvPO3DaFw/PKTYbgpXFpdeaQyFlocU2wJBX01Tma7kx9SozDCeHh7DeNnEmw4dnJH718OKp0dX4YUh4zCVXjf8uFVdpaQg1APJyqkA9ZDifDZcqAfUyqIAfoYhCylWF+qDTMNww1yV6neFVCkG5EbJc+EJmYpcQAcaD/Do7okC95QhDwgIlr6XRV75e5l44zWS1/7SGl1lYa4q/76NCMOmZHwEmorMDdhOpEZlhvHgiyMgAk45ZGaMylBvDgCwc5pyVcYTaCo9OccAtcqm6pqK/N5xBXWOSkTyIz+EVQ2WrOwLz6hXYSqeGy0QlUZEbk92+RqiOlyAmqbCGYdf5wCcA1a2d6GmEnE/4ZyIvJOaHSzT4h7oIa4nZ0zA/SWtUtwYiSY2FDykWPPG+e/XvGZ08mOjpsJ1opSpzAk8+OIuHLWsX6lfeycw3Vn1vNxKnOgvLqxPtMhUihH96TmS5JMAdSYUFv0Vp9OiM47nvbSmqYQlPwIOg4jaT2j0mFs7rCL5rkQHPRBdxl6kqWQiQmOF7i+3jHyYjhUsyy+LvOKHf7CpVxgDCK4d5irz9u25teShwvx9nlApMhambfsy76e3TEtqVGYQY6UantqyF3+6cmZYCjD9WfU8+qsngi34wd1frSZA1jUV+b2T5JMAjlCva9R0gNbXjcdUym1yf1Ulgr9KqZUwo+TVH5PMD3O95SIYkoypRGXUB6OzZHOa2QR/+hfniAAIZOCLc0+AsLL6MgbUmFQpNGyW3RgooImj2xr6qUgi2jqB1KjMIB5+aRcsm+FNK2eu/D7Pqp82plIy0ZMzGp72otAu95dK8iOQrJwKAJSqNvKSHjFxSqs448Iz9BO5vwTrqDTaqlhixqQU/RUq1KslPwrzVCTCePOc6Kx3UYSZ+EAX5KmEaEOMMXGeiowBBfvZh4YUNwYKRLUTToX6OYQHXtyF7qyOY/fvn9F9LOrLTxtTGS/XYtX9ApyQYqB1ob5YNaFrJHxi9yNJiXrAYSqy4o65mEylrql0JvmRX4syKpHJkwqFIUXhyHItRlRyRR5SXDWbNQ8Z83D2J65LJsxm99xfCuzDFrMalTwVeU0vAVMRlr5vFurTkOLXOBhj+M3zIzjp4MFQd8l0Yag3N41MJV7dLwDocd1VrZZqmapYKGT1yNBt3vY3bu6OU1Il3KjUo78UjUpIfglQP6RlegYHL08vNCoKQn0rGflhc6MCBETZ8UkKSvKnetEebbdUfrCqMRAukgON0V+6RqFMwX9/vn9h/ksgvyZqD8E8GeH9fdWM0+THOYLnd0zg1b0lnLlq4Uxvxcmqn1amEs+ocKG+HUxFVkySI5/VwZi6m4qjXItgKjHYhTOO56k0/5oSkdv9MY77K6Gm4h3WgeRJPfrzhAn12QihPkmVYqH7S3Kg1oTuMkk4r6C0flh0mSj73gip/WU1MZUoob6+ph4i/vsZTZr8OEdwz3M7AACnzwajMo1Z9U4r4XhGxdA1dGX0lpMfi1UrUk8B6tFb8Ys/isvUcyRnKuI1VY1Kp91fcaO4AJXaX81aTFSzqZrE/SU2EjyTvVlTETIKu9klFxZSbAnWzujiKsXBfUgj0HydJ/leRNFfVqqp1EFEZxHR80S0gYg+IXj/I0T0HBE9Q0T3EtEBvvcsInrK/XNncO5swt3rd+KY5f2xmlV1Cjyrvh2VgKMwVqrFylHh6MkbrYcUR/RS4fCMSkxdpVS1QsOJgXhhwECdqYTmvWR05dpfRI0HHEdL7i8FoT4s8ixraKE1vAB/eHB9z5EZ9bbI/RV+QIvYkCzxsF6mJSDUS+qE6U1l9UVRZY37kLUpbmYqYqMmqhE2J5MfiUgHcCOAswEcDuBiIjo8MOxJAKsZY0cBuAPAP/neKzHGjnH/nD8tm06AneNlPL1lL95y+KKZ3gqAegLkrmnQVZK4vwCgtw2NuqYqZmQ4MVCv3RW7n7xphRoAwJ+nEk+ob5Wp8ErHIi0pa2ioKLu/4jOV0DwVVaYSOMABhFYd9g59QWmXsDLyzl6agwFkUVpB91pY21/+vrd/TaypNLu/1DLl+V7EIcX1ci68RP9cTX58I4ANjLGNjLEqgO8BuMA/gDF2H2Os6L58BMCyad5jy7j3jzsBAGeumh1GZbDHNSqT1Y7ex7YZJivx3V+AkwU/2WKf+mLVQncH3V+lqppRUS7TIgkFdq6ru79EiY98jaRMJaPAVKSaSsQ8QyNoWvOBH14VWOSe4oZILZkxqpdJ8/ohTEEYfiyO1OJGoe7+4sY6JFPet2aYsQiWiIlqq9xOzDajshTAFt/rYfdaGD4A4Je+13kiWktEjxDRhWGTiOhyd9zakZGR1nacAPc8twPL53fh0EU9035vEepGpbNMZaJigjHEDikG2tNSeKraWaZSMW2pUUlSUJKoWY+or6erVSk2bWRDDFOUYA6Eu7C8EF8F95fIIMkYUrCvPZ8DhLfFFQnvsla69U6RfvdXvIx6h32IQ3+dtRtFfbEBCkR/SQpF1oQhxY1r2m4Pm2BS51wV6kW/PcJvgojeC2A1gH/2Xd6fMbYawCUAbiCig0VzGWNfY4ytZoytHhqa3sTDyYqJ327YhTNXLZqRqsQiTJdRSVKhmKMnbySqHOxHqcNCfalqSZMfM7oGXaNYTCVvhIdA5zKaYkFJcfdIQFFTCemJQkSu4B5+WFUFB7dzX6edcFhwSNW0m4xpVD8VUZn9rMRlJiwFI0k8rIUI++Jqws41PTBWGjAQoYGIjIUopDiY98L3MVeF+mEAy32vlwHYGhxERGcC+CSA8xlj3knIGNvq/n8jgPsBHNvJzSbBPc/tQMW0ce6Ri2d6Kx7md2ehUec1lSR1vzgcTaU199dUxYwsJgnU3VRJNBVZSDEA5A0tRvSXJQwn5ojj/go1KnHcXwIXmmqzrTA9RlaTK2jEdI2gkTxPRaPAQR7RHdEZIxL2VZmNmH2E9XaRGSA+loiE5V9EyZeGIKS5XqAymKQ5N5nK4wBWEtEKIsoCeA+AhiguIjoWwH/CMSg7fdcHiCjn/jwI4GQAz03bzhXx82e2YvG8PI7bf2Cmt+JB1wjzu7MY6bCmMpagQRdHT4sthRljyiHFfEySfvIy9xfAI7bUmYos+z9nqK0l6oXCEav2l2CNqF7zUXqMtGuiwIjJmm7V7OY5snphwT4mgDzCzAzJqJcboOg6YaJ1RUmVohDojIypaEGmMgeNCmPMBHAlgF8BWA/gB4yxdUR0PRHxaK5/BtAD4PZA6PAqAGuJ6GkA9wH4ImNsVhmVsWINv3lhBOcdtbhBgJwNGOzJTYP7y+2lkiCkuNvVVJLm0lQtG6bN1NxfCTQVxpibp9I+phKVoZ8zNKWMeplxyup6YvcXvxbFVILsAfCJ/JKKwyKjIuvDUjMFOoxE9BZn4EvyVAQZ9eHuL3EQgDD8WLCuqE2xyKUmMhaiHJmwyLNOIP5vd4fBGFsDYE3g2nW+n88Mmfc7AEd2dnet4VfrtqNmMZx31JKZ3koTpsWolFtgKjkDNYtFiuFhqDfoUhDqE2gqsjpdfuQyOsoxmIpsvVwm2iAACkwlYfQXvxbZrlfEOCLCkatWs6bC58ky6oNzoroj+scA/h4lahn1IveTM1Z0sItLqlgit5agphi/f7ACwJTZyOA946MHjd/c1FRe0/jZM1ux//wCjlo2b6a30oTBnixGOq2ptCDUt1r+fso1EN0KyY9JNJWoXiocquwC4AwjgqkotROOdn/JGGBY+XogOoorzKBlJdoF4AjrImMUVkCRrxWsfi1jRGLdQxb9JTJC4bW3+PveWNdVFfyu+b10LWisAgwkjKk0ub/450pDil/TGN5TxG837MKFxyyZNVFffnCm0slSLeNlE0SO6B4XvP9K0rDiYkWtlwoAN1FQvZwK4Ij0QHj2O0c+FlMJj9ri+1SP/pJ3o1QqChnijopyf4UZI//awnlCDSdcA6qarOleUV0U/WOAqDwVcUKmVKgX1hUTayXBkv3B/BNhmRhBoICYJYlDnzuB1KhME+54YhgA8K7VyyNGzgwGe3Mo12zvib4TGC/V0JszEulJ3KgkLdUypdhLBXCibwoZ3eu/ooKOMJWaHRH9pZanIkt+VCq14rqiRH9vkYUhBToHoFJxOERTMcSHOCB2f0lL2QvLtMjLumiEpoRMeQ5MI6sAmiPeTNspo6OulTTuN/jZghn6/OeUqbyGYNkMt68dximHDGL5/MJMb0eIoZ7Ol2oZL8cve8/RMlNR7PrIEbelMM89idJU4jCVsmkhL3N/ZWJk1EvcX3yMdL7EKEUZpDAtxlk7JE8lTFORRJuJ3F/8YBXl0nh5J4p5KqbA0BmaBsZEhkLk0hInYgaz3/k+gm4tYUixIFCgfu9G42NNQ8FYIKFRIaJut05XCgX8dsMuvLq3hIuOn50sBXCYCtDZBMjxUrK6XwC8/JKkmgrvT6+iqQDu4R+HqdR4mfoo91c7mYrjCrIjonqk0V+K7q8woyQL8eXrioxDZHHIEIE/TBh35jQf+l7tL0kor38O7/seVqalyaiEaEPiLpRigxWsPMz3EcpAgkylKUqsWVOZdSHFRKQR0SVE9Asi2gngjwC2EdE6IvpnIlrZ2W3u2/juoy9joJCZNQUkRRjsyQLotFExE4UTA77uj4mF+phMJROPqXBDEe3+ipunIhPqnfdU8kxCmYpipeGk0WPVEMFdpeGWUIsxwjP4g62BAV/tL8VkRoD3SBGHCRtN7jVxCLK8rH5AK7FY07qi8iv8MzRm6Te3Exbfe/ZpKvcBOBjANQD2Y4wtZ4wtBPAmOEUdv+iWTUkRwOZdU7jruR245IT9pQfETIO7vzqZAJm0QjHQDvdXPKYSt6WwulAfI6NeQagHogMKWnZ/RUaPhT8B1ywxS4pqDRymxWRDysfzezW7p8I1En7voOsprJlWzdejhIO7mKwQAyDSa5q0ElvMgILGwhIK9c3uQKGmEhKl1gmoPjaeyRhrqpHBGNsN4IcAfkhEyU6L1zhueWgTMpqGS086cKa3IsX87iyow6VakrQS5uhp1f3FhfqM2j/5fGyh3vnFVmEq6smP8pwcFYMAyBkPX0PGnqI1lfDPE5oZ7+WpSFhHSPSX3P0VUi8sBlMxBGG6znhRIIBYJxGF/9ZzYJrdVcHkUJGbTxh6LNRUOKNpdOuVarPI/SUyKEnGzDXsmari9rXDuOCYJVjYN/PNuGQwdA0DhWxn3V9lM1HdLwBub/nkLYV5SHFUbS6Orqz64Q/U2UK0UK8mrgPqTEW2HmMskmkAchdadJVjSUHJEPdXlNstTIsxJAUsRQaM1wuT9UdpdmmJw5aDDbKAcENRC4nA8t+3vm/W0HfF2YOoTIuI/Qhqf8VIvOwEIo0KEb2FiL5ORMe4ry/v/LZeG/jqb15C2bRw+Z8eNNNbUcJgT+eMimnZTi+VhO4vIkJPNnml4qmqhayuhR6uQXRl9FgZ9SVFTSWfcYxVVD4QYyy69pfX9EvuugIkjb4UQ4rDhXqKmMtC802A+JpKVqfQPiyi6Cx+L1HYL9+3qMR+WIfGTMD9FSrUi3JauAESGItg1JowpDgkoiwYqFEX9P1Cvdz4txMqvoAPAfhLANcS0XwAx3R2S68N7Bgv45u/24y3H7sUKxf1zvR2lOAkQHZGw2xHhwAAIABJREFUU+H5JUmFeoDX/0pGiItVU6mVMEdcob6sGP2VMzTYzDlYwvqkAM7TK2Py9VT6s0R1j1QLKbY84yOaH6qL1Gq4+HV5LO3TsX79+ob3TMvG189fjPn2Lqxfv7dp7hdOm4+uLGuad9nhWZi20XQdAP7ujd0wNGp676ZzF6GQrTVdP6qnhq+fvxjDm17EVl9C8hdOn4+sgabx71mpwzx4XsP1gzMmvn7+YuzdugmTO+rf0REF5/rWlzdgu7v2Ylj4+vmLUR55Bet318e+6xANF67ob1j3r4/tgs0a99BVc+Z3TW3H+vVuo78lJk48e6FwXN437rLDMzDt5r+HKOTzeSxbtgyZjPrDoMpv+AhjbC+AjxLRFwEcH2tXcxQ33PMCbMbwt2ceOtNbUcZgTw5PDzf/grcDrdT94ujO6ZhKyFSKVQuFGDXDkrq/VJgKHy96qvbWcw1Fq+4vWd0u//WoCK6wqLmMHt7BcXh4GKsOWIh5/QM4cLCxIV3VtMC2T2DZQAHzu7NNc+1XxzDQncWS/q6G66+MFlGqWThsv+YHNW37BPIZDQcs6G5ca+sY+gtZLA2stXO8jO3jZaxaOg+az6ho2yfQldGx/4LGnLLNu6ZQs+yGh8S9xSoyu4tYuai3Qf/aOVHG9rEyVi2Z5yVLjpdqMEancMjCnobv8+XRKVRMG4f61t20awqmbWPlwvq18VINemD+9rEyRiYqWOUr/SQa98roFEo1W/i9hYExhtHRUQwPD2PFihXK81R8Ab/w3eQTAL6tvPocxRMv78Ztj23B+046cNYmO4ow2JPrmFBfr1Cc3Kj05DMtJT8WYpSH6Yop1JdrTra1jH0AdeYR1aiLZ8rLmIonskvWCuvaGFxDmqcSocmE1f4ql8vIdvdB05rn8lJFYW5ABkBUzYhINoc1GAf/vURz+JXgDHLXUtkTfxlcngkW53NFY4V7CI4T7JfI2av/8wk/F5HwM8lARFiwYAHK5XKseZFGhTH2U99NbgDwH7HuMMdQNW1c86NnsbS/Cx95y77DUgBgsDeLqaoVu4+ICupMJbn7qyenJ89Tqaj1p+fgGfWqtdBKbi+VqLpuqi2FK0pMRW8YK14ngqmo5qlIor9q0oKUJGzn6h3GIbMYY8KZziEaNidsB+L3+D2a/s4ofHzw+PeMY2BXQgMQeM8/tmkLoi/N3ZR/v8I1+eaD4xJIKknqFMbNqJ8EcCcRdbs3fCsRPRT7rq9hfH7NerywYxLXX3CEUpfB2YROthXmFYrnFVpgKjmjhZBitf70HPmMDsbkriU/yjUr0vXF13XGR4cBA2ruL6Ue8bo8pDhx8qPulCkJ69XBWBjjIO/95jksnKmAQo1H+L3E56mUDamuH8I+IDBYFEJVhMZKsKbwYwusSphBmx6ZPqZRYYxdC+A2APcT0W8B/B2AT3RiY/sibl+7Bd/83Wa8/+QVOGPV7M2eD0M9AbIDRqUtmoqRuKCkatdHji6f9qEClQZdgHrCYsWrJRa+Jg9flhm+KMbjudAiyrSE6T98flgUV7hLqv5+05zmB+2GeXHcX0SEj195uTfHNE0MDQ3hvPPOE7qdAG64QtxfTWOB//5/N6NYLHrXDjzwwFCDxdfh+OMf/4jzTvsTnH/GyXjppZca9t3EfgTfC2dz/rFBo3LZZZfhF3f+ONQYA8ANN9zQ8BlaQSyjQkRnAPgggCkAQwD+hjH2YFt2Ur/HWUT0PBFtIKImg0VEOSL6vvv+o0R0oO+9a9zrzxPR29q5ryj8+MlhfPyHz+CUQwZxzTmvm85btw2cqXSir8pYC71UOHpyhlduJS6KVSuWplKI2f3RaSUc/euUVwgDBtSEes4+WhHqc+4ayUOK5UwnlD343m+a4x6L7XB/dXd348Xn16NULAEA7r77bixdutS3t+h7MMZg246LLzieiPCd/7oZU1NTTXtpMkACovKTn/wEZ5x1Ln7+64dw8MEHSz9PmKYSHCs0yhGayowZFQCfBPApxtipAN4J4PtEdHpbdgLALVJ5I4CzARwO4GIiOjww7AMA9jDGDgHwZQD/1517OJye9kcAOAvATdNR9HLXZAWf/PGz+NvvP403rpiPr73vDdKontmMoQ4WlRwvmdAIsXSNIHpyTp/6JD1fpipmbE0FgLJYr9KfHnDaCQOILCrpCfURVYoBuabSjugvpYKUMqMiuE5E4YxAxlRC5gDh7qw/Pf0tuPfu/wEA3Hbbbbj44ovd8Qw3/usX8C//8i/e2Ne//vV49ZWXseXll7Fq1Sp86EMfwnHHHYctW7bguo/9LS4480044ogj8OlPfxoAcPON/46dO7bj3LPegtNOOw0AMDQ0BAagWJrCueeei6OPPhqvf/3r8cPbb/f2CQBr1qzBDTfcgNu/8y38+dvPAQBceOGFeMMb3oAz/2Q1vv/f33DucfPN+NjHPuZ97lu//S1cddVV7vdR/+w9PT34u7/7O5x28gn44HsuwMjILt/35gy69957ceyxx+LII4/E+9//flQqFXzlK1/B1q1bcdppp3mfoRXEcvozxk73/fwsEZ0Np0zLn7S8EwdvBLCBMbYRAIjoewAuAODvNX8BgM+4P98B4D/IeXy4AMD3GGMVAJuIaIO73sNt2lsDPnbH09i8q4inhvfCtGx84JQV+MTZr9tnDQoALOBFJSfan6vCy9630qCsO2fAtJO1FHbcX/E0FUC9pXBJUVNRSVh03ud5LwohxRJ9pj15KgoFKQXuL8acg5uI8I8/W4fnto43vD9VNZHRtaYgAAanAkLO0JqSAmuWjappoztn4PAlffj0/zrCvRcLFffPu/CduPFLX8QHLnknnnnmGbz//e/Hgw8+KDV4YDaef/55fOMb38BNN90EALj6E5/CfgsHsaw/jzPOOAPPPPMM/vrKq3DDl7+Mn//ybqxYvhgA8Pjjj2N4TxEP3XcvlixZgl/8wgmg3bFrN3aU6+67c845B1dccQXKLIMrrroaAHDLLbdg/vz52LB1N84+/RRc9f734p3vfCdOOukkfOy6zwIAbv/BD3Dttde6m61/aVNTUzjuuOPwD//4eXzqM/+Iz/2f63HTjTe6n8mJxrvssstw77334tBDD8X73vc+3Hzzzbj66qvxpS99Cffddx8GBwcF30g8qFYpFp4EjLFtAM6QjYmJpQC2+F4Pu9eEYxhjJoAxAAsU58Ld6+VEtJaI1o6MjCTa6ObRIizG8BcnHoC7P/JmfOq8w/dpgwI4roz+QgajU50R6lvRU4DkLYUZY5iqmh3WVBSZSkZRU+E971usUhwV/cXLmFQt8X68Mi8RmorYKPFoJfHeQiOSZFQlBJ5rSDBl1etfj+Etr+C2227DOeec0zgnxDXHGHDAAQfgxBNP9K7/z50/wrmnnYxjjz0W69atw3PPPRcexcaAw1YdgXvuuQcf//jH8eCDD6J/nriNuJ9hfeUrX8HRRx+N8996KrZvfRUvvvgihoaGcNBBB+HxRx/F3j278cILL+Dkk09291rXVDRNw0UXXQQG4Ly3vxsPPdQYQ7Vp44tYsWIFDj3UiUq99NJL8cADDwj31ApUH93uI6IfAvgpY+wVfpGIsgBOIqJL4VQy/maL+xEG0imOUZnrXGTsawC+BgCrV69OFBTxg/99UpJpsx4LujtTqqWVul8c3dl6peIFrv6jgoppgzHEy6iPqamUajbmd6u4v1xjFRFSXM/Ql2gqKnkqEWVa+DphTMW0ncz+KE1FVAKkrgGQxyj8eG7rOOZ1GVg60JjLValZeH7HBPafX0B/oTExcmSigm1jJRyxpK+hYCIkdogAnP62c/DRj34U999/P0ZHR50pjMHQDdi+Ei7lctkJKYajx3Bs2rQJt3z13/Hzex/EkQctxWWXXYZyuRyab8MArDj4EDzxxBNYs2YNrrnmGpxx5pl4x19djWCgHH95//3345577sHDDz+MsSrh/LPf4uWIXHTRRfjpj+/AwuUH4YILL/TuK9Km+M/+oAUCIvvutAuqj9ZnAbAA3EZEW4noOSLaCOBFABcD+DJj7Jtt2M8wAH8nq2UAtoaNISIDwDwAuxXnpohAp0q1jJVqHtNIiqQ9VTiz6Y7h/uqK6f6qKAr1ng7ShpBiXSMYGsnzVLhxkjAeWffGVjSZKMJBYfkg/P2QOf61OWypuE9458XvxXXXXYcjjzyyYX9L998fv//97wEAv//977Fp0yZHtwmsMT4+jq5CN+b1zcOOHTvwy1/+0nuv0N2DycmJxs/AgJ07tqNQKOC9730vPvrRj+LJJ58MfML6JyYijI2NYWBgAIVCAS+++DyeeXKtN+LP/uzP8Muf/wy//OkPcdG7L2r+PgDYto077rgDAMOan9zhsRmOFQevxObNm7FhwwYAwK233oo3v/nNAIDe3l5MTDR+hqRQ+i1jjJUB3ARH/M4AGARQcsu3tBOPA1hJRCsAvApHeL8kMOZOAJfC0UreCeDXjDFGRHcC+C4RfQnAEgArATzW5v295jHYk8P67ePRA2NivFTDIQt7ogdK4PVUiRlWXIzRn54jLlNRF+rV3Gp1o6LQ816hoKSskGbW0ENdaPU8l+QZ+VKjIrjOJNZI5m4KuxcBWLR4KT784Q83TTnr3Atw909vxzHHHIPjjz8ehx56qLNGwGodffTReN0RR+GMP1mNQ1ceXHc/EfCOP78M77zwfCxbugT33XefuzbDi+vX4cr3vROapiGTyeDf/+NG9/M1750AnHXWWfjqV7+Ko446CgccdAiOOna1N2RgYACHvm4V1q9/Diec8Ebh99Hd3Y1169bhC1/8J+S6e/CzH9/RMC6Xz+O/brkF73rXu2CaJo4//nhcccUVAIDLL78cZ599NhYvXux9hqSI/ejolrjf1tJdw9c2iehKAL8CoAO4hTG2joiuB7CWMXYngP8CcKsrxO+GY3jgjvsBHFHfBPDXjLH2p4a/xjHYk8VoB5hKKw26OLyWwjHDiuN2fQTiMxV1od7VVKKEegX3l/O+rhb9JdH7ZIYpyih5zbaETCWcPfDrtoCq2J77RjAnzN3EjUpg/OTkJF4enWqwQqeeeipOPfVUbByZRFehgLvuuqthzvDuIiYqJv7whz80XP/cDTdhoNBYj6xq2rjkLy/Hxz7yYczvrrtkGQPedPqZ+MDFb/eumZaN57aNNxjEz3zmM1i/bRxEQC6X8xjQjvEydoyXceTSug5z6/d/hJ0TgZIpAer22c9+Flf+/SexY7yMhUPO3G9+85teLbIzTj/Dx5jquOqqq7yIslYRy6gQUQ7AOwAc6J/LGLu+Lbtx1loDYE3g2nW+n8sA3hUy93MAPteuvcxFLOjJYaxUk0b8JMFYqdZSNj3g7/4Y71nBYyoxqxQDcZiKrZanwkurKLq/ZEI94PapjwgHBqK1maTur5yEqfDDU2QcgPa6v7zcFiG7EedohCYohpZpibMftTwV/jokUT+wjuMmiyrTUid6/nH+JMl2xFSFI+6p8VM4obsmnARI/ifFawQ8AbKdEWAV00K5ZrdU9wtowf3lGqFYmkoM9xdjDGVTzf2laYSsrkUK9ZWaBSKFApVR7i8FpiLVVCKEfk+ol+whzP2lRbi/xAbCHdM0p76m6P5hRiIspDh2QcmmtQWJkoLsd/66OamyvseG+zfttT5ucnLSG6myXqcQ97d8GWPsrI7sJMWswKCbqzI6WcXieV0Ro9VQ76XSIlNJGFJcd3+pM5WcoTmx/QruLx5dppo7kzM0Jabi7EFuVLIRa1VNp3pyMN8juEYnNBXGnP/I3F+xkx8l0VZhCM/CZyASVFCGiE2I82DiMJUwCyQ+6JuZRRSjkd07VIuKQJJE47hM5XdEdGT0sBT7KhZ0oP4XL9HSakgx74cyEdOoFF2jEqfAJxEpl79X7aXCkcvo0UxF0le+YS1Drqk4LYnl67Ti/pJ1cLS1DMziOMKOskTur8AYb04Eu4nHVMIP32amImYfYDFYjWhdwWBhXo3Aqgk/VwKrwvup5PPxWqHHZSqnAPhLN5y4Av73xdhRMddJMUvBi0q2U6znFYpbFeo1jdCd1WMzlSTRX4B690evlbDi+vmMphD9pRiirOD+itLGZLpMlFDPGYxoD6XsPKx7cRP06qTQfbZrsgKbAdXRxpyjUtXC6FQV2JtrSigu1yzsmqzC3pNrWLNq2tg5UYG1O9vEGPcWayhWTdBYI/PeMV6GoWkojTTmwoyXapgomzDG6+MZY9ixt4xyl4Hdvn/H/Hqpy8Co7/rOiQo0Aiq7Gj/bzj0llPIGdvsesHbsKaGYN7DXd22yYmJvsQZtLO+1D95TrDp9e8bqh7zoc+8tVlGqWtB8+5+qmNhTrIHGcl5bYxXwzo9xENeonAVMaxXlFNMMr1RLB5hKq+4vwHGBJdVUYhuVrFqf+rJXUVjtlzXKEPA1lZhKJsL9JSkGyZE1tNDcnyj3Fz/YRUylahM+98Aofvj/HYZVBww0vf9X31qLbWMl/OJvGjuU//jJYfztnU/j/o+eigMHG7s4PvzSKD743Ufw3Q+egGMOrpcUeWzTbnzwOw/jvz9wAo5d2Vhq5PNr1uPbD7+KP3727IbrH/qX+/H6pfPw7xevarj+b/e8iC/f8wJe+vw53oE+Ua7hnM/chU+eswofPPYgbyxjDOdcswZ/c8bKhv5Jf//vD2Jhbx63XNb42S689pe47OQDcc3Zzj1tm+Hsf1iDq89ciat9XWK///gr+Pidz+KhT5zudaz82B1P44EXduORfzjDG/f0lr344Hcewi2Xrcbpr3Mqo3/yx8/iV+t2Yu21b/HG/ej3w/hIyHfabigZFSL6LWPsFADr0GhQuIHp68DeUswAunMGujI6RttoVMZdIzCvhf70HN05A5PTEFIMxGAq1Xjur3xGjy4oaVrSxEeOnKF7XTWF69TCi0FytJL8KKtSzLPsw+uOkdAY1UxnXkYwL2s4h7wZyOA33XVEgQ0ZnYQZ/zXLRkag7BvuGjXLhq7pDfczAusTEXSNYNmNn8O0GAzB2hlda9g770MTZGScTVj+sRZruj83erXAOD1wb66phfW9aSdUkx9Pcf+v3uA4xT6LBT3ZtmbVt8v9BQC9CRp1FauOKyn4ixaFQlZRU/GKP6oL9SrthKNyVADHIEg1FQWm4uS6iPcT2TlSklFf8w76cIMkOuyrUgMhZkZ8jiggwdA0WDZrisiqWeI+MVnBAVyzZetTk5ELW9vQCZbtNyrOus1GgBruy/cTNFT8Ho1rsiYXF59nTYNRidtP5V1E1Ov+fC0R/YiIju3M1lLMFJxSLbPT/dWdS+D+itn1kSOvyFTKSZiKglAflaMCuO4vWZ6KghutLSHFIveXGW4c+FyZMRK53PhhGbwfN06iOfVGYkF20/zkD/gOdN/euNEQMRuRcbTsZrbg7L+RnfF5QWPBP2cjq7GbjFqdqdTXtGw7lNGYtvxhph2IG/31KcbYBBGdAuBtAL4F4Kvt31aKmUS763+Nl2vIGlrscvUi9OSM2LW/ipV4XR85CqqaihnfqEQxlXLNUmIqUcmPVUvB/SXLqI9oRyzLqI/OxheHMssYDnd/VcPcX4b4IPev67+PmE24RshuNipCpqJT02FdCzNYWsD9FfJZRUZA5FITMZCawKDNWqYCp6gkAJwL4GbG2E8BZCXjU+yDGOxpb6Xi8ZLZFtcXkMyoTFXNWImPHF1ZVU0luvWvH2rRX3FCimVMxVKM/hLvJ0pTIXKSOYOHPCBnHM71EE0lRL8A6oevGeb+EkQ21ecEXVRMyKKyerNOwQ2MaLyhNTMV07aREezFMUD1sfyQDxoBfp+g/hL8TvjrhjUFxkekvXQKcY3Kq0T0nwDeDWCNW7Zl324ikqIJgz057J6qtq1U9nip1haRHnDcX0k0lTglWji6MoYSUynFzFPJGyp5KmpCvZP8KKn9pcBUpLW/3H3KC1KK2VLd/RWux4gy8b15wkNZ7P4yJe4vj00F5wjcSYDf9eR3U8mMFjUZuVDXmtbIamqeUB8trIu1EpGbjDW2BYBYe+kU4hqEd8Mp9niWW6F4PoC/b/uuUswoFvRkYdkMe10tpFXwro/tQE8+gfurmsz91ZXV1DQVd4xySLGC+0u1u2VUeLJS9JebUS/KnlapcpwJZRzR7q+wqCxDI2hC/ULs/vIOfRm78R3mjDGXqQiMhECD8TSVEA0mGFUlEtWdsWL3V5iw7jdWpvu9BO8NoCH6zLTtpn3OWk2FMVZkjP2IMfai+3obY+yuqHkp9i149b/a5AJrR9dHjp6cgZrFIoVuP6YqyYT6QtbwsvFl8IxKG5MfyzVLWq+LI2foMG0W+gTqMJVooZ4xcbipUu2wEKZS8w5iuaYSNGZhWod/H0FmIDMqHrsxBaG8IsMl0GBkOk9G0wTMKYwFhQj1QaYi0EBE7McQuLVUtZdOIXVdpWgCT4BsV6mWsVKt5RItHLyo5NT/3963x1iSlff9vqr77sf0TPfM7MzsE7M81gteklnsBcmJd0HGcuJdIrBNErw4ICLFTpw4OEASKVFiK1iKAolkRUIYs7aJH8FGu4lJMKyxLcUGMcaLWcCwyy6ww87O9Mz0TD9u33ur6p78UXWqzq17nnXrdjfT5yeNpvveU1Xn9kzXr77v932/z8GpuD9KsFAhUuGCuikNmJOKhQbCz2vy/rJ1PeZivkqsH8YWmgofHKZNYanLsVuN6ZuqeD7VsXxfZTJTaR2AOv2V98RICgr4ueyFd3lKKX1PEamUIyeJ/sHXykqKp/pU8j2Xqr9KEU0oIx/Nur3oU/Gk4jEFbtVSVwXY5iDGco2aCuDmVNwfxeg5+H5x8JSZSf/YjRKEARkdhTk62VAs3VPj0NL1mKe2VJHbKLZrfuRrp/aR9bnojC2bYYChIv3VCtXH5jd7iehumt9STpvlkYSk+ktW9qzvhZkmIZ2m0lBEKlJNKAgmicJYUlzqUynttynTXqQRjbxYYR7wpOIxhTrTX4yxmiOV9EbroqvsDJPcjNIFtoO6dkdjdJuh0VGYg0cgqhQYYyy1abHYcz6nXtO8aLZpSa+jEtvbhjScqs9lFE/n9kWouvFjTfqrqexTcav+UpXyiq9J+1RkJNSYJIrxmGHMVPoOSQsA1CXFQgVaMi3UhzLtZcymIjCZ9jIvHBhSIaJjRPQpInoq+3vKLIiI7iGiPyeiLxPRXxHRTwjvfYSIniWiJ7I/95SP97DDkW4TYUC1lBX3RwmSMatRU0nPY0sq4zHDbpRUilS4QaSpq35gaf7IwSMQFamYGg5FtA1Dv2yFekAe7dgYUqrSX1EyllqtcOSkIkllqUglCFJblOnoRi+kl6+jskcBBJFckqaSpcuaAU3d1MXziAgD25JiORFOVYkp9qrSVA5b+us9AB5njN0J4PHs+zL6AH6KMfa9SM0tP0BEK8L7v8AYuyf788T8t3xjIggIqwv1jBXeHNTXTQ8AC1mkYltWzKu3qmgqXcPNn2MwsktVcXQMI4ULg8oa0l82Ni0aqxUrUtF0xpsE/nTd5I1uJLl5ipB5efEbrixabElu0Hy/0mhCMiNG1fnOz1HufE9fl0dBsey8yv4TsVN+OgLh3mOmJkmZ9jIvHCRSeRBphz6yvx8qL2CMfV2oPHsewCUAx/dsh4cIqzVZtdQ1S4VjqcNHCtuRSm4mOYOmYhepuJCKnqzy+fRWkYo6/RVnuo3NPBXVOWxIKS0Nlgv1qogDKG725V6VyHCc7Hq6ijFZR30Rqciqv2RRgjqyaYZBSX/REFApUlEK9Xlaa7IB0yb6kTZJHlJN5SRj7AKQlioDOKFbTESvRtrN/w3h5V/K0mLvzxozVce+k4jOEdG59fX1OvZ+w2GtJlNJ7qBbV/orF+otSSW3vZ9FUzFEKrujxLrxEShSVkpSiR0ilWyNihAAfY+J+L7MMmUU66MNfryqpFjf36LWR7TNllJSUafMir4TyxLhhkyn0HXUk7VeM92nohDqJb01SufjgEpuxpLqL0nkMy/sKakQ0aeJ6EnJnwcdz3MKwG8A+GnGGP8pvRfAywDci7Qp892q4xljH2SMnWWMnT1+3Ac6MtRlKpk7FNdU/VWUFFuSShZlLFTpqG9ZCvWRG6kUQr0q/WXfTFkMyZreI9dZbDrqxfUibDUVqU2LgZBUne46ggDS1JDYc8LPoTSuzMV9UfTWV3OV96VzB2iUSE5bflzuqFcQkEwDSf3Epq9vE6k091BTqec33RKMsdep3iOii0R0ijF2ISONS4p1ywD+AMC/ZYx9Vjj3hezLIRH9GoB31bj1Qwfu/1W2C3dF3ekv7uG1ZVlS3K84SwUQSMXYqDjO03I24BGIyl6FRx22Q7rEY0TYRiptXaRiM+QrlHuHjZKxtMSXQ9a5nh9nqBqLygaOmpRZrk8kk0/9QGFQOXl+ue8Wv7Zs/eTN3yFSURCQLP0l65Tn1/GaihyPAXg4+/phAI+WFxBRC8DHAfw6Y+x/lt47lf1NSPWYJ+e62xsca4ttDKKx1TwRHXKhvqb0VxAQeg4jhXcqjhIGgF4zJQqjpuIYqeQFAApx3SVS0UUZRaRi6qhXlxSbdBGgsHkpQ6dzpNeVFwjYHDdt4KhJf0mqzGxKkGXpMrXz8LSmItdr5AQ0ZdMi6T9JJMO3gCxSmTKeLEc+h1NTeR+A1xPRUwBen30PIjpLRB/K1vw4gB8E8DZJ6fBHiehLAL4EYA3AL+7t9m8srOYNkLOlwLim4vIkb4KLU3E/W7dQQajvtNJfD3OkUlWoV/eWAJaRCu8xkUYZZjNIQIx25CXFpvRZU5KO4sfq018aTcWY/ppumFTPbZGI3hrhXdbNblofSTQVGWGFpfLjRFF+LPP+isYKq36J+D8VqYR7F6nsafpLB8bYFQAPSF4/B+Ad2de/CeA3FcffP9cNHjKs5bPqR7httfpM6+u7ERaizU1BAAAgAElEQVTbDWkuuCpcSIWvW6zSp5I3P+qvVV1TqTNSmT7XwFJT0XXUWwv1itSZ7ueu1FRivaZSTvekx2gaJjWRh9z6nj/V261vBkFJJ9H1zCg66i0s7VVCfSMMpj3CFAaV5bThPHCQIhWPA4S1uiKVQYTlGqMUIHUqdhXqK7kU56Si/0XcHSW5/mIDU6RSrU9FranYNj+qBm1ZlRQr+lS0aSyuqcjSX4aqMZlLsUlTKftope/JdQ9+zny9ViehUhSk69an0kAtlVA/ma5ijEnTWulaKhUKqJskk0OW/vI4QCisWmYrK97crc/2nmOh5R6pVEl/NcIArTBAP9JfaxDbzZPn6BhLil36VNQlxVxTmbmk2GZypOJYrRFlfvOu0Pw4RURqE0pZP8wo1kQTEudfTmLSkt6p6i81YZXdAFQlxWFAICrOpUqTpdehqUilrL14Q0mPfcexBZ7+mi1SuT4PUmk3sG3pUtwfxQgDsrpBy9BthfkMehmSMcMoHrv1qeQd9ar0l0ufikYPySMV/XmMJcWG9Fc7u6mWLexTQlJfu6qm4tz8KOv5UEQIqn3pOvbLmkae/lIQkCyqkdu/BPl5dWXKoTB5UhXR8M77w1b95XGA0GoEONJt1pD+qm+UMMdSp4Htod0AsZ1hantftSy62wy11V/8Zu4i1LcbAYh0Qr19pKJ1GLbszNdGKpbpL9k8FqNQr7iuqU+lWdIl0mPcnI11HfJhQAioLOxPNxRyqBoaZSm8cp+KrrNfFPXzlJq0r4Zyo0hdRFPuZ5kXPKl4KLG2OLv/V5r+qldTWWiH1vNUdoZxpdQXR88wp543RrpEKkRp5KTqU3GJVIIgnRE/k6aSN1BWT3/xteXrmyZGApJIxWjTokp/KUhF4mxczLRXVYxNW6/oqsvEYWOR5txTlVq6Jkyh/0Ul6E+tMzVeSh4c6oYnFQ8lVhfbMw/qutYf4WivVdOOUiy2m07eX7OQSqcZajvquSmki0sxP6+p+ss2ZZfOqa+uqRBReg5FCs0mUgGmyWFoKEdWRVnGpklF+ktFEDJnY12kkl8jnoxU1N5i6es8SjA1PzImrjX4hCVlslBEP6V1qojGRyoe+4rji+2ZZqqM4jF2RglWatZUFtshRvFYOe1QBE9/VYVtpOKS/gJSsV51Xj4DxTZl11YQglO/i8RpmDFmN09FFakYopxZNJWpVJuhYqzsz6UrEQb40/8kCam9xSZFcNOUSPH6yZghoJT4ptcWpcq5+C8ln6KkmEcisibJcunxvOBJxUOJ1RlNJa/102NXFuqOVOz9v2ZNf3Vbek2FRxUu6S+Az6lXe391HAoLUlKRpa7smh/5munOdpa/pz1W0rHOGEujHKvmx8n5ImOmjiCA9MY8PdiL6W32S134OoGc763c1yIjCWA6vaaNVEp2KdFYPsuer80jEIP1fpSTj0Gj8aTisZ9YW2zj+m5kFRHIcC3z/Traq7/6C7BzKt4ZJZV8vzi6mjQVIDYqupKK+rzD2G7qI0e7GSotVgBLwV9CKs4ux+KkxDEDY/pj8+bHePLmDcif8vPrKau/1MekXe/TArmKiJrBZJd8NFaTVuEtNpnSUo0TFtfGCZNWifHzxhYpNbGqi583NAj684QnFQ8lVrOu+qs71aKVjey4le58IhUrUhnG+QjiKjBFKjyF5dL8CKREoBrSNYzcJkmmQr3cYgWwi1Rk0U5RPWY3j0UWCeiuTURTBJETmWP6K40k7LvwudCvjD4aJT+vWB2p5M2SPFrQEGNenCCsVUZLQklxohXgA4HQ1Ncue4TNC55UPJSYtaueRyorNUcqiw6DuvqjuNKALg6TppJXalnoFiI6jUAt1MeJ0/naTXn6axiPEZC6wkmELlIxe39NRyo5oRn0mGYpguA3e7P1vUS/MZDKKJ6MPAD1z6ZRilRSPy11VAMIkYpuVHFYEvXH6qqyyZJig6BvUVIsI+N5wJOKhxKF/1dFUuGayj6mv7aHcSXfLw5T9VcRqbhXfymt7yO3Dv22ovqLV27ZCP4y/67c5diwF1mfi22U1GyUtQ59VRaQWe2X9hprbs7p+abnmKiaGfn1JyMopiwEyJsrOankxCiPFvj102P0/S9RXtVlWXps6Ob3Qr3HvqKIVKqlv671uaZSb/pryVKoT8YMg2hcyfeLg0cq5W5xjkHV6i+dUO8aqTRCaePiMEqsKr/4OcopNNvqMZk2MrQllRJBmKqy+DGy9Jc+uilZqWhu5vwaUx31yp6WUkpLO6N+MqqJk+mBWvmeBQ1ENcu+WFcuKTYPCJsXPKl4KFH4f1WLVDb6EZohzXRTlyGPVAyDuvh8+lkilW4zTK1YFE1jgwod9Xy9yqalUqRSsceEoyUpKbbt7JcN+XJpvJxIm1loMc2sNJbfSBljdl34pYjIJOyX556Y+lTKQr2q/Bcobv7RWO48XN6DztAyFDSVQqiXR0k+UvHYV/RaITrNYKb010qvNdPkSBlsNZV8Pv0s1V/ZsQOFU3GVjnog1WDq0lR0zY8uDZTTpGIfbQBlw8aKmorm5slR7vXQWc3rrqMloaBcUqyJKKb2Yy4pLjQQg1CfTAr1MrIQU3uma5fNO+eBA0MqRHSMiD5FRE9lfx9VrEuEAV2PCa/fQUSfy47/nWxKpMcMIKJsVn319Ffd5cRAMVLYRCqFQ/EM1V8ZWaicil0sVSbO2wq11veuXmJSoT5xI5Xp6i+79NdMmspUmslOUwFgVW4rXqfsz6Vd3yiVFGt6bvKUllDWG5C6AZGvSc+rjlRCIV1ViP/6qi4d+RzG5sf3AHicMXYngMez72XYZYzdk/35MeH1Xwbw/uz4DQBvn+92DwdWF9uVI5WN/qj2cmIg/YXpNs0jhfl8+oUZIhWeulOJ9f0oRisMpL/EOrSb6uqvYZw4uSrL9BAgJQWdS/DkOaqnv2Sd8S49LmJV1shKU8kig5iL3WYiagQ0pd3oemHKI4L12gcnCk4AmobGcFqo182ByYV6jUeYqDHptJfwEGoqDwJ4JPv6EaRz5q2QzaW/H8DHqhzvocbxGbrqr+9GtVd+cSx2zDNV+Pu9GSIVHjGoyop3R0ml83caIYbxtF08kEYqbs2P04QApDfoWSIVfk5j9ZfEkNI+/VVKM1kcVx6ilRORzmes3HeimWlf7MtSUym5IOu6+8sd9alNvU6otyMLvi7RlTMfQk3lJGPsAgBkf59QrOsQ0Tki+iwRceJYBXCNMcbvMucBnFFdiIjemZ3j3Pr6el37vyGxulDd/2ujP5ofqVjMVOGayixCvSlSSb3F3M/PyUo+XMs1UlGkv6LEWqhvy0qKLau/+F6jCumvcvNjro9YGFFGpfRXSxt5lNJZhgFiUxqMYj58upan4wpdwxTVREmxd1nnPTBZsVb0n6i0kiJKAtRCPY9o/tcXn8d7f/9L0uvOij0lFSL6NBE9KfnzoMNpbmWMnQXw9wF8gIi+B4DsX1BJyYyxDzLGzjLGzh4/ftzxUxwurC21cGVnhLHjEw5jDBv9qPZyYo7FtnmkMK/+mk2oN0QqUezcTQ/o59QPYjdNRRZlAG6RSrsx3Tfjmv6qVFLccBfquYEjj2oiTWpI3GN5OqNufaNUthzF6mqxXHyf0ElM+kuRulN29YdiqbC6U16c/JinAlUuxdn7Tzx3DY8+8R3pdWdFvYMuDGCMvU71HhFdJKJTjLELRHQKwCXFOZ7P/n6GiP4YwKsA/B6AFSJqZNHKzQCer/0DHEKsLbaRjBmu70Y46mAMOYhSF+Ejc4pUFtqhuaS4hkglF+o1kUqVkmnVnPpxNknSxaal3UjLnsuWH8NojNUFh+ovZaTi7v3l0o0vRpw2mkpDZeBoKEMuR0T69VTyMtNUaZUMNfmUSBnKY32jMUNPcd4wmNZKZGTB1zHG8r4WlVAv2r7YOC1UwUFKfz0G4OHs64cBPFpeQERHiaidfb0G4LUAvsLSxPRnALxJd7yHO1YrWrVsZN3084xUTJrKTg2aCo9CVKL67qgqqcgjFdsRwCLyccClaGUQJ9baDO8XETUe23ksUqE+10ZMjZOB1FDSxt14lJMKN4d07DvR3FRT363Jz2NvKGmRKsurtdRzYJqCsK71ExN0Gl159WQzpb6kehYcJFJ5H4DXE9FTAF6ffQ8iOktEH8rWvBzAOSL6IlISeR9j7CvZe+8G8PNE9DRSjeVX93T3Nyi4VYvrsK6im36emoplSfEM6S9+rGrSZD+KK6XXeB9KuQGycD1201SA6Xkmw2hs3e/SbgQYs8mRwEX6y72jvrqmYk5/tRqTwrhtSbHYR6NLOwFpim2iBFnzZD+lqejE95yAhPSXrqR4akiXRCsRSpoL40m5TYtY9qz7/LNgT9NfOjDGrgB4QPL6OQDvyL7+MwCvUBz/DIBXz3OPhxFFV71bBRj3/Toyh5JiIO2qN2kq28MYvVboXO4rgkc5qmv1hwm6q9XTX+UCgIFlb4iItkL0340Sa08yMYXFb5LDeAwifSoKKNyGR8JN2HaWy7ShpFmoL0dGptko+XUEwhwlYyw11be/RjBtH6PaU7Mkvsc68b3cUa+tKgtgU1LcEFJqplHGE3NcNJrSLDhIkYrHAcTxjFQubTlGKnyWysL+lRRvDSIsdWZ7bsojlZGCVEbVJku28/TXJBH08+ICB6E+J4HpqMc2UpHpInwcsLUhpaPdCn8/khynI7LyGGLe52LyCysL9frIpogSchsYjZ0KMBl9qMYhlzvqzSXFxTrxWpPr0s+RJEwf0ZTsXEwPC1XhScVDi5VeE60wwKWtgdNxXFOZR/MjACy2GhjG46lhTSK2hzGWOrORWhgQOs1AGansjCqmv5ry9FeV+SycoMRIhTGWRSqOpFLSRWwjplYjUGgqZj1mMsIxH1cmQH6D1va2BNMd9TqhWiQhXe8HIPSpZOtGWufh6Z4W3VqrwV+CoSUnFZOdi67wYFZ4UvHQgohwYrmNS5tukQpPlx2reZQwB/f/0qXAtgaz2d7n19L0xFQV6nlV2aCU/uLpMJdz8hu/KPqPkjEYs7eP4ecQPcRcOvvL1VL8a9PT8LTFvDn912pMRma2ZchlwtRFUbxSikcp/DXpuUsd9boogO8xmUh/qdeWDSW1kUpWASjuSYSo0ejsYWaFJxUPI25a7uDiplukcmV7iOVOw7r5zhXcqXhLU1a8OYhnTn/xa/Ul6a9RnD4ZViEVfky5VLlfwaCyKylP5gaYtqRSRCrFflzcksslyUPLWS7lCMem4iwnwFL6Syu8l2xXhgZSaQniN28oVPaplO3sNXpFXlKcmNNfoq1KntZSaCV8jW5CpEjgOnuYWeFJxcOIk8sdvOBIKpd3RrnIPw8sWQzq2h5EWJ4x/QWkzZOyiIgTTbdC+ounzPqRglQciIqL8WKDZmHJb0kKEquVoUP6qzwXZRSP0ba4aU1FOEmCRkDa4opytZtNT0wzTKvbEiFFpVsvWsGYplHmhQNjvh91D0xZ1I80qbJm5gLAGEM8HiMM5EPFRKIqbPenz9lqiEO/5lf95UnFw4gq6a+r26N8xv08cKSbksX1rCBAhvrSX6G0pJgTQBWhPo9USmTFU1guOk03q2LaFaIpV0t+mS4zjBOjJsIxNRfFEAlw8BQPd2ywsesv6z/cCcAmZVaI+/rxw/zpPxLEbyOpxGL1lyKllZdDF3qNuqQ4Pe+Y6TUgUafRp8loomJOVaE2KzypeBhxcrmD7WFsNb6X48rOEKsL84tUli1Jpa70l6z6q4hUqmsqqvSXS0pNZiXjOjysXaqoArJIxSH9VRbqbUkFKJ7yTWkpQFL9ZVFpJiUVi/VRMs6P0aWpwoDy1KHeemWyHDpdaxL1x9ohZGKfjC5NJhL4PPtUPKl4GHFyOSsrdkiBXdnnSCVOxtiNkpmrv4C0rFhGqEWk4k5cQWbfX/YU40Tl4v3VzXteipu6e6QyLfY7DfmSTHC0IZXCjDJLS1mk3MoEUcx9MZPEMLvxm/YnTnPkN2ptRZrw+XXmk42AQITSWv2Y4nTK5ViZEmwJkZIuTZYTZUY+s/Rv6eBJxcOIk0sdAMBFyxRYMma42h9hdU6VXwByT7HrfTmpcBJYrCVSCXPHYxFVooryectaTZXqr24u+hfnypsoLSMNmdifVn9VLym2SZ2Vp0YO48RstV+ypbGJVHIrm2icjyLWWcg0J6IEfaTC1+dEoRHBxUbRZMzAmNoIMxSILbXqV6XUinSgvvO/KCgw9enMAk8qHkacWE5JxbZXZaM/AmOFb9g8sNRuIAxIGanwqrA60l9mob4aqXRb4VRH/W6UoBmS0y98VxJl8K9tI5UihVZ8Tt78aIOyh5dz+kuYjWIio0ZACGg6UtEd1xZuvDYWMuK+7KxjwrzfxtQDw6OawghTTwLReJyahRqcj0fxWHtt8TOZ9jgLPKl4GMHTXy9ctyMV3qMyz/QXEWG50zCSynINpLKYaSrlgVp5+qtiMUCvOa3V9EeJ87z7Zpjm9HclpGKbRpOl0Eax/bCw8kwX2/QXvyEOBYIwRSpENFHCPEoShAFpm/lEHcaGVBpiSbHGpJFDnJwZaXQSft1RkhTjAZQuxUJkodFAxHk2yVh9bdGI87AYSnocUCy2G+i1Quv015WddN08hXog1VWuKUklfX2xXYOm0m5gzCSWKkP3nhIRvXY4JdTvjuy74DmICL3m5Ll2XSMVyYRLl0il0wwr6THFsLIkv6ZN2kzUMGyuJVa3cV3FJlJJn/5tIpWC5NJ59oZu/ZgVkzUV+2iJkYWFoSWPqlTrivNltjNeqPfYLxBR2gBpmf7ikcraHCMVICUVVaTCNZV6qr/CiXNyVPHpEtFrTZNKP0qq2b60wlL6y635sZP1ukwQg0NHfacZTDZfxonVtctzZWytYVrZOGbALiri+okYqej6aFrCUz0nC50BY0pyvPpLb4HCCcikBYnakS6dKBKg1djjbN28GpM9qXhYIe1VsSWVLFKZo6YCAEd6rT3RVAr7+0lSKa5RLRrqtRrySKVC5FMmKNdIpRUGaQptNBlt2N54ypVstp+DkxYnMxuhnh/nEqmIFWM26S+xwTIfVqbZV7MhCPWGxsJmSFbajrgHHXGKaS0b8uFl0j795bGvcOmqv7IzQkDASnc+DsUcR7pNXO/LLfnz9FeNkUpZ/9gexmg3gspPfCkRlKq/oriyl5hICPwmbVv9RZSWOIvE5NJRX05/uUYquaZimf5KNRy78mC+Pj1/Ylct1pwmIW3JcmZTn3qF6T9DqxFOkpuiCk1s8tRV04ll2TbkEyXMupCiCjypeFjhpuUOLl4fWs2qv7w9wrGFFoI5VZdwHOmqhXo+JKwOl2QuxJe76mf1FpNFKv0KmgqQ3pzLQj2ReZyv6hxxlp5x6XMZCpMjB5HdSOTyBEzb4gDRat+m9FkaqWirxQqyK8Yq6zv20/QTMxp58vJrU6RSTtnZpL90EUhL6Oa3LaSoggNDKkR0jIg+RURPZX8flaz5ISJ6QvgzIKKHsvc+QkTPCu/ds/ef4sbFmaNdjJIxLu+Yxfr1rQGOZ70t88RKt4XNwXRVFpDOc1lohbX84hSkUk5/RTM1V/ZkJcUzpL/KkUqnEVrNQuHotoL85r6b28XYRiqTvSODqJqmYlscMFH9ZRHdlJ/6xddkECObwgZGH30Mk7EwLVMX1VCW0tIXDIhEmBp0yn+eYk+NzpGgWdKJbC14XHFgSAXAewA8zhi7E8Dj2fcTYIx9hjF2D2PsHgD3A+gD+ENhyS/w9xljT+zJrg8JTh/pAgCev2ZOgV3cHOZlyPPEkW4TyZhJu92v9SOs9OopFOCmlJuDyagondcyS6QSTpUq70bVrfQnNA2HWSoT5xhNkkrHllRK9vu2pDKtqdg9QU9Uf1nYyYjNj3akUowCsNFU+H6GNqmyLKrh7sqqm7voHK0jzrKljKoAgZPK7igBY+ZZN1VxkEjlQQCPZF8/AuAhw/o3Afg/jLH+XHflASCNVADgOxu7xrUvbA7yLvx5glu1XJN01V/fHeXvz4rlbkocm4NpoX4Ww8peqwHGJk0c0/RXteqv3VL1V8cxShOJKe/sd26eTJBkvR02UyenNRW7irPJ9Jd9pDJM0qd+8TXtepEoNB34rQZhFAu9J4Zy5cim+msiraX+uUxFIIZ1XMe74dNfAE4yxi4AQPb3CcP6nwTwW6XXfomI/oqI3k9EykdlInonEZ0jonPr6+uz7fqQ4PQKj1T0pBInY1zeHuLkkfmTis5UMo1UaiIVHqmUrrM9s6aSFQAMJ92FK6W/SkL9bpRYRxkcXYGYXCdQdoTxyEXjpYWmUopwbIsDWkKz5dBCh2lLSoptOvCHcVKktAyRSpQwO8fkMMjLhMVrTa0rEZttSbF6XZom4wPnbojqLyL6NBE9KfnzoON5TgF4BYBPCi+/F8DLANwL4BiAd6uOZ4x9kDF2ljF29vjx4xU+yeHDkW4TS+0GvmMglcvbqUXLXqS/OGmUb/ZAqqnURSqdZqrNlK9Th6YCFJ35jDH0RxWrv0qRytBhPn1+DoGYXOe6iOQwcCAkfqMexAkYs69KEkuKbTSVovkxsarmmmiWtLCByVNaNrNdSkK96uY+UVKs+YzcJTkyVIntVaQye72lAxhjr1O9R0QXiegUY+xCRhqXNKf6cQAfZ4zlv+U8ygEwJKJfA/CuWjbtkeP0StdIKnxC5J6mv6SRyghHaqj8Eq9V1lTqSH8BxQ18GI8xZmmnvSvKJcW7UWI9oCs/h0BMfMyxbdRUCO5JocdYkNqE1mFxQxavN8i78C1MKB1tWvKhZZmm0goDbTUjLxwYWhh5tkPe/Ggp1GdpLZtIyaZKjFcxHgZN5TEAD2dfPwzgUc3at6CU+sqICJSWuzwE4Mk57PFQ48zRrlFTyUlleQ+qv3pyTYUxhmv9CEdrilSA1ENsc7dIU43HDNujeCZvsV6p/2WWZspuKy3p5ZMNt4eJsydZRxKp2Fd/FVVcLg7JRIR2I8AgTvIRyDZEJla72UyZDAJCI6CJaMI0o74RUB7ZGJsrw7Ak1JvLj80lxXaRClC4JOtIpZWTyuHRVN4H4PVE9BSA12ffg4jOEtGH+CIiuh3ALQD+pHT8R4noSwC+BGANwC/uwZ4PFU6vdPD8dVtSmX/662hW3cU7+Dl2RgniMast/QWk+o0YqaRVW9W76QFhJHJGJrm1TIXop+xUvDN0j6K6QgOja0d+R0hjuTokd5ohhtEY/cje9kYkQNthYtz00kZT4etTojBHQryj3qakuClEFbp9NMvpL1NFmUmob3BN5QZKf+nAGLsC4AHJ6+cAvEP4/psAzkjW3T/P/XkAZ1Z6uNaPsDOMlU/BL2wOEAY0d4sWIL2xLHUauLIz2VV/Leuyr6PxkWO508zPCxRRxSwd+5yQ+LkKE8wKpCJUXy20G9p/I+U5JNVf9kJ9RmqjBK4Oye1G2h/jouOIe7Wd3dISSAKAWdzPGjptigd4SiuP0iwiFVOlGP9Mgyg1lDSl66J4rK2EK2sqN4RQ7/HdDV5WfF6TAnv+2gA3LXfmNlWujLXFNi6XIhWeDjtSc6QiVpnVYVjJj+Vksj2DX1mv5E+2XSFS6WWaCmOsQqSSkUqcuJtZZjfvYkCZed/dZpjZ0o8zZ2fzMa3M2qVvqRdxKxibhkx+w+c3bK1PWEZA3FJfXSqc/g7tWAjrTR6paPbKZ9LnmsohSH95HHDcsboAAHj28o5yzfmNPm7OyGcvsLbYmiIVfvOv03vsSLcx0afCiWCm9FeH97+k59qaYVplQVBpM2WV9FenFeZ9M32HGzwwOTnSPf1VilQsjuPRzNYgxigZW6XM0vReSkKtRmB88OHpsmGUmF2Qs/f5/xG75kd9+ovPjeGRrF5TKarPrNNfPlLx2G/cvtYDYCKVXdx8tLdXW8LqQju32ue4mqXDjtY4zni508TmbpR3v+fprxmqvxZaDQQkpr+4puJOVCKpDKK0iqxK+gtIU1+7kVkbmDhW6LnhVVm21WfcjNJlkiZfczWzDbIilcxrrT+ycy1oNYK8+sss1Kfv84cNGwuY/ihOp1hqyK0dBjkJmAwt0yhTo9FMlRT7eSoe+4ylThNri218U0Eqo3iMFzYHexupLE1HKvz7tRp1neVuE/G4SAtt1jBZMggIi+1GTibbMzgrcyLaGkT5TWjRsTRZHNS1O4rRbYbWpqCLgukmH17WsyS1TuaOvOtQccb3ejl7oLCJqBYyV+jdKLFyCmg3wrz50aSpcJK7nqVedet5WmtrGFtFQNsWRNVsBMaqLj68qygprjYHyARPKh5OuGOtp4xULlzfBWPYU1JZXWhjox/l0/kAYH1riEZAtaa/iq769BeXi/azRkNLnaKqbJboR4xUOKk4RyqC2O/qlhwGhF4rxPYwKtJ4ltdfysY1u5Qxc1LhUaldpFKQl1VjJk9/WVSXcU1nI/t/oYsq+Nrr/ciKVPL0l8GkctugleTptKGZpGaBJxUPJ9yxtoBnr8hJhQv4e5n+WltKo5GrQgXY5e0hVhfrtd7n/l9cr+HXm5W4loT+l1nms4ii/05FUuHDyLYHMbYG7j04C+0GtodxXnBgSyoL7Qa2BzH6Dp343IKGl5PbkMpCq4H+KM5cC8x7azeLEmRT+mshT8fxSEW9nq/d6I+sPMsKDUQX/QTYtiCLjkBSfpywx4HA7WsLWN8a5rljEc9dTb099zT9lUUKlwVdZX1riONL9ZY0854YTiYbO6lhpW5srA2WO838Z7k1g+sxT5ltD2Mh/eV2Lu6ltjWIsTmI8u9tsdRuYHuYYHsYodcKrSsAFzvpcbv5eGbzvnn66sqOffqLT8fsW0+lDIXqL/36nhCphAFp/1/wtVd3RhZjkO0ilW4zzB9OdETVaYZW55sFnlQ8nMArwL51Zdoc+tnLO2iFAU7tgZkkB49URHf40IIAAA41SURBVF3l8vaoVj0FKPQZfp0rO+kgslmx3C00let99xs5R7sR5qmSncqkUkRjm7tRnvKzRRpxRM7lzIvtBraHEXYdOuq7eaQymvheh147S39ZjgXg1jeDyOyc3BOiD2NU0y4I0RRhpf+mFhFIK8yjaO26Zpi7LnhS8TgQuON4SirfWN+eeu+pS9t40fGFmZ/eXXBTZgfzwvVizsv61nAOpMIjopRUNvqjWmxgVnqtXJ+5sjPE6gxEtdxJy563K5Ymcy+1zUGEzUGck4wtFtsN7AyT1BPN4dqL7QYG0Rhbgwhti1JfoCCeK1n114JFUUKvlTaF2lZ/iZGNyY+NX/+ahU7CI5Ur20Njf023GeYalS4CEYlYR2piRZ4vKfY4EHjR2iKaIeErFzan3nvq0hbuPLm0p/s5udwBEXKjyzgZY317mJNNXTjaayEMKCeVqzsRji3MTlyriy1c3hmBMYarM0Y/S1kqjT+9H3McUiZa/G8NqkUqW1n6zcVqhms/l7eHzh38/LP2mnbpr2E8xs4wtrrOQjstQbZxJxBTWqbKMk5oYwZ0jQUAae+QeJwME2RhiFRs1s0CTyoeTmg1Atx5YglfeX6SVHZHCc5v7OLOE4t7vp8TS+18zssLmwMkY5Z3/9eFICAcW2jh8lahqRxbmD1SWVtoYxSPsT2MM1KpTlRLnTSVttEfISA4DynrtUI0AsrSX+76zlInTWNtO0YqnIAubQ2dh4JxTcUq/dWyTzvx9ZuDCMN4nBcxmM4NmAskxHOZtCBxrS4aEyMV3Tm5czSRnYt0FXhS8XDG955expef35wYg/uN9W0wBrx4j0kFSC35udEld1E+s1J/scDaYhtXdoYYj1maqqohxbaapdXWt4bY6Eczpb9WF1q4sjPElZ0Rjvbcq9+ICMvdJq5sj7AbJRUilRA7w8RZU+E34QvXB9ZkxM9/KTMwtU1/AWk/lY1us9BuWEUJ4rkBc3+OmEozkaF4XR1ZiJ9HR4C8NLrn0IPkCk8qHs64+8wRXN0Z4YXNQsf42gtbALDnkQqQEggnE54Gm0cF2tpiC+tbQ1zaGiJKWD4NcxZwYnpmfQfJmM2U/jq+1Mb61hBXt6un0Y50m/nP0LVoYLHdzMuRFx1cATiRfOfarnV0lQ9Oy4ocbJ66xRu0jVfYxI3aQBStRpCX6JqaTiciFVOqrG1HFuKUT53+w9Nfto2pVeBJxcMZd59ZBgB88blr+WtPPHcNi+0GXnR870nlttUezm/sYhSP816ZOm74Zdx8tIvzG7sFcdVwDV4A8PVLKSnzyKUKTix1cHl7hMvbw8qkstxp4PxGWtnnKtQvdRoYZeOkXVJn/CY8isdOKTu+dqFl99Q9EU1YpL/EaMulIdOUKus0AxDZnVfcsy6q6VlGKjzVuGCpXVWBJxUPZ7zizAo6zQCffeZq/toXvr2B77vlyJ65E4t4ycklxGOGZy/v4OlL2zh9pGPtkOuCW48t4MrOCF+/mBJAHcTFq9R4pDdrpJKMGb6xvl2dVLpNfDMrF3fVZDhBDuOxU5QjRjUu0zp5c6atq4GYIrOp3BNdrlcsih54NGOKaogoj6xMP2NOOmFAWmFd/HnrIhW+ztYotAo8qXg4o9UIcO/tx/D/nr4MIBWtv3phE3/z1qP7sp+XZBVnX7+4ha9e2MRdp5fncp3bV1OnAP65T6/MXmG2utBCIyB8/tmUoGfRgnjD50Y/qkwq4sTOE44joVeFIoMTDs2nYnTmMliN35BtP6u4v6MWJCGusamk4wRgQ8a8V8REVtyxga9XrhN+brqUGo8gXSx4XHFgSIWI3kxEXyaiMRGd1ax7AxF9jYieJqL3CK/fQUSfI6KniOh3iKg+i1qPKTzwshN46tI2nrq4hT/660sYM+B1d53cl7286PgCGgHhL761gWcu7+CuU/Mhlduyxs8/+fo6jvaaM9neczTCAGeOdvF81mczS/Qj3sirNqCKx93keI414foujgbiDdulFJwToC2prC0V62zSjGJ131GLSj/edW/z2flIYxOJcs2NDAkAMcLT9Ynx4ovGHDMKB4ZUkM6U/3sA/lS1gIhCAL8C4EcA3AXgLUR0V/b2LwN4P2PsTgAbAN4+3+0ebvzoK08jDAi//uffwkc/9y2cWeniFWeO7Mte2o0Q995+DB/5s28iGTPcPad93LGWktfWIMb3nq7vGt+T6VC3rfZmStvdcqzwXKuqbYmRkmufy23C9V0iLlEPcSHVU0fStbZEJH6eVYvSbXGNDXHx8b/HLaoCeeOhmVTS6zJ9oGKdquTrTFMvZ8GBIRXG2FcZY18zLHs1gKcZY88wxkYAfhvAg0REAO4H8LFs3SMAHprfbj2OL7XxD77/VvzGZ7+FL3z7Gn7mh14MMj1OzRFvuPsmAOkv62tfvDaXa3RbIV516woA4DUvXq3tvPfefgwAZiZlMXX1ypurneuVN69k52o7l5yK2sadJ91IjWscL73JvnmWpx9tCUx8gr/1mNn0VPw8NhrES7LKxxdlrhM6cJK665T+3+n2LDp+46umJqhPwLbakf98O3NqfAQO0Ix6S5wB8Jzw/XkA3w9gFcA1xlgsvK78VyCidwJ4JwDceuut89npIcC73/AydJohjvZa+Ml7b9nXvfzEvbfg/EYfr33xmrM7rwv+40N34+Nf+A4evu/22s751vtuw/XdCG+977aZz/W7//g+fP3iVmWn6JefWsIvvfFufF9GLq74+D95Dda3hkYDxjI+/LZ78flvXnXqc3rrfbdhGI+d/u898o9ejYvXB9aE+d/e8iq0LN183/XDL8UDLz+Js9lDgg4fftu9+NyzV4wpxtMrXXz65/+WsX+p0wzxG29/NVYMhQ6vvPkI/v3fvQtvuPuUcY9VQcwUV9V5MaJPA7hJ8ta/YYw9mq35YwDvYoydkxz/ZgA/zBh7R/b9W5FGL/8BwJ8zxl6cvX4LgE8wxl5h2tPZs2fZuXNTl/Lw8PDwUICI/oIxJtW+9zRSYYy9bsZTnAcgPpbcDOB5AJcBrBBRI4tW+OseHh4eHnuIA6OpWOLzAO7MKr1aAH4SwGMsDbc+A+BN2bqHATy6T3v08PDwOLQ4MKRCRG8kovMA7gPwB0T0yez100T0CQDIopCfBfBJAF8F8LuMsS9np3g3gJ8noqeRaiy/utefwcPDw+OwY081lYMIr6l4eHh4uEGnqRyYSMXDw8PD47sfnlQ8PDw8PGqDJxUPDw8Pj9rgScXDw8PDozYceqGeiNYBfKvi4WtIe2QOE/xnvvFx2D4v4D+zK25jjB2XvXHoSWUWENE5VQXEjQr/mW98HLbPC/jPXCd8+svDw8PDozZ4UvHw8PDwqA2eVGbDB/d7A/sA/5lvfBy2zwv4z1wbvKbi4eHh4VEbfKTi4eHh4VEbPKl4eHh4eNQGTyoVQERvIKKvEdHTRPSe/d7PvEFEtxDRZ4joq0T0ZSL6uf3e016BiEIi+ksi+t/7vZe9ABGtENHHiOivs3/v+/Z7T/MGEf2L7P/1k0T0W0RkN/T+uwhE9GEiukRETwqvHSOiTxHRU9nfR+u4licVRxBRCOBXAPwIgLsAvIWI7trfXc0dMYB/yRh7OYAfAPAzh+Azc/wc0jELhwX/FcD/ZYy9DMD34Qb/7ER0BsA/A3CWMXY3gBDpnKYbDR8B8IbSa+8B8Dhj7E4Aj2ffzwxPKu54NYCnGWPPMMZGAH4bwIP7vKe5gjF2gTH2hezrLaQ3mjP7u6v5g4huBvCjAD6033vZCxDRMoAfRDaLiDE2Yoxd299d7QkaALpE1ADQww04NZYx9qcArpZefhDAI9nXjwB4qI5reVJxxxkAzwnfn8chuMFyENHtAF4F4HP7u5M9wQcA/CsA4/3eyB7hRQDWAfxalvL7EBEt7Pem5gnG2HcA/GcA3wZwAcB1xtgf7u+u9gwnGWMXgPTBEcCJOk7qScUdJHntUNRlE9EigN8D8M8ZY5v7vZ95goj+DoBLjLG/2O+97CEaAP4GgP/OGHsVgB3UlBI5qMh0hAcB3AHgNIAFIvqH+7ur7254UnHHeQC3CN/fjBswXC6DiJpICeWjjLHf3+/97AFeC+DHiOibSFOc9xPRb+7vluaO8wDOM8Z4FPoxpCRzI+N1AJ5ljK0zxiIAvw/gNfu8p73CRSI6BQDZ35fqOKknFXd8HsCdRHQHEbWQinqP7fOe5goiIqR59q8yxv7Lfu9nL8AYey9j7GbG2O1I/43/iDF2Qz/BMsZeAPAcEb00e+kBAF/Zxy3tBb4N4AeIqJf9P38AN3hxgoDHADycff0wgEfrOGmjjpMcJjDGYiL6WQCfRFop8mHG2Jf3eVvzxmsBvBXAl4joiey1f80Y+8Q+7sljPvinAD6aPTA9A+Cn93k/cwVj7HNE9DEAX0Ba5fiXuAEtW4jotwD8bQBrRHQewL8D8D4Av0tEb0dKrm+u5VrepsXDw8PDoy749JeHh4eHR23wpOLh4eHhURs8qXh4eHh41AZPKh4eHh4etcGTioeHh4dHbfCk4uHh4eFRGzypeHh4eHjUBk8qHh4HCER0LxH9FRF1iGghm/Nx937vy8PDFr750cPjgIGIfhFAB0AXqRfXf9rnLXl4WMOTiofHAUNmkfJ5AAMAr2GMJfu8JQ8Pa/j0l4fHwcMxAIsAlpBGLB4e3zXwkYqHxwEDET2G1G7/DgCnGGM/u89b8vCwhncp9vA4QCCinwIQM8b+BxGFAP6MiO5njP3Rfu/Nw8MGPlLx8PDw8KgNXlPx8PDw8KgNnlQ8PDw8PGqDJxUPDw8Pj9rgScXDw8PDozZ4UvHw8PDwqA2eVDw8PDw8aoMnFQ8PDw+P2vD/Ac5AMItJ+rxhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, (np.sin(x**2)), label = \"Murat's fav plot\")\n",
    "plt.title(\"$\\sin(x^{2})$ Curve\")\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"$\\sin(x^{2})$\");\n",
    "plt.legend(loc = 5)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transition from `plt` to `ax`\n",
    "\n",
    "<img src='images/styles.png' width = 300 />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.axes()\n",
    "ax.plot(x, np.sin(x))\n",
    "ax.set(xlim=(0, 10), ylim=(-2, 2),\n",
    "       xlabel='x', ylabel='sin(x)',\n",
    "       title='A Simple Plot');\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[For the scatter plots](https://jakevdp.github.io/PythonDataScienceHandbook/04.02-simple-scatter-plots.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Barplots\n",
    "Barplots are used for displaying __one dimensional__, __categorical__ data. You call plt.bar with two arguments:\n",
    "- the x — a list of x-positions for each bar\n",
    "- the y-values — a list of heights for each bar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# an example of a bar plot\n",
    "ax = plt.subplot()\n",
    "our_dogs = pd.Series([\"dengue\", \"dolce\", \"murraysl\", \"dog_a\", \"dog_b\", \"dog_c\"])\n",
    "dogs_age = pd.Series([5, 8, 5, 4, 6, 7])\n",
    "\n",
    "plt.bar(x = our_dogs, height = dogs_age)\n",
    "\n",
    "# set ticks values, as a method of the axes\n",
    "ax.set_xticks(range(0, len(our_dogs)))\n",
    "ax.set_xticklabels(our_dogs)\n",
    "\n",
    "plt.title('age of random dogs')\n",
    "plt.xlabel('dogs')\n",
    "plt.ylabel('age')\n",
    "plt.xticks(rotation = 45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'cat age')"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# utilizing the subplot function and creating subplots\n",
    "our_cats = [\"cat_1\", \"cat_2\", \"cat_3\", \"cat_4\", \"cat_5\", \"cat_6\"]\n",
    "cats_age = [14, 16, 17, 20, 18, 24]\n",
    "fig = plt.figure(figsize=(15, 10))\n",
    "plt.subplot(1,2,1)\n",
    "plt.bar(range(len(our_dogs)), dogs_age, color='pink')\n",
    "plt.title('dog age')\n",
    "plt.xlabel('dog number')\n",
    "plt.subplot(1,2,2)\n",
    "plt.bar(range(len(our_cats)), cats_age, color='green')\n",
    "plt.xlabel('cat number')\n",
    "plt.ylabel('Age')\n",
    "plt.title('cat age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x11da05c88>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# we can create overlaid or side-by-side bargraph. You need\n",
    "# to shift the x value by width to accommodate for two graphs.\n",
    "\n",
    "fig = plt.figure(figsize= (15, 8))\n",
    "# n = 1  # This is our first dataset (out of 2)\n",
    "# t = 2  # Number of datasets\n",
    "# d = 6  # Number of sets of bars\n",
    "# w = 0.8  # Width of each bar\n",
    "\n",
    "# essentially, this list comprehension gives us the position\n",
    "dog_values = [t*element + w*n for element in range(d)]\n",
    "\n",
    "# of the position of dogs\n",
    "plt.bar(dog_values, dogs_age, color='green')\n",
    "\n",
    "\n",
    "n = 2  # This is our second dataset (out of 2)\n",
    "t = 2  # Number of datasets\n",
    "d = 6  # Number of sets of bars\n",
    "w = 0.8  # Width of each bar\n",
    "\n",
    "cat_values = [t*element + w*n for element in range(d)]\n",
    "\n",
    "plt.bar(cat_values, cats_age, color='purple')\n",
    "plt.legend([\"dogs\", \"cats\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.8, 2.8, 4.8, 6.8, 8.8, 10.8]\n",
      "[1.6, 3.6, 5.6, 7.6, 9.6, 11.6]\n"
     ]
    }
   ],
   "source": [
    "print(dog_values)\n",
    "print(cat_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# stacked barplots\n",
    "ax = plt.subplot()\n",
    "music_festivals = [\"Coachella\", \"Govball\", \"EDC\", \"Ultra\"]\n",
    "ticket_sales_in_millon = [114, 62, 120, 116]\n",
    "people_attended_in_thousands = [126, 114, 130, 110]\n",
    "\n",
    "plt.bar(range(len(music_festivals)),\n",
    "        ticket_sales_in_millon)\n",
    "plt.bar(range(len(music_festivals)),\n",
    "        people_attended_in_thousands,\n",
    "        bottom=ticket_sales_in_millon)\n",
    "\n",
    "plt.legend([\"tix sales\", \"number of people\"])\n",
    "ax.set_xticks(range(0, len(music_festivals)))\n",
    "ax.set_xticklabels(music_festivals)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bar plot - animal shelter dataset\n",
    "\n",
    "Let's make a bar plot comparing dogs and cats by month when they are admitted to the shelter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "shelter_df = pd.read_csv('https://data.austintexas.gov/api/views/9t4d-g238/rows.csv?accessType=DOWNLOAD')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "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>Animal ID</th>\n",
       "      <th>Name</th>\n",
       "      <th>DateTime</th>\n",
       "      <th>MonthYear</th>\n",
       "      <th>Date of Birth</th>\n",
       "      <th>Outcome Type</th>\n",
       "      <th>Outcome Subtype</th>\n",
       "      <th>Animal Type</th>\n",
       "      <th>Sex upon Outcome</th>\n",
       "      <th>Age upon Outcome</th>\n",
       "      <th>Breed</th>\n",
       "      <th>Color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A794011</td>\n",
       "      <td>Chunk</td>\n",
       "      <td>05/08/2019 06:20:00 PM</td>\n",
       "      <td>05/08/2019 06:20:00 PM</td>\n",
       "      <td>05/02/2017</td>\n",
       "      <td>Rto-Adopt</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cat</td>\n",
       "      <td>Neutered Male</td>\n",
       "      <td>2 years</td>\n",
       "      <td>Domestic Shorthair Mix</td>\n",
       "      <td>Brown Tabby/White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A776359</td>\n",
       "      <td>Gizmo</td>\n",
       "      <td>07/18/2018 04:02:00 PM</td>\n",
       "      <td>07/18/2018 04:02:00 PM</td>\n",
       "      <td>07/12/2017</td>\n",
       "      <td>Adoption</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dog</td>\n",
       "      <td>Neutered Male</td>\n",
       "      <td>1 year</td>\n",
       "      <td>Chihuahua Shorthair Mix</td>\n",
       "      <td>White/Brown</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Animal ID   Name                DateTime               MonthYear  \\\n",
       "0   A794011  Chunk  05/08/2019 06:20:00 PM  05/08/2019 06:20:00 PM   \n",
       "1   A776359  Gizmo  07/18/2018 04:02:00 PM  07/18/2018 04:02:00 PM   \n",
       "\n",
       "  Date of Birth Outcome Type Outcome Subtype Animal Type Sex upon Outcome  \\\n",
       "0    05/02/2017    Rto-Adopt             NaN         Cat    Neutered Male   \n",
       "1    07/12/2017     Adoption             NaN         Dog    Neutered Male   \n",
       "\n",
       "  Age upon Outcome                    Breed              Color  \n",
       "0          2 years   Domestic Shorthair Mix  Brown Tabby/White  \n",
       "1           1 year  Chihuahua Shorthair Mix        White/Brown  "
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shelter_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "shelter_df.columns = [x.lower().replace(' ','_') for x in shelter_df.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "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>animal_id</th>\n",
       "      <th>name</th>\n",
       "      <th>datetime</th>\n",
       "      <th>monthyear</th>\n",
       "      <th>date_of_birth</th>\n",
       "      <th>outcome_type</th>\n",
       "      <th>outcome_subtype</th>\n",
       "      <th>animal_type</th>\n",
       "      <th>sex_upon_outcome</th>\n",
       "      <th>age_upon_outcome</th>\n",
       "      <th>breed</th>\n",
       "      <th>color</th>\n",
       "      <th>time</th>\n",
       "      <th>days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>A803149</td>\n",
       "      <td>*Birch</td>\n",
       "      <td>08/31/2019 04:26:00 PM</td>\n",
       "      <td>08/31/2019 04:26:00 PM</td>\n",
       "      <td>08/08/2019</td>\n",
       "      <td>Transfer</td>\n",
       "      <td>Partner</td>\n",
       "      <td>Cat</td>\n",
       "      <td>Intact Male</td>\n",
       "      <td>3 weeks</td>\n",
       "      <td>Domestic Shorthair</td>\n",
       "      <td>Brown Tabby</td>\n",
       "      <td>2019-08-31 16:26:00</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>A698049</td>\n",
       "      <td>Luigi</td>\n",
       "      <td>03/16/2015 02:50:00 PM</td>\n",
       "      <td>03/16/2015 02:50:00 PM</td>\n",
       "      <td>06/05/2014</td>\n",
       "      <td>Transfer</td>\n",
       "      <td>Partner</td>\n",
       "      <td>Cat</td>\n",
       "      <td>Spayed Female</td>\n",
       "      <td>9 months</td>\n",
       "      <td>Domestic Medium Hair Mix</td>\n",
       "      <td>Black/White</td>\n",
       "      <td>2015-03-16 14:50:00</td>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>A765349</td>\n",
       "      <td>Einstein</td>\n",
       "      <td>06/08/2018 01:04:00 PM</td>\n",
       "      <td>06/08/2018 01:04:00 PM</td>\n",
       "      <td>01/18/2009</td>\n",
       "      <td>Adoption</td>\n",
       "      <td>Foster</td>\n",
       "      <td>Dog</td>\n",
       "      <td>Neutered Male</td>\n",
       "      <td>9 years</td>\n",
       "      <td>Chihuahua Shorthair Mix</td>\n",
       "      <td>Tricolor</td>\n",
       "      <td>2018-06-08 13:04:00</td>\n",
       "      <td>3285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>A760697</td>\n",
       "      <td>Star</td>\n",
       "      <td>10/26/2017 03:22:00 PM</td>\n",
       "      <td>10/26/2017 03:22:00 PM</td>\n",
       "      <td>10/23/2007</td>\n",
       "      <td>Transfer</td>\n",
       "      <td>Partner</td>\n",
       "      <td>Dog</td>\n",
       "      <td>Intact Male</td>\n",
       "      <td>10 years</td>\n",
       "      <td>Yorkshire Terrier Mix</td>\n",
       "      <td>Brown/Black</td>\n",
       "      <td>2017-10-26 15:22:00</td>\n",
       "      <td>3650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>A789298</td>\n",
       "      <td>*Mr Wrinkles</td>\n",
       "      <td>04/23/2019 11:59:00 AM</td>\n",
       "      <td>04/23/2019 11:59:00 AM</td>\n",
       "      <td>02/15/2019</td>\n",
       "      <td>Adoption</td>\n",
       "      <td>Foster</td>\n",
       "      <td>Dog</td>\n",
       "      <td>Neutered Male</td>\n",
       "      <td>2 months</td>\n",
       "      <td>Labrador Retriever Mix</td>\n",
       "      <td>Brown/White</td>\n",
       "      <td>2019-04-23 11:59:00</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   animal_id          name                datetime               monthyear  \\\n",
       "9    A803149        *Birch  08/31/2019 04:26:00 PM  08/31/2019 04:26:00 PM   \n",
       "10   A698049         Luigi  03/16/2015 02:50:00 PM  03/16/2015 02:50:00 PM   \n",
       "14   A765349      Einstein  06/08/2018 01:04:00 PM  06/08/2018 01:04:00 PM   \n",
       "15   A760697          Star  10/26/2017 03:22:00 PM  10/26/2017 03:22:00 PM   \n",
       "23   A789298  *Mr Wrinkles  04/23/2019 11:59:00 AM  04/23/2019 11:59:00 AM   \n",
       "\n",
       "   date_of_birth outcome_type outcome_subtype animal_type sex_upon_outcome  \\\n",
       "9     08/08/2019     Transfer         Partner         Cat      Intact Male   \n",
       "10    06/05/2014     Transfer         Partner         Cat    Spayed Female   \n",
       "14    01/18/2009     Adoption          Foster         Dog    Neutered Male   \n",
       "15    10/23/2007     Transfer         Partner         Dog      Intact Male   \n",
       "23    02/15/2019     Adoption          Foster         Dog    Neutered Male   \n",
       "\n",
       "   age_upon_outcome                     breed        color  \\\n",
       "9           3 weeks        Domestic Shorthair  Brown Tabby   \n",
       "10         9 months  Domestic Medium Hair Mix  Black/White   \n",
       "14          9 years   Chihuahua Shorthair Mix     Tricolor   \n",
       "15         10 years     Yorkshire Terrier Mix  Brown/Black   \n",
       "23         2 months    Labrador Retriever Mix  Brown/White   \n",
       "\n",
       "                  time  days  \n",
       "9  2019-08-31 16:26:00    21  \n",
       "10 2015-03-16 14:50:00   270  \n",
       "14 2018-06-08 13:04:00  3285  \n",
       "15 2017-10-26 15:22:00  3650  \n",
       "23 2019-04-23 11:59:00    60  "
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Let's drop all the missing values first\n",
    "\n",
    "## Note that here we choose that method for the sake of simplicity.\n",
    "## Normally we should be careful about how to deal with missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "shelter_df.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(26454, 12)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>animal_id</th>\n",
       "      <th>name</th>\n",
       "      <th>datetime</th>\n",
       "      <th>monthyear</th>\n",
       "      <th>date_of_birth</th>\n",
       "      <th>outcome_type</th>\n",
       "      <th>outcome_subtype</th>\n",
       "      <th>animal_type</th>\n",
       "      <th>sex_upon_outcome</th>\n",
       "      <th>age_upon_outcome</th>\n",
       "      <th>breed</th>\n",
       "      <th>color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>A803149</td>\n",
       "      <td>*Birch</td>\n",
       "      <td>08/31/2019 04:26:00 PM</td>\n",
       "      <td>08/31/2019 04:26:00 PM</td>\n",
       "      <td>08/08/2019</td>\n",
       "      <td>Transfer</td>\n",
       "      <td>Partner</td>\n",
       "      <td>Cat</td>\n",
       "      <td>Intact Male</td>\n",
       "      <td>3 weeks</td>\n",
       "      <td>Domestic Shorthair</td>\n",
       "      <td>Brown Tabby</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>A698049</td>\n",
       "      <td>Luigi</td>\n",
       "      <td>03/16/2015 02:50:00 PM</td>\n",
       "      <td>03/16/2015 02:50:00 PM</td>\n",
       "      <td>06/05/2014</td>\n",
       "      <td>Transfer</td>\n",
       "      <td>Partner</td>\n",
       "      <td>Cat</td>\n",
       "      <td>Spayed Female</td>\n",
       "      <td>9 months</td>\n",
       "      <td>Domestic Medium Hair Mix</td>\n",
       "      <td>Black/White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>A765349</td>\n",
       "      <td>Einstein</td>\n",
       "      <td>06/08/2018 01:04:00 PM</td>\n",
       "      <td>06/08/2018 01:04:00 PM</td>\n",
       "      <td>01/18/2009</td>\n",
       "      <td>Adoption</td>\n",
       "      <td>Foster</td>\n",
       "      <td>Dog</td>\n",
       "      <td>Neutered Male</td>\n",
       "      <td>9 years</td>\n",
       "      <td>Chihuahua Shorthair Mix</td>\n",
       "      <td>Tricolor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   animal_id      name                datetime               monthyear  \\\n",
       "9    A803149    *Birch  08/31/2019 04:26:00 PM  08/31/2019 04:26:00 PM   \n",
       "10   A698049     Luigi  03/16/2015 02:50:00 PM  03/16/2015 02:50:00 PM   \n",
       "14   A765349  Einstein  06/08/2018 01:04:00 PM  06/08/2018 01:04:00 PM   \n",
       "\n",
       "   date_of_birth outcome_type outcome_subtype animal_type sex_upon_outcome  \\\n",
       "9     08/08/2019     Transfer         Partner         Cat      Intact Male   \n",
       "10    06/05/2014     Transfer         Partner         Cat    Spayed Female   \n",
       "14    01/18/2009     Adoption          Foster         Dog    Neutered Male   \n",
       "\n",
       "   age_upon_outcome                     breed        color  \n",
       "9           3 weeks        Domestic Shorthair  Brown Tabby  \n",
       "10         9 months  Domestic Medium Hair Mix  Black/White  \n",
       "14          9 years   Chihuahua Shorthair Mix     Tricolor  "
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Let's take a look at the shape and first 3 rows of the dataset\n",
    "print(shelter_df.shape)\n",
    "\n",
    "shelter_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Which animal type has the most number in this data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dog      15218\n",
       "Cat      10818\n",
       "Other      375\n",
       "Bird        43\n",
       "Name: animal_type, dtype: int64"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_animals = shelter_df['animal_type'].value_counts()\n",
    "\n",
    "num_animals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "## barplot with the above result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Dog', 'Cat', 'Other', 'Bird'], dtype='object')"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_animals.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 4 artists>"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(x = num_animals.index , height= num_animals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "## what is the outcome types and especially for animal types what is the distribution of outcomes?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Transfer           16155\n",
       "Adoption            8325\n",
       "Euthanasia          1599\n",
       "Died                 310\n",
       "Missing               40\n",
       "Return to Owner       25\n",
       "Name: outcome_type, dtype: int64"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shelter_df.outcome_type.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "## what is the average number of days for outcomes for different animal_types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "shelter_df."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "animal_type  outcome_type   \n",
       "Bird         Adoption              10\n",
       "             Died                   1\n",
       "             Euthanasia             4\n",
       "             Transfer              28\n",
       "Cat          Adoption            4380\n",
       "             Died                 213\n",
       "             Euthanasia           403\n",
       "             Missing               20\n",
       "             Return to Owner        2\n",
       "             Transfer            5800\n",
       "Dog          Adoption            3912\n",
       "             Died                  91\n",
       "             Euthanasia          1167\n",
       "             Missing               20\n",
       "             Return to Owner       23\n",
       "             Transfer           10005\n",
       "Other        Adoption              23\n",
       "             Died                   5\n",
       "             Euthanasia            25\n",
       "             Transfer             322\n",
       "Name: name, dtype: int64"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shelter_df.groupby(['animal_type', 'outcome_type'])['name'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Which month is the busiest month in terms of action?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "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>animal_id</th>\n",
       "      <th>name</th>\n",
       "      <th>datetime</th>\n",
       "      <th>monthyear</th>\n",
       "      <th>date_of_birth</th>\n",
       "      <th>outcome_type</th>\n",
       "      <th>outcome_subtype</th>\n",
       "      <th>animal_type</th>\n",
       "      <th>sex_upon_outcome</th>\n",
       "      <th>age_upon_outcome</th>\n",
       "      <th>breed</th>\n",
       "      <th>color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>A803149</td>\n",
       "      <td>*Birch</td>\n",
       "      <td>08/31/2019 04:26:00 PM</td>\n",
       "      <td>08/31/2019 04:26:00 PM</td>\n",
       "      <td>08/08/2019</td>\n",
       "      <td>Transfer</td>\n",
       "      <td>Partner</td>\n",
       "      <td>Cat</td>\n",
       "      <td>Intact Male</td>\n",
       "      <td>3 weeks</td>\n",
       "      <td>Domestic Shorthair</td>\n",
       "      <td>Brown Tabby</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>A698049</td>\n",
       "      <td>Luigi</td>\n",
       "      <td>03/16/2015 02:50:00 PM</td>\n",
       "      <td>03/16/2015 02:50:00 PM</td>\n",
       "      <td>06/05/2014</td>\n",
       "      <td>Transfer</td>\n",
       "      <td>Partner</td>\n",
       "      <td>Cat</td>\n",
       "      <td>Spayed Female</td>\n",
       "      <td>9 months</td>\n",
       "      <td>Domestic Medium Hair Mix</td>\n",
       "      <td>Black/White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>A765349</td>\n",
       "      <td>Einstein</td>\n",
       "      <td>06/08/2018 01:04:00 PM</td>\n",
       "      <td>06/08/2018 01:04:00 PM</td>\n",
       "      <td>01/18/2009</td>\n",
       "      <td>Adoption</td>\n",
       "      <td>Foster</td>\n",
       "      <td>Dog</td>\n",
       "      <td>Neutered Male</td>\n",
       "      <td>9 years</td>\n",
       "      <td>Chihuahua Shorthair Mix</td>\n",
       "      <td>Tricolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>A760697</td>\n",
       "      <td>Star</td>\n",
       "      <td>10/26/2017 03:22:00 PM</td>\n",
       "      <td>10/26/2017 03:22:00 PM</td>\n",
       "      <td>10/23/2007</td>\n",
       "      <td>Transfer</td>\n",
       "      <td>Partner</td>\n",
       "      <td>Dog</td>\n",
       "      <td>Intact Male</td>\n",
       "      <td>10 years</td>\n",
       "      <td>Yorkshire Terrier Mix</td>\n",
       "      <td>Brown/Black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>A789298</td>\n",
       "      <td>*Mr Wrinkles</td>\n",
       "      <td>04/23/2019 11:59:00 AM</td>\n",
       "      <td>04/23/2019 11:59:00 AM</td>\n",
       "      <td>02/15/2019</td>\n",
       "      <td>Adoption</td>\n",
       "      <td>Foster</td>\n",
       "      <td>Dog</td>\n",
       "      <td>Neutered Male</td>\n",
       "      <td>2 months</td>\n",
       "      <td>Labrador Retriever Mix</td>\n",
       "      <td>Brown/White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>A805930</td>\n",
       "      <td>*Rachel</td>\n",
       "      <td>11/08/2019 06:37:00 PM</td>\n",
       "      <td>11/08/2019 06:37:00 PM</td>\n",
       "      <td>09/11/2019</td>\n",
       "      <td>Adoption</td>\n",
       "      <td>Foster</td>\n",
       "      <td>Dog</td>\n",
       "      <td>Spayed Female</td>\n",
       "      <td>1 month</td>\n",
       "      <td>Border Collie Mix</td>\n",
       "      <td>Chocolate/White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>A672423</td>\n",
       "      <td>Valentine</td>\n",
       "      <td>02/14/2014 11:44:00 AM</td>\n",
       "      <td>02/14/2014 11:44:00 AM</td>\n",
       "      <td>02/10/2002</td>\n",
       "      <td>Transfer</td>\n",
       "      <td>Partner</td>\n",
       "      <td>Dog</td>\n",
       "      <td>Spayed Female</td>\n",
       "      <td>12 years</td>\n",
       "      <td>Collie Rough Mix</td>\n",
       "      <td>Red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>A777234</td>\n",
       "      <td>Tyson</td>\n",
       "      <td>01/02/2019 12:00:00 AM</td>\n",
       "      <td>01/02/2019 12:00:00 AM</td>\n",
       "      <td>11/25/2017</td>\n",
       "      <td>Transfer</td>\n",
       "      <td>Partner</td>\n",
       "      <td>Dog</td>\n",
       "      <td>Neutered Male</td>\n",
       "      <td>1 year</td>\n",
       "      <td>Pit Bull Mix</td>\n",
       "      <td>Blue/White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>A802723</td>\n",
       "      <td>*Linguine</td>\n",
       "      <td>09/20/2019 06:27:00 PM</td>\n",
       "      <td>09/20/2019 06:27:00 PM</td>\n",
       "      <td>07/09/2019</td>\n",
       "      <td>Adoption</td>\n",
       "      <td>Foster</td>\n",
       "      <td>Dog</td>\n",
       "      <td>Spayed Female</td>\n",
       "      <td>2 months</td>\n",
       "      <td>Miniature Poodle Mix</td>\n",
       "      <td>Tan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>A705205</td>\n",
       "      <td>*Dell</td>\n",
       "      <td>06/20/2015 04:25:00 PM</td>\n",
       "      <td>06/20/2015 04:25:00 PM</td>\n",
       "      <td>03/30/2015</td>\n",
       "      <td>Adoption</td>\n",
       "      <td>Offsite</td>\n",
       "      <td>Cat</td>\n",
       "      <td>Neutered Male</td>\n",
       "      <td>2 months</td>\n",
       "      <td>Domestic Shorthair Mix</td>\n",
       "      <td>White/Blue Tabby</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>A767796</td>\n",
       "      <td>*Koko</td>\n",
       "      <td>06/20/2019 02:18:00 PM</td>\n",
       "      <td>06/20/2019 02:18:00 PM</td>\n",
       "      <td>12/06/2017</td>\n",
       "      <td>Transfer</td>\n",
       "      <td>Partner</td>\n",
       "      <td>Dog</td>\n",
       "      <td>Spayed Female</td>\n",
       "      <td>1 year</td>\n",
       "      <td>American Pit Bull Terrier Mix</td>\n",
       "      <td>Brown/White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>A781976</td>\n",
       "      <td>*Fancy</td>\n",
       "      <td>10/16/2018 02:25:00 PM</td>\n",
       "      <td>10/16/2018 02:25:00 PM</td>\n",
       "      <td>10/08/2017</td>\n",
       "      <td>Transfer</td>\n",
       "      <td>Partner</td>\n",
       "      <td>Dog</td>\n",
       "      <td>Intact Female</td>\n",
       "      <td>1 year</td>\n",
       "      <td>German Shepherd Mix</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>A686459</td>\n",
       "      <td>*Lovey</td>\n",
       "      <td>09/21/2014 05:39:00 PM</td>\n",
       "      <td>09/21/2014 05:39:00 PM</td>\n",
       "      <td>07/15/2014</td>\n",
       "      <td>Adoption</td>\n",
       "      <td>Foster</td>\n",
       "      <td>Cat</td>\n",
       "      <td>Spayed Female</td>\n",
       "      <td>2 months</td>\n",
       "      <td>Domestic Shorthair Mix</td>\n",
       "      <td>Tortie</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>A679096</td>\n",
       "      <td>Copper</td>\n",
       "      <td>12/22/2015 11:50:00 AM</td>\n",
       "      <td>12/22/2015 11:50:00 AM</td>\n",
       "      <td>05/16/2007</td>\n",
       "      <td>Adoption</td>\n",
       "      <td>Foster</td>\n",
       "      <td>Dog</td>\n",
       "      <td>Neutered Male</td>\n",
       "      <td>8 years</td>\n",
       "      <td>Miniature Pinscher Mix</td>\n",
       "      <td>Red</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   animal_id          name                datetime               monthyear  \\\n",
       "9    A803149        *Birch  08/31/2019 04:26:00 PM  08/31/2019 04:26:00 PM   \n",
       "10   A698049         Luigi  03/16/2015 02:50:00 PM  03/16/2015 02:50:00 PM   \n",
       "14   A765349      Einstein  06/08/2018 01:04:00 PM  06/08/2018 01:04:00 PM   \n",
       "15   A760697          Star  10/26/2017 03:22:00 PM  10/26/2017 03:22:00 PM   \n",
       "23   A789298  *Mr Wrinkles  04/23/2019 11:59:00 AM  04/23/2019 11:59:00 AM   \n",
       "33   A805930       *Rachel  11/08/2019 06:37:00 PM  11/08/2019 06:37:00 PM   \n",
       "34   A672423     Valentine  02/14/2014 11:44:00 AM  02/14/2014 11:44:00 AM   \n",
       "41   A777234         Tyson  01/02/2019 12:00:00 AM  01/02/2019 12:00:00 AM   \n",
       "45   A802723     *Linguine  09/20/2019 06:27:00 PM  09/20/2019 06:27:00 PM   \n",
       "46   A705205         *Dell  06/20/2015 04:25:00 PM  06/20/2015 04:25:00 PM   \n",
       "52   A767796         *Koko  06/20/2019 02:18:00 PM  06/20/2019 02:18:00 PM   \n",
       "62   A781976        *Fancy  10/16/2018 02:25:00 PM  10/16/2018 02:25:00 PM   \n",
       "71   A686459        *Lovey  09/21/2014 05:39:00 PM  09/21/2014 05:39:00 PM   \n",
       "75   A679096        Copper  12/22/2015 11:50:00 AM  12/22/2015 11:50:00 AM   \n",
       "\n",
       "   date_of_birth outcome_type outcome_subtype animal_type sex_upon_outcome  \\\n",
       "9     08/08/2019     Transfer         Partner         Cat      Intact Male   \n",
       "10    06/05/2014     Transfer         Partner         Cat    Spayed Female   \n",
       "14    01/18/2009     Adoption          Foster         Dog    Neutered Male   \n",
       "15    10/23/2007     Transfer         Partner         Dog      Intact Male   \n",
       "23    02/15/2019     Adoption          Foster         Dog    Neutered Male   \n",
       "33    09/11/2019     Adoption          Foster         Dog    Spayed Female   \n",
       "34    02/10/2002     Transfer         Partner         Dog    Spayed Female   \n",
       "41    11/25/2017     Transfer         Partner         Dog    Neutered Male   \n",
       "45    07/09/2019     Adoption          Foster         Dog    Spayed Female   \n",
       "46    03/30/2015     Adoption         Offsite         Cat    Neutered Male   \n",
       "52    12/06/2017     Transfer         Partner         Dog    Spayed Female   \n",
       "62    10/08/2017     Transfer         Partner         Dog    Intact Female   \n",
       "71    07/15/2014     Adoption          Foster         Cat    Spayed Female   \n",
       "75    05/16/2007     Adoption          Foster         Dog    Neutered Male   \n",
       "\n",
       "   age_upon_outcome                          breed             color  \n",
       "9           3 weeks             Domestic Shorthair       Brown Tabby  \n",
       "10         9 months       Domestic Medium Hair Mix       Black/White  \n",
       "14          9 years        Chihuahua Shorthair Mix          Tricolor  \n",
       "15         10 years          Yorkshire Terrier Mix       Brown/Black  \n",
       "23         2 months         Labrador Retriever Mix       Brown/White  \n",
       "33          1 month              Border Collie Mix   Chocolate/White  \n",
       "34         12 years               Collie Rough Mix               Red  \n",
       "41           1 year                   Pit Bull Mix        Blue/White  \n",
       "45         2 months           Miniature Poodle Mix               Tan  \n",
       "46         2 months         Domestic Shorthair Mix  White/Blue Tabby  \n",
       "52           1 year  American Pit Bull Terrier Mix       Brown/White  \n",
       "62           1 year            German Shepherd Mix             White  \n",
       "71         2 months         Domestic Shorthair Mix            Tortie  \n",
       "75          8 years         Miniature Pinscher Mix               Red  "
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shelter_df.head(14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'10/26/2017 03:22:00 PM'"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shelter_df.datetime.iloc[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "## How is the distribution of outcomes to months?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "shelter_df['month'] = shelter_df.datetime.map(lambda x: int(x[:2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10    2616\n",
       "7     2574\n",
       "12    2422\n",
       "6     2415\n",
       "11    2291\n",
       "8     2265\n",
       "9     2168\n",
       "5     2168\n",
       "1     2057\n",
       "2     1849\n",
       "3     1836\n",
       "4     1793\n",
       "Name: month, dtype: int64"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shelter_df.month.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Histogram\n",
    "Histograms are like barplots in the sense that it describe __one-dimensional__ data. A histogram divides the variable into bins, counts the number of observations in each bin, and shows the bins on the x-axis and the frequency on the y-axis. It is used for visualizing __continuous__ variables. <br>\n",
    "\n",
    "From the documentation: compute and draw the histogram of x. The return value is a tuple (__n, bins, patches__) or ([n0, n1, ...], bins, [patches0, patches1,...]) if the input contains multiple data."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src= 'images/histogram-basic-example.png' width =350/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set seed for reproducability\n",
    "np.random.seed(2018)\n",
    "\n",
    "# Generate 1000 values from 0 standard normal distribution\n",
    "x = np.random.randn(1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(698,)"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[(x <1) & (x> -1 )].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the distogram with hist() function\n",
    "plt.hist(x)\n",
    "\n",
    "plt.xlabel('Normal random distribution')\n",
    "plt.ylabel('Frequency of Values')\n",
    "plt.title('Histograms')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkkAAAHSCAYAAAD4yV8pAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAXFUlEQVR4nO3df7DdeX3X8de7G7CFgtTdW4rAdrsjYaYyCnqHycrYGiCVqgP+gRVGKjho/ujYllTrsKMzHe0f8fetM3YcM20VtaUWBGWQtlzpxVonwWZhbVkWLjRtwwJ207SlImPp2o9/5N6dJLyT+72551dyH4+ZO7nnnO+P973fvdlnvufc860xRgAAuNZXLHsAAIBVJJIAABoiCQCgIZIAABoiCQCgIZIAABpH5rHRe+65Z9x3333z2DQAwEw99NBDvz7GWLv+/rlE0n333Zfz58/PY9MAADNVVb/a3e/pNgCAhkgCAGiIJACAhkgCAGiIJACAhkgCAGiIJACAhkgCAGiIJACAhkgCAGiIJACAhkgCAGiIJACAhkgCAGiIJACAhkgCAGiIJACAhkgCAGiIJACAhkiCO9zG5nY2NreXPQbAbUckAQA0RBIAQEMkAQA0JkVSVZ2qqkeq6qNV9faq+sp5DwYAsEx7RlJVPTfJdyVZH2O8KMldSV4378EAAJZp6tNtR5J8VVUdSfK0JJ+d30gAAMu3ZySNMT6T5B8nuZjkc0k+P8Z4/7wHAwBYpilPt31Nktck+YYkfzDJ06vqDc1yJ6vqfFWdv3Tp0uwnBQBYoClPt70yyS+PMS6NMX43ybuS/InrFxpjnBljrI8x1tfW1mY9JwDAQk2JpItJjlXV06qqkrwiyaPzHQsAYLmmvCbpQ0nemeTDSX5xZ50zc54LAGCpjkxZaIzxfUm+b86zAACsDO+4DQDQEEkAAA2RBADQEEkAAA2RBADQEEkAAA2RBADQEEkAAA2RBADQEEkAAA2RBADQEEkAAA2RBADQEEkAAA2RBADQEEkAAA2RBADQEEkAAA2RBADQEEkAAA2RBADQEEkAAA2RBADQEEkAAA2RBADQEEkAAA2RBADQEEkAAA2RBADQEEkAAA2RBADQEEkAAA2RBADQEEkAAA2RBADQEEkAAA2RBADQEEkAAA2RBADQEEkAAA2RBADQEEkAAI09I6mqXlhVD1/18dtV9ZZFDAcAsCxH9lpgjPGJJC9Okqq6K8lnkrx7znMBACzVfp9ue0WSXxpj/Oo8hgEAWBX7jaTXJXl790BVnayq81V1/tKlSwefDABgiSZHUlU9Ncmrk7yje3yMcWaMsT7GWF9bW5vVfAAAS7GfM0nfmuTDY4xfm9cwAACrYj+R9Prc4Kk2AIA7zaRIqqqnJTmR5F3zHQcAYDXs+RYASTLG+GKSu+c8CwDAyvCO2wAADZEEANAQSQD7tXV62RMACyCSAAAaIgkAoCGSAAAaIgkAoCGSAAAaIgkAoCGSAAAaIgkAoCGSAAAaIgkAoCGSAAAaIgkAoCGSAAAaIgkAoCGSAOZt6/SyJwBugUgCAGiIJACAhkgCAGiIJACAhkgCAGiIJACAhkgCAGiIJACAhkgCAGiIJACAhkgCAGiIJACAhkgCAGiIJACAhkgCAGiIJACAhkgCAGiIJACAhkgCAGiIJACAhkgCAGiIJACAhkgCAGhMiqSqelZVvbOqPl5Vj1bVA/MeDABgmY5MXO6fJfmpMcZrq+qpSZ42x5kAAJZuz0iqqmcm+aYkb0qSMcaXknxpvmMBACzXlKfb7k9yKcm/qqqPVNUPVdXT5zwXAMBSTYmkI0n+WJJ/McZ4SZL/k+St1y9UVSer6nxVnb906dKMxwQAWKwpkfRYksfGGB/auf3OXImma4wxzowx1scY62tra7OcEQBg4faMpDHG/0ry6ap64c5dr0jysblOBXC72Tp95QO4Y0z97bbvTPKjO7/ZdiHJX5nfSAAAyzcpksYYDydZn/MsAAArwztuAwA0RBIAQEMkAQA0RBIAQEMkAQA0RBIAQEMkAQA0RBIAQEMkAQA0RBIAQEMkAQA0RBIAQEMkAdwOtk5f+QAWRiQBADREEgBAQyQBADREEgBAQyQBADREEgBAQyQBADREEgBAQyQBADREEgBAQyQBADREEgBAQyQBADREEgBAQyQBJMnW6Ssft+v2gZkTScCTNja3s7G5vewxAFaCSAIAaIgkAICGSAIAaIgkAICGSAIAaIgkAICGSAIAaIgkAICGSAIAaIgkAICGSAIAaIgkAICGSAIAaEyKpKr6lar6xap6uKrOz3sogENj6/TB1j3I+nttGw65/ZxJOj7GePEYY31u0wC3hY3N7Ws+v/o2wJ3C020AAI2pkTSSvL+qHqqqk/McCABgFRyZuNzLxhifraqvTbJZVR8fY/zs1QvsxNPJJLn33ntnPCYAwGJNOpM0xvjszp+PJ3l3kpc2y5wZY6yPMdbX1tZmOyUAwILtGUlV9fSqesbu50m+JclH5z0YAMAyTXm67dlJ3l1Vu8v/2Bjjp+Y6FQDAku0ZSWOMC0n+6AJmAQBYGd4CAACgIZIAABoiCQCgIZIAABoiCQCgIZIAABoiCW5jG5vb2djcXvYY11jFmQBuhUgCAGiIJACAhkgCAGiIJACAhkgCAGiIJACAhkgCAGiIJACAhkgCAGiIJACAhkgCAGiIJACAhkgCAGgcWfYAwLU2NrevuX3qxNGZbvf67d3o/lnZ2Nye27YB5smZJACAhkgCmLWt01c+lrU+MBMiCQCgIZIAABoiCQCgIZIAABoiCQCgIZIAABoiCQCgIZIAABoiCQCgIZIAABoiCQCgIZIAABpHlj0A3G42NreTJKdOHJ3rOgcxZX+7y0zZzq5b3d5Bvv5Ff+8AdjmTBADQcCYJYJa2Ts9uua3TyfEHb33dq3XbAW7KmSQAgIZIAgBoiCQAgIZIAgBoTI6kqrqrqj5SVe+d50AAAKtgP2eSvjvJo/MaBABglUyKpKp6XpI/m+SH5jsOAMBqmHom6QeS/K0kvzfHWQAAVsaekVRVfy7J42OMh/ZY7mRVna+q85cuXZrZgAAAyzDlTNLLkry6qn4lyY8neXlV/bvrFxpjnBljrI8x1tfW1mY8JgDAYu0ZSWOMB8cYzxtj3JfkdUl+ZozxhrlPBgCwRK7dBnO0ewV7ptv9np06cXTJkwCH3b4iaYzxwSQfnMskAAArxDtuAwA0PN0GHA5bp5PjD974sb3Wnafd7d9ovlmtf6v7Oeh8cJtyJgkAoCGSAAAaIgkAoCGSAAAaIgkAoCGSAAAaIgkAoCGSAAAaIgkAoCGSAAAaLksCS3T1Fe93P5/H9me13n62N4+vB2CRnEkCAGiIJACAhkgCAGh4TRLAKts6Pd/lgRtyJgkAoCGSAAAaIgkAoCGSAAAaIgkAoCGSAAAaIgkAoCGSAAAaIgkAoOEdt2EJNja3973sqRNHZ7pdAG7OmSQAgIZIAgBoiCQAgIZIAgBoeOE2sFxbp5PjDy5+n8nB9jtlG7vL7HXfKui+nlnNuoxjDDPgTBIAQEMkAQA0RBIAQEMkAQA0RBIAQEMkAQA0RBIAQEMkAQA0RBIAQMM7bsNVNja3kySnThxd6LoH3f7uY7ebjc3tHLt4OQ8cX/YkAF9uzzNJVfWVVfU/qup/VtUjVfV3FzEYAMAyTTmT9DtJXj7G+EJVPSXJz1XVT44xzs15NgCApdkzksYYI8kXdm4+ZedjzHMoAIBlm/TC7aq6q6oeTvJ4ks0xxoeaZU5W1fmqOn/p0qVZzwkAsFCTXrg9xvh/SV5cVc9K8u6qetEY46PXLXMmyZkkWV9fd6YJmJ+t01f+PP7gwZZZNbszL2r9g+4P7nD7eguAMcZvJflgklfNZRoAgBUx5bfb1nbOIKWqvirJK5N8fN6DAQAs05Sn256T5G1VdVeuRNVPjDHeO9+xAACWa8pvt/1CkpcsYBYAgJXhsiQAAA2RBADQEEkAAA2RBADQmPRmknDYbWxuJ0lOnTh60/u6dfaz/WXa7wyrMDPAPDmTBADQEEkAAA2RBADQEEkAAA2RBADQEEkAAA2RBCzf1ukrHzuOXTxzze1umcmP7Xe5qdtallnMdqtf49Xr3Wgb+93u7X48uKOJJACAhkgCAGiIJACAhkgCAGiIJACAhkgCAGgcWfYAsApudEV7V7pfjLMXLidJHjh+7X3nnpj/939jczvHLl7OA/ffPfd9AbcXZ5IAABoiCQCgIZIAABoiCQCgIZIAABoiCQCgIZIAABoiCZi7YxfPLHuE/ds6vewJgCUTSQAADZEEANAQSQAADZEEANAQSQAADZEEANA4suwB4DDZ2Nxe9ghLtfv1nzpxdN/rnr1wOUnywPEv396xi9c+trG5/eR9557Y2ecB/rZ7ct/3333rGwFuO84kAQA0RBIAQEMkAQA0RBIAQEMkAQA0RBIAQEMkAQA0vE8SsFDHLp5Jtu5Ojj84ffn92Dr95HskXbP+vN7jaOv0fLa7yq7+mnc/n3g8223Mcz9wAHueSaqq51fVVlU9WlWPVNV3L2IwAIBlmnIm6Ykkf2OM8eGqekaSh6pqc4zxsTnPBgCwNHueSRpjfG6M8eGdz/93kkeTPHfegwEALNO+XrhdVfcleUmSDzWPnayq81V1/tKlS7OZDgBgSSZHUlV9dZL/kOQtY4zfvv7xMcaZMcb6GGN9bW1tljMCACzcpN9uq6qn5Eog/egY413zHQn2b/dq8J1bueL8KrvZ17qI9Wfh7IXLOffEl8+xCrMB7Jry222V5IeTPDrG+KfzHwkAYPmmPN32siTfnuTlVfXwzsefmfNcAABLtefTbWOMn0tSC5gFAGBluCwJAEBDJAEANEQSAEBDJAEANEQSAEBj0ptJAne+YxfPJEnO3XtyZuvtPnb953tt66a2Tk8f7qAWua87ye737fiDy50DDsiZJACAhkgCAGiIJACAhkgCAGh44TYHtnvl9lMnjs59P7eyjxvNN6srzl+/HVeyX4yzFy4n985x20keuP/uldoWsFjOJAEANEQSAEBDJAEANEQSAEBDJAEANEQSAEBDJAEANEQSAEBDJAEANLzjNnBDxy6eSZKcu/dke3tR+7/Vx7nK1unFb3+/9x1/cH/7utny1+/n6mX3uz8OLWeSAAAaIgkAoCGSAAAaXpPEytvY3P6yz0+dOHqg7dzqsvvZBvNx9sLlmaz3wP13z21fwJ3BmSQAgIZIAgBoiCQAgIZIAgBoiCQAgIZIAgBoiCQAgIZIAgBoiCQAgIZIAgBouCwJcI1jF88kSc7de/KW15+y7u5+lmrrdI5ddOmRa2ydXt52d5c5/uB89wMTOZMEANAQSQAADZEEANDwmiT2bWNzO0ly6sTRfS8zZd39zABX29jcnvwao7MX+uVudP/U7Txw/92T15myLLA8ziQBADT2jKSq+pGqeryqPrqIgQAAVsGUM0n/Osmr5jwHAMBK2TOSxhg/m+Q3FjALAMDK8JokAIDGzCKpqk5W1fmqOn/p0qVZbRYAYClmFkljjDNjjPUxxvra2tqsNgsAsBSebgMAaEx5C4C3Jzmb5IVV9VhVvXn+YwEALNee77g9xnj9IgYBAFglnm6DO8ixi2eWtp9jF88sbP+QJNk6feVjltu4+vaU7c9iBlaWSAIAaIgkAICGSDrENja32/tudP8855jn9mGKsxcu5+yFywdedvexbpmp+5jVMsDBiCQAgIZIAgBoiCQAgIZIAgBoiCQAgIZIAgBoiCQAgIZIAgBoiCQAgIZIAgBoiCQAgIZIAgBoHFn2AHC7OnbxTJLk3L0nD7yd3W3cbJu3ur/d9a52/f72Wq9bbuq6i7Ss/TJjW6f399jufccfXNx+brbufk2Z/1a/Rg5EJJEk2djc3vcy+1nn1Imje95/o2VhVZy9cHkp607d9gP33z23fcBh5Ok2AICGSAIAaIgkAICGSAIAaIgkAICGSAIAaIgkAICGSAIAaIgkAICGSAIAaIgkAICGSAIAaIgkAIDGkWUPALe7YxfPJEnO3Xty0nJTHrt6m1PW6/Z9s/XgjrR1+tYem/L4QeY4/uDstr3X9ncf273v6tvXP7bKVmTW2zaSNja3kySnThxd8iS9Wcx3K9vY2Nx+cvnr1++2t3vfrPZ9o3X22s+UZWHVnL1weW7r7z72wP13t7enrHOrM12//vVz7mf7V697kLlgGTzdBgDQEEkAAA2RBADQEEkAAA2RBADQEEkAAA2RBADQEEkAAA2RBADQmBRJVfWqqvpEVX2qqt4676EAAJZtz0iqqruS/GCSb03yjUleX1XfOO/BAACWacqZpJcm+dQY48IY40tJfjzJa+Y7FgDAck2JpOcm+fRVtx/buQ8A4I5VY4ybL1D1F5L86THGX925/e1JXjrG+M7rljuZ5OTOzRcm+cTsx33SPUl+fY7b52Acn9Xl2Kwux2Z1OTara1bH5uvHGGvX33lkwoqPJXn+Vbefl+Sz1y80xjiT5Mwtj7cPVXV+jLG+iH2xf47P6nJsVpdjs7ocm9U172Mz5em2n0/ygqr6hqp6apLXJXnPvAYCAFgFe55JGmM8UVV/PclPJ7kryY+MMR6Z+2QAAEs05em2jDHel+R9c55lPxbytB63zPFZXY7N6nJsVpdjs7rmemz2fOE2AMBh5LIkAACN2yKSquoPVNVmVX1y58+vucmyz6yqz1TVP1/kjIfZlONTVS+uqrNV9UhV/UJV/cVlzHoY7HUZoar6fVX173ce/1BV3bf4KQ+nCcfme6rqYzs/Ix+oqq9fxpyH1dRLcFXVa6tqVJXfeFuQKcemqr5t5+fnkar6sVns97aIpCRvTfKBMcYLknxg5/aNfH+S/7qQqdg15fh8MclfHmP84SSvSvIDVfWsBc54KEy8jNCbk/zmGOMPJdlI8g8WO+XhNPHYfCTJ+hjjjyR5Z5J/uNgpD6+pl+Cqqmck+a4kH1rshIfXlGNTVS9I8mCSl+38f+Yts9j37RJJr0nytp3P35bkz3cLVdUfT/LsJO9f0FxcsefxGWNsjzE+ufP5Z5M8nuTL3riLA5tyGaGrj9c7k7yiqmqBMx5Wex6bMcbWGOOLOzfP5cr70rEYUy/B9f25Eq//d5HDHXJTjs1fS/KDY4zfTJIxxuOz2PHtEknPHmN8Lkl2/vza6xeoqq9I8k+SfO+CZ2PC8blaVb00yVOT/NICZjtsplxG6MllxhhPJPl8krsXMt3htt9LPL05yU/OdSKutufxqaqXJHn+GOO9ixyMST87R5Mcrar/XlXnqupVs9jxpLcAWISq+i9Jvq556G9P3MR3JHnfGOPT/lE8ezM4PrvbeU6Sf5vkjWOM35vFbFyj+4//+l9hnbIMszf5+15Vb0iynuSb5zoRV7vp8dn5h/hGkjctaiCeNOVn50iSFyT5U7lyBva/VdWLxhi/dZAdr0wkjTFeeaPHqurXquo5Y4zP7fxPtjuN9kCSP1lV35Hkq5M8taq+MMa42euXmGgGxydV9cwk/znJ3xljnJvTqIfdlMsI7S7zWFUdSfL7k/zGYsY71CZd4qmqXpkr//j45jHG7yxoNvY+Ps9I8qIkH9z5h/jXJXlPVb16jHF+YVMeTlP/Xjs3xvjdJL9cVZ/IlWj6+YPs+HZ5uu09Sd648/kbk/yn6xcYY/ylMca9Y4z7kvzNJP9GIC3Mnsdn55I2786V4/KOBc522Ey5jNDVx+u1SX5meMO0Rdjz2Ow8nfMvk7x6Vq+pYLKbHp8xxufHGPeMMe7b+f/MuVw5TgJp/qb8vfYfkxxPkqq6J1eefrtw0B3fLpH095OcqKpPJjmxcztVtV5VP7TUyUimHZ9vS/JNSd5UVQ/vfLx4OePeuXZeY7R7GaFHk/zEGOORqvp7VfXqncV+OMndVfWpJN+Tm/+2KDMy8dj8o1w5E/6OnZ8R18lckInHhyWYeGx+OsnlqvpYkq0k3zvGuHzQfXvHbQCAxu1yJgkAYKFEEgBAQyQBADREEgBAQyQBADREEgBAQyQBADREEgBA4/8DW7AQmKB0lXoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# overlaid histograms for two distributions\n",
    "# plotting two histograms\n",
    "mu1, sigma1 = 0, 0.1  # mean and standard deviation\n",
    "s1 = np.random.normal(mu1, sigma1, 1000)\n",
    "mu2, sigma2 = 0.2, 0.1\n",
    "s2 = np.random.normal(mu2, sigma2, 1000)\n",
    "plt.figure(figsize=(10, 8))\n",
    "plt.hist(s1, bins=200, density=True, alpha=.5)\n",
    "plt.hist(s2, bins=200, density=True, alpha=.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "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>animal_id</th>\n",
       "      <th>name</th>\n",
       "      <th>datetime</th>\n",
       "      <th>monthyear</th>\n",
       "      <th>date_of_birth</th>\n",
       "      <th>outcome_type</th>\n",
       "      <th>outcome_subtype</th>\n",
       "      <th>animal_type</th>\n",
       "      <th>sex_upon_outcome</th>\n",
       "      <th>age_upon_outcome</th>\n",
       "      <th>breed</th>\n",
       "      <th>color</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>A803149</td>\n",
       "      <td>*Birch</td>\n",
       "      <td>08/31/2019 04:26:00 PM</td>\n",
       "      <td>08/31/2019 04:26:00 PM</td>\n",
       "      <td>08/08/2019</td>\n",
       "      <td>Transfer</td>\n",
       "      <td>Partner</td>\n",
       "      <td>Cat</td>\n",
       "      <td>Intact Male</td>\n",
       "      <td>3 weeks</td>\n",
       "      <td>Domestic Shorthair</td>\n",
       "      <td>Brown Tabby</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>A698049</td>\n",
       "      <td>Luigi</td>\n",
       "      <td>03/16/2015 02:50:00 PM</td>\n",
       "      <td>03/16/2015 02:50:00 PM</td>\n",
       "      <td>06/05/2014</td>\n",
       "      <td>Transfer</td>\n",
       "      <td>Partner</td>\n",
       "      <td>Cat</td>\n",
       "      <td>Spayed Female</td>\n",
       "      <td>9 months</td>\n",
       "      <td>Domestic Medium Hair Mix</td>\n",
       "      <td>Black/White</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>A765349</td>\n",
       "      <td>Einstein</td>\n",
       "      <td>06/08/2018 01:04:00 PM</td>\n",
       "      <td>06/08/2018 01:04:00 PM</td>\n",
       "      <td>01/18/2009</td>\n",
       "      <td>Adoption</td>\n",
       "      <td>Foster</td>\n",
       "      <td>Dog</td>\n",
       "      <td>Neutered Male</td>\n",
       "      <td>9 years</td>\n",
       "      <td>Chihuahua Shorthair Mix</td>\n",
       "      <td>Tricolor</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   animal_id      name                datetime               monthyear  \\\n",
       "9    A803149    *Birch  08/31/2019 04:26:00 PM  08/31/2019 04:26:00 PM   \n",
       "10   A698049     Luigi  03/16/2015 02:50:00 PM  03/16/2015 02:50:00 PM   \n",
       "14   A765349  Einstein  06/08/2018 01:04:00 PM  06/08/2018 01:04:00 PM   \n",
       "\n",
       "   date_of_birth outcome_type outcome_subtype animal_type sex_upon_outcome  \\\n",
       "9     08/08/2019     Transfer         Partner         Cat      Intact Male   \n",
       "10    06/05/2014     Transfer         Partner         Cat    Spayed Female   \n",
       "14    01/18/2009     Adoption          Foster         Dog    Neutered Male   \n",
       "\n",
       "   age_upon_outcome                     breed        color  month  \n",
       "9           3 weeks        Domestic Shorthair  Brown Tabby      8  \n",
       "10         9 months  Domestic Medium Hair Mix  Black/White      3  \n",
       "14          9 years   Chihuahua Shorthair Mix     Tricolor      6  "
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shelter_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Convert 'age_upon_outcome' variable to int in days\n",
    "\n",
    "## plot the histogram of days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "def days(x):\n",
    "    if 'day' in x:\n",
    "        return int(x.split(' ')[0])\n",
    "    if 'week' in x:\n",
    "        return int(x.split(' ')[0])*7\n",
    "    if 'month' in x:\n",
    "        return int(x.split(' ')[0])*30\n",
    "    if 'year' in x:\n",
    "        return int(x.split(' ')[0])*365"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3'"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'3 years'.split(' ')[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [],
   "source": [
    "days = shelter_df.age_upon_outcome.map(days)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9           21\n",
       "10         270\n",
       "14        3285\n",
       "15        3650\n",
       "23          60\n",
       "          ... \n",
       "118028      60\n",
       "118029      30\n",
       "118030     365\n",
       "118033    4380\n",
       "118035    4015\n",
       "Name: age_upon_outcome, Length: 26454, dtype: int64"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(days)\n",
    "plt.xlabel('Age in days')\n",
    "plt.ylabel('Frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'10 years'"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shelter_df.age_upon_outcome.iloc[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatterplot - visualizing two dimensional data\n",
    "Scatterplots are usually used for visualizing two dimensional data (observations with two variables). It allows us to examine the relationship between two variables, thus it is sometimes called a correlation plot. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# generate some data -> the sine wave\n",
    "x = np.linspace(0, 10, 30)\n",
    "y = np.sin(x)\n",
    "plt.scatter(x, y, label=\"Function: sin(x)\")\n",
    "plt.title('Scatter Plot in Matplotlib')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# examining correlation with height and weight\n",
    "height = [63, 62, 60, 63, 64, 65, 68, 67, 64, 71, 72, 70, 73]\n",
    "weight = [120, 115, 114, 119, 125, 130, 135, 140, 128, 140, 150, 165, 180]\n",
    "plt.scatter(height, weight, color='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pie Charts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# pie chart\n",
    "music_genre = ['R&B', 'Rock', 'Country', 'House', 'Hip Pop', 'Techno']\n",
    "num_people_like = [15, 5, 3, 7, 18, 3]\n",
    "\n",
    "# Make your plot here\n",
    "plt.figure(figsize=(10, 8))\n",
    "plt.pie(num_people_like, labels=music_genre, autopct=\"%1d%%\")\n",
    "\n",
    "plt.axis('equal')\n",
    "plt.title('Music Preference')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![seaborn](https://d2h0cx97tjks2p.cloudfront.net/blogs/wp-content/uploads/sites/2/2018/07/seaborn.png) \n",
    "\n",
    "# The good, the bad, the seaborn\n",
    "\n",
    "\n",
    "\n",
    "[Data is ugly](https://www.reddit.com/r/dataisugly/)\n",
    "\n",
    "[Data is beatiful](https://www.reddit.com/r/dataisbeautiful/)\n",
    "\n",
    "Learning goals:\n",
    "\n",
    "- Create a list of best practices for data visualization\n",
    "- Identify the differences between matplotlib and seaborn\n",
    "- Create a visualization with seaborn, applying best practices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Best practices for data visualization\n",
    "\n",
    "Documenting best practices:\n",
    "\n",
    "After class, read these articles about the Dos and Don'ts of Visaulizations:\n",
    "- [article 1](https://www.jackhagley.com/What-s-the-difference-between-an-Infographic-and-a-Data-Visualisation)\n",
    "- [article 2](https://thoughtbot.com/blog/analyzing-minards-visualization-of-napoleons-1812-march)\n",
    "- [article 3](http://dataremixed.com/2016/04/the-design-of-everyday-visualizations/)\n",
    "- [article 4](https://visme.co/blog/data-storytelling-tips/)\n",
    "- [article 5](https://www.tableau.com/learn/articles/best-beautiful-data-visualization-examples)\n",
    "- [article 6](files/VisualizationsThatReallyWork.pdf)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Differences between seaborn & matplotlib\n",
    "\n",
    "\n",
    "### Two code examples to accomplish the same plot:\n",
    "\n",
    "**Resources:**\n",
    "- [python graph gallery on seaborn](https://python-graph-gallery.com/seaborn/)\n",
    "- [seaborn](https://seaborn.pydata.org/)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "# Load in data\n",
    "tips = pd.read_csv(\"https://raw.githubusercontent.com/mwaskom/seaborn-data/master/tips.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Matplotlib:\n",
    "\n",
    "# Initialize Figure and Axes object\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# Create violinplot\n",
    "ax.violinplot(tips[\"total_bill\"], vert=False)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Seaborn:\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Load the data\n",
    "tips = sns.load_dataset(\"tips\")\n",
    "# tips = pd.read_csv(\"https://raw.githubusercontent.com/mwaskom/seaborn-data/master/tips.csv\")\n",
    "\n",
    "# Create violinplot\n",
    "\n",
    "sns.violinplot(x = \"total_bill\", data=tips)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 485.359x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(tips, col=\"sex\", hue=\"smoker\")\n",
    "g.map(plt.scatter, \"total_bill\", \"tip\", alpha=.7)\n",
    "g.add_legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda3/lib/python3.7/site-packages/seaborn/axisgrid.py:728: UserWarning: Using the boxplot function without specifying `order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(tips, col=\"day\", height=4, aspect=.5)\n",
    "g.map(sns.boxplot, \"sex\", \"total_bill\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Your turn: Create a visualization with seaborn, applying best practices\n",
    "\n",
    "[exercise from data world](https://data.world/makeovermonday/2018w37-paying-the-president)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_excel('https://query.data.world/s/5qxp2ldwsel3ow2pq5mkvfas2rfaup')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For extra fun:\n",
    "[visualization challenges](http://www.storytellingwithdata.com/blog/2019/3/1/swdchallenge-visualize-this-data)\n",
    "\n",
    "[seaborn cheatsheet](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Python_Seaborn_Cheat_Sheet.pdf)"
   ]
  }
 ],
 "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": "3.7.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
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