python/07-working-with-external-libraries.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**[Python Micro-Course Home Page](https://www.kaggle.com/learn/python)**\n",
    "\n",
    "---\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These exercises accompany the tutorial on [imports](https://www.kaggle.com/colinmorris/working-with-external-libraries).\n",
    "\n",
    "There are only four problems in this last set of exercises, but they're all pretty tricky, so be on guard! If you get stuck, don't hesitate to head to the [Learn Forum](https://kaggle.com/learn-forum) to discuss.\n",
    "\n",
    "Run the setup code below before working on the questions (and run it again if you leave this notebook and come back later)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setup complete.\n"
     ]
    }
   ],
   "source": [
    "from learntools.core import binder; binder.bind(globals())\n",
    "from learntools.python.ex7 import *\n",
    "print('Setup complete.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.\n",
    "\n",
    "After completing [the exercises on lists and tuples](https://www.kaggle.com/kernels/fork/1275177), Jimmy noticed that, according to his `estimate_average_slot_payout` function, the slot machines at the Learn Python Casino are actually rigged *against* the house, and are profitable to play in the long run.\n",
    "\n",
    "Starting with $200 in his pocket, Jimmy has played the slots 500 times, recording his new balance in a list after each spin. He used Python's `matplotlib` library to make a graph of his balance over time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f068f5570b8>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import the jimmy_slots submodule\n",
    "from learntools.python import jimmy_slots\n",
    "# Call the get_graph() function to get Jimmy's graph\n",
    "graph = jimmy_slots.get_graph()\n",
    "graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, he's hit a bit of bad luck recently. He wants to tweet this along with some choice emojis, but, as it looks right now, his followers will probably find it confusing. He's asked if you can help him make the following changes:\n",
    "\n",
    "1. Add the title \"Results of 500 slot machine pulls\"\n",
    "2. Make the y-axis start at 0. \n",
    "3. Add the label \"Balance\" to the y-axis\n",
    "\n",
    "After calling `type(graph)` you see that Jimmy's graph is of type `matplotlib.axes._subplots.AxesSubplot`. Hm, that's a new one. By calling `dir(graph)`, you find three methods that seem like they'll be useful: `.set_title()`, `.set_ylim()`, and `.set_ylabel()`. \n",
    "\n",
    "Use these methods to complete the function `prettify_graph` according to Jimmy's requests. We've already checked off the first request for you (setting a title).\n",
    "\n",
    "(Remember: if you don't know what these methods do, use the `help()` function!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f068d2f4438>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iKJiKiIRZRc96iHMpAFcf15URg9rx2DuLaX+N9i0RkciiYCoiEmb+EM3Kr46ZcfNpPRmW3wpAC/CLSERRMBURCTN/CNYxrY3HY9xzTl+O7paLmcKpiEQOBVMRkTBryK78CrFeDw9d1J9CXyYxHlM4FZGIoGAqIhJmPy0X1YDJFEiI9TJhVAHdW6YRH+PBN24avnHT2LhNu0SJSHgomIqIhJlr4K78qlITYpl8cSFtM5PISo4jzuuh7y2vsG1XWYPXIiKiYCoiEmaVOz81ZF9+FZnJcfznisN4/5qh3H9BPh6DHjfMYmeZtjAVkYalYCoiEmah3vmpLuJjvMTHeDmhV0v+eFYfALpeN5Oycm1hKiINJybcBYiINHXOOcxCu/PT/jinoA2bdpRx63++4IpnPqa03PHqF6tZcufJ4S5NRKKcgqmISJiVOxeW8aW1KT6sPRu3l3L/698AEOcNTI5SOBWRUFJXvohImPldeLvxa3LlMZ355+WDmXPN0bTPTiYpzqtlpUQkpBRMRUTCzB+BLaYQGFowoF0zWqQnMKW4kOyUeNITY1m4anO4SxORKKVgKiISZn5/ZAbTqnLTEvjr2CISYj0cf987LF23NdwliUgUUjAVEQkzvwvfUlH7o01mElOKi8hIiuXIu99i4arN+MZNw1+x3pWIyEFSMBURCbNyf2BWfmPQpXkqk8cUkhzn5fj73gHgppc/r9wkQETkYCiYioiEmYvQMaY16dsmg0ljCjmvoA0XFLbl6dlLaX/NdP7y2jeaHCUiB0XLRYmIhFm5c42iK7+qwvaZFLbPxDmHc45n5i7nz699TWpCTGU4/fKWE0iM84a5UhFpTNRiKiISZpG6XFRdmBl/OKM3d5zZm6nFRaQlxNI6IxEz6H7DTHaUlvPs3OX4xk3j69WazS8itVOLqYhImDW2rvw9eT3GBYVtAXjj6iOJ9Xj4x7zlXPPCZ3S7fiYAMR5jxMQPWL1ppxbpF5EaqcVURCTMyhvBclF1FR/jxRMMqjee2oO+bTJ4dHh//nPFYewo9dM2M0njUEWkRmoxFREJs8ayXNT+GnNoe8Yc2r7y66fGDOSiCR/QrUUqG7btIiMpLozViUgkUoupiEiY+RvRclEHo1/bZjwxsoDFa7aSf8ur/PWDpZWtp1puSkRAwVREJOwidUvSUDi0UzYPXNgPr8e49l//A8A3bhrtr5nOvCUl/OfTldV29b+44Ht846axeM2Whi5ZRBqQuvJFRMIsWrvya3J8zxY8fXEhu8r9PD9/BW8vXENCrJezH50NwNBuuZSW+4n1/tR28vInKwPn7n2b98cNpVVGYlhqF5HQUjAVEQmzctc0uvKrOrRTNgBDuuRQWu5Yvn4bE/67mOyUeB54YxGdr53Bb4/vyt2zFrL49pOYt3Q9he0z+XLlJgbf+QbzrjuG7JT4MD8LEalvCqYiImHmnMPb1JJpkJkRF2N0zEnhjjP7AJAUF8MfZ37F3bMWEuMxOoyfDsDZA/Jof3wyIyZ+wKgnP+TzlZu09JRIlNEYUxGRMIum5aLqw+VDOvLQhf155pJBdGuZWnl8oC+Tgb5MHhk+gIWrNjOgXTPGTp6n5adEooiCqYhImPkdTa4rf19O7tOSQR2ymDymkI45yQD4spIAOKprLn8+L5+Plq3ntS9XB84pnIpEBXXli4iEmXOuSU1+2h9ZKfG8/KvD2LKjDKuS3k/t24ruLVPJSUngqucW8PpXP+IbN01d+yKNnFpMRUTCTF35tUuKiyE3LWGv451yU0lPiuXBC/tT6MvE6zFeD7agikjjpGAqIhJmfgcetZgesIRYLxNGFdCzVRrFk+cxZ/G6cJckIgdIwVREJMwCC+yHu4rGLTUhlqfGFNIpN4XzH5/Dpys2hLskETkACqYiImHmb8LLRdWnzOQ4phYXkdcskdMefI9FP24Od0kisp8UTEVEwszvR2NM60mL9ASmFheRnRLP8Akfara+SCOjYCoiEmZNceenUPJlJzOluJBtu8rwZSUpnIo0IgqmIiJhpuWi6l/3lmlMGlPI6k076dYilY3bSsNdkojUgYKpiEiYabmo0BjQrhmPjxzA4jVb6XvLK2zbVRbukkRkHxRMRUTCTMtFhc7hnXO4/4J8PAY9bpjFzrLycJckIrVQMBURCTOn5aJC6oReLbnzrD4AdL1uJuV+F+aKRKQmCqYiImFW7tSVH2rnFrThupO7A9Bx/HScUzgViUQKpiIiYablohrG2MM7cMXQTgC0v0bhVCQSKZiKiISZdn5qOFce24XRg31AIJyKSGRRMBURCTO/lotqMGbGDaf04Mx+rQF4evaSsNYjIrsLeTA1M5+Zja7y9W/M7Asz+9TMXjezdlXOlZvZguDHS1WOtzezD8zsGzP7h5nFhbpuEZGGouWiGpbHY9x1dh+O7dGcG178nH9//H24SxKRoJAGUzO7HJgF3Gpmb5lZC+BjoMA51wd4Hriryk22O+fygx+nVTn+R+DPzrnOwHqgOJR1i4g0JKflohpcjNfDAxf0Y3DHLK567hPtDiUSIUIWTM0sFbgZGAlcD4wGtjrn3nTObQteNgfI28f9GDCUQIgFmAycXsO1l5jZPDObt2bNmoN/EiIiDUBjTMMjIdbL4yML6NU6nbgYj8KpSAQIZYupH4gD0gCcc0ucc5v3uKYYmFHl64RgsJxjZhXhMwvY4Jyr2LJjBdC6ugd0zj3unCtwzhXk5OTU2xMREQklLRcVPinxMTw1eiC+rCSS47wKpyJhFrJg6pzbSqC19HYCXfn3mFlSxXkzGw4UAHdXuVlb51wBcCFwn5l1BKr731prfIjU0dJ1W/GNm8bnKzeGuxSpgZaLCq9myXFMKS4iMyWOjKRYhVORMArpGFPn3EvAOQTGkeYAVwGY2THAtcBpzrmdVa5fGfx3MfAW0A9YC2SYWUzwsjxgZSjrFokmX6/eAsDIiR/qDTdCqSs//JqnJTC1uIhYr4fmafEsL9m27xuJSL0L5RjTlCoz7jcDXwKpZtYPeIxAKP2xyvXNzCw++Hk2cCjwhQusgPwmcHbw0lHAi6GqWyTaVGy/uGlHKa3SExROI5CWi4oM7bKSmVpcxI5SP4ff9SY/btoR7pJEmpxQtpjGEgigzwM3AicCfyHQdZ8CPLfHslDdgXlm9gmBIHqnc+6L4LnfA78xs0UExpxODGHdIlGlYnebO87sw+adZXTISVY4jTB+F1hfU8Kva4tUnhozkKQ4L4W3v87aLTvxjZumXaJEGkgox5iud86dQKCl82bn3JHOue+dc8c455rvuSyUc+5951xv51zf4L8Tq9zXYudcoXOuk3PunKrd/yJSu2CDKb1bpzNp9EBWbthOz1ZpbNpRGt7CpJLfr678SNKvbTOeGFlAnNdDwW2vAXDfa9+EuSqRpqEhdn7aACxogMcRkWqUB1t6vB4o8GXy6PABfL16M31ueoXtu8rDXJ2AuvIj0aGdsnn4ov4c3jmb43o05y+vf6OeBpEGEPJg6pzb4JxTMBUJk4ouyIqu4iFdc/nzefmYQfcbZrKrzB/O8gTt/BSpjunRnCnFRTx8UX9O6NkCQOFUJMQaosVURMKoYvKTt0rwOaVPK24/ozcAXa6bUXmNhIdzWi4qksV4PfzlgnwO75yNxwLhdGdZOU+++x2+cdP0+hGpRwqmIlGu4j1zz+BzQWFbrjmxGwAdx0/X5I4w0nJRkS8+xsujwwfQt00GcV4PXa+byS3/CczP1etHpP4omIpEOX8wmXqqebVfemRHfj6kIwDtr5ler4/rnGPm/37AN26auj/3oVxjTBuF5PgYnhpdSLeWqfTNS2dqcRGXHRma149IU6VgKhLl/MGWnJq6in97fFeGD2oLwCNvfVsvjzn723Wc8fD7XDb1I1LjA3tjKJzWTMtFNR7pSbG89MvDePGXh3FY52x+f0JXLiwKvH4ee7t+Xj8iTZmCqUiUq6krv4KZcctpvRiW34o/zvwK37hp/P3DZQccJN/9Zi0XPDGHVRt3cNdZfZh73TEM6ZqDmcJpTbRcVONlZtw6rBen9GnJHTO+0u+4yEFSMBWJchXLRVXXlV/B4zHuOacvR3fLBeCaFz6jZZVdojZs21XnN9w3vvqRhFgPb1x9JOcObENCrJdHLhpAQbtmxHpNb9zV0HJRjZvXY/zp3Hz9ASZSDxRMRaKc20dXfoVYr4eHLurPr4/uzD3n9GXrzjJ8WUnc99rX5N/yKh6Dmf9btc/Hm7e0hPw2GSTFxVQeS4zzMmHUQDrnppIQ6+HhtxbhGzeNjdu1yD+oKz8axMV4eOSiAQxsl6k/wEQOgoKpSJSrbrmomiTEerny2C6cPSCPSWMKWb1pJ/e99g3H9mhOn7wMLps6n3e/WVvj7bfuLOPzlZsY6Mvc61x6YixPFxfSKj2Ru2YuBKDvzVrk378fPx+JbIlxXiaMLqBL88AfYAqnIvtPwVQkyu1rjGlNBrRrxgs/H8yLvziUJ0YWMHlMId1apDJ84gd8tGx9tbdZsHwD5X7HgHbNqj2fnRLPPy49hEmjB/LABf3wGIyY+EG1M/dnf7sO37hprFi/bb/qbmx+mpwW5kKkXqQlxPL0xYW0ykgkNSGm8vd6y84yBVWROlAwFYlyrg5jTGvSvWUafdtkAIHZyE8XF9IuK4kxk+ZW+yY7d0kJZtC/hmAKkJMaz1Hdcjm1b2CR/3lL15OWsPfM/efnrwDgsD++yZrNO/e/+EbipzHASqbRIislnqnFRaQlxJKVHMeDb3xDrxtnATD5/SXhLU4kwimYikS5iq78+thZKDc1ganFRSTEeshJjd8rnM5fup5uLdJIS4it0/2dX9iW2dcM5YPxx3BIhyy8np/G5s1bWkLX5qkkxnoZ+eSHUdva5A6wRVsiW6uMRKYUF2IG97zyNYd0yOLILjnc+NLn/O2DZTw9e0nU/k6LHAwFU5Eod6Bd+TVpk5nE1OIiysr9tMlMrHxzLSv389HS9Qz01dxaWp2W6Ykkxnl5YlQBvVqlERcTGJu3dN02zh6Qx6MjBrDox830bp1e2eW/dN3WqHlT/+kPhzAXIvWuQ04KL1x+KM9eegh/+1kRj40YwOCOWYz/12fc8OLnu71+RCRAwVQkyvkPoiu/Jp2bp/LUmEJKtuyic24K67fu4qtVm9m6q5yCaiY+1UVKfAxPjSmkXWZS5bECXzOO7JLDX87vx+crN5KeGGiJPfLut8hIimXhqs318nzCqeLno+WiolPbrCQK22diZiTEenl8ZAG/Prozfzq3LyVbdtGleQr3vrIQ37hp2tZUBAVTkajnr8eu/Kr6tslgwqiBLC3ZRr9bX2X6Zz8AUFDL+NJ9aZYcx9SxRbTJTASgZ6t0AE7q3ZI544/mw2uPZsSgdowe7CPO6+H4+95h2brGPTmqokVby0U1DSnxMVx5bBfO7J/HEyMLWLJuGw+8sQjQtqYioGAqEvUqJteEYjmiQzpm8fCF/fF6jIff+pb8Nhm0ykg8qPtsnpbAv39+KDN+fThxMT/9F5WbmkB8jJdbT+/FTaf1ZOrYIjKSYjni7jdZvWnHwT6VsPlpuagwFyINbnCnbF64fDAz/+9wzi3IA7Q4v4iCqUiU+6lFLjT3f0yP5kwYWcCtw3ryzCWD6uU+s1Li6d4yrdZrugSHEyTHeSm6/XXWb91VL4/d0Pyald+k9WqdTrcWadxxZh9O7NUCgGfnLg9zVSLho2AqEuWcc5iFtqv4qG65jDjER0KsN2SPUZ38Nhk8MaqAuBgP/W59lS07yxr08etDRYu2uvKbNq/HuO/8fA7vnM3v/vkpM4JDY0SaGgVTkShX7ndRvavQ4I7ZPBQcTnDRE3OqXaw/klXMd4nmn5HUTXyMl8dGDKB/2wx+/cyCRvV7LFJfFExFopzfRf8amcf2aM495/Th0+83kpkcBzSesXra+UmqSoqLYdLoQjrkJJMY6200v8ci9UXBVCTK+Z2r16WiItUZ/fL4cPwxzL5mKId3zsZjjSOcVq5jqmQqQelJsUwpLqJ5WjxpVbY1FWkKmsDblUjT5ve7qG8xrZCTGl/ZHdqvbTNivRbxb+ra+Umqk5Maz9SxRSTFxZCdsvcuayLRSsFUJMpiz/bfAAAgAElEQVT5XdMbv5gUF8OTowfSKTewpen8pSXhLqlGFS2mXv1vLHvIa5bE1LGF+J2jdUYiqzY23mXRROpK/xWKRDl/cFZ+U5OeGMvTFxfSIj2Bsx6ZzZc/bAp3SdX6aYxpE/whyT51yk1l8phCNm4vZdAdr1PSSJdFE6krBVORKBcYY9o0Q09OajxTigtpmZ7AiX/5L9+t3RrukvainZ9kX3rnpTNhVAHxMR763/oqm3eUhrskkZBRMBWJctG+XNS+5DVLYkpxEZnJcRx1z1v8sHF7uEvajT+EO3NJ9BjUIYuHL+pPjMfofdMr7CgtD3dJIiGhYCoS5fxOrXGdclOYPKaQlPgYRkz8MKImkmi5KKmro7s3595z+2IG3a6fSWm5P9wlidQ7BVORKOf3O02sIdAdOnFUActLttG7dfpe4XTzjtLKxfm/WNlw41G1XJTsj2H5rbllWC8AOl87A3/FWBCRKKG3K5Eo53dNZ7mofSnqkMUjw/vz5Q+bKGqfuVt36CNvfYvHICMplpPub7jxqFouSvbXiEHt+O3xXQG48aXPcU7hVKKHgqlIlGsKOz/tj6HdAt2hHy4p2a07dM7idfRr24znLxtMZnIcwyd80CBd/urKlwPx8yEdufSIDkyZs5T210wPdzki9UbBVCTKNZWdn/bHsPzW3FqlO3TbrjI++34jBb5mdMpN4emLC9m0vZSOOckhD6fqypcDYWaMO7EbFxS2ARrHLmcidaG3K5Eop6786g2v0h3a44ZZlJY7CtplAtCrdWB5nhXrt9O7dXpIl+fxqytfDpCZcdvpvTm5T0tA4VSig4KpSJRr6stF1aaiO7TCgHbNKj8v6pDFo8MH8OUPm0K6PI+Wi5KD4fUYfz43nyO75OAxhVNp/BRMRaKcczTJnZ/qoqI79JIjOnBa31ZkJsftdv6obrn86bz8yuV5lq7bWjlzf9GPm+ulhopZ1erJlwMVF+Ph0eED6N+2GbFeUziVRk3BVCTKlfsdXqWeGpkZ40/qzv0X9Kv2/Gl9W3Hb6YHxqEfe/RZxMR4ykmI55k/vsLxk20E/frnTGFM5eIlxXiaOHkjn3FQSYj3MW1IS7pJEDoiCqUiU0xjTg3dRUTtuPb0XIw9px1tXD+HvPxtEWkIMIyYe/Mx9LRcl9SU9MZbJFxfSMj2Rsx+dzecrN4a7JJH9pmAqEuW081P9GDGoHbcM60WrjES6t0xj0phCVm/aSbcWqXuF001VFuv/dMWGau/v+w3b8Y2bpuWipF7lpMYzdWwRLdMTOPn+d1m8Zku4SxLZLwqmIlHO77TzUygMaNeMx0cOYPGarfRvm8G2XWWV5x4NLtaflRzHqCf33gL1hY9WcOidbwDwr4+/B9SVL/WndUYiU4qLyEyOY+i9b7Nyw/ZwlyRSZ3q7Eoly6soPncM753D/BfksWL6BHjfMYmdZYOb+B9+V0LdNBi/8fDAxXg8t0hJ2C6dPvvcdXZunMqhDJi98FAym+hlJPapYjzc1Pobh9TDkRKShKJiKRLlyv4JpKJ3QqyV3ntUHgK7XzQws1r9iIwN9mbTLSmZKcSHbS8tpl5WEb9w0tuws44uVmziuZ3MmjBpI37x0AGLUYir1rFfrdCaOHsj367fTq3Wawqk0CgqmIlHOOY1fDLVzC9pw3cndgcBi/bvK/RQE10Tt1iKNSWMGsmZzYDzq2wvX4HdQ4MskJT6GyRcXcvNpPenWIjWcT0GiVGH7TB4dPoCvfthMYfvMkK3HK1JfFExFopy68hvG2MM7cMXQTpVfV12sv3/bZjw+ooDFa7byi799hMegf9sMADKS4hg12EeMBgJLiBzVLZc/n5fP3CUldLt+JqXl/nCXJFIj/U8oEuXK/U4TaxrIlcd24VdDO3FGv9ZkpcTvdu6wztncf0E+HguslJCaEBumKqUpOrVvK/5wem8AOl87g/KKvXBFIkxMuAsQkdByDjz6E7RBmBlXHde1xvMn9GrJ3342iOQ4/dcrDe/CorZs3F7KH2d+Rcfx0/nujpO0lJxEHL1diUS5cqednyLJoA5Z9A5OeBJpaJcP6chlR3YEoP0108NcjcjeFExFopzGmIpIVb8/oSsXFrUFqNwIYv7SEl75fJVm7kvYqT9JJMr5tVyUiFRhZtw6rBdbd5Yx83+rSE2I5axHZgNweOdsdpaVEx/jDXOV0lQpmIpEOb+WixKRPXg9xn3n5bPjTD+rNu3ggTe+oVV6Ig++uYiu181k0R9O1EoREhb6rROJcurKF5HqmBmJcV7aZyfzp3Pzufr4rlx/Sg8AOl07g/99v1Fd+9LgFExFopyWixKRuio+rD1XHN0ZgFMeeJc4r4f3F60Nc1XSlIQ8mJqZz8xGV/n6N2b2hZl9amavm1m7KudGmdk3wY9RVY4PMLPPzGyRmd1vWt9CpM6085OI7I8rj+nM70/oxq+P7kz77GQunPABC5ZvCHdZ0kSENJia2eXALOBWM3vLzFoAHwMFzrk+wPPAXcFrM4EbgSKgELjRzCq2TnkEuAToHPw4IZR1i0QTLRclIvvDzLh8SEeuPLYLU4oLaZuZxOkPvcfCVZtrvM2LC77HN24aS9ZubcBKJRqFLJiaWSpwMzASuB4YDWx1zr3pnNsWvGwOkBf8/HjgVedciXNuPfAqcIKZtQTSnHOznXMOeBo4vYbHvMTM5pnZvDVr1oTqqe3lvUVr8WsXDYlQfue0iLaIHJDctAT+OraI5mnxHH/fOyxbt63a615asBKAIfe8xaqNOxqyRIkyoWwx9QNxQBqAc26Jc27PP7eKgRnBz1sDy6ucWxE81jr4+Z7H9+Kce9w5V+CcK8jJyTn4Z1AHHy9bz0UTPqDD+OkEcrNIZAl05SuYisiBaZOZxJTiIjKSYrlo4py9JkT5/Y55S9cz0NeMlPgYBt3xOiVbd4WpWmnsQhZMnXNbCbSW3k6gK/8eM0uqOG9mw4EC4O6KQ9XdTS3HI0J+mwwuPaIDENhF48PvSjSLUSJKud/hVS4VkYPQpXkqk8cUUrJlF+2ykioX5v9o2Xo6jJ/Oxu2lnFvQhgmjCoiP8TB60od6L5QDEtIxps65l4BzCIwjzQGuAjCzY4BrgdOcczuDl68A2lS5eR6wMng8r5rjEcHMGHdiNy4oDJR+7mOzSY2P4X/fbwxzZSIBWi5KROpD3zYZPDGqgNWbduDLCrQznfnw+5XnC3yZDOqQxcMX9eeLlZso9GVyydPzFFBlv4RyjGlKlRn3m4EvgVQz6wc8RiCU/ljlJrOA48ysWXDS03HALOfcD8BmMxsUnI0/EngxVHUfCDPjttN788ujOjHuxG6kJcZyygPv8uy85fjGTWNnWXm4S5QmzK/lokSkngzumM0nNx7Hm1cP4apju/Db47vSITsZoDKsHt29Ofee25e5S0t45YvVgXMKp1JHFqpxkcFw+XcgG8gClgEXApOB3sAPwUuXOedOC97mYmB88PgfnHOTgscLgKeARAJjUn/l9lF4QUGBmzdvXn0+pTpbvGYL5z42m7Vbfhpj8+3tJwHwxlc/8rOn57HkzpPDUps0PYNuf50jumRz19l9w12KiEShLTvL2LKjjBbpCbsdX7hqM7mp8Vz57ALe/noNzqH3vibMzOY75wr2dV3ItiQNzqw/wcx8wBDn3FPBU8fUcpsngSerOT4P6FX/VYZGh5wU/nHpISxYtoE1W3Zy54yv+Plf57Nk7TYWrt5Mm8xEVm/aQfO0hH3fmchB0nJRIhJKKfExpMTvHSe6tkgF4JGLBjDqyQ/5ePl63lr4I0O65jZ0idKINMTOTxuABQ3wOBGlY04KZw3I47IjO/LLozox6/PVLFy9metO7k7Jll0U3f466zVrURqA03JRIhJGiXFeJowuoGuLVEZPmsvcJSXhLkkiWMiDqXNug3OuyQXTqq46rgv/+dVhfPOHExl7eAeeGFVAXIyHfre+yucrtRexhJZfOz+JSJilJcQyeUwhHXKSOefR2Xy+UhOEpXoN0WLa5JkZvVqnE+sNfLsHd8zmoQv74/UYJ9//LgAT/rs4nCVKFAssF6VkKiLhlZUSz9TiIlpnJHLy/e+yeM2WcJckEUjBNEyO7dGchy7sz6VHdOCEni24bdqXPDt3+b5vKLKftPOTiESKVhmJTCkuJDsljuETPlCPoexFwTSMTujVgmtO6s5fLsjn8M7Z/O6fnzL9sx/2fUOR/eD3a/KTiESODjkpTL64kM07y+iQnaxwKrtRMI0A8TFeHhsxgAHtmvHrZz7Wi1TqlcaYikik6dkqnUmjB7Jy43Z6tkpj047Sva7x+x0vf7KS8x+fjW/cNFZu2M62XWVhqFYakoJphEiKi+HJUQPpmJNCYqxX4VTqjXZ+EpFIVODL5NHhA/h69Wb63PQK23f9tBnNf79ZQ4fx0/nV3z/mo6UbiIvxMPjON+hxw6xqQ6xEDwXTCJKeFMuU4iKap8WTlhCjcCr1wu+085OIRKYhXXO577x+eAy63zCT+UvX4xs3jRETP6R1RiJ/Orcv7/7+KAraNePM/q2J9Rp9bnqFT5Zv0HtklFIwjTA5qfFMHVtEUlwM2SnxfLd2a7hLkkZOXfkiEslO7tOS28/oDcBZj7xPZnIcN5zSgzeuPpIz++eRm5bA3342iD+dm8+fz8vHDIY99B4AU+YsDWfpEgIh2/lJDlxesySmji3k3MfmcNQ9bzH7mqG0TE8Md1nSSGm5KBGJdOcXtsXrMVZv2sGowT5SE2Krve6UPq0A+HrVZj5fuYnr//0/0hJiGJbfuiHLlRBSMI1QnXJTmTymkAuemMMhd7zBR9cfS2ZyXLjLkkbGOQeg5aJEJOKdU9CmTted0qcV9IEdpeWMevJDfv3MAlITYhjarXmIK5SGoK78CNY7L52JowqIj/HQ/9ZX2awB37Kfyv2BYKrlokQk2iTEepkwqoA+eelcPvUjjTmNEgqmEa6oQxaPDO9PjMfofdMr7Cgt3/eNRIKCuVRjTEUkKqUmxPLUmELaZCaREq9Jw9FAwbQRGNqtOfee2xcz6Hb9TErL/eEuSRoJv7ryRSTKZSbHMaW4kPTEWDKT4xROGzkF00ZiWH5rbh3WC4Crn/tELzypk4pgqq58EYlmLdMT+evYIjxmtExPqHyP3LarTO+XjYyCaSMyfFA7fnt8V15csJLzB7bhjulf6gUntVJXvog0Fb7sZKYUF7J1Zxm+rCQmvvsdPW6YhddjzPp8VbjLkzpSMG1kfj6kI5ce0YFn5i7nsXcWkxDrUTiVGlVMftLOTyLSFHRvmcakMYWs3rSTW//zBYW+THq1SuPSKfN5b9HacJcndaDlohoZM2Pcid3onZdOm2ZJXPnsAtZs2olv3DSW3HlyuMuTCFOxXJSCqYg0FQPaNeO5yw5h/bZdHNYpm43bSznvsTn87Ol5bNtVrvfKCKcW00bIzDilTyv6tslganERaYmxZCXH8dR73+EbN41dZZocJQHqyheRpqhX63QO75yDmZGRFJgclZMaT3pirHoZI5yCaSPXKiORKcWFmMFNL38BQJfrZlR24UrTpnVMRUQgNy2BqcVFJMR6yEmNVziNYAqmUaBDTgrPXTaYCSMLGH9SNwDGv/CZXniinZ9ERILaZCYxpbiI0nI/bTIT9R4ZoTTGNEq0z06mfXYy0JxN28t48M1FXHJEB5xzCiVNWLmWixIRqdSleWC77wufmEPn3BTWb91FM233HVHUYhqFrjquCyMPacfj7yym/TXTw12OhJHGmIqI7K5vmwyeGFXA0pJt9Lv1VbbsLAt3SVKFgmkUMjNuOrUnZ/RrDYBv3DS+37Bd3RZNkF/LRYmI7GVwx2wevrA/Xo/R68ZZ2u47giiYRimPx7jr7D4c0z0XgEPvfIPmafEsL9kW5sqkIfm1XJSISLWO6dGce87pAwS2+y7Tdt8Roc7B1MwOM7Mxwc9zzKx96MqS+hDr9fDghf05Pb8VFxS2YUepn8PvepMfN+0Id2nSQCq78vUnqIjIXs7ol8ctw3oC0OnaGZW9TBI+dXq7MrMbgd8D1wQPxQJTQ1WU1J+EWC/3nd+PO87sw1NjBpIU56Xw9tfZsG1XuEuTBqCdn0REajfyEB9XHdsFgA7jp1euZiLhUdd2lDOA04CtAM65lUBqqIqS0OjXthlPjCwgzush/5ZX2aoB31FPOz+JiOzbL4d2YuxhgY7gP7/6dZiradrqGkx3ucA7nAMws+TQlSShdGinbB64sB9ej9HzxlnsLNOA72im5aJERPbNzLj25O6cW5DH/W8s0mThMKprMH3WzB4DMszsZ8BrwBOhK0tC6fieLbjrrMCA7yv+/rFegFHMHxzLr1wqIlI7M+OOM/twYq8WAHpvDJM6BVPn3D3A88A/ga7ADc65B0JZmITWWQPyuPHUHsz6fDVnD8jTgO8o5dfOTyIideb1GPedn8/hnbPxWCCclvsdpZqx32DqOvmpPfBf59xvnXNXA++amS+UhUnojTm0Pf93TGeen79CA76jVEUw9SqYiojUSXyMl8dGDCC/TQaxXqPj+Ol0vnYG67bsDHdpTUJdu/KfA6r+uVAePCaN3K+P7syYQ30AB7RL1MfL1uMbN41l67Q+aiTSclEiIvsvKS6GSaML6dEyjd6t04mP8TDgttfYtKM03KVFvbq+XcU45yrXFwp+rs1lo4CZcf3JPTh7QB6w/2NqXvjoewCOuPtNVmt91Iij5aJERA5MelIsL/7yMF7+1WE8OnwAMR6jz02vHNQuUeV+x64yDQuoTV2D6RozO63iCzMbBqwNTUnS0Dwe484ze3N8z+bA/oXTuUtK6JiTTHKcl+ETPtiv285ZvE6Dy0NMy0WJiBy8o7rl8qfz8jEL7BK1v2NOy/2O5+evoOP46XS5bgZrNmtYQE3qGkwvA8ab2TIzW05gsf1LQ1eWNLQYr4e/nN+PQztl4fVYnQLjxu2lLFy9mWH5rXliVAFLS7bRNy+9Trct9zt+NnkeqfExlddv21WmoFrPKlpMtVyUiMjBOa1vK247vRcAo578EN+4aXV+zxr55Adc/dwn9GyVRkKsh4F/eI2N2zUsoDp1nZX/rXNuENAD6OGcG+ycWxTa0qShJcR6eXxEAb1bpxPn9fDeotobxT9ath7noKBdMwZ3zOahC/vzv5WbOKRD1j67Or5atYnNO8vYUVZOZnIcD725iB43zALgqfe+q7fn1NRVjDFVg6mIyMG7qKgdvz+hG+9/u47UhBhi6tCQ88PG7by3aB2XHdmRl395GI+NKCDWa/S9+RW27yo/qKEB0aius/LjzexC4ArgSjO7wcxuCG1pEg7J8TE8NWYg7bOTuWjCB7zy+Sqeeu+7al9485esByC/bQYAx/Zozj3n9GH24nV0u34mZbV0dcwL3nbS6EI8Ztw9ayGF7TM5sksON738Bf+cvyIEz67pUVe+iEj9unxIR94bN5T3xg2lW8tU4mM8tYbTive7k3u3xOMxjuySw33n9cNj0P2GmfS4YaZ6C6uoa1f+i8AwoIzAtqQVHxKFMpLimFJcSNvMJC6ZMp+bXv6CQzpk7fXCmbukhL556STFxVQeO6NfHrcM6wlAp2tn8OOmHZW3Ky33M39pCb5x05i7pISW6Qkc2imLFy4fzN9/Noh/XDKIx0YMYHDHLH73z0/1Qq0H2vlJRKT+tc5IJC0hlsljCslrlrjbsLQ9zVtSQlKcl+4tf9rJ/eQ+LbnzzD5kp8TRo1UaoAX9K9Q1mOY5585zzt3lnLu34iOklUlY5aYl8LefFXHF0E5cf0oP5ny3jqO75XLji//DN24au8r8LFi+gQJf5l63HXmIj6uO7QJA4e2vE+s1Hnj9GzpfO4OzHpnNmf1aM2fxOn7YuAMzo21WEod0zMLMAsMJRv40nKCuL9SnZy/Ri7oalctFKZeKiNS7rJR4phQXkZYYS1ZyHN+u2bLXNXOXrKd/22bEeHePXOcObMO8647l+csGc0iHus/viHZ1Dabvm1nvkFYiESevWRK/Oa4rxYe155bTevL6Vz8yefZSzKDLdTPYWeanoF2zam/7y6GduOrYLpw9II9Ouanc++rXdGmewln983jh4+9Zu2UXfx1bVO1tU6oMJ0iK8+7zhbqzrJzbpn2Jmf7i3JNfy0WJiIRUq4xEpo4twgyOvvdtvt+wvfLcph2lfLVqE+/WMmcjIdbLE6MK6NUqjbh9DAtoCuoaTA8D5pvZQjP71Mw+M7NPQ1mYRJYRh/j429giXvvNkbuF0QG+6oOpmfGroztzzzl9+fvPinhydAEzfn0E95zTh0mjBzL/umM4tFN2jY9XMZwgOyWejKTYal+oC1dtxjduGv/7fiO7yvzkpMTrL849+DXGVEQk5NpnJ/P0xUWkJsRw6J1vVC4H9fGyDfgdTC2uviGmQqBBphBfVhLJdWiQiWZ1DaYnAp2B44BTgVOC/0oTMrhTNp1yU5g4eiC9WqfRq3UauakJ+7xdRlIcQ7s1x+sxzIyjuuWSlRK/z9vlpiXw17FFxHk95KbG77a71LwlJRx/3zucW5DH3ODA8ucuO4SerdKIj/EwZ/G6A3+i+2n+0sDuV3uua/fA69+E/T+Xn7ryFUxFREKpR6s0Jo0euNtyUPOXlOD1WOUk4do0S45jSnERmSlxZCTF8uAbgfeQpjZrv67LRS11zi0FtgOuyoc0QWkJsbz4i8N45pJDQv5YbTKTmDq2iF3lfo64+01+DO4u9f63geD57LwV3DnjKzpkJ9MuK5mnxgQmbZ3/+Bw+XbEh5PUBPPb2twB0vnZG5bqhP27awb2vfk2sN7wtuJU7P2lLUhGRkCvwZe62HNR/F62lR8s0UuJj9n1joHlaAlOLi4j1erjnla8B9rnKTbSp63JRp5nZN8B3wNvAEmBGCOuSCOf1WJ1faAerS/NUnhpTSHKcl8LbX2fDtl3MXVJCtxapjB7sA2Dx2sAiEZnBvzjzmiVy2oPvsejHzfv9eCvWb6s2TG7cVso5j76/2znnHPOWrqd5WqAFuOP46ZXHKupJjP2pW2brzobdREDLRYmINKyqy0F9vGwDn32/cb9u3y4rmX/9fDDPXnoIN53aAwisclMxZyDa1bUd5VZgEPC1c649cDTwXsiqEtlDfpsMnhhVQFyMh/xbXuXjZRso8DXjhlN68IczevHf3x1VeW2L9MBfnNkp8Rzzp3dYXrKtlnuGLTvLKj93znHBE3NonZHIn1/9Gt+4aZWtju9/u5a5S9bv1gq6eO1WSrbu4spjunDZkR0BuHvWQuYuKSEh1sO/f3EoLdITSE2I4S+vfUPPGwObCDz8VvX7U1QMC6ivvZS1XJSISMM7uU9L7jizNx6DyRcX7vft85olUdg+k9GHtufKYwKr3Nw67YvKxoZoVtdgWuqcWwd4zMzjnHsTyA9hXSJ7qdhdyusxtuwsY6AvE4/HuKioHW0yk3a71pedzJTiQtISYhg+8YNqWyk/XbEB37hp9LpxFi99shKAFeu3s7xkO99v2M5fXv8G2L0VND7GQ5fmqSTEBmZOVmwyUODL5PcndOXCorY8/Na3THpvCX3zMmiZHpitmRIfw59f+5qi9pkc16M5d81cyNQ5S/eq6Yl3FgOBVQ/K6+GvYy0XJSISHucNbMsnNx7HkV1yDup+rji6Excf2p5J7y2h/TXT66m6yFXXYLrBzFKAd4C/mtlfCCy2L9Kgju3RnHvP6Uu3Fqm1zuoH6N4yjUljCvlx0066tUj9qZVzzRZ846Zx2oPvkZkcR/eWafzmHwvwjZvGvKUlADxwQT/+86vD+MVRgVbQ9tdMZ96SEvLbZDD54kJapSeSmhDD7/4ZWJyiY04yZsatw3pxSp+WAHzwXeC+Wmck8kKwW+aZSwbx0EX9OaZ7LtcH14StEAi/JbRIC0woqwjEB0PLRYmIhE9qQuxB34eZcd3J3TlnQB4Q/csi1jWYDiMw8elKYCbwLZqVL2Fyer/WzPy/I8iuw8z+Ae2a8fjIASxes5X+bTMY/6/PGHrv2yTFebliaCfe/u0Qnr10UOW2clf+4xNS42M4qXdLerVO5+rjujJiUDsAPlmxkQJfM7JT4pk6toi0hFgykmL5y/n5WDD4eT3Gn87N56ZTezD/umMq62iZnkhh+0zMjFivhwcv7E+hL7Nyn+UdpeV88+MW1m7ZxRVHd+aXR3UC4I4ZXx1UONVyUSIijZ/HY9xxZm9O6NkCoHLIVzTO2LdoHa9QUFDg5s2bF+4yJELM/N8P/PyvH+F3MPKQdvxqaGdyUn8Ktuu27OTcx2bz7ZrAJKold55cec7vd1z57AJeXLCSKcWFHN450C2zZWcZHmO3LVn3x+YdpVw04QMWrtrMzipjSl+98gg65aZw40uf8/TspXvVsz+en7+Cq5/7hHd+exRts5L2fQMREYlYO8vKGTt5Hu8tWls5VGviqAKKJ8/j29tPiuj5BGY23zlXsM/ragumZraZ6peFMsA559IOvMTQUjCVPS1YvoGs5Li9xqNW+GHjdi6bMp+LD2vPsPzWu50rLffz6YoN9G/brLJ1tD6UbN3FmKfmkhTrZf7S9ewq97P49pPweAy/33HVc5/wr4+/B2D6FYdz0v3/3a+Q+uzc5fzun5/y7u+PIq+ZgqmISGO3dWcZl0yZx64yP1/+sHm3Cbzf3XFSvb5H1ad6Cab1VIgPGOKceyr49RHAfUAf4Hzn3PNVri0HPgt+ucw5d1rweHvgGSAT+AgY4ZzbVdvjKphKYzN3SQlbdpZxVNfcymOl5X4unzqf1778EYDmafE8f9ngvcK1c467Zi3kkbe+5ZMbj2Pifxdz/xuLuP2M3oz/12e8P24orTISG/T5iIhIaH2yfANzl5SwauMOJrz7HT8f0pHfndAt3GVVKyTB1Mxygcqtfpxzy/Zx/eXA/wFJBMalnh+8fRpwNfDSHsF0i7Ffu3IAABC1SURBVHMupZr7eRZ4wTn3jJk9Cvx/e/cfrFld3wf8/WGXDSCLLLBKygIbE2oQR0WvlKkZh2IGsfFXGplR4gBOM9s6/RFn6A9rmyKQ0NQ6jZOaIZKawdgGzdhSaWYSpOpO22lUFiWoxVTq7ABdE1ZXCK5KRD794zkXLstld8nuvc/3rq/XzDP3Od/nnOd8Zz7Ds2/OOd/v94+7+/r9nVsw5Ujxve//IO++5cs58bgNuelz9+ah734/n3vXq/KcE55YdWvnN/bmgvduz4b1Ry071dRn3/WqPPeEA6/SBcDa0915181fyk2fm8Wyv+zjXyvpYIPpik2wX1Ubk1yd5LIkv5TkiiR7u3tnd9+V5KAmaqzZNekLkywG2A8leePT7LutqnZU1Y7du3cfzNfD8I45el1+9edelHe+5idz49tenuOWLDSw6PadsxkAzjzpuFzw/M35/X/wU/k7r3ze458PemcHgMOgqvLLb3xiVpi1PHJ/JSfYfyzJhsyujmYKpAdahueYKVh+pqoWw+fJSR7s7sWHKO5PctpyB3f3Dd290N0Lmzcf2rxhMKJzz9iU37psIRvWzRYa2PvIo3noO9/Pjp3fyrOPPTq3vuOVufFt5+WFpz0773zNT+Yt552RDeuPyrP+kgO0AFgbFmeFueD5m1O1dsPpik2w3917M7tael2Sa6vqvVV1oNEXZ0yXeS9N8r6q+vHMBlo95esPst9wxHnFT5ySf3fpuVl3VOWcq27Ni6/5RD66474snLkpRy0ZkVlVue5nX5gd/+Kn86xVWj4WgPnZsP6oXP/zL8vCmZuetErhWrKiE+x39y1JLknyniSbk1x5gP13TX+/lmR7knOTfCPJiVW1+C/rliS7DrLfcER69Tmn5t+86UXZsunYvPSME5Mkn/zKA0/Zr6pywmGY4BmAteHYDevywSte/qRVCteSZzLB/nfyDCbYr6rjq+rMafPhJHcn2bif/TdV1Y9M709J8ook/7tno7M+neRN066XJ/n4QfYbjlh/66Vb8j//6YW5adv5ufYN5+Sud1807y4BMIATjjn6SasUfnnXQ/Pu0kF7xtNFTaHxm32AA6tqU5KbkpyS2XOi92Z2i/6vJLk5yaYk30vyp919TlX99SQfyOzZ1KOSvK+7Pzh91/PyxHRRX0jy1u5+ZH/nNyofAPhh9v8e/G4uuf5/ZddD38tvvvWl+bv/4fP5P7/8mmxYf7DXJQ+fwzXB/vlJfjXJnswGQH04s6B5VJLLuvsPD6IjW7NkHtPVIpgCAD/svrb727nkN/8o39z7xEwu81gl6nBNF/X+zAYv3ZTkU0l+obtPTfLKJP/qIPvyYJI7D3JfAAAOk+dtPj4f2XZ+fum1L8g/fvXzkyT//OYvZtQl6Q80VHd9d38iSarqmu7+TJJ091cOdsmr7hZMAQDm5KznbsxZz50N8/nuX/wg7//0PfnI7fcNORH/gYLp0knwv7vPZ2NGbQAAlnXlRX81p206Nj977rJTws/dgYLpi6vqzzObS/TY6X2mbesbAgCsIVWVt5x3xry78bT2G0y7e91qdQQAgB9uqz9fAAAALEMwBQBgCIIpAABDEEwBABiCYAoAwBAEUwAAhiCYAgAwBMEUAIAhCKYAAAxBMAUAYAiCKQAAQxBMAQAYgmAKAMAQBFMAAIYgmAIAMATBFACAIQimAAAMQTAFAGAIgikAAEMQTAEAGIJgCgDAEARTAACGIJgCADAEwRQAgCEIpgAADEEwBQBgCIIpAABDEEwBABiCYAoAwBAEUwAAhiCYAgAwBMEUAIAhCKYAAAxBMAUAYAiCKQAAQxBMAQAYgmAKAMAQBFMAAIYgmAIAMATBFACAIQimAAAMQTAFAGAIgikAAEMQTAEAGIJgCgDAEFY8mFbV1qq6Ysn2K6vq81X1aFW9aZ99L6+qr06vy5e0v6yqvlhV91TVr1dVrXS/AQBYXSsaTKvq7UluTXJtVW2vqlOT3JvkiiS/u8++JyW5KslfS3JekquqatP08fVJtiU5a3pdvJL9BgBg9a1fqS+uqo1Jrk7yuiRnJ9meZG93/+n0+WP7HPLqJLd1957p89uSXFxV25Oc0N1/NLX/TpI3JvmDleo7AACrbyWvmD6WZEOSE5Kku3d298P72f+0JPct2b5/ajtter9v+1NU1baq2lFVO3bv3n0ofQcAYJWtWDDt7r1JLktyXWa38t9bVcft55Dlnhvt/bQvd84bunuhuxc2b978jPsMAMD8rOgzpt19S5JLkrwnyeYkV+5n9/uTnL5ke0uSXVP7lmXaAQA4gqxYMK2q46vqzGnz4SR3J9m4n0NuTXJRVW2aBj1dlOTW7v56koer6vxpNP5lST6+Uv0GAGA+VmzwU5Kjk3wgySlJTs5sNP6lVfXyJDcn2ZTkdVV1dXef0917quraJLdPx1+zOBAqyduT3Jjk2MwGPRn4BABwhKnuZR/XPHwnqNqa5ILuvnFFT7SPhYWF3rFjx2qeEgCAZVTVHd29cKD9VmPlpweT3LkK5wEAYA1byVv5SZLuFkwBADig1bhiCgAABySYAgAwBMEUAIAhCKYAAAxBMAUAYAiCKQAAQxBMAQAYgmAKAMAQBFMAAIYgmAIAMATBFACAIQimAAAMQTAFAGAIgikAAEMQTAEAGIJgCgDAEARTAACGIJgCADAEwRQAgCEIpgAADEEwBQBgCIIpAABDEEwBABiCYAoAwBAEUwAAhiCYAgAwBMEUAIAhCKYAAAxBMAUAYAiCKQAAQxBMAQAYgmAKAMAQBFMAAIYgmAIAMATBFACAIQimAAAMQTAFAGAIgikAAEMQTAEAGIJgCgDAEARTAACGIJgCADAEwRQAgCEIpgAADEEwBQBgCIIpAABDEEwBABiCYAoAwBAEUwAAhjC3YFpVW6vqiiXbV1TV7qq6c3r9wpLPLq+qr06vy+fSYQAAVtT6eZy0qt6e5B1JjpvC6Zunjz7a3X9/n31PSnJVkoUkneSOqrqlu7+1il0GAGCFrXowraqNSa5O8rokZyfZnmTvfg55dZLbunvPdPxtSS5OctPK9hQAgNU0j1v5jyXZkOSEJOnund398PTZz1XVXVX1sao6fWo7Lcl9S46/f2p7iqraVlU7qmrH7t27V6j7AACshFUPpt29N8llSa5Lcm1VvbeqjkvyX5Ns7e4XJflvST40HVLLfc3TfPcN3b3Q3QubN29egd4DALBS5jL4qbtvSXJJkvck2Zzkyu7+Znc/Mu3yW0leNr2/P8npSw7fkmTXavUVAIDVserBtKqOr6ozp82Hk9ydZGNV/eiS3V4/tSfJrUkuqqpNVbUpyUVTGwAAR5B5jMo/OskHkpyS5OQk9ya5NMk/rKrXJ3k0yZ4kVyRJd++pqmuT3D4df83iQCgAAI4c1b3s45orf+KqrUku6O4bV+L7FxYWeseOHSvx1QAAPANVdUd3Lxxov3mu/PRgkjvneH4AAAYylwn2k6S7BVMAAB43zyumAADwOMEUAIAhCKYAAAxBMAUAYAiCKQAAQxBMAQAYgmAKAMAQBFMAAIYgmAIAMATBFACAIQimAAAMQTAFAGAIgikAAEMQTAEAGIJgCgDAEARTAACGIJgCADAEwRQAgCEIpgAADEEwBQBgCIIpAABDEEwBABiCYAoAwBAEUwAAhiCYAgAwBMEUAIAhCKYAAAxBMAUAYAiCKQAAQxBMAQAYgmAKAMAQBFMAAIYgmAIAMATBFACAIQimAAAMQTAFAGAIgikAAEMQTAEAGIJgCgDAEARTAACGIJgCADAEwRQAgCEIpgAADEEwBQBgCIIpAABDEEwBABiCYAoAwBAEUwAAhiCYAgAwhCGDaVVtraorlmz/SFV9tKruqarPVtXWuXUOAIAVMVwwraq3J7k1ybVVtb2qTk3yt5N8q7t/IsmvJfnX8+wjAACH3/p5d2CpqtqY5Ookr0tydpLtSfYmeUOSd0+7fSzJ+6uqurvn0E0AAFbAUME0yWNJNiQ5IUm6e2eSVNVpSe6b2h6tqoeSnJzkG0sPrqptSbZNm9+uqj9ZnW4nSU7Ztz+sGWq3tqnf2qZ+a5v6rW2rWb8zD2anoYJpd++tqsuSXJfk1Kp6YZJ/maSW232Z429IcsPK9nJ5VbWjuxfmcW4Ojdqtbeq3tqnf2qZ+a9uI9RsqmCZJd99SVXdldjt/IcmVSe5PcnqS+6tqfZJnJ9kzv14CAHC4DTX4qaqOr6rFS70PJ7k7ycYktyS5fGp/U5JPeb4UAODIMtoV06OTfCCzZx5OTnJvkkuTfDPJh6vqnsyulL55bj18enN5hIDDQu3WNvVb29RvbVO/tW24+tWIFx6neUov6O4b59sTAABWy1C38pd4MMmd8+4EAACrZ8grpgAA/PAZ9YrpmlFVF1fVn0zLpb5z3v3hqarqt6vqgar60pK2k6rqtqr66vR309ReVfXrUz3vqqqXzq/nJElVnV5Vn66qu6vqy1X1i1O7Gg6uqo6pqs9V1R9Ptbt6av+xaXnpr07LTW+Y2i0/PaCqWldVX6iq35+21W+NqKqdVfXFqrqzqnZMbUP/dgqmh6Cq1iX5jSSvSfKCJG+pqhfMt1cs48YkF+/T9s4kn+zus5J8ctpOZrU8a3ptS3L9KvWRp/dokiu7++wk5yf5e9N/Z2o4vkeSXNjdL07ykiQXV9X5mS0r/WtT7b6V2bLTieWnR/WLmc2Ss0j91pa/0d0vWTJf6dC/nYLpoTkvyT3d/bXu/oskH8ls+VQG0t3/PU+d9/YNST40vf9Qkjcuaf+dnvlMkhOr6kdXp6csp7u/3t2fn94vTiN3WtRweFMNvj1tHj29OsmFmS0vnTy1dos1/ViSV1XVcgussEqqakuSn0ny76ftivqtdUP/dgqmh+bxpVIn909tjO+53f31ZBZ8kjxnalfTgU23Bs9N8tmo4Zow3Qa+M8kDSW5L8n+TPNjdj067LK3Pk5afTrK4/DTz874k/ySzJcOTWT3Ub+3oJJ+oqjumZduTwX87R5vHdK05qKVSWVPUdFBVdXyS/5TkHd395/u5EKOGA+nuHyR5SVWdmOTmJGcvt9v0V+0GUlWvTfJAd99RVRcsNi+zq/qN6xXdvauqnpPktqr6yn72HaJ+rpgemsWlUhdtSbJrTn3hmfmzxVsU098HpnY1HVBVHZ1ZKP2P3f2fp2Y1XEO6+8Ek2zN7TvjEaXnp5Mn1ebx2lp8ewiuSvL6qdmb2qNqFmV1BVb81ort3TX8fyOx/DM/L4L+dgumhuT3JWdMIxQ2ZrUh1y5z7xMFZuszt5Uk+vqT9sml04vlJHlq85cF8TM+ofTDJ3d39b5d8pIaDq6rN05XSVNWxSX46s2eEP53Z8tLJU2tn+elBdPc/6+4t3b01s3/fPtXdPx/1WxOq6llVtXHxfZKLknwpg/92msf0EFXV38zs/yDXJfnt7v6VOXeJfVTVTUkuyGyp2z9LclWS/5Lk95KckdnSt5d0954pBL0/s1H830nytu7eMY9+M1NVP5XkfyT5Yp54zu1dmT1nqoYDq6oXZTa4Yl1mF0J+r7uvqarnZXYF7qQkX0jy1u5+pKqOSfLhzJ4j3pPkzd39tfn0nqWmW/n/qLtfq35rw1Snm6fN9Ul+t7t/papOzsC/nYIpAABDcCsfAIAhCKYAAAxBMAUAYAiCKQAAQxBMAQAYgmAKAMAQBFMAAIbw/wEg0a94P/clKQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 792x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def prettify_graph(graph):\n",
    "    \"\"\"Modify the given graph according to Jimmy's requests: add a title, make the y-axis\n",
    "    start at 0, label the y-axis. (And, if you're feeling ambitious, format the tick marks\n",
    "    as dollar amounts using the \"$\" symbol.)\n",
    "    \"\"\"\n",
    "    graph.set_title(\"Results of 500 slot machine pulls\")\n",
    "    # Complete steps 2 and 3 here\n",
    "    graph.set_ylim(0)\n",
    "    graph.set_ylabel(\"Balance\")\n",
    "    # Editing y-labels\n",
    "    ticks = graph.get_yticks()\n",
    "    new_labels = ['${}'.format(int(amt)) for amt in ticks]\n",
    "    graph.set_yticklabels(new_labels)\n",
    "\n",
    "graph = jimmy_slots.get_graph()\n",
    "prettify_graph(graph)\n",
    "graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Bonus:** Can you format the numbers on the y-axis so they look like dollar amounts? e.g. $200 instead of just 200.\n",
    "\n",
    "(We're not going to tell you what method(s) to use here. You'll need to go digging yourself with `dir(graph)` and/or `help(graph)`.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# q1.solution()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. <span title=\"Spicy\" style=\"color: coral\">🌶️🌶️</span>\n",
    "\n",
    "This is a very hard problem. Feel free to skip it if you are short on time:\n",
    "\n",
    "Luigi is trying to perform an analysis to determine the best items for winning races on the Mario Kart circuit. He has some data in the form of lists of dictionaries that look like...\n",
    "\n",
    "    [\n",
    "        {'name': 'Peach', 'items': ['green shell', 'banana', 'green shell',], 'finish': 3},\n",
    "        {'name': 'Bowser', 'items': ['green shell',], 'finish': 1},\n",
    "        # Sometimes the racer's name wasn't recorded\n",
    "        {'name': None, 'items': ['mushroom',], 'finish': 2},\n",
    "        {'name': 'Toad', 'items': ['green shell', 'mushroom'], 'finish': 1},\n",
    "    ]\n",
    "\n",
    "`'items'` is a list of all the power-up items the racer picked up in that race, and `'finish'` was their placement in the race (1 for first place, 3 for third, etc.).\n",
    "\n",
    "He wrote the function below to take a list like this and return a dictionary mapping each item to how many times it was picked up by first-place finishers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def best_items(racers):\n",
    "    \"\"\"Given a list of racer dictionaries, return a dictionary mapping items to the number\n",
    "    of times those items were picked up by racers who finished in first place.\n",
    "    \"\"\"\n",
    "    winner_item_counts = {}\n",
    "    for i in range(len(racers)):\n",
    "        # The i'th racer dictionary\n",
    "        racer = racers[i]\n",
    "        # We're only interested in racers who finished in first\n",
    "        if racer['finish'] == 1:\n",
    "            for i in racer['items']:\n",
    "                # Add one to the count for this item (adding it to the dict if necessary)\n",
    "                if i not in winner_item_counts:\n",
    "                    winner_item_counts[i] = 0\n",
    "                winner_item_counts[i] += 1\n",
    "\n",
    "        # Data quality issues :/ Print a warning about racers with no name set. We'll take care of it later.\n",
    "        if racer['name'] is None:\n",
    "            print(\"WARNING: Encountered racer with unknown name on iteration {}/{} (racer = {})\".format(\n",
    "                i+1, len(racers), racer['name'])\n",
    "                 )\n",
    "    return winner_item_counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "He tried it on a small example list above and it seemed to work correctly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: Encountered racer with unknown name on iteration 3/4 (racer = None)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'green shell': 2, 'mushroom': 1}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample = [\n",
    "    {'name': 'Peach', 'items': ['green shell', 'banana', 'green shell',], 'finish': 3},\n",
    "    {'name': 'Bowser', 'items': ['green shell',], 'finish': 1},\n",
    "    {'name': None, 'items': ['mushroom',], 'finish': 2},\n",
    "    {'name': 'Toad', 'items': ['green shell', 'mushroom'], 'finish': 1},\n",
    "]\n",
    "best_items(sample)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, when he tried running it on his full dataset, the program crashed with a `TypeError`.\n",
    "\n",
    "Can you guess why? Try running the code cell below to see the error message Luigi is getting. Once you've identified the bug, fix it in the cell below (so that it runs without any errors).\n",
    "\n",
    "Hint: Luigi's bug is similar to one we encountered in the [tutorial](https://www.kaggle.com/colinmorris/working-with-external-libraries) when we talked about star imports."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": [
     "raises-exception"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: Encountered racer with unknown name on iteration 4/8 (racer = None)\n",
      "WARNING: Encountered racer with unknown name on iteration 6/8 (racer = None)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'green shell': 4, 'banana': 1, 'red shell': 1, 'blue shell': 1, 'star': 1}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import luigi's full dataset of race data\n",
    "from learntools.python.luigi_analysis import full_dataset\n",
    "\n",
    "# Fix me!\n",
    "def best_items(racers):\n",
    "    winner_item_counts = {}\n",
    "    for i in range(len(racers)):\n",
    "        # The i'th racer dictionary\n",
    "        racer = racers[i]\n",
    "        # We're only interested in racers who finished in first\n",
    "        if racer['finish'] == 1:\n",
    "            for j in racer['items']:\n",
    "                # Add one to the count for this item (adding it to the dict if necessary)\n",
    "                if j not in winner_item_counts:\n",
    "                    winner_item_counts[j] = 0\n",
    "                winner_item_counts[j] += 1\n",
    "\n",
    "        # Data quality issues :/ Print a warning about racers with no name set. We'll take care of it later.\n",
    "        if racer['name'] is None:\n",
    "            print(\"WARNING: Encountered racer with unknown name on iteration {}/{} (racer = {})\".format(\n",
    "                i+1, len(racers), racer['name'])\n",
    "                 )\n",
    "    return winner_item_counts\n",
    "\n",
    "# Try analyzing the imported full dataset\n",
    "best_items(full_dataset)"
   ]
  },
  {
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   "metadata": {},
   "outputs": [],
   "source": [
    "# q2.hint()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# q2.solution()"
   ]
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   "source": [
    "## 3. <span title=\"A bit spicy\" style=\"color: darkgreen \">🌶️</span>\n",
    "\n",
    "Suppose we wanted to create a new type to represent hands in blackjack. One thing we might want to do with this type is overload the comparison operators like `>` and `<=` so that we could use them to check whether one hand beats another. e.g. it'd be cool if we could do this:\n",
    "\n",
    "```python\n",
    ">>> hand1 = BlackjackHand(['K', 'A'])\n",
    ">>> hand2 = BlackjackHand(['7', '10', 'A'])\n",
    ">>> hand1 > hand2\n",
    "True\n",
    "```\n",
    "\n",
    "Well, we're not going to do all that in this question (defining custom classes is a bit beyond the scope of these lessons), but the code we're asking you to write in the function below is very similar to what we'd have to write if we were defining our own `BlackjackHand` class. (We'd put it in the `__gt__` magic method to define our custom behaviour for `>`.)\n",
    "\n",
    "Fill in the body of the `blackjack_hand_greater_than` function according to the docstring."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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       "<span style=\"color:#33cc33\">Correct</span>"
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       "Correct"
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   "source": [
    "def blackjack_hand_greater_than(hand_1, hand_2):\n",
    "    \"\"\"\n",
    "    Return True if hand_1 beats hand_2, and False otherwise.\n",
    "    \n",
    "    In order for hand_1 to beat hand_2 the following must be true:\n",
    "    - The total of hand_1 must not exceed 21\n",
    "    - The total of hand_1 must exceed the total of hand_2 OR hand_2's total must exceed 21\n",
    "    \n",
    "    Hands are represented as a list of cards. Each card is represented by a string.\n",
    "    \n",
    "    When adding up a hand's total, cards with numbers count for that many points. Face\n",
    "    cards ('J', 'Q', and 'K') are worth 10 points. 'A' can count for 1 or 11.\n",
    "    \n",
    "    When determining a hand's total, you should try to count aces in the way that \n",
    "    maximizes the hand's total without going over 21. e.g. the total of ['A', 'A', '9'] is 21,\n",
    "    the total of ['A', 'A', '9', '3'] is 14.\n",
    "    \n",
    "    Examples:\n",
    "    >>> blackjack_hand_greater_than(['K'], ['3', '4'])\n",
    "    True\n",
    "    >>> blackjack_hand_greater_than(['K'], ['10'])\n",
    "    False\n",
    "    >>> blackjack_hand_greater_than(['K', 'K', '2'], ['3'])\n",
    "    False\n",
    "    \"\"\"\n",
    "    def hand_points(hand):\n",
    "        total = 0\n",
    "        aces = 0\n",
    "        for card in hand:\n",
    "            if card in ['J', 'Q', 'K']:\n",
    "                total += 10\n",
    "            elif card == 'A':\n",
    "                total += 1\n",
    "                aces += 1\n",
    "            else:\n",
    "                total += int(card)\n",
    "        if total <= 11:\n",
    "            if aces:\n",
    "                total += 10\n",
    "        return total\n",
    "    \n",
    "    points_1 = hand_points(hand_1)\n",
    "    points_2 = hand_points(hand_2)\n",
    "    return points_1 <= 21 and (points_1 > points_2 or points_2 > 21)\n",
    "\n",
    "q3.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# q3.hint()\n",
    "# q3.solution()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The end\n",
    "\n",
    "You've finished the Python micro-course. Congrats!\n",
    "\n",
    "As always, if you have any questions about these exercises, or anything else you encountered in the course, come to the [Learn Forum](https://kaggle.com/learn-forum).\n",
    "\n",
    "You probably didn't put in all these hours of learning Python just to play silly games of chance, right? If you're interested in applying your newfound Python skills to some data science tasks, check out some of our other lessons on [Kaggle Learn](https://www.kaggle.com/learn/overview). Most of them are in Python, including our courses on...\n",
    "\n",
    "- [Pandas for data manipulation](https://www.kaggle.com/learn/pandas)\n",
    "- [Machine learning with scikit-learn](https://www.kaggle.com/learn/machine-learning)\n",
    "- [Data visualization](https://www.kaggle.com/learn/data-visualisation)\n",
    "- [Deep learning with TensorFlow](https://www.kaggle.com/learn/deep-learning)\n",
    "\n",
    "Happy Pythoning!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "**[Python Micro-Course Home Page](https://www.kaggle.com/learn/python)**\n",
    "\n"
   ]
  }
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