exercise-working-with-external-libraries.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2606228e",
   "metadata": {
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     "start_time": "2021-11-24T15:44:11.017679",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**This notebook is an exercise in the [Python](https://www.kaggle.com/learn/python) course.  You can reference the tutorial at [this link](https://www.kaggle.com/colinmorris/working-with-external-libraries).**\n",
    "\n",
    "---\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94578e60",
   "metadata": {
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    },
    "tags": []
   },
   "source": [
    "# Try It Yourself\n",
    "\n",
    "There are only three problems in this last set of exercises, but they're all pretty tricky, so be on guard! \n",
    "\n",
    "Run the setup code below before working on the questions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d30e0fb9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-24T15:44:11.119077Z",
     "iopub.status.busy": "2021-11-24T15:44:11.117918Z",
     "iopub.status.idle": "2021-11-24T15:44:11.170659Z",
     "shell.execute_reply": "2021-11-24T15:44:11.171242Z",
     "shell.execute_reply.started": "2021-08-09T15:26:48.565121Z"
    },
    "papermill": {
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     "end_time": "2021-11-24T15:44:11.171545",
     "exception": false,
     "start_time": "2021-11-24T15:44:11.096447",
     "status": "completed"
    },
    "tags": []
   },
   "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",
   "id": "6f048f36",
   "metadata": {
    "papermill": {
     "duration": 0.019304,
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     "start_time": "2021-11-24T15:44:11.191306",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 1.\n",
    "\n",
    "After completing the exercises on lists and tuples, 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,
   "id": "32e3bbd7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-24T15:44:11.252202Z",
     "iopub.status.busy": "2021-11-24T15:44:11.251493Z",
     "iopub.status.idle": "2021-11-24T15:44:11.481465Z",
     "shell.execute_reply": "2021-11-24T15:44:11.480784Z",
     "shell.execute_reply.started": "2021-08-09T15:27:11.900358Z"
    },
    "papermill": {
     "duration": 0.251804,
     "end_time": "2021-11-24T15:44:11.481606",
     "exception": false,
     "start_time": "2021-11-24T15:44:11.229802",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "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",
   "id": "63bb952b",
   "metadata": {
    "papermill": {
     "duration": 0.020681,
     "end_time": "2021-11-24T15:44:11.522987",
     "exception": false,
     "start_time": "2021-11-24T15:44:11.502306",
     "status": "completed"
    },
    "tags": []
   },
   "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": null,
   "id": "41718fa4",
   "metadata": {
    "papermill": {
     "duration": 0.020389,
     "end_time": "2021-11-24T15:44:11.564110",
     "exception": false,
     "start_time": "2021-11-24T15:44:11.543721",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c4a5d12",
   "metadata": {
    "papermill": {
     "duration": 0.020834,
     "end_time": "2021-11-24T15:44:11.605545",
     "exception": false,
     "start_time": "2021-11-24T15:44:11.584711",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e04589e1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-24T15:44:11.700665Z",
     "iopub.status.busy": "2021-11-24T15:44:11.673105Z",
     "iopub.status.idle": "2021-11-24T15:44:11.886196Z",
     "shell.execute_reply": "2021-11-24T15:44:11.886793Z",
     "shell.execute_reply.started": "2021-08-09T15:27:57.258346Z"
    },
    "papermill": {
     "duration": 0.260659,
     "end_time": "2021-11-24T15:44:11.886977",
     "exception": false,
     "start_time": "2021-11-24T15:44:11.626318",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:13: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  del sys.path[0]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Results of 500 slot machine pulls'}, ylabel='Balance'>"
      ]
     },
     "execution_count": 3,
     "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": [
    "def prettify_graph(graph):\n",
    "    graph.set_title(\"Results of 500 slot machine pulls\")\n",
    "    # Make the y-axis begin at 0\n",
    "    graph.set_ylim(bottom=0)\n",
    "    # Label the y-axis\n",
    "    graph.set_ylabel(\"Balance\")\n",
    "    # Bonus: format the numbers on the y-axis as dollar amounts\n",
    "    # An array of the values displayed on the y-axis (150, 175, 200, etc.)\n",
    "    ticks = graph.get_yticks()\n",
    "    # Format those values into strings beginning with dollar sign\n",
    "    new_labels = ['${}'.format(int(amt)) for amt in ticks]\n",
    "    # Set the new labels\n",
    "    graph.set_yticklabels(new_labels)\n",
    "\n",
    "graph = jimmy_slots.get_graph()\n",
    "prettify_graph(graph)\n",
    "graph"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebb7293b",
   "metadata": {
    "papermill": {
     "duration": 0.022596,
     "end_time": "2021-11-24T15:44:11.932406",
     "exception": false,
     "start_time": "2021-11-24T15:44:11.909810",
     "status": "completed"
    },
    "tags": []
   },
   "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,
   "id": "3fe637d1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-24T15:44:11.981075Z",
     "iopub.status.busy": "2021-11-24T15:44:11.980404Z",
     "iopub.status.idle": "2021-11-24T15:44:12.060813Z",
     "shell.execute_reply": "2021-11-24T15:44:12.060069Z",
     "shell.execute_reply.started": "2021-08-09T15:28:01.636041Z"
    },
    "papermill": {
     "duration": 0.105916,
     "end_time": "2021-11-24T15:44:12.061111",
     "exception": true,
     "start_time": "2021-11-24T15:44:11.955195",
     "status": "failed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.3333333333333333, \"interactionType\": 1, \"questionType\": 4, \"questionId\": \"1_JimmySlots\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "TypeError",
     "evalue": "can only concatenate str (not \"CodeSolution\") to str",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_19/3401879383.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mq1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcheck\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/learntools/core/problem_view.py\u001b[0m in \u001b[0;36mwrapped\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     22\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minteractions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     25\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/learntools/core/problem_view.py\u001b[0m in \u001b[0;36mwrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mfunctools\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwraps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m         \u001b[0mdisplay\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m         \u001b[0;31m# Don't propagate the return to avoid double printing.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/learntools/core/problem_view.py\u001b[0m in \u001b[0;36mcheck\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    122\u001b[0m                                _congrats=self.problem._congrats)\n\u001b[1;32m    123\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 124\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mCorrect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproblem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_correct_message\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    125\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    126\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_injected_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/learntools/core/problem.py\u001b[0m in \u001b[0;36m_correct_message\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     45\u001b[0m                     )\n\u001b[1;32m     46\u001b[0m                 ):\n\u001b[0;32m---> 47\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m'\\n\\n'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msolution\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     48\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: can only concatenate str (not \"CodeSolution\") to str"
     ]
    }
   ],
   "source": [
    "q1.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f2b9478",
   "metadata": {
    "execution": {
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    "papermill": {
     "duration": null,
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    "tags": []
   },
   "outputs": [],
   "source": [
    "# Check your answer (Run this code cell to receive credit!)\n",
    "q1.solution()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a653362",
   "metadata": {
    "papermill": {
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   "source": [
    "# 2. <span title=\"Spicy\" style=\"color: coral\">🌶️🌶️</span>\n",
    "\n",
    "This is a very challenging problem.  Don't forget that you can receive a hint!\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": null,
   "id": "77e44924",
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   "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",
   "id": "c419dfa0",
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   "source": [
    "He tried it on a small example list above and it seemed to work correctly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed997f5e",
   "metadata": {
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   "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",
   "id": "34b3f393",
   "metadata": {
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   "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": null,
   "id": "811619c9",
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    "tags": [
     "raises-exception"
    ]
   },
   "outputs": [],
   "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 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\n",
    "\n",
    "# Try analyzing the imported full dataset\n",
    "best_items(full_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4bac10a",
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   "outputs": [],
   "source": [
    "#q2.hint()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6484d28c",
   "metadata": {
    "execution": {
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    "tags": []
   },
   "outputs": [],
   "source": [
    "# Check your answer (Run this code cell to receive credit!)\n",
    "q2.solution()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7804c446",
   "metadata": {
    "papermill": {
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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."
   ]
  },
  {
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   "execution_count": null,
   "id": "da9e1971",
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   "source": [
    "def hand_total(hand):\n",
    "    \"\"\"Helper function to calculate the total points of a blackjack hand.\n",
    "    \"\"\"\n",
    "    total = 0\n",
    "    # Count the number of aces and deal with how to apply them at the end.\n",
    "    aces = 0\n",
    "    for card in hand:\n",
    "        if card in ['J', 'Q', 'K']:\n",
    "            total += 10\n",
    "        elif card == 'A':\n",
    "            aces += 1\n",
    "        else:\n",
    "            # Convert number cards (e.g. '7') to ints\n",
    "            total += int(card)\n",
    "    # At this point, total is the sum of this hand's cards *not counting aces*.\n",
    "\n",
    "    # Add aces, counting them as 1 for now. This is the smallest total we can make from this hand\n",
    "    total += aces\n",
    "    # \"Upgrade\" aces from 1 to 11 as long as it helps us get closer to 21\n",
    "    # without busting\n",
    "    while total + 10 <= 21 and aces > 0:\n",
    "        # Upgrade an ace from 1 to 11\n",
    "        total += 10\n",
    "        aces -= 1\n",
    "    return total\n",
    "\n",
    "def blackjack_hand_greater_than(hand_1, hand_2):\n",
    "    total_1 = hand_total(hand_1)\n",
    "    total_2 = hand_total(hand_2)\n",
    "    return total_1 <= 21 and (total_1 > total_2 or total_2 > 21)"
   ]
  },
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   "id": "ea457e50",
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    "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",
    "    pass\n",
    "\n",
    "# Check your answer\n",
    "q3.check()"
   ]
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    "q3.hint()\n",
    "q3.solution()"
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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",
    "    total_1 = total_2 = 0\n",
    "    hand1, hand2 = hand_1, hand_2\n",
    "    for i in range(len(hand_1)):\n",
    "        if hand_1[i] == 'J' or hand_1[i] == 'Q' or hand_1[i] == 'K':\n",
    "            total_1 += 10\n",
    "        elif hand_1[i] == 'A':\n",
    "            total_1 += 1\n",
    "        else:\n",
    "            total_1 += int(hand_1[i])\n",
    "            \n",
    "    while 'A' in hand1:\n",
    "        if total_1 + 10 <= 21:\n",
    "            total_1 += 10\n",
    "        hand1.pop(hand1.index('A'))\n",
    "            \n",
    "    \n",
    "    for i in range(len(hand_2)):\n",
    "        if hand_2[i] == 'J' or hand_2[i] == 'Q' or hand_2[i] == 'K':\n",
    "            total_2 += 10\n",
    "        elif hand_2[i] == 'A':\n",
    "            total_2 += 1\n",
    "        else:\n",
    "            total_2 += int(hand_2[i])\n",
    "    while 'A' in hand2:\n",
    "        if total_2 + 10 <= 21:\n",
    "            total_2 += 10\n",
    "        hand2.pop(hand2.index('A'))\n",
    "\n",
    "    return total_1 <= 21 and (total_1 > total_2 or total_2 > 21)\n",
    "    \n",
    "q3.check()"
   ]
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   "source": [
    "# The End\n",
    "\n",
    "You've finished the Python course. Congrats!\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, we strongly recommend **[this tutorial](https://www.kaggle.com/alexisbcook/titanic-tutorial)**, which will teach you how to make your very first submission to a Kaggle competition.\n",
    "\n",
    "You can also check out some of our other **[Kaggle Courses](https://www.kaggle.com/learn/overview)**. Some good next steps are:\n",
    "\n",
    "1. [Intro to Machine Learning](https://www.kaggle.com/learn/intro-to-machine-learning)\n",
    "2. [Pandas for data manipulation](https://www.kaggle.com/learn/pandas)\n",
    "3. [Data Visualization](https://www.kaggle.com/learn/data-visualization)\n",
    "\n",
    "Happy Pythoning!"
   ]
  },
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   "cell_type": "markdown",
   "id": "f62ff5b3",
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   "source": [
    "---\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "*Have questions or comments? Visit the [Learn Discussion forum](https://www.kaggle.com/learn-forum/161283) to chat with other Learners.*"
   ]
  }
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