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   "source": [
    "\n",
    "# Chapter 8: Mappings & Sets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Coding Exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_00_content.ipynb) of the book. Then, work through the exercises below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Working with Nested Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's write some code to analyze the historic soccer game [Brazil vs. Germany](https://en.wikipedia.org/wiki/Brazil_v_Germany_%282014_FIFA_World_Cup%29) during the 2014 World Cup.\n",
    "\n",
    "Below, `players` consists of two nested `dict` objects, one for each team, that hold `tuple` objects (i.e., records) with information on the players. Besides the jersey number, name, and position, each `tuple` objects contains a `list` object with the times when the player scored."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "players = {\n",
    "    \"Brazil\": [\n",
    "        (12, \"Júlio César\", \"Goalkeeper\", []),\n",
    "        (4, \"David Luiz\", \"Defender\", []),\n",
    "        (6, \"Marcelo\", \"Defender\", []),\n",
    "        (13, \"Dante\", \"Defender\", []),\n",
    "        (23, \"Maicon\", \"Defender\", []),\n",
    "        (5, \"Fernandinho\", \"Midfielder\", []),\n",
    "        (7, \"Hulk\", \"Midfielder\", []),\n",
    "        (8, \"Paulinho\", \"Midfielder\", []),\n",
    "        (11, \"Oscar\", \"Midfielder\", [90]),\n",
    "        (16, \"Ramires\", \"Midfielder\", []),\n",
    "        (17, \"Luiz Gustavo\", \"Midfielder\", []),\n",
    "        (19, \"Willian\", \"Midfielder\", []),\n",
    "        (9, \"Fred\", \"Striker\", []),\n",
    "    ],\n",
    "    \"Germany\": [\n",
    "        (1, \"Manuel Neuer\", \"Goalkeeper\", []),\n",
    "        (4, \"Benedikt Höwedes\", \"Defender\", []),\n",
    "        (5, \"Mats Hummels\", \"Defender\", []),\n",
    "        (16, \"Philipp Lahm\", \"Defender\", []),\n",
    "        (17, \"Per Mertesacker\", \"Defender\", []),\n",
    "        (20, \"Jérôme Boateng\", \"Defender\", []),\n",
    "        (6, \"Sami Khedira\", \"Midfielder\", [29]),\n",
    "        (7, \"Bastian Schweinsteiger\", \"Midfielder\", []),\n",
    "        (8, \"Mesut Özil\", \"Midfielder\", []),\n",
    "        (13, \"Thomas Müller\", \"Midfielder\", [11]),\n",
    "        (14, \"Julian Draxler\", \"Midfielder\", []),\n",
    "        (18, \"Toni Kroos\", \"Midfielder\", [24, 26]),\n",
    "        (9, \"André Schürrle\", \"Striker\", [69, 79]),\n",
    "        (11, \"Miroslav Klose\", \"Striker\", [23]),\n",
    "    ],\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q1.1**: Write a dictionary comprehension to derive a new `dict` object, called `brazilian_players`, that maps a Brazilian player's name to his position!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "brazilian_players = {...}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "brazilian_players"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q1.2**: Generalize the code fragment into a `get_players()` function: Passed a `team` name, it returns a `dict` object like `brazilian_players`. Verify that the function works for the German team!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_players(team):\n",
    "    ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "get_players(\"Germany\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Often, we are given a `dict` object like the one returned from `get_players()`: It is characterized by the observation that a large set of unique keys (i.e., the players' names) is mapped onto a smaller set of non-unique values (i.e., the positions).\n",
    "\n",
    "**Q1.3**: Create a generic `invert()` function that swaps the keys and values of a `mapping` argument passed to it and returns them in a *new* `dict` object! Ensure that *no* key gets lost. Verify your implementation with the `brazilian_players` dictionary!\n",
    "\n",
    "Hints: Think of this as a grouping operation. The *new* values are `list` or `tuple` objects that hold the original keys. You may want to use either the the [defaultdict](https://docs.python.org/3/library/collections.html#collections.defaultdict) type from the [collections](https://docs.python.org/3/library/collections.html) module in the [standard library](https://docs.python.org/3/library/index.html) or the [setdefault()](https://docs.python.org/3/library/stdtypes.html#dict.setdefault) method of the ordinary `dict` type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def invert(mapping):\n",
    "    ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "invert(brazilian_players)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q1.4**: Write a `score_at_minute()` function: It takes two arguments, `team` and `minute`, and returns the number of goals the `team` has scored up until this time in the game.\n",
    "\n",
    "Hints: The function may reference the global `players` for simplicity. Earn bonus points if you can write this in a one-line expression using some *reduction* function and a *generator expression*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def score_at_minute(team, minute):\n",
    "    ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The score at half time was:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_at_minute(\"Brazil\", 45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_at_minute(\"Germany\", 45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The final score was:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_at_minute(\"Brazil\", 90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_at_minute(\"Germany\", 90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q1.5**: Write a `goals_by_player()` function: It takes an argument like the global `players`, and returns a `dict` object mapping the players to the number of goals they scored.\n",
    "\n",
    "Hints: Do *not* \"hard code\" the names of the teams! Earn bonus points if you can solve it in a single dictionary comprehension."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def goals_by_player(players):\n",
    "    ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "goals_by_player(players)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q1.6**: Write a *dictionary comprehension* to filter out the players who did *not* score from the preceding result. Then, write a *set comprehension* that does the same but discards the number of goals scored.\n",
    "\n",
    "Hints: Reference the `goals_by_player()` function from before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "{...}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "{...}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q1.7**: Write a `all_goals()` function: It takes one argument like the global `players` and returns a `list` object containing $2$-element `tuple` objects, where the first element is the minute a player scored and the second his name. The list should be sorted by the time.\n",
    "\n",
    "Hints: You may want to use either the built-in [sorted()](https://docs.python.org/3/library/functions.html#sorted) function or the `list` type's [sort()](https://docs.python.org/3/library/stdtypes.html#list.sort) method. Earn bonus points if you can write a one-line expression with a *generator expression*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def all_goals(players):\n",
    "    ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_goals(players)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q1.8**: Lastly, write a `summary()` function: It takes one argument like the global `players` and prints out a concise report of the goals, the score at the half, and the final result.\n",
    "\n",
    "Hints: Use the `all_goals()` and `score_at_minute()` functions from before.\n",
    "\n",
    "The output should look similar to this:\n",
    "```\n",
    "12' Gerd Müller scores\n",
    "...\n",
    "HALFTIME: TeamA 1 TeamB 2\n",
    "77' Ronaldo scores\n",
    "...\n",
    "FINAL: TeamA 1 TeamB 3\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def summary(players):\n",
    "    ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "summary(players)"
   ]
  }
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