"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."
"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."
"**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!"
"**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!"
"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",
"**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",
"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."
"**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",
"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*."
"**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",
"**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",
"**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",
"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*."
"**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",
