@extends('template')

@section('title')
    Lab 10, Part 2: Ironman data
@stop

@section('content')
# Lab 10, Part 2: Ironman data

<img src="ironman.jpg" style="max-width:80px" alt="The Ironman race logo: a red circle on top of a red rectangel that has two triangular slots cut out of the bottom so that it resembles the letter M.">

In this part of lab, we are working with a subset of the results from the
[2016 Ironman Triathlon](http://www.ironman.com/triathlon/events/americas/ironman/world-championship.aspx#/axzz4NN2nCF1d)
race in Kona, Hawaii.

This race has 3 parts, all completed in order, without a break:
1. A 2.4 mile (3.86 km) **swim**
2. A 112-mile (180.25 km) **bicycle ride**
3. A 26.22 miles (42.20 km) **marathon run**


## Task 0. Familiarize yourself with the data
In your `lab10` folder there is a provided file called `kona.py` that contains a list of dictionaries, where each dictionary corresponds to one athlete. We are only looking at athletes in the 18-24 year old range.

**Create a new file called `ironman.py` **&mdash; at the top of this file import `kona` so you can use the provided list of dictionaries:

```py
from kona import kona
```

Here is a snapshot of what the list of dictionaries from `kona.py` looks like (you can scroll horizontally to see more):

```py
[
    {'swim': 0.95, 'finish': 9.433333333333334, 'run': 3.283333333333333, 'firstname': 'Hans Christian', 'lastname': 'Tungesvik', 'genderRank': '128', 'overallRank': '137', 'bike': 5.083333333333333, 'country': 'NOR', 'divRank': '1'},
    {'swim': 0.95, 'finish': 9.516666666666667, 'run': 3.4833333333333334, 'firstname': 'Kristian', 'lastname': 'Hindkjaer', 'genderRank': '159', 'overallRank': '169', 'bike': 4.933333333333333, 'country': 'DNK', 'divRank': '2'},
    {'swim': 1.05, 'finish': 9.533333333333333, 'run': 3.35, 'firstname': 'Ivan', 'lastname': 'Kharin', 'genderRank': '172', 'overallRank': '183', 'bike': 5.0, 'country': 'RUS', 'divRank': '3'},

[...]

    {'swim': 1.0666666666666667, 'finish': 'DNF', 'run': '---', 'firstname': 'Emily', 'lastname': 'Kempson', 'genderRank': '---', 'overallRank': '---', 'bike': '---', 'country': 'AUS', 'divRank': '---'},
]
```

Notes about the data:
+ Each athlete's dictionary contains 10 `key:value` pairs that include
    their first and last name, country, finish time, overall rank, rank
    by gender, and their times for the run/bike/swim portions of the
    event.
+ Times were converted from a 3:15:23 format to a 3.25 where 3 is the
    number of hours and .25 is fractional number of hours (i.e., 15
    minutes → 1/4 of an hour). Seconds were discarded from our data.
+ *Did Not Finish*
    + For the athletes that did not finish the race, the `finish` key has
        the value `DNF`, short for *Did Not Finish*.
    + For a DNF athlete, many of the key values (e.g., `divRank`, `run`,
        `bike`, `genderRank`) are a string of three dashes: `'---'`. At
        some point, you may have to handle the dictionaries of athletes
        who did not finish, so keep this in mind.



## Task 1A. *printNames* - Prints athlete names

<div class='partnerA'>Partner A</div>

Write a function called `printNames` that takes a list of dictionaries (e.g. the kona data)
and **prints** names each of the 69 athletes in the format `lastname, firstname`.

Example:
```py
printNames(kona)
```

```py
Tungesvik,Hans Christian
Hindkjaer,Kristian
Kharin,Ivan
Mortensen,Mikkel
Geddes,Alexander
Lopes,Andre
Manninen,Juuso
Tissot,Alexis
...
Davis,Jaimee
Talker,Elisa
Fritz,Grant
Kempson,Emily
```

## Task 1B. *getNameTuples* - Return a list of athlete name tuples

<div class='partnerA'>Partner A (yes, A again)</div>

Write a function called `getNameTuples` that takes a list of dictionaries (e.g. the kona data)
and **returns** a **list of tuples** of each of the 69 athletes in the list in the format `(lastname, firstname)`, **sorted** by lastname.

Example:
```py
nameTups = getNameTuples(kona)
nameTups[:10]  # just showing first 10
```

```py
# displayed for readability
[('Akiyama', 'Yuichi'), 
 ('Appleby', 'Matthew'), 
 ('Atkins', 'Wendy'), 
 ('Azuma', 'Tomohiko'), 
 ('Boll', 'Pascal'), 
 ('Bonde', 'Line'), 
 ('Braun', 'Maximilian'), 
 ('Brock', 'Katrine'), 
 ('Callaghan', 'Tom'), 
 ('Carroll', 'Leah')]

```

## Task 1C. *getNameTuplesSortedByFirstname* - Return a list of athlete name tuples, sorted by firstname

<div class='partnerB'>Partner B</div>

This task is similar to Task 1B above, except that the tuples should be sorted by firstname, instead of lastname. This requires writing a helper function, and then using that helper function as a key function to sort by (see Slide 13-13 in <a href="https://cs111.wellesley.edu/content/lectures/lec_sorting/files/lec_sorting.pdf">this set of slides</a>).

Example:
```py
firstnameTups = getNameTuplesSortedByFirstname(kona)
firstnameTups[:10]  # just showing first 10
```

```py
# displayed for readability
[('Reid', 'Aj'), 
 ('Whelan', 'Alex'), 
 ('Geddes', 'Alexander'), 
 ('Jackson', 'Alexander'), 
 ('Tissot', 'Alexis'), 
 ('Lopes', 'Andre'), 
 ('Oleander', 'Anna'), 
 ('Rudson', 'Benjamin'), 
 ('Maas', 'Benjamin'), 
 ('Pleckaitis', 'Braden')]
```

## Task 2. *getNamesCountry* - Returns a sorted list of tuples of athletes in the format (lastname, firstname) from a particular country

<div class='partnerB'>Partner B (yes, again)</div>

Write a function called `getNamesCountry` that takes a list of dictionaries (e.g. the kona data) *and* a 3 letter country abbrevation.

This function should **return** a sorted list of tuples of athlete names in the format (lastname, firstname) from the given country.

__Example: Athletes from BRA:__
```py
getNamesCountry(kona, 'BRA')
```
```py
[('Lopes', 'Andre'), ('Pacheco Venturini', 'Guilherme'), ('Ponte', 'Paula')]
```

__Example: Athletes from JPN:__
```py
getNamesCountry(kona, 'JPN')
```

```py
[('Akiyama', 'Yuichi'), ('Azuma', 'Tomohiko'), ('Shun', 'Hiraya')]
```



## Task 3. *getAllCountries* - Return unique countries

<div class='partnerA'>Partner A</div>

Write a function called `getAllCountries` that takes a list of dictionaries (e.g. the kona data) and **returns** all unique countries represented at the Ironman race, sorted alphabetically.

Example:
```py
print(getAllCountries(kona))
```

```py
['ARG', 'AUS', 'AUT', 'BEL', 'BRA', 
 'CAN', 'CHE', 'DNK', 'ESP', 'FIN', 
 'FRA', 'GBR', 'ITA', 'JPN', 'LTU', 
 'MEX', 'NOR', 'NZL', 'PRI', 'RUS', 
 'SWE', 'USA']
```

There should be 22 unique countries represented across all athletes.



## Task 4. *totalAthletesByCountry* - Return countries & athlete count

<div class='partnerB'>Partner B</div>

Write a function called `totalAthletesByCountry` that takes a list of dictionaries (e.g. the kona data) and **returns** a **dictionary** where each key is a unique country, and the corresponding value is the total number of athletes from that country.

Example:
```py
print(totalAthletesByCountry(kona))
```

Results:
```py
{'NOR': 1, 'DNK': 6, 'RUS': 1, 'NZL': 2, 'BRA': 3, 'FIN': 1, 
 'FRA': 5, 'CHE': 2, 'USA': 22, 'CAN': 4, 'AUT': 2, 'MEX': 1, 
 'ITA': 1, 'GBR': 1, 'AUS': 6, 'PRI': 2, 'JPN': 3, 'LTU': 1, 
 'ARG': 1, 'SWE': 2, 'BEL': 1, 'ESP': 1}
```

(It's okay if your order does not match the example shown, just as long as all the values exist, because order does not matter in dictionaries.)

There are different ways you could approach this task.


## Task 5. *buildDictWithAverageEventTimes* - Average event times for each country's athletes.

<div class='partnerA'>Partner A</div>

Write a function called `buildDictWithAverageEventTimes` that...

+ Takes a list of dictionaries (e.g. the kona data)
+ Returns a dictionary where the keys are unique countries, and the
    corresponding values are a dictionary of average times for each event
    (swim, bike, run).

Example:
```py
print(buildDictWithAverageEventTimes(kona))
```

```
# Note: Line breaks have been added for readability
{
    'NOR': {'swim': 0.95, 'bike': 5.08, 'run': 3.28},
    'DNK': {'swim': 1.12, 'bike': 5.46, 'run': 3.65},
    'RUS': {'swim': 1.05, 'bike': 5.0, 'run': 3.35},
    'NZL': {'swim': 1.14, 'bike': 5.88, 'run': 3.71},
    'BRA': {'swim': 0.99, 'bike': 5.45, 'run': 3.77},
    'FIN': {'swim': 0.88, 'bike': 5.05, 'run': 3.7},
    'FRA': {'swim': 1.03, 'bike': 5.45, 'run': 3.85},
    'CHE': {'swim': 1.03, 'bike': 5.51, 'run': 4.06},
    'USA': {'swim': 1.06, 'bike': 5.86, 'run': 4.23},
    'CAN': {'swim': 1.15, 'bike': 6.03, 'run': 4.35},
    'AUT': {'swim': 1.21, 'bike': 6.48, 'run': 5.1},
    'MEX': {'swim': 1.05, 'bike': 5.92, 'run': 3.35},
    'ITA': {'swim': 1.08, 'bike': 5.65, 'run': 3.75},
    'GBR': {'swim': 1.13, 'bike': 5.7, 'run': 3.65},
    'AUS': {'swim': 0.88, 'bike': 4.87, 'run': 3.57},
    'PRI': {'swim': 1.14, 'bike': 6.02, 'run': 4.79},
    'JPN': {'swim': 1.17, 'bike': 6.1, 'run': 4.67},
    'LTU': {'swim': 1.0, 'bike': 5.75, 'run': 4.35},
    'ARG': {'swim': 1.1, 'bike': 6.18, 'run': 4.2},
    'SWE': {'swim': 1.23, 'bike': 6.85, 'run': 4.88},
    'BEL': {'swim': 0.88, 'bike': 5.1, 'run': 6.8},
    'ESP': {'swim': 1.35, 'bike': 7.7, 'run': 4.55}
}
```

The resulting dictionary should have __22 elements__.

It's okay if your order does not match the example shown, just as long as
all the values exist. It's also fine if your values are slightly
different: there are different approaches to the DNF entries that result
in different values.

__Tips:__
+ Divide the function into two parts: add up all the times, then average
    the times.
+ The `totalAthletesByCountry` and `getAllCountries` functions may or may
    not be useful, depending in your strategy.
+ Your results should be a dictionary of dictionaries; see [this page for
    examples of different ways of constructing nested
    dictionaries](examples-nested-dicts).
+ You will have to handle the *DNF (Did Not Finish)* dictionary entries;
    a boolean expression like `type(x) == str` can be used to determine
    if the time is a string (`DNF` or `---`).


@include('/labs/lab10/_toc')

@stop