Show 05: Rowing

A data table associated with The Boat Race is below. It contains the weight in pounds of each team member who participated in The Boat Race, an annual competition between rivals Cambridge University and Oxford University. This data happens to be from the 1992 race.

Weight (pounds) School Position
188.5 Cambridge Rower
183 Cambridge Rower
194.5 Cambridge Rower
185 Cambridge Rower
214 Cambridge Rower
203.5 Cambridge Rower
186 Cambridge Rower
178.5 Cambridge Rower
109 Cambridge Coxswain
186 Oxford Rower
184.5 Oxford Rower
204 Oxford Rower
184.5 Oxford Rower
195.5 Oxford Rower
202.5 Oxford Rower
174 Oxford Rower
183 Oxford Rower
109.5 Oxford Coxswain

As you were researching rowing, you may have noticed the odd needle-like shape of the racing boats. The data above is for a pair of nine-person boats. (Notice that there are 9 people listed under the Cambridge team, and 9 listed under the Oxford team.)

Below is a photo I took of two nine-person boats for your reference.

rowingeight

To get acclimated with this dataset, start by calculating the mean weight of the Cambridge team. Try this now with just a pencil and paper. [1]

Your result should be about 182.44 pounds.

Remember the complex-looking Approach #3 from the Hobbit exercise? It utilized some handy Python tools, including lists, functions, and variables. I am going to use that code as a reference to create something similar for the Cambridge rowing data. Then we'll compare the code output to the answer you calculated by hand.

Calculating the Cambridge mean

Type this code and save it as mean-cambridge.py.

cambridgeWeights = [188.5, 183, 194.5, 185, 214, 203.5, 186, 178.5, 109]
sumWeights = sum( cambridgeWeights )
teamSize = len( cambridgeWeights )
mean = sumWeights / teamSize
print mean, "pounds"

The Cambridge mean is...

After clicking Run, you should get this:

182.444444444 pounds

Stepping through the code

Line 1:

cambridgeWeights = [188.5, 183, 194.5, 185, 214, 203.5, 186, 178.5, 109]

Lists of data are denoted with a pair of brackets, [ and ]. Each individual data point is separated by a comma, ,. Reading right-to-left, the nine Cambridge teammates' waits are enclosed in a list and then assigned to a new variable called cambridgeWeights. By assigning the list to a variables, you only have to type the data in once. Anytime you want Python to refer to the data, you just mention cambridgeWeights. This saves a lot of typing and makes it easier to read your code.

Line 2:

sumWeights = sum( cambridgeWeights )

Python has a built-in function called sums(), which works with lists. By writing sum(aList) and putting in the name of a specific list, Python will read through the list and add up all the numbers. So you don't have to keep typing sum(cambridgeWeights) every time you need to find the sum of the team, we put the result of sum(cambridgeWeights) into a new variable called sumWeights. Thus, sumWeights has the value 1642.0. Try adding up by hand all the Cambridge weights to confirm that you get the same result.

Line 3:

teamSize = len( cambridgeWeights )

Like sum(), there is also a built-in function that determines the length of a list (i.e. how many pieces of data are in the list). The function is len(). How many individual weights did you add to the list in Line 1? Nine. Thus, the variable teamSize has a value equal to len(cambridgeWeights), which is "9".

Line 4:

mean = sumWeights / teamSize

Using the variables we created earlier, sumWeights and teamSize, divide the former by the latter (i.e. 1642.0 divided by 9) and put the result a variable called mean.

Line 5:

print mean, "pounds"

Display the contents of the mean variable and append the string "pounds" to the end of the line.

Study Drill

  • Using the above code as a reference, create a new script called mean-oxford.py that calculates the average weight of the Oxford crew. Your result should be about 180.4 pounds.
  • Search online for "python sum". How can it be used? Try some sample code.
  • Search online for "python len". How does it work with lists?
[1]Recall that the equation for mean is \(\frac{\text{sum of values}}{\text{number of values summed}}\)