Originally published on my blog.
Here’s today challenge: can you write a commandline tool that allows converting to and from various measurements units?
For instance, you could input “3 miles in meters” and get “4828.03”.
I submitted this challenge to my Python students last weekend, asking them to write the code from scratch. ^{1}
1 hour later, something miraculous happened that I never would have expected.
But let me tell you the full story.
Getting started
I told the students that they could start by writing some “exploratory code”.
“Just hardcode anything you have to and keep everything in the main()
function“, I said.
After a few discussions, we agreed to only write code that converted kilometers to miles, and that we’ll read the values from the command line.
Here’s what we came up with:
import sys
def main():
kilometers = float(sys.argv)
miles = kilometers / 1.609
print(f"{.2f}", miles)
if __name__ == " __main__":
main()
I then pointed out that the code was not generic. Indeed, “kilometers”, “miles” and “1.609” are hardcoded there.
Naming a new function
The students understood there was a threeparameters function waiting to be written. So we went to the drawing board and after a while, we decided to have a function called convert(value, unit_in, unit_out)
.
Note that we did not make any assumption about the body of the function. We just wanted to see how main()
could become more generic, and we were still allowed to hardcode parts of the code:
def convert(value, unit_in, unit_out):
coefficient = 1 / 1.609
result = value * coefficient
return result
def main():
value = float(sys.argv[1])
unit_in = sys.argv[2]
unit_out = sys.argv[3]
result = convert(value, unit_in, unit_out)
print(f"{.2f}", result)
Some notes:
 The
main()
function is now completely generic, and we probably won’t need to change it.  The signature of the
convert
function almost dictated the commandline syntax:
def convert(value, unit_in, unit_out):
...
# Usage: convert.py value unit_in unit_out
$ python3 convert.py 2 meters miles
Computing the coefficient
Now it was time to get rid of the hardcoded coefficient. This time finding a function name was easier:
def get_coefficient(unit_in, unit_out):
...
Then we tried to figure out how to implement it. We knew we would be needing a dictionary, but the structure of it was unknown.
“Back to the drawing board”, I said. “Let’s write down what the dictionary should look like”.
Here’s our first attempt:
units = {
"km": { "miles": 1/1.609, "meters": 1/1000, ....},
"yards": { "miles": 1/1760, "meters": ..., "km": ...}
...
}
“This won’t do”, I said. “Look at what happens if we add a new measurement unit, such as feet
”.
We’ll have to:
 add a new ‘feet’ key to the
units
dictionary,  compute all the coefficient to convert from
feet
to all the other units,  and add a
feet
key to all the other dictionaries
There has to be a better way!
After a short brainstorming session, we decided to limit ourselves to distance measurements, and to always convert to SI units first.
So we draw the new structure of the units
dictionary:
# Coefficients convert from "meters"
distances = {
"km": 1/1000,
"yards": 1.094,
"miles": 1/1609,
}
And then we thought about the algorithm. We found three possibilities:
 If we want to convert from meters, we just have to look up the coefficient in the dictionary
 If we want to convert to meters, we can look up the coefficient in the dictionary and return its inverse
 Otherwise, we combine the two procedures above and return the product of the two coefficients.
“This is looking good”, I said. “Let’s try to implement the algorithm but just for the first case and see what happens”.
Testing the algorithm
I showed my students how they could use Python’s interpreter to check the get_coefficient() function was working properly.
We quickly managed to get the first case working:
def get_coefficient(unit_in, unit_out):
# FIX ME: only works with distances for now
# Coefficients to convert from "meters"
distances = {
"km": 1/1000,
"yards": 1.094,
"miles": 1/1609,
}
if unit_in == "m":
return distances[unit_out]
>>> import conversion
>>> conversion.get_coefficient("m", "km")
0.001
>>> conversion.get_coefficient("m", "yards")
1.094
“Cool, this works”, I said. “Let’s see what happens when the input value is not in meters:”
def get_coefficient(unit_in, unit_out):
# FIX ME: only works with distances for now
# Coefficients to convert from "meters"
distances = {
"km": 1/1000,
"yards": 1.094,
"miles": 1/1609,
}
if unit_in == "m":
return distances[unit_out]
else:
reciprocal_coefficient = 1 / distances[unit_in]
return reciprocal_coefficient * distances[unit_out]
>>> import conversion
>>> conversion.get_coefficient("miles", "yards")
1760
“Look how readable the code is”, I said. “We have a value that’s calledreciprocal_coefficient
and we get it by calling 1 over something else. Isn’t this nice?“.
The miracle
I then pointed out that the ‘else’ after the return was unnecessary.
def get_coefficient(unit_in, unit_out):
# FIX ME: only works with distances for now
# Coefficients to convert from "meters"
distances = {
"km": 1/1000,
"yards": 1.094,
"miles": 1/1609,
}
if unit_in == "m":
return distances[unit_out]
reciprocal_coefficient = 1 / distances[unit_in]
return reciprocal_coefficient * distances[unit_out]
And then it happened. “Hey”, one of the students said, “what if we added meters in the distances dictionary with 1
as value? We could get rid of the first if
too!“.
“Let’s do it”, I said:
def get_coefficient(unit_in, unit_out):
# FIX ME: only works with distances for now
distances = {
"m": 1,
"km": 1/1000,
"yards": 1.094,
"miles": 1/1609,
}
reciprocal_coefficient = 1 / distances[unit_in]
return reciprocal_coefficient * distances[unit_out]
>>> import conversion
>>> conversion.get_coefficient("m", "m")
1
>>> conversion.get_coefficient("km", "m")
1000
>>> conversion.get_coefficient("m", "yards")
1760
And of course, this works. When meters
is either unit_in
or unit_out
, all operations will involve multiplying or dividing by 1.
That was a really nice surprise for several reasons:
 One, when I thought about the problem alone, before starting the workshop, I was pretty sure I would need a much more complex data structure.
 Two, one of the students just refused to believe the code would work, even after having seen it in action in the interpreter ;)
 Three, we killed one comment :)
Lessons learned
We found a beautiful algorithm and a nice data structure, not by trying to solve everything at once, but by slowly building up more and more generic code, getting rid of hardcoded values one after the other, and by carefully thinking about naming.
I hope you find this approach useful, and I highly suggest you try using it next time you implement a new feature.
Cheers!
I'd love to hear what you have to say, so please feel free to leave a comment below, or check out my contact page for more ways to get in touch with me.

I’m using mob programming during my Python classes. It works really well. ↩
Discussion
I'm curious. What makes you think that?