I am looping over objects called events. Each event has a particular object in it. I am calculating the fraction of objects that have a particular characteristic. Imagine the approach as being something like the following:
for event in events:
countCars =+ 1
if event.car.isBlue() is True:
countCarsBlue =+ 1
print("fraction of cars that are blue: {fraction}".format(
fraction = countCarsBlue / countCars))
Now, imagine that I want to calculate the fraction of objects that have the particular characteristic in regions of another of the object's characteristics. So, in my example, I am counting the fraction of cars that are blue. Now, I want to calculate the fraction of cars that are blue in the range of car lengths from 0 m to 1 m, the fraction of cars that are blue in the range of car lengths from 1 m to 2 m, from 2 m to 3 m and from 3 m to 4 m and so on.
Given that I am dealing with a lot of statistics and many more bins than the 4 bins of my simple example, what would be a good way to structure the code for this type of calculation, assuming a constant bin width?
(Would there be a sensible way to do this for variable bin widths?)
First, some code to recreate your example:
import random
class Event(object):
def __init__(self):
self.car = None
class Car(object):
def __init__(self, isBlue, length):
self._isBlue = isBlue
self._length = length
def isBlue(self):
return self._isBlue
def length(self):
return self._length
def __str__(self):
return '{} car of {} m long.'.format('blue' if self.isBlue() else 'non-blue ', self.length())
OK, now I randomly create ten carobjects and add them to an event:
totalNumberOfCars = 10
events = []
for _ in range(totalNumberOfCars):
car = Car(random.choice([True, False]), random.randrange(5, 40)/10.)
print car
event = Event()
event.car = car
events.append(event)
For me, the output was as follows (your output can of course be different):
blue car of 0.5 m long.
non-blue car of 2.3 m long.
non-blue car of 3.8 m long.
blue car of 2.1 m long.
non-blue car of 0.6 m long.
blue car of 0.8 m long.
blue car of 0.5 m long.
blue car of 2.3 m long.
blue car of 3.3 m long.
blue car of 2.1 m long.
Now, if we want to count our events by region, you could do it as follows:
allBlueCars = sum(1 for event in events if event.car.isBlue())
print "Number of blue cars: {}".format(allBlueCars)
maxCarLen = 4
for region in zip(range(maxCarLen ), range(1, maxCarLen +1)):
minlen, maxlen = region
print "Cars between {} and {} m that are blue:".format(minlen, maxlen)
blueCarsInRegion = [str(event.car) for event in events if event.car.isBlue() and minlen <= event.car.length() < maxlen]
if blueCarsInRegion:
print '\n'.join(['\t{}'.format(car) for car in blueCarsInRegion])
else:
print 'no blue cars in this region'
fraction = float(len(blueCarsInRegion)) / allBlueCars
print "fraction of cars that are blue and between {} and {} m long: {}".format(minlen, maxlen, fraction)
print
For the above sample data, that would print:
Number of blue cars: 7
Cars between 0 and 1 m that are blue:
blue car of 0.5 m long.
blue car of 0.8 m long.
blue car of 0.5 m long.
fraction of cars that are blue and between 0 and 1 m long: 0.428571428571
Cars between 1 and 2 m that are blue:
no blue cars in this region
fraction of cars that are blue and between 1 and 2 m long: 0.0
Cars between 2 and 3 m that are blue:
blue car of 2.1 m long.
blue car of 2.3 m long.
blue car of 2.1 m long.
fraction of cars that are blue and between 2 and 3 m long: 0.428571428571
Cars between 3 and 4 m that are blue:
blue car of 3.3 m long.
fraction of cars that are blue and between 3 and 4 m long: 0.142857142857