Sum Values of Python Dictionary (Time/Space Complexity)

user_10296399 0
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I am attempting to solve the following problem:

Given the list of birth dates and death dates, find the year in which the most people were alive.

Here is my code thus far:

b = [1791, 1796, 1691, 1907, 1999, 2001, 1907] # birth dates
d = [1800, 1803, 1692, 1907, 1852, 1980, 2006] # death dates

year_dict = {} # populates dict key as year, val as total living/dead
for birth in b:
    year_dict.setdefault(birth,0) # sets default value of key to 0 
    year_dict[birth] += 1 # will add +1 for each birth and sums duplicates
for death in d:
    year_dict.setdefault(death,0) # sets default value of key to 0
    year_dict[death] += -1 # will add -1 for each death and sums duplicates

The following code returns:

{1791: 1, 1796: 1, 1691: 1, 1907: 1, 1999: 1, 2001: 1, 1800: -1, 1803: -1, 1692: -1, 1852: -1, 1980: -1, 2006: -1}

Now I am looking for a way to create a running sum to find which year has the most people living, example:

Image of desired result

As we can see, the result shows 1796 had the most people alive based on the given data sets. I am having trouble getting the running sum portion which would need to take each key value, and sum it against the previous value. I have tried several different loops and enumeration, but am now stuck. Once I find the best way of resolving this, I will create a function for efficiency.

If there is a more efficient way of doing this taking into account time/space complexity, please let me know. I am trying to learn efficiency with python. I really appreciate your help!!!

space-complexity time-complexity python-3.x dictionary python
2 Answers

Is there a particular data structure you want to house the result in? I got the same result as the imgur link to print to the terminal. It would not be difficult to write it to a dictionary though.

from collections import OrderedDict

b = [1791, 1796, 1691, 1907, 1999, 2001, 1907] # birth dates
d = [1800, 1803, 1692, 1907, 1852, 1980, 2006] # death dates

year_dict = {} # populates dict key as year, val as total living/dead
for birth in b:
    year_dict.setdefault(birth,0) # sets default value of key to 0 
    year_dict[birth] += 1 # will add +1 for each birth and sums duplicates
for death in d:
    year_dict.setdefault(death,0) # sets default value of key to 0
    year_dict[death] += -1 # will add -1 for each death and sums duplicates

year_dict = OrderedDict(sorted(year_dict.items(), key=lambda t: t[0]))
solution_dict = {}

total = 0
print('year net_living running_sum')
for year in year_dict:
    total += year_dict[year]
    solution_dict.update({year:{'net_living': year_dict[year],
                                'running_sum': total}
                                })
    print('{} {:4} {:10}'.format(year, year_dict[year], total))

Outputs:

year net_living running_sum
1691    1          1
1692   -1          0
1791    1          1
1796    1          2
1800   -1          1
1803   -1          0
1852   -1         -1
1907    1          0
1980   -1         -1
1999    1          0
2001    1          1
2006   -1          0

Output of solution_dict

{
1691: {'net_living':  1, 'running_sum':  1},
1692: {'net_living': -1, 'running_sum':  0},
1791: {'net_living':  1, 'running_sum':  1},
1796: {'net_living':  1, 'running_sum':  2},
1800: {'net_living': -1, 'running_sum':  1},
1803: {'net_living': -1, 'running_sum':  0},
1852: {'net_living': -1, 'running_sum': -1},
1907: {'net_living':  1, 'running_sum':  0},
1980: {'net_living': -1, 'running_sum': -1},
1999: {'net_living':  1, 'running_sum':  0},
2001: {'net_living':  1, 'running_sum':  1},
2006: {'net_living': -1, 'running_sum':  0}
}
user_60043380

I would use pandas, and make use of its DataFrame object:

Make the dataframe of people's year of birth and year of death::

born = [1791, 1796, 1691, 1907, 1999, 2001, 1907] # birth dates
died = [1800, 1803, 1692, 1907, 1852, 1980, 2006] # death dates
people = pd.DataFrame({'born': born, 'died': died} for born, died in zip(born, died))

Make a dataframe that includes all years between the first listed birth, and the last listed death:

years = pd.DataFrame(index=np.arange(people['born'].min(), people['died'].max() + 1))

Find the total number of people alive for each of those years:

for year in years.index:
    num_living = ((year > people['born']) & (year < people['died'])).sum()
    years.loc[year, 'total_living'] = num_living

Calling years.tail() yields the following:

    total_living
2002    1.0
2003    1.0
2004    1.0
2005    1.0
2006    0.0

From there, you can simply do an argmax on the 'total_living' column.

To be clear, I assumed a logical situation of people dying after they are born, and (therefore) that there are never negative numbers of people alive.

user_45857130
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