can anybody please give me a hint how to aggregate (sum up) specific elements of multiple lists?
For example I have 20 lists with a similar name pattern, each representing consumption spending over time (i.e. index) of the corresponding household (agent):
c_agent_0 = [10.0, 11.0, ...]
.
.
.
c_agent_19 = [8.0, 9.0, ...]
I need something like value = sum of index whatever's in c_agent_*[whatever]
Use zip to "combine" lists into single one, then sum elements for each index:
c_agent_0 = [10.0, 11.0, 12.0, 13.0, 14.0]
c_agent_1 = [1.1, 12.3, 14.2, 13.2, 14.3]
c_agent_2 = [1.4, 12.1, 14.5, 13.1, 14.2]
zipped_list = zip(c_agent_0, c_agent_1, c_agent_2)
print zipped_list
print [sum(item) for item in zipped_list]
prints
[(10.0, 1.1, 1.4), (11.0, 12.3, 12.1), (12.0, 14.2, 14.5), (13.0, 13.2, 13.1), (14.0, 14.3, 14.2)]
[12.5, 35.4, 40.7, 39.3, 42.5]
You can create a list of lists, and then use numpy arrays:
import numpy as np
c_agent_0 = [10.0, 11.0, 12.0]
c_agent_1 = [13.0, 14.0, 15.0]
c_agent_2 = [16.0, 17.0, 18.0]
mylist = [c_agent_0, c_agent_1, c_agent_2]
mylist = sum(map(np.array, mylist))
print mylist[0]
Note that a list comprehension is most likely faster; this is just a solution with numpy :).
If you want to do this to all the columns, then alecxe's answer is best, if you only want a single one (or a subset), then it's a little wasteful as it does a lot of unnecessary processing.
For a single column (the third in this case):
sum([c_agent[2] for c_agent in c_agents])
We use the sum()
built in to sum, and a simple list comprehension to get the nth item from each list.
As noted in my comment, this assumes you have a list of lists, as opposed to a load of variables:
c_agents = [c_agent_0, c_agent_1, ...]
Using data structures effectively will make your code much, much better.
The previously mentioned zip()
based solution can also easily be modified to work with this data structure:
[sum(column) for column in zip(*c_agent_s)]
Here we use the *
operator to unpack the list as the arguments for zip()
.
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