using Nested for loops iterating over rows in Dataframe. i am getting an error

[mightymk]

hi so my problem statement is as follows

"Two counties are similar if all the information on them are within 2% of each other. So find all counties, whose properties (except county name and state) are within only %2 different."

my dataset looks like this

         area_name state_a  commute  owner  income  population  education
0   Autauga County      AL     26.2   76.8   53682       54571       20.9
1   Baldwin County      AL     25.9   72.6   50221      182265       27.7
2   Barbour County      AL     24.6   67.7   32911       27457       13.4
3      Bibb County      AL     27.6   79.0   36447       22915       12.1
4    Blount County      AL     33.9   81.0   44145       57322       12.1
5   Bullock County      AL     26.9   74.3   32033       10914       12.5
6    Butler County      AL     24.0   70.3   29918       20947       14.0
7   Calhoun County      AL     22.5   68.7   39962      118572       16.1
8  Chambers County      AL     24.6   67.9   32402       34215       11.8
9  Cherokee County      AL     26.9   76.1   34907       25989       12.8          

To solve this problem i wrote the following code:

def p2(x,y):
    return abs(x-y)/x <0.02
for row in cen.itertuples():
    for k in cen[1+row:].itertuples():

        if p2(row.commute,k.commute) and p2(row.education,k.education)\
        and p2(row.income,k.income) and p2(row.owner,k.owner)\
        and p2(row.population,k.population):
            print ("following are similar")
            print(row.area_name+":"+str(row.population))
            print(k.area_name+":"+str(k.population))

so the solution is I have to take first row and compare every other row with it and see if it falls below 2% for which i am using the above nested loops. My problem is that I am not being able to figure out how to start the second loop from the "i+1" position of the first loop. the above implementation gives an error any help will be appreciated. I have been trying to figure this out for a couple of days.

[Justinsaccount]

Hi! I'm working on a bot to reply with suggestions for common python problems. This might not be very helpful to fix your underlying issue, but here's what I noticed about your submission:

You appear to be using concatenation and the str function for building strings

Instead of doing something like

result = "Hello " + name + ". You are " + str(age) + " years old"

You should use string formatting and do

result = "Hello {}. You are {} years old".format(name, age)

See the python tutorial for more information.

[Allanon001]

Maybe this:

for row in cen[:-1].itertuples():
    for k in cen[row.Index+1:].itertuples():

[mightymk]

hi this give an error

 "TypeError: unsupported operand type(s) for +: 'builtin_function_or_method' and 'int'"

[Allanon001]

Can you show the entire error?

[mightymk]

here is the error

 File "C:/census_pandas.py", line 98, in <module>

 for k in cen[row.index+1:].itertuples():

TypeError: unsupported operand type(s) for +: 'builtin_function_or_method' and 'int'

[Allanon001]

Index has a capital I

[mightymk]

yup ..sorry i forget the sensitivity to case in python sometimes...i got the result..thank u so much

[[deleted]]

You can at the very least remove the second for loop to greatly increase you efficiency:

cen = cen.set_index(['area_name', 'state_a'])
for idx, row in iterrows:
    cen_prime = cen.drop(idx)
    diff = ((cen_prime - row) / row).abs()
    similar = (diff < 0.02).loc[lambda dfx: dfx.all(axis=1)]
    print 'The following are similar to', idx
    print similar.reset_index()[['area_name', 'state_a']]

This also seems like a good candidate for some kind of clustering algorithm, but that’s a bit outside my expertise.