data_pro.py

from scipy.special import lpmv as p
from numpy.core.umath import sin, cos
import csv
import urllib2

url = 'https://drive.google.com/open?id=0B6ZzcxI-pS7DWC1NaGo0YU5yMGc'
resp = urllib2.urlopen(url)
data = csv.reader(resp)

ggm02c = []
data_dict = {}

i = 0
curr_epoch = ''
t_data = []
for row in data:
    if 'GGM02C' in row:
        ggm02c.append(row)
    if 'EPOCH' in row:
        data_dict[curr_epoch] = t_data
        t_data = []
        curr_epoch = row
        i = 0
    if i != 0:
        t_data.append(row)
    i += 1
# data_dict = { 'epoch':['geoest, n, m, c, s, cs, ss, epoch', ...], ...}

for k, v in data_dict:
    for row in v:
        s = ''
        t = row.split(',')
        for p in t:
            if p != 'GEOEST' and p != t[-1]:
                s += str(p) + ','
        row = s
# data_dict = { 'epoch':['n, m, c, s, cs, ss', ...], ... }

ttt = []
# noinspection PyRedeclaration
for k, v in data_dict:
    for row in v:
        t = row.split(',')
        tt = []
        tt[0] = int(t[0])
        tt[1] = int(t[1])
        tt[2] = int(t[2]) + int(t[4])
        tt[3] = int(t[3]) + int(t[5])
        ttt.append(tt)
    v = ttt
# data_dict = { 'epoch':[[int(n), int(m), int(c + cs), int(s + ss)], ...], ... }
# [1, 0, (c + cs)_10, (s + ss)_10]
# [2, 0, (c + cs)_20, (s + ss)_20]
# [3, 0, ...]
# [4, 0, ...]
# [5, 0, ...]
# [1, 1, ...]
# [1, 2, ...]
# [1, 3, ...]
# [1, 4, ...]
# [1, 5, ...]
#    .
#    .
#    .
# [5, 5, (c + cs)_55, (s + ss)_55]

G = 6.67e-11
M = 5.972e24
R = 6.371e6
r = R + 500000

# using current EPOCH...


def c(n, m):
    # parse data to find C coefficient of order n and degree m
    pass


def s(n, m):
    # parse data to find S coefficient of order n and degree m
    pass


# noinspection PyShadowingNames
def coeff(n, m, longi):
    return c(n, m) * cos(m * longi) + s(n, m) * sin(m * longi)

data_pro = []

for longi in xrange(-180, 180):
    for lati in xrange(-90, 90+1):
        summ = [[
                    ((R / r) ** n) * (coeff(n, m, long)) * p(n, m, sin(lati))
                    for m in xrange(0, n + 1)]
                for n in xrange(2, 5 + 1)
                ]
        data_pro.append(summ)

# U = (G * M / r) * (1)

Based on how your data looks now the brute force would just be

for item in your_list:
   if item[0] == m and item[1] == m:
       return item[2]

Given how your data is structured now you can do the following

new_dict = {}
for row in data_dict['epoch']:
    if new_dict.get(row[0]):
        new_dict[row[0]].update({row[1]:row[2]})
    else:
        new_dict[row[0]] = {row[1]:row[2]}
return_data = {'epoch':new_dict}

(from SO)