FNB vs Capitec.py

import facebook
from readability_score.calculators.fleschkincaid import *
from readability_score.calculators.dalechall import *
import numpy as np
import requests
from nltk.collocations import *
import nltk
bigram_measures = nltk.collocations.BigramAssocMeasures()
trigram_measures = nltk.collocations.TrigramAssocMeasures()

graph = facebook.GraphAPI(access_token='HAHA NICE TRY', version='2.2')

PAGE_IDS = ["131727087549","34122846784"]
AVERAGE_AGES = {"131727087549":[],"34122846784":[]}
TOKENS = {"131727087549":[],"34122846784":[]}
count = 0
for page_id in PAGE_IDS:
    count = 0
    page = graph.get_object(id=page_id)
    comments = graph.get_connections(id=page_id, connection_name='feed')
    while True:
        if "data" not in comments:
            break
        for comment in comments["data"]:
            if "message" in comment:
                v = comment["message"]
                tokens = nltk.wordpunct_tokenize(v)
                TOKENS[page_id] = TOKENS[page_id] + tokens
                try:
                    print "---------------------------"
                    print v
                    fk = FleschKincaid(v)
                    print fk.min_age
                    AVERAGE_AGES[page_id].append(fk.min_age)
                except:
                    pass

        if count > 25:
            break
        if comments.has_key("paging") and "next" in comments["paging"]:
             print comments["paging"]["next"]
             comments = requests.get(comments["paging"]["next"]).json()
        else:
            print "Done"
        count += 1

print "COUNTS"

print AVERAGE_AGES
for bank in AVERAGE_AGES:
    print bank
    print "OBS", len(AVERAGE_AGES[bank])
    print np.mean(AVERAGE_AGES[bank])