import urllib2
import pandas
from collections import Counter

data = urllib2.urlopen("http://stats202.com/stats202log.txt").readlines()
list_data=[]
for item in data:
    list_data.append(item.split())
df = pandas.DataFrame(list_data)

top_10 = Counter(df[0]).most_common(10)
ip=[]
count=[]
for key, value in top_10:
    ip.append(key)
    count.append(value)
ts = pandas.Series(count, ip)
ts.plot(kind="barh")

status_code=[]
code_counts=[]
status_codes = Counter(df[10]).most_common(10)
for key, value in status_codes:
    status_code.append(key)
    code_counts.append(value)
codes = pandas.Series(code_counts, status_code)
codes.plot(kind="barh")

status_code=[]
code_counts=[]
status_codes = Counter(df[8]).most_common(10)
for key, value in status_codes:
    status_code.append(key)
    code_counts.append(value)
codes = pandas.Series(code_counts, status_code)
codes.plot(kind="barh")

status_code=[]
code_counts=[]
status_codes = Counter(df[6]).most_common(10)
for key, value in status_codes:
    status_code.append(key)
    code_counts.append(value)
codes = pandas.Series(code_counts, status_code)
codes.plot(kind="barh")

status_code=[]
code_counts=[]
status_codes = Counter(df[13]).most_common(10)
for key, value in status_codes:
    status_code.append(key)
    code_counts.append(value)
codes = pandas.Series(code_counts, status_code)
codes.plot(kind="barh")

status_code=[]
code_counts=[]
status_codes = Counter(df[14]).most_common(10)
for key, value in status_codes:
    status_code.append(key)
    code_counts.append(value)
codes = pandas.Series(code_counts, status_code)
codes.plot(kind="barh")