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")
<matplotlib.axes.AxesSubplot at 0x109919790>
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")
<matplotlib.axes.AxesSubplot at 0x109378610>
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")
<matplotlib.axes.AxesSubplot at 0x1091af110>
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")
<matplotlib.axes.AxesSubplot at 0x1091e15d0>
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")
<matplotlib.axes.AxesSubplot at 0x1096b6ad0>
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")
<matplotlib.axes.AxesSubplot at 0x109805790>