In [7]:

    

import numpy as np
import pandas as pd
import pylab as pl
import matplotlib.pyplot as plt

%matplotlib inline

df = pd.read_csv('tmp/session_e.csv')


    

In [2]:

    

df.dtypes


    

    
Out[2]:





Unnamed: 0              int64
Unnamed: 0.1            int64
Unnamed: 0.1.1          int64
Unnamed: 0.1.1          int64
ID                      int64
Serial                  int64
SrcIP                  object
DstIP                  object
SessionID               int64
SrcPort                 int64
DstPort                 int64
Flags                  object
Proto                  object
SrcPortCls0           float64
SrcPortCls1           float64
SrcPortCls2           float64
SrcPortCls3           float64
SrcPortCls4           float64
SrcPortCls5           float64
DstPortCls0           float64
DstPortCls1           float64
DstPortCls2           float64
DstPortCls3           float64
DstPortCls4           float64
DstPortCls5           float64
Bytes                   int64
Bytes Sent              int64
Bytes Received          int64
Packets                 int64
Start Time             object
Elapsed Time (sec)      int64
Bytes2                float64
Bytes3                float64
ElapsedTime2          float64
ElapsedTime3          float64
dtype: object

In [8]:

    

import datetime, time
date_time = []
for x in df['Start Time']:
    date_time.append(int(time.mktime(time.strptime(x,"%Y/%m/%d %H:%M"))))
    
df['Start Time2'] = date_time


    

In [21]:

    

pl.figure(figsize=(12, 6))
p = df.groupby('Start Time2')['Start Time2'].count().plot.bar()
p.tick_params(labelbottom='off',top='off',bottom='off')
p.set_yscale('log')
fig = p.get_figure()
fig.savefig('tmp/datime00.png')


    

In [30]:

    

print min(df['Start Time2'])
print max(df['Start Time2'])


    

    




1486951380
1487038020

In [231]:

    

df['Start Time3'] = df['Start Time2']+100


    

In [236]:

    

c0 = df.groupby('Start Time2')['Start Time2'].count()
c1 = df.groupby('Start Time3')['Start Time3'].count()

tmp = pd.concat([c0,c1],axis=1)
ax= tmp.plot.bar(figsize=(12,6),alpha=0.5,width=2,edgecolor="none")
ax.tick_params(labelbottom='off',top='off',bottom='off')
ax.set_yscale('log')
fig = ax.get_figure()
fig.savefig('tmp/datime01.png')


    

In [16]:

    

df['Start Time4'] = df['Start Time2']+np.floor(10*np.random.rand(len(df['Start Time2'])))
c0 = df.groupby('Start Time2')['Start Time2'].count()
c1 = df.groupby('Start Time4')['Start Time4'].count()

tmp = pd.concat([c0,c1],axis=1)
ax= tmp.plot.bar(figsize=(12,6),alpha=0.5,width=2,edgecolor="none")
ax.tick_params(labelbottom='off',top='off',bottom='off')
ax.set_yscale('log')
fig = ax.get_figure()
fig.savefig('tmp/datime02.png')


    

In [18]:

    

df['Start Time4'] = df['Start Time2']+np.floor(10*np.random.rand(len(df['Start Time2']))-5)
c0 = df.groupby('Start Time2')['Start Time2'].count()
c1 = df.groupby('Start Time4')['Start Time4'].count()

tmp = pd.concat([c0,c1],axis=1)
ax= tmp.plot.bar(figsize=(12,6),alpha=0.5,width=2,edgecolor="none")
ax.tick_params(labelbottom='off',top='off',bottom='off')
ax.set_yscale('log')
fig = ax.get_figure()
fig.savefig('tmp/datime03.png')


    

In [19]:

    

df['Start Time4'] = df['Start Time2']+np.floor(4*np.random.rand(len(df['Start Time2']))-2)
c0 = df.groupby('Start Time2')['Start Time2'].count()
c1 = df.groupby('Start Time4')['Start Time4'].count()

tmp = pd.concat([c0,c1],axis=1)
ax= tmp.plot.bar(figsize=(12,6),alpha=0.5,width=2,edgecolor="none")
ax.tick_params(labelbottom='off',top='off',bottom='off')
ax.set_yscale('log')
fig = ax.get_figure()
fig.savefig('tmp/datime04.png')


    

In [ ]: