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h4cker/dfir/threat-hunting-analysis-main/Detecting Nmap Behavior with Bro HTTP Logs.ipynb

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true,
"scrolled": true
},
"outputs": [],
"source": [
"import json\n",
"from datetime import datetime, timedelta\n",
"import matplotlib.pylab as plot\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib import dates\n",
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"import matplotlib\n",
"matplotlib.style.use('ggplot')\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true,
"scrolled": true
},
"outputs": [],
"source": [
"# Read data from http bro logs\n",
"with open(\"http.log\",'r') as infile:\n",
" file_data = infile.read()\n",
" \n",
"# Split file by newlines\n",
"file_data = file_data.split('\\n')\n",
"\n",
"# Remove comment lines\n",
"http_data = []\n",
"for line in file_data:\n",
" if line[0] is not None and line[0] != \"#\":\n",
" http_data.append(line)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"# Lets analyze user agents\n",
"user_agent_analysis = {}\n",
"user_agent_overall = {}\n",
"for line in http_data:\n",
" # Extract the timestamp\n",
" timestamp = datetime.fromtimestamp(float(line.split('\\t')[0]))\n",
" # Strip second and microsecond from timestamp\n",
" timestamp = str(timestamp.replace(second=0,microsecond=0))\n",
" \n",
" # Extract the user agent\n",
" user_agent = line.split('\\t')[11]\n",
" \n",
" # Update status code analysis variable\n",
" if user_agent not in user_agent_analysis.keys():\n",
" user_agent_analysis[user_agent] = {timestamp: 1}\n",
" else:\n",
" if timestamp not in user_agent_analysis[user_agent].keys():\n",
" user_agent_analysis[user_agent][timestamp] = 1\n",
" else:\n",
" user_agent_analysis[user_agent][timestamp] += 1\n",
" \n",
" # Update overall user agent count\n",
" if user_agent not in user_agent_overall.keys():\n",
" user_agent_overall[user_agent] = 1\n",
" else:\n",
" user_agent_overall[user_agent] += 1"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"scrolled": false
},
"outputs": [
{
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" <td>0.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:44:00</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:45:00</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:46:00</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:47:00</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:48:00</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:49:00</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:50:00</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:51:00</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:52:00</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:53:00</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>12.0</td>\n",
" <td>5.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:54:00</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>70 rows × 7 columns</p>\n",
"</div>"
],
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"... ... \n",
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"... ... \n",
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"\n",
" Wget/1.16.1 (linux-gnu) \n",
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"... ... \n",
"2015-10-21 10:34:00 0.0 \n",
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"2015-10-21 11:54:00 0.0 \n",
"\n",
"[70 rows x 7 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.DataFrame.from_dict(user_agent_analysis,orient='columns').fillna(0)\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"scrolled": false
},
"outputs": [
{
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"<matplotlib.figure.Figure at 0x10d253c10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#df.plot(figsize=(12,9))\n",
"\n",
"ax = df.plot(rot=90,figsize=(12,9))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 10:32:00</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 10:33:00</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 10:34:00</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 10:35:00</th>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 10:36:00</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>44.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 10:37:00</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>43.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 10:38:00</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 10:44:00</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>38.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 10:45:00</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>33.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:10:00</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>25.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:11:00</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>11.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:19:00</th>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:22:00</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>3.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:24:00</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>3.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:36:00</th>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:37:00</th>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:39:00</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>18.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:40:00</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>12.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:41:00</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>8.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:42:00</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:43:00</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:44:00</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:45:00</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:46:00</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:47:00</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:48:00</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:49:00</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:50:00</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:51:00</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:52:00</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:53:00</th>\n",
" <td>0.0</td>\n",
" <td>12.0</td>\n",
" <td>5.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:54:00</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
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"2015-10-21 05:11:00 132.0 \n",
"2015-10-21 05:12:00 21.0 \n",
"2015-10-21 06:03:00 0.0 \n",
"2015-10-21 06:04:00 15.0 \n",
"2015-10-21 06:05:00 3.0 \n",
"2015-10-21 06:10:00 0.0 \n",
"2015-10-21 06:27:00 0.0 \n",
"2015-10-21 06:28:00 0.0 \n",
"2015-10-21 06:29:00 0.0 \n",
"2015-10-21 07:01:00 0.0 \n",
"2015-10-21 07:02:00 0.0 \n",
"2015-10-21 07:42:00 0.0 \n",
"2015-10-21 07:43:00 0.0 \n",
"2015-10-21 07:59:00 0.0 \n",
"2015-10-21 08:00:00 0.0 \n",
"2015-10-21 08:01:00 0.0 \n",
"2015-10-21 10:29:00 0.0 \n",
"2015-10-21 10:31:00 0.0 \n",
"2015-10-21 10:32:00 0.0 \n",
"2015-10-21 10:33:00 0.0 \n",
"2015-10-21 10:34:00 0.0 \n",
"2015-10-21 10:35:00 0.0 \n",
"2015-10-21 10:36:00 0.0 \n",
"2015-10-21 10:37:00 0.0 \n",
"2015-10-21 10:38:00 0.0 \n",
"2015-10-21 10:44:00 0.0 \n",
"2015-10-21 10:45:00 0.0 \n",
"2015-10-21 11:10:00 0.0 \n",
"2015-10-21 11:11:00 0.0 \n",
"2015-10-21 11:19:00 0.0 \n",
"2015-10-21 11:22:00 0.0 \n",
"2015-10-21 11:24:00 0.0 \n",
"2015-10-21 11:36:00 0.0 \n",
"2015-10-21 11:37:00 0.0 \n",
"2015-10-21 11:39:00 0.0 \n",
"2015-10-21 11:40:00 0.0 \n",
"2015-10-21 11:41:00 0.0 \n",
"2015-10-21 11:42:00 0.0 \n",
"2015-10-21 11:43:00 0.0 \n",
"2015-10-21 11:44:00 0.0 \n",
"2015-10-21 11:45:00 0.0 \n",
"2015-10-21 11:46:00 0.0 \n",
"2015-10-21 11:47:00 0.0 \n",
"2015-10-21 11:48:00 0.0 \n",
"2015-10-21 11:49:00 0.0 \n",
"2015-10-21 11:50:00 0.0 \n",
"2015-10-21 11:51:00 0.0 \n",
"2015-10-21 11:52:00 0.0 \n",
"2015-10-21 11:53:00 0.0 \n",
"2015-10-21 11:54:00 0.0 \n",
"\n",
" Wget/1.16.1 (linux-gnu) \n",
"2015-10-21 05:09:00 0.0 \n",
"2015-10-21 05:10:00 0.0 \n",
"2015-10-21 05:11:00 0.0 \n",
"2015-10-21 05:12:00 0.0 \n",
"2015-10-21 06:03:00 0.0 \n",
"2015-10-21 06:04:00 0.0 \n",
"2015-10-21 06:05:00 0.0 \n",
"2015-10-21 06:10:00 0.0 \n",
"2015-10-21 06:27:00 0.0 \n",
"2015-10-21 06:28:00 0.0 \n",
"2015-10-21 06:29:00 0.0 \n",
"2015-10-21 07:01:00 0.0 \n",
"2015-10-21 07:02:00 0.0 \n",
"2015-10-21 07:42:00 1.0 \n",
"2015-10-21 07:43:00 0.0 \n",
"2015-10-21 07:59:00 0.0 \n",
"2015-10-21 08:00:00 0.0 \n",
"2015-10-21 08:01:00 0.0 \n",
"2015-10-21 10:29:00 0.0 \n",
"2015-10-21 10:31:00 0.0 \n",
"2015-10-21 10:32:00 0.0 \n",
"2015-10-21 10:33:00 0.0 \n",
"2015-10-21 10:34:00 0.0 \n",
"2015-10-21 10:35:00 0.0 \n",
"2015-10-21 10:36:00 0.0 \n",
"2015-10-21 10:37:00 0.0 \n",
"2015-10-21 10:38:00 0.0 \n",
"2015-10-21 10:44:00 0.0 \n",
"2015-10-21 10:45:00 0.0 \n",
"2015-10-21 11:10:00 0.0 \n",
"2015-10-21 11:11:00 0.0 \n",
"2015-10-21 11:19:00 0.0 \n",
"2015-10-21 11:22:00 0.0 \n",
"2015-10-21 11:24:00 0.0 \n",
"2015-10-21 11:36:00 0.0 \n",
"2015-10-21 11:37:00 0.0 \n",
"2015-10-21 11:39:00 0.0 \n",
"2015-10-21 11:40:00 0.0 \n",
"2015-10-21 11:41:00 0.0 \n",
"2015-10-21 11:42:00 0.0 \n",
"2015-10-21 11:43:00 0.0 \n",
"2015-10-21 11:44:00 0.0 \n",
"2015-10-21 11:45:00 0.0 \n",
"2015-10-21 11:46:00 0.0 \n",
"2015-10-21 11:47:00 0.0 \n",
"2015-10-21 11:48:00 0.0 \n",
"2015-10-21 11:49:00 0.0 \n",
"2015-10-21 11:50:00 0.0 \n",
"2015-10-21 11:51:00 0.0 \n",
"2015-10-21 11:52:00 0.0 \n",
"2015-10-21 11:53:00 0.0 \n",
"2015-10-21 11:54:00 0.0 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"user_agent_analysis2 = user_agent_analysis\n",
"print(user_agent_analysis2.keys())\n",
"high_volume_user_agents = [\n",
" \"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36\"\n",
"]\n",
"for ua in high_volume_user_agents: \n",
" if ua in user_agent_analysis2.keys():\n",
" del user_agent_analysis2[ua]\n",
"df2 = pd.DataFrame.from_dict(user_agent_analysis2,orient='columns').fillna(0)\n",
"df2"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x110b9ee90>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAskAAAJsCAYAAAAP5D6wAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nV65c4dixY9SsWRNvb2/
"text/plain": [
"<matplotlib.figure.Figure at 0x110ad7290>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df2.plot(rot=90,figsize=(12,9))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true,
"scrolled": true
},
"outputs": [],
"source": [
"# Lets analyze status codes\n",
"status_code_analysis = {}\n",
"status_code_overall = {}\n",
"earliest_time = None\n",
"latest_time = None\n",
"for line in http_data:\n",
" # Extract the timestamp\n",
" timestamp = datetime.fromtimestamp(float(line.split('\\t')[0]))\n",
" # Strip minute, second and microsecond from timestamp\n",
" #timestamp = str(timestamp.replace(minute=0,second=0,microsecond=0))\n",
" timestamp = str(timestamp.replace(second=0,microsecond=0))\n",
" \n",
" # Extract the status code\n",
" status_code = line.split('\\t')[14]\n",
" \n",
" # Update status code analysis variable\n",
" if status_code not in status_code_analysis.keys():\n",
" status_code_analysis[status_code] = {timestamp: 1}\n",
" else:\n",
" if timestamp not in status_code_analysis[status_code].keys():\n",
" status_code_analysis[status_code][timestamp] = 1\n",
" else:\n",
" status_code_analysis[status_code][timestamp] += 1\n",
" \n",
" # Update overall status code count\n",
" if status_code not in status_code_overall.keys():\n",
" status_code_overall[status_code] = 1\n",
" else:\n",
" status_code_overall[status_code] += 1\n",
" \n",
" # Update our earliest and latest time as needed\n",
" if earliest_time is None or timestamp < earliest_time:\n",
" earliest_time = timestamp\n",
" if latest_time is None or timestamp > latest_time:\n",
" latest_time = timestamp"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x110ffde50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Format data for the plot function\n",
"status_label = []\n",
"data = []\n",
"for code in sorted(status_code_overall.keys()):\n",
" status_label.append(str(code) + \" (\" + str(status_code_overall[code]) + \")\")\n",
" data.append(status_code_overall[code])\n",
"\n",
"plot.figure(1,figsize=[8,8])\n",
"patches, texts = plot.pie(data, shadow=True, startangle=90)\n",
"plot.legend(patches, status_label,loc=\"best\")\n",
"plot.title('Status Code Distribution')\n",
"plot.axis('equal')\n",
"plot.tight_layout()\n",
"plot.show()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
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" <th></th>\n",
" <th>-</th>\n",
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" <th>2015-10-21 05:09:00</th>\n",
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" <th>2015-10-21 05:10:00</th>\n",
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" <th>2015-10-21 06:04:00</th>\n",
" <td>4.0</td>\n",
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" <th>2015-10-21 06:05:00</th>\n",
" <td>1.0</td>\n",
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" <td>0.0</td>\n",
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" <th>2015-10-21 06:16:00</th>\n",
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" <td>58.0</td>\n",
" <td>0.0</td>\n",
" <td>6.0</td>\n",
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" <td>2.0</td>\n",
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" <td>0.0</td>\n",
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" <th>2015-10-21 06:18:00</th>\n",
" <td>0.0</td>\n",
" <td>6.0</td>\n",
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" <td>0.0</td>\n",
" <td>3.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 06:19:00</th>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <tr>\n",
" <th>2015-10-21 06:20:00</th>\n",
" <td>0.0</td>\n",
" <td>46.0</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>7.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 06:21:00</th>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 06:24:00</th>\n",
" <td>0.0</td>\n",
" <td>3.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 06:25:00</th>\n",
" <td>0.0</td>\n",
" <td>7.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>3.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 06:26:00</th>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 06:27:00</th>\n",
" <td>0.0</td>\n",
" <td>14.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 06:28:00</th>\n",
" <td>0.0</td>\n",
" <td>28.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 06:29:00</th>\n",
" <td>0.0</td>\n",
" <td>7.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 07:01:00</th>\n",
" <td>4.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 10:34:00</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 10:35:00</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 10:36:00</th>\n",
" <td>0.0</td>\n",
" <td>24.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>18.0</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 10:37:00</th>\n",
" <td>0.0</td>\n",
" <td>9.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>34.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 10:38:00</th>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 10:44:00</th>\n",
" <td>0.0</td>\n",
" <td>32.0</td>\n",
" <td>0.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 10:45:00</th>\n",
" <td>0.0</td>\n",
" <td>30.0</td>\n",
" <td>0.0</td>\n",
" <td>3.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:10:00</th>\n",
" <td>0.0</td>\n",
" <td>21.0</td>\n",
" <td>0.0</td>\n",
" <td>3.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:11:00</th>\n",
" <td>0.0</td>\n",
" <td>10.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:19:00</th>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:22:00</th>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:24:00</th>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:36:00</th>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:37:00</th>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:39:00</th>\n",
" <td>2.0</td>\n",
" <td>14.0</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:40:00</th>\n",
" <td>0.0</td>\n",
" <td>12.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:41:00</th>\n",
" <td>0.0</td>\n",
" <td>8.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:42:00</th>\n",
" <td>0.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:43:00</th>\n",
" <td>0.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:44:00</th>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:45:00</th>\n",
" <td>0.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:46:00</th>\n",
" <td>0.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:47:00</th>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:48:00</th>\n",
" <td>0.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:49:00</th>\n",
" <td>0.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:50:00</th>\n",
" <td>0.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:51:00</th>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:52:00</th>\n",
" <td>0.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:53:00</th>\n",
" <td>2.0</td>\n",
" <td>14.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-21 11:54:00</th>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>70 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" - 200 301 302 304 400 401 404 405 \\\n",
"2015-10-21 05:09:00 8.0 3.0 0.0 4.0 0.0 0.0 2.0 2.0 0.0 \n",
"2015-10-21 05:10:00 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"2015-10-21 05:11:00 8.0 18.0 0.0 13.0 0.0 39.0 11.0 14.0 11.0 \n",
"2015-10-21 05:12:00 0.0 0.0 0.0 0.0 0.0 22.0 0.0 2.0 2.0 \n",
"2015-10-21 06:02:00 1.0 0.0 0.0 0.0 0.0 0.0 1.0 2.0 0.0 \n",
"2015-10-21 06:03:00 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 \n",
"2015-10-21 06:04:00 4.0 1.0 0.0 6.0 0.0 7.0 0.0 0.0 0.0 \n",
"2015-10-21 06:05:00 1.0 376.0 0.0 0.0 0.0 22.0 0.0 0.0 0.0 \n",
"2015-10-21 06:06:00 0.0 1196.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"2015-10-21 06:07:00 0.0 993.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"2015-10-21 06:08:00 0.0 1171.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"2015-10-21 06:09:00 1.0 1015.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 \n",
"2015-10-21 06:10:00 5.0 20.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 \n",
"2015-10-21 06:11:00 5.0 13.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"2015-10-21 06:12:00 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 \n",
"2015-10-21 06:13:00 0.0 13.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"2015-10-21 06:14:00 0.0 63.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"2015-10-21 06:16:00 0.0 58.0 0.0 6.0 0.0 2.0 0.0 0.0 0.0 \n",
"2015-10-21 06:17:00 0.0 13.0 0.0 0.0 0.0 6.0 0.0 0.0 0.0 \n",
"2015-10-21 06:18:00 0.0 6.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 \n",
"2015-10-21 06:19:00 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"2015-10-21 06:20:00 0.0 46.0 0.0 2.0 0.0 7.0 0.0 0.0 0.0 \n",
"2015-10-21 06:21:00 0.0 2.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 \n",
"2015-10-21 06:24:00 0.0 3.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 \n",
"2015-10-21 06:25:00 0.0 7.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 \n",
"2015-10-21 06:26:00 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 \n",
"2015-10-21 06:27:00 0.0 14.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"2015-10-21 06:28:00 0.0 28.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"2015-10-21 06:29:00 0.0 7.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"2015-10-21 07:01:00 4.0 2.0 0.0 4.0 0.0 0.0 0.0 1.0 0.0 \n",
"... ... ... ... ... ... ... ... ... ... \n",
"2015-10-21 10:34:00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"2015-10-21 10:35:00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 \n",
"2015-10-21 10:36:00 0.0 24.0 0.0 0.0 18.0 0.0 2.0 0.0 0.0 \n",
"2015-10-21 10:37:00 0.0 9.0 0.0 0.0 34.0 0.0 0.0 0.0 0.0 \n",
"2015-10-21 10:38:00 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"2015-10-21 10:44:00 0.0 32.0 0.0 6.0 0.0 0.0 0.0 0.0 0.0 \n",
"2015-10-21 10:45:00 0.0 30.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 \n",
"2015-10-21 11:10:00 0.0 21.0 0.0 3.0 0.0 0.0 1.0 0.0 0.0 \n",
"2015-10-21 11:11:00 0.0 10.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 \n",
"2015-10-21 11:19:00 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"2015-10-21 11:22:00 0.0 1.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 \n",
"2015-10-21 11:24:00 0.0 1.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 \n",
"2015-10-21 11:36:00 4.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 \n",
"2015-10-21 11:37:00 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"2015-10-21 11:39:00 2.0 14.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 \n",
"2015-10-21 11:40:00 0.0 12.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"2015-10-21 11:41:00 0.0 8.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"2015-10-21 11:42:00 0.0 6.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"2015-10-21 11:43:00 0.0 6.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"2015-10-21 11:44:00 0.0 5.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"2015-10-21 11:45:00 0.0 6.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"2015-10-21 11:46:00 0.0 6.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"2015-10-21 11:47:00 0.0 5.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"2015-10-21 11:48:00 0.0 6.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"2015-10-21 11:49:00 0.0 6.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"2015-10-21 11:50:00 0.0 6.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"2015-10-21 11:51:00 0.0 5.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"2015-10-21 11:52:00 0.0 6.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"2015-10-21 11:53:00 2.0 14.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 \n",
"2015-10-21 11:54:00 0.0 4.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
" 501 \n",
"2015-10-21 05:09:00 10.0 \n",
"2015-10-21 05:10:00 0.0 \n",
"2015-10-21 05:11:00 39.0 \n",
"2015-10-21 05:12:00 2.0 \n",
"2015-10-21 06:02:00 0.0 \n",
"2015-10-21 06:03:00 0.0 \n",
"2015-10-21 06:04:00 0.0 \n",
"2015-10-21 06:05:00 0.0 \n",
"2015-10-21 06:06:00 0.0 \n",
"2015-10-21 06:07:00 0.0 \n",
"2015-10-21 06:08:00 0.0 \n",
"2015-10-21 06:09:00 0.0 \n",
"2015-10-21 06:10:00 0.0 \n",
"2015-10-21 06:11:00 0.0 \n",
"2015-10-21 06:12:00 0.0 \n",
"2015-10-21 06:13:00 0.0 \n",
"2015-10-21 06:14:00 0.0 \n",
"2015-10-21 06:16:00 0.0 \n",
"2015-10-21 06:17:00 0.0 \n",
"2015-10-21 06:18:00 0.0 \n",
"2015-10-21 06:19:00 0.0 \n",
"2015-10-21 06:20:00 0.0 \n",
"2015-10-21 06:21:00 0.0 \n",
"2015-10-21 06:24:00 0.0 \n",
"2015-10-21 06:25:00 0.0 \n",
"2015-10-21 06:26:00 0.0 \n",
"2015-10-21 06:27:00 0.0 \n",
"2015-10-21 06:28:00 0.0 \n",
"2015-10-21 06:29:00 0.0 \n",
"2015-10-21 07:01:00 1.0 \n",
"... ... \n",
"2015-10-21 10:34:00 1.0 \n",
"2015-10-21 10:35:00 0.0 \n",
"2015-10-21 10:36:00 0.0 \n",
"2015-10-21 10:37:00 0.0 \n",
"2015-10-21 10:38:00 0.0 \n",
"2015-10-21 10:44:00 0.0 \n",
"2015-10-21 10:45:00 0.0 \n",
"2015-10-21 11:10:00 0.0 \n",
"2015-10-21 11:11:00 0.0 \n",
"2015-10-21 11:19:00 0.0 \n",
"2015-10-21 11:22:00 0.0 \n",
"2015-10-21 11:24:00 0.0 \n",
"2015-10-21 11:36:00 0.0 \n",
"2015-10-21 11:37:00 0.0 \n",
"2015-10-21 11:39:00 0.0 \n",
"2015-10-21 11:40:00 0.0 \n",
"2015-10-21 11:41:00 0.0 \n",
"2015-10-21 11:42:00 0.0 \n",
"2015-10-21 11:43:00 0.0 \n",
"2015-10-21 11:44:00 0.0 \n",
"2015-10-21 11:45:00 0.0 \n",
"2015-10-21 11:46:00 0.0 \n",
"2015-10-21 11:47:00 0.0 \n",
"2015-10-21 11:48:00 0.0 \n",
"2015-10-21 11:49:00 0.0 \n",
"2015-10-21 11:50:00 0.0 \n",
"2015-10-21 11:51:00 0.0 \n",
"2015-10-21 11:52:00 0.0 \n",
"2015-10-21 11:53:00 0.0 \n",
"2015-10-21 11:54:00 0.0 \n",
"\n",
"[70 rows x 10 columns]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Output the status codes in table form\n",
"df = pd.DataFrame.from_dict(status_code_analysis,orient='columns').fillna(0)\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x104d01990>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAtAAAAJsCAYAAADUX5SSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n9QzQASm9pYUD6gH6YGH
"text/plain": [
"<matplotlib.figure.Figure at 0x110fd3610>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot the status codes\n",
"df.plot(rot=90,figsize=(12,9))"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['304', '301', '302', '-', '405', '404', '401', '400', '501']\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x111531190>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x104d01890>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Remove the 200 status code and re-plot the status codes\n",
"status_code_analysis2 = status_code_analysis\n",
"if '200' in status_code_analysis2.keys():\n",
" del status_code_analysis2['200']\n",
"print(status_code_analysis2.keys())\n",
"df2 = pd.DataFrame.from_dict(status_code_analysis2,orient='columns').fillna(0)\n",
"df2.plot(rot=90, figsize=(12,9))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Reference in a new issue Copy permalink