From Splunk to Jupyter and into a Pandas Dataframe.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Querying Splunk Data From a Notepad"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Installing and Importing the Splunk SDK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: splunk-sdk in /opt/conda/envs/python2/lib/python2.7/site-packages\r\n"
     ]
    }
   ],
   "source": [
    "!pip install splunk-sdk\n",
    "\n",
    "import splunklib.results as results\n",
    "import splunklib.client as client"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: missingno in /opt/conda/envs/python2/lib/python2.7/site-packages\r\n",
      "Requirement already satisfied: matplotlib in /opt/conda/envs/python2/lib/python2.7/site-packages (from missingno)\r\n",
      "Requirement already satisfied: seaborn in /opt/conda/envs/python2/lib/python2.7/site-packages (from missingno)\r\n",
      "Requirement already satisfied: scipy in /opt/conda/envs/python2/lib/python2.7/site-packages (from missingno)\r\n",
      "Requirement already satisfied: numpy in /opt/conda/envs/python2/lib/python2.7/site-packages (from missingno)\r\n",
      "Requirement already satisfied: six>=1.10 in /opt/conda/envs/python2/lib/python2.7/site-packages (from matplotlib->missingno)\r\n",
      "Requirement already satisfied: python-dateutil in /opt/conda/envs/python2/lib/python2.7/site-packages (from matplotlib->missingno)\r\n",
      "Requirement already satisfied: functools32 in /opt/conda/envs/python2/lib/python2.7/site-packages (from matplotlib->missingno)\r\n",
      "Requirement already satisfied: subprocess32 in /opt/conda/envs/python2/lib/python2.7/site-packages (from matplotlib->missingno)\r\n",
      "Requirement already satisfied: pytz in /opt/conda/envs/python2/lib/python2.7/site-packages (from matplotlib->missingno)\r\n",
      "Requirement already satisfied: cycler>=0.10 in /opt/conda/envs/python2/lib/python2.7/site-packages/cycler-0.10.0-py2.7.egg (from matplotlib->missingno)\r\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=1.5.6 in /opt/conda/envs/python2/lib/python2.7/site-packages (from matplotlib->missingno)\r\n",
      "Requirement already satisfied: pandas in /opt/conda/envs/python2/lib/python2.7/site-packages (from seaborn->missingno)\r\n"
     ]
    }
   ],
   "source": [
    "# Base \n",
    "import io, os, sys, types, datetime, math, StringIO, time\n",
    "\n",
    "# Data Manipulation\n",
    "import random\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Visualization\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "!pip install missingno\n",
    "import missingno\n",
    "import seaborn as sns\n",
    "\n",
    "# Feature Selection and Encoding\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import OneHotEncoder, LabelEncoder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Connection Details for your Splunk instance - TO EDIT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Your Splunk Instance\n",
    "HOST = \"splunk\"\n",
    "PORT = 8089\n",
    "USERNAME = \"admin\"\n",
    "# Update the password in Splunk and edit here as necessary\n",
    "# The default Splunk password is changeme, and it MUST be changed for connection to succeed.\n",
    "PASSWORD = \"password\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Connecting to Splunk Instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connection Successful\n"
     ]
    }
   ],
   "source": [
    "# Create a Service instance and Attempt Connection to Splunk\n",
    "try:\n",
    "    service = client.connect(host=HOST, port=PORT, username=USERNAME, password=PASSWORD)\n",
    "    print \"Connection Successful\"\n",
    "except Exception,e: print str(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining a Function to Query Splunk and Display Search Progress"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Function to Perform a Splunk search\n",
    "def execute_query(searchquery_normal, \n",
    "                  kwargs_normalsearch={\"exec_mode\": \"normal\"}, \n",
    "                  kwargs_options={\"output_mode\": \"csv\", \"count\": 100000}):\n",
    "    # Execute Search\n",
    "    job = service.jobs.create(searchquery_normal, **kwargs_normalsearch)\n",
    "\n",
    "    # A normal search returns the job's SID right away, so we need to poll for completion\n",
    "    while True:\n",
    "        while not job.is_ready():\n",
    "            pass\n",
    "        stats = {\"isDone\": job[\"isDone\"], \"doneProgress\": float(job[\"doneProgress\"])*100, \n",
    "                 \"scanCount\": int(job[\"scanCount\"]), \"eventCount\": int(job[\"eventCount\"]), \n",
    "                 \"resultCount\": int(job[\"resultCount\"])}\n",
    "        status = (\"\\r%(doneProgress)03.1f%%   %(scanCount)d scanned   \" \n",
    "                  \"%(eventCount)d matched   %(resultCount)d results\") % stats\n",
    "\n",
    "        sys.stdout.write(status)\n",
    "        sys.stdout.flush()\n",
    "        if stats[\"isDone\"] == \"1\":\n",
    "            sys.stdout.write(\"\\nDone!\")\n",
    "            break\n",
    "        time.sleep(0.5)\n",
    "\n",
    "    # Get the results and display them\n",
    "    csv_results = job.results(**kwargs_options).read()\n",
    "    job.cancel()\n",
    "    return csv_results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining and Running a Splunk Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "splunk_query = \"\"\"\n",
    "search index=_internal earliest=-4w latest=now source=*metrics.log*\n",
    "| table average_kbps, component, ev, group, host, inst*, load*, log*, name, _time\n",
    "\"\"\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100.0%   908 scanned   908 matched   908 results\n",
      "Done!"
     ]
    }
   ],
   "source": [
    "csv_results = execute_query(splunk_query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "csv_results_pandas = pd.read_csv(StringIO.StringIO(csv_results), encoding='utf8', sep=',', low_memory=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Storing and Displaying the Results in/from a Pandas Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(pandas.core.frame.DataFrame, (908, 11))"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(csv_results_pandas), csv_results_pandas.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>average_kbps</th>\n",
       "      <th>component</th>\n",
       "      <th>ev</th>\n",
       "      <th>group</th>\n",
       "      <th>host</th>\n",
       "      <th>instantaneous_eps</th>\n",
       "      <th>instantaneous_kbps</th>\n",
       "      <th>load_average</th>\n",
       "      <th>log_level</th>\n",
       "      <th>name</th>\n",
       "      <th>_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.183330</td>\n",
       "      <td>Metrics</td>\n",
       "      <td>92.0</td>\n",
       "      <td>thruput</td>\n",
       "      <td>splunk</td>\n",
       "      <td>2.967699</td>\n",
       "      <td>0.779884</td>\n",
       "      <td>0.09</td>\n",
       "      <td>INFO</td>\n",
       "      <td>thruput</td>\n",
       "      <td>2017-07-21T06:07:13.629+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>Metrics</td>\n",
       "      <td>0.0</td>\n",
       "      <td>thruput</td>\n",
       "      <td>splunk</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>INFO</td>\n",
       "      <td>syslog_output</td>\n",
       "      <td>2017-07-21T06:07:13.629+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.180679</td>\n",
       "      <td>Metrics</td>\n",
       "      <td>78.0</td>\n",
       "      <td>thruput</td>\n",
       "      <td>splunk</td>\n",
       "      <td>2.516093</td>\n",
       "      <td>0.779884</td>\n",
       "      <td>NaN</td>\n",
       "      <td>INFO</td>\n",
       "      <td>index_thruput</td>\n",
       "      <td>2017-07-21T06:07:13.629+00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   average_kbps component    ev    group    host  instantaneous_eps  \\\n",
       "0      1.183330   Metrics  92.0  thruput  splunk           2.967699   \n",
       "1      0.000000   Metrics   0.0  thruput  splunk           0.000000   \n",
       "2      1.180679   Metrics  78.0  thruput  splunk           2.516093   \n",
       "\n",
       "   instantaneous_kbps  load_average log_level           name  \\\n",
       "0            0.779884          0.09      INFO        thruput   \n",
       "1            0.000000           NaN      INFO  syslog_output   \n",
       "2            0.779884           NaN      INFO  index_thruput   \n",
       "\n",
       "                           _time  \n",
       "0  2017-07-21T06:07:13.629+00:00  \n",
       "1  2017-07-21T06:07:13.629+00:00  \n",
       "2  2017-07-21T06:07:13.629+00:00  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csv_results_pandas.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>average_kbps</th>\n",
       "      <th>ev</th>\n",
       "      <th>instantaneous_eps</th>\n",
       "      <th>instantaneous_kbps</th>\n",
       "      <th>load_average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>36.000000</td>\n",
       "      <td>203.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.126993</td>\n",
       "      <td>61.423645</td>\n",
       "      <td>3.612268</td>\n",
       "      <td>0.797828</td>\n",
       "      <td>0.241667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.927401</td>\n",
       "      <td>104.286533</td>\n",
       "      <td>4.941300</td>\n",
       "      <td>0.799692</td>\n",
       "      <td>0.164970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.030000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.090000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.297546</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>2.709728</td>\n",
       "      <td>0.779777</td>\n",
       "      <td>0.230000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.642145</td>\n",
       "      <td>81.500000</td>\n",
       "      <td>4.024239</td>\n",
       "      <td>1.117219</td>\n",
       "      <td>0.372500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.178413</td>\n",
       "      <td>669.000000</td>\n",
       "      <td>21.535976</td>\n",
       "      <td>3.183412</td>\n",
       "      <td>0.480000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       average_kbps          ev  instantaneous_eps  instantaneous_kbps  \\\n",
       "count     36.000000  203.000000          36.000000           36.000000   \n",
       "mean       1.126993   61.423645           3.612268            0.797828   \n",
       "std        0.927401  104.286533           4.941300            0.799692   \n",
       "min        0.000000    0.000000           0.000000            0.000000   \n",
       "25%        0.000000    6.000000           0.000000            0.000000   \n",
       "50%        1.297546   15.000000           2.709728            0.779777   \n",
       "75%        1.642145   81.500000           4.024239            1.117219   \n",
       "max        3.178413  669.000000          21.535976            3.183412   \n",
       "\n",
       "       load_average  \n",
       "count     12.000000  \n",
       "mean       0.241667  \n",
       "std        0.164970  \n",
       "min        0.030000  \n",
       "25%        0.090000  \n",
       "50%        0.230000  \n",
       "75%        0.372500  \n",
       "max        0.480000  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csv_results_pandas.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 908 entries, 0 to 907\n",
      "Data columns (total 11 columns):\n",
      "average_kbps          36 non-null float64\n",
      "component             908 non-null object\n",
      "ev                    203 non-null float64\n",
      "group                 904 non-null object\n",
      "host                  908 non-null object\n",
      "instantaneous_eps     36 non-null float64\n",
      "instantaneous_kbps    36 non-null float64\n",
      "load_average          12 non-null float64\n",
      "log_level             908 non-null object\n",
      "name                  671 non-null object\n",
      "_time                 908 non-null object\n",
      "dtypes: float64(5), object(6)\n",
      "memory usage: 78.1+ KB\n"
     ]
    }
   ],
   "source": [
    "csv_results_pandas.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Displaying the Size of the Dataframe in Memory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'78.1 KB'"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def convert_size(size_bytes):\n",
    "   if size_bytes == 0:\n",
    "       return \"0B\"\n",
    "   size_name = (\"Bytes\", \"KB\", \"MB\", \"GB\", \"TB\", \"PB\", \"EB\", \"ZB\", \"YB\")\n",
    "   i = int(math.floor(math.log(size_bytes, 1024)))\n",
    "   p = math.pow(1024, i)\n",
    "   s = round(size_bytes / p, 2)\n",
    "   return \"%s %s\" % (s, size_name[i])\n",
    "convert_size(csv_results_pandas.memory_usage().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Changing the Name of the Variable containing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "joint_df = csv_results_pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How many missing values in our dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TJTBWnb68FpgLmE3SV7X3+a8TnmZmZmZmY0LpwXsbmSGyi6Tna88tCuxLZhRtJun6iJiB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\nT9l06gkcS5Y4WVfSG84c6twimygvA2w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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7d6af7e890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "missingno.matrix(joint_df, figsize = (30,15));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's Eliminate Fields with Missing Values From Our Original Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100.0%   908 scanned   12 matched   12 results\n",
      "Done!"
     ]
    }
   ],
   "source": [
    "splunk_query = \"\"\"\n",
    "search index=_internal earliest=-4w latest=now source=*metrics.log* average_kbps=* load_average=* \n",
    "| table average_kbps, ev, inst*, load*, _time\n",
    "\"\"\" \n",
    "joint_df = execute_query(splunk_query)\n",
    "joint_df = pd.read_csv(StringIO.StringIO(joint_df), encoding='utf8', sep=',', low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7d6849c410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "missingno.matrix(joint_df, figsize = (30,5));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Print the Distribution of Each Feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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At4EXRMSysh7PiYiPHeW9D5blvb0s63iK6fH/zeG/DweA6RFRV44Yuh7o3uD0HIoNMW/L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\njgK2AOe7+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57heR1sXlVx2G86EgAAKeU4zn+487Ft2z+W9Ig2Ka2m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\nAAAAAJAbMdRSJUhKj0UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7d68615750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_all(joint_df):\n",
    "    \n",
    "    fig = plt.figure(figsize=(30, 20)) \n",
    "    fig.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.2, hspace=None)\n",
    "    for feature in joint_df.columns:\n",
    "        num_features = len(joint_df.columns)\n",
    "        num_features = num_features if num_features%2 == 0 else num_features+1\n",
    "        current_feature = joint_df.columns.get_loc(feature)\n",
    "        x_pos_graph = current_feature%num_features/3\n",
    "        y_pos_graph = current_feature%3\n",
    "\n",
    "        if joint_df[feature].dtype == np.object:\n",
    "            plt.subplot2grid((num_features/2,3),(x_pos_graph,y_pos_graph))\n",
    "            sns.countplot(y=feature, data=joint_df);\n",
    "            plt.title(feature + ' - Type: ' + str(joint_df[feature].dtype))\n",
    "        elif joint_df[feature].dtype == np.float64:\n",
    "            plt.subplot2grid((num_features/2,3),(x_pos_graph,y_pos_graph))\n",
    "            sns.distplot(joint_df[feature]);\n",
    "            plt.title(feature + ' - Type: ' + str(joint_df[feature].dtype))\n",
    "        elif joint_df[feature].dtype == np.int64:\n",
    "            plt.subplot2grid((num_features/2,3),(x_pos_graph,y_pos_graph))\n",
    "            sns.countplot(y=feature, data=joint_df);\n",
    "            plt.title(feature + ' - Type: ' + str(joint_df[feature].dtype))\n",
    "\n",
    "if len(joint_df) > 10000:    \n",
    "    plot_all(joint_df.sample(n=10000).drop(['_time'], axis=1))\n",
    "else:\n",
    "    plot_all(joint_df.drop(['_time'], axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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
}