pedro_ulogs_processing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql import SparkSession\n",
    "\n",
    "spark = SparkSession.builder.appName(\"WSDM User Logs Processing\").getOrCreate() \n",
    "df_validation = spark.read.csv(\n",
    "    './data/sample_submission_v2.csv', inferSchema=True, header=True\n",
    ")\n",
    "df_userlogs = spark.read.csv(\n",
    "    './data/user_logs.csv', inferSchema=True, header=True\n",
    ")\n",
    "# Limit userlog 'msno' column to only include those 'msno' values present in 'df_validation', \n",
    "# so we only have the logs \n",
    "# of relevant users (in this case, the users in the validation set). \n",
    "df_validation_userlogs = df_userlogs.join(df_validation, 'msno', how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_validation_userlogs.createOrReplaceTempView('user_logs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR:root:An unexpected error occurred while tokenizing input\n",
      "The following traceback may be corrupted or invalid\n",
      "The error message is: ('EOF in multi-line string', (1, 0))\n",
      "\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-20-6f8030ec547d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m spark.sql(\"\"\"\n\u001b[1;32m      2\u001b[0m \u001b[0mSELECT\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mFROM\u001b[0m \u001b[0muser_logs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \"\"\").show()\n\u001b[0m",
      "\u001b[0;32m~/.virtualenvs/wsdm/lib/python3.5/site-packages/pyspark/sql/dataframe.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(self, n, truncate)\u001b[0m\n\u001b[1;32m    334\u001b[0m         \"\"\"\n\u001b[1;32m    335\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtruncate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbool\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mtruncate\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 336\u001b[0;31m             \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshowString\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    337\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    338\u001b[0m             \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshowString\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtruncate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/wsdm/lib/python3.5/site-packages/py4j/java_gateway.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m   1129\u001b[0m             \u001b[0mproto\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mEND_COMMAND_PART\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1130\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1131\u001b[0;31m         \u001b[0manswer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgateway_client\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend_command\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcommand\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1132\u001b[0m         return_value = get_return_value(\n\u001b[1;32m   1133\u001b[0m             answer, self.gateway_client, self.target_id, self.name)\n",
      "\u001b[0;32m~/.virtualenvs/wsdm/lib/python3.5/site-packages/py4j/java_gateway.py\u001b[0m in \u001b[0;36msend_command\u001b[0;34m(self, command, retry, binary)\u001b[0m\n\u001b[1;32m    881\u001b[0m         \u001b[0mconnection\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_connection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    882\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 883\u001b[0;31m             \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconnection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend_command\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcommand\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    884\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mbinary\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    885\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_create_connection_guard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconnection\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/wsdm/lib/python3.5/site-packages/py4j/java_gateway.py\u001b[0m in \u001b[0;36msend_command\u001b[0;34m(self, command)\u001b[0m\n\u001b[1;32m   1026\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1027\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1028\u001b[0;31m             \u001b[0manswer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msmart_decode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstream\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1029\u001b[0m             \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Answer received: {0}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0manswer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1030\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0manswer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mproto\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mRETURN_MESSAGE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/Cellar/python3/3.5.2_3/Frameworks/Python.framework/Versions/3.5/lib/python3.5/socket.py\u001b[0m in \u001b[0;36mreadinto\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m    573\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    574\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 575\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    576\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    577\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_timeout_occurred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "spark.sql(\"\"\"\n",
    "SELECT * FROM user_logs\n",
    "\"\"\").show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = spark.sql(\"\"\"\n",
    "SELECT\n",
    "    msno,\n",
    "    avg(num_100) as avg_num_100,\n",
    "    max(num_100) as max_num_100,\n",
    "    min(num_100) as min_num_100,\n",
    "    sum(num_100) as sum_num_100,\n",
    "    stddev(num_100) as std_num_100,\n",
    "    \n",
    "    avg(num_25) as avg_num_25,\n",
    "    max(num_25) as max_num_25,\n",
    "    min(num_25) as min_num_25,\n",
    "    sum(num_25) as sum_num_25,\n",
    "    stddev(num_25) as std_num_25,\n",
    "    \n",
    "    avg(num_50) as avg_num_50,\n",
    "    max(num_50) as max_num_50,\n",
    "    min(num_50) as min_num_50,\n",
    "    sum(num_50) as sum_num_50,\n",
    "    stddev(num_50) as std_num_50,\n",
    "    \n",
    "    avg(num_75) as avg_num_75,\n",
    "    max(num_75) as max_num_75,\n",
    "    min(num_75) as min_num_75,\n",
    "    sum(num_75) as sum_num_75,\n",
    "    stddev(num_75) as std_num_75,\n",
    "    \n",
    "    avg(num_985) as avg_num_985,\n",
    "    max(num_985) as max_num_985,\n",
    "    min(num_985) as min_num_985,\n",
    "    sum(num_985) as sum_num_985,\n",
    "    stddev(num_985) as std_num_985,\n",
    "    \n",
    "    avg(num_unq) as avg_num_unq,\n",
    "    max(num_unq) as max_num_unq,\n",
    "    min(num_unq) as min_num_unq,\n",
    "    sum(num_unq) as sum_num_unq,\n",
    "    stddev(num_unq) as std_num_unq,\n",
    "    \n",
    "    avg(total_secs) as avg_total_secs,\n",
    "    max(total_secs) as max_total_secs,\n",
    "    min(total_secs) as min_total_secs,\n",
    "    sum(total_secs) as sum_total_secs,\n",
    "    stddev(total_secs) as std_total_secs\n",
    "FROM user_logs\n",
    "GROUP BY msno, is_churn\n",
    "\"\"\").toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>msno</th>\n",
       "      <th>avg_num_100</th>\n",
       "      <th>max_num_100</th>\n",
       "      <th>min_num_100</th>\n",
       "      <th>sum_num_100</th>\n",
       "      <th>std_num_100</th>\n",
       "      <th>avg_num_25</th>\n",
       "      <th>max_num_25</th>\n",
       "      <th>min_num_25</th>\n",
       "      <th>sum_num_25</th>\n",
       "      <th>...</th>\n",
       "      <th>avg_num_unq</th>\n",
       "      <th>max_num_unq</th>\n",
       "      <th>min_num_unq</th>\n",
       "      <th>sum_num_unq</th>\n",
       "      <th>std_num_unq</th>\n",
       "      <th>avg_total_secs</th>\n",
       "      <th>max_total_secs</th>\n",
       "      <th>min_total_secs</th>\n",
       "      <th>sum_total_secs</th>\n",
       "      <th>std_total_secs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>++4RuqBw0Ss6bQU4oMxaRlbBPoWzoEiIZaxPM04Y4+U=</td>\n",
       "      <td>13.592593</td>\n",
       "      <td>128</td>\n",
       "      <td>0</td>\n",
       "      <td>5872</td>\n",
       "      <td>15.019268</td>\n",
       "      <td>7.356481</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>3178</td>\n",
       "      <td>...</td>\n",
       "      <td>21.379630</td>\n",
       "      <td>133</td>\n",
       "      <td>1</td>\n",
       "      <td>9236</td>\n",
       "      <td>18.703806</td>\n",
       "      <td>3985.793051</td>\n",
       "      <td>32437.844</td>\n",
       "      <td>5.340</td>\n",
       "      <td>1721862.598</td>\n",
       "      <td>4110.228254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>+/eG3UpwFCue/nfZsF6OaTF9/aojAhLmAU9vCeY0oMw=</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>204</td>\n",
       "      <td>13.057565</td>\n",
       "      <td>0.470588</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>8.764706</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>149</td>\n",
       "      <td>7.964997</td>\n",
       "      <td>3471.362294</td>\n",
       "      <td>11295.098</td>\n",
       "      <td>119.440</td>\n",
       "      <td>59013.159</td>\n",
       "      <td>3924.399063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>+/namlXq+u3izRjHCFJV4MgqcXcLidZYszVsROOq/y4=</td>\n",
       "      <td>28.851064</td>\n",
       "      <td>417</td>\n",
       "      <td>0</td>\n",
       "      <td>4068</td>\n",
       "      <td>40.112509</td>\n",
       "      <td>24.304965</td>\n",
       "      <td>129</td>\n",
       "      <td>0</td>\n",
       "      <td>3427</td>\n",
       "      <td>...</td>\n",
       "      <td>50.304965</td>\n",
       "      <td>241</td>\n",
       "      <td>1</td>\n",
       "      <td>7093</td>\n",
       "      <td>39.579386</td>\n",
       "      <td>8149.545170</td>\n",
       "      <td>109681.417</td>\n",
       "      <td>5.878</td>\n",
       "      <td>1149085.869</td>\n",
       "      <td>10610.591422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>+0/X9tkmyHyet9X80G6GTrDFHnJqvai8d1ZPhayT0os=</td>\n",
       "      <td>31.217852</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>20635</td>\n",
       "      <td>24.904138</td>\n",
       "      <td>4.753404</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>3142</td>\n",
       "      <td>...</td>\n",
       "      <td>29.841150</td>\n",
       "      <td>189</td>\n",
       "      <td>1</td>\n",
       "      <td>19725</td>\n",
       "      <td>24.112687</td>\n",
       "      <td>8161.983150</td>\n",
       "      <td>36909.298</td>\n",
       "      <td>0.317</td>\n",
       "      <td>5395070.862</td>\n",
       "      <td>6376.696158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>+09YGn842g6h2EZUXe0VWeC4bBoCbDGfUboitc0vIHw=</td>\n",
       "      <td>45.048889</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>30408</td>\n",
       "      <td>38.858401</td>\n",
       "      <td>3.794074</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "      <td>2561</td>\n",
       "      <td>...</td>\n",
       "      <td>42.265185</td>\n",
       "      <td>218</td>\n",
       "      <td>1</td>\n",
       "      <td>28529</td>\n",
       "      <td>33.547113</td>\n",
       "      <td>11562.865759</td>\n",
       "      <td>44077.115</td>\n",
       "      <td>75.001</td>\n",
       "      <td>7804934.387</td>\n",
       "      <td>9925.265121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           msno  avg_num_100  max_num_100  \\\n",
       "0  ++4RuqBw0Ss6bQU4oMxaRlbBPoWzoEiIZaxPM04Y4+U=    13.592593          128   \n",
       "1  +/eG3UpwFCue/nfZsF6OaTF9/aojAhLmAU9vCeY0oMw=    12.000000           39   \n",
       "2  +/namlXq+u3izRjHCFJV4MgqcXcLidZYszVsROOq/y4=    28.851064          417   \n",
       "3  +0/X9tkmyHyet9X80G6GTrDFHnJqvai8d1ZPhayT0os=    31.217852          132   \n",
       "4  +09YGn842g6h2EZUXe0VWeC4bBoCbDGfUboitc0vIHw=    45.048889          180   \n",
       "\n",
       "   min_num_100  sum_num_100  std_num_100  avg_num_25  max_num_25  min_num_25  \\\n",
       "0            0         5872    15.019268    7.356481          46           0   \n",
       "1            0          204    13.057565    0.470588           3           0   \n",
       "2            0         4068    40.112509   24.304965         129           0   \n",
       "3            0        20635    24.904138    4.753404          82           0   \n",
       "4            0        30408    38.858401    3.794074          79           0   \n",
       "\n",
       "   sum_num_25       ...        avg_num_unq  max_num_unq  min_num_unq  \\\n",
       "0        3178       ...          21.379630          133            1   \n",
       "1           8       ...           8.764706           28            1   \n",
       "2        3427       ...          50.304965          241            1   \n",
       "3        3142       ...          29.841150          189            1   \n",
       "4        2561       ...          42.265185          218            1   \n",
       "\n",
       "   sum_num_unq  std_num_unq  avg_total_secs  max_total_secs  min_total_secs  \\\n",
       "0         9236    18.703806     3985.793051       32437.844           5.340   \n",
       "1          149     7.964997     3471.362294       11295.098         119.440   \n",
       "2         7093    39.579386     8149.545170      109681.417           5.878   \n",
       "3        19725    24.112687     8161.983150       36909.298           0.317   \n",
       "4        28529    33.547113    11562.865759       44077.115          75.001   \n",
       "\n",
       "   sum_total_secs  std_total_secs  \n",
       "0     1721862.598     4110.228254  \n",
       "1       59013.159     3924.399063  \n",
       "2     1149085.869    10610.591422  \n",
       "3     5395070.862     6376.696158  \n",
       "4     7804934.387     9925.265121  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('./data/validation_ulog_features.csv', index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}