Codeup-Curriculum-Access-Anomalies.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Codeup Curriculum Access Log Anomalies\n",
    "### Kwame V. Taylor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Infomation on cohorts can be found in ```cohorts.csv``` for reference."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import math\n",
    "from sklearn import metrics\n",
    "\n",
    "from scipy.stats import entropy\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates #to format dates on our plots\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats\n",
    "\n",
    "# default viz size settings\n",
    "plt.rc('figure', figsize=(14, 9))\n",
    "plt.rc('font', size=15)\n",
    "\n",
    "# This is to make sure matplotlib doesn't throw the following error:\n",
    "# The next line fixes \"TypeError: float() argument must be a string or a number, not 'Timestamp' matplotlib\"\n",
    "pd.plotting.register_matplotlib_converters()\n",
    "\n",
    "# importing GeoIP database for IP geolocation\n",
    "import geoip2.database\n",
    "reader = geoip2.database.Reader('GeoLite2City/GeoLite2-City.mmdb')\n",
    "\n",
    "# import system to check types of errors\n",
    "import sys\n",
    "\n",
    "from wrangle import acquire_access_logs, prepare_logs\n",
    "from preprocessing import add_network_and_host, add_city, add_country\n",
    "from explore import plot_top_cities, plot_city_anomalies, plot_top_countries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Acquire"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>request_method</th>\n",
       "      <th>user_id</th>\n",
       "      <th>cohort_id</th>\n",
       "      <th>ip</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-26</td>\n",
       "      <td>09:55:03</td>\n",
       "      <td>/</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>97.105.19.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-26</td>\n",
       "      <td>09:56:02</td>\n",
       "      <td>java-ii</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>97.105.19.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-26</td>\n",
       "      <td>09:56:05</td>\n",
       "      <td>java-ii/object-oriented-programming</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>97.105.19.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-26</td>\n",
       "      <td>09:56:06</td>\n",
       "      <td>slides/object_oriented_programming</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>97.105.19.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-26</td>\n",
       "      <td>09:56:24</td>\n",
       "      <td>javascript-i/conditionals</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>97.105.19.61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date timestamp                       request_method  user_id  \\\n",
       "0  2018-01-26  09:55:03                                    /        1   \n",
       "1  2018-01-26  09:56:02                              java-ii        1   \n",
       "2  2018-01-26  09:56:05  java-ii/object-oriented-programming        1   \n",
       "3  2018-01-26  09:56:06   slides/object_oriented_programming        1   \n",
       "4  2018-01-26  09:56:24            javascript-i/conditionals        2   \n",
       "\n",
       "  cohort_id            ip  \n",
       "0         8  97.105.19.61  \n",
       "1         8  97.105.19.61  \n",
       "2         8  97.105.19.61  \n",
       "3         8  97.105.19.61  \n",
       "4        22  97.105.19.61  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colnames = ['date', 'timestamp', 'request_method', 'user_id', 'cohort_id', 'ip']\n",
    "\n",
    "df = pd.read_csv('anonymized-curriculum-access.txt', header=None, index_col=False,\n",
    "                 names=colnames, delim_whitespace=True, na_values='\"-\"')\n",
    "#                 usecols=[0, 1, 2, 5])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>request_method</th>\n",
       "      <th>user_id</th>\n",
       "      <th>cohort_id</th>\n",
       "      <th>ip</th>\n",
       "      <th>ds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-26</td>\n",
       "      <td>09:55:03</td>\n",
       "      <td>/</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>97.105.19.61</td>\n",
       "      <td>2018-01-26 09:55:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-26</td>\n",
       "      <td>09:56:02</td>\n",
       "      <td>java-ii</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>97.105.19.61</td>\n",
       "      <td>2018-01-26 09:56:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-26</td>\n",
       "      <td>09:56:05</td>\n",
       "      <td>java-ii/object-oriented-programming</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>97.105.19.61</td>\n",
       "      <td>2018-01-26 09:56:05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-26</td>\n",
       "      <td>09:56:06</td>\n",
       "      <td>slides/object_oriented_programming</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>97.105.19.61</td>\n",
       "      <td>2018-01-26 09:56:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-26</td>\n",
       "      <td>09:56:24</td>\n",
       "      <td>javascript-i/conditionals</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>97.105.19.61</td>\n",
       "      <td>2018-01-26 09:56:24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date timestamp                       request_method  user_id  \\\n",
       "0  2018-01-26  09:55:03                                    /        1   \n",
       "1  2018-01-26  09:56:02                              java-ii        1   \n",
       "2  2018-01-26  09:56:05  java-ii/object-oriented-programming        1   \n",
       "3  2018-01-26  09:56:06   slides/object_oriented_programming        1   \n",
       "4  2018-01-26  09:56:24            javascript-i/conditionals        2   \n",
       "\n",
       "  cohort_id            ip                   ds  \n",
       "0         8  97.105.19.61  2018-01-26 09:55:03  \n",
       "1         8  97.105.19.61  2018-01-26 09:56:02  \n",
       "2         8  97.105.19.61  2018-01-26 09:56:05  \n",
       "3         8  97.105.19.61  2018-01-26 09:56:06  \n",
       "4        22  97.105.19.61  2018-01-26 09:56:24  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge date and timestamp\n",
    "df[\"ds\"] = df[\"date\"] +\" \"+ df[\"timestamp\"]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>request_method</th>\n",
       "      <th>user_id</th>\n",
       "      <th>cohort_id</th>\n",
       "      <th>ip</th>\n",
       "      <th>ds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>/</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>97.105.19.61</td>\n",
       "      <td>2018-01-26 09:55:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>java-ii</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>97.105.19.61</td>\n",
       "      <td>2018-01-26 09:56:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>java-ii/object-oriented-programming</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>97.105.19.61</td>\n",
       "      <td>2018-01-26 09:56:05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>slides/object_oriented_programming</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>97.105.19.61</td>\n",
       "      <td>2018-01-26 09:56:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>javascript-i/conditionals</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>97.105.19.61</td>\n",
       "      <td>2018-01-26 09:56:24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        request_method  user_id cohort_id            ip  \\\n",
       "0                                    /        1         8  97.105.19.61   \n",
       "1                              java-ii        1         8  97.105.19.61   \n",
       "2  java-ii/object-oriented-programming        1         8  97.105.19.61   \n",
       "3   slides/object_oriented_programming        1         8  97.105.19.61   \n",
       "4            javascript-i/conditionals        2        22  97.105.19.61   \n",
       "\n",
       "                    ds  \n",
       "0  2018-01-26 09:55:03  \n",
       "1  2018-01-26 09:56:02  \n",
       "2  2018-01-26 09:56:05  \n",
       "3  2018-01-26 09:56:06  \n",
       "4  2018-01-26 09:56:24  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop date and timestamp\n",
    "df = df.drop(columns=['date', 'timestamp'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "request_method            object\n",
       "user_id                    int64\n",
       "cohort_id                 object\n",
       "ip                        object\n",
       "ds                datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert date column to datetime type\n",
    "df.ds = pd.to_datetime(df.ds)\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>request_method</th>\n",
       "      <th>user_id</th>\n",
       "      <th>cohort_id</th>\n",
       "      <th>ip</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ds</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-26 09:55:03</th>\n",
       "      <td>/</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>97.105.19.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-26 09:56:02</th>\n",
       "      <td>java-ii</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>97.105.19.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-26 09:56:05</th>\n",
       "      <td>java-ii/object-oriented-programming</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>97.105.19.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-26 09:56:06</th>\n",
       "      <td>slides/object_oriented_programming</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>97.105.19.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-26 09:56:24</th>\n",
       "      <td>javascript-i/conditionals</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>97.105.19.61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          request_method  user_id cohort_id  \\\n",
       "ds                                                                            \n",
       "2018-01-26 09:55:03                                    /        1         8   \n",
       "2018-01-26 09:56:02                              java-ii        1         8   \n",
       "2018-01-26 09:56:05  java-ii/object-oriented-programming        1         8   \n",
       "2018-01-26 09:56:06   slides/object_oriented_programming        1         8   \n",
       "2018-01-26 09:56:24            javascript-i/conditionals        2        22   \n",
       "\n",
       "                               ip  \n",
       "ds                                 \n",
       "2018-01-26 09:55:03  97.105.19.61  \n",
       "2018-01-26 09:56:02  97.105.19.61  \n",
       "2018-01-26 09:56:05  97.105.19.61  \n",
       "2018-01-26 09:56:06  97.105.19.61  \n",
       "2018-01-26 09:56:24  97.105.19.61  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set ds as index and sort\n",
    "# this is a very important step!\n",
    "df = df.set_index('ds').sort_index()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(719459, 4)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "/                                                    40122\n",
       "search/search_index.json                             15393\n",
       "javascript-i                                         14551\n",
       "toc                                                  14018\n",
       "java-iii                                             10835\n",
       "                                                     ...  \n",
       "6-regression/8-Project                                   1\n",
       "content/appendix/javascript/functions/models.html        1\n",
       "npm                                                      1\n",
       "examples/css/..%255c                                     1\n",
       "appendix/offer-letters                                   1\n",
       "Name: request_method, Length: 2154, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.request_method.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "97.105.19.58       268648\n",
       "97.105.19.61        60530\n",
       "192.171.117.210      8896\n",
       "71.150.217.33        4919\n",
       "12.106.208.194       4262\n",
       "                    ...  \n",
       "172.58.109.108          1\n",
       "71.78.157.46            1\n",
       "107.77.219.19           1\n",
       "172.56.6.31             1\n",
       "173.239.232.28          1\n",
       "Name: ip, Length: 4064, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ip.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "386"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.cohort_id.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some IP addresses accidentally got put in the ```cohort_id``` column, so I'm going to drop those rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>request_method</th>\n",
       "      <th>user_id</th>\n",
       "      <th>cohort_id</th>\n",
       "      <th>ip</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ds</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-26 09:55:03</th>\n",
       "      <td>/</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>97.105.19.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-26 09:56:02</th>\n",
       "      <td>java-ii</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>97.105.19.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-26 09:56:05</th>\n",
       "      <td>java-ii/object-oriented-programming</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>97.105.19.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-26 09:56:06</th>\n",
       "      <td>slides/object_oriented_programming</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>97.105.19.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-26 09:56:24</th>\n",
       "      <td>javascript-i/conditionals</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>97.105.19.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02 16:48:13</th>\n",
       "      <td>javascript-i/coffee-project</td>\n",
       "      <td>763</td>\n",
       "      <td>62</td>\n",
       "      <td>107.192.148.199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02 16:48:17</th>\n",
       "      <td>javascript-i/mapbox-api</td>\n",
       "      <td>771</td>\n",
       "      <td>62</td>\n",
       "      <td>172.125.226.175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02 16:48:18</th>\n",
       "      <td>javascript-i/coffee-project</td>\n",
       "      <td>771</td>\n",
       "      <td>62</td>\n",
       "      <td>172.125.226.175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02 16:48:28</th>\n",
       "      <td>javascript-i/bom-and-dom/bom</td>\n",
       "      <td>771</td>\n",
       "      <td>62</td>\n",
       "      <td>172.125.226.175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02 16:48:47</th>\n",
       "      <td>javascript-i/bom-and-dom/bom</td>\n",
       "      <td>763</td>\n",
       "      <td>62</td>\n",
       "      <td>107.192.148.199</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>673973 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          request_method  user_id cohort_id  \\\n",
       "ds                                                                            \n",
       "2018-01-26 09:55:03                                    /        1         8   \n",
       "2018-01-26 09:56:02                              java-ii        1         8   \n",
       "2018-01-26 09:56:05  java-ii/object-oriented-programming        1         8   \n",
       "2018-01-26 09:56:06   slides/object_oriented_programming        1         8   \n",
       "2018-01-26 09:56:24            javascript-i/conditionals        2        22   \n",
       "...                                                  ...      ...       ...   \n",
       "2020-11-02 16:48:13          javascript-i/coffee-project      763        62   \n",
       "2020-11-02 16:48:17              javascript-i/mapbox-api      771        62   \n",
       "2020-11-02 16:48:18          javascript-i/coffee-project      771        62   \n",
       "2020-11-02 16:48:28         javascript-i/bom-and-dom/bom      771        62   \n",
       "2020-11-02 16:48:47         javascript-i/bom-and-dom/bom      763        62   \n",
       "\n",
       "                                  ip  \n",
       "ds                                    \n",
       "2018-01-26 09:55:03     97.105.19.61  \n",
       "2018-01-26 09:56:02     97.105.19.61  \n",
       "2018-01-26 09:56:05     97.105.19.61  \n",
       "2018-01-26 09:56:06     97.105.19.61  \n",
       "2018-01-26 09:56:24     97.105.19.61  \n",
       "...                              ...  \n",
       "2020-11-02 16:48:13  107.192.148.199  \n",
       "2020-11-02 16:48:17  172.125.226.175  \n",
       "2020-11-02 16:48:18  172.125.226.175  \n",
       "2020-11-02 16:48:28  172.125.226.175  \n",
       "2020-11-02 16:48:47  107.192.148.199  \n",
       "\n",
       "[673973 rows x 4 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(df[df.cohort_id.str.len() > 4].index)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Drop nulls**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "request_method      0\n",
       "user_id             0\n",
       "cohort_id         420\n",
       "ip                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I noticed there are some requests that are just ```/```. I believe this is when the main page is accessed. I'm going to drop them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Drop bad request_methods**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>request_method</th>\n",
       "      <th>user_id</th>\n",
       "      <th>cohort_id</th>\n",
       "      <th>ip</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ds</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-26 09:55:03</th>\n",
       "      <td>/</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>97.105.19.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-26 10:00:37</th>\n",
       "      <td>/</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "      <td>97.105.19.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-26 10:00:39</th>\n",
       "      <td>javascript-i</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "      <td>97.105.19.61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    request_method  user_id cohort_id            ip\n",
       "ds                                                                 \n",
       "2018-01-26 09:55:03              /        1         8  97.105.19.61\n",
       "2018-01-26 10:00:37              /        6        22  97.105.19.61\n",
       "2018-01-26 10:00:39   javascript-i        6        22  97.105.19.61"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# there are over 10,000 of these '/' requests\n",
    "df.groupby(['request_method']).filter(lambda x : len(x)>10000).head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>request_method</th>\n",
       "      <th>user_id</th>\n",
       "      <th>cohort_id</th>\n",
       "      <th>ip</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ds</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-26 09:56:02</th>\n",
       "      <td>java-ii</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>97.105.19.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-26 09:56:05</th>\n",
       "      <td>java-ii/object-oriented-programming</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>97.105.19.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-26 09:56:06</th>\n",
       "      <td>slides/object_oriented_programming</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>97.105.19.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-26 09:56:24</th>\n",
       "      <td>javascript-i/conditionals</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>97.105.19.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-26 09:56:41</th>\n",
       "      <td>javascript-i/loops</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>97.105.19.61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          request_method  user_id cohort_id  \\\n",
       "ds                                                                            \n",
       "2018-01-26 09:56:02                              java-ii        1         8   \n",
       "2018-01-26 09:56:05  java-ii/object-oriented-programming        1         8   \n",
       "2018-01-26 09:56:06   slides/object_oriented_programming        1         8   \n",
       "2018-01-26 09:56:24            javascript-i/conditionals        2        22   \n",
       "2018-01-26 09:56:41                   javascript-i/loops        2        22   \n",
       "\n",
       "                               ip  \n",
       "ds                                 \n",
       "2018-01-26 09:56:02  97.105.19.61  \n",
       "2018-01-26 09:56:05  97.105.19.61  \n",
       "2018-01-26 09:56:06  97.105.19.61  \n",
       "2018-01-26 09:56:24  97.105.19.61  \n",
       "2018-01-26 09:56:41  97.105.19.61  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(df[df.request_method.str.len() < 2].index)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Split IP address into network and host**\n",
    " * **Reference**: https://docs.oracle.com/cd/E19504-01/802-5753/planning3-18471/index.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>request_method</th>\n",
       "      <th>user_id</th>\n",
       "      <th>cohort_id</th>\n",
       "      <th>ip</th>\n",
       "      <th>network1</th>\n",
       "      <th>network2</th>\n",
       "      <th>host1</th>\n",
       "      <th>host2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ds</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-26 09:56:02</th>\n",
       "      <td>java-ii</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>97.105.19.61</td>\n",
       "      <td>97</td>\n",
       "      <td>105</td>\n",
       "      <td>19</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-26 09:56:05</th>\n",
       "      <td>java-ii/object-oriented-programming</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>97.105.19.61</td>\n",
       "      <td>97</td>\n",
       "      <td>105</td>\n",
       "      <td>19</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-26 09:56:06</th>\n",
       "      <td>slides/object_oriented_programming</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>97.105.19.61</td>\n",
       "      <td>97</td>\n",
       "      <td>105</td>\n",
       "      <td>19</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-26 09:56:24</th>\n",
       "      <td>javascript-i/conditionals</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>97.105.19.61</td>\n",
       "      <td>97</td>\n",
       "      <td>105</td>\n",
       "      <td>19</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-26 09:56:41</th>\n",
       "      <td>javascript-i/loops</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>97.105.19.61</td>\n",
       "      <td>97</td>\n",
       "      <td>105</td>\n",
       "      <td>19</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          request_method  user_id cohort_id  \\\n",
       "ds                                                                            \n",
       "2018-01-26 09:56:02                              java-ii        1         8   \n",
       "2018-01-26 09:56:05  java-ii/object-oriented-programming        1         8   \n",
       "2018-01-26 09:56:06   slides/object_oriented_programming        1         8   \n",
       "2018-01-26 09:56:24            javascript-i/conditionals        2        22   \n",
       "2018-01-26 09:56:41                   javascript-i/loops        2        22   \n",
       "\n",
       "                               ip network1 network2 host1 host2  \n",
       "ds                                                               \n",
       "2018-01-26 09:56:02  97.105.19.61       97      105    19    61  \n",
       "2018-01-26 09:56:05  97.105.19.61       97      105    19    61  \n",
       "2018-01-26 09:56:06  97.105.19.61       97      105    19    61  \n",
       "2018-01-26 09:56:24  97.105.19.61       97      105    19    61  \n",
       "2018-01-26 09:56:41  97.105.19.61       97      105    19    61  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['network1','network2', 'host1', 'host2']] = df.ip.str.split(\".\",expand=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>request_method</th>\n",
       "      <th>user_id</th>\n",
       "      <th>cohort_id</th>\n",
       "      <th>ip</th>\n",
       "      <th>network1</th>\n",
       "      <th>network2</th>\n",
       "      <th>host1</th>\n",
       "      <th>host2</th>\n",
       "      <th>network</th>\n",
       "      <th>host</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ds</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-26 09:56:02</th>\n",
       "      <td>java-ii</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>97.105.19.61</td>\n",
       "      <td>97</td>\n",
       "      <td>105</td>\n",
       "      <td>19</td>\n",
       "      <td>61</td>\n",
       "      <td>97105</td>\n",
       "      <td>1961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-26 09:56:05</th>\n",
       "      <td>java-ii/object-oriented-programming</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>97.105.19.61</td>\n",
       "      <td>97</td>\n",
       "      <td>105</td>\n",
       "      <td>19</td>\n",
       "      <td>61</td>\n",
       "      <td>97105</td>\n",
       "      <td>1961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-26 09:56:06</th>\n",
       "      <td>slides/object_oriented_programming</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>97.105.19.61</td>\n",
       "      <td>97</td>\n",
       "      <td>105</td>\n",
       "      <td>19</td>\n",
       "      <td>61</td>\n",
       "      <td>97105</td>\n",
       "      <td>1961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-26 09:56:24</th>\n",
       "      <td>javascript-i/conditionals</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>97.105.19.61</td>\n",
       "      <td>97</td>\n",
       "      <td>105</td>\n",
       "      <td>19</td>\n",
       "      <td>61</td>\n",
       "      <td>97105</td>\n",
       "      <td>1961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-26 09:56:41</th>\n",
       "      <td>javascript-i/loops</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>97.105.19.61</td>\n",
       "      <td>97</td>\n",
       "      <td>105</td>\n",
       "      <td>19</td>\n",
       "      <td>61</td>\n",
       "      <td>97105</td>\n",
       "      <td>1961</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          request_method  user_id cohort_id  \\\n",
       "ds                                                                            \n",
       "2018-01-26 09:56:02                              java-ii        1         8   \n",
       "2018-01-26 09:56:05  java-ii/object-oriented-programming        1         8   \n",
       "2018-01-26 09:56:06   slides/object_oriented_programming        1         8   \n",
       "2018-01-26 09:56:24            javascript-i/conditionals        2        22   \n",
       "2018-01-26 09:56:41                   javascript-i/loops        2        22   \n",
       "\n",
       "                               ip network1 network2 host1 host2 network  host  \n",
       "ds                                                                             \n",
       "2018-01-26 09:56:02  97.105.19.61       97      105    19    61   97105  1961  \n",
       "2018-01-26 09:56:05  97.105.19.61       97      105    19    61   97105  1961  \n",
       "2018-01-26 09:56:06  97.105.19.61       97      105    19    61   97105  1961  \n",
       "2018-01-26 09:56:24  97.105.19.61       97      105    19    61   97105  1961  \n",
       "2018-01-26 09:56:41  97.105.19.61       97      105    19    61   97105  1961  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['network'] = df['network1'] + df['network2']\n",
    "df['host'] = df['host1'] + df['host2']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**I notice that there is at least one instance of a different user_id under the same exact IP address. Might be a typo, or just something I don't understand about IP addresses.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>request_method</th>\n",
       "      <th>user_id</th>\n",
       "      <th>cohort_id</th>\n",
       "      <th>ip</th>\n",
       "      <th>network</th>\n",
       "      <th>host</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ds</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-26 09:56:02</th>\n",
       "      <td>java-ii</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>97.105.19.61</td>\n",
       "      <td>97105</td>\n",
       "      <td>1961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-26 09:56:05</th>\n",
       "      <td>java-ii/object-oriented-programming</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>97.105.19.61</td>\n",
       "      <td>97105</td>\n",
       "      <td>1961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-26 09:56:06</th>\n",
       "      <td>slides/object_oriented_programming</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>97.105.19.61</td>\n",
       "      <td>97105</td>\n",
       "      <td>1961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-26 09:56:24</th>\n",
       "      <td>javascript-i/conditionals</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>97.105.19.61</td>\n",
       "      <td>97105</td>\n",
       "      <td>1961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-26 09:56:41</th>\n",
       "      <td>javascript-i/loops</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>97.105.19.61</td>\n",
       "      <td>97105</td>\n",
       "      <td>1961</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          request_method  user_id cohort_id  \\\n",
       "ds                                                                            \n",
       "2018-01-26 09:56:02                              java-ii        1         8   \n",
       "2018-01-26 09:56:05  java-ii/object-oriented-programming        1         8   \n",
       "2018-01-26 09:56:06   slides/object_oriented_programming        1         8   \n",
       "2018-01-26 09:56:24            javascript-i/conditionals        2        22   \n",
       "2018-01-26 09:56:41                   javascript-i/loops        2        22   \n",
       "\n",
       "                               ip network  host  \n",
       "ds                                               \n",
       "2018-01-26 09:56:02  97.105.19.61   97105  1961  \n",
       "2018-01-26 09:56:05  97.105.19.61   97105  1961  \n",
       "2018-01-26 09:56:06  97.105.19.61   97105  1961  \n",
       "2018-01-26 09:56:24  97.105.19.61   97105  1961  \n",
       "2018-01-26 09:56:41  97.105.19.61   97105  1961  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(columns=['network1', 'network2', 'host1', 'host2'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "700"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.network.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3727"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.host.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore & Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ip</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>event_count</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4077</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4590</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8447</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57520</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256779</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>473 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              ip\n",
       "event_count     \n",
       "1            537\n",
       "2            511\n",
       "3            313\n",
       "4            238\n",
       "5            184\n",
       "...          ...\n",
       "4077           1\n",
       "4590           1\n",
       "8447           1\n",
       "57520          1\n",
       "256779         1\n",
       "\n",
       "[473 rows x 1 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create dataframe out of value counts\n",
    "ip_counts = pd.DataFrame(df.ip.value_counts()).reset_index()\n",
    "\n",
    "# rename columns\n",
    "ip_counts.columns=['ip', 'event_count']\n",
    "\n",
    "# get the number of ip addresses seen 1, or 2, or 11,998 times. \n",
    "ip_counts.groupby(['event_count']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.149481083203116"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pass the entropy function an array-like object of counts. \n",
    "entropy(ip_counts.event_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Make darden_df for just dates from Darden cohort**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>request_method</th>\n",
       "      <th>user_id</th>\n",
       "      <th>cohort_id</th>\n",
       "      <th>ip</th>\n",
       "      <th>network</th>\n",
       "      <th>host</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ds</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-11-02 16:48:28</th>\n",
       "      <td>javascript-i/bom-and-dom/bom</td>\n",
       "      <td>771</td>\n",
       "      <td>62</td>\n",
       "      <td>172.125.226.175</td>\n",
       "      <td>172125</td>\n",
       "      <td>226175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02 16:48:47</th>\n",
       "      <td>javascript-i/bom-and-dom/bom</td>\n",
       "      <td>763</td>\n",
       "      <td>62</td>\n",
       "      <td>107.192.148.199</td>\n",
       "      <td>107192</td>\n",
       "      <td>148199</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   request_method  user_id cohort_id  \\\n",
       "ds                                                                     \n",
       "2020-11-02 16:48:28  javascript-i/bom-and-dom/bom      771        62   \n",
       "2020-11-02 16:48:47  javascript-i/bom-and-dom/bom      763        62   \n",
       "\n",
       "                                  ip network    host  \n",
       "ds                                                    \n",
       "2020-11-02 16:48:28  172.125.226.175  172125  226175  \n",
       "2020-11-02 16:48:47  107.192.148.199  107192  148199  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>request_method</th>\n",
       "      <th>user_id</th>\n",
       "      <th>cohort_id</th>\n",
       "      <th>ip</th>\n",
       "      <th>network</th>\n",
       "      <th>host</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ds</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-07-13 00:00:19</th>\n",
       "      <td>appendix</td>\n",
       "      <td>633</td>\n",
       "      <td>57</td>\n",
       "      <td>24.26.242.224</td>\n",
       "      <td>2426</td>\n",
       "      <td>242224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-13 00:00:23</th>\n",
       "      <td>appendix/professional-development/linkedin-tips</td>\n",
       "      <td>633</td>\n",
       "      <td>57</td>\n",
       "      <td>24.26.242.224</td>\n",
       "      <td>2426</td>\n",
       "      <td>242224</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      request_method  user_id  \\\n",
       "ds                                                                              \n",
       "2020-07-13 00:00:19                                         appendix      633   \n",
       "2020-07-13 00:00:23  appendix/professional-development/linkedin-tips      633   \n",
       "\n",
       "                    cohort_id             ip network    host  \n",
       "ds                                                            \n",
       "2020-07-13 00:00:19        57  24.26.242.224    2426  242224  \n",
       "2020-07-13 00:00:23        57  24.26.242.224    2426  242224  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "darden_df = df.loc['2020-07-13':'2020-11-02']\n",
    "darden_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(98631, 6)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "darden_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Geolocation Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Add City Feature**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_city(ip):\n",
    "    try:\n",
    "        response = reader.city(ip)\n",
    "        city_name = response.city.name\n",
    "        return city_name\n",
    "    except:\n",
    "        return np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "darden_df['city'] = darden_df.apply(lambda darden_df: add_city(darden_df['ip']),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>request_method</th>\n",
       "      <th>user_id</th>\n",
       "      <th>cohort_id</th>\n",
       "      <th>ip</th>\n",
       "      <th>network</th>\n",
       "      <th>host</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ds</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-07-13 00:00:19</th>\n",
       "      <td>appendix</td>\n",
       "      <td>633</td>\n",
       "      <td>57</td>\n",
       "      <td>24.26.242.224</td>\n",
       "      <td>2426</td>\n",
       "      <td>242224</td>\n",
       "      <td>San Antonio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-13 00:00:23</th>\n",
       "      <td>appendix/professional-development/linkedin-tips</td>\n",
       "      <td>633</td>\n",
       "      <td>57</td>\n",
       "      <td>24.26.242.224</td>\n",
       "      <td>2426</td>\n",
       "      <td>242224</td>\n",
       "      <td>San Antonio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-13 00:04:59</th>\n",
       "      <td>appendix/professional-development/cover-letter</td>\n",
       "      <td>614</td>\n",
       "      <td>56</td>\n",
       "      <td>174.67.1.123</td>\n",
       "      <td>17467</td>\n",
       "      <td>1123</td>\n",
       "      <td>Oklahoma City</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-13 00:05:49</th>\n",
       "      <td>spring/fundamentals/security/authentication</td>\n",
       "      <td>606</td>\n",
       "      <td>56</td>\n",
       "      <td>76.187.7.114</td>\n",
       "      <td>76187</td>\n",
       "      <td>7114</td>\n",
       "      <td>Garland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-13 00:52:09</th>\n",
       "      <td>javascript-i/arrays</td>\n",
       "      <td>672</td>\n",
       "      <td>58</td>\n",
       "      <td>67.11.50.23</td>\n",
       "      <td>6711</td>\n",
       "      <td>5023</td>\n",
       "      <td>Boerne</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      request_method  user_id  \\\n",
       "ds                                                                              \n",
       "2020-07-13 00:00:19                                         appendix      633   \n",
       "2020-07-13 00:00:23  appendix/professional-development/linkedin-tips      633   \n",
       "2020-07-13 00:04:59   appendix/professional-development/cover-letter      614   \n",
       "2020-07-13 00:05:49      spring/fundamentals/security/authentication      606   \n",
       "2020-07-13 00:52:09                              javascript-i/arrays      672   \n",
       "\n",
       "                    cohort_id             ip network    host           city  \n",
       "ds                                                                           \n",
       "2020-07-13 00:00:19        57  24.26.242.224    2426  242224    San Antonio  \n",
       "2020-07-13 00:00:23        57  24.26.242.224    2426  242224    San Antonio  \n",
       "2020-07-13 00:04:59        56   174.67.1.123   17467    1123  Oklahoma City  \n",
       "2020-07-13 00:05:49        56   76.187.7.114   76187    7114        Garland  \n",
       "2020-07-13 00:52:09        58    67.11.50.23    6711    5023         Boerne  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "darden_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "105"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "darden_df.city.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "San Antonio      68077\n",
       "Dallas            5451\n",
       "Boerne            3315\n",
       "New Braunfels     1966\n",
       "Austin            1491\n",
       "                 ...  \n",
       "Whittier             1\n",
       "Floresville          1\n",
       "Bulverde             1\n",
       "Pflugerville         1\n",
       "Marfa                1\n",
       "Name: city, Length: 105, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "darden_df.city.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(98631, 7)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "darden_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_plot_df = darden_df.groupby('city').filter(lambda x : len(x)>900)\n",
    "city_plot_df.city.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "city_plot_df['city'].value_counts().plot(kind='bar')\n",
    "plt.title('Distribution of Top Cities from IP Addresses during Darden')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "city_plot_df['city'].drop(city_plot_df[city_plot_df.city == 'San Antonio'].index).value_counts().plot(kind='bar')\n",
    "plt.title('Top Cities During Darden, Without San Antonio')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_plot_anomalies = darden_df.groupby('city').filter(lambda x : len(x)<4)\n",
    "city_plot_anomalies.city.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "city_plot_anomalies['city'].value_counts().plot(kind='bar')\n",
    "plt.title('Anomaly Cities from IP Addresses During Darden')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Takeaways:** Mexico City showed up (it seems GeoLite couldn't acquire the city in some places and gave me the country instead), so I'm going to see if I can zoom in on geolocations from outside the USA."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create Country feature**\n",
    "\n",
    "Note: Still working with just the Darden duration data so that my geolocation code isn't too computationally expensive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_country(ip):\n",
    "    try:\n",
    "        response = reader.city(ip)\n",
    "        country_name = response.country.name\n",
    "        return country_name\n",
    "    except:\n",
    "        return np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "darden_df['country'] = darden_df.apply(lambda darden_df: add_country(darden_df['ip']),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "darden_df.country.nunique()\n",
    "darden_df.country.value_counts()\n",
    "country_plot_df = darden_df.groupby('country').filter(lambda x : len(x)>700)\n",
    "country_plot_df.country.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>request_method</th>\n",
       "      <th>user_id</th>\n",
       "      <th>cohort_id</th>\n",
       "      <th>ip</th>\n",
       "      <th>network</th>\n",
       "      <th>host</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ds</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-07-13 00:00:19</th>\n",
       "      <td>appendix</td>\n",
       "      <td>633</td>\n",
       "      <td>57</td>\n",
       "      <td>24.26.242.224</td>\n",
       "      <td>2426</td>\n",
       "      <td>242224</td>\n",
       "      <td>San Antonio</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-13 00:00:23</th>\n",
       "      <td>appendix/professional-development/linkedin-tips</td>\n",
       "      <td>633</td>\n",
       "      <td>57</td>\n",
       "      <td>24.26.242.224</td>\n",
       "      <td>2426</td>\n",
       "      <td>242224</td>\n",
       "      <td>San Antonio</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-13 00:04:59</th>\n",
       "      <td>appendix/professional-development/cover-letter</td>\n",
       "      <td>614</td>\n",
       "      <td>56</td>\n",
       "      <td>174.67.1.123</td>\n",
       "      <td>17467</td>\n",
       "      <td>1123</td>\n",
       "      <td>Oklahoma City</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-13 00:05:49</th>\n",
       "      <td>spring/fundamentals/security/authentication</td>\n",
       "      <td>606</td>\n",
       "      <td>56</td>\n",
       "      <td>76.187.7.114</td>\n",
       "      <td>76187</td>\n",
       "      <td>7114</td>\n",
       "      <td>Garland</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-13 00:52:09</th>\n",
       "      <td>javascript-i/arrays</td>\n",
       "      <td>672</td>\n",
       "      <td>58</td>\n",
       "      <td>67.11.50.23</td>\n",
       "      <td>6711</td>\n",
       "      <td>5023</td>\n",
       "      <td>Boerne</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      request_method  user_id  \\\n",
       "ds                                                                              \n",
       "2020-07-13 00:00:19                                         appendix      633   \n",
       "2020-07-13 00:00:23  appendix/professional-development/linkedin-tips      633   \n",
       "2020-07-13 00:04:59   appendix/professional-development/cover-letter      614   \n",
       "2020-07-13 00:05:49      spring/fundamentals/security/authentication      606   \n",
       "2020-07-13 00:52:09                              javascript-i/arrays      672   \n",
       "\n",
       "                    cohort_id             ip network    host           city  \\\n",
       "ds                                                                            \n",
       "2020-07-13 00:00:19        57  24.26.242.224    2426  242224    San Antonio   \n",
       "2020-07-13 00:00:23        57  24.26.242.224    2426  242224    San Antonio   \n",
       "2020-07-13 00:04:59        56   174.67.1.123   17467    1123  Oklahoma City   \n",
       "2020-07-13 00:05:49        56   76.187.7.114   76187    7114        Garland   \n",
       "2020-07-13 00:52:09        58    67.11.50.23    6711    5023         Boerne   \n",
       "\n",
       "                           country  \n",
       "ds                                  \n",
       "2020-07-13 00:00:19  United States  \n",
       "2020-07-13 00:00:23  United States  \n",
       "2020-07-13 00:04:59  United States  \n",
       "2020-07-13 00:05:49  United States  \n",
       "2020-07-13 00:52:09  United States  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "darden_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Look for Suspicious IPs in the City and Country NaNs**\n",
    "\n",
    "It's possible that the city for some IP addresses was unable to be acquired because of suspicious reasons. \n",
    "\n",
    "There are hits from Germany, U.S. Virgin Islands, and Mexico.\n",
    "\n",
    "Let's investigate.\n",
    "\n",
    "Reference: https://ipremoval.sms.symantec.com/ipr/lookup\n",
    "\n",
    "I tested the reference with my own IP address for a quick check of it's validity, and it came back with \"no bad reputation\" for my IP address."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Germany                81\n",
       "U.S. Virgin Islands     4\n",
       "Mexico                  2\n",
       "Name: country, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "darden_df[darden_df.country != 'United States'].country.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "79.220.63.16      62\n",
       "87.158.135.133    19\n",
       "Name: ip, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "darden_df[darden_df.country == 'Germany'].ip.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The IP Address 79.220.63.16 was found to have a negative reputation. Reasons for this assessment include:\n",
    "> * The host has been observed sending spam in a format that is similar to snow shoe spamming techniques.\n",
    "> * The host is unauthorized to send email directly to email servers.\n",
    "\n",
    "> The IP Address 87.158.135.133 was found to have a negative reputation. Reasons for this assessment include:\n",
    "> * The host has been observed sending spam in a format that is similar to snow shoe spamming techniques.\n",
    "> * The host is unauthorized to send email directly to email servers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create bad IPs dataframe**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>request_method</th>\n",
       "      <th>user_id</th>\n",
       "      <th>cohort_id</th>\n",
       "      <th>ip</th>\n",
       "      <th>network</th>\n",
       "      <th>host</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ds</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-10-05 09:02:56</th>\n",
       "      <td>javascript-ii</td>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>79.220.63.16</td>\n",
       "      <td>79220</td>\n",
       "      <td>6316</td>\n",
       "      <td>Munich</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-05 09:03:04</th>\n",
       "      <td>javascript-i</td>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>79.220.63.16</td>\n",
       "      <td>79220</td>\n",
       "      <td>6316</td>\n",
       "      <td>Munich</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-05 09:03:08</th>\n",
       "      <td>javascript-i/introduction</td>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>79.220.63.16</td>\n",
       "      <td>79220</td>\n",
       "      <td>6316</td>\n",
       "      <td>Munich</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-05 09:03:12</th>\n",
       "      <td>javascript-i/introduction/primitive-types</td>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>79.220.63.16</td>\n",
       "      <td>79220</td>\n",
       "      <td>6316</td>\n",
       "      <td>Munich</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-05 09:03:16</th>\n",
       "      <td>javascript-i/introduction/operators</td>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>79.220.63.16</td>\n",
       "      <td>79220</td>\n",
       "      <td>6316</td>\n",
       "      <td>Munich</td>\n",
       "      <td>Germany</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-05 09:08:16</th>\n",
       "      <td>java-ii/file-io</td>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>79.220.63.16</td>\n",
       "      <td>79220</td>\n",
       "      <td>6316</td>\n",
       "      <td>Munich</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-05 09:08:19</th>\n",
       "      <td>java-ii/dependency-management</td>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>79.220.63.16</td>\n",
       "      <td>79220</td>\n",
       "      <td>6316</td>\n",
       "      <td>Munich</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-05 09:08:24</th>\n",
       "      <td>java-ii/junit-tdd</td>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>79.220.63.16</td>\n",
       "      <td>79220</td>\n",
       "      <td>6316</td>\n",
       "      <td>Munich</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-05 09:08:28</th>\n",
       "      <td>search/search_index.json</td>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>79.220.63.16</td>\n",
       "      <td>79220</td>\n",
       "      <td>6316</td>\n",
       "      <td>Munich</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-05 09:10:04</th>\n",
       "      <td>java-i/control-statements-and-loops</td>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>79.220.63.16</td>\n",
       "      <td>79220</td>\n",
       "      <td>6316</td>\n",
       "      <td>Munich</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                request_method  user_id  \\\n",
       "ds                                                                        \n",
       "2020-10-05 09:02:56                              javascript-ii       12   \n",
       "2020-10-05 09:03:04                               javascript-i       12   \n",
       "2020-10-05 09:03:08                  javascript-i/introduction       12   \n",
       "2020-10-05 09:03:12  javascript-i/introduction/primitive-types       12   \n",
       "2020-10-05 09:03:16        javascript-i/introduction/operators       12   \n",
       "...                                                        ...      ...   \n",
       "2020-10-05 09:08:16                            java-ii/file-io       12   \n",
       "2020-10-05 09:08:19              java-ii/dependency-management       12   \n",
       "2020-10-05 09:08:24                          java-ii/junit-tdd       12   \n",
       "2020-10-05 09:08:28                   search/search_index.json       12   \n",
       "2020-10-05 09:10:04        java-i/control-statements-and-loops       12   \n",
       "\n",
       "                    cohort_id            ip network  host    city  country  \n",
       "ds                                                                          \n",
       "2020-10-05 09:02:56        22  79.220.63.16   79220  6316  Munich  Germany  \n",
       "2020-10-05 09:03:04        22  79.220.63.16   79220  6316  Munich  Germany  \n",
       "2020-10-05 09:03:08        22  79.220.63.16   79220  6316  Munich  Germany  \n",
       "2020-10-05 09:03:12        22  79.220.63.16   79220  6316  Munich  Germany  \n",
       "2020-10-05 09:03:16        22  79.220.63.16   79220  6316  Munich  Germany  \n",
       "...                       ...           ...     ...   ...     ...      ...  \n",
       "2020-10-05 09:08:16        22  79.220.63.16   79220  6316  Munich  Germany  \n",
       "2020-10-05 09:08:19        22  79.220.63.16   79220  6316  Munich  Germany  \n",
       "2020-10-05 09:08:24        22  79.220.63.16   79220  6316  Munich  Germany  \n",
       "2020-10-05 09:08:28        22  79.220.63.16   79220  6316  Munich  Germany  \n",
       "2020-10-05 09:10:04        22  79.220.63.16   79220  6316  Munich  Germany  \n",
       "\n",
       "[62 rows x 8 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#add these to bad ips df\n",
    "bad_ips = darden_df[(darden_df.ip == '79.220.63.16') | \n",
    "                    (darden_df.ip == '79.220.63.16')]\n",
    "bad_ips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "request_method      0\n",
       "user_id             0\n",
       "cohort_id           0\n",
       "ip                  0\n",
       "network             0\n",
       "host                0\n",
       "city              185\n",
       "country             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "darden_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>request_method</th>\n",
       "      <th>user_id</th>\n",
       "      <th>cohort_id</th>\n",
       "      <th>ip</th>\n",
       "      <th>network</th>\n",
       "      <th>host</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ds</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-07-13 10:14:49</th>\n",
       "      <td>spring/fundamentals/views</td>\n",
       "      <td>615</td>\n",
       "      <td>56</td>\n",
       "      <td>107.242.125.32</td>\n",
       "      <td>107242</td>\n",
       "      <td>12532</td>\n",
       "      <td>None</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-13 11:19:32</th>\n",
       "      <td>html-css/elements</td>\n",
       "      <td>615</td>\n",
       "      <td>56</td>\n",
       "      <td>107.242.125.32</td>\n",
       "      <td>107242</td>\n",
       "      <td>12532</td>\n",
       "      <td>None</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-13 11:19:46</th>\n",
       "      <td>html-css/forms</td>\n",
       "      <td>615</td>\n",
       "      <td>56</td>\n",
       "      <td>107.242.125.32</td>\n",
       "      <td>107242</td>\n",
       "      <td>12532</td>\n",
       "      <td>None</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-13 11:20:03</th>\n",
       "      <td>html-css/css-i</td>\n",
       "      <td>615</td>\n",
       "      <td>56</td>\n",
       "      <td>107.242.125.32</td>\n",
       "      <td>107242</td>\n",
       "      <td>12532</td>\n",
       "      <td>None</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-13 11:20:04</th>\n",
       "      <td>html-css/css-i/introduction</td>\n",
       "      <td>615</td>\n",
       "      <td>56</td>\n",
       "      <td>107.242.125.32</td>\n",
       "      <td>107242</td>\n",
       "      <td>12532</td>\n",
       "      <td>None</td>\n",
       "      <td>United States</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02 13:33:07</th>\n",
       "      <td>jquery</td>\n",
       "      <td>769</td>\n",
       "      <td>62</td>\n",
       "      <td>98.39.66.72</td>\n",
       "      <td>9839</td>\n",
       "      <td>6672</td>\n",
       "      <td>None</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02 13:33:12</th>\n",
       "      <td>javascript-i</td>\n",
       "      <td>769</td>\n",
       "      <td>62</td>\n",
       "      <td>98.39.66.72</td>\n",
       "      <td>9839</td>\n",
       "      <td>6672</td>\n",
       "      <td>None</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02 13:33:16</th>\n",
       "      <td>javascript-i/bom-and-dom</td>\n",
       "      <td>769</td>\n",
       "      <td>62</td>\n",
       "      <td>98.39.66.72</td>\n",
       "      <td>9839</td>\n",
       "      <td>6672</td>\n",
       "      <td>None</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02 13:33:21</th>\n",
       "      <td>javascript-i/bom-and-dom/bom</td>\n",
       "      <td>769</td>\n",
       "      <td>62</td>\n",
       "      <td>98.39.66.72</td>\n",
       "      <td>9839</td>\n",
       "      <td>6672</td>\n",
       "      <td>None</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02 13:33:58</th>\n",
       "      <td>examples/javascript/defuse-the-bom.html</td>\n",
       "      <td>769</td>\n",
       "      <td>62</td>\n",
       "      <td>98.39.66.72</td>\n",
       "      <td>9839</td>\n",
       "      <td>6672</td>\n",
       "      <td>None</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>185 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              request_method  user_id  \\\n",
       "ds                                                                      \n",
       "2020-07-13 10:14:49                spring/fundamentals/views      615   \n",
       "2020-07-13 11:19:32                        html-css/elements      615   \n",
       "2020-07-13 11:19:46                           html-css/forms      615   \n",
       "2020-07-13 11:20:03                           html-css/css-i      615   \n",
       "2020-07-13 11:20:04              html-css/css-i/introduction      615   \n",
       "...                                                      ...      ...   \n",
       "2020-11-02 13:33:07                                   jquery      769   \n",
       "2020-11-02 13:33:12                             javascript-i      769   \n",
       "2020-11-02 13:33:16                 javascript-i/bom-and-dom      769   \n",
       "2020-11-02 13:33:21             javascript-i/bom-and-dom/bom      769   \n",
       "2020-11-02 13:33:58  examples/javascript/defuse-the-bom.html      769   \n",
       "\n",
       "                    cohort_id              ip network   host  city  \\\n",
       "ds                                                                   \n",
       "2020-07-13 10:14:49        56  107.242.125.32  107242  12532  None   \n",
       "2020-07-13 11:19:32        56  107.242.125.32  107242  12532  None   \n",
       "2020-07-13 11:19:46        56  107.242.125.32  107242  12532  None   \n",
       "2020-07-13 11:20:03        56  107.242.125.32  107242  12532  None   \n",
       "2020-07-13 11:20:04        56  107.242.125.32  107242  12532  None   \n",
       "...                       ...             ...     ...    ...   ...   \n",
       "2020-11-02 13:33:07        62     98.39.66.72    9839   6672  None   \n",
       "2020-11-02 13:33:12        62     98.39.66.72    9839   6672  None   \n",
       "2020-11-02 13:33:16        62     98.39.66.72    9839   6672  None   \n",
       "2020-11-02 13:33:21        62     98.39.66.72    9839   6672  None   \n",
       "2020-11-02 13:33:58        62     98.39.66.72    9839   6672  None   \n",
       "\n",
       "                           country  \n",
       "ds                                  \n",
       "2020-07-13 10:14:49  United States  \n",
       "2020-07-13 11:19:32  United States  \n",
       "2020-07-13 11:19:46  United States  \n",
       "2020-07-13 11:20:03  United States  \n",
       "2020-07-13 11:20:04  United States  \n",
       "...                            ...  \n",
       "2020-11-02 13:33:07  United States  \n",
       "2020-11-02 13:33:12  United States  \n",
       "2020-11-02 13:33:16  United States  \n",
       "2020-11-02 13:33:21  United States  \n",
       "2020-11-02 13:33:58  United States  \n",
       "\n",
       "[185 rows x 8 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "darden_df[darden_df.city.isna()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Takeaways:** Using https://ipremoval.sms.symantec.com/ipr/lookup, I searched for ```107.242.125.32``` and it returned:\n",
    "\n",
    ">The IP Address 107.242.125.32 was found to have a negative reputation. Reasons for this assessment include:\n",
    ">* The host has been observed sending spam in a format that is similar to snow shoe spamming techniques.\n",
    ">* The host is unauthorized to send email directly to email servers.\n",
    "\n",
    "I ran IP address ```98.39.66.72``` as well, but it did not have any bad reputation.\n",
    "\n",
    "Regardless, I see that both of those IP addresses seem to hit each page quickly, in succession.\n",
    "\n",
    "**I can conclude that these two IP addresses are scrapper bots.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The scrapper bots seem to maybe be scrapping the Web Dev curriculum more than the Data Science curriculum, so I'm going to run a hypothesis test to test that. However, first I need to prepare the data for the test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "107.242.125.32     26\n",
       "98.39.66.72        22\n",
       "107.77.201.165     21\n",
       "174.128.181.39     19\n",
       "170.130.2.251      16\n",
       "31.13.189.90        9\n",
       "107.152.98.82       8\n",
       "174.246.201.223     8\n",
       "107.242.125.50      7\n",
       "12.31.71.58         7\n",
       "107.242.125.38      5\n",
       "208.30.107.160      4\n",
       "174.255.132.117     4\n",
       "206.214.78.211      4\n",
       "174.246.196.212     4\n",
       "176.113.73.30       3\n",
       "107.158.15.51       3\n",
       "3.209.254.178       3\n",
       "107.242.125.63      2\n",
       "72.173.98.235       2\n",
       "206.214.78.35       2\n",
       "174.128.180.196     1\n",
       "107.158.15.59       1\n",
       "174.244.67.71       1\n",
       "107.77.201.138      1\n",
       "107.77.201.226      1\n",
       "107.242.125.44      1\n",
       "Name: ip, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "darden_df[darden_df.city.isna()].ip.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I've also found that ```107.77.201.165```, ```174.128.181.39```, ```170.130.2.251```, ```174.246.201.223```,  have bad reputations according to the search as well.\n",
    "\n",
    "I wonder if 107 at the beginning of an IP address means something important.\n",
    "\n",
    "For ```170.130.2.251```, ```31.13.189.90```, ```107.152.98.82``` the search results included another reason for the bad reputation - snow shoe spamming.\n",
    "\n",
    "Turn out that ```12.31.71.58 ```, ```206.214.78.35``` and ```3.209.254.178 ``` are not even valid IPv4 addresses.\n",
    "\n",
    "Now that's enough manual lookup. Time to move on. I'll just store my proven suspicious IPs in a new dataframe named ```bad_ips```."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Add to bad IPs dataframe**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>request_method</th>\n",
       "      <th>user_id</th>\n",
       "      <th>cohort_id</th>\n",
       "      <th>ip</th>\n",
       "      <th>network</th>\n",
       "      <th>host</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>javascript-ii</td>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>79.220.63.16</td>\n",
       "      <td>79220</td>\n",
       "      <td>6316</td>\n",
       "      <td>Munich</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>javascript-ii</td>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>79.220.63.16</td>\n",
       "      <td>79220</td>\n",
       "      <td>6316</td>\n",
       "      <td>Munich</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>javascript-i</td>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>79.220.63.16</td>\n",
       "      <td>79220</td>\n",
       "      <td>6316</td>\n",
       "      <td>Munich</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>javascript-i/introduction</td>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>79.220.63.16</td>\n",
       "      <td>79220</td>\n",
       "      <td>6316</td>\n",
       "      <td>Munich</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>javascript-i/introduction/primitive-types</td>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>79.220.63.16</td>\n",
       "      <td>79220</td>\n",
       "      <td>6316</td>\n",
       "      <td>Munich</td>\n",
       "      <td>Germany</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>javascript-i/loops</td>\n",
       "      <td>127</td>\n",
       "      <td>23</td>\n",
       "      <td>174.128.181.39</td>\n",
       "      <td>174128</td>\n",
       "      <td>18139</td>\n",
       "      <td>None</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>javascript-i/arrays</td>\n",
       "      <td>127</td>\n",
       "      <td>23</td>\n",
       "      <td>174.128.181.39</td>\n",
       "      <td>174128</td>\n",
       "      <td>18139</td>\n",
       "      <td>None</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>javascript-i/arrays/manipulating</td>\n",
       "      <td>127</td>\n",
       "      <td>23</td>\n",
       "      <td>174.128.181.39</td>\n",
       "      <td>174128</td>\n",
       "      <td>18139</td>\n",
       "      <td>None</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>javascript-i/coffee-project</td>\n",
       "      <td>127</td>\n",
       "      <td>23</td>\n",
       "      <td>174.128.181.39</td>\n",
       "      <td>174128</td>\n",
       "      <td>18139</td>\n",
       "      <td>None</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>appendix/pair-programming</td>\n",
       "      <td>127</td>\n",
       "      <td>23</td>\n",
       "      <td>174.128.181.39</td>\n",
       "      <td>174128</td>\n",
       "      <td>18139</td>\n",
       "      <td>None</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>143 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                request_method  user_id cohort_id  \\\n",
       "0                                javascript-ii       12        22   \n",
       "1                                javascript-ii       12        22   \n",
       "2                                 javascript-i       12        22   \n",
       "3                    javascript-i/introduction       12        22   \n",
       "4    javascript-i/introduction/primitive-types       12        22   \n",
       "..                                         ...      ...       ...   \n",
       "138                         javascript-i/loops      127        23   \n",
       "139                        javascript-i/arrays      127        23   \n",
       "140           javascript-i/arrays/manipulating      127        23   \n",
       "141                javascript-i/coffee-project      127        23   \n",
       "142                  appendix/pair-programming      127        23   \n",
       "\n",
       "                 ip network   host    city        country  \n",
       "0      79.220.63.16   79220   6316  Munich        Germany  \n",
       "1      79.220.63.16   79220   6316  Munich        Germany  \n",
       "2      79.220.63.16   79220   6316  Munich        Germany  \n",
       "3      79.220.63.16   79220   6316  Munich        Germany  \n",
       "4      79.220.63.16   79220   6316  Munich        Germany  \n",
       "..              ...     ...    ...     ...            ...  \n",
       "138  174.128.181.39  174128  18139    None  United States  \n",
       "139  174.128.181.39  174128  18139    None  United States  \n",
       "140  174.128.181.39  174128  18139    None  United States  \n",
       "141  174.128.181.39  174128  18139    None  United States  \n",
       "142  174.128.181.39  174128  18139    None  United States  \n",
       "\n",
       "[143 rows x 8 columns]"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bad_ips_append = darden_df[(darden_df.ip == '107.77.201.165') | \n",
    "                    (darden_df.ip == '174.128.181.39') | \n",
    "                    (darden_df.ip == '170.130.2.251') | \n",
    "                    (darden_df.ip == '174.246.201.223') | \n",
    "                    (darden_df.ip == '170.130.2.251') | \n",
    "                    (darden_df.ip == '31.13.189.90') | \n",
    "                    (darden_df.ip == '107.152.98.82')]\n",
    "bad_ips = pd.merge(bad_ips, bad_ips_append, how='outer')\n",
    "bad_ips"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Where am I?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>request_method</th>\n",
       "      <th>user_id</th>\n",
       "      <th>cohort_id</th>\n",
       "      <th>ip</th>\n",
       "      <th>network</th>\n",
       "      <th>host</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ds</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-07-14 16:08:28</th>\n",
       "      <td>1-fundamentals/1.3-pipeline-demo</td>\n",
       "      <td>687</td>\n",
       "      <td>59</td>\n",
       "      <td>99.132.128.255</td>\n",
       "      <td>99132</td>\n",
       "      <td>128255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-14 16:09:31</th>\n",
       "      <td>1-fundamentals/1.1-intro-to-data-science</td>\n",
       "      <td>687</td>\n",
       "      <td>59</td>\n",
       "      <td>99.132.128.255</td>\n",
       "      <td>99132</td>\n",
       "      <td>128255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-14 16:09:32</th>\n",
       "      <td>1-fundamentals/modern-data-scientist.jpg</td>\n",
       "      <td>687</td>\n",
       "      <td>59</td>\n",
       "      <td>99.132.128.255</td>\n",
       "      <td>99132</td>\n",
       "      <td>128255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-14 16:09:32</th>\n",
       "      <td>1-fundamentals/AI-ML-DL-timeline.jpg</td>\n",
       "      <td>687</td>\n",
       "      <td>59</td>\n",
       "      <td>99.132.128.255</td>\n",
       "      <td>99132</td>\n",
       "      <td>128255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-14 16:32:47</th>\n",
       "      <td>1-fundamentals/1.2-data-science-pipeline</td>\n",
       "      <td>687</td>\n",
       "      <td>59</td>\n",
       "      <td>99.132.128.255</td>\n",
       "      <td>99132</td>\n",
       "      <td>128255</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-31 16:45:54</th>\n",
       "      <td>timeseries/modeling-lesson1</td>\n",
       "      <td>780</td>\n",
       "      <td>59</td>\n",
       "      <td>99.132.128.255</td>\n",
       "      <td>99132</td>\n",
       "      <td>128255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02 09:21:45</th>\n",
       "      <td>anomaly-detection/overview</td>\n",
       "      <td>780</td>\n",
       "      <td>59</td>\n",
       "      <td>99.132.128.255</td>\n",
       "      <td>99132</td>\n",
       "      <td>128255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02 09:21:45</th>\n",
       "      <td>anomaly-detection/AnomalyDetectionCartoon.jpeg</td>\n",
       "      <td>780</td>\n",
       "      <td>59</td>\n",
       "      <td>99.132.128.255</td>\n",
       "      <td>99132</td>\n",
       "      <td>128255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02 09:21:50</th>\n",
       "      <td>timeseries/modeling-lesson1</td>\n",
       "      <td>780</td>\n",
       "      <td>59</td>\n",
       "      <td>99.132.128.255</td>\n",
       "      <td>99132</td>\n",
       "      <td>128255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02 09:21:51</th>\n",
       "      <td>timeseries/modeling-lesson2</td>\n",
       "      <td>780</td>\n",
       "      <td>59</td>\n",
       "      <td>99.132.128.255</td>\n",
       "      <td>99132</td>\n",
       "      <td>128255</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>694 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     request_method  user_id  \\\n",
       "ds                                                                             \n",
       "2020-07-14 16:08:28                1-fundamentals/1.3-pipeline-demo      687   \n",
       "2020-07-14 16:09:31        1-fundamentals/1.1-intro-to-data-science      687   \n",
       "2020-07-14 16:09:32        1-fundamentals/modern-data-scientist.jpg      687   \n",
       "2020-07-14 16:09:32            1-fundamentals/AI-ML-DL-timeline.jpg      687   \n",
       "2020-07-14 16:32:47        1-fundamentals/1.2-data-science-pipeline      687   \n",
       "...                                                             ...      ...   \n",
       "2020-10-31 16:45:54                     timeseries/modeling-lesson1      780   \n",
       "2020-11-02 09:21:45                      anomaly-detection/overview      780   \n",
       "2020-11-02 09:21:45  anomaly-detection/AnomalyDetectionCartoon.jpeg      780   \n",
       "2020-11-02 09:21:50                     timeseries/modeling-lesson1      780   \n",
       "2020-11-02 09:21:51                     timeseries/modeling-lesson2      780   \n",
       "\n",
       "                    cohort_id              ip network    host  \n",
       "ds                                                             \n",
       "2020-07-14 16:08:28        59  99.132.128.255   99132  128255  \n",
       "2020-07-14 16:09:31        59  99.132.128.255   99132  128255  \n",
       "2020-07-14 16:09:32        59  99.132.128.255   99132  128255  \n",
       "2020-07-14 16:09:32        59  99.132.128.255   99132  128255  \n",
       "2020-07-14 16:32:47        59  99.132.128.255   99132  128255  \n",
       "...                       ...             ...     ...     ...  \n",
       "2020-10-31 16:45:54        59  99.132.128.255   99132  128255  \n",
       "2020-11-02 09:21:45        59  99.132.128.255   99132  128255  \n",
       "2020-11-02 09:21:45        59  99.132.128.255   99132  128255  \n",
       "2020-11-02 09:21:50        59  99.132.128.255   99132  128255  \n",
       "2020-11-02 09:21:51        59  99.132.128.255   99132  128255  \n",
       "\n",
       "[694 rows x 6 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kwame_df = df[df.ip == '99.132.128.255']\n",
    "kwame_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "San Antonio      16831\n",
       "Burnsville         986\n",
       "Austin             899\n",
       "Boerne             788\n",
       "Houston            411\n",
       "Helotes            111\n",
       "Converse            55\n",
       "Marble Falls        48\n",
       "Hondo               37\n",
       "Port Aransas        20\n",
       "Dallas              20\n",
       "Raleigh             17\n",
       "New Braunfels       16\n",
       "Killeen              7\n",
       "Cibolo               6\n",
       "Phoenix              3\n",
       "Omaha                3\n",
       "Harlingen            2\n",
       "Minneapolis          2\n",
       "Beaumont             1\n",
       "Marfa                1\n",
       "Tulsa                1\n",
       "Name: city, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "darden_students.city.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x137bd6910>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "kwame_df.resample('D').count().plot(legend=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29.4059\n",
      "-98.5078\n"
     ]
    }
   ],
   "source": [
    "response = reader.city('99.132.128.255')\n",
    "print(response.location.latitude)\n",
    "print(response.location.longitude)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Close, but not super precise."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Slice data by cohort/program"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "#program_id == 2\n",
    "web_dev = df[(df.cohort_id == '15') | \n",
    "             (df.cohort_id == '16') | \n",
    "             (df.cohort_id == '18') | \n",
    "             (df.cohort_id == '20') | \n",
    "             (df.cohort_id == '21') | \n",
    "             (df.cohort_id == '22') | \n",
    "             (df.cohort_id == '23') | \n",
    "             (df.cohort_id == '24') | \n",
    "             (df.cohort_id == '25') | \n",
    "             (df.cohort_id == '26') | \n",
    "             (df.cohort_id == '27') | \n",
    "             (df.cohort_id == '28') | \n",
    "             (df.cohort_id == '29') | \n",
    "             (df.cohort_id == '31') | \n",
    "             (df.cohort_id == '32') | \n",
    "             (df.cohort_id == '33') | \n",
    "             (df.cohort_id == '51') | \n",
    "             (df.cohort_id == '52') | \n",
    "             (df.cohort_id == '53') | \n",
    "             (df.cohort_id == '56') | \n",
    "             (df.cohort_id == '57') | \n",
    "             (df.cohort_id == '58') | \n",
    "             (df.cohort_id == '60') | \n",
    "             (df.cohort_id == '61') | \n",
    "             (df.cohort_id == '62') | \n",
    "             (df.cohort_id == '132')]\n",
    "\n",
    "#program_id == 3\n",
    "data_science = df[(df.cohort_id == '30') | \n",
    "                  (df.cohort_id == '34') | \n",
    "                  (df.cohort_id == '55') | \n",
    "                  (df.cohort_id == '59')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add city and country and state features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>request_method</th>\n",
       "      <th>user_id</th>\n",
       "      <th>cohort_id</th>\n",
       "      <th>ip</th>\n",
       "      <th>network</th>\n",
       "      <th>host</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ds</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-08-20 09:40:28</th>\n",
       "      <td>3-sql/1-mysql-overview</td>\n",
       "      <td>470</td>\n",
       "      <td>34</td>\n",
       "      <td>97.105.19.58</td>\n",
       "      <td>97105</td>\n",
       "      <td>1958</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-20 09:40:50</th>\n",
       "      <td>appendix/cli-git-overview</td>\n",
       "      <td>479</td>\n",
       "      <td>34</td>\n",
       "      <td>97.105.19.58</td>\n",
       "      <td>97105</td>\n",
       "      <td>1958</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-20 09:41:02</th>\n",
       "      <td>1-fundamentals/1.1-intro-to-data-science</td>\n",
       "      <td>469</td>\n",
       "      <td>34</td>\n",
       "      <td>97.105.19.58</td>\n",
       "      <td>97105</td>\n",
       "      <td>1958</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-20 09:41:02</th>\n",
       "      <td>1-fundamentals/modern-data-scientist.jpg</td>\n",
       "      <td>469</td>\n",
       "      <td>34</td>\n",
       "      <td>97.105.19.58</td>\n",
       "      <td>97105</td>\n",
       "      <td>1958</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-20 09:41:02</th>\n",
       "      <td>1-fundamentals/AI-ML-DL-timeline.jpg</td>\n",
       "      <td>469</td>\n",
       "      <td>34</td>\n",
       "      <td>97.105.19.58</td>\n",
       "      <td>97105</td>\n",
       "      <td>1958</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-20 09:41:28</th>\n",
       "      <td>1-fundamentals/1.1-intro-to-data-science</td>\n",
       "      <td>480</td>\n",
       "      <td>34</td>\n",
       "      <td>97.105.19.58</td>\n",
       "      <td>97105</td>\n",
       "      <td>1958</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-20 09:41:28</th>\n",
       "      <td>1-fundamentals/AI-ML-DL-timeline.jpg</td>\n",
       "      <td>480</td>\n",
       "      <td>34</td>\n",
       "      <td>97.105.19.58</td>\n",
       "      <td>97105</td>\n",
       "      <td>1958</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-20 09:41:28</th>\n",
       "      <td>1-fundamentals/modern-data-scientist.jpg</td>\n",
       "      <td>480</td>\n",
       "      <td>34</td>\n",
       "      <td>97.105.19.58</td>\n",
       "      <td>97105</td>\n",
       "      <td>1958</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-20 09:41:36</th>\n",
       "      <td>1-fundamentals/1.1-intro-to-data-science</td>\n",
       "      <td>482</td>\n",
       "      <td>34</td>\n",
       "      <td>97.105.19.58</td>\n",
       "      <td>97105</td>\n",
       "      <td>1958</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-20 09:41:36</th>\n",
       "      <td>1-fundamentals/AI-ML-DL-timeline.jpg</td>\n",
       "      <td>482</td>\n",
       "      <td>34</td>\n",
       "      <td>97.105.19.58</td>\n",
       "      <td>97105</td>\n",
       "      <td>1958</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-20 09:41:36</th>\n",
       "      <td>1-fundamentals/modern-data-scientist.jpg</td>\n",
       "      <td>482</td>\n",
       "      <td>34</td>\n",
       "      <td>97.105.19.58</td>\n",
       "      <td>97105</td>\n",
       "      <td>1958</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-20 09:42:10</th>\n",
       "      <td>1-fundamentals/1.1-intro-to-data-science</td>\n",
       "      <td>472</td>\n",
       "      <td>34</td>\n",
       "      <td>97.105.19.58</td>\n",
       "      <td>97105</td>\n",
       "      <td>1958</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               request_method  user_id  \\\n",
       "ds                                                                       \n",
       "2019-08-20 09:40:28                    3-sql/1-mysql-overview      470   \n",
       "2019-08-20 09:40:50                 appendix/cli-git-overview      479   \n",
       "2019-08-20 09:41:02  1-fundamentals/1.1-intro-to-data-science      469   \n",
       "2019-08-20 09:41:02  1-fundamentals/modern-data-scientist.jpg      469   \n",
       "2019-08-20 09:41:02      1-fundamentals/AI-ML-DL-timeline.jpg      469   \n",
       "2019-08-20 09:41:28  1-fundamentals/1.1-intro-to-data-science      480   \n",
       "2019-08-20 09:41:28      1-fundamentals/AI-ML-DL-timeline.jpg      480   \n",
       "2019-08-20 09:41:28  1-fundamentals/modern-data-scientist.jpg      480   \n",
       "2019-08-20 09:41:36  1-fundamentals/1.1-intro-to-data-science      482   \n",
       "2019-08-20 09:41:36      1-fundamentals/AI-ML-DL-timeline.jpg      482   \n",
       "2019-08-20 09:41:36  1-fundamentals/modern-data-scientist.jpg      482   \n",
       "2019-08-20 09:42:10  1-fundamentals/1.1-intro-to-data-science      472   \n",
       "\n",
       "                    cohort_id            ip network  host    city  \\\n",
       "ds                                                                  \n",
       "2019-08-20 09:40:28        34  97.105.19.58   97105  1958  Dallas   \n",
       "2019-08-20 09:40:50        34  97.105.19.58   97105  1958  Dallas   \n",
       "2019-08-20 09:41:02        34  97.105.19.58   97105  1958  Dallas   \n",
       "2019-08-20 09:41:02        34  97.105.19.58   97105  1958  Dallas   \n",
       "2019-08-20 09:41:02        34  97.105.19.58   97105  1958  Dallas   \n",
       "2019-08-20 09:41:28        34  97.105.19.58   97105  1958  Dallas   \n",
       "2019-08-20 09:41:28        34  97.105.19.58   97105  1958  Dallas   \n",
       "2019-08-20 09:41:28        34  97.105.19.58   97105  1958  Dallas   \n",
       "2019-08-20 09:41:36        34  97.105.19.58   97105  1958  Dallas   \n",
       "2019-08-20 09:41:36        34  97.105.19.58   97105  1958  Dallas   \n",
       "2019-08-20 09:41:36        34  97.105.19.58   97105  1958  Dallas   \n",
       "2019-08-20 09:42:10        34  97.105.19.58   97105  1958  Dallas   \n",
       "\n",
       "                           country  \n",
       "ds                                  \n",
       "2019-08-20 09:40:28  United States  \n",
       "2019-08-20 09:40:50  United States  \n",
       "2019-08-20 09:41:02  United States  \n",
       "2019-08-20 09:41:02  United States  \n",
       "2019-08-20 09:41:02  United States  \n",
       "2019-08-20 09:41:28  United States  \n",
       "2019-08-20 09:41:28  United States  \n",
       "2019-08-20 09:41:28  United States  \n",
       "2019-08-20 09:41:36  United States  \n",
       "2019-08-20 09:41:36  United States  \n",
       "2019-08-20 09:41:36  United States  \n",
       "2019-08-20 09:42:10  United States  "
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_science['city'] = data_science.apply(lambda data_science: add_city(data_science['ip']),axis=1)\n",
    "data_science['country'] = data_science.apply(lambda data_science: add_country(data_science['ip']),axis=1)\n",
    "data_science.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>request_method</th>\n",
       "      <th>user_id</th>\n",
       "      <th>cohort_id</th>\n",
       "      <th>ip</th>\n",
       "      <th>network</th>\n",
       "      <th>host</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ds</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-26 09:56:24</th>\n",
       "      <td>javascript-i/conditionals</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>97.105.19.61</td>\n",
       "      <td>97105</td>\n",
       "      <td>1961</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-26 09:56:41</th>\n",
       "      <td>javascript-i/loops</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>97.105.19.61</td>\n",
       "      <td>97105</td>\n",
       "      <td>1961</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-26 09:56:46</th>\n",
       "      <td>javascript-i/conditionals</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>97.105.19.61</td>\n",
       "      <td>97105</td>\n",
       "      <td>1961</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-26 09:56:48</th>\n",
       "      <td>javascript-i/functions</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>97.105.19.61</td>\n",
       "      <td>97105</td>\n",
       "      <td>1961</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-26 09:56:59</th>\n",
       "      <td>javascript-i/loops</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>97.105.19.61</td>\n",
       "      <td>97105</td>\n",
       "      <td>1961</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-26 09:58:26</th>\n",
       "      <td>javascript-i/functions</td>\n",
       "      <td>4</td>\n",
       "      <td>22</td>\n",
       "      <td>97.105.19.61</td>\n",
       "      <td>97105</td>\n",
       "      <td>1961</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-26 09:59:22</th>\n",
       "      <td>mkdocs/search_index.json</td>\n",
       "      <td>5</td>\n",
       "      <td>22</td>\n",
       "      <td>97.105.19.61</td>\n",
       "      <td>97105</td>\n",
       "      <td>1961</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-26 10:00:02</th>\n",
       "      <td>javascript-i/introduction/working-with-data-ty...</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "      <td>97.105.19.61</td>\n",
       "      <td>97105</td>\n",
       "      <td>1961</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-26 10:00:39</th>\n",
       "      <td>javascript-i</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "      <td>97.105.19.61</td>\n",
       "      <td>97105</td>\n",
       "      <td>1961</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-26 10:00:44</th>\n",
       "      <td>javascript-i/introduction/working-with-data-ty...</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "      <td>97.105.19.61</td>\n",
       "      <td>97105</td>\n",
       "      <td>1961</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-26 10:00:49</th>\n",
       "      <td>javascript-i/introduction/variables</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "      <td>97.105.19.61</td>\n",
       "      <td>97105</td>\n",
       "      <td>1961</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-26 10:00:57</th>\n",
       "      <td>javascript-i/introduction/operators</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "      <td>97.105.19.61</td>\n",
       "      <td>97105</td>\n",
       "      <td>1961</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                        request_method  \\\n",
       "ds                                                                       \n",
       "2018-01-26 09:56:24                          javascript-i/conditionals   \n",
       "2018-01-26 09:56:41                                 javascript-i/loops   \n",
       "2018-01-26 09:56:46                          javascript-i/conditionals   \n",
       "2018-01-26 09:56:48                             javascript-i/functions   \n",
       "2018-01-26 09:56:59                                 javascript-i/loops   \n",
       "2018-01-26 09:58:26                             javascript-i/functions   \n",
       "2018-01-26 09:59:22                           mkdocs/search_index.json   \n",
       "2018-01-26 10:00:02  javascript-i/introduction/working-with-data-ty...   \n",
       "2018-01-26 10:00:39                                       javascript-i   \n",
       "2018-01-26 10:00:44  javascript-i/introduction/working-with-data-ty...   \n",
       "2018-01-26 10:00:49                javascript-i/introduction/variables   \n",
       "2018-01-26 10:00:57                javascript-i/introduction/operators   \n",
       "\n",
       "                     user_id cohort_id            ip network  host    city  \\\n",
       "ds                                                                           \n",
       "2018-01-26 09:56:24        2        22  97.105.19.61   97105  1961  Dallas   \n",
       "2018-01-26 09:56:41        2        22  97.105.19.61   97105  1961  Dallas   \n",
       "2018-01-26 09:56:46        3        22  97.105.19.61   97105  1961  Dallas   \n",
       "2018-01-26 09:56:48        3        22  97.105.19.61   97105  1961  Dallas   \n",
       "2018-01-26 09:56:59        2        22  97.105.19.61   97105  1961  Dallas   \n",
       "2018-01-26 09:58:26        4        22  97.105.19.61   97105  1961  Dallas   \n",
       "2018-01-26 09:59:22        5        22  97.105.19.61   97105  1961  Dallas   \n",
       "2018-01-26 10:00:02        6        22  97.105.19.61   97105  1961  Dallas   \n",
       "2018-01-26 10:00:39        6        22  97.105.19.61   97105  1961  Dallas   \n",
       "2018-01-26 10:00:44        6        22  97.105.19.61   97105  1961  Dallas   \n",
       "2018-01-26 10:00:49        6        22  97.105.19.61   97105  1961  Dallas   \n",
       "2018-01-26 10:00:57        6        22  97.105.19.61   97105  1961  Dallas   \n",
       "\n",
       "                           country  \n",
       "ds                                  \n",
       "2018-01-26 09:56:24  United States  \n",
       "2018-01-26 09:56:41  United States  \n",
       "2018-01-26 09:56:46  United States  \n",
       "2018-01-26 09:56:48  United States  \n",
       "2018-01-26 09:56:59  United States  \n",
       "2018-01-26 09:58:26  United States  \n",
       "2018-01-26 09:59:22  United States  \n",
       "2018-01-26 10:00:02  United States  \n",
       "2018-01-26 10:00:39  United States  \n",
       "2018-01-26 10:00:44  United States  \n",
       "2018-01-26 10:00:49  United States  \n",
       "2018-01-26 10:00:57  United States  "
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "web_dev['city'] = web_dev.apply(lambda web_dev: add_city(web_dev['ip']),axis=1)\n",
    "web_dev['country'] = web_dev.apply(lambda web_dev: add_country(web_dev['ip']),axis=1)\n",
    "web_dev.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "San Antonio      16831\n",
       "Burnsville         986\n",
       "Austin             899\n",
       "Boerne             788\n",
       "Houston            411\n",
       "Helotes            111\n",
       "Converse            55\n",
       "Marble Falls        48\n",
       "Hondo               37\n",
       "Port Aransas        20\n",
       "Dallas              20\n",
       "Raleigh             17\n",
       "New Braunfels       16\n",
       "Killeen              7\n",
       "Cibolo               6\n",
       "Phoenix              3\n",
       "Omaha                3\n",
       "Harlingen            2\n",
       "Minneapolis          2\n",
       "Beaumont             1\n",
       "Marfa                1\n",
       "Tulsa                1\n",
       "Name: city, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "darden_students.city.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add state feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_state(ip):\n",
    "    try:\n",
    "        response = reader.city(ip)\n",
    "        state_name = response.subdivisions.most_specific.name\n",
    "        return state_name\n",
    "    except:\n",
    "        return np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>request_method</th>\n",
       "      <th>user_id</th>\n",
       "      <th>cohort_id</th>\n",
       "      <th>ip</th>\n",
       "      <th>network</th>\n",
       "      <th>host</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ds</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-08-20 09:40:28</th>\n",
       "      <td>3-sql/1-mysql-overview</td>\n",
       "      <td>470</td>\n",
       "      <td>34</td>\n",
       "      <td>97.105.19.58</td>\n",
       "      <td>97105</td>\n",
       "      <td>1958</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>United States</td>\n",
       "      <td>Texas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-20 09:40:50</th>\n",
       "      <td>appendix/cli-git-overview</td>\n",
       "      <td>479</td>\n",
       "      <td>34</td>\n",
       "      <td>97.105.19.58</td>\n",
       "      <td>97105</td>\n",
       "      <td>1958</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>United States</td>\n",
       "      <td>Texas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-20 09:41:02</th>\n",
       "      <td>1-fundamentals/1.1-intro-to-data-science</td>\n",
       "      <td>469</td>\n",
       "      <td>34</td>\n",
       "      <td>97.105.19.58</td>\n",
       "      <td>97105</td>\n",
       "      <td>1958</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>United States</td>\n",
       "      <td>Texas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-20 09:41:02</th>\n",
       "      <td>1-fundamentals/modern-data-scientist.jpg</td>\n",
       "      <td>469</td>\n",
       "      <td>34</td>\n",
       "      <td>97.105.19.58</td>\n",
       "      <td>97105</td>\n",
       "      <td>1958</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>United States</td>\n",
       "      <td>Texas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-20 09:41:02</th>\n",
       "      <td>1-fundamentals/AI-ML-DL-timeline.jpg</td>\n",
       "      <td>469</td>\n",
       "      <td>34</td>\n",
       "      <td>97.105.19.58</td>\n",
       "      <td>97105</td>\n",
       "      <td>1958</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>United States</td>\n",
       "      <td>Texas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-20 09:41:28</th>\n",
       "      <td>1-fundamentals/1.1-intro-to-data-science</td>\n",
       "      <td>480</td>\n",
       "      <td>34</td>\n",
       "      <td>97.105.19.58</td>\n",
       "      <td>97105</td>\n",
       "      <td>1958</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>United States</td>\n",
       "      <td>Texas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-20 09:41:28</th>\n",
       "      <td>1-fundamentals/AI-ML-DL-timeline.jpg</td>\n",
       "      <td>480</td>\n",
       "      <td>34</td>\n",
       "      <td>97.105.19.58</td>\n",
       "      <td>97105</td>\n",
       "      <td>1958</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>United States</td>\n",
       "      <td>Texas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-20 09:41:28</th>\n",
       "      <td>1-fundamentals/modern-data-scientist.jpg</td>\n",
       "      <td>480</td>\n",
       "      <td>34</td>\n",
       "      <td>97.105.19.58</td>\n",
       "      <td>97105</td>\n",
       "      <td>1958</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>United States</td>\n",
       "      <td>Texas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-20 09:41:36</th>\n",
       "      <td>1-fundamentals/1.1-intro-to-data-science</td>\n",
       "      <td>482</td>\n",
       "      <td>34</td>\n",
       "      <td>97.105.19.58</td>\n",
       "      <td>97105</td>\n",
       "      <td>1958</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>United States</td>\n",
       "      <td>Texas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-20 09:41:36</th>\n",
       "      <td>1-fundamentals/AI-ML-DL-timeline.jpg</td>\n",
       "      <td>482</td>\n",
       "      <td>34</td>\n",
       "      <td>97.105.19.58</td>\n",
       "      <td>97105</td>\n",
       "      <td>1958</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>United States</td>\n",
       "      <td>Texas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-20 09:41:36</th>\n",
       "      <td>1-fundamentals/modern-data-scientist.jpg</td>\n",
       "      <td>482</td>\n",
       "      <td>34</td>\n",
       "      <td>97.105.19.58</td>\n",
       "      <td>97105</td>\n",
       "      <td>1958</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>United States</td>\n",
       "      <td>Texas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-20 09:42:10</th>\n",
       "      <td>1-fundamentals/1.1-intro-to-data-science</td>\n",
       "      <td>472</td>\n",
       "      <td>34</td>\n",
       "      <td>97.105.19.58</td>\n",
       "      <td>97105</td>\n",
       "      <td>1958</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>United States</td>\n",
       "      <td>Texas</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               request_method  user_id  \\\n",
       "ds                                                                       \n",
       "2019-08-20 09:40:28                    3-sql/1-mysql-overview      470   \n",
       "2019-08-20 09:40:50                 appendix/cli-git-overview      479   \n",
       "2019-08-20 09:41:02  1-fundamentals/1.1-intro-to-data-science      469   \n",
       "2019-08-20 09:41:02  1-fundamentals/modern-data-scientist.jpg      469   \n",
       "2019-08-20 09:41:02      1-fundamentals/AI-ML-DL-timeline.jpg      469   \n",
       "2019-08-20 09:41:28  1-fundamentals/1.1-intro-to-data-science      480   \n",
       "2019-08-20 09:41:28      1-fundamentals/AI-ML-DL-timeline.jpg      480   \n",
       "2019-08-20 09:41:28  1-fundamentals/modern-data-scientist.jpg      480   \n",
       "2019-08-20 09:41:36  1-fundamentals/1.1-intro-to-data-science      482   \n",
       "2019-08-20 09:41:36      1-fundamentals/AI-ML-DL-timeline.jpg      482   \n",
       "2019-08-20 09:41:36  1-fundamentals/modern-data-scientist.jpg      482   \n",
       "2019-08-20 09:42:10  1-fundamentals/1.1-intro-to-data-science      472   \n",
       "\n",
       "                    cohort_id            ip network  host    city  \\\n",
       "ds                                                                  \n",
       "2019-08-20 09:40:28        34  97.105.19.58   97105  1958  Dallas   \n",
       "2019-08-20 09:40:50        34  97.105.19.58   97105  1958  Dallas   \n",
       "2019-08-20 09:41:02        34  97.105.19.58   97105  1958  Dallas   \n",
       "2019-08-20 09:41:02        34  97.105.19.58   97105  1958  Dallas   \n",
       "2019-08-20 09:41:02        34  97.105.19.58   97105  1958  Dallas   \n",
       "2019-08-20 09:41:28        34  97.105.19.58   97105  1958  Dallas   \n",
       "2019-08-20 09:41:28        34  97.105.19.58   97105  1958  Dallas   \n",
       "2019-08-20 09:41:28        34  97.105.19.58   97105  1958  Dallas   \n",
       "2019-08-20 09:41:36        34  97.105.19.58   97105  1958  Dallas   \n",
       "2019-08-20 09:41:36        34  97.105.19.58   97105  1958  Dallas   \n",
       "2019-08-20 09:41:36        34  97.105.19.58   97105  1958  Dallas   \n",
       "2019-08-20 09:42:10        34  97.105.19.58   97105  1958  Dallas   \n",
       "\n",
       "                           country  state  \n",
       "ds                                         \n",
       "2019-08-20 09:40:28  United States  Texas  \n",
       "2019-08-20 09:40:50  United States  Texas  \n",
       "2019-08-20 09:41:02  United States  Texas  \n",
       "2019-08-20 09:41:02  United States  Texas  \n",
       "2019-08-20 09:41:02  United States  Texas  \n",
       "2019-08-20 09:41:28  United States  Texas  \n",
       "2019-08-20 09:41:28  United States  Texas  \n",
       "2019-08-20 09:41:28  United States  Texas  \n",
       "2019-08-20 09:41:36  United States  Texas  \n",
       "2019-08-20 09:41:36  United States  Texas  \n",
       "2019-08-20 09:41:36  United States  Texas  \n",
       "2019-08-20 09:42:10  United States  Texas  "
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_science['state'] = data_science.apply(lambda data_science: add_state(data_science['ip']),axis=1)\n",
    "web_dev['state'] = web_dev.apply(lambda web_dev: add_state(web_dev['ip']),axis=1)\n",
    "data_science.head(12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Quick visualization**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_df = data_science.groupby('city').filter(lambda x : len(x)>100)\n",
    "plot_df.city.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_df['city'].value_counts().plot(kind='bar')\n",
    "plt.title('Which cities are accessing the Data Science curriculum?')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_df = web_dev.groupby('city').filter(lambda x : len(x)>900)\n",
    "plot_df.city.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_df['city'].value_counts().plot(kind='bar')\n",
    "plt.title('Which cities are accessing the Web Dev curriculum?')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_df = data_science.groupby('state').filter(lambda x : len(x)>0)\n",
    "plot_df.state.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_df['state'].drop(plot_df[plot_df.state == 'Texas'].index).value_counts().plot(kind='bar')\n",
    "plt.title('Which States (besides Texas) are accessing the Data Science curriculum?')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Which lesson appears to attract the most traffic consistently across cohorts (per program)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1-fundamentals/modern-data-scientist.jpg                  1559\n",
       "1-fundamentals/AI-ML-DL-timeline.jpg                      1553\n",
       "1-fundamentals/1.1-intro-to-data-science                  1530\n",
       "search/search_index.json                                  1329\n",
       "6-regression/1-overview                                   1122\n",
       "                                                          ... \n",
       "7-classification/confusion-matrix                            1\n",
       "A-clustering/project                                         1\n",
       "regression/ridge-regression                                  1\n",
       "spring/extra-features/error-pages/asdfasdf                   1\n",
       "anomaly-detection/time-series-anomaly-detection-part-1       1\n",
       "Name: request_method, Length: 595, dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_science.request_method.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Takeaway:** The most popular lesson for Data Science cohorts is Fundamentals (1.1): Intro to Data Science. Second to that is Regression: Overview."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "toc                                                      13161\n",
       "javascript-i                                             13112\n",
       "search/search_index.json                                 12107\n",
       "java-iii                                                 10083\n",
       "html-css                                                  9768\n",
       "                                                         ...  \n",
       "00_                                                          1\n",
       "curie-python-assessment.html                                 1\n",
       "content/php_iv/classes-and-objects-ii/overriding.html        1\n",
       "9_Anomaly_Detection                                          1\n",
       "extra-features/json-response                                 1\n",
       "Name: request_method, Length: 1798, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "web_dev.request_method.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Takeaway:** The most popular lesson for Web Dev cohorts is TOC (not sure if that's actually a lesson or not), which has just a handful of hits more than the second most popular lesson, Javascript I."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Is there a cohort that referred to a lesson significantly more that other cohorts seemed to gloss over? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Five most popular lessons in cohort 30:\n",
      "\n",
      "Series([], Name: request_method, dtype: int64)\n",
      "\n",
      "---\n",
      "\n",
      "Five most popular lessons in cohort 34:\n",
      "\n",
      "1-fundamentals/modern-data-scientist.jpg    625\n",
      "1-fundamentals/AI-ML-DL-timeline.jpg        623\n",
      "1-fundamentals/1.1-intro-to-data-science    613\n",
      "search/search_index.json                    550\n",
      "6-regression/1-overview                     521\n",
      "Name: request_method, dtype: int64\n",
      "\n",
      "---\n",
      "\n",
      "Five most popular lessons in cohort 55:\n",
      "\n",
      "6-regression/1-overview                     594\n",
      "search/search_index.json                    479\n",
      "1-fundamentals/modern-data-scientist.jpg    467\n",
      "1-fundamentals/AI-ML-DL-timeline.jpg        464\n",
      "1-fundamentals/1.1-intro-to-data-science    460\n",
      "Name: request_method, dtype: int64\n",
      "\n",
      "---\n",
      "\n",
      "Five most popular lessons in cohort 59:\n",
      "\n",
      "classification/overview                     756\n",
      "classification/scale_features_or_not.svg    587\n",
      "sql/mysql-overview                          510\n",
      "1-fundamentals/modern-data-scientist.jpg    467\n",
      "1-fundamentals/AI-ML-DL-timeline.jpg        466\n",
      "Name: request_method, dtype: int64\n",
      "\n",
      "---\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# data science cohorts\n",
    "cohorts = ['30', '34', '55', '59']\n",
    "for cohort in cohorts:\n",
    "    print('Five most popular lessons in cohort', cohort + ':\\n')\n",
    "    print(data_science[data_science.cohort_id == cohort].request_method.value_counts().head(5))\n",
    "    print('\\n---\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cohort 30 (Ada) seems to not have accessed this curriculum, or the information is missing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bayes' most popular lessons were in Fundamentals and Regression.\n",
    "\n",
    "Curie's were Regression and Fundamentals (in that order).\n",
    "\n",
    "Darden's are (so far) Classification, SQL, and Fundamentals.\n",
    "\n",
    "\n",
    "**Takeaways:** Everyone looks at fundamentals a lot, Curie and Bayes both look at Regression a lot, and Darden is a bit different. We also use the search feature relatively less than previous cohorts."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Are there students who, when active, hardly access the curriculum? If so, what information do you have about these students? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cohort 30\n",
      "0\n",
      "---\n",
      "Cohort 34\n",
      "97\n",
      "---\n",
      "Cohort 55\n",
      "68\n",
      "---\n",
      "Cohort 59\n",
      "48\n",
      "---\n"
     ]
    }
   ],
   "source": [
    "for cohort in cohorts:\n",
    "    print('Cohort', cohort)\n",
    "    print(data_science[data_science.cohort_id == cohort].network.nunique())\n",
    "    print('---')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cohort 30\n",
      "0\n",
      "---\n",
      "Cohort 34\n",
      "23\n",
      "---\n",
      "Cohort 55\n",
      "20\n",
      "---\n",
      "Cohort 59\n",
      "28\n",
      "---\n"
     ]
    }
   ],
   "source": [
    "for cohort in cohorts:\n",
    "    print('Cohort', cohort)\n",
    "    print(data_science[data_science.cohort_id == cohort].user_id.nunique())\n",
    "    print('---')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 23 unique ```user_id```s in Bayes, but I only count 20 students on the Alumni page.\n",
    "\n",
    "There are 20 unique ```user_id```s in Curie, but I only count 16 students on the Alumni page.\n",
    "\n",
    "Perhaps some of the user_ids are instructors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Access counts, Bayes')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_science[data_science.cohort_id == '34'].resample('D').count().plot(legend=False)\n",
    "plt.title('Access counts, Bayes')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_science[data_science.cohort_id == '55'].resample('D').count().plot(legend=False)\n",
    "plt.title('Access counts, Curie')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_science[data_science.cohort_id == '59'].resample('D').count().plot(legend=False)\n",
    "plt.title('Access counts, Darden')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cohort 30\n",
      "Series([], Name: user_id, dtype: int64)\n",
      "---\n",
      "Cohort 34\n",
      "485    2185\n",
      "475    1999\n",
      "476    1856\n",
      "479    1836\n",
      "478    1325\n",
      "482    1303\n",
      "471    1259\n",
      "469    1238\n",
      "473    1182\n",
      "481    1072\n",
      "358    1051\n",
      "466    1045\n",
      "480    1004\n",
      "484    1001\n",
      "483     717\n",
      "468     637\n",
      "472     631\n",
      "474     559\n",
      "467     515\n",
      "470     419\n",
      "477     413\n",
      "487      16\n",
      "650       7\n",
      "Name: user_id, dtype: int64\n",
      "---\n",
      "Cohort 55\n",
      "581    3061\n",
      "576    1515\n",
      "590    1478\n",
      "584    1312\n",
      "579    1220\n",
      "582    1213\n",
      "585    1115\n",
      "580    1096\n",
      "586     888\n",
      "589     776\n",
      "591     749\n",
      "617     743\n",
      "578     710\n",
      "588     628\n",
      "616     574\n",
      "587     535\n",
      "575     531\n",
      "583     431\n",
      "577     258\n",
      "787       1\n",
      "Name: user_id, dtype: int64\n",
      "---\n",
      "Cohort 59\n",
      "685    2335\n",
      "689    1512\n",
      "698    1412\n",
      "699    1336\n",
      "688    1235\n",
      "692    1226\n",
      "681    1147\n",
      "682    1008\n",
      "691     931\n",
      "678     904\n",
      "684     788\n",
      "680     784\n",
      "268     759\n",
      "696     753\n",
      "687     670\n",
      "695     662\n",
      "686     604\n",
      "690     567\n",
      "694     481\n",
      "739     458\n",
      "693     321\n",
      "683     148\n",
      "781      83\n",
      "783      52\n",
      "780      43\n",
      "785      29\n",
      "697      12\n",
      "679      10\n",
      "Name: user_id, dtype: int64\n",
      "---\n"
     ]
    }
   ],
   "source": [
    "for cohort in cohorts:\n",
    "    print('Cohort', cohort)\n",
    "    print(data_science[data_science.cohort_id == cohort].user_id.value_counts())\n",
    "    print('---')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cohort 30\n",
      "Series([], Name: network, dtype: int64)\n",
      "---\n",
      "Cohort 34\n",
      "97105     15793\n",
      "76211       722\n",
      "70226       683\n",
      "6711        530\n",
      "13650       499\n",
      "          ...  \n",
      "73163         1\n",
      "24153         1\n",
      "174197        1\n",
      "174246        1\n",
      "24227         1\n",
      "Name: network, Length: 97, dtype: int64\n",
      "---\n",
      "Cohort 55\n",
      "97105    4902\n",
      "72190    2225\n",
      "99126    1090\n",
      "70112     854\n",
      "64250     846\n",
      "         ... \n",
      "7142        1\n",
      "20936       1\n",
      "16289       1\n",
      "99203       1\n",
      "6397        1\n",
      "Name: network, Length: 68, dtype: int64\n",
      "---\n",
      "Cohort 59\n",
      "9976      2328\n",
      "13650     2279\n",
      "6711      1885\n",
      "968       1638\n",
      "72181     1431\n",
      "70125     1231\n",
      "108239    1230\n",
      "173174    1136\n",
      "6854       986\n",
      "70120      954\n",
      "6669       931\n",
      "76201      897\n",
      "99132      694\n",
      "162197     662\n",
      "72177      575\n",
      "2428       434\n",
      "99203      175\n",
      "192171     111\n",
      "7515       107\n",
      "184203      96\n",
      "17258       64\n",
      "986         55\n",
      "24173       48\n",
      "10448       45\n",
      "104219      37\n",
      "10777       34\n",
      "70117       31\n",
      "173123      25\n",
      "76186       20\n",
      "173149      18\n",
      "10713       17\n",
      "72191       16\n",
      "173127      13\n",
      "184226      10\n",
      "206193      10\n",
      "70114        9\n",
      "72178        7\n",
      "1297         7\n",
      "71146        6\n",
      "174255       4\n",
      "72206        3\n",
      "192198       2\n",
      "72128        2\n",
      "184223       2\n",
      "173173       2\n",
      "16271        1\n",
      "174244       1\n",
      "12132        1\n",
      "Name: network, dtype: int64\n",
      "---\n"
     ]
    }
   ],
   "source": [
    "for cohort in cohorts:\n",
    "    print('Cohort', cohort)\n",
    "    print(data_science[data_science.cohort_id == cohort].network.value_counts())\n",
    "    print('---')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Students with low number of curriculum access logs**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>request_method</th>\n",
       "      <th>user_id</th>\n",
       "      <th>cohort_id</th>\n",
       "      <th>ip</th>\n",
       "      <th>network</th>\n",
       "      <th>host</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ds</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-08-30 12:02:11</th>\n",
       "      <td>appendix/cli/1-intro</td>\n",
       "      <td>487</td>\n",
       "      <td>34</td>\n",
       "      <td>97.105.19.58</td>\n",
       "      <td>97105</td>\n",
       "      <td>1958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-30 12:11:10</th>\n",
       "      <td>appendix/cli/2-listing-files</td>\n",
       "      <td>487</td>\n",
       "      <td>34</td>\n",
       "      <td>97.105.19.58</td>\n",
       "      <td>97105</td>\n",
       "      <td>1958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-30 13:37:40</th>\n",
       "      <td>appendix/cli/0-overview</td>\n",
       "      <td>487</td>\n",
       "      <td>34</td>\n",
       "      <td>97.105.19.58</td>\n",
       "      <td>97105</td>\n",
       "      <td>1958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-30 13:37:58</th>\n",
       "      <td>appendix/cli-git-overview</td>\n",
       "      <td>487</td>\n",
       "      <td>34</td>\n",
       "      <td>97.105.19.58</td>\n",
       "      <td>97105</td>\n",
       "      <td>1958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-30 14:12:22</th>\n",
       "      <td>appendix/cli/3-file-paths</td>\n",
       "      <td>487</td>\n",
       "      <td>34</td>\n",
       "      <td>97.105.19.58</td>\n",
       "      <td>97105</td>\n",
       "      <td>1958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-30 14:19:45</th>\n",
       "      <td>appendix/cli/4-navigating-the-filesystem</td>\n",
       "      <td>487</td>\n",
       "      <td>34</td>\n",
       "      <td>97.105.19.58</td>\n",
       "      <td>97105</td>\n",
       "      <td>1958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-30 14:32:22</th>\n",
       "      <td>appendix/cli/5-creating-files-and-directories</td>\n",
       "      <td>487</td>\n",
       "      <td>34</td>\n",
       "      <td>97.105.19.58</td>\n",
       "      <td>97105</td>\n",
       "      <td>1958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-30 14:38:57</th>\n",
       "      <td>appendix/cli/4-navigating-the-filesystem</td>\n",
       "      <td>487</td>\n",
       "      <td>34</td>\n",
       "      <td>97.105.19.58</td>\n",
       "      <td>97105</td>\n",
       "      <td>1958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-30 14:59:46</th>\n",
       "      <td>appendix/cli/5-creating-files-and-directories</td>\n",
       "      <td>487</td>\n",
       "      <td>34</td>\n",
       "      <td>97.105.19.58</td>\n",
       "      <td>97105</td>\n",
       "      <td>1958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-30 15:15:06</th>\n",
       "      <td>appendix/cli/6-moving-files</td>\n",
       "      <td>487</td>\n",
       "      <td>34</td>\n",
       "      <td>97.105.19.58</td>\n",
       "      <td>97105</td>\n",
       "      <td>1958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-30 15:17:17</th>\n",
       "      <td>appendix/cli/5-creating-files-and-directories</td>\n",
       "      <td>487</td>\n",
       "      <td>34</td>\n",
       "      <td>97.105.19.58</td>\n",
       "      <td>97105</td>\n",
       "      <td>1958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-30 15:20:51</th>\n",
       "      <td>appendix/cli/5-creating-files-and-directories</td>\n",
       "      <td>487</td>\n",
       "      <td>34</td>\n",
       "      <td>97.105.19.58</td>\n",
       "      <td>97105</td>\n",
       "      <td>1958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-30 15:21:02</th>\n",
       "      <td>appendix/cli/5-creating-files-and-directories</td>\n",
       "      <td>487</td>\n",
       "      <td>34</td>\n",
       "      <td>97.105.19.58</td>\n",
       "      <td>97105</td>\n",
       "      <td>1958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-30 15:21:16</th>\n",
       "      <td>appendix/cli/4-navigating-the-filesystem</td>\n",
       "      <td>487</td>\n",
       "      <td>34</td>\n",
       "      <td>97.105.19.58</td>\n",
       "      <td>97105</td>\n",
       "      <td>1958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-30 15:21:58</th>\n",
       "      <td>appendix/cli/6-moving-files</td>\n",
       "      <td>487</td>\n",
       "      <td>34</td>\n",
       "      <td>97.105.19.58</td>\n",
       "      <td>97105</td>\n",
       "      <td>1958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-30 15:43:38</th>\n",
       "      <td>appendix/cli/6-moving-files</td>\n",
       "      <td>487</td>\n",
       "      <td>34</td>\n",
       "      <td>97.105.19.58</td>\n",
       "      <td>97105</td>\n",
       "      <td>1958</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    request_method  user_id  \\\n",
       "ds                                                                            \n",
       "2019-08-30 12:02:11                           appendix/cli/1-intro      487   \n",
       "2019-08-30 12:11:10                   appendix/cli/2-listing-files      487   \n",
       "2019-08-30 13:37:40                        appendix/cli/0-overview      487   \n",
       "2019-08-30 13:37:58                      appendix/cli-git-overview      487   \n",
       "2019-08-30 14:12:22                      appendix/cli/3-file-paths      487   \n",
       "2019-08-30 14:19:45       appendix/cli/4-navigating-the-filesystem      487   \n",
       "2019-08-30 14:32:22  appendix/cli/5-creating-files-and-directories      487   \n",
       "2019-08-30 14:38:57       appendix/cli/4-navigating-the-filesystem      487   \n",
       "2019-08-30 14:59:46  appendix/cli/5-creating-files-and-directories      487   \n",
       "2019-08-30 15:15:06                    appendix/cli/6-moving-files      487   \n",
       "2019-08-30 15:17:17  appendix/cli/5-creating-files-and-directories      487   \n",
       "2019-08-30 15:20:51  appendix/cli/5-creating-files-and-directories      487   \n",
       "2019-08-30 15:21:02  appendix/cli/5-creating-files-and-directories      487   \n",
       "2019-08-30 15:21:16       appendix/cli/4-navigating-the-filesystem      487   \n",
       "2019-08-30 15:21:58                    appendix/cli/6-moving-files      487   \n",
       "2019-08-30 15:43:38                    appendix/cli/6-moving-files      487   \n",
       "\n",
       "                    cohort_id            ip network  host  \n",
       "ds                                                         \n",
       "2019-08-30 12:02:11        34  97.105.19.58   97105  1958  \n",
       "2019-08-30 12:11:10        34  97.105.19.58   97105  1958  \n",
       "2019-08-30 13:37:40        34  97.105.19.58   97105  1958  \n",
       "2019-08-30 13:37:58        34  97.105.19.58   97105  1958  \n",
       "2019-08-30 14:12:22        34  97.105.19.58   97105  1958  \n",
       "2019-08-30 14:19:45        34  97.105.19.58   97105  1958  \n",
       "2019-08-30 14:32:22        34  97.105.19.58   97105  1958  \n",
       "2019-08-30 14:38:57        34  97.105.19.58   97105  1958  \n",
       "2019-08-30 14:59:46        34  97.105.19.58   97105  1958  \n",
       "2019-08-30 15:15:06        34  97.105.19.58   97105  1958  \n",
       "2019-08-30 15:17:17        34  97.105.19.58   97105  1958  \n",
       "2019-08-30 15:20:51        34  97.105.19.58   97105  1958  \n",
       "2019-08-30 15:21:02        34  97.105.19.58   97105  1958  \n",
       "2019-08-30 15:21:16        34  97.105.19.58   97105  1958  \n",
       "2019-08-30 15:21:58        34  97.105.19.58   97105  1958  \n",
       "2019-08-30 15:43:38        34  97.105.19.58   97105  1958  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_science[data_science.user_id == 487]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Takeaways:** Student with user_id 487 in Bayes only ever accessed the appendix. IP ```97.105.19.58```."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "US\n",
      "United States\n",
      "75243\n",
      "Texas\n",
      "Dallas\n",
      "32.9103\n",
      "-96.7283\n"
     ]
    }
   ],
   "source": [
    "response = reader.city('97.105.19.58')\n",
    "print(response.country.iso_code)\n",
    "print(response.country.name)\n",
    "print(response.postal.code)\n",
    "print(response.subdivisions.most_specific.name)\n",
    "print(response.city.name)\n",
    "print(response.location.latitude)\n",
    "print(response.location.longitude)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's someone in Dallas, so perhaps is not a student, but staff. IP doesn't have a bad reputation at least."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>request_method</th>\n",
       "      <th>user_id</th>\n",
       "      <th>cohort_id</th>\n",
       "      <th>ip</th>\n",
       "      <th>network</th>\n",
       "      <th>host</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ds</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-05-07 10:35:52</th>\n",
       "      <td>appendix/professional-development/virtual-meet...</td>\n",
       "      <td>650</td>\n",
       "      <td>34</td>\n",
       "      <td>70.123.225.30</td>\n",
       "      <td>70123</td>\n",
       "      <td>22530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-25 10:19:01</th>\n",
       "      <td>appendix/cli-git-overview</td>\n",
       "      <td>650</td>\n",
       "      <td>34</td>\n",
       "      <td>70.123.225.30</td>\n",
       "      <td>70123</td>\n",
       "      <td>22530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-25 10:20:00</th>\n",
       "      <td>appendix/ds-environment-setup</td>\n",
       "      <td>650</td>\n",
       "      <td>34</td>\n",
       "      <td>70.123.225.30</td>\n",
       "      <td>70123</td>\n",
       "      <td>22530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-03 15:15:21</th>\n",
       "      <td>11-nlp/3-acquire</td>\n",
       "      <td>650</td>\n",
       "      <td>34</td>\n",
       "      <td>70.123.225.30</td>\n",
       "      <td>70123</td>\n",
       "      <td>22530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-06 11:41:32</th>\n",
       "      <td>appendix/ds-environment-setup</td>\n",
       "      <td>650</td>\n",
       "      <td>34</td>\n",
       "      <td>70.123.225.30</td>\n",
       "      <td>70123</td>\n",
       "      <td>22530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-09 13:18:23</th>\n",
       "      <td>python/intro-to-matplotlib</td>\n",
       "      <td>650</td>\n",
       "      <td>34</td>\n",
       "      <td>70.123.225.30</td>\n",
       "      <td>70123</td>\n",
       "      <td>22530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-09 14:06:52</th>\n",
       "      <td>python/intro-to-matplotlib</td>\n",
       "      <td>650</td>\n",
       "      <td>34</td>\n",
       "      <td>70.123.225.30</td>\n",
       "      <td>70123</td>\n",
       "      <td>22530</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                        request_method  \\\n",
       "ds                                                                       \n",
       "2020-05-07 10:35:52  appendix/professional-development/virtual-meet...   \n",
       "2020-06-25 10:19:01                          appendix/cli-git-overview   \n",
       "2020-06-25 10:20:00                      appendix/ds-environment-setup   \n",
       "2020-07-03 15:15:21                                   11-nlp/3-acquire   \n",
       "2020-07-06 11:41:32                      appendix/ds-environment-setup   \n",
       "2020-08-09 13:18:23                         python/intro-to-matplotlib   \n",
       "2020-08-09 14:06:52                         python/intro-to-matplotlib   \n",
       "\n",
       "                     user_id cohort_id             ip network   host  \n",
       "ds                                                                    \n",
       "2020-05-07 10:35:52      650        34  70.123.225.30   70123  22530  \n",
       "2020-06-25 10:19:01      650        34  70.123.225.30   70123  22530  \n",
       "2020-06-25 10:20:00      650        34  70.123.225.30   70123  22530  \n",
       "2020-07-03 15:15:21      650        34  70.123.225.30   70123  22530  \n",
       "2020-07-06 11:41:32      650        34  70.123.225.30   70123  22530  \n",
       "2020-08-09 13:18:23      650        34  70.123.225.30   70123  22530  \n",
       "2020-08-09 14:06:52      650        34  70.123.225.30   70123  22530  "
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_science[data_science.user_id == 650]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What user 650 accesses seems a little random, so I'm going to investigate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "US\n",
      "United States\n",
      "78108\n",
      "Texas\n",
      "Cibolo\n",
      "29.5738\n",
      "-98.2292\n"
     ]
    }
   ],
   "source": [
    "response = reader.city('70.123.225.30')\n",
    "print(response.country.iso_code)\n",
    "print(response.country.name)\n",
    "print(response.postal.code)\n",
    "print(response.subdivisions.most_specific.name)\n",
    "print(response.city.name)\n",
    "print(response.location.latitude)\n",
    "print(response.location.longitude)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The IP Address 70.123.225.30 was found to have a negative reputation. Reasons for this assessment include:\n",
    "> * The host has been observed sending spam in a format that is similar to snow shoe spamming techniques.\n",
    "> * The host is unauthorized to send email directly to email servers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>request_method</th>\n",
       "      <th>user_id</th>\n",
       "      <th>cohort_id</th>\n",
       "      <th>ip</th>\n",
       "      <th>network</th>\n",
       "      <th>host</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ds</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-10-29 22:08:03</th>\n",
       "      <td>appendix/interview_questions_students</td>\n",
       "      <td>787</td>\n",
       "      <td>55</td>\n",
       "      <td>99.126.113.140</td>\n",
       "      <td>99126</td>\n",
       "      <td>113140</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            request_method  user_id cohort_id  \\\n",
       "ds                                                                              \n",
       "2020-10-29 22:08:03  appendix/interview_questions_students      787        55   \n",
       "\n",
       "                                 ip network    host  \n",
       "ds                                                   \n",
       "2020-10-29 22:08:03  99.126.113.140   99126  113140  "
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_science[data_science.user_id == 787]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No bad reputation found and since user 787 is just looking at interview questions once, that seems fairly benign."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ds</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-10-26</th>\n",
       "      <td>6248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-27</th>\n",
       "      <td>14839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-28</th>\n",
       "      <td>3905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-29</th>\n",
       "      <td>10153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-30</th>\n",
       "      <td>5467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-31</th>\n",
       "      <td>3905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-01</th>\n",
       "      <td>3905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02</th>\n",
       "      <td>16401</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            user_id\n",
       "ds                 \n",
       "2020-10-26     6248\n",
       "2020-10-27    14839\n",
       "2020-10-28     3905\n",
       "2020-10-29    10153\n",
       "2020-10-30     5467\n",
       "2020-10-31     3905\n",
       "2020-11-01     3905\n",
       "2020-11-02    16401"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_science[data_science.user_id == 781].resample('D').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>request_method</th>\n",
       "      <th>user_id</th>\n",
       "      <th>cohort_id</th>\n",
       "      <th>ip</th>\n",
       "      <th>network</th>\n",
       "      <th>host</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ds</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-11-02 08:51:05</th>\n",
       "      <td>classification/overview</td>\n",
       "      <td>781</td>\n",
       "      <td>59</td>\n",
       "      <td>108.239.188.205</td>\n",
       "      <td>108239</td>\n",
       "      <td>188205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02 08:51:05</th>\n",
       "      <td>classification/scale_features_or_not.svg</td>\n",
       "      <td>781</td>\n",
       "      <td>59</td>\n",
       "      <td>108.239.188.205</td>\n",
       "      <td>108239</td>\n",
       "      <td>188205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02 08:51:07</th>\n",
       "      <td>anomaly-detection/overview</td>\n",
       "      <td>781</td>\n",
       "      <td>59</td>\n",
       "      <td>108.239.188.205</td>\n",
       "      <td>108239</td>\n",
       "      <td>188205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02 08:51:07</th>\n",
       "      <td>anomaly-detection/AnomalyDetectionCartoon.jpeg</td>\n",
       "      <td>781</td>\n",
       "      <td>59</td>\n",
       "      <td>108.239.188.205</td>\n",
       "      <td>108239</td>\n",
       "      <td>188205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02 09:25:40</th>\n",
       "      <td>classification/overview</td>\n",
       "      <td>781</td>\n",
       "      <td>59</td>\n",
       "      <td>108.239.188.205</td>\n",
       "      <td>108239</td>\n",
       "      <td>188205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02 09:25:41</th>\n",
       "      <td>classification/scale_features_or_not.svg</td>\n",
       "      <td>781</td>\n",
       "      <td>59</td>\n",
       "      <td>108.239.188.205</td>\n",
       "      <td>108239</td>\n",
       "      <td>188205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02 09:25:46</th>\n",
       "      <td>timeseries/modeling-lesson1</td>\n",
       "      <td>781</td>\n",
       "      <td>59</td>\n",
       "      <td>108.239.188.205</td>\n",
       "      <td>108239</td>\n",
       "      <td>188205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02 10:46:05</th>\n",
       "      <td>sql/mysql-overview</td>\n",
       "      <td>781</td>\n",
       "      <td>59</td>\n",
       "      <td>108.239.188.205</td>\n",
       "      <td>108239</td>\n",
       "      <td>188205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02 10:46:15</th>\n",
       "      <td>stats/correlation</td>\n",
       "      <td>781</td>\n",
       "      <td>59</td>\n",
       "      <td>108.239.188.205</td>\n",
       "      <td>108239</td>\n",
       "      <td>188205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02 11:40:07</th>\n",
       "      <td>classification/overview</td>\n",
       "      <td>781</td>\n",
       "      <td>59</td>\n",
       "      <td>108.239.188.205</td>\n",
       "      <td>108239</td>\n",
       "      <td>188205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02 11:40:08</th>\n",
       "      <td>classification/scale_features_or_not.svg</td>\n",
       "      <td>781</td>\n",
       "      <td>59</td>\n",
       "      <td>108.239.188.205</td>\n",
       "      <td>108239</td>\n",
       "      <td>188205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02 11:40:08</th>\n",
       "      <td>classification/scale_features_or_not.svg</td>\n",
       "      <td>781</td>\n",
       "      <td>59</td>\n",
       "      <td>108.239.188.205</td>\n",
       "      <td>108239</td>\n",
       "      <td>188205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02 11:40:13</th>\n",
       "      <td>timeseries/modeling-lesson1</td>\n",
       "      <td>781</td>\n",
       "      <td>59</td>\n",
       "      <td>108.239.188.205</td>\n",
       "      <td>108239</td>\n",
       "      <td>188205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02 12:33:14</th>\n",
       "      <td>timeseries/prep</td>\n",
       "      <td>781</td>\n",
       "      <td>59</td>\n",
       "      <td>108.239.188.205</td>\n",
       "      <td>108239</td>\n",
       "      <td>188205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02 12:33:32</th>\n",
       "      <td>timeseries/explore</td>\n",
       "      <td>781</td>\n",
       "      <td>59</td>\n",
       "      <td>108.239.188.205</td>\n",
       "      <td>108239</td>\n",
       "      <td>188205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02 12:33:48</th>\n",
       "      <td>timeseries/modeling-lesson2</td>\n",
       "      <td>781</td>\n",
       "      <td>59</td>\n",
       "      <td>108.239.188.205</td>\n",
       "      <td>108239</td>\n",
       "      <td>188205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02 12:34:43</th>\n",
       "      <td>sql/database-design</td>\n",
       "      <td>781</td>\n",
       "      <td>59</td>\n",
       "      <td>108.239.188.205</td>\n",
       "      <td>108239</td>\n",
       "      <td>188205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02 12:34:51</th>\n",
       "      <td>timeseries/working-with-time-series-data-with-...</td>\n",
       "      <td>781</td>\n",
       "      <td>59</td>\n",
       "      <td>108.239.188.205</td>\n",
       "      <td>108239</td>\n",
       "      <td>188205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02 12:35:06</th>\n",
       "      <td>timeseries/svr</td>\n",
       "      <td>781</td>\n",
       "      <td>59</td>\n",
       "      <td>108.239.188.205</td>\n",
       "      <td>108239</td>\n",
       "      <td>188205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02 12:35:28</th>\n",
       "      <td>timeseries/sarimax</td>\n",
       "      <td>781</td>\n",
       "      <td>59</td>\n",
       "      <td>108.239.188.205</td>\n",
       "      <td>108239</td>\n",
       "      <td>188205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02 12:35:43</th>\n",
       "      <td>anomaly-detection/time-series-anomaly-detectio...</td>\n",
       "      <td>781</td>\n",
       "      <td>59</td>\n",
       "      <td>108.239.188.205</td>\n",
       "      <td>108239</td>\n",
       "      <td>188205</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                        request_method  \\\n",
       "ds                                                                       \n",
       "2020-11-02 08:51:05                            classification/overview   \n",
       "2020-11-02 08:51:05           classification/scale_features_or_not.svg   \n",
       "2020-11-02 08:51:07                         anomaly-detection/overview   \n",
       "2020-11-02 08:51:07     anomaly-detection/AnomalyDetectionCartoon.jpeg   \n",
       "2020-11-02 09:25:40                            classification/overview   \n",
       "2020-11-02 09:25:41           classification/scale_features_or_not.svg   \n",
       "2020-11-02 09:25:46                        timeseries/modeling-lesson1   \n",
       "2020-11-02 10:46:05                                 sql/mysql-overview   \n",
       "2020-11-02 10:46:15                                  stats/correlation   \n",
       "2020-11-02 11:40:07                            classification/overview   \n",
       "2020-11-02 11:40:08           classification/scale_features_or_not.svg   \n",
       "2020-11-02 11:40:08           classification/scale_features_or_not.svg   \n",
       "2020-11-02 11:40:13                        timeseries/modeling-lesson1   \n",
       "2020-11-02 12:33:14                                    timeseries/prep   \n",
       "2020-11-02 12:33:32                                 timeseries/explore   \n",
       "2020-11-02 12:33:48                        timeseries/modeling-lesson2   \n",
       "2020-11-02 12:34:43                                sql/database-design   \n",
       "2020-11-02 12:34:51  timeseries/working-with-time-series-data-with-...   \n",
       "2020-11-02 12:35:06                                     timeseries/svr   \n",
       "2020-11-02 12:35:28                                 timeseries/sarimax   \n",
       "2020-11-02 12:35:43  anomaly-detection/time-series-anomaly-detectio...   \n",
       "\n",
       "                     user_id cohort_id               ip network    host  \n",
       "ds                                                                       \n",
       "2020-11-02 08:51:05      781        59  108.239.188.205  108239  188205  \n",
       "2020-11-02 08:51:05      781        59  108.239.188.205  108239  188205  \n",
       "2020-11-02 08:51:07      781        59  108.239.188.205  108239  188205  \n",
       "2020-11-02 08:51:07      781        59  108.239.188.205  108239  188205  \n",
       "2020-11-02 09:25:40      781        59  108.239.188.205  108239  188205  \n",
       "2020-11-02 09:25:41      781        59  108.239.188.205  108239  188205  \n",
       "2020-11-02 09:25:46      781        59  108.239.188.205  108239  188205  \n",
       "2020-11-02 10:46:05      781        59  108.239.188.205  108239  188205  \n",
       "2020-11-02 10:46:15      781        59  108.239.188.205  108239  188205  \n",
       "2020-11-02 11:40:07      781        59  108.239.188.205  108239  188205  \n",
       "2020-11-02 11:40:08      781        59  108.239.188.205  108239  188205  \n",
       "2020-11-02 11:40:08      781        59  108.239.188.205  108239  188205  \n",
       "2020-11-02 11:40:13      781        59  108.239.188.205  108239  188205  \n",
       "2020-11-02 12:33:14      781        59  108.239.188.205  108239  188205  \n",
       "2020-11-02 12:33:32      781        59  108.239.188.205  108239  188205  \n",
       "2020-11-02 12:33:48      781        59  108.239.188.205  108239  188205  \n",
       "2020-11-02 12:34:43      781        59  108.239.188.205  108239  188205  \n",
       "2020-11-02 12:34:51      781        59  108.239.188.205  108239  188205  \n",
       "2020-11-02 12:35:06      781        59  108.239.188.205  108239  188205  \n",
       "2020-11-02 12:35:28      781        59  108.239.188.205  108239  188205  \n",
       "2020-11-02 12:35:43      781        59  108.239.188.205  108239  188205  "
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = data_science['2020-10-26']\n",
    "x[x.user_id == 781]\n",
    "\n",
    "#x = data_science['2020-10-27']\n",
    "#x[x.user_id == 781]\n",
    "\n",
    "#x = data_science['2020-10-28']\n",
    "#x[x.user_id == 781]\n",
    "\n",
    "# Lots of time series and classification so far.\n",
    "\n",
    "#x = data_science['2020-10-29']\n",
    "#x[x.user_id == 781]\n",
    "\n",
    "#x = data_science['2020-10-30']\n",
    "#x[x.user_id == 781]\n",
    "\n",
    "#x = data_science['2020-10-30']\n",
    "#x[x.user_id == 781]\n",
    "\n",
    "#x = data_science['2020-10-31']\n",
    "#x[x.user_id == 781]\n",
    "\n",
    "#x = data_science['2020-11-01']\n",
    "#x[x.user_id == 781]\n",
    "\n",
    "x = data_science['2020-11-02']\n",
    "x[x.user_id == 781]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Takeaways:** User 781 isn't listed as having a bad reputation and looks like a real human student.\n",
    "\n",
    "I notice that they usually go on the curriculum mostly just at 3 different times a day, once around 8:30am to 9:30am, and then again around lunch, and then between ~2/3pm and 4/5pm.\n",
    "\n",
    "Perhaps this student doesn't usually do homework/reference the curriculum after class has ended for the day.\n",
    "\n",
    "However it's likely this student's data is incomplete (probably split between different IP addresses/user_ids) because it doesn't begin until 10-26."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>request_method</th>\n",
       "      <th>user_id</th>\n",
       "      <th>cohort_id</th>\n",
       "      <th>ip</th>\n",
       "      <th>network</th>\n",
       "      <th>host</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ds</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-10-25 14:34:13</th>\n",
       "      <td>classification/overview</td>\n",
       "      <td>783</td>\n",
       "      <td>59</td>\n",
       "      <td>70.120.16.59</td>\n",
       "      <td>70120</td>\n",
       "      <td>1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-25 14:34:13</th>\n",
       "      <td>classification/scale_features_or_not.svg</td>\n",
       "      <td>783</td>\n",
       "      <td>59</td>\n",
       "      <td>70.120.16.59</td>\n",
       "      <td>70120</td>\n",
       "      <td>1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-25 14:34:16</th>\n",
       "      <td>timeseries/project</td>\n",
       "      <td>783</td>\n",
       "      <td>59</td>\n",
       "      <td>70.120.16.59</td>\n",
       "      <td>70120</td>\n",
       "      <td>1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-25 14:36:54</th>\n",
       "      <td>timeseries/overview</td>\n",
       "      <td>783</td>\n",
       "      <td>59</td>\n",
       "      <td>70.120.16.59</td>\n",
       "      <td>70120</td>\n",
       "      <td>1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-25 14:40:18</th>\n",
       "      <td>timeseries/acquire</td>\n",
       "      <td>783</td>\n",
       "      <td>59</td>\n",
       "      <td>70.120.16.59</td>\n",
       "      <td>70120</td>\n",
       "      <td>1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-25 14:59:52</th>\n",
       "      <td>timeseries/working-with-time-series-data</td>\n",
       "      <td>783</td>\n",
       "      <td>59</td>\n",
       "      <td>70.120.16.59</td>\n",
       "      <td>70120</td>\n",
       "      <td>1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-27 07:44:50</th>\n",
       "      <td>timeseries/prep</td>\n",
       "      <td>783</td>\n",
       "      <td>59</td>\n",
       "      <td>70.120.16.59</td>\n",
       "      <td>70120</td>\n",
       "      <td>1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-27 07:49:46</th>\n",
       "      <td>timeseries/project</td>\n",
       "      <td>783</td>\n",
       "      <td>59</td>\n",
       "      <td>70.120.16.59</td>\n",
       "      <td>70120</td>\n",
       "      <td>1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-27 07:49:50</th>\n",
       "      <td>timeseries/explore</td>\n",
       "      <td>783</td>\n",
       "      <td>59</td>\n",
       "      <td>70.120.16.59</td>\n",
       "      <td>70120</td>\n",
       "      <td>1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-27 07:49:54</th>\n",
       "      <td>timeseries/prep</td>\n",
       "      <td>783</td>\n",
       "      <td>59</td>\n",
       "      <td>70.120.16.59</td>\n",
       "      <td>70120</td>\n",
       "      <td>1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-27 07:54:14</th>\n",
       "      <td>timeseries/working-with-time-series-data</td>\n",
       "      <td>783</td>\n",
       "      <td>59</td>\n",
       "      <td>70.120.16.59</td>\n",
       "      <td>70120</td>\n",
       "      <td>1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-27 10:14:35</th>\n",
       "      <td>timeseries/project</td>\n",
       "      <td>783</td>\n",
       "      <td>59</td>\n",
       "      <td>70.120.16.59</td>\n",
       "      <td>70120</td>\n",
       "      <td>1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-27 13:02:30</th>\n",
       "      <td>timeseries/working-with-time-series-data</td>\n",
       "      <td>783</td>\n",
       "      <td>59</td>\n",
       "      <td>70.120.16.59</td>\n",
       "      <td>70120</td>\n",
       "      <td>1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-27 13:02:40</th>\n",
       "      <td>timeseries/prep</td>\n",
       "      <td>783</td>\n",
       "      <td>59</td>\n",
       "      <td>70.120.16.59</td>\n",
       "      <td>70120</td>\n",
       "      <td>1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-27 13:45:35</th>\n",
       "      <td>timeseries/project</td>\n",
       "      <td>783</td>\n",
       "      <td>59</td>\n",
       "      <td>70.120.16.59</td>\n",
       "      <td>70120</td>\n",
       "      <td>1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-27 14:29:14</th>\n",
       "      <td>timeseries/explore</td>\n",
       "      <td>783</td>\n",
       "      <td>59</td>\n",
       "      <td>70.120.16.59</td>\n",
       "      <td>70120</td>\n",
       "      <td>1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-27 14:29:37</th>\n",
       "      <td>timeseries/modeling-lesson1</td>\n",
       "      <td>783</td>\n",
       "      <td>59</td>\n",
       "      <td>70.120.16.59</td>\n",
       "      <td>70120</td>\n",
       "      <td>1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-27 14:29:39</th>\n",
       "      <td>timeseries/modeling-lesson2</td>\n",
       "      <td>783</td>\n",
       "      <td>59</td>\n",
       "      <td>70.120.16.59</td>\n",
       "      <td>70120</td>\n",
       "      <td>1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-27 14:29:45</th>\n",
       "      <td>timeseries/modeling-lesson1</td>\n",
       "      <td>783</td>\n",
       "      <td>59</td>\n",
       "      <td>70.120.16.59</td>\n",
       "      <td>70120</td>\n",
       "      <td>1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-27 14:29:58</th>\n",
       "      <td>timeseries/prep</td>\n",
       "      <td>783</td>\n",
       "      <td>59</td>\n",
       "      <td>70.120.16.59</td>\n",
       "      <td>70120</td>\n",
       "      <td>1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-27 15:28:28</th>\n",
       "      <td>timeseries/working-with-time-series-data</td>\n",
       "      <td>783</td>\n",
       "      <td>59</td>\n",
       "      <td>70.120.16.59</td>\n",
       "      <td>70120</td>\n",
       "      <td>1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-27 16:06:58</th>\n",
       "      <td>timeseries/prep</td>\n",
       "      <td>783</td>\n",
       "      <td>59</td>\n",
       "      <td>70.120.16.59</td>\n",
       "      <td>70120</td>\n",
       "      <td>1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-27 16:22:04</th>\n",
       "      <td>timeseries/explore</td>\n",
       "      <td>783</td>\n",
       "      <td>59</td>\n",
       "      <td>70.120.16.59</td>\n",
       "      <td>70120</td>\n",
       "      <td>1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-28 15:15:00</th>\n",
       "      <td>timeseries/project</td>\n",
       "      <td>783</td>\n",
       "      <td>59</td>\n",
       "      <td>70.120.16.59</td>\n",
       "      <td>70120</td>\n",
       "      <td>1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-28 15:15:03</th>\n",
       "      <td>timeseries/project</td>\n",
       "      <td>783</td>\n",
       "      <td>59</td>\n",
       "      <td>70.120.16.59</td>\n",
       "      <td>70120</td>\n",
       "      <td>1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-28 15:25:44</th>\n",
       "      <td>timeseries/explore</td>\n",
       "      <td>783</td>\n",
       "      <td>59</td>\n",
       "      <td>70.120.16.59</td>\n",
       "      <td>70120</td>\n",
       "      <td>1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-29 07:41:17</th>\n",
       "      <td>timeseries/project</td>\n",
       "      <td>783</td>\n",
       "      <td>59</td>\n",
       "      <td>70.120.16.59</td>\n",
       "      <td>70120</td>\n",
       "      <td>1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-29 08:51:16</th>\n",
       "      <td>timeseries/explore</td>\n",
       "      <td>783</td>\n",
       "      <td>59</td>\n",
       "      <td>70.120.16.59</td>\n",
       "      <td>70120</td>\n",
       "      <td>1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-29 08:51:22</th>\n",
       "      <td>timeseries/prep</td>\n",
       "      <td>783</td>\n",
       "      <td>59</td>\n",
       "      <td>70.120.16.59</td>\n",
       "      <td>70120</td>\n",
       "      <td>1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-29 09:36:23</th>\n",
       "      <td>timeseries/explore</td>\n",
       "      <td>783</td>\n",
       "      <td>59</td>\n",
       "      <td>70.120.16.59</td>\n",
       "      <td>70120</td>\n",
       "      <td>1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-29 12:17:49</th>\n",
       "      <td>timeseries/project</td>\n",
       "      <td>783</td>\n",
       "      <td>59</td>\n",
       "      <td>70.120.16.59</td>\n",
       "      <td>70120</td>\n",
       "      <td>1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-29 14:24:47</th>\n",
       "      <td>timeseries/explore</td>\n",
       "      <td>783</td>\n",
       "      <td>59</td>\n",
       "      <td>70.120.16.59</td>\n",
       "      <td>70120</td>\n",
       "      <td>1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-29 14:24:49</th>\n",
       "      <td>timeseries/prep</td>\n",
       "      <td>783</td>\n",
       "      <td>59</td>\n",
       "      <td>70.120.16.59</td>\n",
       "      <td>70120</td>\n",
       "      <td>1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-30 07:36:33</th>\n",
       "      <td>timeseries/modeling-lesson1</td>\n",
       "      <td>783</td>\n",
       "      <td>59</td>\n",
       "      <td>70.120.16.59</td>\n",
       "      <td>70120</td>\n",
       "      <td>1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-30 07:36:34</th>\n",
       "      <td>timeseries/project</td>\n",
       "      <td>783</td>\n",
       "      <td>59</td>\n",
       "      <td>70.120.16.59</td>\n",
       "      <td>70120</td>\n",
       "      <td>1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-30 07:36:38</th>\n",
       "      <td>timeseries/modeling-lesson1</td>\n",
       "      <td>783</td>\n",
       "      <td>59</td>\n",
       "      <td>70.120.16.59</td>\n",
       "      <td>70120</td>\n",
       "      <td>1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-30 10:30:47</th>\n",
       "      <td>timeseries/modeling-lesson2</td>\n",
       "      <td>783</td>\n",
       "      <td>59</td>\n",
       "      <td>70.120.16.59</td>\n",
       "      <td>70120</td>\n",
       "      <td>1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-30 14:50:25</th>\n",
       "      <td>anomaly-detection/overview</td>\n",
       "      <td>783</td>\n",
       "      <td>59</td>\n",
       "      <td>70.120.16.59</td>\n",
       "      <td>70120</td>\n",
       "      <td>1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-30 14:50:25</th>\n",
       "      <td>anomaly-detection/AnomalyDetectionCartoon.jpeg</td>\n",
       "      <td>783</td>\n",
       "      <td>59</td>\n",
       "      <td>70.120.16.59</td>\n",
       "      <td>70120</td>\n",
       "      <td>1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-30 15:15:42</th>\n",
       "      <td>timeseries/modeling-lesson1</td>\n",
       "      <td>783</td>\n",
       "      <td>59</td>\n",
       "      <td>70.120.16.59</td>\n",
       "      <td>70120</td>\n",
       "      <td>1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-31 09:49:48</th>\n",
       "      <td>classification/overview</td>\n",
       "      <td>783</td>\n",
       "      <td>59</td>\n",
       "      <td>70.120.16.59</td>\n",
       "      <td>70120</td>\n",
       "      <td>1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-31 09:49:48</th>\n",
       "      <td>classification/scale_features_or_not.svg</td>\n",
       "      <td>783</td>\n",
       "      <td>59</td>\n",
       "      <td>70.120.16.59</td>\n",
       "      <td>70120</td>\n",
       "      <td>1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-31 09:50:02</th>\n",
       "      <td>timeseries/modeling-lesson2</td>\n",
       "      <td>783</td>\n",
       "      <td>59</td>\n",
       "      <td>70.120.16.59</td>\n",
       "      <td>70120</td>\n",
       "      <td>1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-31 16:02:41</th>\n",
       "      <td>timeseries/working-with-time-series-data</td>\n",
       "      <td>783</td>\n",
       "      <td>59</td>\n",
       "      <td>70.120.16.59</td>\n",
       "      <td>70120</td>\n",
       "      <td>1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-31 16:27:48</th>\n",
       "      <td>timeseries/modeling-lesson1</td>\n",
       "      <td>783</td>\n",
       "      <td>59</td>\n",
       "      <td>70.120.16.59</td>\n",
       "      <td>70120</td>\n",
       "      <td>1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-31 16:41:57</th>\n",
       "      <td>timeseries/working-with-time-series-data</td>\n",
       "      <td>783</td>\n",
       "      <td>59</td>\n",
       "      <td>70.120.16.59</td>\n",
       "      <td>70120</td>\n",
       "      <td>1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02 07:12:26</th>\n",
       "      <td>classification/overview</td>\n",
       "      <td>783</td>\n",
       "      <td>59</td>\n",
       "      <td>70.120.16.59</td>\n",
       "      <td>70120</td>\n",
       "      <td>1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02 07:12:26</th>\n",
       "      <td>classification/scale_features_or_not.svg</td>\n",
       "      <td>783</td>\n",
       "      <td>59</td>\n",
       "      <td>70.120.16.59</td>\n",
       "      <td>70120</td>\n",
       "      <td>1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02 07:12:29</th>\n",
       "      <td>timeseries/project</td>\n",
       "      <td>783</td>\n",
       "      <td>59</td>\n",
       "      <td>70.120.16.59</td>\n",
       "      <td>70120</td>\n",
       "      <td>1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02 07:44:48</th>\n",
       "      <td>timeseries/modeling-lesson2</td>\n",
       "      <td>783</td>\n",
       "      <td>59</td>\n",
       "      <td>70.120.16.59</td>\n",
       "      <td>70120</td>\n",
       "      <td>1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02 08:09:37</th>\n",
       "      <td>anomaly-detection/overview</td>\n",
       "      <td>783</td>\n",
       "      <td>59</td>\n",
       "      <td>70.120.16.59</td>\n",
       "      <td>70120</td>\n",
       "      <td>1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02 08:09:37</th>\n",
       "      <td>anomaly-detection/AnomalyDetectionCartoon.jpeg</td>\n",
       "      <td>783</td>\n",
       "      <td>59</td>\n",
       "      <td>70.120.16.59</td>\n",
       "      <td>70120</td>\n",
       "      <td>1659</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     request_method  user_id  \\\n",
       "ds                                                                             \n",
       "2020-10-25 14:34:13                         classification/overview      783   \n",
       "2020-10-25 14:34:13        classification/scale_features_or_not.svg      783   \n",
       "2020-10-25 14:34:16                              timeseries/project      783   \n",
       "2020-10-25 14:36:54                             timeseries/overview      783   \n",
       "2020-10-25 14:40:18                              timeseries/acquire      783   \n",
       "2020-10-25 14:59:52        timeseries/working-with-time-series-data      783   \n",
       "2020-10-27 07:44:50                                 timeseries/prep      783   \n",
       "2020-10-27 07:49:46                              timeseries/project      783   \n",
       "2020-10-27 07:49:50                              timeseries/explore      783   \n",
       "2020-10-27 07:49:54                                 timeseries/prep      783   \n",
       "2020-10-27 07:54:14        timeseries/working-with-time-series-data      783   \n",
       "2020-10-27 10:14:35                              timeseries/project      783   \n",
       "2020-10-27 13:02:30        timeseries/working-with-time-series-data      783   \n",
       "2020-10-27 13:02:40                                 timeseries/prep      783   \n",
       "2020-10-27 13:45:35                              timeseries/project      783   \n",
       "2020-10-27 14:29:14                              timeseries/explore      783   \n",
       "2020-10-27 14:29:37                     timeseries/modeling-lesson1      783   \n",
       "2020-10-27 14:29:39                     timeseries/modeling-lesson2      783   \n",
       "2020-10-27 14:29:45                     timeseries/modeling-lesson1      783   \n",
       "2020-10-27 14:29:58                                 timeseries/prep      783   \n",
       "2020-10-27 15:28:28        timeseries/working-with-time-series-data      783   \n",
       "2020-10-27 16:06:58                                 timeseries/prep      783   \n",
       "2020-10-27 16:22:04                              timeseries/explore      783   \n",
       "2020-10-28 15:15:00                              timeseries/project      783   \n",
       "2020-10-28 15:15:03                              timeseries/project      783   \n",
       "2020-10-28 15:25:44                              timeseries/explore      783   \n",
       "2020-10-29 07:41:17                              timeseries/project      783   \n",
       "2020-10-29 08:51:16                              timeseries/explore      783   \n",
       "2020-10-29 08:51:22                                 timeseries/prep      783   \n",
       "2020-10-29 09:36:23                              timeseries/explore      783   \n",
       "2020-10-29 12:17:49                              timeseries/project      783   \n",
       "2020-10-29 14:24:47                              timeseries/explore      783   \n",
       "2020-10-29 14:24:49                                 timeseries/prep      783   \n",
       "2020-10-30 07:36:33                     timeseries/modeling-lesson1      783   \n",
       "2020-10-30 07:36:34                              timeseries/project      783   \n",
       "2020-10-30 07:36:38                     timeseries/modeling-lesson1      783   \n",
       "2020-10-30 10:30:47                     timeseries/modeling-lesson2      783   \n",
       "2020-10-30 14:50:25                      anomaly-detection/overview      783   \n",
       "2020-10-30 14:50:25  anomaly-detection/AnomalyDetectionCartoon.jpeg      783   \n",
       "2020-10-30 15:15:42                     timeseries/modeling-lesson1      783   \n",
       "2020-10-31 09:49:48                         classification/overview      783   \n",
       "2020-10-31 09:49:48        classification/scale_features_or_not.svg      783   \n",
       "2020-10-31 09:50:02                     timeseries/modeling-lesson2      783   \n",
       "2020-10-31 16:02:41        timeseries/working-with-time-series-data      783   \n",
       "2020-10-31 16:27:48                     timeseries/modeling-lesson1      783   \n",
       "2020-10-31 16:41:57        timeseries/working-with-time-series-data      783   \n",
       "2020-11-02 07:12:26                         classification/overview      783   \n",
       "2020-11-02 07:12:26        classification/scale_features_or_not.svg      783   \n",
       "2020-11-02 07:12:29                              timeseries/project      783   \n",
       "2020-11-02 07:44:48                     timeseries/modeling-lesson2      783   \n",
       "2020-11-02 08:09:37                      anomaly-detection/overview      783   \n",
       "2020-11-02 08:09:37  anomaly-detection/AnomalyDetectionCartoon.jpeg      783   \n",
       "\n",
       "                    cohort_id            ip network  host  \n",
       "ds                                                         \n",
       "2020-10-25 14:34:13        59  70.120.16.59   70120  1659  \n",
       "2020-10-25 14:34:13        59  70.120.16.59   70120  1659  \n",
       "2020-10-25 14:34:16        59  70.120.16.59   70120  1659  \n",
       "2020-10-25 14:36:54        59  70.120.16.59   70120  1659  \n",
       "2020-10-25 14:40:18        59  70.120.16.59   70120  1659  \n",
       "2020-10-25 14:59:52        59  70.120.16.59   70120  1659  \n",
       "2020-10-27 07:44:50        59  70.120.16.59   70120  1659  \n",
       "2020-10-27 07:49:46        59  70.120.16.59   70120  1659  \n",
       "2020-10-27 07:49:50        59  70.120.16.59   70120  1659  \n",
       "2020-10-27 07:49:54        59  70.120.16.59   70120  1659  \n",
       "2020-10-27 07:54:14        59  70.120.16.59   70120  1659  \n",
       "2020-10-27 10:14:35        59  70.120.16.59   70120  1659  \n",
       "2020-10-27 13:02:30        59  70.120.16.59   70120  1659  \n",
       "2020-10-27 13:02:40        59  70.120.16.59   70120  1659  \n",
       "2020-10-27 13:45:35        59  70.120.16.59   70120  1659  \n",
       "2020-10-27 14:29:14        59  70.120.16.59   70120  1659  \n",
       "2020-10-27 14:29:37        59  70.120.16.59   70120  1659  \n",
       "2020-10-27 14:29:39        59  70.120.16.59   70120  1659  \n",
       "2020-10-27 14:29:45        59  70.120.16.59   70120  1659  \n",
       "2020-10-27 14:29:58        59  70.120.16.59   70120  1659  \n",
       "2020-10-27 15:28:28        59  70.120.16.59   70120  1659  \n",
       "2020-10-27 16:06:58        59  70.120.16.59   70120  1659  \n",
       "2020-10-27 16:22:04        59  70.120.16.59   70120  1659  \n",
       "2020-10-28 15:15:00        59  70.120.16.59   70120  1659  \n",
       "2020-10-28 15:15:03        59  70.120.16.59   70120  1659  \n",
       "2020-10-28 15:25:44        59  70.120.16.59   70120  1659  \n",
       "2020-10-29 07:41:17        59  70.120.16.59   70120  1659  \n",
       "2020-10-29 08:51:16        59  70.120.16.59   70120  1659  \n",
       "2020-10-29 08:51:22        59  70.120.16.59   70120  1659  \n",
       "2020-10-29 09:36:23        59  70.120.16.59   70120  1659  \n",
       "2020-10-29 12:17:49        59  70.120.16.59   70120  1659  \n",
       "2020-10-29 14:24:47        59  70.120.16.59   70120  1659  \n",
       "2020-10-29 14:24:49        59  70.120.16.59   70120  1659  \n",
       "2020-10-30 07:36:33        59  70.120.16.59   70120  1659  \n",
       "2020-10-30 07:36:34        59  70.120.16.59   70120  1659  \n",
       "2020-10-30 07:36:38        59  70.120.16.59   70120  1659  \n",
       "2020-10-30 10:30:47        59  70.120.16.59   70120  1659  \n",
       "2020-10-30 14:50:25        59  70.120.16.59   70120  1659  \n",
       "2020-10-30 14:50:25        59  70.120.16.59   70120  1659  \n",
       "2020-10-30 15:15:42        59  70.120.16.59   70120  1659  \n",
       "2020-10-31 09:49:48        59  70.120.16.59   70120  1659  \n",
       "2020-10-31 09:49:48        59  70.120.16.59   70120  1659  \n",
       "2020-10-31 09:50:02        59  70.120.16.59   70120  1659  \n",
       "2020-10-31 16:02:41        59  70.120.16.59   70120  1659  \n",
       "2020-10-31 16:27:48        59  70.120.16.59   70120  1659  \n",
       "2020-10-31 16:41:57        59  70.120.16.59   70120  1659  \n",
       "2020-11-02 07:12:26        59  70.120.16.59   70120  1659  \n",
       "2020-11-02 07:12:26        59  70.120.16.59   70120  1659  \n",
       "2020-11-02 07:12:29        59  70.120.16.59   70120  1659  \n",
       "2020-11-02 07:44:48        59  70.120.16.59   70120  1659  \n",
       "2020-11-02 08:09:37        59  70.120.16.59   70120  1659  \n",
       "2020-11-02 08:09:37        59  70.120.16.59   70120  1659  "
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_science[data_science.user_id == 783]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This user's low number are also due to the date starting late, at 10-25."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>request_method</th>\n",
       "      <th>user_id</th>\n",
       "      <th>cohort_id</th>\n",
       "      <th>ip</th>\n",
       "      <th>network</th>\n",
       "      <th>host</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ds</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-10-22 14:21:19</th>\n",
       "      <td>timeseries/overview</td>\n",
       "      <td>780</td>\n",
       "      <td>59</td>\n",
       "      <td>99.132.128.255</td>\n",
       "      <td>99132</td>\n",
       "      <td>128255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-22 15:35:22</th>\n",
       "      <td>timeseries/acquire</td>\n",
       "      <td>780</td>\n",
       "      <td>59</td>\n",
       "      <td>99.132.128.255</td>\n",
       "      <td>99132</td>\n",
       "      <td>128255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-22 18:40:09</th>\n",
       "      <td>regression/acquire-and-prep</td>\n",
       "      <td>780</td>\n",
       "      <td>59</td>\n",
       "      <td>99.132.128.255</td>\n",
       "      <td>99132</td>\n",
       "      <td>128255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-22 18:41:01</th>\n",
       "      <td>regression/split-and-scale</td>\n",
       "      <td>780</td>\n",
       "      <td>59</td>\n",
       "      <td>99.132.128.255</td>\n",
       "      <td>99132</td>\n",
       "      <td>128255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-22 18:42:11</th>\n",
       "      <td>regression/explore</td>\n",
       "      <td>780</td>\n",
       "      <td>59</td>\n",
       "      <td>99.132.128.255</td>\n",
       "      <td>99132</td>\n",
       "      <td>128255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-22 18:43:55</th>\n",
       "      <td>regression/evaluate</td>\n",
       "      <td>780</td>\n",
       "      <td>59</td>\n",
       "      <td>99.132.128.255</td>\n",
       "      <td>99132</td>\n",
       "      <td>128255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-22 18:44:54</th>\n",
       "      <td>regression/feature-engineering</td>\n",
       "      <td>780</td>\n",
       "      <td>59</td>\n",
       "      <td>99.132.128.255</td>\n",
       "      <td>99132</td>\n",
       "      <td>128255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-22 18:47:04</th>\n",
       "      <td>regression/model</td>\n",
       "      <td>780</td>\n",
       "      <td>59</td>\n",
       "      <td>99.132.128.255</td>\n",
       "      <td>99132</td>\n",
       "      <td>128255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-22 18:47:40</th>\n",
       "      <td>regression/project</td>\n",
       "      <td>780</td>\n",
       "      <td>59</td>\n",
       "      <td>99.132.128.255</td>\n",
       "      <td>99132</td>\n",
       "      <td>128255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-22 18:47:44</th>\n",
       "      <td>clustering/wrangle</td>\n",
       "      <td>780</td>\n",
       "      <td>59</td>\n",
       "      <td>99.132.128.255</td>\n",
       "      <td>99132</td>\n",
       "      <td>128255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-22 18:51:13</th>\n",
       "      <td>clustering/explore</td>\n",
       "      <td>780</td>\n",
       "      <td>59</td>\n",
       "      <td>99.132.128.255</td>\n",
       "      <td>99132</td>\n",
       "      <td>128255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-22 18:52:08</th>\n",
       "      <td>clustering/explore</td>\n",
       "      <td>780</td>\n",
       "      <td>59</td>\n",
       "      <td>99.132.128.255</td>\n",
       "      <td>99132</td>\n",
       "      <td>128255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-22 18:52:30</th>\n",
       "      <td>clustering/model</td>\n",
       "      <td>780</td>\n",
       "      <td>59</td>\n",
       "      <td>99.132.128.255</td>\n",
       "      <td>99132</td>\n",
       "      <td>128255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-23 17:12:48</th>\n",
       "      <td>timeseries/overview</td>\n",
       "      <td>780</td>\n",
       "      <td>59</td>\n",
       "      <td>99.132.128.255</td>\n",
       "      <td>99132</td>\n",
       "      <td>128255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-24 00:23:16</th>\n",
       "      <td>classification/logistic-regression</td>\n",
       "      <td>780</td>\n",
       "      <td>59</td>\n",
       "      <td>99.132.128.255</td>\n",
       "      <td>99132</td>\n",
       "      <td>128255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-24 12:30:10</th>\n",
       "      <td>appendix/data_science_community</td>\n",
       "      <td>780</td>\n",
       "      <td>59</td>\n",
       "      <td>99.132.128.255</td>\n",
       "      <td>99132</td>\n",
       "      <td>128255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-24 12:52:29</th>\n",
       "      <td>clustering/model</td>\n",
       "      <td>780</td>\n",
       "      <td>59</td>\n",
       "      <td>99.132.128.255</td>\n",
       "      <td>99132</td>\n",
       "      <td>128255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-24 19:26:29</th>\n",
       "      <td>clustering/explore</td>\n",
       "      <td>780</td>\n",
       "      <td>59</td>\n",
       "      <td>99.132.128.255</td>\n",
       "      <td>99132</td>\n",
       "      <td>128255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-24 19:26:41</th>\n",
       "      <td>clustering/wrangle</td>\n",
       "      <td>780</td>\n",
       "      <td>59</td>\n",
       "      <td>99.132.128.255</td>\n",
       "      <td>99132</td>\n",
       "      <td>128255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-24 19:27:01</th>\n",
       "      <td>clustering/explore</td>\n",
       "      <td>780</td>\n",
       "      <td>59</td>\n",
       "      <td>99.132.128.255</td>\n",
       "      <td>99132</td>\n",
       "      <td>128255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-25 19:40:07</th>\n",
       "      <td>timeseries/acquire</td>\n",
       "      <td>780</td>\n",
       "      <td>59</td>\n",
       "      <td>99.132.128.255</td>\n",
       "      <td>99132</td>\n",
       "      <td>128255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-26 12:02:48</th>\n",
       "      <td>anomaly-detection/overview</td>\n",
       "      <td>780</td>\n",
       "      <td>59</td>\n",
       "      <td>99.132.128.255</td>\n",
       "      <td>99132</td>\n",
       "      <td>128255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-26 12:02:48</th>\n",
       "      <td>anomaly-detection/AnomalyDetectionCartoon.jpeg</td>\n",
       "      <td>780</td>\n",
       "      <td>59</td>\n",
       "      <td>99.132.128.255</td>\n",
       "      <td>99132</td>\n",
       "      <td>128255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-26 12:06:14</th>\n",
       "      <td>timeseries/working-with-time-series-data</td>\n",
       "      <td>780</td>\n",
       "      <td>59</td>\n",
       "      <td>99.132.128.255</td>\n",
       "      <td>99132</td>\n",
       "      <td>128255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-27 09:29:18</th>\n",
       "      <td>timeseries/working-with-time-series-data</td>\n",
       "      <td>780</td>\n",
       "      <td>59</td>\n",
       "      <td>99.132.128.255</td>\n",
       "      <td>99132</td>\n",
       "      <td>128255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-27 14:55:16</th>\n",
       "      <td>timeseries/prep</td>\n",
       "      <td>780</td>\n",
       "      <td>59</td>\n",
       "      <td>99.132.128.255</td>\n",
       "      <td>99132</td>\n",
       "      <td>128255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-27 14:55:46</th>\n",
       "      <td>timeseries/acquire</td>\n",
       "      <td>780</td>\n",
       "      <td>59</td>\n",
       "      <td>99.132.128.255</td>\n",
       "      <td>99132</td>\n",
       "      <td>128255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-27 16:00:16</th>\n",
       "      <td>timeseries/acquire</td>\n",
       "      <td>780</td>\n",
       "      <td>59</td>\n",
       "      <td>99.132.128.255</td>\n",
       "      <td>99132</td>\n",
       "      <td>128255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-27 16:00:16</th>\n",
       "      <td>timeseries/acquire</td>\n",
       "      <td>780</td>\n",
       "      <td>59</td>\n",
       "      <td>99.132.128.255</td>\n",
       "      <td>99132</td>\n",
       "      <td>128255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-27 16:28:37</th>\n",
       "      <td>timeseries/working-with-time-series-data</td>\n",
       "      <td>780</td>\n",
       "      <td>59</td>\n",
       "      <td>99.132.128.255</td>\n",
       "      <td>99132</td>\n",
       "      <td>128255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-27 16:28:46</th>\n",
       "      <td>timeseries/prep</td>\n",
       "      <td>780</td>\n",
       "      <td>59</td>\n",
       "      <td>99.132.128.255</td>\n",
       "      <td>99132</td>\n",
       "      <td>128255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-27 16:53:00</th>\n",
       "      <td>timeseries/prep</td>\n",
       "      <td>780</td>\n",
       "      <td>59</td>\n",
       "      <td>99.132.128.255</td>\n",
       "      <td>99132</td>\n",
       "      <td>128255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-28 13:06:13</th>\n",
       "      <td>timeseries/prep</td>\n",
       "      <td>780</td>\n",
       "      <td>59</td>\n",
       "      <td>99.132.128.255</td>\n",
       "      <td>99132</td>\n",
       "      <td>128255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-28 22:45:33</th>\n",
       "      <td>python/overview</td>\n",
       "      <td>780</td>\n",
       "      <td>59</td>\n",
       "      <td>99.132.128.255</td>\n",
       "      <td>99132</td>\n",
       "      <td>128255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-28 22:45:38</th>\n",
       "      <td>python/intro-to-matplotlib</td>\n",
       "      <td>780</td>\n",
       "      <td>59</td>\n",
       "      <td>99.132.128.255</td>\n",
       "      <td>99132</td>\n",
       "      <td>128255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-29 10:17:18</th>\n",
       "      <td>timeseries/prep</td>\n",
       "      <td>780</td>\n",
       "      <td>59</td>\n",
       "      <td>99.132.128.255</td>\n",
       "      <td>99132</td>\n",
       "      <td>128255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-30 11:09:15</th>\n",
       "      <td>timeseries/explore</td>\n",
       "      <td>780</td>\n",
       "      <td>59</td>\n",
       "      <td>99.132.128.255</td>\n",
       "      <td>99132</td>\n",
       "      <td>128255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-30 13:57:37</th>\n",
       "      <td>timeseries/modeling-lesson1</td>\n",
       "      <td>780</td>\n",
       "      <td>59</td>\n",
       "      <td>99.132.128.255</td>\n",
       "      <td>99132</td>\n",
       "      <td>128255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-31 16:45:54</th>\n",
       "      <td>timeseries/modeling-lesson1</td>\n",
       "      <td>780</td>\n",
       "      <td>59</td>\n",
       "      <td>99.132.128.255</td>\n",
       "      <td>99132</td>\n",
       "      <td>128255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02 09:21:45</th>\n",
       "      <td>anomaly-detection/overview</td>\n",
       "      <td>780</td>\n",
       "      <td>59</td>\n",
       "      <td>99.132.128.255</td>\n",
       "      <td>99132</td>\n",
       "      <td>128255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02 09:21:45</th>\n",
       "      <td>anomaly-detection/AnomalyDetectionCartoon.jpeg</td>\n",
       "      <td>780</td>\n",
       "      <td>59</td>\n",
       "      <td>99.132.128.255</td>\n",
       "      <td>99132</td>\n",
       "      <td>128255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02 09:21:50</th>\n",
       "      <td>timeseries/modeling-lesson1</td>\n",
       "      <td>780</td>\n",
       "      <td>59</td>\n",
       "      <td>99.132.128.255</td>\n",
       "      <td>99132</td>\n",
       "      <td>128255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02 09:21:51</th>\n",
       "      <td>timeseries/modeling-lesson2</td>\n",
       "      <td>780</td>\n",
       "      <td>59</td>\n",
       "      <td>99.132.128.255</td>\n",
       "      <td>99132</td>\n",
       "      <td>128255</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     request_method  user_id  \\\n",
       "ds                                                                             \n",
       "2020-10-22 14:21:19                             timeseries/overview      780   \n",
       "2020-10-22 15:35:22                              timeseries/acquire      780   \n",
       "2020-10-22 18:40:09                     regression/acquire-and-prep      780   \n",
       "2020-10-22 18:41:01                      regression/split-and-scale      780   \n",
       "2020-10-22 18:42:11                              regression/explore      780   \n",
       "2020-10-22 18:43:55                             regression/evaluate      780   \n",
       "2020-10-22 18:44:54                  regression/feature-engineering      780   \n",
       "2020-10-22 18:47:04                                regression/model      780   \n",
       "2020-10-22 18:47:40                              regression/project      780   \n",
       "2020-10-22 18:47:44                              clustering/wrangle      780   \n",
       "2020-10-22 18:51:13                              clustering/explore      780   \n",
       "2020-10-22 18:52:08                              clustering/explore      780   \n",
       "2020-10-22 18:52:30                                clustering/model      780   \n",
       "2020-10-23 17:12:48                             timeseries/overview      780   \n",
       "2020-10-24 00:23:16              classification/logistic-regression      780   \n",
       "2020-10-24 12:30:10                 appendix/data_science_community      780   \n",
       "2020-10-24 12:52:29                                clustering/model      780   \n",
       "2020-10-24 19:26:29                              clustering/explore      780   \n",
       "2020-10-24 19:26:41                              clustering/wrangle      780   \n",
       "2020-10-24 19:27:01                              clustering/explore      780   \n",
       "2020-10-25 19:40:07                              timeseries/acquire      780   \n",
       "2020-10-26 12:02:48                      anomaly-detection/overview      780   \n",
       "2020-10-26 12:02:48  anomaly-detection/AnomalyDetectionCartoon.jpeg      780   \n",
       "2020-10-26 12:06:14        timeseries/working-with-time-series-data      780   \n",
       "2020-10-27 09:29:18        timeseries/working-with-time-series-data      780   \n",
       "2020-10-27 14:55:16                                 timeseries/prep      780   \n",
       "2020-10-27 14:55:46                              timeseries/acquire      780   \n",
       "2020-10-27 16:00:16                              timeseries/acquire      780   \n",
       "2020-10-27 16:00:16                              timeseries/acquire      780   \n",
       "2020-10-27 16:28:37        timeseries/working-with-time-series-data      780   \n",
       "2020-10-27 16:28:46                                 timeseries/prep      780   \n",
       "2020-10-27 16:53:00                                 timeseries/prep      780   \n",
       "2020-10-28 13:06:13                                 timeseries/prep      780   \n",
       "2020-10-28 22:45:33                                 python/overview      780   \n",
       "2020-10-28 22:45:38                      python/intro-to-matplotlib      780   \n",
       "2020-10-29 10:17:18                                 timeseries/prep      780   \n",
       "2020-10-30 11:09:15                              timeseries/explore      780   \n",
       "2020-10-30 13:57:37                     timeseries/modeling-lesson1      780   \n",
       "2020-10-31 16:45:54                     timeseries/modeling-lesson1      780   \n",
       "2020-11-02 09:21:45                      anomaly-detection/overview      780   \n",
       "2020-11-02 09:21:45  anomaly-detection/AnomalyDetectionCartoon.jpeg      780   \n",
       "2020-11-02 09:21:50                     timeseries/modeling-lesson1      780   \n",
       "2020-11-02 09:21:51                     timeseries/modeling-lesson2      780   \n",
       "\n",
       "                    cohort_id              ip network    host  \n",
       "ds                                                             \n",
       "2020-10-22 14:21:19        59  99.132.128.255   99132  128255  \n",
       "2020-10-22 15:35:22        59  99.132.128.255   99132  128255  \n",
       "2020-10-22 18:40:09        59  99.132.128.255   99132  128255  \n",
       "2020-10-22 18:41:01        59  99.132.128.255   99132  128255  \n",
       "2020-10-22 18:42:11        59  99.132.128.255   99132  128255  \n",
       "2020-10-22 18:43:55        59  99.132.128.255   99132  128255  \n",
       "2020-10-22 18:44:54        59  99.132.128.255   99132  128255  \n",
       "2020-10-22 18:47:04        59  99.132.128.255   99132  128255  \n",
       "2020-10-22 18:47:40        59  99.132.128.255   99132  128255  \n",
       "2020-10-22 18:47:44        59  99.132.128.255   99132  128255  \n",
       "2020-10-22 18:51:13        59  99.132.128.255   99132  128255  \n",
       "2020-10-22 18:52:08        59  99.132.128.255   99132  128255  \n",
       "2020-10-22 18:52:30        59  99.132.128.255   99132  128255  \n",
       "2020-10-23 17:12:48        59  99.132.128.255   99132  128255  \n",
       "2020-10-24 00:23:16        59  99.132.128.255   99132  128255  \n",
       "2020-10-24 12:30:10        59  99.132.128.255   99132  128255  \n",
       "2020-10-24 12:52:29        59  99.132.128.255   99132  128255  \n",
       "2020-10-24 19:26:29        59  99.132.128.255   99132  128255  \n",
       "2020-10-24 19:26:41        59  99.132.128.255   99132  128255  \n",
       "2020-10-24 19:27:01        59  99.132.128.255   99132  128255  \n",
       "2020-10-25 19:40:07        59  99.132.128.255   99132  128255  \n",
       "2020-10-26 12:02:48        59  99.132.128.255   99132  128255  \n",
       "2020-10-26 12:02:48        59  99.132.128.255   99132  128255  \n",
       "2020-10-26 12:06:14        59  99.132.128.255   99132  128255  \n",
       "2020-10-27 09:29:18        59  99.132.128.255   99132  128255  \n",
       "2020-10-27 14:55:16        59  99.132.128.255   99132  128255  \n",
       "2020-10-27 14:55:46        59  99.132.128.255   99132  128255  \n",
       "2020-10-27 16:00:16        59  99.132.128.255   99132  128255  \n",
       "2020-10-27 16:00:16        59  99.132.128.255   99132  128255  \n",
       "2020-10-27 16:28:37        59  99.132.128.255   99132  128255  \n",
       "2020-10-27 16:28:46        59  99.132.128.255   99132  128255  \n",
       "2020-10-27 16:53:00        59  99.132.128.255   99132  128255  \n",
       "2020-10-28 13:06:13        59  99.132.128.255   99132  128255  \n",
       "2020-10-28 22:45:33        59  99.132.128.255   99132  128255  \n",
       "2020-10-28 22:45:38        59  99.132.128.255   99132  128255  \n",
       "2020-10-29 10:17:18        59  99.132.128.255   99132  128255  \n",
       "2020-10-30 11:09:15        59  99.132.128.255   99132  128255  \n",
       "2020-10-30 13:57:37        59  99.132.128.255   99132  128255  \n",
       "2020-10-31 16:45:54        59  99.132.128.255   99132  128255  \n",
       "2020-11-02 09:21:45        59  99.132.128.255   99132  128255  \n",
       "2020-11-02 09:21:45        59  99.132.128.255   99132  128255  \n",
       "2020-11-02 09:21:50        59  99.132.128.255   99132  128255  \n",
       "2020-11-02 09:21:51        59  99.132.128.255   99132  128255  "
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_science[data_science.user_id == 780]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This user's low number are also due to the date starting late, at 10-22."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>request_method</th>\n",
       "      <th>user_id</th>\n",
       "      <th>cohort_id</th>\n",
       "      <th>ip</th>\n",
       "      <th>network</th>\n",
       "      <th>host</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ds</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-10-26 14:35:01</th>\n",
       "      <td>timeseries/acquire</td>\n",
       "      <td>785</td>\n",
       "      <td>59</td>\n",
       "      <td>72.181.127.233</td>\n",
       "      <td>72181</td>\n",
       "      <td>127233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-27 09:13:52</th>\n",
       "      <td>working-with-time-series-data</td>\n",
       "      <td>785</td>\n",
       "      <td>59</td>\n",
       "      <td>72.181.127.233</td>\n",
       "      <td>72181</td>\n",
       "      <td>127233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-27 09:13:55</th>\n",
       "      <td>working-with-time-series-data</td>\n",
       "      <td>785</td>\n",
       "      <td>59</td>\n",
       "      <td>72.181.127.233</td>\n",
       "      <td>72181</td>\n",
       "      <td>127233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-27 09:13:57</th>\n",
       "      <td>working-with-time-series-data</td>\n",
       "      <td>785</td>\n",
       "      <td>59</td>\n",
       "      <td>72.181.127.233</td>\n",
       "      <td>72181</td>\n",
       "      <td>127233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-27 09:14:02</th>\n",
       "      <td>working-with-time-series-data</td>\n",
       "      <td>785</td>\n",
       "      <td>59</td>\n",
       "      <td>72.181.127.233</td>\n",
       "      <td>72181</td>\n",
       "      <td>127233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-27 09:14:14</th>\n",
       "      <td>sql/mysql-overview</td>\n",
       "      <td>785</td>\n",
       "      <td>59</td>\n",
       "      <td>72.181.127.233</td>\n",
       "      <td>72181</td>\n",
       "      <td>127233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-27 09:14:14</th>\n",
       "      <td>classification/overview</td>\n",
       "      <td>785</td>\n",
       "      <td>59</td>\n",
       "      <td>72.181.127.233</td>\n",
       "      <td>72181</td>\n",
       "      <td>127233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-27 09:14:14</th>\n",
       "      <td>classification/scale_features_or_not.svg</td>\n",
       "      <td>785</td>\n",
       "      <td>59</td>\n",
       "      <td>72.181.127.233</td>\n",
       "      <td>72181</td>\n",
       "      <td>127233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-27 09:14:20</th>\n",
       "      <td>timeseries/overview</td>\n",
       "      <td>785</td>\n",
       "      <td>59</td>\n",
       "      <td>72.181.127.233</td>\n",
       "      <td>72181</td>\n",
       "      <td>127233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-27 13:36:29</th>\n",
       "      <td>timeseries/acquire</td>\n",
       "      <td>785</td>\n",
       "      <td>59</td>\n",
       "      <td>72.181.127.233</td>\n",
       "      <td>72181</td>\n",
       "      <td>127233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-27 13:37:23</th>\n",
       "      <td>timeseries/working-with-time-series-data</td>\n",
       "      <td>785</td>\n",
       "      <td>59</td>\n",
       "      <td>72.181.127.233</td>\n",
       "      <td>72181</td>\n",
       "      <td>127233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-27 13:41:33</th>\n",
       "      <td>timeseries/prep</td>\n",
       "      <td>785</td>\n",
       "      <td>59</td>\n",
       "      <td>72.181.127.233</td>\n",
       "      <td>72181</td>\n",
       "      <td>127233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-27 13:41:36</th>\n",
       "      <td>timeseries/project</td>\n",
       "      <td>785</td>\n",
       "      <td>59</td>\n",
       "      <td>72.181.127.233</td>\n",
       "      <td>72181</td>\n",
       "      <td>127233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-27 13:41:39</th>\n",
       "      <td>timeseries/explore</td>\n",
       "      <td>785</td>\n",
       "      <td>59</td>\n",
       "      <td>72.181.127.233</td>\n",
       "      <td>72181</td>\n",
       "      <td>127233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-27 13:42:20</th>\n",
       "      <td>timeseries/acquire</td>\n",
       "      <td>785</td>\n",
       "      <td>59</td>\n",
       "      <td>72.181.127.233</td>\n",
       "      <td>72181</td>\n",
       "      <td>127233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-29 10:37:41</th>\n",
       "      <td>timeseries/overview</td>\n",
       "      <td>785</td>\n",
       "      <td>59</td>\n",
       "      <td>72.181.127.233</td>\n",
       "      <td>72181</td>\n",
       "      <td>127233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-29 11:50:06</th>\n",
       "      <td>timeseries/acquire</td>\n",
       "      <td>785</td>\n",
       "      <td>59</td>\n",
       "      <td>24.173.208.242</td>\n",
       "      <td>24173</td>\n",
       "      <td>208242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-30 09:12:46</th>\n",
       "      <td>timeseries/explore</td>\n",
       "      <td>785</td>\n",
       "      <td>59</td>\n",
       "      <td>72.181.127.233</td>\n",
       "      <td>72181</td>\n",
       "      <td>127233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-30 10:02:52</th>\n",
       "      <td>timeseries/acquire</td>\n",
       "      <td>785</td>\n",
       "      <td>59</td>\n",
       "      <td>72.181.127.233</td>\n",
       "      <td>72181</td>\n",
       "      <td>127233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-30 10:04:08</th>\n",
       "      <td>timeseries/modeling-lesson1</td>\n",
       "      <td>785</td>\n",
       "      <td>59</td>\n",
       "      <td>72.181.127.233</td>\n",
       "      <td>72181</td>\n",
       "      <td>127233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-30 16:35:15</th>\n",
       "      <td>timeseries/acquire</td>\n",
       "      <td>785</td>\n",
       "      <td>59</td>\n",
       "      <td>72.181.127.233</td>\n",
       "      <td>72181</td>\n",
       "      <td>127233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02 09:09:29</th>\n",
       "      <td>classification/overview</td>\n",
       "      <td>785</td>\n",
       "      <td>59</td>\n",
       "      <td>72.181.127.233</td>\n",
       "      <td>72181</td>\n",
       "      <td>127233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02 09:09:30</th>\n",
       "      <td>classification/scale_features_or_not.svg</td>\n",
       "      <td>785</td>\n",
       "      <td>59</td>\n",
       "      <td>72.181.127.233</td>\n",
       "      <td>72181</td>\n",
       "      <td>127233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02 09:10:00</th>\n",
       "      <td>anomaly-detection/overview</td>\n",
       "      <td>785</td>\n",
       "      <td>59</td>\n",
       "      <td>72.181.127.233</td>\n",
       "      <td>72181</td>\n",
       "      <td>127233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02 09:10:04</th>\n",
       "      <td>anomaly-detection/AnomalyDetectionCartoon.jpeg</td>\n",
       "      <td>785</td>\n",
       "      <td>59</td>\n",
       "      <td>72.181.127.233</td>\n",
       "      <td>72181</td>\n",
       "      <td>127233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02 09:11:28</th>\n",
       "      <td>classification/overview</td>\n",
       "      <td>785</td>\n",
       "      <td>59</td>\n",
       "      <td>72.181.127.233</td>\n",
       "      <td>72181</td>\n",
       "      <td>127233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02 09:11:28</th>\n",
       "      <td>classification/scale_features_or_not.svg</td>\n",
       "      <td>785</td>\n",
       "      <td>59</td>\n",
       "      <td>72.181.127.233</td>\n",
       "      <td>72181</td>\n",
       "      <td>127233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02 09:11:34</th>\n",
       "      <td>timeseries/acquire</td>\n",
       "      <td>785</td>\n",
       "      <td>59</td>\n",
       "      <td>72.181.127.233</td>\n",
       "      <td>72181</td>\n",
       "      <td>127233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02 10:07:28</th>\n",
       "      <td>timeseries/overview</td>\n",
       "      <td>785</td>\n",
       "      <td>59</td>\n",
       "      <td>72.181.127.233</td>\n",
       "      <td>72181</td>\n",
       "      <td>127233</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     request_method  user_id  \\\n",
       "ds                                                                             \n",
       "2020-10-26 14:35:01                              timeseries/acquire      785   \n",
       "2020-10-27 09:13:52                   working-with-time-series-data      785   \n",
       "2020-10-27 09:13:55                   working-with-time-series-data      785   \n",
       "2020-10-27 09:13:57                   working-with-time-series-data      785   \n",
       "2020-10-27 09:14:02                   working-with-time-series-data      785   \n",
       "2020-10-27 09:14:14                              sql/mysql-overview      785   \n",
       "2020-10-27 09:14:14                         classification/overview      785   \n",
       "2020-10-27 09:14:14        classification/scale_features_or_not.svg      785   \n",
       "2020-10-27 09:14:20                             timeseries/overview      785   \n",
       "2020-10-27 13:36:29                              timeseries/acquire      785   \n",
       "2020-10-27 13:37:23        timeseries/working-with-time-series-data      785   \n",
       "2020-10-27 13:41:33                                 timeseries/prep      785   \n",
       "2020-10-27 13:41:36                              timeseries/project      785   \n",
       "2020-10-27 13:41:39                              timeseries/explore      785   \n",
       "2020-10-27 13:42:20                              timeseries/acquire      785   \n",
       "2020-10-29 10:37:41                             timeseries/overview      785   \n",
       "2020-10-29 11:50:06                              timeseries/acquire      785   \n",
       "2020-10-30 09:12:46                              timeseries/explore      785   \n",
       "2020-10-30 10:02:52                              timeseries/acquire      785   \n",
       "2020-10-30 10:04:08                     timeseries/modeling-lesson1      785   \n",
       "2020-10-30 16:35:15                              timeseries/acquire      785   \n",
       "2020-11-02 09:09:29                         classification/overview      785   \n",
       "2020-11-02 09:09:30        classification/scale_features_or_not.svg      785   \n",
       "2020-11-02 09:10:00                      anomaly-detection/overview      785   \n",
       "2020-11-02 09:10:04  anomaly-detection/AnomalyDetectionCartoon.jpeg      785   \n",
       "2020-11-02 09:11:28                         classification/overview      785   \n",
       "2020-11-02 09:11:28        classification/scale_features_or_not.svg      785   \n",
       "2020-11-02 09:11:34                              timeseries/acquire      785   \n",
       "2020-11-02 10:07:28                             timeseries/overview      785   \n",
       "\n",
       "                    cohort_id              ip network    host  \n",
       "ds                                                             \n",
       "2020-10-26 14:35:01        59  72.181.127.233   72181  127233  \n",
       "2020-10-27 09:13:52        59  72.181.127.233   72181  127233  \n",
       "2020-10-27 09:13:55        59  72.181.127.233   72181  127233  \n",
       "2020-10-27 09:13:57        59  72.181.127.233   72181  127233  \n",
       "2020-10-27 09:14:02        59  72.181.127.233   72181  127233  \n",
       "2020-10-27 09:14:14        59  72.181.127.233   72181  127233  \n",
       "2020-10-27 09:14:14        59  72.181.127.233   72181  127233  \n",
       "2020-10-27 09:14:14        59  72.181.127.233   72181  127233  \n",
       "2020-10-27 09:14:20        59  72.181.127.233   72181  127233  \n",
       "2020-10-27 13:36:29        59  72.181.127.233   72181  127233  \n",
       "2020-10-27 13:37:23        59  72.181.127.233   72181  127233  \n",
       "2020-10-27 13:41:33        59  72.181.127.233   72181  127233  \n",
       "2020-10-27 13:41:36        59  72.181.127.233   72181  127233  \n",
       "2020-10-27 13:41:39        59  72.181.127.233   72181  127233  \n",
       "2020-10-27 13:42:20        59  72.181.127.233   72181  127233  \n",
       "2020-10-29 10:37:41        59  72.181.127.233   72181  127233  \n",
       "2020-10-29 11:50:06        59  24.173.208.242   24173  208242  \n",
       "2020-10-30 09:12:46        59  72.181.127.233   72181  127233  \n",
       "2020-10-30 10:02:52        59  72.181.127.233   72181  127233  \n",
       "2020-10-30 10:04:08        59  72.181.127.233   72181  127233  \n",
       "2020-10-30 16:35:15        59  72.181.127.233   72181  127233  \n",
       "2020-11-02 09:09:29        59  72.181.127.233   72181  127233  \n",
       "2020-11-02 09:09:30        59  72.181.127.233   72181  127233  \n",
       "2020-11-02 09:10:00        59  72.181.127.233   72181  127233  \n",
       "2020-11-02 09:10:04        59  72.181.127.233   72181  127233  \n",
       "2020-11-02 09:11:28        59  72.181.127.233   72181  127233  \n",
       "2020-11-02 09:11:28        59  72.181.127.233   72181  127233  \n",
       "2020-11-02 09:11:34        59  72.181.127.233   72181  127233  \n",
       "2020-11-02 10:07:28        59  72.181.127.233   72181  127233  "
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_science[data_science.user_id == 785]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This user's low number are also due to the date starting late, at 10-22."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>request_method</th>\n",
       "      <th>user_id</th>\n",
       "      <th>cohort_id</th>\n",
       "      <th>ip</th>\n",
       "      <th>network</th>\n",
       "      <th>host</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ds</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-07-13 15:20:48</th>\n",
       "      <td>3-sql/1-mysql-overview</td>\n",
       "      <td>697</td>\n",
       "      <td>59</td>\n",
       "      <td>136.50.70.27</td>\n",
       "      <td>13650</td>\n",
       "      <td>7027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-13 15:20:50</th>\n",
       "      <td>1-fundamentals/1.1-intro-to-data-science</td>\n",
       "      <td>697</td>\n",
       "      <td>59</td>\n",
       "      <td>136.50.70.27</td>\n",
       "      <td>13650</td>\n",
       "      <td>7027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-13 15:20:50</th>\n",
       "      <td>1-fundamentals/modern-data-scientist.jpg</td>\n",
       "      <td>697</td>\n",
       "      <td>59</td>\n",
       "      <td>136.50.70.27</td>\n",
       "      <td>13650</td>\n",
       "      <td>7027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-13 15:20:50</th>\n",
       "      <td>1-fundamentals/AI-ML-DL-timeline.jpg</td>\n",
       "      <td>697</td>\n",
       "      <td>59</td>\n",
       "      <td>136.50.70.27</td>\n",
       "      <td>13650</td>\n",
       "      <td>7027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-13 15:20:59</th>\n",
       "      <td>1-fundamentals/1.2-data-science-pipeline</td>\n",
       "      <td>697</td>\n",
       "      <td>59</td>\n",
       "      <td>136.50.70.27</td>\n",
       "      <td>13650</td>\n",
       "      <td>7027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-13 15:20:59</th>\n",
       "      <td>1-fundamentals/DataToAction_v2.jpg</td>\n",
       "      <td>697</td>\n",
       "      <td>59</td>\n",
       "      <td>136.50.70.27</td>\n",
       "      <td>13650</td>\n",
       "      <td>7027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-13 15:21:01</th>\n",
       "      <td>1-fundamentals/1.1-intro-to-data-science</td>\n",
       "      <td>697</td>\n",
       "      <td>59</td>\n",
       "      <td>136.50.70.27</td>\n",
       "      <td>13650</td>\n",
       "      <td>7027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-13 15:21:02</th>\n",
       "      <td>1-fundamentals/AI-ML-DL-timeline.jpg</td>\n",
       "      <td>697</td>\n",
       "      <td>59</td>\n",
       "      <td>136.50.70.27</td>\n",
       "      <td>13650</td>\n",
       "      <td>7027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-13 15:21:02</th>\n",
       "      <td>1-fundamentals/modern-data-scientist.jpg</td>\n",
       "      <td>697</td>\n",
       "      <td>59</td>\n",
       "      <td>136.50.70.27</td>\n",
       "      <td>13650</td>\n",
       "      <td>7027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-13 15:21:12</th>\n",
       "      <td>2-storytelling/3-tableau</td>\n",
       "      <td>697</td>\n",
       "      <td>59</td>\n",
       "      <td>136.50.70.27</td>\n",
       "      <td>13650</td>\n",
       "      <td>7027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-13 15:21:16</th>\n",
       "      <td>2-storytelling/3.3-creating-charts</td>\n",
       "      <td>697</td>\n",
       "      <td>59</td>\n",
       "      <td>136.50.70.27</td>\n",
       "      <td>13650</td>\n",
       "      <td>7027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-13 15:26:39</th>\n",
       "      <td>4-python/8.4.1-pandas-overview</td>\n",
       "      <td>697</td>\n",
       "      <td>59</td>\n",
       "      <td>136.50.70.27</td>\n",
       "      <td>13650</td>\n",
       "      <td>7027</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               request_method  user_id  \\\n",
       "ds                                                                       \n",
       "2020-07-13 15:20:48                    3-sql/1-mysql-overview      697   \n",
       "2020-07-13 15:20:50  1-fundamentals/1.1-intro-to-data-science      697   \n",
       "2020-07-13 15:20:50  1-fundamentals/modern-data-scientist.jpg      697   \n",
       "2020-07-13 15:20:50      1-fundamentals/AI-ML-DL-timeline.jpg      697   \n",
       "2020-07-13 15:20:59  1-fundamentals/1.2-data-science-pipeline      697   \n",
       "2020-07-13 15:20:59        1-fundamentals/DataToAction_v2.jpg      697   \n",
       "2020-07-13 15:21:01  1-fundamentals/1.1-intro-to-data-science      697   \n",
       "2020-07-13 15:21:02      1-fundamentals/AI-ML-DL-timeline.jpg      697   \n",
       "2020-07-13 15:21:02  1-fundamentals/modern-data-scientist.jpg      697   \n",
       "2020-07-13 15:21:12                  2-storytelling/3-tableau      697   \n",
       "2020-07-13 15:21:16        2-storytelling/3.3-creating-charts      697   \n",
       "2020-07-13 15:26:39            4-python/8.4.1-pandas-overview      697   \n",
       "\n",
       "                    cohort_id            ip network  host  \n",
       "ds                                                         \n",
       "2020-07-13 15:20:48        59  136.50.70.27   13650  7027  \n",
       "2020-07-13 15:20:50        59  136.50.70.27   13650  7027  \n",
       "2020-07-13 15:20:50        59  136.50.70.27   13650  7027  \n",
       "2020-07-13 15:20:50        59  136.50.70.27   13650  7027  \n",
       "2020-07-13 15:20:59        59  136.50.70.27   13650  7027  \n",
       "2020-07-13 15:20:59        59  136.50.70.27   13650  7027  \n",
       "2020-07-13 15:21:01        59  136.50.70.27   13650  7027  \n",
       "2020-07-13 15:21:02        59  136.50.70.27   13650  7027  \n",
       "2020-07-13 15:21:02        59  136.50.70.27   13650  7027  \n",
       "2020-07-13 15:21:12        59  136.50.70.27   13650  7027  \n",
       "2020-07-13 15:21:16        59  136.50.70.27   13650  7027  \n",
       "2020-07-13 15:26:39        59  136.50.70.27   13650  7027  "
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_science[data_science.user_id == 697]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "User 697 only accessed the curriculum on the first day of class for Darden. Looking at the access times and how they are in quick succession, I wonder if this a bot that tried to hide in all of the first-day-of-class traffic."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I looked up the IP address and the search returned this:\n",
    "    \n",
    "> The IP Address 136.50.70.27 was found to have a negative reputation. Reasons for this assessment include:\n",
    "> \n",
    "> * The host has been observed sending spam in a format that is similar to snow shoe spamming techniques.\n",
    "> * The host is unauthorized to send email directly to email servers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I can add this IP to my list of suspicious IPs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>request_method</th>\n",
       "      <th>user_id</th>\n",
       "      <th>cohort_id</th>\n",
       "      <th>ip</th>\n",
       "      <th>network</th>\n",
       "      <th>host</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ds</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-07-13 14:39:21</th>\n",
       "      <td>13-advanced-topics/1-tidy-data</td>\n",
       "      <td>679</td>\n",
       "      <td>59</td>\n",
       "      <td>24.28.146.155</td>\n",
       "      <td>2428</td>\n",
       "      <td>146155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-13 14:39:36</th>\n",
       "      <td>1-fundamentals/1.1-intro-to-data-science</td>\n",
       "      <td>679</td>\n",
       "      <td>59</td>\n",
       "      <td>24.28.146.155</td>\n",
       "      <td>2428</td>\n",
       "      <td>146155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-13 14:39:37</th>\n",
       "      <td>1-fundamentals/AI-ML-DL-timeline.jpg</td>\n",
       "      <td>679</td>\n",
       "      <td>59</td>\n",
       "      <td>24.28.146.155</td>\n",
       "      <td>2428</td>\n",
       "      <td>146155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-13 14:39:37</th>\n",
       "      <td>1-fundamentals/modern-data-scientist.jpg</td>\n",
       "      <td>679</td>\n",
       "      <td>59</td>\n",
       "      <td>24.28.146.155</td>\n",
       "      <td>2428</td>\n",
       "      <td>146155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-13 15:49:31</th>\n",
       "      <td>1-fundamentals/1.1-intro-to-data-science</td>\n",
       "      <td>679</td>\n",
       "      <td>59</td>\n",
       "      <td>24.28.146.155</td>\n",
       "      <td>2428</td>\n",
       "      <td>146155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-13 15:49:32</th>\n",
       "      <td>1-fundamentals/modern-data-scientist.jpg</td>\n",
       "      <td>679</td>\n",
       "      <td>59</td>\n",
       "      <td>24.28.146.155</td>\n",
       "      <td>2428</td>\n",
       "      <td>146155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-13 15:49:32</th>\n",
       "      <td>1-fundamentals/AI-ML-DL-timeline.jpg</td>\n",
       "      <td>679</td>\n",
       "      <td>59</td>\n",
       "      <td>24.28.146.155</td>\n",
       "      <td>2428</td>\n",
       "      <td>146155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-14 08:05:15</th>\n",
       "      <td>1-fundamentals/1.1-intro-to-data-science</td>\n",
       "      <td>679</td>\n",
       "      <td>59</td>\n",
       "      <td>24.28.146.155</td>\n",
       "      <td>2428</td>\n",
       "      <td>146155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-14 08:05:15</th>\n",
       "      <td>1-fundamentals/AI-ML-DL-timeline.jpg</td>\n",
       "      <td>679</td>\n",
       "      <td>59</td>\n",
       "      <td>24.28.146.155</td>\n",
       "      <td>2428</td>\n",
       "      <td>146155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-14 08:05:15</th>\n",
       "      <td>1-fundamentals/modern-data-scientist.jpg</td>\n",
       "      <td>679</td>\n",
       "      <td>59</td>\n",
       "      <td>24.28.146.155</td>\n",
       "      <td>2428</td>\n",
       "      <td>146155</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               request_method  user_id  \\\n",
       "ds                                                                       \n",
       "2020-07-13 14:39:21            13-advanced-topics/1-tidy-data      679   \n",
       "2020-07-13 14:39:36  1-fundamentals/1.1-intro-to-data-science      679   \n",
       "2020-07-13 14:39:37      1-fundamentals/AI-ML-DL-timeline.jpg      679   \n",
       "2020-07-13 14:39:37  1-fundamentals/modern-data-scientist.jpg      679   \n",
       "2020-07-13 15:49:31  1-fundamentals/1.1-intro-to-data-science      679   \n",
       "2020-07-13 15:49:32  1-fundamentals/modern-data-scientist.jpg      679   \n",
       "2020-07-13 15:49:32      1-fundamentals/AI-ML-DL-timeline.jpg      679   \n",
       "2020-07-14 08:05:15  1-fundamentals/1.1-intro-to-data-science      679   \n",
       "2020-07-14 08:05:15      1-fundamentals/AI-ML-DL-timeline.jpg      679   \n",
       "2020-07-14 08:05:15  1-fundamentals/modern-data-scientist.jpg      679   \n",
       "\n",
       "                    cohort_id             ip network    host  \n",
       "ds                                                            \n",
       "2020-07-13 14:39:21        59  24.28.146.155    2428  146155  \n",
       "2020-07-13 14:39:36        59  24.28.146.155    2428  146155  \n",
       "2020-07-13 14:39:37        59  24.28.146.155    2428  146155  \n",
       "2020-07-13 14:39:37        59  24.28.146.155    2428  146155  \n",
       "2020-07-13 15:49:31        59  24.28.146.155    2428  146155  \n",
       "2020-07-13 15:49:32        59  24.28.146.155    2428  146155  \n",
       "2020-07-13 15:49:32        59  24.28.146.155    2428  146155  \n",
       "2020-07-14 08:05:15        59  24.28.146.155    2428  146155  \n",
       "2020-07-14 08:05:15        59  24.28.146.155    2428  146155  \n",
       "2020-07-14 08:05:15        59  24.28.146.155    2428  146155  "
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_science[data_science.user_id == 679]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This one looks like it could one of the pieces of the same snowshoe technique as user 697.\n",
    "\n",
    "I looked it up and that was confirmed.\n",
    "\n",
    "I will add it to the suspicious IPs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizations per Program"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_plot_df = data_science.groupby('city').filter(lambda x : len(x)>100)\n",
    "city_plot_df.city.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1IAAAJ/CAYAAAByY+JbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzde/xt93wn/tdbMqnELzdElYogBkFHNdOLdoYiNIymjaamrZlxKT86qqXVRsj0MINEx6UjioyatC5Vt5YgTRNKdDAjqi0icY24C04cyUlE4zN/fNbX2dnZ3+93f06+33NynOfz8diP73ev9Vmftfbea19e6/NZn1WttQAAALC8G+3uDQAAANjTCFIAAACDBCkAAIBBghQAAMAgQQoAAGCQIAUAADBIkII1VNUjqqpV1X125zp3x3bszvVeH1V186r6s6r64rTt79rd27SZquo+0+N8xJLlj5jKb9ncLVtzG36jqi6sqm9P23LE7tqW7zdVdfGy+3xVbfH87zD6XuL6G9lf4YZIkGKvMPMFuXK7pqq2VtVHqupPq+rnqqo2eJ1bquoXNrLOzTA9N1uq6pDdvS0b5HlJHpbkpUn+Q5JnLSo0fYG3JW/32YXbv7J9R1fVGVX16aq6sqqumPbXF1bVnddZ9ojpNb3HrtreZVXVzyZ5cZILkzwu/TW6dLdu1JJmQuhpc9Pn96Wrp2kvr6rbDK7j0Kq6aqrn4Rv7CNhd5vaP71bVt6b39l9W1SOrav/rWf8h03v+Phu0yYvWccuq+u/T59C3quqbVfWJqnptVR2/WeuFG7J9d/cGwC7250nenqSSHJjkTkl+Icl/THJuVZ3QWrtspvwrk7w2ydU7sa4/SPKnSf5qcLnrs86dcZ/0bT0jyWVz83b1tmyEY5Kc3Vp75jrlfjvJ/zdz/y5JTkryl0neNFf2Yxu3eeurqv+SZEuSryV5zbT+SnLX9JD4hKo6tLX2rSTnJdk/yXdmqjgi/TW9OMk/zFX/2an8P2/aA1jbMdPfR7XWvrGbtmEzfD7JU6f/D0x/Xz0qyYOq6kdaa19bsp5fS7Jfks8keXSSV23wdrK6Re+ljfQP6Qd6kuSAJIcneUCSVyR5WlU9tLX2jztZ9yHp7/kkedf12chFquq2Sf5vkoOSvDrJS6ZZRyZ5cPpn6fzn5jLulKRtxDbC7iBIsbf5+9batX6YVNWTkzw3yZPTg9axK/Naa9ckuWZXbFhVHdha+9auXOd6bkjbMuCWSdb9gd5au1bAnY7knpTkn+b3kV2pqh6V5BlJ/jbJL7bWvjk3//fSfzBVkrTWvpvkqmXrb621kfKb4JbTdqz7Gk1H6b/TWttdoW/EN+f2m5dU1VeTPCHJI5P84ZL1PDr9tX9zkhdW1R1aa5/a2E0ds4e9Djtt9L20E76w4LPl6VV1Qno4Oauq7tpa27qJ27CzfjfJLZL8QmvtzXPznlRVP7wzlbbWvn29twx2I1372Ou11q5prf1Okr9L8nNV9TMr8xadI1RVN566UFxUVdur6rKq+nBV/eE0/4iqWjnC9p9mu3TM1NGmblv3q6q/q6rLk5y52jpn7Dut+7PVzy/5p6r69/OFVupfMP1adU9lVo5ifmZmW7estS3Vz0N6cVV9burG9Lnp/s1WWd99q+p3q+pT03Z/vKr+04LHt1BV3aSqnjOz/Jernwd125kyW6bnuHLt5/0Ry65nlXXvW1W/X1UXTF2uvj51x7n7XLnvnXtUVb8yvTZXVdUl07R1D1xV1X7pXREvT/Kw+RCVJK21K1trJ7bWtk3LXOu8junv307F/9fM8/Cu+e1csP6HTfvjt6Z9+/9U1S8tKPfgqnp3VX2terfDS6rqTVX1L9d4bCvvi0dO9+e364zp/mFV9Yqq+kqSK5L88Mzyr6yqr0z7wKeq6tlVdcDcelbO+zmqejfIL1XvFvmOqrrTVOb4qvr7adsvrqrHrvqiXD9nT3+PXKZwVd0zyT3SW7Jfnd4y8shVyt6mql5XvXvVtqo6s6rusErZG1XVU6vqM9M++eGq+rVVyq73OhxcVadW1Sen1+HSqvrzqrr9XD1rfk7OlFtqX6qqH6qql0zzr65+DuTpVXWLuXI3raoXTPvHyvv1g1X1lDWe+pVlr3OO1Oy06l3wPjo97s9WP6hxvbXWXp9+MO+HkvznmXXfqKqeVlXnVf/Mu3p6/C+pmc/a6p/Pn5nu/sHMe+vimTK/UVV/U1VfmOr5UlW9qpY/P+6O0993rPIYPj8/rap+tKpeP/Oe/dy0r9xhpszCc6Sqd23+y2m/+Pa0Hz2t5j5Hq+pdUx23mureOr3fz57fh6by+1XV71XVP0z75Ter6vyqesJcuaX2c9AiBTv8SZKfSe+m8HdrlHtxepedP0vygiT7pH/J3Heaf2n6eR+vTPKeJKevUs/RSR6a5H+m/3BaxqlJbpLerWLlR+mfV9WNW2tnLFnHrJeld9X4xSRPSu9KliT/tNoCVXVwkvem/zh8RZK/T/KjSR6f5L5V9eNTl7NZz07vMvOyJN+eyp5RVZ9srf3vtTZw+uI8O8lPJ3lDeteYO051PKCqjp6+xN+U5JO57vP+3nWeg/W8OskvJzkn/Xm/ZfqPnfdV1b9prX1orvxD0rsNvjjJl5P8fHpYvW1W+VE846en+l/ZWtvZ84bOS3++T0p/Dt4zTf/KWgtV1X9L8rQkf53k5CTfTd8vXl9VT2itvXgqd+8kb0ny4STPSe8Oeqsk90/fJz6+yipW3hePTfJvpv8Xbdc56c/bf03f1y+vHd2KDk5/DT6e3nXuqUl+uqrut6C15E/TA+mzkxyW5HeSnF1VJ6f/aH1J+v776CQvq6oLWmtrve93xsqPz2W79T06PbS8sbV2RVW9Lf2gwH+ZWkuS9PNh0l/n26SfC3hBknunB+hF59o8P8lvTcu8IL1l4cVJPr3Gtix6HVbe+4enP3cfTf/x/xtJ/s/0XvzstPx6n5NL70tVdXiS96V3efyTJJ+a5j8+yc9O61056PD6JP82/bPmH9O70N05fX9ZtlVwkccl+cFp/ZcleXiSU6vq862111yPele8PP399+Ak/22atl+SpyR5Y3oL5RVJ/nX6fvIzVfVjrbWr07v+Pin9eZ7tmnz5TP2/m+T9Sf5Heov93ZL8evpn9t1ba19fZ/tWWkUfU1UvnFq2V1VV/27a7iumx/bJ9M+2B07rXrWVtaoeND2OT6Z/3n8jyU8leWb6gYYT5ha5Sfq+/f70z73bpe/vb66qu009K1YOVJ2dvi/8TXq32auS3D3J8UlOm8qN7Ofs7Vprbm7f97f0D86W5HfXKHPPqcwbZ6Y9Ypp2n5lp30jy9iXW2ZKcsca8luT+C+YtWufKtM8mOXhm+sHTtG8k2X+9da9S95Zp2hFLln/WNO035sr+52n6f12w/IeS7Dcz/dbpgerPl3geHzPV8dy56Q+epr9y2ed9yX1ky8y0Y6Zpf5GkZqb/SPo5Ru+ZmXbEVPaaJPecmV7pPwpakp9cZxt+cyr35J3Y7kesNW3Bds4+zpV9/9kLyv9Vkm1JDpzuP38qe4vR53ha/oxMPQwXTU/yqgXzXj3Ne9Dc9D+cpj96wf585txr9sRp+reSHD4z/bD0H1PL7Isrz91pc9MvTv8xe/Ppdrv00HxZeqvS3Zao+8bp7+MzZqYdN63v2Lmyz56mP3Ju+gun6e+amXan9FD8jiT7zL3m383ce3+d1+GPklyZ5F/NTb/ttI/Mbvu6n5PL7kvpIeKrSX54bvrR6e/DLdP9g6f6/ngn983rvG9mpn0xySEz0w9IPzjwviXrbkneuk6ZbUm+PnO/MvO5PjP90VN9v7xg39yySt03WTDtftMyv7fE9t8+yTen8pekvyd/O8mPLSi78tx8NcmtF8y/0dx7Z3Z/vXF6gD8vyb5zyz0p1/0+eteix5AeQFuSB85M+72s/jk3u01L7+dubrr2wQ7bpr8HrVPum0nuWlV3u57r+8fW2rmDy7ykzXT3mv5/aZJD07/wd4VfTP+SnG9pe1n6kfdfXLDMH7d+5DRJ0lr7QvrR5jsuKLtofd9NP2L9Pa21t6WfvH1cVW3WZ9nKY3lWa63NrPufkrw1/ajwYXPLnNNa+/uZsi29BWS2vtWs7Hvb1iy18X4t/QfGn1bvtvm9W3qLwYHpR4STvv8nyUPnu9lskP8+e2d6bX8+yYdaa2+fK/uc7Gg5m/c/Zl+z7GiZe3Nr7ZKVia23/F2U5fbFtdw5/X1xaXpLzyvS3w/HtdY+ssTyx6e/j2dbp9+W/mP0UXNlfyG9Je/P5qafuqDe49J/kD+/TUfmk2TaR89ZY3vmX4dK30/OS/KFuX3kivTWgAfMLLLM5+S6+9LUOvDv0vfDq+bWe3F6q8XKeq9MP0DzEwNd1pb1v9rMQEStte3pj/n67jeztmXm+6d1VyZJVe1TfWS+myd551TkJ5atuLV2xVTPjaZuazdPb7H75jL1tNY+neRfpbc0JsmvpreAnV+9G/OPzRR/YPoBhedNn/XzdX13ftqMY9Jb/v5XkkPmXu+V9/8D5pb5bnpL26yV52j29fm1JFvTW7YWbtNO7Ofs5QQp2GHZH7G/nf6D58PV++G/vKp25sf8al2g1rJo9LgLpr+7qu/27ZJc1Oa6Uk33L1plOxZ1Ifp6kpstmL5ofV9si0/A/mj6j/ybL1HPzrhd+pf0ouf9IzNlZl2f12hl3ztwqa3bOHdJ/7F9YXaEgZXbn0xlfnD6e1p6C+MfJ/lGVb29qp64IFDurPn3xWHpI4J9dL5g6wNWfCnL7XMr+89nFpTdmuX2xbVcnP4j8Jj0bnZ3bK0duSD8rebR6c/356vqyKo6Mr2V4ZwkPz/9kFtx+ySfmA1GSdJa+1KuO/LmynNz4YJ1XrBg2opFr8PN0n9Ezu8jl2bHD+AVy3xOLrMv3Sn9t8rK8zN/u9PKeqeDNb+d3nXsM9XPZ3pRVd1vjce5rOvzGbasgzL3/VNVv1xV/yc9JG7NjqCe9Od3KdXPU31Xehi4LDuev4OXrae1dnFr7QmttcPTu2D+cnrL792TvLWqbjoVXQkv892el3GX6e8rct3XemUf/sG5Zb7YWpsfJGSlq+Ls63PHJBcuKDtrdD9nL+ccKdjhR6a/F61VqLX25ulo54PSfzDdP/1L/j1Vdf/Zlpd1bN+JbWwLpo1c/2p3vedXG/lvmW3f0Ot7DdqZdS96jZa1Es5+9HrUsTMqUxeyrP5afTRJWmtfr6p/nX6e0zHp56O8IMkzqupBrbX3XZ8NmY70z2/bzljtcVyffXEtV+xEC3NfcdXtkvzstA2rHWB5eHrXvRWr7Wfzj2Pl/tBnxxqvw7lZ3PI1v/y6n5NL7ksr631VVj+X9MqZ9b60qt6c3vX33kl+Kf1yAX/RWrvOwDwDNnX00um5OjD9XLCVacendyv+v+nn/HwuvRvqPunnMi518G56jv8mvfXuxPSDCVem7xOvXbaeWVNof336OZSvTm+helD667TWPrfu5k5/n5LrXrphxRfn7q/12szv4+tt09B+DoIU7PDo6e/b1is4HQl/VZJXTV0BTknvf31c+pfLZjkqvYvLrJUjeLNHTL+R5Ka5rkVH7ke/7D6d5E5Vte9sq9TUNedfZu0T2HfGp9JHUzykXfsaX0l/PrZl+ZP5d2bdD0x/jucH4Dhq+jvfwnFUrmtl2nrPzf9OPz/gF6rqZm39E8BXM/qafiLJzyW5pLW27jWzppaQd023VNWPJPlgkqen/4DdSF9NP6/prvMzqurQ9JPAV/vBtad4ZPoPuMfkui1KSR984NHZEaQ+neRfVtU+s61SVfVD6S0Ms1ZO6r9Lrrv/3SXLu3TatoOWDYzLfE4usS99Mn1/3m9gvV9KH+Dg5VW1T/oANL9SVc9rrX1gmTp2g1+f/s5+//yH9OD0s7PBthZfkHut9/yvpoevY1tr3/u8qqqbZKBVaw3vn9Zx6+n+ysHIH83a3UcX+cT0d6cPTKzh40nuUlU/0FYfdn14P2fvpmsfe72p7/l/Tx+x7+1tjVHkVvqpz06bzsNY6cIwG14uz+Iwc308fjpnYGV7Dk4fTeqyJO+eKffxJD9VM0NDTz86F40atzKy07Lb+lfp3R9+fW76Y6bpf7lkPcv6q/TPqhNnJ1bVself1G9Zp8/99V13kjx1+iG4su67pZ+383ftuqPrHVN9GOuVspX+43G2voWm1synpR+Z/ouquk4Xv+rDSj+7qtY6l2/0NX3l9PfZ0w/P+XXeYub/Rd0oL0w/wr3R+/vKuQtnJvnRqvq5udknpu8bG73P7TJTV7dHJPlwa+3lrbU3zN/Sr293t6llIemDL/xg+oXEZ/3+glW8JdMAJrOv7bSP3n/Z7Zxeh1cn+fFaMCT+VOctpr9LfU4usy9NBxPenuT4qvrJBeusla6AVXVAzQ2HPwW1lYMgG75/boTq15H6vfSWlhfPzLom/bW70UzZSg+Z89Z6z6+E7fnWmZOyfKvWfapfT2x++o3SRypNdnQV/Zv0g1u/M4X7+WXWav09O/3gyYkzXQVnl91/0efikl6dHhyv8/ytbNPIfg6JFin2PvesqodP/x+Y3r/+F9JH4/mb9KNqazkwyZeq6i3pPwq+mn6OzOPT+6+fOVP2/UnuX1W/nz7KUWutvfZ6bv/X0odffUX6l+Ij04do/fW5rjinpR8JfmdVvTL9qvePSR/h75Zzdb5/+nvq1EXjqiQfWeME+eemDz/74unH2IfSA82j049EPneV5XbWGUn+U5Lfn7q/nJc+9PFvpJ9wf9IGr+97WmvnVNXrkvz7JIdW1VuzY/jzq9JHgpv3j+nP+4vTz985Lv0H6yuX6fbWWntFVd0mfcj0T1bVa9J/oNwovQXhhPThq5+zei25IL0V5zeqant60P5qa+2diwq31j5QVX+QfiHgf6iq16f/qPuhJD+W3mVnv6n4/6x+8c2/Sd+f9k/ysPT3xvzgBxvlpPSuX39VVX+c3krxb6f1npflLx9wQ/SA9GHM/2SNMm9MH43w0Uk+kP4e+9X01+LH0rtd3id9QJBrtc621i6c9sUnpO+Xb0zff56Qvq+OdCN9WvoQ/a+b3hfvT3J1+ufng9Jbkh6R5T8nl92XHp9+SYrzqurPpjpvlN7CftxUdkt6i/i7q+ov07vJbk1/zzw+veX4Pdm9bj3z/bN/+mf3A5L8ePo+ffxcq/sb0i+R8c7pcf+L9O+ra4XF5Htdbj+Z5N9X1afSPxuvaK2dmX6g4UlJ3l5Vp6e/Zsekd2dftjX/d9MvNXBm+iUvvpn+WfjQ9M+Iv83UmtZa215Vj562/yNVtTL8+WHpLfzPTz8YcB2tD/v/H9MPOl00fdd9Mv077M7pg7L8YqYWzEF/lB76nj7T3fGq9NbuO2XHgYVl93Mw/Lnb3nHLjiFsV27XpH8RfDT9R9jPrbLcIzIz3Gr6j8nnpPdZ/3r6CFEXp58Ye8e5Ze+Y/kG9bWW9M/NaVh8a/VrrnJt2//Qfu5dM6/5Ikl9dpZ6npP84+Xb6AAiPWlT3VPb30rv9fCczQ+iuUf6w9BPEPz8t8/n0I6k3X++xzMx7V5KLl3z9bjI9759O/0L7anorym0XlF31uV1yH9kyN33f9CP9H5uey2+kf8nffa7cESvLJ/mV9KPg304/r+GZSf7F4PYcPe2bn0n/st+efr2d58/ua1llqPP0L/y/n5b93pDYWWOY5PSuVGdPj3Fl289K8viZMsent3J8fipzaXpr6EOXfFxnZI3hz9dY7nbTa/7VaR/4dPow4AfMlduSBcP5r/O4l9oXs/bw5x8Z3eemZV8/1Xn3dcpdlB6I95/uH57+Q3Vbemg+M8kdMjec9FT2Ruk/Dlc+Dz6SPjLZdZ6rJV6HA9KvM/bh9Jajb6W/N/5nkp+Yyiz1OTmyL6UPKPOH6a3tV03PxYfTfxwfNZW5Wfo5Vv8wzb8y/Uf4C5P80MBnwCPWmrbsczVXts3dLk9/b/9V+mfzdYY5n5Z7TPqBkavSD8ycnt7qdJ3PufRA9r/TB5Ros/t0egD74DTva+nnRh2+aH9ZZTt+Mv2aTh9ID2nfmZ7j9yV5cpIfWLDMj0+P72vT63tJktckuf3ce+c6608fMORVSb6Q/n7/Svq1nU5OctP13rtZ5f2ePrz609K/+1f2ow/kupfzWHc/d3NrrfVrbABw/UytZZ9J8ozW2pbdujEAwKZzjhQAAMAgQQoAAGCQIAUAADDIOVIAAACD9trhz29+85u3I444YndvBgAAcAP1wQ9+8GuttcMWzdtrg9QRRxyR888/f3dvBgAAcANVVZ9dbZ5zpAAAAAYJUgAAAIMEKQAAgEGCFAAAwCBBCgAAYJAgBQAAMEiQAgAAGCRIAQAADBKkAAAABglSAAAAgwQpAACAQYIUAADAIEEKAABgkCAFAAAwaN0gVVW/VFXvraqvV9VVVXVRVT29qvabKVNVdVJVfa6qrqyq86rqHgvqOqqq3lFV26vqi1X1zKraZ67MhtUFAACwGZZpkbpZkr9N8utJjk3yiiRPS/L8mTInJjk5yalJHpLk8iTnVtUtVwpU1aFJzk3SkhyX5JlJfifJM+bWt5F1AQAAbLh91yvQWnvZ3KS/raqDkvznqvrNJD+QHn6e01o7LUmq6n1JLk7yhCRPn5Z7XJL9kxzfWtuW5Jypni1V9dzW2raquvFG1bUTzwUAAMBSdvYcqa8nWenad68kByV53crM1toVSc5Mb8FacWySs+dCzmvTA9G9N6EuAACATbF0kKqqfarqgKr6mSRPTPKS1lpLcuck1yT5xNwiH5vmrbhzkgtnC7TWLkmyfabcRtYFAACwKUZapK6Ybu9J8u4kT5mmH5rk8tbaNXPltyY5YGZQikOTXLag3q3TvI2u6zqq6rFVdX5VnX/ppZeuVgwAAGBNI0HqXkn+TfqgDsclOW1mXltQvhbMW63cMmV2pq5raa2d3lo7urV29GGHHbZaMQAAgDWtO9jEitba30///l1VfS3Jn1bV89JbgQ6sqn3mWpIOSbK9tfad6f7Wadq8g7OjdWkj6wIAANgUSwepOSuh6nbp5yrtk+TIJBfNlJk/j+nCzJ2/VFW3SXKTmXIbWdeGOuLEt21Gtd9z8SkP3tT6AQCAjbOzo/b99PT3M0nem2RbkhNWZlbVAenXgDprZpmzkjywqg6cmfawJFemn3OVDa4LAABgU6zbIlVVf51+8duPpo+o99Pp50n9RWvtU1OZU5KcXFVb01uEnpwe0l40U9VL00f7e1NVnZrk9km2JHn+yjDmrbWrNqouAACAzbJM174PJHlEkiOS/HOSTyd5anqYWXFKeth5apKbJTk/yTGtta+sFGitba2q+6UPUnFm+rlML0gPQNmkugAAADZc9UtB7X2OPvrodv755y9d3jlSAACwd6mqD7bWjl40b2fPkQIAANhrCVIAAACDBCkAAIBBghQAAMAgQQoAAGCQIAUAADBIkAIAABgkSAEAAAwSpAAAAAYJUgAAAIMEKQAAgEGCFAAAwCBBCgAAYJAgBQAAMEiQAgAAGCRIAQAADBKkAAAABglSAAAAgwQpAACAQYIUAADAIEEKAABgkCAFAAAwSJACAAAYJEgBAAAMEqQAAAAGCVIAAACDBCkAAIBBghQAAMAgQQoAAGCQIAUAADBIkAIAABgkSAEAAAwSpAAAAAYJUgAAAIMEKQAAgEGCFAAAwCBBCgAAYJAgBQAAMEiQAgAAGCRIAQAADBKkAAAABglSAAAAgwQpAACAQYIUAADAIEEKAABgkCAFAAAwSJACAAAYJEgBAAAMEqQAAAAGCVIAAACDBCkAAIBBghQAAMAgQQoAAGCQIAUAADBIkAIAABgkSAEAAAwSpAAAAAYJUgAAAIMEKQAAgEGCFAAAwCBBCgAAYJAgBQAAMEiQAgAAGCRIAQAADBKkAAAABq0bpKrqhKp6S1V9oaour6oPVtWvzJW5uKra3O3LC+o6qqreUVXbq+qLVfXMqtpnrkxV1UlV9bmqurKqzquqe+xMXQAAAJth3yXKPDnJZ5I8KcnXkjwoyWuq6uattRfNlHtNktn7V89WUlWHJjk3yQVJjktyhyTPSw9zT58pemKSk5M8JcmF0/rPraq7tda+PFgXAADAhlsmSD2ktfa1mfvvrKpbpQec2eD0pdba+9eo53FJ9k9yfGttW5JzquqgJFuq6rmttW1VdeP0IPWc1tppSVJV70tycZInZEdIWreuJR4XAADATlm3a99ciFrxoSS3GFzXsUnOngs5r00PRPee7t8ryUFJXjez/iuSnDktP1IXAADAptjZwSbuld6tbtajqurqqvpmVb2hqm47N//O6V31vqe1dkmS7dO8lTLXJPnE3LIfmymzbF0AAACbYpmufddSVfdLPy/pUTOT35zk/Uk+n+QuSf4gyXuq6u6ttW9OZQ5NctmCKrdO81bKXN5au2ZBmQOqar/W2tVL1rVo2x+b5LFJcvjhh6/6GAEAANYy1CJVVUekDyrx5tbaGSvTW2u/1Vr789bae1prpyd5YJJbJXnkXBVtUbVz01crMz9vmbquvfLWTm+tHd1aO/qwww5brRgAAMCalg5SVXXTJGcluSTJw9cq21r7SJKLktxzZvLWJIcsKH5wdrQubU1y4IJhzA9Jsr219p2BugAAADbFUkGqqg5I8tYk+yV58DQAxDJmW4cuzNz5S1V1myQ3yY7znS5Msk+SI+fqmT8napm6AAAANsUyF+TdN8nrk9wxybGtta8usczdktwpyQdnJp+V5IFVdeDMtIcluTLJu6f7702yLckJM3UdkOQh0/IjdQEAAGyKZQab+OP0i/D+VpKbVtVPzsz7UJL7p3f1e2uSL6a3FD09vQvgGTNlX5rkiUneVFWnJrl9ki1Jnr8yjHlr7aqqOiXJyVW1NTsuyHujXPuaVevWBQAAsFmWCVIPmP7+0YJ5t0vyufRrSr0w/bylryf56yQnzYaa1trWacS/09KvC3VZkhekB6BZp6QHp6cmuVmS85Mc01r7yk7UBQAAsOHWDVKttSOWqOd+y6ystXZBkvuuU6YledZ0u151AQAAbIadvfTWkFIAACAASURBVCAvAADAXkuQAgAAGCRIAQAADBKkAAAABglSAAAAgwQpAACAQYIUAADAIEEKAABgkCAFAAAwSJACAAAYJEgBAAAMEqQAAAAGCVIAAACDBCkAAIBBghQAAMAgQQoAAGCQIAUAADBIkAIAABgkSAEAAAwSpAAAAAYJUgAAAIMEKQAAgEGCFAAAwCBBCgAAYJAgBQAAMEiQAgAAGCRIAQAADBKkAAAABglSAAAAgwQpAACAQYIUAADAIEEKAABgkCAFAAAwSJACAAAYJEgBAAAMEqQAAAAGCVIAAACDBCkAAIBBghQAAMAgQQoAAGCQIAUAADBIkAIAABgkSAEAAAwSpAAAAAYJUgAAAIMEKQAAgEGCFAAAwCBBCgAAYJAgBQAAMEiQAgAAGLTv7t4Adp0jTnzbptZ/8SkP3tT6AQDghkKLFAAAwCBBCgAAYJAgBQAAMEiQAgAAGCRIAQAADBKkAAAABglSAAAAgwQpAACAQYIUAADAIEEKAABgkCAFAAAwSJACAAAYJEgBAAAMEqQAAAAGrRukquqEqnpLVX2hqi6vqg9W1a/MlamqOqmqPldVV1bVeVV1jwV1HVVV76iq7VX1xap6ZlXts1l1AQAAbIZlWqSenOTyJE9K8vNJ/jbJa6rqN2fKnJjk5CSnJnnIVP7cqrrlSoGqOjTJuUlakuOSPDPJ7yR5xtz6NrIuAACADbfvEmUe0lr72sz9d1bVrdID1ouq6sbp4ec5rbXTkqSq3pfk4iRPSPL0abnHJdk/yfGttW1Jzqmqg5Jsqarntta2bWRdO/NkAAAALGPdFqm5ELXiQ0luMf1/ryQHJXndzDJXJDkzybEzyxyb5Oy5kPPa9EB0702oCwAAYFPs7GAT90pywfT/nZNck+QTc2U+Ns3LTLkLZwu01i5Jsn2m3EbWBQAAsCmGg1RV3S/9vKQXT5MOTXJ5a+2auaJbkxxQVfvNlLtsQZVbp3kbXdeibX9sVZ1fVedfeumlqxUDAABY01CQqqojkrwmyZtba2fMzGqLii+Yt1q5ZcrsTF3X0lo7vbV2dGvt6MMOO2y1YgAAAGtaOkhV1U2TnJXkkiQPn5m1NcmBC4YePyTJ9tbad2bKHbKg6oOzo3VpI+sCAADYFEsFqao6IMlbk+yX5MHTABArLkyyT5Ij5xabP4/pwsydv1RVt0lyk5lyG1kXAADApljmgrz7Jnl9kjsmOba19tW5Iu9Nsi3JCTPLHJB+DaizZsqdleSBVXXgzLSHJbkyybs3oS4AAIBNscx1pP44yYOS/FaSm1bVT87M+1Br7aqqOiXJyVW1Nb1F6MnpIe1FM2VfmuSJSd5UVacmuX2SLUmevzKM+UbWBQAAsFmWCVIPmP7+0YJ5t0u/WO4p6WHnqUluluT8JMe01r6yUrC1tnUa8e+09OtCXZbkBekBaNZG1gUAALDh1g1SrbUjlijTkjxruq1V7oIk991VdQEAAGyGnb0gLwAAwF5LkAIAABgkSAEAAAwSpAAAAAYJUgAAAIMEKQAAgEGCFAAAwCBBCgAAYJAgBQAAMEiQAgAAGCRIAQAADBKkAAAABglSAAAAgwQpAACAQYIUAADAIEEKAABgkCAFAAAwSJACAAAYJEgBAAAMEqQAAAAGCVIAAACDBCkAAIBBghQAAMAgQQoAAGCQIAUAADBIkAIAABgkSAEAAAwSpAAAAAYJUgAAAIMEKQAAgEGCFAAAwCBBCgAAYJAgBQAAMEiQAgAAGCRIAQAADBKkAAAABglSAAAAgwQpAACAQYIUAADAIEEKAABgkCAFAAAwSJACAAAYJEgBAAAMEqQAAAAGCVIAAACDBCkAAIBBghQAAMAgQQoAAGCQIAUAADBIkAIAABgkSAEAAAwSpAAAAAYJUgAAAIMEKQAAgEGCFAAAwCBBCgAAYJAgBQAAMEiQAgAAGCRIAQAADBKkAAAABglSAAAAgwQpAACAQYIUAADAIEEKAABgkCAFAAAwaKkgVVVHVtXLquofq+qaqnrXgjIXV1Wbu315QbmjquodVbW9qr5YVc+sqn3mylRVnVRVn6uqK6vqvKq6x87UBQAAsNH2XbLcXZM8KMn7k+y3RrnXJHnRzP2rZ2dW1aFJzk1yQZLjktwhyfPSA93TZ4qemOTkJE9JcmGSJyc5t6ru1lr78mBdAAAAG2rZIHVma+3NSVJVb0hy81XKfam19v416nlckv2THN9a25bknKo6KMmWqnpua21bVd04PUg9p7V22rTO9yW5OMkTsiMkrVvXko8NAABgyFJd+1pr392g9R2b5Oy5kPPa9EB07+n+vZIclOR1M+u/IsmZ0/IjdQEAAGy4jR5s4lFVdXVVfbOq3lBVt52bf+f0rnrf01q7JMn2ad5KmWuSfGJu2Y/NlFm2LgAAgA23bNe+Zbw5/Ryqzye5S5I/SPKeqrp7a+2bU5lDk1y2YNmt07yVMpe31q5ZUOaAqtqvtXb1knVdS1U9Nsljk+Twww9f9nEBAABcy4a1SLXWfqu19uettfe01k5P8sAkt0ryyPmiCxavuemrlZmft0xds9t4emvt6Nba0YcddtiiIgAAAOvatOtItdY+kuSiJPecmbw1ySELih+cHa1LW5McuGAY80OSbG+tfWegLgAAgA23Ky7IO9s6dGHmzl+qqtskuUl2nO90YZJ9khw5V8/8OVHL1AUAALDhNi1IVdXdktwpyQdnJp+V5IFVdeDMtIcluTLJu6f7702yLckJM3UdkOQh0/IjdQEAAGy4pQabmILMg6a7t05yUFX90nT/7Ul+NsnDk7w1yRfTW4qenuSSJGfMVPXSJE9M8qaqOjXJ7ZNsSfL8lWHMW2tXVdUpSU6uqq3ZcUHeG+XaF/tdty4AAIDNsOyofbdI8vq5aSv3b5fkc1OZF6aft/T1JH+d5KTZUNNa21pV90tyWvp1oS5L8oL0ADTrlPTg9NQkN0tyfpJjWmtf2Ym6AAAANtRSQaq1dnF2jJq3mvstWdcFSe67TpmW5FnT7XrVBQAAsNF2xWATAAAA31cEKQAAgEGCFAAAwCBBCgAAYJAgBQAAMEiQAgAAGCRIAQAADBKkAAAABglSAAAAgwQpAACAQYIUAADAIEEKAABgkCAFAAAwSJACAAAYJEgBAAAMEqQAAAAGCVIAAACDBCkAAIBBghQAAMAgQQoAAGCQIAUAADBIkAIAABgkSAEAAAwSpAAAAAYJUgAAAIMEKQAAgEGCFAAAwCBBCgAAYJAgBQAAMEiQAgAAGCRIAQAADBKkAAAABglSAAAAgwQpAACAQYIUAADAIEEKAABgkCAFAAAwSJACAAAYJEgBAAAMEqQAAAAGCVIAAACDBCkAAIBBghQAAMAgQQoAAGCQIAUAADBIkAIAABgkSAEAAAwSpAAAAAYJUgAAAIMEKQAAgEGCFAAAwCBBCgAAYJAgBQAAMEiQAgAAGCRIAQAADBKkAAAABglSAAAAgwQpAACAQYIUAADAIEEKAABgkCAFAAAwSJACAAAYJEgBAAAMEqQAAAAGCVIAAACDlgpSVXVkVb2sqv6xqq6pqnctKFNVdVJVfa6qrqyq86rqHgvKHVVV76iq7VX1xap6ZlXts1l1AQAAbLRlW6TumuRBST4+3RY5McnJSU5N8pAklyc5t6puuVKgqg5Ncm6SluS4JM9M8jtJnrGJdQEAAGyoZYPUma2127TWTkjy0fmZVXXj9PDznNbaaa21c5OckB5ynjBT9HFJ9k9yfGvtnNbaS9ODz5Or6qCNrgsAAGAzLBWkWmvfXafIvZIclOR1M8tckeTMJMfOlDs2ydmttW0z016bHojuvQl1AQAAbLiNGmzizkmuSfKJuekfm+bNlrtwtkBr7ZIk22fKbWRdAAAAG26jgtShSS5vrV0zN31rkgOqar+ZcpctWH7rNG+j67qWqnpsVZ1fVedfeumlaz4gAACA1Wzk8OdtwbRaMG+1csuU2Zm6dhRu7fTW2tGttaMPO+ywRUUAAADWtVFBamuSAxcMPX5Iku2tte/MlDtkwfIHZ0fr0kbWBQAAsOE2KkhdmGSfJEfOTZ8/j+nCzJ2/VFW3SXKTmXIbWRcAAMCG26gg9d4k29KHKU+SVNUB6deAOmum3FlJHlhVB85Me1iSK5O8exPqAgAA2HD7LlNoCjIPmu7eOslBVfVL0/23t9a2V9UpSU6uqq3pLUJPTg9qL5qp6qVJnpjkTVV1apLbJ9mS5Pkrw5i31q7aqLoAAAA2w1JBKsktkrx+btrK/dsluTjJKelh56lJbpbk/CTHtNa+srJAa21rVd0vyWnp14W6LMkL0gPQrI2sCwAAYEMtFaRaaxdnx6h5q5VpSZ413dYqd0GS++6qugAAADbaRg5/DgAAsFcQpAAAAAYJUgAAAIMEKQAAgEGCFAAAwCBBCgAAYJAgBQAAMEiQAgAAGCRIAQAADBKkAAAABglSAAAAgwQpAACAQYIUAADAIEEKAABgkCAFAAAwSJACAAAYJEgBAAAMEqQAAAAGCVIAAACDBCkAAIBBghQAAMAgQQoAAGCQIAUAADBIkAIAABgkSAEAAAwSpAAAAAYJUgAAAIMEKQAAgEGCFAAAwCBBCgAAYJAgBQAAMEiQAgAAGCRIAQAADBKkAAAABglSAAAAgwQpAACAQYIUAADAIEEKAABgkCAFAAAwSJACAAAYJEgBAAAMEqQAAAAGCVIAAACDBCkAAIBBghQAAMAgQQoAAGCQIAUAADBIkAIAABgkSAEAAAwSpAAAAAYJUgAAAIMEKQAAgEGCFAAAwCBBCgAAYJAgBQAAMEiQAgAAGCRIAQAADBKkAAAABglSAAAAgwQpAACAQYIUAADAIEEKAABgkCAFAAAwSJACAAAYJEgBAAAM2rAgVVWPqKq24Pa4mTJVVSdV1eeq6sqqOq+q7rGgrqOq6h1Vtb2qvlhVz6yqfebKLFUXAADARtt3E+q8b5IrZ+5/eub/E5OcnOQpSS5M8uQk51bV3VprX06Sqjo0yblJLkhyXJI7JHleeuh7+khdAAAAm2EzgtQHWmuXz0+sqhunh5/ntNZOm6a9L8nFSZ6QHSHpcUn2T3J8a21bknOq6qAkW6rqua21bQN1AQAAbLhdeY7UvZIclOR1KxNaa1ckOTPJsTPljk1y9hSiVrw2PVzde7AuAACADbcZQepTVfXPVXVRVf3/M9PvnOSaJJ+YK/+xad5suQtnC7TWLkmyfabcsnUBAABsuI3s2vel9HOW/m+SfZL8SpKXVtUBrbUXJDk0yeWttWvmltua5ICq2q+1dvVU7rIF9W+d5mWgrmupqscmeWySHH744TvzGAEAADYuSLXWzk5y9syks6rqB5I8var+aKXYgkVrwbzVyi1TZrV5aa2dnuT0JDn66KMXlgEAAFjPZp8j9YYkN01yRHpr0YHzw5gnOSTJ9tbad6b7W6dp8w7OjpaqZesCAADYcLtqsImWft7TPkmOnJs3f07UhZk7z6mqbpPkJjPllq0LAABgw212kHpokq8l+WyS9ybZluSElZlVdUCShyQ5a2aZs5I8sKoOnJn2sPRrU717ur9sXQAAABtuw86Rqqo3pg808U/prUUPm25PbK19N8lVVXVKkpOramt2XET3RkleNFPVS5M8McmbqurUJLdPsiXJ81eGRG+tLVsXAADAhtvIUfsuSvKoJLdJH/ThgiT/sbX2ypkyp6SHnacmuVmS85Mc01r7ykqB1trWqrpfktPSrwt1WZIXpIepjNQFAACwGTZy1L6Tkpy0TpmW5FnTba1yFyS570bUBQAAsNF21WATAAAA3zcEKQAAgEGCFAAAwCBBCgAAYJAgBQAAMEiQAgAAGCRIAQAADBKkAAAABglSAAAAgwQpAACAQYIUAADAIEEKAABgkCAFAAAwSJACAAAYJEgBAAAMEqQAAAAGCVIAAACDBCkAAIBBghQAAMAgQQoAAGCQIAUAADBIkAIAABgkSAEAAAwSpAAAAAYJUgAAAIMEKQAAgEGCFAAAwCBBCgAAYJAgBQAAMEiQAgAAGCRIAQAADBKkAAAABglSAAAAgwQpAACAQYIUAADAIEEKAABgkCAFAAAwSJACAAAYJEgBAAAMEqQAAAAGCVIAAACDBCkAAIBBghQAAMAgQQoAAGCQIAUAADBIkAIAABi07+7eABhxxIlv29T6Lz7lwZtaPwAA3x+0SAEAAAwSpAAAAAYJUgAAAIMEKQAAgEGCFAAAwCBBCgAAYJAgBQAAMMh1pGAX2uzrYCWuhQUAsCsIUsCQ74cw6MLOAMD1pWsfAADAIC1SAHug74dWNY9hfVo3AW64BCkA2Et9P3TVBdhdBCkAYI8lDAK7iyAFALAb6SIKeyaDTQAAAAwSpAAAAAYJUgAAAIMEKQAAgEF79GATVXVUkhcl+akklyV5eZJntNau2a0bBgCwFzFgBnujPTZIVdWhSc5NckGS45LcIcnz0lvZnr4bNw0AAPg+t8cGqSSPS7J/kuNba9uSnFNVByXZUlXPnaYBAABsuD05SB2b5Oy5wPTaJKcmuXeSM3fLVgEAsMfRPZFRe3KQunOSd85OaK1dUlXbp3mCFAAAe4XNDoLJ5ofBPe0x7Mmj9h2aPsDEvK3TPAAAgE1RrbXdvQ07paq+k+R3W2t/NDf9C0nOaK09bcEyj03y2OnunZJctImbePMkX9vE+ncFj+GGYU9/DHv69iceww2Fx3DDsKc/hj19+xOP4YbCY9j9dsX237a1dtiiGXty176tSQ5ZMP3gLG6pSmvt9CSnb+ZGraiq81trR++KdW0Wj+GGYU9/DHv69iceww2Fx3DDsKc/hj19+xOP4YbCY9j9dvf278ld+y5MPxfqe6rqNkluMs0DAADYFHtykDoryQOr6sCZaQ9LcmWSd++eTQIAAPYGe3KQemmSbyd5U1Xdfzr/aUuS599AriG1S7oQbjKP4YZhT38Me/r2Jx7DDYXHcMOwpz+GPX37E4/hhsJj2P126/bvsYNNJElVHZXktCQ/lX5e1MuTbGmtXbNbNwwAAPi+tkcHKQAAgN1hT+7aBwAAsFsIUgDcoFTVjdeZf+tdtS0A86rqX+zubeCGQZDaQFV1q6p6aFU9Zvp7q929TXuLqtq3qn5gbtoDquq3q+qeu2u7gJ3ylqrab9GMqjoyyd/t4u0BNsCe/Dupqu5VVWdV1beSXFVV36qqt1fVT+3ubWP3cY7UBqiqfZK8KMljkuwzM+ua9NFEfrO19t3dsW17i6p6Y5JvttYeNd1/YpIXpo/suE+S41trb92Nmzhk+hF59yQ3TfKNJB9urV29e7dq71NV+ya5zg/61tr23bA5e42quijJRenv23+emX7XJOck+afW2s/tru0bMbWu/dskP5xkvqWttdZesuu3au9SVdck+XiSX2ytXTg37yeSvLe1ts/ChW9AquroJMf/v/bOPN7Wsfz/748kDTKfUKZI5dtoCElIxkiRUBKVBiqFDCljUaTkG0nmX2WKDJlDiIqcb4YUKnOmzOQg5/P747qXvc7aa+3p7LOe59n7er9e+7X3eu7H8Vl7P+t57uu+r+tz0fta2rz/qkZG0+dJktYGziXuS6cBDwCvAT4CvBH4gO3fVKdw5pA0L7A48Ffbz1atZyRIEvFZWBS43vbTlejIQGrmkfQtYBfgm8ApDHzANgf2Aw62vVd1CkeGpCnAK23fXl6LuOktC1xi+5wq9Q2FpHuBHW3/sry+GzjZ9tckHQG803YjVo0k7QrsAbwaEGDgCeAA2wdXqW00FFfN5Ymb3LG27y+7CQ/YfrJadb2R9GrgAGLCMoX4G8xAEyZdAJLeBuwJrEA8cFaxPVXSt4Hf2T6/UoE9KKl7lwNTgS1sTy+TyAuAK4HNm7CwIOk9wBnAAj1OcVOuJQBJy9B9Eo/t8/qvaGRImg7cBiwMbGv79LaxRgRSkr5AuBQ/TLyXQde/7TX7rWukNH2eJOka4C5gM3dMnMtC7qK231WJuFEiaV/gZbZ3L6/fB5wFvAK4D1jX9l8qlDgskrYHvgEsRMyRVizPtjOAK2wf2jcxtvNrJr+ID9cuPcZ2Ae6qWuMI38d5wGFtr/cD/gv8rXzfpmqNQ2ifBryn/PxWYpVrqfJ6TWK3qnKdI3gfXynaDydWsd9Yvh9e/gZfrlrjCN7Dq4BTy/t4tnxfroydCnyvao3D6D8JeBL4AbGQ8MnOr6o1jvB9rA88TwQkewPT2/4OewHnVa1xGP1LAHcC/w9YA3gc+Dnwkqq1jeI9TAV+D7wdeGnVembifSwL3Fg+y9O7fL1QtcZh9E8n2qQcVN7DdxhYSF6p7vqLzn8QLV5mr1rLGPU3ep4EPEMEGN3G1gWeqVrjKN7L39vnc8D/AecDKwOXAGdXrXEY/V8rc759y7Oh/dn2ZeD3/dQzO8l4MAW4ocfYDWW8CSxHaWwmaTbgC8DXbR9UVjC+AhxfnbwheYCYeP0OWA+40/Y/ytjLiQ9aE9gB+I7tPduO3QJcIekx4iZxWCXKRs73gXcD7weuIm54Lc4jHpq7VKBrpKwLfNX20VULmUkOBI63vV1JUdy7bezPwOerkTUybN9RVkqvAD4G/NR2rTV34Y1EeuL1VQuZSX5CpLhuAtxMl92QBvCC7V0lXQccAywnaYuqRY2CKcBJbkt1bRhNnyc9BizVY2zpMt4UFgH+CSBpUWKh53O2r5H0feC4KsWNgB2AvcrctHMn+RZgmX6KyUBqfLgV2AK4qMvYFsQftgnMTaQNQKRkzUesAANcCuxchagRchrwXUlvB7YlUiBavJNIhWgCiwKX9Rj7LfX+G7TYhEizvKzLTe5OIg+7zjwN3FO1iHHgTQwErJ053E8Qn+/aIOmgHkPXEbsJT7SdY9u79UfZTHEDkXrSdN5JpFg2ps60F7ZPkfRX4FfEtdWUdOnzid2zS6oWMkaaPk86DThQ0hPAL21PK/WPHwG+DZxQqbrR8SQx3wN4H/Co7WvK62lEil+dWYj47HZjOl1Sj2clGUiND98CTpa0GPBLYndkCrAZkVbWlFWve4gUjiuBDwB/s31vGZubGXcW6sbuxORwReDHRI1Li+WJnOwmcBewDtCtaHXtMl53Xs5AQN7JXERqTZ05BNhe0kWucfHzCHgQeH2Psf+hftfSZkOMPdUxbqAJgdQXgOMl3WH78qrFzAT/oM+Tk1mJ7RtKzd0pxKJbE4rFDweOKrbbF9NlB8T2zX1XNXKaPk/aDZifCJhOkPQUkcYOkQ7ehPtRi8uB3Uvt4C5EfVSLZYC7K1E1cv4OrE73RYX3ErvmfSMDqXHA9qkl7Wpf4IfAS4nahOuA9WxfXKW+UXAscJCk9xOB1B5tYysDf61E1Qgo6Q779RjbpM9yZobDgMMkzcfgh802RGpf3bkW2JowBujkI8DV/ZUzal5LpDrcIukyBk9YmrIbcjKwn6SbiTodABfDgN2I9KbaYHvJqjXMAi4mVncvlfQ8sdgzA7brntIEsRN+kKSptv9ZtZgxsC0RDL6I7UclrUcswr2hElWjo5WpsDdR49hOy5SotoYZTZ8n2X4G+Lik/YkF24UJY4Zr3eEE2QC+StSenkykebeXEmxNpFPXmUOBIyQ9R8yTAKZI+jSwE1Hb3DfStW+cKbVFCwD/buJqtqStiZvEnwmnNZfjRxIFfE3avm4kkrYjHpaLEA9HAf8C9mlC3U5xKvsNUa92GnAE8X7eSARS77V9bXUKh0bS7cOcYtu9dnpqQ+mrdjphOnE/8eC/h0iLuIiwgn6+OoUTH0n7MMxuh+19+6Nm7Ei6FlgMmBe4g+67IbV1LCvPtXNtD9opL4tWG9o+sf/KRo6kNRj+WmrErmcT50mS3gtMtf1Ul7FXAsvbrnsAMizFtXaaa+6KKulrxILCKxhw1v0PsK/77G6cgVTSWCQ9xChSMhqy8gvM0B+htep1jxv0YZW0KuGMtTKxSmrgD8Cutq+qUttkQ9JawFrExOURopVBrVd/iz37ArY/12XsSOAh29/sv7LJiaRhi89tb9sPLWOh9JFapa0OpH1seeAa19z+fCLSpN5FE/EaKr//txC12eeXXdo5geeaEOBKmouon209235v+/F+68jUvjFSip4Ps33PEEXSLZqSCgS82IR0Mbr3CqlTDvbhNCO3fdSUoOlu6p+r3JUSLK0m6eXEKvZjzia2lWD7EppXoL4lg9OXWlxJpPFmINUn6hwkjZBBveDamJ8uKZd1Q9L/I1Kufme7tmn2vRimd9H9ktZxvXsXDXUNvYrYDWkExQTqQML97uWUPkzAo0QWw5+Y0eW1ljj6UXYzL+krGUiNnc0IR7t7GLpIGhpSGF2KWA8jeuW8rMdptVlxsb1P1RrGg9JYbqTY9o9nmZhxpuSVP1O1juGQtAExQXmi/DwkrnHz0U5Kit9rqf/CSDuLAPf2GPtXGa8lkk4F9rD9j/LzkNj+aB9kjRuSFiAWRx7plipXFyRtDGzcduibJYuhnTmB1Yi6zrozD7HLP4+kR4jU6SvL11TbdTfx+ThhONHiEOI97Eu43h0IfLACXT0p6XxrtB36TKmra2dOoqb8xn7pGgcOIOqIvkjU3rXXPZ5FtMaoVSA1kudyO/18RmcgNUbaC6MnUJH0XsCGwKeJIHEHwgp6K6J/wpeqkzYyGrpV/aPhT3kRE66EtWIEu7Lt1HGH9tdEGuI15edWbVo3al3U3ULSIkRfuPW7DVPv93E/0deuWyuA5YDOCXGdWJAopIcwipkQu+aSNgf2oa1Hi6RbiX4up1WlawimEM3ZWyzFYCv654gV7W9Rc2xvBCDpbYQz2XsIE5CDgf9I+oPttSuUOBxN7F20EgPzHhOL5p19vJ4D/kY0iW0KWwO72z6uS4uSf9Db7bVKhnsut9PXZ1vWSCUvIukWovP78YSbzoq2rytjJxAFiINqFupASUc8gI6t3Rg9YgAAIABJREFUattTJZ0L/Ml2rVZYJhIjMGhop3ZmDZIWB+6z/Vz5eUhs39kHWTOFpPOIoONAejRRrWtxegnMPwd8zPa5bcc3AH4BHGV716r0TTYkbUksrp1PWIY/ALwG2JxogP5x2ydXp3BoivvmFxrortYTSUsQAdWnynfXuUZH0gPAZ2yfI+mTwPdtz1/G1gLOsv2qIf+RCinPuA9NgObaSHoG2Mj2b0og9TywQpkvrQ+canuualXOyEiey+308xmdO1LjRNn1eC9hENAtheaIvosaPYsCt9p+QdI0In2jxc+JCUwtAykiNaBRW9UTiabvynbcdE0EVYMc7UrAXtu0sg5WBbazPWx6WQ3ZC3gHcI6khwnDlYWJJsIXkfVR/WZPInj9fMfxE4v5xzcIK+XaUZ7NrwWWIHYOGomktxBpiKsRc43XEA2frwT+t3yvM43uXdT0Z1wHNxFpr936Va4PTO2vnOFpf0ZLekmdUlkzkBoHit3zGYRzSDdMWEDXnfuIPGyA24mbdeuDtlQlikZOE7equyJpHiJgfQ8xcXyEeEgeZXuQ7XAy7txOOAENcmci0lGuob4pce08SAPq07phexqwjqR1iWad8xNNnmvvONjOSFJeG7KztjTRe6YbpxM97mqJ7WnlnlrX1O6RcgPxeT6OSL+/uhTbN4Wm9y4absG8SfXL3wJOL2ZQpxFz1HdI+jAx96hVrVoX7pV0InB8Hep8M7VvHJA0FXiW2PW4uam9WSQdAzxse1dJXwG+B5xKvLfNgZNsf7pKjb1o4lZ1NyQtBfyWyO+/ioEUmncTE+M1bf+j5z9QEXUuBB0tZcV05R42t6sCF9t+Rf+VjY6SjrUDsIHt2ruSTUR6pLzOC7waeBx4tG5prt2QdCdwpO0Du4ztAXze9qhSb/qJpB8AC9nesmotY0XSScTi2sLA9cTi2hXAFbb/XaW2maUJvYtGsmBe59TKTiR9lCjlWKzt8L3AznXPYigOkJ8grPP/RDSXP7mq51wGUuOApKeBTWxfWLWWmUHSQkTvlpvK668SDVRfDlwM7Gf76Qol9qQ0jPyD7S91CaQOA95ue/VqVQ6PpLOBJYlO7/e2HX8tUZ9wu+2Ne/33VVGCjxEXgtbtgVMKuN9RXh5P2Gv/s+O0OYGPEp+Rd1BzJJ1GFEvPRbiSde5m2vbmfRc2Qkoa5aYM3pk9w3ZnwXejkLQSYQTyedu/r1rPcCgaC+8B7A/8kljgmUIU338DONA1bixcnmW7EFkX5xH62yc/jdlNkLQksSvSSvFbCrgFuNz2F6rUNpGZKAvmnUhahoE+TLe4QUFBsdDfBtiEmHucCRxnu1vK4qzT0aDfWW2R9Htite6EqrVMVorV7enEJPg04mG5HRGU7Ap8sAmBrqQngE/a/lWXsU2Jm8Sr+69saOpcCDoSJO3NQA3dUAHh7YTTVF9v1GOhFNgPie01+6FltEiaQtRCvQ24g4Gd2SWI1fh1bNfZuW9YJG0FfNX28lVrGQ5JsxFB1I7EwlqLZ4BDCee+2qbOlYWeoajd4s5wlJqp9xKLO7U3mwCQtAqRlrgM3WvJ39V3USNkoiyYA0jaCzja9r+6jC1M1Nbu139lY0PSq4jPwfbAO4l6u+OJcohB73Hc//8ZSM08kt5B/NF2rKsL1mSgyVvVLSQ9Dnza9i+7jG1G3Pzm7r+yiU3poTYHEUA9AbyPwb1lnmvKKmSZ+C4MPNGwOgoAJP0MWJ2YuFzbdnxFYsHkctufqErfeFBSjk+rs1NZJ23tJRYmdndusv1otaomB5JWZsBsYlWinvkR4Goixe/KbunIdUHS2sQC5yXAOkSGxcuJ93IP8Zn+VHUKh2YiLZhLegFYpUf6+vLANXUPytuRtAaxM/URYBpxba1JZDJ81vbPZun/PwOpmac0+XsFscLyPF26pNue0m9dI6GkxI34IqjTipGkxejirtbwreozgTcSqX3tLjWLAxcAf7W9SVX6eiHpFbb/0/p5uPNb5yazhpIW16obvKBqPaNF0XD0i7Z/0WXs48D/2p6v/8pGR4/PwhzAm4nm59Ntr9RfVaNH0tbAue7SgFfSfMCGtk/sv7LJQ9lVu5eBJrxX2P5LtapGTglErgJ2Y8bU+8WBC4ED6nwNTaQF83ItrdS+SNU2tjFwjO1etWC1oFw32xBGJUsQxmjHAGc62pi8hKjz38L2wrNSS7r2jQ+H09ymi3+hudpfdFeTdCmwve2/2b4VuLVaaWPmK8ClwG0lJ7tVi7A8sV29U4XahuJJSa0VrqcY/pqq7WqXpNWA+WyfVV4vQEx6lyVWU3ev+86U7f8Wg4Dam2L04GVAr520J4lgpAn0+iyImBR/qL9yxsxxxL12UCBFpE8fB9R2EgwvpovuDKxAtPr4sO2/SNqRWIGve63aUrZH06+vbixL1NO16mlfCZHmXWrw9qXe19DFxP30UkmNWjAHUPTu+mR5aeDHpZSgnTmJJtYX9VPbSJD0/lZKfZnvvZfYyTyeKHm4s4zNLukk21tK+gWRjjxLyUBqHLC9T9UaxortbarWMBM8w8BEcQ3CCavR2L5D0puIJosrEik0NxMTleNr7Gr0KcJmvvVzU4NzgIOBcxjoc/JDYC3gV8QK2LPA1ytRNjq+C+wp6coG1hP9AdhN0qXtBjeSXkmsaP+hMmWjo9tnYRoxAbim7gF5G0OZyMxPl0llnZD0LmIi/BDRz2gNIliHuMfuTKQF1ZZWECVpDmKy2zJgubHGz4V2pgGz2bak+wiTjFbvqycIS/E60+QFc4D/MLAQIsI19JGOc54j0uLq2K7nbEkfcTj+/hvYgHDQffFvUp4PZxJBFoTN/izv/5WpfeNIg29wjUTSlUTwdDGxU/NzIm+/G7a9W7+0Jc2lpJV9zPYFJTXr38CnbJ8s6dPA123Xva9ay7VvVWBu4Dq6O5XV0rWvpNFcRui9iIGd2XWJScAatq+vTuHEp6T4tBxCtwHOJQKRduYkanb+anud/qkbHZKuIrRvAsxGTBhbqWWbAIfaXmyof6MOSNqVcE98NfE5MBGEHGD74Cq1DYeki4Czbf9I0gnAysAXib/FIcB/ba9cpcbJgqTjgP1tdzrT1hZJPydcXLewfWaX8QWIIHBZ4KO2z+2XttyRGie63OAAHpdU6xucRtAwsh3Xq3nkdsTuwcbEA2UtYregGyZWshuBpDcysCP1L+A623+rVtXIKA/Jk4nVoibaVM9BrJ5CBCKzE5NIiJTRWZpvPY4sQNgit79uBLb/LOkNhGX1ioR7333AkcD33fC+OQ1hCrEw2GIpYKGOc54jAt1v9UvUGFkO2Nj2dEmdu2sPE++11ih6Ox5IfAZOYcDJcnPgQEnP2j6sQonDcSgDuwNfJ3b9Ww549wAfrkLUaJkIC+a2t239XAKQeYFHutVA1oitiOfyqZI+YfuU1oCkJYhraQHg/f1O080dqXGg3OAOofsN7nPATnW9wal7w8he2DVtHqkhmqg2CUVjwp8SKy+zETUWryLyys8APuOaN1eV9Cdi4vIIkQ53EnBZU0w/iv7f2N5d0vHAki49yCRtDhxiu+5pKBMaSSvZ/mPVOoZDA/3VutHaTbgeOMxdWh7UhWKlv73tv1atZSxIup9wb/25BvcZ3BbYxzVuKAwg6TaisfyeXca+DWxue+n+KxsbJaBdmnDu+1sTgpFeC+Y0YEewk/Is24ewom9xK9HK4LRKRI0ASYcDnyXcjU+U9HbCDfIFwqTr5r5rasjcptZMtBtcEykOLvc14WY8FMX2+QPADkTj0WmS5iQCqx8RzllbValxJCiaRm5B9HZ4O/Ag0cjzFNtXDvXfVo2kDxK9yJ4g0uI2tn1+GTuOaMi7UYUSR02ZtCwMPNjQXUIAJG0IfA14TxPsecsi207EtXQOkV42BdiIaJR8DJEatybRP26W2vSON5Lmsd3Z6Ll2SPoJkbGwDnAnEUi1DHwuBy6y/dXqFA6PpGmEO+KgHnbFWvwc24N6M1VJue98oXwtAdxPLAju2zTn1iYvmHciaUuiFOJ8Br+X9YCP2z65OoVDI+kQwkTiUOAzRNbOOrbvqUSQ7fyayS9iu/H9PcbWBqZVrXGyfBEFxK8n8mRn+Kpa2wj1P0nsOnUb2w54smqNY3hPbwT2Am4iVo3uqlrTCDS/nghel+k4/lli57NyjSN8HxsAfyRSXl8AlivHfwpsVbW+Dq0vIVJ+/gY8Xa6XzcrYesCN5T3cDHyiar0jfE8HASf1GDuZqM2BcCu7oWq9Q7yPLwC7tr1+B5GO9QJRf/e6qjUOo39eYGp5Vl9F7PD/kUjr+xMwV9UaR/AebgUO6jF2EHBr1Rp7XDfTiTrm7xCT9ucJe+3K9Y3yvdwGfLvH2LeBv1etcRTv5SaiJ1a3sSOJ/nCV6xzmPXyr3H+uBuatUkvWSI0PdxErXYNWiohA6q7+yhk7ZQVpVXp3Hq+jmwuSFgGOAtbvNkyk0dR+BZtI5etlmPEvYoLZKGzfIulYQvtOwGsrljQsjiLcQYW4to+qQM6YKL1/jiVWHo8gnB9b3Ap8GqjTDsjOxMPxQsJ5aXHgREkrEdfNLcSK6ekuT9IGsC3w8R5jxwG/IFoenAJs1i9RY+BLRAuAFocR96NdiNrT7xA1DLXE9qOKhrafIHamniZSj48GTrTdq7a2ThwGHFb6dv2SAQOWzQgzkC9XJ60nXyDSVr/SOtC6L0naviG/9xaLEgY43fgtcf9qCksDvXZgTyeup1qh6Nfaed8X0ZPvls7SR/fRij4DqfGhiTe4QUh6DdEnZ1nigm1dme0Xby0DKeKBuBwx4bqZKIJuIocDuxTb52daB4t73C7U9/c/CEkLEZ+BzQmHpkeJCXJtUwYAJG0/zCm2/eO+iJk59gQOtr1HqQtpD6T+QlxPdWIbov7sa60DbSko5wCbunlpibMDb6J7X5Y3E3WQEPeraV3OqQuLUYxLJC1ILLatZfu3kp4j0o5rjSPt+5jy1TgcbnfPAnszYKsvIqD9vO2jq9TXg6UZPP85g+j983qgSTV3E2bBnJijrkDsFHayQhmvG7W1n89Aahxo6A2uG4cQhZOLErnjKxEfqK2I7tEfqE7asKwKbGf71KqFjJYuzolvAO6WdDFRWzSFuFE/Q6Sh1BpJnyeCp/cQK79nAQcQdQhNmAgPNSls3cibEEgtTvcHJcSkvW5915YAft1x7Jzy/dCGXDudnEw4qs3OQI3UgoTT6H4MBLfLESmNdeVZBpogr0n0pGnVOj4CzFOFqLFQ/haDGjq7ATU7tn8q6Wii59LCRPbCPTXeoZ2TuFbaaS0QvrzPWmaWCbFgXjgO2KcssHW+l28Q7pC1wjXu15qB1DjRwBtcN1YnCvhaqWWyfRdwgKTZiN2QdasSNwwPMnCDbhqdKT3Pl6/2nhpPlu+bEsX2deZ7xIR4M+D8hqVvYHu2zmOS5iGu/d2ALfsuamzcDbwTuLTL2ArA3/srZ1jmZPBnuPX68T5rGS92JHabvkW0amjxLFGn1vos/5HIBqgr1wA7SLqHmDBeYPuFMvZ6YtGwthQ31AOIPlJT6N5guNap35L2Ao62/S/is31329jCxELiflXpG4JNJa3Q9no2YkFqs5Ju2aLWO/0TaMEcYhHnpcDuwL5tx58hnt91vI5qS7r2jQMdN7jOsTrf4GZA0pPABravlPQYUYz+6zL2PuAs23NVKrIHJQVoB0J/re3BJzqSXtGE1d2xIOmzRLPeNarWMhySdifMG7YnUiqfIHoyzUPU5OznGrlMFavwgwlXtRazAT8kiunvbjte60lXJ2UV+22EM9b9RO+ZR6pVNXIkLQucTQRNdwNr2761jF0I3G/7kxVKHBJJJwEbEingXVO/bZ/Qb12jQdILwCru0uJD0vLANa6Zk2X5TI8U101/C0kvBd4F3E4sNDd5wfxFJM0LvIWB93KT7UerVdU8MpAaB5p4g+uGpBuAb9k+VdEJ/i7bW5axHwCbuKa9NiSdRqQizgVcC3Ra8tr25n0XNgmRNAV4pe3by2sRjoPLApfYPmeo/77OFJvhX9l+VdVahqP83n8EfJ5wN5qd2Ol8CfAT2ztUKG8QE2XSNZGRND/RuNNtx95KBFIPVadsaCQ9QrgONmnXYAbK52Ml29d2GduYcMJrTNPtJlEycp4hFmrrvHOcVECm9o0PLVe4bryOKLJvAucRxZSnEqkoZ5VUjueJYuPdKtQ2HAsA/yg/v5SoQ2gEkjYAfmf7ifLzkNg+rw+yZobjibSxVs74vsTOyN+BL0r6jO3jq5E2dsru8s7EqmTtKZPdHcoiyFrA/EQ9y6Wt3YQ60S2lciIg6W2E8ccKxPNgFUcj2G8Tn/vzKxU4Cmw/3OXYjVVoGSVPE3btjULSJ4HWTp+BH0vqzLiYE3gr3Q1NknHA9vTSL/Q1VWsZKyOZW7TTgHlGbcgdqTHScYNbHfg/InWmnRdvcLY37aO8caHkNX+YKAq9uEkP/CZRVhpXtn1N+bndMbGT2q/ES7qfyBk/s6zkPUC4xx0kaV+iwe07qlXZmx42q3MQu53TCPe4C/ouLGkcktYnUuKuJmrV9gZWKIHUXsTnflQTnCqQNKyJj+2P9kPLWCjNVN8HfMj2aHY+K0XSZkRTc4j62MuIxZB2niOMSo7oFugm40PZ9fsu0duuCYsHMzCCuUU7tZ9n1InckRo7/yGa+UFcmI/T/QZ3Pg2wrC5pQGsTBgetVZcHiP4Iv2lqDnBDWJKBYu0lqxQyTszNwGdjeWA+wsIaYjJZ934b3WxWpxEr2hcweMGkthSTjM8RDorzEfeoK4GjbHemvybjz4HA8ba3K25xe7eN/ZlIu2wC3Xb45yOabT9MsUavE13cUN9O9Ju5jO6p37XLuLB9GnAagKTjgP1Lj7uk/3yD2NX/s6R7ifnRDM8J2++qQtgImQhzi1qSgdQYmUg3OEnvJIrPlwb+C/ybCA7nJ+opbpO0he0/V6dyaLo8NAdhe9d+aBkDTwEnSzrK9oXdTpC0LvBZosFh3bmHqIe6krDM/5vte8vY3NS7X05Xm9Wy0LAm0Xh0E+KzUWskLUUshEwBriL6nLyGcGT6oqQ1bf+j97+QjANvYqBfV2dw/gQRjNQe22t2Oy5pUeBXwA/6q2hEdLqhTifmPGt3OdfUO3Ud29vCi/ei1xFtSq633bgm7Q3lpvLVSGzfOfxZyVjIQGocaN3gmkhpwnsh4diyPnBZaVyIpJcR6RDfBS6U9FbbD1Ymdmg6H5oA8xK9ch4n6tTqGkh9hXDDGirH/SJidXtnav7AB44FDpL0fiKQ2qNtbGUa1IRR0kqE3flHiSDkEWreULiNHxAr7yu3BbJIei2xU/59op9RMut4kPhsd+N/aFYTz0HYvlvSgYSrYq1MZGxPuBX40iz8G8BCRPC3IjBV0hnAFbYPrVLfRKbJ87xulPndp4jazUWBHWzfJmlz4AbbjXlOV00GUuNEqSfahFgpmrNzvMb5418i3GhW67QNL/1/zpf0eyIN5YvAXv2XODy9HpplInwU9U6h+Sjw/aHSJ21b0k+Ar1LzQMr2gSX1YUXi+jq2bXg+woK4tkh6CxE8bUE0iX2OqJHaCTjczWkMuwbwyfYgCsD2vaVW7biu/1UynpwM7CfpZuD35ZglLUN8jo+pTNn48QLx3EtmIZK+BuxPLGxexoz94X5L3LMykOoDkhYgFmofaWJdWrn/XExkiFxHPCtarW1WIxZAt65EXAPJQGockPQFoq7i38BtdOlRUWPWIYpUe9Z92H5M0o+JYtdaBlK9sP1HSQcTNtDLV62nB4sTvU2G46/ExL722D4ROLHL8VoGtJJeTwROWxJpif8lHjR7AZcTOwf/16AgCmLFulfBcKspZjJr+SZxPV1O9I8COIvYUbiIaBJbe0ofqU7mAN5MTO4HWXLXieKQuIDtz3UZOxJ4yPY3+69sVOwA7FVMezo/17cAy1SgaUgmmlNc2a3Zh7bftaRbib/LaVXpGgOHEc+0jYjSgvY56+VEsJ6MkAykxoddiFX3zzdsogVRFzV1BOddR813QobgYaIouq48Q6QgDseryrmNoKQOvJbuO7QjCRz7yd+JwOKPhDnD6a3GhJLmrlLYTHAZsL+ka9vz4yUtTtRJ1aofiqRRrYCWYL3WlF39DSWtRVjQL0Ckh15i++JKxY2Om+geeIsIoj7TXzmjZkt6LwJeSXwe6h5ILUQ8h7sxnS732Rrwa0bhFEfvhZ/KkbQlYZp0PpFm/wCR7r05UeP8EttNSftejXAffKxLUP4A0aA3GSEZSI0PU4CTGhhEQWztPj6C855kZJP9SpD0ii6HWyum+wF/6a+iUTEV+CBw7jDnbczIgt5KkbQIkU65frdh6vnAvJPYGXwLkeZwn6QLG/qZbvEVIv3nNklTiQfkFGJn9m4iVbFOHN/xujVxV5dj0GXHs66UJp61ClxHSTeziWnAPZ2pozVlEaCXzn+V8brzd6LVSrfr6L2MLKuh30ykOrU9CbfTzqyKE8uu5jdoTv3sNKKtTTdey2BXy2QIMpAaH84HVqKZD8qhmgl3O7euPEXvFdN7gQ/1V86oOBw4VdLVtk/odkJZrd+WWP2qO0cDyxET9ZtpQKqr7SUlrQJ8DPhI+f5oKeI+nwamwdm+Q9KbiILiFYlVxpuJ2qjjW6YyNWKutp/fRDQGPwY4gzBtmEKkF3+Kgd46jaEs9nyaeG/3Ayc2xUnL9uVVa5hJ7ifuSZd1GVsOeKi/csbEocARkp4DflmOTZH0aeJeu11lynrQlOt7hCxN1Ch343Rgm/5JmWkuBr4u6TfE3AmidvNlRF1zrVMs60Y25B0HJK1OrMD/grhAB0XzNUxlAl5s0vYYURMyFLMDc9e1SZukbejd++ca28/3XdQokHQIcZO+juhVdBfxfhYD1iWcdX5ge5ee/0hNkPQ4sJ3tYZt41pHSRHgtIh3oQ8A8xN/iF8APbf+pQnljpvSUWgq4u8bumwBI+i1wtu3vdxnbGfig7dX7LmwElM/yRrbb6yjmIlLg3kA4iM4NPA28y/atlQgdJaUP1qYM7kl2Rt13bkt7jM8BH7N9btvxDYjP9VE1bo/xIsVwYi/gFQwsbP4H2Nf2wZUJ64GkjwEX2H6k7dhiwL/ar5mSxbCN7drWDEq6EzjS9oFdxvYgSjsW77+y0VPaFlxF7EpdTCzQnk04ic5BOL3e3/tfSNrJQGocKMFIi85fqKhxl2hJew9/1gC2951VWiY7kjYi0rHeDbysHH6WuOEdavvXVWkbDZJuA3ayXSs75LEgaQ5gA8KIYkPiwXOr7TdXKmwIJG1BBIAvJSa5P5f0TeDrxEMS4Exg67r2oJH0H+DD3fqqlZ5qv7LdLZ23ckoa5Zm292s7ti9Rg/MZ28dKWpCYwNxo+xMVSR0xkqYQ5hhvA+5goD5kCeB6YB3btd3VkTQnMVF8P1Ezex+xQzsf8b4+VOrZak8JyldhoN7u97ZHkp7fdyS9AKxi+5ry+iVEhsKKtqe2nbcScHXd5kmS3gtMtf2UpH2IVh77EzuCrVTpzYi0vgObND+SNC+xkzlD7SbhINw4J8IqyUBqHCg7UkMyAVIjGkPZUehmcPCfCuSMmrLy22r4+nDdV3s7KUW5OwAbDOUG2TQkvZIIULawvVHVerohaTvgJ8Tux5NE7cThRNrJ/kRq31uJfP8jbH+9GqVDI+kWwiVxiy5jpwLvaN/xqROSHgE+0bHzcROA7be0HfsEsZPQq89UbZD0M6I+ZxPb17YdX5FIa7q8IQHhukS91/xEQNU0049GURaZV+4IpJ4HVmhIIPViIFjmFfsDOzJjfdEzRNrlN4dqYZJMXLJGahwYLkgqTW+TWUjp9r4rkSfeq8C1VjfpXpTA6YGqdcwEmxApiXdKupbBqa623YRarxkouzc/L1915UvE7uVOAJK2Ak4AdrT9o3LOBZL+S/RWq2UgReg6uQQgZzNQI/VBosaoztfP7ERaMQCS5iNMbw7vOO8OwomtCWwAfLE9iAKwfW1Ja/rfamSNjrLDOWiXswkM42o5HXgCuH6C1SVVzYt14banA3tK+h5hSrQwsbN5U8vhtc6UZ/GIAz3b75qFciYUGUjNIopl8qZEncUaRJpNMuv4MrA7cBDwbeBbRKPILYh0ptrmXk9AFgD+UX5+KbBghVomG0sRK6YtziImA522yX8iXAprie3Tyyr17sQ9dCHCMOBaoslwLxvoOnArcc9vmQ9tWL53TuCnEOk0TeBlxA5nN55kIGW09jTY9ON4hnayFGEY8Gvg47afIhl3StB0ZdU6xsBfmDGQEtF099fE7mwyRjK1bxyR9HJixXRLYD1iEnkT8LM6FoJOJMrK9VHEqu+LqQNlO/4cohZh9yo1TgbK73th4AnbvSZeySyi6ak0E4FifPNT4MfEzvKXiWDjze2mN5J+Aixue70qdI4GSZcQwdS67XV1Jd31IuAZ2++vSl83Jprph6R3AKcQrqhnE06DCxJtMT5D7DAvQjRbPcn2DhVJfZFyP1qptZPZdj9a3vb/tZ1Xy/tR0b8f8M+RnN+E3nYtSgnBc3Q8G5LRkztSM0m5GNcjgqcPEm469xFB1Mdsn1KhvMnEksCfbb8g6XnCaQ3b0yUdQTx8MpCa9cxGpCxtRLgPJv2n2+pYrpj1CdvHS1qYqBOch+j9tkNHENWaADelOH1nwjr8bkkXMVBovy6xsr1GddJ6sibws45juwDLMNj045tA3Wu8DiHqGn/YduwR4KBiib637dVLKcHOxPVXBy4sqcTtXNJxrM5z0V6NnDsxDeptRz4Txo06X7y1RtKaRPC0KTAvkcN/AnASUdDdcgZK+sPDwKvKz3cB7ySakUL8fXo1n0vGEdv/LTaxtXRUmyQ0feICgKSPEPV2r6O7eUxtc/iLRfIgm+S28YdoTn0Utv8saRmhM5z2AAAOaklEQVRigr4i4d53H3Ak4fL17yr19WAJBqe0bgrcbPtYiL9D2blqQkC7CvDdHmN/ZSB9/ToGzIqqpgm/1+FYk0iFTpKu1P5hWmMuISL6S4mb26WlGLFVH5X0l6uIB/x5RF+QfUqR93PEylwTmyU3le8SRblX1tkSeYIyESYuFKvhvQhr7UY0dZ7olM9yk3b1J5rpxz2E++ZFXca2LeMQC4e1qHlpkh34EDxT1zYRST3IQGrsXEk0JlyT2O14o6Rf2m6y21qT2Qd4bfn5ACKlZhsGGs59qRJVk5N1iDqpOyRdR6QBtacRNNK1rwlMkIkLhBnAd+pqzz4ZmAAuXxPN9GNP4CRJbyHqfls1UhsByxLGSgBr00wzhKQaMsVvJslAaoyUXORFiPS+LQj710Ml/RY4l7w4+0YxOHgKuAagNFbckRndy5L+sQBwS8frJBkNc5G7yFXTdJevHwE/LRkiLdOP2xm8o7MOYQpVa2yfJumfxK7gx5jRyXLblpOl7e2rU5nUFUkP0X1e2pn2DYDtKbNe1cQgXfvGCUlLEze3zYn0AYi0vyOBc5rSNb2JFMOPZwiHpjQ4SJKGI+lI4EnbX6taSxI00eWr9LjqNP24sW18QeBGojHyj6tROTySXgZ8BLjG9m1V60maR0mXHs0O80TJbpjlZCA1C5D0diKo+ijRq+Ux2/NVq2piI+nvwK62z6haS5IkM4ekzYhau8uJ1NzOps7YPq/fuiYzvaz0k/4g6RlgPduXV60lSZIBMpCaxUhaFdjCdtbozEIkbUf00VgvDQ6qRdJBw51je9d+aEmaSenfMhSuW8+ZiU4GUtUi6Rrgp7Z/WrWWJEkGyEAqmRBIOg1YlWiwmAYHFSLp9i6H5wVeDTwOPGr79f1VlTQJSYsPd47tO/uhJQkykKqWsih7PPBV4ALbg+pakiTpP2k2kUwU0uCgJthestvx0r3+KGLnMEl6kkFSrcnV12o4k+jPdxZgSY/S8bdIg4Ak6T+5I5UkSd+QtBXwVdvLV60lqS+Slh3uHNs390PLZKWHy9cCRL1aunz1mZGYBaRBQJL0n9yRSpKknzwMvLFqEUntuYnhdz6yRmrWcji5+1QbbO9TtYYkSQaTO1JJoyk9pJYBXgrcZNuSFibyyN8M3A0c0+qxkcx6JL2iy+E5iL/HYcB02yv1V1XSJCSt3uXwfETPn3WAHW3/ur+qkqR6JM0BvJX4PDwC3Gj7uWpVJcnkJQOpcab02pij87jt/1QgZ0IjaQmiOWSrb9etRK+N84E5gX8CSxEmB2vbvqL/KicfxXGt241FwL3AhzKwTcaKpG8Bi9neumotSdJPJO0K7EE801QOPw4cYPvgyoQlySQmU/vGAUmvBg4ANgGmMHCDayfTUMaf7xBB64bAU8A+wHnA9cCmtp+TNCfwK2BvYK2KdE42PsXgQGoacA/RUPL5/ktKJhCXAdkvLplUSPoKcCBwJHAK4Uz7GmBz4EBJz9o+rEKJSTIpyR2pcUDSScRk/mjgZqL7+wzYPqHfuiY6ku4FdrZ9cnm9DPA3YAPbF7SdtyFwbBZDV4OkeYidwbttP1i1nqTZSPo+sVAyrEV6kkwUJN0GnGp7zy5j3wY2t710/5UlyeQmd6TGh3UJJ7KjqxYyyVgIaO9Z9M/yvXOy/hAwf18UTWIkbQF8iKhXO8P2zyV9E/g6Jd1V0pnA1rafrk5pUnckndrl8BzAm4A3ENdUkkwmFiV2Y7vxW2Dn/klJkqRFBlLjw9NE2lLSX0RH092qhEx2JG0H/AS4FngSOE7SCsA2wJ7ETu1by897khPhZGgW7HJsGnAlsJPt8/qsJ0mq5i7CaOU3XcbWLuNJkvSZDKTGh0OA7SVdZHt61WImGZuWCTvAbEQwtZmkldvOWaLvqiYfXwIOtb0TvNgv6gTCXe1H5ZwLJP2XaMibgVTSE9trVq0hSWrGYcBhkuYDfknUSE0BNiMWrL5cnbQkmbxkjdQ4IOlg4KNEbdRlRMPCdmx7t74Lm+AUd7iRYttp+DGLkPQ0sKHty8rruQg3qVVt/77tvNWAi23PWY3SpGlIWgCYF3jE9sNV60mSqig7/3sDixCLhgL+BeyTpQVJUg0ZSI0Dkm4f5hTbfn1fxCRJBZSgdmXb15TXLwGeB1awPbXtvJWAqzOoTYZD0uaEE+cybYdvBfayfVolopKkYiSJqJdaCLgPuMc5kUuSysjUvnHA9pJVa0iSGtDtYZ4P+GTUSNoS+DnRE+5AZrR6PlnSS1punUkymShN5+8G/gs8mEFUklRL7kglSTLTlB2px4iHe4sFuhybHZg7d6SSoZB0E/A725/vMnYk8B7bb+m/siSpDkkbEKl97yB6U77L9lRJRwFX2P5ZpQKTZBKSO1LjRNluX5VIQxlU/2H7iL6LSpL+sW/VApIJxdLAV3uMnU4U1yfJpEHS1sCxxE7tEcBxbcO3AZ8GMpBKkj6TgdQ4IOk1wCXAsgwUgMKMaU0ZSCUTFtsZSCXjyQPACsDFXcZWKONJMpnYEzjY9h6lBrU9kPoLsEs1spJkcpOB1PhwCOFQtihwN7AS8aDfCtga+EB10pIkSRrHccA+ZcLYafX8DaJuKkkmE4vTfWEBosfaq/uoJUmSQgZS48PqwI6Egw5E7dldwAGSZiN2o9atStxkQNKctqdVrSNJknFhP+ClwO7MmDb6DPA9YP8qRCVJhdwNvBO4tMvYCsDf+ysnSRLIQGq8mAd4yPZ0SU8QK6ctrgayh9Ss53FJ1wFXlq+rbD9asaYkScZAaWy+p6TvAW8BFiYWqm7Kz3UySTkG2FvSA8CZ5ZgkrQXsSiw+JEnSZzKQGh9uJx70ELnKHwd+XV5vBDxShahJxseA1YD3AzsRD5ibGQisfmf7ngr1JUkySkrQdGX7MUlrArvaXr8aVUlSCd8lygdOAF4ox64m3Pt+YvuwqoQlyWQm7c/HAUkHAgva/oyk9YGzgAeJhqSLAbvZ/l6VGicTkuYiHBTfC6xFpD3Ydi4cJEmNkTQPsB4xYbwdOMv282VsM2J3fzngVttvqkxoklSEpKWI59oCxCLtpbZvrVZVkkxeMpCaBUhaEfgwYYN+se3zK5Y0aZD0CmAVYnfqvYTxx/PA1bY3qFJbkiS9kfRW4CKi8W6LqcCmwC+Iz/VfgAOAU0r6X5JMaCQtCTxj+/62Y9t3nPak7f/XX2VJkkAGUskEodRSrEYU4z4M/I5ICboCuD67vydJvZF0DtGHb2vgesKl7H+J5qMvA3bIhqPJZELSu4nn2Adtn1uOvYRYHOxstbK+7YsqEZokk5gMpMaZsiPyaeBNwP3AibbvrFbVxEfSdMLR6xjgaNs3VCwpSZJRIOk+YEfbp7YdW4poNvpZ20dXJi5JKkDSScBctjdsO9YKpFawPbUcOxyYYnuzapQmyeQla0bGiKRDgI1sL9N2bC7gWuANwKPA3MDOkt6VOcyznPWIVL7VgD9K+g9wFbEjdQVwne0XhvjvkySpltcAd3Qca72+vq9KkqQerMbIXH8vAg6fxVqSJOnCbFULaDBrAp1pJrsQqSnb2V4AWISYCHyzv9ImH7Yvsv0N26sTAezGwB/K998TgW2SJPWmV4rEf/uqIknqwYLADBktZUHwa0RfqRYPl3OTJOkzuSM1dpYArus4tilws+1jAWw/VHau9iXpC5LmB97DgNnEO4k88rQ+T5L6c6GkbkHTJZ3HbU/pcl6STCSeBObvPGj7kI5DC5RzkyTpMxlIjZ3ZgWmtF5LmA97M4O31O4CF+idrciLpSCJ4ehMwHfgzUaR7IHCl7X9XKC9JkuHJBackmZHriF6UZw1z3kYMXthNkqQPZCA1dm4F1gAuKa9bxaAXdpw3hWzI2w/eDJxBBE9X236qYj1JkowC2xlIJcmM/Bj4paTLe9mbS9qKcLr8SF+VJUkCpGvfmJG0DfBT4kb3APBlYmv9za0GkuW8nwCL216vCp1JkiRJkjQTST8AdgSuIUwl7iZqCV8HrEP0Svyh7Z0qE5kkk5gMpGYCSXsAOwDzEI0jd7B9Y9v4gsCNwL62f1yNysmDpJcBnwJWABYl/h63SdocuMH2XysVmCRJkiSjRNLGRDC1CtFTDeBZwkjpUNtnV6UtSSY7GUglEwJJywAXE4591xFplyvanirpR8CrbW9docQkSZIkGTOlh9T8hIHSv7OlR5JUT9qfJxOFw4C7CDfFdRno+A5wOeHklyRJkiSNxPYLth+0/UAGUUlSD9JsIpkorAZsZvuxsmrXzgPAwhVoSpIkSZIkSSYouSOVTBSmAS/vMfZa4LE+akmSJEmSJEkmOBlIJROFi4GvS5q77ZiLAcWXgPOqkZUkSZIkSZJMRNJsIpkQSFoUuIrYlboY2Bw4G/gfYA5gZdv3V6cwSZIkSZIkmUjkjlQyIbB9N/B24EjCcOIfRF3UacDyGUQlSZIkSZIk40nuSCVJkiRJkiRJkoyS3JFKkiRJkiRJkiQZJWl/njQWSZeO4nTbXmuWiUmSJEmSJEkmFRlIJU3m4RGcszDwbiBzWJMkSZIkSZJxIwOppLHY3qzXmKTFgN2ADYF/Az/ol64kSZIkSZJk4pNmE8mEQtLSwB7AVsCDwCHAT2w/U6mwJEmSJEmSZEKRO1LJhEDS/wB7ApsBdwM7Asfafq5SYUmSJEmSJMmEJF37kkYjaXlJZwA3AO8EPgO8wfaRGUQlSZIkSZIks4rckUoai6TzgXWIIGoL26dVLClJkiRJkiSZJGSNVNJYJE0vPz4CTB/qXADbU2atoiRJkiRJkmSykDtSSZPZt2oBSZIkSZIkyeQkd6SSJEmSJEmSJElGSZpNJEmSJEmSJEmSjJIMpJIkSZIkSZIkSUZJBlJJkiRJkiRJkiSjJAOpJEmSJEmSJEmSUZKBVJIkSZIkSZIkySj5/12k/htl49xiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1008x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "city_plot_df['city'].value_counts().plot(kind='bar')\n",
    "plt.title('Distribution of Top Cities from IP Addresses in Data Science')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_science['country'].value_counts().plot(kind='bar')\n",
    "plt.title('Distribution of Countries from IP Addresses in Data Science')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "web_dev['country'].value_counts().plot(kind='bar')\n",
    "plt.title('Distribution of Countries from IP Addresses in Data Science')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What lessons are accessed least?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "curie-statistics-assessment                               1\n",
       "appendix/open_data/www.census.gov                         1\n",
       "statistics-assessment                                     1\n",
       "12-distributed-ml/spark-topics                            1\n",
       "appendix/open_data/www.stat.ucla.edu/data                 1\n",
       "python/handling-duplicate-values                          1\n",
       "3-vocabulary                                              1\n",
       "regression/least-angle-regression                         1\n",
       "appendix/univariate_regression_in_excel                   1\n",
       "4-python/pandas-lesson                                    1\n",
       "12-distributed-ml/6.2-prepare-part-2                      1\n",
       "appendix/www.opensecrets.org                              1\n",
       "spring/extra-features/error-pages                         1\n",
       "7.4.1-pandas-overview                                     1\n",
       "explore                                                   1\n",
       "appendix/cli/2-Overview                                   1\n",
       "scale_features_or_not.svg                                 1\n",
       "12-distributed-ml/3-getting-started                       1\n",
       "9-timeseries/6.1-parametric-modeling                      1\n",
       "misleading1_fox.jpg                                       1\n",
       "12-distributed-ml/6.3-prepare-part-3                      1\n",
       "itc%20-%20ml                                              1\n",
       "classification/project-old-exec                           1\n",
       "itc-ml                                                    1\n",
       "misleading3_deaths.jpg                                    1\n",
       "5-detecting-with-clustering                               1\n",
       "distributed-ml/read-jdbc                                  1\n",
       "stats/probability-and-event-interactions                  1\n",
       "12-distributed-ml/6.1-prepare-part-1                      1\n",
       "python/custom-sorting-functions                           1\n",
       "index.html                                                1\n",
       "git/cli                                                   1\n",
       "advanced-topics/html-forms                                1\n",
       "mkdocs/search_index.json                                  1\n",
       "appendix/open_data/www.openstreetmap.org                  1\n",
       "capstones                                                 1\n",
       ",%20https://github.com/RaulCPena                          1\n",
       "7.4.2-series                                              1\n",
       "appendix/open_data/www.databasefootball.com               1\n",
       "regression/project/null                                   1\n",
       "misleading1_baseball.jpg                                  1\n",
       "appendix/open_data/www.census.gov/geo/www/tiger           1\n",
       "%20https://github.com/RaulCPena                           1\n",
       "javascript-i/bom-and-dom/dom                              1\n",
       "advanced-topics/json-responses                            1\n",
       "7-classification/confusion-matrix                         1\n",
       "A-clustering/project                                      1\n",
       "regression/ridge-regression                               1\n",
       "spring/extra-features/error-pages/asdfasdf                1\n",
       "anomaly-detection/time-series-anomaly-detection-part-1    1\n",
       "Name: request_method, dtype: int64"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_science.request_method.value_counts().tail(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Appendix_LeastAngleRegression                            1\n",
       "appendix/interview_questions_instructors                 1\n",
       "appendix/extra-exercises                                 1\n",
       "fundamentals/cli/file-paths                              1\n",
       "working-with-time-series-data                            1\n",
       "explore                                                  1\n",
       "content/laravel/quickstart/resource-controllers.html     1\n",
       "quiz115                                                  1\n",
       "examples/css/%252e%252e%255c                             1\n",
       "one                                                      1\n",
       "10_Appendix_EntityLabeling                               1\n",
       "user/913                                                 1\n",
       "4-stats/2.5-distributions-and-testing                    1\n",
       "appendix/open_data/www.stat.ucla.edu/data                1\n",
       "appendix/intellij-intro                                  1\n",
       "codeupexercises                                          1\n",
       "students/743/notes                                       1\n",
       "distributed-ml/spark-execution-diagram.svg               1\n",
       "java-iii/jdbc/poop                                       1\n",
       "mysql/java-iii                                           1\n",
       "nlp/prepare                                              1\n",
       "student-page                                             1\n",
       "4-stats/2.1-probability_and_event_interactions           1\n",
       "5-stats                                                  1\n",
       "dendogram_sample.jpeg                                    1\n",
       "further-reading/javascript/array-splice                  1\n",
       "examples/html/welcome                                    1\n",
       "133                                                      1\n",
       "series                                                   1\n",
       "appendix/testing-code.md                                 1\n",
       "11-nlp/word-2-vec.md                                     1\n",
       "11-distributed-ml/exercises                              1\n",
       "timeseries/sarimax                                       1\n",
       "introduction/variables                                   1\n",
       "appendix/cli/intro                                       1\n",
       "5-stats/2-inferential-stats                              1\n",
       "professional-development/cover-letter                    1\n",
       "html-css/css-ii/boostrap-grid-system                     1\n",
       "content/php_iv/exceptions/throwing.html                  1\n",
       "annotations                                              1\n",
       "130                                                      1\n",
       "quic/115                                                 1\n",
       "content/laravel/quickstart/controllers.html              1\n",
       "registration                                             1\n",
       "Exploration_Lecture                                      1\n",
       "00_                                                      1\n",
       "curie-python-assessment.html                             1\n",
       "content/php_iv/classes-and-objects-ii/overriding.html    1\n",
       "9_Anomaly_Detection                                      1\n",
       "extra-features/json-response                             1\n",
       "Name: request_method, dtype: int64"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "web_dev.request_method.value_counts().tail(50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I notice a lot of cross-program crossover access here, particularly from Web Dev users accessing the DS curriculum."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Which lesson appears to attract the most traffic consistently across cohorts (per program)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1-fundamentals/modern-data-scientist.jpg             1559\n",
       "1-fundamentals/AI-ML-DL-timeline.jpg                 1553\n",
       "1-fundamentals/1.1-intro-to-data-science             1530\n",
       "search/search_index.json                             1329\n",
       "6-regression/1-overview                              1122\n",
       "classification/overview                               856\n",
       "10-anomaly-detection/AnomalyDetectionCartoon.jpeg     742\n",
       "10-anomaly-detection/1-overview                       739\n",
       "3-sql/1-mysql-overview                                700\n",
       "1-fundamentals/DataToAction_v2.jpg                    660\n",
       "classification/scale_features_or_not.svg              637\n",
       "1-fundamentals/1.2-data-science-pipeline              636\n",
       "sql/mysql-overview                                    612\n",
       "appendix/cli-git-overview                             578\n",
       "5-stats/4.2-compare-means                             551\n",
       "5-stats/3-probability-distributions                   524\n",
       "3-sql/7-functions                                     479\n",
       "5-stats/2-simulation                                  448\n",
       "6-regression/5.0-evaluate                             440\n",
       "4-python/3-data-types-and-variables                   433\n",
       "Name: request_method, dtype: int64"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_science.request_method.value_counts().head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "toc                                                                          13161\n",
       "javascript-i                                                                 13112\n",
       "search/search_index.json                                                     12107\n",
       "java-iii                                                                     10083\n",
       "html-css                                                                      9768\n",
       "java-ii                                                                       9453\n",
       "spring                                                                        9160\n",
       "jquery                                                                        8469\n",
       "mysql                                                                         8454\n",
       "java-i                                                                        8220\n",
       "javascript-ii                                                                 7911\n",
       "appendix                                                                      6234\n",
       "mysql/tables                                                                  5852\n",
       "mysql/databases                                                               5495\n",
       "html-css/elements                                                             5380\n",
       "javascript-i/introduction/working-with-data-types-operators-and-variables     5307\n",
       "javascript-i/functions                                                        5267\n",
       "javascript-i/javascript-with-html                                             5256\n",
       "java-iii/jsp-and-jstl                                                         5140\n",
       "mysql/users                                                                   5127\n",
       "Name: request_method, dtype: int64"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "web_dev.request_method.value_counts().head(20)"
   ]
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}