Discovery Analytics PE.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Discovery Analytics\n",
    "\n",
    "Discovery is all about finding previously unknown anomalies or patterns in your data. This can be **clustering**, looking for **trends**, using **regression** to find relationships between variables, etc. Discovery also means testing those proposed relationships through hypothesis testing. In this exercise we will try our hand at clustering the data to look for user or workstation groups, some simple regression to see if in increased number of total flows is related to the number of unique ASs accessed in a day, and finally look for trends in the time series.\n",
    "- - -\n",
    "\n",
    "We'll start with some basic import statements and readability settings and retreive our data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>l_ipn</th>\n",
       "      <th>r_asn</th>\n",
       "      <th>f</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2006-07-01</td>\n",
       "      <td>0</td>\n",
       "      <td>701</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2006-07-01</td>\n",
       "      <td>0</td>\n",
       "      <td>714</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2006-07-01</td>\n",
       "      <td>0</td>\n",
       "      <td>1239</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2006-07-01</td>\n",
       "      <td>0</td>\n",
       "      <td>1680</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2006-07-01</td>\n",
       "      <td>0</td>\n",
       "      <td>2514</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2006-07-01</td>\n",
       "      <td>0</td>\n",
       "      <td>3320</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2006-07-01</td>\n",
       "      <td>0</td>\n",
       "      <td>3561</td>\n",
       "      <td>13</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>20796</th>\n",
       "      <td>2006-09-30</td>\n",
       "      <td>8</td>\n",
       "      <td>33322</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20797</th>\n",
       "      <td>2006-09-30</td>\n",
       "      <td>8</td>\n",
       "      <td>35845</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20798</th>\n",
       "      <td>2006-09-30</td>\n",
       "      <td>8</td>\n",
       "      <td>36351</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20799</th>\n",
       "      <td>2006-09-30</td>\n",
       "      <td>8</td>\n",
       "      <td>36856</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20800</th>\n",
       "      <td>2006-09-30</td>\n",
       "      <td>9</td>\n",
       "      <td>2152</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20801</th>\n",
       "      <td>2006-09-30</td>\n",
       "      <td>9</td>\n",
       "      <td>8070</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20802</th>\n",
       "      <td>2006-09-30</td>\n",
       "      <td>9</td>\n",
       "      <td>8075</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20803 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             date  l_ipn  r_asn   f\n",
       "0      2006-07-01      0    701   1\n",
       "1      2006-07-01      0    714   1\n",
       "2      2006-07-01      0   1239   1\n",
       "3      2006-07-01      0   1680   1\n",
       "4      2006-07-01      0   2514   1\n",
       "5      2006-07-01      0   3320   1\n",
       "6      2006-07-01      0   3561  13\n",
       "...           ...    ...    ...  ..\n",
       "20796  2006-09-30      8  33322   1\n",
       "20797  2006-09-30      8  35845   1\n",
       "20798  2006-09-30      8  36351   3\n",
       "20799  2006-09-30      8  36856   4\n",
       "20800  2006-09-30      9   2152   1\n",
       "20801  2006-09-30      9   8070   1\n",
       "20802  2006-09-30      9   8075   2\n",
       "\n",
       "[20803 rows x 4 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from pandas import DataFrame as df\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from pprint import pprint\n",
    "\n",
    "# formatting \n",
    "pd.set_option(\"max_rows\", 15)\n",
    "\n",
    "# read in our test data\n",
    "dta = pd.read_csv(\"data/network_traffic.csv\")\n",
    "\n",
    "#display our DataFrame\n",
    "dta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quick review: \n",
    "Look at the ouput below and see if you remember how to ensure our data types are correct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>l_ipn</th>\n",
       "      <th>r_asn</th>\n",
       "      <th>f</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20803.000000</td>\n",
       "      <td>20803.000000</td>\n",
       "      <td>20803.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.228236</td>\n",
       "      <td>12138.320338</td>\n",
       "      <td>93.914243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.278183</td>\n",
       "      <td>9766.324252</td>\n",
       "      <td>5764.995269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>4323.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>8764.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>17676.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>40092.000000</td>\n",
       "      <td>784234.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              l_ipn         r_asn              f\n",
       "count  20803.000000  20803.000000   20803.000000\n",
       "mean       4.228236  12138.320338      93.914243\n",
       "std        3.278183   9766.324252    5764.995269\n",
       "min        0.000000      3.000000       1.000000\n",
       "25%        1.000000   4323.000000       1.000000\n",
       "50%        4.000000   8764.000000       2.000000\n",
       "75%        7.000000  17676.000000       8.000000\n",
       "max        9.000000  40092.000000  784234.000000"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dta.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Put your code here to make our categorical variables categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>l_ipn</th>\n",
       "      <th>r_asn</th>\n",
       "      <th>f</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20803</td>\n",
       "      <td>20803.0</td>\n",
       "      <td>20803.0</td>\n",
       "      <td>20803.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>92</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>2006-07-26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3561.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>372</td>\n",
       "      <td>3980.0</td>\n",
       "      <td>483.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>93.914243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5764.995269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>784234.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              date    l_ipn    r_asn              f\n",
       "count        20803  20803.0  20803.0   20803.000000\n",
       "unique          92     10.0   2005.0            NaN\n",
       "top     2006-07-26      0.0   3561.0            NaN\n",
       "freq           372   3980.0    483.0            NaN\n",
       "mean           NaN      NaN      NaN      93.914243\n",
       "std            NaN      NaN      NaN    5764.995269\n",
       "min            NaN      NaN      NaN       1.000000\n",
       "25%            NaN      NaN      NaN       1.000000\n",
       "50%            NaN      NaN      NaN       2.000000\n",
       "75%            NaN      NaN      NaN       8.000000\n",
       "max            NaN      NaN      NaN  784234.000000"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# %load solutions/fix_types.py\n",
    "dta.l_ipn = dta.l_ipn.astype('category')\n",
    "dta.r_asn = dta.r_asn.astype('category')\n",
    "dta.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20803 entries, 0 to 20802\n",
      "Data columns (total 4 columns):\n",
      "date     20803 non-null object\n",
      "l_ipn    20803 non-null category\n",
      "r_asn    20803 non-null category\n",
      "f        20803 non-null int64\n",
      "dtypes: category(2), int64(1), object(1)\n",
      "memory usage: 482.1+ KB\n"
     ]
    }
   ],
   "source": [
    "dta.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have our data cleaned up a little and see that we have no Null values (bonus!), let's think about how to procede. We saw before that there is some interesting data in the `f` field. From our summary we see that the three bottom quartiles contain data from 0 to 8, but our max value is amost 800,000!\n",
    "\n",
    "We can take a couple approaches to see where these large values are coming from. They might be the work of one or two computers, it may have been a couple anomalous days, and it may be tied to certain autonomous systems. Let's compare the `f` field by host (IP Address)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">f</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
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       "      <th>25%</th>\n",
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       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>l_ipn</th>\n",
       "      <th></th>\n",
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       "      <th>0</th>\n",
       "      <td>3980.0</td>\n",
       "      <td>26.426382</td>\n",
       "      <td>232.733282</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>5059.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2159.0</td>\n",
       "      <td>90.639648</td>\n",
       "      <td>466.006782</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>4718.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2416.0</td>\n",
       "      <td>81.007036</td>\n",
       "      <td>299.501027</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>47.25</td>\n",
       "      <td>5214.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1186.0</td>\n",
       "      <td>4.134907</td>\n",
       "      <td>16.190804</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>313.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1308.0</td>\n",
       "      <td>898.636850</td>\n",
       "      <td>22963.981816</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>30.00</td>\n",
       "      <td>784234.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1249.0</td>\n",
       "      <td>4.627702</td>\n",
       "      <td>15.580845</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>322.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1305.0</td>\n",
       "      <td>5.114176</td>\n",
       "      <td>23.323676</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>530.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2233.0</td>\n",
       "      <td>15.836095</td>\n",
       "      <td>41.948103</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13.00</td>\n",
       "      <td>1057.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2230.0</td>\n",
       "      <td>53.902691</td>\n",
       "      <td>341.605457</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.00</td>\n",
       "      <td>7902.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2737.0</td>\n",
       "      <td>39.743149</td>\n",
       "      <td>108.232832</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>21.00</td>\n",
       "      <td>1027.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            f                                                          \n",
       "        count        mean           std  min  25%  50%    75%       max\n",
       "l_ipn                                                                  \n",
       "0      3980.0   26.426382    232.733282  1.0  1.0  1.0   2.00    5059.0\n",
       "1      2159.0   90.639648    466.006782  1.0  1.0  2.0   5.00    4718.0\n",
       "2      2416.0   81.007036    299.501027  1.0  2.0  6.0  47.25    5214.0\n",
       "3      1186.0    4.134907     16.190804  1.0  1.0  2.0   2.00     313.0\n",
       "4      1308.0  898.636850  22963.981816  1.0  1.0  4.0  30.00  784234.0\n",
       "5      1249.0    4.627702     15.580845  1.0  1.0  2.0   4.00     322.0\n",
       "6      1305.0    5.114176     23.323676  1.0  1.0  2.0   3.00     530.0\n",
       "7      2233.0   15.836095     41.948103  1.0  2.0  4.0  13.00    1057.0\n",
       "8      2230.0   53.902691    341.605457  1.0  2.0  4.0   9.00    7902.0\n",
       "9      2737.0   39.743149    108.232832  1.0  2.0  6.0  21.00    1027.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We'll use groupby again and look at the data\n",
    "dta.groupby('l_ipn').describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll make a note that IP 4 has some very different behavior compared to the rest (not necessarily malicious). Let's see if there is any way to meaningfully group these IPs.\n",
    "_ _ _ \n",
    "I'd like to visualize this data to look for anything that jumps out to me for grouping purposes. One thing we can do is compare the number of unique ASs accessed by an IP and the total number of flows. The next two commands show us those two quantities by IP."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "l_ipn\n",
       "0    935\n",
       "1    631\n",
       "2    403\n",
       "3    476\n",
       "4    382\n",
       "5    418\n",
       "6    496\n",
       "7    466\n",
       "8    582\n",
       "9    514\n",
       "Name: unique, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#this returns a Series object with the number of unique ASs by IP\n",
    "dta.groupby('l_ipn').r_asn.describe()['unique']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "l_ipn\n",
       "0     105177\n",
       "1     195691\n",
       "2     195713\n",
       "3       4904\n",
       "4    1175417\n",
       "5       5780\n",
       "6       6674\n",
       "7      35362\n",
       "8     120203\n",
       "9     108777\n",
       "Name: f, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#this returns a Series with the sum of flows per IP\n",
    "dta.groupby('l_ipn').f.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>asns</th>\n",
       "      <th>flows</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>l_ipn</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>935</td>\n",
       "      <td>105177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>631</td>\n",
       "      <td>195691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>403</td>\n",
       "      <td>195713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>476</td>\n",
       "      <td>4904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>382</td>\n",
       "      <td>1175417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>418</td>\n",
       "      <td>5780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>496</td>\n",
       "      <td>6674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>466</td>\n",
       "      <td>35362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>582</td>\n",
       "      <td>120203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>514</td>\n",
       "      <td>108777</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       asns    flows\n",
       "l_ipn               \n",
       "0       935   105177\n",
       "1       631   195691\n",
       "2       403   195713\n",
       "3       476     4904\n",
       "4       382  1175417\n",
       "5       418     5780\n",
       "6       496     6674\n",
       "7       466    35362\n",
       "8       582   120203\n",
       "9       514   108777"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we'll make two Series for our unique count and total flows\n",
    "asns = dta.groupby('l_ipn').r_asn.describe()['unique']\n",
    "flows = dta.groupby('l_ipn').f.sum()\n",
    "# now we'll make a new DataFrame with the two lists together\n",
    "dataf = df({'asns':asns, 'flows':flows}, index = flows.index)\n",
    "dataf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>asns</th>\n",
       "      <th>flows</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>530.300000</td>\n",
       "      <td>1.953698e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>161.908788</td>\n",
       "      <td>3.520110e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>382.000000</td>\n",
       "      <td>4.904000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>430.000000</td>\n",
       "      <td>1.384600e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>486.000000</td>\n",
       "      <td>1.069770e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>565.000000</td>\n",
       "      <td>1.768190e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>935.000000</td>\n",
       "      <td>1.175417e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             asns         flows\n",
       "count   10.000000  1.000000e+01\n",
       "mean   530.300000  1.953698e+05\n",
       "std    161.908788  3.520110e+05\n",
       "min    382.000000  4.904000e+03\n",
       "25%    430.000000  1.384600e+04\n",
       "50%    486.000000  1.069770e+05\n",
       "75%    565.000000  1.768190e+05\n",
       "max    935.000000  1.175417e+06"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataf.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this new DataFrame, we can plot the data and visually check for groups:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe319c62940>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataf.plot.scatter('asns', 'flows')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the plot above, we can see a couple of possible anomalies to dig into. Let's add a little more detal to see the plot better and know where to look:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# some pre-processing for the plot\n",
    "data = zip(dataf.asns, dataf.flows)\n",
    "colors = (\"red\", \"green\", \"blue\", \"black\", \"aqua\", \"purple\", \"pink\", \"brown\", \"gray\", \"teal\")\n",
    "ips = dataf.index\n",
    " \n",
    "# Create plot using matplotlib instead of Pandas\n",
    "fig = plt.figure(figsize=[12, 7])\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    " \n",
    "for data, color, ip in zip(data, colors, ips):\n",
    "    x, y = data\n",
    "    ax.scatter(x, y, alpha=0.8, c=color, edgecolors='none', s=200, label=ip)\n",
    " \n",
    "plt.title('Flows per number of ASNs')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like most of the datapoints follow a roughly straight line indicating that there may be a positive correlation between the two variables. The interesting-looking points are the IPs 0, 2, and 4.\n",
    "\n",
    "There is arguably a lot of aggregation happening here, we really have more granular data we could look at. What if we plot the number of unique number of asns accessed against flows, but treated each day as an individual data point?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2006-07-01    146\n",
       "2006-07-02     86\n",
       "2006-07-03    174\n",
       "2006-07-04    133\n",
       "2006-07-05    187\n",
       "2006-07-06    167\n",
       "2006-07-07    133\n",
       "             ... \n",
       "2006-09-24     88\n",
       "2006-09-25    122\n",
       "2006-09-26    182\n",
       "2006-09-27    145\n",
       "2006-09-28    122\n",
       "2006-09-29    113\n",
       "2006-09-30    164\n",
       "Name: unique, Length: 92, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asns = dta.groupby(['date']).r_asn.describe()['unique']\n",
    "asns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2006-07-01     5058\n",
       "2006-07-02     7437\n",
       "2006-07-03     7248\n",
       "2006-07-04     8280\n",
       "2006-07-05    27189\n",
       "2006-07-06     6391\n",
       "2006-07-07     8971\n",
       "              ...  \n",
       "2006-09-24     4118\n",
       "2006-09-25     5215\n",
       "2006-09-26    13963\n",
       "2006-09-27     4143\n",
       "2006-09-28    11149\n",
       "2006-09-29    25281\n",
       "2006-09-30    15485\n",
       "Name: f, Length: 92, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flows = dta.groupby(['date']).f.sum()\n",
    "flows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like both **series** are what we need. Now we put them into a new **dataframe**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>asns</th>\n",
       "      <th>flows</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-07-01</th>\n",
       "      <td>146</td>\n",
       "      <td>5058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-07-02</th>\n",
       "      <td>86</td>\n",
       "      <td>7437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-07-03</th>\n",
       "      <td>174</td>\n",
       "      <td>7248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-07-04</th>\n",
       "      <td>133</td>\n",
       "      <td>8280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-07-05</th>\n",
       "      <td>187</td>\n",
       "      <td>27189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-07-06</th>\n",
       "      <td>167</td>\n",
       "      <td>6391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-07-07</th>\n",
       "      <td>133</td>\n",
       "      <td>8971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-09-24</th>\n",
       "      <td>88</td>\n",
       "      <td>4118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-09-25</th>\n",
       "      <td>122</td>\n",
       "      <td>5215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-09-26</th>\n",
       "      <td>182</td>\n",
       "      <td>13963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-09-27</th>\n",
       "      <td>145</td>\n",
       "      <td>4143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-09-28</th>\n",
       "      <td>122</td>\n",
       "      <td>11149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-09-29</th>\n",
       "      <td>113</td>\n",
       "      <td>25281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-09-30</th>\n",
       "      <td>164</td>\n",
       "      <td>15485</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>92 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            asns  flows\n",
       "date                   \n",
       "2006-07-01   146   5058\n",
       "2006-07-02    86   7437\n",
       "2006-07-03   174   7248\n",
       "2006-07-04   133   8280\n",
       "2006-07-05   187  27189\n",
       "2006-07-06   167   6391\n",
       "2006-07-07   133   8971\n",
       "...          ...    ...\n",
       "2006-09-24    88   4118\n",
       "2006-09-25   122   5215\n",
       "2006-09-26   182  13963\n",
       "2006-09-27   145   4143\n",
       "2006-09-28   122  11149\n",
       "2006-09-29   113  25281\n",
       "2006-09-30   164  15485\n",
       "\n",
       "[92 rows x 2 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newf = df({'asns':asns, 'flows':flows}, index=flows.index)\n",
    "newf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot this new data and see what we have."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe31b203908>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "newf.plot.scatter('asns', 'flows', s=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the data appears flat, maybe due to those two obvious anomalies stretching our __*y*__-axis.  \n",
    "We see those points, but cannot get any more information or be sure which points we are looking at. In the next cell, we'll use *bokeh* to get a tooltip added to our scatterplot to show us that dates in question."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"bd4ce518-844e-4bea-aa71-c08010b461df\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id != null && id in Bokeh.index) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var id = msg.content.text.trim();\n",
       "            if (id in Bokeh.index) {\n",
       "              Bokeh.index[id].model.document.clear();\n",
       "              delete Bokeh.index[id];\n",
       "            }\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
       "   */\n",
       "  function handleAddOutput(event, handle) {\n",
       "    var output_area = handle.output_area;\n",
       "    var output = handle.output;\n",
       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
       "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      var bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      var script_attrs = bk_div.children[0].attributes;\n",
       "      for (var i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[toinsert.length - 1]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(\"bd4ce518-844e-4bea-aa71-c08010b461df\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "    }\n",
       "    finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.info(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(js_urls, callback) {\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = js_urls.length;\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var s = document.createElement('script');\n",
       "      s.src = url;\n",
       "      s.async = false;\n",
       "      s.onreadystatechange = s.onload = function() {\n",
       "        root._bokeh_is_loading--;\n",
       "        if (root._bokeh_is_loading === 0) {\n",
       "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "          run_callbacks()\n",
       "        }\n",
       "      };\n",
       "      s.onerror = function() {\n",
       "        console.warn(\"failed to load library \" + url);\n",
       "      };\n",
       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "    }\n",
       "  };var element = document.getElementById(\"bd4ce518-844e-4bea-aa71-c08010b461df\");\n",
       "  if (element == null) {\n",
       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'bd4ce518-844e-4bea-aa71-c08010b461df' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.13.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.13.0.min.js\"];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.13.0.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.13.0.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i].call(root, root.Bokeh);\n",
       "      }if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"bd4ce518-844e-4bea-aa71-c08010b461df\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"bd4ce518-844e-4bea-aa71-c08010b461df\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n    }\n    finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.info(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(js_urls, callback) {\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = js_urls.length;\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var s = document.createElement('script');\n      s.src = url;\n      s.async = false;\n      s.onreadystatechange = s.onload = function() {\n        root._bokeh_is_loading--;\n        if (root._bokeh_is_loading === 0) {\n          console.log(\"Bokeh: all BokehJS libraries loaded\");\n          run_callbacks()\n        }\n      };\n      s.onerror = function() {\n        console.warn(\"failed to load library \" + url);\n      };\n      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.getElementsByTagName(\"head\")[0].appendChild(s);\n    }\n  };var element = document.getElementById(\"bd4ce518-844e-4bea-aa71-c08010b461df\");\n  if (element == null) {\n    console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'bd4ce518-844e-4bea-aa71-c08010b461df' but no matching script tag was found. \")\n    return false;\n  }\n\n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.13.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.13.0.min.js\"];\n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    \n    function(Bokeh) {\n      \n    },\n    function(Bokeh) {\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.13.0.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.13.0.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.css\");\n    }\n  ];\n\n  function run_inline_js() {\n    \n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"bd4ce518-844e-4bea-aa71-c08010b461df\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(js_urls, function() {\n      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"bd41c656-c25a-41a8-a5fc-409cb4895bd2\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = {\"a6343da7-d2b3-4956-bc0f-9622eaee45fc\":{\"roots\":{\"references\":[{\"attributes\":{\"formatter\":{\"id\":\"252fb896-04d6-4199-99e8-3a263e58cc0a\",\"type\":\"BasicTickFormatter\"},\"plot\":{\"id\":\"651451f5-820f-4461-847f-42bce70a2d7a\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"c41075c6-f976-4143-93a1-b5580170b681\",\"type\":\"BasicTicker\"}},\"id\":\"c21cb19a-0cb1-45dd-817a-d86a05b19d14\",\"type\":\"LinearAxis\"},{\"attributes\":{\"fill_color\":{\"value\":\"#1f77b4\"},\"line_color\":{\"value\":\"#1f77b4\"},\"size\":{\"units\":\"screen\",\"value\":10},\"x\":{\"field\":\"asns\"},\"y\":{\"field\":\"flows\"}},\"id\":\"8b065311-2dae-4b59-8c8e-703bdaaa6f40\",\"type\":\"Circle\"},{\"attributes\":{},\"id\":\"c38768da-aff9-4fae-b495-5b321283dc3c\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"f93c92e3-28b2-4002-8b65-b35707de9eab\",\"type\":\"HoverTool\"}]},\"id\":\"0beaab5e-f76a-4e85-8317-5d887aba327d\",\"type\":\"Toolbar\"},{\"attributes\":{\"callback\":null,\"renderers\":\"auto\",\"tooltips\":[[\"Date\",\"@date\"],[\"Flows\",\"@flows\"]]},\"id\":\"f93c92e3-28b2-4002-8b65-b35707de9eab\",\"type\":\"HoverTool\"},{\"attributes\":{},\"id\":\"252fb896-04d6-4199-99e8-3a263e58cc0a\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"plot\":null,\"text\":\"\"},\"id\":\"12b844ae-4678-456d-8ff0-45f94c68ff62\",\"type\":\"Title\"},{\"attributes\":{\"formatter\":{\"id\":\"3dc922ca-8611-42a0-84c4-1637bf17bad8\",\"type\":\"BasicTickFormatter\"},\"plot\":{\"id\":\"651451f5-820f-4461-847f-42bce70a2d7a\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"eb4c1938-f7ef-4d13-a8a9-4d59c4a9cc46\",\"type\":\"BasicTicker\"}},\"id\":\"105b51a5-c2b4-4c30-8fce-a57a62747df1\",\"type\":\"LinearAxis\"},{\"attributes\":{\"dimension\":1,\"plot\":{\"id\":\"651451f5-820f-4461-847f-42bce70a2d7a\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"c41075c6-f976-4143-93a1-b5580170b681\",\"type\":\"BasicTicker\"}},\"id\":\"ad72a4fc-e3b2-4725-9a35-a966ce8dfd7f\",\"type\":\"Grid\"},{\"attributes\":{\"callback\":null,\"data\":{\"asns\":[146,86,174,133,187,167,133,129,82,165,154,117,133,143,112,107,107,150,183,137,194,123,162,185,222,226,203,191,136,93,170,173,204,176,150,110,102,193,139,186,196,195,134,84,123,130,165,122,110,133,125,138,151,173,158,127,101,93,192,194,153,118,122,119,67,93,108,143,163,123,115,91,184,159,157,169,133,89,96,125,134,153,141,152,114,88,122,182,145,122,113,164],\"date\":[\"2006-07-01\",\"2006-07-02\",\"2006-07-03\",\"2006-07-04\",\"2006-07-05\",\"2006-07-06\",\"2006-07-07\",\"2006-07-08\",\"2006-07-09\",\"2006-07-10\",\"2006-07-11\",\"2006-07-12\",\"2006-07-13\",\"2006-07-14\",\"2006-07-15\",\"2006-07-16\",\"2006-07-17\",\"2006-07-18\",\"2006-07-19\",\"2006-07-20\",\"2006-07-21\",\"2006-07-22\",\"2006-07-23\",\"2006-07-24\",\"2006-07-25\",\"2006-07-26\",\"2006-07-27\",\"2006-07-28\",\"2006-07-29\",\"2006-07-30\",\"2006-07-31\",\"2006-08-01\",\"2006-08-02\",\"2006-08-03\",\"2006-08-04\",\"2006-08-05\",\"2006-08-06\",\"2006-08-07\",\"2006-08-08\",\"2006-08-09\",\"2006-08-10\",\"2006-08-11\",\"2006-08-12\",\"2006-08-13\",\"2006-08-14\",\"2006-08-15\",\"2006-08-16\",\"2006-08-17\",\"2006-08-18\",\"2006-08-19\",\"2006-08-20\",\"2006-08-21\",\"2006-08-22\",\"2006-08-23\",\"2006-08-24\",\"2006-08-25\",\"2006-08-26\",\"2006-08-27\",\"2006-08-28\",\"2006-08-29\",\"2006-08-30\",\"2006-08-31\",\"2006-09-01\",\"2006-09-02\",\"2006-09-03\",\"2006-09-04\",\"2006-09-05\",\"2006-09-06\",\"2006-09-07\",\"2006-09-08\",\"2006-09-09\",\"2006-09-10\",\"2006-09-11\",\"2006-09-12\",\"2006-09-13\",\"2006-09-14\",\"2006-09-15\",\"2006-09-16\",\"2006-09-17\",\"2006-09-18\",\"2006-09-19\",\"2006-09-20\",\"2006-09-21\",\"2006-09-22\",\"2006-09-23\",\"2006-09-24\",\"2006-09-25\",\"2006-09-26\",\"2006-09-27\",\"2006-09-28\",\"2006-09-29\",\"2006-09-30\"],\"flows\":[5058,7437,7248,8280,27189,6391,8971,9240,4380,6320,5020,5429,3753,4283,10839,4758,13214,15244,11754,8499,13023,3928,26769,10926,6497,9888,7785,14514,4485,4309,9667,10163,10020,4905,12395,4441,7427,4602,12687,10342,9983,13588,10778,6152,8407,14900,12159,31044,7270,9433,11091,25218,23246,17390,11629,15776,14467,4412,9804,10159,6728,7286,7861,4664,3140,3247,5341,3147,5588,6898,2850,2706,6624,8633,12519,8936,3425,17121,277477,788297,8139,31305,9310,5202,8914,4118,5215,13963,4143,11149,25281,15485]},\"selected\":{\"id\":\"f7866e51-ea3c-4884-ac7d-b00cbae30278\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"c38768da-aff9-4fae-b495-5b321283dc3c\",\"type\":\"UnionRenderers\"}},\"id\":\"2c2c60d7-70f3-43f7-b084-a84b6c4e0c21\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"aa9a9fdd-dcf4-43f3-8cc3-419f9f38f0cd\",\"type\":\"LinearScale\"},{\"attributes\":{\"plot\":{\"id\":\"651451f5-820f-4461-847f-42bce70a2d7a\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"eb4c1938-f7ef-4d13-a8a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Application\",\"version\":\"0.13.0\"}};\n",
       "  var render_items = [{\"docid\":\"a6343da7-d2b3-4956-bc0f-9622eaee45fc\",\"roots\":{\"651451f5-820f-4461-847f-42bce70a2d7a\":\"bd41c656-c25a-41a8-a5fc-409cb4895bd2\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "651451f5-820f-4461-847f-42bce70a2d7a"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bokeh.models import ColumnDataSource, HoverTool\n",
    "from bokeh.plotting import figure, show \n",
    "from bokeh.io import output_notebook\n",
    "\n",
    "# allows bokeh to show in a jupyter notebook\n",
    "output_notebook()\n",
    "\n",
    "# turns our dataframe into a dict that bokeh can handle\n",
    "source = ColumnDataSource(newf)\n",
    "\n",
    "# adds the date to a tooltip\n",
    "for date in newf.index:\n",
    "    hover = HoverTool(tooltips=[('Date', '@date'), ('Flows', '@flows')])\n",
    "\n",
    "# creates our figure with our custom tooltips\n",
    "p = figure(plot_width=600, plot_height=300,\n",
    "           tools=[hover])\n",
    "\n",
    "# makes a scatter plot of # of ASNs and total flows\n",
    "# one point for each date\n",
    "p.circle(x='asns', y='flows', size=10, source=source)\n",
    "\n",
    "# shows our figure \n",
    "show(p)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that both of those points have over 200k flows. We'll take a note of 9/17 and 9/18. Just to be certain, let's look at everything else"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"d5d24d6d-3f2f-401d-885f-595d400975a7\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id != null && id in Bokeh.index) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var id = msg.content.text.trim();\n",
       "            if (id in Bokeh.index) {\n",
       "              Bokeh.index[id].model.document.clear();\n",
       "              delete Bokeh.index[id];\n",
       "            }\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
       "   */\n",
       "  function handleAddOutput(event, handle) {\n",
       "    var output_area = handle.output_area;\n",
       "    var output = handle.output;\n",
       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
       "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      var bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      var script_attrs = bk_div.children[0].attributes;\n",
       "      for (var i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[toinsert.length - 1]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(\"d5d24d6d-3f2f-401d-885f-595d400975a7\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "    }\n",
       "    finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.info(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(js_urls, callback) {\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = js_urls.length;\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var s = document.createElement('script');\n",
       "      s.src = url;\n",
       "      s.async = false;\n",
       "      s.onreadystatechange = s.onload = function() {\n",
       "        root._bokeh_is_loading--;\n",
       "        if (root._bokeh_is_loading === 0) {\n",
       "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "          run_callbacks()\n",
       "        }\n",
       "      };\n",
       "      s.onerror = function() {\n",
       "        console.warn(\"failed to load library \" + url);\n",
       "      };\n",
       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "    }\n",
       "  };var element = document.getElementById(\"d5d24d6d-3f2f-401d-885f-595d400975a7\");\n",
       "  if (element == null) {\n",
       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'd5d24d6d-3f2f-401d-885f-595d400975a7' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.13.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.13.0.min.js\"];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.13.0.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.13.0.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i].call(root, root.Bokeh);\n",
       "      }if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"d5d24d6d-3f2f-401d-885f-595d400975a7\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"d5d24d6d-3f2f-401d-885f-595d400975a7\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n    }\n    finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.info(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(js_urls, callback) {\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = js_urls.length;\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var s = document.createElement('script');\n      s.src = url;\n      s.async = false;\n      s.onreadystatechange = s.onload = function() {\n        root._bokeh_is_loading--;\n        if (root._bokeh_is_loading === 0) {\n          console.log(\"Bokeh: all BokehJS libraries loaded\");\n          run_callbacks()\n        }\n      };\n      s.onerror = function() {\n        console.warn(\"failed to load library \" + url);\n      };\n      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.getElementsByTagName(\"head\")[0].appendChild(s);\n    }\n  };var element = document.getElementById(\"d5d24d6d-3f2f-401d-885f-595d400975a7\");\n  if (element == null) {\n    console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'd5d24d6d-3f2f-401d-885f-595d400975a7' but no matching script tag was found. \")\n    return false;\n  }\n\n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.13.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.13.0.min.js\"];\n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    \n    function(Bokeh) {\n      \n    },\n    function(Bokeh) {\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.13.0.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.13.0.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.css\");\n    }\n  ];\n\n  function run_inline_js() {\n    \n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"d5d24d6d-3f2f-401d-885f-595d400975a7\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(js_urls, function() {\n      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"17643f7c-e344-4cde-99ec-b3087c425aba\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = {\"a4efd522-4a58-4537-8ccb-9fe516b9805d\":{\"roots\":{\"references\":[{\"attributes\":{},\"id\":\"402f1f84-be09-488a-a7ca-c6b4c09c5810\",\"type\":\"LinearScale\"},{\"attributes\":{\"callback\":null,\"renderers\":\"auto\",\"tooltips\":[[\"Date\",\"@date\"],[\"Flows\",\"@flows\"]]},\"id\":\"87731c39-0b4b-4266-88c5-10f91c5cf9bb\",\"type\":\"HoverTool\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"size\":{\"units\":\"screen\",\"value\":10},\"x\":{\"field\":\"asns\"},\"y\":{\"field\":\"flows\"}},\"id\":\"b0133fa2-adee-478f-ae6d-8350d44a08a5\",\"type\":\"Circle\"},{\"attributes\":{},\"id\":\"62ecae88-eeba-47b6-8ee5-65c3035079e3\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"dimension\":1,\"plot\":{\"id\":\"46ae2cf6-632b-4d6c-9e0c-18c33d672605\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"d110ac84-b9ea-410c-bde2-953b2907d593\",\"type\":\"BasicTicker\"}},\"id\":\"2c968b98-a5d5-48b6-b6a6-152419c43c9c\",\"type\":\"Grid\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"87731c39-0b4b-4266-88c5-10f91c5cf9bb\",\"type\":\"HoverTool\"}]},\"id\":\"2d7a7d56-e794-4c03-81cf-ac56937276fb\",\"type\":\"Toolbar\"},{\"attributes\":{},\"id\":\"a50f3ffe-3d91-4980-8798-73a96c9b5a39\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"callback\":null,\"data\":{\"asns\":[146,86,174,133,187,167,133,129,82,165,154,117,133,143,112,107,107,150,183,137,194,123,162,185,222,226,203,191,136,93,170,173,204,176,150,110,102,193,139,186,196,195,134,84,123,130,165,122,110,133,125,138,151,173,158,127,101,93,192,194,153,118,122,119,67,93,108,143,163,123,115,91,184,159,157,169,133,89,134,153,141,152,114,88,122,182,145,122,113,164],\"date\":[\"2006-07-01\",\"2006-07-02\",\"2006-07-03\",\"2006-07-04\",\"2006-07-05\",\"2006-07-06\",\"2006-07-07\",\"2006-07-08\",\"2006-07-09\",\"2006-07-10\",\"2006-07-11\",\"2006-07-12\",\"2006-07-13\",\"2006-07-14\",\"2006-07-15\",\"2006-07-16\",\"2006-07-17\",\"2006-07-18\",\"2006-07-19\",\"2006-07-20\",\"2006-07-21\",\"2006-07-22\",\"2006-07-23\",\"2006-07-24\",\"2006-07-25\",\"2006-07-26\",\"2006-07-27\",\"2006-07-28\",\"2006-07-29\",\"2006-07-30\",\"2006-07-31\",\"2006-08-01\",\"2006-08-02\",\"2006-08-03\",\"2006-08-04\",\"2006-08-05\",\"2006-08-06\",\"2006-08-07\",\"2006-08-08\",\"2006-08-09\",\"2006-08-10\",\"2006-08-11\",\"2006-08-12\",\"2006-08-13\",\"2006-08-14\",\"2006-08-15\",\"2006-08-16\",\"2006-08-17\",\"2006-08-18\",\"2006-08-19\",\"2006-08-20\",\"2006-08-21\",\"2006-08-22\",\"2006-08-23\",\"2006-08-24\",\"2006-08-25\",\"2006-08-26\",\"2006-08-27\",\"2006-08-28\",\"2006-08-29\",\"2006-08-30\",\"2006-08-31\",\"2006-09-01\",\"2006-09-02\",\"2006-09-03\",\"2006-09-04\",\"2006-09-05\",\"2006-09-06\",\"2006-09-07\",\"2006-09-08\",\"2006-09-09\",\"2006-09-10\",\"2006-09-11\",\"2006-09-12\",\"2006-09-13\",\"2006-09-14\",\"2006-09-15\",\"2006-09-16\",\"2006-09-19\",\"2006-09-20\",\"2006-09-21\",\"2006-09-22\",\"2006-09-23\",\"2006-09-24\",\"2006-09-25\",\"2006-09-26\",\"2006-09-27\",\"2006-09-28\",\"2006-09-29\",\"2006-09-30\"],\"flows\":[5058,7437,7248,8280,27189,6391,8971,9240,4380,6320,5020,5429,3753,4283,10839,4758,13214,15244,11754,8499,13023,3928,26769,10926,6497,9888,7785,14514,4485,4309,9667,10163,10020,4905,12395,4441,7427,4602,12687,10342,9983,13588,10778,6152,8407,14900,12159,31044,7270,9433,11091,25218,23246,17390,11629,15776,14467,4412,9804,10159,6728,7286,7861,4664,3140,3247,5341,3147,5588,6898,2850,2706,6624,8633,12519,8936,3425,17121,8139,31305,9310,5202,8914,4118,5215,13963,4143,11149,25281,15485]},\"selected\":{\"id\":\"f6c1aec4-135d-4aeb-b55d-dc8e4a2d5e1c\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"6609c042-55de-4fc4-aa21-e95f322f74e2\",\"type\":\"UnionRenderers\"}},\"id\":\"59cff60e-920f-4268-9dbe-7c9f90d4bdd9\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"25954442-5ade-465d-af4b-b0648492dd7b\",\"type\":\"BasicTicker\"},{\"attributes\":{\"below\":[{\"id\":\"3e7d1318-e7c5-4b18-8634-b66269621371\",\"type\":\"LinearAxis\"}],\"left\":[{\"id\":\"5f8dc85f-2d8b-4c5b-ac2c-58df9bb15a17\",\"type\":\"LinearAxis\"}],\"plot_height\":300,\"renderers\":[{\"id\":\"3e7d1318-e7c5-4b18-8634-b66269621371\",\"type\":\"LinearAxis\"},{\"id\":\"e7d2172e-6f6b-4c63-afa2-ee86a90aceb6\",\"type\":\"Grid\"},{\"id\":\"5f8dc85f-2d8b-4c5b-ac2c-58df9bb15a17\",\"type\":\"LinearAxis\"},{\"id\":\"2c968b98-a5d5-48b6-b6a6-152419c43c9c\",\"type\":\"Grid\"},{\"id\":\"1d971c26-516d-4275-9333-8b893b905c21\",\"type\":\"GlyphRenderer\"}],\"title\":{\"id\":\"36665a42-7375-4056-b67b-a799ef00a5d2\",\"type\":\"Title\"},\"toolbar\":{\"id\":\"2d7a7d56-e794-4c03-81cf-ac56937276fb\",\"type\":\"Toolbar\"},\"x_range\":{\"id\":\"d3cb99d2-5313-444e-81a5-9f9393c3e2bf\",\"type\":\"DataRange1d\"},\"x_scale\":{\"id\":\"7a45a416-9f0b-430f-a385-08d7950fcc5c\",\"type\":\"Linear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Application\",\"version\":\"0.13.0\"}};\n",
       "  var render_items = [{\"docid\":\"a4efd522-4a58-4537-8ccb-9fe516b9805d\",\"roots\":{\"46ae2cf6-632b-4d6c-9e0c-18c33d672605\":\"17643f7c-e344-4cde-99ec-b3087c425aba\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "46ae2cf6-632b-4d6c-9e0c-18c33d672605"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# hide those two dates and run the visualization again\n",
    "adj_frame = newf.loc[(newf.index != '2006-09-17') & (newf.index != '2006-09-18')]\n",
    "\n",
    "# allows bokeh to show in a jupyter notebook\n",
    "output_notebook()\n",
    "\n",
    "# turns our dataframe into a dict that bokeh can handle\n",
    "source = ColumnDataSource(adj_frame)\n",
    "\n",
    "# adds the date to a tooltip\n",
    "for date in newf.index:\n",
    "    hover = HoverTool(tooltips=[('Date', '@date'), ('Flows', '@flows')])\n",
    "\n",
    "# creates our figure with our custom tooltips\n",
    "p = figure(plot_width=600, plot_height=300,\n",
    "           tools=[hover])\n",
    "\n",
    "# makes a scatter plot of # of ASNs and total flows\n",
    "# one point for each date\n",
    "p.circle(x='asns', y='flows', size=10, source=source)\n",
    "\n",
    "# shows our figure \n",
    "show(p)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the data isn't as flat as it looked previously. There appears to be a generally positive correlation between the number of ASNs and the number of flows, as we thought before. There does appear to be a couple groups in the data, however. Lets try visualizing by date to look for trends."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"0d4ec3a2-33ed-4b8b-916f-0493896d834f\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
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       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
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       "\n",
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       "   * Handle when an output is cleared or removed\n",
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       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
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       "    // Clean up Bokeh references\n",
       "    if (id != null && id in Bokeh.index) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
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       "\n",
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       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var id = msg.content.text.trim();\n",
       "            if (id in Bokeh.index) {\n",
       "              Bokeh.index[id].model.document.clear();\n",
       "              delete Bokeh.index[id];\n",
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       "        }\n",
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       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
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       "\n",
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       "      return\n",
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       "\n",
       "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
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       "      var bk_div = document.createElement(\"div\");\n",
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       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
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       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
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       "        CLASS_NAME,\n",
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       "      this.keyboard_manager.register_events(toinsert);\n",
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       "      render(props, toinsert[toinsert.length - 1]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
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       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(\"0d4ec3a2-33ed-4b8b-916f-0493896d834f\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "    }\n",
       "    finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.info(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(js_urls, callback) {\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = js_urls.length;\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var s = document.createElement('script');\n",
       "      s.src = url;\n",
       "      s.async = false;\n",
       "      s.onreadystatechange = s.onload = function() {\n",
       "        root._bokeh_is_loading--;\n",
       "        if (root._bokeh_is_loading === 0) {\n",
       "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "          run_callbacks()\n",
       "        }\n",
       "      };\n",
       "      s.onerror = function() {\n",
       "        console.warn(\"failed to load library \" + url);\n",
       "      };\n",
       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "    }\n",
       "  };var element = document.getElementById(\"0d4ec3a2-33ed-4b8b-916f-0493896d834f\");\n",
       "  if (element == null) {\n",
       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '0d4ec3a2-33ed-4b8b-916f-0493896d834f' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.13.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.13.0.min.js\"];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.13.0.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.13.0.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i].call(root, root.Bokeh);\n",
       "      }if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"0d4ec3a2-33ed-4b8b-916f-0493896d834f\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"0d4ec3a2-33ed-4b8b-916f-0493896d834f\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n    }\n    finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.info(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(js_urls, callback) {\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = js_urls.length;\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var s = document.createElement('script');\n      s.src = url;\n      s.async = false;\n      s.onreadystatechange = s.onload = function() {\n        root._bokeh_is_loading--;\n        if (root._bokeh_is_loading === 0) {\n          console.log(\"Bokeh: all BokehJS libraries loaded\");\n          run_callbacks()\n        }\n      };\n      s.onerror = function() {\n        console.warn(\"failed to load library \" + url);\n      };\n      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.getElementsByTagName(\"head\")[0].appendChild(s);\n    }\n  };var element = document.getElementById(\"0d4ec3a2-33ed-4b8b-916f-0493896d834f\");\n  if (element == null) {\n    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '0d4ec3a2-33ed-4b8b-916f-0493896d834f' but no matching script tag was found. \")\n    return false;\n  }\n\n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.13.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.13.0.min.js\"];\n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    \n    function(Bokeh) {\n      \n    },\n    function(Bokeh) {\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.13.0.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.13.0.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.css\");\n    }\n  ];\n\n  function run_inline_js() {\n    \n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"0d4ec3a2-33ed-4b8b-916f-0493896d834f\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(js_urls, function() {\n      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"9564ed99-e71b-4d2e-8343-4d3142082451\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = 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Application\",\"version\":\"0.13.0\"}};\n",
       "  var render_items = [{\"docid\":\"8c9330d0-23eb-4bc9-81d9-8a7277f0925e\",\"roots\":{\"34d30ca9-7728-4b34-a7ff-5f8600ef36a9\":\"9564ed99-e71b-4d2e-8343-4d3142082451\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "34d30ca9-7728-4b34-a7ff-5f8600ef36a9"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# allows bokeh to show in a jupyter notebook\n",
    "output_notebook()\n",
    "\n",
    "# turns our dataframe into a dict that bokeh can handle\n",
    "source = ColumnDataSource(newf)\n",
    "\n",
    "# adds the date to a tooltip\n",
    "for date in newf.index:\n",
    "    hover = HoverTool(tooltips=[('Date', '@date'), ('Flows', '@flows')])\n",
    "\n",
    "# creates our figure with our custom tooltips\n",
    "p = figure(plot_width=600, plot_height=300,\n",
    "           tools=[hover])\n",
    "\n",
    "# makes a scatter plot of total flows\n",
    "# one point for each date\n",
    "p.line(x='date', y='flows', source=source)\n",
    "\n",
    "# shows our figure \n",
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we'll make two Series for our unique count and total flows\n",
    "asns = dta.groupby(['l_ipn', 'date').r_asn.describe()['unique']\n",
    "flows = dta.groupby('l_ipn', 'date').f.sum()\n",
    "# now we'll make a new DataFrame with the two lists together\n",
    "dataf = df({'asns':asns, 'flows':flows}, index = flows.index)\n",
    "dataf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dta.groupby('l_ipn').r_asn.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a list\n",
    "ary = []\n",
    "\n",
    "# populate the list with separate dataFrames for each IP\n",
    "for i in range(10):\n",
    "    ary.append(dta[dta['l_ipn'] == i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# describe the data for IP: 0\n",
    "ary[0].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "stds = []\n",
    "for frame in ary:\n",
    "    stds.append(frame.f.std())\n",
    "\n",
    "stds1 = pd.DataFrame(stds)\n",
    "stds1.describe()"
   ]
  }
 ],
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