Scratch.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, time\n",
    "import datetime as dt\n",
    "import pandas as pd\n",
    "from multiprocessing import Pool, cpu_count\n",
    "import Settings\n",
    "SOURCE = Settings.NETFLOW_PROS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm.notebook import tqdm\n",
    "from tqdm.notebook import tnrange"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Starting conversion\n",
      "[INFO] 7 files found in directory D:\\Users\\Geovanni\\Sync\\PhD\\Data\\Conv\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e62d7a19aeb34a4fa77bcdd0823d8e33",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=6.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "INTERVAL = 5\n",
    "if __name__ == '__main__':\n",
    "    conv_files = list(filter(lambda x: x.startswith('feather_'), os.listdir(SOURCE)))\n",
    "    print('[INFO] Starting conversion')\n",
    "    print(f'[INFO] {len(conv_files)} files found in directory {os.getcwd()}{SOURCE[1:]}')\n",
    "    df = pd.read_feather(SOURCE+'/'+conv_files[0])\n",
    "    df['Duration'] = df['Duration'].map(lambda x: dt.timedelta(seconds=x))\n",
    "    df['Datetime_lastseen'] = df['Datetime_firstseen']+df['Duration']\n",
    "    if True:\n",
    "        dfs=[df]\n",
    "        for file in tqdm(conv_files[1:]):\n",
    "            tempdf = pd.read_feather(SOURCE+'/'+file)\n",
    "            tempdf['Duration'] = tempdf['Duration'].map(lambda x: dt.timedelta(seconds=x))\n",
    "            tempdf['Datetime_lastseen'] = tempdf['Datetime_firstseen']+df['Duration']\n",
    "            dfs.append(tempdf)\n",
    "        df = pd.concat(dfs, ignore_index=True)\n",
    "#     df.sort_values(by=['Datetime_firstseen'], inplace=True)\n",
    "#     df.reset_index(inplace=True, drop=True)\n",
    "\n",
    "#     pool = Pool(processes=4)\n",
    "#     dfs = pool.map(process_nfdump, conv_files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9496587, 14)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Datetime_firstseen</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Protocol</th>\n",
       "      <th>Src_IP_Addr</th>\n",
       "      <th>Src_Port</th>\n",
       "      <th>Direc</th>\n",
       "      <th>Dst_IP_Addr</th>\n",
       "      <th>Dst_Port</th>\n",
       "      <th>Flags</th>\n",
       "      <th>Tos</th>\n",
       "      <th>Packets</th>\n",
       "      <th>Bytes</th>\n",
       "      <th>Flows</th>\n",
       "      <th>Datetime_lastseen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-02-07 23:54:53.840</td>\n",
       "      <td>00:04:50.310000</td>\n",
       "      <td>UDP</td>\n",
       "      <td>134.193.92.68</td>\n",
       "      <td>500</td>\n",
       "      <td>-&gt;</td>\n",
       "      <td>209.85.235.189</td>\n",
       "      <td>500</td>\n",
       "      <td>........</td>\n",
       "      <td>0</td>\n",
       "      <td>121</td>\n",
       "      <td>52824</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-02-07 23:59:44.150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-02-07 23:57:52.820</td>\n",
       "      <td>00:01:51.696000</td>\n",
       "      <td>TCP</td>\n",
       "      <td>172.217.9.132</td>\n",
       "      <td>443</td>\n",
       "      <td>-&gt;</td>\n",
       "      <td>134.193.198.19</td>\n",
       "      <td>62831</td>\n",
       "      <td>...AP...</td>\n",
       "      <td>128</td>\n",
       "      <td>2521</td>\n",
       "      <td>1800000</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-02-07 23:59:44.516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-02-07 23:58:01.226</td>\n",
       "      <td>00:01:43.250000</td>\n",
       "      <td>TCP</td>\n",
       "      <td>172.217.1.238</td>\n",
       "      <td>443</td>\n",
       "      <td>-&gt;</td>\n",
       "      <td>134.193.198.19</td>\n",
       "      <td>62833</td>\n",
       "      <td>...AP...</td>\n",
       "      <td>0</td>\n",
       "      <td>1225</td>\n",
       "      <td>990522</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-02-07 23:59:44.476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-02-07 23:58:34.242</td>\n",
       "      <td>00:01:10.006000</td>\n",
       "      <td>TCP</td>\n",
       "      <td>134.193.254.218</td>\n",
       "      <td>49345</td>\n",
       "      <td>-&gt;</td>\n",
       "      <td>150.199.7.126</td>\n",
       "      <td>443</td>\n",
       "      <td>...APRS.</td>\n",
       "      <td>0</td>\n",
       "      <td>715</td>\n",
       "      <td>41665</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-02-07 23:59:44.248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-07 23:58:43.858</td>\n",
       "      <td>00:01:01.010000</td>\n",
       "      <td>TCP</td>\n",
       "      <td>136.34.135.223</td>\n",
       "      <td>51053</td>\n",
       "      <td>-&gt;</td>\n",
       "      <td>134.193.136.193</td>\n",
       "      <td>443</td>\n",
       "      <td>...AP.SF</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>1832</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-02-07 23:59:44.868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9496582</th>\n",
       "      <td>2020-02-08 00:34:43.987</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>TCP</td>\n",
       "      <td>45.227.254.30</td>\n",
       "      <td>49116</td>\n",
       "      <td>-&gt;</td>\n",
       "      <td>204.56.1.75</td>\n",
       "      <td>33898</td>\n",
       "      <td>......S.</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9496583</th>\n",
       "      <td>2020-02-08 00:34:43.987</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>TCP</td>\n",
       "      <td>134.49.252.240</td>\n",
       "      <td>58357</td>\n",
       "      <td>-&gt;</td>\n",
       "      <td>134.193.25.80</td>\n",
       "      <td>80</td>\n",
       "      <td>......S.</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9496584</th>\n",
       "      <td>2020-02-08 00:34:43.987</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>TCP</td>\n",
       "      <td>92.63.194.115</td>\n",
       "      <td>40352</td>\n",
       "      <td>-&gt;</td>\n",
       "      <td>134.193.157.232</td>\n",
       "      <td>35543</td>\n",
       "      <td>......S.</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9496585</th>\n",
       "      <td>2020-02-08 00:34:43.987</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>TCP</td>\n",
       "      <td>198.209.56.129</td>\n",
       "      <td>443</td>\n",
       "      <td>-&gt;</td>\n",
       "      <td>134.193.244.195</td>\n",
       "      <td>52995</td>\n",
       "      <td>...AP...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>109</td>\n",
       "      <td>1</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9496586</th>\n",
       "      <td>2020-02-08 00:34:43.987</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>TCP</td>\n",
       "      <td>198.108.66.85</td>\n",
       "      <td>40219</td>\n",
       "      <td>-&gt;</td>\n",
       "      <td>134.193.35.225</td>\n",
       "      <td>20000</td>\n",
       "      <td>......S.</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9496587 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Datetime_firstseen        Duration Protocol      Src_IP_Addr  \\\n",
       "0       2020-02-07 23:54:53.840 00:04:50.310000      UDP    134.193.92.68   \n",
       "1       2020-02-07 23:57:52.820 00:01:51.696000      TCP    172.217.9.132   \n",
       "2       2020-02-07 23:58:01.226 00:01:43.250000      TCP    172.217.1.238   \n",
       "3       2020-02-07 23:58:34.242 00:01:10.006000      TCP  134.193.254.218   \n",
       "4       2020-02-07 23:58:43.858 00:01:01.010000      TCP   136.34.135.223   \n",
       "...                         ...             ...      ...              ...   \n",
       "9496582 2020-02-08 00:34:43.987        00:00:00      TCP    45.227.254.30   \n",
       "9496583 2020-02-08 00:34:43.987        00:00:00      TCP   134.49.252.240   \n",
       "9496584 2020-02-08 00:34:43.987        00:00:00      TCP    92.63.194.115   \n",
       "9496585 2020-02-08 00:34:43.987        00:00:00      TCP   198.209.56.129   \n",
       "9496586 2020-02-08 00:34:43.987        00:00:00      TCP    198.108.66.85   \n",
       "\n",
       "        Src_Port Direc      Dst_IP_Addr Dst_Port     Flags  Tos  Packets  \\\n",
       "0            500    ->   209.85.235.189      500  ........    0      121   \n",
       "1            443    ->   134.193.198.19    62831  ...AP...  128     2521   \n",
       "2            443    ->   134.193.198.19    62833  ...AP...    0     1225   \n",
       "3          49345    ->    150.199.7.126      443  ...APRS.    0      715   \n",
       "4          51053    ->  134.193.136.193      443  ...AP.SF    0       16   \n",
       "...          ...   ...              ...      ...       ...  ...      ...   \n",
       "9496582    49116    ->      204.56.1.75    33898  ......S.    0        1   \n",
       "9496583    58357    ->    134.193.25.80       80  ......S.    0        1   \n",
       "9496584    40352    ->  134.193.157.232    35543  ......S.    0        1   \n",
       "9496585      443    ->  134.193.244.195    52995  ...AP...    0        1   \n",
       "9496586    40219    ->   134.193.35.225    20000  ......S.    0        1   \n",
       "\n",
       "           Bytes  Flows       Datetime_lastseen  \n",
       "0          52824      1 2020-02-07 23:59:44.150  \n",
       "1        1800000      1 2020-02-07 23:59:44.516  \n",
       "2         990522      1 2020-02-07 23:59:44.476  \n",
       "3          41665      1 2020-02-07 23:59:44.248  \n",
       "4           1832      1 2020-02-07 23:59:44.868  \n",
       "...          ...    ...                     ...  \n",
       "9496582       40      1                     NaT  \n",
       "9496583       44      1                     NaT  \n",
       "9496584       40      1                     NaT  \n",
       "9496585      109      1                     NaT  \n",
       "9496586       40      1                     NaT  \n",
       "\n",
       "[9496587 rows x 14 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "display(df.shape)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-02-07 23:30:00.056000\t->\t2020-02-08 01:04:30.056000\n"
     ]
    }
   ],
   "source": [
    "interval_sum = pd.DataFrame(pd.date_range(start=df.Datetime_firstseen.min(), \n",
    "                                           end=df.Datetime_lastseen.max(), \n",
    "                                           freq=f'{INTERVAL}S'))\n",
    "interval_sum[1] = [0] * len(interval_sum)\n",
    "interval_sum.columns = ['datespace', 'sum of data transfer']\n",
    "print(f'{interval_sum.datespace.min()}\\t->\\t{interval_sum.datespace.max()}')\n",
    "interval_sum.set_index('datespace', inplace=True)\n",
    "\n",
    "# interval_sum = interval_sum.iloc[:int(len(interval_sum)*.25),:]\n",
    "# interval_sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "def calculate_resampled_bytes(row, starttime, inter):\n",
    "    num_interval_transfer = math.ceil(row['Duration'].total_seconds()/inter)\n",
    "    avg_rate_of_transfer = row['Bytes']/num_interval_transfer\n",
    "    list_of_bytes_across_intervals = [avg_rate_of_transfer]*num_interval_transfer\n",
    "    return list_of_bytes_across_intervals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ffd9be7d9f034a7db8c7e2499ec14e17",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=1135.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At 2020-02-07 23:30:00.056000\t0\t0%\n",
      "At 2020-02-07 23:34:45.056000\t57\t5%\n",
      "At 2020-02-07 23:39:30.056000\t114\t10%\n",
      "At 2020-02-07 23:44:15.056000\t171\t15%\n",
      "At 2020-02-07 23:48:55.056000\t227\t20%\n",
      "At 2020-02-07 23:53:40.056000\t284\t25%\n",
      "At 2020-02-07 23:58:25.056000\t341\t30%\n",
      "At 2020-02-08 00:03:10.056000\t398\t35%\n",
      "At 2020-02-08 00:07:50.056000\t454\t40%\n",
      "At 2020-02-08 00:12:35.056000\t511\t45%\n",
      "At 2020-02-08 00:17:20.056000\t568\t50%\n",
      "At 2020-02-08 00:22:05.056000\t625\t55%\n",
      "At 2020-02-08 00:26:45.056000\t681\t60%\n",
      "At 2020-02-08 00:31:30.056000\t738\t65%\n",
      "At 2020-02-08 00:36:15.056000\t795\t70%\n",
      "At 2020-02-08 00:41:00.056000\t852\t75%\n",
      "At 2020-02-08 00:45:40.056000\t908\t80%\n",
      "At 2020-02-08 00:50:25.056000\t965\t85%\n",
      "At 2020-02-08 00:55:10.056000\t1022\t90%\n",
      "At 2020-02-08 00:59:55.056000\t1079\t95%\n",
      "\n"
     ]
    }
   ],
   "source": [
    "current_point = 0\n",
    "stop=False\n",
    "progress = [x for x in range(0,105,5)]\n",
    "interval_index_list = list(interval_sum.index)\n",
    "interval_sum_length = interval_sum.shape[0]\n",
    "for interval_datetime in tqdm(interval_index_list):\n",
    "    if interval_index_list.index(interval_datetime)/interval_sum_length >= progress[0]/100:\n",
    "        print(f'At {interval_datetime}\\t{interval_index_list.index(interval_datetime)}\\t{progress.pop(0)}%')\n",
    "    current_interval_index = interval_index_list.index(interval_datetime)\n",
    "    df_inside_interval = df[(df.Datetime_firstseen >= interval_datetime) & (df.Datetime_firstseen < interval_datetime+dt.timedelta(seconds=INTERVAL))]\n",
    "#     for ind, row in tqdm(df_inside_interval.iterrows(), total=df_inside_interval.shape[0], leave=False, position=1):\n",
    "    for ind, row in df_inside_interval.iterrows():\n",
    "        if row['Datetime_firstseen'] + row['Duration'] >= interval_datetime+dt.timedelta(seconds=5):\n",
    "            resampled_bytes = calculate_resampled_bytes(row, interval_datetime, INTERVAL)\n",
    "            for pos, transfer in enumerate(resampled_bytes):\n",
    "                try:\n",
    "                    interval_sum.iloc[current_interval_index+pos]+=transfer                    \n",
    "                except:\n",
    "                    date_range = pd.date_range(start=interval_sum.index[-1], \n",
    "                                               periods=len(resampled_bytes)-pos+1, \n",
    "                                               freq=f'{INTERVAL}S').tolist()[1:]\n",
    "                    interval_sum = pd.concat([interval_sum, pd.DataFrame({'sum of data transfer':resampled_bytes[pos:]}, index=date_range)])\n",
    "                    break\n",
    "        else:\n",
    "            interval_sum.loc[interval_datetime] += row['Bytes']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "184813620806.99988\n",
      "184813620807\n"
     ]
    }
   ],
   "source": [
    "print(sum(interval_sum['sum of data transfer']))\n",
    "print(sum(df['Bytes']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "interval_sum.reset_index().to_feather('./feather_intervale_sum')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f48b5bb1c8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "interval_sum.iloc[:,:].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total bytes transfered:\t401153114\n",
      "# of records:\t23943\n"
     ]
    }
   ],
   "source": [
    "start_interval = '2020-02-08 00:04:40.000'\n",
    "end_interval = '2020-02-08 00:04:45.056'\n",
    "df_subset = df[(df['Datetime_firstseen']>=pd.to_datetime(start_interval))&(df['Datetime_firstseen']<pd.to_datetime(end_interval))]\n",
    "\n",
    "print('Total bytes transfered:\\t', end='')\n",
    "print(sum(df_subset['Bytes']))\n",
    "\n",
    "print('# of records:\\t', end='')\n",
    "print(df_subset.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Feb-07-2020 23:59:38.00', 'Feb-07-2020 23:59:38.87', 'Feb-07-2020 23:59:39.75', 'Feb-07-2020 23:59:40.62', 'Feb-07-2020 23:59:41.50', 'Feb-07-2020 23:59:42.37', 'Feb-07-2020 23:59:43.25', 'Feb-07-2020 23:59:44.12', 'Feb-07-2020 23:59:44.99']\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1944 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "num_intervals = 300\n",
    "start=100\n",
    "disp = df.iloc[start:num_intervals+start,:].sample(frac=1)\n",
    "# disp = df.sample(frac=1).iloc[:num_intervals,:]\n",
    "intervals = disp[['Datetime_firstseen','Datetime_lastseen','Duration']]\n",
    "\n",
    "min_date=disp.Datetime_firstseen.min() - dt.timedelta(microseconds=disp.Datetime_firstseen.min().microsecond)\n",
    "max_date=disp.Datetime_lastseen.max() - dt.timedelta(microseconds=disp.Datetime_firstseen.min().microsecond) + dt.timedelta(seconds=1)\n",
    "\n",
    "\n",
    "idx=0\n",
    "\n",
    "viridis = plt.cm.get_cmap('viridis', num_intervals)\n",
    "              \n",
    "fig, ax = plt.subplots(figsize=(20,27))\n",
    "for x_s, dur, x_e in zip(intervals['Datetime_firstseen'],intervals['Duration'], intervals['Datetime_lastseen']):\n",
    "#     print(x_s, dur.total_seconds(), x_e)\n",
    "    x_s = (x_s - min_date).total_seconds()\n",
    "    ax.add_patch(plt.Rectangle((x_s,idx),\n",
    "                               dur.total_seconds(),\n",
    "                               .2 ,\n",
    "                               color=viridis(idx / num_intervals)))\n",
    "    idx+=1\n",
    "\n",
    "    \n",
    "ax.autoscale()\n",
    "\n",
    "date_range = pd.date_range(start=min_date, end=max_date, periods=len(ax.xaxis.get_majorticklocs()))\n",
    "date_range = ['{:%b-%d-%Y %H:%M:%S}.{:02.0f}'.format(x, x.microsecond / 10000.0) for x in date_range]\n",
    "print(date_range)\n",
    "ax.xaxis.set_ticklabels(date_range)\n",
    "# ax.set_ylim(-2,4)\n",
    "plt.savefig('./networkflow')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "ip_host = pd.read_feather(\"./feather_hostnames\")\n",
    "ip_host.set_index('ip_addr', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Datetime_firstseen</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Protocol</th>\n",
       "      <th>Src_IP_Addr</th>\n",
       "      <th>Src_Port</th>\n",
       "      <th>Direc</th>\n",
       "      <th>Dst_IP_Addr</th>\n",
       "      <th>Dst_Port</th>\n",
       "      <th>Flags</th>\n",
       "      <th>Tos</th>\n",
       "      <th>Packets</th>\n",
       "      <th>Bytes</th>\n",
       "      <th>Flows</th>\n",
       "      <th>Datetime_lastseen</th>\n",
       "      <th>Source_Host</th>\n",
       "      <th>Dest_Host</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-02-07 23:54:53.840</td>\n",
       "      <td>00:04:50.310000</td>\n",
       "      <td>UDP</td>\n",
       "      <td>134.193.92.68</td>\n",
       "      <td>500</td>\n",
       "      <td>-&gt;</td>\n",
       "      <td>209.85.235.189</td>\n",
       "      <td>500</td>\n",
       "      <td>........</td>\n",
       "      <td>0</td>\n",
       "      <td>121</td>\n",
       "      <td>52824</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-02-07 23:59:44.150</td>\n",
       "      <td>UMKC</td>\n",
       "      <td>oc-in-f189.1e100.net</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-02-07 23:57:52.820</td>\n",
       "      <td>00:01:51.696000</td>\n",
       "      <td>TCP</td>\n",
       "      <td>172.217.9.132</td>\n",
       "      <td>443</td>\n",
       "      <td>-&gt;</td>\n",
       "      <td>134.193.198.19</td>\n",
       "      <td>62831</td>\n",
       "      <td>...AP...</td>\n",
       "      <td>128</td>\n",
       "      <td>2521</td>\n",
       "      <td>1800000</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-02-07 23:59:44.516</td>\n",
       "      <td>dfw25s26-in-f4.1e100.net</td>\n",
       "      <td>UMKC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-02-07 23:58:01.226</td>\n",
       "      <td>00:01:43.250000</td>\n",
       "      <td>TCP</td>\n",
       "      <td>172.217.1.238</td>\n",
       "      <td>443</td>\n",
       "      <td>-&gt;</td>\n",
       "      <td>134.193.198.19</td>\n",
       "      <td>62833</td>\n",
       "      <td>...AP...</td>\n",
       "      <td>0</td>\n",
       "      <td>1225</td>\n",
       "      <td>990522</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-02-07 23:59:44.476</td>\n",
       "      <td>lax17s02-in-f14.1e100.net</td>\n",
       "      <td>UMKC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-02-07 23:58:34.242</td>\n",
       "      <td>00:01:10.006000</td>\n",
       "      <td>TCP</td>\n",
       "      <td>134.193.254.218</td>\n",
       "      <td>49345</td>\n",
       "      <td>-&gt;</td>\n",
       "      <td>150.199.7.126</td>\n",
       "      <td>443</td>\n",
       "      <td>...APRS.</td>\n",
       "      <td>0</td>\n",
       "      <td>715</td>\n",
       "      <td>41665</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-02-07 23:59:44.248</td>\n",
       "      <td>UMKC</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-07 23:58:43.858</td>\n",
       "      <td>00:01:01.010000</td>\n",
       "      <td>TCP</td>\n",
       "      <td>136.34.135.223</td>\n",
       "      <td>51053</td>\n",
       "      <td>-&gt;</td>\n",
       "      <td>134.193.136.193</td>\n",
       "      <td>443</td>\n",
       "      <td>...AP.SF</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>1832</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-02-07 23:59:44.868</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>UMKC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Datetime_firstseen        Duration Protocol      Src_IP_Addr Src_Port  \\\n",
       "0 2020-02-07 23:54:53.840 00:04:50.310000      UDP    134.193.92.68      500   \n",
       "1 2020-02-07 23:57:52.820 00:01:51.696000      TCP    172.217.9.132      443   \n",
       "2 2020-02-07 23:58:01.226 00:01:43.250000      TCP    172.217.1.238      443   \n",
       "3 2020-02-07 23:58:34.242 00:01:10.006000      TCP  134.193.254.218    49345   \n",
       "4 2020-02-07 23:58:43.858 00:01:01.010000      TCP   136.34.135.223    51053   \n",
       "\n",
       "  Direc      Dst_IP_Addr Dst_Port     Flags  Tos  Packets    Bytes  Flows  \\\n",
       "0    ->   209.85.235.189      500  ........    0      121    52824      1   \n",
       "1    ->   134.193.198.19    62831  ...AP...  128     2521  1800000      1   \n",
       "2    ->   134.193.198.19    62833  ...AP...    0     1225   990522      1   \n",
       "3    ->    150.199.7.126      443  ...APRS.    0      715    41665      1   \n",
       "4    ->  134.193.136.193      443  ...AP.SF    0       16     1832      1   \n",
       "\n",
       "        Datetime_lastseen                Source_Host             Dest_Host  \n",
       "0 2020-02-07 23:59:44.150                       UMKC  oc-in-f189.1e100.net  \n",
       "1 2020-02-07 23:59:44.516   dfw25s26-in-f4.1e100.net                  UMKC  \n",
       "2 2020-02-07 23:59:44.476  lax17s02-in-f14.1e100.net                  UMKC  \n",
       "3 2020-02-07 23:59:44.248                       UMKC               Unknown  \n",
       "4 2020-02-07 23:59:44.868                    Unknown                  UMKC  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Source_Host'] = df.loc[:,'Src_IP_Addr'].map(lambda x: ip_host.loc[x,'hostnames'])\n",
    "df['Dest_Host'] = df.loc[:,'Dst_IP_Addr'].map(lambda x: ip_host.loc[x,'hostnames'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2020-02-07 23:54:53.840</td>\n",
       "      <td>00:04:50.310000</td>\n",
       "      <td>UDP</td>\n",
       "      <td>134.193.92.68</td>\n",
       "      <td>500</td>\n",
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       "      <td>500</td>\n",
       "      <td>........</td>\n",
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       "      <td>2020-02-07 23:59:44.150</td>\n",
       "      <td>UMKC</td>\n",
       "      <td>oc-in-f189.1e100.net</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-02-07 23:57:52.820</td>\n",
       "      <td>00:01:51.696000</td>\n",
       "      <td>TCP</td>\n",
       "      <td>172.217.9.132</td>\n",
       "      <td>443</td>\n",
       "      <td>-&gt;</td>\n",
       "      <td>134.193.198.19</td>\n",
       "      <td>62831</td>\n",
       "      <td>...AP...</td>\n",
       "      <td>128</td>\n",
       "      <td>2521</td>\n",
       "      <td>1800000</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-02-07 23:59:44.516</td>\n",
       "      <td>dfw25s26-in-f4.1e100.net</td>\n",
       "      <td>UMKC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-02-07 23:58:01.226</td>\n",
       "      <td>00:01:43.250000</td>\n",
       "      <td>TCP</td>\n",
       "      <td>172.217.1.238</td>\n",
       "      <td>443</td>\n",
       "      <td>-&gt;</td>\n",
       "      <td>134.193.198.19</td>\n",
       "      <td>62833</td>\n",
       "      <td>...AP...</td>\n",
       "      <td>0</td>\n",
       "      <td>1225</td>\n",
       "      <td>990522</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-02-07 23:59:44.476</td>\n",
       "      <td>lax17s02-in-f14.1e100.net</td>\n",
       "      <td>UMKC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-02-07 23:58:34.242</td>\n",
       "      <td>00:01:10.006000</td>\n",
       "      <td>TCP</td>\n",
       "      <td>134.193.254.218</td>\n",
       "      <td>49345</td>\n",
       "      <td>-&gt;</td>\n",
       "      <td>150.199.7.126</td>\n",
       "      <td>443</td>\n",
       "      <td>...APRS.</td>\n",
       "      <td>0</td>\n",
       "      <td>715</td>\n",
       "      <td>41665</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-02-07 23:59:44.248</td>\n",
       "      <td>UMKC</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-07 23:58:43.858</td>\n",
       "      <td>00:01:01.010000</td>\n",
       "      <td>TCP</td>\n",
       "      <td>136.34.135.223</td>\n",
       "      <td>51053</td>\n",
       "      <td>-&gt;</td>\n",
       "      <td>134.193.136.193</td>\n",
       "      <td>443</td>\n",
       "      <td>...AP.SF</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>1832</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-02-07 23:59:44.868</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>UMKC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Datetime_firstseen        Duration Protocol      Src_IP_Addr Src_Port  \\\n",
       "0 2020-02-07 23:54:53.840 00:04:50.310000      UDP    134.193.92.68      500   \n",
       "1 2020-02-07 23:57:52.820 00:01:51.696000      TCP    172.217.9.132      443   \n",
       "2 2020-02-07 23:58:01.226 00:01:43.250000      TCP    172.217.1.238      443   \n",
       "3 2020-02-07 23:58:34.242 00:01:10.006000      TCP  134.193.254.218    49345   \n",
       "4 2020-02-07 23:58:43.858 00:01:01.010000      TCP   136.34.135.223    51053   \n",
       "\n",
       "  Direc      Dst_IP_Addr Dst_Port     Flags  Tos  Packets    Bytes  Flows  \\\n",
       "0    ->   209.85.235.189      500  ........    0      121    52824      1   \n",
       "1    ->   134.193.198.19    62831  ...AP...  128     2521  1800000      1   \n",
       "2    ->   134.193.198.19    62833  ...AP...    0     1225   990522      1   \n",
       "3    ->    150.199.7.126      443  ...APRS.    0      715    41665      1   \n",
       "4    ->  134.193.136.193      443  ...AP.SF    0       16     1832      1   \n",
       "\n",
       "        Datetime_lastseen                Source_Host             Dest_Host  \n",
       "0 2020-02-07 23:59:44.150                       UMKC  oc-in-f189.1e100.net  \n",
       "1 2020-02-07 23:59:44.516   dfw25s26-in-f4.1e100.net                  UMKC  \n",
       "2 2020-02-07 23:59:44.476  lax17s02-in-f14.1e100.net                  UMKC  \n",
       "3 2020-02-07 23:59:44.248                       UMKC               Unknown  \n",
       "4 2020-02-07 23:59:44.868                    Unknown                  UMKC  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Geovanni.TheBeast-Snipes\\Anaconda3\\envs\\dsci\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    },
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-02-07 23:30:00.534</td>\n",
       "      <td>00:30:00.074000</td>\n",
       "      <td>TCP</td>\n",
       "      <td>18.215.66.244</td>\n",
       "      <td>443</td>\n",
       "      <td>-&gt;</td>\n",
       "      <td>134.193.19.39</td>\n",
       "      <td>61753</td>\n",
       "      <td>...AP...</td>\n",
       "      <td>0</td>\n",
       "      <td>380</td>\n",
       "      <td>40711</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-02-08 00:00:00.608</td>\n",
       "      <td>ec2-18-215-66-244.compute-1.amazonaws.com</td>\n",
       "      <td>UMKC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-02-07 23:30:00.624</td>\n",
       "      <td>00:29:59.946000</td>\n",
       "      <td>TCP</td>\n",
       "      <td>172.217.9.174</td>\n",
       "      <td>443</td>\n",
       "      <td>-&gt;</td>\n",
       "      <td>134.193.255.84</td>\n",
       "      <td>61175</td>\n",
       "      <td>...AP.S.</td>\n",
       "      <td>128</td>\n",
       "      <td>3626</td>\n",
       "      <td>1300000</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-02-08 00:00:00.570</td>\n",
       "      <td>dfw25s27-in-f14.1e100.net</td>\n",
       "      <td>UMKC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-02-07 23:30:00.650</td>\n",
       "      <td>00:30:00.044000</td>\n",
       "      <td>TCP</td>\n",
       "      <td>162.159.128.235</td>\n",
       "      <td>443</td>\n",
       "      <td>-&gt;</td>\n",
       "      <td>134.193.97.48</td>\n",
       "      <td>54320</td>\n",
       "      <td>...AP...</td>\n",
       "      <td>0</td>\n",
       "      <td>906</td>\n",
       "      <td>43179</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-02-08 00:00:00.694</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>UMKC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-02-07 23:30:10.987</td>\n",
       "      <td>00:29:59.928000</td>\n",
       "      <td>TCP</td>\n",
       "      <td>75.87.144.226</td>\n",
       "      <td>59710</td>\n",
       "      <td>-&gt;</td>\n",
       "      <td>134.193.136.77</td>\n",
       "      <td>8000</td>\n",
       "      <td>...AP.S.</td>\n",
       "      <td>0</td>\n",
       "      <td>23308</td>\n",
       "      <td>1200000</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-02-08 00:00:10.915</td>\n",
       "      <td>cpe-75-87-144-226.kc.res.rr.com</td>\n",
       "      <td>UMKC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-07 23:30:15.762</td>\n",
       "      <td>00:29:51.518000</td>\n",
       "      <td>TCP</td>\n",
       "      <td>3.222.53.17</td>\n",
       "      <td>443</td>\n",
       "      <td>-&gt;</td>\n",
       "      <td>134.193.177.99</td>\n",
       "      <td>62714</td>\n",
       "      <td>...AP.S.</td>\n",
       "      <td>0</td>\n",
       "      <td>188</td>\n",
       "      <td>19984</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-02-08 00:00:07.280</td>\n",
       "      <td>ec2-3-222-53-17.compute-1.amazonaws.com</td>\n",
       "      <td>UMKC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Datetime_firstseen        Duration Protocol      Src_IP_Addr Src_Port  \\\n",
       "0 2020-02-07 23:30:00.534 00:30:00.074000      TCP    18.215.66.244      443   \n",
       "1 2020-02-07 23:30:00.624 00:29:59.946000      TCP    172.217.9.174      443   \n",
       "2 2020-02-07 23:30:00.650 00:30:00.044000      TCP  162.159.128.235      443   \n",
       "3 2020-02-07 23:30:10.987 00:29:59.928000      TCP    75.87.144.226    59710   \n",
       "4 2020-02-07 23:30:15.762 00:29:51.518000      TCP      3.222.53.17      443   \n",
       "\n",
       "  Direc     Dst_IP_Addr Dst_Port     Flags  Tos  Packets    Bytes  Flows  \\\n",
       "0    ->   134.193.19.39    61753  ...AP...    0      380    40711      1   \n",
       "1    ->  134.193.255.84    61175  ...AP.S.  128     3626  1300000      1   \n",
       "2    ->   134.193.97.48    54320  ...AP...    0      906    43179      1   \n",
       "3    ->  134.193.136.77     8000  ...AP.S.    0    23308  1200000      1   \n",
       "4    ->  134.193.177.99    62714  ...AP.S.    0      188    19984      1   \n",
       "\n",
       "        Datetime_lastseen                                Source_Host Dest_Host  \n",
       "0 2020-02-08 00:00:00.608  ec2-18-215-66-244.compute-1.amazonaws.com      UMKC  \n",
       "1 2020-02-08 00:00:00.570                  dfw25s27-in-f14.1e100.net      UMKC  \n",
       "2 2020-02-08 00:00:00.694                                    Unknown      UMKC  \n",
       "3 2020-02-08 00:00:10.915            cpe-75-87-144-226.kc.res.rr.com      UMKC  \n",
       "4 2020-02-08 00:00:07.280    ec2-3-222-53-17.compute-1.amazonaws.com      UMKC  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_umkc = df[df.Dest_Host == 'UMKC']\n",
    "df_umkc.sort_values(by=['Datetime_firstseen'], inplace=True)\n",
    "df_umkc.reset_index(drop=True, inplace=True)\n",
    "df_umkc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f4bd22fb48>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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otRVK1TyBqXnfYRZu3Fu19YtIYSUD3cyGArcD5wOjgDFmNipntiVAvbu/D3gcuLHShVZDrXUb1MKG5W9/PpMv3jWv2mX0S1z2pETKFaWFfibQ5O4b3L0DmAhclDmDu89098Ph0/nAyMqWWR364YtInEQJ9JOBrRnPm8NphVwCTO1PUbUi3eUSsYmua7mISDVFCfR8aZY3uczsq0A9cFOB1y81swYza2hpaYleZZWku1yqW4YcYw53dHL91NW0H+2qdikSM1ECvRk4JeP5SGBb7kxm9gngCuBCdz+Sb0HuPt7d6929fsSIEX2pN70lGcz+7VrpQ5djw69nreeu2Rt4cP7mapciMRMl0BcBp5nZqWZ2PDAamJQ5g5mdAdxFEOa7Kl9mYQN5oDCpwxalt1o44JzS0dkNQGe3vlBSnpKB7u6dwGXANGA18Ki7rzSza8zswnC2m4A3AY+Z2atmNqnA4mKlO0zoIWqiJ0q+Yx219BEn/YQ2GTiRTv139ynAlJxpV2U8/kSF66oJ+mFJNfRcB6fKhUjs6EzRIrzWBqJXmvboa1LPwfiEfu9kwCjQi0hqCz1p/55yDdSxjv2HOzja1d3v5aS/d8f6ByVlU6AXkfQGehw8/eprPL64udpllOTuvP+a5/nhY0srsKwKFCTHJAV6Uf27Hnpreyd3zGqiW6MV+uxfJr7KDyoQkgMtFcJPvdprRG/5y8pzQpu701mB1r8kW2wDfTDOyuzv9dCvfnolNz7byOy1tX8S1bFkIL45UbtJ2o92UTd2Mo82bC04T74T2n7w2DL+/IpEnIAtAyh2gT6oN7goc525u8oH248CGk98LCh2j9s1Ow6ydW9wqaOW1uCcu3Ez1pVcZubG4YlXerqdlm7d3/dCC1jefCBdm8RX7AJ9MPW3Dz01jn2o/perpv1oFx+7eRbzN+wZ0PX0tNB7f1nOu3UuH7lxJhDt3IZSN0C/6PaX+lxnIf/3thf55C2zK75cGVyKmiJ6fli9f1p7Dh1hVmP2SbF7D3dkPe8OuzwH+iYPnV3dbGg5NCDLXt9yiAfynIJ+4PBRpizfPiDrTNm0uy39+Ehn365rsm7nITbubuO7v3ul6Hy/emEdXTl7Uq3hHlYh7p6+Nnx3iRBOSa1iSM6Mhzs602eIZm4cnl+1s2QdpWze08bTr76Wfn710yu4bvKqXvPtO9y/9RSz59CRrGvTPDBvExfe9mKflvXkkuasf09K445W3nn55PTeULlmr20p2hVWrq5uZ1dre8WWF0VsAz31w7htZlPPePEKSy81z6/04gkL+ca9i7K+pF+fsDBrnq4IrbFV2w7SdqQTCLpoPnLjCyxrLm+X+sZpjZzzi9l9+iKX+sJ9ZtyL/OSpFb2mf/ehV/jO717htf2vR1rP2p2tkedNOfvmWenHP31mdVnvzbWnrYPFmwvfmGPayp1MX70za9oPH1tWdJn3z9vMF++ax/OrdkYemVKohT7qqml87tcv8T9Lt6WDdevew/zj/Q382yPlHxQ+kBHOn7p1Dv8y8dX089/O28zdczeWvcyUTbvbeGZZeQd/P/jT6Vyc8fv4ydMrWdZ8oE/r/7dHlmb9e1ImLtpCt8Nzq3b2em1Xazsfum46TbsK3/D76xMW8qPHi3/m5fj5s2s487oZ7Dk0eF1ZsQ30zP6+nz/byH9PX9crUIvp6OwueTW7nh9f9vS9bR2s3HYwPU/d2Mn89/TefaKpDc3QAoHefrSLC8bN5dth63Hhhr1s3ft63mVlLxd2HewJ4gVhK/EjN86kcUf0O9Qv3LSXj99cfDf79QL/R5v3Bq3nxxq2Fv2RBPU6n7xlDh++4YW8dzva1drOvz3yatHPY13GOurGTqZu7OSi68ynadehdD355LbQtxTZQHZ2dbMm/L/O3JB2dntW2GWOTOns6i56FuiK1w7yvYeX8D9Lg/e3h3slW/a29Z45dM7Ns9L/rpTpq3Zy+jXPsSDsZmo/2r/RMTPX7OKD1z6f/nzOvWU2lz20hIkLt2RtOEpZuHEvv38l/xDUbz+4uOBrUaU+1mufWdXrM562cictrUe496VN/VpHrn1tHQXv4DVt5Q4gGO02WGIb6JnunL2eW6avZfbaFpZs2ccvnmss+Z4Lxs3lL3/ybPGZCpyx94Frn08/TmXALdPX9np76rXNBX6Q28IW6yub94XzZw9XO9LZxb62jl7ve3jhFs782QyW52nhRDnYlqn1SLQv21VPr8gK0a6uoNZbp6/jE7+cU/S9103uaV3nu9vRDVPW8OSS15i8LOjCadp1iBfX7c6ap9zjypOXbadu7OSsPZBSXV/uPRfGCtbZs9KLbn+Jq5/u2VP5xr2LeHjhluB9ZB8Qv+yhJem9rsseWpKePrdpd/rfYWZ8+e753DB1TcHupNT3bu3Owt1pG3a3ccv0tfzX/6yk/WgXW/ce5lv3NwBEagH/88NLuOS+RUXnuXbyKva0ddC8L9hwHQ0/+7G/X86PnyivRfv9R5fmPfg6dcUOvv/o0qwg7ur2Pu99//XPZgzKMM8v37Og4B28OrtSx9AGbyhHIgI902fveJlfvdDE3HUt6V385n2HOZQTXLmtmpTOrm7qxk7m3pc28my4hS2WA91FvnCLw6C+4smeIGjc0Urd2Mls2XOYc34RtI4PHenktf2v9+pf/Yf7FnFGxsYjZeKioJ/vsoeDln1meZnDOR9dtJWv/WZBr/f3pU///nnZ/ejbDmR31aR+7EBWl9HB9qPc82Lx3ftUxZ3hQYdP/HI2X82tO89/85y1wWe8aXcbM9fsom7sZJ5buYOXm3YzcVEQtmsy9lis8KKAoBvp3Vf2DA3M/GiXbt3PbzP+D15syt7g5A6jfc/V0wDS3yGAtTta03sBnV3dvLx+D3fOXs9Ft+U/yBn1Y5q8bDv3vrSJ++dtoiGjWynK+yct3caMNT3Hgj55y2zqxk5m3vo9rHjtAN3dnj6PYohZr269fYd7NzhKKTbk+HcLgs9t58F23vUfU9LPo9i0p6fhtKv1CLsijtrp6Oxm5pq+XSR29fZgTz3fhif1fd64u41FmwbnPryJC/SUr/1mIZ+6JWg5/u3PZ/LFO/NvRa+fupq/uHIqo64KWuttHUFr6ZfPrU2HWNFAL7Pp+PjiIIw/9+uXs6Zf8eTy9MYh1Qf4UlPxkRmb9xTvM//RE8uYu243v3yuMf3vGyiZG8yNGQczn12+o9e8W3Lq3h32MV719MqCy0+FwCOLen7gF09YyIdveIGzb56VDvBLH1jMl+/pvREDONzRlVVbKfk21vnOWnX3vH3ouV04Q4dY3j70NQW6ycrd7N72QlO6VZi7DoCbphXeG0hJ7Q2MuXs+n/nVi9zz4ob0saBhQ4akR+ukdHU7N01bk+6OaWk9wtd+s4CXczZ4mYYNKRw789YH3/kNLcHnlOp+KsXdmdWYfb5HqmXc0dnN7MbC54LcNG0N3yyxl1J6/b2npT6Liycs5AsF8qfSEhvokB0yq8Itaa67Zm/gSGc3hzu6WLBhT3pLOyRjN6nYRZLKyXN3T7eud+ccKDF6B0BKvhEJWe/NKC/fF2vcC00c7hjYu9/cM3dj3o3btgO9D4RePCE7cPeH/bBHOrv52ZT8Bz9T/64fP7G8zzVePWklH8s40FpKvkAvdNZqvk/ucEf2XuGwIdbz+URI63x7UnfOXl9w/oPtnVnnPOTu6d8+cz33v1zeTTNWbjvI1r3BZ/jRm2b2er1h8z5un7mee+ZuAOBD101n7rrd6W6ffHK7IPZntPJTG+5U6/a4cMxv25FO6sZO5q4C//4H87TkJ7wU7BleP3V1rwPemcrZyD++uJntGd/pYo29apx/kuhAh+yhb9v2v87ana3szdMvDfCl8fPToXrg9Z6DPY4X3CAUWlauWY27eGXLvoIHSIaYZQXIgoxx03fP3cgVT+YPstzhilNX9G4Rp7g7k5dtL7jh6I/HFzfzzv+Yws6D7fznpJ6W9q15DvAe7XIenL85PV/mBa3Gz9mQd/nFurYgOOiVKTV7f0ZARX2re/76Mr9DAMOGDok8vBFg6orew0JvmLqm6Hsy+42H5Om7zRxp9KPHS4+ead4XbWTS86t3ZfUlp/47WlqPsCOney430D/40+m9lpf6Thw3NJg3dTzg9plNedc/K0+XyV2zN9B+NHuvbG9bR/rYVUru76FQwB860skPHlvKl+9e0Ot7le+rUo1LNUS6HnqcZQ59+6cHF7Os+QDD33RCwfm78vwwM7s+VryWfaCpUF98rm/cu4ibv3B6wdfbO7uyAiR1wC3ldwu28L9PPrHX+z516xzem2d6PvU/nc6eCBug3YeO0Nnl/OmJb4i03EwfvXEmRzqLf5E7u7u5MhwK+Z8XvifSFQrLjeVUy+nm53ofrC4V1C2tR+h2L9jCyt0TcfJ3uXw344AowFNLXkvXtb6ldKtw58Hyh7v94vmef+/WvYd7hdV9L29KP360ofSoktRxoFJyz15NjY760HW9wzpzL2zRpr1ZNU4Ju+g6OoNpqRb6mLvnA8FeSMrdczbwqff8KW9/2xsLfj9WbT+Y9RudumIHU1fsYNMNn05P68p588dunsW4MWdw4el/lp62q7Wdi38TjKLbuLuNG6au4fIL/gojdVDcyd1Mq4U+wFJb+dzujkzdJbJl7O+zW8qrC7Tc8/nvGb3DJeWlpj1ZX+yoF3k62uUs2ZL9Y5qxemdWCz+lVJinDtzU/3Q6Z10/o1dfdxSlwhyyg+rQkc5I4bZky/68/6a+WLuz+DDLD103nb/+2YyCwxYn5zuhKs9vN7cl2LB5X9YB8oGwP2MY4d1zN/Ku/5hSZO6BdbDACVEPZXSP5OtbfmD+Zv7pwcVA0FK/aVr+vZLrpqzma2H3XaE95c/d8XLevYxnM/Z+8nUV/uSpFVz51PL0Xta9L23KOtZx15wNvPfqaeku1x89vqzX6J1qBLoN1Ek5pdTX13tDQ+F+tkIu//3yXq3XuPizE9/Qa3TIQPnnc/6ccS/k3z0tZuP1F3Dq5YMXAn/0h8dH7rYqx0dOG87cdYUPzFXaq1edy/uv6T0i6Vj2k8+M4tpnih//qYRffOF0/r0PV+S8/x/OZNhQ48t35z+InrLphk9HOu/hs2eczC1fen/6ee575l1+Died+Adl15nLzBa7e32+1yK10M3sPDNrNLMmMxub5/UTzOyR8PUFZlbXv5ILe3JJ7V8bu5DBCnOgT2EODPoFmgYizIFBDXPIHm8ugcEIc6BPYQ7B6JNSYQ69g7mQ1B72xt1teUfV3fRsI3VjJ/fqtq2kki10MxsKrAXOBZqBRcAYd1+VMc93gPe5+z+Z2Wjgs+7+pWLL7WsLvS9nCIqI1JLMPvxy9beFfibQ5O4b3L0DmAhclDPPRcBvw8ePAx+3gb4ilYhITA1UwzRKoJ8MZF6CrDmclnced+8EDgBvy12QmV1qZg1m1tDS0rebPowbc0af3iciUis+/8GRA7LcKMMW87W0c/tposyDu48HxkPQ5RJh3b1cePqfZQ0nEhGRQJQWejNwSsbzkUDumLr0PGY2DDgRGJyLF4iICBAt0BcBp5nZqWZ2PDAamJQzzyTg6+HjzwMveLXGQ4qIHKNKdrm4e6eZXQZMA4YCE9x9pZldAzS4+yTgN8ADZtZE0DIfPZBFi4hIb5FO/Xf3KcCUnGlXZTxuB75Q2dJERKQcx9Sp/yIiSaZAFxFJCAW6iEhCKNBFRBKialdbNLMWoLzbp/QYDgzu1Zf6Jg51xqFGiEedqrFy4lBntWp8h7uPyPdC1QK9P8ysodDFaWpJHOqMQ40QjzpVY+XEoc5arFFdLiIiCaFAFxFJiLgG+vhqFxBRHOqMQ40QjzpVY+XEoc6aqzGWfegiItJbXFvoIiKSQ4EuIpIU7h6rP8B5QCPQBIyt4HInALuAFRnT/gh4HlgX/v3WcLoB48IalgEfyHjP18P51wFfz5j+QWB5+J5x9HR3RV4HwTXnZwKrgZXAv9RonW8AFgJLwzr/K5z3VGBBuIxHgOPD6SeEz5vC1+sy6rk8nN4IfKrU96DcdRBcQXQJ8Ewt1ghsCj+PVwmublpzn3c4/S0Et59cQ/D9/Jsaq/Oz4f9h6n60JqsAAARASURBVM9B4F9rrMb0OvqcY9UK5j6G7lBgPfBO4HiCwBhVoWV/lCCMMgP9RsIfIjAW+Hn4+AJgaviBnAUsyPjgNoR/vzV8nPrwFoZfcgvfe3656wBOoucH9GaCm3ePqsE6DXhT+Ppx4bSzgEeB0eH0O4Fvh4+/A9wZPh4NPBI+HhV+xicQhOD68DtQ8HvQh3V8H3iInkCvqRoJAn14zne1pj7vcPpvgW+Fj48nCPiaqzMjR3YA76jVGvucY9UM6D6E7t8A0zKeXw5cXsHl15Ed6I3ASeHjk4DG8PFdwJjc+YAxwF0Z0+8Kp50ErMmYnp6v3HXk1Ps0cG4t1wm8EXgF+GuCs+qG5X6WBNfa/5vw8bBwPsv9fFPzFfoehO8pZx0jgRnAOcAzfXj/YNS4id6BXmuf92nARsIWaQ3XmZrvk8BLtVxjX//ErQ89yg2rK+lP3H07QPj3H5eoo9j05jzT+7IOAMysDjiDoPVbc3Wa2VAze5WgG+t5gtbqfg9uIp677EI3GS+3/reVuY47gB8B3eE85b5/MGo04DkzW2xml4bz1drn/SGgBbjXzJaY2T1m9oc1WGfqPaOBh/v4/kH7jfdF3AI90s2oB0GhOsqd3pd1YGZvAp4A/tXdD9Zine7e5e7vJ2gFnwn8VZFlV6rOYvXnvvZGoMXdF2dMK+f9g1EjwAXu/gHgfOC7ZvbRPPOkVOvzHkLQXflrdz8DaCPoWqi1Oj28jeaFwGN9eX8fpvdlHX0Wt0CPcsPqStppZicBhH/vKlFHsekj80wvex1mdhxBmP/O3X9fq3Wmnrj7fmAWQR/hW8KbiOfOV+gm4+XWv7vMdXzKzDYBEwm6XW6twRpXhf+Pu4AnCTaOtfZ5LwOa3X1BOO1xgoCvtTq3EWwYX3H3nX18/6D9dvoiboEe5YbVlZR58+uvE/RZp6ZfbIGzgAPhrtQ04JNm9lYzeytBX9208LVWMzvLzAy4OGdZkdZBcCDnN8Bqd/9lDdfZaWZvATCzPwA+QTDyYSbBTcTzLSO17MybjE8CRpvZCWZ2KkFf7UIKfA/C90RdxzPuPtLd68L3v+DuX6mxGmcDbwr/H/+Q4HNaUc5nMRift7svA7aa2V+E83ycYENUa3VuJ+jbTnW39OX9g1Fj3/WnA74afwiODK8l6JO9ooLLfRjYDhwl2HJeQtDfOYNguNEM4I/CeQ24PaxhOVCfsZx/IBiG1AR8M2N6PcGPcT1wGz1DmiKvA/hbgl2yZfQMv7qgBut8H8FQwGXhsq4K530nQdg1EezynhBOf0P4vCl8/Z0Z9VwRLruRcNRAse9BH9dxNj2jXGqpxr8jGB2TGv55RbmfxWB83uH09wMN4Wf+FMEIkJqqk6CbbQ9wYsZya6rG/uaYTv0XEUmIuHW5iIhIAQp0EZGEUKCLiCSEAl1EJCEU6CIiCaFAFxFJCAW6iEhC/H/KIwtOcQPbEwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_umkc.loc[:,'Bytes'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f4b79e0f08>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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U83uZ2W+j+XPNrDDuQutt3l1F4fipvLOi5Zd2u6tq2LRzf6O2x95dzT89t4Dft/MwyKuLKwCYuqiCP3xYzqS3VjU6jj5n1ZaGl/p1bXxA7KmqYcG67S3O/+OHGygcP5VNu/a32Kfev/5+EUCzE2OpHDzOmr7eu1/5mIdmrezQ8uav3Urh+Kkpw6W65mCo1J+YPlDrzZ54DtQmpvdU1TR6pVO5q4q/nfhu2k8Ht3Y7t0VNbR1Pzl7DgVZepiQ/8Ktr6hpCoulhmdo6Z/H6Hezaf4C5q+I5Gb27qoaHZq2kts6Zv3YrazbvafbVBS9+UN7wgbv8Fl51ZbIztHP/Aapqmm/TO6cuZdR98Z1TqKqpZWfS9l28fkfK9bbVrhbqBzj/njf4wl0Hn+CTt+nwCdO48an51NY5P/nTUj7dkf7x2ZXSfojJzPKAicBFQDkwz8ymuPvSpG43ANvc/bNmNhb4KfDNzih4WcVOAK7+zVyW3nEJ75VtYcayjdxz5Wm4O3ura7no57Oo2LGflXddxs9eK+Wikcc23PBvllZy9gn92X+gjqfmrGHQkb2ZvWoLd339c1Ts2MfCddv53185iY8rdvHTVz/m5guKeOK9NY1OND095+AnUDfu3M/Tc9byyzcOnqAr37qPA7V1bNy5n/eid478amYZH63fwd7qGiZcdirD+vcBEseu/+LfD+6xb9q1n/fKtnDBqYMa2ia/mzjhecsLixhy9OGcPrQfo04ZhBn8bn45V597Av0OPwx3bzjB6Z441ndYXuPn7xlLN3LntGVce94J3P5yYhPura6hrs6pdcdIhPSMZZs4LM/4fOEAeuYfXMaW3VUMOKJnQ7Bu3l3N03PW8sjbqxqeUJ6Z+wlXnjWUTTurGHRUL6pq6ti1/wBXPjgbgPtnLGfu6q2M+/wwZq/awksLEk+Sd339c3zt9MEcFh22qK1zTr71lUb17ztQy8zSTXz7scQhm++OOokfjR7BD19YyIJ12/nKfW8y/9YLAaiqqaNXfg/MjKfmrGVe0slfgH3Vtby8cAOvLf2U/z/2TD7ZupdPd+5n3da9XHNeYUO/91dvpd/hh3Ht5Pe54qwh3HxBETc98yEzlm3ktpeWcO6JA5izaiszbxnF8QP60MPgwVkruffVUpbecQl9eubz/Wc/YPqSjay556vsrWocJD97rZRfv3nwifO98f+LAUf0ZOPO/eTn9WDI0Yc3CtmZH2/iydlr+KuiAq46eyjrt+1jwouLuPDUYzmmby+uOnsoNz5ZwnsrEzscTZ8gncQe5w+eT7wK+utTBjFvzTYgcUI1uX9VTeO037qnmm8+PJsHxp5BTa0z6a1VTP0oseNzwYhBPHJNMWu37uW4o3rzyNuJ++3Ds1byjeJhjQL0V2+swMyYtbySr502mBHHHcXnC/uzdU81989YTv8+PfnuqJPomdeD/LweuDun3PoqAGvu+SqL1+/ga798h+u+WMiPRo/g8J551NTWkdfDqKlzamqdaR9V8PTctVx//nBGHHckx/TtxTtlm/nSZwey90AtUxZsoN/hh/Fvf/iI/n0Oa6it6TmoA7XOx5/uZPQDbwNw9xWfY8wZnwHgz0s3MmfVFh59ZzWPvrOaVXddxsuLNjBxZhlTbvqrRk/kby2v5MsnF9BVLN3LCjM7D7jd3S+JpicAuPvdSX2mR31mm1k+8ClQ4K0svLi42EtKStpc8IU/n0XZpt3pO8oh48je+eza3/L5isH9elORxb2qokF9G8579Mrv0Swwc0XyOKT9igb15eYLivib0z/Truub2Xx3L07XL5PDMkOAdUnT5VFbyj7uXgPsAI5JUdSNZlZiZiWVlalP7KRz/fnDG02fWHAEAH17xftNCv0OPyx9p3Y6Kaq5q33xpGabJK0Rxx3JaUP7dUI18fnC8AENlz87qG+jeScc04czhh3d1SVxenSbffnkAoqO7cuI444E4EtFzffcBvfr3aytMw/LDziiZ5uv85l+vSk6tm/6jh104sDGj42h/Q9v9MqxqT498xpNH3dU89uyqeEDM3v8DezbK6N+megVjeHCU4+l6Ni+nZov9TJJxFR3s6Z75Jn0wd0nAZMgseeewbqb+dYXjudbXzi+PVcVETlkZLLnXg4MS5oeCjT9RE5Dn+iwTD9gKyIikhWZhPs8oMjMhptZT2AsMKVJnynAtdHlq4A3WjveLiIinSvtYRl3rzGzm4DpQB4w2d2XmNkdQIm7TwF+AzxlZmUk9tjHdmbRIiLSuozOQrr7NGBak7bbki7vB74Rb2kiItJeh8wnVEVEDiUKdxGRACncRUQCpHAXEQlQ2q8f6LQVm1UCa9t59YFAKF8KrbF0P6GMAzSW7qojYznB3dN+SU3Wwr0jzKwkk+9WyAUaS/cTyjhAY+muumIsOiwjIhIghbuISIByNdwnZbuAGGks3U8o4wCNpbvq9LHk5DF3ERFpXa7uuYuISCsU7iIiIar/Be+u+gOOBn4HfAwsA84DBgB/BlZE//tHfQ34BVAGLALOSlrOtVH/FcC1Se1nAx9F1/kFBw89pVxHJ4zldmA9sCD6uyyp/4SorlLgkqT20VFbGTA+qX04MDeq+bdAz6i9VzRdFs0v7OA4TkmqdwGwE/jnXNsurYwj57ZJtMz/CywBFgPPAr3bs/64xtgJY3kcWJ20Xc7ozvevaHn/FI1hCfDPra0j2+Po0AZr543zBPCd6HJPEgF5b/2dCxgP/DS6fBnwSnQjnQvMTRroquh//+hy/Q36PomQtei6l0btKdfRCWO5HbglRd+RwEISD8LhwEoSX6GcF10+MVrGQmBkdJ3ngbHR5YeA70aXvwc8FF0eC/w2xu2TR+I3cE/I1e2SYhw5t01I/HTlauDwpPVe19b1xznGThjL48BVKfp3y/sX8Jckgr0PiW/UnQEUtbSObI+jU4M8xY1zVLSRrUl7KTA4ujwYKI0uPwyMa9oPGAc8nNT+cNQ2GPg4qb2hX0vr6ISx3E7qIJkATEianh5txPOA6U37RRt3M5AftTf0q79udDk/6mcdGU/S+i8G3s3V7dLCOHJum3Dwd4kHRMv7E3BJW9cf5xhjHsvFtBzu3fL+ReJrzR9Nmv5/wL+2tI5sj6Orj7mfCFQCj5nZh2b2qJkdARzr7hUA0f9BUf+Wfpy7tfbyFO20so64xwJwk5ktMrPJZta/nWM5BtjuiR8cbzqWjH6QvJ3GknjZDLm5XVKNA3Jsm7j7euC/gE+Aimh589ux/jjHGNtY3P21aPad0Xa538zqf5G6u96/FgNfNrNjzKwPiT3zYa2sI6vj6OpwzwfOAh509zOBPSReYrSkpR/ebmt7Z2hpLA8CJwFnkLgj/yzqH+dYOmWc0c8oXg68kK5rC+vvDtsl1ThybptET0BjSBxK+QxwBHBpO9af9W2VaixmdjWJVwojgM+T2Kv/Uf1Vsl1zKu6+DPgpiWPer5I4lFXTylWyOo6uDvdyoNzd50bTvyMRkBvNbDBA9H9TUv9UP87dWvvQFO20so5Yx+LuG9291t3rgEeAc9o5ls3A0dEPjjcdS2f9IPmlwAfuvjGazsXt0mwcObpNLgRWu3ulux8AXgS+2I71xznGWMfi7hWeUAU8Rvu3S5fdv9z9N+5+lrt/mcTtu6KVdWR1HF0a7u7+KbDOzE6Jmi4AltL4B7avBV6KLk8BrrGEc0m8nKsgcdzwYjPrH+0VXEziuGAFsMvMzjUzA65psqxU64h1LPUbIPJ1Ei/l6tc/1sx6mdlwEidi3qeFHyD3xMG1mSR+cDzV7VI/ljh/kHwcjQ9l5Nx2STWOHN0mnwDnmlmf6Darf6y0df1xjjHOsSxLCisD/pbG26Vb3r/MbFD0/3jgChL3s+75OGnvyYUOnJQ4Aygh8dagP5I4W3wM8DqJZ8HXgQFRXwMmkjir/xFQnLSc60m8XagM+HZSezGJO8lK4FccfCtRynV0wlieimpdFG2QwUn9fxzVVUp0FjxqvwxYHs37cVL7iSQeiGUkDjH0itp7R9Nl0fwTYxhLH2AL0C+pLee2SwvjyNVt8h8k3ma7OBpDr/asP64xdsJY3oi2y2LgaaBvDty/3ibxJLsQuKA7P0709QMiIgHSJ1RFRAKkcBcRCZDCXUQkQAp3EZEAKdxFRAKkcBcRCZDCXUQkQP8DYxQBcpcAPakAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_umkc.loc[600000:900000,'Bytes'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1100000000"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_umkc.loc[600000:900000,'Bytes'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}