tsdb.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Iguazio Time Series DB Usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import v3io_frames as v3f\n",
    "import numpy as np\n",
    "from datetime import datetime, timedelta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a sample time-series dataset and write to the DB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 60 entries, 2018-10-02 14:05:00+03:00 to 2018-10-02 19:00:00+03:00\n",
      "Freq: 300S\n",
      "Data columns (total 3 columns):\n",
      "cpu     60 non-null float64\n",
      "mem     60 non-null float64\n",
      "disk    60 non-null float64\n",
      "dtypes: float64(3)\n",
      "memory usage: 1.9 KB\n",
      "None                                 cpu       mem      disk\n",
      "2018-10-02 14:05:00+03:00  0.933897 -0.916895 -1.207371\n",
      "2018-10-02 14:10:00+03:00  0.876833 -1.964795  0.670975\n",
      "2018-10-02 14:15:00+03:00  1.540077 -1.997942  0.918253\n",
      "2018-10-02 14:20:00+03:00  0.101722 -0.948778  0.147619\n",
      "2018-10-02 14:25:00+03:00  2.425123 -0.686299  0.036415\n"
     ]
    }
   ],
   "source": [
    "# create v3io frames adapter (same one for kv/tsdb/obj/stream/sql)\n",
    "client = v3f.Client()\n",
    "\n",
    "# create random+cumsum time series with 3 metrics (high,low,close)\n",
    "end = datetime.now().replace(minute=0, second=0, microsecond=0)\n",
    "rng = pd.date_range(end=end, periods=60, freq='300s', tz='Israel')\n",
    "df = pd.DataFrame(np.random.randn(len(rng), 3), index=rng, columns=['cpu','mem','disk'])\n",
    "df = df.cumsum()\n",
    "print(df.info(), df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'num_frames': 1, 'num_rows': 60}"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client.write(backend='tsdb', table='clustermon',dfs=df, labels={'node':'11'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading from the TSDB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfs = []; key_list = []\n",
    "for df in client.read(backend='tsdb', table='clustermon', step='10m', aggragators='avg,max,count',start=\"now-5h\", end='now'):  # , filter=\"__name__=='cpu'\"\n",
    "    #print(df.labels)\n",
    "    #df.index = df.index.tz_localize('Israel')\n",
    "    dfs += [df]\n",
    "    key_list += [(df.labels['node'], df.labels['__name__'], df.labels['Aggregator'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"9\" halign=\"left\"></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>metric</th>\n",
       "      <th colspan=\"3\" halign=\"left\">high</th>\n",
       "      <th colspan=\"3\" halign=\"left\">low</th>\n",
       "      <th colspan=\"3\" halign=\"left\">close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>func</th>\n",
       "      <th>avg</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>avg</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>avg</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>node</th>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"26\" valign=\"top\">11</th>\n",
       "      <th>2018-09-29 21:44:17</th>\n",
       "      <td>-1.477705</td>\n",
       "      <td>-0.739035</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.896848</td>\n",
       "      <td>1.277099</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.188171</td>\n",
       "      <td>2.212375</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-29 21:54:17</th>\n",
       "      <td>-1.582295</td>\n",
       "      <td>-1.116608</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.460278</td>\n",
       "      <td>0.537020</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.116329</td>\n",
       "      <td>1.288640</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-29 22:04:17</th>\n",
       "      <td>-1.847861</td>\n",
       "      <td>-1.543047</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.561068</td>\n",
       "      <td>-0.393600</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.984575</td>\n",
       "      <td>-0.334985</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-29 22:14:17</th>\n",
       "      <td>-1.153288</td>\n",
       "      <td>-1.147944</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.435780</td>\n",
       "      <td>-0.551891</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-2.951615</td>\n",
       "      <td>-2.175035</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-29 22:24:17</th>\n",
       "      <td>-2.994355</td>\n",
       "      <td>-2.196948</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.813749</td>\n",
       "      <td>-1.175142</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-5.235545</td>\n",
       "      <td>-4.897679</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-29 22:34:17</th>\n",
       "      <td>-3.340669</td>\n",
       "      <td>-3.081341</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-2.103707</td>\n",
       "      <td>-1.682563</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-6.527413</td>\n",
       "      <td>-5.574587</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-29 22:44:17</th>\n",
       "      <td>-2.042713</td>\n",
       "      <td>-1.982671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-2.219642</td>\n",
       "      <td>-2.163848</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-6.928804</td>\n",
       "      <td>-5.925800</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-29 22:54:17</th>\n",
       "      <td>-1.959080</td>\n",
       "      <td>-1.505773</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.496473</td>\n",
       "      <td>-0.365911</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-6.050278</td>\n",
       "      <td>-5.728095</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-29 23:04:17</th>\n",
       "      <td>-1.016556</td>\n",
       "      <td>-0.907506</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.229547</td>\n",
       "      <td>-0.078180</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-5.315916</td>\n",
       "      <td>-4.878538</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-29 23:14:17</th>\n",
       "      <td>1.090523</td>\n",
       "      <td>1.346521</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-2.300728</td>\n",
       "      <td>-2.037302</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-5.716884</td>\n",
       "      <td>-5.057786</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-29 23:24:17</th>\n",
       "      <td>0.404178</td>\n",
       "      <td>0.449059</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-4.563454</td>\n",
       "      <td>-4.095960</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-4.881752</td>\n",
       "      <td>-4.728078</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-29 23:34:17</th>\n",
       "      <td>-1.351067</td>\n",
       "      <td>-0.688478</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-5.450044</td>\n",
       "      <td>-5.012864</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-4.305286</td>\n",
       "      <td>-3.190759</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-29 23:44:17</th>\n",
       "      <td>-1.244462</td>\n",
       "      <td>-0.569408</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-4.122179</td>\n",
       "      <td>-3.719279</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-2.486222</td>\n",
       "      <td>-1.975777</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-29 23:54:17</th>\n",
       "      <td>-1.111861</td>\n",
       "      <td>-0.970525</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-3.199483</td>\n",
       "      <td>-2.916697</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.310328</td>\n",
       "      <td>0.225623</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-30 00:04:17</th>\n",
       "      <td>-0.596996</td>\n",
       "      <td>-0.356807</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-3.374890</td>\n",
       "      <td>-3.048844</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.832145</td>\n",
       "      <td>2.204527</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-30 00:14:17</th>\n",
       "      <td>0.164371</td>\n",
       "      <td>0.322530</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-2.975351</td>\n",
       "      <td>-2.772099</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.985780</td>\n",
       "      <td>1.996321</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-30 00:24:17</th>\n",
       "      <td>1.394435</td>\n",
       "      <td>1.722966</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-5.000560</td>\n",
       "      <td>-3.755242</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.282094</td>\n",
       "      <td>3.824943</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-30 00:34:17</th>\n",
       "      <td>2.815268</td>\n",
       "      <td>2.845040</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-5.278948</td>\n",
       "      <td>-5.227248</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.232761</td>\n",
       "      <td>4.528897</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-30 00:44:17</th>\n",
       "      <td>1.853097</td>\n",
       "      <td>1.854282</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-5.050500</td>\n",
       "      <td>-4.878113</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.570316</td>\n",
       "      <td>5.160681</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-30 00:54:17</th>\n",
       "      <td>0.654906</td>\n",
       "      <td>1.258009</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-5.393068</td>\n",
       "      <td>-4.744838</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.416613</td>\n",
       "      <td>5.600179</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-30 01:04:17</th>\n",
       "      <td>0.575960</td>\n",
       "      <td>0.995346</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-6.098739</td>\n",
       "      <td>-5.545104</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.075697</td>\n",
       "      <td>6.092829</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-30 01:14:17</th>\n",
       "      <td>1.565484</td>\n",
       "      <td>1.938605</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-4.781674</td>\n",
       "      <td>-4.676875</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.369579</td>\n",
       "      <td>7.851801</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-30 01:24:17</th>\n",
       "      <td>-1.136471</td>\n",
       "      <td>-0.945249</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-6.484813</td>\n",
       "      <td>-6.143703</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.264717</td>\n",
       "      <td>6.327879</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-30 01:34:17</th>\n",
       "      <td>-1.383439</td>\n",
       "      <td>-0.872658</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-6.910115</td>\n",
       "      <td>-6.223143</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.090548</td>\n",
       "      <td>7.834331</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-30 01:44:17</th>\n",
       "      <td>-1.929805</td>\n",
       "      <td>-0.624254</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-7.282795</td>\n",
       "      <td>-7.194025</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.365278</td>\n",
       "      <td>7.535174</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-30 01:54:17</th>\n",
       "      <td>-3.114681</td>\n",
       "      <td>-2.737228</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-6.934316</td>\n",
       "      <td>-6.431206</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.381555</td>\n",
       "      <td>9.055512</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                              \\\n",
       "metric                        high                       low                   \n",
       "func                           avg       max count       avg       max count   \n",
       "node Date                                                                      \n",
       "11   2018-09-29 21:44:17 -1.477705 -0.739035   2.0  0.896848  1.277099   2.0   \n",
       "     2018-09-29 21:54:17 -1.582295 -1.116608   2.0  0.460278  0.537020   2.0   \n",
       "     2018-09-29 22:04:17 -1.847861 -1.543047   2.0 -0.561068 -0.393600   2.0   \n",
       "     2018-09-29 22:14:17 -1.153288 -1.147944   2.0 -1.435780 -0.551891   2.0   \n",
       "     2018-09-29 22:24:17 -2.994355 -2.196948   2.0 -1.813749 -1.175142   2.0   \n",
       "     2018-09-29 22:34:17 -3.340669 -3.081341   2.0 -2.103707 -1.682563   2.0   \n",
       "     2018-09-29 22:44:17 -2.042713 -1.982671   2.0 -2.219642 -2.163848   2.0   \n",
       "     2018-09-29 22:54:17 -1.959080 -1.505773   2.0 -1.496473 -0.365911   2.0   \n",
       "     2018-09-29 23:04:17 -1.016556 -0.907506   2.0 -0.229547 -0.078180   2.0   \n",
       "     2018-09-29 23:14:17  1.090523  1.346521   2.0 -2.300728 -2.037302   2.0   \n",
       "     2018-09-29 23:24:17  0.404178  0.449059   2.0 -4.563454 -4.095960   2.0   \n",
       "     2018-09-29 23:34:17 -1.351067 -0.688478   2.0 -5.450044 -5.012864   2.0   \n",
       "     2018-09-29 23:44:17 -1.244462 -0.569408   2.0 -4.122179 -3.719279   2.0   \n",
       "     2018-09-29 23:54:17 -1.111861 -0.970525   2.0 -3.199483 -2.916697   2.0   \n",
       "     2018-09-30 00:04:17 -0.596996 -0.356807   2.0 -3.374890 -3.048844   2.0   \n",
       "     2018-09-30 00:14:17  0.164371  0.322530   2.0 -2.975351 -2.772099   2.0   \n",
       "     2018-09-30 00:24:17  1.394435  1.722966   2.0 -5.000560 -3.755242   2.0   \n",
       "     2018-09-30 00:34:17  2.815268  2.845040   2.0 -5.278948 -5.227248   2.0   \n",
       "     2018-09-30 00:44:17  1.853097  1.854282   2.0 -5.050500 -4.878113   2.0   \n",
       "     2018-09-30 00:54:17  0.654906  1.258009   2.0 -5.393068 -4.744838   2.0   \n",
       "     2018-09-30 01:04:17  0.575960  0.995346   2.0 -6.098739 -5.545104   2.0   \n",
       "     2018-09-30 01:14:17  1.565484  1.938605   2.0 -4.781674 -4.676875   2.0   \n",
       "     2018-09-30 01:24:17 -1.136471 -0.945249   2.0 -6.484813 -6.143703   2.0   \n",
       "     2018-09-30 01:34:17 -1.383439 -0.872658   2.0 -6.910115 -6.223143   2.0   \n",
       "     2018-09-30 01:44:17 -1.929805 -0.624254   2.0 -7.282795 -7.194025   2.0   \n",
       "     2018-09-30 01:54:17 -3.114681 -2.737228   2.0 -6.934316 -6.431206   2.0   \n",
       "\n",
       "                                                    \n",
       "metric                       close                  \n",
       "func                           avg       max count  \n",
       "node Date                                           \n",
       "11   2018-09-29 21:44:17  2.188171  2.212375   2.0  \n",
       "     2018-09-29 21:54:17  1.116329  1.288640   2.0  \n",
       "     2018-09-29 22:04:17 -0.984575 -0.334985   2.0  \n",
       "     2018-09-29 22:14:17 -2.951615 -2.175035   2.0  \n",
       "     2018-09-29 22:24:17 -5.235545 -4.897679   2.0  \n",
       "     2018-09-29 22:34:17 -6.527413 -5.574587   2.0  \n",
       "     2018-09-29 22:44:17 -6.928804 -5.925800   2.0  \n",
       "     2018-09-29 22:54:17 -6.050278 -5.728095   2.0  \n",
       "     2018-09-29 23:04:17 -5.315916 -4.878538   2.0  \n",
       "     2018-09-29 23:14:17 -5.716884 -5.057786   2.0  \n",
       "     2018-09-29 23:24:17 -4.881752 -4.728078   2.0  \n",
       "     2018-09-29 23:34:17 -4.305286 -3.190759   2.0  \n",
       "     2018-09-29 23:44:17 -2.486222 -1.975777   2.0  \n",
       "     2018-09-29 23:54:17 -0.310328  0.225623   2.0  \n",
       "     2018-09-30 00:04:17  1.832145  2.204527   2.0  \n",
       "     2018-09-30 00:14:17  1.985780  1.996321   2.0  \n",
       "     2018-09-30 00:24:17  3.282094  3.824943   2.0  \n",
       "     2018-09-30 00:34:17  4.232761  4.528897   2.0  \n",
       "     2018-09-30 00:44:17  4.570316  5.160681   2.0  \n",
       "     2018-09-30 00:54:17  5.416613  5.600179   2.0  \n",
       "     2018-09-30 01:04:17  6.075697  6.092829   2.0  \n",
       "     2018-09-30 01:14:17  7.369579  7.851801   2.0  \n",
       "     2018-09-30 01:24:17  6.264717  6.327879   2.0  \n",
       "     2018-09-30 01:34:17  7.090548  7.834331   2.0  \n",
       "     2018-09-30 01:44:17  7.365278  7.535174   2.0  \n",
       "     2018-09-30 01:54:17  8.381555  9.055512   2.0  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb = pd.concat(dfs, keys=key_list, names=['node', 'metric','func'])\n",
    "comb.columns = ['']\n",
    "comb.unstack(level=['metric','func'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21f756d88d0>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "comb.unstack(level=['node','metric','func']).plot(figsize=[15,7])"
   ]
  }
 ],
 "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": "3.6.3"
  },
  "toc-autonumbering": false
 },
 "nbformat": 4,
 "nbformat_minor": 2
}